diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index de4fded6ae6e66995aa9f1687a9d598017416f7a..3dad41a88c8212b7445c32f241d887306d3c19ad 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -41,7 +41,7 @@ TensorFlow coding style. #### General guidelines and philosophy for contribution * Include unit tests when you contribute new features, as they help to - a) prove that your code works correctly, b) guard against future breaking + a) prove that your code works correctly, and b) guard against future breaking changes to lower the maintenance cost. * Bug fixes also generally require unit tests, because the presence of bugs usually indicates insufficient test coverage. @@ -51,7 +51,7 @@ TensorFlow coding style. non-backward-compatible API changes without a major release. Reviewers of your pull request will comment on any API compatibility issues. * When you contribute a new feature to TensorFlow, the maintenance burden is (by - default) transferred to the TensorFlow team. This means that benefit of + default) transferred to the TensorFlow team. This means that benefit of the contribution must be compared against the cost of maintaining the feature. * Full new features (e.g., a new op implementing a cutting-edge algorithm) typically will live in @@ -68,8 +68,8 @@ Include a license at the top of new files. * [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1) * [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1) * [Bash license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/ci_sanity.sh#L2) -* [HTML license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/dist/index.html#L2) -* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/components/tf_backend/backend.ts#L1) +* [HTML license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/tf-backend.html#L2) +* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/backend.ts#L1) Bazel BUILD files also need to include a license section, e.g., [BUILD example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/BUILD#L61). @@ -163,7 +163,7 @@ There are two ways to run TensorFlow unit tests. bazel test ${flags} //tensorflow/python/... ``` -2. Using [Docker](www.docker.com) and TensorFlow's CI scripts. +2. Using [Docker](https://www.docker.com) and TensorFlow's CI scripts. ```bash # Install Docker first, then this will build and run cpu tests diff --git a/README.md b/README.md index c754c3f0db088be8b638c8bc508e1dd765d960d8..ef5bdc66ef03131318e1dde627e0224cca9137fd 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,10 @@ ----------------- -| **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** | -|-----------------|---------------------|------------------|-------------------|---------------| -| [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | + +| **`Documentation`** | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** | +|-----------------|---------------------|------------------|-------------------|---------------|---------------| +| [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) **TensorFlow** is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while @@ -21,20 +22,6 @@ organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. -**If you want to contribute to TensorFlow, be sure to review the [contribution -guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's -[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to -uphold this code.** - -**We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for -tracking requests and bugs. So please see -[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions -and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** - -The TensorFlow project strives to abide by generally accepted best practices in open-source software development: - -[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486) - ## Installation *See [Installing TensorFlow](https://www.tensorflow.org/get_started/os_setup.html) for instructions on how to install our release binaries or how to build from source.* @@ -75,6 +62,22 @@ $ python >>> sess.close() ``` +## Contribution guidelines + +**If you want to contribute to TensorFlow, be sure to review the [contribution +guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's +[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to +uphold this code.** + +**We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for +tracking requests and bugs. So please see +[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions +and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** + +The TensorFlow project strives to abide by generally accepted best practices in open-source software development: + +[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486) + ## For more information * [TensorFlow Website](https://www.tensorflow.org) diff --git a/RELEASE.md b/RELEASE.md index 0fad3b5d41cffd3e99e2b8900894bb02d94eb567..6f54dee58f75c29a16545ba25de12fe059baf1eb 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -21,7 +21,7 @@ newcomers. * Other: * Add `tf.contrib.distributions.Kumaraswamy`. * `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`. - * Add auto_correlation to distributions. + * Add `auto_correlation` to distributions. * Add `tf.contrib.distributions.Autoregressive`. * Add SeparableConv1D layer. * Add convolutional Flipout layers. @@ -31,12 +31,12 @@ newcomers. * Output variance over trees predictions for classifications tasks. * For `pt` and `eval` commands, allow writing tensor values to filesystem as numpy files. * gRPC: Propagate truncated errors (instead of returning gRPC internal error). - * Augment parallel_interleave to support 2 kinds of prefetching. + * Augment `parallel_interleave` to support 2 kinds of prefetching. * Improved XLA support for C64-related ops log, pow, atan2, tanh. * Add probabilistic convolutional layers. ## API Changes -* Introducing prepare_variance boolean with default setting to False for backward compatibility. +* Introducing `prepare_variance` boolean with default setting to False for backward compatibility. * Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`. ## Known Bugs @@ -633,7 +633,7 @@ answered questions, and were part of inspiring discussions. * Fixed LIBXSMM integration. * Make decode_jpeg/decode_png/decode_gif handle all formats, since users frequently try to decode an image as the wrong type. * Improve implicit broadcasting lowering. -* Improving stability of GCS/Bigquery clients by a faster retrying of stale transmissions. +* Improving stability of GCS/BigQuery clients by a faster retrying of stale transmissions. * Remove OpKernelConstruction::op_def() as part of minimizing proto dependencies. * VectorLaplaceDiag distribution added. * Android demo no longer requires libtensorflow_demo.so to run (libtensorflow_inference.so still required) diff --git a/tensorflow/SECURITY.md b/SECURITY.md similarity index 97% rename from tensorflow/SECURITY.md rename to SECURITY.md index 074eed2951526d53ab62515b7b869569a9708299..9f252e68182c549e6f3b00a3372181e151389bc4 100644 --- a/tensorflow/SECURITY.md +++ b/SECURITY.md @@ -233,7 +233,7 @@ v//Fw6ZeY+HmRDFdirjD7wXtIuER4vqCryIqR6Xe9X8oJXz9L/Jhslc= ### Known vulnerabilities -| Type | Versions affected | Reported by | Additional Information | -|------|:-----------------:|---------------------------------------| -| out of bounds read| <=1.4 | @zhangbo5891001 | [issue report](https://github.com/tensorflow/tensorflow/issues/14959) | +| Type | Versions affected | Reported by | Additional Information | +|-------------------|:-----------------:|-----------------------|-----------------------------| +| out of bounds read| <=1.4 | Blade Team of Tencent | [issue report](https://github.com/tensorflow/tensorflow/issues/14959) | diff --git a/configure b/configure index 9c21d2b03a27714f05094667691e74c16fa89f35..66b66ba54ed68a9aa0ee556f84f68c3a83a495ab 100755 --- a/configure +++ b/configure @@ -8,7 +8,8 @@ if [ -z "$PYTHON_BIN_PATH" ]; then fi # Set all env variables -"$PYTHON_BIN_PATH" configure.py +CONFIGURE_DIR=$(dirname "$0") +"$PYTHON_BIN_PATH" "${CONFIGURE_DIR}/configure.py" "$@" echo "Configuration finished" diff --git a/configure.py b/configure.py index 27519b4aba013e53f688513b798de4238e545022..d14edef1be9e31137c96bed7aebf7ba158b3274f 100644 --- a/configure.py +++ b/configure.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import argparse import errno import os import platform @@ -32,10 +33,6 @@ except ImportError: from distutils.spawn import find_executable as which # pylint: enable=g-import-not-at-top -_TF_BAZELRC = os.path.join(os.path.dirname(os.path.abspath(__file__)), - '.tf_configure.bazelrc') -_TF_WORKSPACE = os.path.join(os.path.dirname(os.path.abspath(__file__)), - 'WORKSPACE') _DEFAULT_CUDA_VERSION = '9.0' _DEFAULT_CUDNN_VERSION = '7' _DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,5.2' @@ -51,6 +48,11 @@ _SUPPORTED_ANDROID_NDK_VERSIONS = [10, 11, 12, 13, 14, 15] _DEFAULT_PROMPT_ASK_ATTEMPTS = 10 +_TF_WORKSPACE_ROOT = os.path.abspath(os.path.dirname(__file__)) +_TF_BAZELRC_FILENAME = '.tf_configure.bazelrc' +_TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME) +_TF_WORKSPACE = os.path.join(_TF_WORKSPACE_ROOT, 'WORKSPACE') + class UserInputError(Exception): pass @@ -119,22 +121,6 @@ def sed_in_place(filename, old, new): f.write(newdata) -def remove_line_with(filename, token): - """Remove lines that contain token from file. - - Args: - filename: string for filename. - token: string token to check if to remove a line from file or not. - """ - with open(filename, 'r') as f: - filedata = f.read() - - with open(filename, 'w') as f: - for line in filedata.strip().split('\n'): - if token not in line: - f.write(line + '\n') - - def write_to_bazelrc(line): with open(_TF_BAZELRC, 'a') as f: f.write(line + '\n') @@ -245,25 +231,30 @@ def setup_python(environ_cp): environ_cp['PYTHON_BIN_PATH'] = python_bin_path # Write tools/python_bin_path.sh - with open('tools/python_bin_path.sh', 'w') as f: + with open(os.path.join( + _TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'), 'w') as f: f.write('export PYTHON_BIN_PATH="%s"' % python_bin_path) -def reset_tf_configure_bazelrc(): +def reset_tf_configure_bazelrc(workspace_path): """Reset file that contains customized config settings.""" open(_TF_BAZELRC, 'w').close() - - home = os.path.expanduser('~') - if not os.path.exists('.bazelrc'): - if os.path.exists(os.path.join(home, '.bazelrc')): - with open('.bazelrc', 'a') as f: - f.write('import %s/.bazelrc\n' % home.replace('\\', '/')) + bazelrc_path = os.path.join(workspace_path, '.bazelrc') + + data = [] + if os.path.exists(bazelrc_path): + with open(bazelrc_path, 'r') as f: + data = f.read().splitlines() + with open(bazelrc_path, 'w') as f: + for l in data: + if _TF_BAZELRC_FILENAME in l: + continue + f.write('%s\n' % l) + if is_windows(): + tf_bazelrc_path = _TF_BAZELRC.replace("\\", "/") else: - open('.bazelrc', 'w').close() - - remove_line_with('.bazelrc', 'tf_configure') - with open('.bazelrc', 'a') as f: - f.write('import %workspace%/.tf_configure.bazelrc\n') + tf_bazelrc_path = _TF_BAZELRC + f.write('import %s\n' % tf_bazelrc_path) def cleanup_makefile(): @@ -271,7 +262,8 @@ def cleanup_makefile(): These files could interfere with Bazel parsing. """ - makefile_download_dir = 'tensorflow/contrib/makefile/downloads' + makefile_download_dir = os.path.join( + _TF_WORKSPACE_ROOT, 'tensorflow', 'contrib', 'makefile', 'downloads') if os.path.isdir(makefile_download_dir): for root, _, filenames in os.walk(makefile_download_dir): for f in filenames: @@ -445,7 +437,7 @@ def convert_version_to_int(version): def check_bazel_version(min_version): - """Check installed bezel version is at least min_version. + """Check installed bazel version is at least min_version. Args: min_version: string for minimum bazel version. @@ -456,7 +448,7 @@ def check_bazel_version(min_version): if which('bazel') is None: print('Cannot find bazel. Please install bazel.') sys.exit(0) - curr_version = run_shell(['bazel', '--batch', 'version']) + curr_version = run_shell(['bazel', '--batch', '--bazelrc=/dev/null', 'version']) for line in curr_version.split('\n'): if 'Build label: ' in line: @@ -502,7 +494,8 @@ def set_cc_opt_flags(environ_cp): for opt in cc_opt_flags.split(): write_to_bazelrc('build:opt --copt=%s' % opt) # It should be safe on the same build host. - write_to_bazelrc('build:opt --host_copt=-march=native') + if not is_ppc64le(): + write_to_bazelrc('build:opt --host_copt=-march=native') write_to_bazelrc('build:opt --define with_default_optimizations=true') # TODO(mikecase): Remove these default defines once we are able to get # TF Lite targets building without them. @@ -827,6 +820,28 @@ def set_gcc_host_compiler_path(environ_cp): write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path) +def reformat_version_sequence(version_str, sequence_count): + """Reformat the version string to have the given number of sequences. + + For example: + Given (7, 2) -> 7.0 + (7.0.1, 2) -> 7.0 + (5, 1) -> 5 + (5.0.3.2, 1) -> 5 + + Args: + version_str: String, the version string. + sequence_count: int, an integer. + Returns: + string, reformatted version string. + """ + v = version_str.split('.') + if len(v) < sequence_count: + v = v + (['0'] * (sequence_count - len(v))) + + return '.'.join(v[:sequence_count]) + + def set_tf_cuda_version(environ_cp): """Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION.""" ask_cuda_version = ( @@ -837,6 +852,7 @@ def set_tf_cuda_version(environ_cp): # Configure the Cuda SDK version to use. tf_cuda_version = get_from_env_or_user_or_default( environ_cp, 'TF_CUDA_VERSION', ask_cuda_version, _DEFAULT_CUDA_VERSION) + tf_cuda_version = reformat_version_sequence(str(tf_cuda_version), 2) # Find out where the CUDA toolkit is installed default_cuda_path = _DEFAULT_CUDA_PATH @@ -893,6 +909,7 @@ def set_tf_cudnn_version(environ_cp): tf_cudnn_version = get_from_env_or_user_or_default( environ_cp, 'TF_CUDNN_VERSION', ask_cudnn_version, _DEFAULT_CUDNN_VERSION) + tf_cudnn_version = reformat_version_sequence(str(tf_cudnn_version), 1) default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH') ask_cudnn_path = (r'Please specify the location where cuDNN %s library is ' @@ -1031,7 +1048,10 @@ def set_tf_tensorrt_install_path(environ_cp): for lib_file in possible_files: if is_compatible(lib_file, cuda_ver, cudnn_ver): - ver_str = nvinfer_pattern.search(lib_file).group(1) + matches = nvinfer_pattern.search(lib_file) + if len(matches.groups()) == 0: + continue + ver_str = matches.group(1) ver = convert_version_to_int(ver_str) if len(ver_str) else 0 if ver > highest_ver[0]: highest_ver = [ver, ver_str, lib_file] @@ -1054,12 +1074,22 @@ def set_tf_tensorrt_install_path(environ_cp): break # Reset and Retry - print('Invalid path to TensorRT. None of the following files can be found:') - print(trt_install_path) - print(os.path.join(trt_install_path, 'lib')) - print(os.path.join(trt_install_path, 'lib64')) - if search_result: - print(libnvinfer_path_from_ldconfig) + if possible_files: + print('TensorRT libraries found in one the following directories', + 'are not compatible with selected cuda and cudnn installations') + print(trt_install_path) + print(os.path.join(trt_install_path, 'lib')) + print(os.path.join(trt_install_path, 'lib64')) + if search_result: + print(libnvinfer_path_from_ldconfig) + else: + print( + 'Invalid path to TensorRT. None of the following files can be found:') + print(trt_install_path) + print(os.path.join(trt_install_path, 'lib')) + print(os.path.join(trt_install_path, 'lib64')) + if search_result: + print(libnvinfer_path_from_ldconfig) else: raise UserInputError('Invalid TF_TENSORRT setting was provided %d ' @@ -1195,7 +1225,7 @@ def set_host_c_compiler(environ_cp): environ_cp, var_name='HOST_C_COMPILER', var_default=default_c_host_compiler, - ask_for_var=('Please specify which C compiler should be used as the host' + ask_for_var=('Please specify which C compiler should be used as the host ' 'C compiler.'), check_success=os.path.exists, error_msg='Invalid C compiler path. %s cannot be found.', @@ -1339,13 +1369,20 @@ def config_info_line(name, help_text): def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", + type=str, + default=_TF_WORKSPACE_ROOT, + help="The absolute path to your active Bazel workspace.") + args = parser.parse_args() + # Make a copy of os.environ to be clear when functions and getting and setting # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.5.4') + check_bazel_version('0.10.0') - reset_tf_configure_bazelrc() + reset_tf_configure_bazelrc(args.workspace) cleanup_makefile() setup_python(environ_cp) @@ -1400,6 +1437,10 @@ def main(): if is_linux(): set_tf_tensorrt_install_path(environ_cp) set_tf_cuda_compute_capabilities(environ_cp) + if 'LD_LIBRARY_PATH' in environ_cp and environ_cp.get( + 'LD_LIBRARY_PATH') != '1': + write_action_env_to_bazelrc('LD_LIBRARY_PATH', + environ_cp.get('LD_LIBRARY_PATH')) set_tf_cuda_clang(environ_cp) if environ_cp.get('TF_CUDA_CLANG') == '1': @@ -1453,7 +1494,6 @@ def main(): 'more details.') config_info_line('mkl', 'Build with MKL support.') config_info_line('monolithic', 'Config for mostly static monolithic build.') - config_info_line('tensorrt', 'Build with TensorRT support.') if __name__ == '__main__': main() diff --git a/tensorflow/BUILD b/tensorflow/BUILD index c225cc1a74ca34b818a9e9bed878c9a0d5b22cc0..d152281d5d760d5afb8bc1605441fdfcb9c919bf 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -483,6 +483,7 @@ filegroup( "//tensorflow/contrib/factorization:all_files", "//tensorflow/contrib/factorization/examples:all_files", "//tensorflow/contrib/factorization/kernels:all_files", + "//tensorflow/contrib/feature_column:all_files", "//tensorflow/contrib/ffmpeg:all_files", "//tensorflow/contrib/ffmpeg/default:all_files", "//tensorflow/contrib/framework:all_files", @@ -542,7 +543,6 @@ filegroup( "//tensorflow/contrib/model_pruning:all_files", "//tensorflow/contrib/model_pruning/examples/cifar10:all_files", "//tensorflow/contrib/nccl:all_files", - "//tensorflow/contrib/ndlstm:all_files", "//tensorflow/contrib/nearest_neighbor:all_files", "//tensorflow/contrib/nn:all_files", "//tensorflow/contrib/opt:all_files", @@ -682,6 +682,7 @@ filegroup( "//tensorflow/tools/docs:all_files", "//tensorflow/tools/git:all_files", "//tensorflow/tools/graph_transforms:all_files", + "//tensorflow/tools/integration_tests/gcs_smoke_test:all_files", "//tensorflow/tools/mlpbtxt:all_files", "//tensorflow/tools/proto_text:all_files", "//tensorflow/tools/quantization:all_files", @@ -787,6 +788,7 @@ tf_cc_shared_object( }), deps = [ "//tensorflow/c:c_api", + "//tensorflow/c:c_api_experimental", "//tensorflow/c:exported_symbols.lds", "//tensorflow/c:version_script.lds", "//tensorflow/c/eager:c_api", diff --git a/tensorflow/__init__.py b/tensorflow/__init__.py index 083634bd7964b0c12e10a1f3c71be5eab597a6c4..78ad6aec19f3bbbfcb389012ac1577573b3e4901 100644 --- a/tensorflow/__init__.py +++ b/tensorflow/__init__.py @@ -21,7 +21,7 @@ from __future__ import division from __future__ import print_function # pylint: disable=wildcard-import -from tensorflow.python import * +from tensorflow.python import * # pylint: disable=redefined-builtin # pylint: enable=wildcard-import from tensorflow.python.util.lazy_loader import LazyLoader diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD index c46cb32aa46af474c889095564d46c5f2399c3ad..29ed957c9aa8cbe515f5f43bdccbf8c94f47c459 100644 --- a/tensorflow/c/BUILD +++ b/tensorflow/c/BUILD @@ -6,23 +6,21 @@ licenses(["notice"]) # Apache 2.0 load( "//tensorflow:tensorflow.bzl", "tf_cc_test", + "tf_cuda_cc_test", "tf_copts", "tf_cuda_library", "tf_custom_op_library", ) -# For platform specific build config -load( - "//tensorflow/core:platform/default/build_config.bzl", - "tf_kernel_tests_linkstatic", -) - # ----------------------------------------------------------------------------- # Public targets filegroup( name = "headers", - srcs = ["c_api.h"], + srcs = [ + "c_api.h", + "c_api_experimental.h", + ], visibility = ["//tensorflow:__subpackages__"], ) @@ -33,7 +31,11 @@ filegroup( "*.cc", "*.h", ], - exclude = ["*test*"], + exclude = [ + "c_api_experimental.cc", + "c_api_experimental.h", + "*test*", + ], ), visibility = ["//visibility:public"], ) @@ -91,9 +93,33 @@ tf_cuda_library( "//tensorflow/core:lib", "//tensorflow/core:lib_internal", ], + }) + select({ + "//tensorflow:with_xla_support": [ + "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/jit", + ], + "//conditions:default": [], }), ) +tf_cuda_library( + name = "c_api_experimental", + srcs = [ + "c_api_experimental.cc", + ], + hdrs = [ + "c_api_experimental.h", + ], + copts = tf_copts(), + visibility = ["//visibility:public"], + deps = [ + ":c_api", + ":c_api_internal", + "//tensorflow/compiler/jit/legacy_flags:mark_for_compilation_pass_flags", + "//tensorflow/core:protos_all_cc", + ], +) + exports_files( [ "version_script.lds", @@ -135,15 +161,21 @@ tf_cuda_library( testonly = 1, srcs = ["c_test_util.cc"], hdrs = ["c_test_util.h"], + visibility = [ + "//learning/brain:__subpackages__", + "//tensorflow:__subpackages__", + ], deps = [ ":c_api", + ":c_api_experimental", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_options", "//tensorflow/core:test", ], ) -tf_cc_test( +tf_cuda_cc_test( name = "c_api_test", size = "small", srcs = ["c_api_test.cc"], diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index e7fb1dec53b56544679173e79729a9d3b950fc89..8b9b3da21c7ae7932cc3e7d86b56f71e6b36a704 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -30,6 +30,7 @@ limitations under the License. #endif #include "tensorflow/c/c_api_internal.h" #include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/common_runtime/eval_const_tensor.h" #include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/allocation_description.pb.h" #include "tensorflow/core/framework/log_memory.h" @@ -64,6 +65,7 @@ using tensorflow::AllocationDescription; using tensorflow::DataType; using tensorflow::Graph; using tensorflow::GraphDef; +using tensorflow::mutex_lock; using tensorflow::NameRangeMap; using tensorflow::NameRangesForNode; using tensorflow::NewSession; @@ -72,11 +74,13 @@ using tensorflow::NodeBuilder; using tensorflow::NodeDef; using tensorflow::OpDef; using tensorflow::OpRegistry; +using tensorflow::OutputTensor; using tensorflow::PartialTensorShape; using tensorflow::RunMetadata; using tensorflow::RunOptions; using tensorflow::Session; using tensorflow::Status; +using tensorflow::string; using tensorflow::Tensor; using tensorflow::TensorBuffer; using tensorflow::TensorId; @@ -87,8 +91,6 @@ using tensorflow::error::Code; using tensorflow::errors::FailedPrecondition; using tensorflow::errors::InvalidArgument; using tensorflow::gtl::ArraySlice; -using tensorflow::mutex_lock; -using tensorflow::string; using tensorflow::strings::StrCat; extern "C" { @@ -109,6 +111,10 @@ TF_Status* TF_NewStatus() { return new TF_Status; } void TF_DeleteStatus(TF_Status* s) { delete s; } void TF_SetStatus(TF_Status* s, TF_Code code, const char* msg) { + if (code == TF_OK) { + s->status = Status::OK(); + return; + } s->status = Status(static_cast(code), tensorflow::StringPiece(msg)); } @@ -198,8 +204,8 @@ TF_Tensor* TF_NewTensor(TF_DataType dtype, const int64_t* dims, int num_dims, // (any alignment requirements will be taken care of by TF_TensorToTensor // and TF_TensorFromTensor). // - // Other types have the same representation, so copy only if it is safe to do - // so. + // Other types have the same representation, so copy only if it is safe to + // do so. buf->data_ = allocate_tensor("TF_NewTensor", len); std::memcpy(buf->data_, data, len); buf->deallocator_ = deallocate_buffer; @@ -211,7 +217,13 @@ TF_Tensor* TF_NewTensor(TF_DataType dtype, const int64_t* dims, int num_dims, buf->deallocator_ = deallocator; buf->deallocator_arg_ = deallocator_arg; } - return new TF_Tensor{dtype, TensorShape(dimvec), buf}; + TF_Tensor* ret = new TF_Tensor{dtype, TensorShape(dimvec), buf}; + size_t elem_size = TF_DataTypeSize(dtype); + if (elem_size > 0 && len < (elem_size * ret->shape.num_elements())) { + delete ret; + return nullptr; + } + return ret; } TF_Tensor* TF_TensorMaybeMove(TF_Tensor* tensor) { @@ -698,6 +710,58 @@ void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output, Status LoadLibrary(const char* library_filename, void** result, const void** buf, size_t* len); +// TODO(josh11b,mrry): Change Session to be able to use a Graph* +// directly, instead of requiring us to serialize to a GraphDef and +// call Session::Extend(). +bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) + EXCLUSIVE_LOCKS_REQUIRED(session->mu) { + if (session->graph != nullptr) { + session->graph->mu.lock(); + const Graph& graph = session->graph->graph; + + status->status = session->graph->sessions[session]; + if (!status->status.ok()) { + session->graph->mu.unlock(); + return false; + } + + const auto num_nodes = graph.num_node_ids(); + if (session->last_num_graph_nodes < num_nodes) { + status->status = tensorflow::ValidateNoCycles(session->graph->graph); + if (!status->status.ok()) { + session->graph->mu.unlock(); + return false; + } + + GraphDef graph_def; + *graph_def.mutable_versions() = graph.versions(); + // Fill graph_def with nodes with ids in the range + // [session->last_num_graph_nodes, num_nodes), that is the nodes + // added since the last TF_SessionRun() call. + for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) { + Node* const node = graph.FindNodeId(id); + if (node != nullptr && node->IsOp()) { + NodeDef* const node_def = graph_def.add_node(); + *node_def = node->def(); + } + } + *graph_def.mutable_library() = graph.flib_def().ToProto(); + session->graph->mu.unlock(); + status->status = session->session->Extend(graph_def); + if (!status->status.ok()) { + // Contract is we always delete input_values[i]. + return false; + } + // Note: session->session is not modified if Extend() fails, so + // we only set last_num_graph_nodes if it succeeds. + session->last_num_graph_nodes = num_nodes; + } else { + session->graph->mu.unlock(); + } + } + return true; +} + } // namespace tensorflow static void TF_Run_Setup(int noutputs, TF_Tensor** c_outputs, @@ -2398,11 +2462,7 @@ void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx, // TF_Session functions ---------------------------------------------- TF_Session::TF_Session(tensorflow::Session* s, TF_Graph* g) - : session(s), graph(g), last_num_graph_nodes(0), device_mgr(nullptr) { - if (s->LocalDeviceManager(&device_mgr).ok()) { - devices = device_mgr->ListDevices(); - } -} + : session(s), graph(g), last_num_graph_nodes(0), extend_before_run(true) {} TF_Session* TF_NewSession(TF_Graph* graph, const TF_SessionOptions* opt, TF_Status* status) { @@ -2502,58 +2562,6 @@ void TF_DeleteSession(TF_Session* s, TF_Status* status) { delete s; } -// TODO(josh11b,mrry): Change Session to be able to use a Graph* -// directly, instead of requiring us to serialize to a GraphDef and -// call Session::Extend(). -static bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { - if (session->graph != nullptr) { - mutex_lock session_lock(session->mu); - session->graph->mu.lock(); - const Graph& graph = session->graph->graph; - - status->status = session->graph->sessions[session]; - if (!status->status.ok()) { - session->graph->mu.unlock(); - return false; - } - - const auto num_nodes = graph.num_node_ids(); - if (session->last_num_graph_nodes < num_nodes) { - status->status = tensorflow::ValidateNoCycles(session->graph->graph); - if (!status->status.ok()) { - session->graph->mu.unlock(); - return false; - } - - GraphDef graph_def; - *graph_def.mutable_versions() = graph.versions(); - // Fill graph_def with nodes with ids in the range - // [session->last_num_graph_nodes, num_nodes), that is the nodes - // added since the last TF_SessionRun() call. - for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) { - Node* const node = graph.FindNodeId(id); - if (node != nullptr && node->IsOp()) { - NodeDef* const node_def = graph_def.add_node(); - *node_def = node->def(); - } - } - *graph_def.mutable_library() = graph.flib_def().ToProto(); - session->graph->mu.unlock(); - status->status = session->session->Extend(graph_def); - if (!status->status.ok()) { - // Contract is we always delete input_values[i]. - return false; - } - // Note: session->session is not modified if Extend() fails, so - // we only set last_num_graph_nodes if it succeeds. - session->last_num_graph_nodes = num_nodes; - } else { - session->graph->mu.unlock(); - } - } - return true; -} - void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options, const TF_Output* inputs, TF_Tensor* const* input_values, int ninputs, const TF_Output* outputs, @@ -2563,8 +2571,12 @@ void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options, // TODO(josh11b,mrry): Change Session to be able to use a Graph* // directly, instead of requiring us to serialize to a GraphDef and // call Session::Extend(). - if (!ExtendSessionGraphHelper(session, status)) { - return; + { + mutex_lock l(session->mu); + if (session->extend_before_run && + !tensorflow::ExtendSessionGraphHelper(session, status)) { + return; + } } TF_Run_Setup(noutputs, output_values, status); @@ -2600,8 +2612,12 @@ void TF_SessionPRunSetup(TF_Session* session, const TF_Output* inputs, const char** handle, TF_Status* status) { *handle = nullptr; - if (!ExtendSessionGraphHelper(session, status)) { - return; + { + mutex_lock l(session->mu); + if (session->extend_before_run && + !tensorflow::ExtendSessionGraphHelper(session, status)) { + return; + } } std::vector input_names(ninputs); @@ -2643,8 +2659,12 @@ void TF_SessionPRun(TF_Session* session, const char* handle, // TODO(josh11b,mrry): Change Session to be able to use a Graph* // directly, instead of requiring us to serialize to a GraphDef and // call Session::Extend(). - if (!ExtendSessionGraphHelper(session, status)) { - return; + { + mutex_lock l(session->mu); + if (session->extend_before_run && + !tensorflow::ExtendSessionGraphHelper(session, status)) { + return; + } } TF_Run_Setup(noutputs, output_values, status); @@ -2672,6 +2692,24 @@ void TF_SessionPRun(TF_Session* session, const char* handle, output_values, target_names, nullptr, status); } +unsigned char TF_TryEvaluateConstant(TF_Graph* graph, TF_Output output, + TF_Tensor** result, TF_Status* status) { + *result = nullptr; + mutex_lock l(graph->mu); + OutputTensor tensor(&output.oper->node, output.index); + bool evaluated; + Tensor result_tensor; + status->status = EvaluateConstantTensor( + tensor, graph->refiner, *graph->graph.op_registry(), + graph->graph.versions().producer(), &evaluated, &result_tensor); + if (evaluated) { + DCHECK(status->status.ok()); + *result = TF_TensorFromTensor(result_tensor, status); + if (!status->status.ok()) evaluated = false; + } + return evaluated; +} + TF_ApiDefMap* TF_NewApiDefMap(TF_Buffer* op_list_buffer, TF_Status* status) { tensorflow::OpList op_list; if (!op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length)) { diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index d2e45341bf1b9ee4579f84064550ce26041dd04a..b32f574628c4d1dc5c3bb3f1265a1b12adee28bc 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -226,6 +226,10 @@ typedef struct TF_Tensor TF_Tensor; // (*deallocator)(data, len, deallocator_arg) // Clients must provide a custom deallocator function so they can pass in // memory managed by something like numpy. +// +// May return NULL (and invoke the deallocator) if the provided data buffer +// (data, len) is inconsistent with a tensor of the given TF_DataType +// and the shape specified by (dima, num_dims). TF_CAPI_EXPORT extern TF_Tensor* TF_NewTensor( TF_DataType, const int64_t* dims, int num_dims, void* data, size_t len, void (*deallocator)(void* data, size_t len, void* arg), @@ -1271,23 +1275,33 @@ TF_CAPI_EXPORT extern void TF_FunctionGetAttrValueProto( // Deleting a function does not remove it from any graphs it was copied to. TF_CAPI_EXPORT extern void TF_DeleteFunction(TF_Function* func); +// Attempts to evaluate `output`. This will only be possible if `output` doesn't +// depend on any graph inputs (this function is safe to call if this isn't the +// case though). +// +// If the evaluation is successful, this function returns true and `output`s +// value is returned in `result`. Otherwise returns false. An error status is +// returned if something is wrong with the graph or input. Note that this may +// return false even if no error status is set. +TF_CAPI_EXPORT extern unsigned char TF_TryEvaluateConstant(TF_Graph* graph, + TF_Output output, + TF_Tensor** result, + TF_Status* status); + // TODO(josh11b): Register OpDef, available to all operations added // to this graph. -// The following two may both benefit from a subgraph-definition API -// that re-uses most of the graph-definition API. -// TODO(andydavis): Add functions to a graph. - // -------------------------------------------------------------------------- // API for driving Graph execution. typedef struct TF_Session TF_Session; -// Return a new execution session with the associated graph, or NULL on error. +// Return a new execution session with the associated graph, or NULL on +// error. Does not take ownership of any input parameters. // -// *graph must be a valid graph (not deleted or nullptr). This function will -// prevent the graph from being deleted until TF_DeleteSession() is called. -// Does not take ownership of opts. +// *`graph` must be a valid graph (not deleted or nullptr). `graph` will be be +// kept alive for the lifetime of the returned TF_Session. New nodes can still +// be added to `graph` after this call. TF_CAPI_EXPORT extern TF_Session* TF_NewSession(TF_Graph* graph, const TF_SessionOptions* opts, TF_Status* status); diff --git a/tensorflow/c/c_api_experimental.cc b/tensorflow/c/c_api_experimental.cc new file mode 100644 index 0000000000000000000000000000000000000000..be7f85a5bb06dce84579b109d506ded049042b50 --- /dev/null +++ b/tensorflow/c/c_api_experimental.cc @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/c/c_api_experimental.h" + +#include "tensorflow/c/c_api_internal.h" +#include "tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h" +#include "tensorflow/core/protobuf/config.pb.h" + +void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) { + tensorflow::ConfigProto& config = options->options.config; + auto* optimizer_options = + config.mutable_graph_options()->mutable_optimizer_options(); + if (enable) { + optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1); + + // These XLA flags are needed to trigger XLA properly from C (more generally + // non-Python) clients. If this API is called again with `enable` set to + // false, it is safe to keep these flag values as is. + tensorflow::legacy_flags::MarkForCompilationPassFlags* flags = + tensorflow::legacy_flags::GetMarkForCompilationPassFlags(); + flags->tf_xla_cpu_global_jit = true; + flags->tf_xla_min_cluster_size = 1; + } else { + optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF); + } +} diff --git a/tensorflow/c/c_api_experimental.h b/tensorflow/c/c_api_experimental.h new file mode 100644 index 0000000000000000000000000000000000000000..5a7b007e40aa199889b2d00b2bde5976c19e2966 --- /dev/null +++ b/tensorflow/c/c_api_experimental.h @@ -0,0 +1,66 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_C_C_API_EXPERIMENTAL_H_ +#define TENSORFLOW_C_C_API_EXPERIMENTAL_H_ + +#include +#include + +#include "tensorflow/c/c_api.h" + +// -------------------------------------------------------------------------- +// Experimental C API for TensorFlow. +// +// The API here is subject to changes in the future. + +// Macro to control visibility of exported symbols in the shared library (.so, +// .dylib, .dll). +// This duplicates the TF_EXPORT macro definition in +// tensorflow/core/platform/macros.h in order to keep this .h file independent +// of any other includes.$a +#ifdef SWIG +#define TF_CAPI_EXPORT +#else +#if defined(COMPILER_MSVC) +#ifdef TF_COMPILE_LIBRARY +#define TF_CAPI_EXPORT __declspec(dllexport) +#else +#define TF_CAPI_EXPORT __declspec(dllimport) +#endif // TF_COMPILE_LIBRARY +#else +#define TF_CAPI_EXPORT __attribute__((visibility("default"))) +#endif // COMPILER_MSVC +#endif // SWIG + +#ifdef __cplusplus +extern "C" { +#endif + +// When `enable` is true, set +// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also +// set XLA flag values to prepare for XLA compilation. Otherwise set +// global_jit_level to OFF. +// +// This API is syntax sugar over TF_SetConfig(), and is used by clients that +// cannot read/write the tensorflow.ConfigProto proto. +TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options, + unsigned char enable); + +#ifdef __cplusplus +} /* end extern "C" */ +#endif + +#endif // TENSORFLOW_C_C_API_EXPERIMENTAL_H_ diff --git a/tensorflow/c/c_api_function.cc b/tensorflow/c/c_api_function.cc index 46271e0514f473099848a8573cb7cb6fad33f7dc..384e6c8cb97022264c5327da5ca5861057608fbe 100644 --- a/tensorflow/c/c_api_function.cc +++ b/tensorflow/c/c_api_function.cc @@ -44,8 +44,12 @@ class NodeNameMapping { public: NodeNameMapping() = default; - // Normalize the input/output name and make it unique. - string GetIOName(const string& name); + // Normalize the input name and make it unique. This is the same as the + // function for output, expect that it adds a name mapping for the name. + string GetInputName(const string& name); + + // Normalize the output name and make it unique. + string GetOutputName(const string& name); // Make the node name unique. string Uniquify(const string& name); @@ -107,7 +111,13 @@ string NodeNameMapping::UniquifyHelper(const string& name) const { } } -string NodeNameMapping::GetIOName(const string& name) { +string NodeNameMapping::GetInputName(const string& name) { + const string& input_name = GetOutputName(name); + name_mapping_[name] = input_name; + return input_name; +} + +string NodeNameMapping::GetOutputName(const string& name) { const string& input_name = UniquifyHelper(Normalize(name)); // Record that we used this name, but don't add it to name_mapping_ // since this name is not for a node. @@ -214,10 +224,11 @@ Status FillFunctionBody( // Add control inputs. for (const Edge* edge : control_edges) { - // Add this control input only if the src node is in the body. + // Add this control input only if the src node is in the body or a part of + // the inputs. const string normalized = node_names.Lookup(edge->src()->name()); // If we did not find a name for the source of control edge, this - // source must be outside of the body. Raise an error. + // source must be outside of the body, and not an input. Raise an error. if (normalized.empty()) { return InvalidArgument( "The source of control edge ", edge->DebugString(), @@ -279,7 +290,7 @@ Status GraphToFunctionDef(const Graph& fn_body, const string& fn_name, TF_RETURN_IF_ERROR(node_names.UseOutputName(output_names[i])); argdef->set_name(output_names[i]); } else { - argdef->set_name(node_names.GetIOName(node->name())); + argdef->set_name(node_names.GetOutputName(node->name())); } } @@ -289,7 +300,7 @@ Status GraphToFunctionDef(const Graph& fn_body, const string& fn_name, int idx = inputs[i].index; OpDef::ArgDef* argdef = fdef->mutable_signature()->add_input_arg(); argdef->set_type(node->output_type(idx)); - const string& input_name = node_names.GetIOName(node->name()); + const string& input_name = node_names.GetInputName(node->name()); argdef->set_name(input_name); tensor_renaming[strings::StrCat(node->name(), ":", idx)] = input_name; } @@ -467,7 +478,7 @@ Status ComputeBodyNodes( return Status::OK(); } -} // anonymous namespace +} // namespace } // namespace tensorflow using tensorflow::Node; diff --git a/tensorflow/c/c_api_function_test.cc b/tensorflow/c/c_api_function_test.cc index dbce66d2317a8e89288fab932cf69055f8b5a7f0..7ca50119eafe299b307f06c555aec1388e7e82e2 100644 --- a/tensorflow/c/c_api_function_test.cc +++ b/tensorflow/c/c_api_function_test.cc @@ -331,6 +331,11 @@ class CApiFunctionTest : public ::testing::Test { << "Failed to find expected edge " << e.ToString() << " in fdef: " << fdef.DebugString(); } + for (const EdgeSpec& e : c_edges) { + ASSERT_TRUE(a_edges.find(e) != a_edges.end()) + << "Failed to find expected control edge " << e.ToString() + << " in fdef: " << fdef.DebugString(); + } // If caller specified all edges, check that we have seen all if (is_exact_edges) { @@ -980,7 +985,7 @@ TEST_F(CApiFunctionTest, ControlDependency) { VerifyFDef( {"add_0", "scalar"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}), {{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}}, - {{"scalar", "add_0"}}); + {{"^scalar", "add_0:2"}}); } TEST_F(CApiFunctionTest, ControlDependencyOutsideOfBody) { @@ -1023,12 +1028,17 @@ TEST_F(CApiFunctionTest, ControlDependencyOutsideOfBody_FromInputNode) { TF_Operation* add = AddWithCtrlDependency(feed1, feed2, func_graph_, feed1, s_); EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); - Define(-1, {}, {feed1, feed2}, {add}, {}, true); - EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_)); - EXPECT_EQ(string("The source of control edge [id=3 feed1:-1 -> add:-1] " - "is not in the body. Encountered while creating " - "function 'MyFunc'"), - string(TF_Message(s_))); + Define(-1, {}, {feed1, feed2}, {add}, {}); + + // Use, run, and verify + TF_Operation* two = ScalarConst(2, host_graph_, s_); + TF_Operation* func_feed = Placeholder(host_graph_, s_); + TF_Operation* func_op = Use({two, func_feed}); + Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3); + VerifyFDef( + {"add_0"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}), + {{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}}, + {{"^feed1", "add_0:2"}}); } TEST_F(CApiFunctionTest, DuplicateInputsAreNotAllowed) { diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h index 91667056e0eeb224b4b8a034766f11a123cd1a03..25233931de4ec221d73a4d7432a21445088e3d7c 100644 --- a/tensorflow/c/c_api_internal.h +++ b/tensorflow/c/c_api_internal.h @@ -129,10 +129,11 @@ struct TF_Session { tensorflow::mutex mu; int last_num_graph_nodes; - // NOTE(ashankar): Experimental fields to help keep the - // buffers of a TF_Tensor pinned in device memory. - const tensorflow::DeviceMgr* device_mgr; // Owned by session. - std::vector devices; // Owned by device_mgr. + // If true, TF_SessionRun and similar methods will call + // ExtendSessionGraphHelper before running the graph (this is the default + // public behavior). Can be set to false if the caller needs to call + // ExtendSessionGraphHelper manually. + bool extend_before_run GUARDED_BY(mu); }; struct TF_ImportGraphDefOptions { @@ -212,6 +213,8 @@ void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output, void RecordMutation(TF_Graph* graph, const TF_Operation& op, const char* mutation_type); +bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status); + } // end namespace tensorflow #endif // TENSORFLOW_C_C_API_INTERNAL_H_ diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index 01954eb235f1a93d943c2ec7ea4c5ca44785d402..028f146be31790b211e546978302e81afe26b231 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -57,6 +57,52 @@ static void ExpectHasSubstr(StringPiece s, StringPiece expected) { << "'" << s << "' does not contain '" << expected << "'"; } +// Returns the GPU device name if there is one (with arbitrary tie breaking if +// there are more than one), or "" otherwise. +string GPUDeviceName(TF_Session* session) { + std::unique_ptr status( + TF_NewStatus(), TF_DeleteStatus); + TF_Status* s = status.get(); + std::unique_ptr list( + TF_SessionListDevices(session, s), TF_DeleteDeviceList); + TF_DeviceList* device_list = list.get(); + + CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + + const int num_devices = TF_DeviceListCount(device_list); + LOG(INFO) << "There are " << num_devices << " devices."; + for (int i = 0; i < num_devices; ++i) { + const char* device_name = TF_DeviceListName(device_list, i, s); + CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + const char* device_type = TF_DeviceListType(device_list, i, s); + CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + LOG(INFO) << "Device " << i << " has name " << device_name << ", type " + << device_type; + if (string(device_type) == DEVICE_GPU) { + return device_name; + } + } + // No GPU device found. + return ""; +} + +string GPUDeviceName() { + std::unique_ptr status( + TF_NewStatus(), TF_DeleteStatus); + TF_Status* s = status.get(); + std::unique_ptr graph(TF_NewGraph(), + TF_DeleteGraph); + + TF_SessionOptions* opts = TF_NewSessionOptions(); + TF_Session* sess = TF_NewSession(graph.get(), opts, s); + TF_DeleteSessionOptions(opts); + + const string gpu_device_name = GPUDeviceName(sess); + TF_DeleteSession(sess, s); + CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + return gpu_device_name; +} + TEST(CAPI, Version) { EXPECT_STRNE("", TF_Version()); } TEST(CAPI, Status) { @@ -94,6 +140,17 @@ TEST(CAPI, Tensor) { EXPECT_TRUE(deallocator_called); } +void NoOpDeallocator(void* data, size_t, void*) {} + +TEST(CAPI, MalformedTensor) { + // See https://github.com/tensorflow/tensorflow/issues/7394 + // num_dims = 0 implies a scalar, so should be backed by at least 4 bytes of + // data. + TF_Tensor* t = + TF_NewTensor(TF_FLOAT, nullptr, 0, nullptr, 0, &NoOpDeallocator, nullptr); + ASSERT_TRUE(t == nullptr); +} + TEST(CAPI, AllocateTensor) { const int num_bytes = 6 * sizeof(float); int64_t dims[] = {2, 3}; @@ -123,6 +180,10 @@ TEST(CAPI, MaybeMove) { } TEST(CAPI, LibraryLoadFunctions) { + // TODO(b/73318067): Fix linking for the GPU test generated by the + // tf_cuda_cc_test() bazel rule and remove the next line. + if (!GPUDeviceName().empty()) return; + // Load the library. TF_Status* status = TF_NewStatus(); TF_Library* lib = @@ -912,6 +973,70 @@ TEST(CAPI, Session) { TF_DeleteStatus(s); } +// If `device` is non-empty, run Min op on that device. +// Otherwise run it on the default device (CPU). +void RunMinTest(const string& device, bool use_XLA) { + TF_Status* s = TF_NewStatus(); + TF_Graph* graph = TF_NewGraph(); + + // Make a placeholder operation. + TF_Operation* feed = Placeholder(graph, s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + + // Make a constant operation with the scalar "0", for axis. + TF_Operation* one = ScalarConst(0, graph, s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + + // Create a session for this graph. + CSession csession(graph, s, use_XLA); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + + if (!device.empty()) { + LOG(INFO) << "Setting op Min on device " << device; + } + TF_Operation* min = MinWithDevice(feed, one, graph, device, s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + + // Run the graph. + csession.SetInputs({{feed, Int32Tensor({3, 2, 5})}}); + csession.SetOutputs({min}); + csession.Run(s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Tensor* out = csession.output_tensor(0); + ASSERT_TRUE(out != nullptr); + EXPECT_EQ(TF_INT32, TF_TensorType(out)); + EXPECT_EQ(0, TF_NumDims(out)); // scalar + ASSERT_EQ(sizeof(int32), TF_TensorByteSize(out)); + int32* output_contents = static_cast(TF_TensorData(out)); + EXPECT_EQ(2, *output_contents); + + // Clean up + csession.CloseAndDelete(s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_DeleteGraph(graph); + TF_DeleteStatus(s); +} + +TEST(CAPI, Session_Min_CPU) { RunMinTest(/*device=*/"", /*use_XLA=*/false); } + +TEST(CAPI, Session_Min_XLA_CPU) { RunMinTest(/*device=*/"", /*use_XLA=*/true); } + +TEST(CAPI, Session_Min_GPU) { + const string gpu_device = GPUDeviceName(); + // Skip this test if no GPU is available. + if (gpu_device.empty()) return; + + RunMinTest(gpu_device, /*use_XLA=*/false); +} + +TEST(CAPI, Session_Min_XLA_GPU) { + const string gpu_device = GPUDeviceName(); + // Skip this test if no GPU is available. + if (gpu_device.empty()) return; + + RunMinTest(gpu_device, /*use_XLA=*/true); +} + TEST(CAPI, SessionPRun) { TF_Status* s = TF_NewStatus(); TF_Graph* graph = TF_NewGraph(); @@ -1956,7 +2081,7 @@ TEST_F(CApiAttributesTest, Tensor) { } TEST_F(CApiAttributesTest, StringTensor) { - // Create the string-Tensor "atttribute" value. + // Create the string-Tensor "attribute" value. char encoded[] = { 0, 0, 0, 0, 0, 0, 0, 0, // array[uint64] offsets 1, // varint encoded string length @@ -2054,6 +2179,10 @@ TEST_F(CApiAttributesTest, Errors) { } TEST(TestApiDef, TestCreateApiDef) { + // TODO(b/73318067): Fix linking for the GPU test generated by the + // tf_cuda_cc_test() bazel rule and remove the next line. + if (!GPUDeviceName().empty()) return; + TF_Status* status = TF_NewStatus(); TF_Library* lib = TF_LoadLibrary("tensorflow/c/test_op.so", status); @@ -2084,6 +2213,10 @@ TEST(TestApiDef, TestCreateApiDef) { } TEST(TestApiDef, TestCreateApiDefWithOverwrites) { + // TODO(b/73318067): Fix linking for the GPU test generated by the + // tf_cuda_cc_test() bazel rule and remove the next line. + if (!GPUDeviceName().empty()) return; + TF_Status* status = TF_NewStatus(); TF_Library* lib = TF_LoadLibrary("tensorflow/c/test_op.so", status); diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc index 37439ff0beac5a5220460465e954b6c093ee1ba9..53346a8cdf26d98683579bfd5f0514d4b5fcc86b 100644 --- a/tensorflow/c/c_test_util.cc +++ b/tensorflow/c/c_test_util.cc @@ -15,11 +15,13 @@ limitations under the License. #include "tensorflow/c/c_test_util.h" +#include "tensorflow/c/c_api_experimental.h" #include "tensorflow/core/framework/function.pb.h" #include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/framework/tensor.pb.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/public/session_options.h" using tensorflow::GraphDef; using tensorflow::NodeDef; @@ -32,6 +34,10 @@ static void DoubleDeallocator(void* data, size_t, void* arg) { delete[] static_cast(data); } +static void FloatDeallocator(void* data, size_t, void* arg) { + delete[] static_cast(data); +} + TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) { int64_t num_values = 1; for (int i = 0; i < num_dims; ++i) { @@ -76,13 +82,21 @@ TF_Tensor* DoubleTensor(double v) { &DoubleDeallocator, nullptr); } +TF_Tensor* FloatTensor(float v) { + const int num_bytes = sizeof(float); + float* values = new float[1]; + values[0] = v; + return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes, + &FloatDeallocator, nullptr); +} + // All the *Helper methods are used as a workaround for the restrictions that // one cannot call ASSERT_* methods in non-void-returning functions (when // exceptions are disabled during compilation) void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name, - TF_Operation** op) { + TF_DataType dtype, TF_Operation** op) { TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name); - TF_SetAttrType(desc, "dtype", TF_INT32); + TF_SetAttrType(desc, "dtype", dtype); *op = TF_FinishOperation(desc, s); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); ASSERT_NE(*op, nullptr); @@ -90,7 +104,14 @@ void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name, TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name) { TF_Operation* op; - PlaceholderHelper(graph, s, name, &op); + PlaceholderHelper(graph, s, name, TF_INT32, &op); + return op; +} + +TF_Operation* PlaceholderFloat(TF_Graph* graph, TF_Status* s, + const char* name) { + TF_Operation* op; + PlaceholderHelper(graph, s, name, TF_FLOAT, &op); return op; } @@ -124,8 +145,15 @@ TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s, return Const(tensor.get(), graph, s, name); } -void AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, - const char* name, TF_Operation** op, bool check) { +TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s, + const char* name) { + unique_tensor_ptr tensor(FloatTensor(v), TF_DeleteTensor); + return Const(tensor.get(), graph, s, name); +} + +void AddOpHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name, TF_Operation** op, + bool check) { TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name); TF_Output add_inputs[2] = {{l, 0}, {r, 0}}; TF_AddInputList(desc, add_inputs, 2); @@ -139,14 +167,14 @@ void AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name) { TF_Operation* op; - AddHelper(l, r, graph, s, name, &op, true); + AddOpHelper(l, r, graph, s, name, &op, true); return op; } TF_Operation* AddNoCheck(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name) { TF_Operation* op; - AddHelper(l, r, graph, s, name, &op, false); + AddOpHelper(l, r, graph, s, name, &op, false); return op; } @@ -160,6 +188,36 @@ TF_Operation* AddWithCtrlDependency(TF_Operation* l, TF_Operation* r, return TF_FinishOperation(desc, s); } +// If `op_device` is non-empty, set the created op on that device. +void BinaryOpHelper(const char* op_name, TF_Operation* l, TF_Operation* r, + TF_Graph* graph, TF_Status* s, const char* name, + TF_Operation** op, const string& op_device, bool check) { + TF_OperationDescription* desc = TF_NewOperation(graph, op_name, name); + if (!op_device.empty()) { + TF_SetDevice(desc, op_device.c_str()); + } + TF_AddInput(desc, {l, 0}); + TF_AddInput(desc, {r, 0}); + *op = TF_FinishOperation(desc, s); + if (check) { + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + ASSERT_NE(*op, nullptr); + } +} + +TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + const string& op_device, TF_Status* s, + const char* name) { + TF_Operation* op; + BinaryOpHelper("Min", l, r, graph, s, name, &op, op_device, true); + return op; +} + +TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name) { + return MinWithDevice(l, r, graph, /*op_device=*/"", s, name); +} + TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s, const char* name) { TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name); @@ -369,8 +427,9 @@ std::vector GetFuncNames(const tensorflow::GraphDef& graph_def) { return names; } -CSession::CSession(TF_Graph* graph, TF_Status* s) { +CSession::CSession(TF_Graph* graph, TF_Status* s, bool use_XLA) { TF_SessionOptions* opts = TF_NewSessionOptions(); + TF_EnableXLACompilation(opts, use_XLA); session_ = TF_NewSession(graph, opts, s); TF_DeleteSessionOptions(opts); } diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h index 6acc2fec0063a8592e8e22a00b530df05a08cdb8..8cf060f73f1187f8f5281211785451be74882828 100644 --- a/tensorflow/c/c_test_util.h +++ b/tensorflow/c/c_test_util.h @@ -44,8 +44,14 @@ TF_Tensor* Int32Tensor(int32_t v); TF_Tensor* DoubleTensor(double v); +TF_Tensor* FloatTensor(float v); + +// TODO(hongm): Change Placeholder() to take in a TF_DataType parameter, and +// unify with PlaceholderFloat. TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name = "feed"); +TF_Operation* PlaceholderFloat(TF_Graph* graph, TF_Status* s, + const char* name = "feed"); TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s, const char* name = "const"); @@ -56,6 +62,9 @@ TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s, TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s, const char* name = "scalar"); +TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s, + const char* name = "scalar"); + TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name = "add"); @@ -69,6 +78,14 @@ TF_Operation* AddWithCtrlDependency(TF_Operation* l, TF_Operation* r, TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s, const char* name = "add"); +TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name = "min"); + +// If `op_device` is non-empty, set the created op on that device. +TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + const string& op_device, TF_Status* s, + const char* name = "min"); + TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s, const char* name = "neg"); @@ -108,7 +125,7 @@ std::vector GetFuncNames(const tensorflow::GraphDef& graph_def); class CSession { public: - CSession(TF_Graph* graph, TF_Status* s); + CSession(TF_Graph* graph, TF_Status* s, bool use_XLA = false); explicit CSession(TF_Session* session); ~CSession(); @@ -124,6 +141,8 @@ class CSession { TF_Tensor* output_tensor(int i) { return output_values_[i]; } + TF_Session* mutable_session() { return session_; } + private: void DeleteInputValues(); void ResetOutputValues(); diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD index e62310d811462f88af93505393b622d9a87c72d3..e55cb672e97e1403a3dd864c91c176426eb3f067 100644 --- a/tensorflow/c/eager/BUILD +++ b/tensorflow/c/eager/BUILD @@ -6,6 +6,7 @@ load( "tf_cuda_cc_test", "tf_cc_test", "tf_copts", + "tfe_xla_copts", "tf_cuda_library", ) @@ -16,7 +17,7 @@ tf_cuda_library( "c_api_internal.h", ], hdrs = ["c_api.h"], - copts = tf_copts(), + copts = tf_copts() + tfe_xla_copts(), visibility = ["//visibility:public"], deps = select({ "//tensorflow:android": [ @@ -33,7 +34,15 @@ tf_cuda_library( "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", ], - }) + ["//tensorflow/core:gpu_runtime"], + }) + select({ + "//tensorflow:with_xla_support": [ + "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/jit", + ], + "//conditions:default": [], + }) + [ + "//tensorflow/core:gpu_runtime", + ], ) tf_cuda_library( @@ -56,12 +65,14 @@ tf_cuda_library( tf_cuda_cc_test( name = "c_api_test", srcs = ["c_api_test.cc"], + extra_copts = tfe_xla_copts(), tags = [ "guitar", "multi_gpu", ], deps = [ ":c_api", + "//tensorflow/c:c_test_util", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index d65b592895950cea3b528478e5bd6257ac688cc6..dfe2089d601b4cfd917fee0806285283ae5533ec 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -25,15 +25,21 @@ limitations under the License. #include "tensorflow/c/c_api_internal.h" #include "tensorflow/c/eager/c_api_internal.h" #include "tensorflow/c/eager/runtime.h" +#ifdef TENSORFLOW_EAGER_USE_XLA +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#endif // TENSORFLOW_EAGER_USE_XLA #include "tensorflow/core/common_runtime/copy_tensor.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" +#include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/rendezvous.h" #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/refcount.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" #include "tensorflow/core/platform/mutex.h" @@ -44,15 +50,37 @@ using tensorflow::int64; using tensorflow::string; namespace { -bool IsCPU(tensorflow::Device* d) { +bool IsCPU(const tensorflow::Device* d) { return d == nullptr || d->tensorflow_gpu_device_info() == nullptr; } -string DeviceName(tensorflow::Device* d) { +bool IsXLA(const tensorflow::Device* d) { + if (d == nullptr) return false; + const auto& device_type = d->attributes().device_type(); + return device_type.find("XLA") != std::string::npos; +} + +string DeviceName(const tensorflow::Device* d) { return (d == nullptr) ? "cpu:0" : d->name(); } + +#ifdef TENSORFLOW_EAGER_USE_XLA +std::atomic_int_fast64_t func_id_generator(0); +#endif // TENSORFLOW_EAGER_USE_XLA } // namespace +TFE_ContextDevicePlacementPolicy PlacementPolicy( + bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy) { + if (!soft_placement) { + return original_policy; + } + if (original_policy == TFE_DEVICE_PLACEMENT_EXPLICIT || + original_policy == TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32) { + return TFE_DEVICE_PLACEMENT_SILENT; + } + return original_policy; +} + extern "C" { TFE_ContextOptions* TFE_NewContextOptions() { return new TFE_ContextOptions; } @@ -70,22 +98,15 @@ void TFE_ContextOptionsSetDevicePlacementPolicy( void TFE_DeleteContextOptions(TFE_ContextOptions* options) { delete options; } TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) { - TF_Graph* graph = TF_NewGraph(); - TF_Session* session = TF_NewSession(graph, &opts->session_options, status); - if (status->status.ok()) { - if (session->device_mgr == nullptr || session->devices.empty()) { - status->status = tensorflow::errors::InvalidArgument( - "Provided TF_SessionOptions are not compatible with eager execution " - "(perhaps the TF_SessionOptions alluded to session execution in a " - "remote address space?)"); - } - } + std::vector devices; + status->status = tensorflow::DeviceFactory::AddDevices( + opts->session_options.options, "/job:localhost/replica:0/task:0", + &devices); if (!status->status.ok()) { - TF_DeleteGraph(graph); return nullptr; } - - return new TFE_Context(*opts, session); + return new TFE_Context(*opts, std::unique_ptr( + new tensorflow::DeviceMgr(devices))); } void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { @@ -94,15 +115,14 @@ void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { tensorflow::mutex_lock ml(ctx->cache_mu); tensorflow::gtl::STLDeleteValues(&ctx->kernel_cache); } - TF_Graph* graph = ctx->session->graph; - TF_DeleteSession(ctx->session, status); - TF_DeleteGraph(graph); ctx->rendezvous->Unref(); delete ctx; } TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) { - return TF_SessionListDevices(ctx->session, status); + TF_DeviceList* list = new TF_DeviceList; + ctx->device_manager->ListDeviceAttributes(&list->response); + return list; } void TFE_ContextClearCaches(TFE_Context* ctx) { @@ -131,7 +151,7 @@ TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) { tensorflow::Tensor tensor; status->status = tensorflow::TF_TensorToTensor(t, &tensor); if (!status->status.ok()) return nullptr; - return new TFE_TensorHandle(tensor, nullptr); + return new TFE_TensorHandle(tensor, nullptr, nullptr); } void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { delete h; } @@ -140,17 +160,22 @@ TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) { return static_cast(h->t.dtype()); } -int TFE_TensorHandleNumDims(TFE_TensorHandle* h) { return h->t.dims(); } +int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) { + status->status = tensorflow::Status::OK(); + return h->t.dims(); +} -int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index) { +int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index, + TF_Status* status) { + status->status = tensorflow::Status::OK(); return h->t.dim_size(dim_index); } -const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h) { - // This might be a bit confusing as a tensor on CPU can sometimes return - // "CPU:0" and sometimes "/job:localhost/replica:0/task:0/cpu:0". - // TODO(ashankar): Figure out which one would be nicer. - return (h->d == nullptr) ? "CPU:0" : h->d->name().c_str(); +const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) { + status->status = tensorflow::Status::OK(); + return (h->op_device == nullptr) + ? "/job:localhost/replica:0/task:0/device:CPU:0" + : h->op_device->name().c_str(); } TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) { @@ -172,19 +197,23 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, TFE_Context* ctx, const char* device_name, TF_Status* status) { - tensorflow::Device* dstd = ctx->devices()[0]; + tensorflow::Device* dstd = ctx->devices[0]; if (device_name != nullptr && strlen(device_name) > 0) { - status->status = ctx->session->device_mgr->LookupDevice(device_name, &dstd); + status->status = ctx->device_manager->LookupDevice(device_name, &dstd); if (!status->status.ok()) return nullptr; } - tensorflow::Device* srcd = h->d == nullptr ? ctx->devices()[0] : h->d; + tensorflow::Device* srcd = h->d == nullptr ? ctx->devices[0] : h->d; bool is_same_device = (srcd == dstd) || (DeviceName(srcd) == DeviceName(dstd)); const bool dst_cpu = IsCPU(dstd); const bool src_cpu = IsCPU(srcd); - if (is_same_device) { - return new TFE_TensorHandle(h->t, dst_cpu ? nullptr : dstd); + // both_on_cpu can be true and yet is_same_device is false, if one of src/dst + // has device type XLA_CPU, and the other CPU. + const bool both_on_cpu = src_cpu && dst_cpu; + if (is_same_device || both_on_cpu) { + dstd = dst_cpu ? nullptr : dstd; + return new TFE_TensorHandle(h->t, dstd, dstd); } tensorflow::Tensor* src = &(h->t); if (!dst_cpu && (src->dtype() != tensorflow::DT_VARIANT && @@ -203,7 +232,8 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, } tensorflow::Tensor dst(dstd->GetAllocator(attr), src->dtype(), src->shape()); if (src->shape().num_elements() == 0) { - return new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd); + dstd = dst_cpu ? nullptr : dstd; + return new TFE_TensorHandle(dst, dstd, dstd); } tensorflow::DeviceContext* src_device_context = nullptr; if (!src_cpu) { @@ -231,7 +261,8 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, }); n.WaitForNotification(); return (TF_GetCode(status) == TF_OK) - ? new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd) + ? new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd, + dst_cpu ? nullptr : dstd) : nullptr; } @@ -256,8 +287,7 @@ void TFE_DeleteOp(TFE_Op* op) { delete op; } void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) { tensorflow::Device* d = nullptr; if (device_name != nullptr && strlen(device_name) > 0) { - status->status = - op->ctx->session->device_mgr->LookupDevice(device_name, &d); + status->status = op->ctx->device_manager->LookupDevice(device_name, &d); if (!status->status.ok()) return; } op->device = d; @@ -265,22 +295,29 @@ void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) { const char* TFE_OpGetDevice(TFE_Op* op, TF_Status* status) { tensorflow::Device* device = - (op->device == nullptr) ? op->ctx->devices()[0] : op->device; + (op->device == nullptr) ? op->ctx->devices[0] : op->device; return device->name().c_str(); } +void TFE_OpSetXLACompilation(TFE_Op* op, unsigned char enable) { + op->use_xla = enable; +#ifndef TENSORFLOW_EAGER_USE_XLA + LOG(WARNING) << "This call is a no-op, as the TensorFlow library is not " + "built with XLA support."; +#endif // TENSORFLOW_EAGER_USE_XLA +} + void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status) { // Questionable heuristic ... - // - // Motivation: After an 'op' is placed on GPU because some of its earlier - // inputs are on GPU, we want to keep the 'op' there, even if some later - // inputs of it are not on GPU. - if (IsCPU(op->device) && !IsCPU(h->d)) { + // - If a device was explicitly set on the op, always use that. + // - If not, place on the first non-host device seen. + if (op->device == nullptr && !IsCPU(h->d)) { op->device = h->d; } if (!status->status.ok()) return; op->inputs.push_back(h->t); op->input_devices.push_back(h->d); + op->input_op_devices.push_back(h->op_device); op->attrs.NumInputs(op->inputs.size()); } @@ -430,6 +467,19 @@ void TFE_OpSetAttrShapeList(TFE_Op* op, const char* attr_name, proto.get(), num_values)); } +void TFE_OpSetAttrFunctionList(TFE_Op* op, const char* attr_name, + const TFE_Op** value, int num_values) { + std::unique_ptr funcs( + new tensorflow::NameAttrList[num_values]); + for (int i = 0; i < num_values; i++) { + funcs[i].set_name(value[i]->name); + value[i]->attrs.FillAttrValueMap(funcs[i].mutable_attr()); + } + op->attrs.Set(attr_name, + tensorflow::gtl::ArraySlice( + funcs.get(), num_values)); +} + namespace { tensorflow::Status ValidateInputTypeAndPlacement( @@ -483,7 +533,8 @@ tensorflow::Status ValidateInputTypeAndPlacement( } // We are only here if the policy is warn or silent copies, so we should // trigger a copy. - TFE_TensorHandle original{op->inputs[i], op->input_devices[i]}; + TFE_TensorHandle original{op->inputs[i], op->input_devices[i], + op->device}; TF_Status* s = TF_NewStatus(); TFE_TensorHandle* copied_tensor = TFE_TensorHandleCopyToDevice( &original, ctx, expected_device->name().c_str(), s); @@ -511,17 +562,298 @@ tensorflow::Status ValidateInputTypeAndPlacement( } return tensorflow::Status::OK(); } + +#ifdef TENSORFLOW_EAGER_USE_XLA +// Synthesizes and returns a wrapper function over `op`, which must be a +// primitive op (e.g. matmul). +// +// The wrapper function conforms to the function signature expected by +// _XlaLaunchOp, with input params ordered by . For example, if the op has input params , they will be reordered to as the input params to the synthesized function. +// +// It populates `const_input_types`, `arg_input_types` and +// `op_input_to_func_input` based on the reordering results, that the caller can +// use them to build an _XlaLaunchOp. On error, it returns NULL, and sets +// `status` accordingly. +const tensorflow::FunctionDef* OpToFunction( + TFE_Op* op, std::vector* const_input_types, + std::vector* arg_input_types, + tensorflow::gtl::FlatMap* op_input_to_func_input, + TF_Status* status) { + DCHECK(!op->is_function()); + + tensorflow::FunctionDef fdef; + + // Get the OpDef of the op we are trying to encapsulate. + TFE_Context* ctx = op->ctx; + const tensorflow::OpRegistrationData* op_data; + { + tensorflow::tf_shared_lock l(ctx->functions_mu); + status->status = ctx->func_lib_def.LookUp(op->name, &op_data); + if (!status->status.ok()) { + return nullptr; + } + } + const tensorflow::OpDef& op_def = op_data->op_def; + + tensorflow::OpDef* signature = fdef.mutable_signature(); + + // Handle constant inputs. + const std::unordered_set const_inputs( + *tensorflow::XlaOpRegistry::CompileTimeConstantInputs(op->name)); + + // First add place holders for the input args, so that we can refer to them by + // position in the next loop. Also tally up the resource inputs. + int num_resource_inputs = 0; + for (int i = 0; i < op_def.input_arg_size(); ++i) { + if (op_def.input_arg(i).type() == tensorflow::DT_RESOURCE) { + ++num_resource_inputs; + } + signature->add_input_arg(); + } + + // Now we map the input params from `op_def` to `signature`, where the param + // ordering for `signature` is: . + int const_index = 0; + int arg_index = const_inputs.size(); + int resource_index = op_def.input_arg_size() - num_resource_inputs; + for (int i = 0; i < op_def.input_arg_size(); ++i) { + const tensorflow::OpDef::ArgDef& op_input_arg = op_def.input_arg(i); + tensorflow::OpDef::ArgDef* func_input_arg = nullptr; + if (const_inputs.find(op_input_arg.name()) != const_inputs.end()) { + VLOG(1) << "For const input, mapping op input " << i << " to func input " + << const_index; + (*op_input_to_func_input)[i] = const_index; + func_input_arg = signature->mutable_input_arg(const_index++); + const_input_types->push_back( + static_cast(op->inputs[i].dtype())); + } else if (op_input_arg.type() == tensorflow::DT_RESOURCE) { + VLOG(1) << "For resource input, mapping op input " << i + << " to func input " << resource_index; + (*op_input_to_func_input)[i] = resource_index; + func_input_arg = signature->mutable_input_arg(resource_index++); + } else { + VLOG(1) << "For arg input, mapping op input " << i << " to func input " + << arg_index; + (*op_input_to_func_input)[i] = arg_index; + func_input_arg = signature->mutable_input_arg(arg_index++); + arg_input_types->push_back( + static_cast(op->inputs[i].dtype())); + } + + func_input_arg->set_name(op_input_arg.name()); + func_input_arg->set_type(op->inputs[i].dtype()); + } + VLOG(1) << "Added OpDef Inputs: " << fdef.DebugString(); + + // Resources args are at the end of the function input params, and we should + // have iterated over all of them. + DCHECK_EQ(signature->input_arg_size(), resource_index); + + // Make the synthesized function's name unique. + signature->set_name(tensorflow::strings::StrCat( + op_def.name(), func_id_generator.fetch_add(1))); + + // Add the node def and set its input names to match op_def's names. + const tensorflow::NodeDef& ndef = op->attrs.BuildNodeDef(); + DCHECK_EQ(signature->input_arg_size(), ndef.input_size()); + *fdef.add_node_def() = ndef; + for (int i = 0; i < op_def.input_arg_size(); ++i) { + fdef.mutable_node_def(0)->set_input(i, op_def.input_arg(i).name()); + } + VLOG(1) << "Added NodeDef: " << fdef.DebugString(); + + // Fix the output names and set output types. + for (int i = 0; i < op_def.output_arg_size(); ++i) { + tensorflow::OpDef::ArgDef* arg = signature->add_output_arg(); + const tensorflow::OpDef::ArgDef& op_def_arg = op_def.output_arg(i); + const string& out_tensor_name = tensorflow::strings::StrCat( + ndef.name(), ":", op_def_arg.name(), ":", 0); + arg->set_name(op_def_arg.name()); + (*fdef.mutable_ret())[op_def_arg.name()] = out_tensor_name; + const string& type_attr = op_def_arg.type_attr(); + if (!type_attr.empty()) { + auto i = ndef.attr().find(type_attr); + if (i == ndef.attr().end()) { + status->status = tensorflow::errors::InvalidArgument( + tensorflow::strings::StrCat("Could not find attr ", type_attr, + " in NodeDef ", ndef.DebugString())); + return nullptr; + } + arg->set_type(i->second.type()); + } + } + VLOG(1) << "Fixed Output names and all types: " << fdef.DebugString(); + + tensorflow::mutex_lock l(ctx->functions_mu); + status->status = ctx->func_lib_def.AddFunctionDef(fdef); + if (!status->status.ok()) return nullptr; + const auto ret = ctx->func_lib_def.Find(signature->name()); + DCHECK(ret != nullptr); + return ret; +} + +// Builds an _XLALaunchOp as a wrapper over 'op', so that 'op' can be executed +// via XLA. +std::unique_ptr BuildXlaLaunch(TFE_Op* op, TF_Status* status) { + VLOG(1) << "Creating _XlaLaunchOp for TFE_Op " << op->name; + auto launch_op = + std::unique_ptr(TFE_NewOp(op->ctx, "_XlaLaunch", status)); + if (TF_GetCode(status) != TF_OK) return nullptr; + if (op->device) { + TFE_OpSetDevice(launch_op.get(), op->device->name().c_str(), status); + if (TF_GetCode(status) != TF_OK) return nullptr; + } + + const tensorflow::FunctionDef* fdef; + { + tensorflow::tf_shared_lock l(op->ctx->functions_mu); + fdef = op->ctx->func_lib_def.Find(op->name); + } + std::vector const_input_types; + std::vector arg_input_types; + tensorflow::gtl::FlatMap op_input_to_func_input; + if (fdef == nullptr) { + // See if this is a primitive op, and if so create a function for it, so + // that _XlaLaunchOp can access it. + fdef = OpToFunction(op, &const_input_types, &arg_input_types, + &op_input_to_func_input, status); + if (!status->status.ok()) return nullptr; + } else { + // TODO(hongm): XlaOpRegistry::CompileTimeConstantInputs() does not work for + // functions, so we need to find another way to handle constant inputs. + for (int i = const_input_types.size(); + i < fdef->signature().input_arg_size(); ++i) { + VLOG(1) << "Adding Targs from input arg " << i; + const tensorflow::OpDef::ArgDef& arg = fdef->signature().input_arg(i); + arg_input_types.push_back(static_cast(arg.type())); + } + } + DCHECK(fdef != nullptr); + + // Copy inputs and their devices. + // Since input param reordering may have occurred between `op` and `launch_op` + // via `op_input_to_func_input`, adjust the actual inputs accordingly. + launch_op->inputs = op->inputs; + launch_op->input_devices = op->input_devices; + launch_op->input_op_devices = op->input_op_devices; + if (!op_input_to_func_input.empty()) { + DCHECK_EQ(op->inputs.size(), op_input_to_func_input.size()); + if (!op->input_devices.empty()) { + DCHECK_EQ(op->input_devices.size(), op_input_to_func_input.size()); + } + for (int i = 0; i < op_input_to_func_input.size(); ++i) { + VLOG(1) << "mapping op input " << i << " to func input " + << op_input_to_func_input[i]; + + launch_op->inputs[op_input_to_func_input[i]] = op->inputs[i]; + if (!op->input_devices.empty()) { + launch_op->input_devices[op_input_to_func_input[i]] = + op->input_devices[i]; + } + } + } + launch_op->attrs.NumInputs(op->inputs.size()); + + TFE_OpSetAttrTypeList(launch_op.get(), "Tconstants", const_input_types.data(), + const_input_types.size()); + + // Set Targs and Nresources attrs. + TFE_OpSetAttrTypeList(launch_op.get(), "Targs", arg_input_types.data(), + arg_input_types.size()); + const int num_resource_inputs = fdef->signature().input_arg_size() - + const_input_types.size() - + arg_input_types.size(); + TFE_OpSetAttrInt(launch_op.get(), "Nresources", num_resource_inputs); + + // Set Tresults attr. + std::vector tresults; + for (const tensorflow::OpDef::ArgDef& arg : fdef->signature().output_arg()) { + tresults.push_back(static_cast(arg.type())); + } + TFE_OpSetAttrTypeList(launch_op.get(), "Tresults", tresults.data(), + tresults.size()); + + // Set function attr. + tensorflow::AttrValue attr_value; + tensorflow::NameAttrList* func = attr_value.mutable_func(); + func->set_name(fdef->signature().name()); + launch_op->attrs.Set("function", attr_value); + + return launch_op; +} +#endif // TENSORFLOW_EAGER_USE_XLA + +tensorflow::Device* SelectDevice(const tensorflow::NodeDef& ndef, + TFE_Context* ctx, TF_Status* status) { + tensorflow::DeviceSet ds; + for (tensorflow::Device* d : ctx->devices) { + ds.AddDevice(d); + } + tensorflow::DeviceTypeVector final_devices; + status->status = tensorflow::SupportedDeviceTypesForNode( + ds.PrioritizedDeviceTypeList(), ndef, &final_devices); + if (!status->status.ok()) { + return nullptr; + } + if (final_devices.empty()) { + status->status = tensorflow::errors::Internal( + "Could not find valid device for node ", ndef.DebugString()); + return nullptr; + } + for (tensorflow::Device* d : ctx->devices) { + if (d->device_type() == final_devices[0].type_string()) { + return d; + } + } + status->status = tensorflow::errors::Unknown( + "Could not find a device for node ", ndef.DebugString()); + return nullptr; +} + } // namespace void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, TF_Status* status) { +#ifdef TENSORFLOW_EAGER_USE_XLA + std::unique_ptr xla_launch_op; + if (op->use_xla && op->name != "_XlaLaunch") { + xla_launch_op = BuildXlaLaunch(op, status); + if (!status->status.ok()) { + return; + } + op = xla_launch_op.get(); + } +#endif // TENSORFLOW_EAGER_USE_XLA TFE_Context* ctx = op->ctx; - // TODO(ashankar): ASSUMPTION: ctx->devices()[0] is always CPU - tensorflow::Device* device = - (op->device == nullptr) ? ctx->devices()[0] : op->device; + tensorflow::Device* device = op->device; + // Ensure all resource-touching ops run in the device the resource is, + // regardless of anything else that has been specified. This is identical to + // the graph mode behavior. + for (int i = 0; i < op->inputs.size(); ++i) { + if (op->inputs[i].dtype() == tensorflow::DT_RESOURCE && + op->input_op_devices[i] != device) { + tensorflow::Device* d = op->input_op_devices[i] == nullptr + ? ctx->devices[0] + : op->input_op_devices[i]; + VLOG(1) << "Changing device of operation " << op->name << " to " + << d->name() << " because input #" << i + << " is a resource in this device."; + device = d; + op->device = d; + } + } + if (!ctx->soft_placement && device == nullptr) { + // TODO(ashankar): ASSUMPTION: ctx->devices[0] is always CPU + device = ctx->devices[0]; + } + std::vector outputs(1); const tensorflow::MemoryTypeVector* output_memory_types = nullptr; - tensorflow::Fprint128 cache_key = op->attrs.CacheKey(device->name()); + tensorflow::Fprint128 cache_key = + op->attrs.CacheKey(device == nullptr ? "unspecified" : device->name()); tensorflow::KernelAndDevice* kernel; { tensorflow::tf_shared_lock l(ctx->cache_mu); @@ -529,6 +861,17 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, } if (kernel == nullptr) { const tensorflow::NodeDef& ndef = op->attrs.BuildNodeDef(); + if (ctx->soft_placement && device == nullptr) { + device = SelectDevice(ndef, ctx, status); + if (!status->status.ok()) { + return; + } + } + CHECK(device != nullptr); + if (ctx->log_device_placement) { + LOG(INFO) << "Executing op " << ndef.op() << " in device " + << device->name(); + } kernel = new tensorflow::KernelAndDevice(ctx->rendezvous); // Knowledge of the implementation of Init (and in-turn // FunctionLibraryRuntime::CreateKernel) tells us that ctx->func_lib_def @@ -542,12 +885,37 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, delete kernel; return; } + // Update output_dtypes inside `kernel`. + const tensorflow::OpDef* op_def = nullptr; + const tensorflow::FunctionDef* function_def = + ctx->func_lib_def.Find(ndef.op()); + if (function_def != nullptr) { + op_def = &(function_def->signature()); + } + if (op_def == nullptr) { + status->status = OpDefForOp(ndef.op().c_str(), &op_def); + if (!status->status.ok()) { + return; + } + } + tensorflow::DataTypeVector input_dtypes; + status->status = InOutTypesForNode(ndef, *op_def, &input_dtypes, + kernel->output_dtypes()); + if (!status->status.ok()) { + return; + } tensorflow::mutex_lock ml(ctx->cache_mu); tensorflow::gtl::InsertOrUpdate(&(ctx->kernel_cache), cache_key, kernel); } + if (device == nullptr) { + // TODO(apassos) debug how the assignment below might return a different + // device from the one requested above. + device = kernel->device(); + } + std::vector copied_tensors; status->status = ValidateInputTypeAndPlacement( - ctx, ctx->devices()[0], device, op, kernel->kernel(), &copied_tensors); + ctx, ctx->devices[0], device, op, kernel->kernel(), &copied_tensors); output_memory_types = &kernel->kernel()->output_memory_types(); if (!status->status.ok()) { for (auto* t : copied_tensors) { @@ -586,13 +954,13 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, auto* step_stats = ctx->run_metadata.mutable_step_stats(); // Lazily initialize the RunMetadata with information about all devices if // this is the first call. - while (step_stats->dev_stats_size() < ctx->devices().size()) { + while (step_stats->dev_stats_size() < ctx->devices.size()) { step_stats->add_dev_stats(); } // Find the current device's index. int device_idx = 0; - for (int i = 0; i < ctx->devices().size(); ++i) { - if (ctx->devices()[i] == device) { + for (int i = 0; i < ctx->devices.size(); ++i) { + if (ctx->devices[i] == device) { device_idx = i; break; } @@ -610,7 +978,7 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, (*output_memory_types)[i] == tensorflow::HOST_MEMORY) { d = nullptr; } - retvals[i] = new TFE_TensorHandle(outputs[i], d); + retvals[i] = new TFE_TensorHandle(outputs[i], d, device); } } @@ -636,7 +1004,7 @@ void TFE_ContextAddFunction(TFE_Context* ctx, TF_Function* function, } // extern "C" TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t) { - return new TFE_TensorHandle(t, nullptr); + return new TFE_TensorHandle(t, nullptr, nullptr); } const tensorflow::Tensor* TFE_TensorHandleUnderlyingTensorInHostMemory( diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 6a2aff1591d551d4859ef9686604680923802101..9610ca1b3bd6c0a77268709abaa9f899d476bde9 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -61,7 +61,8 @@ TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig( // Controls how to act when we try to run an operation on a given device but // some input tensors are not on that device. typedef enum TFE_ContextDevicePlacementPolicy { - // Running operations with input tensors on the wrong device will fail. + // Running operations with input tensors on the wrong device will fail. When + // soft placement is enabled acts like TFE_DEVICE_PLACEMENT_SILENT. TFE_DEVICE_PLACEMENT_EXPLICIT = 0, // Copy the tensor to the right device but log a warning. TFE_DEVICE_PLACEMENT_WARN = 1, @@ -69,7 +70,8 @@ typedef enum TFE_ContextDevicePlacementPolicy { // operation will be blocked till the copy completes. TFE_DEVICE_PLACEMENT_SILENT = 2, // Default placement policy which silently copies int32 tensors but not other - // dtypes. + // dtypes. When soft placement is enabled acts like + // TFE_DEVICE_PLACEMENT_SILENT. TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3, } TFE_ContextDevicePlacementPolicy; @@ -87,7 +89,8 @@ typedef struct TFE_Context TFE_Context; TF_CAPI_EXPORT extern TFE_Context* TFE_NewContext( const TFE_ContextOptions* opts, TF_Status* status); -TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status); +TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx, + TF_Status* status); TF_CAPI_EXPORT extern TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status); @@ -118,9 +121,13 @@ TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status); TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h); TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h); -TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h); -TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index); -TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h); +TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h, + TF_Status* status); +TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, + int dim_index, + TF_Status* status); +TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName( + TFE_TensorHandle* h, TF_Status* status); TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status); @@ -130,10 +137,9 @@ TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, // that shares the underlying buffer. Otherwise, it currently requires at least // one of the source or destination devices to be CPU (i.e., for the source or // destination tensor to be placed in host memory). -TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, - TFE_Context* ctx, - const char* device_name, - TF_Status* status); +TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice( + TFE_TensorHandle* h, TFE_Context* ctx, const char* device_name, + TF_Status* status); // Description of the TensorFlow op to execute. // @@ -148,7 +154,8 @@ TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorH // the additional sanity checks there seem unnecessary; typedef struct TFE_Op TFE_Op; -TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name, +TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx, + const char* op_or_function_name, TF_Status* status); TF_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op); @@ -158,10 +165,20 @@ TF_CAPI_EXPORT extern void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_CAPI_EXPORT extern const char* TFE_OpGetDevice(TFE_Op* op, TF_Status* status); -TF_CAPI_EXPORT extern void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status); +// When 'enable' is set to 1, and if TensorFlow library is built with XLA +// support, a subsequent TFE_Execute() call on `op` will run the op via XLA. +// +// If the library is not built with XLA support, this call would be a no-op. +TF_CAPI_EXPORT extern void TFE_OpSetXLACompilation(TFE_Op* op, + unsigned char enable); -TF_CAPI_EXPORT extern TF_AttrType TFE_OpGetAttrType(TFE_Op* op, const char* attr_name, - unsigned char* is_list, TF_Status* status); +TF_CAPI_EXPORT extern void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, + TF_Status* status); + +TF_CAPI_EXPORT extern TF_AttrType TFE_OpGetAttrType(TFE_Op* op, + const char* attr_name, + unsigned char* is_list, + TF_Status* status); // Get an attribute type given an op name; a fusion of TFE_NewOp and // TFE_OpGetAttrType for use from Python without the overhead of the individual // calls and memory management of TFE_Op. @@ -169,10 +186,13 @@ TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType( TFE_Context* ctx, const char* op_or_function_name, const char* attr_name, unsigned char* is_list, TF_Status* status); -TF_CAPI_EXPORT extern void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, +TF_CAPI_EXPORT extern void TFE_OpSetAttrString(TFE_Op* op, + const char* attr_name, const char* value); -TF_CAPI_EXPORT extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value); -TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value); +TF_CAPI_EXPORT extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, + int64_t value); +TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, + float value); TF_CAPI_EXPORT extern void TFE_OpSetAttrBool(TFE_Op* op, const char* attr_name, unsigned char value); TF_CAPI_EXPORT extern void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name, @@ -181,7 +201,8 @@ TF_CAPI_EXPORT extern void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name, // -1 and `dims` can be null. If a dimension is unknown, the // corresponding entry in the `dims` array must be -1. TF_CAPI_EXPORT extern void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name, - const int64_t* dims, const int num_dims, + const int64_t* dims, + const int num_dims, TF_Status* out_status); // Sets the attribute attr_name to be a function specified by 'function'. @@ -192,19 +213,33 @@ TF_CAPI_EXPORT extern void TFE_OpSetAttrFunction(TFE_Op* op, const char* attr_name, const TFE_Op* value); -TF_CAPI_EXPORT extern void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name, - const char** value, int num_values); -TF_CAPI_EXPORT extern void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name, - const int64_t* values, int num_values); -TF_CAPI_EXPORT extern void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name, - const float* values, int num_values); -TF_CAPI_EXPORT extern void TFE_OpSetAttrBoolList(TFE_Op* op, const char* attr_name, - const unsigned char* values, int num_values); -TF_CAPI_EXPORT extern void TFE_OpSetAttrTypeList(TFE_Op* op, const char* attr_name, - const TF_DataType* values, int num_values); -TF_CAPI_EXPORT extern void TFE_OpSetAttrShapeList(TFE_Op* op, const char* attr_name, - const int64_t** dims, const int* num_dims, - int num_values, TF_Status* out_status); +TF_CAPI_EXPORT extern void TFE_OpSetAttrStringList(TFE_Op* op, + const char* attr_name, + const char** value, + int num_values); +TF_CAPI_EXPORT extern void TFE_OpSetAttrIntList(TFE_Op* op, + const char* attr_name, + const int64_t* values, + int num_values); +TF_CAPI_EXPORT extern void TFE_OpSetAttrFloatList(TFE_Op* op, + const char* attr_name, + const float* values, + int num_values); +TF_CAPI_EXPORT extern void TFE_OpSetAttrBoolList(TFE_Op* op, + const char* attr_name, + const unsigned char* values, + int num_values); +TF_CAPI_EXPORT extern void TFE_OpSetAttrTypeList(TFE_Op* op, + const char* attr_name, + const TF_DataType* values, + int num_values); +TF_CAPI_EXPORT extern void TFE_OpSetAttrShapeList( + TFE_Op* op, const char* attr_name, const int64_t** dims, + const int* num_dims, int num_values, TF_Status* out_status); +TF_CAPI_EXPORT extern void TFE_OpSetAttrFunctionList(TFE_Op* op, + const char* attr_name, + const TFE_Op** value, + int num_values); // Execute the operation defined by 'op' and return handles to computed // tensors in 'retvals'. @@ -219,9 +254,9 @@ TF_CAPI_EXPORT extern void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, // Add a function (serialized FunctionDef protocol buffer) to ctx so // that it can be invoked using TFE_Execute. -TF_CAPI_EXPORT extern void TFE_ContextAddFunctionDef(TFE_Context* ctx, - const char* serialized_function_def, - size_t size, TF_Status* status); +TF_CAPI_EXPORT extern void TFE_ContextAddFunctionDef( + TFE_Context* ctx, const char* serialized_function_def, size_t size, + TF_Status* status); // Adds a function (created from TF_GraphToFunction or // TF_FunctionImportFunctionDef) to the context, allowing it to be executed with diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h index f2abffb7bc042ffb6d0c5f360fe96588c61ff176..f701f3483e2d1bef1ccdac2e98fd870befa233d4 100644 --- a/tensorflow/c/eager/c_api_internal.h +++ b/tensorflow/c/eager/c_api_internal.h @@ -43,15 +43,26 @@ struct TFE_ContextOptions { TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32}; }; +TFE_ContextDevicePlacementPolicy PlacementPolicy( + bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy); + struct TFE_Context { - explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s) - : policy(opts.policy), - session(s), - rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)), + explicit TFE_Context(const TFE_ContextOptions& opts, + std::unique_ptr device_mgr) + : soft_placement( + opts.session_options.options.config.allow_soft_placement()), + policy(PlacementPolicy(soft_placement, opts.policy)), + device_manager(std::move(device_mgr)), + devices(device_manager->ListDevices()), + rendezvous( + new tensorflow::IntraProcessRendezvous(device_manager.get())), pflr(new tensorflow::ProcessFunctionLibraryRuntime( - session->device_mgr, opts.session_options.options.env, - TF_GRAPH_DEF_VERSION, &func_lib_def, {})) {} + device_manager.get(), opts.session_options.options.env, + TF_GRAPH_DEF_VERSION, &func_lib_def, {})), + log_device_placement( + opts.session_options.options.config.log_device_placement()) {} + const bool soft_placement; const TFE_ContextDevicePlacementPolicy policy; // Note: we cannot use C++11 thread_local here as there is no concept of a @@ -60,8 +71,9 @@ struct TFE_Context { std::unordered_map thread_local_policies GUARDED_BY(policy_map_mu); - // TFE_Context is an extension of TF_Session. And TF_Session needs a TF_Graph. - TF_Session* const session; + std::unique_ptr device_manager; + // Devices owned by device_manager + const std::vector devices; tensorflow::Rendezvous* const rendezvous; tensorflow::mutex functions_mu; @@ -82,17 +94,18 @@ struct TFE_Context { return pflr->GetFLR(d->name()); } - const std::vector& devices() { return session->devices; } - // Whether we should compute RunMetadata. std::atomic should_store_metadata{false}; tensorflow::mutex metadata_mu; tensorflow::RunMetadata run_metadata GUARDED_BY(metadata_mu); + + const bool log_device_placement; }; struct TFE_TensorHandle { - TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d) - : t(t), d(d) {} + TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d, + tensorflow::Device* op_device) + : t(t), d(d), op_device(op_device) {} tensorflow::Tensor t; // TODO(ashankar): d == nullptr iff local CPU @@ -104,6 +117,10 @@ struct TFE_TensorHandle { // TODO(ashankar): Reference count TFE_Context to ensure that 'd' of a // TFE_TensorHandle does not outlive the TFE_Context from which it came? tensorflow::Device* d; + + // Device in which the op producing this tensor was executed. Equals to d for + // constant tensors. + tensorflow::Device* op_device; }; struct TFE_Op { @@ -120,7 +137,9 @@ struct TFE_Op { const tensorflow::AttrTypeMap* attr_types; std::vector inputs; std::vector input_devices; + std::vector input_op_devices; tensorflow::Device* device; + bool use_xla = false; }; #endif // TENSORFLOW_C_EAGER_C_API_INTERNAL_H_ diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index b0409af87c25b7eb106a69260f9f0b4c30317490..00fb7e68d00dd2ef316bf89b8f253cf6c7c63f00 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -60,6 +60,38 @@ TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) { return op; } +TFE_TensorHandle* TestAxisTensorHandle() { + int64_t dims[] = {1}; + int data[] = {1}; + TF_Tensor* t = TF_AllocateTensor( + TF_INT32, &dims[0], sizeof(dims) / sizeof(int64_t), sizeof(data)); + memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t)); + TF_Status* status = TF_NewStatus(); + TFE_TensorHandle* th = TFE_NewTensorHandle(t, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeleteTensor(t); + TF_DeleteStatus(status); + return th; +} + +TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input, + TFE_TensorHandle* axis) { + TF_Status* status = TF_NewStatus(); + + TFE_Op* op = TFE_NewOp(ctx, "Min", status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_OpAddInput(op, input, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_OpAddInput(op, axis, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_OpSetAttrBool(op, "keep_dims", 1); + TFE_OpSetAttrType(op, "Tidx", TF_INT32); + TF_DeleteStatus(status); + TFE_OpSetAttrType(op, "T", TFE_TensorHandleDataType(input)); + + return op; +} + // If there is a GPU device, returns true and sets 'gpu_device_name' // accordingly. bool GetGPUDeviceName(TFE_Context* ctx, string* gpu_device_name) { @@ -410,7 +442,7 @@ TEST(CAPI, SetAndGetOpDevices) { TF_DeleteStatus(status); } -TEST(CAPI, Execute) { +TEST(CAPI, Execute_MatMul_CPU) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); TFE_Context* ctx = TFE_NewContext(opts, status); @@ -443,6 +475,117 @@ TEST(CAPI, Execute) { TF_DeleteStatus(status); } +TEST(CAPI, Execute_Min_CPU) { + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_Context* ctx = TFE_NewContext(opts, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* input = TestMatrixTensorHandle(); + TFE_TensorHandle* axis = TestAxisTensorHandle(); + TFE_Op* minOp = MinOp(ctx, input, axis); + TFE_TensorHandle* retvals[2] = {nullptr}; + int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_Execute(minOp, &retvals[0], &num_retvals, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteOp(minOp); + TFE_DeleteTensorHandle(input); + TFE_DeleteTensorHandle(axis); + TFE_DeleteContext(ctx, status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + ASSERT_EQ(1, num_retvals); + + TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + TFE_DeleteTensorHandle(retvals[0]); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + float output[2] = {0}; + EXPECT_EQ(sizeof(output), TF_TensorByteSize(t)); + memcpy(&output[0], TF_TensorData(t), TF_TensorByteSize(t)); + TF_DeleteTensor(t); + EXPECT_EQ(1, output[0]); + EXPECT_EQ(3, output[1]); + TF_DeleteStatus(status); +} + +#ifdef TENSORFLOW_EAGER_USE_XLA +TEST(CAPI, Execute_MatMul_XLA_CPU) { + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_Context* ctx = TFE_NewContext(opts, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* m = TestMatrixTensorHandle(); + TFE_Op* matmul = MatMulOp(ctx, m, m); + + TFE_OpSetXLACompilation(matmul, true); + + TFE_TensorHandle* retvals[2] = {nullptr}; + int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_Execute(matmul, &retvals[0], &num_retvals, status); + // Running a primitive TF operator via XLA is not yet supported. + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + TFE_DeleteOp(matmul); + TFE_DeleteTensorHandle(m); + TFE_DeleteContext(ctx, status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + EXPECT_EQ(1, num_retvals); + + TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + TFE_DeleteTensorHandle(retvals[0]); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + float product[4] = {0}; + EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); + memcpy(&product[0], TF_TensorData(t), TF_TensorByteSize(t)); + TF_DeleteTensor(t); + EXPECT_EQ(7, product[0]); + EXPECT_EQ(10, product[1]); + EXPECT_EQ(15, product[2]); + EXPECT_EQ(22, product[3]); + + TF_DeleteStatus(status); +} + +TEST(CAPI, Execute_Min_XLA_CPU) { + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_Context* ctx = TFE_NewContext(opts, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* input = TestMatrixTensorHandle(); + TFE_TensorHandle* axis = TestAxisTensorHandle(); + TFE_Op* minOp = MinOp(ctx, input, axis); + + TFE_OpSetXLACompilation(minOp, true); + + TFE_TensorHandle* retvals[2] = {nullptr}; + int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_Execute(minOp, &retvals[0], &num_retvals, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteOp(minOp); + TFE_DeleteTensorHandle(input); + TFE_DeleteTensorHandle(axis); + TFE_DeleteContext(ctx, status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + ASSERT_EQ(1, num_retvals); + + TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + TFE_DeleteTensorHandle(retvals[0]); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + float output[2] = {0}; + EXPECT_EQ(sizeof(output), TF_TensorByteSize(t)); + memcpy(&output[0], TF_TensorData(t), TF_TensorByteSize(t)); + TF_DeleteTensor(t); + EXPECT_EQ(1, output[0]); + EXPECT_EQ(3, output[1]); + TF_DeleteStatus(status); +} +#endif // TENSORFLOW_EAGER_USE_XLA + TEST(CAPI, ExecuteWithTracing) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); @@ -484,7 +627,69 @@ TEST(CAPI, ExecuteWithTracing) { TF_DeleteStatus(status); } -TEST(CAPI, Function) { +TEST(CAPI, Function_ident_CPU) { + // First create a simple identity function. + TF_Graph* function_graph = TF_NewGraph(); + TF_OperationDescription* arg_descr = + TF_NewOperation(function_graph, "Placeholder", "arg"); + TF_SetAttrType(arg_descr, "dtype", TF_INT32); + TF_Status* status = TF_NewStatus(); + TF_Operation* arg = TF_FinishOperation(arg_descr, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_OperationDescription* id_descr = + TF_NewOperation(function_graph, "Identity", "id"); + TF_SetAttrType(id_descr, "T", TF_INT32); + TF_AddInput(id_descr, {arg, 0}); + TF_Operation* id = TF_FinishOperation(id_descr, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_Output input{arg, 0}; + TF_Output output{id, 0}; + TF_Function* fn = + TF_GraphToFunction(function_graph, "ident", 0, 1, &id, 1, &input, 1, + &output, nullptr, nullptr, "test", status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteGraph(function_graph); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_Context* ctx = TFE_NewContext(opts, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_DeleteContextOptions(opts); + TFE_ContextAddFunction(ctx, fn, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteFunction(fn); + + TF_Tensor* t = + TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32)); + *reinterpret_cast(TF_TensorData(t)) = 42; + TFE_TensorHandle* h = TFE_NewTensorHandle(t, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteTensor(t); + + TFE_Op* op = TFE_NewOp(ctx, "ident", status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_OpAddInput(op, h, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + + std::vector result; + result.push_back(nullptr); + int num_retvals = 1; + TFE_Execute(op, result.data(), &num_retvals, status); + TFE_DeleteOp(op); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + ASSERT_EQ(num_retvals, 1); + + TF_Tensor* r = TFE_TensorHandleResolve(result[0], status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + EXPECT_EQ(*reinterpret_cast(TF_TensorData(r)), 42); + TFE_DeleteTensorHandle(h); + TF_DeleteTensor(r); + TFE_DeleteTensorHandle(result[0]); + TFE_DeleteContext(ctx, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteStatus(status); +} + +#ifdef TENSORFLOW_EAGER_USE_XLA +TEST(CAPI, Function_ident_XLA_CPU) { // First create a simple identity function. TF_Graph* function_graph = TF_NewGraph(); TF_OperationDescription* arg_descr = @@ -526,6 +731,9 @@ TEST(CAPI, Function) { TFE_OpAddInput(op, h, status); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + // Now run it via XLA. + TFE_OpSetXLACompilation(op, true); + std::vector result; result.push_back(nullptr); int num_retvals = 1; @@ -544,6 +752,7 @@ TEST(CAPI, Function) { ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); } +#endif // TENSORFLOW_EAGER_USE_XLA string MatMulFunction() { tensorflow::FunctionDef def; @@ -723,7 +932,8 @@ TEST(CAPI, Variables) { ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); ASSERT_EQ(1, num_retvals); EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(value_handle)); - EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle)); + EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle, status)); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float value = 0.0f; TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); @@ -765,7 +975,8 @@ void BM_ReadVariable(int iters) { CHECK_EQ(1, num_retvals); CHECK(h); CHECK_EQ(TF_FLOAT, TFE_TensorHandleDataType(h)); - CHECK_EQ(0, TFE_TensorHandleNumDims(h)); + CHECK_EQ(0, TFE_TensorHandleNumDims(h, status)); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); h = nullptr; } tensorflow::testing::StopTiming(); diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc index 12abfcba2f02782172ab8b978487461770f44167..9b46cf8245901934c9c4d41a2b7c10c1c5bf7cbd 100644 --- a/tensorflow/c/eager/runtime.cc +++ b/tensorflow/c/eager/runtime.cc @@ -41,17 +41,26 @@ const uint32 kIsList = 1U << 31; } // namespace +Status OpDefForOp(const char* op_name, const OpDef** op_def) { + const OpRegistrationData* op_reg_data = nullptr; + Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data); + if (s.ok()) { + *op_def = &op_reg_data->op_def; + } + return s; +} + Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out) { mutex_lock l(g_op_name_to_attr_type_map_lock); *out = gtl::FindPtrOrNull(*OpNameToAttrTypeMap(), op_name); if (*out != nullptr) return Status::OK(); - const OpRegistrationData* op_reg_data = nullptr; - Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data); + const OpDef* op_def = nullptr; + Status s = OpDefForOp(op_name, &op_def); if (!s.ok()) return s; std::unique_ptr m(new AttrTypeMap); // TODO(agarwal): Avoid having to create this "registry" at runtime, // perhaps can be done at op registration time? - for (const auto& attr : op_reg_data->op_def.attr()) { + for (const auto& attr : op_def->attr()) { string type = attr.type(); const bool is_list = (type.length() > 6 && type.compare(0, 4, "list") == 0); if (is_list) { @@ -293,7 +302,18 @@ Status KernelAndDevice::Run(std::vector* input_tensors, params.runner = &runner; OpKernelContext context(¶ms); - device_->Compute(kernel_.get(), &context); + + if (kernel_->def().op() == "_Recv") { + // TODO(apassos) do not special-case _Recv. Currently the GPU device fails + // if trying to run _Recv->Compute(), specifically checking for _Recv. To go + // around this we call _Recv->ComputeAsync, to mimic graph mode behavior. + AsyncOpKernel* async = kernel_->AsAsync(); + Notification done; + device_->ComputeAsync(async, &context, [&done]() { done.Notify(); }); + done.WaitForNotification(); + } else { + device_->Compute(kernel_.get(), &context); + } if (!context.status().ok()) return context.status(); output_tensors->clear(); @@ -315,7 +335,7 @@ Status KernelAndDevice::Run(std::vector* input_tensors, allocator_pair.second->GetRecordsAndUnRef(); } auto* ms = stats->mutable_memory_stats(); - ms->set_temp_memory_size(context.temp_memory_size()); + ms->set_temp_memory_size(context.temp_memory_allocated()); for (const auto& alloc_id : context.persistent_alloc_ids()) { ms->mutable_persistent_tensor_alloc_ids()->Add(alloc_id); } diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h index 4d20b5244a46fcde2eed0a429dced2a77b86aedd..985ed96735ea578d738f36bddb6a70647e200906 100644 --- a/tensorflow/c/eager/runtime.h +++ b/tensorflow/c/eager/runtime.h @@ -39,6 +39,9 @@ namespace tensorflow { // represent the TF_AttrType type of the values in the list. typedef std::unordered_map AttrTypeMap; +// Look up OpDef for `op_name`. +Status OpDefForOp(const char* op_name, const OpDef** op_def); + // Returns the AttrTypeMap for the TensorFlow operation named op_name. Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out); @@ -180,12 +183,17 @@ class KernelAndDevice { const OpKernel* kernel() const { return kernel_.get(); } + Device* device() const { return device_; } + + DataTypeVector* output_dtypes() { return &output_dtypes_; } + private: std::unique_ptr kernel_; Device* device_; FunctionLibraryRuntime* flib_; checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_; Rendezvous* rendez_; + DataTypeVector output_dtypes_; }; } // namespace tensorflow diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc index 6e37cdb5f4beea53d4a2ded0705ae482d0bc2d68..26683f50ec5905a105e24c4165896102b085fded 100644 --- a/tensorflow/c/python_api.cc +++ b/tensorflow/c/python_api.cc @@ -99,4 +99,15 @@ void RemoveAllControlInputs(TF_Graph* graph, TF_Operation* op) { } } +void SetRequireShapeInferenceFns(TF_Graph* graph, bool require) { + mutex_lock l(graph->mu); + graph->refiner.set_require_shape_inference_fns(require); +} + +void ExtendSession(TF_Session* session, TF_Status* status) { + mutex_lock l(session->mu); + session->extend_before_run = false; + ExtendSessionGraphHelper(session, status); +} + } // namespace tensorflow diff --git a/tensorflow/c/python_api.h b/tensorflow/c/python_api.h index aa9d9e06b28c54cb8869eb547d36ee3cb0d4e6b8..13b680b3a24afa2d285ea18207578aff4350f6d5 100644 --- a/tensorflow/c/python_api.h +++ b/tensorflow/c/python_api.h @@ -37,6 +37,20 @@ void UpdateEdge(TF_Graph* graph, TF_Output new_src, TF_Input dst, void RemoveAllControlInputs(TF_Graph* graph, TF_Operation* op); +// Sets whether ops missing a shape inference function should trigger an +// error. The default is true. +void SetRequireShapeInferenceFns(TF_Graph* graph, bool require); + +// Extends `session` with any new operations added to its associated graph. +// Usually this happens automatically in TF_SessionRun. After this is called, +// TF_SessionRun will no longer extend the session on every call. +// +// We expose this here to allow fine-grained synchronization in multi-threaded +// workloads, which is required since the Python implementation depends on the +// above mutation methods. This allows us to prevent modifications to nodes in +// the graph after the session has been made aware of them. +void ExtendSession(TF_Session* session, TF_Status* status); + } // namespace tensorflow #endif // TENSORFLOW_C_PYTHON_API_H_ diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc index a40ad1ffc3b262840e6ca0043139b1b61e04510d..39893f5ccd50864f2a22956781a040368ffad27f 100644 --- a/tensorflow/cc/framework/cc_op_gen.cc +++ b/tensorflow/cc/framework/cc_op_gen.cc @@ -697,7 +697,8 @@ string OpInfo::GetOpAttrStruct() const { attr_comment = MakeComment(attr_comment, " "); strings::StrAppend(&setters, attr_comment); - strings::StrAppend(&setters, " Attrs ", attr_func_def, " x) {\n"); + strings::StrAppend(&setters, " TF_MUST_USE_RESULT Attrs ", attr_func_def, + " x) {\n"); strings::StrAppend(&setters, " Attrs ret = *this;\n"); strings::StrAppend(&setters, " ret.", api_def_attr.rename_to(), "_ = x;\n"); diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index 13a3bba5e6d5ca19ff3f0eca76665ba7d3ab628d..1c23f3257e1ea3898d7039c01434c2cf30efa935 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -48,8 +48,8 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op, REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad); Status LogSoftmaxGrad(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { + const std::vector& grad_inputs, + std::vector* grad_outputs) { auto softmax = Exp(scope, op.output(0)); auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true)); auto mul = Mul(scope, sum, softmax); @@ -107,11 +107,10 @@ Status BiasAddGradHelper(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { string data_format; - BiasAddGrad::Attrs input_attrs; TF_RETURN_IF_ERROR( GetNodeAttr(op.output(0).node()->attrs(), "data_format", &data_format)); - input_attrs.DataFormat(data_format); - auto dx_1 = BiasAddGrad(scope, grad_inputs[0], input_attrs); + auto dx_1 = + BiasAddGrad(scope, grad_inputs[0], BiasAddGrad::DataFormat(data_format)); grad_outputs->push_back(Identity(scope, grad_inputs[0])); grad_outputs->push_back(dx_1); return scope.status(); @@ -130,19 +129,16 @@ Status Conv2DGrad(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "use_cudnn_on_gpu", &use_cudnn_on_gpu)); - Conv2DBackpropInput::Attrs input_attrs; - input_attrs.DataFormat(data_format); - input_attrs.UseCudnnOnGpu(use_cudnn_on_gpu); - auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)), - op.input(1), grad_inputs[0], - strides, padding, input_attrs); + auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)), op.input(1), + grad_inputs[0], strides, padding, + Conv2DBackpropInput::DataFormat(data_format) + .UseCudnnOnGpu(use_cudnn_on_gpu)); grad_outputs->push_back(dx_1); - Conv2DBackpropFilter::Attrs filter_attrs; - filter_attrs.DataFormat(data_format); - filter_attrs.UseCudnnOnGpu(use_cudnn_on_gpu); - auto dx_2 = Conv2DBackpropFilter(scope, op.input(0), - Shape(scope, op.input(1)), grad_inputs[0], - strides, padding, filter_attrs); + auto dx_2 = + Conv2DBackpropFilter(scope, op.input(0), Shape(scope, op.input(1)), + grad_inputs[0], strides, padding, + Conv2DBackpropFilter::DataFormat(data_format) + .UseCudnnOnGpu(use_cudnn_on_gpu)); grad_outputs->push_back(dx_2); return scope.status(); } @@ -160,13 +156,9 @@ Status MaxPoolGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); - internal::MaxPoolGrad::Attrs grad_attrs; - grad_attrs.DataFormat(data_format); - auto dx = internal::MaxPoolGrad(scope, op.input(0), - op.output(0), - grad_inputs[0], - ksize, strides, - padding, grad_attrs); + auto dx = internal::MaxPoolGrad( + scope, op.input(0), op.output(0), grad_inputs[0], ksize, strides, padding, + internal::MaxPoolGrad::DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -180,15 +172,9 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op, auto attrs = op.output(0).node()->attrs(); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); - MaxPoolGradV2::Attrs grad_attrs; - grad_attrs.DataFormat(data_format); - auto dx = MaxPoolGradV2(scope, op.input(0), - op.output(0), - grad_inputs[0], - op.input(1), - op.input(2), - padding, - grad_attrs); + auto dx = MaxPoolGradV2(scope, op.input(0), op.output(0), grad_inputs[0], + op.input(1), op.input(2), padding, + MaxPoolGradV2::DataFormat(data_format)); grad_outputs->push_back(dx); grad_outputs->push_back(NoGradient()); grad_outputs->push_back(NoGradient()); @@ -196,13 +182,74 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper); +Status MaxPool3DGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + MaxPool3DGrad::Attrs grad_attrs; + grad_attrs.DataFormat(data_format); + auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], + ksize, strides, padding, grad_attrs); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("MaxPool3D", MaxPool3DGradHelper); + +Status AvgPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + internal::AvgPoolGrad::Attrs grad_attrs; + grad_attrs.DataFormat(data_format); + auto dx = + internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], + ksize, strides, padding, grad_attrs); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("AvgPool", AvgPoolGradHelper); + +Status AvgPool3DGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + AvgPool3DGrad::Attrs grad_attrs; + grad_attrs.DataFormat(data_format); + auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], + ksize, strides, padding, grad_attrs); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("AvgPool3D", AvgPool3DGradHelper); + Status LRNGradHelper(const Scope& scope, const Operation& op, const std::vector& grad_inputs, - std::vector* grad_outputs){ - internal::LRNGrad::Attrs grad_attrs; - - auto dx = internal::LRNGrad(scope, grad_inputs[0], op.input(0), op.output(0), - grad_attrs); + std::vector* grad_outputs) { + auto dx = internal::LRNGrad(scope, grad_inputs[0], op.input(0), op.output(0)); grad_outputs->push_back(dx); return scope.status(); } diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index 0cfe5f6e3c49f7c4a3cafbf48ff4e54a0ffd0d47..c4eba7ecb017fe4628140d75a63bc7f0f09deb7f 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -31,8 +31,11 @@ using ops::Elu; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; +using ops::AvgPool; +using ops::AvgPool3D; using ops::MaxPool; using ops::MaxPoolV2; +using ops::MaxPool3D; using ops::Placeholder; using ops::Relu; using ops::Relu6; @@ -70,9 +73,9 @@ class NNGradTest : public ::testing::Test { // Sets tensor with random values, ensuring that the max value is largest by // a reasonable amount. - // This is an issue for MaxPool and MaxPoolV2, in which perturbations by the - // numeric gradient computation in the gradient checker can change the max - // value if values are too close together. + // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which + // perturbations by the numeric gradient computation in the gradient checker + // can change the max value if values are too close together. template void SetRandomValuesWithBumpedMax(Tensor* tensor) { auto tensor_flat = tensor->flat(); @@ -203,6 +206,41 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { RunTest(x, x_init_value, y, y_shape); } +TEST_F(NNGradTest, MaxPool3DGradHelper) { + TensorShape x_shape({1, 3, 3, 3, 1}); + TensorShape y_shape({1, 1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one MaxPool3D. + const std::vector ksize{1, 3, 3, 3, 1}; + const std::vector strides{1, 3, 3, 3, 1}; + auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); + Tensor x_init_value = Tensor(DT_FLOAT, x_shape); + SetRandomValuesWithBumpedMax(&x_init_value); + RunTest(x, x_init_value, y, y_shape); +} + +TEST_F(NNGradTest, AvgPoolGradHelper) { + TensorShape x_shape({1, 2, 2, 1}); + TensorShape y_shape({1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one AvgPool. + const std::vector ksize{1, 2, 2, 1}; + const std::vector strides{1, 2, 2, 1}; + auto y = AvgPool(scope_, x, ksize, strides, "SAME"); + RunTest(x, x_shape, y, y_shape); +} + +TEST_F(NNGradTest, AvgPool3DGradHelper) { + TensorShape x_shape({1, 3, 3, 3, 1}); + TensorShape y_shape({1, 1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one AvgPool3D. + const std::vector ksize{1, 3, 3, 3, 1}; + const std::vector strides{1, 3, 3, 3, 1}; + auto y = AvgPool3D(scope_, x, ksize, strides, "SAME"); + RunTest(x, x_shape, y, y_shape); +} + TEST_F(NNGradTest, LRN){ TensorShape x_shape({1, 1, 2, 1}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); diff --git a/tensorflow/cc/profiler/profiler.h b/tensorflow/cc/profiler/profiler.h index 6077c45c5854fd5812ccb7c91522f93ed4e54883..64edbb5766c3604fbe0f15c2299843718381aa3f 100644 --- a/tensorflow/cc/profiler/profiler.h +++ b/tensorflow/cc/profiler/profiler.h @@ -61,18 +61,18 @@ class Profiler { /// Adds tracing information `run_meta` to profiler. A `run_meta` is /// generated by a TensorFlow session run call. `step` is the key /// to the `run_meta`. When calling ProfileXXX methods, caller can specify - /// `step` in `options` to seletively profile the corresponding `run_meta`. + /// `step` in `options` to selectively profile the corresponding `run_meta`. /// Multiple different `run_meta` can be keyed by the same `step` in order /// to group them together. void AddStep(int64 step, const RunMetadata& run_meta); /// Profiles the model by organizing nodes in graph structure. - /// Each node is an op and the nodes are contected by the op inputs/outputs. + /// Each node is an op and the nodes are connected by the op inputs/outputs. GraphNodeProto ProfileGraph(const Options& options); /// Profiles the model by organizing nodes in name scope structure. /// Each node is an op, and nodes are organized by the ops' name - /// scope, similar to a filesystem tree. + /// scope, similar to a file system tree. /// E.g. /foo is the root of operation /foo/matmul_1 and foo/conv_2. GraphNodeProto ProfileNameScope(const Options& options); diff --git a/tensorflow/cc/tools/BUILD b/tensorflow/cc/tools/BUILD index 97f66e79b8ad9f383b22f56e9385fc6d2080e1f8..f413a5cc52e9eb4bc393b8186f5b591681fa2e5e 100644 --- a/tensorflow/cc/tools/BUILD +++ b/tensorflow/cc/tools/BUILD @@ -32,6 +32,7 @@ tf_cc_test( deps = [ ":freeze_saved_model", "//tensorflow/cc:cc_ops", + "//tensorflow/cc:resource_variable_ops", "//tensorflow/core:core_cpu", "//tensorflow/core:framework_internal", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/cc/tools/freeze_saved_model.cc b/tensorflow/cc/tools/freeze_saved_model.cc index ddf372cdef21e1b3892c9a03714478d5a5785517..4ddddcb5863c9ffb1e5367db750b0d2ffd29cd5e 100644 --- a/tensorflow/cc/tools/freeze_saved_model.cc +++ b/tensorflow/cc/tools/freeze_saved_model.cc @@ -75,16 +75,13 @@ void GetNodeNameToNodeDefMap( // variable nodes to convert. void GetReachableNodesAndVariables( GraphDef* graph_def, const std::unordered_set& outputs, + const std::unordered_map& name_to_node_map, std::unordered_set* reachable_node_names, std::unordered_set* variable_node_names) { // TODO(suharshs): Add support for ResourceVariables. static const std::unordered_set* kVariableTypes = - new std::unordered_set({"Variable", "VariableV2"}); - // name_to_node_map is needed to get the inputs from the NodeDef corresponding - // the a string node name. These inputs are used when doing our backwards - // traversal. - std::unordered_map name_to_node_map; - GetNodeNameToNodeDefMap(graph_def, &name_to_node_map); + new std::unordered_set({"Variable", "VariableV2", "VarHandleOp"}); + std::queue nodes_to_visit; for (const string& tensor_name : outputs) { // We need to strip off the tensor part to get the node name. @@ -99,7 +96,7 @@ void GetReachableNodesAndVariables( continue; } reachable_node_names->insert(node_name); - NodeDef* node = name_to_node_map[node_name]; + NodeDef* node = name_to_node_map.at(node_name); if (kVariableTypes->find(node->op()) != kVariableTypes->end()) { variable_node_names->insert(node->name()); } @@ -111,7 +108,9 @@ void GetReachableNodesAndVariables( // Gets a map from variable name to variable value. Status GetVariableNameToTensorMap( - Session* session, std::unordered_set variable_names_set, + Session* session, + const std::unordered_map& name_to_node_map, + std::unordered_set variable_names_set, std::unordered_map* variable_name_to_value_map) { if (variable_names_set.empty()) { return Status::OK(); @@ -120,8 +119,14 @@ Status GetVariableNameToTensorMap( std::vector tensor_names; for (const string& node_name : variable_names_set) { variable_names.push_back(node_name); - // We need to run tensors, so append ":0". - tensor_names.push_back(node_name + ":0"); + NodeDef* node_def = name_to_node_map.at(node_name); + if (node_def->op() == "VarHandleOp") { + // If this is a resource variable, we have to run the corresponding + // ReadVariableOp. + tensor_names.push_back(node_name + "/Read/ReadVariableOp:0"); + } else { + tensor_names.push_back(node_name + ":0"); + } } std::vector outputs; TF_RETURN_IF_ERROR( @@ -143,6 +148,15 @@ void ConvertVariableToConstant(const NodeDef& variable_node, (*const_node->mutable_attr())["value"].mutable_tensor()); } +// Converts a ReadVariableOp NodeDef to an Identity NodeDef. +void ConvertReadVariableOpToIdentity(const NodeDef& node, + NodeDef* identity_node) { + identity_node->set_name(node.name()); + identity_node->set_op("Identity"); + (*identity_node->mutable_attr())["T"] = node.attr().at("dtype"); + identity_node->add_input(node.input(0)); +} + // Freezes the subgraph of all nodes needed by `outputs`. Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, const std::unordered_set& outputs, @@ -155,14 +169,19 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, if (graph_def.node_size() == 0) { return Status::OK(); } + // name_to_node_map is needed to get the inputs from the NodeDef corresponding + // the a string node name. These inputs are used when doing our backwards + // traversal. + std::unordered_map name_to_node_map; + GetNodeNameToNodeDefMap(&graph_def, &name_to_node_map); std::unordered_set reachable_node_names; std::unordered_set variable_node_names; - GetReachableNodesAndVariables(&graph_def, outputs, &reachable_node_names, - &variable_node_names); + GetReachableNodesAndVariables(&graph_def, outputs, name_to_node_map, + &reachable_node_names, &variable_node_names); std::unordered_map variable_to_value_map; - TF_RETURN_IF_ERROR( - GetVariableNameToTensorMap(saved_model_bundle.session.get(), - variable_node_names, &variable_to_value_map)); + TF_RETURN_IF_ERROR(GetVariableNameToTensorMap( + saved_model_bundle.session.get(), name_to_node_map, variable_node_names, + &variable_to_value_map)); // We copy the nodes in the same order they were in the original graph_def. for (const NodeDef& node : graph_def.node()) { if (reachable_node_names.find(node.name()) == reachable_node_names.end()) { @@ -171,6 +190,12 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, if (variable_node_names.find(node.name()) != variable_node_names.end()) { ConvertVariableToConstant(node, variable_to_value_map[node.name()], frozen_graph_def->add_node()); + } else if (node.op() == "ReadVariableOp" && + variable_node_names.find(node.input(0)) != + variable_node_names.end()) { + // If the node is a ReadVariableOp, its input VarHandleOp will be + // converted to a Constant, so we will need to convert it to an Identity. + ConvertReadVariableOpToIdentity(node, frozen_graph_def->add_node()); } else { // If the node isn't a variable, just copy the node as-is. *frozen_graph_def->add_node() = node; diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc index 52a81a50284aec36bba4e56a0232c886cb0cb6cf..cd35fd3b95deec669218cfa4f25fea2c3ac9e56e 100644 --- a/tensorflow/cc/tools/freeze_saved_model_test.cc +++ b/tensorflow/cc/tools/freeze_saved_model_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/cc/tools/freeze_saved_model.h" +#include "tensorflow/cc/ops/resource_variable_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/function_testlib.h" #include "tensorflow/core/framework/graph.pb.h" @@ -113,6 +114,160 @@ class FreezeTest : public ::testing::Test { test::ExpectTensorEqual(unfrozen_outputs[0], frozen_outputs[0]); } + + void TestFreezeGraphWithoutDependentVariables(bool use_resource) { + // Test freezing a graph with variables that are not needed by the outputs + // in the SignatureDef. The resulting graph shouldn't be frozen, but + // non-dependent nodes should be pruned. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output b = ops::Const(scope.WithOpName("b"), 10.0f, {}); + Output c = ops::Mul(scope.WithOpName("c"), a, b); + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + Output read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + } else { + Output var = + ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), var, a); + } + + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + GraphDef expected_graph_def; + Scope expected_scope = Scope::NewRootScope(); + Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {}); + Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {}); + Output expected_c = + ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b); + TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def)); + + GraphDefEqual(frozen_graph_def, expected_graph_def); + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } + + void TestFreezeGraphWithDependentVariables(bool use_resource) { + // Test freezing a graph with variables that are needed by outputs in the + // SignatureDef. The variables should be frozen. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output read_var; + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + } else { + Output read_var = + ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a); + } + Output c = ops::Mul(scope.WithOpName("c"), a, read_var); + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + // If using normal variables there should be 3 nodes in the resulting + // graph_def. If using resource variables there should be 4 nodes in the + // resulting graph_def. + // In both cases, none should be variables. + size_t expected_nodes = use_resource ? 4 : 3; + EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes); + for (const NodeDef& node : frozen_graph_def.node()) { + EXPECT_NE(node.op(), "Variable") << node.name(); + EXPECT_NE(node.op(), "VariableV2") << node.name(); + EXPECT_NE(node.op(), "VarHandleOp") << node.name(); + EXPECT_NE(node.op(), "ReadVariableOp") << node.name(); + } + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } + + void TestFreezeGraphWithAndWithoutDependentVariables(bool use_resource) { + // Test freezing a graph with some variables that are needed and not needed + // by + // the outputs in the SignatureDef. The resulting graph should only freeze + // dependent variables. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output read_var; + + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + Output var_1 = + ops::VarHandleOp(scope.WithOpName("var_1"), DataType::DT_FLOAT, {}); + Output read_var_1 = + ops::ReadVariableOp(scope.WithOpName("var_1/Read/ReadVariableOp"), + var, DataType::DT_FLOAT); + auto assign_1 = + ops::AssignVariableOp(scope.WithOpName("assign_1"), var_1, a); + } else { + read_var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a); + Output var_1 = + ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT); + Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var_1, a); + } + + Output c = ops::Mul(scope.WithOpName("c"), a, read_var); + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + // There should be 3 nodes in the resulting graph_def, and none should be + // variables. + size_t expected_nodes = use_resource ? 4 : 3; + EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes); + for (const NodeDef& node : frozen_graph_def.node()) { + EXPECT_NE(node.op(), "Variable") << node.name(); + EXPECT_NE(node.op(), "VariableV2") << node.name(); + EXPECT_NE(node.op(), "VarHandleOp") << node.name(); + EXPECT_NE(node.op(), "ReadVariableOp") << node.name(); + } + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } }; TEST_F(FreezeTest, InputsAndOutputsSingleSignatureDef) { @@ -196,111 +351,28 @@ TEST_F(FreezeTest, GraphDefWithNoVariables) { GraphDefEqual(frozen_graph_def, graph_def); } -TEST_F(FreezeTest, GraphDefWithVariablesNotNeededByOutputs) { - // Test freezing a graph with variables that are not needed by the outputs in - // the SignatureDef. The resulting graph shouldn't be frozen, but - // non-dependent nodes should be pruned. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output b = ops::Const(scope.WithOpName("b"), 10.0f, {}); - Output c = ops::Mul(scope.WithOpName("c"), a, b); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); - - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); - - GraphDef expected_graph_def; - Scope expected_scope = Scope::NewRootScope(); - Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {}); - Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {}); - Output expected_c = - ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b); - TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def)); - - GraphDefEqual(frozen_graph_def, expected_graph_def); - - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithoutDependentVariables) { + TestFreezeGraphWithoutDependentVariables(false); } -TEST_F(FreezeTest, GraphDefWithVariablesNeededByOutputs) { - // Test freezing a graph with variables that are needed by outputs in the - // SignatureDef. The variables should be frozen. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output c = ops::Mul(scope.WithOpName("c"), a, var); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); - - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); - - // There should be 3 nodes in the resulting graph_def, and none should be - // variables. - EXPECT_EQ(frozen_graph_def.node_size(), 3); - for (const NodeDef& node : frozen_graph_def.node()) { - EXPECT_NE(node.op(), "Variable") << node.name(); - EXPECT_NE(node.op(), "VariableV2") << node.name(); - } - - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithoutDependentResourceVariables) { + TestFreezeGraphWithoutDependentVariables(true); } -TEST_F(FreezeTest, GraphDefWithVariablesNeededAndNotNeededByOutputs) { - // Test freezing a graph with some variables that are needed and not needed by - // the outputs in the SignatureDef. The resulting graph should only freeze - // dependent variables. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output c = ops::Mul(scope.WithOpName("c"), a, var); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - Output var_1 = - ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT); - Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); +TEST_F(FreezeTest, GraphDefWithDependentVariables) { + TestFreezeGraphWithDependentVariables(false); +} - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); +TEST_F(FreezeTest, GraphDefWithDependentResourceVariables) { + TestFreezeGraphWithDependentVariables(true); +} - // There should be 3 nodes in the resulting graph_def, and none should be - // variables. - EXPECT_EQ(frozen_graph_def.node_size(), 3); - for (const NodeDef& node : frozen_graph_def.node()) { - EXPECT_NE(node.op(), "Variable") << node.name(); - EXPECT_NE(node.op(), "VariableV2") << node.name(); - } +TEST_F(FreezeTest, GraphDefWithAndWithoutDependentVariables) { + TestFreezeGraphWithAndWithoutDependentVariables(false); +} - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithAndWithoutDependentResourceVariables) { + TestFreezeGraphWithAndWithoutDependentVariables(true); } } // namespace diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index 0540260efd83e18258ec6e93c514d14e328791b1..0900e87ebabd378e6237b77ca0ef01677c07c244 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -132,7 +132,9 @@ tf_library( config = "test_graph_tfadd.config.pbtxt", cpp_class = "AddComp", graph = "test_graph_tfadd.pbtxt", - tags = ["manual"], + tags = [ + "manual", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -143,7 +145,9 @@ tf_library( config = "test_graph_tfunknownop.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -155,7 +159,9 @@ tf_library( config = "test_graph_tfunknownop2.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -166,7 +172,9 @@ tf_library( config = "test_graph_tfunknownop3.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + ], ) # Utility library for benchmark binaries, used by the *_benchmark rules that are @@ -189,7 +197,6 @@ cc_library( name = "benchmark_extra_android", tags = [ "manual", - "notap", ], visibility = ["//visibility:public"], ) diff --git a/tensorflow/compiler/aot/tests/BUILD b/tensorflow/compiler/aot/tests/BUILD index 7dfd49cc3b92f83fd64ca62bd2230938ce2d0a65..28aab6eb614ca7123d9e00f7f5cc3661b62e23f7 100644 --- a/tensorflow/compiler/aot/tests/BUILD +++ b/tensorflow/compiler/aot/tests/BUILD @@ -74,7 +74,9 @@ tf_library( # compile but the others in this directory succeed, you may need to # expand the "required by all tf_library targets" list in tfcompile.bzl. include_standard_runtime_deps = False, - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -84,7 +86,9 @@ tf_library( cpp_class = "AddWithCkptComp", freeze_checkpoint = "test_graph_tfadd_with_ckpt.ckpt", graph = "test_graph_tfadd_with_ckpt.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -95,7 +99,9 @@ tf_library( freeze_checkpoint = "test_graph_tfadd_with_ckpt_saver.ckpt", freeze_saver = "test_graph_tfadd_with_ckpt_saver.saver", graph = "test_graph_tfadd_with_ckpt_saver.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -104,7 +110,9 @@ tf_library( config = "test_graph_tffunction.config.pbtxt", cpp_class = "FunctionComp", graph = "test_graph_tffunction.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -113,7 +121,9 @@ tf_library( config = "test_graph_tfgather.config.pbtxt", cpp_class = "GatherComp", graph = "test_graph_tfgather.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -122,7 +132,9 @@ tf_library( config = "test_graph_tfmatmul.config.pbtxt", cpp_class = "foo::bar::MatMulComp", graph = "test_graph_tfmatmul.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_library( @@ -131,7 +143,9 @@ tf_library( config = "test_graph_tfmatmulandadd.config.pbtxt", cpp_class = "MatMulAndAddComp", graph = "test_graph_tfmatmulandadd.pb", - tags = ["manual"], + tags = [ + "manual", + ], tfcompile_flags = "--gen_name_to_index --gen_program_shape", ) @@ -141,13 +155,17 @@ tf_library( config = "test_graph_tfsplits.config.pbtxt", cpp_class = "SplitsComp", graph = "test_graph_tfsplits.pb", - tags = ["manual"], + tags = [ + "manual", + ], ) tf_cc_test( name = "tfcompile_test", srcs = ["tfcompile_test.cc"], - tags = ["manual"], + tags = [ + "manual", + ], deps = [ ":test_graph_tfadd", ":test_graph_tfadd_with_ckpt", diff --git a/tensorflow/compiler/aot/tfcompile.bzl b/tensorflow/compiler/aot/tfcompile.bzl index 58572fea3db5599cc282944e15c866dcf5f25de0..9dff1be09fede6f65f82c2f36d94be07e781949f 100644 --- a/tensorflow/compiler/aot/tfcompile.bzl +++ b/tensorflow/compiler/aot/tfcompile.bzl @@ -103,6 +103,7 @@ def tf_library(name, graph, config, # Now run freeze_graph to convert variables into constants. freeze_args = (" --input_graph=$(location " + graph + ")" + + " --checkpoint_version=1" + " --input_binary=" + str(not graph.endswith(".pbtxt")) + " --input_checkpoint=$(location " + freeze_checkpoint + ")" + " --output_graph=$(location " + freeze_file + ")" + @@ -223,9 +224,6 @@ def tf_library(name, graph, config, # TODO(cwhipkey): only depend on kernel code that the model actually needed. "//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_1d", "//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_2d", - "//tensorflow/compiler/xla/service/cpu:cpu_runtime_avx", - "//tensorflow/compiler/xla/service/cpu:cpu_runtime_neon", - "//tensorflow/compiler/xla/service/cpu:cpu_runtime_sse4_1", "//tensorflow/compiler/xla/service/cpu:runtime_conv2d", "//tensorflow/compiler/xla/service/cpu:runtime_matmul", "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_conv2d", diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index a711319607f4ff2b83aa0ebe50e215b3d0e2258e..955d12dc203faa567b51a7b0b6f50ad6d8a94a54 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -29,7 +29,10 @@ load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured") # Target that bundles up the XLA CPU and GPU JIT devices. cc_library( name = "jit", - visibility = [":friends"], + visibility = [ + ":friends", + "//learning/tfx:__subpackages__", + ], deps = [ ":xla_cpu_device", ":xla_cpu_jit", @@ -102,12 +105,17 @@ cc_library( cc_library( name = "xla_interpreter_device", srcs = ["xla_interpreter_device.cc"], + visibility = [":friends"], deps = [ + ":jit_compilation_passes", ":xla_device", "//tensorflow/compiler/jit/kernels:xla_launch_op", "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/tf2xla/kernels:xla_ops", + "//tensorflow/compiler/xla/service:interpreter_plugin", # buildcleaner: keep + "//tensorflow/core:lib", ], - alwayslink = True, + alwayslink = 1, ) cc_library( @@ -200,6 +208,7 @@ cc_library( name = "graph_to_functiondef", srcs = ["graph_to_functiondef.cc"], hdrs = ["graph_to_functiondef.h"], + visibility = [":friends"], deps = [ "//tensorflow/core:core_cpu", "//tensorflow/core:framework", diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 9c372a012789fc25ca0a711349c09ca62edc6754..2d175c40f9dfaef4e5024b77a6ecb8d6022e7a56 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -381,12 +381,24 @@ class Encapsulator { Node* send_from_host = nullptr; }; + // Creates an outside_compilation subgraph for outside_compilation_id if + // none exists yet. Returns the (possible newly created) subgraph for + // outside_compilation_id. + OutsideCompilationSubgraph* LookupOrCreateOutsideCompilationSubgraph( + const string& outside_compilation_id); + // Builds a ParallelCheck op that compares the output of the original // subgraph with the encapsulated subgraph. Status BuildParallelCheckOp( const std::unordered_map& node_images, Graph* graph_out); + // Builds a placeholder node used to provide the key input to a RecvAtHost + // or SendFromHost node. This placeholder node will be removed by a later + // pass. + Status AddHostComputeKeyPlaceholder(OutsideCompilationSubgraph* oc_subgraph, + Graph* graph_out); + // Builds a _RecvAtHost node producing all the inputs of an // outside_compilation subgraph and stores it in oc_subgraph.recv_at_host. Status AddRecvAtHostNode(const string& subgraph_name, @@ -413,6 +425,10 @@ class Encapsulator { // NodeDef for the function call node. NodeDef call_node_def_; + // Placeholder node simulating the host compute key in the output graph. + // Not owned. + Node* host_compute_key_placeholder_ = nullptr; + // Function call node(s) in the output graph. Not owned. // If parallel_checking is enabled, 'call_node_inputs' is the function call // node to which inputs should be fed, and 'call_node_outputs' is the @@ -712,39 +728,44 @@ Status Encapsulator::Subgraph::RecordResult( return Status::OK(); } -void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( - const string& outside_compilation_id, const Edge* edge) { +Encapsulator::Subgraph::OutsideCompilationSubgraph* +Encapsulator::Subgraph::LookupOrCreateOutsideCompilationSubgraph( + const string& outside_compilation_id) { auto iter = outside_compilation_subgraphs_ .emplace(outside_compilation_id, OutsideCompilationSubgraph()) .first; - OutsideCompilationSubgraph& outside_subgraph = iter->second; + OutsideCompilationSubgraph* outside_subgraph = &iter->second; + return outside_subgraph; +} + +void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( + const string& outside_compilation_id, const Edge* edge) { + OutsideCompilationSubgraph* outside_subgraph = + LookupOrCreateOutsideCompilationSubgraph(outside_compilation_id); if (edge->IsControlEdge()) { - outside_subgraph.control_inputs.insert(edge->src()); + outside_subgraph->control_inputs.insert(edge->src()); } else { - int input_index = outside_subgraph.inputs.size(); - outside_subgraph.inputs.emplace(NodeSlot(edge->src(), edge->src_output()), - input_index); + int input_index = outside_subgraph->inputs.size(); + outside_subgraph->inputs.emplace(NodeSlot(edge->src(), edge->src_output()), + input_index); } } void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( const string& outside_compilation_id, const Edge* edge) { - auto subgraph_iter = - outside_compilation_subgraphs_ - .emplace(outside_compilation_id, OutsideCompilationSubgraph()) - .first; - OutsideCompilationSubgraph& outside_subgraph = subgraph_iter->second; + OutsideCompilationSubgraph* outside_subgraph = + LookupOrCreateOutsideCompilationSubgraph(outside_compilation_id); if (edge->IsControlEdge()) { - outside_subgraph.control_outputs.insert(edge->dst()); + outside_subgraph->control_outputs.insert(edge->dst()); } else { DataType dtype = edge->dst()->input_type(edge->dst_input()); auto output_iter = - outside_subgraph.outputs_by_src + outside_subgraph->outputs_by_src .emplace(NodeSlot(edge->src(), edge->src_output(), dtype), - outside_subgraph.outputs_by_src.size()) + outside_subgraph->outputs_by_src.size()) .first; int output_index = output_iter->second; - outside_subgraph.outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = + outside_subgraph->outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = output_index; } } @@ -1060,9 +1081,36 @@ Status Encapsulator::Subgraph::AddFunctionCallNode( return Status::OK(); } +Status Encapsulator::Subgraph::AddHostComputeKeyPlaceholder( + OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + TensorShapeProto shape_proto; + TensorShape shape({2}); + shape.AsProto(&shape_proto); + GraphDefBuilder::Options options(graph_out, /*status=*/nullptr); + NodeDef key_def; + NodeDefBuilder builder( + strings::StrCat(call_node_def_.name(), "_key_placeholder"), + "Placeholder"); + builder.Attr("dtype", DT_STRING); + builder.Attr("shape", shape_proto); + builder.Attr("_host_compute_call_node", call_node_def_.name()); + Status s = builder.Finalize(&key_def); + if (!s.ok()) return s; + + host_compute_key_placeholder_ = graph_out->AddNode(key_def, &s); + if (!s.ok()) return s; + host_compute_key_placeholder_->set_assigned_device_name(device_); + + return Status::OK(); +} + Status Encapsulator::Subgraph::AddRecvAtHostNode( const string& subgraph_name, const string& oc_subgraph_name, OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + if (host_compute_key_placeholder_ == nullptr) { + TF_RETURN_IF_ERROR(AddHostComputeKeyPlaceholder(oc_subgraph, graph_out)); + } + std::vector dtypes(oc_subgraph->inputs.size(), DT_INVALID); for (const auto& input : oc_subgraph->inputs) { @@ -1078,15 +1126,21 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_recv"), kRecvAtHostOp); + builder.Device(device_); builder.Attr("Toutputs", dtypes); + // TODO(misard) For now we only support TPU device 0. + builder.Attr("device_ordinal", 0); builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, "_", oc_subgraph_name)); + builder.Input(host_compute_key_placeholder_->name(), 0, DT_STRING); Status s = builder.Finalize(&recv_def); if (!s.ok()) return s; oc_subgraph->recv_at_host = graph_out->AddNode(recv_def, &s); if (!s.ok()) return s; oc_subgraph->recv_at_host->set_assigned_device_name(device_); + graph_out->AddEdge(host_compute_key_placeholder_, 0, + oc_subgraph->recv_at_host, 0); // Add a control dependency forcing the RecvAtHost to run before the subgraph // completes. This has no effect on execution order but prevents the @@ -1101,6 +1155,10 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( const std::unordered_map& node_images, const string& subgraph_name, const string& oc_subgraph_name, OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + if (host_compute_key_placeholder_ == nullptr) { + TF_RETURN_IF_ERROR(AddHostComputeKeyPlaceholder(oc_subgraph, graph_out)); + } + std::vector dtypes(oc_subgraph->outputs_by_src.size(), DT_INVALID); std::vector inputs( oc_subgraph->outputs_by_src.size()); @@ -1120,16 +1178,22 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_send"), kSendFromHostOp); + builder.Device(device_); builder.Attr("Tinputs", dtypes); builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, "_", oc_subgraph_name)); + // TODO(misard) For now we only support TPU device 0. + builder.Attr("device_ordinal", 0); builder.Input(inputs); + builder.Input(host_compute_key_placeholder_->name(), 0, DT_STRING); Status s = builder.Finalize(&send_def); if (!s.ok()) return s; oc_subgraph->send_from_host = graph_out->AddNode(send_def, &s); if (!s.ok()) return s; oc_subgraph->send_from_host->set_assigned_device_name(device_); + graph_out->AddEdge(host_compute_key_placeholder_, 0, + oc_subgraph->send_from_host, inputs.size()); // Add a control dependency forcing the SendFromHost to run before the // subgraph completes. This has no effect on execution order but prevents the @@ -1709,7 +1773,9 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( std::unique_ptr graph_out(new Graph(graph_in.op_registry())); graph_out->set_versions(graph_in.versions()); - static_shape_out->resize(send_node->num_inputs()); + // The final input to the send node is the dynamic key, which we don't include + // in the static shapes. + static_shape_out->resize(send_node->num_inputs() - 1); // We don't use the standard ReverseDFS because we want to cut off traversal // whenever we find an output with fully defined shape. @@ -1750,9 +1816,14 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( // continue. TensorShapeProto proto; context->ShapeHandleToProto(shape, &proto); - dummy_node_images[src_node] = AddDummyShapedNode( - src_node->output_type(src_port), proto, graph_out.get()); - if (n == send_node) { + if (dummy_node_images.find(src_node) == dummy_node_images.end()) { + dummy_node_images[src_node] = AddDummyShapedNode( + src_node->output_type(src_port), proto, graph_out.get()); + } + // The final input to the send node is the dynamic key, which we + // don't include in the static shapes. + if (n == send_node && + in_edge->dst_input() < static_shape_out->size()) { (*static_shape_out)[in_edge->dst_input()] = proto; } } else { diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index aed9cae0f1799c4524da8ee309344849798755d5..d7bea56a7244665c571c23d49c6769a163b86e9e 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -246,26 +246,32 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, << diff << "\nActual: " << actual.DebugString(); \ } while (false) -// TODO(misard): remove these fake registrations once there are real Ops to be -// compiled. +// These dummy Op registrations are here because the real Op registrations live +// in contrib and there can't be a dependence from this test to contrib. REGISTER_OP("_XlaHostCompute") .Input("inputs: Tinputs") .Output("outputs: Toutputs") .Attr("Tinputs: list(type) >= 0") .Attr("Toutputs: list(type) >= 0") .Attr("key: string") + .Attr("shape_inference_graph: string = ''") + .Attr("shapes: list(shape) >= 0") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaSendFromHost") - .Input("input: Tinputs") + .Input("inputs: Tinputs") + .Input("dynamic_key: string") .Attr("Tinputs: list(type) >= 0") .Attr("key: string") + .Attr("device_ordinal: int") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaRecvAtHost") - .Output("output: Toutputs") + .Input("dynamic_key: string") + .Output("outputs: Toutputs") .Attr("Toutputs: list(type) >= 0") .Attr("key: string") + .Attr("device_ordinal: int") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("InputTest") @@ -327,43 +333,71 @@ Node* InputShaped(const GraphDefBuilder::Options& opts) { return ops::SourceOp("InputTestShaped", opts); } -Node* KnownShape(const gtl::ArraySlice& shape, - const GraphDefBuilder::Options& opts) { +Node* KnownShapeBase(DataType dtype, const gtl::ArraySlice& shape, + const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const", opts.op_registry()); TensorProto value; - value.set_dtype(DT_FLOAT); + value.set_dtype(dtype); for (int dim : shape) { value.mutable_tensor_shape()->add_dim()->set_size(dim); } return opts.WithAttr("value", value) - .WithAttr("dtype", DT_FLOAT) + .WithAttr("dtype", dtype) + .FinalizeBuilder(&node_builder); +} + +Node* KnownShape(const gtl::ArraySlice& shape, + const GraphDefBuilder::Options& opts) { + return KnownShapeBase(DT_FLOAT, shape, opts); +} + +Node* KeyPlaceholderShape(const GraphDefBuilder::Options& opts) { + return KnownShapeBase(DT_STRING, {2}, opts); +} + +Node* KeyPlaceholder(const string& call_node, + const GraphDefBuilder::Options& opts) { + if (opts.HaveError()) return nullptr; + NodeBuilder node_builder(opts.GetNameForOp("Placeholder"), "Placeholder", + opts.op_registry()); + TensorShapeProto shape; + shape.add_dim()->set_size(2); + return opts.WithAttr("shape", shape) + .WithAttr("dtype", DT_STRING) + .WithAttr("_host_compute_call_node", call_node) .FinalizeBuilder(&node_builder); } -Node* RecvAtHost(const string& key, const gtl::ArraySlice& dtypes, +Node* RecvAtHost(ops::NodeOut key_input, const string& key, + const gtl::ArraySlice& dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaRecvAtHost"), "_XlaRecvAtHost", opts.op_registry()); + node_builder.Input(std::move(key_input)); return opts.WithAttr("Toutputs", dtypes) .WithAttr("key", key) + .WithAttr("device_ordinal", 0) .FinalizeBuilder(&node_builder); } -Node* SendFromHost(const string& key, const std::vector& inputs, +Node* SendFromHost(ops::NodeOut key_input, const string& key, + const std::vector& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaSendFromHost"), "_XlaSendFromHost", opts.op_registry()); node_builder.Input(inputs); + node_builder.Input(std::move(key_input)); std::vector dtypes; for (const auto& node : inputs) { dtypes.push_back(node.dt); } - return opts.WithAttr("key", key) - .WithAttr("Tinputs", dtypes) + return opts.WithAttr("Tinputs", dtypes) + .WithAttr("key", key) + .WithAttr("device_ordinal", 0) .FinalizeBuilder(&node_builder); } @@ -809,13 +843,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -855,12 +892,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { node_builder.Input(a).Input(b); Node* call = b2.opts().FinalizeBuilder(&node_builder); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -921,13 +962,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { string shape_string_expected_1; { GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape1.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape1.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape1.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape1.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape1.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape1_graph; TF_EXPECT_OK(shape1.ToGraphDef(&shape1_graph)); EXPECT_TRUE(shape1_graph.SerializeToString(&shape_string_expected_1)); @@ -936,17 +980,21 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { string shape_string_expected_2; { GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape2.opts().WithName("KnownShape/_0")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), shape2.opts().WithName("E")); Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {DT_FLOAT, DT_FLOAT}, shape2.opts().WithName("outside_compilation_F1_O2_recv")); Node* h = Binary(ops::NodeOut(recv2, 0), e, shape2.opts().WithName("H")); - SendFromHost("host_compute_channel_F1_O2", {h}, - shape2.opts().WithName("outside_compilation_F1_O2_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {h}, shape2.opts().WithName("outside_compilation_F1_O2_send")); GraphDef shape2_graph; TF_EXPECT_OK(shape2.ToGraphDef(&shape2_graph)); EXPECT_TRUE(shape2_graph.SerializeToString(&shape_string_expected_2)); @@ -997,25 +1045,30 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { node_builder.Input(a).Input(b); Node* call = b2.opts().FinalizeBuilder(&node_builder); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_recv")); Node* g = Binary(e, ops::NodeOut(recv2, 1), b2.opts().WithName("G").WithControlInputs({recv2, e})); Node* h = Binary(ops::NodeOut(recv2, 0), e, b2.opts().WithName("H")); - Node* send2 = - SendFromHost("host_compute_channel_F1_O2", {h}, - b2.opts().WithName("outside_compilation_F1_O2_send")); + Node* send2 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", {h}, + b2.opts().WithName("outside_compilation_F1_O2_send")); Node* s = NoOp(b2.opts() .WithName("F1_sequencer") @@ -1073,13 +1126,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -1138,12 +1194,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* a = InputShaped(b2.opts().WithName("A")); Node* b = InputShaped(b2.opts().WithName("B")); + Node* key_constant1 = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant1, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant1, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1153,14 +1213,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* s1 = NoOp( b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1})); - Node* recv2 = - RecvAtHost("host_compute_channel_F2_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F2_O1_recv")); + Node* key_constant2 = + KeyPlaceholder("F2", b2.opts().WithName("F2_key_placeholder")); + Node* recv2 = RecvAtHost( + ops::NodeOut(key_constant2, 0), "host_compute_channel_F2_O1", + {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_recv")); Node* h = Binary(ops::NodeOut(call1, 1), recv2, b2.opts().WithName("H").WithControlInput(s1)); - Node* send2 = - SendFromHost("host_compute_channel_F2_O1", {h}, - b2.opts().WithName("outside_compilation_F2_O1_send")); + Node* send2 = SendFromHost( + ops::NodeOut(key_constant2, 0), "host_compute_channel_F2_O1", {h}, + b2.opts().WithName("outside_compilation_F2_O1_send")); NodeBuilder node_builder2("F2", "F2", lib_def.get()); node_builder2.Input(e).Input(call1); @@ -1237,9 +1299,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { Node* b = Input(b2.opts().WithName("B")); Node* e = Unary(a, b2.opts().WithName("E")); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* send1 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1313,13 +1377,15 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { Node* a = InputShaped(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(a, b2.opts().WithName("E").WithControlInput(recv1)); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1385,9 +1451,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); @@ -1458,11 +1526,14 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1572,13 +1643,15 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); - Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_0")); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - shape.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); + Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_1")); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = BinaryUnknownShape(known, recv, shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -1619,13 +1692,16 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { Node* call = b2.opts().WithControlInputs({c}).FinalizeBuilder(&node_builder); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = BinaryUnknownShape( c, ops::NodeOut(recv, 0), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index 79b02baba83cb47f4f2f16544ad711a4b6937d90..a0211acbbe9eec77d30c7d14293650de8826f41c 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -190,6 +190,9 @@ Status FindCompilationCandidates( pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice); for (Node* node : graph.op_nodes()) { + VLOG(2) << "FindCompilationCandidates(): Processing " + << node->DebugString(); + DeviceType device_type(""); TF_RETURN_IF_ERROR( DeviceTypeOfDevice(node->assigned_device_name(), &device_type)); @@ -216,6 +219,13 @@ Status FindCompilationCandidates( !IsCompilableWhile(*node, jit_device_type, 0, lib_runtime)) { continue; } + // _Arg nodes in a top-level function represent feeds. + // Do not compile them. + if (node->type_string() == "_Arg") { + VLOG(2) << "Skipping jit compilation for '_Arg'-typed node " + << node->DebugString(); + continue; + } // _Retval nodes in a top-level function represent fetches. // Do not compile them. if (node->type_string() == "_Retval") { @@ -304,6 +314,7 @@ Status MarkForCompilationPass::Run( static_cast(flags->tf_xla_auto_jit); } bool cpu_global_jit = flags->tf_xla_cpu_global_jit; + VLOG(1) << "flags->tf_xla_cpu_global_jit = " << flags->tf_xla_cpu_global_jit; const FunctionLibraryDefinition* fld = options.flib_def; auto is_compilable = [global_jit_level, cpu_global_jit, fld]( diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc index 454f0aeae98d7afd51f12b2cfb1810de275a57f7..1a8858cccef623185709ab5dc2187a313dd130f7 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/core/framework/op.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/graph_def_builder.h" +#include "tensorflow/core/graph/graph_def_builder_util.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" @@ -80,7 +81,7 @@ TEST(XlaCompilationTest, Chains) { ops::UnaryOp("UncompilableUnary", c, builder.opts().WithName("D")); Node* e = ops::UnaryOp("Relu", d, builder.opts().WithName("E")); ops::UnaryOp("Relu", e, builder.opts().WithName("F")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -105,7 +106,7 @@ TEST(XlaCompilationTest, UncompilableCycles) { Node* b = ops::UnaryOp("UncompilableUnary", a, builder.opts().WithName("B")); ops::BinaryOp("MatMul", a, b, builder.opts().WithName("C")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -125,7 +126,7 @@ TEST(XlaCompilationTest, CompilableCycles) { .WithAttr("value", Tensor())); Node* b = ops::UnaryOp("Relu", a, builder.opts().WithName("B")); ops::BinaryOp("MatMul", a, b, builder.opts().WithName("C")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -148,7 +149,7 @@ TEST(XlaCompilationTest, UnsupportedTypes) { .WithAttr("value", Tensor(DT_COMPLEX128, TensorShape()))); Node* b = ops::UnaryOp("Neg", a, builder.opts().WithName("B")); ops::BinaryOp("MatMul", a, b, builder.opts().WithName("C")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -177,7 +178,7 @@ TEST(XlaCompilationTest, ConcatWithConstArg) { concat_builder.Input(dim).Input({a, a}).Attr("N", 2); builder.opts().FinalizeBuilder(&concat_builder); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -212,7 +213,7 @@ TEST(XlaCompilationTest, FunctionCalls) { Node* c = ops::UnaryOp("Relu", b, builder.opts().WithName("C")); ops::UnaryOp("UncompilableFn", c, builder.opts().WithName("D")); ops::BinaryOp("NoInlineFn", c, c, builder.opts().WithName("E")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph, &flib_def)); @@ -244,7 +245,7 @@ TEST(XlaCompilationTest, MetadataOpsDontStartClusters) { Node* c = ops::UnaryOp("Rank", b, builder.opts().WithName("C")); Node* d = ops::UnaryOp("Size", c, builder.opts().WithName("D")); ops::UnaryOp("Shape", d, builder.opts().WithName("E")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); auto clusters = GetClusters(*graph); @@ -330,7 +331,7 @@ TEST(XlaCompilationTest, SymbolicGradients) { d_builder.Input({c, c}); builder.opts().FinalizeBuilder(&d_builder); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -382,7 +383,7 @@ TEST(XlaCompilationTest, CyclesWithAllDifferentScopes) { ops::BinaryOp( "MatMul", a, b, builder.opts().WithName("C").WithAttr(kXlaScopeAttr, "ScopeC")); - TF_CHECK_OK(builder.ToGraph(graph.get())); + TF_CHECK_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -413,7 +414,7 @@ TEST(XlaCompilationTest, CyclesWithSplittingScopes) { ops::BinaryOp( "Add", b, c, builder.opts().WithName("D").WithAttr(kXlaScopeAttr, "Scope2")); - TF_CHECK_OK(builder.ToGraph(graph.get())); + TF_CHECK_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -443,7 +444,7 @@ TEST(XlaCompilationTest, CyclesWithDifferentScopesAndBridge) { "Relu", a, builder.opts().WithName("B").WithAttr(kXlaScopeAttr, "ScopeB")); ops::BinaryOp("MatMul", a, b, builder.opts().WithName("C")); - TF_CHECK_OK(builder.ToGraph(graph.get())); + TF_CHECK_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); @@ -484,7 +485,7 @@ TEST(XlaCompilationTest, Resources) { Node* c = ops::UnaryOp("ResourceOutput", b, builder.opts().WithName("C")); Node* d = ops::UnaryOp("ResourceInput", c, builder.opts().WithName("D")); ops::UnaryOp("Relu", d, builder.opts().WithName("E")); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); auto clusters = GetClusters(*graph); @@ -541,7 +542,7 @@ TEST(XlaCompilationTest, Retval) { .WithAttr("T", DT_FLOAT) .WithAttr("index", 0)); - TF_EXPECT_OK(builder.ToGraph(graph.get())); + TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); } TF_ASSERT_OK(MarkForCompilation(&graph)); diff --git a/tensorflow/compiler/jit/xla_interpreter_device.cc b/tensorflow/compiler/jit/xla_interpreter_device.cc index 2614deefd8823dcb8f38e9e22ae4e78145d0d96a..a329451b14a785b17913e3838a6571b62b422804 100644 --- a/tensorflow/compiler/jit/xla_interpreter_device.cc +++ b/tensorflow/compiler/jit/xla_interpreter_device.cc @@ -25,8 +25,8 @@ namespace tensorflow { const char* const DEVICE_XLA_INTERPRETER = "XLA_INTERPRETER"; const char* const DEVICE_INTERPRETER_XLA_JIT = "XLA_INTERPRETER_JIT"; -constexpr std::array kExecAllTypes = { - {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}}; +constexpr std::array kExecAllTypes = { + {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_BOOL}}; class XlaInterpreterDeviceFactory : public DeviceFactory { public: diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 25e329b6aadbab7219d7120ce5f51b3a6f5884e9..782bf82d4149968d5e5fbfb93bbd4ff1dcd75494 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -479,8 +479,9 @@ tf_xla_py_test( tf_xla_py_test( name = "reverse_sequence_op_test", - size = "small", + size = "medium", srcs = ["reverse_sequence_op_test.py"], + tags = ["optonly"], deps = [ ":xla_test", "//tensorflow/python:array_ops", @@ -639,6 +640,7 @@ tf_xla_py_test( name = "variable_ops_test", size = "small", srcs = ["variable_ops_test.py"], + tags = ["optonly"], deps = [ ":xla_test", "//tensorflow/python:array_ops", @@ -677,6 +679,19 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "scatter_nd_op_test", + size = "medium", + srcs = ["scatter_nd_op_test.py"], + tags = ["optonly"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "xla_device_test", size = "small", @@ -801,6 +816,17 @@ tf_library( tfcompile_flags = ["--xla_cpu_multi_thread_eigen=false"], ) +tf_xla_py_test( + name = "fake_quant_ops_test", + size = "medium", + srcs = ["fake_quant_ops_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + ], +) + # ----------------------------------------------------------------------------- filegroup( diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 30a6d3a74d64f90ad33062df6d1e16e3a575bd63..6bcfed7b69fcee838acd5045a3b337809b5a52c8 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -71,7 +71,7 @@ class BinaryOpsTest(XLATestCase): expected=np.array([[[[False, True], [True, False]]]], dtype=dtype)) self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([3, 3, -1.5, -8, 44], dtype=dtype), np.array([2, -2, 7, -4, 0], dtype=dtype), expected=np.array( @@ -108,57 +108,57 @@ class BinaryOpsTest(XLATestCase): [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4, np.pi], dtype=dtype)) self._testBinary( - gen_math_ops._reciprocal_grad, + gen_math_ops.reciprocal_grad, np.array([4, -3, -2, 1], dtype=dtype), np.array([5, -6, 7, -8], dtype=dtype), expected=np.array([-80, 54, -28, 8], dtype=dtype)) self._testBinary( - gen_math_ops._sigmoid_grad, + gen_math_ops.sigmoid_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-60, -36, -14, 0], dtype=dtype)) self._testBinary( - gen_math_ops._rsqrt_grad, + gen_math_ops.rsqrt_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-160, -81, -28, -4], dtype=dtype)) self._testBinary( - gen_math_ops._sqrt_grad, + gen_math_ops.sqrt_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([0.625, 1, 1.75, 4], dtype=dtype)) self._testBinary( - gen_nn_ops._softplus_grad, + gen_nn_ops.softplus_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array( [3.97322869, 2.99258232, 1.99817801, 0.99966466], dtype=dtype)) self._testBinary( - gen_nn_ops._softsign_grad, + gen_nn_ops.softsign_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array( [0.11111111, 0.06122449, 0.03125, 0.01234568], dtype=dtype)) self._testBinary( - gen_math_ops._tanh_grad, + gen_math_ops.tanh_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-75, -48, -21, 0], dtype=dtype)) self._testBinary( - gen_nn_ops._elu_grad, + gen_nn_ops.elu_grad, np.array([1, 2, 3, 4, 5, 6], dtype=dtype), np.array([-.6, -.4, -.2, 0, .2, .4], dtype=dtype), expected=np.array([0.4, 1.2, 2.4, 4, 5, 6], dtype=dtype)) self._testBinary( - gen_nn_ops._selu_grad, + gen_nn_ops.selu_grad, np.array([1, 2, 3, 4, 5, 6], dtype=dtype), np.array([-.6, -.4, -.2, .2, .4, .6], dtype=dtype), expected=np.array( @@ -166,20 +166,20 @@ class BinaryOpsTest(XLATestCase): 4.202803949422, 5.2535049367774, 6.30420592413], dtype=dtype)) self._testBinary( - gen_nn_ops._relu_grad, + gen_nn_ops.relu_grad, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype), np.array([0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9], dtype=dtype), expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10], dtype=dtype)) self._testBinary( - gen_nn_ops._relu6_grad, + gen_nn_ops.relu6_grad, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtype), np.array( [0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9, 6.1, 10.0], dtype=dtype), expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10, 0, 0], dtype=dtype)) self._testBinary( - gen_nn_ops._softmax_cross_entropy_with_logits, + gen_nn_ops.softmax_cross_entropy_with_logits, np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtype), np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]], dtype=dtype), expected=[ @@ -191,7 +191,7 @@ class BinaryOpsTest(XLATestCase): equality_test=self.ListsAreClose) self._testBinary( - gen_nn_ops._sparse_softmax_cross_entropy_with_logits, + gen_nn_ops.sparse_softmax_cross_entropy_with_logits, np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]], dtype=dtype), np.array([2, 1, 7], dtype=np.int32), @@ -207,7 +207,7 @@ class BinaryOpsTest(XLATestCase): def testIntOps(self): for dtype in self.int_types: self._testBinary( - gen_math_ops._truncate_div, + gen_math_ops.truncate_div, np.array([3, 3, -1, -9, -8], dtype=dtype), np.array([2, -2, 7, 2, -4], dtype=dtype), expected=np.array([1, -1, 0, -4, 2], dtype=dtype)) @@ -369,7 +369,7 @@ class BinaryOpsTest(XLATestCase): expected=np.array([[[[False, True], [True, False]]]], dtype=dtype)) self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([3, 3j, -1.5j, -8, 2 + 3j, 2 + 4j], dtype=dtype), np.array([2, -2, 7j, -4j, 4 - 6j, 1 + 2j], dtype=dtype), expected=np.array( @@ -378,7 +378,7 @@ class BinaryOpsTest(XLATestCase): # Test inf/nan scenarios. self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([4 + 3j, 4, 3j, -4, -4j, 2 - 3j], dtype=dtype), np.array([0, 0, 0, 0, 0, 0], dtype=dtype), expected=np.array( @@ -418,19 +418,19 @@ class BinaryOpsTest(XLATestCase): lhs = np.array([4 + 2j, -3 - 1j, 2j, 1], dtype=dtype) rhs = np.array([5, -6j, 7 - 3j, -8j], dtype=dtype) self._testBinary( - gen_math_ops._reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs) + gen_math_ops.reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs) self._testBinary( - gen_math_ops._sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs)) + gen_math_ops.sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs)) self._testBinary( - gen_math_ops._rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2) + gen_math_ops.rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2) self._testBinary( - gen_math_ops._sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs)) + gen_math_ops.sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs)) self._testBinary( - gen_math_ops._tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs)) + gen_math_ops.tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs)) def testComplexMath(self): for dtype in self.complex_types: @@ -538,7 +538,7 @@ class BinaryOpsTest(XLATestCase): if dtype not in self.complex_types: # floordiv unsupported for complex. self._testBinary( - gen_math_ops._floor_div, + gen_math_ops.floor_div, np.array([3, 3, -1, -9, -8], dtype=dtype), np.array([2, -2, 7, 2, -4], dtype=dtype), expected=np.array([1, -2, -1, -5, 2], dtype=dtype)) @@ -554,12 +554,12 @@ class BinaryOpsTest(XLATestCase): def _testRemainder(self, dtype): """Test cases for remainder operators.""" self._testBinary( - gen_math_ops._floor_mod, + gen_math_ops.floor_mod, np.array([3, 3, -1, -8], dtype=dtype), np.array([2, -2, 7, -4], dtype=dtype), expected=np.array([1, -1, 6, 0], dtype=dtype)) self._testBinary( - gen_math_ops._truncate_mod, + gen_math_ops.truncate_mod, np.array([3, 3, -1, -8], dtype=dtype), np.array([2, -2, 7, -4], dtype=dtype), expected=np.array([1, 1, -1, 0], dtype=dtype)) @@ -1045,6 +1045,20 @@ class BinaryOpsTest(XLATestCase): ], equality_test=self.ListsAreClose) + def splitvOp(x, y): # pylint: disable=invalid-name + return array_ops.split(value=y, num_or_size_splits=[2, 3], axis=x) + for axis in [1, -1]: + self._testBinary( + splitvOp, + np.int32(axis), + np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], + dtype=dtype), + expected=[ + np.array([[0, 1], [5, 6]], dtype=dtype), + np.array([[2, 3, 4], [7, 8, 9]], dtype=dtype), + ], + equality_test=self.ListsAreClose) + def testTile(self): for dtype in self.numeric_types: self._testBinary( diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py index 81734082d9aab86f8bc763681265ef64ef32bd31..f10973e19f1945515b776cf86349445ed7334629 100644 --- a/tensorflow/compiler/tests/concat_ops_test.py +++ b/tensorflow/compiler/tests/concat_ops_test.py @@ -301,7 +301,7 @@ class ConcatOffsetTest(XLATestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) diff --git a/tensorflow/compiler/tests/fake_quant_ops_test.py b/tensorflow/compiler/tests/fake_quant_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dfe9400ef0f55ca011d4e23ba5d735899ca2e054 --- /dev/null +++ b/tensorflow/compiler/tests/fake_quant_ops_test.py @@ -0,0 +1,452 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.platform import googletest + + +class FakeQuantWithMinMaxArgsTest(XLATestCase): + """Test cases for FakeQuantWithMinMaxArgs operation.""" + + # 8 bits, wide range. + def testOp_with8BitsNoScalingNoNudging(self): + self._TestOp(0.0, 255.0, 8, False, 0.0, 255.0, 1.0) + + def testOp_with8BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 128.0, 8, False, 0.0, 127.5, 0.5) + + def testOp_with8BitsScalingAndNudgingUp(self): + self._TestOp(-128.0, -0.5, 8, False, -127.5, 0.0, 0.5) + + def testOp_with8BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 127.4, 8, False, 0.0, 127.5, 0.5) + + # 8 bits, narrow range. + def testOp_with8BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 254.0, 8, True, 0.0, 254.0, 1.0) + + def testOp_with8BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 127.1, 8, True, 0.0, 127.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-127.1, -0.1, 8, True, -127.0, 0.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 126.9, 8, True, 0.0, 127.0, 0.5) + + # 7 bits, wide range. + def testOp_with7BitsNoScalingNoNudging(self): + self._TestOp(0.0, 127.0, 7, False, 0.0, 127.0, 1.0) + + def testOp_with7BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 64.0, 7, False, 0.0, 63.5, 0.5) + + def testOp_with7BitsScalingAndNudgingUp(self): + self._TestOp(-64.0, -0.5, 7, False, -63.5, 0.0, 0.5) + + def testOp_with7BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 63.4, 7, False, 0.0, 63.5, 0.5) + + # 7 bits, narrow range. + def testOp_with7BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 126.0, 7, True, 0.0, 126.0, 1.0) + + def testOp_with7BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 63.1, 7, True, 0.0, 63.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-63.1, -0.1, 7, True, -63.0, 0.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 62.9, 7, True, 0.0, 63.0, 0.5) + + def _TestOp(self, input_min, input_max, num_bits, narrow_range, + expected_nudged_input_min, expected_nudged_input_max, + expected_step): + inputs = np.array( + [ + expected_nudged_input_min - expected_step, + expected_nudged_input_min - 0.01, expected_nudged_input_min, + expected_nudged_input_min + 0.01, + expected_nudged_input_min + expected_step - 0.01, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step + 0.01, + expected_nudged_input_max - 0.01, expected_nudged_input_max, + expected_nudged_input_max + 0.01, + expected_nudged_input_max + expected_step + ], + dtype=np.float32) + expected = np.array( + [ + expected_nudged_input_min, expected_nudged_input_min, + expected_nudged_input_min, expected_nudged_input_min, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step, + expected_nudged_input_max, expected_nudged_input_max, + expected_nudged_input_max, expected_nudged_input_max + ], + dtype=np.float32) + + with self.test_session() as session: + with self.test_scope(): + input_placeholder = array_ops.placeholder( + dtypes.float32, inputs.shape, name="inputs") + outputs = array_ops.fake_quant_with_min_max_args( + input_placeholder, + min=input_min, + max=input_max, + num_bits=num_bits, + narrow_range=narrow_range) + result = session.run(outputs, {input_placeholder: inputs}) + self.assertAllCloseAccordingToType( + result, expected, rtol=1e-3, atol=1e-5, bfloat16_rtol=0.03) + + +class FakeQuantWithMinMaxArgsGradientTest(XLATestCase): + """Test cases for FakeQuantWithMinMaxArgsGradient operation.""" + + # 8 bits, wide range. + def testOp_with8BitsNoScalingNoNudging(self): + self._TestOp(0.0, 255.0, 8, False, 0.0, 255.0, 1.0) + + def testOp_with8BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 128.0, 8, False, 0.0, 127.5, 0.5) + + def testOp_with8BitsScalingAndNudgingUp(self): + self._TestOp(-128.0, -0.5, 8, False, -127.5, 0.0, 0.5) + + def testOp_with8BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 127.4, 8, False, 0.0, 127.5, 0.5) + + # 8 bits, narrow range. + def testOp_with8BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 254.0, 8, True, 0.0, 254.0, 1.0) + + def testOp_with8BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 127.1, 8, True, 0.0, 127.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-127.1, -0.1, 8, True, -127.0, 0.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 126.9, 8, True, 0.0, 127.0, 0.5) + + # 7 bits, wide range. + def testOp_with7BitsNoScalingNoNudging(self): + self._TestOp(0.0, 127.0, 7, False, 0.0, 127.0, 1.0) + + def testOp_with7BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 64.0, 7, False, 0.0, 63.5, 0.5) + + def testOp_with7BitsScalingAndNudgingUp(self): + self._TestOp(-64.0, -0.5, 7, False, -63.5, 0.0, 0.5) + + def testOp_with7BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 63.4, 7, False, 0.0, 63.5, 0.5) + + # 7 bits, narrow range. + def testOp_with7BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 126.0, 7, True, 0.0, 126.0, 1.0) + + def testOp_with7BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 63.1, 7, True, 0.0, 63.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-63.1, -0.1, 7, True, -63.0, 0.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 62.9, 7, True, 0.0, 63.0, 0.5) + + def _TestOp(self, input_min, input_max, num_bits, narrow_range, + expected_nudged_input_min, expected_nudged_input_max, + expected_step): + inputs = np.array( + [ + expected_nudged_input_min - expected_step, + expected_nudged_input_min - 0.01, expected_nudged_input_min, + expected_nudged_input_min + 0.01, + expected_nudged_input_min + expected_step - 0.01, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step + 0.01, + expected_nudged_input_max - 0.01, expected_nudged_input_max, + expected_nudged_input_max + 0.01, + expected_nudged_input_max + expected_step + ], + dtype=np.float32) + gradients = np.arange(1, len(inputs) + 1, dtype=np.float32) + expected_backprops = np.array( + [0.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 0.0], + dtype=np.float32) + + with self.test_session() as session: + with self.test_scope(): + gradient_placeholder = array_ops.placeholder( + dtypes.float32, gradients.shape, name="gradients") + input_placeholder = array_ops.placeholder( + dtypes.float32, inputs.shape, name="inputs") + outputs = gen_array_ops.fake_quant_with_min_max_args_gradient( + gradient_placeholder, + input_placeholder, + min=input_min, + max=input_max, + num_bits=num_bits, + narrow_range=narrow_range) + backprops = session.run(outputs, { + gradient_placeholder: gradients, + input_placeholder: inputs + }) + self.assertAllCloseAccordingToType( + backprops, + expected_backprops, + rtol=1e-3, + atol=1e-5, + bfloat16_rtol=0.03) + + +class FakeQuantWithMinMaxVarsTest(XLATestCase): + """Test cases for FakeQuantWithMinMaxVars operation.""" + + # 8 bits, wide range. + def testOp_with8BitsNoScalingNoNudging(self): + self._TestOp(0.0, 255.0, 8, False, 0.0, 255.0, 1.0) + + def testOp_with8BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 128.0, 8, False, 0.0, 127.5, 0.5) + + def testOp_with8BitsScalingAndNudgingUp(self): + self._TestOp(-128.0, -0.5, 8, False, -127.5, 0.0, 0.5) + + def testOp_with8BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 127.4, 8, False, 0.0, 127.5, 0.5) + + # 8 bits, narrow range. + def testOp_with8BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 254.0, 8, True, 0.0, 254.0, 1.0) + + def testOp_with8BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 127.1, 8, True, 0.0, 127.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-127.1, -0.1, 8, True, -127.0, 0.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 126.9, 8, True, 0.0, 127.0, 0.5) + + # 7 bits, wide range. + def testOp_with7BitsNoScalingNoNudging(self): + self._TestOp(0.0, 127.0, 7, False, 0.0, 127.0, 1.0) + + def testOp_with7BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 64.0, 7, False, 0.0, 63.5, 0.5) + + def testOp_with7BitsScalingAndNudgingUp(self): + self._TestOp(-64.0, -0.5, 7, False, -63.5, 0.0, 0.5) + + def testOp_with7BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 63.4, 7, False, 0.0, 63.5, 0.5) + + # 7 bits, narrow range. + def testOp_with7BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 126.0, 7, True, 0.0, 126.0, 1.0) + + def testOp_with7BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 63.1, 7, True, 0.0, 63.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-63.1, -0.1, 7, True, -63.0, 0.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 62.9, 7, True, 0.0, 63.0, 0.5) + + def _TestOp(self, input_min, input_max, num_bits, narrow_range, + expected_nudged_input_min, expected_nudged_input_max, + expected_step): + inputs = np.array( + [ + expected_nudged_input_min - expected_step, + expected_nudged_input_min - 0.01, expected_nudged_input_min, + expected_nudged_input_min + 0.01, + expected_nudged_input_min + expected_step - 0.01, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step + 0.01, + expected_nudged_input_max - 0.01, expected_nudged_input_max, + expected_nudged_input_max + 0.01, + expected_nudged_input_max + expected_step + ], + dtype=np.float32) + expected = np.array( + [ + expected_nudged_input_min, expected_nudged_input_min, + expected_nudged_input_min, expected_nudged_input_min, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step, + expected_nudged_input_max, expected_nudged_input_max, + expected_nudged_input_max, expected_nudged_input_max + ], + dtype=np.float32) + + with self.test_session() as session: + with self.test_scope(): + input_placeholder = array_ops.placeholder( + dtypes.float32, inputs.shape, name="inputs") + min_placeholder = array_ops.placeholder(dtypes.float32, (), name="min") + max_placeholder = array_ops.placeholder(dtypes.float32, (), name="max") + outputs = array_ops.fake_quant_with_min_max_vars( + input_placeholder, + min_placeholder, + max_placeholder, + num_bits=num_bits, + narrow_range=narrow_range) + result = session.run( + outputs, { + input_placeholder: inputs, + min_placeholder: input_min, + max_placeholder: input_max + }) + self.assertAllCloseAccordingToType( + result, expected, rtol=1e-3, atol=1e-5, bfloat16_rtol=0.03) + + +class FakeQuantWithMinMaxVarsGradientTest(XLATestCase): + """Test cases for FakeQuantWithMinMaxVarsGradient operation.""" + + # 8 bits, wide range. + def testOp_with8BitsNoScalingNoNudging(self): + self._TestOp(0.0, 255.0, 8, False, 0.0, 255.0, 1.0) + + def testOp_with8BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 128.0, 8, False, 0.0, 127.5, 0.5) + + def testOp_with8BitsScalingAndNudgingUp(self): + self._TestOp(-128.0, -0.5, 8, False, -127.5, 0.0, 0.5) + + def testOp_with8BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 127.4, 8, False, 0.0, 127.5, 0.5) + + # 8 bits, narrow range. + def testOp_with8BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 254.0, 8, True, 0.0, 254.0, 1.0) + + def testOp_with8BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 127.1, 8, True, 0.0, 127.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-127.1, -0.1, 8, True, -127.0, 0.0, 0.5) + + def testOp_with8BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 126.9, 8, True, 0.0, 127.0, 0.5) + + # 7 bits, wide range. + def testOp_with7BitsNoScalingNoNudging(self): + self._TestOp(0.0, 127.0, 7, False, 0.0, 127.0, 1.0) + + def testOp_with7BitsScalingAndNudgingDown(self): + self._TestOp(0.5, 64.0, 7, False, 0.0, 63.5, 0.5) + + def testOp_with7BitsScalingAndNudgingUp(self): + self._TestOp(-64.0, -0.5, 7, False, -63.5, 0.0, 0.5) + + def testOp_with7BitsScalingAndNudgingBetween(self): + self._TestOp(-0.1, 63.4, 7, False, 0.0, 63.5, 0.5) + + # 7 bits, narrow range. + def testOp_with7BitsNarrowRangeNoScalingNoNudging(self): + self._TestOp(0.0, 126.0, 7, True, 0.0, 126.0, 1.0) + + def testOp_with7BitsNarrowRangeScalingAndNudgingDown(self): + self._TestOp(0.1, 63.1, 7, True, 0.0, 63.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingUp(self): + self._TestOp(-63.1, -0.1, 7, True, -63.0, 0.0, 0.5) + + def testOp_with7BitsNarrowRangeScalingAndNudgingBetween(self): + self._TestOp(-0.1, 62.9, 7, True, 0.0, 63.0, 0.5) + + def _TestOp(self, input_min, input_max, num_bits, narrow_range, + expected_nudged_input_min, expected_nudged_input_max, + expected_step): + inputs = np.array( + [ + expected_nudged_input_min - expected_step, + expected_nudged_input_min - 0.01, expected_nudged_input_min, + expected_nudged_input_min + 0.01, + expected_nudged_input_min + expected_step - 0.01, + expected_nudged_input_min + expected_step, + expected_nudged_input_min + expected_step + 0.01, + expected_nudged_input_max - 0.01, expected_nudged_input_max, + expected_nudged_input_max + 0.01, + expected_nudged_input_max + expected_step + ], + dtype=np.float32) + gradients = np.arange(1, len(inputs) + 1, dtype=np.float32) + expected_backprops_wrt_input = np.array( + [0.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 0.0], + dtype=np.float32) + expected_backprops_wrt_min = 1.0 + 2.0 + expected_backprops_wrt_max = 10.0 + 11.0 + + with self.test_session() as session: + with self.test_scope(): + gradient_placeholder = array_ops.placeholder( + dtypes.float32, gradients.shape, name="gradients") + input_placeholder = array_ops.placeholder( + dtypes.float32, inputs.shape, name="inputs") + min_placeholder = array_ops.placeholder(dtypes.float32, (), name="min") + max_placeholder = array_ops.placeholder(dtypes.float32, (), name="max") + outputs = array_ops.fake_quant_with_min_max_vars_gradient( + gradient_placeholder, + input_placeholder, + min_placeholder, + max_placeholder, + num_bits=num_bits, + narrow_range=narrow_range) + backprops_wrt_input, backprops_wrt_min, backprops_wrt_max = session.run( + outputs, { + gradient_placeholder: gradients, + input_placeholder: inputs, + min_placeholder: input_min, + max_placeholder: input_max + }) + self.assertAllCloseAccordingToType( + backprops_wrt_input, + expected_backprops_wrt_input, + rtol=1e-3, + atol=1e-5, + bfloat16_rtol=0.03) + self.assertAllCloseAccordingToType( + backprops_wrt_min, + expected_backprops_wrt_min, + rtol=1e-3, + atol=1e-5, + bfloat16_rtol=0.03) + self.assertAllCloseAccordingToType( + backprops_wrt_max, + expected_backprops_wrt_max, + rtol=1e-3, + atol=1e-5, + bfloat16_rtol=0.03) + + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/compiler/tests/gather_test.py b/tensorflow/compiler/tests/gather_test.py index 13cbe6f312f5175edaec28fa7a8f28064194b0e9..1a8c4519118f69ce51ca9a5eb95a9d706c7766cc 100644 --- a/tensorflow/compiler/tests/gather_test.py +++ b/tensorflow/compiler/tests/gather_test.py @@ -122,6 +122,20 @@ class GatherTest(xla_test.XLATestCase): gather_np = np.take(params, indices, axis=axis) self.assertAllEqual(gather_np, gather_value) + def testIndicesWithDifferentDimensions(self): + with self.test_session(): + for dtype in self.numeric_tf_types: + params = array_ops.placeholder(dtype=dtype) + indices = array_ops.placeholder(dtype=np.int32) + with self.test_scope(): + gather = array_ops.gather(params, indices) + self.assertAllEqual( + 7, gather.eval(feed_dict={params: [4, 7, 2], indices: 1})) + self.assertAllEqual( + [7], gather.eval(feed_dict={params: [4, 7, 2], indices: [1]})) + self.assertAllEqual( + [[7]], gather.eval(feed_dict={params: [4, 7, 2], indices: [[1]]})) + class GatherBenchmark(test.Benchmark): """Microbenchmarks for the gather op.""" diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py index 538fa8e8e570b83ed681ecc0501285520cabdecb..3bc41b7cfd72bec7572097f8c53eef314a4369f6 100644 --- a/tensorflow/compiler/tests/image_ops_test.py +++ b/tensorflow/compiler/tests/image_ops_test.py @@ -426,7 +426,7 @@ class ResizeBilinearTest(XLATestCase): with self.test_session() as sess, self.test_scope(): dtype = dtype or np.float32 grads = array_ops.placeholder(np.float32) - resized = gen_image_ops._resize_bilinear_grad( + resized = gen_image_ops.resize_bilinear_grad( grads, np.zeros([1, input_shape[0], input_shape[1], 1], dtype=dtype), align_corners=True) diff --git a/tensorflow/compiler/tests/lrn_ops_test.py b/tensorflow/compiler/tests/lrn_ops_test.py index 5d8d89224d4a778d84803811710bb095872e86b2..69bd8f7230d4394c45764d02a88fb0ec097c5756 100644 --- a/tensorflow/compiler/tests/lrn_ops_test.py +++ b/tensorflow/compiler/tests/lrn_ops_test.py @@ -115,11 +115,11 @@ class LRNTest(XLATestCase): out_image = constant_op.constant(out_image_vals, shape=shape) out_grads = constant_op.constant(out_grads_vals, shape=shape) with ops.device(CPU_DEVICE): - expected = gen_nn_ops._lrn_grad(out_grads, in_image, out_image, - depth_radius, bias, alpha, beta) + expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image, + depth_radius, bias, alpha, beta) with self.test_scope(): - actual = gen_nn_ops._lrn_grad(out_grads, in_image, out_image, - depth_radius, bias, alpha, beta) + actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image, + depth_radius, bias, alpha, beta) expected_val = expected.eval() actual_val = actual.eval() self.assertAllClose(actual_val, expected_val, rtol=1e-3) diff --git a/tensorflow/compiler/tests/pooling_ops_3d_test.py b/tensorflow/compiler/tests/pooling_ops_3d_test.py index eb48fe555a0b182ea7983cbd8c3b217d56350408..4eed903963a34a253ea5c409782d9a89a97a4fdf 100644 --- a/tensorflow/compiler/tests/pooling_ops_3d_test.py +++ b/tensorflow/compiler/tests/pooling_ops_3d_test.py @@ -33,7 +33,7 @@ from tensorflow.python.platform import test # MaxPoolGrad. def _AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding): del outputs # Unused by average-pooling gradients. - return gen_nn_ops._avg_pool3d_grad( + return gen_nn_ops.avg_pool3d_grad( inputs.get_shape().as_list(), output_gradients, ksize=ksize, @@ -263,7 +263,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding1_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[1, 3, 3, 3, 1], ksize=[1, 1, 1], strides=[1, 1, 1], @@ -272,7 +272,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_1_6_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 3, 6, 3], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -281,7 +281,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_1_7_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 5, 7, 3], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -290,7 +290,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_2_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 2, 2, 2, 3], ksize=[2, 2, 2], strides=[2, 2, 2], @@ -299,7 +299,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding1_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 2, 4, 1], ksize=[1, 1, 1], strides=[1, 1, 1], @@ -308,7 +308,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding2_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 2, 4, 1], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -317,7 +317,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding2_2_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 5, 2, 4, 3], ksize=[2, 2, 2], strides=[2, 2, 2], @@ -326,7 +326,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding3_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[1, 3, 3, 7, 1], ksize=[3, 3, 3], strides=[1, 1, 1], diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py index 7c19a99c4eb4be3ca34b3ce949216e557b0a681d..fe270af3d636c0824621f36360ce9e7d14d8fc91 100644 --- a/tensorflow/compiler/tests/pooling_ops_test.py +++ b/tensorflow/compiler/tests/pooling_ops_test.py @@ -292,8 +292,15 @@ class PoolGradTest(XLATestCase): CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0" - def _VerifyOneTest(self, pool_func, pool_grad_func, input_sizes, ksize, - strides, padding, data_format): + def _VerifyOneTest(self, + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + data_format, + pool_grad_grad_func=None): """Verifies the output values of the pooling gradient function. Args: @@ -304,9 +311,19 @@ class PoolGradTest(XLATestCase): strides: The stride dimensions padding: Padding type. data_format: The data format we use to run the pooling operation. + pool_grad_grad_func: Second-order gradient function, if available. """ total_size = np.prod(input_sizes) - x = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_sizes) + # TODO(b/73062247): MaxPoolGradGrad can confuse gradients when x is equally + # maximal at 16 bits. Switch to np.random.randn when resolved. + x = np.arange(1, total_size + 1, dtype=np.float32) + x *= (np.random.randint(2, size=total_size) * 2 - 1) # Flip signs randomly + # Verify some specifically interesting values... + x[np.random.choice(total_size)] = np.inf + x[np.random.choice(total_size)] = -np.inf + # TODO(b/74222344): Fix nan handling for max pool grad. + # x[np.random.choice(total_size)] = np.nan + x = x.reshape(input_sizes) with self.test_session() as sess: # Use the forward pool function to compute some corresponding outputs # (needed for the CPU device, and we need the shape in both cases). @@ -323,6 +340,8 @@ class PoolGradTest(XLATestCase): output_gradient_vals = np.arange( 1, output_vals.size + 1, dtype=np.float32) output_gradient_vals = output_gradient_vals.reshape(output_vals.shape) + output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32) + output_grad_grad_vals = output_grad_grad_vals.reshape(x.shape) # Use the Tensorflow CPU pooling gradient to compute the expected input # gradients. @@ -342,18 +361,36 @@ class PoolGradTest(XLATestCase): {inputs: x, output_gradients: output_gradient_vals}) + output_grad_gradients = array_ops.placeholder( + dtypes.float32, shape=expected_input_gradient_vals.shape) + if pool_grad_grad_func is not None: + expected_grad_gradients = pool_grad_grad_func( + inputs, + outputs, + output_grad_gradients, + ksize=ksize, + strides=strides, + padding=padding, + data_format="NHWC") + expected_grad_gradients_vals = sess.run(expected_grad_gradients, { + inputs: x, + output_grad_gradients: output_grad_grad_vals + }) + # Run the gradient op on the XLA device with self.test_scope(): outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape) xla_inputs = inputs xla_outputs = outputs xla_output_gradients = output_gradients + xla_output_grad_gradients = output_grad_gradients xla_ksize = ksize xla_strides = strides if data_format == "NCHW": xla_inputs = NHWCToNCHW(inputs) xla_outputs = NHWCToNCHW(outputs) xla_output_gradients = NHWCToNCHW(output_gradients) + xla_output_grad_gradients = NHWCToNCHW(output_grad_gradients) xla_ksize = NHWCToNCHW(ksize) xla_strides = NHWCToNCHW(strides) actual_input_gradients = pool_grad_func( @@ -366,22 +403,54 @@ class PoolGradTest(XLATestCase): data_format=data_format) if data_format == "NCHW": actual_input_gradients = NCHWToNHWC(actual_input_gradients) - actual = sess.run(actual_input_gradients, { + if pool_grad_grad_func is not None: + actual_grad_gradients = pool_grad_grad_func( + xla_inputs, + xla_outputs, + xla_output_grad_gradients, + ksize=xla_ksize, + strides=xla_strides, + padding=padding, + data_format=data_format) + if data_format == "NCHW": + actual_grad_gradients = NCHWToNHWC(actual_grad_gradients) + actual_input_gradients_vals = sess.run(actual_input_gradients, { inputs: x, outputs: output_vals, output_gradients: output_gradient_vals }) - # Compare the Tensorflow and XLA results. self.assertAllClose( - expected_input_gradient_vals.flatten(), - actual.flatten(), + expected_input_gradient_vals, + actual_input_gradients_vals, rtol=1e-4, atol=1e-6) - self.assertShapeEqual(actual, inputs) - - def _VerifyValues(self, pool_func, pool_grad_func, input_sizes, ksize, - strides, padding): + self.assertShapeEqual(actual_input_gradients_vals, inputs) + + if pool_grad_grad_func is not None: + actual_grad_gradients_vals = sess.run( + actual_grad_gradients, { + inputs: x, + outputs: output_vals, + output_grad_gradients: output_grad_grad_vals + }) + + # Compare the Tensorflow and XLA results. + self.assertAllClose( + expected_grad_gradients_vals, + actual_grad_gradients_vals, + rtol=1e-4, + atol=1e-6) + self.assertShapeEqual(actual_grad_gradients_vals, outputs) + + def _VerifyValues(self, + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + pool_grad_grad_func=None): """Verifies the output values of the pooling function. Args: @@ -391,12 +460,20 @@ class PoolGradTest(XLATestCase): ksize: The kernel size dimensions strides: The stride dimensions padding: Padding type. + pool_grad_grad_func: Second-order gradient function, if available. """ for data_format in GetTestConfigs(): - self._VerifyOneTest(pool_func, pool_grad_func, input_sizes, ksize, - strides, padding, data_format) - - def _TestPooling(self, forward_op, backward_op): + self._VerifyOneTest( + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + data_format, + pool_grad_grad_func=pool_grad_grad_func) + + def _TestPooling(self, forward_op, backward_op, pool_grad_grad_func=None): # VALID padding self._VerifyValues( forward_op, @@ -404,7 +481,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 3, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding self._VerifyValues( @@ -413,7 +491,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 2, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, non square window self._VerifyValues( @@ -422,7 +501,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 2, 2, 1], ksize=[1, 1, 2, 1], strides=[1, 1, 1, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # VALID padding, uneven stride self._VerifyValues( @@ -431,14 +511,16 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 1, 2, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) self._VerifyValues( forward_op, backward_op, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 2, 1, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, size 4 input self._VerifyValues( @@ -447,7 +529,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 4, 4, 4], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, size 8 input self._VerifyValues( @@ -456,10 +539,14 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) def testMaxPool(self): - self._TestPooling(nn_ops.max_pool, gen_nn_ops._max_pool_grad) + self._TestPooling( + nn_ops.max_pool, + gen_nn_ops.max_pool_grad, + pool_grad_grad_func=gen_nn_ops.max_pool_grad_grad) def testAvgPool(self): # Wrapper around AvgPoolGrad that ignores extra arguments needed by @@ -467,7 +554,7 @@ class PoolGradTest(XLATestCase): def AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding, data_format): del outputs # Unused by average-pooling gradients. - return gen_nn_ops._avg_pool_grad( + return gen_nn_ops.avg_pool_grad( inputs.get_shape().as_list(), output_gradients, ksize=ksize, @@ -483,7 +570,7 @@ class PoolGradTest(XLATestCase): def testMaxPoolKernelSmallerThanStrideValid(self): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 7, 7, 1], ksize=[1, 2, 2, 1], strides=[1, 3, 3, 1], @@ -492,7 +579,7 @@ class PoolGradTest(XLATestCase): def testMaxPoolKernelSmallerThanStrideSame(self): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 3, 3, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -500,7 +587,7 @@ class PoolGradTest(XLATestCase): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 4, 4, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], diff --git a/tensorflow/compiler/tests/scatter_nd_op_test.py b/tensorflow/compiler/tests/scatter_nd_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..638946e234daf28dc4a34e6c33fc0f78b8e8699b --- /dev/null +++ b/tensorflow/compiler/tests/scatter_nd_op_test.py @@ -0,0 +1,188 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.ops.tf.scatter_nd.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools + +import numpy as np + +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import errors +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +def _AsType(v, vtype): + return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v) + + +def _FlatInnerDims(tensor, ndims=2): + shape = list(tensor.shape) + return tensor.reshape( + [functools.reduce(lambda x, y: x * y, shape[:-ndims + 1], 1)] + + shape[-ndims + 1:]) + + +def _FlatOuterDims(tensor, ndims=2): + shape = list(tensor.shape) + return tensor.reshape( + shape[:ndims - 1] + + [functools.reduce(lambda x, y: x * y, shape[ndims - 1:], 1)]) + + +def _NumpyScatterNd(ref, indices, updates, op): + ixdim = indices.shape[-1] + num_updates = indices.size // ixdim + total_nd = len(ref.shape) + slice_size = 1 + for i in range(ixdim, total_nd): + slice_size *= ref.shape[i] + flat_indices = _FlatInnerDims(indices) + flat_updates = updates.reshape((num_updates, slice_size)) + output_flat = _FlatOuterDims(ref, ixdim + 1) + for ix_updates, ix_output in enumerate(flat_indices): + ix_output = tuple(ix_output) + output_flat[ix_output] = op(output_flat[ix_output], + flat_updates[ix_updates]) + return output_flat.reshape(ref.shape) + + +def _NumpyUpdate(indices, updates, shape): + ref = np.zeros(shape, dtype=updates.dtype) + return _NumpyScatterNd(ref, indices, updates, lambda p, u: u) + + +class ScatterNdTest(XLATestCase): + + def _VariableRankTest(self, + np_scatter, + tf_scatter, + vtype, + itype, + repeat_indices=False): + np.random.seed(8) + ref_shapes = [(3, 6), (3, 6), (3, 6, 9), (3, 6, 9), (3, 6, 9), (3, 6, 9)] + indices_shapes = [(2,), (2, 2), (2,), (2, 2), (2, 3), (2, 3, 3)] + for ref_shape, indices_shape in zip(ref_shapes, indices_shapes): + num_updates = indices_shape[0] + ixdim = indices_shape[-1] + + indexable_area_shape = () + for i in range(ixdim): + indexable_area_shape += (ref_shape[i],) + all_indices = [ + list(coord) + for coord, _ in np.ndenumerate(np.empty(indexable_area_shape, vtype)) + ] + np.random.shuffle(all_indices) + indices = np.array(all_indices[:num_updates]) + + if num_updates > 1 and repeat_indices: + indices = indices[:num_updates // 2] + for _ in range(num_updates - num_updates // 2): + indices = np.append( + indices, [indices[np.random.randint(num_updates // 2)]], axis=0) + np.random.shuffle(indices) + indices = _AsType(indices[:num_updates], itype) + + updates_shape = (num_updates,) + for i in range(ixdim, len(ref_shape)): + updates_shape += (ref_shape[i],) + updates = _AsType(np.random.randn(*(updates_shape)), vtype) + + # Scatter via numpy + np_out = np_scatter(indices, updates, ref_shape) + # Scatter via tensorflow + tf_out = tf_scatter(indices, updates, ref_shape) + + self.assertAllClose(np_out, tf_out) + + def _VariableRankTests(self, np_scatter, tf_scatter): + for vtype in self.numeric_types: + for itype in set([np.int32, np.int64]).intersection(set(self.int_types)): + self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) + + def _runScatterNd(self, indices, updates, shape): + with self.test_session(): + updates_placeholder = array_ops.placeholder(updates.dtype) + indices_placeholder = array_ops.placeholder(indices.dtype) + with self.test_scope(): + output = array_ops.scatter_nd(indices_placeholder, updates_placeholder, + shape) + feed_dict = {updates_placeholder: updates, indices_placeholder: indices} + return output.eval(feed_dict=feed_dict) + + def testSimple(self): + indices = np.array([[4], [3], [1], [7]], dtype=np.int32) + updates = np.array([9, 10, 11, 12], dtype=np.float32) + expected = np.array([0, 11, 0, 10, 9, 0, 0, 12], dtype=np.int32) + self.assertAllEqual(expected, self._runScatterNd(indices, updates, [8])) + + def testSimple2(self): + indices = np.array([[1, 0], [1, 1]], dtype=np.int32) + updates = np.array([11., 12.], dtype=np.float32) + expected = np.array([[0., 0.], [11., 12.], [0., 0.]], dtype=np.float32) + self.assertAllEqual(expected, self._runScatterNd(indices, updates, [3, 2])) + + def testSimple3(self): + indices = np.array([[1]], dtype=np.int32) + updates = np.array([[11., 12.]], dtype=np.float32) + expected = np.array([[0., 0.], [11., 12.], [0., 0.]]) + self.assertAllEqual(expected, self._runScatterNd(indices, updates, [3, 2])) + + def testVariableRankUpdate(self): + self._VariableRankTests(_NumpyUpdate, self._runScatterNd) + + def testExtraIndicesDimensions(self): + indices = np.zeros([1, 1, 2], np.int32) + updates = np.zeros([1, 1], np.int32) + expected = np.zeros([2, 2], dtype=np.int32) + self.assertAllEqual(expected, self._runScatterNd(indices, updates, [2, 2])) + + def testRank3InvalidShape1(self): + indices = np.zeros([3, 2, 2], np.int32) + updates = np.zeros([2, 2, 2], np.int32) + with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError, + "Must have updates.shape"): + self._runScatterNd(indices, updates, [2, 2, 2]) + + def testRank3InvalidShape2(self): + indices = np.zeros([2, 2, 1], np.int32) + updates = np.zeros([2, 2], np.int32) + with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError, + "Must have updates.shape"): + self._runScatterNd(indices, updates, [2, 2, 2]) + + def testScatterOutOfRange(self): + updates = np.array([-3, -4, -5]).astype(np.float32) + + # Indices all in range, no problem. + indices = np.array([[2], [0], [5]], dtype=np.int32) + self._runScatterNd(indices, updates, [6]) + + # Indices out of range should not fail. It produces implementation-defined + # output. + indices = np.array([[-1], [0], [5]], dtype=np.int32) + self._runScatterNd(indices, updates, [6]) + indices = np.array([[2], [0], [6]], dtype=np.int32) + self._runScatterNd(indices, updates, [6]) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py index 260a04421b62310c109d8f0ea72875a50c234bb0..4a9c0e7471f9cdb2a47b54705495d2dda9748890 100644 --- a/tensorflow/compiler/tests/segment_reduction_ops_test.py +++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py @@ -60,6 +60,14 @@ class SegmentReductionOpsTest(XLATestCase): np.array([0, 1, 2, 3, 4, 5], dtype=dtype), np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4)) + def testUnsortedSegmentSum1DIndices1DDataNegativeIndices(self): + for dtype in self.numeric_types: + self.assertAllClose( + np.array([6, 3, 0, 6], dtype=dtype), + self.UnsortedSegmentSum( + np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), + np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) + def testUnsortedSegmentSum1DIndices2DDataDisjoint(self): for dtype in self.numeric_types: data = np.array( diff --git a/tensorflow/compiler/tests/slice_ops_test.py b/tensorflow/compiler/tests/slice_ops_test.py index a7cbfb04003c397212a35e16c6b23d7c2a18f7df..305ca0c6b78d3ef985deb38816f9388e7983906b 100644 --- a/tensorflow/compiler/tests/slice_ops_test.py +++ b/tensorflow/compiler/tests/slice_ops_test.py @@ -20,6 +20,7 @@ from __future__ import print_function from tensorflow.compiler.tests.xla_test import XLATestCase from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.platform import googletest @@ -137,6 +138,34 @@ class StridedSliceTest(XLATestCase): self.assertAllEqual([6, 4], result) + def test2DDegenerate(self): + for dtype in self.numeric_types: + with self.test_session(): + i = array_ops.placeholder(dtype, shape=[2, 3]) + with self.test_scope(): + o = array_ops.strided_slice(i, [-1, 0], [0, 3]) + params = { + i: [[0, 1, 2], + [3, 4, 5]] + } + result = o.eval(feed_dict=params) + + self.assertEqual(tensor_shape.TensorShape((0, 3)), result.shape) + + def test2DDegenerateNegativeStride(self): + for dtype in self.numeric_types: + with self.test_session(): + i = array_ops.placeholder(dtype, shape=[2, 3]) + with self.test_scope(): + o = array_ops.strided_slice(i, [0, 0], [-1, 3], [-1, 1]) + params = { + i: [[0, 1, 2], + [3, 4, 5]] + } + result = o.eval(feed_dict=params) + + self.assertEqual(tensor_shape.TensorShape((0, 3)), result.shape) + def test3D(self): for dtype in self.numeric_types: with self.test_session(): diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py index c013f4b50a4cf95be8028248c52b10b1c3be2bd3..92518aadc4bf5c601cfb4192c093799784b6aa72 100644 --- a/tensorflow/compiler/tests/spacetobatch_op_test.py +++ b/tensorflow/compiler/tests/spacetobatch_op_test.py @@ -75,11 +75,11 @@ class SpaceToBatchTest(XLATestCase): for dtype in self.float_types: # outputs = space_to_batch(inputs) placeholder = array_ops.placeholder(dtype) - x_tf = gen_array_ops._space_to_batch( + x_tf = gen_array_ops.space_to_batch( placeholder, paddings, block_size=block_size) self.assertAllEqual(sess.run(x_tf, {placeholder: inputs}), outputs) # inputs = batch_to_space(outputs) - x_tf = gen_array_ops._batch_to_space( + x_tf = gen_array_ops.batch_to_space( placeholder, paddings, block_size=block_size) self.assertAllEqual(sess.run(x_tf, {placeholder: outputs}), inputs) diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py index 2b9c2279737ccee531d488d27ccdb0cafa1dc8fc..94342f9567ca71274609e63b0482d55637c98d51 100644 --- a/tensorflow/compiler/tests/stack_ops_test.py +++ b/tensorflow/compiler/tests/stack_ops_test.py @@ -34,33 +34,33 @@ class StackOpTest(XLATestCase): with self.test_session(), self.test_scope(): size = array_ops.placeholder(dtypes.int32) v = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, v) + h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, v) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval({size: 5, v: [[4.0, 5.0]]})) def testStackPushPopSwap(self): with self.test_session(), self.test_scope(): a = np.arange(2000) x = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True) + h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose(a, c1.eval({x: a})) def testMultiStack(self): with self.test_session(), self.test_scope(): v = array_ops.placeholder(dtypes.float32) - h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, v) + h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c1 = gen_data_flow_ops.stack_push_v2(h1, v) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="bar") + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval({v: 4.0})) @@ -69,15 +69,15 @@ class StackOpTest(XLATestCase): with self.test_session() as sess, self.test_scope(): v1 = array_ops.placeholder(dtypes.float32) v2 = array_ops.placeholder(dtypes.float32) - h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") + h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, v1) + c1 = gen_data_flow_ops.stack_push_v2(h1, v1) with ops.control_dependencies([c1]): - c2 = gen_data_flow_ops._stack_push_v2(h2, v2) + c2 = gen_data_flow_ops.stack_push_v2(h2, v2) with ops.control_dependencies([c2]): - pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) out1, out2 = sess.run([pop1, pop2], {v1: 4.0, v2: 5.0}) self.assertAllClose(out1, 4.0) @@ -86,17 +86,17 @@ class StackOpTest(XLATestCase): def testCloseStack(self): with self.test_session() as sess, self.test_scope(): size = array_ops.placeholder(dtypes.int32) - h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close_v2(h) + h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1, {size: 5}) def testPushCloseStack(self): with self.test_session() as sess, self.test_scope(): v = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, v) + h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, v) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1, {v: [[4.0, 5.0]]}) diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index a62925a1818da00cb0a9e82e1281db20fb38b208..7624d6e4b2e2ece6a61155743fc8b866f6903f32 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -338,7 +338,7 @@ class TensorArrayTest(xla_test.XLATestCase): w0 = ta.write(0, [[4.0, 5.0]]) # Test reading wrong datatype. - r0_bad = gen_data_flow_ops._tensor_array_read_v3( + r0_bad = gen_data_flow_ops.tensor_array_read_v3( handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow) with self.assertRaisesOpError("TensorArray dtype is "): r0_bad.eval() diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 3c7dfef03dfb5d86dd63fd4aa84ad56081833035..eb20ca501c80b01c76198e1ad54173f1c601714d 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -58,6 +58,15 @@ xla_proto_library( ], ) +xla_proto_library( + name = "host_compute_metadata_proto", + srcs = ["host_compute_metadata.proto"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:protos_all_cc", + ], +) + cc_library( name = "tf2xla", srcs = ["tf2xla.cc"], @@ -149,6 +158,7 @@ cc_library( ":common", ":dump_graph", ":functionalize_control_flow", + ":host_compute_metadata_proto", ":sharding_util", ":tf2xla_util", "//tensorflow/compiler/tf2xla/lib:util", @@ -312,6 +322,7 @@ tf_cc_test( "//tensorflow/cc:cc_ops", "//tensorflow/cc:function_ops", "//tensorflow/cc:ops", + "//tensorflow/cc:resource_variable_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", diff --git a/tensorflow/compiler/tf2xla/const_analysis.cc b/tensorflow/compiler/tf2xla/const_analysis.cc index 0249500910c6ae441f038fe9ad6178794f1997ac..6f46532419d3389bafe8c3bf41fa41e8a3e173b7 100644 --- a/tensorflow/compiler/tf2xla/const_analysis.cc +++ b/tensorflow/compiler/tf2xla/const_analysis.cc @@ -37,7 +37,7 @@ Status BackwardsConstAnalysis(const Graph& g, }; Status status; - std::unordered_set must_be_const; + std::unordered_set must_be_const; auto visit = [&status, &metadata_ops, &must_be_const, compile_time_const_args](Node* node) { if (!status.ok()) return; @@ -55,7 +55,7 @@ Status BackwardsConstAnalysis(const Graph& g, compile_time_const_args->at(index) = true; return; } - for (Node* pred : node->in_nodes()) { + for (const Node* pred : node->in_nodes()) { must_be_const.insert(pred); } return; @@ -64,7 +64,7 @@ Status BackwardsConstAnalysis(const Graph& g, // Mark any compile-time constant operator arguments as const. const std::unordered_set* const_inputs = XlaOpRegistry::CompileTimeConstantInputs(node->type_string()); - if (!const_inputs) return; + if (!const_inputs || const_inputs->empty()) return; NameRangeMap input_name_ranges; status = diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index bf304102ede610e952a424f0b24505a14692f8ed..8b7beef83ec2ed0df780d6a9cb2a4bcf737d008b 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -285,7 +285,8 @@ Status BuildLoopBody(const Graph& graph, Frame* frame, Status FunctionalizeLoop(Graph* graph, Frame* frame, FunctionLibraryDefinition* library) { VLOG(2) << "Frame " << frame->name << " before: " - << dump_graph::DumpGraphToFile("functionalize_before", *graph); + << dump_graph::DumpGraphToFile("functionalize_before", *graph, + library); // Split loop-varying Enter nodes with multiple successors. If the same // Tensor is fed as input to multiple loop arguments, we may end up with a @@ -470,7 +471,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, TF_RETURN_IF_ERROR(BuildLoopBody(*graph, frame, &arg_types, &body_graph)); VLOG(2) << "Frame " << frame->name << " condition: " - << dump_graph::DumpGraphToFile("loop_condition", *cond_graph) + << dump_graph::DumpGraphToFile("loop_condition", *cond_graph, library) << " body: " << dump_graph::DumpGraphToFile("loop_body", *body_graph); static std::atomic sequence_num(0LL); @@ -551,7 +552,8 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, frame->parent->nodes.insert(while_node); VLOG(2) << "Frame " << frame->name << " after: " - << dump_graph::DumpGraphToFile("functionalize_after", *graph); + << dump_graph::DumpGraphToFile("functionalize_after", *graph, + library); return Status::OK(); } @@ -581,14 +583,16 @@ class FunctionalizeCond { // CondArgNode represents a input to the conditional and its corresponding // switch nodes. struct CondArgNode { - explicit CondArgNode(Node* input) : input(input) {} + explicit CondArgNode(Node* src, int src_output) + : src(src), src_output(src_output) {} string ToString() const { - return strings::StrCat("input=", input->name(), - " switches=", NodesToString(switch_nodes)); + return strings::StrCat("src=", src->name(), ":", src_output, + " switches=", NodesToString(switches)); } - Node* input; - std::vector switch_nodes; + Node* src; + int src_output; + std::vector switches; }; using CondArgNodes = std::vector; @@ -602,15 +606,23 @@ class FunctionalizeCond { int count; }; - struct PredicateSwitches { - explicit PredicateSwitches(Node* predicate) : predicate(predicate) {} + // Group of switch nodes that will be part of the same XlaIf. + struct SwitchCluster { + explicit SwitchCluster(const Edge* predicate_edge) + : predicate_edge(predicate_edge) {} + string ToString() const { + return strings::StrCat(name, " predicate=", predicate_edge->src()->name(), + " switches=", NodesToString(switches)); + } - Node* predicate; + string name; + const Edge* predicate_edge; std::vector switches; }; - FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library) - : library_(library), graph_(graph) {} + FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library, + bool dump_graphs) + : library_(library), graph_(graph), dump_graphs_(dump_graphs) {} // Perform the actual cond functionalization. Iterate over groups of switch // nodes (linked by common predicate), from innermost to outermost, and @@ -621,40 +633,38 @@ class FunctionalizeCond { // frontier (the nodes where the cond ends). StatusOr, std::unordered_set>> - DetermineBranchMapAndFrontier(const std::vector& switches); + DetermineBranchMapAndFrontier(const SwitchCluster& switch_cluster); // Returns XlaIf node created from subgraph of merge and switch nodes. This // encapsulates the process of extracting the bodies needed for the then and // else branch, creates a XlaIf node, removing the nodes of the branches from // the graph and replacing the merge node with a XlaIf. StatusOr ConvertToXlaIf(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, - const std::vector& merge_nodes, - Node* predicate); + const SwitchCluster& switch_cluster, + const std::vector& switches); // Builds a XlaIfOp to replace the Switch-Graph-Merge cluster with. StatusOr BuildAndAddXlaIfOp(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, - const std::vector& merge_nodes, - Node* predicate); + const SwitchCluster& switch_cluster, + const std::vector& merge_nodes); // Extracts a function body corresponding to the given input edge of the merge // node. Status ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, + const std::vector& switches, const std::vector& merge_nodes, int input_edge, Graph* body); // Adds all the input edges to `if_node` corresponding to the arguments. - Status AddInputEdges(const CondArgNodes& cond_arg_nodes, Node* predicate, - Node* if_node); + Status AddInputEdges(const CondArgNodes& cond_arg_nodes, + const Edge* predicate_edge, Node* if_node); // Adds all output edges from the `if_node`. Status AddOutputEdges(const std::vector& outputs, Node* if_node); - // Returns the switches of graph_ (along with grouping predicates) in - // postorder. Dead switch nodes are skipped and removed from the graph. - std::vector DeterminePredicateSwitchOrder(); + // Returns the switch clusters of graph_ in postorder. Dead switch nodes are + // skipped and removed from the graph. + StatusOr> DeterminePredicateSwitchOrder(); // Update the state for destination based on the state of source and the node // being updated. @@ -677,6 +687,7 @@ class FunctionalizeCond { FunctionLibraryDefinition* library_; Graph* graph_; + bool dump_graphs_; }; bool IsDeadSwitch(const Node* node) { @@ -724,10 +735,13 @@ Status FunctionalizeCond::ValidateFrontier( ") in both Else and Then branch should be in Both."); } } - if (pending[kBoth].empty() && pending[kThenBranch].empty() && - pending[kElseBranch].empty()) { - return errors::Internal("Unexpected empty frontier for switch nodes"); - } + // An empty frontier indicates a dead switch. Above we attempt to remove dead + // switch nodes, but not all are removed so don't treat it as an error yet. + // TODO(jpienaar): Find out why dead switch nodes remain. + // if (pending[kBoth].empty() && pending[kThenBranch].empty() && + // pending[kElseBranch].empty()) { + // return errors::Internal("Unexpected empty frontier for switch nodes"); + // } return Status::OK(); } @@ -745,8 +759,8 @@ Status FunctionalizeCond::Join(const ForwardFlowNode& src_state, if (IsMerge(dst)) { dst_state->branch = Branch::kBoth; } else { - return errors::Internal("Illegal merge: ", src_state.ToString(), " with ", - dst_state->ToString(), " for ", + return errors::Internal("Illegal merge:\n", src_state.ToString(), + " with ", dst_state->ToString(), " for\n", dst->DebugString()); } } @@ -754,48 +768,217 @@ Status FunctionalizeCond::Join(const ForwardFlowNode& src_state, return Status::OK(); } -std::vector +StatusOr> FunctionalizeCond::DeterminePredicateSwitchOrder() { + struct Cluster { + bool operator==(const Cluster& other) const { + return representative == other.representative; + } + int representative = -1; + }; + + // Perform a DFS over the graph and + // * Determine the reverse topological order of the nodes (there should be no + // cycles at this point so the post-order numbering corresponds to the + // reverse topological sorting); + // * Identify dead switches; + // * Initialize the cluster's representative; + std::vector> clusters(graph_->num_node_ids()); std::vector dead_switches; std::vector switch_order; - DFS(*graph_, nullptr, [this, &dead_switches, &switch_order](Node* n) { + std::vector rev_topo_sorted_nodes; + DFS(*graph_, nullptr, [&](Node* n) { + clusters[n->id()].Get().representative = n->id(); if (IsSwitch(n)) { if (IsDeadSwitch(n)) { dead_switches.push_back(n); } else { + rev_topo_sorted_nodes.push_back(n); switch_order.push_back(n); } + } else if (n->IsOp()) { + // Exclude src and sink nodes from further consideration. + rev_topo_sorted_nodes.push_back(n); } }); + std::vector switch_clusters; + // Return early if there are no switches in the graph. + if (switch_order.empty()) { + return switch_clusters; + } + // Remove all dead switch nodes. for (Node* n : dead_switches) { VLOG(2) << "Removing dead switch: " << n->DebugString(); graph_->RemoveNode(n); } - std::vector predicate_switch_order; - if (switch_order.empty()) { - return predicate_switch_order; + // Identify switch nodes that are part of the same control flow context by + // considering the operands of operations: an operation is part of the same + // control context as its operands unless the operation is a switch. Control + // dependencies are considered part of the same control flow context if the + // switch depth is the same (see comment below). + + // entry_cluster records the input cluster to a switch node. This is used when + // merging with a merge node where the dst's cluster is merged with the entry + // cluster of the merge node's cluster (which corresponds to a switch cluster + // and so has an entry cluster). + std::unordered_map*> entry_cluster; + + // Returns the output cluster of a node. Where the output cluster is cluster + // where the output of the node is used. For non-merge nodes this is simply + // the cluster they are part of, while for merge nodes it is the entry cluster + // of the cluster they are part of (this will correspond to the entry node of + // a switch node that dominates the merge). + auto find_output_cluster = [&](Node* n) { + UnionFind* cluster = &clusters[n->id()]; + if (!IsMerge(n)) return cluster; + auto it = entry_cluster.find(clusters[n->id()].Get().representative); + // If the cluster is not found in the entry_cluster map then an + // instruction not dominated by a switch node has been merged into the + // cluster of the merge. This indicates a failure of the clustering. + CHECK(it != entry_cluster.end()) + << "Unable to find entry for n=" << n->id() << " (" + << cluster->Get().representative << ")"; + return it->second; + }; + + // TODO(jpienaar): This could be combined with DetermineBranchMapAndFrontier. + std::vector switch_depth(graph_->num_node_ids()); + for (auto it = rev_topo_sorted_nodes.rbegin(); + it != rev_topo_sorted_nodes.rend(); ++it) { + Node* n = *it; + + // Compute switch depth. + int new_switch_depth = 0; + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + new_switch_depth = std::max( + new_switch_depth, switch_depth[src->id()] - (IsMerge(src) ? 1 : 0)); + } + switch_depth[n->id()] = new_switch_depth + (IsSwitch(n) ? 1 : 0); + + // Only merge the input operands of a switch. The switch's clustering itself + // is determined by the interaction of the switch's outputs. + if (IsSwitch(n)) { + Node* input; + TF_CHECK_OK(n->input_node(0, &input)); + entry_cluster[n->id()] = find_output_cluster(input); + UnionFind* cluster = entry_cluster[n->id()]; + int cluster_depth = switch_depth[cluster->Get().representative]; + // Merge the inputs of the switch node with one another. This results in + // predicates and control input residing in the same cluster. + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + UnionFind* src_cluster = find_output_cluster(src); + int src_cluster_depth = switch_depth[src_cluster->Get().representative]; + if (cluster_depth != src_cluster_depth) { + return errors::InvalidArgument( + "Unable to functionalize control flow in graph: Switch ('", + n->name(), "') has operands ('", input->name(), "' and '", + src->name(), "') that have different switch depths (", + cluster_depth, " != ", src_cluster_depth, ")"); + } + cluster->Merge(src_cluster); + } + continue; + } + + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + if (!src->IsOp()) continue; + UnionFind* cluster = find_output_cluster(src); + // Merge a node with its data operands and with its control operands if + // the src and dst are in the same ControlContext. The ControlContext is + // not explicitly available here, and instead the switch depth is used as + // a proxy here. Due to the invariant that control edges can only be from + // a containing scope to an inner scope or from the inner scope to its + // containing scope (for exit nodes), the switch depth will only match if + // the src and dst are in the same ControlContext. Control edges between + // ControlContexts are handled during the extraction. + int src_id = cluster->Get().representative; + int src_depth = switch_depth[src_id]; + if (!e->IsControlEdge() || new_switch_depth == src_depth) { + if (src_depth != new_switch_depth) { + return errors::InvalidArgument( + "Unable to functionalize control flow in graph: Operand ('", + src->name(), "') and operator ('", n->name(), + "') have different switch depths (", src_depth, + " != ", new_switch_depth, ")"); + } + cluster->Merge(&clusters[n->id()]); + } + } + } + + if (dump_graphs_) { + // Mark the switch cluster each node is part of. + for (Node* n : graph_->nodes()) { + n->ClearAttr("_XlaFunctionalizeSwitchGroup"); + n->AddAttr("_XlaFunctionalizeSwitchGroup", + clusters[n->id()].Get().representative); + } + LOG(INFO) << "FunctionalizeControlFlow (with_clusters): " + << dump_graph::DumpGraphToFile("functionalize_clustered", *graph_, + library_); + } + + // Verify all the nodes of a cluster are at the same depth. + std::unordered_map> cluster_to_depth_node; + for (Node* n : graph_->nodes()) { + int depth = switch_depth[n->id()]; + int cluster_rep = clusters[n->id()].Get().representative; + auto it = cluster_to_depth_node.find(cluster_rep); + if (it == cluster_to_depth_node.end()) { + cluster_to_depth_node[cluster_rep] = std::make_pair(depth, n); + } else { + if (it->second.first != depth) { + return errors::Internal( + "Illegal clustering created, mismatch in depths:", "\n\t", + n->DebugString(), "(", clusters[n->id()].Get().representative, + ") at depth=", depth, " vs\n\t", it->second.second->DebugString(), + "(", clusters[n->id()].Get().representative, ") at depth ", + it->second.first); + } + } } + struct Hash { + size_t operator()(const std::pair& item) const { + return Hash64Combine(hash()(item.first), + std::hash()(item.second.representative)); + } + }; + // Merge Switch nodes with common predicate. - std::unordered_map predicate_index; + std::unordered_map, int, Hash> predicate_index; // The nodes in switch_order are in reverse topological order, but the // clustered switches need not be (i.e., when considered as a cluster one // element of a cluster may be later in the topological order than another // node whose cluster is later in the topological order of clustered // switches). for (auto it = switch_order.rbegin(); it != switch_order.rend(); ++it) { - Node* pred; - TF_CHECK_OK((*it)->input_node(1, &pred)); - if (predicate_index.find(pred) == predicate_index.end()) { - predicate_index[pred] = predicate_switch_order.size(); - predicate_switch_order.emplace_back(pred); + const Edge* pred_edge; + TF_CHECK_OK((*it)->input_edge(1, &pred_edge)); + // The predicate can be preceded by a identity node. Look through identity + // nodes to predicate. + while (pred_edge->src()->IsIdentity()) { + TF_CHECK_OK(pred_edge->src()->input_edge(0, &pred_edge)); } - predicate_switch_order[predicate_index[pred]].switches.push_back(*it); + auto repr = std::make_pair(pred_edge->src(), clusters[(*it)->id()].Get()); + if (predicate_index.find(repr) == predicate_index.end()) { + predicate_index[repr] = switch_clusters.size(); + switch_clusters.emplace_back(pred_edge); + // Generate a name by concatenating with the cluster representative as + // there could be multiple switch clusters with the same predicate. + switch_clusters[predicate_index[repr]].name = strings::StrCat( + pred_edge->src()->name(), "_", repr.second.representative, "_If"); + } + switch_clusters[predicate_index[repr]].switches.push_back(*it); } - return predicate_switch_order; + + return switch_clusters; } StatusOr> @@ -843,10 +1026,10 @@ StatusOr< std::pair, std::unordered_set>> FunctionalizeCond::DetermineBranchMapAndFrontier( - const std::vector& switches) { + const SwitchCluster& switch_cluster) { std::unordered_map branch_map; std::unordered_set frontier; - std::vector stack = switches; + std::vector stack = switch_cluster.switches; std::vector visited(graph_->num_node_ids(), false); while (!stack.empty()) { Node* n = stack.back(); @@ -869,9 +1052,12 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( ForwardFlowNode& ffn = branch_map[out]; if (IsSwitch(n)) { int index = e->IsControlEdge() ? Branch::kNeither : e->src_output(); - TF_RETURN_IF_ERROR(Join(ForwardFlowNode(Branch(index)), out, &ffn)); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + Join(ForwardFlowNode(Branch(index)), out, &ffn), " when joining ", + e->DebugString()); } else { - TF_RETURN_IF_ERROR(Join(branch_map[n], out, &ffn)); + TF_RETURN_WITH_CONTEXT_IF_ERROR(Join(branch_map[n], out, &ffn), + " when joining ", e->DebugString()); } if (IsMerge(out)) { if (out->in_edges().size() == ffn.count) { @@ -888,7 +1074,7 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } } - if (VLOG_IS_ON(2)) { + if (dump_graphs_) { for (const auto& kv : branch_map) { // Append attribute to the graph if running with logging to make the // changes clearer in the visualization. @@ -900,41 +1086,50 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } Status FunctionalizeCond::FunctionalizeInternal() { - std::vector predicate_switch_order = - DeterminePredicateSwitchOrder(); + TF_ASSIGN_OR_RETURN(std::vector predicate_switch_order, + DeterminePredicateSwitchOrder()); // Iterate from innermost set of clustered switches to outermost, replacing // matching switch->merge subgraphs with single XlaIf nodes. for (auto it = predicate_switch_order.rbegin(); it != predicate_switch_order.rend(); ++it) { auto& ps = *it; - VLOG(3) << "Flow down from: " << NodesToString(ps.switches) << " (" - << ps.predicate->name() << ")"; + VLOG(3) << "Flow down from: " << ps.ToString(); std::unordered_map branch_map; std::unordered_set frontier; TF_ASSIGN_OR_RETURN(std::tie(branch_map, frontier), - DetermineBranchMapAndFrontier(ps.switches)); + DetermineBranchMapAndFrontier(ps)); - VLOG(2) << "FunctionalizeControlFlow (before XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_bc", *graph_); + if (dump_graphs_) + LOG(INFO) << "FunctionalizeControlFlow (before XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_bc", *graph_, + library_); TF_RETURN_IF_ERROR(ValidateFrontier(branch_map, frontier)); + struct Hash { + size_t operator()(const std::pair& item) const { + return Hash64Combine(hash()(item.first), + std::hash()(item.second)); + } + }; + // Sort the merge and switch nodes using NodeCmp. The switch-nodes are // further grouped (post sorting) by input to the switch node as in the // functionalized form each input will be passed in only once. This grouping // should retain the sorted order. CondArgNodes cond_arg_nodes; - std::unordered_map input_index; std::sort(ps.switches.begin(), ps.switches.end(), NodeCmp()); + std::unordered_map, int, Hash> input_index; for (Node* switch_node : ps.switches) { - Node* in; - TF_RETURN_IF_ERROR(switch_node->input_node(0, &in)); - if (input_index.find(in) == input_index.end()) { - input_index[in] = cond_arg_nodes.size(); - cond_arg_nodes.emplace_back(in); + const Edge* e; + TF_RETURN_IF_ERROR(switch_node->input_edge(0, &e)); + std::pair key = std::make_pair(e->src(), e->src_output()); + if (input_index.find(key) == input_index.end()) { + input_index[key] = cond_arg_nodes.size(); + cond_arg_nodes.emplace_back(key.first, key.second); } - cond_arg_nodes.at(input_index.at(in)).switch_nodes.push_back(switch_node); + cond_arg_nodes.at(input_index.at(key)).switches.push_back(switch_node); } std::vector merge_nodes(frontier.begin(), frontier.end()); std::sort(merge_nodes.begin(), merge_nodes.end(), NodeCmp()); @@ -943,9 +1138,8 @@ Status FunctionalizeCond::FunctionalizeInternal() { EnsureDominanceAndReturnNonDominatedControlNodes( branch_map, ps.switches)); - TF_ASSIGN_OR_RETURN( - Node * if_node, - ConvertToXlaIf(cond_arg_nodes, ps.switches, merge_nodes, ps.predicate)); + TF_ASSIGN_OR_RETURN(Node * if_node, + ConvertToXlaIf(cond_arg_nodes, ps, merge_nodes)); for (Node* old : old_control_nodes) { graph_->AddControlEdge(old, if_node); } @@ -954,25 +1148,26 @@ Status FunctionalizeCond::FunctionalizeInternal() { graph_->RemoveNode(del_kv.first); } for (auto& kv : cond_arg_nodes) { - for (Node* node : kv.switch_nodes) { + for (Node* node : kv.switches) { graph_->RemoveNode(node); } } - VLOG(2) << "FunctionalizeControlFlow (after XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_ac", *graph_); + if (dump_graphs_) + LOG(INFO) << "FunctionalizeControlFlow (after XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_ac", *graph_, + library_); } return Status::OK(); } StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( - const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, - const std::vector& merge_nodes, Node* predicate) { - VLOG(2) << "Build if op for " << NodesToString(merge_nodes) << " with input " - << NodesToString(switch_nodes); + const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, + const std::vector& merge_nodes) { + VLOG(2) << "Build if op for " << switch_cluster.name; NodeDef if_def; // Create a new If node using the name of the merge node. - NodeDefBuilder builder(strings::StrCat(predicate->name(), "_If"), "XlaIf"); + NodeDefBuilder builder(switch_cluster.name, "XlaIf"); string branch[] = {"else_branch", "then_branch"}; for (int i = 0; i < 2; ++i) { static std::atomic sequence_num(0LL); @@ -982,12 +1177,9 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( body_name.set_name( strings::StrCat("_functionalize_if_", branch[i], "_", id)); auto body = xla::MakeUnique(graph_->op_registry()); - TF_RETURN_IF_ERROR( - ExtractBody(cond_arg_nodes, switch_nodes, merge_nodes, i, body.get())); + TF_RETURN_IF_ERROR(ExtractBody(cond_arg_nodes, switch_cluster.switches, + merge_nodes, i, body.get())); VLOG(3) << "Body " << branch[i] << ": " << DebugString(body.get()); - VLOG(4) << "FunctionalizeControlFlow (" << branch[i] << "): " - << dump_graph::DumpGraphToFile( - strings::StrCat("functionalize_", branch[i]), *body); FunctionDef body_fdef; TF_RETURN_IF_ERROR(GraphToFunctionDef(*body, body_name.name(), &body_fdef)); TF_RETURN_IF_ERROR(library_->AddFunctionDef(body_fdef)); @@ -999,7 +1191,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( DataTypeVector in_arg_types; for (auto& kv : cond_arg_nodes) { bool inserted = false; - for (const Node* arg : kv.switch_nodes) { + for (const Node* arg : kv.switches) { const Edge* in_edge; TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); if (in_edge->IsControlEdge()) { @@ -1026,10 +1218,12 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( builder.Attr("Tout", out_type); builder.Attr("Tcond", DT_BOOL); - builder.Device(predicate->assigned_device_name()); + builder.Device(switch_cluster.predicate_edge->src()->assigned_device_name()); // Conditional should be the first input ... - builder.Input( - NodeDefBuilder::NodeOut(predicate->name(), 0, predicate->output_type(0))); + builder.Input(NodeDefBuilder::NodeOut( + switch_cluster.predicate_edge->src()->name(), + switch_cluster.predicate_edge->src_output(), + switch_cluster.predicate_edge->src()->output_type(0))); // ... followed by the other inputs. builder.Input(inputs); @@ -1039,7 +1233,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( } Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, + const std::vector& switches, const std::vector& merge_nodes, int input_edge, Graph* body) { VLOG(2) << "ExtractBody for " << NodesToString(merge_nodes) << " along edge " @@ -1049,7 +1243,7 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, int arg_count = 0; for (auto& kv : cond_arg_nodes) { Node* arg_node = nullptr; - for (const auto* arg : kv.switch_nodes) { + for (const auto* arg : kv.switches) { DataType dtype = arg->input_type(0); if (arg_node == nullptr) { TF_ASSIGN_OR_RETURN(arg_node, BuildArgNode(body, dtype, arg_count++)); @@ -1073,8 +1267,7 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, node_map.at(in->id()) = body->CopyNode(in); } - if (std::find(switch_nodes.begin(), switch_nodes.end(), in) == - switch_nodes.end()) { + if (std::find(switches.begin(), switches.end(), in) == switches.end()) { body->AddEdge(node_map.at(in->id()), in_edge->src_output(), node_map.at(node->id()), 0); } else { @@ -1090,24 +1283,17 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, } Status FunctionalizeCond::AddInputEdges(const CondArgNodes& cond_arg_nodes, - Node* predicate, Node* if_node) { + const Edge* predicate_edge, + Node* if_node) { VLOG(3) << "AddInputEdges for " << if_node->name(); int index = 0; - graph_->AddEdge(predicate, 0, if_node, index++); - for (auto& kv : cond_arg_nodes) { - bool inserted = false; - for (const Node* arg : kv.switch_nodes) { - const Edge* in_edge; - TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); - if (in_edge->IsControlEdge()) { - graph_->AddControlEdge(in_edge->src(), if_node); - } else { - if (!inserted) { - graph_->AddEdge(in_edge->src(), in_edge->src_output(), if_node, - index++); - inserted = true; - } - } + graph_->AddEdge(predicate_edge->src(), predicate_edge->src_output(), if_node, + index++); + for (auto& arg : cond_arg_nodes) { + if (arg.src_output == Graph::kControlSlot) { + graph_->AddControlEdge(arg.src, if_node); + } else { + graph_->AddEdge(arg.src, arg.src_output, if_node, index++); } } return Status::OK(); @@ -1128,10 +1314,10 @@ Status FunctionalizeCond::AddOutputEdges(const std::vector& outputs, return errors::Unimplemented("Output of index (", edge->src_output(), ") of merge node ", node->name()); } - graph_->RemoveEdge(edge); int src_output = dst_input == Graph::kControlSlot ? Graph::kControlSlot : i; + graph_->RemoveEdge(edge); graph_->AddEdge(if_node, src_output, dst, dst_input); } } @@ -1139,16 +1325,17 @@ Status FunctionalizeCond::AddOutputEdges(const std::vector& outputs, } StatusOr FunctionalizeCond::ConvertToXlaIf( - const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, - const std::vector& merge_nodes, Node* predicate) { - VLOG(1) << "ConvertToXlaIf for " << NodesToString(switch_nodes) << " -> " + const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, + const std::vector& merge_nodes) { + VLOG(1) << "ConvertToXlaIf for " << switch_cluster.ToString() << " -> " << NodesToString(merge_nodes); // Extract bodies and builds a If operator. TF_ASSIGN_OR_RETURN( Node * if_node, - BuildAndAddXlaIfOp(cond_arg_nodes, switch_nodes, merge_nodes, predicate)); - TF_RETURN_IF_ERROR(AddInputEdges(cond_arg_nodes, predicate, if_node)); + BuildAndAddXlaIfOp(cond_arg_nodes, switch_cluster, merge_nodes)); + TF_RETURN_IF_ERROR( + AddInputEdges(cond_arg_nodes, switch_cluster.predicate_edge, if_node)); TF_RETURN_IF_ERROR(AddOutputEdges(merge_nodes, if_node)); return if_node; @@ -1157,18 +1344,20 @@ StatusOr FunctionalizeCond::ConvertToXlaIf( Status FunctionalizeCond::Functionalize(Graph* graph, FunctionLibraryDefinition* library) { VLOG(1) << "FunctionalizeCond::Functionalize"; - FunctionalizeCond fc(graph, library); + FunctionalizeCond fc(graph, library, /*dump_graphs=*/VLOG_IS_ON(2)); return fc.FunctionalizeInternal(); } } // namespace -// Transformation that converts Tensorflow's graph control flow constructs into +// Transformation that converts TensorFlow's graph control flow constructs into // functional equivalents. Status FunctionalizeControlFlow(Graph* graph, FunctionLibraryDefinition* library) { VLOG(2) << "FunctionalizeControlFlow (initial): " - << dump_graph::DumpGraphToFile("functionalize_initial", *graph); + << dump_graph::DumpGraphToFile("functionalize_initial", *graph, + library); + // Note: BuildControlFlowInfo() requires that the graph's source node is // connected to all source nodes in the graph. Many graphs violate this // invariant. @@ -1180,7 +1369,8 @@ Status FunctionalizeControlFlow(Graph* graph, for (Node* node : graph->op_nodes()) { const ControlFlowInfo& cf = cf_info[node->id()]; - VLOG(2) << "node: " << node->name() << " frame_name: " << cf.frame_name + VLOG(2) << "node: " << node->name() << " (" << node->id() + << ") frame_name: " << cf.frame_name << " frame: " << (cf.frame ? cf.frame->name() : "---") << " parent_frame: " << (cf.parent_frame ? cf.parent_frame->name() : "---"); @@ -1248,7 +1438,8 @@ Status FunctionalizeControlFlow(Graph* graph, TF_RETURN_IF_ERROR(FunctionalizeCond::Functionalize(graph, library)); VLOG(2) << "FunctionalizeControlFlow (final): " - << dump_graph::DumpGraphToFile("functionalize_final", *graph); + << dump_graph::DumpGraphToFile("functionalize_final", *graph, + library); return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index 71f12a13339b9b5495631b8f9350579f6a0785a3..bc7276c3afd5060d6faeceb4d479416299ecc5da 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -38,10 +38,11 @@ namespace { // Returns the names of the "then" and "else" functions for the XlaIf node in a // graph. -Status FindIfThenAndElse(const GraphDef& graph, NameAttrList* then_fn, - NameAttrList* else_fn) { +Status FindIfThenAndElse(const GraphDef& graph, string* op_name, + NameAttrList* then_fn, NameAttrList* else_fn) { for (const NodeDef& node : graph.node()) { if (node.op() == "XlaIf") { + *op_name = node.name(); const NameAttrList* result; TF_RETURN_IF_ERROR(GetNodeAttr(node, "then_branch", &result)); *then_fn = *result; @@ -96,9 +97,10 @@ TEST(FunctionalizeControlFlow, Conditional) { GraphDef graph_def; graph.ToGraphDef(&graph_def); + string op_name; NameAttrList then_fn; NameAttrList else_fn; - TF_EXPECT_OK(FindIfThenAndElse(graph_def, &then_fn, &else_fn)); + TF_EXPECT_OK(FindIfThenAndElse(graph_def, &op_name, &then_fn, &else_fn)); InstantiationResultForTest else_result; TF_EXPECT_OK( InstantiateFunctionForTest(else_fn.name(), library, &else_result)); @@ -109,7 +111,7 @@ TEST(FunctionalizeControlFlow, Conditional) { auto y = ops::Placeholder(scope.WithOpName("y"), DT_INT32); auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32); auto less = ops::Less(scope.WithOpName("cond/Less"), y, x); - auto if_op = ops::XlaIf(scope.WithOpName("cond/Less_If"), less, + auto if_op = ops::XlaIf(scope.WithOpName(op_name), less, std::initializer_list{less, y, x}, then_fn, else_fn, {DT_INT32}); GraphDef expected; diff --git a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md index 91351421bcacd26c41b5c9f98ea833730e4aef30..20179b67991d3d23d678cf1df2642e029ea037fd 100644 --- a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md @@ -3,6 +3,7 @@ Operator | Type Constraint ------------------------------------- | --------------- `Abs` | `T={double,float,int32,int64}` +`Acos` | `T={complex64,double,float,int32,int64}` `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` @@ -15,10 +16,12 @@ Operator | Type Constraint `ApproximateEqual` | `T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMax` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={float}` `ArgMin` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` +`Asin` | `T={complex64,double,float,int32,int64}` `Asinh` | `T={complex64,double,float}` `AssignAddVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignSubVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignVariableOp` | `dtype={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Atan` | `T={complex64,double,float,int32,int64}` `Atan2` | `T={double,float}` `Atanh` | `T={complex64,double,float}` `AvgPool` | `T={double,float}` @@ -75,6 +78,10 @@ Operator | Type Constraint `FFT` | `FFT2D` | `FFT3D` | +`FakeQuantWithMinMaxArgs` | +`FakeQuantWithMinMaxArgsGradient` | +`FakeQuantWithMinMaxVars` | +`FakeQuantWithMinMaxVarsGradient` | `Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` @@ -84,6 +91,7 @@ Operator | Type Constraint `FusedBatchNormGradV2` | `U={float}`
`T={float}` `FusedBatchNormV2` | `U={float}`
`T={float}` `Gather` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` +`GatherNd` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` @@ -117,14 +125,18 @@ Operator | Type Constraint `LogicalNot` | `LogicalOr` | `MatMul` | `T={complex64,double,float}` +`MatrixBandPart` | `Tindex={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixSetDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradGrad` | `T={float}` +`MaxPoolGradGradV2` | `T={float}` `MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` `MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` @@ -186,6 +198,7 @@ Operator | Type Constraint `Round` | `T={complex64,double,float,int32,int64}` `Rsqrt` | `T={complex64,double,float}` `RsqrtGrad` | `T={complex64,double,float}` +`ScatterNd` | `Tindices={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Select` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Selu` | `T={double,float}` `SeluGrad` | `T={double,float}` @@ -198,6 +211,7 @@ Operator | Type Constraint `Sinh` | `T={complex64,double,float}` `Size` | `out_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Slice` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Snapshot` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Softmax` | `T={double,float}` `SoftmaxCrossEntropyWithLogits` | `T={double,float}` `Softplus` | `T={double,float,int32,int64,uint32,uint64}` diff --git a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md index b9bdb829d773825005a8921f48d28b6892d8f0cd..55f0538dba7c1941dfea88e0631cd299e51f76d0 100644 --- a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md @@ -3,6 +3,7 @@ Operator | Type Constraint ------------------------------------- | --------------- `Abs` | `T={double,float,int32,int64}` +`Acos` | `T={complex64,double,float,int32,int64}` `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` @@ -15,10 +16,12 @@ Operator | Type Constraint `ApproximateEqual` | `T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMax` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMin` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` +`Asin` | `T={complex64,double,float,int32,int64}` `Asinh` | `T={complex64,double,float}` `AssignAddVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignSubVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignVariableOp` | `dtype={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Atan` | `T={complex64,double,float,int32,int64}` `Atan2` | `T={double,float}` `Atanh` | `T={complex64,double,float}` `AvgPool` | `T={double,float}` @@ -75,6 +78,10 @@ Operator | Type Constraint `FFT` | `FFT2D` | `FFT3D` | +`FakeQuantWithMinMaxArgs` | +`FakeQuantWithMinMaxArgsGradient` | +`FakeQuantWithMinMaxVars` | +`FakeQuantWithMinMaxVarsGradient` | `Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` @@ -84,6 +91,7 @@ Operator | Type Constraint `FusedBatchNormGradV2` | `U={float}`
`T={float}` `FusedBatchNormV2` | `U={float}`
`T={float}` `Gather` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` +`GatherNd` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` @@ -117,14 +125,18 @@ Operator | Type Constraint `LogicalNot` | `LogicalOr` | `MatMul` | `T={complex64,double,float}` +`MatrixBandPart` | `Tindex={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixSetDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradGrad` | `T={float}` +`MaxPoolGradGradV2` | `T={float}` `MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` `MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` @@ -183,6 +195,7 @@ Operator | Type Constraint `Round` | `T={complex64,double,float,int32,int64}` `Rsqrt` | `T={complex64,double,float}` `RsqrtGrad` | `T={complex64,double,float}` +`ScatterNd` | `Tindices={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Select` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Selu` | `T={double,float}` `SeluGrad` | `T={double,float}` @@ -195,6 +208,7 @@ Operator | Type Constraint `Sinh` | `T={complex64,double,float}` `Size` | `out_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Slice` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Snapshot` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Softmax` | `T={double,float}` `SoftmaxCrossEntropyWithLogits` | `T={double,float}` `Softplus` | `T={double,float,int32,int64,uint32,uint64}` diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 1418d95956e1536292d58dfc4c2b53c53421fa94..b20c1ffc7d8956f3f5530ee63e9b711a26439be5 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -130,11 +130,11 @@ Status GraphCompiler::Compile() { // Set up inputs from outputs of previous nodes. for (auto* e : n->in_edges()) { if (e->IsControlEdge()) continue; - Node* src = e->src(); + const Node* src = e->src(); TF_RET_CHECK(src->id() < output_registry.size()); const NodeOutputs& src_outputs = output_registry[src->id()]; - tensor_inputs_[e->dst_input()] = src_outputs[e->src_output()]; + tensor_inputs_.at(e->dst_input()) = src_outputs.at(e->src_output()); } OpKernelContext op_context(¶ms, n->num_outputs()); diff --git a/tensorflow/compiler/tf2xla/graph_compiler.h b/tensorflow/compiler/tf2xla/graph_compiler.h index ba00160b6d78c1e55cc2e053cd5285344e0179fb..127562eb23d775f17179cc9ee968ec2255cf3a14 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.h +++ b/tensorflow/compiler/tf2xla/graph_compiler.h @@ -70,7 +70,7 @@ class GraphCompiler { private: // Partially sets params. This partially set params can be reused - // across multple nodes visit. + // across multiple nodes visit. void PartiallySetupParams(OpKernelContext::Params* params); // Tests if a node is a functional node. A functional node represents a diff --git a/tensorflow/compiler/tf2xla/host_compute_metadata.proto b/tensorflow/compiler/tf2xla/host_compute_metadata.proto new file mode 100644 index 0000000000000000000000000000000000000000..43ab371a217e6c4521a160715104c96e3c8782c6 --- /dev/null +++ b/tensorflow/compiler/tf2xla/host_compute_metadata.proto @@ -0,0 +1,38 @@ +syntax = "proto3"; + +package tensorflow.tf2xla; +option cc_enable_arenas = true; +option java_outer_classname = "Tf2XlaProtos"; +option java_multiple_files = true; +option java_package = "org.tensorflow.tf2xla"; + +import "tensorflow/core/framework/tensor_shape.proto"; +import "tensorflow/core/framework/types.proto"; + +// TensorMetadata indicates the type and shape of a Tensor that is +// part of a host compute transfer. +message TensorMetadata { + DataType type = 1; + TensorShapeProto shape = 2; +} + +// HostTransferMetadata describes a transfer either from host to device +// or device to host. It has a key that is unique to the computation, +// and metadata about the list of tensors being transferred. +message HostTransferMetadata { + // The key used to identify this transfer. + string key = 1; + + // For each Tensor being transferred, its type and shape. + repeated TensorMetadata metadata = 2; +} + +// HostComputeMetadata describes all the sends and recvs +// from all host compute transfer ops in a computation. +message HostComputeMetadata { + // Metadata about each device_to_host transfer + repeated HostTransferMetadata device_to_host = 1; + + // Metadata about each host_to_device transfer + repeated HostTransferMetadata host_to_device = 2; +} diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 4c6b29bd015d048f842906cc509a6ed564629b73..d2fa933cf9c085f92b2f442827a94d72938e4bb2 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -32,6 +32,7 @@ tf_kernel_library( "dynamic_stitch_op.cc", "elu_op.cc", "extract_image_patches_op.cc", + "fake_quantize_ops.cc", "fft_ops.cc", "fill_op.cc", "function_ops.cc", @@ -64,6 +65,7 @@ tf_kernel_library( "reverse_op.cc", "reverse_sequence_op.cc", "scan_ops.cc", + "scatter_nd_op.cc", "segment_reduction_ops.cc", "select_op.cc", "sendrecv_ops.cc", @@ -96,12 +98,15 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:batch_dot", "//tensorflow/compiler/tf2xla/lib:cholesky", + "//tensorflow/compiler/tf2xla/lib:scatter", "//tensorflow/compiler/tf2xla/lib:triangular_solve", "//tensorflow/compiler/tf2xla/lib:util", + "//tensorflow/compiler/tf2xla/lib:while_loop", "//tensorflow/compiler/tf2xla/ops:sendrecv_ops", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index 344a2ab2b6835c518c41de6f7a30fb2a34d130d2..cbade79e85eed10ecb5ead7151ee778c86a0de37 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -159,7 +159,9 @@ class BatchToSpaceNDOp : public XlaOpKernel { block_shape, crops); } }; -REGISTER_XLA_OP(Name("BatchToSpaceND").CompileTimeConstInput("crops"), +REGISTER_XLA_OP(Name("BatchToSpaceND") + .CompileTimeConstInput("block_shape") + .CompileTimeConstInput("crops"), BatchToSpaceNDOp); class BatchToSpaceOp : public XlaOpKernel { diff --git a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..453a32c494b42e9922bc35fc526f3306530054fd --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc @@ -0,0 +1,289 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/core/platform/macros.h" + +namespace tensorflow { +namespace { + +// Gymnastics with nudged zero point is to ensure that the real zero maps to +// an integer, which is required for e.g. zero-padding in convolutional layers. +void CpuNudge(const float min, const float max, const float quant_min, + const float quant_max, float* nudged_min, float* nudged_max, + float* scale) { + *scale = (max - min) / (quant_max - quant_min); + + const float zero_point_from_min = quant_min - min / *scale; + float nudged_zero_point; + if (zero_point_from_min <= quant_min) { + nudged_zero_point = quant_min; + } else if (zero_point_from_min >= quant_max) { + nudged_zero_point = quant_max; + } else { + nudged_zero_point = std::round(zero_point_from_min); + } + + *nudged_min = (quant_min - nudged_zero_point) * (*scale); + *nudged_max = (quant_max - nudged_zero_point) * (*scale); +} + +// An XLA version of CpuNudge(). +void XlaNudge(xla::ComputationBuilder* b, const DataType data_type, + const xla::ComputationDataHandle& min, + const xla::ComputationDataHandle& max, + const float quant_min_value, const float quant_max_value, + xla::ComputationDataHandle* nudged_min, + xla::ComputationDataHandle* nudged_max, + xla::ComputationDataHandle* scale) { + *scale = b->Div(b->Sub(max, min), + XlaHelpers::FloatLiteral(b, data_type, + quant_max_value - quant_min_value)); + xla::ComputationDataHandle quant_min = + XlaHelpers::FloatLiteral(b, data_type, quant_min_value); + xla::ComputationDataHandle zero_point_from_min = + b->Sub(quant_min, b->Div(min, *scale)); + xla::ComputationDataHandle quant_max = + XlaHelpers::FloatLiteral(b, data_type, quant_max_value); + xla::ComputationDataHandle nudged_zero_point = + b->Select(b->Le(zero_point_from_min, quant_min), quant_min, + b->Select(b->Ge(zero_point_from_min, quant_max), quant_max, + b->Round(zero_point_from_min))); + *nudged_min = b->Mul(b->Sub(quant_min, nudged_zero_point), *scale); + *nudged_max = b->Mul(b->Sub(quant_max, nudged_zero_point), *scale); +} + +xla::ComputationDataHandle Quantize( + xla::ComputationBuilder* b, const xla::ComputationDataHandle& input, + const DataType data_type, + const xla::ComputationDataHandle& nudged_input_min, + const xla::ComputationDataHandle& nudged_input_max, + const xla::ComputationDataHandle& input_scale) { + xla::ComputationDataHandle one = XlaHelpers::FloatLiteral(b, data_type, 1.0f); + xla::ComputationDataHandle inv_scale = b->Div(one, input_scale); + xla::ComputationDataHandle half = + XlaHelpers::FloatLiteral(b, data_type, 0.5f); + + xla::ComputationDataHandle clamped = + b->Clamp(nudged_input_min, input, nudged_input_max); + xla::ComputationDataHandle clamped_shifted = + b->Sub(clamped, nudged_input_min); + xla::ComputationDataHandle rounded = + b->Floor(b->Add(b->Mul(clamped_shifted, inv_scale), half)); + return b->Add(b->Mul(rounded, input_scale), nudged_input_min); +} + +class FakeQuantWithMinMaxArgsOp : public XlaOpKernel { + public: + explicit FakeQuantWithMinMaxArgsOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + int num_bits; + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits)); + OP_REQUIRES(ctx, num_bits >= 2 && num_bits <= 16, + errors::InvalidArgument("num_bits is out of range, expected " + "between 2 and 16, was: ", + num_bits)); + bool narrow_range; + OP_REQUIRES_OK(ctx, ctx->GetAttr("narrow_range", &narrow_range)); + quant_min_ = narrow_range ? 1 : 0; + quant_max_ = (1 << num_bits) - 1; + + float input_min, input_max; + OP_REQUIRES_OK(ctx, ctx->GetAttr("min", &input_min)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("max", &input_max)); + CpuNudge(input_min, input_max, quant_min_, quant_max_, &nudged_input_min_, + &nudged_input_max_, &input_scale_); + } + + void Compile(XlaOpKernelContext* ctx) override { + xla::ComputationDataHandle input = ctx->Input(0); + const DataType data_type = ctx->input_type(0); + + xla::ComputationBuilder* b = ctx->builder(); + xla::ComputationDataHandle nudged_input_min = + XlaHelpers::FloatLiteral(b, data_type, nudged_input_min_); + xla::ComputationDataHandle nudged_input_max = + XlaHelpers::FloatLiteral(b, data_type, nudged_input_max_); + xla::ComputationDataHandle input_scale = + XlaHelpers::FloatLiteral(b, data_type, input_scale_); + xla::ComputationDataHandle output = Quantize( + b, input, data_type, nudged_input_min, nudged_input_max, input_scale); + ctx->SetOutput(0, output); + } + + private: + float quant_min_; + float quant_max_; + float nudged_input_min_; + float nudged_input_max_; + float input_scale_; +}; + +REGISTER_XLA_OP(Name("FakeQuantWithMinMaxArgs"), FakeQuantWithMinMaxArgsOp); + +class FakeQuantWithMinMaxArgsGradOp : public XlaOpKernel { + public: + explicit FakeQuantWithMinMaxArgsGradOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + int num_bits; + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits)); + OP_REQUIRES(ctx, num_bits >= 2 && num_bits <= 16, + errors::InvalidArgument("num_bits is out of range, expected " + "between 2 and 16, was: ", + num_bits)); + bool narrow_range; + OP_REQUIRES_OK(ctx, ctx->GetAttr("narrow_range", &narrow_range)); + const float quant_min = narrow_range ? 1 : 0; + const float quant_max = (1 << num_bits) - 1; + + float input_min, input_max, scale; + OP_REQUIRES_OK(ctx, ctx->GetAttr("min", &input_min)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("max", &input_max)); + CpuNudge(input_min, input_max, quant_min, quant_max, &nudged_input_min_, + &nudged_input_max_, &scale); + } + + void Compile(XlaOpKernelContext* ctx) override { + xla::ComputationDataHandle gradient = ctx->Input(0); + const TensorShape gradient_shape = ctx->InputShape(0); + xla::ComputationDataHandle input = ctx->Input(1); + const DataType data_type = ctx->input_type(1); + + xla::ComputationBuilder* b = ctx->builder(); + xla::ComputationDataHandle nudged_input_min = + XlaHelpers::FloatLiteral(b, data_type, nudged_input_min_); + xla::ComputationDataHandle nudged_input_max = + XlaHelpers::FloatLiteral(b, data_type, nudged_input_max_); + + xla::ComputationDataHandle between_nudged_min_max = + b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); + xla::ComputationDataHandle zeroes = b->Broadcast( + XlaHelpers::Zero(b, data_type), gradient_shape.dim_sizes()); + xla::ComputationDataHandle output = + b->Select(between_nudged_min_max, gradient, zeroes); + ctx->SetOutput(0, output); + } + + private: + float nudged_input_min_; + float nudged_input_max_; +}; + +REGISTER_XLA_OP(Name("FakeQuantWithMinMaxArgsGradient"), + FakeQuantWithMinMaxArgsGradOp); + +class FakeQuantWithMinMaxVarsOp : public XlaOpKernel { + public: + explicit FakeQuantWithMinMaxVarsOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + int num_bits; + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits)); + OP_REQUIRES(ctx, num_bits >= 2 && num_bits <= 16, + errors::InvalidArgument("num_bits is out of range, expected " + "between 2 and 16, was: ", + num_bits)); + bool narrow_range; + OP_REQUIRES_OK(ctx, ctx->GetAttr("narrow_range", &narrow_range)); + quant_min_ = narrow_range ? 1 : 0; + quant_max_ = (1 << num_bits) - 1; + } + + void Compile(XlaOpKernelContext* ctx) override { + xla::ComputationDataHandle input = ctx->Input(0); + const DataType data_type = ctx->input_type(0); + xla::ComputationDataHandle input_min = ctx->Input(1); + xla::ComputationDataHandle input_max = ctx->Input(2); + + xla::ComputationBuilder* b = ctx->builder(); + xla::ComputationDataHandle nudged_input_min, nudged_input_max, input_scale; + XlaNudge(b, data_type, input_min, input_max, quant_min_, quant_max_, + &nudged_input_min, &nudged_input_max, &input_scale); + + xla::ComputationDataHandle output = Quantize( + b, input, data_type, nudged_input_min, nudged_input_max, input_scale); + ctx->SetOutput(0, output); + } + + private: + float quant_min_; + float quant_max_; +}; + +REGISTER_XLA_OP(Name("FakeQuantWithMinMaxVars"), FakeQuantWithMinMaxVarsOp); + +class FakeQuantWithMinMaxVarsGradOp : public XlaOpKernel { + public: + explicit FakeQuantWithMinMaxVarsGradOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + int num_bits; + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits)); + OP_REQUIRES(ctx, num_bits >= 2 && num_bits <= 16, + errors::InvalidArgument("num_bits is out of range, expected " + "between 2 and 16, was: ", + num_bits)); + bool narrow_range; + OP_REQUIRES_OK(ctx, ctx->GetAttr("narrow_range", &narrow_range)); + quant_min_ = narrow_range ? 1 : 0; + quant_max_ = (1 << num_bits) - 1; + } + + void Compile(XlaOpKernelContext* ctx) override { + xla::ComputationDataHandle gradient = ctx->Input(0); + const TensorShape gradient_shape = ctx->InputShape(0); + xla::ComputationDataHandle input = ctx->Input(1); + const DataType data_type = ctx->input_type(1); + xla::ComputationDataHandle input_min = ctx->Input(2); + xla::ComputationDataHandle input_max = ctx->Input(3); + + xla::ComputationBuilder* b = ctx->builder(); + xla::ComputationDataHandle nudged_input_min, nudged_input_max, input_scale; + XlaNudge(b, data_type, input_min, input_max, quant_min_, quant_max_, + &nudged_input_min, &nudged_input_max, &input_scale); + + xla::ComputationDataHandle between_nudged_min_max = + b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); + xla::ComputationDataHandle zero = XlaHelpers::Zero(b, data_type); + xla::ComputationDataHandle zeroes = + b->Broadcast(zero, gradient_shape.dim_sizes()); + xla::ComputationDataHandle output0 = + b->Select(between_nudged_min_max, gradient, zeroes); + ctx->SetOutput(0, output0); + + xla::ComputationDataHandle below_min = b->Lt(input, nudged_input_min); + xla::ComputationDataHandle output1 = + b->ReduceAll(b->Select(below_min, gradient, zeroes), zero, + *ctx->GetOrCreateAdd(data_type)); + ctx->SetOutput(1, output1); + + xla::ComputationDataHandle above_max = b->Gt(input, nudged_input_max); + xla::ComputationDataHandle output2 = + b->ReduceAll(b->Select(above_max, gradient, zeroes), zero, + *ctx->GetOrCreateAdd(data_type)); + ctx->SetOutput(2, output2); + } + + private: + float quant_min_; + float quant_max_; +}; + +REGISTER_XLA_OP(Name("FakeQuantWithMinMaxVarsGradient"), + FakeQuantWithMinMaxVarsGradOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index e9af1e9c2fcb4922ea3570516419abd04a611a04..7945c05af40df21a798a2cff51fe7f8e935793f6 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" +#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" @@ -32,12 +33,12 @@ Status XlaGather(const xla::ComputationDataHandle& input, DataType dtype, DataType index_type, xla::ComputationBuilder* builder, xla::ComputationDataHandle* gather_output) { - // If the indices are N-dimensional, then the last dimension of indices should - // be of size N and correspond to the N indices. - int64 num_axes = 1; + // If the indices are N-dimensional, then the minor dimension of indices + // should be of size N and correspond to the N indices. + int64 num_index_dims = 1; if (indices_are_nd) { CHECK_GE(indices_shape.dims(), 1); - num_axes = indices_shape.dim_size(indices_shape.dims() - 1); + num_index_dims = indices_shape.dim_size(indices_shape.dims() - 1); indices_shape.RemoveLastDims(1); } @@ -46,15 +47,15 @@ Status XlaGather(const xla::ComputationDataHandle& input, // input, the output is returned with shape: // input.shape[:axis] + indices.shape + input.shape[axis+1:] - const int num_indices = indices_shape.num_elements(); + const int64 num_indices = indices_shape.num_elements(); TensorShape input_shape_pre_axis(input_shape); input_shape_pre_axis.RemoveDimRange(axis, input_shape.dims()); TensorShape input_shape_post_axis(input_shape); - input_shape_post_axis.RemoveDimRange(0, axis + num_axes); + input_shape_post_axis.RemoveDimRange(0, axis + num_index_dims); // Each slice of the input tensor has shape: // [, 1, ..., 1, ] TensorShape slice_shape(input_shape); - for (int64 i = 0; i < num_axes; ++i) { + for (int64 i = 0; i < num_index_dims; ++i) { slice_shape.set_dim(axis + i, 1); } @@ -79,7 +80,7 @@ Status XlaGather(const xla::ComputationDataHandle& input, return Status::OK(); } - for (int64 i = 0; i < num_axes; ++i) { + for (int64 i = 0; i < num_index_dims; ++i) { if (input_shape.dim_size(axis + i) == 0) { return errors::InvalidArgument("Gather dimension ", axis + i, " is of size zero in tensor with shape ", @@ -91,57 +92,30 @@ Status XlaGather(const xla::ComputationDataHandle& input, // iteration. If there is an axis dimension, we must leave it alone. std::vector flat_indices_shape = {num_indices}; if (indices_are_nd) { - flat_indices_shape.push_back(num_axes); + flat_indices_shape.push_back(num_index_dims); } // Specify the shape of the loop-carried Tensor tuple. - xla::PrimitiveType ptype; - TF_CHECK_OK(DataTypeToPrimitiveType(dtype, &ptype)); - xla::PrimitiveType idxtype; - TF_CHECK_OK(DataTypeToPrimitiveType(index_type, &idxtype)); - std::vector tuple_shapes( - {// The iteration counter i is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(idxtype, {}), - // The input array has shape input_shape. Loop invariant. - xla::ShapeUtil::MakeShape(ptype, input_shape.dim_sizes()), - // The gather indices are reshaped to flat_indices_shape. Loop invariant. - xla::ShapeUtil::MakeShape(idxtype, flat_indices_shape), - // The output array, which is updated on each loop iteration. - xla::ShapeUtil::MakeShape(ptype, loop_out_shape.dim_sizes())}); - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); // Construct the initial values of the loop-carried Tensors. - auto init_i = XlaHelpers::Zero(builder, index_type); + auto flat_indices = builder->Reshape(indices, flat_indices_shape); auto init_out = builder->Broadcast(XlaHelpers::Zero(builder, dtype), loop_out_shape.dim_sizes()); - auto flat_indices = builder->Reshape(indices, flat_indices_shape); - auto init = builder->Tuple({init_i, input, flat_indices, init_out}); - - // Construct the while loop condition (i < num_indices) - std::unique_ptr condb = - builder->CreateSubBuilder("GatherWhileCond"); - condb->Lt(condb->GetTupleElement( - condb->Parameter(0, tuple_shape, "GatherWhileTuple"), 0), - XlaHelpers::IntegerLiteral(condb.get(), index_type, num_indices)); - auto cond_status = condb->Build(); - auto cond = cond_status.ConsumeValueOrDie(); + auto init = {input, flat_indices, init_out}; // Construct the while loop body's function. The implementation of gather is: // for i in range(num_indices): // index = dynamic-slice(indices, i) // xi = dynamic-slice(input, index) // output = dynamic-update-slice(output, xi, i) - std::unique_ptr bodyb = - builder->CreateSubBuilder("GatherWhileBody"); - { - // The four loop carried values. - auto loop_tuple = bodyb->Parameter(0, tuple_shape, "GatherWhileTuple"); - auto i = bodyb->GetTupleElement(loop_tuple, 0); - auto input = bodyb->GetTupleElement(loop_tuple, 1); - auto indices = bodyb->GetTupleElement(loop_tuple, 2); - auto output = bodyb->GetTupleElement(loop_tuple, 3); - - auto zero_index = XlaHelpers::Zero(bodyb.get(), index_type); + auto body_fn = [&](xla::ComputationDataHandle i, + gtl::ArraySlice loop_vars, + xla::ComputationBuilder* bodyb) { + auto input = loop_vars[0]; + auto indices = loop_vars[1]; + auto output = loop_vars[2]; + + auto zero_index = XlaHelpers::Zero(bodyb, index_type); // Slice the i-th index from the indices array. xla::ComputationDataHandle index; @@ -150,7 +124,7 @@ Status XlaGather(const xla::ComputationDataHandle& input, // Slice out the entire nd index, if applicable. indices_offset = bodyb->Pad(indices_offset, zero_index, xla::MakeEdgePaddingConfig({{0, 1}})); - index = bodyb->DynamicSlice(indices, indices_offset, {1, num_axes}); + index = bodyb->DynamicSlice(indices, indices_offset, {1, num_index_dims}); index = bodyb->Collapse(index, {0, 1}); } else { index = bodyb->DynamicSlice(indices, indices_offset, {1}); @@ -174,16 +148,16 @@ Status XlaGather(const xla::ComputationDataHandle& input, // Update the output Tensor auto updated_output = bodyb->DynamicUpdateSlice(output, slice_i, out_index); - bodyb->Tuple({bodyb->Add(i, XlaHelpers::One(bodyb.get(), index_type)), - input, indices, updated_output}); - } - auto body_status = bodyb->Build(); - auto body = body_status.ConsumeValueOrDie(); + return std::vector{input, indices, + updated_output}; + }; // Construct the While loop, extract and reshape the output. - auto gather_while = builder->While(cond, body, init); - auto result = builder->GetTupleElement(gather_while, 3); - *gather_output = builder->Reshape(result, out_shape.dim_sizes()); + xla::PrimitiveType ptype; + TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(index_type, &ptype)); + TF_ASSIGN_OR_RETURN(auto outputs, XlaForEachIndex(num_indices, ptype, body_fn, + init, "gather", builder)); + *gather_output = builder->Reshape(outputs[2], out_shape.dim_sizes()); return Status::OK(); } @@ -250,9 +224,10 @@ class GatherNdOp : public XlaOpKernel { errors::InvalidArgument("params must be at least a vector")); OP_REQUIRES(context, TensorShapeUtils::IsVectorOrHigher(indices_shape), errors::InvalidArgument("indices must be at least a vector")); - const int64 num_axes = indices_shape.dim_size(indices_shape.dims() - 1); + const int64 num_index_dims = + indices_shape.dim_size(indices_shape.dims() - 1); OP_REQUIRES( - context, num_axes <= params_shape.dims(), + context, num_index_dims <= params_shape.dims(), errors::InvalidArgument( "index innermost dimension length must be <= params rank; saw: ", indices_shape.dim_size(indices_shape.dims() - 1), " vs. ", diff --git a/tensorflow/compiler/tf2xla/kernels/identity_op.cc b/tensorflow/compiler/tf2xla/kernels/identity_op.cc index d2b1f7913ecc9113284827b53de8fb0e5b711322..39af662b638cb9d723118e58fcfc983633fed497 100644 --- a/tensorflow/compiler/tf2xla/kernels/identity_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/identity_op.cc @@ -40,6 +40,7 @@ REGISTER_XLA_OP(Name("Identity").CompilationOnly(), IdentityOp); REGISTER_XLA_OP(Name("IdentityN").CompilationOnly(), IdentityOp); REGISTER_XLA_OP(Name("PreventGradient"), IdentityOp); REGISTER_XLA_OP(Name("StopGradient"), IdentityOp); +REGISTER_XLA_OP(Name("Snapshot"), IdentityOp); } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index d4fb5dd4e06c7c70591262c0d63a91c383a2a6e0..086a9491aa93ebfae99f296dd355ae2e322084ec 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -525,5 +525,172 @@ class AvgPool3DGradOp : public AvgPoolGradOp { REGISTER_XLA_OP(Name("AvgPool3DGrad").CompileTimeConstInput("orig_input_shape"), AvgPool3DGradOp); +class MaxPoolGradGradOp : public XlaOpKernel { + public: + MaxPoolGradGradOp(OpKernelConstruction* ctx, int num_spatial_dims) + : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { + if (ctx->num_inputs() == 3) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); + } + OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding_)); + } + + int num_dims() const { return num_spatial_dims_ + 2; } + + void Compile(XlaOpKernelContext* ctx) override { + if (ctx->num_inputs() != 3) { + OP_REQUIRES( + ctx, ctx->num_inputs() == 5, + errors::InvalidArgument("Must supply ksize and stride arguments.")); + const TensorShape ksize_shape = ctx->InputShape(3); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), + errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(3, &ksize_)); + + const TensorShape stride_shape = ctx->InputShape(4); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), + errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(4, &stride_)); + } + + OP_REQUIRES(ctx, ksize_.size() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + OP_REQUIRES(ctx, stride_.size() == num_dims(), + errors::InvalidArgument("Sliding window strides field must " + "specify ", + num_dims(), " dimensions")); + + const TensorShape tensor_in_shape = ctx->InputShape(0); + const TensorShape tensor_out_shape = ctx->InputShape(1); + const TensorShape out_backprop_shape = ctx->InputShape(2); + + // For maxpooling, tensor_in should have num_dims() dimensions. + OP_REQUIRES(ctx, tensor_in_shape.dims() == num_dims(), + errors::InvalidArgument("tensor_in must be ", num_dims(), + "-dimensional")); + OP_REQUIRES(ctx, tensor_out_shape.dims() == num_dims(), + errors::InvalidArgument("tensor_out must be ", num_dims(), + "-dimensional")); + // For maxpooling, out_backprop should have num_dims() dimensions. + OP_REQUIRES(ctx, out_backprop_shape.dims() == num_dims(), + errors::InvalidArgument("out_backprop must be ", num_dims(), + "-dimensional")); + + // What we want to compute: + // Given y = MaxPool(x), and xs_grad = MaxPoolGrad(x, y, ys_grad) + // MaxPoolGradGrad computes {ys_grad}_grad given x, y, and {xs_grad}_grad. + // + // In the regular TF op, this amounts to selecting for each window the + // incoming backprop value from xs_grad_grad that corresponds to the maximal + // value in the corresponding window of x. + // + // TODO(b/73062247): What we really want is a ReduceWindow with different + // arrays for index selection vs return value selection--a select-to-gather. + // + // Here, we implement a bitwise hack: we use the hi 16 bits of input for + // separate max pooling alongside each of the hi and lo 16 bits of + // out_backprop packed into 16 lo bits, which we then glue back together at + // the end to get a full 32 bits of gradient. + // + // This could select the wrong backprop value for two x values that are + // equally maximal up to the first 16 bits, in which case we are taking the + // latter. + // + // Note that in principle we could use 32 separate maxpools to recover each + // of 32 bits of the gradient while preserving 31 bits of input for the max + // pooling criteria; here, we just truncate to the first 16 bits of input. + + auto input = ctx->Input(0); + auto out_backprop = ctx->Input(2); + + auto b = ctx->builder(); + + auto sixteen = b->ConstantR0(16); + // in (f32) -> round to bf16 -> f32 for correct bitwidth -> 16-high-bit u32 + auto in_hi = b->BitcastConvertType( + b->ConvertElementType(b->ConvertElementType(input, xla::BF16), + xla::F32), + xla::U32); + auto bp_int = b->BitcastConvertType(out_backprop, xla::U32); + auto bp_hi = b->ShiftRightLogical(bp_int, sixteen); + auto bp_lo = b->ShiftRightLogical(b->ShiftLeft(bp_int, sixteen), sixteen); + auto in_hi_bp_hi = b->Add(in_hi, bp_hi); // Want an unsigned add. + auto in_hi_bp_lo = b->Add(in_hi, bp_lo); // Want an unsigned add. + + auto init_value = XlaHelpers::MinValue(b, DT_FLOAT); + // We will reduce by taking the maximal value up to 16 bits (ignoring the lo + // 16 bits of packed-in hi/lo backprop value). + auto rb = b->CreateSubBuilder("GreaterOrEqOf_ByFirst16Bits"); + { + // F32 parameters to satisfy lowering type restriction for reduce opcode. + const xla::Shape scalar = xla::ShapeUtil::MakeShape(xla::F32, {}); + auto lhs = rb->Parameter(0, scalar, "lhs"); + auto rhs = rb->Parameter(1, scalar, "rhs"); + auto sixteen = rb->ConstantR0(16); + auto lhs_criteria = rb->ShiftLeft( + rb->ShiftRightLogical(rb->BitcastConvertType(lhs, xla::S32), sixteen), + sixteen); + auto rhs_criteria = rb->ShiftLeft( + rb->ShiftRightLogical(rb->BitcastConvertType(rhs, xla::S32), sixteen), + sixteen); + // Must use a F32 comparison, because S32 would not work for negatives. + rb->Select(rb->Ge(rb->BitcastConvertType(lhs_criteria, xla::F32), + rb->BitcastConvertType(rhs_criteria, xla::F32)), + lhs, rhs); + } + auto reduce = rb->BuildAndNoteError(); + xla::Padding xla_padding = + (padding_ == VALID) ? xla::Padding::kValid : xla::Padding::kSame; + auto pooled_hi = + b->ReduceWindow(b->BitcastConvertType(in_hi_bp_hi, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); + auto pooled_lo = + b->ReduceWindow(b->BitcastConvertType(in_hi_bp_lo, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); + auto grads_hi = + b->ShiftLeft(b->BitcastConvertType(pooled_hi, xla::U32), sixteen); + auto grads_lo = b->ShiftRightLogical( + b->ShiftLeft(b->BitcastConvertType(pooled_lo, xla::U32), sixteen), + sixteen); + auto grads = b->Add(grads_hi, grads_lo); // Want an unsigned add. + + xla::PrimitiveType element_type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(2), &element_type)); + ctx->SetOutput(0, b->BitcastConvertType(grads, element_type)); + } + + protected: + const int num_spatial_dims_; + std::vector ksize_; + std::vector stride_; + Padding padding_; + TensorFormat data_format_ = FORMAT_NHWC; +}; + +class MaxPool2DGradGradOp : public MaxPoolGradGradOp { + public: + explicit MaxPool2DGradGradOp(OpKernelConstruction* ctx) + : MaxPoolGradGradOp(ctx, /*num_spatial_dims=*/2) { + string data_format; + OP_REQUIRES_OK(ctx, ctx->GetAttr("data_format", &data_format)); + OP_REQUIRES(ctx, FormatFromString(data_format, &data_format_), + errors::InvalidArgument("Invalid data format")); + } +}; +REGISTER_XLA_OP(Name("MaxPoolGradGrad").TypeConstraint("T", DT_FLOAT), + MaxPool2DGradGradOp); +REGISTER_XLA_OP(Name("MaxPoolGradGradV2") + .TypeConstraint("T", DT_FLOAT) + .CompileTimeConstInput("ksize") + .CompileTimeConstInput("strides"), + MaxPool2DGradGradOp); + } // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..8433a29c4e203cac726ee6bf7f67a863447326ed --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc @@ -0,0 +1,121 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/scatter.h" +#include "tensorflow/compiler/tf2xla/shape_util.h" +#include "tensorflow/compiler/tf2xla/type_util.h" +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/core/framework/kernel_def_builder.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +// Check whether updates.shape = indices.shape[:batch_dim] + +// buffer_shape[num_index_dims:] +Status ValidateUpdateShape(const TensorShape& buffer_shape, + const TensorShape& indices_shape, + const TensorShape& updates_shape) { + if (indices_shape.dims() < 1) { + return errors::InvalidArgument( + "indices shape must have >= 1 dimension; got ", + indices_shape.DebugString()); + } + + const int64 num_index_dims = indices_shape.dim_size(indices_shape.dims() - 1); + const int64 batch_dim = indices_shape.dims() - 1; + + auto shape_err = [&]() { + return errors::InvalidArgument( + "Must have updates.shape = indices.shape[:batch_dim] + ", + "buffer_shape[num_index_dims:], got updates.shape: ", + updates_shape.DebugString(), + ", indices.shape: ", indices_shape.DebugString(), + ", buffer_shape: ", buffer_shape.DebugString(), + ", num_index_dims: ", num_index_dims, ", and batch_dim: ", batch_dim); + }; + + if (updates_shape.dims() < batch_dim) return shape_err(); + if (buffer_shape.dims() < + num_index_dims + (updates_shape.dims() - batch_dim)) { + return shape_err(); + } + if (updates_shape.dims() != + batch_dim + buffer_shape.dims() - num_index_dims) { + return shape_err(); + } + for (int d = 0; d < batch_dim; ++d) { + if (updates_shape.dim_size(d) != indices_shape.dim_size(d)) { + return shape_err(); + } + } + for (int d = 0; d < updates_shape.dims() - batch_dim; ++d) { + if (updates_shape.dim_size(d + batch_dim) != + buffer_shape.dim_size(d + num_index_dims)) { + return shape_err(); + } + } + return Status::OK(); +} + +class ScatterNdOp : public XlaOpKernel { + public: + explicit ScatterNdOp(OpKernelConstruction* context) : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + DataType dtype = context->input_type(1); + + TensorShape indices_shape = context->InputShape(0); + TensorShape updates_shape = context->InputShape(1); + + TensorShape buffer_shape; + OP_REQUIRES_OK(context, context->ConstantInputAsShape(2, &buffer_shape)); + + OP_REQUIRES( + context, TensorShapeUtils::IsVectorOrHigher(buffer_shape), + errors::InvalidArgument("Output must be at least 1-D, ", + "got shape: ", buffer_shape.DebugString())); + + OP_REQUIRES( + context, + buffer_shape.num_elements() > 0 || (indices_shape.num_elements() == 0 && + updates_shape.num_elements() == 0), + errors::InvalidArgument( + "Indices and updates specified for empty output. indices shape: ", + indices_shape.DebugString())); + + OP_REQUIRES_OK(context, ValidateUpdateShape(buffer_shape, indices_shape, + updates_shape)); + + xla::ComputationBuilder* builder = context->builder(); + auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype), + buffer_shape.dim_sizes()); + auto indices = context->Input(0); + auto updates = context->Input(1); + auto result = + XlaScatter(buffer, updates, indices, + /*indices_are_vectors=*/true, /*combiner=*/{}, builder); + OP_REQUIRES_OK(context, result.status()); + context->SetOutput(0, result.ValueOrDie()); + } +}; + +REGISTER_XLA_OP(Name("ScatterNd").CompileTimeConstInput("shape"), ScatterNdOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_op_helpers.h b/tensorflow/compiler/tf2xla/kernels/scatter_op_helpers.h deleted file mode 100644 index a5ab7de17adb734014fe2dcbd60ae5c219c8e486..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/tf2xla/kernels/scatter_op_helpers.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -// Helper methods for XLA Scatter Ops. -#ifndef TENSORFLOW_COMPILER_TF2XLA_KERNELS_SCATTER_OP_HELPERS_H_ -#define TENSORFLOW_COMPILER_TF2XLA_KERNELS_SCATTER_OP_HELPERS_H_ - -#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/util/bcast.h" - -namespace tensorflow { - -// Adds to builder an XLA computation that performs a scatter-add of input (of -// shape input_shape) keyed on indices (of shape indices_shape). The shape -// of the Tensor returned by this is num_segments input_shape[indices.dims():] -// -static xla::ComputationDataHandle XlaComputeScatterAddDynamicSlice( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& input, - const TensorShape& input_shape, const xla::ComputationDataHandle& indices, - const TensorShape& indices_shape, int64 num_segments, DataType dtype, - xla::ComputationBuilder* builder); - -} // namespace tensorflow - -#endif // TENSORFLOW_COMPILER_TF2XLA_KERNELS_SCATTER_OP_HELPERS_H_ diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc index c220edd588071ef262621784015d34cd475b2918..80d6df6c48b0141734dcee1c2a3c413926931feb 100644 --- a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -13,125 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include -#include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" -#include "tensorflow/compiler/tf2xla/shape_util.h" -#include "tensorflow/compiler/tf2xla/type_util.h" +#include "tensorflow/compiler/tf2xla/lib/scatter.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/computation_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" -#include "tensorflow/core/framework/kernel_def_builder.h" -#include "tensorflow/core/framework/types.h" namespace tensorflow { - -xla::ComputationDataHandle XlaComputeScatterAddDynamicSlice( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& input, - const TensorShape& input_shape, const xla::ComputationDataHandle& indices, - const TensorShape& indices_shape, int64 num_segments, DataType dtype, - xla::ComputationBuilder* builder) { - // Flatten data for dynamic indexing via indices_1d. - TensorShape input_shape_i(input_shape); - for (int64 d = 0; d < indices_shape.dims(); ++d) { - input_shape_i.RemoveDim(0); - } - TensorShape flat_shape({indices_shape.num_elements()}); - flat_shape.AppendShape(input_shape_i); - - // output is same as flattened input shape with dim_size(0) = num_segments. - TensorShape out_shape(flat_shape); - out_shape.set_dim(0, num_segments); - - // Slices from the input data are same shape as the input data, except dim 0. - TensorShape slice_shape(flat_shape); - slice_shape.set_dim(0, 1); - TensorShape loop_out_slice_shape(out_shape); - loop_out_slice_shape.set_dim(0, 1); - - // Construct the initial values of the loop-carried variables - // Flatten the indices into 1-D for ease of iteration. - auto indices_1d = builder->Reshape(indices, {indices_shape.num_elements()}); - // Flatten the data for ease of indexing via values in indices_1d. - auto data_flat = builder->Reshape(input, flat_shape.dim_sizes()); - - auto init_i = builder->ConstantR0(0); - auto init_out = builder->Broadcast(XlaHelpers::Zero(builder, dtype), - out_shape.dim_sizes()); - - xla::PrimitiveType ptype; - TF_CHECK_OK(DataTypeToPrimitiveType(dtype, &ptype)); - - std::vector tuple_shapes( - {// The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The flattened input data is loop invariant. - xla::ShapeUtil::MakeShape(ptype, flat_shape.dim_sizes()), - // The scatter indices tensor is loop invariant. - xla::ShapeUtil::MakeShape(xla::S32, {indices_shape.num_elements()}), - // The output data array is updated each loop iteration. - xla::ShapeUtil::MakeShape(ptype, out_shape.dim_sizes())}); - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - - auto init = builder->Tuple({init_i, data_flat, indices_1d, init_out}); - - // Construct the while loop condition (i < num_indices) - xla::ComputationBuilder condb(ctx->builder()->client(), - "ScatterAddWhileCond"); - condb.Lt(condb.GetTupleElement( - condb.Parameter(0, tuple_shape, "ScatterAddWhileTuple"), 0), - condb.ConstantR0(indices_shape.num_elements())); - auto cond_status = condb.Build(); - auto cond = cond_status.ConsumeValueOrDie(); - - // Construct the while loop body's function. The implementation of scatter is: - // for i in range(num_indices): - // index = dynamic-slice(indices, i) - // xi = dynamic-slice(input, i) - // output = dynamic-update-slice(output, xi, index) - xla::ComputationBuilder bodyb(ctx->builder()->client(), - "ScatterAddWhileBody"); - { - auto input_tuple = bodyb.Parameter(0, tuple_shape, "ScatterAddWhileTuple"); - auto i = bodyb.GetTupleElement(input_tuple, 0); - auto data = bodyb.GetTupleElement(input_tuple, 1); - auto idcs = bodyb.GetTupleElement(input_tuple, 2); - auto output = bodyb.GetTupleElement(input_tuple, 3); - - // Index into the data array at i. - auto zero = bodyb.ConstantR1({0}); - std::vector index_vals(flat_shape.dims(), zero); - index_vals[0] = bodyb.Reshape(i, {1}); - auto index = bodyb.ConcatInDim(index_vals, 0); - - auto data_slice = - bodyb.Reshape(bodyb.DynamicSlice(data, index, slice_shape.dim_sizes()), - loop_out_slice_shape.dim_sizes()); - - // Index into the output array. - std::vector out_index_vals(out_shape.dims(), - zero); - out_index_vals[0] = bodyb.DynamicSlice(idcs, bodyb.Reshape(i, {1}), {1}); - auto out_index = bodyb.ConcatInDim(out_index_vals, 0); - - // Slice the output array, update value, and update the output slice. - auto updated_output = bodyb.DynamicUpdateSlice( - output, - bodyb.Add(data_slice, - bodyb.DynamicSlice(output, out_index, - loop_out_slice_shape.dim_sizes())), - out_index); - - auto ip1 = bodyb.Add(i, bodyb.ConstantR0(1)); - bodyb.Tuple({ip1, data, idcs, updated_output}); - } - auto body_status = bodyb.Build(); - auto body = body_status.ConsumeValueOrDie(); - - auto gather_while = builder->While(cond, body, init); - return builder->GetTupleElement(gather_while, 3); -} - namespace { class UnsortedSegmentSum : public XlaOpKernel { @@ -153,10 +41,10 @@ class UnsortedSegmentSum : public XlaOpKernel { // as data with the first indices.rank dimensions are replaced // by a single dimension with size num_segments. auto data = ctx->Input(0); - auto data_shape = ctx->InputShape(0); + TensorShape data_shape = ctx->InputShape(0); auto indices = ctx->Input(1); - auto indices_shape = ctx->InputShape(1); + TensorShape indices_shape = ctx->InputShape(1); int64 num_segments; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &num_segments)); @@ -174,10 +62,21 @@ class UnsortedSegmentSum : public XlaOpKernel { d, " differs ", data_shape.dim_size(d), " vs. ", indices_shape.dim_size(d))); } - auto result = XlaComputeScatterAddDynamicSlice( - ctx, data, data_shape, indices, indices_shape, num_segments, dtype_, - ctx->builder()); - ctx->SetOutput(0, result); + xla::ComputationBuilder* builder = ctx->builder(); + TensorShape buffer_shape = data_shape; + buffer_shape.RemoveDimRange(0, indices_shape.dims()); + buffer_shape.InsertDim(0, num_segments); + auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype_), + buffer_shape.dim_sizes()); + + auto combiner = + [](xla::ComputationDataHandle a, xla::ComputationDataHandle b, + xla::ComputationBuilder* builder) { return builder->Add(a, b); }; + + auto result = XlaScatter(buffer, /*updates=*/data, indices, + /*indices_are_vectors=*/false, combiner, builder); + OP_REQUIRES_OK(ctx, result.status()); + ctx->SetOutput(0, result.ValueOrDie()); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 79c435c90a1f57250be90c2c2523bf3d7d231461..43c15e753805352875034dfd2c70a2a1ed9a4114 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -111,27 +111,24 @@ class SplitVOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { const int32 num_split = num_outputs(); + const TensorShape input_shape = ctx->InputShape(0); const TensorShape index_shape = ctx->InputShape(2); - xla::Literal literal_index; - OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &literal_index)); - int32 split_dim; - OP_REQUIRES(ctx, index_shape.dims() == 0, - errors::InvalidArgument("split_dim input to Split Op must be a " - "scalar")); - split_dim = literal_index.Get({}); + int64 split_dim_orig; + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &split_dim_orig)); + int64 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims() + : split_dim_orig; + OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(), + errors::InvalidArgument("-input rank(-", input_shape.dims(), + ") <= split_dim < input rank (", + input_shape.dims(), "), but got ", + split_dim_orig)); xla::ComputationDataHandle input = ctx->Input(0); - const TensorShape input_shape = ctx->InputShape(0); OP_REQUIRES(ctx, input_shape.dims() > 0, errors::InvalidArgument("Can't split a 0 dimensional input")); - OP_REQUIRES( - ctx, 0 <= split_dim && split_dim < input_shape.dims(), - errors::InvalidArgument("0 <= split_dim < number of input dimensions (", - input_shape.dims(), "), but got ", split_dim)); - OP_REQUIRES( ctx, num_split > 0, errors::InvalidArgument( diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index 91c169428c7a88a8d107a97445aeea999946e3e9..6204aa4e27000fddec7f5b82b2198d37956f6aba 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -77,13 +77,14 @@ class StridedSliceOp : public XlaOpKernel { for (int i = 0; i < begin.size(); ++i) { if (strides[i] > 0) { slice_begin.push_back(begin[i]); - slice_end.push_back(end[i]); + slice_end.push_back(std::max(end[i], begin[i])); slice_strides.push_back(strides[i]); } else { // Negative stride: swap begin and end, add 1 because the interval // is semi-open, and mark the dimension to be reversed. slice_begin.push_back(input_shape.dim_size(i) - begin[i] - 1); - slice_end.push_back(input_shape.dim_size(i) - end[i] - 1); + slice_end.push_back(std::max(input_shape.dim_size(i) - end[i] - 1, + input_shape.dim_size(i) - begin[i] - 1)); slice_strides.push_back(-strides[i]); dimensions_to_reverse.push_back(i); } diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 0c5ad9e5255ffc3dfcfb83335060ae833937b3ce..7cb47f908d4ff43f455f1e77c53cd3cc956579ee 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -60,11 +60,13 @@ XLAJIT_MAKE_UNARY( b->Add(XlaHelpers::One(b, input_type(0)), x)))); // acosh(x) = log(x + sqrt(x^2 - 1)) +// = log(x + sqrt((x+1)*(x-1))) XLAJIT_MAKE_UNARY( Acosh, - b->Log(b->Add(x, b->Pow(b->Sub(b->Mul(x, x), - XlaHelpers::One(b, input_type(0))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); + b->Log(b->Add(x, + b->Pow(b->Mul(b->Add(x, XlaHelpers::One(b, input_type(0))), + b->Sub(x, XlaHelpers::One(b, input_type(0)))), + XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); // asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) XLAJIT_MAKE_UNARY( diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index d184f59e01788829d0ba97092c14d36e5188e4e8..488fda74bf7b5c1d66f8d706a1be3cc1fc29a492 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -49,6 +49,25 @@ cc_library( ], ) +cc_library( + name = "scatter", + srcs = ["scatter.cc"], + hdrs = ["scatter.h"], + deps = [ + ":util", + ":while_loop", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:computation", + "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/core:lib", + ], +) + cc_library( name = "triangular_solve", srcs = ["triangular_solve.cc"], @@ -107,6 +126,21 @@ cc_library( ], ) +cc_library( + name = "while_loop", + srcs = ["while_loop.cc"], + hdrs = ["while_loop.h"], + deps = [ + ":util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:computation", + "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/core:lib", + ], +) + # ----------------------------------------------------------------------------- filegroup( diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc new file mode 100644 index 0000000000000000000000000000000000000000..45699233ea8b2a75e3850098250307b95546cc28 --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -0,0 +1,192 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/scatter.h" + +#include +#include + +#include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/tf2xla/lib/while_loop.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/gtl/array_slice.h" + +namespace tensorflow { + +xla::StatusOr XlaScatter( + const xla::ComputationDataHandle& buffer, + const xla::ComputationDataHandle& updates, + const xla::ComputationDataHandle& indices, bool indices_are_vectors, + const std::function& combiner, + xla::ComputationBuilder* builder) { + TF_ASSIGN_OR_RETURN(std::unique_ptr buffer_shape, + builder->GetShape(buffer)); + TF_ASSIGN_OR_RETURN(std::unique_ptr updates_shape, + builder->GetShape(updates)); + TF_ASSIGN_OR_RETURN(std::unique_ptr indices_shape, + builder->GetShape(indices)); + gtl::ArraySlice indices_dims = + xla::AsInt64Slice(indices_shape->dimensions()); + gtl::ArraySlice buffer_dims = + xla::AsInt64Slice(buffer_shape->dimensions()); + + // If the indices are N-dimensional, the minor dimension of indices contains + // the indices to update. Otherwise the indices are all scalars. + int64 num_index_dims = 1; + if (indices_are_vectors) { + TF_RET_CHECK(!indices_dims.empty()); + num_index_dims = indices_dims.back(); + if (num_index_dims > xla::ShapeUtil::Rank(*buffer_shape)) { + return errors::InvalidArgument( + "The size of the minor dimension of the indices (shape: ", + xla::ShapeUtil::HumanString(*indices_shape), + ") must be <= the rank of the buffer (shape: ", + xla::ShapeUtil::HumanString(*buffer_shape), ")"); + } + indices_dims.pop_back(); + } + + int64 num_indices = 1; + for (int64 dim : indices_dims) { + num_indices *= dim; + } + + // Degenerate case: nothing to update. Return the buffer unchanged. + if (num_indices == 0) { + return buffer; + } + + // If any of the indexed dimensions are zero in the buffer, the update cannot + // succeed since it updates a slice of size 1. + for (int64 i = 0; i < num_index_dims; ++i) { + if (xla::ShapeUtil::GetDimension(*buffer_shape, i) == 0) { + return errors::InvalidArgument( + "Scatter dimension ", i, " is of size zero in tensor with shape ", + xla::ShapeUtil::HumanString(*buffer_shape)); + } + } + + // Shape of the non-indexed dimensions of the buffer. + std::vector buffer_shape_post_axes( + buffer_dims.begin() + num_index_dims, buffer_dims.end()); + + // Flatten the major dimensions of indices and updates into a single dimension + // for ease of iteration. + std::vector flat_indices_shape({num_indices}); + if (indices_are_vectors) { + flat_indices_shape.push_back(num_index_dims); + } + + std::vector flat_updates_shape({num_indices}); + flat_updates_shape.insert(flat_updates_shape.end(), + buffer_shape_post_axes.begin(), + buffer_shape_post_axes.end()); + + // Construct the initial values of the loop-carried Tensors. + auto flat_indices = builder->Reshape(indices, flat_indices_shape); + auto flat_updates = builder->Reshape(updates, flat_updates_shape); + auto init = {flat_indices, flat_updates, buffer}; + + // Constructs the loop body. The implementation of scatter is essentially: + // for i in range(num_indices): + // index = dynamic-slice(indices, i) + // update = dynamic-slice(updates, i) + // buffer = dynamic-update-slice(buffer, update, index) + auto body_fn = [&](xla::ComputationDataHandle i, + gtl::ArraySlice loop_vars, + xla::ComputationBuilder* body_builder) { + auto indices = loop_vars[0]; + auto updates = loop_vars[1]; + auto buffer = loop_vars[2]; + + auto zero_index = body_builder->ConstantLiteral( + xla::Literal::Zero(indices_shape->element_type())); + + // Slice the i-th index from the indices array. + xla::ComputationDataHandle index; + auto indices_offset = body_builder->Reshape(i, {1}); + if (indices_are_vectors) { + indices_offset = body_builder->Pad(indices_offset, zero_index, + xla::MakeEdgePaddingConfig({{0, 1}})); + + index = body_builder->DynamicSlice(indices, indices_offset, + {1, num_index_dims}); + index = body_builder->Collapse(index, {0, 1}); + } else { + index = body_builder->DynamicSlice(indices, indices_offset, {1}); + } + + // Discard updates with negative indices, since some users expect this. + auto index_in_range = + body_builder->ReduceAll(body_builder->Le(zero_index, index), + body_builder->ConstantR0(true), + xla::CreateScalarAndComputation(body_builder)); + + // Make the index in bounds to prevent implementation defined behavior. + index = body_builder->Max(index, zero_index); + index = body_builder->Pad( + index, zero_index, + xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); + + // Slice the i-th index from the updates array. + auto updates_offset = body_builder->Reshape(i, {1}); + updates_offset = body_builder->Pad( + updates_offset, zero_index, + xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); + std::vector flat_updates_slice_shape({1}); + flat_updates_slice_shape.insert(flat_updates_slice_shape.end(), + buffer_shape_post_axes.begin(), + buffer_shape_post_axes.end()); + auto update = body_builder->DynamicSlice(updates, updates_offset, + flat_updates_slice_shape); + + // Unflatten the major (iteration) dimensions of the slice to their + // original shape. + std::vector updates_slice_shape(num_index_dims, 1); + updates_slice_shape.insert(updates_slice_shape.end(), + buffer_shape_post_axes.begin(), + buffer_shape_post_axes.end()); + update = body_builder->Reshape(update, updates_slice_shape); + + // Apply the update to the buffer. If there is a combiner, use it to merge + // the current values with the update. + auto current_value = + body_builder->DynamicSlice(buffer, index, updates_slice_shape); + if (combiner) { + update = combiner(current_value, update, body_builder); + } + // Use the current value instead of the update if the index is out of + // bounds. + update = body_builder->Select(index_in_range, update, current_value); + // Apply the update. + buffer = body_builder->DynamicUpdateSlice(buffer, update, index); + + return std::vector{indices, updates, buffer}; + }; + + TF_ASSIGN_OR_RETURN( + auto outputs, XlaForEachIndex(num_indices, indices_shape->element_type(), + body_fn, init, "scatter", builder)); + return outputs[2]; +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h new file mode 100644 index 0000000000000000000000000000000000000000..41e6d3b195ebf90662c7b9b42c53fcb0133ab29e --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_SCATTER_H_ +#define TENSORFLOW_COMPILER_TF2XLA_LIB_SCATTER_H_ + +#include + +#include "tensorflow/compiler/xla/client/computation.h" +#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/statusor.h" + +namespace tensorflow { + +// Builds an XLA computation that performs a scatter operation on `buffer`, +// returning an updated buffer. +// For each i0, i1, ..., sets +// buffer[indices[i0, i1, ...], ...] := updates[i0, i1, ...] +// +// If `indices_are_vectors` is false, then each index in indices is a scalar, +// and the shape of `indices` must be a prefix of the shape of updates. +// Otherwise, `indices_are_vectors`, then indices are multidimensional and the +// minor dimension of `indices` represents a vector of indices. +// +// If any indices are negative, the corresponding update is discarded. +// +// If a `combiner` is provided, updates are combined with the existing values in +// the buffer using the combiner function. Otherwise, the updates replace the +// existing values. The order of updates is implementation-defined. +xla::StatusOr XlaScatter( + const xla::ComputationDataHandle& buffer, + const xla::ComputationDataHandle& updates, + const xla::ComputationDataHandle& indices, bool indices_are_vectors, + const std::function& combiner, + xla::ComputationBuilder* builder); + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_SCATTER_H_ diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index 9b7492f8cf6e86498d7e2f5d42e42ea978c664d8..f579669bbd852b514e021ce71d635f8ce5e4fe4d 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -57,6 +57,61 @@ xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* builder, } } +xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* builder, + xla::PrimitiveType type, + int64 value) { + xla::Literal literal; + switch (type) { + case xla::U8: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::U32: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::U64: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::S8: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::S32: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::S64: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::F32: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::F64: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::C64: + literal = std::move(*xla::Literal::CreateR0(value)); + break; + case xla::PRED: + LOG(FATAL) << "pred element type is not integral"; + case xla::S16: + case xla::U16: + LOG(FATAL) << "u16/s16 literals not yet implemented"; + case xla::BF16: + literal = std::move( + *xla::Literal::CreateR0(static_cast(value))); + break; + case xla::F16: + literal = std::move( + *xla::Literal::CreateR0(static_cast(value))); + break; + case xla::TUPLE: + LOG(FATAL) << "tuple element type is not integral"; + case xla::OPAQUE: + LOG(FATAL) << "opaque element type is not integral"; + default: + LOG(FATAL) << "unhandled element type " << type; + } + return builder->ConstantLiteral(literal); +} + xla::StatusOr SliceInMinorDims( xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, gtl::ArraySlice start, gtl::ArraySlice end) { diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index 7f93102ee78bec60018814975a0badfeb7874aa6..51f8baaf00bd8fd25baa1a87be8cb0089dfb22b5 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -32,6 +32,11 @@ xla::ComputationDataHandle Zeros(xla::ComputationBuilder* builder, xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* builder, xla::PrimitiveType type, double value); +// Returns a integer scalar constant of 'type' with 'value'. +// If 'type' is complex, returns a real value with zero imaginary component. +xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* builder, + xla::PrimitiveType type, int64 value); + // Performs a slice in the minor dimensions of a Tensor. xla::StatusOr SliceInMinorDims( xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc new file mode 100644 index 0000000000000000000000000000000000000000..86c02ac2e65c12d3527c4022df0cc603e522ef7a --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -0,0 +1,125 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/while_loop.h" +#include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" + +namespace tensorflow { + +xla::StatusOr> XlaWhileLoop( + const LoopConditionFunction& condition_function, + const LoopBodyFunction& body_function, + gtl::ArraySlice initial_values, + StringPiece name, xla::ComputationBuilder* builder) { + int arity = initial_values.size(); + std::vector var_shapes; + var_shapes.reserve(arity); + for (const xla::ComputationDataHandle& input : initial_values) { + TF_ASSIGN_OR_RETURN(auto shape, builder->GetShape(input)); + var_shapes.push_back(std::move(*shape)); + } + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(var_shapes); + + // Unpacks a tuple into its component parts. + auto unpack_tuple = [](xla::ComputationDataHandle tuple, int arity, + xla::ComputationBuilder* builder) { + std::vector elements(arity); + for (int i = 0; i < arity; ++i) { + elements[i] = builder->GetTupleElement(tuple, i); + } + return elements; + }; + + // Build the condition. + std::unique_ptr cond_builder = + builder->CreateSubBuilder(strings::StrCat(name, "_condition")); + { + auto parameter = cond_builder->Parameter(0, tuple_shape, "parameter"); + + TF_ASSIGN_OR_RETURN( + auto result, + condition_function(unpack_tuple(parameter, arity, cond_builder.get()), + cond_builder.get())); + TF_RETURN_IF_ERROR(cond_builder->SetReturnValue(result)); + } + TF_ASSIGN_OR_RETURN(auto cond, cond_builder->Build()); + + // Build the body. + std::unique_ptr body_builder = + builder->CreateSubBuilder(strings::StrCat(name, "_body")); + { + auto parameter = body_builder->Parameter(0, tuple_shape, "parameter"); + + TF_ASSIGN_OR_RETURN( + auto result, + body_function(unpack_tuple(parameter, arity, body_builder.get()), + body_builder.get())); + + TF_RET_CHECK(result.size() == initial_values.size()); + body_builder->Tuple(result); + } + TF_ASSIGN_OR_RETURN(auto body, body_builder->Build()); + + auto outputs = builder->While(cond, body, builder->Tuple(initial_values)); + + return unpack_tuple(outputs, arity, builder); +} + +xla::StatusOr> XlaForEachIndex( + int64 num_iterations, xla::PrimitiveType num_iterations_type, + const ForEachIndexBodyFunction& body_function, + gtl::ArraySlice initial_values, + StringPiece name, xla::ComputationBuilder* builder) { + auto while_cond_fn = [&](gtl::ArraySlice values, + xla::ComputationBuilder* cond_builder) + -> xla::StatusOr { + return cond_builder->Lt( + values[0], + IntegerLiteral(cond_builder, num_iterations_type, num_iterations)); + }; + auto while_body_fn = [&](gtl::ArraySlice values, + xla::ComputationBuilder* body_builder) + -> xla::StatusOr> { + xla::ComputationDataHandle iteration = values[0]; + + std::vector updated_values; + updated_values.reserve(values.size()); + updated_values.push_back(body_builder->Add( + iteration, + body_builder->ConstantLiteral(xla::Literal::One(num_iterations_type)))); + + values.remove_prefix(1); + TF_ASSIGN_OR_RETURN(std::vector body_outputs, + body_function(iteration, values, body_builder)); + updated_values.insert(updated_values.end(), body_outputs.begin(), + body_outputs.end()); + return updated_values; + }; + + std::vector values; + values.reserve(initial_values.size() + 1); + values.push_back( + builder->ConstantLiteral(xla::Literal::Zero(num_iterations_type))); + values.insert(values.end(), initial_values.begin(), initial_values.end()); + + TF_ASSIGN_OR_RETURN(values, XlaWhileLoop(while_cond_fn, while_body_fn, values, + name, builder)); + values.erase(values.begin(), values.begin() + 1); + return values; +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h new file mode 100644 index 0000000000000000000000000000000000000000..2e67a0c99b6deb65fa16ab2dec1727f5cb5fcb92 --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -0,0 +1,74 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ +#define TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ + +#include +#include + +#include "tensorflow/compiler/xla/client/computation.h" +#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/array_slice.h" + +namespace tensorflow { + +// Function that builds a loop condition. Takes as input a sequence of input +// values, and returns a boolean value representing if the condition succeeds. +typedef std::function( + gtl::ArraySlice, xla::ComputationBuilder*)> + LoopConditionFunction; + +// Function that builds a loop body. Takes as input a sequence of input values +// and returns a sequence of output values. +typedef std::function>( + gtl::ArraySlice, xla::ComputationBuilder*)> + LoopBodyFunction; + +// Helper function for building an XLA while loop, where the values carried by +// the loop are a tuple of values, e.g., (a, b, c): +// while( +// condition: (a, b, c) -> bool, +// body: (a, b, c) -> (a, b, c) +// init: (a, b, c) +// ) +// 'name' is a descriptive name for the loop. +xla::StatusOr> XlaWhileLoop( + const LoopConditionFunction& condition_function, + const LoopBodyFunction& body_function, + gtl::ArraySlice initial_values, + StringPiece name, xla::ComputationBuilder* builder); + +// Builds an XLA loop that repeats a computation `num_iterations` times. +// +// The body function (ForEachIndexBodyFunction) takes as input a pair of +// (current iteration number, loop-carried values), and returns an updated +// vector of the loop-carried values. +typedef std::function>( + xla::ComputationDataHandle, gtl::ArraySlice, + xla::ComputationBuilder*)> + ForEachIndexBodyFunction; + +xla::StatusOr> XlaForEachIndex( + int64 num_iterations, xla::PrimitiveType num_iterations_type, + const ForEachIndexBodyFunction& body_function, + gtl::ArraySlice initial_values, + StringPiece name, xla::ComputationBuilder* builder); + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index fcbd157c6191655865d5e250fdf71338780bc2a6..2c3cd658e0462368ac0b51938979b7a6815a7574 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -40,20 +40,20 @@ Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) { return Status::OK(); } -Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type, - Tensor* host_tensor) { +Status CopyLiteralToHostTensor(const xla::Literal& literal, + Tensor* host_tensor) { + TF_RET_CHECK(xla::ShapeUtil::IsArray(literal.shape()) && + xla::ShapeUtil::ElementsIn(literal.shape()) == + host_tensor->NumElements()); xla::PrimitiveType primitive_type; - TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(target_type, &primitive_type)); + TF_RETURN_IF_ERROR( + DataTypeToPrimitiveType(host_tensor->dtype(), &primitive_type)); if (literal.shape().element_type() != primitive_type) { return errors::InvalidArgument( "Cannot convert literal of type ", xla::PrimitiveType_Name(literal.shape().element_type()), - " to tensor of type ", DataTypeString(target_type)); + " to tensor of type ", DataTypeString(host_tensor->dtype())); } - - TensorShape shape; - TF_RETURN_IF_ERROR(XLAShapeToTensorShape(literal.shape(), &shape)); - *host_tensor = Tensor(target_type, shape); size_t total_bytes = host_tensor->TotalBytes(); if (total_bytes > 0) { const void* src_ptr = literal.untyped_data(); @@ -63,4 +63,12 @@ Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type, return Status::OK(); } +Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type, + Tensor* host_tensor) { + TensorShape shape; + TF_RETURN_IF_ERROR(XLAShapeToTensorShape(literal.shape(), &shape)); + *host_tensor = Tensor(target_type, shape); + return CopyLiteralToHostTensor(literal, host_tensor); +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index fe08e83c2391a8b24696961cacfd909d46e49e7d..f283b0236811f8d52e8fe2982a74c11c92cd20d8 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -29,7 +29,8 @@ namespace tensorflow { // unsupported type. Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); -// Copies 'literal' to 'host_tensor', which is allocated of type . +// Copies 'literal' to freshly allocated 'host_tensor', which is allocated of +// type . // Fails if the literal's primitive type != // DataTypeToPrimitiveType(target_type). Note that is not // derivable from the type of , because multiple tensorflow types map @@ -38,6 +39,12 @@ Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type, Tensor* host_tensor); +// Copies the contents of 'literal' to a previously allocated tensor +// 'host_tensor'. The tensor and the literal must have the same number of +// elements and the same type. +Status CopyLiteralToHostTensor(const xla::Literal& literal, + Tensor* host_tensor); + } // namespace tensorflow #endif // TENSORFLOW_COMPILER_TF2XLA_LITERAL_UTIL_H_ diff --git a/tensorflow/compiler/tf2xla/python/BUILD b/tensorflow/compiler/tf2xla/python/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..f0a2ef0651ff6115bd201a3b1c34b3c061a22a3d --- /dev/null +++ b/tensorflow/compiler/tf2xla/python/BUILD @@ -0,0 +1,24 @@ +licenses(["notice"]) # Apache 2.0 + +package( + default_visibility = [ + "//learning/tfx:__subpackages__", + "//tensorflow:internal", + ], +) + +load( + "//tensorflow/core:platform/default/build_config.bzl", + "tf_py_clif_cc", +) + +tf_py_clif_cc( + name = "xla_op_registry", + srcs = ["xla_op_registry.clif"], + pyclif_deps = [ + "//tensorflow/core:framework/kernel_def_pyclif", + ], + deps = [ + "//tensorflow/compiler/tf2xla:xla_compiler", + ], +) diff --git a/tensorflow/compiler/tf2xla/python/xla_op_registry.clif b/tensorflow/compiler/tf2xla/python/xla_op_registry.clif new file mode 100644 index 0000000000000000000000000000000000000000..e1ee6cc656a314876fc1fabbebe1bee39a6b2831 --- /dev/null +++ b/tensorflow/compiler/tf2xla/python/xla_op_registry.clif @@ -0,0 +1,7 @@ +from "third_party/tensorflow/core/framework/kernel_def_pyclif.h" import * # KernelDef + +from "third_party/tensorflow/compiler/tf2xla/xla_op_registry.h": + namespace `tensorflow`: + def `XlaOpRegistry::DeviceKernels` as + device_kernels(device: str, include_compilation_only_kernels: bool) -> + list diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index c5b4ec5b15f90eb43c61cddb7bfd7640fa237a3d..0dc5118c9c659cc1529515f34c9eb43fd07a69e8 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -109,6 +109,12 @@ XlaCompiler::XlaCompiler(XlaCompiler::Options options) local_flib_runtime_ = local_pflr_->GetFLR(device_->name()); flib_runtime_ = pflr_->GetFLR(device_->name()); + + // The default variable representation shape is the identity function. + if (!options_.variable_representation_shape_fn) { + options_.variable_representation_shape_fn = + [](const TensorShape& shape, DataType type) { return shape; }; + } } XlaCompiler::~XlaCompiler() = default; @@ -153,7 +159,8 @@ std::unique_ptr XlaCompiler::GetGraph(const FunctionBody* fbody) { std::unique_ptr graph(new Graph(options_.flib_def)); CopyGraph(*fbody->graph, graph.get()); OptimizerOptions opts; - opts.set_do_common_subexpression_elimination(true); + opts.set_opt_level(OptimizerOptions::L0); + opts.set_do_common_subexpression_elimination(false); opts.set_do_function_inlining(true); opts.set_do_constant_folding(true); GraphOptimizer optimizer(opts); @@ -184,8 +191,7 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, CheckSignature(fbody->arg_types, args), "Signature check failure while compiling: ", function.name()); - std::unique_ptr graph(new Graph(options_.flib_def)); - CopyGraph(*fbody->graph, graph.get()); + std::unique_ptr graph = GetGraph(fbody); // _Arg and _Retval nodes don't exist in the stored subgraph for the function; // they are added by the function body looked up. Therefore, they don't have @@ -213,15 +219,6 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, *graph); } - // Optimize the graph before running the compiler. - OptimizerOptions opts; - opts.set_do_common_subexpression_elimination(true); - opts.set_do_function_inlining(true); - opts.set_do_constant_folding(true); - GraphOptimizer optimizer(opts); - optimizer.Optimize(flib_runtime_, flib_runtime_->env(), - /*device=*/nullptr, &graph, /*shape_map=*/nullptr); - VLOG(1) << "===================================================="; TF_RETURN_IF_ERROR( CompileGraph(options, function_id, std::move(graph), args, result)); @@ -232,8 +229,8 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, } // Computes the XLA shape for argument 'arg'. -/*static*/ Status XlaCompiler::XLAShapeForArgument( - const XlaCompiler::Argument& arg, xla::Shape* xla_shape) { +Status XlaCompiler::XLAShapeForArgument(const XlaCompiler::Argument& arg, + xla::Shape* xla_shape) { switch (arg.kind) { case XlaCompiler::Argument::kConstant: return TensorShapeToXLAShape(arg.type, arg.constant_value.shape(), @@ -244,8 +241,12 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, TF_RET_CHECK(arg.initialized); switch (arg.resource_kind) { - case XlaResource::kVariable: - return TensorShapeToXLAShape(arg.type, arg.shape, xla_shape); + case XlaResource::kVariable: { + TensorShape representation_shape = + options_.variable_representation_shape_fn(arg.shape, arg.type); + return TensorShapeToXLAShape(arg.type, representation_shape, + xla_shape); + } case XlaResource::kTensorArray: { if (arg.tensor_array_size < 0) { return errors::InvalidArgument( @@ -319,16 +320,125 @@ Status ExecuteGraph(XlaContext* xla_context, std::unique_ptr graph, return Status::OK(); } +// Builds the XLA computation. +// +// `retvals` is the list of retvals produced by _Retval operators, in index +// order. `variable_map` is a map from variable ID numbers to XlaOpContext +// variable states, generated by the symbolic evaluation. +// If `return_updated_values_for_all_resources` is true, all resources will be +// included in `resource_updates`, regardless of whether their value changed. +// Sets `*num_nonconst_outputs` to the number of outputs of the `computation`. +// Sets `*resource_updates` to a description of resources whose values are +// written by the computation; the variable writes are the last +// `resource_updates.size()` return values from the computation. Each entry in +// `resource_updates` is a (input_index, type) pair, where `input_index` is the +// index of a resource variable argument to the computation, and `type` is the +// type of the final output. +Status BuildComputation( + const std::vector& args, + const std::vector& arg_cores, + const std::vector& retvals, + const std::vector>& resources, + bool return_updated_values_for_all_resources, + xla::ComputationBuilder* builder, xla::Computation* computation, + int* num_computation_outputs, int* num_nonconst_outputs, + std::vector* resource_updates) { + std::vector elems; + elems.reserve(retvals.size()); + for (const XlaExpression& retval : retvals) { + if (!retval.has_constant_value()) { + elems.push_back(retval.handle()); + } + } + *num_nonconst_outputs = elems.size(); + + // Add return values for resources whose values have changed. + std::vector arg_resources; + arg_resources.reserve(resources.size()); + for (const auto& resource : resources) { + if (resource->arg_num() >= 0) { + arg_resources.push_back(resource.get()); + } + } + std::sort(arg_resources.begin(), arg_resources.end(), + [](const XlaResource* a, const XlaResource* b) { + return a->arg_num() < b->arg_num(); + }); + + // Attach a common operator name as metadata. This has no semantic effect — it + // merely makes the HLO graph more readable when visualized via TensorBoard, + // since TensorBoard forms groups out of operators with similar names. + xla::OpMetadata retval_metadata; + retval_metadata.set_op_name("XLA_Retvals"); + builder->SetOpMetadata(retval_metadata); + + for (const XlaResource* resource : arg_resources) { + const XlaCompiler::Argument& arg = args[resource->arg_num()]; + const int core = arg_cores[resource->arg_num()]; + DCHECK_LT(resource->arg_num(), arg_cores.size()); + bool modified = + resource->value().handle() != resource->initial_value().handle(); + // TensorArray gradients were modified if their values changed or there are + // any newly created gradients. + for (const auto& grad : resource->tensor_array_gradients()) { + modified = modified || + grad.second->value().handle() != + grad.second->initial_value().handle() || + arg.tensor_array_gradients.count(grad.first) == 0; + } + if (return_updated_values_for_all_resources || modified) { + resource_updates->emplace_back(); + XlaCompiler::ResourceUpdate& update = resource_updates->back(); + update.input_index = resource->arg_num(); + update.type = resource->type(); + update.shape = resource->shape(); + update.modified = modified; + for (const auto& grad : resource->tensor_array_gradients()) { + update.tensor_array_gradients_accessed.insert(grad.first); + } + + // Request that the value be returned on a specific core. + xla::ScopedShardingAssignment assign_sharding( + builder, core == -1 ? tensorflow::gtl::optional() + : xla::sharding_builder::AssignDevice(core)); + + xla::ComputationDataHandle handle; + TF_RETURN_IF_ERROR(resource->Pack(&handle, builder)); + + // Since we can't change the sharding metadata of as this point, + // create a tuple/get-tuple-element combination so that sharding + // assignment will be placed on this value, which will cause the resource + // update to be returned from the same device that provided the resource. + handle = builder->GetTupleElement(builder->Tuple({handle}), 0); + + elems.push_back(handle); + } + } + + *num_computation_outputs = elems.size(); + + // Builds the XLA computation. + builder->Tuple(elems); + builder->ClearOpMetadata(); + + xla::StatusOr computation_status = builder->Build(); + if (!computation_status.ok()) { + return computation_status.status(); + } + *computation = computation_status.ConsumeValueOrDie(); + return Status::OK(); +} + +} // namespace + // Builds XLA computations for each of the arguments to the computation. // `args` are the arguments to the computation. -Status BuildArguments(const Graph& graph, - const std::vector& args, - bool use_tuple_arg, xla::ComputationBuilder* builder, - XlaContext* context, std::vector* arg_cores, - std::vector* arg_expressions, - std::vector* input_mapping, - std::vector* input_shapes, - bool is_entry_computation) { +Status XlaCompiler::BuildArguments( + const Graph& graph, const std::vector& args, + bool use_tuple_arg, xla::ComputationBuilder* builder, XlaContext* context, + std::vector* arg_cores, std::vector* arg_expressions, + std::vector* input_mapping, std::vector* input_shapes, + bool is_entry_computation) { arg_expressions->resize(args.size()); *arg_cores = std::vector(args.size(), -1); @@ -383,8 +493,8 @@ Status BuildArguments(const Graph& graph, std::vector arg_shapes(input_mapping->size()); for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { // Computes the shapes of non-constant arguments. - TF_RETURN_IF_ERROR(XlaCompiler::XLAShapeForArgument( - args[(*input_mapping)[i]], &arg_shapes[i])); + TF_RETURN_IF_ERROR( + XLAShapeForArgument(args[(*input_mapping)[i]], &arg_shapes[i])); } if (use_tuple_arg) { @@ -413,6 +523,13 @@ Status BuildArguments(const Graph& graph, } } + // Attach a common operator name as metadata. This has no semantic effect — it + // merely makes the HLO graph more readable when visualized via TensorBoard, + // since TensorBoard forms groups out of operators with similar names. + xla::OpMetadata arg_metadata; + arg_metadata.set_op_name("XLA_Args"); + builder->SetOpMetadata(arg_metadata); + // Build parameter handles for non-constant arguments. std::vector arg_handles(input_mapping->size()); if (use_tuple_arg) { @@ -451,6 +568,8 @@ Status BuildArguments(const Graph& graph, } } + builder->ClearOpMetadata(); + // Fill in the handles in non-constant arguments. VLOG(2) << "XLA computation inputs:"; for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { @@ -481,108 +600,6 @@ Status BuildArguments(const Graph& graph, return Status::OK(); } -// Builds the XLA computation. -// -// `retvals` is the list of retvals produced by _Retval operators, in index -// order. `variable_map` is a map from variable ID numbers to XlaOpContext -// variable states, generated by the symbolic evaluation. -// If `return_updated_values_for_all_resources` is true, all resources will be -// included in `resource_updates`, regardless of whether their value changed. -// Sets `*num_nonconst_outputs` to the number of outputs of the `computation`. -// Sets `*resource_updates` to a description of resources whose values are -// written by the computation; the variable writes are the last -// `resource_updates.size()` return values from the computation. Each entry in -// `resource_updates` is a (input_index, type) pair, where `input_index` is the -// index of a resource variable argument to the computation, and `type` is the -// type of the final output. -Status BuildComputation( - const std::vector& args, - const std::vector& arg_cores, - const std::vector& retvals, - const std::vector>& resources, - bool return_updated_values_for_all_resources, - xla::ComputationBuilder* builder, xla::Computation* computation, - int* num_computation_outputs, int* num_nonconst_outputs, - std::vector* resource_updates) { - std::vector elems; - elems.reserve(retvals.size()); - for (const XlaExpression& retval : retvals) { - if (!retval.has_constant_value()) { - elems.push_back(retval.handle()); - } - } - *num_nonconst_outputs = elems.size(); - - // Add return values for resources whose values have changed. - std::vector arg_resources; - arg_resources.reserve(resources.size()); - for (const auto& resource : resources) { - if (resource->arg_num() >= 0) { - arg_resources.push_back(resource.get()); - } - } - std::sort(arg_resources.begin(), arg_resources.end(), - [](const XlaResource* a, const XlaResource* b) { - return a->arg_num() < b->arg_num(); - }); - - for (const XlaResource* resource : arg_resources) { - const XlaCompiler::Argument& arg = args[resource->arg_num()]; - const int core = arg_cores[resource->arg_num()]; - DCHECK_LT(resource->arg_num(), arg_cores.size()); - bool modified = - resource->value().handle() != resource->initial_value().handle(); - // TensorArray gradients were modified if their values changed or there are - // any newly created gradients. - for (const auto& grad : resource->tensor_array_gradients()) { - modified = modified || - grad.second->value().handle() != - grad.second->initial_value().handle() || - arg.tensor_array_gradients.count(grad.first) == 0; - } - if (return_updated_values_for_all_resources || modified) { - resource_updates->emplace_back(); - XlaCompiler::ResourceUpdate& update = resource_updates->back(); - update.input_index = resource->arg_num(); - update.type = resource->type(); - update.shape = resource->shape(); - update.modified = modified; - for (const auto& grad : resource->tensor_array_gradients()) { - update.tensor_array_gradients_accessed.insert(grad.first); - } - - // Request that the value be returned on a specific core. - xla::ScopedShardingAssignment assign_sharding( - builder, core == -1 ? tensorflow::gtl::optional() - : xla::sharding_builder::AssignDevice(core)); - - xla::ComputationDataHandle handle; - TF_RETURN_IF_ERROR(resource->Pack(&handle, builder)); - - // Since we can't change the sharding metadata of as this point, - // create a tuple/get-tuple-element combination so that sharding - // assignment will be placed on this value, which will cause the resource - // update to be returned from the same device that provided the resource. - handle = builder->GetTupleElement(builder->Tuple({handle}), 0); - - elems.push_back(handle); - } - } - - *num_computation_outputs = elems.size(); - - // Builds the XLA computation. - builder->Tuple(elems); - xla::StatusOr computation_status = builder->Build(); - if (!computation_status.ok()) { - return computation_status.status(); - } - *computation = computation_status.ConsumeValueOrDie(); - return Status::OK(); -} - -} // namespace - Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, string const& name, std::unique_ptr graph, @@ -607,7 +624,8 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, xla::ComputationBuilder builder(client(), name); XlaContext* context = new XlaContext(this, &builder, options_.allow_cpu_custom_calls, - options.resolve_compile_time_constants); + options.resolve_compile_time_constants, + &options_.variable_representation_shape_fn); core::ScopedUnref context_unref(context); std::vector arg_expressions; @@ -656,6 +674,14 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, VLOG(2) << "XLA output shape: " << xla::ShapeUtil::HumanString(result->xla_output_shape); + // Copy the host transfer metadata to the result. + for (const auto& send : host_compute_sends_) { + *result->host_compute_metadata.add_device_to_host() = send.second; + } + for (const auto& recv : host_compute_recvs_) { + *result->host_compute_metadata.add_host_to_device() = recv.second; + } + // Tensorflow expects a major-to-minor order of results. xla::LayoutUtil::SetToDefaultLayout(&result->xla_output_shape); @@ -690,4 +716,59 @@ Status XlaCompiler::GetChannelHandle(const string& key, return Status::OK(); } +namespace { + +void SetTransfer(const string& key, const std::vector& types, + const std::vector& shapes, + tf2xla::HostTransferMetadata* transfer) { + transfer->set_key(key); + CHECK(types.size() == shapes.size()); + for (int i = 0; i < types.size(); ++i) { + tf2xla::TensorMetadata* metadata = transfer->add_metadata(); + metadata->set_type(types[i]); + shapes[i].AsProto(metadata->mutable_shape()); + } +} + +} // namespace + +Status XlaCompiler::SetDeviceToHostMetadata( + const string& key, const std::vector& types, + const std::vector& shapes) { + if (host_compute_sends_.find(key) != host_compute_sends_.end()) { + return errors::InvalidArgument( + "Duplicate calls to SetDeviceToHostMetadata with key ", key); + } + tf2xla::HostTransferMetadata& transfer = host_compute_sends_[key]; + SetTransfer(key, types, shapes, &transfer); + return Status::OK(); +} + +Status XlaCompiler::GetDeviceToHostShapes( + const string& key, std::vector* shapes) const { + const auto iter = host_compute_sends_.find(key); + if (iter == host_compute_sends_.end()) { + return errors::InvalidArgument( + "No host compute send shapes registered for key ", key); + } + shapes->clear(); + for (int i = 0; i < iter->second.metadata_size(); ++i) { + TensorShape shape(iter->second.metadata(i).shape()); + shapes->push_back(shape); + } + return Status::OK(); +} + +Status XlaCompiler::SetHostToDeviceMetadata( + const string& key, const std::vector& types, + const std::vector& shapes) { + if (host_compute_recvs_.find(key) != host_compute_sends_.end()) { + return errors::InvalidArgument( + "Duplicate calls to SetHostToDeviceMetadata with key ", key); + } + tf2xla::HostTransferMetadata& transfer = host_compute_recvs_[key]; + SetTransfer(key, types, shapes, &transfer); + return Status::OK(); +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index b86c82c0ab5ce379d35a13043857f459199e2ad2..a70d2637e0b578ddb57dc990cd9550798e675e1d 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_XLA_COMPILER_H_ #define TENSORFLOW_COMPILER_TF2XLA_XLA_COMPILER_H_ +#include "tensorflow/compiler/tf2xla/host_compute_metadata.pb.h" #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/core/common_runtime/device.h" @@ -29,6 +30,9 @@ limitations under the License. #include "tensorflow/core/public/version.h" namespace tensorflow { + +class XlaContext; + // The XlaCompiler class is responsible for compilation of a self-contained // subgraph of a TensorFlow computation using the XLA linear algebra runtime. // It does a symbolic execution of the graph starting from specific input @@ -213,6 +217,10 @@ class XlaCompiler { // containing both constant and non-constant results. std::vector outputs; + // TensorFlow shapes and types of sends/recvs from HostCompute Ops to their + // matching RecvAtHost/SendFromHost Ops in the outer graph. + tf2xla::HostComputeMetadata host_compute_metadata; + // Resources whose values were updated by the computation, ordered // by return value position. Resource updates follow the non-constant // results in the outputs of XLA computation. @@ -239,6 +247,12 @@ class XlaCompiler { // for CPU. bool allow_cpu_custom_calls = false; + // If set, the XLA representation of variables represented to XLA as the + // shape given by this shape function. Variables are reshaped to this shape + // on write, and reshaped to their original shape on read. + std::function + variable_representation_shape_fn; + // If not nullptr, populate_resource_manager is called with the // compilation device's resource manager when the compilation // device is created, and can be used to create metadata objects @@ -278,7 +292,7 @@ class XlaCompiler { // Returns the shape of the XLA parameter for an argument 'arg'. // See the class comment for more details about the argument passing // convention. - static Status XLAShapeForArgument(const Argument& arg, xla::Shape* xla_shape); + Status XLAShapeForArgument(const Argument& arg, xla::Shape* xla_shape); // Retrieves the channel handle associated with `key`. Allocates // a new channel handle if none exists. @@ -287,6 +301,22 @@ class XlaCompiler { // same XlaCompiler. Status GetChannelHandle(const string& key, xla::ChannelHandle* channel); + // Sets the shapes and types for the device to host transfer associated with + // 'key'. + Status SetDeviceToHostMetadata(const string& key, + const std::vector& types, + const std::vector& shapes); + + // Gets the shapes the device to host transfer associated with 'key'. + Status GetDeviceToHostShapes(const string& key, + std::vector* shapes) const; + + // Sets the shapes and types for the host to device transfer associated with + // 'key'. + Status SetHostToDeviceMetadata(const string& key, + const std::vector& types, + const std::vector& shapes); + const Options& options() const { return options_; } xla::Client* client() const { return options_.client; } FunctionLibraryRuntime* flib_runtime() const { return flib_runtime_; } @@ -299,6 +329,17 @@ class XlaCompiler { // Returns the optimized graph object in this function body. std::unique_ptr GetGraph(const FunctionBody* fbody); + // Builds XLA computations for each of the arguments to the computation. + // `args` are the arguments to the computation. + Status BuildArguments(const Graph& graph, + const std::vector& args, + bool use_tuple_arg, xla::ComputationBuilder* builder, + XlaContext* context, std::vector* arg_cores, + std::vector* arg_expressions, + std::vector* input_mapping, + std::vector* input_shapes, + bool is_entry_computation); + // Graph compiler needs to know how to get an optimized graph from a function // body. friend class GraphCompiler; @@ -339,6 +380,9 @@ class XlaCompiler { std::unordered_map channels_; + std::unordered_map host_compute_sends_; + std::unordered_map host_compute_recvs_; + TF_DISALLOW_COPY_AND_ASSIGN(XlaCompiler); }; diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc index 65de4dbad75b7fb76a041bc799fc31dc5cb80d74..a18eeacd41808884fac9ec5d617cb0d274ea27d8 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/cc/framework/ops.h" #include "tensorflow/cc/ops/data_flow_ops.h" #include "tensorflow/cc/ops/function_ops.h" +#include "tensorflow/cc/ops/resource_variable_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" @@ -683,5 +684,128 @@ TEST_F(XlaCompilerTest, LocalFunctionWithWrongArgumentsFail) { << status.error_message(); } +// Tests a simple graph that reads and writes a variable. +TEST_F(XlaCompilerTest, Variables) { + Scope scope = Scope::NewRootScope().ExitOnError(); + auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0); + auto var = ops::_Arg(scope.WithOpName("V"), DT_RESOURCE, 1); + auto write = ops::AssignAddVariableOp(scope, var, a); + auto read = ops::ReadVariableOp( + scope.WithControlDependencies(std::vector{write}), var, + DT_INT32); + auto read_plus_one = ops::Add(scope, read, ops::Const(scope, 1)); + auto d = ops::_Retval(scope.WithOpName("D"), read_plus_one, 0); + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + + // Builds a description of the arguments. + std::vector args(2); + args[0].kind = XlaCompiler::Argument::kParameter; + args[0].type = DT_INT32; + args[0].shape = TensorShape({2}); + args[1].kind = XlaCompiler::Argument::kResource; + args[1].resource_kind = XlaResource::kVariable; + args[1].initialized = true; + args[1].type = DT_INT32; + args[1].shape = TensorShape({2}); + + // Compiles the graph. + XlaCompiler compiler(DefaultOptions()); + + XlaCompiler::CompilationResult result; + TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(), "add", + std::move(graph), args, &result)); + + // Tests that the generated computation works. + std::unique_ptr param0_literal = + xla::Literal::CreateR1({7, 42}); + std::unique_ptr param1_literal = + xla::Literal::CreateR1({-3, 101}); + std::unique_ptr param0_data = + client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); + std::unique_ptr param1_data = + client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); + + std::unique_ptr actual = + client_ + ->Execute(*result.computation, {param0_data.get(), param1_data.get()}) + .ConsumeValueOrDie(); + std::unique_ptr actual_literal = + client_->Transfer(*actual).ConsumeValueOrDie(); + + std::unique_ptr expected0 = + xla::Literal::CreateR1({5, 144}); + std::unique_ptr expected1 = + xla::Literal::CreateR1({4, 143}); + std::unique_ptr expected_literal = + xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal); +} + +// Tests a simple graph that reads and writes a variable, with a +// variable_representation_shape_fn passed to the compiler that flattens all +// variable tensors to vectors. +TEST_F(XlaCompilerTest, VariableRepresentationShapeFunction) { + Scope scope = Scope::NewRootScope().ExitOnError(); + auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0); + auto var = ops::_Arg(scope.WithOpName("V"), DT_RESOURCE, 1); + auto write = ops::AssignAddVariableOp(scope, var, a); + auto read = ops::ReadVariableOp( + scope.WithControlDependencies(std::vector{write}), var, + DT_INT32); + auto read_plus_one = ops::Add(scope, read, ops::Const(scope, 1)); + auto d = ops::_Retval(scope.WithOpName("D"), read_plus_one, 0); + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + + // Builds a description of the arguments. + std::vector args(2); + args[0].kind = XlaCompiler::Argument::kParameter; + args[0].type = DT_INT32; + args[0].shape = TensorShape({2, 2}); + args[1].kind = XlaCompiler::Argument::kResource; + args[1].resource_kind = XlaResource::kVariable; + args[1].initialized = true; + args[1].type = DT_INT32; + args[1].shape = TensorShape({2, 2}); + + // Compiles the graph. + XlaCompiler::Options options = DefaultOptions(); + options.variable_representation_shape_fn = [](const TensorShape& shape, + DataType type) { + return TensorShape({shape.num_elements()}); + }; + XlaCompiler compiler(options); + + XlaCompiler::CompilationResult result; + TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(), "add", + std::move(graph), args, &result)); + + // Tests that the generated computation works. + std::unique_ptr param0_literal = + xla::Literal::CreateR2({{4, 55}, {1, -3}}); + std::unique_ptr param1_literal = + xla::Literal::CreateR1({22, 11, 33, 404}); + std::unique_ptr param0_data = + client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); + std::unique_ptr param1_data = + client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); + + std::unique_ptr actual = + client_ + ->Execute(*result.computation, {param0_data.get(), param1_data.get()}) + .ConsumeValueOrDie(); + std::unique_ptr actual_literal = + client_->Transfer(*actual).ConsumeValueOrDie(); + + std::unique_ptr expected0 = + xla::Literal::CreateR2({{27, 67}, {35, 402}}); + std::unique_ptr expected1 = + xla::Literal::CreateR1({26, 66, 34, 401}); + std::unique_ptr expected_literal = + xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 73878955e3fd54c103c0b07faf7f5ee5bcd84de0..8423921086fec1cf534cf613102fc3839035cb85 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -62,13 +62,16 @@ void XlaContext::set_args(std::vector args) { args_ = std::move(args); } -XlaContext::XlaContext(XlaCompiler* compiler, xla::ComputationBuilder* builder, - bool allow_cpu_custom_calls, - bool resolve_compile_time_constants) +XlaContext::XlaContext( + XlaCompiler* compiler, xla::ComputationBuilder* builder, + bool allow_cpu_custom_calls, bool resolve_compile_time_constants, + const std::function* + variable_representation_shape_fn) : compiler_(compiler), builder_(builder), allow_cpu_custom_calls_(allow_cpu_custom_calls), - resolve_compile_time_constants_(resolve_compile_time_constants) {} + resolve_compile_time_constants_(resolve_compile_time_constants), + variable_representation_shape_fn_(variable_representation_shape_fn) {} string XlaContext::DebugString() { return "TLA JIT context"; } @@ -115,6 +118,11 @@ Status XlaContext::CreateResource( return Status::OK(); } +TensorShape XlaContext::VariableRepresentationShape(const TensorShape& shape, + DataType type) const { + return (*variable_representation_shape_fn_)(shape, type); +} + const xla::Computation* XlaContext::GetOrCreateMax(const DataType type) { return LookupOrCreate(type, &max_func_, [this, type] { const string type_string = DataTypeString(type); diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index fac0352ae81e24597e1045981ac47a7cd09481da..00fbaba37c542954f690b310a184cff985a05156 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -44,7 +44,9 @@ class XlaContext : public ResourceBase { // Creates a new XlaContext. XlaContext(XlaCompiler* compiler, xla::ComputationBuilder* builder, - bool allow_cpu_custom_calls, bool resolve_compile_time_constants); + bool allow_cpu_custom_calls, bool resolve_compile_time_constants, + const std::function* + variable_representation_shape_fn); // Virtual method defined by ResourceBase. string DebugString() override; @@ -86,6 +88,11 @@ class XlaContext : public ResourceBase { return resources_; } + // Returns the XLA shape to be used to represent a variable of TF `shape` + // and `type`. + TensorShape VariableRepresentationShape(const TensorShape& shape, + DataType type) const; + // Get an XLA lambda to compute Max. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. @@ -133,6 +140,11 @@ class XlaContext : public ResourceBase { // Holds ownership of resources. The resources are not ordered. std::vector> resources_; + // A function that describes how variable shapes should be represented + // in XLA. Variable values will be reshaped to this shape. Must be non-null. + const std::function* + variable_representation_shape_fn_; + // Cache of prebuilt computations indexed by their type. using ComputationMap = std::map; diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index 77e24162676045b88dc8b62d2c6a4ecc1e738e96..f048662953e20b2a612271e2daeef6e370c4822a 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -135,58 +135,9 @@ xla::ComputationDataHandle XlaHelpers::Epsilon(xla::ComputationBuilder* b, xla::ComputationDataHandle XlaHelpers::IntegerLiteral( xla::ComputationBuilder* b, DataType data_type, int64 value) { - xla::Literal literal; xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - switch (type) { - case xla::U8: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::U32: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::U64: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::S8: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::S32: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::S64: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::F32: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::F64: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::C64: - literal = std::move(*xla::Literal::CreateR0(value)); - break; - case xla::PRED: - LOG(FATAL) << "pred element type is not integral"; - case xla::S16: - case xla::U16: - LOG(FATAL) << "u16/s16 literals not yet implemented"; - case xla::BF16: - literal = std::move( - *xla::Literal::CreateR0(static_cast(value))); - break; - case xla::F16: - literal = std::move( - *xla::Literal::CreateR0(static_cast(value))); - break; - case xla::TUPLE: - LOG(FATAL) << "tuple element type is not integral"; - case xla::OPAQUE: - LOG(FATAL) << "opaque element type is not integral"; - default: - LOG(FATAL) << "unhandled element type " << type; - } - return b->ConstantLiteral(literal); + return ::tensorflow::IntegerLiteral(b, type, value); } xla::ComputationDataHandle XlaHelpers::FloatLiteral(xla::ComputationBuilder* b, diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index ee29158646fa96fe554d089e11d50afb47e3e300..c4bb90d58755f16672ca7c6a6738065be6330485 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -302,10 +302,19 @@ Status XlaOpKernelContext::ReadVariableInput( "Type mismatch for read of variable ", variable->name(), ". Expected ", DataTypeString(type), "; got ", DataTypeString(variable->type())); } - *value = variable->value(); if (shape) { *shape = variable->shape(); } + + XlaContext& xla_context = XlaContext::Get(context_); + TensorShape representation_shape = xla_context.VariableRepresentationShape( + variable->shape(), variable->type()); + if (representation_shape == variable->shape()) { + *value = variable->value(); + } else { + *value = + builder()->Reshape(variable->value(), variable->shape().dim_sizes()); + } return Status::OK(); } @@ -400,8 +409,8 @@ Status XlaOpKernelContext::GetResourceInput(int index, XlaResource** resource) { return Status::OK(); } -Status XlaOpKernelContext::AssignVariable( - int input_index, DataType type, const xla::ComputationDataHandle& handle) { +Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, + xla::ComputationDataHandle handle) { TF_RET_CHECK(handle.handle() != 0); const XlaExpression* expression = @@ -419,6 +428,13 @@ Status XlaOpKernelContext::AssignVariable( XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), &shape)); TF_RETURN_IF_ERROR(variable->SetTypeAndShape(type, shape)); + + XlaContext& xla_context = XlaContext::Get(context_); + TensorShape representation_shape = + xla_context.VariableRepresentationShape(shape, type); + if (shape != representation_shape) { + handle = builder()->Reshape(handle, representation_shape.dim_sizes()); + } return variable->SetValue(handle); } diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index e1fd0f55c6d2501b4813c90171630a8df567f78a..4e4b97e0cec8d16b9b5686a779b1285906765dbd 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -175,7 +175,7 @@ class XlaOpKernelContext { // variable has been initialized with a different type or with a // different shape. Status AssignVariable(int input_index, DataType type, - const xla::ComputationDataHandle& handle); + xla::ComputationDataHandle handle); // Helper routines for the OP_REQUIRES macros void CtxFailure(const Status& s); diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc index 0dde6a986c61bdd5b0b2e6d7a16b29ab95be98ab..bbe808595d958346bd55bf8419306bf3de4cd1d0 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.cc +++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc @@ -255,6 +255,8 @@ void XlaOpRegistry::RegisterCompilationKernels() { std::vector XlaOpRegistry::DeviceKernels( const string& compilation_device_name, bool include_compilation_only_kernels) { + // Ensure compilation kernels registered. + RegisterCompilationKernels(); std::vector kernels; XlaOpRegistry& registry = Instance(); mutex_lock lock(registry.mutex_); diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 34e733bc8d80b364cec1783006eba0a5468b55ea..c7cb69215fb051b7f87c3be3b0b419b9c1b8998c 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -372,7 +372,6 @@ tf_cc_test( cc_library( name = "array2d", - srcs = ["array2d.cc"], hdrs = ["array2d.h"], visibility = ["//visibility:public"], deps = [ diff --git a/tensorflow/compiler/xla/array.h b/tensorflow/compiler/xla/array.h index 71aa057cd3a1c273c0e851497a78f94ba37c778e..24b58bec11bd8d8b5c79ac84c5f43c509644b51d 100644 --- a/tensorflow/compiler/xla/array.h +++ b/tensorflow/compiler/xla/array.h @@ -121,6 +121,44 @@ class Array { CHECK(idx == num_elements()); } + // Creates a 1D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array(std::initializer_list values) + : Array(ToInt64Vector({values.size()})) { + int64 idx = 0; + for (const auto& it1 : values) { + values_[idx] = static_cast(it1); + ++idx; + } + CHECK(idx == num_elements()); + } + + // Creates a 2D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array(std::initializer_list> values) + : Array(ToInt64Vector({values.size(), values.begin()->size()})) { + int64 idx = 0; + for (const auto& it1 : values) { + for (const auto& it2 : it1) { + values_[idx] = static_cast(it2); + ++idx; + } + } + CHECK(idx == num_elements()); + } + // Creates a 3D array from the given nested initializer list. The outer // initializer list is the first dimension, and so on. Array(InitializerList3D values) @@ -138,6 +176,30 @@ class Array { CHECK(idx == num_elements()); } + // Creates a 3D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array(std::initializer_list>> + values) + : Array(ToInt64Vector({values.size(), values.begin()->size(), + values.begin()->begin()->size()})) { + int64 idx = 0; + for (const auto& it1 : values) { + for (const auto& it2 : it1) { + for (const auto& it3 : it2) { + values_[idx] = static_cast(it3); + ++idx; + } + } + } + CHECK(idx == num_elements()); + } + // Creates a 4D array from the given nested initializer list. The outer // initializer list is the first dimension, and so on. Array(InitializerList4D values) @@ -158,6 +220,34 @@ class Array { CHECK(idx == num_elements()); } + // Creates a 4D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array(std::initializer_list< + std::initializer_list>>> + values) + : Array(ToInt64Vector({values.size(), values.begin()->size(), + values.begin()->begin()->size(), + values.begin()->begin()->begin()->size()})) { + int64 idx = 0; + for (const auto& it1 : values) { + for (const auto& it2 : it1) { + for (const auto& it3 : it2) { + for (const auto& it4 : it3) { + values_[idx] = static_cast(it4); + ++idx; + } + } + } + } + CHECK(idx == num_elements()); + } + Array(const Array& other) : sizes_(other.sizes_), values_(new T[num_elements()]) { std::copy(&other.values_[0], &other.values_[0] + num_elements(), @@ -185,7 +275,7 @@ class Array { // Fills the array with the sequence i*multiplier for i=0,1,... void FillWithMultiples(const T& multiplier) { for (int64 i = 0; i < num_elements(); ++i) { - values_[i] = i * multiplier; + values_[i] = static_cast(i) * multiplier; } } diff --git a/tensorflow/compiler/xla/array2d.h b/tensorflow/compiler/xla/array2d.h index bb85fbee9b97fd6b9b0bf7223a9b820989dcbfa7..a17e81f44832f272fd93dce9f854042b4a84fde4 100644 --- a/tensorflow/compiler/xla/array2d.h +++ b/tensorflow/compiler/xla/array2d.h @@ -25,6 +25,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array.h" +#include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/strings/str_util.h" @@ -52,6 +53,17 @@ class Array2D : public Array { Array2D(std::initializer_list> values) : Array(values) {} + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array2D(std::initializer_list> values) + : Array(values) {} + Array2D(const Array2D& other) : Array(other) {} int64 n1() const { return this->dim(0); } @@ -86,9 +98,23 @@ class Array2D : public Array { // Returns a linspace-populated Array2D in the range [from, to] (inclusive) // with dimensions n1 x n2. -std::unique_ptr> MakeLinspaceArray2D(float from, float to, - int64 n1, int64 n2); - +template +std::unique_ptr> MakeLinspaceArray2D(double from, double to, + int64 n1, int64 n2) { + auto array = MakeUnique>(n1, n2); + int64 count = n1 * n2; + NativeT step = + static_cast((count > 1) ? (to - from) / (count - 1) : 0); + auto set = [&array, n1, n2](int64 index, NativeT value) { + (*array)(index / n2, index % n2) = value; + }; + for (int64 i = 0; i < count - 1; ++i) { + set(i, (static_cast(from) + + static_cast(i) * static_cast(step))); + } + set(count - 1, static_cast(to)); + return array; +} } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_ARRAY2D_H_ diff --git a/tensorflow/compiler/xla/array2d_test.cc b/tensorflow/compiler/xla/array2d_test.cc index c08e42c20ee684dfad8268aa8223440fbfad8a33..93034a719bfbd6724c007059715754677f3f1e62 100644 --- a/tensorflow/compiler/xla/array2d_test.cc +++ b/tensorflow/compiler/xla/array2d_test.cc @@ -63,6 +63,20 @@ TEST(Array2dTest, InitializerListCtor) { EXPECT_EQ(arr(1, 2), 6); } +TEST(Array2dTest, InitializerListCtorHalf) { + Array2D arr = {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}; + + EXPECT_EQ(arr.n1(), 2); + EXPECT_EQ(arr.n2(), 3); + + EXPECT_EQ(arr(0, 0), static_cast(1)); + EXPECT_EQ(arr(0, 1), static_cast(2)); + EXPECT_EQ(arr(0, 2), static_cast(3)); + EXPECT_EQ(arr(1, 0), static_cast(4)); + EXPECT_EQ(arr(1, 1), static_cast(5)); + EXPECT_EQ(arr(1, 2), static_cast(6)); +} + TEST(Array2dTest, Accessors) { Array2D arr = {{1, 2, 3}, {4, 5, 6}}; diff --git a/tensorflow/compiler/xla/array3d.h b/tensorflow/compiler/xla/array3d.h index a1c5840a5f3874e27043c821ed4684da2fa6c542..0e9a0722ae43e1dc6ecddde9cbc3daf1db058840 100644 --- a/tensorflow/compiler/xla/array3d.h +++ b/tensorflow/compiler/xla/array3d.h @@ -57,6 +57,19 @@ class Array3D : public Array { values) : Array(values) {} + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array3D( + std::initializer_list>> + values) + : Array(values) {} + int64 n1() const { return this->dim(0); } int64 n2() const { return this->dim(1); } int64 n3() const { return this->dim(2); } diff --git a/tensorflow/compiler/xla/array3d_test.cc b/tensorflow/compiler/xla/array3d_test.cc index 6b5f4b343b2113652758bbd5ce0fc803239c1266..691ff6c03594a98a12e0fdd2151c4c2a2c9c128a 100644 --- a/tensorflow/compiler/xla/array3d_test.cc +++ b/tensorflow/compiler/xla/array3d_test.cc @@ -69,6 +69,29 @@ TEST(Array3dTest, InitializerListCtor) { EXPECT_EQ(arr(2, 3, 1), 24); } +TEST(Array3dTest, InitializerListCtorHalf) { + Array3D arr = { + {{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}}, + {{9.0f, 10.0f}, {11.0f, 12.0f}, {13.0f, 14.0f}, {15.0f, 16.0f}}, + {{17.0f, 18.0f}, {19.0f, 20.0f}, {21.0f, 22.0f}, {23.0f, 24.0f}}}; + + EXPECT_EQ(arr.n1(), 3); + EXPECT_EQ(arr.n2(), 4); + EXPECT_EQ(arr.n3(), 2); + EXPECT_EQ(arr.num_elements(), 24); + + EXPECT_EQ(arr(0, 0, 0), static_cast(1)); + EXPECT_EQ(arr(0, 0, 1), static_cast(2)); + EXPECT_EQ(arr(0, 1, 0), static_cast(3)); + EXPECT_EQ(arr(0, 3, 1), static_cast(8)); + EXPECT_EQ(arr(1, 0, 0), static_cast(9)); + EXPECT_EQ(arr(1, 1, 1), static_cast(12)); + EXPECT_EQ(arr(2, 0, 0), static_cast(17)); + EXPECT_EQ(arr(2, 1, 1), static_cast(20)); + EXPECT_EQ(arr(2, 2, 0), static_cast(21)); + EXPECT_EQ(arr(2, 3, 1), static_cast(24)); +} + TEST(Array3dTest, Fill) { Array3D fullof7(2, 3, 4, 7); for (int64 n1 = 0; n1 < fullof7.n1(); ++n1) { diff --git a/tensorflow/compiler/xla/array4d.h b/tensorflow/compiler/xla/array4d.h index f8b2b2afe5fed9c465c2a1f39308b7f44311b16a..a75fffc605aa0df3e1e2eeb6d3129718cbbba0e4 100644 --- a/tensorflow/compiler/xla/array4d.h +++ b/tensorflow/compiler/xla/array4d.h @@ -82,6 +82,19 @@ class Array4D : public Array { values) : Array(values) {} + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array4D(std::initializer_list>>> + values) + : Array(values) {} + // Numerically-named aliases for the various dimensions. This matches the // dimension names used in array3d. int64 n4() const { return this->dim(3); } diff --git a/tensorflow/compiler/xla/array4d_test.cc b/tensorflow/compiler/xla/array4d_test.cc index 3bc8148c911df0aeade364e4ac2e2ee828bacb53..927733ea1eab43feff643c35535cc6d9ea59ba5a 100644 --- a/tensorflow/compiler/xla/array4d_test.cc +++ b/tensorflow/compiler/xla/array4d_test.cc @@ -97,6 +97,36 @@ TEST(Array3dTest, InitializerListCtor) { EXPECT_EQ(arr(2, 3, 1, 0), 24); } +TEST(Array3dTest, InitializerListCtorHalf) { + Array4D arr = { + {{{1.0f}, {2.0f}}, {{3.0f}, {4.0f}}, {{5.0f}, {6.0f}}, {{7.0f}, {8.0f}}}, + {{{9.0f}, {10.0f}}, + {{11.0f}, {12.0f}}, + {{13.0f}, {14.0f}}, + {{15.0f}, {16.0f}}}, + {{{17.0f}, {18.0f}}, + {{19.0f}, {20.0f}}, + {{21.0f}, {22.0f}}, + {{23.0f}, {24.0f}}}}; + + EXPECT_EQ(arr.n1(), 3); + EXPECT_EQ(arr.n2(), 4); + EXPECT_EQ(arr.n3(), 2); + EXPECT_EQ(arr.n4(), 1); + EXPECT_EQ(arr.num_elements(), 24); + + EXPECT_EQ(arr(0, 0, 0, 0), static_cast(1)); + EXPECT_EQ(arr(0, 0, 1, 0), static_cast(2)); + EXPECT_EQ(arr(0, 1, 0, 0), static_cast(3)); + EXPECT_EQ(arr(0, 3, 1, 0), static_cast(8)); + EXPECT_EQ(arr(1, 0, 0, 0), static_cast(9)); + EXPECT_EQ(arr(1, 1, 1, 0), static_cast(12)); + EXPECT_EQ(arr(2, 0, 0, 0), static_cast(17)); + EXPECT_EQ(arr(2, 1, 1, 0), static_cast(20)); + EXPECT_EQ(arr(2, 2, 0, 0), static_cast(21)); + EXPECT_EQ(arr(2, 3, 1, 0), static_cast(24)); +} + TEST(Array4dTest, Fill) { Array4D fullof7(2, 3, 4, 5, 7); fullof7.Each([](tensorflow::gtl::ArraySlice idx, int* cell) { diff --git a/tensorflow/compiler/xla/array_test.cc b/tensorflow/compiler/xla/array_test.cc index 8b9419477479d952126fd831eb44899e7649ca71..e8356c9832d34135f5ffb1a5c7a9d6db6db3a051 100644 --- a/tensorflow/compiler/xla/array_test.cc +++ b/tensorflow/compiler/xla/array_test.cc @@ -60,6 +60,25 @@ TEST(ArrayTest, InitializerListCtor) { EXPECT_EQ(arr(1, 2), 6); } +TEST(ArrayTest, InitializerListCtorHalf) { + Array d2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); + EXPECT_EQ(d2.dim(0), 2); + EXPECT_EQ(d2.dim(1), 3); + + Array d3({{{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}}); + EXPECT_EQ(d3.dim(0), 3); + EXPECT_EQ(d3.dim(1), 2); + EXPECT_EQ(d3.dim(2), 1); + + Array d4( + {{{{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}}, + {{{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}, {{1.0f}, {4.0f}}}}); + EXPECT_EQ(d4.dim(0), 2); + EXPECT_EQ(d4.dim(1), 3); + EXPECT_EQ(d4.dim(2), 2); + EXPECT_EQ(d4.dim(3), 1); +} + TEST(ArrayTest, IndexingReadWrite) { Array arr({2, 3}); diff --git a/tensorflow/compiler/xla/client/compile_only_client.cc b/tensorflow/compiler/xla/client/compile_only_client.cc index c7e2c4367b89ca2112022fa40449ae3ebe28463e..59662c95ac15e7c23790c5b5ff5d75a694613aeb 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.cc +++ b/tensorflow/compiler/xla/client/compile_only_client.cc @@ -39,16 +39,15 @@ CompileOnlyClient::CompileAheadOfTime( return compiler_service_->CompileAheadOfTime(service_instances, options); } -int64 CompileOnlyClient::PointerSizeForTriple( - tensorflow::StringPiece target_triple) { - llvm::Triple triple(llvm::Triple::normalize( - llvm::StringRef(target_triple.data(), target_triple.size()))); - if (triple.isArch64Bit()) { +int64 CompileOnlyClient::PointerSizeForTriple(tensorflow::StringPiece triple) { + llvm::Triple llvm_triple( + llvm::Triple::normalize(llvm::StringRef(triple.data(), triple.size()))); + if (llvm_triple.isArch64Bit()) { return 8; - } else if (triple.isArch32Bit()) { + } else if (llvm_triple.isArch32Bit()) { return 4; } else { - CHECK(triple.isArch16Bit()); + CHECK(llvm_triple.isArch16Bit()); return 2; } } diff --git a/tensorflow/compiler/xla/client/computation.cc b/tensorflow/compiler/xla/client/computation.cc index 4baea8df6e3331200ee52f500fb7b961428e56be..e6c57bda0f0c4cb969939883efebcf3a6d6be381 100644 --- a/tensorflow/compiler/xla/client/computation.cc +++ b/tensorflow/compiler/xla/client/computation.cc @@ -64,4 +64,14 @@ void Computation::ResetWithoutFreeing() { parent_ = nullptr; } +StatusOr Computation::GetProgramShape() const { + GetComputationShapeRequest request; + *request.mutable_computation() = handle_; + GetComputationShapeResponse response; + + TF_RETURN_IF_ERROR(parent_->GetComputationShape(&request, &response)); + + return std::move(*response.mutable_program_shape()); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/computation.h b/tensorflow/compiler/xla/client/computation.h index b595172486950bf08b057625d7b2dd97ac9b2278..a53fc9e9cf34704bd08ddb5bf062c1ec1107f5fb 100644 --- a/tensorflow/compiler/xla/client/computation.h +++ b/tensorflow/compiler/xla/client/computation.h @@ -60,6 +60,10 @@ class Computation { // Returns true if this object is a null Computation. bool IsNull() const { return parent_ == nullptr; } + // Returns the "program shape" (parameter and return shapes) for this + // computation. + StatusOr GetProgramShape() const; + private: void ResetWithoutFreeing(); diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc index 46f2ed4836eda6bf6d5b68f2e29ac6888cd1749b..39d02f0863f78d4094f2cc4805f534713fb7e929 100644 --- a/tensorflow/compiler/xla/client/computation_builder.cc +++ b/tensorflow/compiler/xla/client/computation_builder.cc @@ -233,6 +233,26 @@ StatusOr> ComputationBuilder::GetShape( return status_or_shape; } +StatusOr ComputationBuilder::GetProgramShape() { + TF_RETURN_IF_ERROR(first_error_); + + GetComputationShapeRequest request; + *request.mutable_computation() = computation_.handle(); + GetComputationShapeResponse response; + + VLOG(2) << "making get-program-shape-request"; + Status status = client_->stub()->GetComputationShape(&request, &response); + VLOG(2) << "done with get-program-shape-request"; + + if (!status.ok()) { + first_error_ = status; + return status; + } + + TF_RET_CHECK(response.has_program_shape()); + return std::move(*response.mutable_program_shape()); +} + ComputationDataHandle ComputationBuilder::CheckShape( const ComputationDataHandle& operand, const Shape& expected_shape) { std::unique_ptr actual_shape = GetShape(operand).ConsumeValueOrDie(); @@ -388,7 +408,7 @@ ComputationDataHandle ComputationBuilder::Reshape( ComputationDataHandle ComputationBuilder::Collapse( const ComputationDataHandle& operand, - tensorflow::gtl::ArraySlice dims_to_collapse) { + tensorflow::gtl::ArraySlice dimensions) { if (!first_error_.ok()) { return ComputationDataHandle(); } @@ -396,8 +416,8 @@ ComputationDataHandle ComputationBuilder::Collapse( // Don't support out-of-order collapse here. // Checks that the collapsed dimensions are in order and consecutive. for (tensorflow::gtl::ArraySlice::size_type i = 1; - i < dims_to_collapse.size(); ++i) { - if (dims_to_collapse[i] - 1 != dims_to_collapse[i - 1]) { + i < dimensions.size(); ++i) { + if (dimensions[i] - 1 != dimensions[i - 1]) { NoteError(InvalidArgument( "Collapsed dimensions are not in order and consecutive.")); return ComputationDataHandle(); @@ -414,9 +434,9 @@ ComputationDataHandle ComputationBuilder::Collapse( VLOG(3) << "original shape: " << ShapeUtil::HumanString(*original_shape); VLOG(3) << "dims to collapse: " - << tensorflow::str_util::Join(dims_to_collapse, ","); + << tensorflow::str_util::Join(dimensions, ","); - if (dims_to_collapse.size() <= 1) { + if (dimensions.size() <= 1) { // Not collapsing anything, trivially we can return the operand versus // enqueueing a trivial reshape. return operand; @@ -424,7 +444,7 @@ ComputationDataHandle ComputationBuilder::Collapse( std::vector new_sizes; for (int i = 0; i < ShapeUtil::Rank(*original_shape); ++i) { - if (i <= dims_to_collapse.front() || i > dims_to_collapse.back()) { + if (i <= dimensions.front() || i > dimensions.back()) { new_sizes.push_back(original_shape->dimensions(i)); } else { new_sizes.back() *= original_shape->dimensions(i); @@ -733,13 +753,13 @@ ComputationDataHandle ComputationBuilder::Infeed(const Shape& shape, } void ComputationBuilder::Outfeed(const ComputationDataHandle& operand, - const Shape& shape, + const Shape& shape_with_layout, const string& outfeed_config) { OpRequest op_request; OutfeedRequest* request = op_request.mutable_outfeed_request(); request->set_outfeed_config(outfeed_config); *request->mutable_operand() = operand; - *request->mutable_shape() = shape; + *request->mutable_shape() = shape_with_layout; RunOpAndNoteError(&op_request); } @@ -769,6 +789,20 @@ ComputationDataHandle ComputationBuilder::CustomCall( return RunOpAndParseResponse(&op_request); } +ComputationDataHandle ComputationBuilder::HostCompute( + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, const Shape& shape) { + OpRequest op_request; + HostComputeRequest* request = op_request.mutable_host_compute_request(); + for (const ComputationDataHandle& operand : operands) { + *request->add_operands() = operand; + } + *request->mutable_shape() = shape; + request->set_channel_name(channel_name); + request->set_cost_estimate_ns(cost_estimate_ns); + return RunOpAndParseResponse(&op_request); +} + ComputationDataHandle ComputationBuilder::Complex( const ComputationDataHandle& real, const ComputationDataHandle& imag, tensorflow::gtl::ArraySlice broadcast_dimensions) { @@ -834,6 +868,14 @@ ComputationDataHandle ComputationBuilder::Or( return BinaryOp(BINOP_OR, lhs, rhs, broadcast_dimensions); } +// TODO(b/65209188): Create a dedicated lowering for Xor +ComputationDataHandle ComputationBuilder::Xor( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return Or(And(Not(lhs), rhs, broadcast_dimensions), + And(lhs, Not(rhs), broadcast_dimensions)); +} + ComputationDataHandle ComputationBuilder::Not( const ComputationDataHandle& operand) { return UnaryOp(UNOP_NOT, operand); @@ -1200,6 +1242,22 @@ ComputationDataHandle ComputationBuilder::While( return RunOpAndParseResponse(&op_request); } +ComputationDataHandle ComputationBuilder::Gather( + const ComputationDataHandle& input, + const ComputationDataHandle& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds) { + OpRequest op_request; + GatherRequest* gather_request = op_request.mutable_gather_request(); + *gather_request->mutable_input() = input; + *gather_request->mutable_gather_indices() = gather_indices; + *gather_request->mutable_dimension_numbers() = dimension_numbers; + for (int64 window_bound : window_bounds) { + gather_request->add_window_bounds(window_bound); + } + return RunOpAndParseResponse(&op_request); +} + ComputationDataHandle ComputationBuilder::Conditional( const ComputationDataHandle& predicate, const ComputationDataHandle& true_operand, @@ -1332,15 +1390,16 @@ ComputationDataHandle ComputationBuilder::BatchNormInference( ComputationDataHandle ComputationBuilder::BatchNormGrad( const ComputationDataHandle& operand, const ComputationDataHandle& scale, - const ComputationDataHandle& mean, const ComputationDataHandle& var, + const ComputationDataHandle& batch_mean, + const ComputationDataHandle& batch_var, const ComputationDataHandle& grad_output, float epsilon, int64 feature_index) { OpRequest op_request; BatchNormGradRequest* request = op_request.mutable_batch_norm_grad_request(); *request->mutable_operand() = operand; *request->mutable_scale() = scale; - *request->mutable_mean() = mean; - *request->mutable_variance() = var; + *request->mutable_mean() = batch_mean; + *request->mutable_variance() = batch_var; *request->mutable_grad_output() = grad_output; request->set_epsilon(epsilon); request->set_feature_index(feature_index); diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h index ea4cdb76673b1c99036224bcd754ce4fe1360945..2141ebc2065a1a80d2fe820a7b6fe15434c89e28 100644 --- a/tensorflow/compiler/xla/client/computation_builder.h +++ b/tensorflow/compiler/xla/client/computation_builder.h @@ -101,6 +101,9 @@ class ComputationBuilder { StatusOr> GetShape( const ComputationDataHandle& operand); + // Retrieves the (inferred) result for the current computation's shape. + StatusOr GetProgramShape(); + // Checks that the operand has the given expected shape. Returns the operand // if yes, fails with a CHECK error if no. ComputationDataHandle CheckShape(const ComputationDataHandle& operand, @@ -195,9 +198,8 @@ class ComputationBuilder { tensorflow::gtl::ArraySlice new_sizes); // Enqueues an operation onto the computation that collapses the operand, from - // minor to major order, then reshapes it into the shape with the given - // dimension sizes, also from major to minor. Conceptually, this is a limited - // form of "shape casting". + // first to last dimension (C order), then reshapes it to the given dimension + // sizes. Conceptually, this is a limited form of "shape casting". ComputationDataHandle Reshape(const ComputationDataHandle& operand, tensorflow::gtl::ArraySlice new_sizes); @@ -443,6 +445,16 @@ class ComputationBuilder { tensorflow::gtl::ArraySlice operands, const Shape& shape); + // Enqueues a pseudo-op to represent host-side computation data-dependencies. + // During code generation, host send and receive operations will be generated + // to transfer |operands| to the host and a single result of |shape| back to + // the device. Host send/recv operations are emitted using |channel_name|. + // Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO + // instruction scheduling. + ComputationDataHandle HostCompute( + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, const Shape& shape); + // The following methods enqueue element-wise binary arithmetic operations // onto the computation. The shapes of the operands have to match unless one // of the operands is a scalar, or an explicit broadcast dimension is given @@ -500,6 +512,10 @@ class ComputationBuilder { const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + ComputationDataHandle Xor( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + ComputationDataHandle Not(const ComputationDataHandle& operand); ComputationDataHandle ShiftLeft( @@ -705,6 +721,13 @@ class ComputationBuilder { const int exponent_bits, const int mantissa_bits); + // Enqueues a Gather node onto the computation. + ComputationDataHandle Gather( + const ComputationDataHandle& input, + const ComputationDataHandle& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + // Enqueues a Send node onto the computation, to send the given operand to // a Recv instruction that shares the same channel handle. void Send(const ComputationDataHandle& operand, const ChannelHandle& handle); @@ -853,7 +876,7 @@ class ComputationBuilder { Window* window); // Internal helper method that does the building for an arbitrary unary op. - ComputationDataHandle UnaryOp(UnaryOperation binop, + ComputationDataHandle UnaryOp(UnaryOperation unop, const ComputationDataHandle& operand); // Internal helper method that does the building for an arbitrary binary op. diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index 5f2b55713e342aa3d0251386d57cb52481fe748d..b63a1465ea755b906853860d47768ecbeaa0dcdd 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -31,14 +31,43 @@ limitations under the License. namespace xla { namespace { +// Calculates the number of bytes required to store the data within the +// specified shape. In case of a (nested) tuple shape this is the total byte +// size of all sub-shapes within the tuple. +int64 DataSizeOfShape(const Shape& shape) { + if (ShapeUtil::IsArray(shape)) { + return ShapeUtil::ByteSizeOf(shape); + } + + int64 total_size = 0; + for (const Shape& s : shape.tuple_shapes()) { + total_size += DataSizeOfShape(s); + } + return total_size; +} + +// Create a ComputationDataHandle for an op what generates fake data with the +// given shape. +ComputationDataHandle BuildFakeDataOpOnDevice(const Shape& shape, + ComputationBuilder* builder) { + if (ShapeUtil::IsArray(shape)) { + return builder->Broadcast( + builder->ConstantLiteral(Literal::One(shape.element_type())), + AsInt64Slice(shape.dimensions())); + } + std::vector parts; + for (const Shape& s : shape.tuple_shapes()) { + parts.push_back(BuildFakeDataOpOnDevice(s, builder)); + } + return builder->Tuple(parts); +} + std::unique_ptr MakeFakeDataViaDeviceOrDie(const Shape& shape, Client* client) { ComputationBuilder b( client, tensorflow::strings::StrCat("make_fake_", ShapeUtil::HumanString(shape))); - // TODO(b/26811613): Replace this when RNG is supported on all backends. - b.Broadcast(b.ConstantLiteral(Literal::One(shape.element_type())), - AsInt64Slice(shape.dimensions())); + BuildFakeDataOpOnDevice(shape, &b); Computation computation = b.Build().ConsumeValueOrDie(); auto execution_options = CreateDefaultExecutionOptions(); @@ -51,7 +80,7 @@ std::unique_ptr MakeFakeDataViaDeviceOrDie(const Shape& shape, std::unique_ptr MakeFakeDataOrDie(const Shape& shape, Client* client) { - if (ShapeUtil::ByteSizeOf(shape) < (1LL << 20)) { + if (DataSizeOfShape(shape) < (1LL << 20)) { StatusOr> literal_status = MakeFakeLiteral(shape); if (!literal_status.ok()) { // If we got an Unimplemented error, fall back to making the fake data via diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index ef98dbb6403beedb0c08ab9a0fc9e7d4ee31ab3b..91396f055fe4a3ecbd436139be9470e2a35e1c63 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -172,7 +172,9 @@ StatusOr> LocalExecutable::Run( std::unique_ptr result, executable_->ExecuteOnStreamWrapper( &service_options, run_options.execution_profile(), arguments)); - return ScopedShapedBuffer::MakeScoped(result.get(), run_options.allocator()); + + return MakeUnique(std::move(*result), + run_options.allocator()); } StatusOr> LocalExecutable::ExecuteAndDump( diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index b52a30f5a0b92e0094e6b0de3241c10a5a909cad..de0ed13c43f87966c272102b2e9af9ff3be63aea 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -69,7 +69,7 @@ class LocalExecutable { // of the computation. tensorflow::Status ValidateExecutionOptions( const tensorflow::gtl::ArraySlice arguments, - const ExecutableRunOptions& options, const Backend& backend); + const ExecutableRunOptions& run_options, const Backend& backend); // Records the computation in a SessionModule proto with the arguments used to // invoke it, and the result. Enabled by flag: --tla_dump_executions_to. diff --git a/tensorflow/compiler/xla/index_util.h b/tensorflow/compiler/xla/index_util.h index 0b9188e8524d6f1367541496dc5a86a250a0d530..142006f2626e83d3254f2de65fc28fd5d6694e53 100644 --- a/tensorflow/compiler/xla/index_util.h +++ b/tensorflow/compiler/xla/index_util.h @@ -37,7 +37,7 @@ class IndexUtil { static int64 MultidimensionalIndexToLinearIndex( const Shape& shape, tensorflow::gtl::ArraySlice multi_index); - // Coverts a linear index into multidimensional index (eg {x, y, z}) based on + // Converts a linear index into multidimensional index (eg {x, y, z}) based on // the shape and its layout. The first index in the returned multidimensional // index is dimension 0. static std::vector LinearIndexToMultidimensionalIndex( diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 89279b659c75ce4775581dfbfa8d830f54ae6fe8..d247aeb41f77b87f75b40476a294d057ca898034 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -223,7 +223,7 @@ Status Literal::CopySliceFromInternal( Literal::StrideConfig stride_config(src_literal.shape(), shape(), copy_size); - auto copy_proc = [&](const std::vector& indexes) { + auto copy_proc = [&](tensorflow::gtl::ArraySlice indexes) { // Map from multi-dimensional index, to source index. std::transform(indexes.begin(), indexes.end(), src_base.begin(), src_indexes.begin(), std::plus()); @@ -234,7 +234,8 @@ Status Literal::CopySliceFromInternal( int64 src_index = linear_index(src_literal.shape(), src_indexes); int64 dest_index = linear_index(shape(), dest_indexes); - StridedCopy(data(), dest_index, stride_config.dest_stride, + // `this->` is needed to workaround MSVC bug: #16882 + StridedCopy(this->data(), dest_index, stride_config.dest_stride, src_literal.data(), src_index, stride_config.source_stride, stride_config.minor_loop_size); return true; @@ -247,6 +248,28 @@ Status Literal::CopySliceFromInternal( return Status::OK(); } +Status Literal::CopyElementFrom(const Literal& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index) { + DCHECK_EQ(shape().element_type(), src_literal.shape().element_type()); + const int64 src_linear_index = IndexUtil::MultidimensionalIndexToLinearIndex( + src_literal.shape(), src_index); + const int64 dest_linear_index = + IndexUtil::MultidimensionalIndexToLinearIndex(shape(), dest_index); + const int64 primitive_size = + ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); + + char* dest_address = + static_cast(untyped_data()) + dest_linear_index * primitive_size; + const char* source_address = + static_cast(src_literal.untyped_data()) + + src_linear_index * primitive_size; + if (dest_address != source_address) { + memcpy(dest_address, source_address, primitive_size); + } + return Status::OK(); +} + std::vector Literal::DecomposeTuple() { CHECK(ShapeUtil::IsTuple(shape())); std::vector elements; @@ -342,7 +365,7 @@ Status Literal::Piece::CopyFrom(const Literal::Piece& src) { #undef COPY_ELEMENTS default: return Unimplemented( - "Unhandled primitive type %s", + "Copying a Literal object with element type %s is not implemented.", PrimitiveType_Name(subshape().element_type()).c_str()); } } @@ -490,7 +513,10 @@ Status Literal::CopySliceFrom(const Literal& src_literal, default: break; } - return Unimplemented("Unhandled primitive type %d", shape().element_type()); + return Unimplemented( + "Copying a slice from a Literal object with element type %d is not " + "implemented.", + shape().element_type()); } /* static */ Literal Literal::Zero(PrimitiveType primitive_type) { @@ -807,9 +833,10 @@ std::unique_ptr Literal::Slice( DimensionVector result_dimensions; for (int64 dnum = 0; dnum < ShapeUtil::Rank(shape()); ++dnum) { CHECK_GE(start_indices[dnum], 0); - CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)); + CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)) + << "dnum = " << dnum; int64 dimension = limit_indices[dnum] - start_indices[dnum]; - CHECK_GT(dimension, 0); + CHECK_GE(dimension, 0) << "dnum = " << dnum; result_dimensions.push_back(dimension); } const auto result_shape = @@ -1008,6 +1035,49 @@ void Literal::SortSparseElements(const ShapeIndex& shape_index) { piece(shape_index).SortSparseElements(); } +Literal Literal::GetFirstScalarLiteral() const { + CHECK(ShapeUtil::IsArray(shape_)); + CHECK_GT(ShapeUtil::ElementsIn(shape_), 0); + switch (shape_.element_type()) { + case PRED: + return std::move(*Literal::CreateR0(GetFirstElement())); + // 8 bit types. + case S8: + return std::move(*Literal::CreateR0(GetFirstElement())); + case U8: + return std::move(*Literal::CreateR0(GetFirstElement())); + // 16 bit types. + case BF16: + return std::move( + *Literal::CreateR0(GetFirstElement())); + case F16: + return std::move(*Literal::CreateR0(GetFirstElement())); + case S16: + return std::move(*Literal::CreateR0(GetFirstElement())); + case U16: + return std::move(*Literal::CreateR0(GetFirstElement())); + // 32 bit types. + case F32: + return std::move(*Literal::CreateR0(GetFirstElement())); + case S32: + return std::move(*Literal::CreateR0(GetFirstElement())); + case U32: + return std::move(*Literal::CreateR0(GetFirstElement())); + // 64 bit types. + case C64: + return std::move( + *Literal::CreateR0(GetFirstElement())); + case F64: + return std::move(*Literal::CreateR0(GetFirstElement())); + case S64: + return std::move(*Literal::CreateR0(GetFirstElement())); + case U64: + return std::move(*Literal::CreateR0(GetFirstElement())); + default: + LOG(FATAL) << "Unhandled primitive type " << shape_.element_type(); + } +} + void Literal::Piece::SortSparseElements() { switch (subshape().element_type()) { case PRED: @@ -1257,11 +1327,17 @@ string Literal::ToString(bool print_layout) const { /* static */ std::unique_ptr Literal::MakeTupleOwned( std::vector> elements) { - std::vector element_ptrs; + std::vector element_shapes; + element_shapes.reserve(elements.size()); for (const auto& element : elements) { - element_ptrs.push_back(element.get()); + element_shapes.push_back(element->shape()); + } + auto literal = MakeUnique(ShapeUtil::MakeTupleShape(element_shapes)); + for (int64 i = 0; i < elements.size(); ++i) { + TF_CHECK_OK( + literal->MoveFrom(std::move(*elements[i]), /*dest_shape_index=*/{i})); } - return MakeTuple(element_ptrs); + return literal; } void Literal::EachCellAsString( @@ -1344,8 +1420,8 @@ StatusOr> ConvertIfDestTypeMatches( return ConvertToC64(src_literal); // Other types are not yet supported. default: - return InvalidArgument( - "Unimplemented: Convert from type %s to type %s", + return Unimplemented( + "Converting from type %s to type %s is not implemented.", PrimitiveType_Name(src_literal.shape().element_type()).c_str(), PrimitiveType_Name(primitive_dest_type).c_str()); } @@ -1374,10 +1450,29 @@ StatusOr> Literal::Convert( #undef CONVERT_IF_DEST_TYPE_MATCHES // Other types are not yet supported. default: - return InvalidArgument("Unimplemented: Convert from type %s to type %s", - PrimitiveType_Name(shape().element_type()).c_str(), - PrimitiveType_Name(primitive_dest_type).c_str()); + return Unimplemented( + "Converting from type %s to type %s is not implemented.", + PrimitiveType_Name(shape().element_type()).c_str(), + PrimitiveType_Name(primitive_dest_type).c_str()); + } +} + +StatusOr> Literal::ConvertToShape( + const Shape& dest_shape) const { + if (!ShapeUtil::IsTuple(dest_shape)) { + return Convert(dest_shape.element_type()); + } + std::vector elements; + for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { + auto element = LiteralView::Create(*this, {i}); + TF_ASSIGN_OR_RETURN( + auto new_element, + element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i}))); + elements.push_back(std::move(*new_element)); } + auto converted = MakeUnique(); + *converted = Literal::MoveIntoTuple(&elements); + return std::move(converted); } template @@ -1564,6 +1659,92 @@ bool Literal::IsAllComplex(complex64 value) const { } } +bool Literal::IsAllFirst() const { + for (const auto& pair : pieces_) { + const Piece& piece = pair.second; + if (!ShapeUtil::IsArray(piece.subshape())) { + continue; + } + + // Empty shapes are not all the first element since there is no first + // element. + if (ShapeUtil::HasZeroElements(piece.subshape())) { + return false; + } + auto piece_is_all = [&]() { + switch (piece.subshape().element_type()) { + case PRED: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 8 bit types + case S8: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U8: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 16 bit types + case BF16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case F16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 32 bit types + case F32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 64 bit types + case C64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case F64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + default: + return false; + } + }; + + if (!piece_is_all()) { + return false; + } + } + return true; +} + bool Literal::IsZero(tensorflow::gtl::ArraySlice indices) const { CHECK(ShapeUtil::IsArray(shape())); switch (shape().element_type()) { diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index 2b68b8f177dcae19f55a45f2f284ea10f7ce6e52..d5254877331cbde7e981dfb9c0708347fc9d4dd1 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -262,6 +262,11 @@ class Literal { tensorflow::gtl::ArraySlice dest_base, tensorflow::gtl::ArraySlice copy_size); + // Copies one element from src_literal[src_index] to (*this)[dest_index]. + Status CopyElementFrom(const Literal& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index); + // Returns a vector containing the tuple elements of this Literal as separate // Literals. This Literal must be tuple-shaped and can be a nested tuple. The // elements are moved into the new Literals; no data is copied. Upon return @@ -333,6 +338,11 @@ class Literal { StatusOr> Convert( PrimitiveType primitive_dest_type) const; + // Converts this literal to the given shape. Returns an error is the + // conversion is not possible. + StatusOr> ConvertToShape( + const Shape& dest_shape) const; + // Creates a scalar literal value zero of the given primitive type. static Literal Zero(PrimitiveType primitive_type); @@ -451,6 +461,9 @@ class Literal { template NativeT GetFirstElement() const; + // Returns a literal scalar representing the first element. + Literal GetFirstScalarLiteral() const; + // As Get(), but determines the correct type and converts the value // into text. string GetAsString(tensorflow::gtl::ArraySlice multi_index, @@ -485,6 +498,27 @@ class Literal { static std::unique_ptr MakeTupleOwned( std::vector> elements); + // This overload lets you pass a braced list of unique_ptrs to + // MakeTupleOwned: + // + // Literal::MakeTupleOwned(Literal::CreateR1(...), ...). + // + // Simply relying on the MakeTupleOwned(std::vector>) + // overload doesn't work because std::initializer_list's elements are always + // const. + // + // The arguments to this function must all be unique_ptr. + template + static std::unique_ptr MakeTupleOwned( + std::unique_ptr... elements) { + std::array, sizeof...(Ts)> arr{ + std::move(elements)...}; + std::vector> v; + v.insert(v.begin(), std::make_move_iterator(arr.begin()), + std::make_move_iterator(arr.end())); + return MakeTupleOwned(std::move(v)); + } + // Returns a string representation of the literal value. // Warning: this function can take minutes for multi-million element Literals. string ToString(bool print_layout = false) const; @@ -581,6 +615,9 @@ class Literal { // This literal must have a dense layout. bool IsAllComplex(complex64 value) const; + // Literal consists entirely of the first element of the literal. + bool IsAllFirst() const; + // Returns whether this literal is zero at the specified index. This literal // must be an array with a dense layout. bool IsZero(tensorflow::gtl::ArraySlice indices) const; @@ -1242,7 +1279,7 @@ Status Literal::Populate(const FnType& generator) { int64 minor_dimension_size = ShapeUtil::GetDimension(this_shape, stride_config.minor_dimension); - auto init_function = [&](const std::vector& indexes) { + auto init_function = [&](tensorflow::gtl::ArraySlice indexes) { const int64 index = IndexUtil::MultidimensionalIndexToLinearIndex(shape(), indexes); std::copy(indexes.begin(), indexes.end(), minor_scan_indexes.begin()); diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc index b3583c2eb75de8297d5e7507430491f119bd4462..04e45f00491b0bef94f3c0af1c875b2d007194fd 100644 --- a/tensorflow/compiler/xla/literal_util_test.cc +++ b/tensorflow/compiler/xla/literal_util_test.cc @@ -30,6 +30,7 @@ limitations under the License. namespace xla { namespace { +using tensorflow::gtl::ArraySlice; using ::testing::ElementsAre; using ::testing::HasSubstr; @@ -214,11 +215,11 @@ TEST_F(LiteralUtilTest, CreateSparse) { std::vector expected_values = {8, 9, 7, 10}; EXPECT_EQ(literal->sparse_indices()->data(), - tensorflow::gtl::ArraySlice( - expected_indices.data(), expected_indices.num_elements())); - EXPECT_EQ(tensorflow::gtl::ArraySlice(literal->data().data(), - expected_values.size()), - tensorflow::gtl::ArraySlice(expected_values)); + ArraySlice(expected_indices.data(), + expected_indices.num_elements())); + EXPECT_EQ( + ArraySlice(literal->data().data(), expected_values.size()), + ArraySlice(expected_values)); } TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) { @@ -290,7 +291,7 @@ TEST_F(LiteralUtilTest, EachCellR2F32) { // clang-format on std::vector> seen; literal->EachCellAsString( - [&seen](tensorflow::gtl::ArraySlice indices, const string& value) { + [&seen](ArraySlice indices, const string& value) { seen.emplace_back(indices[0], indices[1], value); }); @@ -501,6 +502,24 @@ TEST_F(LiteralUtilTest, IsAllComplex) { ->IsAllComplex({8.0f, 9.0f})); } +TEST_F(LiteralUtilTest, IsAllFirst) { + // IsAllComplex always returns false when the literal is not complex. + EXPECT_FALSE(Literal::CreateR1({false, true})->IsAllFirst()); + EXPECT_TRUE(Literal::CreateR1({false, false})->IsAllFirst()); + EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); + + complex64 c8_9 = {8, 9}; + complex64 c7_9 = {7, 9}; + EXPECT_TRUE(Literal::CreateR2({{c8_9}, {c8_9}})->IsAllFirst()); + EXPECT_FALSE(Literal::CreateR2({{c7_9}, {c8_9}})->IsAllFirst()); +} + TEST_F(LiteralUtilTest, IsZero) { auto scalar_zero = Literal::CreateR0(0.0f); auto scalar_one = Literal::CreateR0(1.0f); @@ -604,11 +623,10 @@ TEST_F(LiteralUtilTest, TransposeR4) { // clang-format on auto reshape = original->Transpose(/*permutation=*/{2, 3, 0, 1}); - reshape->EachCell( - [&](tensorflow::gtl::ArraySlice indices, float value) { - EXPECT_EQ(value, original->Get( - {indices[2], indices[3], indices[0], indices[1]})); - }); + reshape->EachCell([&](ArraySlice indices, float value) { + EXPECT_EQ(value, original->Get( + {indices[2], indices[3], indices[0], indices[1]})); + }); } TEST_F(LiteralUtilTest, TestR4RelayoutEquivalence) { @@ -845,7 +863,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) { const int64 zero_base[] = {0, 0, 0, 0}; const int64 step[] = {1, 1, 1, 1}; uint32 seqnr = 0; - auto init_proc = [&](const std::vector& indexes) { + auto init_proc = [&](ArraySlice indexes) { source->Set(indexes, ++seqnr); return true; }; @@ -861,7 +879,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) { std::vector source_indexes(TF_ARRAYSIZE(dimensions), 0); std::vector blank_indexes(TF_ARRAYSIZE(dimensions), 0); bool matched = true; - auto check_proc = [&](const std::vector& indexes) { + auto check_proc = [&](ArraySlice indexes) { std::copy(indexes.begin(), indexes.end(), source_indexes.begin()); std::transform(source_indexes.begin(), source_indexes.end(), src_base, source_indexes.begin(), std::plus()); @@ -1049,7 +1067,7 @@ TEST_F(LiteralUtilTest, Populate) { primitive_util::NativeToPrimitiveType(), data.dimensions, data.layout); auto literal = Literal::CreateFromShape(shape); - auto generator = [&](tensorflow::gtl::ArraySlice indexes) -> uint32 { + auto generator = [&](ArraySlice indexes) -> uint32 { // Offsets from linear index just to avoid R0 literals to be initialized // with zero. return IndexUtil::MultidimensionalIndexToLinearIndex(literal->shape(), @@ -1061,7 +1079,7 @@ TEST_F(LiteralUtilTest, Populate) { std::vector zero_base(data.dimensions.size(), 0); std::vector step(data.dimensions.size(), 1); bool matched = true; - auto check_function = [&](const std::vector& indexes) { + auto check_function = [&](ArraySlice indexes) { auto value = literal->Get(indexes); matched = matched && (value == generator(indexes)); return matched; @@ -1214,15 +1232,15 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { EXPECT_EQ(*conv, *c64); EXPECT_EQ(s32->Convert(TUPLE).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(s32->Convert(S16).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(s32->Convert(U16).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(c64->Convert(F32).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(c64->Convert(S32).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); } TEST_F(LiteralUtilTest, CopyFromProto_Bool) { diff --git a/tensorflow/compiler/xla/map_util.h b/tensorflow/compiler/xla/map_util.h index 0ad0b9133075fdb29effe1be2c007a851f420a33..8db8c6f3de84a6c46625eadbb6b0f83d2262e5f7 100644 --- a/tensorflow/compiler/xla/map_util.h +++ b/tensorflow/compiler/xla/map_util.h @@ -65,6 +65,25 @@ MaybeFind(const Collection& collection, return {it->second}; } +// Returns a const reference to the value associated with the given key if it +// exists, otherwise returns a const reference to the provided default value. +// +// WARNING: If a temporary object is passed as the default "value," +// this function will return a reference to that temporary object, +// which will be destroyed at the end of the statement. A common +// example: if you have a map with string values, and you pass a char* +// as the default "value," either use the returned value immediately +// or store it in a string (not string&). +template +const typename Collection::value_type::second_type& FindOrDefault( + const Collection& collection, + const typename Collection::value_type::first_type& key, + const typename Collection::value_type::second_type& value) { + auto it = collection.find(key); + if (it != collection.end()) return it->second; + return value; +} + // Inserts the key-value pair into the collection. Dies if key was already // present. template diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 3b0d8377395ca2a91fb007b784773e6df9c8d6c0..b21ab3044fae7136071f50bdba6e74b799a309d5 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -278,6 +278,12 @@ const Computation& LocalComputation::computation() const { return computation_; } +StatusOr LocalComputation::GetReturnValueShape() const { + TF_ASSIGN_OR_RETURN(ProgramShape program_shape, + computation_.GetProgramShape()); + return std::move(*program_shape.mutable_result()); +} + LocalComputationBuilder::LocalComputationBuilder(const string& computation_name) : builder_(GetOrCreateLocalClient(), computation_name) {} @@ -303,6 +309,11 @@ std::unique_ptr LocalComputationBuilder::GetShape( return builder_.GetShape(operand).ConsumeValueOrDie(); } +StatusOr LocalComputationBuilder::GetReturnValueShape() { + TF_ASSIGN_OR_RETURN(ProgramShape program_shape, builder_.GetProgramShape()); + return program_shape.result(); +} + ComputationDataHandle LocalComputationBuilder::Infeed(const Shape& shape) { return builder_.Infeed(shape); } @@ -357,6 +368,12 @@ ComputationDataHandle LocalComputationBuilder::Slice( return builder_.Slice(operand, start_indices, limit_indices, strides); } +ComputationDataHandle LocalComputationBuilder::SliceInDim( + const ComputationDataHandle& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno) { + return builder_.SliceInDim(operand, start_index, limit_index, stride, dimno); +} + ComputationDataHandle LocalComputationBuilder::DynamicSlice( const ComputationDataHandle& operand, const ComputationDataHandle& start_indices, diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 4c6a504f4cd83533185cdadf60ae2c53a0d5e911..a7375c8965e9041226ffee08dab6ffafa25312af 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -102,11 +102,16 @@ class CompiledLocalComputation { class LocalComputation { public: LocalComputation(Computation computation); + StatusOr Compile( const std::vector& argument_shapes, const ExecutableBuildOptions* build_options); + const Computation& computation() const; + // Returns the return-value shape for this computation. + StatusOr GetReturnValueShape() const; + private: Computation computation_; }; @@ -133,6 +138,9 @@ class LocalComputationBuilder { std::unique_ptr GetShape(const ComputationDataHandle& operand); + // Returns the shape of the current return value for the computation. + StatusOr GetReturnValueShape(); + ComputationDataHandle Infeed(const Shape& shape); void Outfeed(const ComputationDataHandle& operand, const Shape& shape, @@ -162,6 +170,10 @@ class LocalComputationBuilder { tensorflow::gtl::ArraySlice limit_indices, tensorflow::gtl::ArraySlice strides); + ComputationDataHandle SliceInDim(const ComputationDataHandle& operand, + int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + ComputationDataHandle DynamicSlice( const ComputationDataHandle& operand, const ComputationDataHandle& start_indices, diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 114754bde4033a13e217bd6552ebffbde7c3503b..b5354131c94930b75ea66036ddb61ecd3993414f 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -201,6 +201,15 @@ tensorflow::ImportNumpy(); } } +%typemap(out) StatusOr { + if ($1.ok()) { + $result = numpy::PyShapeInfoFromXlaShape($1.ConsumeValueOrDie()); + } else { + PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str()); + return NULL; + } +} + %typemap(out) Status { if (!$1.ok()) { PyErr_SetString( @@ -823,6 +832,21 @@ tensorflow::ImportNumpy(); } Py_DECREF(o); + o = PyObject_GetAttrString($input, "result_shape"); + if (o == nullptr) { + return nullptr; + } + if (o != Py_None) { + StatusOr statusor = numpy::XlaShapeFromPyShape(o); + if (!statusor.ok()) { + PyErr_SetString(PyExc_TypeError, tensorflow::strings::StrCat("ExecutableBuildOptions.result_shape could not be created from Python shape value: ", statusor.status().ToString()).c_str()); + Py_DECREF(o); + return NULL; + } + build_options.set_result_layout(statusor.ValueOrDie()); + } + Py_DECREF(o); + $1 = &build_options; } } @@ -843,6 +867,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::CompiledLocalComputation::ExecuteWithShapedBuffers; %unignore xla::swig::LocalComputation; %unignore xla::swig::LocalComputation::Compile; +%unignore xla::swig::LocalComputation::GetReturnValueShape; %unignore xla::swig::LocalComputationBuilder; %unignore xla::swig::LocalComputationBuilder::LocalComputationBuilder; %unignore xla::swig::LocalComputationBuilder::Build; @@ -850,6 +875,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::ClearOpMetadata; %unignore xla::swig::LocalComputationBuilder::Parameter; %unignore xla::swig::LocalComputationBuilder::GetShape; +%unignore xla::swig::LocalComputationBuilder::GetReturnValueShape; %unignore xla::swig::LocalComputationBuilder::Infeed; %unignore xla::swig::LocalComputationBuilder::Outfeed; %unignore xla::swig::LocalComputationBuilder::ConstantLiteral; @@ -860,6 +886,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Collapse; %unignore xla::swig::LocalComputationBuilder::CrossReplicaSum; %unignore xla::swig::LocalComputationBuilder::Slice; +%unignore xla::swig::LocalComputationBuilder::SliceInDim; %unignore xla::swig::LocalComputationBuilder::DynamicSlice; %unignore xla::swig::LocalComputationBuilder::DynamicUpdateSlice; %unignore xla::swig::LocalComputationBuilder::ConcatInDim; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index f8cee5d5665cf95b19e037658b88ffddd5efa511..90cda42f3227c80826ffbf4e5473647c2795544d 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -30,9 +30,9 @@ from tensorflow.compiler.xla import xla_data_pb2 from tensorflow.compiler.xla.python import pywrap_xla as c_api -# Most functions are snake_case for consistency with other modules, -# whereas method names of ComputationBuilder and LocalComputation are -# CamelCase for consistency with XLA. +# Most functions are snake_case for consistency with other modules, whereas +# method names of ComputationBuilder and LocalComputation are CamelCase for +# consistency with XLA. # pylint: disable=invalid-name @@ -123,24 +123,34 @@ _BINARY_OPS = [ 'Pow', ] + XLA_ELEMENT_TYPE_TO_DTYPE = { - xla_data_pb2.F32: np.dtype(np.float32), - xla_data_pb2.F64: np.dtype(np.float64), - xla_data_pb2.S32: np.dtype(np.int32), - xla_data_pb2.S64: np.dtype(np.int64), - xla_data_pb2.U32: np.dtype(np.uint32), - xla_data_pb2.U64: np.dtype(np.uint64), - xla_data_pb2.PRED: np.dtype(np.bool), + xla_data_pb2.PRED: np.dtype('bool'), + xla_data_pb2.S8: np.dtype('int8'), + xla_data_pb2.S16: np.dtype('int16'), + xla_data_pb2.S32: np.dtype('int32'), + xla_data_pb2.S64: np.dtype('int64'), + xla_data_pb2.U8: np.dtype('uint8'), + xla_data_pb2.U16: np.dtype('uint16'), + xla_data_pb2.U32: np.dtype('uint32'), + xla_data_pb2.U64: np.dtype('uint64'), + xla_data_pb2.F16: np.dtype('float16'), + xla_data_pb2.F32: np.dtype('float32'), + xla_data_pb2.F64: np.dtype('float64'), + xla_data_pb2.C64: np.dtype('complex64'), xla_data_pb2.TUPLE: np.dtype(np.object), } # Note the conversion on the key. Numpy has a known issue wherein dtype hashing # doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus, # when keying by dtype in this dict, we use the string form of dtypes. -DTYPE_TO_XLA_ELEMENT_TYPE = { - str(v): k - for k, v in XLA_ELEMENT_TYPE_TO_DTYPE.items() -} +DTYPE_TO_XLA_ELEMENT_TYPE = {str(dt): et + for et, dt in XLA_ELEMENT_TYPE_TO_DTYPE.items()} + + +def dtype_to_etype(dtype): + """Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE.""" + return DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))] class LocalBuffer(object): @@ -195,6 +205,12 @@ class Shape(object): self._minor_to_major = minor_to_major self._check_minor_to_major() + def __eq__(self, other): + # pylint: disable=protected-access + return (self.np_dtype == other.np_dtype and + self._dimensions == other._dimensions and + self._minor_to_major == other._minor_to_major) + def __repr__(self): return ('xla_client.Shape(np_dtype={!r}, dimensions={!r}, ' 'minor_to_major={!r})').format(self.np_dtype, self._dimensions, @@ -354,17 +370,44 @@ class LocalComputation(object): # Ensure a reference to C-based destructor for use in __del__. if is_compiled: + assert isinstance(c_local_computation, c_api.CompiledLocalComputation) self._delete = c_api.DeleteCompiledLocalComputation else: + assert isinstance(c_local_computation, c_api.LocalComputation) self._delete = c_api.DeleteLocalComputation def Compile(self, argument_shapes=(), compile_options=None, layout_fn=None): + """Compiles an un-compiled local computation. + + Local computations are the result of a "LocalComputationBuild'ing" process + -- they start in uncompiled form, and via a call to Compile() turn into a + compiled local computation. + + Raises: + ValueError: if this is already a compiled local computation. + + Arguments: + argument_shapes: parameter shapes -- they are first laid out by layout_fn + if layout_fn is provided. Otherwise, the default layout for those shapes + will be used. + compile_options: options to use for compilation, includes an optional + laid out result shape for the computation. + layout_fn: lambda that is used to lay out the argument/result shapes. + + Returns: + A newly *compiled* local computation instance. + """ if self.is_compiled: raise ValueError('Attempt to compile a compiled local XLA computation.') + if layout_fn: argument_shapes = [ shape.map_leaves(layout_fn) for shape in argument_shapes ] + result_shape = _wrap_shape(self.c_local_computation.GetReturnValueShape()) + result_shape = result_shape.map_leaves(layout_fn) + compile_options = compile_options or CompileOptions() + compile_options.result_shape = result_shape return LocalComputation( self.c_local_computation.Compile(argument_shapes, compile_options), is_compiled=True) @@ -606,6 +649,9 @@ class ComputationBuilder(object): def GetShape(self, operand): return _wrap_shape(self._client.GetShape(_unwrap_data_handle(operand))) + def GetReturnValueShape(self): + return _wrap_shape(self._client.GetReturnValueShape()) + def GetComputationStats(self): raise NotImplementedError() @@ -620,7 +666,7 @@ class ComputationBuilder(object): representing the configuration of the padding operation. Returns: - A ComputationDataHandle representing the added pad op. + A ComputationDataHandle representing the added Pad op. """ if not isinstance(padding_config, xla_data_pb2.PaddingConfig): padding_config = GetPaddingConfigFromTriples(padding_config) @@ -630,7 +676,20 @@ class ComputationBuilder(object): padding_config)) def Reshape(self, operand, dimensions, new_sizes): - """Reshape op.""" + """Enqueues a reshape op onto the computation. + + Args: + operand: ComputationDataHandle representing the array to be reshaped. + dimensions: sequence of integers encoding the order in which dimensions + are collapsed or None, in which case dimensions are flattened in order. + new_sizes: sequence of integers encoding the new dimension sizes (shape). + + Returns: + A ComputationDataHandle representing the added Reshape op. + """ + if dimensions is None: + ndim = len(self.GetShape(operand).dimensions()) + dimensions = tuple(range(ndim)) return _wrap_data_handle( self._client.Reshape( _unwrap_data_handle(operand), dimensions, new_sizes)) @@ -736,11 +795,27 @@ class ComputationBuilder(object): strides = [1] * len(start_indices) return _wrap_data_handle( self._client.Slice( - _unwrap_data_handle(operand), - start_indices, - limit_indices, + _unwrap_data_handle(operand), start_indices, limit_indices, strides)) + def SliceInDim(self, operand, start_index, limit_index, stride, dimno): + """Enqueues a slice-in-dimension operation onto the computation. + + Args: + operand: ComputationDataHandle for the N dimensional array to be sliced. + start_index: an integer containing the start index of the slice. + limit_index: an integer containing the end index of the slice. + stride: an integer containing the stride size for the slice. + dimno: an integer indicating the dimension along which to slice. + + Returns: + A ComputationDataHandle representing the added Slice op. + """ + return _wrap_data_handle( + self._client.SliceInDim( + _unwrap_data_handle(operand), start_index, limit_index, stride, + dimno)) + def DynamicSlice(self, operand, start_indices, slice_sizes): """Enqueues a slice op with dynamic start indices onto the computation. diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 65720c6ef9ec1cd7a816bcf719960fa803dd45a1..4c16c1f8b07a28d8098e92e27f81a126ed9bdf0c 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -86,7 +86,8 @@ class ComputationsWithConstantsTest(LocalComputationTest): def testConstantScalarSumF32(self): c = self._NewComputation() - c.Add(c.ConstantF32Scalar(1.11), c.ConstantF32Scalar(3.14)) + root = c.Add(c.ConstantF32Scalar(1.11), c.ConstantF32Scalar(3.14)) + self.assertEqual(c.GetShape(root), c.GetReturnValueShape()) self._ExecuteAndCompareClose(c, expected=4.25) def testConstantScalarSumF64(self): @@ -761,6 +762,23 @@ class SingleOpTest(LocalComputationTest): [3, 2]) self._ExecuteAndCompareExact(c, expected=[[4, 5], [7, 8]]) + def testSliceInDim(self): + c = self._NewComputation() + c.SliceInDim( + c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])), + start_index=1, + limit_index=2, + stride=1, + dimno=1) + self._ExecuteAndCompareExact(c, expected=[[2], [5], [8]]) + c.SliceInDim( + c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])), + start_index=0, + limit_index=3, + stride=2, + dimno=0) + self._ExecuteAndCompareExact(c, expected=[[1, 2, 3], [7, 8, 9]]) + def testDynamicSlice(self): c = self._NewComputation() c.DynamicSlice( @@ -881,6 +899,13 @@ class EmbeddedComputationsTest(LocalComputationTest): c.Mul(c.ParameterFromNumpy(NumpyArrayF32(0)), c.ConstantF32Scalar(2.0)) return c.Build() + def _CreateMulF32ByParamComputation(self): + """Computation (f32) -> f32 that multiplies one parameter by the other.""" + c = self._NewComputation("mul_f32_by_param") + c.Mul(c.ParameterFromNumpy(NumpyArrayF32(0)), + c.ParameterFromNumpy(NumpyArrayF32(0))) + return c.Build() + def _CreateMulF64By2Computation(self): """Computation (f64) -> f64 that multiplies its parameter by 2.""" c = self._NewComputation("mul_f64_by2") @@ -1021,6 +1046,14 @@ class EmbeddedComputationsTest(LocalComputationTest): self._CreateBinaryDivF64Computation(), [0]) self._ExecuteAndCompareClose(c, expected=[0.2, 0.4, 0.75, 1.0]) + def DISABLED_testMapWithStaticOperands(self): + c = self._NewComputation() + factor = c.ConstantF32Scalar(3.0) + c.Map([c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0]))], + self._CreateMulF32ByParamComputation(), [0], + static_operands=[factor]) + self._ExecuteAndCompareClose(c, expected=[3.0, 6.0, 9.0, 12.0]) + def testSelectAndScatterF32(self): c = self._NewComputation() c.SelectAndScatter(c.Constant(NumpyArrayF32([[1., 2., 6.], [4., 5., 3.]])), diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index a9acdae380af5b7f9efb3d08302fc717108f5e40..8711b8aa2ef47103f0ec5972f790843273c54f8c 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -30,29 +30,23 @@ limitations under the License. namespace xla { -/* static */ std::unique_ptr> ReferenceUtil::TransposeArray2D( - const Array2D& operand) { - auto result = MakeUnique>(operand.width(), operand.height()); - for (int64 w = 0; w < operand.width(); ++w) { - for (int64 h = 0; h < operand.height(); ++h) { - (*result)(w, h) = operand(h, w); - } - } - - return result; -} - -/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( - const Array2D& lhs, const Array2D& rhs) { +namespace { + +template +std::unique_ptr> MatmulArray2DImpl( + const Array2D& lhs, const Array2D& rhs, + const std::function& impl_fn) { CHECK_EQ(lhs.width(), rhs.height()); int m = lhs.height(); int n = rhs.width(); int k = lhs.width(); - auto result = MakeUnique>(m, n); + auto result = MakeUnique>(m, n); // Because Eigen is a header-oriented library, make sure that the Eigen code // is the same as the code used by the CPU backend (otherwise the linker will // randomly pick *some* definition). - __xla_cpu_runtime_EigenSingleThreadedMatMulF32( + impl_fn( /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m, k, /*transpose_lhs=*/0, @@ -60,22 +54,24 @@ namespace xla { return result; } +} // namespace + +/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( + const Array2D& lhs, const Array2D& rhs) { + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF16); +} + +/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( + const Array2D& lhs, const Array2D& rhs) { + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF32); +} + /* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( const Array2D& lhs, const Array2D& rhs) { - CHECK_EQ(lhs.width(), rhs.height()); - int m = lhs.height(); - int n = rhs.width(); - int k = lhs.width(); - auto result = MakeUnique>(m, n); - // Because Eigen is a header-oriented library, make sure that the Eigen code - // is the same as the code used by the CPU backend (otherwise the linker will - // randomly pick *some* definition). - __xla_cpu_runtime_EigenSingleThreadedMatMulF64( - /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m, - k, - /*transpose_lhs=*/0, - /*transpose_rhs=*/0); - return result; + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF64); } /* static */ std::unique_ptr> ReferenceUtil::Array2DF32ToF64( diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h index 3ec96f2f38b8f91e1549419b60481327fa9bbd5f..57b0218882d818db8d21bb60f93a14235a944bbe 100644 --- a/tensorflow/compiler/xla/reference_util.h +++ b/tensorflow/compiler/xla/reference_util.h @@ -39,10 +39,22 @@ namespace xla { class ReferenceUtil { public: // Returns the result of a transpose operation on the input matrix. - static std::unique_ptr> TransposeArray2D( - const Array2D& operand); + template + static std::unique_ptr> TransposeArray2D( + const Array2D& operand) { + auto result = MakeUnique>(operand.width(), operand.height()); + for (int64 w = 0; w < operand.width(); ++w) { + for (int64 h = 0; h < operand.height(); ++h) { + (*result)(w, h) = operand(h, w); + } + } + + return result; + } // Returns the result of a matrix multiply `lhs x rhs`. + static std::unique_ptr> MatmulArray2D( + const Array2D& lhs, const Array2D& rhs); static std::unique_ptr> MatmulArray2D( const Array2D& lhs, const Array2D& rhs); static std::unique_ptr> MatmulArray2D( diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 0f2d0a9e96e20007aa24a22832bdca4f0add372d..0e272e1eea8800c8cf7a0101338449961b6b4b57 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -43,6 +43,118 @@ filegroup( ]), ) +cc_library( + name = "bfloat16_support", + srcs = ["bfloat16_support.cc"], + hdrs = ["bfloat16_support.h"], + deps = [ + ":hlo", + ], +) + +cc_library( + name = "bfloat16_conversion_folding", + srcs = ["bfloat16_conversion_folding.cc"], + hdrs = ["bfloat16_conversion_folding.h"], + deps = [ + ":bfloat16_support", + ":hlo", + ":hlo_pass", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "bfloat16_conversion_folding_test", + srcs = ["bfloat16_conversion_folding_test.cc"], + deps = [ + ":bfloat16_conversion_folding", + ":bfloat16_support", + ":hlo", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", + ], +) + +cc_library( + name = "bfloat16_normalization", + srcs = ["bfloat16_normalization.cc"], + hdrs = ["bfloat16_normalization.h"], + deps = [ + ":bfloat16_support", + ":hlo", + ":hlo_pass", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "bfloat16_normalization_test", + srcs = ["bfloat16_normalization_test.cc"], + deps = [ + ":bfloat16_normalization", + ":bfloat16_support", + ":hlo", + ":hlo_verifier", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", + ], +) + +cc_library( + name = "bfloat16_propagation", + srcs = ["bfloat16_propagation.cc"], + hdrs = ["bfloat16_propagation.h"], + deps = [ + ":bfloat16_support", + ":hlo", + ":hlo_dataflow_analysis", + ":hlo_dce", + ":hlo_pass", + ":tuple_simplifier", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_tree", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "bfloat16_propagation_test", + srcs = ["bfloat16_propagation_test.cc"], + deps = [ + ":bfloat16_propagation", + ":bfloat16_support", + ":hlo", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep + ], +) + cc_library( name = "shape_inference", srcs = ["shape_inference.cc"], @@ -70,7 +182,8 @@ tf_cc_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep + "//tensorflow/core:lib", ], ) @@ -643,6 +756,7 @@ cc_library( hdrs = ["llvm_compiler.h"], deps = [ ":compiler", + "//tensorflow/core:lib_internal", "@llvm//:core", ], ) @@ -875,6 +989,7 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", ], ) @@ -951,6 +1066,38 @@ tf_cc_test( ], ) +cc_library( + name = "hlo_module_group_metadata", + srcs = ["hlo_module_group_metadata.cc"], + hdrs = ["hlo_module_group_metadata.h"], + deps = [ + ":hlo", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +cc_library( + name = "hlo_module_group_util", + srcs = ["hlo_module_group_util.cc"], + hdrs = ["hlo_module_group_util.h"], + deps = [ + ":hlo", + ":hlo_module_group_metadata", + ":hlo_reachability", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + cc_library( name = "hlo_scheduling", srcs = ["hlo_scheduling.cc"], @@ -1102,6 +1249,41 @@ tf_cc_test( ], ) +cc_library( + name = "conditional_simplifier", + srcs = ["conditional_simplifier.cc"], + hdrs = ["conditional_simplifier.h"], + deps = [ + ":call_inliner", + ":hlo", + ":hlo_pass", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "conditional_simplifier_test", + srcs = ["conditional_simplifier_test.cc"], + deps = [ + ":conditional_simplifier", + ":hlo", + ":hlo_matchers", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) + cc_library( name = "while_loop_simplifier", srcs = ["while_loop_simplifier.cc"], @@ -2238,6 +2420,24 @@ cc_library( ":hlo", ":hlo_proto", "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:util", + ], +) + +tf_cc_test( + name = "hlo_proto_util_test", + srcs = ["hlo_proto_util_test.cc"], + deps = [ + ":hlo", + ":hlo_proto", + ":hlo_proto_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index fb857559f972a220a19b108baa4c441e09b90e1f..ecaa474336850c0ea3d2636826a7c62ecc5fe17e 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -122,6 +122,8 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault { Status HandleBitcast(HloInstruction* bitcast) override; + Status HandleBitcastConvert(HloInstruction* bitcast) override; + Status HandleBroadcast(HloInstruction* broadcast) override; Status HandleConcatenate(HloInstruction* concatenate) override; @@ -411,6 +413,13 @@ Status AlgebraicSimplifierVisitor::HandleBitcast(HloInstruction* bitcast) { return Status::OK(); } +Status AlgebraicSimplifierVisitor::HandleBitcastConvert( + HloInstruction* bitcast) { + // Eliminate bitcast converts between same shape. + ReplaceInstructionIfSameShape(bitcast, bitcast->mutable_operand(0)); + return Status::OK(); +} + Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy) { // If a copy feeds a copy, make it a single copy. if (copy->operand(0)->opcode() == HloOpcode::kCopy) { @@ -516,6 +525,18 @@ Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant) { return ReplaceInstruction( constant, BuildTupleConstant(computation_, constant->literal())); } + + // If a literal is all the same element replace it with a scalar broadcast. + if (ShapeUtil::ElementsIn(constant->shape()) > 1 && + constant->literal().IsAllFirst()) { + std::unique_ptr unique_scalar = + MakeUnique(constant->literal().GetFirstScalarLiteral()); + HloInstruction* scalar = computation_->AddInstruction( + HloInstruction::CreateConstant(std::move(unique_scalar))); + return ReplaceWithNewInstruction( + constant, + HloInstruction::CreateBroadcast(constant->shape(), scalar, {})); + } return Status::OK(); } @@ -1604,6 +1625,14 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( if (IsAll(start_indices, 0) && SameShape(dynamic_update_slice, update)) { return ReplaceInstruction(dynamic_update_slice, update); } + + // If any dimension of update is 0, elide the DynamicUpdateSlice. This + // optimization becomes invalid should we later prefer to warn about out of + // bound indices. + if (ShapeUtil::HasZeroElements(update->shape())) { + return ReplaceInstruction(dynamic_update_slice, + dynamic_update_slice->mutable_operand(0)); + } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index e43ea50af45318adf2c95aa69b3e53a5225c5579..451294ef5d8367686d7fc22b7f5ebfde89d14d42 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -61,13 +61,12 @@ TEST_F(AlgebraicSimplifierTest, AddZero) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param0, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kAdd); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -83,13 +82,12 @@ TEST_F(AlgebraicSimplifierTest, AddConstOnLHS) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, constant, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kAdd); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Add(param0, op::Constant())); } @@ -110,13 +108,12 @@ TEST_F(AlgebraicSimplifierTest, AddReassociateMergeConstants) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, add1, constant2)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kAdd); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Add(param0, op::Add(constant1, constant2))); } @@ -133,13 +130,12 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR0Operand) { builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, bcast, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kAdd); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -156,17 +152,47 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) { builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, bcast, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kAdd); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } +TEST_F(AlgebraicSimplifierTest, ConstantToBroadcast) { + HloComputation::Builder builder(TestName()); + builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR1({3.14f, 3.14f, 3.14f}))); + + auto computation = module().AddEntryComputation(builder.Build()); + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, op::Constant()); + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + root = computation->root_instruction(); + EXPECT_THAT(root, op::Broadcast(op::Constant())); + EXPECT_EQ(3.14f, root->operand(0)->literal().GetFirstElement()); +} + +TEST_F(AlgebraicSimplifierTest, ConstantNotToBroadcast) { + HloComputation::Builder builder(TestName()); + builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR1({3.14, 3.14, 4}))); + + auto computation = module().AddEntryComputation(builder.Build()); + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, op::Constant()); + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_FALSE(simplifier.Run(&module()).ValueOrDie()); + root = computation->root_instruction(); + EXPECT_THAT(root, op::Constant()); +} + // Test that A - 0 is simplified to A TEST_F(AlgebraicSimplifierTest, SubZero) { Shape r0f32 = ShapeUtil::MakeShape(F32, {}); @@ -178,13 +204,12 @@ TEST_F(AlgebraicSimplifierTest, SubZero) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kSubtract, param0, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kSubtract); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -200,13 +225,12 @@ TEST_F(AlgebraicSimplifierTest, SubConstCanonicalization) { builder.AddInstruction(HloInstruction::CreateBinary( r0f32, HloOpcode::kSubtract, param0, constant)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root->opcode(), HloOpcode::kSubtract); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Add(param0, op::Negate(constant))); } @@ -226,15 +250,14 @@ TEST_F(AlgebraicSimplifierTest, LhsDivOfDiv) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, div, param2)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(op::Divide(param0, param1), param2)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Divide(param0, op::Multiply(param1, param2))); @@ -255,15 +278,14 @@ TEST_F(AlgebraicSimplifierTest, RhsDivOfDiv) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, div)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(param0, op::Divide(param1, param2))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Divide(op::Multiply(param0, param2), param1)); @@ -289,8 +311,7 @@ TEST_F(AlgebraicSimplifierTest, DivOfDivAndDiv) { builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kDivide, div0, div1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT( computation->root_instruction(), @@ -298,7 +319,7 @@ TEST_F(AlgebraicSimplifierTest, DivOfDivAndDiv) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT( computation->root_instruction(), @@ -320,15 +341,14 @@ TEST_F(AlgebraicSimplifierTest, DivOfExp) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, exp)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(param0, op::Exp(param1))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Multiply(param0, op::Exp(op::Negate(param1)))); @@ -349,15 +369,14 @@ TEST_F(AlgebraicSimplifierTest, DivOfPower) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, power)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(param0, op::Power(param1, param2))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Multiply(param0, op::Power(param1, op::Negate(param2)))); @@ -380,15 +399,14 @@ TEST_F(AlgebraicSimplifierTest, DivOfBroadcastingPower) { builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kDivide, param0, power)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(param0, op::Power(param1, param2))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); ASSERT_THAT(computation->root_instruction(), op::Multiply(param0, op::Power(param1, op::Negate(param2)))); @@ -411,12 +429,11 @@ TEST_F(AlgebraicSimplifierTest, DivideByConstant) { builder.AddInstruction(HloInstruction::CreateBinary(r1f32, HloOpcode::kDivide, param0, constant)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Multiply(param0, op::Divide(op::Constant(), constant))); @@ -438,11 +455,10 @@ TEST_F(AlgebraicSimplifierTest, PowerOfPower) { builder.AddInstruction(HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, inner_power, exp2)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Power(base, op::Multiply(exp1, exp2))); } @@ -451,24 +467,23 @@ TEST_F(AlgebraicSimplifierTest, PowerOfPower) { // numbers. TEST_F(AlgebraicSimplifierTest, PowerOfPowerComplex) { Shape r0c64 = ShapeUtil::MakeShape(C64, {}); - Shape r1f32 = ShapeUtil::MakeShape(F32, {7}); + Shape r1c64 = ShapeUtil::MakeShape(C64, {7}); HloComputation::Builder builder(TestName()); HloInstruction* base = builder.AddInstruction( - HloInstruction::CreateParameter(0, r1f32, "param0")); + HloInstruction::CreateParameter(0, r1c64, "param0")); HloInstruction* exp1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r0c64, "param1")); HloInstruction* exp2 = builder.AddInstruction( HloInstruction::CreateParameter(2, r0c64, "param2")); HloInstruction* inner_power = builder.AddInstruction( - HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, base, exp1)); - builder.AddInstruction(HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, + HloInstruction::CreateBinary(r1c64, HloOpcode::kPower, base, exp1)); + builder.AddInstruction(HloInstruction::CreateBinary(r1c64, HloOpcode::kPower, inner_power, exp2)); - auto module = CreateNewModule(); - module->AddEntryComputation(builder.Build()); + module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_FALSE(simplifier.Run(&module()).ValueOrDie()); } // Test that A/1 is simplified to A for a scalar. @@ -482,13 +497,12 @@ TEST_F(AlgebraicSimplifierTest, DivOneScalar) { HloInstruction* div = builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, one)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, div); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -504,13 +518,12 @@ TEST_F(AlgebraicSimplifierTest, DivOneArray) { HloInstruction* div = builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kDivide, param0, one)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, div); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -529,13 +542,12 @@ TEST_F(AlgebraicSimplifierTest, ComplexOfRealImagC) { HloInstruction* cplx = builder.AddInstruction( HloInstruction::CreateBinary(r2c64, HloOpcode::kComplex, real, imag)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, cplx); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -554,13 +566,12 @@ TEST_F(AlgebraicSimplifierTest, RealOfComplex) { HloInstruction* real = builder.AddInstruction( HloInstruction::CreateUnary(r2f32, HloOpcode::kReal, cplx)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, real); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param0); } @@ -579,13 +590,12 @@ TEST_F(AlgebraicSimplifierTest, ImagOfComplex) { HloInstruction* imag = builder.AddInstruction( HloInstruction::CreateUnary(r2f32, HloOpcode::kImag, cplx)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, imag); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_EQ(root, param1); } @@ -607,13 +617,12 @@ TEST_F(AlgebraicSimplifierTest, SelectMakeTuple) { HloInstruction* add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, get, param2)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, add); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Add(param1, param2)); } @@ -633,15 +642,14 @@ TEST_F(AlgebraicSimplifierTest, ExpDiv) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, exp0, exp1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Divide(op::Exp(param0), op::Exp(param1))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Exp(op::Subtract(param0, param1))); @@ -662,15 +670,14 @@ TEST_F(AlgebraicSimplifierTest, ExpMul) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kMultiply, exp0, exp1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Multiply(op::Exp(param0), op::Exp(param1))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Exp(op::Add(param0, param1))); @@ -689,15 +696,14 @@ TEST_F(AlgebraicSimplifierTest, PowExp) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, exp0, param1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(op::Exp(param0), param1)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Exp(op::Multiply(param0, param1))); @@ -716,15 +722,14 @@ TEST_F(AlgebraicSimplifierTest, LnPow) { builder.AddInstruction( HloInstruction::CreateUnary(r0f32, HloOpcode::kLog, pow)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Log(op::Power(param0, param1))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Multiply(op::Log(param0), param1)); @@ -741,14 +746,13 @@ TEST_F(AlgebraicSimplifierTest, LnExp) { builder.AddInstruction( HloInstruction::CreateUnary(r0f32, HloOpcode::kLog, exp0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Log(op::Exp(param0))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_EQ(computation->root_instruction(), param0); } @@ -770,15 +774,14 @@ TEST_F(AlgebraicSimplifierTest, LnExpDiv) { builder.AddInstruction( HloInstruction::CreateUnary(r0f32, HloOpcode::kLog, div)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Log(op::Divide(op::Exp(param0), op::Exp(param1)))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Subtract(param0, param1)); } @@ -795,14 +798,13 @@ TEST_F(AlgebraicSimplifierTest, Pow0Scalar) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(param0, zero)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); HloInstruction* root = computation->root_instruction(); EXPECT_THAT(root, op::Constant()); @@ -820,14 +822,13 @@ TEST_F(AlgebraicSimplifierTest, Pow0Vector) { builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, param0, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(param0, zero)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); HloInstruction* root = computation->root_instruction(); EXPECT_THAT(root, op::Broadcast()); @@ -849,14 +850,13 @@ TEST_F(AlgebraicSimplifierTest, Pow1) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, one)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(param0, one)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_EQ(computation->root_instruction(), param0); } @@ -872,14 +872,13 @@ TEST_F(AlgebraicSimplifierTest, Pow2) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, two)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(param0, two)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Multiply(param0, param0)); } @@ -895,14 +894,13 @@ TEST_F(AlgebraicSimplifierTest, PowNegative1) { builder.AddInstruction(HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, negative_one)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Power(param0, negative_one)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); HloInstruction* root = computation->root_instruction(); EXPECT_THAT(root, op::Divide(op::Broadcast(), param0)); @@ -941,16 +939,15 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedConvolution) { dim->set_base_dilation(1); dim->set_window_reversal(false); // Create add computation. - std::unique_ptr module = CreateNewModule(); builder.AddInstruction(HloInstruction::CreateConvolve( ShapeUtil::MakeShape(F32, {3, 3, 3}), lhs, rhs, window, dnums)); - module->AddEntryComputation(builder.Build()); + module().AddEntryComputation(builder.Build()); HloPassFix simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Convolution(lhs, rhs)); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Broadcast(op::Constant())); } @@ -969,7 +966,6 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedReduceWindow) { dim->set_base_dilation(1); } // Create add computation. - std::unique_ptr module = CreateNewModule(); HloComputation* add_computation = nullptr; { HloComputation::Builder builder(TestName() + ".add"); @@ -980,20 +976,20 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedReduceWindow) { HloInstruction::CreateParameter(1, scalar_shape, "p1")); builder.AddInstruction( HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); - add_computation = module->AddEmbeddedComputation(builder.Build()); + add_computation = module().AddEmbeddedComputation(builder.Build()); } builder.AddInstruction(HloInstruction::CreateReduceWindow( ShapeUtil::MakeShape(F32, {5, 2}), param, builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), window, add_computation)); - module->AddEntryComputation(builder.Build()); + module().AddEntryComputation(builder.Build()); HloPassFix simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + EXPECT_THAT(module().entry_computation()->root_instruction(), op::ReduceWindow(param, op::Constant())); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Broadcast(op::Constant())); } @@ -1014,14 +1010,13 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedPad) { builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), padding)); - std::unique_ptr module = CreateNewModule(); - module->AddEntryComputation(builder.Build()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + module().AddEntryComputation(builder.Build()); + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Pad(param, op::Constant())); HloPassFix simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - EXPECT_THAT(module->entry_computation()->root_instruction(), + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Broadcast(op::Constant())); } @@ -1039,17 +1034,16 @@ TEST_F(AlgebraicSimplifierTest, ReshapeBroadcast) { ShapeUtil::MakeShape(F32, {3, 2}), broadcast)); auto computation = builder.Build(); - auto module = CreateNewModule(); - module->AddEntryComputation(std::move(computation)); + module().AddEntryComputation(std::move(computation)); - EXPECT_THAT(module->entry_computation()->root_instruction(), + EXPECT_THAT(module().entry_computation()->root_instruction(), op::Reshape(op::Broadcast(op::Reshape(op)))); HloPassFix simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); - EXPECT_THAT(module->entry_computation()->root_instruction(), op); + EXPECT_THAT(module().entry_computation()->root_instruction(), op); } // Test that convert(A, $TYPE) is simplified to A if A is of type $TYPE. @@ -1060,14 +1054,13 @@ TEST_F(AlgebraicSimplifierTest, ConvertBetweenSameType) { builder.AddInstruction( HloInstruction::CreateConvert(ShapeUtil::MakeShape(F32, {}), input)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Convert(input)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), input); } @@ -1081,14 +1074,13 @@ TEST_F(AlgebraicSimplifierTest, RemoveCopy) { builder.AddInstruction( HloInstruction::CreateUnary(param0->shape(), HloOpcode::kCopy, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Copy(param0)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param0); } @@ -1102,14 +1094,13 @@ TEST_F(AlgebraicSimplifierTest, RemoveUnaryConcatenate) { builder.AddInstruction( HloInstruction::CreateConcatenate(param0->shape(), {param0}, 0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Concatenate(param0)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param0); } @@ -1132,8 +1123,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) { builder.AddInstruction(HloInstruction::CreateConcatenate( result_shape, {empty_literal, param0, param0, empty_slice, param1}, 0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT( computation->root_instruction(), @@ -1141,7 +1131,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Concatenate(param0, param0, param1)); @@ -1163,15 +1153,14 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) { builder.AddInstruction(HloInstruction::CreateConcatenate( result_shape, {empty_literal, empty_slice}, 0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Concatenate(empty_literal, empty_slice)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_EQ(computation->root_instruction(), empty_literal); } @@ -1188,14 +1177,13 @@ TEST_F(AlgebraicSimplifierTest, ConcatenateOfBroadcastBecomesPad) { HloInstruction* broadcast = builder.AddInstruction( HloInstruction::CreateBroadcast(r1f32, param1, {})); builder.AddInstruction(HloInstruction::CreateConcatenate( - param0->shape(), {broadcast, param0}, 0)); + ShapeUtil::MakeShape(F32, {200}), {broadcast, param0}, 0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Pad(param0, param1)); } @@ -1209,8 +1197,7 @@ TEST_F(AlgebraicSimplifierTest, CopyWithDifferentLayout) { HloInstruction* copy = builder.AddInstruction( HloInstruction::CreateUnary(param0->shape(), HloOpcode::kCopy, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); // Set to different layouts. *param0->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1}); @@ -1220,7 +1207,7 @@ TEST_F(AlgebraicSimplifierTest, CopyWithDifferentLayout) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(&module()).ValueOrDie()); // Copy has not been removed. EXPECT_THAT(computation->root_instruction(), op::Copy(param0)); @@ -1236,8 +1223,7 @@ TEST_F(AlgebraicSimplifierTest, CopyWithSameLayout) { HloInstruction* copy = builder.AddInstruction( HloInstruction::CreateUnary(param0->shape(), HloOpcode::kCopy, param0)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); // Set to same layouts. *param0->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1}); @@ -1247,7 +1233,7 @@ TEST_F(AlgebraicSimplifierTest, CopyWithSameLayout) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); // Copy has been removed. EXPECT_THAT(computation->root_instruction(), param0); @@ -1268,14 +1254,13 @@ TEST_F(AlgebraicSimplifierTest, NoBitcastAdded) { *reshape->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1, 2, 3, 4, 5}); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(param0)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(&module()).ValueOrDie()); // Reshape is not replaced with a bitcast. EXPECT_THAT(computation->root_instruction(), op::Reshape(param0)); @@ -1314,8 +1299,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeReplacedWithBitcast) { builder.AddInstruction(HloInstruction::CreateTuple( {transformable_reshape, dimensions_wrong_reshape, layout_wrong_reshape})); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Tuple(transformable_reshape, dimensions_wrong_reshape, @@ -1323,7 +1307,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeReplacedWithBitcast) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, bitcasting_callback()); - simplifier.Run(module.get()).ValueOrDie(); + simplifier.Run(&module()).ValueOrDie(); // Verify that only the first reshape is replaced. EXPECT_THAT( @@ -1344,8 +1328,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeAfterEffectiveUnary) { builder.AddInstruction( HloInstruction::CreateBinary(ShapeUtil::MakeShape(F32, {1, 2, 3, 4, 5}), HloOpcode::kMaximum, movable_reshape, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Maximum(op::Reshape(param), zero)); @@ -1353,7 +1336,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeAfterEffectiveUnary) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, bitcasting_callback()); - simplifier.Run(module.get()).ValueOrDie(); + simplifier.Run(&module()).ValueOrDie(); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Maximum(param, zero))); } @@ -1371,8 +1354,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeToScalarNotHoistedAfterEffectiveUnary) { HloInstruction::CreateConstant(Literal::CreateR1({1., 2., 3.}))); builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(F32, {3}), HloOpcode::kMaximum, reshape, zero)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Maximum(op::Reshape(param), zero)); @@ -1380,7 +1362,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeToScalarNotHoistedAfterEffectiveUnary) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, bitcasting_callback()); - simplifier.Run(module.get()).ValueOrDie(); + simplifier.Run(&module()).ValueOrDie(); EXPECT_THAT(computation->root_instruction(), op::Maximum(op::Reshape(param), zero)); @@ -1405,9 +1387,8 @@ TEST_F(AlgebraicSimplifierTest, FailureToSinkReshapeDoesntAffectChangedBit) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, bitcasting_callback()); - auto module = CreateNewModule(); - module->AddEntryComputation(builder.Build()); - EXPECT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + module().AddEntryComputation(builder.Build()); + EXPECT_TRUE(simplifier.Run(&module()).ValueOrDie()); } // Regression test for a bug where if we failed to sink a reshape, we'd set the @@ -1424,14 +1405,14 @@ TEST_F(AlgebraicSimplifierTest, FailureToSinkBroadcastDoesntAffectChangedBit) { builder.AddInstruction(HloInstruction::CreateConstant( Literal::CreateR2({{0, 0}, {0, 0}}))))); - builder.AddInstruction(HloInstruction::CreateBroadcast( - ShapeUtil::MakeShape(F32, {2, 2, 2}), add, /*broadcast_dimensions=*/{0})); + builder.AddInstruction( + HloInstruction::CreateBroadcast(ShapeUtil::MakeShape(F32, {2, 2, 2}), add, + /*broadcast_dimensions=*/{0, 1})); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, bitcasting_callback()); - auto module = CreateNewModule(); - module->AddEntryComputation(builder.Build()); - EXPECT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + module().AddEntryComputation(builder.Build()); + EXPECT_TRUE(simplifier.Run(&module()).ValueOrDie()); } TEST_F(AlgebraicSimplifierTest, TransposeEqualsBitcast1) { @@ -1448,14 +1429,13 @@ TEST_F(AlgebraicSimplifierTest, TransposeEqualsBitcast1) { *transpose->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1, 2, 3}); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Transpose(param)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); // Verify that the reshape is replaced. EXPECT_THAT(computation->root_instruction(), op::Bitcast(param)); @@ -1475,14 +1455,13 @@ TEST_F(AlgebraicSimplifierTest, TransposeEqualsBitcast2) { *transpose->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({3, 1, 2, 0}); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Transpose(param)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); // Verify that the reshape is replaced. EXPECT_THAT(computation->root_instruction(), op::Bitcast(param)); @@ -1501,15 +1480,14 @@ TEST_F(AlgebraicSimplifierTest, ReshapesMerged) { builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {1, 2, 1, 1, 2, 1}), reshape1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Reshape(param0))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Reshape(param0)); } @@ -1529,14 +1507,13 @@ TEST_F(AlgebraicSimplifierTest, CopiesMerged) { ShapeUtil::MakeShapeWithLayout(F32, {2, 2, 2}, {0, 2, 1}), HloOpcode::kCopy, copy1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Copy(op::Copy(param0))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Copy(param0)); } @@ -1554,14 +1531,13 @@ TEST_F(AlgebraicSimplifierTest, TransposesMerged) { builder.AddInstruction(HloInstruction::CreateTranspose( ShapeUtil::MakeShape(F32, {4, 3, 2}), transpose1, {1, 0, 2})); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Transpose(transpose1)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Transpose(param0)); EXPECT_EQ(std::vector({2, 1, 0}), @@ -1576,17 +1552,16 @@ TEST_F(AlgebraicSimplifierTest, ReshapeAndBroadcastMerged) { auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {1, 5, 1}), param0)); builder.AddInstruction(HloInstruction::CreateBroadcast( - ShapeUtil::MakeShape(F32, {1, 2, 3, 5, 1}), reshape1, {0, 2, 3})); + ShapeUtil::MakeShape(F32, {1, 2, 3, 5, 1}), reshape1, {0, 3, 2})); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Broadcast(op::Reshape(param0))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Broadcast(param0)); } @@ -1601,15 +1576,14 @@ TEST_F(AlgebraicSimplifierTest, BroadcastAndReshapeMerged) { builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {2, 3, 7, 2, 1, 3, 2}), broadcast1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param0))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Broadcast(param0)); } @@ -1623,15 +1597,14 @@ TEST_F(AlgebraicSimplifierTest, BroadcastAndReshape_1_3x1_3) { builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(F32, {3}), broadcast)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); @@ -1646,15 +1619,14 @@ TEST_F(AlgebraicSimplifierTest, BroadcastAndReshape_4_3x2x4_6x1x1x4) { builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {6, 1, 1, 4}), broadcast)); - auto module = CreateNewModule(); - HloComputation* computation = module->AddEntryComputation(builder.Build()); + HloComputation* computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Broadcast(param)); EXPECT_THAT(computation->root_instruction()->dimensions(), @@ -1670,15 +1642,14 @@ TEST_F(AlgebraicSimplifierTest, BroadcastAndReshape_1_3x2x1_6x1x1x1) { builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {6, 1, 1, 1}), broadcast)); - auto module = CreateNewModule(); - HloComputation* computation = module->AddEntryComputation(builder.Build()); + HloComputation* computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Broadcast(param)); const std::vector broadcast_dims = @@ -1696,15 +1667,14 @@ TEST_F(AlgebraicSimplifierTest, BroadcastAndReshape_4_3x2x4x2_6x8) { builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(F32, {6, 8}), broadcast)); - auto module = CreateNewModule(); - HloComputation* computation = module->AddEntryComputation(builder.Build()); + HloComputation* computation = module().AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Reshape(op::Broadcast(param))); @@ -2410,12 +2380,11 @@ TEST_F(AlgebraicSimplifierTest, IteratorInvalidation) { call_builder.AddInstruction( HloInstruction::CreateCall(r1f32, {zero, one}, dot_computation.get())); - auto module = CreateNewModule(); - module->AddEmbeddedComputation(std::move(dot_computation)); - module->AddEntryComputation(call_builder.Build()); + module().AddEmbeddedComputation(std::move(dot_computation)); + module().AddEntryComputation(call_builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); } // Test that a constant with tuple shape becomes a tuple of constants. @@ -2428,12 +2397,11 @@ TEST_F(AlgebraicSimplifierTest, ConstantTupleBecomesTupleOfConstants) { Literal::CreateR1(constant_vector).get()}); builder.AddInstruction(HloInstruction::CreateConstant(std::move(value))); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Tuple(op::Constant(), op::Constant())); } @@ -2453,11 +2421,10 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicSlice) { HloInstruction::CreateConstant(Literal::CreateR1({0, 0, 0}))), /*slice_sizes=*/{10, 100, 1000})); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Parameter()); } @@ -2487,11 +2454,10 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicUpdateSlice) { builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR1({0, 0, 0}))))); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::DynamicSlice(op::Parameter(), op::Parameter())); } @@ -2554,15 +2520,16 @@ TEST_P(PadReduceWindowEffectiveBroadcastTest, DoIt) { PaddingConfig padding = window_util::MakeSymmetricPadding( decorate_spatials(param.symmetric_pad_spatials, 0, 0)); + TF_ASSERT_OK_AND_ASSIGN( + const Shape pad_shape, + ShapeInference::InferPadShape(input->shape(), + ShapeUtil::MakeShape(F32, {}), padding)); HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad( - ShapeUtil::MakeShape( - F32, decorate_spatials(param.reduce_window_spatials, 128, 2048)), - input, + pad_shape, input, builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), padding)); - std::unique_ptr module = CreateNewModule(); HloComputation* add_computation = nullptr; { HloComputation::Builder builder(TestName() + ".add"); @@ -2573,24 +2540,24 @@ TEST_P(PadReduceWindowEffectiveBroadcastTest, DoIt) { HloInstruction::CreateParameter(1, scalar_shape, "p1")); builder.AddInstruction( HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); - add_computation = module->AddEmbeddedComputation(builder.Build()); + add_computation = module().AddEmbeddedComputation(builder.Build()); } - TF_ASSERT_OK_AND_ASSIGN( - const Shape output_shape, - ShapeInference::InferPadShape(input_shape, ShapeUtil::MakeShape(F32, {}), - padding)); Window window = window_util::MakeWindow( decorate_spatials(param.reduce_window_spatials, 1, 1)); auto zero = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + TF_ASSERT_OK_AND_ASSIGN(const Shape output_shape, + ShapeInference::InferReduceWindowShape( + pad->shape(), zero->shape(), window, + add_computation->ComputeProgramShape())); builder.AddInstruction(HloInstruction::CreateReduceWindow( output_shape, pad, zero, window, add_computation)); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(&module())); ASSERT_TRUE(run_successful); EXPECT_TRUE( @@ -2667,11 +2634,10 @@ TEST_P(DotStrengthReductionTest, DotStrengthReduction) { dot_dnums.add_rhs_contracting_dimensions(0); builder.AddInstruction( HloInstruction::CreateDot(dot_shape, lhs, rhs, dot_dnums)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - TF_ASSERT_OK_AND_ASSIGN(bool changed, simplifier.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool changed, simplifier.Run(&module())); const bool dot_should_be_transformed = m == 1 || k == 1 || n == 1; const bool computation_should_be_modified = dot_should_be_transformed || (transpose_lhs && transpose_rhs); @@ -2699,7 +2665,7 @@ struct DotOfConcatTestSpec { }; class DotOfConcatSimplificationTest - : public HloTestBase, + : public HloVerifiedTestBase, public ::testing::WithParamInterface {}; // Test that we transform @@ -2745,11 +2711,10 @@ TEST_P(DotOfConcatSimplificationTest, ConstantLHS) { builder.AddInstruction( HloInstruction::CreateDot(dot_shape, lhs, rhs, dot_dnums)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(&module())); ASSERT_TRUE(run_successful); EXPECT_TRUE( @@ -2790,17 +2755,17 @@ TEST_P(DotOfConcatSimplificationTest, ConstantRHS) { HloInstruction* lhs2 = builder.AddInstruction( HloInstruction::CreateParameter(2, lhs2_shape, "lhs2")); HloInstruction* lhs3 = builder.AddInstruction( - HloInstruction::CreateParameter(3, lhs2_shape, "lhs3")); + HloInstruction::CreateParameter(3, lhs3_shape, "lhs3")); Shape lhs_shape = ShapeUtil::MakeShape(F32, {spec.m, spec.k}); HloInstruction* lhs = builder.AddInstruction(HloInstruction::CreateConcatenate( lhs_shape, {lhs0, lhs1, lhs2, lhs3}, 1)); - Shape rhs_shape = ShapeUtil::MakeShape(F32, {spec.k, spec.m}); + Shape rhs_shape = ShapeUtil::MakeShape(F32, {spec.k, spec.n}); auto* rhs = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( - /*from=*/10.0, /*to=*/10000.0, /*rows=*/spec.k, /*cols=*/spec.m))); + /*from=*/10.0, /*to=*/10000.0, /*rows=*/spec.k, /*cols=*/spec.n))); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); @@ -2810,11 +2775,10 @@ TEST_P(DotOfConcatSimplificationTest, ConstantRHS) { builder.AddInstruction( HloInstruction::CreateDot(dot_shape, lhs, rhs, dot_dnums)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); + auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(&module())); ASSERT_TRUE(run_successful); EXPECT_TRUE( ShapeUtil::Equal(computation->root_instruction()->shape(), dot_shape)); @@ -2836,6 +2800,29 @@ DotOfConcatTestSpec kDotOfConcatTestSpecs[] = { {/*m=*/1, /*k=*/16, /*n=*/1}, // }; +// Test that DynamicUpdateSlice update param with any dimension equal to zero +// gets removed. +TEST_F(AlgebraicSimplifierTest, DynamicUpdateSliceZeroUpdate) { + HloComputation::Builder builder(TestName()); + const Shape dslice_shape = ShapeUtil::MakeShape(F32, {10}); + HloInstruction* const operand = builder.AddInstruction( + HloInstruction::CreateParameter(0, dslice_shape, "operand")); + const Shape update_shape = ShapeUtil::MakeShape(F32, {0}); + HloInstruction* const update = builder.AddInstruction( + HloInstruction::CreateParameter(1, update_shape, "update")); + HloInstruction* const start_indices = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR1({0}))); + builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( + dslice_shape, operand, update, start_indices)); + const HloComputation* const computation = + module().AddEntryComputation(builder.Build()); + + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_THAT(computation->root_instruction(), operand); +} + INSTANTIATE_TEST_CASE_P(DotOfConcatSimplificationTestInstantiation, DotOfConcatSimplificationTest, ::testing::ValuesIn(kDotOfConcatTestSpecs)); diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc index 4e80679c11dfdf7fdf8077a9f354139a4cab6803..4f819a743c48f30df8dde00ece72a0b4e1748802 100644 --- a/tensorflow/compiler/xla/service/allocation_tracker.cc +++ b/tensorflow/compiler/xla/service/allocation_tracker.cc @@ -34,40 +34,54 @@ StatusOr AllocationTracker::Register( std::unique_ptr shaped_buffer, const string& tag) { tensorflow::mutex_lock lock(mutex_); VLOG(2) << "Register"; - return RegisterInternal(std::move(shaped_buffer), tag); + std::vector> replicated_buffers; + replicated_buffers.emplace_back(std::move(shaped_buffer)); + return RegisterInternal(std::move(replicated_buffers), tag); +} + +StatusOr AllocationTracker::RegisterReplicatedBuffers( + std::vector> replicated_buffers, + const string& tag) { + tensorflow::mutex_lock lock(mutex_); + VLOG(2) << "RegisterReplicatedBuffers"; + return RegisterInternal(std::move(replicated_buffers), tag); } StatusOr AllocationTracker::RegisterInternal( - std::unique_ptr shaped_buffer, const string& tag) { + std::vector> replicated_buffers, + const string& tag) { VLOG(2) << "RegisterInternal(" - << "tag: \"" << tag << "\" " - << "shaped_buffer: " << *shaped_buffer; - if (shaped_buffer->platform() != backend_->platform()) { - return InvalidArgument( - "AllocationTracker for platform %s cannot register buffer from " - "platform %s", - backend_->platform()->Name().c_str(), - shaped_buffer->platform()->Name().c_str()); + << "tag: \"" << tag << "\" with " << replicated_buffers.size() + << " shaped_buffers."; + for (const auto& shaped_buffer : replicated_buffers) { + VLOG(2) << "shaped_buffer:" << *shaped_buffer; + if (shaped_buffer->platform() != backend_->platform()) { + return InvalidArgument( + "AllocationTracker for platform %s cannot register buffer from " + "platform %s", + backend_->platform()->Name().c_str(), + shaped_buffer->platform()->Name().c_str()); + } } int64 handle = next_handle_++; - std::vector shape_indices; - ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), - [this, &shape_indices](const Shape& /*subshape*/, - const ShapeIndex& index) { - shape_indices.push_back(index); - }); - for (const ShapeIndex& index : shape_indices) { - AddAllocationOrIncrementRefCount(shaped_buffer->buffer(index), - shaped_buffer->device_ordinal()); + for (auto& shaped_buffer : replicated_buffers) { + std::vector shape_indices; + ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), + [this, &shape_indices](const Shape& /*subshape*/, + const ShapeIndex& index) { + shape_indices.push_back(index); + }); + for (const ShapeIndex& index : shape_indices) { + AddAllocationOrIncrementRefCount(shaped_buffer->buffer(index), + shaped_buffer->device_ordinal()); + } + handle_to_shaped_buffers_[handle].emplace_back(std::move(shaped_buffer)); } + GlobalDataHandle result; result.set_handle(handle); - - handle_to_shaped_buffer_[handle] = std::move(shaped_buffer); - VLOG(2) << "handle: " << handle; - return result; } @@ -75,23 +89,35 @@ tensorflow::Status AllocationTracker::Unregister(const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); VLOG(2) << "Unregister(" << "handle: " << data.handle() << ")"; - TF_ASSIGN_OR_RETURN(ShapedBuffer * shaped_buffer, ResolveInternal(data)); - std::vector shape_indices; - ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), - [this, &shape_indices](const Shape& /*subshape*/, - const ShapeIndex& index) { - shape_indices.push_back(index); - }); - for (const ShapeIndex& index : shape_indices) { - TF_RETURN_IF_ERROR(DecrementRefCount(shaped_buffer->buffer(index), - shaped_buffer->device_ordinal())); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + for (const auto& shaped_buffer : replicated_buffers) { + std::vector shape_indices; + ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), + [this, &shape_indices](const Shape& /*subshape*/, + const ShapeIndex& index) { + shape_indices.push_back(index); + }); + for (const ShapeIndex& index : shape_indices) { + TF_RETURN_IF_ERROR(DecrementRefCount(shaped_buffer->buffer(index), + shaped_buffer->device_ordinal())); + } } + return Reset(data); +} - // Keep a nullptr as a tombstone for unregistered handles. This enables better - // error messages. That is, "handle has been deallocated" versus "handle does - // not exist". - handle_to_shaped_buffer_.at(data.handle()).reset(); - +Status AllocationTracker::Reset(const GlobalDataHandle& data) { + // Keep a nullptr as a tombstone for unregistered handles. This enables + // better error messages. That is, "handle has been deallocated" versus + // "handle does not exist". + auto it = handle_to_shaped_buffers_.find(data.handle()); + if (it == handle_to_shaped_buffers_.end()) { + return NotFound("no allocation record for global data handle: %lld", + data.handle()); + } + for (auto& shaped_buffer : it->second) { + shaped_buffer.reset(); + } return tensorflow::Status::OK(); } @@ -99,7 +125,11 @@ StatusOr> AllocationTracker::DeconstructTuple( const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); - TF_ASSIGN_OR_RETURN(ShapedBuffer * shaped_buffer, ResolveInternal(data)); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + // We only need to care about replica id 0 here, since the GlobalDataHandle is + // the same for all buffers across replicas. + const ShapedBuffer* shaped_buffer = replicated_buffers[0]; if (!ShapeUtil::IsTuple(shaped_buffer->on_host_shape())) { return InvalidArgument("global data handle %lld is not a tuple", data.handle()); @@ -109,7 +139,7 @@ StatusOr> AllocationTracker::DeconstructTuple( TF_RET_CHECK(ShapeUtil::IsTuple(shaped_buffer->on_device_shape())); if (ShapeUtil::IsNestedTuple(shaped_buffer->on_device_shape())) { - return Unimplemented("deconstructing nested tuples not yet supported"); + return Unimplemented("Deconstructing nested tuples is not implemented."); } std::vector element_handles; @@ -122,37 +152,55 @@ StatusOr> AllocationTracker::DeconstructTuple( shaped_buffer->platform(), shaped_buffer->device_ordinal()); element_buffer->set_buffer(shaped_buffer->buffer(/*index=*/{i}), /*index=*/{}); + std::vector> replicated_buffers; + replicated_buffers.emplace_back(std::move(element_buffer)); TF_ASSIGN_OR_RETURN( GlobalDataHandle element_handle, - RegisterInternal(std::move(element_buffer), "deconstructed tuple")); + RegisterInternal(std::move(replicated_buffers), "deconstructed tuple")); element_handles.push_back(element_handle); } return std::move(element_handles); } -StatusOr AllocationTracker::Resolve( +StatusOr> AllocationTracker::Resolve( const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); return AllocationTracker::ResolveInternal(data); } -StatusOr AllocationTracker::ResolveInternal( +StatusOr AllocationTracker::ResolveForReplica( + const GlobalDataHandle& data, int replica_id) { + tensorflow::mutex_lock lock(mutex_); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + if (replica_id >= replicated_buffers.size()) { + return InvalidArgument( + "Requesting buffer for replica %d, but found buffers only for %lu " + "replicas.", + replica_id, replicated_buffers.size()); + } + return replicated_buffers[replica_id]; +} + +StatusOr> AllocationTracker::ResolveInternal( const GlobalDataHandle& data) { VLOG(2) << "resolve:" << data.handle(); - auto it = handle_to_shaped_buffer_.find(data.handle()); - if (it == handle_to_shaped_buffer_.end()) { + auto it = handle_to_shaped_buffers_.find(data.handle()); + if (it == handle_to_shaped_buffers_.end()) { return NotFound("no allocation record for global data handle: %lld", data.handle()); } - ShapedBuffer* shaped_buffer = it->second.get(); - - if (shaped_buffer == nullptr) { - return InvalidArgument("global data handle %lld was previously deallocated", - data.handle()); + std::vector replicated_buffers; + for (const auto& shaped_buffer : it->second) { + if (shaped_buffer == nullptr) { + return InvalidArgument( + "global data handle %lld was previously deallocated", data.handle()); + } + replicated_buffers.push_back(shaped_buffer.get()); } - return shaped_buffer; + return replicated_buffers; } void AllocationTracker::AddAllocationOrIncrementRefCount( diff --git a/tensorflow/compiler/xla/service/allocation_tracker.h b/tensorflow/compiler/xla/service/allocation_tracker.h index 807af8694972083d097604a67ee46d2f73d9545a..038aee8541b297d6f91fe2b3bce7455fd9a7084e 100644 --- a/tensorflow/compiler/xla/service/allocation_tracker.h +++ b/tensorflow/compiler/xla/service/allocation_tracker.h @@ -43,10 +43,17 @@ class AllocationTracker { AllocationTracker(Backend* backend) : backend_(backend), next_handle_(1) {} // Registers a shaped buffer of device memory, and returns a corresponding - // handle that can be used for talking to XLA clients. + // handle that can be used for talking to XLA clients. The given shaped buffer + // will be treated as the buffer corresponding to the only replica. StatusOr Register( std::unique_ptr shaped_buffer, const string& tag); + // Registers a vector of shaped buffers of device memory, one per replica, and + // returns a corresponding handle that can be used for talking to XLA clients. + StatusOr RegisterReplicatedBuffers( + std::vector> replicated_buffers, + const string& tag); + // Unregister the allocation for the given data handle. Status Unregister(const GlobalDataHandle& data); @@ -54,9 +61,17 @@ class AllocationTracker { StatusOr> DeconstructTuple( const GlobalDataHandle& Data); - // Resolve a handle from an XLA client to a shaped buffer, or provide an error - // status to say whether it was not found (or found, but found deallocated). - StatusOr Resolve(const GlobalDataHandle& data); + // Resolve a handle from an XLA client to a vector of shaped buffers, one per + // replica, or provide an error status to say whether any of those buffers + // were not found (or found, but found deallocated). + StatusOr> Resolve( + const GlobalDataHandle& data); + + // Resolves a handle from an XLA client and replica id to a shaped buffer, or + // provide an error status to say whether it was not found (or found, but + // found deallocated). + StatusOr ResolveForReplica(const GlobalDataHandle& data, + int replica_id); private: // Data structure encapsulating single memory allocation on the device. @@ -74,13 +89,17 @@ class AllocationTracker { // Internal helper which resolves the given GlobalDataHandle to a // ShapedBuffer. - StatusOr ResolveInternal(const GlobalDataHandle& data) - EXCLUSIVE_LOCKS_REQUIRED(mutex_); + StatusOr> ResolveInternal( + const GlobalDataHandle& data) EXCLUSIVE_LOCKS_REQUIRED(mutex_); - // Internal helper which registers a shaped buffer. + // Internal helper which registers a vector of shaped buffers, one per + // replica. StatusOr RegisterInternal( - std::unique_ptr shaped_buffer, const string& tag) - EXCLUSIVE_LOCKS_REQUIRED(mutex_); + std::vector> replicated_buffers, + const string& tag) EXCLUSIVE_LOCKS_REQUIRED(mutex_); + + // Resets the shaped buffers corresponding to the given handle. + Status Reset(const GlobalDataHandle& data) EXCLUSIVE_LOCKS_REQUIRED(mutex_); // Adds the given device address to the allocation tracker, or if it already // exists, then increment it's reference count. @@ -111,9 +130,10 @@ class AllocationTracker { tensorflow::gtl::FlatMap opaque_to_allocation_map_ GUARDED_BY(mutex_); - // A map from data handle to ShapedBuffer. - tensorflow::gtl::FlatMap> - handle_to_shaped_buffer_ GUARDED_BY(mutex_); + // A map from data handle to a vector of shaped buffers that represent the + // buffers for different replicas. + tensorflow::gtl::FlatMap>> + handle_to_shaped_buffers_ GUARDED_BY(mutex_); TF_DISALLOW_COPY_AND_ASSIGN(AllocationTracker); }; diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.cc b/tensorflow/compiler/xla/service/batchnorm_expander.cc index 27ddfd47aa3096afd3e245af1ac3cedd9b48ce4a..84c9db32932becd9b701929b392efa4998d03067 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander.cc +++ b/tensorflow/compiler/xla/service/batchnorm_expander.cc @@ -153,6 +153,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining( std::vector added_instructions; auto add = [&](std::unique_ptr inst) { HloInstruction* added_inst = computation_->AddInstruction(std::move(inst)); + added_inst->set_metadata(batch_norm->metadata()); added_instructions.push_back(added_inst); return added_inst; }; @@ -334,6 +335,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormInference( std::vector added_instructions; auto add = [&](std::unique_ptr inst) { HloInstruction* added_inst = computation_->AddInstruction(std::move(inst)); + added_inst->set_metadata(batch_norm->metadata()); added_instructions.push_back(added_inst); return added_inst; }; @@ -419,6 +421,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( std::vector added_instructions; auto add = [&](std::unique_ptr inst) { HloInstruction* added_inst = computation_->AddInstruction(std::move(inst)); + added_inst->set_metadata(batch_norm->metadata()); added_instructions.push_back(added_inst); return added_inst; }; diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc new file mode 100644 index 0000000000000000000000000000000000000000..432448e9bbc7db30ed67a0130d52b060032362d5 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc @@ -0,0 +1,188 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_conversion_folding.h" + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +class BFloat16ConversionFoldingVisitor : public DfsHloVisitorWithDefault { + public: + explicit BFloat16ConversionFoldingVisitor( + HloComputation* computation, const BFloat16Support* bfloat16_support) + : computation_(computation), bfloat16_support_(bfloat16_support) {} + + Status DefaultAction(HloInstruction* hlo) override; + + static bool Run(HloComputation* computation, + const BFloat16Support* bfloat16_support) { + BFloat16ConversionFoldingVisitor visitor(computation, bfloat16_support); + TF_CHECK_OK(computation->Accept(&visitor)); + return visitor.changed_; + } + + private: + // Checks if the HLO has a BF16 -> F32 conversion as input, or a F32 -> BF16 + // conversion as output, and folds them to the HLO itself if feasible. + Status TryFoldBF16Conversions(HloInstruction* hlo); + + // Folds the F32 -> BF16 conversions from the HLO's output. + // + // Precondition: all of the HLO's users are F32 -> BF16 conversions. + Status FoldOutputConversions(HloInstruction* hlo); + + // Folds the BF16 -> F32 conversion operand to the HLO. + // + // Precondition: the operand is a F32 -> BF16 conversion. + Status FoldOperandConversion(HloInstruction* hlo, int64 operand_index); + + HloComputation* computation_; + const BFloat16Support* bfloat16_support_; + bool changed_ = false; +}; + +Status BFloat16ConversionFoldingVisitor::FoldOutputConversions( + HloInstruction* hlo) { + std::vector materialized_users = hlo->users(); + hlo->mutable_shape()->set_element_type(BF16); + for (auto user : materialized_users) { + CHECK_EQ(user->opcode(), HloOpcode::kConvert); + TF_RETURN_IF_ERROR(user->ReplaceAllUsesWith(hlo)); + changed_ = true; + } + return Status::OK(); +} + +Status BFloat16ConversionFoldingVisitor::FoldOperandConversion( + HloInstruction* hlo, int64 operand_index) { + // The operand is a convert from BF16 to F32. + auto operand = hlo->mutable_operand(operand_index); + CHECK_EQ(operand->opcode(), HloOpcode::kConvert); + TF_RETURN_IF_ERROR( + hlo->ReplaceOperandWith(operand_index, operand->mutable_operand(0))); + changed_ = true; + return Status::OK(); +} + +Status BFloat16ConversionFoldingVisitor::TryFoldBF16Conversions( + HloInstruction* hlo) { + std::vector bf16_to_f32_operands; + bool has_other_f32_operands = false; + for (int64 i = 0; i < hlo->operands().size(); ++i) { + auto operand = hlo->operand(i); + if (operand->shape().element_type() == F32) { + if (operand->opcode() == HloOpcode::kConvert && + operand->operand(0)->shape().element_type() == BF16 && + bfloat16_support_->SupportsBF16Operand(*hlo, i)) { + // Operand is a convert from BF16 to F32 and we support BF16 input + // directly in the current HLO at the operand index. + bf16_to_f32_operands.push_back(i); + } else { + has_other_f32_operands = true; + } + continue; + } + } + + bool fold_output_conversion = hlo->user_count() > 0 && + hlo->shape().element_type() == F32 && + bfloat16_support_->SupportsBF16Output(*hlo) && + hlo != computation_->root_instruction(); + if (fold_output_conversion) { + for (auto user : hlo->users()) { + if (user->opcode() == HloOpcode::kConvert && + user->shape().element_type() == BF16) { + continue; + } + // We should not change the output type if any user is not a conversion + // from F32 to BF16. + fold_output_conversion = false; + break; + } + } + + if (!bfloat16_support_->SupportsMixedPrecisions(*hlo)) { + if (has_other_f32_operands || + (!fold_output_conversion && hlo->shape().element_type() == F32)) { + // Some of the operands/output will remain F32, but we cannot use mixed + // precisions, so we cannot do anything here. + return Status::OK(); + } + } + + if (fold_output_conversion) { + TF_RETURN_IF_ERROR(FoldOutputConversions(hlo)); + } + + for (int64 i : bf16_to_f32_operands) { + TF_RETURN_IF_ERROR(FoldOperandConversion(hlo, i)); + } + return Status::OK(); +} + +Status BFloat16ConversionFoldingVisitor::DefaultAction(HloInstruction* hlo) { + // Do not fold BF16 conversions for instructions related to tuples, entry and + // exit of a computation, fusion, convert, and control flow. + if (hlo->opcode() == HloOpcode::kTuple || // + hlo->opcode() == HloOpcode::kGetTupleElement || // + hlo->opcode() == HloOpcode::kInfeed || // + hlo->opcode() == HloOpcode::kOutfeed || // + hlo->opcode() == HloOpcode::kSend || // + hlo->opcode() == HloOpcode::kSendDone || // + hlo->opcode() == HloOpcode::kRecv || // + hlo->opcode() == HloOpcode::kRecvDone || // + hlo->opcode() == HloOpcode::kConstant || // + hlo->opcode() == HloOpcode::kParameter || // + hlo->opcode() == HloOpcode::kFusion || // + hlo->opcode() == HloOpcode::kConvert || // + hlo->opcode() == HloOpcode::kCall || // + hlo->opcode() == HloOpcode::kCustomCall || // + hlo->opcode() == HloOpcode::kWhile || // + hlo->opcode() == HloOpcode::kConditional) { + return Status::OK(); + } + if (hlo == computation_->root_instruction() && + !bfloat16_support_->SupportsMixedPrecisions(*hlo)) { + // If hlo is the root instruction, we cannot change its output, so folding + // can only happen when it supports mixed precision so that we can change + // its operands. + return Status::OK(); + } + return TryFoldBF16Conversions(hlo); +} + +StatusOr BFloat16ConversionFolding::Run(HloModule* module) { + XLA_VLOG_LINES( + 2, "BFloat16ConversionFolding::Run(), before:\n" + module->ToString()); + bool changed = false; + for (auto* comp : module->MakeNonfusionComputations()) { + if (BFloat16ConversionFoldingVisitor::Run(comp, bfloat16_support_)) { + changed = true; + } + } + XLA_VLOG_LINES( + 2, "BFloat16ConversionFolding::Run(), after:\n" + module->ToString()); + return changed; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding.h b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.h new file mode 100644 index 0000000000000000000000000000000000000000..c9398387098fad84ba28735c30e426fedd9b0cb0 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_CONVERSION_FOLDING_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_CONVERSION_FOLDING_H_ + +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { + +// A pass which folds F32 <-> BF16 conversions to their operands or users, when +// it is supported by the backend. +// +// This pass follows the passed-in backend-specific BF16 support rules, but can +// introduce mixed precision in individual HLOs which breaks the assumption of +// some other HLO passes. So it should be used at the end of the HLO +// optimization pipeline followed by a DCE pass. If other passes are needed +// after this pass, run BFloat16MixedPrecisionRemoval first to undo some of the +// changed made by this pass. +class BFloat16ConversionFolding : public HloPassInterface { + public: + explicit BFloat16ConversionFolding(const BFloat16Support* bfloat16_support) + : bfloat16_support_(bfloat16_support) {} + + ~BFloat16ConversionFolding() override = default; + tensorflow::StringPiece name() const override { return "bfloat16-fold"; } + + // Run BF16 conversion folding on the given computation. Returns whether the + // computation was changed. + StatusOr Run(HloModule* module) override; + + private: + const BFloat16Support* bfloat16_support_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_CONVERSION_FOLDING_H_ diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..cb37759439debf41a305ec7dccaa548e1bf234cd --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc @@ -0,0 +1,209 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_conversion_folding.h" +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +class TestBFloat16Support : public BFloat16Support { + public: + TestBFloat16Support() {} + ~TestBFloat16Support() override {} + + bool SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const override { + if (hlo.opcode() == HloOpcode::kAdd || + hlo.opcode() == HloOpcode::kSubtract || + hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + return false; + } + + bool SupportsBF16Output(const HloInstruction& hlo) const override { + if (hlo.opcode() == HloOpcode::kAdd || + hlo.opcode() == HloOpcode::kSubtract || + hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + return false; + } + + bool SupportsMixedPrecisions(const HloInstruction& hlo) const override { + if (hlo.opcode() == HloOpcode::kAdd || hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + return false; + } +}; + +class BFloat16ConversionFoldingTest : public HloTestBase { + protected: + bool FoldConversions(HloModule* module) { + TestBFloat16Support bfloat16_support_; + BFloat16ConversionFolding fold(&bfloat16_support_); + StatusOr result = fold.Run(module); + EXPECT_IS_OK(result.status()); + return result.ValueOrDie(); + } +}; + +TEST_F(BFloat16ConversionFoldingTest, FoldIfSupported) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(f32_shape, HloOpcode::kAdd, a, b)); + HloInstruction* convert0 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, add0)); + HloInstruction* convert1 = builder.AddInstruction( + HloInstruction::CreateConvert(f32_shape, convert0)); + + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(f32_shape, HloOpcode::kAdd, convert1, c)); + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, add1)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(FoldConversions(module.get())); + + EXPECT_EQ(computation->root_instruction(), add1); + EXPECT_EQ(add0->shape().element_type(), BF16); + EXPECT_EQ(add1->shape().element_type(), BF16); + EXPECT_EQ(add1->operand(0), add0); +} + +TEST_F(BFloat16ConversionFoldingTest, DoNotFoldIfUnsupported) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* mul0 = builder.AddInstruction( + HloInstruction::CreateBinary(f32_shape, HloOpcode::kMultiply, a, b)); + HloInstruction* convert0 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, mul0)); + HloInstruction* convert1 = builder.AddInstruction( + HloInstruction::CreateConvert(f32_shape, convert0)); + + HloInstruction* mul1 = builder.AddInstruction(HloInstruction::CreateBinary( + f32_shape, HloOpcode::kMultiply, convert1, c)); + HloInstruction* convert2 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, mul1)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(FoldConversions(module.get())); + + EXPECT_EQ(computation->root_instruction(), convert2); + EXPECT_EQ(mul0->shape().element_type(), F32); + EXPECT_EQ(mul1->shape().element_type(), F32); + EXPECT_EQ(mul1->operand(0), convert1); +} + +TEST_F(BFloat16ConversionFoldingTest, DoNotFoldUnsupportedMixedPrecision) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* sub0 = builder.AddInstruction( + HloInstruction::CreateBinary(f32_shape, HloOpcode::kSubtract, a, b)); + HloInstruction* convert0 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, sub0)); + HloInstruction* convert1 = builder.AddInstruction( + HloInstruction::CreateConvert(f32_shape, convert0)); + + HloInstruction* sub1 = builder.AddInstruction(HloInstruction::CreateBinary( + f32_shape, HloOpcode::kSubtract, convert1, c)); + HloInstruction* convert2 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, sub1)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(FoldConversions(module.get())); + + EXPECT_EQ(computation->root_instruction(), convert2); + EXPECT_EQ(sub0->shape().element_type(), F32); + EXPECT_EQ(sub1->shape().element_type(), F32); + EXPECT_EQ(sub1->operand(0), convert1); +} + +TEST_F(BFloat16ConversionFoldingTest, DoNotFoldTuple) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + HloInstruction* convert0 = + builder.AddInstruction(HloInstruction::CreateConvert(f32_shape, b)); + + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({a, convert0})); + HloInstruction* gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(f32_shape, tuple, 0)); + HloInstruction* convert1 = + builder.AddInstruction(HloInstruction::CreateConvert(bf16_shape, gte)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(FoldConversions(module.get())); + + EXPECT_EQ(computation->root_instruction(), convert1); + EXPECT_EQ(gte->shape().element_type(), F32); + EXPECT_EQ(tuple->operand(1), convert0); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization.cc b/tensorflow/compiler/xla/service/bfloat16_normalization.cc new file mode 100644 index 0000000000000000000000000000000000000000..14c54ddd135af024327f63418b410da1ed3c4fd4 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_normalization.cc @@ -0,0 +1,381 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_normalization.h" + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +class BFloat16NormalizationVisitor : public DfsHloVisitorWithDefault { + public: + explicit BFloat16NormalizationVisitor(HloComputation* computation, + const BFloat16Support* bfloat16_support) + : computation_(computation), bfloat16_support_(bfloat16_support) {} + + Status DefaultAction(HloInstruction* hlo) override; + + // Special handling for cross-replica-sum which can have a tuple output. + Status HandleCrossReplicaSum(HloInstruction* crs) override; + + static bool Run(HloComputation* computation, + const BFloat16Support* bfloat16_support) { + BFloat16NormalizationVisitor visitor(computation, bfloat16_support); + TF_CHECK_OK(computation->Accept(&visitor)); + return visitor.changed_; + } + + private: + // Checks if the HLO uses BF16 in an unsupported way, and if so, inserts + // conversions between F32 and BF16 to make it supported. + Status HandleInstruction(HloInstruction* hlo); + + // Inserts a conversion HLO that changes the given HLO's output type. + Status InsertConvertAfterOutput(HloInstruction* hlo, PrimitiveType to, + HloComputation* computation); + + // Changes the output type to the specified type, then inserts a conversion + // to the original type. + Status ChangeOutputTypeThenInsertConvertBack(HloInstruction* hlo, + PrimitiveType to, + HloComputation* computation); + + // Inserts a conversion HLO that changes the given HLO's operand type. + Status InsertConvertBeforeOperand(HloInstruction* hlo, int64 operand_idx, + PrimitiveType to, + HloComputation* computation); + + // Inserts conversion HLOs to replace the called computations' BF16 + // operands/outputs to F32. + Status ConvertCalledComputations( + HloInstruction* hlo, + tensorflow::gtl::ArraySlice bf16_called_comps); + + HloComputation* computation_; + const BFloat16Support* bfloat16_support_; + bool changed_ = false; +}; + +Status BFloat16NormalizationVisitor::InsertConvertAfterOutput( + HloInstruction* hlo, PrimitiveType to, HloComputation* computation) { + bool is_root = computation->root_instruction() == hlo; + std::vector materialized_users = hlo->users(); + // Use inst's shape temporarily, in order to pass checks in ReplaceUseWith. + auto convert = computation->AddInstruction( + HloInstruction::CreateConvert(hlo->shape(), hlo)); + for (auto* user : materialized_users) { + TF_RETURN_IF_ERROR(hlo->ReplaceUseWith(user, convert)); + } + if (is_root) { + computation->set_root_instruction(convert); + } + convert->mutable_shape()->set_element_type(to); + changed_ = true; + return Status::OK(); +} + +Status BFloat16NormalizationVisitor::ChangeOutputTypeThenInsertConvertBack( + HloInstruction* hlo, PrimitiveType to, HloComputation* computation) { + auto original_type = hlo->shape().element_type(); + hlo->mutable_shape()->set_element_type(to); + return InsertConvertAfterOutput(hlo, original_type, computation); +} + +Status BFloat16NormalizationVisitor::InsertConvertBeforeOperand( + HloInstruction* hlo, int64 operand_idx, PrimitiveType to, + HloComputation* computation) { + auto operand = hlo->mutable_operand(operand_idx); + auto convert = computation->AddInstruction(HloInstruction::CreateConvert( + ShapeUtil::ChangeElementType(operand->shape(), to), operand)); + TF_RETURN_IF_ERROR(hlo->ReplaceOperandWith(operand_idx, convert)); + changed_ = true; + return Status::OK(); +} + +Status BFloat16NormalizationVisitor::ConvertCalledComputations( + HloInstruction* hlo, + tensorflow::gtl::ArraySlice bf16_called_comps) { + std::map cloned_computations; + for (auto& comp : bf16_called_comps) { + auto cloned = comp->parent()->AddEmbeddedComputation(comp->Clone()); + cloned_computations[comp] = cloned; + changed_ = true; + } + hlo->ReplaceCalledComputations([&](HloComputation* comp) { + auto it = cloned_computations.find(comp); + if (it != cloned_computations.end()) { + return it->second; + } + return comp; + }); + for (auto& comp_pair : cloned_computations) { + auto comp = comp_pair.second; + if (comp->root_instruction()->shape().element_type() == BF16) { + TF_RETURN_IF_ERROR( + InsertConvertAfterOutput(comp->root_instruction(), F32, comp)); + } + for (auto* param : comp->parameter_instructions()) { + if (param->shape().element_type() == BF16) { + // This changes the parameter to F32 then inserts a convert after it. + TF_RETURN_IF_ERROR( + ChangeOutputTypeThenInsertConvertBack(param, F32, comp)); + } + } + } + return Status::OK(); +} + +Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( + HloInstruction* crs) { + if (!ShapeUtil::IsTuple(crs->shape())) { + return HandleInstruction(crs); + } + + std::vector operand_types(crs->operand_count()); + std::vector output_types(crs->operand_count()); + int64 f32_count = 0; + int64 bf16_count = 0; + bool has_unsupported_bf16_operand = false; + bool has_unsupported_bf16_output = false; + for (int64 i = 0; i < crs->operand_count(); ++i) { + operand_types[i] = crs->operand(i)->shape().element_type(); + output_types[i] = ShapeUtil::GetSubshape(crs->shape(), {i}).element_type(); + if (operand_types[i] == F32) { + f32_count += 1; + } else if (operand_types[i] == BF16) { + bf16_count += 1; + if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + has_unsupported_bf16_operand = true; + } + } + if (output_types[i] == F32) { + f32_count += 1; + } else if (output_types[i] == BF16) { + bf16_count += 1; + if (!bfloat16_support_->SupportsBF16Output(*crs)) { + has_unsupported_bf16_output = true; + } + } + } + + if (bf16_count == 0) { + return Status::OK(); + } + + auto should_convert_operand = [&](int64 i) { + if (operand_types[i] != BF16) { + return false; + } + if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + return true; + } + if (bfloat16_support_->SupportsMixedPrecisions(*crs)) { + return false; + } + return has_unsupported_bf16_operand || has_unsupported_bf16_output || + f32_count > 0; + }; + + for (int64 i = 0; i < crs->operand_count(); ++i) { + if (should_convert_operand(i)) { + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(crs, i, F32, computation_)); + f32_count += 1; + bf16_count -= 1; + } + } + + if (!has_unsupported_bf16_output && + (bfloat16_support_->SupportsMixedPrecisions(*crs) || f32_count == 0 || + bf16_count == 0)) { + return Status::OK(); + } + + std::vector materialized_users = crs->users(); + std::vector output_elements(crs->operand_count()); + auto original_shape = crs->shape(); + for (int64 i = 0; i < crs->operand_count(); ++i) { + auto subshape = ShapeUtil::GetMutableSubshape(crs->mutable_shape(), {i}); + if (output_types[i] != BF16) { + output_elements[i] = computation_->AddInstruction( + HloInstruction::CreateGetTupleElement(*subshape, crs, i)); + continue; + } + subshape->set_element_type(F32); + auto gte = computation_->AddInstruction( + HloInstruction::CreateGetTupleElement(*subshape, crs, i)); + output_elements[i] = + computation_->AddInstruction(HloInstruction::CreateConvert( + ShapeUtil::ChangeElementType(*subshape, BF16), gte)); + } + auto tuple = computation_->AddInstruction( + HloInstruction::CreateTuple(output_elements)); + + // Use the crs' shape temporarily, in order to pass checks in + // ReplaceUseWith. + *tuple->mutable_shape() = crs->shape(); + for (auto* user : materialized_users) { + TF_RETURN_IF_ERROR(crs->ReplaceUseWith(user, tuple)); + } + *tuple->mutable_shape() = original_shape; + return Status::OK(); +} + +Status BFloat16NormalizationVisitor::HandleInstruction(HloInstruction* hlo) { + int f32_count = 0; + int bf16_count = 1; + + for (int64 i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == F32) { + f32_count += 1; + } else if (hlo->operand(i)->shape().element_type() == BF16) { + bf16_count += 1; + } + } + + if (hlo->shape().element_type() == F32) { + f32_count += 1; + } else if (hlo->shape().element_type() == BF16) { + bf16_count += 1; + } + + std::vector bf16_called_comps; + for (auto* comp : hlo->called_computations()) { + bool comp_has_bf16 = false; + if (comp->root_instruction()->shape().element_type() == F32) { + f32_count += 1; + } else if (comp->root_instruction()->shape().element_type() == BF16) { + bf16_count += 1; + comp_has_bf16 = true; + } + for (auto* param : comp->parameter_instructions()) { + if (param->shape().element_type() == F32) { + f32_count += 1; + } else if (param->shape().element_type() == BF16) { + bf16_count += 1; + comp_has_bf16 = true; + } + } + if (comp_has_bf16) { + bf16_called_comps.push_back(comp); + } + } + + // Resolve unsupported BF16 operands. + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16 && + !bfloat16_support_->SupportsBF16Operand(*hlo, i)) { + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); + bf16_count -= 1; + f32_count += 1; + } + } + + // Resolve unsupported BF16 output. + if (hlo->shape().element_type() == BF16 && + !bfloat16_support_->SupportsBF16Output(*hlo)) { + TF_RETURN_IF_ERROR( + ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_)); + bf16_count -= 1; + f32_count += 1; + } + + // Resolve unsupported mixed precision after resolving unsupported BF16 + // operands and output, because the numbers of BF16 operands/output and F32 + // operands/output may have changed. + if (bfloat16_support_->SupportsMixedPrecisions(*hlo) || bf16_count == 0 || + f32_count == 0) { + return Status::OK(); + } + // See if we can change everything to BF16. + if (hlo->called_computations().empty() && + hlo->shape().element_type() == BF16) { + bool can_use_bf16 = true; + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16) { + continue; + } + if ((bfloat16_support_->EffectiveOperandPrecisionIsBF16(*hlo, i) || + bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, + i)) && + bfloat16_support_->SupportsBF16Operand(*hlo, i)) { + continue; + } + can_use_bf16 = false; + break; + } + if (can_use_bf16) { + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == F32) { + TF_RETURN_IF_ERROR( + InsertConvertBeforeOperand(hlo, i, BF16, computation_)); + } + } + return Status::OK(); + } + } + if (hlo->shape().element_type() == BF16) { + TF_RETURN_IF_ERROR( + ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_)); + } + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16) { + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); + } + } + return ConvertCalledComputations(hlo, bf16_called_comps); +} + +Status BFloat16NormalizationVisitor::DefaultAction(HloInstruction* hlo) { + // Do not change instructions related to entry and exit of a computation, + // tuples, fusion, convert, and control flow. + if (hlo->opcode() == HloOpcode::kTuple || // + hlo->opcode() == HloOpcode::kGetTupleElement || // + hlo->opcode() == HloOpcode::kInfeed || // + hlo->opcode() == HloOpcode::kOutfeed || // + hlo->opcode() == HloOpcode::kConstant || // + hlo->opcode() == HloOpcode::kParameter || // + hlo->opcode() == HloOpcode::kFusion || // + hlo->opcode() == HloOpcode::kConvert || // + hlo->opcode() == HloOpcode::kCall || // + hlo->opcode() == HloOpcode::kCustomCall || // + hlo->opcode() == HloOpcode::kWhile || // + hlo->opcode() == HloOpcode::kConditional) { + return Status::OK(); + } + return HandleInstruction(hlo); +} + +StatusOr BFloat16Normalization::Run(HloModule* module) { + XLA_VLOG_LINES( + 2, "BFloat16Normalization::Run(), before:\n" + module->ToString()); + bool changed = false; + for (auto* comp : module->MakeComputationPostOrder()) { + if (BFloat16NormalizationVisitor::Run(comp, bfloat16_support_)) { + changed = true; + } + } + XLA_VLOG_LINES(2, + "BFloat16Normalization::Run(), after:\n" + module->ToString()); + return changed; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization.h b/tensorflow/compiler/xla/service/bfloat16_normalization.h new file mode 100644 index 0000000000000000000000000000000000000000..2a60fe0af3218484acb95e6c69815d551350764c --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_normalization.h @@ -0,0 +1,92 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_NORMALIZATION_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_NORMALIZATION_H_ + +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { + +// A pass which adds F32 <-> BF16 conversions for HLO instructions that do not +// support BF16 input/output or mixed precision, according to the passed-in +// backend-specific BF16 support rules. +class BFloat16Normalization : public HloPassInterface { + public: + explicit BFloat16Normalization(const BFloat16Support* bfloat16_support) + : bfloat16_support_(bfloat16_support) {} + + ~BFloat16Normalization() override = default; + tensorflow::StringPiece name() const override { return "bf16-normalization"; } + + // Run BF16 normalization on the given computation. Returns whether the + // computation was changed. + StatusOr Run(HloModule* module) override; + + private: + const BFloat16Support* bfloat16_support_; +}; + +// A pass that unconditionally removes the mixed F32/BF16 uses in HLO +// instructions (excluding convert) by adding F32 <-> BF16 conversions. Unlike +// BFloat16Normalization, this pass does not use a backend-specific +// BFloat16Support, and does not change HLOs that have BF16 data if they do not +// use mixed precision; it removes mixed precision even if the backend supports +// it. This pass is used to make the HLO module valid for other HLO passes which +// do not support mixed precision. +class BFloat16MixedPrecisionRemoval : public HloPassInterface { + public: + BFloat16MixedPrecisionRemoval() {} + + ~BFloat16MixedPrecisionRemoval() override = default; + + tensorflow::StringPiece name() const override { + return "bf16-mixed-precision-removal"; + } + + // Run mixed precision removal on the given computation. Returns whether the + // computation was changed. + StatusOr Run(HloModule* module) override { + BFloat16Normalization normalization(&no_mixed_precision_support_); + return normalization.Run(module); + } + + private: + class BFloat16SupportForMixedPrecisionRemoval : public BFloat16Support { + public: + BFloat16SupportForMixedPrecisionRemoval() {} + + ~BFloat16SupportForMixedPrecisionRemoval() override = default; + + bool SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const override { + return true; + } + + bool SupportsBF16Output(const HloInstruction& hlo) const override { + return true; + } + + bool SupportsMixedPrecisions(const HloInstruction& hlo) const override { + return false; + } + } no_mixed_precision_support_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_NORMALIZATION_H_ diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..1afaefd9df9c5771fb9e134ae9050f3abb00ea4a --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc @@ -0,0 +1,287 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_normalization.h" +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_verifier.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +class TestBFloat16Support : public BFloat16Support { + public: + TestBFloat16Support() {} + ~TestBFloat16Support() override {} + + bool SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const override { + if (hlo.opcode() == HloOpcode::kAdd || + hlo.opcode() == HloOpcode::kSubtract || + hlo.opcode() == HloOpcode::kReduce || + hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + if (hlo.opcode() == HloOpcode::kDot) { + // Test that only the first operand of kDot supports BF16. + return operand_index == 0; + } + return false; + } + + bool SupportsBF16Output(const HloInstruction& hlo) const override { + if (hlo.opcode() == HloOpcode::kAdd || hlo.opcode() == HloOpcode::kReduce || + hlo.opcode() == HloOpcode::kSubtract || + hlo.opcode() == HloOpcode::kDot || hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + return false; + } + + bool SupportsMixedPrecisions(const HloInstruction& hlo) const override { + if (hlo.opcode() == HloOpcode::kAdd || hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kGetTupleElement) { + return true; + } + return false; + } +}; + +class BFloat16NormalizationTest : public HloTestBase { + protected: + bool Normalize(HloModule* module) { + TestBFloat16Support bfloat16_support_; + BFloat16Normalization normalization(&bfloat16_support_); + StatusOr result = normalization.Run(module); + EXPECT_IS_OK(result.status()); + + HloVerifier verifier(/*allow_mixed_precision=*/true); + EXPECT_IS_OK(verifier.Run(module).status()); + + return result.ValueOrDie(); + } +}; + +TEST_F(BFloat16NormalizationTest, NoopIfSupported) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(bf16_shape, HloOpcode::kAdd, a, b)); + + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(f32_shape, HloOpcode::kAdd, add0, c)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction(), add1); + EXPECT_EQ(add0->shape().element_type(), BF16); + EXPECT_EQ(add1->shape().element_type(), F32); +} + +TEST_F(BFloat16NormalizationTest, ResolveIfUnsupportedBF16) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* mul0 = builder.AddInstruction( + HloInstruction::CreateBinary(bf16_shape, HloOpcode::kMultiply, a, b)); + + HloInstruction* mul1 = builder.AddInstruction( + HloInstruction::CreateBinary(bf16_shape, HloOpcode::kMultiply, mul0, c)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction()->opcode(), HloOpcode::kConvert); + EXPECT_EQ(computation->root_instruction()->operand(0), mul1); + EXPECT_EQ(mul0->shape().element_type(), F32); + EXPECT_EQ(mul1->shape().element_type(), F32); + EXPECT_EQ(mul1->operand(0)->opcode(), HloOpcode::kConvert); +} + +TEST_F(BFloat16NormalizationTest, ResolveUnsupportedMixedPrecisionSubtraction) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32_shape, "c")); + + HloInstruction* sub0 = builder.AddInstruction( + HloInstruction::CreateBinary(bf16_shape, HloOpcode::kSubtract, a, b)); + + HloInstruction* sub1 = builder.AddInstruction( + HloInstruction::CreateBinary(bf16_shape, HloOpcode::kSubtract, sub0, c)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction()->opcode(), HloOpcode::kConvert); + EXPECT_EQ(computation->root_instruction()->operand(0), sub1); + EXPECT_EQ(sub0->shape().element_type(), F32); + EXPECT_EQ(sub1->shape().element_type(), F32); + EXPECT_EQ(sub1->operand(0)->opcode(), HloOpcode::kConvert); +} + +TEST_F(BFloat16NormalizationTest, ResolveUnsupportedMixedPrecisionReduce) { + Shape f32_input_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape f32_output_shape = ShapeUtil::MakeShape(F32, {4}); + + Shape bf16_scalar_shape = ShapeUtil::MakeShape(BF16, {}); + + auto reduce_comp_builder = HloComputation::Builder("reduce_comp"); + auto reduce_comp_param0 = reduce_comp_builder.AddInstruction( + HloInstruction::CreateParameter(0, bf16_scalar_shape, "param0")); + auto reduce_comp_param1 = reduce_comp_builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_scalar_shape, "param1")); + reduce_comp_builder.AddInstruction( + HloInstruction::CreateBinary(bf16_scalar_shape, HloOpcode::kAdd, + reduce_comp_param0, reduce_comp_param1)); + + auto module = CreateNewModule(); + auto reduce_computation = + module->AddEmbeddedComputation(reduce_comp_builder.Build()); + + auto builder = HloComputation::Builder(TestName()); + HloInstruction* input = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_input_shape, "a")); + HloInstruction* init = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_scalar_shape, "init")); + HloInstruction* reduce = builder.AddInstruction(HloInstruction::CreateReduce( + f32_output_shape, input, init, {0}, reduce_computation)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction(), reduce); + EXPECT_EQ(reduce->called_computations().size(), 1); + EXPECT_EQ(reduce->called_computations()[0]->num_parameters(), 2); + EXPECT_EQ(reduce->called_computations()[0] + ->parameter_instruction(0) + ->shape() + .element_type(), + F32); + EXPECT_EQ(reduce->called_computations()[0] + ->parameter_instruction(1) + ->shape() + .element_type(), + F32); + EXPECT_EQ(reduce->called_computations()[0] + ->root_instruction() + ->shape() + .element_type(), + F32); + EXPECT_EQ(reduce->shape().element_type(), F32); + EXPECT_EQ(reduce->operand(0), input); + EXPECT_EQ(input->shape().element_type(), F32); + EXPECT_EQ(reduce->operand(1)->opcode(), HloOpcode::kConvert); + EXPECT_EQ(reduce->operand(1)->shape().element_type(), F32); +} + +TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) { + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {2, 4}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + + HloInstruction* crs = + builder.AddInstruction(HloInstruction::CreateCrossReplicaSum( + ShapeUtil::MakeTupleShape({f32_shape, bf16_shape}), {a, b})); + HloInstruction* gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(bf16_shape, crs, 1)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction(), gte); + EXPECT_EQ(gte->shape().element_type(), BF16); + EXPECT_EQ(crs->operand(1)->shape().element_type(), F32); + EXPECT_EQ(ShapeUtil::GetSubshape(crs->shape(), {1}).element_type(), F32); +} + +// Tests that the normalization should not cause unsupported mixed precision due +// to resolving unsupported BF16 operand. +TEST_F(BFloat16NormalizationTest, DoNotAddUnsupportedMixedPrecision) { + auto builder = HloComputation::Builder(TestName()); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {4, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, bf16_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + + DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(1); + dot_dnums.add_rhs_contracting_dimensions(0); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateDot(bf16_shape, a, b, dot_dnums)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction()->opcode(), HloOpcode::kConvert); + EXPECT_EQ(dot->shape().element_type(), F32); + EXPECT_EQ(dot->operand(0)->shape().element_type(), F32); + EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConvert); + EXPECT_EQ(dot->operand(1)->shape().element_type(), F32); + EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConvert); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc new file mode 100644 index 0000000000000000000000000000000000000000..531f36e8c5473ef684e654ed6b89c4d5ef04b401 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -0,0 +1,683 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_propagation.h" + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/map_util.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_dce.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/tuple_simplifier.h" +#include "tensorflow/compiler/xla/shape_tree.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/gtl/cleanup.h" +#include "tensorflow/core/platform/logging.h" + +namespace xla { + +BFloat16Propagation::BFloat16Propagation( + const BFloat16Support* bfloat16_support) + : bfloat16_support_(bfloat16_support) {} + +void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision( + HloInstruction* fusion) { + CHECK_EQ(fusion->opcode(), HloOpcode::kFusion); + if (!bfloat16_support_->SupportsMixedPrecisions(*fusion)) { + return; + } + + // We are depending on the fusion node itself having already been analyzed + // for whether it can output BF16 and this has been adjusted in the output + // shape, and now we're looking to update the interior of the fusion node to + // match the new output shape, as well as recursively process the whole fusion + // node even if the output shape was not modified. + auto root = fusion->fused_instructions_computation()->root_instruction(); + + // Adjust root's element types according to the fusion's output shape. + ShapeUtil::ForEachMutableSubshape( + root->mutable_shape(), [&](Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() != F32) { + return; + } + if (ShapeUtil::GetSubshape(fusion->shape(), index).element_type() == + BF16) { + subshape->set_element_type(BF16); + changed_ = true; + VLOG(2) << "Fused root " << root->ToString() << " at shape index " + << index << " changed to BF16 precision for fusion " + << fusion->ToString(); + } + }); + + // Propagate BF16 in the fusion computation. + auto insts = + fusion->fused_instructions_computation()->MakeInstructionPostOrder(); + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); + } + computations_visited_in_mutation_pass_.insert( + fusion->fused_instructions_computation()); +} + +void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision( + HloInstruction* while_hlo) { + CHECK_EQ(while_hlo->opcode(), HloOpcode::kWhile); + + // We are depending on the while node itself having already been analyzed for + // whether it can output BF16 and this has been adjusted in the output shape, + // and now we're looking to update the body and condition computations to + // match the new output shape, as well as recursively process the whole while + // node even if the output shape was not modified. + HloComputation* body = while_hlo->while_body(); + auto body_root = body->root_instruction(); + HloComputation* condition = while_hlo->while_condition(); + + ShapeUtil::ForEachMutableSubshape( + body_root->mutable_shape(), + [this, while_hlo, body_root](Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() != F32) { + return; + } + if (ShapeUtil::GetSubshape(while_hlo->shape(), index).element_type() == + BF16) { + subshape->set_element_type(BF16); + changed_ = true; + VLOG(2) << "While body root " << body_root->ToString() + << " at shape index " << index + << " changed to BF16 precision for while " + << while_hlo->ToString(); + } + }); + + auto body_insts = body->MakeInstructionPostOrder(); + for (auto inst_it = body_insts.rbegin(); inst_it != body_insts.rend(); + ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); + } + computations_visited_in_mutation_pass_.insert(body); + + auto condition_insts = condition->MakeInstructionPostOrder(); + for (auto inst_it = condition_insts.rbegin(); + inst_it != condition_insts.rend(); ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); + } + computations_visited_in_mutation_pass_.insert(condition); +} + +bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, + const ShapeIndex& index) const { + auto value_set = dataflow_->GetValueSet(&hlo, index); + for (const HloValue* value : value_set.values()) { + if (ContainsKey(values_that_must_be_kept_as_f32_, value)) { + return false; + } + if (value->shape().element_type() == BF16) { + continue; + } + for (const HloUse& use : value->uses()) { + if (!ContainsKey(instructions_visited_in_mutation_pass_, + use.instruction)) { + // We don't know yet whether use.instruction will consume BF16 since it + // hasn't been visited. Although we visit instructions in reverse + // topological order, this is still possible because there may be + // unvisited instruction that alias the same buffer. In this case, we + // aggressively skip this use, and if this causes inconsistency (e.g., + // one use is in BF16 but another use is in F32), it will be resolved at + // the end of the BFloat16Propagation pass. + continue; + } + // Any visited user that can accept BF16 has already been updated if + // necessary, e.g., the output has been changed to BF16 if it propagates + // precision, or a called computation's parameters have been changed to + // BF16 for fusions or whiles. + if (use.instruction->opcode() == HloOpcode::kFusion) { + const auto* fused_parameter = + use.instruction->fused_parameter(use.operand_number); + if (ShapeUtil::GetSubshape(fused_parameter->shape(), use.operand_index) + .element_type() != BF16) { + return false; + } + continue; + } else if (use.instruction->opcode() == HloOpcode::kWhile) { + const auto* cond_parameter = + use.instruction->while_condition()->parameter_instruction( + use.operand_number); + if (ShapeUtil::GetSubshape(cond_parameter->shape(), use.operand_index) + .element_type() != BF16) { + return false; + } + const auto* body_parameter = + use.instruction->while_body()->parameter_instruction( + use.operand_number); + if (ShapeUtil::GetSubshape(body_parameter->shape(), use.operand_index) + .element_type() != BF16) { + return false; + } + continue; + } + if (bfloat16_support_->EffectiveOperandPrecisionIsBF16( + *use.instruction, use.operand_number)) { + continue; + } + // If the op propagates precision and it outputs a BF16, then it's OK to + // supply BF16 also as the input. In the backward mutation pass, the users + // shapes should have already been processed. + PrimitiveType user_output_type = PRIMITIVE_TYPE_INVALID; + if (use.instruction->opcode() == HloOpcode::kTuple || + (use.instruction->opcode() == HloOpcode::kCrossReplicaSum && + ShapeUtil::IsTuple(use.instruction->shape()))) { + user_output_type = ShapeUtil::GetSubshape( + ShapeUtil::GetSubshape(use.instruction->shape(), + {use.operand_number}), + use.operand_index) + .element_type(); + } else { + user_output_type = use.instruction->shape().element_type(); + } + if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision( + *use.instruction, use.operand_number) && + user_output_type == BF16) { + continue; + } + return false; + } + } + return true; +} + +void BFloat16Propagation::DetermineAndMutateInstructionPrecision( + HloInstruction* hlo, bool skip_parameters) { + // We handle any fusion computation or while body/condition after the + // instruction is handled, because we need to know the output shape of a + // fusion or while before propagating inside its computations. + bool postpone_processing_called_computations = false; + auto cleaner = tensorflow::gtl::MakeCleanup( + [this, hlo, &postpone_processing_called_computations] { + if (!postpone_processing_called_computations) { + if (hlo->opcode() == HloOpcode::kFusion) { + DetermineAndMutateFusionComputationPrecision(hlo); + } else if (hlo->opcode() == HloOpcode::kWhile) { + DetermineAndMutateWhileComputationsPrecision(hlo); + } + } + instructions_visited_in_mutation_pass_.insert(hlo); + }); + + if (hlo->opcode() == HloOpcode::kWhile && + (caller_counts_[hlo->while_condition()] > 1 || + caller_counts_[hlo->while_body()] > 1)) { + postpone_processing_called_computations = true; + return; + } + + // Do not change precision for instructions related to entry and exit of a + // computation, and control flow, because this pass might break the interfaces + // or assumptions for them. + if (hlo->opcode() == HloOpcode::kInfeed || // + hlo->opcode() == HloOpcode::kOutfeed || // + hlo->opcode() == HloOpcode::kSend || // + hlo->opcode() == HloOpcode::kSendDone || // + hlo->opcode() == HloOpcode::kRecv || // + hlo->opcode() == HloOpcode::kRecvDone || // + hlo->opcode() == HloOpcode::kCustomCall || // + hlo->opcode() == HloOpcode::kCall || // + hlo->opcode() == HloOpcode::kConditional || // + (hlo->opcode() == HloOpcode::kParameter && skip_parameters)) { + return; + } + + // Prevent root instructions from having their output modified by recording + // all F32 output values as needing to stay as F32. + CHECK(hlo->parent() != nullptr); + if (hlo == hlo->parent()->root_instruction()) { + if (!hlo->parent()->IsFusionComputation()) { + ShapeUtil::ForEachSubshape(hlo->shape(), [&](const Shape& subshape, + const ShapeIndex& index) { + if (subshape.element_type() != F32) { + return; + } + for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { + // Since we use HloValues from the dataflow analysis, this can also + // affect HLO instructions beyond the root, e.g., if the root is a + // Tuple HLO, then its operands are also affected. + values_that_must_be_kept_as_f32_.insert(value); + } + }); + } + return; + } + + if (!ContainsKey(consider_using_bfloat16_, hlo)) { + return; + } + + if (!bfloat16_support_->SupportsBF16Output(*hlo)) { + return; + } + + ShapeUtil::ForEachMutableSubshape( + hlo->mutable_shape(), + [hlo, this](Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() == F32 && + AllUsersConsumeBF16(*hlo, index)) { + subshape->set_element_type(BF16); + changed_ = true; + VLOG(2) << "HloInstruction output at shape index " << index + << " changed to BF16 precision: " << hlo->ToString(); + } + }); +} + +bool BFloat16Propagation::InstructionIsCandidateForBF16Output( + HloInstruction* hlo) { + if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) && + hlo->opcode() != HloOpcode::kTuple && + hlo->opcode() != HloOpcode::kGetTupleElement && + hlo->shape().element_type() != BF16) { + for (int64 i = 0; i < hlo->operand_count(); ++i) { + if (!bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, + i) || + !ContainsKey(consider_using_bfloat16_, hlo->operand(i))) { + return false; + } + } + } + return true; +} + +void BFloat16Propagation::AdjustCalledComputationParameters( + HloInstruction* hlo) { + auto adjust_computation = + [this, hlo](HloComputation* computation, + tensorflow::gtl::ArraySlice operands) { + // Adjust parameters. + CHECK_EQ(operands.size(), computation->num_parameters()); + for (int64 i = 0; i < operands.size(); ++i) { + auto parameter = computation->parameter_instruction(i); + ShapeUtil::ForEachMutableSubshape( + parameter->mutable_shape(), + [this, i, hlo, &operands, parameter](Shape* subshape, + const ShapeIndex& index) { + if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) { + return; + } + PrimitiveType operand_type = + ShapeUtil::GetSubshape(operands[i]->shape(), index) + .element_type(); + if (subshape->element_type() == operand_type) { + return; + } + CHECK(operand_type == F32 || operand_type == BF16); + subshape->set_element_type(operand_type); + changed_ = true; + VLOG(2) << "Called computation parameter " + << parameter->ToString() << " at shape index " << index + << " adjusted to match operand in HLO " + << hlo->ToString(); + }); + } + }; + + switch (hlo->opcode()) { + case HloOpcode::kFusion: + adjust_computation(hlo->fused_instructions_computation(), + hlo->operands()); + break; + case HloOpcode::kWhile: + adjust_computation(hlo->while_condition(), hlo->operands()); + adjust_computation(hlo->while_body(), hlo->operands()); + break; + default: + break; + } +} + +void BFloat16Propagation::AdjustCalledComputationRoot(HloInstruction* hlo) { + auto adjust_computation = [this, hlo](HloComputation* computation, + const Shape& output_shape) { + // Adjust root. + HloInstruction* root = computation->root_instruction(); + ShapeUtil::ForEachMutableSubshape( + root->mutable_shape(), [this, hlo, root, &output_shape]( + Shape* subshape, const ShapeIndex& index) { + if (!ShapeUtil::IsLeafIndex(hlo->shape(), index)) { + return; + } + const PrimitiveType output_type = + ShapeUtil::GetSubshape(output_shape, index).element_type(); + if (subshape->element_type() == output_type) { + return; + } + CHECK(output_type == F32 || output_type == BF16); + subshape->set_element_type(output_type); + // It's possible that output_type is F32, but the root instruction's + // type is BF16; e.g., a fusion node's output was changed to BF16 + // initially but then adjusted back to F32, and the fusion computation + // is now being adjusted after the fusion node. + if (output_type == F32) { + for (const auto* value : + dataflow_->GetValueSet(root, index).values()) { + // We rely on the fact that this adjustment works in reverse + // topological order so that called computation will be + // processed later. Adding the value to + // values_that_must_be_kept_as_f32_ will ensure the + // correctness of the adjustment for HLOs that will be + // processed later. + values_that_must_be_kept_as_f32_.insert(value); + } + } + changed_ = true; + VLOG(2) << "Called computation root " << root->ToString() + << " at shape index " << index + << " adjusted to match output shape of " << hlo->ToString(); + }); + }; + + switch (hlo->opcode()) { + case HloOpcode::kFusion: + adjust_computation(hlo->fused_instructions_computation(), hlo->shape()); + break; + case HloOpcode::kWhile: + adjust_computation(hlo->while_condition(), hlo->shape()); + adjust_computation(hlo->while_body(), hlo->shape()); + break; + default: + break; + } +} + +bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( + HloComputation* computation, + tensorflow::gtl::FlatSet* visited_computations) { + bool parameter_changed = false; + auto insts = computation->MakeInstructionPostOrder(); + // Do the adjustment on each instruction in the computation in reverse + // topological order. + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + auto hlo = *inst_it; + auto adjust_hlo_output = [this, hlo, ¶meter_changed]( + Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() != F32 && subshape->element_type() != BF16) { + return; + } + PrimitiveType type = BF16; + for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { + if (value->shape().element_type() == BF16) { + continue; + } + CHECK_EQ(value->shape().element_type(), F32); + type = F32; + break; + } + // It's possible that a user has been changed from BF16 to F32 + // during this final adjustment pass, so we need to check + // AllUsersConsumeBF16() again. + if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) { + type = F32; + } + if (type == F32) { + for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { + // We rely on the fact that this adjustment works in reverse + // topological order. Adding the value to + // values_that_must_be_kept_as_f32_ will ensure the correctness + // of the adjustment for HLOs that will be processed later. + values_that_must_be_kept_as_f32_.insert(value); + } + } + if (type != subshape->element_type()) { + subshape->set_element_type(type); + VLOG(2) << "HloInstruction output at shape index " << index + << " adjusted to " << *subshape << ": " << hlo->ToString(); + if (hlo->opcode() == HloOpcode::kParameter) { + parameter_changed = true; + } + } + }; + ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_hlo_output); + AdjustCalledComputationRoot(hlo); + if (hlo->opcode() == HloOpcode::kWhile) { + // We need to run on the while body and condition repeatedly until a fixed + // point is reached, i.e., the parameters do not change any more. We may + // need more than one iteration because the while input and output alias + // each other, so changing one input parameter requires changing the + // corresponding output element and thus may transitively require changing + // another input parameter. A fixed point will be reached because the + // parameters can only be changed from BF16 to F32, not the other way + // around. + tensorflow::gtl::FlatSet visited_in_while; + while (ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_condition(), + &visited_in_while) || + ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_body(), + &visited_in_while)) { + visited_in_while.clear(); + ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), + adjust_hlo_output); + AdjustCalledComputationRoot(hlo); + } + visited_computations->insert(visited_in_while.begin(), + visited_in_while.end()); + } + } + // Now adjust parameters of called computations. + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + AdjustCalledComputationParameters(*inst_it); + } + return parameter_changed; +} + +Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers( + HloModule* module) { + std::list computations_topological_order = + module->MakeComputationPostOrder(); + tensorflow::gtl::FlatSet resolved; + for (auto comp_it = computations_topological_order.rbegin(); + comp_it != computations_topological_order.rend(); ++comp_it) { + if (ContainsKey(resolved, *comp_it)) { + continue; + } + ResolveInconsistencyOfAliasingBuffersHelper(*comp_it, &resolved); + } + + // We could have changed a fusion computation's root shape to have a different + // precision than the fusion node's output, if the fusion root does not + // define a buffer (e.g., a tuple). Now we add conversions after such fusion + // roots to make them match the fusion output. If the fusion output is a + // (possibly nested) tuple, we first create get-tuple-elements, then convert + // the unmatching leaf nodes, and finally create a new tuple as the fusion + // computation's root. If tuples and get-tuple-elements are created, we will + // run tuple simplifier and dead code elimination at the end (dead code is not + // allowed in fusion computation). E.g., + // + // (1) (2) (3) + // a b a b a b + // |\ | |\ | |\ | + // \ add -> |add -> | add + // \ | \ | convert | + // tuple tuple \ | + // / \ tuple + // gte gte + // | | + // convert | + // \ / + // tuple + // (1) a is F32 but tuple is BF16 + // (2) after adding conversion + // (3) after tuple simplifier and DCE. + bool needs_tuple_simplifier = false; + for (auto computation : computations_topological_order) { + auto insts = computation->MakeInstructionPostOrder(); + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + auto hlo = *inst_it; + if (hlo->opcode() != HloOpcode::kFusion) { + continue; + } + auto fusion_computation = hlo->fused_instructions_computation(); + auto fusion_root = fusion_computation->root_instruction(); + if (ShapeUtil::Compatible(fusion_root->shape(), hlo->shape())) { + continue; + } + ShapeTree converted_outputs(hlo->shape()); + // Iterate through nodes in the shape tree in pre-order and initialize + // each non-root node with a corresponding get-tuple-element. For a leaf + // node, if its shape does not match the fusion output, create a + // conversion node to overwrite the node value. + for (auto it = converted_outputs.begin(); it != converted_outputs.end(); + ++it) { + ShapeIndex output_index = it->first; + HloInstruction*& output = it->second; + const Shape subshape = + ShapeUtil::GetSubshape(hlo->shape(), output_index); + if (output_index.empty()) { + output = fusion_root; + } else { + ShapeIndex parent_index = output_index; + parent_index.pop_back(); + output = fusion_computation->AddInstruction( + HloInstruction::CreateGetTupleElement( + subshape, converted_outputs.element(parent_index), + output_index.back())); + } + if (ShapeUtil::IsTuple(subshape)) { + continue; + } + if (!ShapeUtil::Compatible( + subshape, + ShapeUtil::GetSubshape(fusion_root->shape(), output_index))) { + output = fusion_computation->AddInstruction( + HloInstruction::CreateConvert(subshape, output)); + } + } + // Iterate through nodes in the shape tree in reverse pre-order and create + // a tuple instruction for each non-leaf node where the elements are the + // values of its child nodes. + for (auto it = converted_outputs.rbegin(); it != converted_outputs.rend(); + ++it) { + ShapeIndex output_index = it->first; + HloInstruction*& output = it->second; + const Shape& subshape = + ShapeUtil::GetSubshape(hlo->shape(), output_index); + if (!ShapeUtil::IsTuple(subshape)) { + continue; + } + std::vector elements( + ShapeUtil::TupleElementCount(subshape)); + ShapeIndex child_index = output_index; + for (int64 i = 0; i < elements.size(); ++i) { + child_index.push_back(i); + elements[i] = converted_outputs.element(child_index); + child_index.pop_back(); + } + output = fusion_computation->AddInstruction( + HloInstruction::CreateTuple(elements)); + } + fusion_computation->set_root_instruction(converted_outputs.element({})); + needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape()); + } + } + + // We may have converted some constants from F32 to BF16, so adjust the + // constant literals in such cases. We do this here instead of when the + // constant node's is changed because 1) the HloInstruction interface does not + // allow resetting the literal so we have to create a new kConstant + // instruction to replace the old one, which invalidates dataflow analysis, + // and 2) it's possible that a kConstant's output gets changed to BF16 at the + // beginning but later on adjusted back to F32, so converting literals here + // can avoid repeated conversions. + // + // TODO(b/73833576): Consider resetting literal in HloInstruction. + bool needs_dce = needs_tuple_simplifier; + for (auto computation : computations_topological_order) { + for (auto hlo : computation->MakeInstructionPostOrder()) { + if (hlo->opcode() != HloOpcode::kConstant) { + continue; + } + if (!ShapeUtil::Equal(hlo->literal().shape(), hlo->shape())) { + TF_ASSIGN_OR_RETURN(auto converted_literal, + hlo->literal().ConvertToShape(hlo->shape())); + auto new_constant = computation->AddInstruction( + HloInstruction::CreateConstant(std::move(converted_literal))); + TF_RETURN_IF_ERROR(hlo->ReplaceAllUsesWith(new_constant)); + needs_dce = true; + } + } + } + + if (needs_tuple_simplifier) { + TupleSimplifier tuple_simplifier; + TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); + } + if (needs_dce) { + HloDCE dce; + TF_RETURN_IF_ERROR(dce.Run(module).status()); + } + return Status::OK(); +} + +// The algorithm first does a forward pass (parameters to root) to determine a +// set of instructions to consider using bfloat16, then does a backward pass to +// determine the precisions of those instructions according to the need of +// their users. +StatusOr BFloat16Propagation::Run(HloModule* module) { + TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module)); + + std::list computations_topological_order = + module->MakeComputationPostOrder(); + // The first step is a forward pass (parameters to root), where we determine + // the potential candidate instructions to use bfloat16 in the outputs that + // are not likely to cause overhead from extra explicit conversions. This is + // done forwardly because we determine whether an HLO is a candidate partially + // based on whether its operands are candidates. + for (auto computation : computations_topological_order) { + for (auto inst : computation->MakeInstructionPostOrder()) { + if (InstructionIsCandidateForBF16Output(inst)) { + consider_using_bfloat16_.insert(inst); + } + } + } + + // The second step is a backward pass (root to parameters), where we modify + // the precisions of the instructions identified in the first step when + // feasible. This is done backwardly because we determine the precision of an + // HLO's output based on how it is later used. + // + // The precision of an instruction is determined by its users, so we do the + // propagation in reverse topological order. + for (auto comp_it = computations_topological_order.rbegin(); + comp_it != computations_topological_order.rend(); ++comp_it) { + if ((*comp_it)->IsFusionComputation()) { + // Fusion computations are handled when visiting the fusion instruction. + continue; + } + auto insts = (*comp_it)->MakeInstructionPostOrder(); + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, + /*skip_parameters=*/true); + } + } + + if (!changed_) { + return false; + } + + // It's possible that an instruction does not define a buffer, but the + // defining instruction's shape has changed. So we need to adjust the output + // shapes of instructions according to the HLO values they refer to. + TF_RETURN_IF_ERROR(ResolveInconsistencyOfAliasingBuffers(module)); + return true; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h new file mode 100644 index 0000000000000000000000000000000000000000..89a5ac5db1549877a135182ae8df57fa6bf9d579 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h @@ -0,0 +1,159 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_ + +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { + +// HLO pass which reduces the precision of some HLO instructions to BF16 +// according to the backend-specific BFloat16Support rule provided by the +// caller. +// +// This pass can be used to reduce instruction precision without affecting the +// numerical accuracy of the module, i.e., the final output of the module would +// be bitwise identical to that without this pass; this is possible if the +// backend already reduces precision to BF16 on some HLO instructions. +// +// This pass will not modify the signature of a computation, unless it is a +// fusion computation or its only caller is a while. +// +// !!! WARNING !!! This pass can introduce mixed precision in individual HLOs, +// which has two issues: +// +// 1) It does not guarantee to respect the passed-in BFloat16Support +// specification in terms of mixed precision, so the backend may not support an +// HLO that has mixed precision produced by this pass. To address this issue, +// run BFloat16Normalization with the same BFloat16Support after this pass. +// +// 2) In general, mixed precision may break the assumptions of some other HLO +// passes even if the specific backend supports the individual HLOs. Such +// assumptions include that there are no HLOs using mixed precision, or that the +// precision of an HLO's output is determined by its inputs. It should be used +// at the end of the HLO optimization pipeline but before +// BFloat16ConversionFolding. If other passes are needed after this pass, run +// BFloat16MixedPrecisionRemoval first to undo some of the changes made by this +// pass. +class BFloat16Propagation : public HloPassInterface { + public: + explicit BFloat16Propagation(const BFloat16Support* bfloat16_support); + + ~BFloat16Propagation() override = default; + + tensorflow::StringPiece name() const override { + return "bfloat16-propagation"; + } + + // Runs the pass on the given module. Returns whether the module was changed + // (precision reductions were added). + StatusOr Run(HloModule* module) override; + + private: + // *************************** + // Function called and state produced by the forward analysis pass (from + // parameters to root) that determines the candidate HLOs to use BF16 outputs. + + // Determines whether we should consider changing the precision of the given + // instruction in the forward pass. + bool InstructionIsCandidateForBF16Output(HloInstruction* hlo); + + // The set of instructions to consider using bfloat16, computed in the forward + // pass. + tensorflow::gtl::FlatSet consider_using_bfloat16_; + + // *************************** + // Functions called and state produced by the backward mutation pass (from + // root to parameters). + + // Determines the precision for the given instruction in the mutation pass. + void DetermineAndMutateInstructionPrecision(HloInstruction* hlo, + bool skip_parameters); + + // Special handling in the mutation pass for fusion computations. + // + // Precondition: hlo->opcode() == kFusion + void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion); + + // Special handling in the mutation pass for while computations. + // + // Precondition: hlo->opcode() == kWhile + void DetermineAndMutateWhileComputationsPrecision(HloInstruction* while_hlo); + + // The set of HloInstructions that have been visited in the mutation pass. + tensorflow::gtl::FlatSet + instructions_visited_in_mutation_pass_; + + // The set of HloComputations that have been visited in the mutation pass. + tensorflow::gtl::FlatSet + computations_visited_in_mutation_pass_; + + // *************************** + // Functions called by the final inconsistency resolving pass. + + // Adjusts the output shapes of HloInstructions such that if two + // HloInstructions have aliasing buffers in their outputs, they must have the + // same precision. + Status ResolveInconsistencyOfAliasingBuffers(HloModule* module); + + // Resolves inconsistency of aliasing buffers for the given computation, and + // recursively runs on a while instruction's condition and body until a fixed + // point is reached. + bool ResolveInconsistencyOfAliasingBuffersHelper( + HloComputation* computation, + tensorflow::gtl::FlatSet* visited_computations); + + // Makes the parameters of called computations match how they are called by + // the given HLO. + void AdjustCalledComputationParameters(HloInstruction* hlo); + + // Makes the root instructions of called computations match how they are used + // by the given HLO. + void AdjustCalledComputationRoot(HloInstruction* hlo); + + // *************************** + // Functions called and state used by two or more passes. + + // Returns whether all uses of the given HloInstruction can consume BF16 + // input. + bool AllUsersConsumeBF16(const HloInstruction& hlo, + const ShapeIndex& index) const; + + // The set of F32 HLO values that must be kept in F32. + tensorflow::gtl::FlatSet values_that_must_be_kept_as_f32_; + + // Mapping from each HloComputation to the number of callers to it in the + // module. Populated at the beginning of this pass. + tensorflow::gtl::FlatMap caller_counts_; + + const BFloat16Support* bfloat16_support_; + std::unique_ptr dataflow_; + + bool changed_ = false; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_ diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..5950b004b3da439c442eec6e5e09ea2307fcb018 --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -0,0 +1,620 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_propagation.h" +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +// A class specifying the BF16 support used to test the propagation pass. It +// specifies that BF16 and mixed precision are supported in all HloInstructions, +// and that kDot reduces its operands precision to BF16. +class TestBFloat16Support : public BFloat16Support { + public: + TestBFloat16Support() {} + ~TestBFloat16Support() override {} + + bool SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const override { + return true; + } + + bool SupportsBF16Output(const HloInstruction& hlo) const override { + return true; + } + + bool SupportsMixedPrecisions(const HloInstruction& hlo) const override { + return true; + } + + bool EffectiveOperandPrecisionIsBF16(const HloInstruction& hlo, + int64 operand_index) const override { + return hlo.opcode() == HloOpcode::kDot; + } +}; + +class BFloat16PropagationTest : public HloTestBase { + protected: + // Runs the propagation pass on the given module, and returns whether the + // module is changed after this pass. + bool PropagatePrecision(HloModule* module) { + TestBFloat16Support bfloat16_support; + BFloat16Propagation propagation(&bfloat16_support); + StatusOr result = propagation.Run(module); + EXPECT_IS_OK(result.status()); + return result.ValueOrDie(); + } + + // Returns whether the given HloInstruction's output element type is BF16 or + // the only use of it is converting to BF16. + bool OutputsBF16(const HloInstruction* inst) { + if (inst->shape().element_type() == BF16) { + return true; + } + return inst->user_count() == 1 && + inst->users()[0]->opcode() == HloOpcode::kConvert && + inst->users()[0]->shape().element_type() == BF16; + } +}; + +// Tests that BF16 can propagate through select over non-tuple buffers, but not +// through add where reducing operand precision can affect the result. +TEST_F(BFloat16PropagationTest, PropagateThroughSelectButNotAdd) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* c = + builder.AddInstruction(HloInstruction::CreateParameter(2, shape, "c")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, add0, b)); + HloInstruction* pred = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kEq, a, b)); + HloInstruction* sel = builder.AddInstruction( + HloInstruction::CreateTernary(shape, HloOpcode::kSelect, pred, c, add1)); + HloInstruction* xpose = + builder.AddInstruction(HloInstruction::CreateTranspose( + ShapeUtil::MakeShape(F32, {4, 2}), sel, {1, 0})); + HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, xpose, a)); + HloInstruction* root = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, dot, dot)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), root); + EXPECT_TRUE(OutputsBF16(xpose)); + EXPECT_TRUE(OutputsBF16(sel)); + EXPECT_TRUE(OutputsBF16(add1)); + EXPECT_FALSE(OutputsBF16(add0)); + EXPECT_FALSE(OutputsBF16(a)); + EXPECT_FALSE(OutputsBF16(b)); + EXPECT_FALSE(OutputsBF16(c)); +} + +// Tests that if a constant is converted to BF16 then its literal must also be +// converted. +TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + Array2D array_a(4, 4); + array_a.FillUnique(1.0f); + Array2D array_b(4, 4); + array_b.FillUnique(10.0f); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromArray(array_a))); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromArray(array_b))); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, a, b)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(dot->operand(0))); + EXPECT_TRUE(OutputsBF16(dot->operand(1))); + EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant); + EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant); + LiteralTestUtil::ExpectEqual( + dot->operand(0)->literal(), + *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_a))); + LiteralTestUtil::ExpectEqual( + dot->operand(1)->literal(), + *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_b))); +} + +// Tests that BF16 can be propagated through nested tuples. +TEST_F(BFloat16PropagationTest, PropagateThroughTuples) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, a)); + HloInstruction* add2 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, b, b)); + HloInstruction* xpose = + builder.AddInstruction(HloInstruction::CreateTranspose( + ShapeUtil::MakeShape(F32, {4, 2}), add1, {1, 0})); + + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1, add2})); + HloInstruction* tuple1 = + builder.AddInstruction(HloInstruction::CreateTuple({tuple0, xpose})); + + HloInstruction* lhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(xpose->shape(), tuple1, 1)); + HloInstruction* rhs = + builder.AddInstruction(HloInstruction::CreateGetTupleElement( + add0->shape(), + builder.AddInstruction(HloInstruction::CreateGetTupleElement( + tuple0->shape(), tuple1, 0)), + 0)); + HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, lhs, rhs)); + + HloInstruction* output_tuple = + builder.AddInstruction(HloInstruction::CreateTuple({dot, add2})); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), output_tuple); + EXPECT_TRUE(OutputsBF16(xpose)); + EXPECT_TRUE(OutputsBF16(add0)); + EXPECT_TRUE(OutputsBF16(add1)); + EXPECT_FALSE(OutputsBF16(add2)); +} + +// Tests that even if an instruction does not define a buffer in its output, its +// shape must match the defining instruction. +TEST_F(BFloat16PropagationTest, SameValueReferencedTwice) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, a)); + + HloInstruction* lhs = builder.AddInstruction(HloInstruction::CreateTranspose( + ShapeUtil::MakeShape(F32, {4, 2}), add1, {1, 0})); + + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1})); + HloInstruction* rhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(add1->shape(), tuple, 1)); + + // lhs is the transpose of add1, and rhs is a get-tuple-element aliasing add1. + HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, lhs, rhs)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(add0)); + EXPECT_TRUE(OutputsBF16(add1)); + EXPECT_TRUE(OutputsBF16(lhs)); + // rhs is a get-tuple-element, which does not define a buffer, but its shape + // should also be adjusted accordingly. + EXPECT_TRUE(OutputsBF16(rhs)); +} + +// Tests that a non-fusion computation's root should not be changed. +TEST_F(BFloat16PropagationTest, DoNotChangeComputationRoot) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b)); + + HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, add, add)); + + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({add, dot})); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), tuple); + EXPECT_FALSE(OutputsBF16(add)); +} + +// Tests that BF16 is propagated properly through fused computations. +TEST_F(BFloat16PropagationTest, PropagateThroughFusion) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + + auto builder_f0 = HloComputation::Builder("fusion0"); + HloInstruction* a_f0 = + builder_f0.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b_f0 = + builder_f0.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* tuple_f0 = + builder_f0.AddInstruction(HloInstruction::CreateTuple({a_f0, b_f0})); + auto comp_f0 = module->AddEmbeddedComputation(builder_f0.Build()); + auto fusion0 = builder.AddInstruction(HloInstruction::CreateFusion( + tuple_f0->shape(), HloInstruction::FusionKind::kCustom, {add, add}, + comp_f0)); + + auto builder_f1 = HloComputation::Builder("fusion1"); + HloInstruction* p_f1 = builder_f1.AddInstruction( + HloInstruction::CreateParameter(0, tuple_f0->shape(), "param")); + HloInstruction* a_f1 = builder_f1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, p_f1, 0)); + HloInstruction* b_f1 = builder_f1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, p_f1, 1)); + HloInstruction* dot = builder_f1.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, a_f1, b_f1)); + auto comp_f1 = module->AddEmbeddedComputation(builder_f1.Build()); + auto fusion1 = builder.AddInstruction(HloInstruction::CreateFusion( + dot->shape(), HloInstruction::FusionKind::kCustom, {fusion0}, comp_f1)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), fusion1); + EXPECT_TRUE(OutputsBF16(add)); + EXPECT_TRUE(OutputsBF16(a_f0)); + EXPECT_TRUE(OutputsBF16(b_f0)); + EXPECT_TRUE(OutputsBF16(a_f1)); + EXPECT_TRUE(OutputsBF16(b_f1)); +} + +// Tests that if 1) the root instruction of a fusion is a tuple, 2) the fusion +// outputs are only used by a dot, and 3) one element of the tuple is used by +// an add in the fusion computation, then the propagation pass should create a +// convert in the fusion computation to keep the add's operand in F32 but change +// the fusion output to BF16. E.g., the following fusion computation +// (F32, F32) fusion_computation(F32 a, F32 b) +// = tuple(F32 a, F32 add(F32 a, F32 b)) +// will be changed to +// (BF16, BF16) fusion_computation(F32 a, F32 b) +// = tuple(BF16 convert(a), BF16 add(F32 a, F32 b)) +TEST_F(BFloat16PropagationTest, ConvertTupleFusionElementIfUsedByAdd) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + + auto builder_f = HloComputation::Builder("fusion0"); + HloInstruction* a_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* add_f = builder_f.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a_f, b_f)); + HloInstruction* tuple_f = + builder_f.AddInstruction(HloInstruction::CreateTuple({a_f, add_f})); + auto comp_f = module->AddEmbeddedComputation(builder_f.Build()); + auto fusion = builder.AddInstruction(HloInstruction::CreateFusion( + tuple_f->shape(), HloInstruction::FusionKind::kCustom, {add, add}, + comp_f)); + + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, fusion, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, fusion, 1)); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, gte0, gte1)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(gte0)); + EXPECT_TRUE(OutputsBF16(gte1)); + EXPECT_FALSE(OutputsBF16(a_f)); + EXPECT_FALSE(OutputsBF16(b_f)); + EXPECT_TRUE(OutputsBF16(add_f)); + auto new_fusion_root = comp_f->root_instruction(); + EXPECT_EQ(new_fusion_root->opcode(), HloOpcode::kTuple); + EXPECT_EQ(new_fusion_root->operand(1), add_f); + EXPECT_EQ(new_fusion_root->operand(0)->opcode(), HloOpcode::kConvert); + EXPECT_TRUE(OutputsBF16(new_fusion_root->operand(0))); +} + +// A select over tuples does not define the leaf buffers, so the types in +// on_true and on_false must match, so that as long as one of them is F32, the +// other must be F32 as well. +TEST_F(BFloat16PropagationTest, SelectOverTuples) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param")); + HloInstruction* pred = builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(PRED, {}), "pred")); + + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, add0, param)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({param, add0})); + HloInstruction* tuple1 = + builder.AddInstruction(HloInstruction::CreateTuple({param, add1})); + HloInstruction* sel = builder.AddInstruction(HloInstruction::CreateTernary( + tuple0->shape(), HloOpcode::kSelect, pred, tuple0, tuple1)); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, sel, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, sel, 1)); + HloInstruction* xpose = + builder.AddInstruction(HloInstruction::CreateTranspose( + ShapeUtil::MakeShape(F32, {4, 2}), gte0, {1, 0})); + HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, xpose, gte1)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_FALSE(OutputsBF16(add0)); + EXPECT_FALSE(OutputsBF16(add1)); + EXPECT_FALSE(OutputsBF16(gte0)); + EXPECT_FALSE(OutputsBF16(gte1)); + EXPECT_TRUE(OutputsBF16(xpose)); +} + +// Tests that BF16 is propagated properly through while computations. +TEST_F(BFloat16PropagationTest, PropagateThroughWhile) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1})); + + auto builder_cond = HloComputation::Builder("cond"); + auto cond_param = builder_cond.AddInstruction( + HloInstruction::CreateParameter(0, tuple->shape(), "cond_param")); + auto cond_lhs = builder_cond.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond_param, 0)); + auto cond_rhs = builder_cond.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond_param, 1)); + // This add should prevent RHS from using BF16 + auto cond_add_rhs = builder_cond.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, cond_rhs, cond_rhs)); + auto cond_dot = builder_cond.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond_lhs, cond_add_rhs)); + builder_cond.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond = module->AddEmbeddedComputation(builder_cond.Build()); + + auto builder_body = HloComputation::Builder("body"); + auto body_param = builder_body.AddInstruction( + HloInstruction::CreateParameter(0, tuple->shape(), "body_param")); + auto body_lhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 0)); + auto body_rhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 1)); + auto body_dot = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + builder_body.AddInstruction( + HloInstruction::CreateTuple({body_dot, body_rhs})); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while_hlo = builder.AddInstruction( + HloInstruction::CreateWhile(tuple->shape(), cond, body, tuple)); + + auto lhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while_hlo, 0)); + auto rhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while_hlo, 1)); + auto dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(lhs)); + EXPECT_FALSE(OutputsBF16(rhs)); + EXPECT_TRUE(OutputsBF16(body_dot)); + EXPECT_TRUE(OutputsBF16(body_lhs)); + EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_TRUE(OutputsBF16(cond_lhs)); + EXPECT_FALSE(OutputsBF16(cond_rhs)); + EXPECT_TRUE(OutputsBF16(add0)); + EXPECT_FALSE(OutputsBF16(add1)); +} + +// Tests that BF16 is not propagated through multiple whiles that invoke the +// same computation as long as one while prevents the propagation. +TEST_F(BFloat16PropagationTest, DoNotPropagateWhilesCallingSameComputation) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add2 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add3 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1})); + HloInstruction* tuple1 = + builder.AddInstruction(HloInstruction::CreateTuple({add2, add3})); + + // Condition computation for the first while. + auto builder_cond0 = HloComputation::Builder("cond0"); + auto cond0_param = builder_cond0.AddInstruction( + HloInstruction::CreateParameter(0, tuple0->shape(), "cond0_param")); + auto cond0_lhs = builder_cond0.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond0_param, 0)); + auto cond0_rhs = builder_cond0.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond0_param, 1)); + // This add should prevent RHS from using BF16 + auto cond0_add_rhs = + builder_cond0.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, cond0_rhs, cond0_rhs)); + auto cond0_dot = builder_cond0.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond0_lhs, cond0_add_rhs)); + builder_cond0.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond0.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond0_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond0.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond0_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond0 = module->AddEmbeddedComputation(builder_cond0.Build()); + + // Condition computation for the second while. + auto builder_cond1 = HloComputation::Builder("cond1"); + auto cond1_param = builder_cond1.AddInstruction( + HloInstruction::CreateParameter(0, tuple1->shape(), "cond1_param")); + auto cond1_lhs = builder_cond1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond1_param, 0)); + auto cond1_rhs = builder_cond1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond1_param, 1)); + // This add should prevent LHS from using BF16 + auto cond1_add_lhs = + builder_cond1.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, cond1_lhs, cond1_lhs)); + auto cond1_dot = builder_cond1.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond1_add_lhs, cond1_rhs)); + builder_cond1.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond1.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond1_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond1.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond1_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond1 = module->AddEmbeddedComputation(builder_cond1.Build()); + + // Body computation shared by both whiles. + auto builder_body = HloComputation::Builder("body"); + auto body_param = builder_body.AddInstruction( + HloInstruction::CreateParameter(0, tuple0->shape(), "body_param")); + auto body_lhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 0)); + auto body_rhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 1)); + auto body_dot = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + builder_body.AddInstruction( + HloInstruction::CreateTuple({body_dot, body_rhs})); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while0 = builder.AddInstruction( + HloInstruction::CreateWhile(tuple0->shape(), cond0, body, tuple0)); + auto while1 = builder.AddInstruction( + HloInstruction::CreateWhile(tuple1->shape(), cond1, body, tuple1)); + + auto lhs = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while0, 0)), + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while0, 1)))); + auto rhs = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while1, 0)), + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while1, 1)))); + auto dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + EXPECT_FALSE(OutputsBF16(body_dot)); + EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_FALSE(OutputsBF16(body_lhs)); + EXPECT_FALSE(OutputsBF16(cond0_lhs)); + EXPECT_FALSE(OutputsBF16(cond0_rhs)); + EXPECT_FALSE(OutputsBF16(cond1_lhs)); + EXPECT_FALSE(OutputsBF16(cond1_rhs)); + EXPECT_TRUE(OutputsBF16(cond0_add_rhs)); + EXPECT_TRUE(OutputsBF16(cond1_add_lhs)); + EXPECT_EQ(computation->root_instruction(), dot); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc new file mode 100644 index 0000000000000000000000000000000000000000..07b4b14b5ec1bdbc01345091105df69368b0b2fb --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_support.cc @@ -0,0 +1,112 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/bfloat16_support.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" + +namespace xla { + +bool BFloat16Support::SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const { + switch (hlo.opcode()) { + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kCustomCall: + case HloOpcode::kGetTupleElement: + case HloOpcode::kTuple: + case HloOpcode::kWhile: + return true; + case HloOpcode::kConvert: + CHECK_EQ(operand_index, 0); + return hlo.operand(0)->shape().element_type() == BF16; + default: + break; + } + return false; +} + +bool BFloat16Support::SupportsBF16Output(const HloInstruction& hlo) const { + switch (hlo.opcode()) { + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kCustomCall: + case HloOpcode::kGetTupleElement: + case HloOpcode::kTuple: + case HloOpcode::kWhile: + return true; + case HloOpcode::kConvert: + return hlo.shape().element_type() == BF16; + default: + break; + } + return false; +} + +bool BFloat16Support::SupportsMixedPrecisions(const HloInstruction& hlo) const { + switch (hlo.opcode()) { + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kConvert: + case HloOpcode::kCustomCall: + case HloOpcode::kGetTupleElement: + case HloOpcode::kTuple: + case HloOpcode::kWhile: + return true; + default: + break; + } + return false; +} + +/* static */ +bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( + const HloInstruction& hlo, int64 operand_index) { + switch (hlo.opcode()) { + case HloOpcode::kAbs: + case HloOpcode::kBroadcast: + case HloOpcode::kClamp: + case HloOpcode::kConcatenate: + case HloOpcode::kConvert: + case HloOpcode::kCopy: + case HloOpcode::kGetTupleElement: + case HloOpcode::kMaximum: + case HloOpcode::kMinimum: + case HloOpcode::kPad: + case HloOpcode::kReshape: + case HloOpcode::kReverse: + case HloOpcode::kSlice: + case HloOpcode::kSort: + case HloOpcode::kTranspose: + case HloOpcode::kTuple: + return true; + case HloOpcode::kDynamicSlice: + return operand_index == 0; + case HloOpcode::kDynamicUpdateSlice: + return operand_index == 0 || operand_index == 1; + case HloOpcode::kSelect: + return operand_index == 1 || operand_index == 2; + default: + break; + } + return false; +} + +bool BFloat16Support::EffectiveOperandPrecisionIsBF16( + const HloInstruction& hlo, int64 operand_index) const { + return false; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_support.h b/tensorflow/compiler/xla/service/bfloat16_support.h new file mode 100644 index 0000000000000000000000000000000000000000..82c2745f444e4f9c544c78cb36dafc11f678518a --- /dev/null +++ b/tensorflow/compiler/xla/service/bfloat16_support.h @@ -0,0 +1,60 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_SUPPORT_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_SUPPORT_H_ + +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" + +namespace xla { + +class BFloat16Support { + public: + BFloat16Support() {} + virtual ~BFloat16Support() {} + + // Returns whether the backend supports BF16 operand for the HLO instruction + // at the given index. + virtual bool SupportsBF16Operand(const HloInstruction& hlo, + int64 operand_index) const; + + // Returns whether the backend supports BF16 output for the HLO instruction. + virtual bool SupportsBF16Output(const HloInstruction& hlo) const; + + // Returns whether the backend support mixed precision: the operands, output, + // and parameters/output of the called computations can have different + // precisions (BF16 and F32). + virtual bool SupportsMixedPrecisions(const HloInstruction& hlo) const; + + // Returns whether the given HLO preserves its BF16 operand precision at the + // given index, so even if the output is F32, elements in the output that + // depend on the BF16 operand will still have BF16 effective precision even if + // they have F32 format. Similarly, this also means if the output is BF16 then + // increasing the operand precision from BF16 to F32 will not change the + // output. This typically includes HLOs that pass elements from the operand to + // the output without arithmetic operations. + static bool EffectiveOperandPrecisionIsOutputPrecision( + const HloInstruction& hlo, int64 operand_index); + + // Returns if the backend only uses BF16 precision for the operand at the + // specified index, even if the operand is F32. + virtual bool EffectiveOperandPrecisionIsBF16(const HloInstruction& hlo, + int64 operand_index) const; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_SUPPORT_H_ diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 774b11478c6d2faf0eb5db29df3cfd3cc1e98d5b..fb18c9d828708ce7f6606143e0e305f654b912fa 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -45,6 +45,185 @@ using ::tensorflow::gtl::FlatMap; using ::tensorflow::gtl::FlatSet; using ::tensorflow::strings::Appendf; using ::tensorflow::strings::HumanReadableNumBytes; +using ::tensorflow::strings::Printf; +using ::tensorflow::strings::StrAppend; + +namespace { + +template +string ColocatedBufferSetsToString(const T& container, const char* title) { + string result; + StrAppend(&result, title, "\n"); + for (const auto& it : container) { + StrAppend(&result, "\t", it->ToString(), "\n"); + } + return result; +} + +// Walk the call graph of the HLO module and place each computation into either +// thread_local_computations or global_computations depending upon whether the +// computation requires thread-local allocations or global allocations. The +// elements in thread_local_computations and global_computations are in post +// order (if computation A has an instruction which calls computation B, then A +// will appear after B in the vector). +Status GatherComputationsByAllocationType( + const HloModule* module, + std::vector* thread_local_computations, + std::vector* global_computations) { + // Create a worklist of computations paired with whether the allocation must + // be thread-local. + std::deque> worklist; + worklist.push_back(std::make_pair(module->entry_computation(), + /*is_thread_local*/ false)); + + // Sets for quickly checking membership. Computations are returned in vectors + // for stable iteration. + FlatSet thread_local_set; + FlatSet global_set; + + while (!worklist.empty()) { + auto worklist_front = worklist.front(); + worklist.pop_front(); + const HloComputation* computation = worklist_front.first; + bool is_thread_local = worklist_front.second; + bool in_thread_local_set = thread_local_set.count(computation) > 0; + bool in_global_set = global_set.count(computation) > 0; + + // If the computation has already been added to the respective set, then + // nothing to do. + if ((is_thread_local && in_thread_local_set) || + (!is_thread_local && in_global_set)) { + continue; + } + + // If the computation has already been added to the other set this is an + // error condition because the global call to the computation (eg, + // while/call) may return a reference to one of the thread-local buffers to + // the calling computation which will become a dangling reference when the + // thread-local is deallocated with the call return. + if ((is_thread_local && in_global_set) || + (!is_thread_local && in_thread_local_set)) { + return InvalidArgument( + "computation %s has conflicting allocation requirements (global " + "and thread-local)", + computation->name().c_str()); + } + + if (is_thread_local) { + thread_local_set.insert(computation); + } else { + global_set.insert(computation); + } + + for (auto* instruction : computation->instructions()) { + for (HloComputation* subcomputation : + instruction->called_computations()) { + switch (instruction->opcode()) { + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kWhile: + // Call and while must be called from a computation with global + // allocations as they may return references to buffers inside the + // called computation which cannot be thread-local. + if (is_thread_local) { + return InvalidArgument( + "computation %s cannot contain call/while op because it " + "requires thread-local buffer allocations", + computation->name().c_str()); + } + worklist.push_back(std::make_pair(subcomputation, + false)); // Not thread local. + break; + case HloOpcode::kMap: + case HloOpcode::kReduce: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: + case HloOpcode::kFusion: + // Map/reduce etc computations are always thread-local. + worklist.push_back(std::make_pair(subcomputation, + true)); // Thread local. + break; + default: + return InternalError( + "Unexpected calling opcode: %s", + HloOpcodeString(instruction->opcode()).c_str()); + } + } + } + } + + // Add the computations to the vectors in post order. + for (auto* computation : module->MakeComputationPostOrder()) { + if (thread_local_set.count(computation) > 0) { + thread_local_computations->push_back(computation); + } else if (global_set.count(computation) > 0) { + global_computations->push_back(computation); + } + // If the computation is not reachable from the entry computation, then it + // will not appear in either thread_local_set or global_set. We don't bother + // assigning buffers for these. + } + return Status::OK(); +} + +// Checks that points-to set of 'instruction' is unambiguous and distinct +// (ensured by CopyInsertion), then adds the buffer from the points-to set at +// 'index' to 'colocated_set'. +const LogicalBuffer* AddBufferToColocatedSet( + const HloInstruction* instruction, const ShapeIndex& index, + const TuplePointsToAnalysis& points_to_analysis, + std::vector* colocated_set) { + // CopyInsertion ensures root points-to set is unambiguous and distinct. + const auto& points_to = points_to_analysis.GetPointsToSet(instruction); + DCHECK(!points_to.IsAmbiguous()); + colocated_set->push_back(points_to.element(index)[0]); + return colocated_set->back(); +} + +// Given the interference map of a graph (the list of interfering node indices +// for each node), perform graph coloring such that interfering nodes are +// assigned to different colors. Returns the assigned color of the nodes, where +// the colors are represented as integer values [0, color_count). +std::vector ColorInterferenceGraph( + const std::vector>& interference_map) { + const int64 node_count = interference_map.size(); + + // Sort the nodes such that we assign nodes with more interference first. This + // relies on the common heuristic of assigning the most constrained node + // first, but it would be good to investigate other ordering heuristics too. + std::vector nodes(node_count); + std::iota(nodes.begin(), nodes.end(), 0); + std::sort(nodes.begin(), nodes.end(), + [&interference_map](const int64 i, const int64 j) { + return interference_map[i].size() > interference_map[j].size(); + }); + + const int64 kColorUnassigned = -1; + std::vector assigned_colors(node_count, kColorUnassigned); + for (int64 node : nodes) { + // Mark the colors that are already assigned to the neighbors. + std::vector available_colors(node_count, true); + for (int64 neighbor : interference_map[node]) { + int64 color = assigned_colors[neighbor]; + if (color != kColorUnassigned) { + available_colors[color] = false; + } + } + + // Find the color that is not yet assigned to the neighbors. + int64 color = kColorUnassigned; + for (color = 0; color < available_colors.size(); ++color) { + if (available_colors[color]) { + break; + } + } + CHECK_NE(color, kColorUnassigned); + assigned_colors[node] = color; + } + return assigned_colors; +} + +} // namespace size_t BufferAllocation::Slice::Hasher::operator()(Slice s) const { uint64 h = std::hash()(s.index()); @@ -93,6 +272,9 @@ BufferAllocationProto BufferAllocation::ToProto() const { proto.set_color(color_.value()); if (is_entry_computation_parameter_) { proto.set_is_entry_computation_parameter(true); + for (int64 idx : param_shape_index()) { + proto.add_parameter_shape_index(idx); + } proto.set_parameter_number(parameter_number_); } proto.set_maybe_live_out(maybe_live_out_); @@ -110,27 +292,132 @@ BufferAllocationProto BufferAllocation::ToProto() const { return proto; } +std::pair> +BufferAllocation::ComputePeakMemoryLogicalBuffers() const { + if (HeapTraces().empty()) { + // Just return the largest LogicalBuffer in the allocation. + const LogicalBuffer* largest_buffer = nullptr; + int64 largest_size = 0; + for (const auto& pair : assigned_buffers()) { + const LogicalBuffer* buffer = pair.first; + int64 size = pair.second.size; + if (largest_buffer == nullptr) { + largest_buffer = buffer; + largest_size = size; + continue; + } + // Tie-break with LogicalBuffer::Id so the return value is stable relative + // to changing addresses. + if (size > largest_size || + ((size == largest_size) && (largest_buffer->id() > buffer->id()))) { + largest_buffer = buffer; + largest_size = size; + } + } + CHECK(largest_buffer != nullptr) + << "No logical buffers in allocation: " << ToString(); + return {largest_size, {largest_buffer}}; + } + + // Create a map from LogicalBuffer::Id to LogicalBuffer* for the logical + // buffers in this allocation. + tensorflow::gtl::FlatMap + id_to_buffer; + tensorflow::gtl::FlatMap buffer_sizes; + for (const auto& pair : assigned_buffers()) { + const LogicalBuffer* buffer = pair.first; + const OffsetSize& offset_size = pair.second; + id_to_buffer[buffer->id()] = buffer; + buffer_sizes[buffer] = offset_size.size; + } + + // Returns how much the given event increases the total size of live + // buffers. Can be negative. + auto memory_delta = [this, &id_to_buffer, &buffer_sizes]( + const HeapSimulatorTrace::Event& event) -> int64 { + const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id()); + const int64 buffer_size = buffer_sizes.at(buffer); + if (event.kind() == HeapSimulatorTrace::Event::ALLOC) { + return buffer_size; + } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) { + // Sharing a buffer does not change the live set size for the purposes of + // the heap simulator. Even though the shared-with buffer may be smaller, + // the entire allocation remains live. + return 0; + } else if (event.kind() == HeapSimulatorTrace::Event::FREE) { + return -1 * buffer_size; + } + LOG(FATAL) << "Unknown event kind: " << event.kind(); + }; + + int64 total_max_live_size = 0; + std::vector live_buffers_vector; + for (const HeapSimulatorTrace& heap_trace : HeapTraces()) { + // First compute the size of the maximal live set. + int64 max_live_size = 0; + int64 live_size = 0; + for (const auto& event : heap_trace.events()) { + live_size += memory_delta(event); + if (max_live_size < live_size) { + max_live_size = live_size; + } + } + + // Next gather the set of logical buffers live at the earliest point of + // maximal live set size. + tensorflow::gtl::FlatSet live_buffers; + live_size = 0; + for (const auto& event : heap_trace.events()) { + const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id()); + if (event.kind() == HeapSimulatorTrace::Event::ALLOC) { + InsertOrDie(&live_buffers, buffer); + } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) { + // Nothing to do. + } else if (event.kind() == HeapSimulatorTrace::Event::FREE) { + CHECK(ContainsKey(live_buffers, buffer)); + live_buffers.erase(buffer); + } + + live_size += memory_delta(event); + if (live_size == max_live_size) { + break; + } + } + CHECK_EQ(live_size, max_live_size); + total_max_live_size += max_live_size; + + live_buffers_vector.insert(live_buffers_vector.end(), live_buffers.begin(), + live_buffers.end()); + } + + // Stabily sort the live buffers. + std::sort(live_buffers_vector.begin(), live_buffers_vector.end(), + [](const LogicalBuffer* a, const LogicalBuffer* b) { + return a->id() < b->id(); + }); + return {total_max_live_size, live_buffers_vector}; +} + string BufferAllocation::ToString() const { string output; - tensorflow::strings::StrAppend( - &output, tensorflow::strings::Printf("allocation %lld: %p, size %lld", - index_, this, size())); + Appendf(&output, "allocation %lld: %p, size %lld", index_, this, size()); if (color().value() != 0) { - tensorflow::strings::StrAppend(&output, ", color ", color().value()); + StrAppend(&output, ", color ", color().value()); } if (is_entry_computation_parameter()) { - tensorflow::strings::StrAppend(&output, ", parameter ", parameter_number()); + StrAppend(&output, ", parameter ", parameter_number(), " at ShapeIndex ", + param_shape_index().ToString()); } if (is_thread_local()) { - tensorflow::strings::StrAppend(&output, ", thread-local"); + StrAppend(&output, ", thread-local"); } if (maybe_live_out()) { - tensorflow::strings::StrAppend(&output, ", maybe-live-out"); + StrAppend(&output, ", maybe-live-out"); } if (IsPreallocatedTempBuffer()) { - tensorflow::strings::StrAppend(&output, ", preallocated-temp"); + StrAppend(&output, ", preallocated-temp"); } - tensorflow::strings::StrAppend(&output, ":\n"); + StrAppend(&output, ":\n"); // Dump the assigned buffers ordered by id. std::vector sorted_buffers; for (const auto& buffer_offset_size : assigned_buffers_) { @@ -142,12 +429,11 @@ string BufferAllocation::ToString() const { }); for (const LogicalBuffer* buffer : sorted_buffers) { const OffsetSize& offset_size = FindOrDie(assigned_buffers_, buffer); - tensorflow::strings::StrAppend( - &output, - tensorflow::strings::Printf( - " %s [%lld,%lld]: %s\n", buffer->ToString().c_str(), - offset_size.offset, offset_size.size, - ShapeUtil::HumanStringWithLayout(buffer->shape()).c_str())); + StrAppend(&output, + tensorflow::strings::Printf( + " %s [%lld,%lld]: %s\n", buffer->ToString().c_str(), + offset_size.offset, offset_size.size, + ShapeUtil::HumanStringWithLayout(buffer->shape()).c_str())); } return output; } @@ -345,6 +631,7 @@ void BufferAssignment::AddAssignment(BufferAllocation* allocation, // Combines allocations of temporary buffers of the same color into one big // BufferAllocation. void BufferAssignment::CombineTempAllocations() { + VLOG(1) << "CombineTempAllocations()"; FlatMap combined_allocation_map; @@ -366,11 +653,16 @@ void BufferAssignment::CombineTempAllocations() { if (combined_it == combined_allocation_map.end()) { // We have found the first temp allocation of this color. Collect // the other temp allocations of the same color into it. + VLOG(1) << "Combined temp allocation for color " << color + << " is: " << temp_allocation; combined_allocation_map.emplace(color, temp_allocation); continue; } auto* combined_allocation = &combined_it->second; + VLOG(1) << "Combined allocation absorbing temp allocation: " + << temp_allocation; + // Each temp allocation is placed end-to-end, accounting for alignment. // The offset of each buffer in the combined allocation is computed from // the base offset of the allocation. @@ -384,6 +676,10 @@ void BufferAssignment::CombineTempAllocations() { const int64 size = buffer_offset_size.second.size; combined_allocation->AddAssignment(*buffer, base + offset, size); } + if (!temp_allocation.HeapTraces().empty()) { + CHECK_EQ(temp_allocation.HeapTraces().size(), 1); + combined_allocation->AddHeapTrace(temp_allocation.HeapTraces().front()); + } } // Replace all existing temporary allocations with the new combined // allocations. @@ -513,123 +809,13 @@ BufferAssignmentProto BufferAssignment::ToProto() const { for (const BufferAllocation& allocation : Allocations()) { BufferAllocationProto proto_allocation = allocation.ToProto(); proto.add_buffer_allocations()->Swap(&proto_allocation); - } - for (const HeapSimulatorTrace& trace : heap_simulator_traces_) { - *proto.add_heap_simulator_traces() = trace; - } - return proto; -} - -namespace { - -// Walk the call graph of the HLO module and place each computation into either -// thread_local_computations or global_computations depending upon whether the -// computation requires thread-local allocations or global allocations. The -// elements in thread_local_computations and global_computations are in post -// order (if computation A has an instruction which calls computation B, then A -// will appear after B in the vector). -Status GatherComputationsByAllocationType( - const HloModule* module, - std::vector* thread_local_computations, - std::vector* global_computations) { - // Create a worklist of computations paired with whether the allocation must - // be thread-local. - std::deque> worklist; - worklist.push_back(std::make_pair(module->entry_computation(), - /*is_thread_local*/ false)); - - // Sets for quickly checking membership. Computations are returned in vectors - // for stable iteration. - FlatSet thread_local_set; - FlatSet global_set; - - while (!worklist.empty()) { - auto worklist_front = worklist.front(); - worklist.pop_front(); - const HloComputation* computation = worklist_front.first; - bool is_thread_local = worklist_front.second; - bool in_thread_local_set = thread_local_set.count(computation) > 0; - bool in_global_set = global_set.count(computation) > 0; - - // If the computation has already been added to the respective set, then - // nothing to do. - if ((is_thread_local && in_thread_local_set) || - (!is_thread_local && in_global_set)) { - continue; - } - - // If the computation has already been added to the other set this is an - // error condition because the global call to the computation (eg, - // while/call) may return a reference to one of the thread-local buffers to - // the calling computation which will become a dangling reference when the - // thread-local is deallocated with the call return. - if ((is_thread_local && in_global_set) || - (!is_thread_local && in_thread_local_set)) { - return InvalidArgument( - "computation %s has conflicting allocation requirements (global " - "and thread-local)", - computation->name().c_str()); - } - - if (is_thread_local) { - thread_local_set.insert(computation); - } else { - global_set.insert(computation); - } - - for (auto* instruction : computation->instructions()) { - for (HloComputation* subcomputation : - instruction->called_computations()) { - switch (instruction->opcode()) { - case HloOpcode::kCall: - case HloOpcode::kConditional: - case HloOpcode::kWhile: - // Call and while must be called from a computation with global - // allocations as they may return references to buffers inside the - // called computation which cannot be thread-local. - if (is_thread_local) { - return InvalidArgument( - "computation %s cannot contain call/while op because it " - "requires thread-local buffer allocations", - computation->name().c_str()); - } - worklist.push_back(std::make_pair(subcomputation, - false)); // Not thread local. - break; - case HloOpcode::kMap: - case HloOpcode::kReduce: - case HloOpcode::kReduceWindow: - case HloOpcode::kSelectAndScatter: - case HloOpcode::kFusion: - // Map/reduce etc computations are always thread-local. - worklist.push_back(std::make_pair(subcomputation, - true)); // Thread local. - break; - default: - return InternalError( - "Unexpected calling opcode: %s", - HloOpcodeString(instruction->opcode()).c_str()); - } - } - } - } - - // Add the computations to the vectors in post order. - for (auto* computation : module->MakeComputationPostOrder()) { - if (thread_local_set.count(computation) > 0) { - thread_local_computations->push_back(computation); - } else if (global_set.count(computation) > 0) { - global_computations->push_back(computation); + for (const HeapSimulatorTrace& heap_trace : allocation.HeapTraces()) { + *proto.add_heap_simulator_traces() = heap_trace; } - // If the computation is not reachable from the entry computation, then it - // will not appear in either thread_local_set or global_set. We don't bother - // assigning buffers for these. } - return Status::OK(); + return proto; } -} // namespace - /* static */ StatusOr> BufferAssigner::Run( const HloModule* module, std::unique_ptr hlo_ordering, @@ -840,7 +1026,7 @@ Status BufferAssigner::AssignBuffersForComputation( /*is_thread_local=*/false, /*is_reusable=*/false); allocation->set_entry_computation_parameter( - instruction->parameter_number()); + instruction->parameter_number(), buffer->index()); VLOG(3) << "New allocation #" << allocation->index() << " for entry computation parameter: " << *buffer; continue; @@ -1061,7 +1247,8 @@ void BufferAssigner::AssignBuffersFromHeapSimulator( assignment->AddAssignment(allocation, buffer, chunk.offset, chunk.size); } - assignment->heap_simulator_traces_.push_back(result.debug_trace); + VLOG(1) << "Ran heap simulation for allocation: " << allocation->ToString(); + allocation->AddHeapTrace(result.debug_trace); } // Adds the 'colocated_set' of buffers to 'colocated_buffer_sets', maintaining @@ -1082,7 +1269,8 @@ void BufferAssigner::AddSetToColocatedBufferSets( if (colocated_set.empty()) { return; } - + VLOG(5) << ColocatedBufferSetsToString(colocated_set, + "Adding colocated buffer set"); // Find existing sets that overlap with at least one buffer from the // colocated_set. The resulting 'overlap_set_indices' will have at most // colocated_buffer_sets->size() entries, and will be in increasing order. @@ -1090,6 +1278,10 @@ void BufferAssigner::AddSetToColocatedBufferSets( for (size_t index = 0; index < colocated_buffer_sets->size(); ++index) { for (const LogicalBuffer* buffer : colocated_set) { if ((*colocated_buffer_sets)[index].count(buffer) > 0) { + VLOG(5) << "Found overlap with existing set on buffer " + << buffer->ToString() << "\n" + << ColocatedBufferSetsToString((*colocated_buffer_sets)[index], + "Overlapping set"); overlap_set_indices.push_back(index); break; } @@ -1101,6 +1293,7 @@ void BufferAssigner::AddSetToColocatedBufferSets( colocated_buffer_sets->emplace_back(); colocated_buffer_sets->back().insert(colocated_set.begin(), colocated_set.end()); + VLOG(5) << "No overlap found, new group created"; return; } @@ -1112,6 +1305,8 @@ void BufferAssigner::AddSetToColocatedBufferSets( first->insert(overlap_set.begin(), overlap_set.end()); } first->insert(colocated_set.begin(), colocated_set.end()); + VLOG(5) << ColocatedBufferSetsToString( + *first, "Result of the colocated buffer set merging"); // Remove overlap sets that we just merged. The offset accounts for the fact // that as elements are erased, the indices need to be adjusted. Keep in mind @@ -1122,158 +1317,85 @@ void BufferAssigner::AddSetToColocatedBufferSets( } } -// Conceptually the same as AddSetToColocatedBufferSets, but specific to the -// colocated buffers for while instructions. 'colocated_set' contains the -// buffers for a single while instruction that must be colocated. The idea here -// is to apply a memory-saving heuristic for separate while instructions whose -// buffers are disjoint in liveness, by using the colocation mechanism to force -// buffer sharing. This often reduces memory for multi-layer RNNs. -// -// TODO(b/32491382): We should be able to remove this heuristic after we -// implement module-level liveness analysis, which would let us directly detect -// buffer sharing opportunities between the while instruction buffer and the -// buffers from the predicate and body computation, as well as sharing across -// different while instructions. -void BufferAssigner::AddWhileSetToColocatedBufferSets( - const std::vector& colocated_set, - const LogicalBuffer* while_init_buffer, - const LogicalBuffer* while_result_buffer, const HloInstruction* while_hlo, - const HloComputation& computation, const BufferLiveness& buffer_liveness, - const LogicalBuffer::SizeFunction& buffer_size, - std::vector* colocated_buffer_sets) { - CHECK(!colocated_set.empty()); - const TuplePointsToAnalysis& points_to_analysis = - buffer_liveness.points_to_analysis(); - - // Parallel while loops cannot safely share colocated buffer sets. - if (buffer_liveness.hlo_ordering().SequentialOrder(computation) == nullptr) { - AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets); - return; - } - - // Scan 'colocated_buffer_sets' in reverse order for locality; colocated sets - // are added in postorder over computations and instructions. - const int64 init_buffer_size = buffer_size(*while_init_buffer); - const bool is_live_out = buffer_liveness.MaybeLiveOut(*while_result_buffer); - for (int i = colocated_buffer_sets->size() - 1; i >= 0; --i) { - const ColocatedBufferSet& predecessor_set = (*colocated_buffer_sets)[i]; - - // Skip predecessor sets not associated with while loops. - if (std::all_of(predecessor_set.begin(), predecessor_set.end(), - [](const LogicalBuffer* buffer) { - return buffer->instruction()->opcode() != - HloOpcode::kWhile; - })) { - continue; - } - - // Skip predecessor sets already associated with 'while_hlo'. - if (std::any_of(predecessor_set.begin(), predecessor_set.end(), - [&while_hlo](const LogicalBuffer* buffer) { - return buffer->instruction() == while_hlo; - })) { - continue; - } - - // Skip predecessor sets with entry parameter if the while result is live - // out. - if (is_live_out && - std::any_of(predecessor_set.begin(), predecessor_set.end(), - [](const LogicalBuffer* buffer) { - auto* instruction = buffer->instruction(); - auto* computation = instruction->parent(); - auto* module = computation->parent(); - return instruction->opcode() == HloOpcode::kParameter && - computation == module->entry_computation(); - })) { - continue; - } +std::vector +BufferAssigner::MergeColocatedBufferSets( + const std::vector& colocated_buffer_sets, + const BufferLiveness& buffer_liveness, + const LogicalBuffer::SizeFunction& buffer_size) { + VLOG(1) << "colocation sets count before coalescing:" + << colocated_buffer_sets.size(); + + // Returns true if the given buffer is for the entry parameter. + auto is_entry_parameter = [](const LogicalBuffer& buffer) { + auto* instruction = buffer.instruction(); + auto* computation = instruction->parent(); + auto* module = computation->parent(); + return instruction->opcode() == HloOpcode::kParameter && + computation == module->entry_computation(); + }; - // Build vector of predecessor while result and init buffers, which are - // checked for liveness interference below. We must check both the result - // and init buffers because they're aliased together, but - // TuplePointsToAnalysis is unaware of this aliasing. - std::vector predecessor_while_buffers; - for (const LogicalBuffer* buffer : predecessor_set) { - const HloInstruction* instruction = buffer->instruction(); - if (instruction->opcode() == HloOpcode::kWhile && - buffer_size(*buffer) == init_buffer_size && - instruction->parent() == &computation) { - predecessor_while_buffers.push_back(buffer); - // Add the init buffer at the same index, which must also exist in the - // predecessor set, and must be unambiguous. - const PointsToSet& init_points_to = - points_to_analysis.GetPointsToSet(instruction->operand(0)); - const auto& init_buffers = init_points_to.element(buffer->index()); - CHECK_EQ(init_buffers.size(), 1); - CHECK_GT(predecessor_set.count(init_buffers[0]), 0); - predecessor_while_buffers.push_back(init_buffers[0]); + // Returns true if the two colocated buffer sets (specified by their indices + // into the colocated_buffer_sets) can be merged into a single set. + auto cannot_merge_buffer_sets = [&colocated_buffer_sets, &buffer_liveness, + &buffer_size, + &is_entry_parameter](int64 i, int64 j) { + for (auto& buffer_a : colocated_buffer_sets[i]) { + for (auto& buffer_b : colocated_buffer_sets[j]) { + // Do not merge if the set includes live outs or entry parameters. + if (buffer_liveness.MaybeLiveOut(*buffer_a) || + is_entry_parameter(*buffer_a) || + buffer_liveness.MaybeLiveOut(*buffer_b) || + is_entry_parameter(*buffer_b)) { + return true; + } + // Do not merge if the buffers interfere with each other. + if (buffer_a->id() != buffer_b->id() && + buffer_liveness.MayInterfere(*buffer_a, *buffer_b)) { + return true; + } + // Do not merge if the buffer sizes are different. + if (buffer_size(*buffer_a) != buffer_size(*buffer_b)) { + return true; + } } } - if (predecessor_while_buffers.empty()) { - continue; - } - - // Skip predecessor set if the live range of any predecessor - // buffers overlaps with 'while_init_buffer' or - // 'while_result_buffer' (we need to check both since they're - // aliased together, but the points-to analysis is unaware of this - // aliasing). Note that tuple element buffer forwarding can cause - // the same buffer to appear on both sides of the interference - // comparison below. - auto may_interfere_with_init_or_result = [&](const LogicalBuffer* buffer) { - if (while_init_buffer->id() != buffer->id() && - buffer_liveness.MayInterfere(*while_init_buffer, *buffer)) { - return true; - } + return false; + }; - if (while_result_buffer->id() != buffer->id() && - buffer_liveness.MayInterfere(*while_result_buffer, *buffer)) { - return true; + // Build the interference map among the colocated buffer sets (nodes), by + // adding an edge between any two nodes that cannot be merged into a single + // colocated buffer set. + std::vector> interference_map( + colocated_buffer_sets.size()); + for (int64 i = 0; i < colocated_buffer_sets.size(); ++i) { + for (int64 j = i + 1; j < colocated_buffer_sets.size(); ++j) { + if (cannot_merge_buffer_sets(i, j)) { + interference_map[i].push_back(j); + interference_map[j].push_back(i); } - - return false; - }; - - if (std::any_of(predecessor_while_buffers.begin(), - predecessor_while_buffers.end(), - may_interfere_with_init_or_result)) { - continue; } + } - // All our checks have passed; merge 'predecessor_set' with 'colocated_set', - // and add the merged set to 'colocated_buffer_sets'. This forces the - // colocation of buffers across different while instructions. - FlatSet unique; - unique.insert(predecessor_set.begin(), predecessor_set.end()); - unique.insert(colocated_set.begin(), colocated_set.end()); - std::vector merged_set(unique.begin(), unique.end()); - AddSetToColocatedBufferSets(merged_set, colocated_buffer_sets); - return; + // Assign a color to each colocation set in colocated_buffer_sets, such that + // the sets that can be merged are assigned with the same color. + auto assigned_colors = ColorInterferenceGraph(interference_map); + + // Merge the buffer sets with the same color. + CHECK(!assigned_colors.empty()); + int64 num_sets = + *std::max_element(assigned_colors.begin(), assigned_colors.end()) + 1; + std::vector new_colocated_buffer_sets(num_sets); + for (int64 i = 0; i < colocated_buffer_sets.size(); ++i) { + const auto& buffer_set = colocated_buffer_sets[i]; + new_colocated_buffer_sets[assigned_colors[i]].insert(buffer_set.begin(), + buffer_set.end()); } - // Failed to merge into predecessor set; add 'colocated_set' as-is. - AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets); + VLOG(1) << "colocation sets count after coalescing:" + << colocated_buffer_sets.size(); + return new_colocated_buffer_sets; } -namespace { - -// Checks that points-to set of 'instruction' is unambiguous and distinct -// (ensured by CopyInsertion), then adds the buffer from the points-to set at -// 'index' to 'colocated_set'. -const LogicalBuffer* AddBufferToColocatedSet( - const HloInstruction* instruction, const ShapeIndex& index, - const TuplePointsToAnalysis& points_to_analysis, - std::vector* colocated_set) { - // CopyInsertion ensures root points-to set is unambiguous and distinct. - const auto& points_to = points_to_analysis.GetPointsToSet(instruction); - DCHECK(!points_to.IsAmbiguous()); - colocated_set->push_back(points_to.element(index)[0]); - return colocated_set->back(); -} - -} // namespace - // Builds sets of buffers in 'colocated_buffer_sets' which should be colocated // in the same allocation (currently just supports kWhile, kCall, and // kConditional). @@ -1299,12 +1421,11 @@ void BufferAssigner::BuildColocatedBufferSets( const Shape& /*subshape*/, const ShapeIndex& index) { std::vector colocated_set; // Add while.init. - auto* init_buffer = - AddBufferToColocatedSet(while_hlo->operand(0), index, - points_to_analysis, &colocated_set); + AddBufferToColocatedSet(while_hlo->operand(0), index, + points_to_analysis, &colocated_set); // Add while.result. - auto* result_buffer = AddBufferToColocatedSet( - while_hlo, index, points_to_analysis, &colocated_set); + AddBufferToColocatedSet(while_hlo, index, points_to_analysis, + &colocated_set); // Add while.cond.parameter. AddBufferToColocatedSet( while_hlo->while_condition()->parameter_instruction(0), index, @@ -1317,10 +1438,7 @@ void BufferAssigner::BuildColocatedBufferSets( AddBufferToColocatedSet( while_hlo->while_body()->root_instruction(), index, points_to_analysis, &colocated_set); - AddWhileSetToColocatedBufferSets( - colocated_set, init_buffer, result_buffer, while_hlo, - *computation, buffer_liveness, buffer_size, - colocated_buffer_sets); + AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets); }); } else if (opcode == HloOpcode::kCall) { const HloInstruction* call_hlo = instruction; @@ -1400,6 +1518,22 @@ void BufferAssigner::BuildColocatedBufferSets( } } } + + if (colocated_buffer_sets->empty()) { + return; + } + + // Try to find more coalescing opportunities among the colocated buffer sets. + // + // TODO(b/32491382): We should be able to remove this by using the + // module-level liveness analysis, which would let us directly detect buffer + // sharing opportunities between the while instruction buffer and the buffers + // from the predicate and body computation, as well as sharing across + // different while instructions. + std::vector new_colocated_buffer_sets = + MergeColocatedBufferSets(*colocated_buffer_sets, buffer_liveness, + buffer_size); + std::swap(*colocated_buffer_sets, new_colocated_buffer_sets); } // Assigns all colocated buffer sets in 'colocated_buffer_sets' to the same @@ -1411,14 +1545,17 @@ void BufferAssigner::AssignColocatedBufferSets( FlatSet* colocated_allocations) { for (const ColocatedBufferSet& colocated_buffer_set : colocated_buffer_sets) { BufferAllocation* allocation = nullptr; - // Set 'entry_parameter_number' if entry param in 'colocated_buffer_set'. + // Set 'entry_parameter_number' and 'entry_parameter_shape_idx' if entry + // param in 'colocated_buffer_set'. int64 entry_parameter_number = -1; + const ShapeIndex* entry_parameter_shape_idx = nullptr; for (const LogicalBuffer* buffer : colocated_buffer_set) { const HloInstruction* instruction = buffer->instruction(); const HloComputation* computation = instruction->parent(); if (instruction->opcode() == HloOpcode::kParameter && computation == computation->parent()->entry_computation()) { entry_parameter_number = instruction->parameter_number(); + entry_parameter_shape_idx = &buffer->index(); break; } } @@ -1439,7 +1576,8 @@ void BufferAssigner::AssignColocatedBufferSets( // body computation (which updates in place). // Set 'entry_computation_parameter' to indicate that it contains // an entry parameter, and to prevent reuse in MaybeAssignBuffer. - allocation->set_entry_computation_parameter(entry_parameter_number); + allocation->set_entry_computation_parameter( + entry_parameter_number, *entry_parameter_shape_idx); } colocated_allocations->insert(allocation->index()); } else { diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index 08a40bfeb2a2a78c25805308e73154c6cc667f21..3086d0e2ca0026547134285b8ceb357390fc7ece 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -91,6 +91,13 @@ class BufferAllocation { return parameter_number_; } + // If this allocation is for a parameter of the entry computation, this + // function returns which subshape of the parameter the allocation is for. + const ShapeIndex& param_shape_index() const { + CHECK(is_entry_computation_parameter_); + return param_shape_index_; + } + // Returns whether this allocation is assigned a LogicalBuffer which may // be live out of the entry computation. bool maybe_live_out() const { return maybe_live_out_; } @@ -185,6 +192,37 @@ class BufferAllocation { !is_thread_local(); } + // Add a heap trace which was used to assign slices to logical buffers in this + // allocation. A single BufferAllocation may include multiple heap traces + // in the case of the temporary block where there is a heap trace per + // computation. + void AddHeapTrace(const HeapSimulatorTrace& heap_trace) { + heap_traces_.push_back(heap_trace); + } + + // Return the set of heap traces used to assign slices to logical buffers in + // this allocation. + const std::vector HeapTraces() const { + return heap_traces_; + } + + // Compute and return the LogicalBuffers which are live at the point of peak + // memory usage for the given allocation. The point of peak memory usage is + // the point at which the total size of all live logical buffers is + // maximal. If peak memory is reached at multiple points, the set of logical + // buffers live at the earliest maximal point is returned. The vector is + // stabily asserted by LogicalBuffer::Index. + // + // The return value is a pair of total size of the logical buffers at peak, + // and the buffers themselves. + std::pair> + ComputePeakMemoryLogicalBuffers() const; + + // Get the number of bytes lost to fragmentation. This is equal to the + // difference between the size of the allocation and the size of the maximal + // live set. + int64 fragmentation_bytes() const { return fragmentation_bytes_; } + bool operator==(const BufferAllocation& other) const { return index_ == other.index_; } @@ -203,9 +241,11 @@ class BufferAllocation { // Adds a LogicalBuffer to the set assigned to this buffer. void AddAssignment(const LogicalBuffer& buffer, int64 offset, int64 size); - void set_entry_computation_parameter(int64 parameter_number) { + void set_entry_computation_parameter(int64 parameter_number, + ShapeIndex param_shape_index) { is_entry_computation_parameter_ = true; parameter_number_ = parameter_number; + param_shape_index_ = std::move(param_shape_index); } void set_maybe_live_out(bool value) { maybe_live_out_ = value; } void set_index(Index index) { index_ = index; } @@ -235,6 +275,10 @@ class BufferAllocation { // indicates the index (starting from 0) of the parameter. int64 parameter_number_ = 0; + // If this buffer is for an entry computation parameter, which subshape of the + // parameter is it for? + ShapeIndex param_shape_index_; + // Whether the allocation contains a LogicalBuffer which may be live-out of // the entry computation. Note that this flag is conservatively computed by // TuplePointsToAnalysis. That is, an allocation marked `maybe_live_out_` @@ -244,6 +288,9 @@ class BufferAllocation { // Mapping from the set of buffers assigned to this allocation to their // logical offsets and sizes. tensorflow::gtl::FlatMap assigned_buffers_; + + int64 fragmentation_bytes_ = 0; + std::vector heap_traces_; }; // Add stream operators for nicer output of CHECK/RET_CHECK failures. @@ -428,7 +475,6 @@ class BufferAssignment { LogicalBuffer::AlignmentFunction color_alignment_; Stats stats_; - std::vector heap_simulator_traces_; TF_DISALLOW_COPY_AND_ASSIGN(BufferAssignment); }; @@ -528,15 +574,13 @@ class BufferAssigner { const std::vector& colocated_set, std::vector* colocated_buffer_sets); - // Conceptually the same as AddSetToColocatedBufferSets, but specific to the - // colocated buffers for while instructions. - void AddWhileSetToColocatedBufferSets( - const std::vector& colocated_set, - const LogicalBuffer* while_init_buffer, - const LogicalBuffer* while_result_buffer, const HloInstruction* while_hlo, - const HloComputation& computation, const BufferLiveness& buffer_liveness, - const LogicalBuffer::SizeFunction& buffer_size, - std::vector* colocated_buffer_sets); + // Given a list of colocated buffer sets (each colocated buffer set represents + // the logical buffers that would be assigned to the same physical buffer), + // try to merge the sets if the buffers can be shared. Returns the merged set. + std::vector MergeColocatedBufferSets( + const std::vector& colocated_buffer_sets, + const BufferLiveness& buffer_liveness, + const LogicalBuffer::SizeFunction& buffer_size); // Split a set of buffers into several sets, each of which contains buffers // colored with the same color. diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index 6fc9d783f1b34de8c0f93c6aa342591891d08eaf..513a8785bbd52b0a3bfa3642bbfc62b1035ffb17 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -37,14 +37,16 @@ limitations under the License. #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/macros.h" namespace xla { - namespace { +using ::testing::UnorderedElementsAre; + // DFS visitor that collects the instructions referenced by a computation // without descending into nested computations, i.e., only from the operands. class InstructionListVisitor : public DfsHloVisitorWithDefault { @@ -101,6 +103,22 @@ class BufferAssignmentTest : public HloTestBase { .ConsumeValueOrDie(); } + std::unique_ptr RunBufferAssignmentWithInstructionSequence( + HloModule* module, + tensorflow::gtl::ArraySlice instruction_sequence, + int64 alignment = 1) { + SequentialHloOrdering::HloModuleSequence module_sequence; + module_sequence[module->entry_computation()] = + std::vector(instruction_sequence.begin(), + instruction_sequence.end()); + return BufferAssigner::Run( + module, + xla::MakeUnique(module, module_sequence), + backend().compiler()->BufferSizeBytesFunction(), + [alignment](LogicalBuffer::Color) { return alignment; }) + .ConsumeValueOrDie(); + } + // Builds an x+1.0 computation to use in a Map. std::unique_ptr BuildMapComputationPlus1(const string& name) { auto builder = HloComputation::Builder(name); @@ -614,7 +632,7 @@ TEST_F(BufferAssignmentTest, TrivialMap) { BufferAllocation map_buffer = GetAssignedOutputAllocation(*buffers, map); EXPECT_NE(param0_buffer.index(), map_buffer.index()); - // The final computation node of the map is an add of an f32 parm and a + // The final computation node of the map is an add of an f32 param and a // constant. EXPECT_EQ(HloOpcode::kAdd, inner_last->opcode()); const BufferAllocation& inner_add_buffer = @@ -1370,7 +1388,7 @@ TEST_F(BufferAssignmentTest, AmbiguousBufferAsOutput) { auto element_slices = assignment->GetAllSlices(select, /*index=*/{0}); EXPECT_EQ(2, element_slices.size()); EXPECT_THAT(element_slices, - ::testing::UnorderedElementsAre( + UnorderedElementsAre( assignment->GetUniqueSlice(tuple_param0, /*index=*/{0}) .ConsumeValueOrDie(), assignment->GetUniqueSlice(tuple_param1, /*index=*/{0}) @@ -1473,6 +1491,98 @@ TEST_F(BufferAssignmentTest, OneTempAllocation) { } } +TEST_F(BufferAssignmentTest, TrivialPeakBuffers) { + // paramscalar ------- (mul) -- (add) -- (sub) + // / / / + // param0[100] -------/ / / + // / / + // param1[100] --------------/--------/ + auto builder = HloComputation::Builder(TestName()); + auto paramscalar = + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32vec100_, "")); + auto param1 = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32vec100_, "")); + auto mul = builder.AddInstruction(HloInstruction::CreateBinary( + f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); + auto add = builder.AddInstruction( + HloInstruction::CreateBinary(f32vec100_, HloOpcode::kAdd, mul, param1)); + builder.AddInstruction(HloInstruction::CreateBinary( + f32vec100_, HloOpcode::kSubtract, add, param1)); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + auto buffers = RunBufferAssignment(module.get()); + + // Trivially, the set of peak memory logical buffer(s) of an allocation with a + // single logical buffer should be exactly the logical buffer in that + // allocation. + const BufferAllocation& mul_buffer = GetTopLevelAllocation(*buffers, mul); + int64 peak_size; + std::vector peak_buffers; + + std::tie(peak_size, peak_buffers) = + mul_buffer.ComputePeakMemoryLogicalBuffers(); + EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(f32vec100_)); + ASSERT_EQ(peak_buffers.size(), 1); + EXPECT_EQ(peak_buffers[0]->instruction(), mul); +} + +TEST_F(BufferAssignmentTest, PeakBuffers) { + // Compute the peak liveness buffers of the following sequence: + // + // %param = ... + // %log = log(%param) + // %rev = reverse(%log) + // %neg = neg(%param) + // %concat = concat(%rev, %neg) + // ROOT %root = slice(concat) + // + // In the temporary block, the set of live buffers at peak memory use should + // be {%rev, %neg, %concat}. This occurs right at the concat itself. + auto builder = HloComputation::Builder(TestName()); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32vec100_, "")); + auto log = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec100_, HloOpcode::kLog, param)); + auto rev = builder.AddInstruction( + HloInstruction::CreateReverse(f32vec100_, log, {0})); + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param)); + const Shape concat_shape = ShapeUtil::MakeShape(F32, {200}); + auto concat = builder.AddInstruction( + HloInstruction::CreateConcatenate(concat_shape, {rev, neg}, 0)); + // Make the root tiny so no interior nodes can share its buffer. + auto root = builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {1}), concat, {0}, {1}, {1})); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + auto buffers = RunBufferAssignmentWithInstructionSequence( + module.get(), {param, log, rev, neg, concat, root}); + + // The temporary buffer should hold the 4 interior instructions. + const BufferAllocation& buffer = GetTopLevelAllocation(*buffers, concat); + EXPECT_FALSE(buffer.IsInputOrOutput()); + EXPECT_TRUE(buffer.IsPreallocatedTempBuffer()); + ASSERT_EQ(buffer.assigned_buffers().size(), 4); + + int64 peak_size; + std::vector peak_buffers; + std::tie(peak_size, peak_buffers) = buffer.ComputePeakMemoryLogicalBuffers(); + + // The peak live set should be concat and its inputs. + EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(ShapeUtil::MakeShape(F32, {400}))); + ASSERT_EQ(peak_buffers.size(), 3); + std::vector peak_instructions; + for (const LogicalBuffer* logical_buffer : peak_buffers) { + peak_instructions.push_back(logical_buffer->instruction()); + } + EXPECT_THAT(peak_instructions, UnorderedElementsAre(rev, neg, concat)); +} + class WhileBufferAssignmentTest : public HloTestBase { protected: std::unique_ptr BuildWhileConditionComputation( @@ -1587,6 +1697,192 @@ TEST_F(WhileBufferAssignmentTest, TwoForwardWhileLoops) { assignment->GetUniqueSlice(while1, {1}).ConsumeValueOrDie()); } +// Tests that two colocated buffer sets are not merged if an entry parameter +// buffer belongs to either of the colocation sets (b/73267882). +// +// %param --> %while.0 --> %mul --> %while.1 --> %broadcast +// +// %while.0 body just forwards the init value, so the loop carried variable +// remains the constant, whereas %while.1 changes the loop carried variable. +TEST_F(WhileBufferAssignmentTest, ColocatedBufferWithEntryParameter) { + const Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + + const char* module_str = R"( +HloModule test_module + +%cond.v0 { + %param = s32[] parameter(0) + ROOT %constant = pred[] constant(true) +} + +%cond.v1 { + %param.0 = s32[] parameter(0) + ROOT %constant.0 = pred[] constant(true) +} + +%body.v0 { + ROOT %param.1 = s32[] parameter(0) +} + +%body.v1 { + %param.2 = s32[] parameter(0) + ROOT add = s32[] add(%param.2, %param.2) +} + +ENTRY %test_module { + %param.3 = s32[] parameter(0) + %while.0 = s32[] while(%param.3), condition=%cond.v0, body=%body.v0 + %mul = s32[] multiply(%while.0, %while.0) + %while.1 = s32[] while(%mul), condition=%cond.v1, body=%body.v1 + ROOT %bcast = s32[1024,1024]{1,0} broadcast(s32[] %while.1), dimensions={} +})"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + tools::Parse(module_str)); + + // Run CopyInsertion and check if the graph constructed above doesn't need + // any copies inserted for BufferAssignment to run. + int64 instruction_count = module->instruction_count(); + CopyInsertion copy_insertion; + ASSERT_IS_OK(copy_insertion.Run(module.get()).status()); + ASSERT_EQ(instruction_count, module->instruction_count()); + + // Get the instructions in the module. + const HloInstruction* bcast = module->entry_computation()->root_instruction(); + const HloInstruction* param = + module->entry_computation()->parameter_instruction(0); + ASSERT_EQ(bcast->opcode(), HloOpcode::kBroadcast); + const HloInstruction* while1 = bcast->operand(0); + ASSERT_EQ(while1->opcode(), HloOpcode::kWhile); + const HloInstruction* while0 = while1->operand(0)->operand(0); + ASSERT_EQ(while0->opcode(), HloOpcode::kWhile); + + // Run buffer assignment. + auto assignment = RunBufferAssignment(module.get()); + TF_ASSERT_OK_AND_ASSIGN(auto slice_param, + assignment->GetUniqueSlice(param, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while0, + assignment->GetUniqueSlice(while0, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while1, + assignment->GetUniqueSlice(while1, {})); + + // The parameter slice is part of the while0's colocation set (init value), + // but not merged into the while1's colocation set. + EXPECT_EQ(slice_param, slice_while0); + EXPECT_NE(slice_param, slice_while1); +} + +// Tests that the colocated buffers for while instructions are properly assigned +// during buffer assignment such that the result tuple elements are not assigned +// to the same buffer. +// +// %infeed --> %while.0 --> %while.1 --+ +// +-- %tuple +// %zero --> %add --> %while.2 --+ +// +// Execution Order: +// %infeed -> %while.0 -> %while.1 -> %zero -> %add -> %while.2 -> %tuple +// +// The HLO computation used in this test requires specific ordering to expose +// the bug (b/72496031). During buffer assignment, the visitation order of +// colocated buffers is %while.2 -> while.0 -> while.1, and the buffer +// assignment was coalescing the colocated buffers for all 3 while instructions, +// therefore assigning the same buffer to the two result tuple elements. +TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { + const Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + + // Builds a condition computation: x -> x < 4 + auto build_cond = [&]() { + auto builder = HloComputation::Builder("cond"); + auto const4 = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(4))); + auto param = + builder.AddInstruction(HloInstruction::CreateParameter(0, r0s32, "x")); + builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, param, const4)); + return builder.Build(); + }; + + // Builds a body computation: x -> x + 9 + auto build_body = [&]() { + auto builder = HloComputation::Builder("body"); + auto const9 = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(9))); + auto param = + builder.AddInstruction(HloInstruction::CreateParameter(0, r0s32, "x")); + builder.AddInstruction( + HloInstruction::CreateBinary(r0s32, HloOpcode::kAdd, param, const9)); + return builder.Build(); + }; + + // Build the entry computation as described in the comment above. + auto module = xla::MakeUnique(TestName()); + auto builder = HloComputation::Builder("entry"); + + auto infeed = builder.AddInstruction(HloInstruction::CreateInfeed(r0s32, "")); + auto cond0 = module->AddEmbeddedComputation(build_cond()); + auto body0 = module->AddEmbeddedComputation(build_body()); + auto while0 = builder.AddInstruction( + HloInstruction::CreateWhile(r0s32, cond0, body0, infeed)); + + auto cond1 = module->AddEmbeddedComputation(build_cond()); + auto body1 = module->AddEmbeddedComputation(build_body()); + auto while1 = builder.AddInstruction( + HloInstruction::CreateWhile(r0s32, cond1, body1, while0)); + + auto zero = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(0))); + auto add = builder.AddInstruction( + HloInstruction::CreateBinary(r0s32, HloOpcode::kAdd, zero, zero)); + auto cond2 = module->AddEmbeddedComputation(build_cond()); + auto body2 = module->AddEmbeddedComputation(build_body()); + auto while2 = builder.AddInstruction( + HloInstruction::CreateWhile(r0s32, cond2, body2, add)); + + auto tuple = + builder.AddInstruction(HloInstruction::CreateTuple({while2, while1})); + module->AddEntryComputation(builder.Build()); + + // Run CopyInsertion and check if the graph constructed above doesn't need + // any copies inserted for BufferAssignment to run. + int64 instruction_count = module->instruction_count(); + CopyInsertion copy_insertion; + ASSERT_IS_OK(copy_insertion.Run(module.get()).status()); + ASSERT_EQ(instruction_count, module->instruction_count()); + + // Create a sequential order among all the instructions in the entry + // computation, since the issue this test stresses depends on the order the + // nodes are traversed during BufferAssignment. + SequentialHloOrdering::HloModuleSequence sequence; + sequence[module->entry_computation()] = {infeed, while0, while1, zero, + add, while2, tuple}; + TF_ASSERT_OK_AND_ASSIGN( + auto assignment, + BufferAssigner::Run( + module.get(), + xla::MakeUnique(module.get(), sequence), + backend().compiler()->BufferSizeBytesFunction(), + [](LogicalBuffer::Color) { return 1; })); + + // The result tuple elements must be assigned with different buffers. + TF_ASSERT_OK_AND_ASSIGN(auto slice0, assignment->GetUniqueSlice(tuple, {0})); + TF_ASSERT_OK_AND_ASSIGN(auto slice1, assignment->GetUniqueSlice(tuple, {1})); + EXPECT_NE(slice0, slice1); + + // while0 and while1 result buffers must be equal to slice1. + TF_ASSERT_OK_AND_ASSIGN(auto slice_while0, + assignment->GetUniqueSlice(while0, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while1, + assignment->GetUniqueSlice(while1, {})); + EXPECT_EQ(slice1, slice_while0); + EXPECT_EQ(slice1, slice_while1); + + // while2 result buffer must be equal to slice0. + TF_ASSERT_OK_AND_ASSIGN(auto slice_while2, + assignment->GetUniqueSlice(while2, {})); + EXPECT_EQ(slice0, slice_while2); +} + TEST_F(WhileBufferAssignmentTest, OneForwardBackwardWhileLoopSet) { auto module = xla::MakeUnique(TestName()); auto builder = HloComputation::Builder("entry"); diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc index e7749252ce44f0daf7016f72d80401695eaaacb9..37982aaef9eddd64ef6b57ad5a9cf8dd6a565097 100644 --- a/tensorflow/compiler/xla/service/buffer_liveness.cc +++ b/tensorflow/compiler/xla/service/buffer_liveness.cc @@ -117,11 +117,12 @@ bool BufferLiveness::live_range_strictly_before(const LogicalBuffer& a, // If the root instruction aliases the buffer 'a', the live range of 'a' is // until the end of the computation and can never be strictly before another - // buffer. This is needed to prevent the root instruction's buffers from - // being reused by later instructions even when the root is not the last - // instruction in the schedule. + // buffer defined in the same computation. This is needed to prevent the + // root instruction's buffers from being reused by later instructions even + // when the root is not the last instruction in the schedule. if (alias.instruction()->parent()->root_instruction() == - alias.instruction()) { + alias.instruction() && + alias.instruction()->parent() == b.instruction()->parent()) { return false; } } diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc index dab73596e1639eed62151197048ee8d29570b20a..6664496ab6c603c35c7dce923fcf94c54d1ce714 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.cc +++ b/tensorflow/compiler/xla/service/compile_only_service.cc @@ -72,8 +72,7 @@ CompileOnlyService::CompileAheadOfTime( VersionedComputationHandle versioned_handle = user_computation->GetVersionedHandle(); - // TODO(b/63773457): Track DebugOptions in AotCompilationOptions. - DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags(); + const DebugOptions& debug_options = options.debug_options(); // Dump computation proto state if flag is set. const string& directory_path = debug_options.xla_dump_computations_to(); diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index e2e9d2a0c048fec6c6ffbeef1223ae0e6aef50d1..0392d4af48a040c4a648f7bf9bf21a62ce03a990 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -86,4 +86,7 @@ Compiler::GetPlatformCompilers() { return compilers->at(platform->id()).get(); } +AotCompilationOptions::AotCompilationOptions() + : debug_options_(legacy_flags::GetDebugOptionsFromFlags()) {} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index 74fd24edf88d44b2dfdc87556b0af43987e69e08..33e19efc72c6d30ccd7e0b3a13f664a4f42208bf 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -79,11 +79,15 @@ class AotCompilationOptions { device_allocator_ = device_allocator; } + const DebugOptions& debug_options() const { return debug_options_; } + DebugOptions* mutable_debug_options() { return &debug_options_; } + protected: - AotCompilationOptions() = default; + AotCompilationOptions(); private: DeviceMemoryAllocator* device_allocator_ = nullptr; + DebugOptions debug_options_; }; // Abstract compiler interface that is subclassed for compilation on a diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.cc b/tensorflow/compiler/xla/service/conditional_simplifier.cc new file mode 100644 index 0000000000000000000000000000000000000000..f35de080853f7ec986565cb2df1050946ac3f244 --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier.cc @@ -0,0 +1,106 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" + +#include +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" + +namespace xla { + +// Tries to replace a conditional with a call operation of the corresponding +// computation. If the given conditional has a constant predicate, tries to +// replace it with a call to its true/false computation as appropirate and then +// inline that computation. +// +// Returns true if it made a change to the graph. +static StatusOr TryRemoveConditional(HloInstruction* conditional) { + CHECK_EQ(conditional->opcode(), HloOpcode::kConditional); + // Do not remove conditionals that contain side-effecting instructions or + // have control predecessors/successors in either true/false computation. + if (!conditional->parent()->IsRemovable(conditional) || + conditional->HasSideEffect()) { + VLOG(2) << "Not attempting to remove conditional as it is not removable or " + "has side effect: " + << conditional->ToShortString(); + return false; + } + + if (conditional->operand(0)->opcode() != HloOpcode::kConstant) { + VLOG(2) << "Not attempting to remove conditional as its predicate is not a " + "compile-time constant: " + << conditional->ToShortString(); + return false; + } + + auto computation = conditional->parent(); + HloInstruction* call_op; + if (conditional->operand(0)->literal().Get({})) { + call_op = computation->AddInstruction(HloInstruction::CreateCall( + conditional->shape(), {conditional->mutable_operand(1)}, + conditional->true_computation())); + } else { + call_op = computation->AddInstruction(HloInstruction::CreateCall( + conditional->shape(), {conditional->mutable_operand(2)}, + conditional->false_computation())); + } + + TF_RETURN_IF_ERROR(computation->ReplaceInstruction(conditional, call_op)); + TF_RETURN_IF_ERROR(CallInliner::Inline(call_op).status()); + + return true; +} + +StatusOr ConditionalSimplifier::Run(HloModule* module) { + XLA_VLOG_LINES( + 3, "ConditionalSimplifier::Run(), before:\n" + module->ToString()); + bool changed = false; + + // Gather all the conditional ops in our module. We do this ahead of time so + // we don't have to worry about mutating the lists of computations or + // instructions as we iterate. + std::vector conditional_ops; + for (auto* comp : module->computations()) { + for (auto* instr : comp->instructions()) { + if (instr->opcode() == HloOpcode::kConditional) { + conditional_ops.push_back(instr); + } + } + } + + for (HloInstruction* conditional_op : conditional_ops) { + TF_ASSIGN_OR_RETURN(bool result, TryRemoveConditional(conditional_op)); + changed |= result; + } + + XLA_VLOG_LINES(3, + "ConditionalSimplifier::Run(), after:\n" + module->ToString()); + return changed; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.h b/tensorflow/compiler/xla/service/conditional_simplifier.h new file mode 100644 index 0000000000000000000000000000000000000000..063261e26d06e21a297e8e3c405898a17221b7ca --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier.h @@ -0,0 +1,38 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ + +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/stringpiece.h" + +namespace xla { + +// HLO pass that removes kConditional with a constant predicate, replacing them +// with their true or false computation as appropriate. +class ConditionalSimplifier : public HloPassInterface { + public: + tensorflow::StringPiece name() const override { + return "simplify-conditional"; + } + StatusOr Run(HloModule* module) override; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ diff --git a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..868348547d9f5cbdc7576c7fc0697d72c3a3e557 --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc @@ -0,0 +1,153 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" + +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +namespace op = xla::testing::opcode_matchers; + +class ConditionalSimplifierTest : public HloVerifiedTestBase { + public: + // Makes a computation that contains a conditional with constant predicate. + HloComputation* MakeConditional(HloModule* module); +}; + +HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) { + HloComputation::Builder builder(TestName()); + + // true_computation returns param+1. + HloComputation* true_computation; + { + HloComputation::Builder true_computation_builder(TestName() + + ".true_computation"); + auto param = + true_computation_builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(S32, {}), "param")); + auto one = true_computation_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1))); + + true_computation_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, one)); + + true_computation = + module->AddEmbeddedComputation(true_computation_builder.Build()); + } + + // false_computation returns param+42. + HloComputation* false_computation; + { + HloComputation::Builder false_computation_builder(TestName() + + ".false_computation"); + auto param = false_computation_builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(S32, {}), + "param")); + auto forty_two = false_computation_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42))); + + false_computation_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, forty_two)); + false_computation = + module->AddEmbeddedComputation(false_computation_builder.Build()); + } + + auto false_instrn = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(false))); + auto false_param = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(S32, {}), "false_param")); + auto one = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1))); + + builder.AddInstruction(HloInstruction::CreateConditional( + ShapeUtil::MakeShape(S32, {}), false_instrn, one, true_computation, + false_param, false_computation)); + + return module->AddEntryComputation(builder.Build()); +} + +TEST_F(ConditionalSimplifierTest, ConditionalGetsInlined) { + HloComputation* computation = MakeConditional(&module()); + ASSERT_TRUE(ConditionalSimplifier().Run(&module()).ValueOrDie()); + EXPECT_THAT(computation->root_instruction(), + op::Add(op::Parameter(), op::Constant())); +} + +TEST_F(ConditionalSimplifierTest, ConditionalWithControlDependency) { + HloComputation* computation = MakeConditional(&module()); + + auto* true_op = computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(true))); + TF_ASSERT_OK( + true_op->AddControlDependencyTo(computation->root_instruction())); + + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsSend) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + + auto* true_computation = conditional->true_computation(); + auto* send = true_computation->AddInstruction(HloInstruction::CreateSend( + true_computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(true))), + /*channel_id=*/0)); + true_computation->AddInstruction(HloInstruction::CreateSendDone(send)); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsRecv) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + + auto* true_computation = conditional->true_computation(); + auto* recv = true_computation->AddInstruction(HloInstruction::CreateRecv( + ShapeUtil::MakeShape(F32, {1}), /*channel_id=*/0)); + true_computation->AddInstruction(HloInstruction::CreateRecvDone(recv)); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + auto* false_computation = conditional->false_computation(); + false_computation->AddInstruction( + HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config")); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index cd983bc03e993caed883916de01d75dffdbc4bab..df73c285971e237b6f5492f8a7c587f23646ec1e 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -58,6 +58,45 @@ bool ValueIsReadOnly(const HloValue& value) { return IsConstantValue(value) || IsEntryParameterValue(value); } +// Data structure describing the action which should be taken on parts of a +// computation buffers, with respect to the adding of special case copies. +struct SpecialCaseCopyPolicy { + // Insert a copy if the same buffer is found at multiple indices within the + // output tuple. + bool copy_root_replicated_buffers = false; + // If true, insert a copy if a buffer coming from a constant or a parameter + // is found wihtin the output tuple. + bool copy_parameters_and_constants = false; +}; + +SpecialCaseCopyPolicy GetSpecialCaseCopyPolicy(const CallGraphNode& node, + HloModule* module, + HloComputation* computation) { + SpecialCaseCopyPolicy policy; + if (computation == module->entry_computation()) { + policy.copy_parameters_and_constants = true; + policy.copy_root_replicated_buffers = true; + } + for (const CallSite& site : node.caller_callsites()) { + // The kWhile instruction does not have an handling here, as the + // AddCopiesForWhile() API takes care of adding its own copies. + if (site.instruction()->opcode() == HloOpcode::kConditional) { + policy.copy_parameters_and_constants = true; + policy.copy_root_replicated_buffers = true; + } + } + return policy; +} + +bool ShouldCopyRootValue(const HloValue& value, + const SpecialCaseCopyPolicy& policy) { + if (policy.copy_parameters_and_constants) { + return IsConstantValue(value) || + value.defining_instruction()->opcode() == HloOpcode::kParameter; + } + return false; +} + // Deep copy the given instructions 'from' and 'to' at the ShapeIndexes given in // 'indices_to_copy'. Add control edges from the respective kCopy instructions // in deep copy of 'from' to the respective kCopy instruction in the deep copy @@ -729,7 +768,8 @@ class CopyRemover { // has a different operand (the operand of the elided copy). for (const HloUse* copy_use : copy_value_node->uses) { operand_node->uses.push_back(copy_use); - if (copy_use->instruction->opcode() == HloOpcode::kCopy) { + if (copy_use->instruction->opcode() == HloOpcode::kCopy && + ContainsKey(copy_map_, copy_use->instruction)) { copy_map_.at(copy_use->instruction).src = operand_node; } } @@ -956,7 +996,8 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { } TF_RET_CHECK(node.context() == CallContext::kSequential); - const bool is_entry = computation == module->entry_computation(); + SpecialCaseCopyPolicy policy = + GetSpecialCaseCopyPolicy(node, module, computation); HloInstruction* root = computation->root_instruction(); // Mark nondistinct/ambiguous indices. @@ -969,27 +1010,26 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { for (const HloBuffer* buffer : buffers_at_index) { buffer_seen_before |= !seen.insert(buffer).second; } - if (buffers_at_index.size() > 1 || (buffer_seen_before && is_entry)) { - VLOG(2) << "Index " << index << " of root of computation " + if (buffers_at_index.size() > 1 || + (buffer_seen_before && policy.copy_root_replicated_buffers)) { + VLOG(2) << "Index " << index << " of computation " << computation->name() << " (" << root->name() << ") has ambiguous or non-distinct buffer. Copying."; add_index_to_copy(root, index); } }); - // For entry instructions, mark any parameter or constant values. - if (is_entry) { - for (const auto& pair : - alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) { - const ShapeIndex& index = pair.first; - const HloValueSet& value_set = pair.second; - for (const HloValue* value : value_set.values()) { - if (ValueIsReadOnly(*value)) { - VLOG(2) << "Root of entry computation (" << root->name() - << ") has constant or entry parameter value at index " - << index << ". Copying."; - add_index_to_copy(root, index); - } + for (const auto& pair : + alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) { + const ShapeIndex& index = pair.first; + const HloValueSet& value_set = pair.second; + for (const HloValue* value : value_set.values()) { + if (ShouldCopyRootValue(*value, policy)) { + VLOG(2) << "Root of (" << root->name() << ") of computation(" + << computation->name() + << ") has constant or parameter value at index " << index + << ". Copying."; + add_index_to_copy(root, index); } } } @@ -1011,7 +1051,6 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { instruction->parent()->set_root_instruction(deep_copy); } } - return Status::OK(); } @@ -1155,7 +1194,7 @@ bool IsWhileBody(const HloComputation* computation, HloModule* module) { std::unique_ptr call_graph = CallGraph::Build(module); TF_ASSIGN_OR_RETURN(std::unique_ptr dataflow, - HloDataflowAnalysis::Run(module)); + HloDataflowAnalysis::Run(*module)); bool changed = false; diff --git a/tensorflow/compiler/xla/service/copy_insertion_test.cc b/tensorflow/compiler/xla/service/copy_insertion_test.cc index 128ee726ea6e4a8b63727fdc9762d865cee1c985..153f062d015e49db11c4c9ae0a2a61e76c020f02 100644 --- a/tensorflow/compiler/xla/service/copy_insertion_test.cc +++ b/tensorflow/compiler/xla/service/copy_insertion_test.cc @@ -1724,8 +1724,58 @@ void BM_ParallelWhiles(int num_iters, int num_whiles) { } } +std::unique_ptr MakeBenchmarkWhileBody( + const int num_tuple_inputs) { + auto builder = HloComputation::Builder("benchmark_loop_body"); + const Shape element_shape = ShapeUtil::MakeShape(F32, {}); + std::vector input_shape(num_tuple_inputs, element_shape); + const Shape loop_state_shape = ShapeUtil::MakeTupleShape(input_shape); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, loop_state_shape, "loop_state")); + std::vector gte_nodes(num_tuple_inputs); + for (int i = 0; i < num_tuple_inputs; ++i) { + gte_nodes[i] = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(element_shape, param, i)); + } + builder.AddInstruction(HloInstruction::CreateTuple(gte_nodes)); + return builder.Build(); +} + +void BM_ManyElementTuple(int num_iters, const int num_tuple_inputs) { + tensorflow::testing::StopTiming(); + HloModuleConfig config; + config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); + CopyInsertion copy_insertion; + const Shape element_shape = ShapeUtil::MakeShape(F32, {}); + std::vector tuple_params(num_tuple_inputs); + for (int i = 0; i < num_iters; ++i) { + auto builder = HloComputation::Builder("BM_ParallelWhiles"); + HloModule module("BM_ManyElementTuple", VersionedComputationHandle(), + config); + for (int j = 0; j < num_tuple_inputs; ++j) { + tuple_params[j] = builder.AddInstruction( + HloInstruction::CreateParameter(j, element_shape, "")); + } + HloInstruction* init = + builder.AddInstruction(HloInstruction::CreateTuple(tuple_params)); + HloComputation* condition = + module.AddEmbeddedComputation(MakeTrivialCondition(init->shape())); + HloComputation* body = + module.AddEmbeddedComputation(MakeBenchmarkWhileBody(num_tuple_inputs)); + HloInstruction* xla_while = builder.AddInstruction( + HloInstruction::CreateWhile(init->shape(), condition, body, init)); + builder.AddInstruction(HloInstruction::CreateGetTupleElement( + ShapeUtil::MakeShape(F32, {}), xla_while, 0)); + module.AddEntryComputation(builder.Build()); + tensorflow::testing::StartTiming(); + ASSERT_IS_OK(copy_insertion.Run(&module).status()); + tensorflow::testing::StopTiming(); + } +} + BENCHMARK(BM_SequentialWhiles)->Arg(512)->Arg(1024)->Arg(2048)->Arg(4096); BENCHMARK(BM_ParallelWhiles)->Arg(512)->Arg(1024)->Arg(2048)->Arg(4096); +BENCHMARK(BM_ManyElementTuple)->Arg(1024)->Arg(12288); TEST_F(CopyInsertionTest, SimpleControlFlowTest) { const string& hlo_string = R"( diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 2f0259163120dd5d62a5d1289deada8dc59c2c6c..38a54fcb644f26355916abcff0ec18f7094856dc 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -105,6 +105,7 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:buffer_liveness", "//tensorflow/compiler/xla/service:call_inliner", + "//tensorflow/compiler/xla/service:conditional_simplifier", "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", @@ -159,13 +160,11 @@ cc_library( deps = [ ":compiler_functor", ":cpu_runtime", - ":cpu_runtime_avx", - ":cpu_runtime_neon", - ":cpu_runtime_sse4_1", ":custom_call_target_registry", ":disassembler", ":external_constant_pool", ":orc_jit_memory_mapper", + ":runtime_fp16", ":runtime_conv2d", ":runtime_fft", ":runtime_fork_join", @@ -185,6 +184,20 @@ cc_library( ] + ORC_JIT_MEMORY_MAPPER_TARGETS, ) +cc_library( + name = "runtime_fp16", + srcs = [ + "runtime_fp16.cc", + ], + hdrs = [ + "runtime_fp16.h", + ], + copts = runtime_copts(), + deps = [ + "//tensorflow/core:framework_lite", + ], +) + cc_library( name = "cpu_executable", srcs = ["cpu_executable.cc"], @@ -408,9 +421,6 @@ cc_library( hdrs = ["compiler_functor.h"], deps = [ ":cpu_runtime", - ":cpu_runtime_avx", - ":cpu_runtime_neon", - ":cpu_runtime_sse4_1", ":disassembler", ":llvm_ir_runtime", "//tensorflow/compiler/xla:statusor", @@ -430,43 +440,6 @@ cc_library( ], ) -cc_library( - name = "cpu_runtime_sse4_1", - srcs = ["cpu_runtime_sse4_1.cc"], - hdrs = ["cpu_runtime_sse4_1.h"], - copts = ["-DEIGEN_AVOID_STL_ARRAY"], - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/core:framework_lite", - "//third_party/eigen3", - ], -) - -cc_library( - name = "cpu_runtime_avx", - srcs = ["cpu_runtime_avx.cc"], - hdrs = ["cpu_runtime_avx.h"], - copts = ["-DEIGEN_AVOID_STL_ARRAY"], - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/core:framework_lite", - "//third_party/eigen3", - ], -) - -cc_library( - name = "cpu_runtime_neon", - srcs = ["cpu_runtime_neon.cc"], - hdrs = ["cpu_runtime_neon.h"], - # runtime_copts() enables -mfpu=neon - copts = ["-DEIGEN_AVOID_STL_ARRAY"] + runtime_copts(), - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/core:framework_lite", - "//third_party/eigen3", - ], -) - cc_library( name = "cpu_runtime", srcs = [ @@ -497,6 +470,7 @@ cc_library( "llvm_ir_runtime.h", ], deps = [ + ":vector_support_library", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:lib", "@llvm//:core", @@ -541,7 +515,6 @@ cc_library( cc_library( name = "runtime_matvec", - srcs = ["runtime_matvec.cc"], hdrs = ["runtime_matvec.h"], copts = runtime_copts(), deps = [ @@ -852,6 +825,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "@llvm//:core", + "@llvm//:support", ], ) diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc index 04b4a8c5c80eeefdbe10001ba5c462affbc9b21d..61b2da7a7dce7f6fba46a23cc8e5462a3899a18c 100644 --- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc +++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc @@ -37,9 +37,6 @@ limitations under the License. #include "llvm/Transforms/IPO/PassManagerBuilder.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h" #include "tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -50,15 +47,6 @@ limitations under the License. namespace xla { namespace cpu { -/* static */ CompilerFunctor::VectorIntrinsics -CompilerFunctor::AllIntrinsics() { - VectorIntrinsics intrinsics; - intrinsics.sse_intrinsics = true; - intrinsics.avx_intrinsics = true; - intrinsics.neon_intrinsics = true; - return intrinsics; -} - /* Create filtered versions of the LLVM Pass Managers to filter out some of the expensive passes. Profiling: @@ -105,8 +93,8 @@ class FilteredPassManager : public llvm::legacy::PassManager { }; } // anonymous namespace -llvm::object::OwningBinary CompilerFunctor:: -operator()(llvm::Module& module) const { +std::unique_ptr CompilerFunctor::operator()( + llvm::Module& module) const { FilteredPassManager module_passes(disable_expensive_passes_); FilteredFunctionPassManager function_passes(&module, disable_expensive_passes_); @@ -169,112 +157,32 @@ operator()(llvm::Module& module) const { codegen_passes.run(module); // Construct ObjectFile from machine code buffer. - std::unique_ptr memory_buffer( + return std::unique_ptr( new llvm::ObjectMemoryBuffer(std::move(stream_buffer))); - llvm::Expected> - object_file_or_error = llvm::object::ObjectFile::createObjectFile( - memory_buffer->getMemBufferRef()); - CHECK(object_file_or_error); - - std::unique_ptr object_file = - std::move(object_file_or_error.get()); - if (VLOG_IS_ON(2)) { - StatusOr disassembly_status = - disassembler_->DisassembleObjectFile(*object_file); - if (disassembly_status.ok()) { - auto result = disassembly_status.ValueOrDie(); - XLA_VLOG_LINES(2, result.text); - VLOG(2) << "compiled code size: " << result.code_size_bytes << " bytes"; - } - } - - return llvm::object::OwningBinary( - std::move(object_file), std::move(memory_buffer)); } -namespace { -// Returns the set of vectorized library functions supported for the target. -std::vector VectorFunctionsForTargetLibraryInfoImpl( - llvm::Triple::ArchType arch, llvm::StringRef feature_string, - CompilerFunctor::VectorIntrinsics const& available_intrinsics) { - std::vector vector_functions; - - const llvm::VecDesc four_wide_vector_functions_neon[] = { - {"expf", runtime::kExpV4F32NEONSymbolName, 4}, - {"llvm.exp.f32", runtime::kExpV4F32NEONSymbolName, 4}, - - {"logf", runtime::kLogV4F32NEONSymbolName, 4}, - {"llvm.log.f32", runtime::kLogV4F32NEONSymbolName, 4}, - }; - - const llvm::VecDesc four_wide_vector_functions_sse[] = { - {"expf", runtime::kExpV4F32SSESymbolName, 4}, - {"llvm.exp.f32", runtime::kExpV4F32SSESymbolName, 4}, - - {"logf", runtime::kLogV4F32SSESymbolName, 4}, - {"llvm.log.f32", runtime::kLogV4F32SSESymbolName, 4}, - }; - - const llvm::VecDesc eight_wide_vector_functions_avx[] = { - {"expf", runtime::kExpV8F32AVXSymbolName, 8}, - {"llvm.exp.f32", runtime::kExpV8F32AVXSymbolName, 8}, - - {"logf", runtime::kLogV8F32AVXSymbolName, 8}, - {"llvm.log.f32", runtime::kLogV8F32AVXSymbolName, 8}, - }; - - // These functions are generated by XLA as LLVM IR, so they're always - // available. - const llvm::VecDesc ir_vector_functions[] = { +static std::vector VectorFunctionsForTargetLibraryInfoImpl() { + std::vector result = { {"tanhf", runtime::kTanhV4F32SymbolName, 4}, {"llvm.tanh.f32", runtime::kTanhV4F32SymbolName, 4}, {"tanhf", runtime::kTanhV8F32SymbolName, 8}, {"llvm.tanh.f32", runtime::kTanhV8F32SymbolName, 8}, - }; - llvm::SmallVector features; - feature_string.split(features, ',', -1, /*KeepEmpty=*/false); - auto has_feature = [&features](const llvm::StringRef feature) { - return std::find(features.begin(), features.end(), feature) != - features.end(); - }; + {"expf", runtime::kExpV4F32SymbolName, 4}, + {"llvm.exp.f32", runtime::kExpV4F32SymbolName, 4}, - switch (arch) { - case llvm::Triple::x86: - case llvm::Triple::x86_64: { - if (has_feature("+sse4.1") && available_intrinsics.sse_intrinsics) { - vector_functions.insert(vector_functions.end(), - std::begin(four_wide_vector_functions_sse), - std::end(four_wide_vector_functions_sse)); - } - if (has_feature("+avx") && available_intrinsics.avx_intrinsics) { - vector_functions.insert(vector_functions.end(), - std::begin(eight_wide_vector_functions_avx), - std::end(eight_wide_vector_functions_avx)); - } - break; - } - case llvm::Triple::arm: - case llvm::Triple::aarch64: { - if (has_feature("+neon") && available_intrinsics.neon_intrinsics) { - vector_functions.insert(vector_functions.end(), - std::begin(four_wide_vector_functions_neon), - std::end(four_wide_vector_functions_neon)); - } - break; - } - default: - break; - } + {"expf", runtime::kExpV8F32SymbolName, 8}, + {"llvm.exp.f32", runtime::kExpV8F32SymbolName, 8}, - vector_functions.insert(vector_functions.end(), - std::begin(ir_vector_functions), - std::end(ir_vector_functions)); + {"logf", runtime::kLogV4F32SymbolName, 4}, + {"llvm.log.f32", runtime::kLogV4F32SymbolName, 4}, - return vector_functions; + {"logf", runtime::kLogV8F32SymbolName, 8}, + {"llvm.log.f32", runtime::kLogV8F32SymbolName, 8}, + }; + return result; } -} // namespace void CompilerFunctor::AddTargetInfoPasses( llvm::legacy::PassManagerBase* passes) const { @@ -282,9 +190,7 @@ void CompilerFunctor::AddTargetInfoPasses( auto target_library_info_impl = MakeUnique(target_triple); target_library_info_impl->addVectorizableFunctions( - VectorFunctionsForTargetLibraryInfoImpl( - target_triple.getArch(), target_machine_->getTargetFeatureString(), - available_intrinsics_)); + VectorFunctionsForTargetLibraryInfoImpl()); passes->add( new llvm::TargetLibraryInfoWrapperPass(*target_library_info_impl)); passes->add(createTargetTransformInfoWrapperPass( diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.h b/tensorflow/compiler/xla/service/cpu/compiler_functor.h index 8cdd049e7b773bdc455db627ff1749997d621ee4..c38b896c5019b48fd2a16a51abd59e12ebdb29eb 100644 --- a/tensorflow/compiler/xla/service/cpu/compiler_functor.h +++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.h @@ -31,21 +31,10 @@ namespace cpu { // Orc JIT compile layer. class CompilerFunctor { public: - // Describes the set of vector intrinsics available to the generated code. - struct VectorIntrinsics { - bool sse_intrinsics; - bool avx_intrinsics; - bool neon_intrinsics; - }; - - // Returns a VectorIntrinsics where all intrinsics are available. - static VectorIntrinsics AllIntrinsics(); - explicit CompilerFunctor( llvm::TargetMachine* target_machine, const Disassembler* disassembler, int opt_level, bool optimize_for_size, bool enable_fast_math, bool disable_expensive_passes, - const VectorIntrinsics& available_intrinsics, LLVMCompiler::ModuleHook pre_optimization_hook = nullptr, LLVMCompiler::ModuleHook post_optimization_hook = nullptr) : target_machine_(target_machine), @@ -54,12 +43,11 @@ class CompilerFunctor { optimize_for_size_(optimize_for_size), enable_fast_math_(enable_fast_math), disable_expensive_passes_(disable_expensive_passes), - available_intrinsics_(available_intrinsics), pre_optimization_hook_(pre_optimization_hook), post_optimization_hook_(post_optimization_hook) {} // Compile a Module to an ObjectFile. - llvm::object::OwningBinary operator()( + std::unique_ptr operator()( llvm::Module& module) const; // NOLINT private: @@ -78,7 +66,6 @@ class CompilerFunctor { const bool optimize_for_size_; const bool enable_fast_math_; const bool disable_expensive_passes_; - const VectorIntrinsics available_intrinsics_; LLVMCompiler::ModuleHook pre_optimization_hook_; LLVMCompiler::ModuleHook post_optimization_hook_; }; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index d13a97bcc9a84afb22556389b4cdcd985f58d445..0d15be5a23ec6e3ced551d6f9d05078d17b2612c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -47,6 +47,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h" #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h" #include "tensorflow/compiler/xla/service/cpu/cpu_copy_insertion.h" @@ -275,6 +276,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) { pass.AddPass(); pass.AddPass(); pass.AddPass(); + pass.AddPass(); } pipeline.AddPass( [](const HloInstruction& dot, @@ -888,13 +890,11 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, options::OptimizeForSizeRequested(module->config()), module->config().debug_options().xla_enable_fast_math(), module->config().debug_options().xla_llvm_disable_expensive_passes(), - CompilerFunctor::AllIntrinsics(), pre_optimization_ir_dump_hook, - post_optimization_ir_dump_hook); - llvm::object::OwningBinary object_file = + pre_optimization_ir_dump_hook, post_optimization_ir_dump_hook); + std::unique_ptr object_file = compiler_functor(llvm_module); - llvm::StringRef object_file_data_ref = object_file.getBinary()->getData(); - ObjectFileData object_file_data(object_file_data_ref.begin(), - object_file_data_ref.end()); + ObjectFileData object_file_data(object_file->getBufferStart(), + object_file->getBufferEnd()); BufferSizes buffer_sizes; for (const BufferAllocation& allocation : assignment->Allocations()) { diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index 802d0a6fb46890b31d14b1fbf3b2e7d6520caccc..c053703c3524a47ee1de9681c1b986edbf109430 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -63,7 +63,7 @@ CpuExecutable::CpuExecutable( assignment_(std::move(assignment)) { // Resolve symbols in the constructor rather than at execution time to avoid // races because FindSymbol is not thread safe. - llvm::JITSymbol sym = jit_->FindSymbol(entry_function_name); + llvm::JITSymbol sym = jit_->FindCompiledSymbol(entry_function_name); // We expect to find the symbol provided with entry_function_name; otherwise // this is an internal error. CHECK(sym) << "Symbol " << entry_function_name << " not found."; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc index 482e04052d5a914eab0e5bff2c7a83f3b698052f..0fc5a746bbbc7685ff5d4647111a750e7d7b1c19 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc @@ -30,7 +30,6 @@ bool CanBeLoopFused(const HloInstruction& hlo) { // These are the only ones we fuse since we rely on effective elemental IR // generation. return hlo.IsElementwise() || // - hlo.opcode() == HloOpcode::kBitcast || hlo.opcode() == HloOpcode::kBroadcast || hlo.opcode() == HloOpcode::kConcatenate || hlo.opcode() == HloOpcode::kDynamicSlice || diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 595c3f55b321f47e2312b93e0c238c7637495d77..6ed1cd31b18f6360bdd7fd41bd5be2e657b310a5 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -77,7 +77,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Basic_1) { EXPECT_THAT(computation->root_instruction(), op::Fusion()); } -TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) { +TEST_F(InstructionFusionTest, DotOperationNoFusion_Bitcast) { HloComputation::Builder builder(TestName()); HloInstruction* arg0 = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {2, 512, 2, 128}), "arg0")); @@ -94,8 +94,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) { auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); EXPECT_EQ(dot, computation->root_instruction()); - EXPECT_TRUE(CpuInstructionFusion().Run(module.get()).ValueOrDie()); - EXPECT_THAT(computation->root_instruction(), op::Fusion()); + EXPECT_FALSE(CpuInstructionFusion().Run(module.get()).ValueOrDie()); } TEST_F(InstructionFusionTest, DotOperationFusion_Reshape) { @@ -244,35 +243,33 @@ class OpcodeFusionTest : public InstructionFusionTest { } }; -TEST_F(OpcodeFusionTest, Exponential_Bitcast_Negate) { +TEST_F(OpcodeFusionTest, Exponential_Reshape_Negate) { HloComputation::Builder builder(TestName()); Shape param_shape = ShapeUtil::MakeShape(F32, {1, 4}); Shape result_shape = ShapeUtil::MakeShape(F32, {4}); HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "param")); - // InstructionFusion::ShouldFuse() precludes fusing a bitcast whose operand - // is a parameter, so create an operand between the parameter and bitcast. HloInstruction* exp1 = builder.AddInstruction( HloInstruction::CreateUnary(param_shape, HloOpcode::kExp, param0)); - HloInstruction* bitcast2 = builder.AddInstruction( - HloInstruction::CreateUnary(result_shape, HloOpcode::kBitcast, exp1)); + HloInstruction* reshape2 = + builder.AddInstruction(HloInstruction::CreateReshape(result_shape, exp1)); builder.AddInstruction( - HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, bitcast2)); + HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, reshape2)); auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); RunFusionAndCheckOpcodesWereFused( - module.get(), {HloOpcode::kNegate, HloOpcode::kBitcast, HloOpcode::kExp, + module.get(), {HloOpcode::kNegate, HloOpcode::kReshape, HloOpcode::kExp, HloOpcode::kParameter}); } -TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { +TEST_F(OpcodeFusionTest, Broadcast_Reshape_DynamicSlice_Tanh) { HloComputation::Builder builder(TestName()); Shape param_shape = ShapeUtil::MakeShape(F32, {8}); Shape starts_shape = ShapeUtil::MakeShape(F32, {2}); Shape broadcast_shape = ShapeUtil::MakeShape(F32, {1, 8, 8}); - Shape bitcast_shape = ShapeUtil::MakeShape(F32, {8, 8}); + Shape reshape_shape = ShapeUtil::MakeShape(F32, {8, 8}); Shape dynamic_slice_shape = ShapeUtil::MakeShape(F32, {4, 4}); HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "param")); @@ -280,11 +277,11 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { HloInstruction::CreateParameter(1, starts_shape, "starts")); HloInstruction* broadcast2 = builder.AddInstruction( HloInstruction::CreateBroadcast(broadcast_shape, param0, {1})); - HloInstruction* bitcast3 = builder.AddInstruction(HloInstruction::CreateUnary( - bitcast_shape, HloOpcode::kBitcast, broadcast2)); + HloInstruction* reshape3 = builder.AddInstruction( + HloInstruction::CreateReshape(reshape_shape, broadcast2)); HloInstruction* dynamic_slice4 = builder.AddInstruction(HloInstruction::CreateDynamicSlice( - dynamic_slice_shape, bitcast3, param1, {4, 4})); + dynamic_slice_shape, reshape3, param1, {4, 4})); builder.AddInstruction(HloInstruction::CreateUnary( dynamic_slice_shape, HloOpcode::kTanh, dynamic_slice4)); @@ -293,7 +290,7 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { RunFusionAndCheckOpcodesWereFused( module.get(), - {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kBitcast, + {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kReshape, HloOpcode::kBroadcast, HloOpcode::kParameter, HloOpcode::kParameter}); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 1ef45dbec39a0880ebb123ba3fcd1fd6c89eb39a..9a3bd68c80c6e8bcdb231c63ba025d1f73619eb7 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -31,17 +31,25 @@ XfeedManager* GetXfeedManager() { return manager; } +extern const char* const kEigenMatMulF16SymbolName = + "__xla_cpu_runtime_EigenMatMulF16"; extern const char* const kEigenMatMulF32SymbolName = "__xla_cpu_runtime_EigenMatMulF32"; extern const char* const kEigenMatMulF64SymbolName = "__xla_cpu_runtime_EigenMatMulF64"; +extern const char* const kEigenConvF16SymbolName = + "__xla_cpu_runtime_EigenConvF16"; extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; +extern const char* const kEigenSingleThreadedMatMulF16SymbolName = + "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF32"; extern const char* const kEigenSingleThreadedMatMulF64SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF64"; +extern const char* const kEigenSingleThreadedConvF16SymbolName = + "__xla_cpu_runtime_EigenSingleThreadedConvF16"; extern const char* const kEigenSingleThreadedConvF32SymbolName = "__xla_cpu_runtime_EigenSingleThreadedConvF32"; extern const char* const kAcquireInfeedBufferForDequeueSymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index 3e1f08071119c938619d02777513e5b834077118..e61d6ea28b633398863357541e056ee887582f9c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -41,12 +41,16 @@ namespace runtime { // the actual symbol. // 2. When using ahead-of-time compilation, the linker can resolve the name // because it is a symbol in the cpu_runtime library. +extern const char* const kEigenMatMulF16SymbolName; extern const char* const kEigenMatMulF32SymbolName; extern const char* const kEigenMatMulF64SymbolName; +extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; +extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; +extern const char* const kEigenSingleThreadedConvF16SymbolName; extern const char* const kEigenSingleThreadedConvF32SymbolName; extern const char* const kAcquireInfeedBufferForDequeueSymbolName; extern const char* const kReleaseInfeedBufferAfterDequeueSymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc deleted file mode 100644 index b1c1142e8d988be2ca00809b4be505466071c72f..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc +++ /dev/null @@ -1,43 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h" - -#define EIGEN_USE_THREADS - -#include "third_party/eigen3/Eigen/Core" - -#ifdef TF_XLA_HAS_AVX -xla::cpu::runtime::V8F32AVX __xla_cpu_runtime_ExpV8F32AVX( - xla::cpu::runtime::V8F32AVX x) { - return Eigen::internal::pexp(x); -} - -xla::cpu::runtime::V8F32AVX __xla_cpu_runtime_LogV8F32AVX( - xla::cpu::runtime::V8F32AVX x) { - return Eigen::internal::plog(x); -} -#endif // TF_XLA_HAS_AVX - -namespace xla { -namespace cpu { -namespace runtime { - -const char *const kExpV8F32AVXSymbolName = "__xla_cpu_runtime_ExpV8F32AVX"; -const char *const kLogV8F32AVXSymbolName = "__xla_cpu_runtime_LogV8F32AVX"; - -} // namespace runtime -} // namespace cpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h deleted file mode 100644 index e5c782f93f54dc9f8f76fce7e4735a60e8847583..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h +++ /dev/null @@ -1,60 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This header declares functions which may be called by the generated code on -// the CPU. Calls to these functions must be resolved explicitly in the JIT in -// xla::cpu::SimpleResolver. It also defines a per-CpuExecutable context -// which is used to cache expensive state and resources utilized by the -// aforementioned functions. - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_AVX_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_AVX_H_ - -#include "tensorflow/core/platform/macros.h" - -#if defined(__AVX__) -#include -#define TF_XLA_HAS_AVX -#endif - -namespace xla { -namespace cpu { -namespace runtime { - -extern const char *const kExpV8F32AVXSymbolName; -extern const char *const kLogV8F32AVXSymbolName; - -#ifdef TF_XLA_HAS_AVX -typedef __m256 V8F32AVX; -#endif -} // namespace runtime -} // namespace cpu -} // namespace xla - -extern "C" { - -#ifdef TF_XLA_HAS_AVX -// The following functions are vectorized versions of a selection of libm -// library functions. -// References to these functions are created by the LLVM vectorizer. -xla::cpu::runtime::V8F32AVX __xla_cpu_runtime_ExpV8F32AVX( - xla::cpu::runtime::V8F32AVX x); - -xla::cpu::runtime::V8F32AVX __xla_cpu_runtime_LogV8F32AVX( - xla::cpu::runtime::V8F32AVX x); -#endif -} - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_AVX_H_ diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.cc deleted file mode 100644 index 8099b722f10ecb83f7cf6c58ba2abb783478b97f..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.cc +++ /dev/null @@ -1,46 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h" - -#define EIGEN_USE_THREADS - -#include "third_party/eigen3/Eigen/Core" - -#ifdef TF_XLA_HAS_NEON - -xla::cpu::runtime::V4F32NEON __xla_cpu_runtime_ExpV4F32NEON( - xla::cpu::runtime::V4F32NEON x) { - return Eigen::internal::pexp(x); -} - -xla::cpu::runtime::V4F32NEON __xla_cpu_runtime_LogV4F32NEON( - xla::cpu::runtime::V4F32NEON x) { - Eigen::internal::Packet4f p = x; - return Eigen::internal::plog(p); -} - -#endif // TF_XLA_HAS_NEON - -namespace xla { -namespace cpu { -namespace runtime { - -const char *const kExpV4F32NEONSymbolName = "__xla_cpu_runtime_ExpV4F32NEON"; -const char *const kLogV4F32NEONSymbolName = "__xla_cpu_runtime_LogV4F32NEON"; - -} // namespace runtime -} // namespace cpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h deleted file mode 100644 index 2f5d1a872aaf3868d6d27f88a4f05c778d45660f..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h +++ /dev/null @@ -1,62 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_NEON_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_NEON_H_ - -// This header declares functions which may be called by the generated code on -// the CPU. Calls to these functions must be resolved explicitly in the JIT in -// xla::cpu::SimpleResolver. - -#include "tensorflow/core/platform/macros.h" - -#ifdef __ARM_NEON__ -// For the other runtimes (AVX, SSE4.1) we define the vector type directly using -// __attribute__((__vector_size__(*))). Unfortunately, the typedef for the ARM -// NEON SIMD types is not portable, so the type has to come from -#include -#define TF_XLA_HAS_NEON -#endif // __ARM_NEON__ - -namespace xla { -namespace cpu { -namespace runtime { - -extern const char *const kExpV4F32NEONSymbolName; -extern const char *const kLogV4F32NEONSymbolName; - -#ifdef TF_XLA_HAS_NEON -typedef float32x4_t V4F32NEON; -#endif // TF_XLA_HAS_NEON - -} // namespace runtime -} // namespace cpu -} // namespace xla - -extern "C" { - -#ifdef TF_XLA_HAS_NEON -// The following functions are vectorized versions of a selection of libm -// library functions. -// References to these functions are created by the LLVM vectorizer. -xla::cpu::runtime::V4F32NEON __xla_cpu_runtime_ExpV4F32NEON( - xla::cpu::runtime::V4F32NEON x); - -xla::cpu::runtime::V4F32NEON __xla_cpu_runtime_LogV4F32NEON( - xla::cpu::runtime::V4F32NEON x); -#endif // TF_XLA_HAS_NEON -} - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_NEON_H_ diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc deleted file mode 100644 index d8ecf231cc8c859ac88e1ef1478f7107cd86a052..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h" - -#define EIGEN_USE_THREADS - -#include "third_party/eigen3/Eigen/Core" - -#ifdef TF_XLA_HAS_SSE4_1 - -xla::cpu::runtime::V4F32SSE __xla_cpu_runtime_ExpV4F32SSE( - xla::cpu::runtime::V4F32SSE x) { - Eigen::internal::Packet4f p = x; - return Eigen::internal::pexp(p); -} - -xla::cpu::runtime::V4F32SSE __xla_cpu_runtime_LogV4F32SSE( - xla::cpu::runtime::V4F32SSE x) { - Eigen::internal::Packet4f p = x; - return Eigen::internal::plog(p); -} - -#endif // TF_XLA_HAS_SSE4_1 - -namespace xla { -namespace cpu { -namespace runtime { - -const char *const kExpV4F32SSESymbolName = "__xla_cpu_runtime_ExpV4F32SSE"; -const char *const kLogV4F32SSESymbolName = "__xla_cpu_runtime_LogV4F32SSE"; - -} // namespace runtime -} // namespace cpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h deleted file mode 100644 index aeb1eda23f76a6b5cb520b6673e0a011fa1130c7..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This header declares functions which may be called by the generated code on -// the CPU. Calls to these functions must be resolved explicitly in the JIT in -// xla::cpu::SimpleResolver. It also defines a per-CpuExecutable context -// which is used to cache expensive state and resources utilized by the -// aforementioned functions. - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_SSE4_1_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_SSE4_1_H_ - -#include "tensorflow/core/platform/macros.h" - -// MSVC does not have __SSE4_1__ macro. Eigen enables EIGEN_VECTORIZE_SSE4_1 -// when __AVX__ is defined, we should do the same. -#if defined(__SSE4_1__) || (defined(_MSC_VER) && defined(__AVX__)) -#include -#define TF_XLA_HAS_SSE4_1 -#endif - -namespace xla { -namespace cpu { -namespace runtime { - -extern const char *const kExpV4F32SSESymbolName; -extern const char *const kLogV4F32SSESymbolName; - -#ifdef TF_XLA_HAS_SSE4_1 -typedef __m128 V4F32SSE; -#endif - -} // namespace runtime -} // namespace cpu -} // namespace xla - -extern "C" { - -#ifdef TF_XLA_HAS_SSE4_1 -// The following functions are vectorized versions of a selection of libm -// library functions. -// References to these functions are created by the LLVM vectorizer. -xla::cpu::runtime::V4F32SSE __xla_cpu_runtime_ExpV4F32SSE( - xla::cpu::runtime::V4F32SSE x); - -xla::cpu::runtime::V4F32SSE __xla_cpu_runtime_LogV4F32SSE( - xla::cpu::runtime::V4F32SSE x); -#endif -} - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_SSE4_1_H_ diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index c9fc586b9a4c06eb9e1f111d8f9bd2f717990aab..6f06256e08e8e3342e77c7c79a2a47465b89eca3 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -549,7 +549,7 @@ DotOpEmitter::DotOpEmitter( const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features) { PrimitiveType type = target_array.GetShape().element_type(); - TF_RET_CHECK(F32 == type || F64 == type || C64 == type); + TF_RET_CHECK(F16 == type || F32 == type || F64 == type || C64 == type); DotOpEmitter dot_emitter(dot, transpose_lhs, transpose_rhs, target_array, lhs_array, rhs_array, addend_array, executable_run_options_value, ir_builder, @@ -919,6 +919,12 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() { llvm::Type* float_type; const char* fn_name; switch (type) { + case F16: + fn_name = multi_threaded_eigen + ? runtime::kEigenMatMulF16SymbolName + : runtime::kEigenSingleThreadedMatMulF16SymbolName; + float_type = ir_builder_->getHalfTy(); + break; case F32: fn_name = multi_threaded_eigen ? runtime::kEigenMatMulF32SymbolName @@ -1051,7 +1057,8 @@ static bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, // The inputs and the output must // 1) be matrices with no padding, and // 2) have an allowed element type. - return output_shape.element_type() == F32 && + PrimitiveType output_primitive_type = output_shape.element_type(); + return (output_primitive_type == F32 || output_primitive_type == F16) && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 0b2d3d47463b745049807e9afa55360434ad522b..3b8056d50500cac381a1c5ad6b05028476504a47 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -801,7 +801,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { auto rhs = dot->operand(1); TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( /*instruction=*/*dot, /*operands=*/{lhs, rhs}, - /*supported_types=*/{F32, F64, C64})); + /*supported_types=*/{F16, F32, F64, C64})); const DotDimensionNumbers& dnums = dot->dot_dimension_numbers(); if (dnums.lhs_batch_dimensions_size() > 0 || dnums.rhs_batch_dimensions_size() > 0) { @@ -849,7 +849,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { const auto& window = convolution->window(); TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( /*instruction=*/*convolution, /*operands=*/{lhs, rhs}, - /*supported_types=*/{F32, C64})); + /*supported_types=*/{F16, F32, C64})); const ConvolutionDimensionNumbers& dnums = convolution->convolution_dimension_numbers(); @@ -928,25 +928,30 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { int64 rhs_col_dilation = one_dim_convolution ? 1 : window.dimensions(1).window_dilation(); - // Args have been computed, make the call. - llvm::Type* float_ptr_type = ir_builder_.getFloatTy()->getPointerTo(); + PrimitiveType primitive_type = lhs->shape().element_type(); + llvm::Type* ir_ptr_type = primitive_type == F16 + ? ir_builder_.getHalfTy()->getPointerTo() + : ir_builder_.getFloatTy()->getPointerTo(); llvm::Type* int64_type = ir_builder_.getInt64Ty(); llvm::Type* int8_ptr_type = ir_builder_.getInt8Ty()->getPointerTo(); llvm::FunctionType* conv_type = llvm::FunctionType::get( ir_builder_.getVoidTy(), - {int8_ptr_type, float_ptr_type, float_ptr_type, float_ptr_type, - int64_type, int64_type, int64_type, int64_type, - int64_type, int64_type, int64_type, int64_type, - int64_type, int64_type, int64_type, int64_type, - int64_type, int64_type, int64_type, int64_type, - int64_type, int64_type, int64_type, int64_type}, + {int8_ptr_type, ir_ptr_type, ir_ptr_type, ir_ptr_type, int64_type, + int64_type, int64_type, int64_type, int64_type, int64_type, + int64_type, int64_type, int64_type, int64_type, int64_type, + int64_type, int64_type, int64_type, int64_type, int64_type, + int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); bool multi_threaded_eigen = hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); const char* fn_name = - (multi_threaded_eigen - ? runtime::kEigenConvF32SymbolName - : runtime::kEigenSingleThreadedConvF32SymbolName); + primitive_type == F16 + ? (multi_threaded_eigen + ? runtime::kEigenConvF16SymbolName + : runtime::kEigenSingleThreadedConvF16SymbolName) + : (multi_threaded_eigen + ? runtime::kEigenConvF32SymbolName + : runtime::kEigenSingleThreadedConvF32SymbolName); llvm::Function* conv_func = llvm::cast( module_->getOrInsertFunction(fn_name, conv_type)); conv_func->setCallingConv(llvm::CallingConv::C); @@ -956,9 +961,9 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { conv_func, { GetExecutableRunOptionsArgument(), ir_builder_.CreateBitCast( - GetEmittedValueFor(convolution), float_ptr_type), - ir_builder_.CreateBitCast(lhs_address, float_ptr_type), - ir_builder_.CreateBitCast(rhs_address, float_ptr_type), + GetEmittedValueFor(convolution), ir_ptr_type), + ir_builder_.CreateBitCast(lhs_address, ir_ptr_type), + ir_builder_.CreateBitCast(rhs_address, ir_ptr_type), ir_builder_.getInt64(input_batch), ir_builder_.getInt64(input_rows), ir_builder_.getInt64(input_cols), @@ -1332,7 +1337,7 @@ IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator( if (ShapeUtil::ElementIsComplex(root_shape)) { // TODO(b/65408531): Complex add could by done via bitcast to // Complex multiply would be more challenging. We could perhaps use a - // strided load to get all reals in a vector, all imags in a vector, or use + // strided load to get all reals in a vector, all images in a vector, or use // CreateShuffleVector on a bitcast to float x [2N]. *failure_reason = "complex values not supported"; return nullptr; @@ -2069,7 +2074,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( /*instruction=*/*root, /*operands=*/{lhs, rhs}, - /*supported_types=*/{F32})); + /*supported_types=*/{F16, F32})); llvm_ir::IrArray lhs_array(GetIrArrayFor(lhs)); llvm_ir::IrArray rhs_array(GetIrArrayFor(rhs)); diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.cc b/tensorflow/compiler/xla/service/cpu/ir_function.cc index ca8c290dd1c4959e42026c3917d37f8fc95a1011..2d6f2f3818a7bd4424aaa7d918ca86abef15c0e9 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_function.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_function.cc @@ -209,9 +209,9 @@ std::vector GetArrayFunctionCallArguments( parameter_addresses[i], ir_builder->getInt8PtrTy(), AsStringRef(tensorflow::strings::StrCat(name, "_parameter_", i, "_address_as_i8ptr"))); - llvm::Value* slot_in_param_adresses = ir_builder->CreateInBoundsGEP( + llvm::Value* slot_in_param_addresses = ir_builder->CreateInBoundsGEP( parameter_addresses_buffer, {ir_builder->getInt64(i)}); - ir_builder->CreateStore(parameter_as_i8ptr, slot_in_param_adresses); + ir_builder->CreateStore(parameter_as_i8ptr, slot_in_param_addresses); } const auto to_int8_ptr = [=](llvm::Value* ptr) { diff --git a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc index 0336fa61312e5cd626ae38ddd29875bff256212a..2e5cc96098241415b82f225afc81981f3e1069e0 100644 --- a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc @@ -20,6 +20,8 @@ limitations under the License. #include "llvm/IR/Intrinsics.h" #include "llvm/IR/Verifier.h" #include "llvm/Transforms/Utils/Cloning.h" +#include "tensorflow/compiler/xla/service/cpu/vector_support_library.h" +#include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -28,6 +30,10 @@ namespace runtime { const char* const kTanhV4F32SymbolName = "__xla_cpu_runtime_TanhV4F32"; const char* const kTanhV8F32SymbolName = "__xla_cpu_runtime_TanhV8F32"; +const char* const kExpV4F32SymbolName = "__xla_cpu_runtime_ExpV4F32"; +const char* const kExpV8F32SymbolName = "__xla_cpu_runtime_ExpV8F32"; +const char* const kLogV4F32SymbolName = "__xla_cpu_runtime_LogV4F32AVX"; +const char* const kLogV8F32SymbolName = "__xla_cpu_runtime_LogV8F32AVX"; namespace { llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, @@ -42,27 +48,23 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, } llvm::LLVMContext* context = &module->getContext(); - llvm::Type* float_type = llvm::Type::getFloatTy(*context); - llvm::VectorType* vector_type = - llvm::VectorType::get(float_type, vector_width); llvm::BasicBlock* vector_tanh_body = llvm::BasicBlock::Create(*context, "body", vector_tanh_function); llvm::IRBuilder<> ir_builder(vector_tanh_body); - llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); ir_builder.setFastMathFlags(fast_math_flags); + VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "tanh_f32"); + llvm::Value* input = &*vector_tanh_function->arg_begin(); - CHECK_EQ(input->getType(), vector_type); + CHECK_EQ(input->getType(), vsl.vector_type()); // This implements the same rational interpolant as implemented in Eigen3. - llvm::Value* input_clamped = llvm_ir::EmitFloatMin( - llvm_ir::EmitFloatMax(input, llvm::ConstantFP::get(vector_type, -9.0), - &ir_builder), - llvm::ConstantFP::get(vector_type, 9.0), &ir_builder); + llvm::Value* input_clamped = + vsl.Clamp(input, /*low=*/GetIeeeF32(-9.0), /*high=*/GetIeeeF32(9.0)); std::array numerator_coeffs{ -2.76076847742355e-16f, 2.00018790482477e-13f, -8.60467152213735e-11f, @@ -73,31 +75,230 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, 1.19825839466702e-06f, 1.18534705686654e-04f, 2.26843463243900e-03f, 4.89352518554385e-03f}; - llvm::Value* input_squared = - ir_builder.CreateFMul(input_clamped, input_clamped); - llvm::Value* numerator = - llvm::ConstantFP::get(vector_type, numerator_coeffs[0]); + llvm::Value* input_squared = vsl.Mul(input_clamped, input_clamped); + llvm::Value* numerator = vsl.SplatFloat(GetIeeeF32(numerator_coeffs[0])); for (int i = 1; i < numerator_coeffs.size(); i++) { - numerator = ir_builder.CreateFAdd( - ir_builder.CreateFMul(input_squared, numerator), - llvm::ConstantFP::get(vector_type, numerator_coeffs[i])); + numerator = + vsl.MulAdd(input_squared, numerator, GetIeeeF32(numerator_coeffs[i])); } - numerator = ir_builder.CreateFMul(input_clamped, numerator); - llvm::Value* denominator = - llvm::ConstantFP::get(vector_type, denominator_coeffs[0]); + numerator = vsl.Mul(input_clamped, numerator); + + llvm::Value* denominator = vsl.SplatFloat(GetIeeeF32(denominator_coeffs[0])); for (int i = 1; i < denominator_coeffs.size(); i++) { - denominator = ir_builder.CreateFAdd( - ir_builder.CreateFMul(input_squared, denominator), - llvm::ConstantFP::get(vector_type, denominator_coeffs[i])); + denominator = vsl.MulAdd(input_squared, denominator, + GetIeeeF32(denominator_coeffs[i])); } - llvm::Value* result = ir_builder.CreateFDiv(numerator, denominator); + llvm::Value* result = vsl.Div(numerator, denominator); ir_builder.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_tanh_function)); return vector_tanh_function; } + +llvm::Function* EmitVectorF32ExpIfNeeded(llvm::Module* module, + llvm::StringRef function_name, + int vector_width, + bool enable_fast_math) { + llvm::Function* vector_exp_function = module->getFunction(function_name); + if (vector_exp_function == nullptr) { + // If the function declaration is not present in the module, there can't be + // any calls to resolve. Don't emit the function in this case. + return nullptr; + } + + llvm::LLVMContext* context = &module->getContext(); + + llvm::BasicBlock* vector_exp_body = + llvm::BasicBlock::Create(*context, "body", vector_exp_function); + + llvm::IRBuilder<> ir_builder(vector_exp_body); + llvm::FastMathFlags fast_math_flags; + fast_math_flags.setFast(); + ir_builder.setFastMathFlags(fast_math_flags); + + VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "exp_f32"); + + // This implements the same polynomial approximation as implemented in Eigen3. + + const llvm::APFloat half = GetIeeeF32(0.5); + const llvm::APFloat one = GetIeeeF32(1.0); + + const llvm::APFloat exp_hi = GetIeeeF32(88.3762626647950); + const llvm::APFloat exp_lo = GetIeeeF32(-88.3762626647949); + + const llvm::APFloat cephes_LOG2EF = GetIeeeF32(1.44269504088896341); + const llvm::APFloat cephes_exp_C1 = GetIeeeF32(0.693359375); + const llvm::APFloat cephes_exp_C2 = GetIeeeF32(-2.12194440e-4); + + const llvm::APFloat cephes_exp_p0 = GetIeeeF32(1.9875691500E-4); + const llvm::APFloat cephes_exp_p1 = GetIeeeF32(1.3981999507E-3); + const llvm::APFloat cephes_exp_p2 = GetIeeeF32(8.3334519073E-3); + const llvm::APFloat cephes_exp_p3 = GetIeeeF32(4.1665795894E-2); + const llvm::APFloat cephes_exp_p4 = GetIeeeF32(1.6666665459E-1); + const llvm::APFloat cephes_exp_p5 = GetIeeeF32(5.0000001201E-1); + + llvm::Value* input = &*vector_exp_function->arg_begin(); + llvm::Value* input_clamped = + vsl.Clamp(input, /*low=*/exp_lo, /*high=*/exp_hi); + llvm::Value* fx = vsl.Floor(vsl.MulAdd(input_clamped, cephes_LOG2EF, half)); + llvm::Value* tmp = vsl.Mul(cephes_exp_C1, fx); + llvm::Value* z = vsl.Mul(cephes_exp_C2, fx); + llvm::Value* x = vsl.Sub(input_clamped, tmp); + x = vsl.Sub(x, z); + z = vsl.Mul(x, x); + + llvm::Value* y = vsl.MulAdd(x, cephes_exp_p0, cephes_exp_p1); + y = vsl.MulAdd(y, x, cephes_exp_p2); + y = vsl.MulAdd(y, x, cephes_exp_p3); + y = vsl.MulAdd(y, x, cephes_exp_p4); + y = vsl.MulAdd(y, x, cephes_exp_p5); + y = vsl.MulAdd(y, z, x); + y = vsl.Add(one, y); + + // VectorSupportLibrary (intentionally) can't juggle more than one type at a + // time so drop down to IRBuilder for this bit. + llvm::Value* vector_constant_0x7f = + ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + llvm::Value* vector_constant_23 = + ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + llvm::Type* i32_vector_type = + llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + // fx is clamped so we don't have to worry about it being out of range for + // i32. + llvm::Value* emm0 = ir_builder.CreateFPToSI(fx, i32_vector_type); + emm0 = ir_builder.CreateAdd(emm0, vector_constant_0x7f); + emm0 = ir_builder.CreateShl(emm0, vector_constant_23); + llvm::Value* emm0_f32 = ir_builder.CreateBitCast(emm0, vsl.vector_type()); + + llvm::Value* result = vsl.Max(vsl.Mul(y, emm0_f32), input); + + ir_builder.CreateRet(result); + + DCHECK(!llvm::verifyFunction(*vector_exp_function)); + return vector_exp_function; +} + +llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, + llvm::StringRef function_name, + int vector_width, + bool enable_fast_math) { + llvm::Function* vector_log_function = module->getFunction(function_name); + if (vector_log_function == nullptr) { + // If the function declaration is not present in the module, there can't be + // any calls to resolve. Don't emit the function in this case. + return nullptr; + } + + llvm::LLVMContext* context = &module->getContext(); + + llvm::BasicBlock* vector_log_body = + llvm::BasicBlock::Create(*context, "body", vector_log_function); + + llvm::IRBuilder<> ir_builder(vector_log_body); + llvm::FastMathFlags fast_math_flags; + fast_math_flags.setFast(); + ir_builder.setFastMathFlags(fast_math_flags); + + llvm::Value* input = &*vector_log_function->arg_begin(); + VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "log_f32"); + + const llvm::APFloat half = GetIeeeF32(0.5); + const llvm::APFloat one = GetIeeeF32(1.0); + + // This implements the same polynomial approximation as implemented in Eigen3. + // Returns NaN for x < 0, -INF for x = 0 + const llvm::APFloat cephes_SQRTHF = GetIeeeF32(0.707106781186547524); + const llvm::APFloat cephes_log_p0 = GetIeeeF32(7.0376836292E-2); + const llvm::APFloat cephes_log_p1 = GetIeeeF32(-1.1514610310E-1); + const llvm::APFloat cephes_log_p2 = GetIeeeF32(1.1676998740E-1); + const llvm::APFloat cephes_log_p3 = GetIeeeF32(-1.2420140846E-1); + const llvm::APFloat cephes_log_p4 = GetIeeeF32(+1.4249322787E-1); + const llvm::APFloat cephes_log_p5 = GetIeeeF32(-1.6668057665E-1); + const llvm::APFloat cephes_log_p6 = GetIeeeF32(+2.0000714765E-1); + const llvm::APFloat cephes_log_p7 = GetIeeeF32(-2.4999993993E-1); + const llvm::APFloat cephes_log_p8 = GetIeeeF32(+3.3333331174E-1); + const llvm::APFloat cephes_log_q1 = GetIeeeF32(-2.12194440e-4); + const llvm::APFloat cephes_log_q2 = GetIeeeF32(0.693359375); + + // The smallest non denormalized float number. + const llvm::APFloat min_norm_pos = GetIeeeF32FromBitwiseRep(0x00800000); + const llvm::APFloat minus_inf = GetIeeeF32FromBitwiseRep(0xff800000); + const llvm::APFloat inv_mant_mask = GetIeeeF32FromBitwiseRep(~0x7f800000); + + // invalid_mask is set if x is negative or NaN (and therefore output + // must be NaN). + llvm::Value* invalid_mask = vsl.FCmpULEMask(input, vsl.GetZeroVector()); + llvm::Value* iszero_mask = vsl.FCmpEQMask(input, vsl.GetZeroVector()); + + // Cut off denormalized stuff. + input = vsl.Max(min_norm_pos, input); + + // VectorSupportLibrary (intentionally) can't juggle more than one type at a + // time so drop down to IRBuilder for this bit. + llvm::Value* vector_constant_0x7f = + ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + llvm::Value* vector_constant_23 = + ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + llvm::Type* i32_vector_type = + llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + + llvm::Value* emm0 = ir_builder.CreateLShr( + ir_builder.CreateBitCast(input, i32_vector_type), vector_constant_23); + + // Keep only the fractional part. + input = vsl.FloatAnd(input, inv_mant_mask); + input = vsl.FloatOr(input, half); + + emm0 = ir_builder.CreateSub(emm0, vector_constant_0x7f); + llvm::Value* e = + vsl.Add(one, ir_builder.CreateSIToFP(emm0, vsl.vector_type())); + + // part2: + // if( x < SQRTHF ) { + // e -= 1; + // x = x + x - 1.0; + // } else { x = x - 1.0; } + llvm::Value* mask = vsl.FCmpOLTMask(input, cephes_SQRTHF); + llvm::Value* tmp = vsl.FloatAnd(input, mask); + input = vsl.Sub(input, one); + e = vsl.Sub(e, vsl.FloatAnd(mask, one)); + input = vsl.Add(input, tmp); + + llvm::Value* x2 = vsl.Mul(input, input); + llvm::Value* x3 = vsl.Mul(x2, input); + + llvm::Value *y, *y1, *y2; + y = vsl.MulAdd(input, cephes_log_p0, cephes_log_p1); + y1 = vsl.MulAdd(input, cephes_log_p3, cephes_log_p4); + y2 = vsl.MulAdd(input, cephes_log_p6, cephes_log_p7); + y = vsl.MulAdd(y, input, cephes_log_p2); + y1 = vsl.MulAdd(y1, input, cephes_log_p5); + y2 = vsl.MulAdd(y2, input, cephes_log_p8); + y = vsl.MulAdd(y, x3, y1); + y = vsl.MulAdd(y, x3, y2); + y = vsl.Mul(y, x3); + + y1 = vsl.Mul(cephes_log_q1, e); + tmp = vsl.Mul(half, x2); + y = vsl.Add(y, y1); + input = vsl.Sub(input, tmp); + y2 = vsl.Mul(cephes_log_q2, e); + input = vsl.Add(input, y); + input = vsl.Add(input, y2); + + // Negative arg will be NAN, 0 will be -INF. + llvm::Value* or_lhs = + vsl.FloatAndNot(iszero_mask, vsl.FloatOr(input, invalid_mask)); + llvm::Value* or_rhs = vsl.FloatAnd(iszero_mask, minus_inf); + llvm::Value* result = vsl.FloatOr(or_lhs, or_rhs); + + ir_builder.CreateRet(result); + + DCHECK(!llvm::verifyFunction(*vector_log_function)); + return vector_log_function; +} } // namespace void RewriteIRRuntimeFunctions(llvm::Module* module, bool enable_fast_math) { @@ -108,11 +309,28 @@ void RewriteIRRuntimeFunctions(llvm::Module* module, bool enable_fast_math) { EmitVectorF32TanhIfNeeded(module, kTanhV8F32SymbolName, /*vector_width=*/8, enable_fast_math); + auto* exp_v4f32 = + EmitVectorF32ExpIfNeeded(module, kExpV4F32SymbolName, + /*vector_width=*/4, enable_fast_math); + auto* exp_v8f32 = + EmitVectorF32ExpIfNeeded(module, kExpV8F32SymbolName, + /*vector_width=*/8, enable_fast_math); + + auto* log_v4f32 = + EmitVectorF32LogIfNeeded(module, kLogV4F32SymbolName, + /*vector_width=*/4, enable_fast_math); + auto* log_v8f32 = + EmitVectorF32LogIfNeeded(module, kLogV8F32SymbolName, + /*vector_width=*/8, enable_fast_math); + // Gather all the call sites, force inline them and then delete the vector // function bodies. + // + // TODO(b/73081976): Should we avoid inlining these intrinsics in some cases? std::vector calls_to_inline; - for (auto* function : {tanh_v4f32, tanh_v8f32}) { + for (auto* function : + {tanh_v4f32, tanh_v8f32, exp_v4f32, exp_v8f32, log_v4f32, log_v8f32}) { if (function != nullptr) { for (auto* user : function->users()) { calls_to_inline.push_back(llvm::cast(user)); @@ -125,7 +343,8 @@ void RewriteIRRuntimeFunctions(llvm::Module* module, bool enable_fast_math) { CHECK(llvm::InlineFunction(call_to_inline, inline_function_info)); } - for (auto* function : {tanh_v4f32, tanh_v8f32}) { + for (auto* function : + {tanh_v4f32, tanh_v8f32, exp_v4f32, exp_v8f32, log_v4f32, log_v8f32}) { if (function != nullptr) { function->eraseFromParent(); } diff --git a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h index 7f31fb98b0d03c16ef40bff9822227e01f6be46b..5553972677512617ccb6ac4f57a4d33400b664e3 100644 --- a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTINE_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTINE_H_ +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTIME_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTIME_H_ #include "llvm/IR/Module.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" @@ -25,6 +25,10 @@ namespace runtime { extern const char* const kTanhV4F32SymbolName; extern const char* const kTanhV8F32SymbolName; +extern const char* const kExpV4F32SymbolName; +extern const char* const kExpV8F32SymbolName; +extern const char* const kLogV4F32SymbolName; +extern const char* const kLogV8F32SymbolName; // The following CPU runtime functions have LLVM-IR only implementations: // @@ -40,4 +44,4 @@ void RewriteIRRuntimeFunctions(llvm::Module* module, bool enable_fast_math); } // namespace cpu } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTINE_H_ +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_LLVM_IR_RUNTIME_H_ diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc index cd997f07890cdc1d9a546ede58cc1d992b6416ae..07a9f0efcb64db4b2ff0c6518d4b48eee9a505e0 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc @@ -394,7 +394,7 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions( for (auto& entry : *function_names_) { tensorflow::mutex_lock lock(jit_mutex_); HloInstruction* instruction = entry.first; - llvm::JITSymbol sym = jit_->FindSymbol(entry.second); + llvm::JITSymbol sym = jit_->FindCompiledSymbol(entry.second); TF_RET_CHECK(sym); InsertOrDie( &functions, instruction, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc index c2f64eb27a554d17ebe2a94dba334fe378bd7254..3905e7ff2a14d25813e345399e692f9e0f4bd0af 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc @@ -34,7 +34,26 @@ TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConvF32( int64 lhs_col_dilation, int64 rhs_row_dilation, int64 rhs_col_dilation) { const xla::ExecutableRunOptions* run_options = static_cast(run_options_ptr); - tensorflow::xla::EigenConvF32Impl( + tensorflow::xla::EigenConvImpl( + *run_options->intra_op_thread_pool(), out, lhs, rhs, input_batch, + input_rows, input_cols, input_channels, kernel_rows, kernel_cols, + kernel_channels, kernel_filters, output_rows, output_cols, row_stride, + col_stride, padding_top, padding_bottom, padding_left, padding_right, + lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation); +} + +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConvF16( + const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 input_batch, int64 input_rows, int64 input_cols, + int64 input_channels, int64 kernel_rows, int64 kernel_cols, + int64 kernel_channels, int64 kernel_filters, int64 output_rows, + int64 output_cols, int64 row_stride, int64 col_stride, int64 padding_top, + int64 padding_bottom, int64 padding_left, int64 padding_right, + int64 lhs_row_dilation, int64 lhs_col_dilation, int64 rhs_row_dilation, + int64 rhs_col_dilation) { + const xla::ExecutableRunOptions* run_options = + static_cast(run_options_ptr); + tensorflow::xla::EigenConvImpl( *run_options->intra_op_thread_pool(), out, lhs, rhs, input_batch, input_rows, input_cols, input_channels, kernel_rows, kernel_cols, kernel_channels, kernel_filters, output_rows, output_cols, row_stride, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h index 05ae094691fd9a7ca83b902145c0750fafdc529a..39e20ed45639040110b99ddb52eb6f6dab26dfaa 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h @@ -34,6 +34,20 @@ extern void __xla_cpu_runtime_EigenConvF32( tensorflow::int64 lhs_col_dilation, tensorflow::int64 rhs_row_dilation, tensorflow::int64 rhs_col_dilation); +extern void __xla_cpu_runtime_EigenConvF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, + tensorflow::int64 input_batch, tensorflow::int64 input_rows, + tensorflow::int64 input_cols, tensorflow::int64 input_channels, + tensorflow::int64 kernel_rows, tensorflow::int64 kernel_cols, + tensorflow::int64 kernel_channels, tensorflow::int64 kernel_filters, + tensorflow::int64 output_rows, tensorflow::int64 output_cols, + tensorflow::int64 row_stride, tensorflow::int64 col_stride, + tensorflow::int64 padding_top, tensorflow::int64 padding_bottom, + tensorflow::int64 padding_left, tensorflow::int64 padding_right, + tensorflow::int64 lhs_row_dilation, tensorflow::int64 lhs_col_dilation, + tensorflow::int64 rhs_row_dilation, tensorflow::int64 rhs_col_dilation); + } // extern "C" #endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_H_ diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h index 02f45fee0f1b8cd1125ec6a97f01e0028137bb69..85af63bb032ce33bdd188d6e5bcd78a726d5d9fa 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h @@ -24,26 +24,27 @@ limitations under the License. namespace tensorflow { namespace xla { -template -void EigenConvF32Impl(const EigenDevice& device, float* out, float* lhs, - float* rhs, int64 input_batch, int64 input_rows, - int64 input_cols, int64 input_channels, int64 kernel_rows, - int64 kernel_cols, int64 kernel_channels, - int64 kernel_filters, int64 output_rows, - int64 output_cols, int64 row_stride, int64 col_stride, - int64 padding_top, int64 padding_bottom, - int64 padding_left, int64 padding_right, - int64 lhs_row_dilation, int64 lhs_col_dilation, - int64 rhs_row_dilation, int64 rhs_col_dilation) { - const Eigen::TensorMap, +template +void EigenConvImpl(const EigenDevice& device, ScalarType* out, ScalarType* lhs, + ScalarType* rhs, int64 input_batch, int64 input_rows, + int64 input_cols, int64 input_channels, int64 kernel_rows, + int64 kernel_cols, int64 kernel_channels, + int64 kernel_filters, int64 output_rows, int64 output_cols, + int64 row_stride, int64 col_stride, int64 padding_top, + int64 padding_bottom, int64 padding_left, + int64 padding_right, int64 lhs_row_dilation, + int64 lhs_col_dilation, int64 rhs_row_dilation, + int64 rhs_col_dilation) { + const Eigen::TensorMap, Eigen::Aligned> input(lhs, input_batch, input_rows, input_cols, input_channels); - const Eigen::TensorMap, + const Eigen::TensorMap, Eigen::Aligned> kernel(rhs, kernel_rows, kernel_cols, kernel_channels, kernel_filters); - Eigen::TensorMap, Eigen::Aligned> + Eigen::TensorMap, + Eigen::Aligned> output(out, input_batch, output_rows, output_cols, kernel_filters); Eigen::array, 1> contract_dims; @@ -75,7 +76,7 @@ void EigenConvF32Impl(const EigenDevice& device, float* out, float* lhs, row_stride, rhs_col_dilation, rhs_row_dilation, lhs_col_dilation, lhs_row_dilation, padding_left, padding_right, padding_top, - padding_bottom, 0.0f) + padding_bottom, static_cast(0.0f)) .reshape(pre_contract_dims) .contract(kernel.reshape(kernel_dims), contract_dims) .reshape(post_contract_dims); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc b/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc new file mode 100644 index 0000000000000000000000000000000000000000..af0275c8bd00c82220fbe116eb90d2692393713b --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc @@ -0,0 +1,133 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/cpu/runtime_fp16.h" +#include "tensorflow/core/platform/macros.h" + +namespace { +using tensorflow::uint16; +using tensorflow::uint32; + +// Helper class that lets us access the underlying bit representation +// of a float without breaking C++ strict aliasing. +class AliasedFloatInt { + public: + static_assert(sizeof(float) == sizeof(uint32), ""); + + static AliasedFloatInt FromFloat(float f) { + AliasedFloatInt value; + value.set_float(f); + return value; + } + + static AliasedFloatInt FromUInt(uint32 u) { + AliasedFloatInt value; + value.set_uint(u); + return value; + } + + void set_float(float f) { memcpy(&value_, &f, sizeof(f)); } + float as_float() const { + float f; + memcpy(&f, &value_, sizeof(f)); + return f; + } + + void set_uint(uint32 u) { value_ = u; } + uint32 as_uint() const { return value_; } + + private: + uint32 value_; +}; +} // namespace + +// __gnu_f2h_ieee and __gnu_h2f_ieee are marked as weak symbols so if XLA is +// built with compiler-rt (that also defines these symbols) we don't get a +// duplicate definition linker error. Making these symbols weak also ensures +// that the compiler-rt definitions "win", but that isn't essential. + +// Algorithm copied from Eigen. +uint16 TF_ATTRIBUTE_WEAK __gnu_f2h_ieee(float float_value) { + AliasedFloatInt f = AliasedFloatInt::FromFloat(float_value); + + const AliasedFloatInt f32infty = AliasedFloatInt::FromUInt(255 << 23); + const AliasedFloatInt f16max = AliasedFloatInt::FromUInt((127 + 16) << 23); + const AliasedFloatInt denorm_magic = + AliasedFloatInt::FromUInt(((127 - 15) + (23 - 10) + 1) << 23); + unsigned int sign_mask = 0x80000000u; + uint32 o = static_cast(0x0u); + + unsigned int sign = f.as_uint() & sign_mask; + f.set_uint(f.as_uint() ^ sign); + + // NOTE all the integer compares in this function can be safely + // compiled into signed compares since all operands are below + // 0x80000000. Important if you want fast straight SSE2 code + // (since there's no unsigned PCMPGTD). + + if (f.as_uint() >= + f16max.as_uint()) { // result is Inf or NaN (all exponent bits set) + o = (f.as_uint() > f32infty.as_uint()) ? 0x7e00 + : 0x7c00; // NaN->qNaN and Inf->Inf + } else { // (De)normalized number or zero + if (f.as_uint() < (113 << 23)) { // resulting FP16 is subnormal or zero + // use a magic value to align our 10 mantissa bits at the bottom of + // the float. as long as FP addition is round-to-nearest-even this + // just works. + f.set_float(f.as_float() + denorm_magic.as_float()); + + // and one integer subtract of the bias later, we have our final float! + o = static_cast(f.as_uint() - denorm_magic.as_uint()); + } else { + unsigned int mant_odd = + (f.as_uint() >> 13) & 1; // resulting mantissa is odd + + // update exponent, rounding bias part 1 + f.set_uint(f.as_uint() + (static_cast(15 - 127) << 23) + + 0xfff); + // rounding bias part 2 + f.set_uint(f.as_uint() + mant_odd); + // take the bits! + o = static_cast(f.as_uint() >> 13); + } + } + + o |= static_cast(sign >> 16); + return o; +} + +// Algorithm copied from Eigen. +float TF_ATTRIBUTE_WEAK __gnu_h2f_ieee(uint16 h) { + const AliasedFloatInt magic = AliasedFloatInt::FromUInt(113 << 23); + const unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift + AliasedFloatInt o; + + o.set_uint((h & 0x7fff) << 13); // exponent/mantissa bits + unsigned int exp = shifted_exp & o.as_uint(); // just the exponent + o.set_uint(o.as_uint() + ((127 - 15) << 23)); // exponent adjust + + // handle exponent special cases + if (exp == shifted_exp) { // Inf/NaN? + o.set_uint(o.as_uint() + ((128 - 16) << 23)); // extra exp adjust + } else if (exp == 0) { // Zero/Denormal? + o.set_uint(o.as_uint() + (1 << 23)); // extra exp adjust + o.set_float(o.as_float() - magic.as_float()); // renormalize + } + + o.set_uint(o.as_uint() | (h & 0x8000) << 16); // sign bit + return o.as_float(); +} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fp16.h b/tensorflow/compiler/xla/service/cpu/runtime_fp16.h new file mode 100644 index 0000000000000000000000000000000000000000..01d92d031904af99884c2583a8c7b5086b289d44 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_fp16.h @@ -0,0 +1,27 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_ + +#include "tensorflow/core/platform/types.h" + +// Converts an F32 value to a F16. +extern "C" tensorflow::uint16 __gnu_f2h_ieee(float); + +// Converts an F16 value to a F32. +extern "C" float __gnu_h2f_ieee(tensorflow::uint16); + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_ diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc index bff57d33ae23fbba8c664cbd18df77e4c35eb592..39b13183ff093611a42b3931d45f64eadb420622 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc @@ -63,30 +63,41 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, C.device(*run_options->intra_op_thread_pool()) = A.contract(B, dims); } +template +void MatMulImpl(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, + int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { + if (m == 1 || n == 1) { + // Despite being single threaded, this version of matrix * vector is faster. + xla::EigenMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); + } else { + MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); + } +} + } // namespace +void __xla_cpu_runtime_EigenMatMulF16(const void* run_options_ptr, + Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 m, int64 n, + int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); +} + void __xla_cpu_runtime_EigenMatMulF32(const void* run_options_ptr, float* out, float* lhs, float* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - // Despite being single threaded, this version of matrix * vector is faster. - xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); } void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - // Despite being single threaded, this version of matrix * vector is faster. - xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h index fdb644651dd5d0fa0345580f52ed0fb051672285..b5156434f6d568012b8f51ba9b14d64ce418cec7 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h @@ -25,6 +25,12 @@ extern "C" { // order. 'out' is a pointer to a buffer sufficiently large to hold the result // of the operation. Following standard nomenclature: lhs is m x k, // rhs is k x n, and out is m x n. +extern void __xla_cpu_runtime_EigenMatMulF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs); + extern void __xla_cpu_runtime_EigenMatMulF32( const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc b/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc deleted file mode 100644 index 435820cdd36e2a906d9dfbe2555f4c0df623c729..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc +++ /dev/null @@ -1,110 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include -#include - -#include "third_party/eigen3/Eigen/Core" -#include "tensorflow/compiler/xla/service/cpu/runtime_matvec.h" - -using tensorflow::int32; -using tensorflow::int64; - -namespace { - -// Does mat * x or mat^T * x. -template -void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols, - int32 transpose) { - // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to - // be faster (b/30223679). See also: the matmul op kernel in TensorFlow, - // which implements the same optimization. - using Matrix = Eigen::Matrix; - using MatrixMap = Eigen::Map; - - using Vector = Eigen::Matrix; - using VectorMap = Eigen::Map; - - auto x = VectorMap(x_buf, cols); - auto out = VectorMap(out_buf, rows); - - int64 mat_rows = rows; - int64 mat_cols = cols; - - if (transpose) { - std::swap(mat_rows, mat_cols); - } - - auto mat = MatrixMap(mat_buf, mat_rows, mat_cols); - - if (transpose) { - out = mat.transpose() * x; - } else { - out = mat * x; - } -} - -// Converts matmul-style args to matvec. -template -void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k, - int32 transpose_lhs, int32 transpose_rhs) { - // If the input is in the form x * A, where x is the vector, then bring A back - // over to the left hand side. We make use of the identity - // - // (x * A)^T = A^T * x^T - // - // We do not need to take the transpose of x or of the result since taking - // the transpose of a vector does not change the memory layout. - const int64 cols = k; - - T* mat; - T* vec; - int64 rows; - bool transpose_mat; - - bool is_mat_vec = (n == 1); - - if (is_mat_vec) { - mat = lhs; - vec = rhs; - rows = m; - transpose_mat = transpose_lhs; - } else { - mat = rhs; - vec = lhs; - rows = n; - transpose_mat = !transpose_rhs; - } - - MatVec(out, mat, vec, rows, cols, transpose_mat); -} - -} // namespace - -namespace xla { - -void EigenMatVecF32(float* out, float* lhs, float* rhs, int64 m, int64 n, - int64 k, int32 transpose_lhs, int32 transpose_rhs) { - assert((m == 1 || n == 1) && "not a matrix-vector multiply"); - DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); -} - -void EigenMatVecF64(double* out, double* lhs, double* rhs, int64 m, int64 n, - int64 k, int32 transpose_lhs, int32 transpose_rhs) { - assert((m == 1 || n == 1) && "not a matrix-vector multiply"); - DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); -} - -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h index 1bd8dfb377acc1f7cfbe9a92773f87f0ef25de3a..70eb98c54169824e220d9287753c0849362eade6 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h @@ -16,10 +16,86 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_ +#include "third_party/eigen3/Eigen/Core" + #include "tensorflow/core/platform/types.h" namespace xla { +namespace detail { + +using tensorflow::int32; +using tensorflow::int64; + +// Does mat * x or mat^T * x. +template +void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols, + int32 transpose) { + // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to + // be faster (b/30223679). See also: the matmul op kernel in TensorFlow, + // which implements the same optimization. + using Matrix = Eigen::Matrix; + using MatrixMap = Eigen::Map; + + using Vector = Eigen::Matrix; + using VectorMap = Eigen::Map; + + auto x = VectorMap(x_buf, cols); + auto out = VectorMap(out_buf, rows); + + int64 mat_rows = rows; + int64 mat_cols = cols; + + if (transpose) { + std::swap(mat_rows, mat_cols); + } + + auto mat = MatrixMap(mat_buf, mat_rows, mat_cols); + + if (transpose) { + out = mat.transpose() * x; + } else { + out = mat * x; + } +} + +// Converts matmul-style args to matvec. +template +void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k, + int32 transpose_lhs, int32 transpose_rhs) { + // If the input is in the form x * A, where x is the vector, then bring A back + // over to the left hand side. We make use of the identity + // + // (x * A)^T = A^T * x^T + // + // We do not need to take the transpose of x or of the result since taking + // the transpose of a vector does not change the memory layout. + const int64 cols = k; + + T* mat; + T* vec; + int64 rows; + bool transpose_mat; + + bool is_mat_vec = (n == 1); + + if (is_mat_vec) { + mat = lhs; + vec = rhs; + rows = m; + transpose_mat = transpose_lhs; + } else { + mat = rhs; + vec = lhs; + rows = n; + transpose_mat = !transpose_rhs; + } + + MatVec(out, mat, vec, rows, cols, transpose_mat); +} + +} // namespace detail + // Performs a matrix-vector multiplication using Eigen. 'lhs' and 'rhs' are // pointers to buffers containing input matrices in column-major order. 'out' is // a pointer to a buffer sufficiently large to hold the result of the @@ -30,15 +106,15 @@ namespace xla { // // TODO(b/64684907): Compare runtime performance of these functions with dot // simplification. -void EigenMatVecF32(float* out, float* lhs, float* rhs, tensorflow::int64 m, - tensorflow::int64 n, tensorflow::int64 k, - tensorflow::int32 transpose_lhs, - tensorflow::int32 transpose_rhs); - -void EigenMatVecF64(double* out, double* lhs, double* rhs, tensorflow::int64 m, - tensorflow::int64 n, tensorflow::int64 k, - tensorflow::int32 transpose_lhs, - tensorflow::int32 transpose_rhs); +template +void EigenMatVec(T* out, T* lhs, T* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, + tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs) { + assert((m == 1 || n == 1) && "not a matrix-vector multiply"); + detail::DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc index d0b0e11ac0f9fd06e384c2bb5e6296edd0825f5c..5afccc6a86e2df468e3e3e874cf0f4d4e1342a88 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc @@ -21,6 +21,24 @@ limitations under the License. using tensorflow::int64; +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void +__xla_cpu_runtime_EigenSingleThreadedConvF16( + const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 input_batch, int64 input_rows, int64 input_cols, + int64 input_channels, int64 kernel_rows, int64 kernel_cols, + int64 kernel_channels, int64 kernel_filters, int64 output_rows, + int64 output_cols, int64 row_stride, int64 col_stride, int64 padding_top, + int64 padding_bottom, int64 padding_left, int64 padding_right, + int64 lhs_row_dilation, int64 lhs_col_dilation, int64 rhs_row_dilation, + int64 rhs_col_dilation) { + tensorflow::xla::EigenConvImpl( + Eigen::DefaultDevice(), out, lhs, rhs, input_batch, input_rows, + input_cols, input_channels, kernel_rows, kernel_cols, kernel_channels, + kernel_filters, output_rows, output_cols, row_stride, col_stride, + padding_top, padding_bottom, padding_left, padding_right, + lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation); +} + TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedConvF32( const void* run_options_ptr, float* out, float* lhs, float* rhs, @@ -30,7 +48,7 @@ __xla_cpu_runtime_EigenSingleThreadedConvF32( int64 row_stride, int64 col_stride, int64 padding_top, int64 padding_bottom, int64 padding_left, int64 padding_right, int64 lhs_row_dilation, int64 lhs_col_dilation, int64 rhs_row_dilation, int64 rhs_col_dilation) { - tensorflow::xla::EigenConvF32Impl( + tensorflow::xla::EigenConvImpl( Eigen::DefaultDevice(), out, lhs, rhs, input_batch, input_rows, input_cols, input_channels, kernel_rows, kernel_cols, kernel_channels, kernel_filters, output_rows, output_cols, row_stride, col_stride, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h index 8ae1a42149bde26ca2f510ad47e76ae47f34a977..f216bd0152aa93b8753d881938c63a9cabea899b 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h @@ -20,6 +20,20 @@ limitations under the License. extern "C" { +extern void __xla_cpu_runtime_EigenSingleThreadedConvF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, + tensorflow::int64 input_batch, tensorflow::int64 input_rows, + tensorflow::int64 input_cols, tensorflow::int64 input_channels, + tensorflow::int64 kernel_rows, tensorflow::int64 kernel_cols, + tensorflow::int64 kernel_channels, tensorflow::int64 kernel_filters, + tensorflow::int64 output_rows, tensorflow::int64 output_cols, + tensorflow::int64 row_stride, tensorflow::int64 col_stride, + tensorflow::int64 padding_top, tensorflow::int64 padding_bottom, + tensorflow::int64 padding_left, tensorflow::int64 padding_right, + tensorflow::int64 lhs_row_dilation, tensorflow::int64 lhs_col_dilation, + tensorflow::int64 rhs_row_dilation, tensorflow::int64 rhs_col_dilation); + extern void __xla_cpu_runtime_EigenSingleThreadedConvF32( const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, float* lhs, float* rhs, tensorflow::int64 input_batch, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc index ee8eb081556d60fcf6537b1036a4a5825c4c7bf6..17303e2f0d34e531a3a56aa147608b949e0f43ae 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc @@ -57,26 +57,38 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, C = A.contract(B, dims); } +template +void SingleThreadedMatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, + int64 m, int64 n, int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + if (m == 1 || n == 1) { + xla::EigenMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); + } else { + MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); + } +} + } // namespace +void __xla_cpu_runtime_EigenSingleThreadedMatMulF16( + const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); +} + void __xla_cpu_runtime_EigenSingleThreadedMatMulF32( const void* run_options_ptr, float* out, float* lhs, float* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); } void __xla_cpu_runtime_EigenSingleThreadedMatMulF64( const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h index 029eb9514287d8c69cde2cfb06e0d56e78d6f165..9371a62242328a67618321e2b1d112956c06ee4b 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h @@ -25,6 +25,12 @@ extern "C" { // 'out' is a pointer to a buffer sufficiently large to hold the result of the // operation. Following standard nomenclature: lhs is m x k, rhs is k x n, and // out is m x n. +extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs); + extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF32( const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n, diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index de5e9b411905a37a7db7d05f51cca2802c1526ed..80c24eaccfc2a83f8f3f311d60860715668d0c08 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -21,20 +21,19 @@ limitations under the License. #include #include "llvm/ExecutionEngine/ExecutionEngine.h" +#include "llvm/ExecutionEngine/JITSymbol.h" #include "llvm/ExecutionEngine/SectionMemoryManager.h" #include "llvm/IR/Mangler.h" #include "llvm/Support/CodeGen.h" #include "llvm/Support/Host.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_neon.h" -#include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h" #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" #include "tensorflow/compiler/xla/service/cpu/orc_jit_memory_mapper.h" #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h" #include "tensorflow/compiler/xla/service/cpu/runtime_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_fork_join.h" +#include "tensorflow/compiler/xla/service/cpu/runtime_fp16.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" @@ -46,36 +45,6 @@ namespace xla { namespace cpu { namespace { -// A simple SymbolResolver that delegates to the host dynamic linker. -class SimpleResolver : public llvm::LegacyJITSymbolResolver { - public: - explicit SimpleResolver(ExternalConstantPool* external_constant_pool) - : external_constant_pool_(external_constant_pool) {} - - llvm::JITSymbol findSymbol(const std::string& name) override { - if (const uint8* from_constant_pool = - external_constant_pool_->Find(string(name))) { - return llvm::JITEvaluatedSymbol( - reinterpret_cast(from_constant_pool), - llvm::JITSymbolFlags::None); - } - - void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name); - if (func_addr == nullptr) { - return nullptr; - } - llvm::JITEvaluatedSymbol symbol_info(reinterpret_cast(func_addr), - llvm::JITSymbolFlags::None); - return symbol_info; - } - llvm::JITSymbol findSymbolInLogicalDylib(const std::string& name) override { - return nullptr; - } - - private: - ExternalConstantPool* external_constant_pool_; -}; - llvm::SmallVector DetectMachineAttributes() { llvm::SmallVector result; llvm::StringMap host_features; @@ -100,27 +69,6 @@ llvm::StringRef GetHostCpuName() { cpu_name.consume_back("-avx512"); return cpu_name; } - -CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() { - CompilerFunctor::VectorIntrinsics intrinsics; -#ifdef TF_XLA_HAS_SSE4_1 - intrinsics.sse_intrinsics = true; -#else - intrinsics.sse_intrinsics = false; -#endif -#ifdef TF_XLA_HAS_AVX - intrinsics.avx_intrinsics = true; -#else - intrinsics.avx_intrinsics = false; -#endif -#ifdef TF_XLA_HAS_NEON - intrinsics.neon_intrinsics = true; -#else - intrinsics.neon_intrinsics = false; -#endif - return intrinsics; -} - } // namespace SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options, @@ -139,49 +87,71 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options, /*MAttrs=*/DetectMachineAttributes()))), disassembler_(*target_machine_), data_layout_(target_machine_->createDataLayout()), - object_layer_([] { - return std::make_shared( - orc_jit_memory_mapper::GetInstance()); - }), - compile_layer_( - object_layer_, - CompilerFunctor(target_machine_.get(), &disassembler_, opt_level, - optimize_for_size, enable_fast_math, - disable_expensive_passes, GetAvailableIntrinsics(), - std::move(pre_optimization_hook), - std::move(post_optimization_hook))) { + execution_session_(string_pool_), + symbol_resolver_(llvm::orc::createLegacyLookupResolver( + [this](const std::string& name) -> llvm::JITSymbol { + return this->ResolveRuntimeSymbol(name); + }, + [](llvm::Error Err) { + cantFail(std::move(Err), "lookupFlags failed"); + })), + object_layer_(execution_session_, + [this](llvm::orc::VModuleKey) { + llvm::orc::RTDyldObjectLinkingLayer::Resources result; + result.MemMgr = + std::make_shared( + orc_jit_memory_mapper::GetInstance()); + result.Resolver = symbol_resolver_; + return result; + }), + compile_layer_(object_layer_, + CompilerFunctor(target_machine_.get(), &disassembler_, + opt_level, optimize_for_size, + enable_fast_math, disable_expensive_passes, + std::move(pre_optimization_hook), + std::move(post_optimization_hook))) { VLOG(1) << "CPU target: " << target_machine_->getTargetCPU().str() << " features: " << target_machine_->getTargetFeatureString().str(); } -SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule( - std::unique_ptr module) { - auto handle = cantFail(compile_layer_.addModule( - std::move(module), MakeUnique(external_constant_pool()))); - module_handles_.push_back(handle); - return handle; +llvm::JITSymbol SimpleOrcJIT::ResolveRuntimeSymbol(const std::string& name) { + if (const uint8* from_constant_pool = + external_constant_pool_.Find(string(name))) { + return llvm::JITEvaluatedSymbol( + reinterpret_cast(from_constant_pool), + llvm::JITSymbolFlags::None); + } + + void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name); + if (func_addr == nullptr) { + return nullptr; + } + llvm::JITEvaluatedSymbol symbol_info(reinterpret_cast(func_addr), + llvm::JITSymbolFlags::None); + return symbol_info; } -void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) { - module_handles_.erase( - std::remove(module_handles_.begin(), module_handles_.end(), handle), - module_handles_.end()); - cantFail(compile_layer_.removeModule(handle)); +SimpleOrcJIT::VModuleKeyT SimpleOrcJIT::AddModule( + std::unique_ptr module) { + auto key = execution_session_.allocateVModule(); + cantFail(compile_layer_.addModule(key, std::move(module))); + module_keys_.push_back(key); + return key; } -llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string& name) { - std::string mangled_name; - { - llvm::raw_string_ostream mangled_name_stream(mangled_name); - llvm::Mangler::getNameWithPrefix(mangled_name_stream, name, data_layout_); - } +void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::VModuleKeyT key) { + module_keys_.erase(std::remove(module_keys_.begin(), module_keys_.end(), key), + module_keys_.end()); + cantFail(compile_layer_.removeModule(key)); +} +llvm::JITSymbol SimpleOrcJIT::FindCompiledSymbol(const std::string& name) { // Resolve symbol from last module to first, allowing later redefinitions of // symbols shadow earlier ones. - for (auto& handle : - llvm::make_range(module_handles_.rbegin(), module_handles_.rend())) { + for (auto& key : + llvm::make_range(module_keys_.rbegin(), module_keys_.rend())) { if (auto symbol = - compile_layer_.findSymbolIn(handle, mangled_name, + compile_layer_.findSymbolIn(key, name, /*ExportedSymbolsOnly=*/true)) { return symbol; } @@ -208,29 +178,24 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(AcquireInfeedBufferForDequeue); REGISTER_CPU_RUNTIME_SYMBOL(AcquireOutfeedBufferForPopulation); + REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenFft); + REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF64); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); -#ifdef TF_XLA_HAS_NEON - REGISTER_CPU_RUNTIME_SYMBOL(ExpV4F32NEON); - REGISTER_CPU_RUNTIME_SYMBOL(LogV4F32NEON); -#endif -#ifdef TF_XLA_HAS_SSE4_1 - REGISTER_CPU_RUNTIME_SYMBOL(ExpV4F32SSE); - REGISTER_CPU_RUNTIME_SYMBOL(LogV4F32SSE); -#endif -#ifdef TF_XLA_HAS_AVX - REGISTER_CPU_RUNTIME_SYMBOL(ExpV8F32AVX); - REGISTER_CPU_RUNTIME_SYMBOL(LogV8F32AVX); -#endif REGISTER_CPU_RUNTIME_SYMBOL(ParallelForkJoin); REGISTER_CPU_RUNTIME_SYMBOL(ReleaseInfeedBufferAfterDequeue); REGISTER_CPU_RUNTIME_SYMBOL(ReleaseOutfeedBufferAfterPopulation); + registry->Register("__gnu_f2h_ieee", reinterpret_cast(__gnu_f2h_ieee)); + registry->Register("__gnu_h2f_ieee", reinterpret_cast(__gnu_h2f_ieee)); + #undef REGISTER_CPU_RUNTIME_SYMBOL // Register both the f32 (float) and f64 (double) versions of a libm symbol. @@ -273,15 +238,15 @@ bool RegisterKnownJITSymbols() { REGISTER_LIBM_SYMBOL(ilogb, int (*)(double)); REGISTER_LIBM_SYMBOL(ldexp, double (*)(double, int)); REGISTER_LIBM_SYMBOL(lgamma, double (*)(double)); - REGISTER_LIBM_SYMBOL(llrint, long long (*)(double)); - REGISTER_LIBM_SYMBOL(llround, long long (*)(double)); + REGISTER_LIBM_SYMBOL(llrint, long long (*)(double)); // NOLINT(runtime/int) + REGISTER_LIBM_SYMBOL(llround, long long (*)(double)); // NOLINT(runtime/int) REGISTER_LIBM_SYMBOL(log, double (*)(double)); REGISTER_LIBM_SYMBOL(log10, double (*)(double)); REGISTER_LIBM_SYMBOL(log1p, double (*)(double)); REGISTER_LIBM_SYMBOL(log2, double (*)(double)); REGISTER_LIBM_SYMBOL(logb, double (*)(double)); - REGISTER_LIBM_SYMBOL(lrint, long (*)(double)); - REGISTER_LIBM_SYMBOL(lround, long (*)(double)); + REGISTER_LIBM_SYMBOL(lrint, long (*)(double)); // NOLINT(runtime/int) + REGISTER_LIBM_SYMBOL(lround, long (*)(double)); // NOLINT(runtime/int) REGISTER_LIBM_SYMBOL(modf, double (*)(double, double*)); REGISTER_LIBM_SYMBOL(nan, double (*)(const char*)); REGISTER_LIBM_SYMBOL(nearbyint, double (*)(double)); @@ -292,7 +257,8 @@ bool RegisterKnownJITSymbols() { REGISTER_LIBM_SYMBOL(remquo, double (*)(double, double, int*)); REGISTER_LIBM_SYMBOL(rint, double (*)(double)); REGISTER_LIBM_SYMBOL(round, double (*)(double)); - REGISTER_LIBM_SYMBOL(scalbln, double (*)(double, long)); + REGISTER_LIBM_SYMBOL(scalbln, + double (*)(double, long)); // NOLINT(runtime/int) REGISTER_LIBM_SYMBOL(scalbn, double (*)(double, int)); REGISTER_LIBM_SYMBOL(sin, double (*)(double)); #ifdef __APPLE__ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h index ded01e9e4d7442296f7406dd035e6ab385458238..aaeff2de8785b99d271f13b261c63118bcf7bd4a 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h @@ -21,8 +21,10 @@ limitations under the License. #include #include "llvm/ADT/Triple.h" +#include "llvm/ExecutionEngine/Orc/Core.h" #include "llvm/ExecutionEngine/Orc/IRCompileLayer.h" #include "llvm/ExecutionEngine/Orc/RTDyldObjectLinkingLayer.h" +#include "llvm/ExecutionEngine/Orc/SymbolStringPool.h" #include "llvm/IR/Module.h" #include "llvm/Target/TargetMachine.h" #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h" @@ -44,11 +46,9 @@ namespace cpu { class SimpleOrcJIT { public: using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer; - using CompileFtor = - std::function( - llvm::Module&)>; + using CompileFtor = std::function; using CompileLayerT = llvm::orc::IRCompileLayer; - using ModuleHandleT = CompileLayerT::ModuleHandleT; + using VModuleKeyT = llvm::orc::VModuleKey; // Create a new JIT, targeting the host architecture. // The |target_options| parameter allows customization of certain code @@ -78,16 +78,16 @@ class SimpleOrcJIT { return target_machine_->getTargetTriple(); } - // Add a module to the JIT. Returns an opaque handle that can be used to later + // Add a module to the JIT. Returns an opaque key that can be used to later // remove this module. - ModuleHandleT AddModule(std::unique_ptr module); + VModuleKeyT AddModule(std::unique_ptr module); // Remove a module from the JIT and free the memory associated with it. - void RemoveModule(ModuleHandleT handle); + void RemoveModule(VModuleKeyT key); // Get the runtime address of the compiled symbol whose name is given. Returns // nullptr if the symbol cannot be found. - llvm::JITSymbol FindSymbol(const std::string& name); + llvm::JITSymbol FindCompiledSymbol(const std::string& name); llvm::TargetMachine* target_machine() const { return target_machine_.get(); } @@ -96,10 +96,15 @@ class SimpleOrcJIT { } private: - std::vector module_handles_; + llvm::JITSymbol ResolveRuntimeSymbol(const std::string& name); + + std::vector module_keys_; std::unique_ptr target_machine_; const Disassembler disassembler_; const llvm::DataLayout data_layout_; + llvm::orc::SymbolStringPool string_pool_; + llvm::orc::ExecutionSession execution_session_; + std::shared_ptr symbol_resolver_; ObjLayerT object_layer_; CompileLayerT compile_layer_; ExternalConstantPool external_constant_pool_; diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc index 128b465be239130918687d8e2ba0458684086ee1..cd1165e23812861ba9951546b7dd744529232196 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/vector_support_library.h" +#include "llvm/Support/raw_ostream.h" #include "tensorflow/compiler/xla/service/cpu/target_machine_features.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" @@ -35,8 +36,27 @@ VectorSupportLibrary::VectorSupportLibrary(PrimitiveType primitive_type, vector_pointer_type_ = llvm::PointerType::getUnqual(vector_type_); } +static string TypeToString(llvm::Type* type) { + std::string o; + llvm::raw_string_ostream ostream(o); + type->print(ostream); + return ostream.str(); +} + +void VectorSupportLibrary::AssertCorrectTypes( + std::initializer_list values) { + for (llvm::Value* v : values) { + llvm::Type* type = v->getType(); + if (type != scalar_type() && type != vector_type()) { + LOG(FATAL) << "Expected either " << TypeToString(scalar_type()) << " or " + << TypeToString(vector_type()) << " but got " + << TypeToString(type); + } + } +} + llvm::Value* VectorSupportLibrary::Mul(llvm::Value* lhs, llvm::Value* rhs) { - CHECK(lhs->getType() == scalar_type() || lhs->getType() == vector_type()); + AssertCorrectTypes({lhs, rhs}); return MulInternal(lhs, rhs); } @@ -50,10 +70,128 @@ llvm::Value* VectorSupportLibrary::MulInternal(llvm::Value* lhs, } llvm::Value* VectorSupportLibrary::Add(llvm::Value* lhs, llvm::Value* rhs) { - CHECK(lhs->getType() == scalar_type() || lhs->getType() == vector_type()); + AssertCorrectTypes({lhs, rhs}); return AddInternal(lhs, rhs); } +llvm::Value* VectorSupportLibrary::Sub(llvm::Value* lhs, llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + return ir_builder()->CreateFSub(lhs, rhs); +} + +llvm::Value* VectorSupportLibrary::Max(llvm::Value* lhs, llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + if (scalar_type_->isFloatingPointTy()) { + return llvm_ir::EmitFloatMax(lhs, rhs, ir_builder_); + } else { + LOG(FATAL) << "Max for integers is unimplemented"; + } +} + +llvm::Value* VectorSupportLibrary::Floor(llvm::Value* a) { + AssertCorrectTypes({a}); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, {a}, + {a->getType()}, ir_builder()); +} + +llvm::Value* VectorSupportLibrary::Div(llvm::Value* lhs, llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + if (scalar_type_->isFloatingPointTy()) { + return ir_builder()->CreateFDiv(lhs, rhs, name()); + } else { + LOG(FATAL) << "Division for integers is unimplemented"; + } +} + +llvm::Value* VectorSupportLibrary::Clamp(llvm::Value* a, + const llvm::APFloat& low, + const llvm::APFloat& high) { + AssertCorrectTypes({a}); + llvm::Type* type = a->getType(); + CHECK(low.compare(high) == llvm::APFloat::cmpLessThan); + CHECK(scalar_type_->isFloatingPointTy()); + return llvm_ir::EmitFloatMin( + llvm_ir::EmitFloatMax(a, GetConstantFloat(type, low), ir_builder_), + GetConstantFloat(type, high), ir_builder_); +} + +llvm::Value* VectorSupportLibrary::FCmpEQMask(llvm::Value* lhs, + llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + return I1ToFloat(ir_builder()->CreateFCmpOEQ(lhs, rhs, name())); +} + +llvm::Value* VectorSupportLibrary::FCmpOLTMask(llvm::Value* lhs, + llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + return I1ToFloat(ir_builder()->CreateFCmpOLT(lhs, rhs, name())); +} + +llvm::Value* VectorSupportLibrary::FCmpULEMask(llvm::Value* lhs, + llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + return I1ToFloat(ir_builder()->CreateFCmpULE(lhs, rhs, name())); +} + +llvm::Value* VectorSupportLibrary::I1ToFloat(llvm::Value* i1) { + bool is_vector = llvm::isa(i1->getType()); + llvm::Type* integer_type = IntegerTypeForFloatSize(is_vector); + return ir_builder()->CreateBitCast( + ir_builder()->CreateSExt(i1, integer_type, name()), + is_vector ? vector_type() : scalar_type(), name()); +} + +llvm::Type* VectorSupportLibrary::IntegerTypeForFloatSize(bool vector) { + CHECK(scalar_type()->isFloatingPointTy()); + const llvm::DataLayout& data_layout = + ir_builder()->GetInsertBlock()->getModule()->getDataLayout(); + int64 float_size_bits = data_layout.getTypeSizeInBits(scalar_type()); + llvm::Type* scalar_int_type = ir_builder()->getIntNTy(float_size_bits); + if (vector) { + return llvm::VectorType::get(scalar_int_type, vector_size()); + } else { + return scalar_int_type; + } +} + +llvm::Value* VectorSupportLibrary::BroadcastScalar(llvm::Value* x) { + CHECK_EQ(x->getType(), scalar_type()); + return ir_builder()->CreateVectorSplat(vector_size(), x, name()); +} + +llvm::Value* VectorSupportLibrary::FloatAnd(llvm::Value* lhs, + llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + llvm::Type* int_type = + IntegerTypeForFloatSize(lhs->getType() == vector_type()); + return ir_builder()->CreateBitCast( + ir_builder()->CreateAnd( + ir_builder()->CreateBitCast(lhs, int_type, name()), + ir_builder()->CreateBitCast(rhs, int_type, name()), name()), + vector_type()); +} + +llvm::Value* VectorSupportLibrary::FloatNot(llvm::Value* lhs) { + AssertCorrectTypes({lhs}); + llvm::Type* int_type = + IntegerTypeForFloatSize(lhs->getType() == vector_type()); + return ir_builder()->CreateBitCast( + ir_builder()->CreateNot( + ir_builder()->CreateBitCast(lhs, int_type, name()), name()), + vector_type()); +} + +llvm::Value* VectorSupportLibrary::FloatOr(llvm::Value* lhs, llvm::Value* rhs) { + AssertCorrectTypes({lhs, rhs}); + llvm::Type* int_type = + IntegerTypeForFloatSize(lhs->getType() == vector_type()); + return ir_builder()->CreateBitCast( + ir_builder()->CreateOr(ir_builder()->CreateBitCast(lhs, int_type, name()), + ir_builder()->CreateBitCast(rhs, int_type, name()), + name()), + vector_type(), name()); +} + llvm::Value* VectorSupportLibrary::AddInternal(llvm::Value* lhs, llvm::Value* rhs) { if (scalar_type_->isFloatingPointTy()) { @@ -93,6 +231,7 @@ llvm::Value* VectorSupportLibrary::LoadScalar(llvm::Value* pointer) { void VectorSupportLibrary::StoreVector(llvm::Value* value, llvm::Value* pointer) { + AssertCorrectTypes({value}); if (pointer->getType() != vector_pointer_type()) { pointer = ir_builder()->CreateBitCast(pointer, vector_pointer_type()); } @@ -102,6 +241,7 @@ void VectorSupportLibrary::StoreVector(llvm::Value* value, void VectorSupportLibrary::StoreScalar(llvm::Value* value, llvm::Value* pointer) { + AssertCorrectTypes({value}); if (pointer->getType() != scalar_pointer_type()) { pointer = ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); @@ -230,6 +370,9 @@ std::vector VectorSupportLibrary::ComputeHorizontalSums( std::vector VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums( std::vector vectors, llvm::Value* init_values) { + // vectors are N llvm vector values, each with N elements. + int64 lane_width = vectors.size(); + while (vectors.size() != 2) { std::vector new_vectors; for (int i = 0; i < vectors.size(); i += 2) { @@ -250,10 +393,14 @@ VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums( high = AddInternal(ExtractHighHalf(init_values), high); } + // `low` has the first `lane_width / 2` horizontal reductions, and `high` has + // the next `lane_width / 2` horizontal reductions. + std::vector results; - for (int i = 0; i < 8; i++) { + for (int i = 0; i < lane_width; i++) { llvm::Value* scalar_result = ir_builder()->CreateExtractElement( - i < 4 ? low : high, ir_builder()->getInt32(i % 4), name()); + i < (lane_width / 2) ? low : high, + ir_builder()->getInt32(i % (lane_width / 2)), name()); results.push_back(scalar_result); } diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.h b/tensorflow/compiler/xla/service/cpu/vector_support_library.h index 8fbac2a6670f8ef18c00877a1566bd4ab896a7c8..6479bf76aab581ae3ec2923d98dab53720cab203 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.h +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.h @@ -26,6 +26,16 @@ limitations under the License. namespace xla { namespace cpu { + +// Simple wrappers around llvm::APFloat::APFloat to make the calling code more +// obvious. + +inline llvm::APFloat GetIeeeF32(float f) { return llvm::APFloat(f); } +inline llvm::APFloat GetIeeeF32FromBitwiseRep(int32 bitwise_value) { + return llvm::APFloat(llvm::APFloat::IEEEsingle(), + llvm::APInt(/*numBits=*/32, /*val=*/bitwise_value)); +} + // A thin wrapper around llvm_util.h to make code generating vector math flow // more readable. class VectorSupportLibrary { @@ -41,16 +51,96 @@ class VectorSupportLibrary { llvm::Value* Mul(int64 lhs, llvm::Value* rhs) { return Mul(ir_builder()->getInt64(lhs), rhs); } + llvm::Value* Mul(const llvm::APFloat& lhs, llvm::Value* rhs) { + return Mul(GetConstantFloat(rhs->getType(), lhs), rhs); + } + + // If your call resolved to these then you probably wanted the versions taking + // APFloat. + llvm::Value* Mul(double lhs, llvm::Value* rhs) = delete; + llvm::Value* Mul(float lhs, llvm::Value* rhs) = delete; llvm::Value* Add(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* Add(int64 lhs, llvm::Value* rhs) { return Add(ir_builder()->getInt64(lhs), rhs); } + llvm::Value* Add(const llvm::APFloat& lhs, llvm::Value* rhs) { + return Add(GetConstantFloat(rhs->getType(), lhs), rhs); + } + + // If your call resolved to these then you probably wanted the versions taking + // APFloat. + llvm::Value* Add(double lhs, llvm::Value* rhs) = delete; + llvm::Value* Add(float lhs, llvm::Value* rhs) = delete; + + llvm::Value* Sub(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* Sub(llvm::Value* lhs, const llvm::APFloat& rhs) { + return Sub(lhs, GetConstantFloat(lhs->getType(), rhs)); + } + llvm::Value* Max(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* Max(const llvm::APFloat& lhs, llvm::Value* rhs) { + return Max(GetConstantFloat(rhs->getType(), lhs), rhs); + } + llvm::Value* Div(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* MulAdd(llvm::Value* a, llvm::Value* b, llvm::Value* c) { return Add(c, Mul(a, b)); } + llvm::Value* MulAdd(llvm::Value* a, llvm::Value* b, const llvm::APFloat& c) { + return Add(GetConstantFloat(vector_type(), c), Mul(a, b)); + } + + llvm::Value* MulAdd(llvm::Value* a, const llvm::APFloat& b, + const llvm::APFloat& c) { + return Add(GetConstantFloat(a->getType(), c), + Mul(a, GetConstantFloat(a->getType(), b))); + } + + llvm::Value* Floor(llvm::Value* a); + + llvm::Value* Clamp(llvm::Value* a, const llvm::APFloat& low, + const llvm::APFloat& high); + llvm::Value* SplatFloat(const llvm::APFloat& d) { + return GetConstantFloat(vector_type(), d); + } + + // These compare instructions return a floating point typed mask instead of an + // i1. For instance, on a vector typed input, lanes where the predicate is + // true get a float with all ones and other lanes get a float with all zeros. + // This is slightly odd from the perspective of LLVM's type system, but it + // makes kernel IR generation code written using VectorSupportLibrary (its + // raison d'etre) less cluttered. + + llvm::Value* FCmpEQMask(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* FCmpULEMask(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* FCmpOLTMask(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* FCmpOLTMask(llvm::Value* lhs, const llvm::APFloat& rhs) { + return FCmpOLTMask(lhs, GetConstantFloat(lhs->getType(), rhs)); + } + + // These boolean operations operate on the bitwise values of the floating + // point inputs. They return a (vector of) float(s) but like in the mask + // generating predicates above this type system oddity makes the kernel IR + // generation code less cluttered. + llvm::Value* FloatAnd(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* FloatAnd(llvm::Value* lhs, const llvm::APFloat& rhs) { + return FloatAnd(lhs, GetConstantFloat(lhs->getType(), rhs)); + } + llvm::Value* FloatOr(llvm::Value* lhs, llvm::Value* rhs); + llvm::Value* FloatOr(llvm::Value* lhs, const llvm::APFloat& rhs) { + return FloatOr(lhs, GetConstantFloat(lhs->getType(), rhs)); + } + llvm::Value* FloatNot(llvm::Value* lhs); + llvm::Value* FloatAndNot(llvm::Value* lhs, llvm::Value* rhs) { + return FloatAnd(FloatNot(lhs), rhs); + } + + llvm::Value* BroadcastScalar(llvm::Value* x); + llvm::Value* BroadcastScalar(const llvm::APFloat& d) { + return BroadcastScalar(GetConstantFloat(scalar_type(), d)); + } + llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, llvm::Value* offset_elements); llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, @@ -144,6 +234,11 @@ class VectorSupportLibrary { llvm::Value* AddReduce(llvm::Value* vector); + // Checks that each value in `values` is either of type scalar_type() or + // vector_type(). This LOG(FATAL)'s so it should only be called in cases + // where a mismatching type is a programmer bug. + void AssertCorrectTypes(std::initializer_list values); + // Perform an X86 AVX style horizontal add between `lhs` and `rhs`. The // resulting IR for an 8-float wide vector is expected to lower to a single // vhaddps instruction on a CPU that supports vhaddps, and not be too bad in @@ -163,6 +258,16 @@ class VectorSupportLibrary { std::vector ComputeAvxOptimizedHorizontalSums( std::vector vectors, llvm::Value* init_values); + llvm::Type* IntegerTypeForFloatSize(bool vector); + llvm::Value* I1ToFloat(llvm::Value* i1); + llvm::Value* GetConstantFloat(llvm::Type* type, const llvm::APFloat& f) { + llvm::Constant* scalar_value = llvm::ConstantFP::get(type->getContext(), f); + if (llvm::isa(type)) { + return llvm::ConstantVector::getSplat(vector_size(), scalar_value); + } + return scalar_value; + } + int64 vector_size_; PrimitiveType primitive_type_; llvm::IRBuilder<>* ir_builder_; diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h index a803b3171f9afa6297553c5507c4f9aa45e420ab..56723e765048698baedc50ae7b189d0287ee56b8 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h @@ -190,6 +190,7 @@ class DfsHloVisitorBase { virtual Status HandleInfeed(HloInstructionPtr hlo) = 0; virtual Status HandleOutfeed(HloInstructionPtr hlo) = 0; + virtual Status HandleHostCompute(HloInstructionPtr hlo) = 0; virtual Status HandleRng(HloInstructionPtr hlo) = 0; virtual Status HandleReverse(HloInstructionPtr hlo) = 0; virtual Status HandleSort(HloInstructionPtr hlo) = 0; @@ -213,6 +214,7 @@ class DfsHloVisitorBase { virtual Status HandleSelectAndScatter(HloInstructionPtr hlo) = 0; virtual Status HandleWhile(HloInstructionPtr hlo) = 0; virtual Status HandleConditional(HloInstructionPtr hlo) = 0; + virtual Status HandleGather(HloInstructionPtr hlo) = 0; virtual Status HandlePad(HloInstructionPtr hlo) = 0; diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h index 170adb3d241b3648bc53f96dde9866f0b794f80a..ecda5288ee17a3856ce95f0caa327c3524fd180b 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h @@ -103,6 +103,9 @@ class DfsHloVisitorWithDefaultBase Status HandleOutfeed(HloInstructionPtr outfeed) override { return DefaultAction(outfeed); } + Status HandleHostCompute(HloInstructionPtr host_compute) override { + return DefaultAction(host_compute); + } Status HandleReverse(HloInstructionPtr reverse) override { return DefaultAction(reverse); } @@ -185,6 +188,9 @@ class DfsHloVisitorWithDefaultBase Status HandleSendDone(HloInstructionPtr send_done) override { return DefaultAction(send_done); } + Status HandleGather(HloInstructionPtr gather) override { + return DefaultAction(gather); + } // Invoked to inform the visitor that the traversal has completed, and that // the root was "root". diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 4468adbadbf823f1420a8b665a26f66cb7d36b43..111c29593e6fb7350496d0ab8da4b53e181c7d8e 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -226,7 +226,7 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( if (primitive_util::IsIntegralType(to_type)) { return ir_builder_->CreateIntCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_), - primitive_util::IsSignedIntegralType(to_type)); + primitive_util::IsSignedIntegralType(from_type)); } if (primitive_util::IsFloatingPointType(to_type)) { if (to_type == BF16) { @@ -1003,6 +1003,30 @@ StatusOr ElementalIrEmitter::EmitReducePrecision( ir_builder_); } +static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder, + llvm::Value* lhs, llvm::Value* rhs, + llvm::Value* shift_result, + bool saturate_to_sign_bit) { + llvm::IntegerType* integer_type = + llvm::cast(lhs->getType()); + unsigned integer_bitsize = integer_type->getBitWidth(); + llvm::ConstantInt* integer_bitsize_constant = + llvm::ConstantInt::get(integer_type, integer_bitsize); + llvm::ConstantInt* zero = llvm::ConstantInt::get(integer_type, 0); + llvm::ConstantInt* minus_one = llvm::ConstantInt::get(integer_type, -1); + llvm::Value* saturated_value; + if (saturate_to_sign_bit) { + saturated_value = ir_builder->CreateSelect( + ir_builder->CreateICmpSLT(lhs, zero), minus_one, zero); + } else { + saturated_value = zero; + } + llvm::Value* shift_amt_in_range = + ir_builder->CreateICmpULT(rhs, integer_bitsize_constant, "shft.chk"); + return ir_builder->CreateSelect(shift_amt_in_range, shift_result, + saturated_value); +} + StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( const HloInstruction* op, llvm::Value* lhs_value, llvm::Value* rhs_value, bool is_signed) const { @@ -1050,12 +1074,27 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( return ir_builder_->CreateAnd(lhs_value, rhs_value); case HloOpcode::kOr: return ir_builder_->CreateOr(lhs_value, rhs_value); - case HloOpcode::kShiftLeft: - return ir_builder_->CreateShl(lhs_value, rhs_value); + + // Shifting out bits >= the number of bits in the type being shifted + // produces a poison value in LLVM which is basically "deferred undefined + // behavior" -- doing something observable with such a value precipitates + // UB. We replace the poison value with a constant to avoid this deferred + // UB. case HloOpcode::kShiftRightArithmetic: - return ir_builder_->CreateAShr(lhs_value, rhs_value); + return SaturateShiftIfNecessary( + ir_builder_, lhs_value, rhs_value, + ir_builder_->CreateAShr(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/true); + case HloOpcode::kShiftLeft: + return SaturateShiftIfNecessary( + ir_builder_, lhs_value, rhs_value, + ir_builder_->CreateShl(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); case HloOpcode::kShiftRightLogical: - return ir_builder_->CreateLShr(lhs_value, rhs_value); + return SaturateShiftIfNecessary( + ir_builder_, lhs_value, rhs_value, + ir_builder_->CreateLShr(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); default: return Unimplemented("binary integer op '%s'", HloOpcodeString(op->opcode()).c_str()); @@ -1683,6 +1722,14 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( SetToFirstInsertPoint(if_data.after_block, ir_builder_); return ir_builder_->CreateLoad(ret_value_addr); }; + case HloOpcode::kBitcast: + CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), + ShapeUtil::ElementsIn(hlo->operand(0)->shape())); + return [this, hlo, &operand_to_generator](const IrArray::Index& index) { + const HloInstruction* operand = hlo->operand(0); + return operand_to_generator.at(operand)(index.SourceIndexOfBitcast( + hlo->shape(), operand->shape(), ir_builder_)); + }; case HloOpcode::kReshape: CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), ShapeUtil::ElementsIn(hlo->operand(0)->shape())); diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index 78dc0ad4fcd167c93f19d0c2b18ea72d666897ef..a99e2b7794a399047fb5a77a140bd333214e3f23 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -38,14 +38,7 @@ namespace xla { GenericTransferManager::GenericTransferManager(se::Platform::Id platform_id, size_t pointer_size) - : platform_id_(platform_id), pointer_size_(pointer_size) { - // We currently only support kHostPlatformId for CPU, kCudaPlatformId for - // GPU and kInterpreterPlatformId for Interpreter. Before supporting other - // platforms, we need to test this transfer manager on them. - CHECK(platform_id_ == se::host::kHostPlatformId || - platform_id_ == se::interpreter::kInterpreterPlatformId || - platform_id_ == se::cuda::kCudaPlatformId); -} + : platform_id_(platform_id), pointer_size_(pointer_size) {} se::Platform::Id GenericTransferManager::PlatformId() const { return platform_id_; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 9da4fb97fa27a238fead74985cb481a9be1f4a65..a1ea5884a478a0a21af3df338aa79da3686b9d2c 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -397,6 +397,7 @@ tf_cc_test( "//tensorflow/compiler/xla/service:hlo_matchers", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", ], ) @@ -437,8 +438,10 @@ tf_cc_test( ":fusion_merger", ":instruction_fusion", "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla/service:hlo_matchers", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", ], ) @@ -510,6 +513,7 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:buffer_liveness", "//tensorflow/compiler/xla/service:call_inliner", + "//tensorflow/compiler/xla/service:conditional_simplifier", "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index ed78fef4113bd9f7048ca3c8c2d4e38c5ec4762a..2029c303d47e9a62135b003c3bd9be6f8b3438d4 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -98,6 +98,14 @@ StatusOr> BufferAllocations::Builder::Build( } } + if (VLOG_IS_ON(2)) { + for (BufferAllocation::Index i = 0; i < num_buffers; ++i) { + const auto& buf = buffer_allocations->buffers_[i]; + VLOG(2) << "Buffer " << i << " -> " << buf.opaque() << " (" << buf.size() + << "B)"; + } + } + return std::move(buffer_allocations); } diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc index f76f15929d12eed63d8964acd61fb3fea3945006..461747b699b542ae0c8735aea34cc9e57c1fb387 100644 --- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc @@ -45,7 +45,7 @@ ConvolutionThunk::ConvolutionThunk( const BufferAllocation::Slice& scratch_buffer, const Shape& input_shape, const Shape& filter_shape, const Shape& output_shape, const Window& window, const ConvolutionDimensionNumbers& dim_nums, int64 algorithm, - const HloInstruction* hlo) + bool tensor_ops_enabled, const HloInstruction* hlo) : Thunk(Kind::kConvolution, hlo), convolution_kind_(convolution_kind), input_buffer_(input_buffer), @@ -58,29 +58,30 @@ ConvolutionThunk::ConvolutionThunk( output_shape_(output_shape), window_(window), dim_nums_(dim_nums), - algorithm_(algorithm) {} + algorithm_(algorithm), + tensor_ops_enabled_(tensor_ops_enabled) {} Status ConvolutionThunk::ExecuteOnStream( const BufferAllocations& buffer_allocations, se::Stream* stream) { - se::DeviceMemory input_data( - buffer_allocations.GetDeviceAddress(input_buffer_)); - se::DeviceMemory filter_data( - buffer_allocations.GetDeviceAddress(filter_buffer_)); - se::DeviceMemory output_data( - buffer_allocations.GetDeviceAddress(output_buffer_)); + se::DeviceMemoryBase input_data = + buffer_allocations.GetDeviceAddress(input_buffer_); + se::DeviceMemoryBase filter_data = + buffer_allocations.GetDeviceAddress(filter_buffer_); + se::DeviceMemoryBase output_data = + buffer_allocations.GetDeviceAddress(output_buffer_); se::DeviceMemoryBase scratch = buffer_allocations.GetDeviceAddress(scratch_buffer_); se::dnn::AlgorithmConfig algorithm_config( - se::dnn::AlgorithmDesc(algorithm_, /*use_tensor_ops=*/false)); + se::dnn::AlgorithmDesc(algorithm_, tensor_ops_enabled_)); TF_RETURN_IF_ERROR(RunCudnnConvolution( convolution_kind_, input_shape_, filter_shape_, output_shape_, input_data, filter_data, output_data, scratch, window_, dim_nums_, algorithm_config, stream)); - // Figure out which of output/input/filter is the result produced by this op, - // and write the result tuple. + // Figure out which of output/input/filter is the result produced by + // this op, and write the result tuple. void* result_ptr = [&] { switch (convolution_kind_) { case CudnnConvKind::kForward: diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h index ca9ef5277b3369dea3f698d1bcf0ad190d2c5217..900d9cb6243088b56a1825fb3ab8c06cf8d74726 100644 --- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h @@ -59,7 +59,7 @@ class ConvolutionThunk : public Thunk { const Shape& input_shape, const Shape& filter_shape, const Shape& output_shape, const Window& window, const ConvolutionDimensionNumbers& dim_nums, int64 algorithm, - const HloInstruction* hlo); + bool tensor_ops_enabled, const HloInstruction* hlo); ConvolutionThunk(const ConvolutionThunk&) = delete; ConvolutionThunk& operator=(const ConvolutionThunk&) = delete; @@ -99,6 +99,7 @@ class ConvolutionThunk : public Thunk { const Window window_; const ConvolutionDimensionNumbers dim_nums_; int64 algorithm_; + bool tensor_ops_enabled_; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc index 621b2d510fa98af40b89badebef5e45902f23d4c..1792893ae401bf16d2dd9e861607e8f3821a505e 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc @@ -135,15 +135,6 @@ std::vector GetAlgorithms(CudnnConvKind kind, break; } - // Remove any algorithms with tensor math enabled. These have lower precision - // than regular algorithms, and we don't yet have a way to turn this on/off in - // XLA. - algorithms.erase(std::remove_if(algorithms.begin(), algorithms.end(), - [&](const AlgorithmDesc& a) { - return a.tensor_ops_enabled(); - }), - algorithms.end()); - return algorithms; } @@ -172,7 +163,7 @@ string NumBytesToString(int64 bytes) { // cache misses and doing extra work. Overall, caching doesn't seem worth the // trouble, but we may want to revisit this if we ever find a model where // caching would speed up compilation a lot. -optional> +optional> CudnnConvolutionAlgorithmPicker::PickBestAlgorithm( CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, const Shape& output_shape, const Window& window, @@ -222,6 +213,7 @@ CudnnConvolutionAlgorithmPicker::PickBestAlgorithm( ShouldIncludeWinogradNonfusedAlgo(input_shape, output_shape, dnums); se::dnn::ProfileResult best_result; int64 best_result_bytes_used = 0; + for (const AlgorithmDesc& alg : GetAlgorithms(kind, use_winograd_nonfused, stream_exec_)) { ScratchAllocator scratch_allocator(device_ordinal, allocator); @@ -229,14 +221,12 @@ CudnnConvolutionAlgorithmPicker::PickBestAlgorithm( VLOG(3) << "Trying algorithm " << AlgorithmToString(alg) << " for " << instr->ToString(); - bool launch_ok = - RunCudnnConvolution(kind, input_shape, filter_shape, output_shape, - se::DeviceMemory(input_buf.ValueOrDie()), - se::DeviceMemory(filter_buf.ValueOrDie()), - se::DeviceMemory(output_buf.ValueOrDie()), - &scratch_allocator, window, dnums, - AlgorithmConfig(alg), &stream, &profile_result) - .ok(); + bool launch_ok = RunCudnnConvolution( + kind, input_shape, filter_shape, output_shape, + input_buf.ValueOrDie(), filter_buf.ValueOrDie(), + output_buf.ValueOrDie(), &scratch_allocator, window, + dnums, AlgorithmConfig(alg), &stream, &profile_result) + .ok(); if (launch_ok && profile_result.is_valid()) { int64 scratch_bytes_used = scratch_allocator.TotalAllocatedBytes(); @@ -260,8 +250,9 @@ CudnnConvolutionAlgorithmPicker::PickBestAlgorithm( << AlgorithmToString(best_result.algorithm()) << ", takes " << best_result.elapsed_time_in_ms() << "ms, and uses " << best_result_bytes_used << "B of scratch memory."; - return std::make_pair(best_result.algorithm().algo_id(), - best_result_bytes_used); + return std::make_tuple(best_result.algorithm().algo_id(), + best_result.algorithm().tensor_ops_enabled(), + best_result_bytes_used); } LOG(WARNING) << "All algorithms tried for convolution " << instr->ToString() @@ -277,19 +268,19 @@ StatusOr CudnnConvolutionAlgorithmPicker::RunOnInstruction( const auto& lhs_shape = instr->operand(0)->shape(); const auto& rhs_shape = instr->operand(1)->shape(); const auto& conv_result_shape = instr->shape().tuple_shapes(0); - optional> alg_and_scratch_bytes; + optional> alg_scratch_and_tc; if (call_target == kCudnnConvForwardCallTarget) { - alg_and_scratch_bytes = PickBestAlgorithm( + alg_scratch_and_tc = PickBestAlgorithm( CudnnConvKind::kForward, /*input_shape=*/lhs_shape, /*filter_shape=*/rhs_shape, /*output_shape=*/conv_result_shape, instr->window(), instr->convolution_dimension_numbers(), instr); } else if (call_target == kCudnnConvBackwardInputCallTarget) { - alg_and_scratch_bytes = PickBestAlgorithm( + alg_scratch_and_tc = PickBestAlgorithm( CudnnConvKind::kBackwardInput, /*input_shape=*/conv_result_shape, /*filter_shape=*/rhs_shape, /*output_shape=*/lhs_shape, instr->window(), instr->convolution_dimension_numbers(), instr); } else if (call_target == kCudnnConvBackwardFilterCallTarget) { - alg_and_scratch_bytes = PickBestAlgorithm( + alg_scratch_and_tc = PickBestAlgorithm( CudnnConvKind::kBackwardFilter, /*input_shape=*/lhs_shape, /*filter_shape=*/conv_result_shape, /*output_shape=*/rhs_shape, instr->window(), instr->convolution_dimension_numbers(), instr); @@ -298,17 +289,20 @@ StatusOr CudnnConvolutionAlgorithmPicker::RunOnInstruction( << instr->ToString(); } - if (!alg_and_scratch_bytes.has_value()) { + if (!alg_scratch_and_tc.has_value()) { return false; } int64 algorithm; + bool tensor_ops_enabled; int64 scratch_bytes; - std::tie(algorithm, scratch_bytes) = *alg_and_scratch_bytes; + + std::tie(algorithm, tensor_ops_enabled, scratch_bytes) = *alg_scratch_and_tc; VLOG(1) << "Setting cudnn conv to use algorithm " << algorithm << " and " << NumBytesToString(scratch_bytes) - << " of scratch memory: " << instr->ToString(); + << " of scratch memory: " << instr->ToString() + << " tensor_ops_enabled: " << tensor_ops_enabled; // Replace instr with a new CustomCall which has the correct algorithm, and // whose output shape has the appropriate amount of scratch memory. @@ -318,10 +312,15 @@ StatusOr CudnnConvolutionAlgorithmPicker::RunOnInstruction( ShapeUtil::MakeShape(U8, {scratch_bytes})}); HloInstruction* algorithm_hlo = computation->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(algorithm))); + HloInstruction* tensor_ops_enabled_hlo = + computation->AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR0(tensor_ops_enabled))); + HloInstruction* new_call = computation->AddInstruction(HloInstruction::CreateCustomCall( new_call_shape, - {instr->mutable_operand(0), instr->mutable_operand(1), algorithm_hlo}, + {instr->mutable_operand(0), instr->mutable_operand(1), algorithm_hlo, + tensor_ops_enabled_hlo}, instr->custom_call_target())); new_call->set_window(instr->window()); new_call->set_convolution_dimension_numbers( diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h index 10e49daee5df187e5ad90b7adf8c92aa9a63ba21..516210ec2e500cf03774d27408300ac3346e7b4f 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h @@ -47,7 +47,7 @@ class CudnnConvolutionAlgorithmPicker : public HloPassInterface { private: StatusOr RunOnComputation(HloComputation* computation); StatusOr RunOnInstruction(HloInstruction* instr); - tensorflow::gtl::optional> PickBestAlgorithm( + tensorflow::gtl::optional> PickBestAlgorithm( CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, const Shape& output_shape, const Window& window, const ConvolutionDimensionNumbers& dnums, HloInstruction* instr); diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc index f5f52cf62bf6edb7925ec3b22fc1772ffbfbf089..e4ae839e1dd4cb3a744a3f6a3329cabdaeb3f38d 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc @@ -70,42 +70,17 @@ class ScratchBufAllocator : public se::ScratchAllocator { bool allocated_ = false; }; -} // anonymous namespace - -string CudnnConvKindToString(CudnnConvKind kind) { - switch (kind) { - case CudnnConvKind::kForward: - return "forward"; - case CudnnConvKind::kBackwardFilter: - return "backward_filter"; - case CudnnConvKind::kBackwardInput: - return "backward_input"; - } -} - -Status RunCudnnConvolution(CudnnConvKind kind, const Shape& input_shape, - const Shape& filter_shape, const Shape& output_shape, - DeviceMemory input_buf, - DeviceMemory filter_buf, - DeviceMemory output_buf, - DeviceMemoryBase scratch_buf, const Window& window, - const ConvolutionDimensionNumbers& dnums, - AlgorithmConfig algorithm, Stream* stream, - ProfileResult* profile_result /*= nullptr*/) { - ScratchBufAllocator scratch_allocator(scratch_buf); - return RunCudnnConvolution(kind, input_shape, filter_shape, output_shape, - input_buf, filter_buf, output_buf, - &scratch_allocator, window, dnums, algorithm, - stream, profile_result); -} - +template Status RunCudnnConvolution( CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, - const Shape& output_shape, DeviceMemory input_buf, - DeviceMemory filter_buf, DeviceMemory output_buf, + const Shape& output_shape, DeviceMemory input_buf, + DeviceMemory filter_buf, DeviceMemory output_buf, se::ScratchAllocator* scratch_allocator, const Window& window, const ConvolutionDimensionNumbers& dnums, AlgorithmConfig algorithm, Stream* stream, ProfileResult* profile_result /*= nullptr*/) { + VLOG(3) << "Convolution Algorithm: " << algorithm.algorithm().algo_id(); + VLOG(3) << "tensor_ops_enabled: " + << algorithm.algorithm().tensor_ops_enabled(); VLOG(3) << "Convolution kind: " << CudnnConvKindToString(kind); VLOG(3) << "input shape: { " << ShapeUtil::HumanString(input_shape) << " }"; VLOG(3) << "filter shape: { " << ShapeUtil::HumanString(filter_shape) << " }"; @@ -121,8 +96,16 @@ Status RunCudnnConvolution( // tensorflow/python/ops/nn_ops.py). const int effective_num_dimensions = std::max(2, num_dimensions); - CHECK_EQ(F32, output_shape.element_type()) - << ShapeUtil::HumanString(output_shape); + if (std::is_same::value) { + CHECK_EQ(F32, output_shape.element_type()) + << ShapeUtil::HumanString(output_shape); + } else if (std::is_same::value) { + CHECK_EQ(F16, output_shape.element_type()) + << ShapeUtil::HumanString(output_shape); + } else { + LOG(FATAL) << ShapeUtil::HumanString(output_shape); + } + CHECK_EQ(num_dimensions, dnums.input_spatial_dimensions_size()); CHECK_EQ(num_dimensions, dnums.kernel_spatial_dimensions_size()); CHECK_EQ(num_dimensions, dnums.output_spatial_dimensions_size()); @@ -217,5 +200,63 @@ Status RunCudnnConvolution( return Status::OK(); } +} // anonymous namespace + +string CudnnConvKindToString(CudnnConvKind kind) { + switch (kind) { + case CudnnConvKind::kForward: + return "forward"; + case CudnnConvKind::kBackwardFilter: + return "backward_filter"; + case CudnnConvKind::kBackwardInput: + return "backward_input"; + } +} + +Status RunCudnnConvolution( + CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, + const Shape& output_shape, perftools::gputools::DeviceMemoryBase input_buf, + perftools::gputools::DeviceMemoryBase filter_buf, + perftools::gputools::DeviceMemoryBase output_buf, + perftools::gputools::DeviceMemoryBase scratch_buf, const Window& window, + const ConvolutionDimensionNumbers& dnums, + perftools::gputools::dnn::AlgorithmConfig algorithm, + perftools::gputools::Stream* stream, + perftools::gputools::dnn::ProfileResult* profile_result) { + ScratchBufAllocator scratch_allocator(scratch_buf); + return RunCudnnConvolution(kind, input_shape, filter_shape, output_shape, + input_buf, filter_buf, output_buf, + &scratch_allocator, window, dnums, algorithm, + stream, profile_result); +} + +Status RunCudnnConvolution( + CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, + const Shape& output_shape, perftools::gputools::DeviceMemoryBase input_buf, + perftools::gputools::DeviceMemoryBase filter_buf, + perftools::gputools::DeviceMemoryBase output_buf, + perftools::gputools::ScratchAllocator* scratch_allocator, + const Window& window, const ConvolutionDimensionNumbers& dnums, + perftools::gputools::dnn::AlgorithmConfig algorithm, + perftools::gputools::Stream* stream, + perftools::gputools::dnn::ProfileResult* profile_result) { + PrimitiveType output_primitive_type = output_shape.element_type(); + CHECK(output_primitive_type == F32 || output_primitive_type == F16) + << ShapeUtil::HumanString(output_shape); + if (output_primitive_type == F32) { + return RunCudnnConvolution( + kind, input_shape, filter_shape, output_shape, + se::DeviceMemory(input_buf), se::DeviceMemory(filter_buf), + se::DeviceMemory(output_buf), scratch_allocator, window, dnums, + algorithm, stream, profile_result); + } + return RunCudnnConvolution(kind, input_shape, filter_shape, output_shape, + se::DeviceMemory(input_buf), + se::DeviceMemory(filter_buf), + se::DeviceMemory(output_buf), + scratch_allocator, window, dnums, algorithm, + stream, profile_result); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h index b101f76510c129fd22b246e5f0348848192ecbba..3dbfa2730da359d3c7937140508017c4a7b02d6c 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h @@ -55,7 +55,10 @@ string CudnnConvKindToString(CudnnConvKind kind); // Note that depending on the value of CudnnConvKind, the result of this call // may be written into input_buf, filter_buf, or output_buf! // -// At the moment we only support cudnn convolutions over floats. +// At the moment we only support cudnn convolutions over float and half, and +// convolution with half data type is implemented with cudnn PSEUDO_HALF +// configuration, that is, the input values are half and the internal +// computation type is float. // // We provide one overload which takes a scratch buffer, and another which takes // an allocator which is responsible for allocating the scratch space. In @@ -69,10 +72,9 @@ string CudnnConvKindToString(CudnnConvKind kind); // that size, if you like. Status RunCudnnConvolution( CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, - const Shape& output_shape, - perftools::gputools::DeviceMemory input_buf, - perftools::gputools::DeviceMemory filter_buf, - perftools::gputools::DeviceMemory output_buf, + const Shape& output_shape, perftools::gputools::DeviceMemoryBase input_buf, + perftools::gputools::DeviceMemoryBase filter_buf, + perftools::gputools::DeviceMemoryBase output_buf, perftools::gputools::DeviceMemoryBase scratch_buf, const Window& window, const ConvolutionDimensionNumbers& dnums, perftools::gputools::dnn::AlgorithmConfig algorithm, @@ -81,10 +83,9 @@ Status RunCudnnConvolution( Status RunCudnnConvolution( CudnnConvKind kind, const Shape& input_shape, const Shape& filter_shape, - const Shape& output_shape, - perftools::gputools::DeviceMemory input_buf, - perftools::gputools::DeviceMemory filter_buf, - perftools::gputools::DeviceMemory output_buf, + const Shape& output_shape, perftools::gputools::DeviceMemoryBase input_buf, + perftools::gputools::DeviceMemoryBase filter_buf, + perftools::gputools::DeviceMemoryBase output_buf, perftools::gputools::ScratchAllocator* scratch_allocator, const Window& window, const ConvolutionDimensionNumbers& dnums, perftools::gputools::dnn::AlgorithmConfig algorithm, diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc index c137fbc97e29e24edb3603c611a5c8f093bc62a6..3cd30b754c3242f00c704de1afab2282ed827b41 100644 --- a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc +++ b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc @@ -45,6 +45,7 @@ void MaybeResolveTupleElements(HloInstruction* instruction, // Returns the bytes read by fusion parameter 'param', by returning the byte // size of 'param' shape (or the cumulative byte sizes of all leaf tuple // elements if 'param' is tuple-shaped). +// // In the special case where all users of 'param' (or all users of a leaf // tuple element if 'param' is tuple-shaped) are Slice instructions, the size // of each slice instruction is accumulated instead, to give a more accurate @@ -63,11 +64,10 @@ double CalculateBytesReadByFusionParameter(HloInstruction* param) { // Slice for a more accurate estimate of bytes read. double bytes = 0.0; for (auto& instruction : instructions) { - if (std::all_of(instruction->users().begin(), instruction->users().end(), - [](const HloInstruction* instruction) { - return instruction->opcode() == HloOpcode::kSlice || - instruction->opcode() == HloOpcode::kDynamicSlice; - })) { + if (c_all_of(instruction->users(), [](const HloInstruction* instruction) { + return instruction->opcode() == HloOpcode::kSlice || + instruction->opcode() == HloOpcode::kDynamicSlice; + })) { // All users are slice: accumulate bytes of all user slice instructions. for (auto& user : instruction->users()) { bytes += ShapeUtil::ByteSizeOf(user->shape()); @@ -199,6 +199,7 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { ++total_visited_; // Skip 'fusion' instruction if there are no users into which we can merge. if (fusion->users().empty()) { + VLOG(3) << "Not merging " << fusion->name() << ": Has no users."; ++num_fail_no_users_; return Status::OK(); } @@ -208,24 +209,27 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // Input fusion instructions need to be rooted at a particular HLO (e.g. // kReduce), so they shouldn't be further fused either. if (fusion->fusion_kind() != HloInstruction::FusionKind::kLoop) { + VLOG(3) << "Not merging " << fusion->name() << ": Is not loop fusion."; ++num_fail_not_loop_fusion_; return Status::OK(); } // Skip multiple output fusion. It's not yet supported. if (fusion->IsMultiOutputFusion()) { + VLOG(3) << "Not merging " << fusion->name() << ": Is multi-output fusion."; ++num_fail_not_loop_fusion_; return Status::OK(); } // Skip 'fusion' instruction if we cannot merge into all of its users. // Merging into all users enables the removal of 'fusion' from the // computation. - if (!std::all_of(fusion->users().begin(), fusion->users().end(), - [](const HloInstruction* instruction) { - return instruction->opcode() == HloOpcode::kFusion && - instruction->fusion_kind() == - HloInstruction::FusionKind::kLoop; - })) { + if (!c_all_of(fusion->users(), [](const HloInstruction* user) { + return user->opcode() == HloOpcode::kFusion && + (user->fusion_kind() == HloInstruction::FusionKind::kLoop || + user->fusion_kind() == HloInstruction::FusionKind::kInput); + })) { + VLOG(3) << "Not merging " << fusion->name() + << ": Some of its users are not loop/input fusion kernels."; ++num_fail_merge_all_users_; return Status::OK(); } @@ -233,18 +237,17 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // Skip 'fusion' instruction if any of its fused instructions are expensive. // This is done to avoid the duplication of expensive instructions, which // would occur if 'fusion' were merged into multiple users. + // // If 'fusion' has just one user, then an earlier fusion pass chose not to // fuse this producer/comsumer pair (likely because of expensive instruction // re-use by the consumer), and so we honor that choice here as well. - if (!std::all_of(fusion->fused_instructions().begin(), - fusion->fused_instructions().end(), - [](const HloInstruction* instruction) { - if (instruction->opcode() != HloOpcode::kParameter && - GpuInstructionFusion::IsExpensive(*instruction)) { - return false; - } - return true; - })) { + if (c_any_of(fusion->fused_instructions(), + [](const HloInstruction* instruction) { + return instruction->opcode() != HloOpcode::kParameter && + GpuInstructionFusion::IsExpensive(*instruction); + })) { + VLOG(3) << "Not merging " << fusion->name() + << ": Contains one or more expensive instructions."; ++num_fail_expensive_fused_instruction_; return Status::OK(); } @@ -253,6 +256,8 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // exceeds the threshold value. if (CalculateFlopsToBytesRatio(fusion) > FusionMerger::GetThresholdFlopsToBytesRatio()) { + VLOG(3) << "Not merging " << fusion->name() + << ": flops-to-bytes ratio is not favorable."; ++num_fail_flops_to_byte_ratio_; return Status::OK(); } @@ -265,6 +270,9 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { const double merged_to_current_bytes_ratio = merged_bytes_transferred / std::max(1.0, current_bytes_transferred); if (merged_to_current_bytes_ratio > 1.10) { + VLOG(3) << "Not merging " << fusion->name() + << ": merged-to-current-bytes-ratio of " + << merged_to_current_bytes_ratio << " is not favorable."; ++num_fail_net_bytes_transferred_ratio_; return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc index deef5966b80d1b7f16e9982eed9ac5c7131e9d73..c0def275253fce84ac99322f0e5f8c127a985c3b 100644 --- a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc +++ b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc @@ -16,13 +16,17 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/fusion_merger.h" #include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" namespace xla { namespace gpu { namespace { +namespace op = xla::testing::opcode_matchers; + class FusionMergerTest : public HloTestBase { protected: FusionMergerTest() : module_(CreateNewModule()) {} @@ -459,6 +463,43 @@ TEST_F(FusionMergerTest, BytesTransferredThresholdNotExeceeded) { EXPECT_TRUE(FusionMerger().Run(module_.get()).ValueOrDie()); } +// Check that we're willing to merge f1_computation into f2_computation, even +// though f2 is an input fusion node. +TEST_F(FusionMergerTest, WillMergeIntoInputFusion) { + const char* const kModule = R"( + HloModule m + + f1_computation { + f1_p0 = f32[10]{0} parameter(0) + ROOT f1_root = f32[10]{0} add(f1_p0, f1_p0) + } + + add_computation { + add_lhs = f32[] parameter(0) + add_rhs = f32[] parameter(1) + ROOT add_root = f32[] add(add_lhs, add_rhs) + } + + f2_computation { + f2_p0 = f32[10]{0} parameter(0) + f2_mul = f32[10]{0} multiply(f2_p0, f2_p0) + f2_zero = f32[] constant(0) + ROOT f2_root = f32[] reduce(f2_mul, f2_zero), dimensions={0}, + to_apply=add_computation + } + + ENTRY entry { + p0 = f32[10]{0} parameter(0) + f1 = f32[10]{0} fusion(p0), kind=kLoop, calls=f1_computation + ROOT f2 = f32[] fusion(f1), kind=kInput, calls=f2_computation + } + )"; + auto module = tools::Parse(kModule).ValueOrDie(); + EXPECT_TRUE(FusionMerger().Run(module.get()).ValueOrDie()); + EXPECT_THAT(module->entry_computation()->root_instruction(), + op::Fusion(op::Parameter())); +} + } // namespace } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc index 8e3aebbc12b5e6d746700956b9743bc94db50167..ca54b2eed8a3f7839f88107180be30448d979b97 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc @@ -108,11 +108,13 @@ bool DoGemmWithAlgorithm(MatrixDescriptor lhs_matrix, return stream ->ThenBlasGemmWithAlgorithm( lhs_transpose, rhs_transpose, output_matrix.num_rows, - output_matrix.num_cols, /*size of reduce dim=*/k, /*alpha=*/1.0, - lhs_data, /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data, - /*leading dim of RHS=*/rhs_matrix.num_rows, /*beta=*/0.0, - &output_data, /*leading dim of output=*/output_matrix.num_rows, - computation_type, algorithm, output_profile_result) + output_matrix.num_cols, /*size of reduce dim=*/k, + /*alpha=*/static_cast(1.0f), lhs_data, + /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data, + /*leading dim of RHS=*/rhs_matrix.num_rows, + /*beta=*/static_cast(0.0f), &output_data, + /*leading dim of output=*/output_matrix.num_rows, computation_type, + algorithm, output_profile_result) .ok(); } @@ -137,9 +139,9 @@ StatusOr DoGemmAutotune( // for all algorithms if we're targeting < sm_50. But because we pass a // non-null ProfileResult, DoGemmWithAlgorithm should always return true, // and the actual success-ness is returned in ProfileResult::is_valid. - DCHECK(DoGemmWithAlgorithm(lhs_matrix, rhs_matrix, output_matrix, - computation_type, algorithm, stream, - &profile_result)); + CHECK(DoGemmWithAlgorithm(lhs_matrix, rhs_matrix, output_matrix, + computation_type, algorithm, stream, + &profile_result)); if (profile_result.is_valid() && profile_result.elapsed_time_in_ms() < best_result.elapsed_time_in_ms()) { @@ -161,6 +163,8 @@ StatusOr DoGemmAutotune( // DoGemm/DoGemmWithAlgorithm/DoGemmAutotune. auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm) { switch (type) { + case F16: + return &DoGemm; case F32: return &DoGemm; case F64: @@ -172,6 +176,8 @@ auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm) { auto GetGemmWithAlgorithmFn(PrimitiveType type) -> decltype(&DoGemmWithAlgorithm) { switch (type) { + case F16: + return &DoGemmWithAlgorithm; case F32: return &DoGemmWithAlgorithm; case F64: @@ -182,6 +188,8 @@ auto GetGemmWithAlgorithmFn(PrimitiveType type) } auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune) { switch (type) { + case F16: + return &DoGemmAutotune; case F32: return &DoGemmAutotune; case F64: @@ -196,6 +204,10 @@ auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune) { // separately from the precision of the inputs and result. se::blas::ComputationType GetBlasComputationType(PrimitiveType type) { switch (type) { + case F16: + // Use F32 as computation type for F16 as we currently only implement the + // cuDNN pseudo half configuration for half precision. + return se::blas::ComputationType::kF32; case F32: return se::blas::ComputationType::kF32; case F64: @@ -315,6 +327,9 @@ tensorflow::Status GemmThunk::ExecuteOnStream( stream, /*output_profile_result=*/nullptr); } + + // Autotune will fail when CUDA 8 and GPU sm_50 or older are used. + // Use the older Gemm API in this case. return GetGemmFn(element_type)(lhs_matrix, rhs_matrix, output_matrix, stream); }; diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 28ebd034ee0c89137f4e6eb417d8a37f4a00af7a..b41eb72d835c057345384a5d950cd27cff9ad6d0 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h" @@ -176,6 +177,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module, pass.AddPass(); pass.AddPass(); pass.AddPass(); + pass.AddPass(); } pipeline.AddPass( @@ -241,6 +243,22 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module, TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); } + { + HloPassPipeline pipeline("layout_assignment"); + pipeline.AddPass( + hlo_module->mutable_entry_computation_layout()); + + // The LayoutAssignment pass may leave behind kCopy instructions which are + // duplicate or NOPs, so remove them with algebraic simplification and CSE. + pipeline.AddPass>( + /*is_layout_sensitive=*/true, + /*valid_bitcast_callback=*/[](const Shape&, const Shape&) { + return true; + }); + pipeline.AddPass(/*is_layout_sensitive=*/true); + TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); + } + { HloPassFix fusion("fusion"); fusion.AddInvariantChecker(); @@ -277,15 +295,6 @@ tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) { HloPassPipeline pipeline("GPU-ir-emit-prepare"); pipeline.AddInvariantChecker(); - pipeline.AddPass( - hlo_module->mutable_entry_computation_layout()); - - // The LayoutAssignment pass may leave behind kCopy instructions which are - // duplicate or NOPs, so remove them with algebraic simplification and CSE. - pipeline.AddPass>( - /*is_layout_sensitive=*/true, - [](const Shape&, const Shape&) { return true; }); - pipeline.AddPass(/*is_layout_sensitive=*/true); // Copy insertion should be performed immediately before IR emission to avoid // inserting unnecessary copies (later pass adds an instruction which // materializes the value) or missing a necessary copy (later pass removes an diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc index 88bf5a74fa03618d6f61365450f05e6f5d1a0c86..9db85bc788bde46c890a46ce9b0902ddce3f5675 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc @@ -49,7 +49,7 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { TF_ASSIGN_OR_RETURN(bool changed, generic_copy_insertion.Run(module)); TF_ASSIGN_OR_RETURN(std::unique_ptr dataflow, - HloDataflowAnalysis::Run(module)); + HloDataflowAnalysis::Run(*module)); // Make sure all operands of a library call are in memory instead of constants // in IR. @@ -79,9 +79,9 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); } } else if (IsCustomCallToDnnConvolution(*hlo)) { - // The last argument to a CUDNN convolution is its algorithm, which must - // be an HLO constant -- it shouldn't be copied. - for (int64 i = 0; i < hlo->operand_count() - 1; ++i) { + // The last two arguments to a CUDNN convolution are two HLO constants for + // cudnn algorithm and tensor_ops_enabled flag, which shouldn't be copied. + for (int64 i = 0; i < hlo->operand_count() - 2; ++i) { TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); } } else if (ImplementedAsLibraryCall(*hlo)) { diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index f5d67b9ea9498df3f023ea9a694a63b468c5be18..04b37d913e0bc8f8226057f107da05fd1e675010 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -46,12 +46,14 @@ namespace { class HloExecutionProfiler { public: // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler(bool do_profile, HloExecutionProfile* profile, - se::Stream* stream, - const HloComputation* computation) + explicit HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation) : do_profile_(do_profile), profile_(profile), stream_(stream), + sub_streams_(sub_streams), computation_(computation) { if (do_profile_) { clock_rate_ghz_ = @@ -70,6 +72,7 @@ class HloExecutionProfiler { CHECK(!finished_execution_) << "Call FinishExecution only once!"; finished_execution_ = true; if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); stream_->ThenStopTimer(execution_timer_.get()); stream_->BlockHostUntilDone().IgnoreError(); profile_->set_total_cycles_executed( @@ -88,6 +91,7 @@ class HloExecutionProfiler { // that the hlo_instruction took to execute in the profile. void FinishOperation(const HloInstruction* hlo_instruction) { if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); stream_->ThenStopTimer(per_op_timer_.get()); stream_->BlockHostUntilDone().IgnoreError(); profile_->SetCyclesTakenBy( @@ -100,6 +104,7 @@ class HloExecutionProfiler { double clock_rate_ghz_; HloExecutionProfile* profile_; se::Stream* stream_; + const std::vector::SmartPtr>& sub_streams_; const HloComputation* computation_; std::unique_ptr execution_timer_; std::unique_ptr per_op_timer_; @@ -147,13 +152,9 @@ Status GpuExecutable::ExecuteThunks( LOG(WARNING) << "PROFILING: profiling is enabled"; } - HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream, - hlo_module_->entry_computation()); - - uint64 start_micros = tensorflow::Env::Default()->NowMicros(); - // Stream 0 indicates `main_stream` and substreams start from stream 1. std::vector::SmartPtr> sub_streams; + sub_streams.reserve(thunk_schedule_->StreamCount() - 1); while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) { sub_streams.emplace_back(); TF_ASSIGN_OR_RETURN( @@ -161,6 +162,10 @@ Status GpuExecutable::ExecuteThunks( run_options->BorrowStream(main_stream->parent()->device_ordinal())); } + HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream, + sub_streams, hlo_module_->entry_computation()); + uint64 start_micros = tensorflow::Env::Default()->NowMicros(); + // The next event enqueued on stream N must not run until the thunk at // last_blocking_thunk_for_stream[N] completes. std::map last_blocking_thunk_for_stream; @@ -262,9 +267,16 @@ StatusOr> GpuExecutable::ExecuteOnStream( ++i) { const BufferAllocation& allocation = assignment_->GetAllocation(i); if (allocation.is_entry_computation_parameter()) { - auto param_no = allocation.parameter_number(); - buffer_allocations_builder.RegisterBuffer( - i, arguments[param_no]->root_buffer()); + // The caller must give us a buffer for ShapeIndex {} of every parameter. + // It can optionally give us a buffer for other ShapeIndices, but we + // ignore them: Because we can't rely on these sub-buffers' addresses + // being available, our generated code can't use them. Instead, it must + // chase pointers starting at the tuple root. + if (allocation.param_shape_index().empty()) { + auto param_no = allocation.parameter_number(); + buffer_allocations_builder.RegisterBuffer( + i, arguments[param_no]->root_buffer()); + } } } se::StreamExecutor* executor = run_options->stream()->parent(); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h index 1fe7970e7d94ad4a4cad6aabcfc84a1356753443..3d34311b4368d17cb074aaf33c71fc865e96387e 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h @@ -66,13 +66,14 @@ class HloToIrBindings { } llvm::Value* GetTempBufferBase() const { return temp_buffer_base_; } + void SetTempBufferBase(llvm::Value* v) { temp_buffer_base_ = v; } // A helper method that returns the base pointer of the IrArray containing the // output of "inst".at the given ShapeIndex. llvm::Value* GetBasePointer(const HloInstruction& hlo, const ShapeIndex& shape_index = {}) const { auto it = base_ptrs_.find(&hlo); - CHECK(it != base_ptrs_.end()); + CHECK(it != base_ptrs_.end()) << hlo.ToString(); return it->second.element(shape_index); } @@ -113,7 +114,7 @@ class HloToIrBindings { std::unordered_map> base_ptrs_; // The address of the memory block that contains all temporary buffers. - llvm::Value* temp_buffer_base_; + llvm::Value* temp_buffer_base_ = nullptr; llvm_ir::AliasAnalysis alias_analysis_; }; diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index b5962f069bf499c913bd5479f263a7cb77c00555..84504d29e08f4aa487bf495887c16b5c8a4f5684 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -26,6 +26,7 @@ namespace { bool IsFusile(const HloInstruction& hlo) { return (hlo.IsElementwise() && hlo.operand_count() > 0) || + hlo.opcode() == HloOpcode::kBitcast || hlo.opcode() == HloOpcode::kBroadcast || hlo.opcode() == HloOpcode::kConcatenate || hlo.opcode() == HloOpcode::kDynamicSlice || @@ -70,17 +71,6 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return false; } - // We may need to know original operand layout to emit input fusion, and so - // far, we merely use the layout of an operand of the fusion node, which means - // we must fuse only elementwise operations. This restriction should be lifted - // later if we need to fuse other operations, e.g. transpose, for performance. - if ((IsReductionToVector(*consumer) || - (HloOpcode::kFusion == consumer->opcode() && - HloInstruction::FusionKind::kInput == consumer->fusion_kind())) && - !producer->IsElementwise()) { - return false; - } - // Cost condition: not fuse (simple, expensive producers) and (consumers who // reuse operand elements). if (producer->opcode() != HloOpcode::kFusion && diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 2d6dad27a59978da6e4719afc50ebee5e641dde0..c81dbb7bf357558021ca4e4a4e28922d0e4f733d 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" namespace op = xla::testing::opcode_matchers; @@ -163,5 +164,79 @@ TEST_F(InstructionFusionTest, GetTupleElementFused) { EXPECT_EQ(HloOpcode::kGetTupleElement, fused_root->operand(1)->opcode()); } +// Tests that broadcasts fused into a fusion with a reduce root. +TEST_F(InstructionFusionTest, BroadcastIntoReduce) { + auto module = tools::Parse(R"( + HloModule test_module + + add { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(lhs, rhs) + } + + ENTRY BroadcastIntoReduce { + constant = f32[] constant(1) + broadcast = f32[16,16,16,16]{3,2,1,0} broadcast(constant), dimensions={} + constant.1 = f32[] constant(0) + ROOT reduce = f32[] reduce(broadcast, constant.1), dimensions={0,1,2,3}, + to_apply=add + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Reduce(op::Broadcast(op::Parameter()), op::Parameter())); +} + +TEST_F(InstructionFusionTest, BitcastIntoAdd) { + auto module = tools::Parse(R"( + HloModule test_module + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + bitcast = f32[4,1]{1,0} bitcast(p0) + ROOT add = f32[4,1] add(bitcast, p1) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Add(op::Bitcast(op::Parameter()), op::Parameter())); +} + +TEST_F(InstructionFusionTest, AddIntoBitcast) { + auto module = tools::Parse(R"( + HloModule test_module + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + add = f32[4,1] add(p0, p1) + ROOT bitcast = f32[4,1,1] bitcast(add) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Bitcast(op::Add(op::Parameter(), op::Parameter()))); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 2f65edffea81db7dba1f8545f92b27ea622044e7..1b89dfa7ae40d45d10c0838308a8e5e4bc037244 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -49,8 +49,10 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, // The inputs and the output must // 1) be matrices with no padding and a non-zero number of elements, // 2) have an allowed element type. - bool type_is_allowed = (output_shape.element_type() == F32 || - output_shape.element_type() == F64); + PrimitiveType output_primitive_type = output_shape.element_type(); + bool type_is_allowed = + (output_primitive_type == F16 || output_primitive_type == F32 || + output_primitive_type == F64); return type_is_allowed && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape) && diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h index 7ad9680bfb4a2ec0d43e2fe86fd138a4a46e2935..59455f389e733fee2d6cace7486f919a0c5e834e 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h @@ -63,10 +63,11 @@ bool IsCustomCallToDnnBatchNorm(const HloInstruction& hlo); // strings. // // These CustomCalls have window() and convolution_dimension_numbers() set like -// regular convolution ops. They have the same LHS and RHS operands, plus one -// additional int64 operand, representing which cudnn algorithm to run. This -// operand must be an HLO constant. A value of -1 means that the implementation -// is free to choose the best algorithm it can. +// regular convolution ops. They have the same LHS and RHS operands, plus two +// additional constant operands: an int64 operand for the cudnn algorithm and +// a bool operand for whether tensor_ops is enabled. A value of -1 for the cudnn +// algorithm means that the implementation is free to choose the best algorithm +// it can. // // These calls output a tuple (conv_result, scratch_memory), where conv_result // is the actual result of the convolution, and scratch_memory is temporary diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index a3df67a87344d6ece2ea9047321ad9542c13f8cf..1e0db2821a2c212d0f212ae94ab69231bc6053ea 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -17,6 +17,7 @@ limitations under the License. #include #include +#include #include "tensorflow/core/platform/logging.h" // IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc" @@ -438,6 +439,32 @@ Status IrEmitter::HandleSelect(HloInstruction* select) { return IrEmitter::DefaultAction(select); } +namespace { +llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { + return ir_builder->CreateExtractValue(x, {0}); +} + +llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { + return ir_builder->CreateExtractValue(x, {1}); +} + +std::pair MultiplyComplex( + llvm::Value* lhs_value, llvm::Value* rhs_value, + llvm::IRBuilder<>* ir_builder) { + llvm::Value* lhs_real = Real(lhs_value, ir_builder); + llvm::Value* lhs_imag = Imag(lhs_value, ir_builder); + llvm::Value* rhs_real = Real(rhs_value, ir_builder); + llvm::Value* rhs_imag = Imag(rhs_value, ir_builder); + llvm::Value* real_result1 = ir_builder->CreateFMul(lhs_real, rhs_real); + llvm::Value* real_result2 = ir_builder->CreateFMul(lhs_imag, rhs_imag); + llvm::Value* real_result = ir_builder->CreateFSub(real_result1, real_result2); + llvm::Value* imag_result1 = ir_builder->CreateFMul(lhs_real, rhs_imag); + llvm::Value* imag_result2 = ir_builder->CreateFMul(lhs_imag, rhs_real); + llvm::Value* imag_result = ir_builder->CreateFAdd(imag_result1, imag_result2); + return {real_result, imag_result}; +} +} // namespace + Status IrEmitter::HandleDot(HloInstruction* dot) { auto lhs_instruction = dot->operand(0); auto rhs_instruction = dot->operand(1); @@ -456,21 +483,10 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { rhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto real = [&](llvm::Value* x) { - return ir_builder_.CreateExtractValue(x, {0}); - }; - auto imag = [&](llvm::Value* x) { - return ir_builder_.CreateExtractValue(x, {1}); - }; - llvm::Value* real_result = ir_builder_.CreateFSub( - ir_builder_.CreateFMul(real(lhs_value), real(rhs_value)), - ir_builder_.CreateFMul(imag(lhs_value), imag(rhs_value))); - llvm::Value* imag_result = ir_builder_.CreateFAdd( - ir_builder_.CreateFMul(real(lhs_value), imag(rhs_value)), - ir_builder_.CreateFMul(imag(lhs_value), real(rhs_value))); + auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); result = llvm::ConstantAggregateZero::get(lhs_array.GetElementLlvmType()); - result = ir_builder_.CreateInsertValue(result, real_result, {0}); - result = ir_builder_.CreateInsertValue(result, imag_result, {1}); + result = ir_builder_.CreateInsertValue(result, value.first, {0}); + result = ir_builder_.CreateInsertValue(result, value.second, {1}); } else { result = ir_builder_.CreateFMul(lhs_value, rhs_value); } @@ -548,20 +564,13 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { llvm::Value* accum = ir_builder_.CreateLoad(accum_address); llvm::Value* updated_accum; if (ShapeUtil::ElementIsComplex(lhs_shape)) { -#define REAL(x) ir_builder_.CreateExtractValue(x, {0}) -#define IMAG(x) ir_builder_.CreateExtractValue(x, {1}) - llvm::Value* product_real = ir_builder_.CreateFSub( - ir_builder_.CreateFMul(REAL(lhs_element), REAL(rhs_element)), - ir_builder_.CreateFMul(IMAG(lhs_element), IMAG(rhs_element))); - llvm::Value* product_imag = ir_builder_.CreateFAdd( - ir_builder_.CreateFMul(REAL(lhs_element), IMAG(rhs_element)), - ir_builder_.CreateFMul(IMAG(lhs_element), REAL(rhs_element))); - updated_accum = ir_builder_.CreateInsertValue( - accum, ir_builder_.CreateFAdd(REAL(accum), product_real), {0}); - updated_accum = ir_builder_.CreateInsertValue( - updated_accum, ir_builder_.CreateFAdd(IMAG(accum), product_imag), {1}); -#undef IMAG -#undef REAL + auto value = MultiplyComplex(lhs_element, rhs_element, &ir_builder_); + llvm::Value* accum_real = Real(accum, &ir_builder_); + llvm::Value* real_sum = ir_builder_.CreateFAdd(accum_real, value.first); + updated_accum = ir_builder_.CreateInsertValue(accum, real_sum, {0}); + llvm::Value* accum_imag = Imag(accum, &ir_builder_); + llvm::Value* imag_sum = ir_builder_.CreateFAdd(accum_imag, value.second); + updated_accum = ir_builder_.CreateInsertValue(updated_accum, imag_sum, {1}); } else { llvm::Value* product = ir_builder_.CreateFMul(lhs_element, rhs_element); updated_accum = ir_builder_.CreateFAdd(accum, product); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index c81dfbf6c2a34aeb6d92ded23b8e264ebec30d54..4cfb613ae994fb541107a61128baeb7dbcae264f 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -75,6 +75,10 @@ namespace gpu { namespace { using llvm_ir::IrName; +using tensorflow::gtl::ArraySlice; +using tensorflow::gtl::nullopt; +using tensorflow::gtl::optional; +using tensorflow::strings::StrCat; // If a dimensions is smaller than this, untiled transposition may be more // efficient. @@ -137,6 +141,38 @@ void UpdateLaunchDimensions(const LaunchDimensions& launch_dims, Thunk* thunk, llvm::MDString::get(llvm_context, "reqntidx"), llvm::ConstantAsMetadata::get(threads_per_block_ir_value)})); } + +// Tries to get a Slice for the given instruction at the given index, but +// returns nullopt if we might not know the slice's address at runtime without +// dereferencing a containing tuple. +// +// In particular, when XLA accepts a parameter of tuple type, the caller has the +// option of telling XLA what are the values inside of the tuple, or just giving +// XLA a pointer to the top-level tuple and letting us chase the pointers on the +// GPU. We therefore cannot rely having these pointers to parameter sub-buffers +// being present when we run the program. +optional GetKnownAtRuntimeSlice( + const HloInstruction* instr, const ShapeIndex& index, + const BufferAssignment& buffer_assn) { + auto maybe_slice = buffer_assn.GetUniqueSlice(instr, index); + if (!maybe_slice.ok()) { + return nullopt; + } + // BufferAllocation gives a slice and alloc to every buffer accessed by XLA, + // but we don't necessarily know the runtime address of sub-buffers of input + // parameters. + const BufferAllocation::Slice& slice = maybe_slice.ValueOrDie(); + const BufferAllocation* alloc = slice.allocation(); + if (alloc->IsInputOrOutput() && !alloc->maybe_live_out() && + !alloc->param_shape_index().empty()) { + return nullopt; + } + + // Otherwise, we will know the address of this slice at runtime without having + // to dereference a tuple. + return slice; +} + } // namespace IrEmitterUnnested::IrEmitterUnnested(const HloModuleConfig& hlo_module_config, @@ -154,16 +190,20 @@ Status IrEmitterUnnested::Postprocess(HloInstruction* hlo) { } namespace { -bool ImplementedAsHostToDeviceMemcpy(const HloInstruction& hlo) { - // `hlo` needs to satisfy three conditions to be implemented as a +bool ImplementedAsHostToDeviceMemcpy(const BufferAssignment& buffer_assignment, + const HloInstruction& hlo) { + // `hlo` needs to satisfy the following conditions to be implemented as a // host-to-device cuMemcpy. // // 1. `hlo` is a kCopy instruction. // 2. `hlo`'s only operand is a kConstant instruction. // 3. `hlo` and its operand have the same shape (thus the same layout too). + // 4. The address of `hlo`'s buffer is known at runtime (without dereferencing + // pointers in a tuple). return hlo.opcode() == HloOpcode::kCopy && hlo.operand(0)->opcode() == HloOpcode::kConstant && - ShapeUtil::Equal(hlo.operand(0)->shape(), hlo.shape()); + ShapeUtil::Equal(hlo.operand(0)->shape(), hlo.shape()) && + GetKnownAtRuntimeSlice(&hlo, {}, buffer_assignment).has_value(); } bool ImplementedAsDeviceToDeviceMemcpy( @@ -177,13 +217,15 @@ bool ImplementedAsDeviceToDeviceMemcpy( // instance) which means the source buffer also resides on the device. return hlo.opcode() == HloOpcode::kCopy && ShapeUtil::Equal(hlo.operand(0)->shape(), hlo.shape()) && - buffer_assignment.HasTopLevelAllocation(hlo.operand(0)); + GetKnownAtRuntimeSlice(&hlo, {}, buffer_assignment).has_value() && + GetKnownAtRuntimeSlice(hlo.operand(0), {}, buffer_assignment) + .has_value(); } } // namespace llvm::Function* IrEmitterUnnested::BuildKernelPrototype( const HloInstruction& inst, - tensorflow::gtl::ArraySlice escaped_hlos) { + tensorflow::gtl::ArraySlice args) { // Compute the kernel name. The opcode string may contain "-" which cannot be // in a PTX function name, so sanitize the name before uniquifying it. string kernel_name = ir_emitter_context_->name_uniquer()->GetUniqueName( @@ -192,43 +234,32 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( // Create the kernel and add it to the module. llvm::Module* module = ir_emitter_context_->llvm_module(); llvm::LLVMContext& context = module->getContext(); - int num_escaped_hlos = escaped_hlos.size(); llvm::FunctionType* kernel_type = llvm::FunctionType::get( /*Result=*/llvm::Type::getVoidTy(context), - std::vector(num_escaped_hlos + 1, - ir_builder_.getInt8PtrTy()), + std::vector(args.size(), ir_builder_.getInt8PtrTy()), /*isVarArg=*/false); llvm::Function* kernel = llvm::Function::Create(kernel_type, llvm::GlobalValue::ExternalLinkage, kernel_name.c_str(), module); - // Add dereferenceable information to each of the escaped HLO parameters. - for (size_t arg_no = 0; arg_no < escaped_hlos.size(); ++arg_no) { - const HloInstruction* escaped_hlo = escaped_hlos[arg_no]; - const Shape& escaped_hlo_shape = escaped_hlo->shape(); - int64 escaped_hlo_size = llvm_ir::ByteSizeOf( - escaped_hlo_shape, ir_emitter_context_->llvm_module()->getDataLayout()); - kernel->addDereferenceableAttr(arg_no + 1, escaped_hlo_size); - } - - // The last argument is a pointer to the temporary buffer memory block. - // We know that it doesn't alias any of the escaped arguments (the inputs + - // the result). We also know how many bytes can be dereferenced in it. - const llvm::Argument& temp_buffer = *std::prev(kernel->arg_end()); - int64 temp_buffer_arg_no = temp_buffer.getArgNo(); - int64 temp_allocation_total_size = - ir_emitter_context_->buffer_assignment().temp_allocation_total_size(); - if (temp_allocation_total_size != 0) { - kernel->addDereferenceableAttr(temp_buffer_arg_no + 1, - temp_allocation_total_size); - } - kernel->addParamAttr(temp_buffer_arg_no, llvm::Attribute::NoAlias); + // Add dereferenceable and alignment information to each of the kernel's + // parameters. + auto arg_it = kernel->arg_begin(); + for (size_t arg_no = 0; arg_no < args.size(); ++arg_no) { + const BufferAllocation* alloc = args[arg_no]; + llvm::Argument* fn_arg = &*arg_it; + ++arg_it; - // All arguments to a kernel must be aligned to kCudaMallocAlignBytes. - for (int64 i = 0; i < kernel->arg_size(); ++i) { + kernel->addDereferenceableAttr(arg_no + 1, alloc->size()); kernel->addParamAttr( - i, llvm::Attribute::get(context, llvm::Attribute::Alignment, - kCudaMallocAlignBytes)); + arg_no, llvm::Attribute::get(context, llvm::Attribute::Alignment, + kCudaMallocAlignBytes)); + + if (alloc->IsPreallocatedTempBuffer()) { + fn_arg->setName("temp_buf"); + } else { + fn_arg->setName(llvm_ir::AsStringRef(StrCat("alloc", alloc->index()))); + } } // TODO(b/65380986): Investigate if adding fast math flags for generated @@ -245,10 +276,9 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( // Update the insert point to the entry basic block. llvm::BasicBlock* entry_bb = - llvm::BasicBlock::Create(context, - "entry", // The name of the basic block. - kernel); // The parent/owner of "entry_bb". - // Emit a "return void" at entry_bb's end, and sets the insert point before + llvm::BasicBlock::Create(context, /*Name=*/"entry", /*Parent=*/kernel); + + // Emit a "return void" at entry_bb's end, and set the insert point before // that return instruction. ir_builder_.SetInsertPoint(llvm::ReturnInst::Create(context, entry_bb)); @@ -393,6 +423,11 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) { CHECK(algorithm_inst->IsConstant()) << algorithm_inst->ToString(); int64 algorithm = algorithm_inst->literal().Get({}); + const HloInstruction* tensor_ops_enabled_inst = custom_call->operand(3); + CHECK(tensor_ops_enabled_inst->IsConstant()) + << tensor_ops_enabled_inst->ToString(); + bool tensor_ops_enabled = tensor_ops_enabled_inst->literal().Get({}); + const auto& target = custom_call->custom_call_target(); std::unique_ptr thunk; if (target == kCudnnConvForwardCallTarget) { @@ -407,7 +442,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) { /*filter_shape=*/rhs_shape, /*output_shape=*/conv_result_shape, // custom_call->window(), custom_call->convolution_dimension_numbers(), - algorithm, custom_call); + algorithm, tensor_ops_enabled, custom_call); } else if (target == kCudnnConvBackwardInputCallTarget) { thunk = MakeUnique( CudnnConvKind::kBackwardInput, @@ -420,7 +455,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) { /*filter_shape=*/rhs_shape, /*output_shape=*/lhs_shape, // custom_call->window(), custom_call->convolution_dimension_numbers(), - algorithm, custom_call); + algorithm, tensor_ops_enabled, custom_call); } else if (target == kCudnnConvBackwardFilterCallTarget) { thunk = MakeUnique( CudnnConvKind::kBackwardFilter, @@ -433,7 +468,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) { /*filter_shape=*/conv_result_shape, /*output_shape=*/rhs_shape, // custom_call->window(), custom_call->convolution_dimension_numbers(), - algorithm, custom_call); + algorithm, tensor_ops_enabled, custom_call); } else { LOG(FATAL) << "Unexpected custom call target: " << custom_call->custom_call_target(); @@ -482,39 +517,6 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(root->Accept(&fused_emitter)); Shape input_shape = root->operand(0)->shape(); - // EmitReductionToVector requires the input shape to have a layout, but - // fused instructions don't have one. So we determine its layout from - // the fusion's operands. The choice of the layout only affects - // performance but not correctness. - auto choose_input_layout = []( - tensorflow::gtl::ArraySlice operands, - Shape* input_shape) -> Status { - // Prefer the layout of an operand whose shape is compatible with - // input_shape. - for (const HloInstruction* operand : operands) { - if (ShapeUtil::Compatible(*input_shape, operand->shape())) { - return LayoutUtil::CopyLayoutBetweenShapes(operand->shape(), - input_shape); - } - } - // If no operand has a compatible shape, prefer an operand that has - // the same rank at least. - for (const HloInstruction* operand : operands) { - if (ShapeUtil::Rank(*input_shape) == - ShapeUtil::Rank(operand->shape())) { - // Do not use CopyLayoutBetweenShapes because input_shape and - // operand->shape() may be incompatible. - *input_shape->mutable_layout() = operand->shape().layout(); - return Status::OK(); - } - } - // When all the above fails, which is rare, set the default layout. - LayoutUtil::SetToDefaultLayout(input_shape); - return Status::OK(); - }; - TF_RETURN_IF_ERROR( - choose_input_layout(fusion->operands(), &input_shape)); - return EmitReductionToVector( root, input_shape, fused_emitter.GetGenerator(root->operand(0)), fused_emitter.GetGenerator(root->operand(1)), root->dimensions(), @@ -864,7 +866,8 @@ int64 EmitTranspose021Tiled(llvm_ir::IrArray input, llvm_ir::IrArray output, } // namespace Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) { - if (ImplementedAsHostToDeviceMemcpy(*copy)) { + if (ImplementedAsHostToDeviceMemcpy(ir_emitter_context_->buffer_assignment(), + *copy)) { thunk_sequence_->emplace_back(BuildHostToDeviceCopyThunk(copy)); return Status::OK(); } @@ -1926,62 +1929,207 @@ Status IrEmitterUnnested::HandleInfeed(HloInstruction* infeed) { return Status::OK(); } -llvm::Function* IrEmitterUnnested::EmitBasePointersForHloAndItsOperands( - const HloInstruction& hlo, std::vector* io_hlos) { - const BufferAssignment& buffer_assignment = - ir_emitter_context_->buffer_assignment(); - // GetTupleElement instructions are implemented by emitting IR that indexes - // and loads the target tuple element pointer from its operand (possibly - // recursively). For this reason, GetTupleElement instructions are associated - // with their operand buffer in 'io_hlos' and 'non_io_hlos' below. - std::vector non_io_hlos; - for (const HloInstruction* operand : hlo.operands()) { - const HloInstruction* to_lookup = operand->LatestNonGteAncestor(); - if (buffer_assignment.HasTopLevelAllocation(to_lookup) && - buffer_assignment.GetUniqueTopLevelSlice(to_lookup) - .ConsumeValueOrDie() - .allocation() - ->IsInputOrOutput()) { - io_hlos->push_back(operand); - } else { - non_io_hlos.push_back(operand); +// Figures out how to access the buffers for all subshapes of hlo's operands and +// for hlo itself (i.e. all the buffers produced by HLO). +// +// Returns a map keyed on the pair {HloInstruction, ShapeIndex}. The value for +// this key is a pair {Slice, ShapeIndex}, where the slice tells you the root +// buffer to look in, and the ShapeIndex describes how to dereference starting +// at that buffer to get to the buffer in question. +// +// For example, if {hlo, {1}} is mapped to {slice, {3, 4}}, then the buffer for +// hlo at ShapeIndex {1} (i.e. the buffer for the second tuple element of hlo) +// is found at slice[3][4]. That is, slice is a void***, which we dereference +// twice -- first at index 3, and then at index 4 -- to get the address of our +// buffer. +// +// This function conservatively assumes that we'll touch all sub-buffers of +// every operand and of the output. +static std::map, + std::pair> +GetHloBufferSlices(const HloInstruction* hlo, + const BufferAssignment& buffer_assn) { + std::map, + std::pair> + slices; + + // Tries to find a slice plus an array of indices i1, ..., iN such that the + // sub-buffer for instr at index can be found at slice[i1]...[iN]. + auto find_slice_for = [&](const HloInstruction* instr, + const ShapeIndex& index) + -> optional> { + // Simple, common case: Is the buffer for instr known at runtime? If so, + // we're done. + auto slice = GetKnownAtRuntimeSlice(instr, index, buffer_assn); + if (slice.has_value()) { + return {{*slice, ShapeIndex()}}; } - } - CHECK_NE(HloOpcode::kGetTupleElement, hlo.opcode()); - if (buffer_assignment.HasTopLevelAllocation(&hlo) && - buffer_assignment.GetUniqueTopLevelSlice(&hlo) - .ConsumeValueOrDie() - .allocation() - ->IsInputOrOutput()) { - io_hlos->push_back(&hlo); - } else { - non_io_hlos.push_back(&hlo); + // If we don't know the buffer for instr at index, see if we know the buffer + // for instr at index without its last element. If so, we can dynamically + // find the buffer for instr by dereferencing a pointer in that buffer. + // Continue looking this way until we run out of elements in 'index'. + ShapeIndex new_index = index; + ShapeIndex gte_indices; + while (!new_index.empty()) { + gte_indices.push_front(new_index.back()); + new_index.pop_back(); + auto slice = GetKnownAtRuntimeSlice(instr, new_index, buffer_assn); + if (slice.has_value()) { + return {{*slice, gte_indices}}; + } + } + + // If *that* didn't work, check whether instr is a GTE instruction. If it + // is, see if we can get a buffer for its parent, and continue walking up + // parents until we find a defined buffer or we hit something that's not a + // GTE. + const HloInstruction* parent = instr; + while (parent->opcode() == HloOpcode::kGetTupleElement) { + gte_indices.push_front(parent->tuple_index()); + parent = parent->operand(0); + + auto slice = GetKnownAtRuntimeSlice(parent, {}, buffer_assn); + if (slice.has_value()) { + return {{*slice, gte_indices}}; + } + } + + return nullopt; + }; + + // Adds entries for all subshapes of instr to `slices`. + auto add_slices_for = [&](const HloInstruction* instr) { + // GPU constants don't have buffers; don't bother looking for one. + if (instr->IsConstant()) { + return; + } + + ShapeUtil::ForEachSubshape( + instr->shape(), [&](const Shape& /*shape*/, const ShapeIndex& index) { + if (slices.count({instr, index})) { + // HLOs can have duplicate operands; don't bother redoing work. + return; + } + auto maybe_slice = find_slice_for(instr, index); + if (maybe_slice.has_value()) { + slices[{instr, index}] = *maybe_slice; + } else { + VLOG(1) << "Couldn't find buffer for " << instr->ToString() + << " at index " << index.ToString(); + } + }); + }; + + add_slices_for(hlo); + for (const HloInstruction* operand : hlo->operands()) { + // Conservatively assume we'll need the buffers for all subshapes of the + // operand. + add_slices_for(operand); } - llvm::Function* kernel = BuildKernelPrototype(hlo, *io_hlos); - // bindings_ is reused because the bindings of kConstant to their underlying - // llvm::Constant can be shared for all HLOs in this computation. - bindings_.EmitBasePointersForHlos(*io_hlos, non_io_hlos); - return kernel; + return slices; +} + +Status IrEmitterUnnested::HandleGather(HloInstruction* gather) { + // TODO(b/72710576): Gather is not implemented on GPUs + return Unimplemented("Gather is not implemented on GPUs."); } std::unique_ptr IrEmitterUnnested::BuildKernelThunk( const HloInstruction* inst) { - std::vector io_hlos; - llvm::Function* kernel = - EmitBasePointersForHloAndItsOperands(*inst, &io_hlos); + const BufferAssignment& buffer_assn = + ir_emitter_context_->buffer_assignment(); + + std::map, + std::pair> + hlo_slices = GetHloBufferSlices(inst, buffer_assn); + + // Figure out which buffer allocations need to be passed as arguments to our + // kernel. This is simply all of the allocations referenced in hlo_slices, + // plus the XLA temp buffer (if we have it). We always include the temp + // buffer because even if the kernel itself doesn't use it, a nested + // subcomputation within the kernel (e.g. a kMap's computation) might. + std::unordered_set buffers_needed; + for (const auto& kv : hlo_slices) { + buffers_needed.insert(kv.second.first.allocation()); + } + tensorflow::gtl::optional temp_buffer; + for (const BufferAllocation& alloc : buffer_assn.Allocations()) { + if (alloc.IsPreallocatedTempBuffer()) { + if (!temp_buffer.has_value()) { + temp_buffer = &alloc; + } else { + LOG(FATAL) << "Multiple temp buffers found, but only one is allowed!"; + } + } + } + if (temp_buffer.has_value()) { + buffers_needed.insert(*temp_buffer); + } + + // We'll pass a pointer to each of the elements of `buffers` to our kernel, in + // this order. + std::vector buffers(buffers_needed.begin(), + buffers_needed.end()); + std::sort(buffers.begin(), buffers.end(), + [](const BufferAllocation* a, const BufferAllocation* b) { + return a->index() < b->index(); + }); + + llvm::Function* kernel = BuildKernelPrototype(*inst, buffers); + + // Build a map from a BufferAllocation to the corresponding argument in our + // kernel. + std::unordered_map kernel_args; + { + auto arg_it = kernel->arg_begin(); + auto buffers_it = buffers.begin(); + for (; arg_it != kernel->arg_end(); ++arg_it, ++buffers_it) { + kernel_args[*buffers_it] = arg_it; + } + } + + // For each buffer our kernel might want to touch, bind it to a value derived + // from our kernel args. + for (const auto& kv : hlo_slices) { + const HloInstruction* instr = kv.first.first; + const ShapeIndex& index = kv.first.second; + const BufferAllocation::Slice& slice = kv.second.first; + const ShapeIndex& gte_index = kv.second.second; + + VLOG(3) << "Buffer for " << instr->ToString() << " at " << index.ToString() + << " is found in slice " << slice.ToString() << " at GTE index " + << gte_index.ToString(); + + llvm::Value* loc = + ir_builder_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), + {ir_builder_.getInt64(slice.offset())}); + + // If gte_index is nonempty, we have to dereference `loc` to get to the + // value we're ultimately interested in. + llvm::Type* int8_double_pointer = + llvm::PointerType::get(ir_builder_.getInt8PtrTy(), /*AddressSpace=*/0); + for (int64 idx : gte_index) { + loc = ir_builder_.CreateBitCast(loc, int8_double_pointer); + loc = ir_builder_.CreateLoad( + ir_builder_.CreateInBoundsGEP(loc, {ir_builder_.getInt64(idx)})); + } + + bindings_.BindHloToIrValue(*instr, loc, index); + } - // Compute the input buffer indices. - std::vector io_buffers; - io_buffers.reserve(io_hlos.size()); - for (const HloInstruction* io_hlo : io_hlos) { - io_buffers.push_back(GetAllocationSlice(*io_hlo->LatestNonGteAncestor())); + // Bind the temp buffer so that nested subcomputations can find it if they + // need. + if (temp_buffer.has_value()) { + bindings_.SetTempBufferBase(kernel_args.at(*temp_buffer)); + } else { + bindings_.SetTempBufferBase( + llvm::ConstantPointerNull::get(ir_builder_.getInt8PtrTy())); } - // Create a KernelThunk that launches the kernel that implements "inst". - return MakeUnique(io_buffers, - llvm_ir::AsString(kernel->getName()), inst); + return MakeUnique(buffers, llvm_ir::AsString(kernel->getName()), + inst); } std::unique_ptr IrEmitterUnnested::BuildHostToDeviceCopyThunk( diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index 56ab8208cee6f53afce365baa213fd2f5a6425a0..b83a2337e2decd9d4fba3d40fcf33f131fca8a3c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -67,6 +67,7 @@ class IrEmitterUnnested : public IrEmitter { Status HandleDot(HloInstruction* dot) override; Status HandleFft(HloInstruction* fft) override; Status HandleFusion(HloInstruction* fusion) override; + Status HandleGather(HloInstruction* gather) override; Status HandleGetTupleElement(HloInstruction* get_tuple_element) override; Status HandleReduce(HloInstruction* reduce) override; Status HandleSelectAndScatter(HloInstruction* instruction) override; @@ -93,14 +94,10 @@ class IrEmitterUnnested : public IrEmitter { std::unique_ptr BuildThunk(const HloInstruction* hlo); // Builds the prototype of the IR kernel for `inst` and adds it to the module. + // This kernel takes as arguments pointers to the given buffer allocations. llvm::Function* BuildKernelPrototype( const HloInstruction& inst, - tensorflow::gtl::ArraySlice escaped_hlos); - - // Emits the base pointers for `hlo` and its operands. `io_hlos` will store - // all input/output HLOs among `hlo` and its operands. - llvm::Function* EmitBasePointersForHloAndItsOperands( - const HloInstruction& hlo, std::vector* io_hlos); + tensorflow::gtl::ArraySlice args); // EmitColumnReduction and EmitRowReduction emit code for column and row // reduction of a matrix and/or 3D tensor. Row and column reduction have diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc index 96606993696354f36e143b3b994bbe6afb902df3..c20a781a33fe89af4740ed31dd5bfb1a64473057 100644 --- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc @@ -29,10 +29,10 @@ namespace xla { namespace gpu { KernelThunk::KernelThunk( - tensorflow::gtl::ArraySlice io_buffers, + tensorflow::gtl::ArraySlice args, const string& kernel_name, const HloInstruction* hlo_instruction) : Thunk(Kind::kKernel, hlo_instruction), - io_buffers_(io_buffers.begin(), io_buffers.end()), + args_(args.begin(), args.end()), kernel_name_(kernel_name) {} tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable) { @@ -42,7 +42,7 @@ tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable) { return tensorflow::Status::OK(); } - loader_spec_.reset(new se::MultiKernelLoaderSpec(io_buffers_.size() + 1)); + loader_spec_.reset(new se::MultiKernelLoaderSpec(args_.size())); tensorflow::StringPiece ptx = executable.ptx(); // Convert tensorflow::StringPiece to se::port::StringPiece because // StreamExecutor uses the latter. @@ -81,15 +81,16 @@ tensorflow::Status KernelThunk::ExecuteOnStream( kernel = &it->second; } + VLOG(3) << "Launching " << kernel->name(); // Launch the kernel with potentially multiple blocks and threads. static constexpr int kKernelArgsLimit = 1024; auto kernel_args = MakeUnique>(); - for (const BufferAllocation::Slice io_buffer : io_buffers_) { - kernel_args->add_device_memory_argument( - buffer_allocations.GetDeviceAddress(io_buffer)); + for (const BufferAllocation* arg : args_) { + const auto& buf = buffer_allocations.GetDeviceAddress(arg->index()); + kernel_args->add_device_memory_argument(buf); + VLOG(3) << " Arg: alloc #" << arg->index() << ": " << buf.opaque() << " (" + << buf.size() << "B)"; } - kernel_args->add_device_memory_argument( - buffer_allocations.GetTempBufferBase()); if (!stream->parent()->Launch( stream, se::ThreadDim(launch_dimensions.threads_per_block()), se::BlockDim(launch_dimensions.block_count()), *kernel, diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h index 350b5aaf360b0dad7f7b04d73f4c32bad55d3ce9..9ae455e2fcc253a7a08ff95764721048a16b0bf7 100644 --- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h @@ -46,7 +46,7 @@ class KernelThunk : public Thunk { // Constructs a thunk for the given kernel. // // `hlo_instruction` is as in Thunk. Other arguments are as the class members. - KernelThunk(tensorflow::gtl::ArraySlice io_buffers, + KernelThunk(tensorflow::gtl::ArraySlice args, const string& kernel_name, const HloInstruction* hlo_instruction); KernelThunk(const KernelThunk&) = delete; KernelThunk& operator=(const KernelThunk&) = delete; @@ -63,8 +63,8 @@ class KernelThunk : public Thunk { perftools::gputools::Stream* stream) override; private: - // The indices of the input/output buffers. - const std::vector io_buffers_; + // Buffers passed to the kernel as arguments. + const std::vector args_; // Entry kernel name for the computation. const string kernel_name_; diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc index cfabae791d26d0eb49826085ad7ad166a19109a1..defd281d74bd38f7da3f268e0f55970fc1af8263 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc @@ -252,7 +252,7 @@ void EmitBitcodeToFile(const Module& module, tensorflow::StringPiece filename) { LOG(FATAL) << "opening bitcode file for writing: " << error_code.message(); } - llvm::WriteBitcodeToFile(&module, outfile.os()); + llvm::WriteBitcodeToFile(module, outfile.os()); outfile.keep(); } diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index cde5877e29f36abc61c5417ce960e2c7699e2749..3dd4c4a0794e5c41b877078c4e69c6c9584ce6c0 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -27,38 +27,6 @@ namespace xla { using tensorflow::gtl::FlatMap; using tensorflow::gtl::FlatSet; -namespace { - -// Returns the set of buffers that may be sources of all operands of the given -// instruction. The returned buffers are guaranteed to have no duplicates, and -// to be sorted in a deterministic order. -std::vector UniqueOperandSourceBuffers( - const HloInstruction* instruction, - const TuplePointsToAnalysis& points_to_analysis) { - std::vector buffers; - for (const HloInstruction* operand : instruction->operands()) { - points_to_analysis.GetPointsToSet(operand).ForEachElement( - [&](const ShapeIndex& /*index*/, - const PointsToSet::BufferList& points_to) { - buffers.insert(buffers.end(), points_to.begin(), points_to.end()); - }); - } - - // Sort and then remove duplicates from buffers. - std::sort(buffers.begin(), buffers.end(), - [](const LogicalBuffer* a, const LogicalBuffer* b) { - return a->id() < b->id(); - }); - buffers.erase(std::unique(buffers.begin(), buffers.end(), - [](const LogicalBuffer* a, const LogicalBuffer* b) { - return a->id() == b->id(); - }), - buffers.end()); - return buffers; -} - -} // namespace - /*static*/ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloModule& module, @@ -93,6 +61,7 @@ Status HeapSimulator::RunComputation( const HloComputation& computation, const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis) { + VLOG(3) << "Computation:\n" << computation.ToString(); // The goal here is to minimize memory usage, assuming the given sequential // ordering of instructions. The strategy is to walk through the instruction // sequence, calling Alloc and Free on the underlying heap algorithm. The @@ -101,7 +70,51 @@ Status HeapSimulator::RunComputation( // 'live_buffers' tracks the liveness of each buffer that we assign, by // associating it with a set of HloInstructions that need to be visited. When // the set becomes empty, the buffer is no longer used, and can be freed. + // 'used_buffers' is the reverse map - it tracks which buffers were used by an + // instruction, so that we can remove the instructions from a buffer's live + // set after they are visited. FlatMap> live_buffers; + FlatMap> used_buffers; + auto add_user_to_buffer = [this, &live_buffers, &used_buffers]( + const HloInstruction* user, + const LogicalBuffer* buffer) { + if (!IgnoreBuffer(buffer)) { + VLOG(4) << " Adding user " << user->name() << " to buffer " + << buffer->ToString(); + live_buffers[buffer].insert(user); + used_buffers[user].insert(buffer); + } + }; + + // Initialize live_buffers for each buffer that we're going to assign. The + // set of instructions that need to be visited contains all users of all + // aliases, that is, all users of all instructions that have the buffer + // contained in their points-to set. + for (const HloInstruction* instruction : instruction_sequence) { + const PointsToSet& points_to = + points_to_analysis.GetPointsToSet(instruction); + const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet(); + for (const HloInstruction* user : instruction->users()) { + if (user->opcode() != HloOpcode::kGetTupleElement) { + for (const LogicalBuffer* buffer : buffer_set) { + add_user_to_buffer(user, buffer); + } + } else { + // A GetTupleElement doesn't need to keep all of its operand's buffers + // alive. It only needs the buffers that relate to the element its + // extracting, and the tuple it's extracting from, but not the buffers + // for the other elements. + for (const LogicalBuffer* buffer : points_to.element({})) { + add_user_to_buffer(user, buffer); + } + const PointsToSet& gte_points_to = + points_to_analysis.GetPointsToSet(user); + for (const LogicalBuffer* buffer : gte_points_to.CreateFlattenedSet()) { + add_user_to_buffer(user, buffer); + } + } + } + } const HloInstruction* root = computation.root_instruction(); auto output_source_buffers = @@ -114,34 +127,17 @@ Status HeapSimulator::RunComputation( buffers_defined_by_instruction = points_to_analysis.GetBuffersDefinedByInstruction(instruction); - // Initialize live_buffers for each buffer that we're going to assign. The - // set of instructions that need to be visited contains all users of all - // aliases. The alias itself is not necessary; if it has users, the users - // are necessarily scheduled after the alias. And if it has no users, it is - // either a dead value or an output, both of which are handled below. - // - // We ignore control dependencies here. The reasoning is that the control - // dependencies have already been accounted for in the ordering of the given - // 'instruction_sequence', and should not otherwise artificially extend the - // lifetime of buffers that aren't already connected by a data dependency. + VLOG(3) << "Instruction: " << instruction->ToString(); + for (const LogicalBuffer* buffer : buffers_defined_by_instruction) { + VLOG(4) << " Defines: " << buffer->ToString() + << (IgnoreBuffer(buffer) ? " (Ignored)" : ""); + } + dead_buffers_to_free.clear(); for (const LogicalBuffer* buffer : buffers_defined_by_instruction) { if (IgnoreBuffer(buffer)) { continue; } - FlatSet* live_set = nullptr; - for (const BufferAlias& alias : - points_to_analysis.GetBufferAliases(*buffer)) { - const std::vector& users = - alias.instruction()->users(); - if (!users.empty()) { - if (live_set == nullptr) { - live_set = &live_buffers[buffer]; - } - live_set->insert(users.begin(), users.end()); - } - } - // Add a nullptr sentry to ensure entry parameters and output source // buffers are not freed until the very end. const bool entry_parameter = @@ -165,11 +161,12 @@ Status HeapSimulator::RunComputation( // have no instructions left to visit are moved from live_buffers to // operand_buffers_to_free. operand_buffers_to_free.clear(); - for (const LogicalBuffer* operand_buffer : - UniqueOperandSourceBuffers(instruction, points_to_analysis)) { + for (const LogicalBuffer* operand_buffer : used_buffers[instruction]) { if (IgnoreBuffer(operand_buffer)) { continue; } + VLOG(4) << " Removing user " << instruction->name() << " from buffer " + << operand_buffer->ToString(); auto it = live_buffers.find(operand_buffer); FlatSet* live_set = &it->second; live_set->erase(instruction); @@ -178,6 +175,11 @@ Status HeapSimulator::RunComputation( operand_buffers_to_free.push_back(operand_buffer); } } + // Sort to get a deterministic iteration order. + std::sort(operand_buffers_to_free.begin(), operand_buffers_to_free.end(), + [](const LogicalBuffer* x, const LogicalBuffer* y) { + return x->id() < y->id(); + }); // Allocate buffers defined by this instruction. This is the latest point // that we can allocate; right before the buffer is first used. This must @@ -203,6 +205,8 @@ Status HeapSimulator::RunComputation( CanShareOperandBufferWithUser( operand_buffer->instruction(), operand_buffer->index(), buffer->instruction(), buffer->index(), points_to_analysis)) { + VLOG(3) << " Sharing: " << buffer->ToString() << " with " + << operand_buffer->ToString(); ShareBuffer(buffer, operand_buffer, instruction); shared = true; break; @@ -211,6 +215,7 @@ Status HeapSimulator::RunComputation( } if (!shared) { + VLOG(3) << " Allocating: " << buffer->ToString(); Alloc(buffer, instruction); } } @@ -225,6 +230,7 @@ Status HeapSimulator::RunComputation( // sub-computations will never be run concurrently. if (module_sequence_ != nullptr) { if (instruction->opcode() == HloOpcode::kCall || + instruction->opcode() == HloOpcode::kConditional || instruction->opcode() == HloOpcode::kWhile) { for (const HloComputation* called_computation : instruction->called_computations()) { @@ -243,20 +249,34 @@ Status HeapSimulator::RunComputation( // Free buffers that are no longer live. This is the earliest point that we // can de-allocate; right after the last use of the buffer. for (const LogicalBuffer* buffer : dead_buffers_to_free) { + VLOG(3) << " Freeing dead: " << buffer->ToString(); Free(buffer, instruction); } for (const LogicalBuffer* buffer : operand_buffers_to_free) { + VLOG(3) << " Freeing operand: " << buffer->ToString(); Free(buffer, instruction); } } // Any remaining live buffers must be entry parameters or output source - // buffers, which had a nullptr sentry added. Free them now. + // buffers, which had a nullptr sentry added. Free them now, in a + // deterministic order. + std::vector to_free; + to_free.reserve(live_buffers.size()); for (const auto& buffer_pending : live_buffers) { const LogicalBuffer* buffer = buffer_pending.first; const FlatSet& pending = buffer_pending.second; CHECK_EQ(pending.size(), 1) << *buffer; CHECK(*pending.begin() == nullptr) << *buffer; + to_free.push_back(buffer); + } + + std::sort(to_free.begin(), to_free.end(), + [](const LogicalBuffer* x, const LogicalBuffer* y) { + return x->id() < y->id(); + }); + for (const LogicalBuffer* buffer : to_free) { + VLOG(3) << "Freeing pending: " << buffer->ToString(); Free(buffer, root); } diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 387b649a731ebcbfd8307807469f39f22d192b06..688a271712ac243666ba4ff02932aa4f7f7ed21c 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -410,6 +410,56 @@ TEST_F(HeapSimulatorTest, MultiplyDotDotTuple) { }); } +TEST_F(HeapSimulatorTest, IndependentTupleElements) { + auto builder = HloComputation::Builder(TestName()); + auto paramA = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32scalar_, "paramA")); + auto paramB = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32scalar_, "paramB")); + auto mul = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kMultiply, paramA, paramB)); + auto add = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kAdd, paramA, paramB)); + auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({mul, add})); + auto element0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 0)); + auto broadcast = builder.AddInstruction( + HloInstruction::CreateBroadcast(f32vec4_, element0, {0})); + auto sub = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kSubtract, paramA, paramB)); + auto element1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 1)); + auto output = builder.AddInstruction( + HloInstruction::CreateTuple({broadcast, sub, element1})); + + HeapSimulatorTracker tracker(TestName(), builder.Build(), + {paramA, paramB, mul, add, tuple, element0, + broadcast, sub, element1, output}); + tracker.ExpectCallSequence({ + {kAlloc, tracker.BufferAt(paramA, {})}, + {kAlloc, tracker.BufferAt(paramB, {})}, + {kAlloc, tracker.BufferAt(mul, {})}, + {kAlloc, tracker.BufferAt(add, {})}, + {kAlloc, tracker.BufferAt(tuple, {})}, + {kAlloc, tracker.BufferAt(broadcast, {})}, + // The mul can be freed right after the broadcast happens, even though + // The other GetTupleElement is still alive. + {kFree, tracker.BufferAt(mul, {})}, + {kAlloc, tracker.BufferAt(sub, {})}, + // The temporary tuple is now dead. + {kFree, tracker.BufferAt(tuple, {})}, + {kAlloc, tracker.BufferAt(output, {})}, + // All params and outputs are freed at the end. + {kFree, tracker.BufferAt(paramA, {})}, + {kFree, tracker.BufferAt(paramB, {})}, + {kFree, tracker.BufferAt(add, {})}, + {kFree, tracker.BufferAt(broadcast, {})}, + {kFree, tracker.BufferAt(sub, {})}, + {kFree, tracker.BufferAt(output, {})}, + {kFinish, nullptr}, + }); +} + TEST_F(HeapSimulatorTest, WholeModule) { HeapSimulatorTracker tracker(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 0e9a852788e978f79fa6f6c802f855a4c476583f..66fd3170513c3e0ce222008080cd7e438f41ff26 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -129,6 +129,10 @@ message HloInstructionProto { // FFT length. repeated int64 fft_length = 32; + + // Gather dimension numbers. + xla.GatherDimensionNumbers gather_dimension_numbers = 33; + repeated int64 gather_window_bounds = 34; } // Serialization of HloComputation. @@ -141,6 +145,9 @@ message HloComputationProto { // The name of the root of the computation. string root_name = 3; + + // The program shape (with layout) of this computation. + xla.ProgramShape program_shape = 4; } // Serialization of HloModule. @@ -151,6 +158,9 @@ message HloModuleProto { // The array of computations is always in a valid dependency order, where // callees appear before their callers. repeated HloComputationProto computations = 3; + + // The program shape (with layout) of the entry computation. + xla.ProgramShape program_shape = 4; } // Serialization of HloOrdering. @@ -200,6 +210,7 @@ message BufferAllocationProto { bool is_reusable = 4; bool is_entry_computation_parameter = 5; int64 parameter_number = 6; + repeated int64 parameter_shape_index = 10; bool maybe_live_out = 7; int64 color = 8; repeated Assigned assigned = 9; diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc index 6d2a3aa5b531650a658502531e050702ffbd3760..30e32a46d7dd0923f738939c33407ac7484b5bbe 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc @@ -419,7 +419,7 @@ StatusOr> HloAliasAnalysis::Run( auto alias_analysis = WrapUnique(new HloAliasAnalysis(module)); TF_ASSIGN_OR_RETURN( alias_analysis->dataflow_analysis_, - HloDataflowAnalysis::Run(module, /*ssa_form=*/true, + HloDataflowAnalysis::Run(*module, /*ssa_form=*/true, /*bitcast_defines_value=*/false)); BufferValueMap buffer_map(alias_analysis->dataflow_analysis()); diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index 5432419e4a2dd2916da32ac6566851bf52fd68ca..f99c7cf5e495eaf83e0dda859ef31a7487bc6ffe 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -399,6 +399,7 @@ HloComputationProto HloComputation::ToProto() const { proto.add_instructions()->Swap(&instruction_proto); } proto.set_root_name(root_instruction()->name()); + *proto.mutable_program_shape() = ComputeProgramShape(); return proto; } @@ -509,13 +510,14 @@ StatusOr HloComputation::DeepCopyInstruction( "Can't deep copy instruction %s: instruction is not in computation %s", instruction->name().c_str(), name().c_str()); } - if (indices_to_copy != nullptr && !ShapeUtil::Compatible(instruction->shape(), indices_to_copy->shape())) { return FailedPrecondition( "Can't deep copy instruction %s: given shape tree of indices to copy " - "has incompatible shape", - instruction->name().c_str()); + "has incompatible shapes: %s vs. %s", + instruction->name().c_str(), + ShapeUtil::HumanString(instruction->shape()).c_str(), + ShapeUtil::HumanString(indices_to_copy->shape()).c_str()); } ShapeIndex index; @@ -531,7 +533,6 @@ ProgramShape HloComputation::ComputeProgramShape() const { } *program_shape.mutable_result() = root_instruction_->shape(); - LayoutUtil::ClearLayout(&program_shape); return program_shape; } diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index 061c59abe5e315917161ed737f89de53d71bb1b6..dd9d346999f0eae448d74628278c802ccd3f51b4 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -77,6 +77,14 @@ class HloComputation { return last_added_instruction_; } + Status ForEachInstruction( + const std::function& func) const { + for (const auto& instruction : instructions_) { + TF_RETURN_IF_ERROR(func(instruction.get())); + } + return Status::OK(); + } + private: const string name_; HloInstruction* last_added_instruction_; @@ -240,7 +248,7 @@ class HloComputation { ShapeTree* copies_added = nullptr); // Computes and returns the ProgramShape of this computation (shape of - // parameters and result without layout). + // parameters and result with layout). ProgramShape ComputeProgramShape() const; // Return whether `*this` and `other` are functionally equivalent. diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc index 53450991b6fad5b9651d9d23b55c908e6b68e5dd..35ecd4428d0dfde2de445ea34472d2c78148c6c9 100644 --- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc +++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc @@ -35,7 +35,10 @@ limitations under the License. namespace xla { StatusOr HloConstantFolding::Run(HloModule* module) { - auto evaluator = MakeUnique(); + // Limit the constant folding to 0 iterations to skip folding loops. This + // retains the behavior from before while loop support in HloEvaluator and may + // be revised. + auto evaluator = MakeUnique(/*max_loop_iterations=*/0); XLA_VLOG_LINES(2, "HloConstantFolding::Run(), before:\n" + module->ToString()); diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc index 9cd5a1e2b71a7aa768e478289e8e4cc13030fcc3..4ec2ef27bf59b0c877ec38e55ef5c12debeec227 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc @@ -229,6 +229,10 @@ Status HloCostAnalysis::HandleOutfeed(const HloInstruction*) { return Status::OK(); } +Status HloCostAnalysis::HandleHostCompute(const HloInstruction*) { + return Status::OK(); +} + Status HloCostAnalysis::HandleMap(const HloInstruction* map) { // Compute properties of the mapped function. TF_ASSIGN_OR_RETURN(const Properties sub_properties, @@ -529,6 +533,11 @@ Status HloCostAnalysis::HandleConditional(const HloInstruction* conditional) { return Status::OK(); } +Status HloCostAnalysis::HandleGather(const HloInstruction* gather) { + // Gather does not issue any flops. + return Status::OK(); +} + Status HloCostAnalysis::FinishVisit(const HloInstruction*) { return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h index e5783539e5436f09fa58bf7889118380ee90fea0..d17678d20f2a23fd98d18b77d5fb25853901a789 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h @@ -71,6 +71,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleCrossReplicaSum(const HloInstruction* crs) override; Status HandleInfeed(const HloInstruction* infeed) override; Status HandleOutfeed(const HloInstruction* outfeed) override; + Status HandleHostCompute(const HloInstruction* host_compute) override; Status HandleRng(const HloInstruction* random) override; Status HandleReverse(const HloInstruction* reverse) override; Status HandleSort(const HloInstruction* sort) override; @@ -99,6 +100,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleTranspose(const HloInstruction* transpose) override; Status HandleWhile(const HloInstruction* xla_while) override; Status HandleConditional(const HloInstruction* conditional) override; + Status HandleGather(const HloInstruction* gather) override; Status FinishVisit(const HloInstruction* root) override; Status Preprocess(const HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index d25fc5d7418ae40c7167f88d6172906482a58925..934e43ba4879628362009267c671ec4cb0d79c52 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -38,12 +38,12 @@ namespace xla { using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; -HloDataflowAnalysis::HloDataflowAnalysis(HloModule* module, bool ssa_form, +HloDataflowAnalysis::HloDataflowAnalysis(const HloModule& module, bool ssa_form, bool bitcast_defines_value) : module_(module), ssa_form_(ssa_form), bitcast_defines_value_(bitcast_defines_value), - call_graph_(CallGraph::Build(module)) {} + call_graph_(CallGraph::Build(&module)) {} bool HloDataflowAnalysis::ValueIsDefinedAt(const HloInstruction* instruction, const ShapeIndex& index) const { @@ -115,9 +115,9 @@ void HloDataflowAnalysis::DeleteMarkedValues() { } string HloDataflowAnalysis::ToString() const { - string out = StrCat("HloDataflowAnalysis, module ", module_->name(), "\n"); + string out = StrCat("HloDataflowAnalysis, module ", module_.name(), "\n"); StrAppend(&out, " Instruction value sets:\n"); - for (const HloComputation* computation : module_->computations()) { + for (const HloComputation* computation : module_.computations()) { for (const HloInstruction* instruction : computation->instructions()) { StrAppend(&out, " ", instruction->name(), ":\n"); if (ShapeUtil::IsTuple(instruction->shape())) { @@ -585,16 +585,23 @@ bool HloDataflowAnalysis::UpdateInstructionValueSet( void HloDataflowAnalysis::Propagate() { std::queue worklist; + tensorflow::gtl::FlatSet workset; + auto add_to_worklist = [&worklist, &workset](HloInstruction* instruction) { + if (workset.insert(instruction).second) { + worklist.push(instruction); + } + }; - for (HloComputation* computation : module_->computations()) { + for (HloComputation* computation : module_.computations()) { for (HloInstruction* instruction : computation->instructions()) { - worklist.push(instruction); + add_to_worklist(instruction); } } while (!worklist.empty()) { HloInstruction* instruction = worklist.front(); worklist.pop(); + workset.erase(workset.find(instruction)); VLOG(3) << "Worklist top: " << instruction->name(); VLOG(3) << ToString(); @@ -608,9 +615,10 @@ void HloDataflowAnalysis::Propagate() { VLOG(4) << "New value set for " << instruction->name() << ": " << GetInstructionValueSet(instruction); - // Instruction value was updated. Add users to work list. + // Instruction value was updated. Add users to work list if we haven't + // already. for (HloInstruction* user : instruction->users()) { - worklist.push(user); + add_to_worklist(user); // If user sequentially calls a computation, then the respective // parameter(s) of the computation need to be updated. @@ -625,10 +633,10 @@ void HloDataflowAnalysis::Propagate() { // Note that the same instruction can be used in both operand 1 and // operand 2. if (user->operand(1) == instruction) { - worklist.push(user->true_computation()->parameter_instruction(0)); + add_to_worklist(user->true_computation()->parameter_instruction(0)); } if (user->operand(2) == instruction) { - worklist.push(user->false_computation()->parameter_instruction(0)); + add_to_worklist(user->false_computation()->parameter_instruction(0)); } } else { for (HloComputation* called_computation : user->called_computations()) { @@ -636,7 +644,7 @@ void HloDataflowAnalysis::Propagate() { call_graph_->GetNode(called_computation); if (call_graph_node.context() == CallContext::kSequential) { for (int64 operand_number : user->OperandIndices(instruction)) { - worklist.push( + add_to_worklist( called_computation->parameter_instruction(operand_number)); } } @@ -652,13 +660,13 @@ void HloDataflowAnalysis::Propagate() { for (const CallSite& callsite : call_graph_node.caller_callsites()) { if ((callsite.instruction()->opcode() == HloOpcode::kCall) || (callsite.instruction()->opcode() == HloOpcode::kConditional)) { - worklist.push(callsite.instruction()); + add_to_worklist(callsite.instruction()); } else if (callsite.instruction()->opcode() == HloOpcode::kWhile) { // Add the while itself, and the body and condition parameters. - worklist.push(callsite.instruction()); - worklist.push( + add_to_worklist(callsite.instruction()); + add_to_worklist( callsite.instruction()->while_body()->parameter_instruction(0)); - worklist.push( + add_to_worklist( callsite.instruction()->while_condition()->parameter_instruction( 0)); } @@ -678,7 +686,7 @@ InstructionValueSet& HloDataflowAnalysis::GetInstructionValueSet( } Status HloDataflowAnalysis::InitializeInstructionValueSets() { - for (const HloComputation* computation : module_->computations()) { + for (const HloComputation* computation : module_.computations()) { const CallGraphNode& call_graph_node = call_graph_->GetNode(computation); for (HloInstruction* instruction : computation->instructions()) { // Create an empty shape tree. @@ -779,9 +787,9 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() { /* static */ StatusOr> HloDataflowAnalysis::Run( - HloModule* module, bool ssa_form, bool bitcast_defines_value) { - VLOG(1) << "HloDataflowAnalysis::Run on module " << module->name(); - XLA_VLOG_LINES(2, module->ToString()); + const HloModule& module, bool ssa_form, bool bitcast_defines_value) { + VLOG(1) << "HloDataflowAnalysis::Run on module " << module.name(); + XLA_VLOG_LINES(2, module.ToString()); auto dataflow_analysis = WrapUnique( new HloDataflowAnalysis(module, ssa_form, bitcast_defines_value)); @@ -798,7 +806,7 @@ StatusOr> HloDataflowAnalysis::Run( // lookup is faster. std::vector> value_positions( dataflow_analysis->next_value_id_); - for (const HloComputation* computation : module->computations()) { + for (const HloComputation* computation : module.computations()) { for (HloInstruction* instruction : computation->instructions()) { for (const auto& pair : dataflow_analysis->GetInstructionValueSet(instruction)) { @@ -850,7 +858,7 @@ Status HloDataflowAnalysis::Verify() const { // For each value in each value set, verify that the value set's position // appears in the value's positions(). - for (const auto& computation : module_->computations()) { + for (const auto& computation : module_.computations()) { for (const auto& instruction : computation->instructions()) { for (const auto& pair : GetInstructionValueSet(instruction)) { const ShapeIndex& index = pair.first; diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h index 89d318188f0855c7924836a51cfe98d531e08cb4..7b8a74b096ff48733717e78ada5bb56a28caed72 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h @@ -60,7 +60,7 @@ class HloDataflowAnalysis { // a new HLO value in the analysis. If false then Bitcast forwards the // value of its operand. static StatusOr> Run( - HloModule* module, bool ssa_form = false, + const HloModule& module, bool ssa_form = false, bool bitcast_defines_value = false); // Returns true if 'instruction' defines an HLO value at the given shape index @@ -119,7 +119,7 @@ class HloDataflowAnalysis { string ToString() const; protected: - HloDataflowAnalysis(HloModule* module, bool ssa_form, + HloDataflowAnalysis(const HloModule& module, bool ssa_form, bool bitcast_defines_value = false); // Returns a new HloValue defined at the given instruction and shape index. @@ -180,7 +180,7 @@ class HloDataflowAnalysis { // Verify various invariants of the dataflow analysis. Status Verify() const; - HloModule* const module_; + const HloModule& module_; const bool ssa_form_; const bool bitcast_defines_value_; diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc index e714b2567fd1b3eab607a19f0bb7e3288150dc64..7bf3a1a06045c79621d75b653bf42220705a69d4 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc @@ -50,7 +50,7 @@ class HloDataflowAnalysisTest : public HloTestBase, bool bitcast_defines_value = false) { hlo_graph_dumper::MaybeDumpHloModule(*module_, "Before dataflow analysis"); analysis_ = - HloDataflowAnalysis::Run(module_.get(), ssa_form, bitcast_defines_value) + HloDataflowAnalysis::Run(*module_, ssa_form, bitcast_defines_value) .ConsumeValueOrDie(); return *analysis_; } diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc index 1e5f0f797a13fd7e7ce1cc934387a274a74153bc..fcd723af146e2227b8661b1a4993f1338f7de389 100644 --- a/tensorflow/compiler/xla/service/hlo_dce.cc +++ b/tensorflow/compiler/xla/service/hlo_dce.cc @@ -40,7 +40,7 @@ StatusOr HloDCE::Run(HloModule* module) { VLOG(2) << "Before dce:"; XLA_VLOG_LINES(2, module->ToString()); - for (auto* computation : module->MakeNonfusionComputations()) { + for (auto* computation : module->MakeComputationPostOrder()) { std::unordered_set live_instructions; TF_RETURN_IF_ERROR(computation->root_instruction()->Accept( [&live_instructions](HloInstruction* instruction) { diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index ab604064d5e34a1e21f3209cc4d27fc05a9ca057..a839f8066ee47459e73e4ec4c30f7fa8f9150bc8 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -34,8 +34,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_query.h" #include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/compiler/xla/status.h" -#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" @@ -53,12 +51,22 @@ namespace xla { namespace { +using tensorflow::gtl::ArraySlice; +using tensorflow::gtl::FlatSet; +using tensorflow::gtl::optional; + template struct is_complex_t : public std::false_type {}; template <> struct is_complex_t : public std::true_type {}; +template +struct is_complex64_t : public std::false_type {}; + +template <> +struct is_complex64_t : public std::true_type {}; + template StatusOr> Compare(const Shape& shape, HloOpcode opcode, const Literal& lhs_literal, @@ -101,11 +109,10 @@ StatusOr> Compare(const Shape& shape, HloOpcode opcode, } auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - return compare_op(lhs_literal.Get(multi_index), - rhs_literal.Get(multi_index)); - })); + TF_RETURN_IF_ERROR(result->Populate([&](ArraySlice multi_index) { + return compare_op(lhs_literal.Get(multi_index), + rhs_literal.Get(multi_index)); + })); return std::move(result); } @@ -132,11 +139,10 @@ StatusOr> Compare( } auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - return compare_op(lhs_literal.Get(multi_index), - rhs_literal.Get(multi_index)); - })); + TF_RETURN_IF_ERROR(result->Populate([&](ArraySlice multi_index) { + return compare_op(lhs_literal.Get(multi_index), + rhs_literal.Get(multi_index)); + })); return std::move(result); } @@ -161,8 +167,8 @@ StatusOr> ElementWiseUnaryOpImpl( auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return unary_op(operand_literal.Get(multi_index)); })); return std::move(result); @@ -174,7 +180,7 @@ StatusOr> ElementWiseUnaryOpImpl( // with the base index. void IterateThroughWindow( const Shape& window_shape, const Window& window, const Shape& base_shape, - const tensorflow::gtl::ArraySlice& window_count_index, + const ArraySlice& window_count_index, const std::function&)>& f) { const int64 rank = ShapeUtil::Rank(base_shape); DimensionVector window_index(rank); @@ -250,17 +256,37 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { template < typename NativeT, - typename std::enable_if::value || - is_complex_t::value>::type* = nullptr> + typename std::enable_if::value>::type* = nullptr> Status HandleAbs(HloInstruction* abs) { TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs], - ElementWiseUnaryOp(abs, [](ElementwiseT elem_operand) { + ElementWiseUnaryOp(abs, [](NativeT elem_operand) { return std::abs(elem_operand); })); return Status::OK(); } + template < + typename NativeT, + typename std::enable_if::value>::type* = nullptr> + Status HandleAbs(HloInstruction* abs) { + const Literal& operand_literal = + parent_->GetEvaluatedLiteralFor(abs->operand(0)); + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[abs], + (ElementWiseUnaryOpImpl( + abs, [](NativeT elem_operand) { return std::abs(elem_operand); }, + operand_literal))); + + return Status::OK(); + } + Status HandleAbs(HloInstruction* abs) override { + // If the operand is of C64 type, the return type of abs will be F32. + // However, ElementwiseT would still be the return type, F32, and thus + // specifying the ElementwiseT explicitly as C64 is needed below. + if (abs->operand(0)->shape().element_type() == C64) { + return HandleAbs(abs); + } return HandleAbs(abs); } @@ -308,13 +334,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { operand_to_broadcast.shape().dimensions(i)); } - return output->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - for (int64 i = 0; i < broadcast->dimensions().size(); ++i) { - broadcast_indices[i] = multi_index[broadcast->dimensions(i)]; - } - return operand_to_broadcast.Get(broadcast_indices); - }); + return output->Populate([&](ArraySlice multi_index) { + for (int64 i = 0; i < broadcast->dimensions().size(); ++i) { + broadcast_indices[i] = multi_index[broadcast->dimensions(i)]; + } + return operand_to_broadcast.Get(broadcast_indices); + }); } template < @@ -588,14 +613,25 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } - template < - typename NativeT, - typename std::enable_if::value>::type* = nullptr> + template ::value>::type* = + nullptr> Status HandleMaximum(HloInstruction* maximum) { TF_ASSIGN_OR_RETURN( parent_->evaluated_[maximum], ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) { - return std::fmax(lhs, rhs); + return std::max(lhs, rhs); + })); + return Status::OK(); + } + + template ::value>::type* = nullptr> + Status HandleMaximum(HloInstruction* maximum) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[maximum], + ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) { + return ((lhs >= rhs) || std::isnan(lhs)) ? lhs : rhs; })); return Status::OK(); } @@ -611,18 +647,30 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return HandleMaximum(maximum); } - template < - typename NativeT, - typename std::enable_if::value>::type* = nullptr> + template ::value>::type* = + nullptr> Status HandleMinimum(HloInstruction* minimum) { TF_ASSIGN_OR_RETURN(parent_->evaluated_[minimum], ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el, ElementwiseT rhs_el) { - return std::fmin(lhs_el, rhs_el); + return std::min(lhs_el, rhs_el); })); return Status::OK(); } + template ::value>::type* = nullptr> + Status HandleMinimum(HloInstruction* minimum) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[minimum], + ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el, + ElementwiseT rhs_el) { + return ((lhs_el <= rhs_el) || std::isnan(lhs_el)) ? lhs_el : rhs_el; + })); + return Status::OK(); + } + template < typename NativeT, typename std::enable_if::value>::type* = nullptr> @@ -742,7 +790,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { TF_ASSIGN_OR_RETURN( parent_->evaluated_[shl], ElementWiseBinaryOp(shl, [](NativeT lhs_elem, NativeT rhs_elem) { - return lhs_elem << rhs_elem; + return IsShiftOutOfBounds(rhs_elem) ? 0 + : (lhs_elem << rhs_elem); })); return Status::OK(); } @@ -767,8 +816,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { TF_ASSIGN_OR_RETURN( parent_->evaluated_[shr], ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) { - return static_cast(static_cast(lhs_elem) >> - rhs_elem); + SignedT lhs_signed = static_cast(lhs_elem); + if (IsShiftOutOfBounds(rhs_elem)) { + return lhs_signed < 0 ? static_cast(-1) : 0; + } else { + return lhs_signed >> rhs_elem; + } })); return Status::OK(); } @@ -794,6 +847,10 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { TF_ASSIGN_OR_RETURN( parent_->evaluated_[shr], ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) { + // If shift amount is greater than the number of bits, then return 0. + if (IsShiftOutOfBounds(rhs_elem)) { + return static_cast(0); + } return static_cast(static_cast(lhs_elem) >> rhs_elem); })); @@ -869,8 +926,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand); auto result = Literal::CreateFromShape(result_shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice out_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice out_index) { std::vector from_index(out_index.begin(), out_index.end()); for (const int64 dim : reverse_dimensions) { from_index[dim] = result_shape.dimensions(dim) - 1 - out_index[dim]; @@ -945,7 +1002,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { DimensionVector rhs_index(rhs_rank); DimensionVector rhs_spatial_index(dnums.kernel_spatial_dimensions_size()); - auto func = [&](tensorflow::gtl::ArraySlice out_index) { + auto func = [&](ArraySlice out_index) { ElementwiseT result_val = static_cast(0); std::fill(lhs_index.begin(), lhs_index.end(), 0); @@ -1028,55 +1085,118 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { CHECK(ShapeUtil::IsArray(lhs->shape())); CHECK(ShapeUtil::IsArray(rhs->shape())); - // Dot only supports operands of rank 1 and 2. - const auto dot_rank = ShapeUtil::Rank(dot->shape()); + const auto& dnums = dot->dot_dimension_numbers(); + const auto lhs_rank = ShapeUtil::Rank(lhs->shape()); const auto rhs_rank = ShapeUtil::Rank(rhs->shape()); - CHECK(lhs_rank > 0 && lhs_rank <= 2); - CHECK(rhs_rank > 0 && rhs_rank <= 2); - CHECK_EQ(dot_rank, lhs_rank + rhs_rank - 2); CHECK(ShapeUtil::SameElementType(lhs->shape(), rhs->shape())); CHECK(ShapeUtil::SameElementType(lhs->shape(), dot->shape())); - // Check contracted dimensions are the same. - // - // Determine the index of the contracted dimensions for input tensors. - // dimensions -1 of lhs and dimension 0 of rhs are contracted. - const int64 lhs_contracted_dimension = - ShapeUtil::GetDimensionNumber(lhs->shape(), -1); - const int64 rhs_contracted_dimension = 0; - CHECK_EQ(lhs->shape().dimensions(lhs_contracted_dimension), - rhs->shape().dimensions(rhs_contracted_dimension)) + // There must be 1 and only 1 Contracting dimension for lhs and rhs. + CHECK_EQ(dnums.lhs_contracting_dimensions_size(), 1); + CHECK_EQ(dnums.rhs_contracting_dimensions_size(), 1); + const int64 lhs_contracting_dimension = dnums.lhs_contracting_dimensions(0); + const int64 rhs_contracting_dimension = dnums.rhs_contracting_dimensions(0); + // Contracted dimension sizes must be the same. + CHECK_EQ(lhs->shape().dimensions(lhs_contracting_dimension), + rhs->shape().dimensions(rhs_contracting_dimension)) << "lhs contracted dimension: " - << lhs->shape().dimensions(lhs_contracted_dimension) + << lhs->shape().dimensions(lhs_contracting_dimension) << " rhs contracted dimension: " - << rhs->shape().dimensions(rhs_contracted_dimension); + << rhs->shape().dimensions(rhs_contracting_dimension); const int64 contracted_dimension_size = - lhs->shape().dimensions(lhs_contracted_dimension); + lhs->shape().dimensions(lhs_contracting_dimension); const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs); const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs); auto result = Literal::CreateFromShape(dot->shape()); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + + CHECK_EQ(dnums.lhs_batch_dimensions_size(), + dnums.rhs_batch_dimensions_size()); + + std::vector lhs_non_contracting_dims; + for (int64 i = 0; i < lhs_rank; i++) { + if (i != lhs_contracting_dimension) { + lhs_non_contracting_dims.push_back(i); + } + } + + std::vector rhs_non_batch_non_contracting_dims; + FlatSet batch_dims_set(dnums.rhs_batch_dimensions().begin(), + dnums.rhs_batch_dimensions().end()); + for (int64 i = 0; i < rhs_rank; i++) { + if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) { + rhs_non_batch_non_contracting_dims.push_back(i); + } + } + + const int64 batch_dim_size = dnums.lhs_batch_dimensions_size(); + const int64 lhs_non_contracting_size = lhs_non_contracting_dims.size(); + + DimensionVector lhs_index(lhs_rank); + DimensionVector rhs_index(rhs_rank); + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice result_index) { ElementwiseT result_val = static_cast(0); - std::vector lhs_index(lhs_rank, 0); - std::vector rhs_index(rhs_rank, 0); - // Set index for non-contracted dimension for lhs and rhs. - if (lhs_rank > 1) { - lhs_index[0] = multi_index[0]; + // Find the corresponding non-contracting indices for lhs and rhs. + // + // For `result_index`, its batch dimension, if exists, will be at the + // same dimension as the batch dimension of lhs and rhs. More + // specifically: + // - For lhs, the non-contracting dimensions, including the batch + // dimension have the same index as the `result_index`. + // - For rhs, the batch dimension is set seperately from other + // non-contracting dimensions, since these other non-contracting + // dimensions in rhs follow the non-contracting dimensions of lhs in + // the resulting index. + // + // As an example, for a resulting index: + // result_index [result_batch, result_x, result_y] + // the effecting lhs and rhs indices are: + // lhs [result_batch, lhs_non_contracting_dim, contracting_dim + // rhs [result_batch, contracting_dim, rhs_non_contracting_dim] + // `result_x` is only affected by the lhs_non_contracting_dim and + // likewise `result_y` only depends on rhs_non_contracting_dim. + // + // so we can look up the lhs and rhs indices by: + // + // lhs: + // batch index is the same as `result_batch`. + // non-contracting dimension is the same as + // result_index[lhs_non_contracting_dim] + // rhs: + // batch index: the same as `result_batch`. + // non-contracting dimension index: *not* the same as + // result_index[rhs_non_contractng_dim], since the + // non-contracting dimensions of lhs are included in the + // result_index first. Instead, the non_contracting_dim of rhs must + // be calculated as following: + // lhs_non_contracting_dimensions_size + + // (rhs_non_batch_non_contracting_dim - batch_dim_size) - 1 + // + // Note that (rhs_non_batch_contracting_dim - batch_dim_size) is + // the index offset to the result_index that only depends on + // the non_batch and non-contracting dimensions of rhs. -1 at the + // end translates size to index. + for (auto i : lhs_non_contracting_dims) { + lhs_index[i] = result_index[i]; + } + for (auto i : dnums.rhs_batch_dimensions()) { + rhs_index[i] = result_index[i]; } - if (rhs_rank > 1) { - rhs_index[1] = multi_index[multi_index.size() - 1]; + for (auto i : rhs_non_batch_non_contracting_dims) { + const int64 rhs_non_batch_non_contracting_dim = + lhs_non_contracting_size + (i - batch_dim_size) - 1; + rhs_index[i] = result_index[rhs_non_batch_non_contracting_dim]; } // Accumulates resulting product along the contracted dimension. for (int64 i = 0; i < contracted_dimension_size; ++i) { - lhs_index[lhs_contracted_dimension] = i; - rhs_index[rhs_contracted_dimension] = i; + lhs_index[lhs_contracting_dimension] = i; + rhs_index[rhs_contracting_dimension] = i; result_val += static_cast(lhs_literal.Get(lhs_index)) * @@ -1112,9 +1232,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get({}); auto result = Literal::CreateFromShape(pad->shape()); TF_RETURN_IF_ERROR(result->Populate( - [&scalar](tensorflow::gtl::ArraySlice multi_index) { - return scalar; - })); + [&scalar](ArraySlice multi_index) { return scalar; })); const Literal& evaluated_operand = parent_->GetEvaluatedLiteralFor(pad->operand(0)); @@ -1127,7 +1245,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // corresponding index of the resulting padded literal. const PaddingConfig& pad_config = pad->padding_config(); - auto func = [&](const std::vector& input_index) { + auto func = [&](ArraySlice input_index) { for (auto i = 0; i < input_index.size(); ++i) { // Interior padding occurs logically before edge padding, so in the case // of negative edge padding elements are removed from the @@ -1277,9 +1395,9 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(map->shape()); - HloEvaluator embedded_evaluator; - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { std::vector> arg_literals; arg_literals.reserve(operands.size()); @@ -1339,6 +1457,11 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map)); break; } + case F16: { + TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], + MapImpl(map)); + break; + } case F32: { TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map)); break; @@ -1364,7 +1487,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { Status HandleReduce(HloInstruction* reduce) override { auto arg = reduce->operand(0); auto init_value = reduce->operand(1); - tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); + ArraySlice dimensions(reduce->dimensions()); HloComputation* function = reduce->to_apply(); TF_RET_CHECK(ShapeUtil::Rank(reduce->shape()) == ShapeUtil::Rank(arg->shape()) - dimensions.size()); @@ -1407,10 +1530,10 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { } } - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); // For each resulting dimension, calculate and assign computed value. - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { ReturnT result_val = init_scalar; std::vector base(arg_dimensions.size()); @@ -1418,7 +1541,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { base[result_to_arg_index[i]] = multi_index[i]; } - auto func = [&](const std::vector& input_index) { + auto func = [&](ArraySlice input_index) { auto curr_val = arg_literal.Get(input_index); // Evaluate computation with specified literal operands. @@ -1464,9 +1587,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // Initialize result array with the init value. TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice output_index) { - return init_scalar; - })); + [&](ArraySlice output_index) { return init_scalar; })); std::vector window_dimension_sizes; for (const auto& window_dimension : window.dimensions()) { @@ -1483,7 +1604,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { int64 rank = ShapeUtil::Rank(operand_literal.shape()); - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); DimensionVector source_index(rank); std::fill(source_index.begin(), source_index.end(), 0); @@ -1499,8 +1620,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // 2. Using the selected index, scatter value from `source` to result. We // do this by iterating through the window, and compare each index with // the selected index. - tensorflow::gtl::optional selected_val; - tensorflow::gtl::optional> selected_index; + optional selected_val; + optional> selected_index; IterateThroughWindow( window_shape, window, operand_literal.shape(), source_index, @@ -1594,10 +1715,10 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { DimensionVector window_index(window.dimensions_size()); DimensionVector operand_index(ShapeUtil::Rank(operand_literal.shape())); - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); // For each resulting dimension, calculate and assign computed value. - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice output_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice output_index) { ReturnT result_val = init_scalar; std::fill(window_index.begin(), window_index.end(), 0); @@ -1647,7 +1768,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { const int64 rank = ShapeUtil::Rank(operand->shape()); const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand); - auto func = [&](tensorflow::gtl::ArraySlice out_index) { + auto func = [&](ArraySlice out_index) { DimensionVector operand_index(rank); for (int64 i = 0; i < rank; ++i) { operand_index[i] = @@ -1828,8 +1949,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { std::vector operand_indices(start.size()); auto result = Literal::CreateFromShape(result_shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { for (int64 i = 0; i < operand_indices.size(); ++i) { CHECK_GE(multi_index[i] + start[i], 0); // Mod is only used here to be consistent with the existing @@ -1849,17 +1970,26 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { StatusOr> DynamicUpdateSlice( const Literal& operand_literal, const Literal& update_literal, const Literal& start_indices_literal) { - auto start_indices_typed = start_indices_literal.data(); - const std::vector start(start_indices_typed.begin(), - start_indices_typed.end()); - auto result = operand_literal.CloneToUnique(); - std::vector result_index(ShapeUtil::Rank(result->shape()), 0); + auto start_indices_typed = start_indices_literal.data(); + const auto rank = ShapeUtil::Rank(result->shape()); + std::vector start(rank, 0); + for (int64 i = 0; i < rank; ++i) { + // All other implementations currently wrap-around the index, so this + // should do so as well. + start[i] = (start_indices_typed[i] % result->shape().dimensions(i)); + start[i] += (start[i] < 0) * result->shape().dimensions(i); + } + std::vector result_index(rank, 0); - auto func = [&](const std::vector& update_index) { + auto func = [&](ArraySlice update_index) { std::transform(update_index.begin(), update_index.end(), start.begin(), result_index.begin(), std::plus()); - + // Same as above, wrap-around only to match other implementations' + // semantics. + std::transform(result_index.begin(), result_index.end(), + result->shape().dimensions().begin(), result_index.begin(), + std::modulus()); result->Set(result_index, update_literal.Get(update_index)); return true; @@ -1912,8 +2042,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return ConvertBinaryFunction(binary_op)( lhs_literal.Get(multi_index), rhs_literal.Get(multi_index)); @@ -1950,8 +2080,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return ternary_op(lhs_literal.Get(multi_index), rhs_literal.Get(multi_index), ehs_literal.Get(multi_index)); @@ -1960,10 +2090,19 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return std::move(result); } + template + static bool IsShiftOutOfBounds(NativeT rhs) { + typedef typename std::make_unsigned::type UnsignedT; + UnsignedT lhs_size_unsigned = sizeof(NativeT) * CHAR_BIT; + UnsignedT rhs_unsigned = static_cast(rhs); + return rhs_unsigned >= lhs_size_unsigned; + } + HloEvaluator* parent_; }; // class HloEvaluator::TypedVisitor -HloEvaluator::HloEvaluator() { +HloEvaluator::HloEvaluator(int64 max_loop_iterations) + : max_loop_iterations_(max_loop_iterations) { typed_visitors_[PRED] = MakeUnique>(this); typed_visitors_[U8] = MakeUnique>(this); typed_visitors_[U16] = MakeUnique([](HloInstruction*) { @@ -1977,9 +2116,7 @@ HloEvaluator::HloEvaluator() { }); typed_visitors_[S32] = MakeUnique>(this); typed_visitors_[S64] = MakeUnique>(this); - typed_visitors_[F16] = MakeUnique([](HloInstruction*) { - return Unimplemented("HloEvaluator: unhandled primitive type: F16."); - }); + typed_visitors_[F16] = MakeUnique>(this); typed_visitors_[F32] = MakeUnique>(this); typed_visitors_[F64] = MakeUnique>(this); typed_visitors_[C64] = MakeUnique>(this); @@ -1999,8 +2136,7 @@ HloEvaluator::HloEvaluator() { template StatusOr> HloEvaluator::Evaluate( - const HloModule& module, - tensorflow::gtl::ArraySlice arg_literals) { + const HloModule& module, ArraySlice arg_literals) { XLA_VLOG_LINES(2, "HloEvaluator::Evaluate module:\n" + module.ToString()); evaluated_.clear(); @@ -2017,8 +2153,7 @@ StatusOr> HloEvaluator::Evaluate( template StatusOr> HloEvaluator::Evaluate( - const HloComputation& computation, - tensorflow::gtl::ArraySlice arg_literals) { + const HloComputation& computation, ArraySlice arg_literals) { XLA_VLOG_LINES( 2, "HloEvaluator::Evaluate computation:\n" + computation.ToString()); @@ -2034,8 +2169,7 @@ StatusOr> HloEvaluator::Evaluate( template StatusOr> HloEvaluator::Evaluate( - HloInstruction* instruction, - tensorflow::gtl::ArraySlice arg_literals) { + HloInstruction* instruction, ArraySlice arg_literals) { TF_RET_CHECK(hlo_query::AllOperandsAreParametersOrConstants(*instruction)); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(instruction->shape())); @@ -2160,8 +2294,7 @@ Status HloEvaluator::HandleTranspose(HloInstruction* transpose) { } Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { - tensorflow::gtl::ArraySlice operands( - concatenate->operands()); + ArraySlice operands(concatenate->operands()); // The result concatenate dimension is going to be the sum of all // concatenate dimensions of the operands taking part of the operation. const Shape& reference_shape = operands[0]->shape(); @@ -2333,6 +2466,340 @@ Status HloEvaluator::HandleTuple(HloInstruction* tuple) { return Status::OK(); } +// Returns an ShapeUtil::IndexIterationSpace that iterates over the output +// gather dimensions while keeping the rest of the output dimensions clamped to +// 0. +ShapeUtil::IndexIterationSpace IterationSpaceForOutputGatherIndices( + const Shape& output_shape, const GatherDimensionNumbers& dim_numbers) { + int64 output_rank = output_shape.dimensions_size(); + std::vector index_base(output_rank, 0); + std::vector index_count; + index_count.reserve(output_rank); + for (int64 i = 0; i < output_rank; i++) { + bool is_output_gather_dim = + !c_binary_search(dim_numbers.output_window_dims(), i); + index_count.push_back(is_output_gather_dim ? output_shape.dimensions(i) + : 1); + } + + return {std::move(index_base), std::move(index_count), + std::vector(output_rank, 1)}; +} + +// Return an ShapeUtil::IndexIterationSpace that iterates over the output window +// dimensions while keeping the rest of the output dimensions clamped to 0. +ShapeUtil::IndexIterationSpace IterationSpaceForOutputWindowIndices( + int64 output_rank, ArraySlice window_bounds, + const GatherDimensionNumbers& dim_numbers) { + std::vector index_base(output_rank, 0); + std::vector index_count(output_rank, 1); + int64 window_bounds_idx = 0; + for (int64 i = 0; i < output_rank; i++) { + bool is_output_window_dim = + c_binary_search(dim_numbers.output_window_dims(), i); + if (is_output_window_dim) { + while (c_binary_search(dim_numbers.elided_window_dims(), + window_bounds_idx)) { + window_bounds_idx++; + } + index_count[i] = window_bounds[window_bounds_idx++]; + } + } + + return {std::move(index_base), std::move(index_count), + std::vector(output_rank, 1)}; +} + +// This functor computes the contribution of gather_indices to an input index +// corresponding to an output index. That is, given an output index I, it picks +// out the gather output indices in I and uses them to look up a gather index, +// G, from the gather indices tensor, and expands G into the input space +// according to gather_dims_to_operand_dims. +class OutputGatherIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit OutputGatherIndexToInputIndex( + const GatherDimensionNumbers* dim_numbers, const Shape& input_shape, + const Shape& output_shape, const Literal* gather_indices) + : dim_numbers_(*dim_numbers), gather_indices_(*gather_indices) { + for (int64 i = 0; i < output_shape.dimensions_size(); i++) { + output_dim_is_gather_dims_.push_back( + !c_binary_search(dim_numbers_.output_window_dims(), i)); + } + + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + int64 index_of_input_dim_in_index_vector = + std::distance(dim_numbers_.gather_dims_to_operand_dims().begin(), + c_find(dim_numbers_.gather_dims_to_operand_dims(), i)); + if (index_of_input_dim_in_index_vector == + dim_numbers_.gather_dims_to_operand_dims_size()) { + input_dim_value_to_index_vector_.push_back(-1); + } else { + input_dim_value_to_index_vector_.push_back( + index_of_input_dim_in_index_vector); + } + } + + index_vector_index_.resize(gather_indices_.shape().dimensions_size()); + input_index_.resize(input_shape.dimensions_size()); + int64 index_vector_size = + gather_indices_.shape().dimensions(dim_numbers_.index_vector_dim()); + index_vector_.resize(index_vector_size); + } + + // Returns the contribution of gather_indices to the input index corresponding + // to output_index. See gather_inner_loop_body. + // + // This is conceptually a stateless transformation from output_index to the + // gather input index, but: + // + // - Instead of allocating memory to represent the gather input index on + // every invocation we reuse the same storage for the result + // (input_index_), mutating it in place. + // - Instead of allocating buffers for temporary values like + // index_vector_index_ and index_vector on every invocation, we reuse the + // same storage for all invocations. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()(ArraySlice output_index) { + PropagateOutputIndexGatherDimsToIndexVectorIndex(output_index); + TF_RETURN_IF_ERROR(FetchIndexVector()); + PropagateIndexVectorToInputIndex(); + return ArraySlice(input_index_); + } + + private: + // Propagates the gather index dimensions from the output index into + // index_vector_index_ by mutating index_vector_index_ in place. Does not + // update the dim_numbers.index_vector_dim() dimension -- that's the dimension + // we iterate over in FetchIndexVector. + void PropagateOutputIndexGatherDimsToIndexVectorIndex( + ArraySlice output_index) { + int64 index_vector_index_i = 0; + for (int64 i = 0, e = output_index.size(); i < e; i++) { + if (!output_dim_is_gather_dims_[i]) { + continue; + } + + if (index_vector_index_i == dim_numbers_.index_vector_dim()) { + index_vector_index_i++; + } + + index_vector_index_[index_vector_index_i++] = output_index[i]; + } + } + + // Populates index_vector_ by iterating over gather_indices_ according to + // index_vector_index_. + Status FetchIndexVector() { + int64 index_vector_dim = dim_numbers_.index_vector_dim(); + for (int64 i = 0, e = index_vector_.size(); i < e; i++) { + index_vector_index_[index_vector_dim] = i; + TF_ASSIGN_OR_RETURN(index_vector_[i], gather_indices_.GetIntegralAsS64( + index_vector_index_)); + } + return Status::OK(); + } + + // Populates input_index_. + void PropagateIndexVectorToInputIndex() { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_index_vector_[i] != -1) { + input_index_[i] = index_vector_[input_dim_value_to_index_vector_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i of + // the input index from the index vector. See + // PropagateIndexVectorToInputIndex. + std::vector input_dim_value_to_index_vector_; + + // output_dim_is_gather_dims_[i] is true iff the output index i is a gather + // dimension. + std::vector output_dim_is_gather_dims_; + + // The buffer into which we construct an index into gather_indices_ to fetch + // the index vector. + std::vector index_vector_index_; + + // The index vector fetched from gather_indices_. + std::vector index_vector_; + + // The result computed by this functor. operator() returns an ArraySlice into + // this vector. + std::vector input_index_; + + const GatherDimensionNumbers& dim_numbers_; + const Literal& gather_indices_; +}; + +// This functor computes the contribution of the window indices in an output +// index to an input index. That is, given an output index I it picks out the +// output window indices in I and expands it into a window index into the input +// shape. +class OutputWindowIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit OutputWindowIndexToInputIndex( + const GatherDimensionNumbers& dim_numbers, const Shape& input_shape, + const Shape& output_shape) { + std::vector window_index_to_output_index; + int64 output_index_count = 0; + for (int64 i = 0; i < output_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.output_window_dims(), i)) { + window_index_to_output_index.push_back(output_index_count++); + } else { + output_index_count++; + } + } + + int64 window_dim_count = 0; + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.elided_window_dims(), i)) { + input_dim_value_to_output_index_.push_back(-1); + } else { + input_dim_value_to_output_index_.push_back( + window_index_to_output_index[window_dim_count++]); + } + } + + input_index_.resize(input_shape.dimensions_size()); + } + + // Returns the contribution of the window indices to the input index + // corresponding to output_index. See gather_inner_loop_body. + // + // This is conceptually a stateless transformation from output_index to the + // window input index, but instead of allocating memory to represent the + // gather input index on every invocation we reuse the same storage for the + // result (input_index_), mutating it in place. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()(ArraySlice output_index) { + PropagateOutputIndexWindowDimsToInputIndex(output_index); + return ArraySlice(input_index_); + } + + private: + // Propagates window dimensions from the output index to input_index_ by + // mutating input_index_ in place. + void PropagateOutputIndexWindowDimsToInputIndex( + ArraySlice output_index) { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_output_index_[i] != -1) { + input_index_[i] = output_index[input_dim_value_to_output_index_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i of + // the input index from the output index. See + // PropagateOutputIndexToInputIndex. + std::vector input_dim_value_to_output_index_; + + // The result computed by this functor. operator() returns an ArraySlice into + // this vector. + std::vector input_index_; +}; + +// Rehapes the gather indices input to have a trailing degenerate `1` dimension +// if necessary. Hands over the ownership of the newly created literal (if +// there is one) to `reshaped_gather_indices`. +static StatusOr> ReshapedGatherIndices( + int64 index_vector_dim, const Literal& gather_indices, + std::unique_ptr* reshaped_gather_indices) { + if (gather_indices.shape().dimensions_size() != index_vector_dim) { + return std::cref(gather_indices); + } + + std::vector new_shape(gather_indices.shape().dimensions().begin(), + gather_indices.shape().dimensions().end()); + new_shape.push_back(1); + TF_ASSIGN_OR_RETURN(*reshaped_gather_indices, + gather_indices.Reshape(new_shape)); + return std::cref(**reshaped_gather_indices); +} + +Status HloEvaluator::HandleGather(HloInstruction* gather) { + std::unique_ptr result = Literal::CreateFromShape(gather->shape()); + const Shape& shape = gather->shape(); + const GatherDimensionNumbers& dim_numbers = + gather->gather_dimension_numbers(); + const Literal& operand = GetEvaluatedLiteralFor(gather->operand(0)); + std::unique_ptr reshaped_gather_indices; + TF_ASSIGN_OR_RETURN( + const Literal& gather_indices, + ReshapedGatherIndices(dim_numbers.index_vector_dim(), + GetEvaluatedLiteralFor(gather->operand(1)), + &reshaped_gather_indices)); + + // We iterate over the gather dimensions in the output shape in an outer loop + // nest, and iterate over the window dimensions in the output shape in an + // inner loop nest. + + ShapeUtil::IndexIterationSpace gather_indices_iteration_space = + IterationSpaceForOutputGatherIndices(shape, dim_numbers); + ShapeUtil::IndexIterationSpace window_indices_iteration_space = + IterationSpaceForOutputWindowIndices( + shape.dimensions_size(), gather->gather_window_bounds(), dim_numbers); + + // Scratch buffers that hold an index in the output shape and the + // corresponding index in the input shape. + std::vector input_index(operand.shape().dimensions_size()); + std::vector output_index(gather->shape().dimensions_size()); + + OutputGatherIndexToInputIndex output_gather_index_to_input_index( + &gather->gather_dimension_numbers(), /*input_shape=*/operand.shape(), + /*output_shape=*/shape, &gather_indices); + OutputWindowIndexToInputIndex output_window_index_to_input_index( + gather->gather_dimension_numbers(), /*input_shape=*/operand.shape(), + /*output_shape=*/shape); + + auto gather_inner_loop_body = + [&](ArraySlice output_window_index, + ArraySlice input_gather_index, + ArraySlice output_gather_index) -> StatusOr { + TF_ASSIGN_OR_RETURN( + ArraySlice input_window_index, + output_window_index_to_input_index(output_window_index)); + for (int i = 0, e = output_index.size(); i < e; i++) { + output_index[i] = output_gather_index[i] + output_window_index[i]; + } + for (int i = 0, e = input_index.size(); i < e; i++) { + input_index[i] = input_gather_index[i] + input_window_index[i]; + } + TF_RETURN_IF_ERROR( + result->CopyElementFrom(operand, input_index, output_index)); + return true; + }; + + auto gather_outer_loop_body = + [&](ArraySlice output_gather_index) -> StatusOr { + TF_ASSIGN_OR_RETURN( + ArraySlice input_gather_index, + output_gather_index_to_input_index(output_gather_index)); + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + shape, window_indices_iteration_space, + std::bind(gather_inner_loop_body, std::placeholders::_1, + input_gather_index, output_gather_index))); + return true; + }; + + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + shape, gather_indices_iteration_space, gather_outer_loop_body)); + evaluated_[gather] = std::move(result); + return Status::OK(); +} + Status HloEvaluator::HandleGetTupleElement(HloInstruction* get_tuple_element) { const auto result_shape = get_tuple_element->shape(); const int64 index = get_tuple_element->tuple_index(); @@ -2363,6 +2830,84 @@ Status HloEvaluator::HandleCopy(HloInstruction* copy) { return Status::OK(); } +Status HloEvaluator::HandleCall(HloInstruction* call) { + auto* computation = call->to_apply(); + auto operands = call->operands(); + + std::vector arg_literals; + arg_literals.reserve(operands.size()); + for (auto operand : operands) { + const Literal& arg_literal = GetEvaluatedLiteralFor(operand); + arg_literals.push_back(&arg_literal); + } + + HloEvaluator embedded_evaluator; + std::unique_ptr result = + embedded_evaluator.Evaluate(*computation, arg_literals) + .ConsumeValueOrDie(); + + evaluated_[call] = std::move(result); + return Status::OK(); +} + +Status HloEvaluator::HandleConditional(HloInstruction* conditional) { + const auto& pred = GetEvaluatedLiteralFor(conditional->operand(0)); + const auto& true_computation_arg = + GetEvaluatedLiteralFor(conditional->operand(1)); + const auto& false_computation_arg = + GetEvaluatedLiteralFor(conditional->operand(2)); + + auto* true_computation = conditional->true_computation(); + auto* false_computation = conditional->false_computation(); + + auto result = Literal::CreateFromShape(conditional->shape()); + HloEvaluator embedded_evaluator; + if (pred.Get({})) { + result = embedded_evaluator + .Evaluate(*true_computation, + {&true_computation_arg}) + .ConsumeValueOrDie(); + } else { + result = embedded_evaluator + .Evaluate(*false_computation, + {&false_computation_arg}) + .ConsumeValueOrDie(); + } + + evaluated_[conditional] = std::move(result); + return Status::OK(); +} + +Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) { + HloComputation* cond_comp = while_hlo->while_condition(); + HloComputation* body_comp = while_hlo->while_body(); + // Initialize the loop carried valued with the input to the While instruction. + auto lcv = GetEvaluatedLiteralFor(while_hlo->operand(0)).CloneToUnique(); + bool keep_going = true; + int64 iteration_count = 0; + HloEvaluator cond_evaluator(max_loop_iterations_); + HloEvaluator loop_body_evaluator(max_loop_iterations_); + while (keep_going) { + if (max_loop_iterations_ >= 0 && iteration_count++ > max_loop_iterations_) { + return InvalidArgument("Loop %s exceeded loop iteration limit (%lld).", + while_hlo->name().c_str(), max_loop_iterations_); + } + TF_ASSIGN_OR_RETURN(auto cond_val, cond_evaluator.Evaluate( + *cond_comp, {lcv.get()})); + keep_going = cond_val->GetFirstElement(); + if (keep_going) { + TF_ASSIGN_OR_RETURN(auto body_val, loop_body_evaluator.Evaluate( + *body_comp, {lcv.get()})); + VLOG(3) << "Loop iteration result: " << body_val->ToString(); + lcv = std::move(body_val); + cond_evaluator.ResetVisitStates(); + loop_body_evaluator.ResetVisitStates(); + } + } + evaluated_[while_hlo] = std::move(lcv); + return Status::OK(); +} + Status HloEvaluator::Preprocess(HloInstruction* hlo) { VLOG(2) << "About to visit HLO: " << hlo->ToString(); return Status::OK(); @@ -2376,28 +2921,27 @@ Status HloEvaluator::Postprocess(HloInstruction* hlo) { // Explicit instantiation of templatized Evaluate* methods. // -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(const HloModule& module, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(const HloModule& module, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( - const HloModule& module, - tensorflow::gtl::ArraySlice> arg_literals); + const HloModule& module, ArraySlice> arg_literals); -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(const HloComputation& computation, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(const HloComputation& computation, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( const HloComputation& computation, - tensorflow::gtl::ArraySlice> arg_literals); + ArraySlice> arg_literals); -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(HloInstruction* instruction, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(HloInstruction* instruction, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( HloInstruction* instruction, - tensorflow::gtl::ArraySlice> arg_literals); + ArraySlice> arg_literals); } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index 3b2b697e492a78a06a4e5ae6bf056ff8676f2ff5..410e5ce7afacebd058c8061f8426c8e317d3d074 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -36,7 +36,10 @@ namespace xla { // This class is not thread-safe. class HloEvaluator : public DfsHloVisitorWithDefault { public: - HloEvaluator(); + // Only evaluate up to max_loop_iterations per while-loop execution if + // specified. + explicit HloEvaluator(int64 max_loop_iterations = -1); + // Evaluates an HLO module and an array of pointers to literals. // Returns the evaluated result as a literal if successful. // Precondition: The indices of arg_literals correspond to the parameter @@ -149,10 +152,18 @@ class HloEvaluator : public DfsHloVisitorWithDefault { Status HandleTuple(HloInstruction* tuple) override; + Status HandleGather(HloInstruction* gather) override; + Status HandleGetTupleElement(HloInstruction* get_tuple_element) override; Status HandleCopy(HloInstruction* copy) override; + Status HandleConditional(HloInstruction* conditional) override; + + Status HandleCall(HloInstruction* call) override; + + Status HandleWhile(HloInstruction* while_hlo) override; + private: // Returns the already-evaluated literal result for the instruction. // A Constant instruction is considered evaluated and its literal will be @@ -190,6 +201,9 @@ class HloEvaluator : public DfsHloVisitorWithDefault { // Must be cleared for each evaluation. std::vector arg_literals_; + // Max loop iterations to execute with no maximum if negative. + int64 max_loop_iterations_; + TF_DISALLOW_COPY_AND_ASSIGN(HloEvaluator); }; diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index 97765d65909cee192f65069777f8f195081603b2..685cacd7f74c00789296dee16f0a6a94c35a4393 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -1729,6 +1729,207 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutionsWithConstantOperand) { *result.ValueOrDie()); } +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV1) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,3] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 3} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV2) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV2 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[3,2] gather(operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3, 1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{1, 3}, {4, 6}, {7, 9}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherMultipleBatchDims) { + const char* hlo_text = R"( +HloModule TensorFlowGatherMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,3,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=2, + window_bounds={3, 1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 2}, {2, 1}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR3( + {{{1, 3}, {4, 6}, {7, 9}}, {{3, 2}, {6, 5}, {9, 8}}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherNd) { + const char* hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1,2} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{-1, 1}, {-4, 4}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, + EvaluateGather_TensorFlowGatherNdNonDefaultIndexVectorDim) { + const char* hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1,2} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{-2, 2}, {-1, 1}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_DynamicSlice) { + const char* hlo_text = R"( +HloModule DynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[1,1] gather(operand, indices), + output_window_dims={0,1}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{5}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_BatchDynamicSlice) { + const char* hlo_text = R"( +HloModule BatchDynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,1,1] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{2, 1}, {1, 1}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR3({{{8}}, {{5}}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_ZeroDimBounds) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,0] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,0] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 0} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = Literal::CreateR2({{}, {}, {}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{}, {}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + INSTANTIATE_TEST_CASE_P(HloEvaluatorTest_Instantiation, HloEvaluatorTest, ::testing::ValuesIn(use_bf16_params)); diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 44fcd36370dcd0cf77601aa1cd2b92810947bd5f..1dc72355cf179e996caab4d6b52068dc99d02244 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -157,52 +157,60 @@ enum ColorScheme { kDashedBorder, }; +// Graphviz attributes/colors that make up a color scheme. +struct NodeColors { + const char* style; + const char* fill_color; + const char* stroke_color; + const char* font_color; +}; + +NodeColors NodeColorsForScheme(ColorScheme color) { + switch (color) { + case kBlue: + return NodeColors{"filled", "#bbdefb", "#8aacc8", "black"}; + case kBrown: + return NodeColors{"filled", "#bcaaa4", "#8c7b75", "black"}; + case kDarkBlue: + return NodeColors{"filled", "#1565c0", "#003c8f", "white"}; + case kDarkGreen: + return NodeColors{"filled", "#2e7d32", "#005005", "white"}; + case kDarkRed: + return NodeColors{"filled", "#b71c1c", "#7f0000", "white"}; + case kGray: + return NodeColors{"filled", "#cfd8dc", "#9ea7aa", "black"}; + case kGreen: + return NodeColors{"filled", "#c8e6c9", "#97b498", "black"}; + case kOrange: + return NodeColors{"filled", "#ffe0b2", "#cbae82", "black"}; + case kPurple: + return NodeColors{"filled", "#e1bee7", "#af8eb5", "black"}; + case kRed: + return NodeColors{"filled", "#ffcdd2", "#cb9ca1", "black"}; + case kWhite: + return NodeColors{"filled", "white", "black", "black"}; + case kYellow: + return NodeColors{"filled", "#fff9c4", "#cbc693", "black"}; + case kDashedBorder: + // "filled,dashed" looks the same as "dashed", since we have a white + // background. But we use "filled,dashed" so that when you hover over + // any part of the node (not just the text inside the node), our css + // :hover rule is triggered. + return NodeColors{"filled,dashed", "white", "#757575", "#757575"}; + } +} + // Given a ColorScheme, returns an attribute string for a node of that color. // Sets the node's style and fill/stroke/text colors. // // Colors are from https://material.io/color. string NodeColorAttributes(ColorScheme color) { - using std::make_tuple; - - const char *style, *fill_color, *stroke_color, *font_color; - std::tie(style, fill_color, stroke_color, font_color) = [color] { - switch (color) { - case kBlue: - return make_tuple("filled", "#bbdefb", "#8aacc8", "black"); - case kBrown: - return make_tuple("filled", "#bcaaa4", "#8c7b75", "black"); - case kDarkBlue: - return make_tuple("filled", "#1565c0", "#003c8f", "white"); - case kDarkGreen: - return make_tuple("filled", "#2e7d32", "#005005", "white"); - case kDarkRed: - return make_tuple("filled", "#b71c1c", "#7f0000", "white"); - case kGray: - return make_tuple("filled", "#cfd8dc", "#9ea7aa", "black"); - case kGreen: - return make_tuple("filled", "#c8e6c9", "#97b498", "black"); - case kOrange: - return make_tuple("filled", "#ffe0b2", "#cbae82", "black"); - case kPurple: - return make_tuple("filled", "#e1bee7", "#af8eb5", "black"); - case kRed: - return make_tuple("filled", "#ffcdd2", "#cb9ca1", "black"); - case kWhite: - return make_tuple("filled", "white", "black", "black"); - case kYellow: - return make_tuple("filled", "#fff9c4", "#cbc693", "black"); - case kDashedBorder: - // "filled,dashed" looks the same as "dashed", since we have a white - // background. But we use "filled,dashed" so that when you hover over - // any part of the node (not just the text inside the node), our css - // :hover rule is triggered. - return make_tuple("filled,dashed", "white", "#757575", "#757575"); - } - }(); + NodeColors node_colors = NodeColorsForScheme(color); return Printf( - R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", style, - font_color, stroke_color, fill_color); + R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", + node_colors.style, node_colors.font_color, node_colors.stroke_color, + node_colors.fill_color); } // Replaces <> with <>, so that this string is safe(er) for use in a @@ -604,11 +612,21 @@ tooltip = " "; StrAppend(&subcomp_label, "
", extra_info); } - // Subcomputation's fill/stroke color is light/dark red/gray, depending on - // whether or not the subcomputation's fusion node is highlighted. bool highlight = filter_.Highlight(parent_instr); - const char* fillcolor = highlight ? "#ffcdd2" : "#f5f5f5"; - const char* strokecolor = highlight ? "#b71c1c" : "#c2c2c2"; + const char* fillcolor; + const char* strokecolor; + if (debug_options_.xla_hlo_graph_sharding_color() && !highlight) { + // Use the sharding color, if the node isn't highlighted. + NodeColors node_colors = + NodeColorsForScheme(GetInstructionColor(parent_instr)); + fillcolor = node_colors.fill_color; + strokecolor = node_colors.stroke_color; + } else { + // Subcomputation's fill/stroke color is light/dark red/gray, depending on + // whether or not the subcomputation's fusion node is highlighted. + fillcolor = highlight ? "#ffcdd2" : "#f5f5f5"; + strokecolor = highlight ? "#b71c1c" : "#c2c2c2"; + } style = Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s;")", fillcolor, strokecolor); @@ -782,6 +800,14 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( auto stringify_constant = [](const HloInstruction* constant) { const auto& shape = constant->shape(); + // If the shape has a dimension of size zero, print it as e.g. + // "{} (f32[42, 0, 10])". The alternative, calling Literal::ToString(), + // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which + // is just noise. + if (ShapeUtil::HasZeroElements(shape)) { + return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); + } + // Print the literal value of constants with <= K elements. optional elem_count; if (!ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)) { @@ -940,6 +966,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kConcatenate: case HloOpcode::kCopy: case HloOpcode::kDynamicSlice: + case HloOpcode::kGather: case HloOpcode::kPad: case HloOpcode::kReshape: case HloOpcode::kReverse: @@ -988,6 +1015,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kCustomCall: + case HloOpcode::kHostCompute: case HloOpcode::kWhile: return kDarkGreen; case HloOpcode::kConstant: diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 277648f07206ecf28479c3f63521732f8f6d8e0f..af9d772b0070be0c10e728940721d72d1ab6c04f 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -801,6 +801,22 @@ static string FusionNodeName(HloInstruction::FusionKind fusion_kind) { return instruction; } +HloInstruction* HloInstruction::AddFusionOperand(HloInstruction* new_operand) { + CHECK_EQ(opcode(), HloOpcode::kFusion); + CHECK_EQ(operand_count(), + fused_instructions_computation()->parameter_instructions().size()); + const int64 param_no = operand_count(); + // Name the parameter after the instruction it represents in the outer + // (non-fusion) computation. + string param_name = StrCat(new_operand->name(), ".param_", param_no); + HloInstruction* fused_parameter = + fused_instructions_computation()->AddParameter( + HloInstruction::CreateParameter(param_no, new_operand->shape(), + param_name)); + AppendOperand(new_operand); + return fused_parameter; +} + void HloInstruction::MergeFusionInstruction( HloInstruction* instruction_to_merge) { CHECK_EQ(opcode_, HloOpcode::kFusion); @@ -993,13 +1009,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal( // Clone's operand was not already an operand of the fusion // instruction. Add it as an operand and add a corresponding fused // parameter instruction. - int64 param_no = fused_parameters.size(); - // Name the parameter after the instruction it represents in the outer - // (non-fusion) computation. - string param_name = StrCat(operand->name(), ".param_", param_no); - fused_param = fused_instructions_computation()->AddParameter( - CreateParameter(param_no, operand->shape(), param_name)); - AppendOperand(operand); + fused_param = AddFusionOperand(operand); } TF_CHECK_OK(clone->ReplaceOperandWith(operand_num, fused_param)); } @@ -1084,6 +1094,7 @@ bool HloInstruction::HasSideEffect() const { case HloOpcode::kInfeed: case HloOpcode::kOutfeed: case HloOpcode::kTrace: + case HloOpcode::kHostCompute: return true; default: { // Check if any of the called computations has a side effect. @@ -1121,6 +1132,19 @@ bool HloInstruction::HasSideEffect() const { return instruction; } +/* static */ std::unique_ptr HloInstruction::CreateHostCompute( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) { + std::unique_ptr instruction = + WrapUnique(new HloInstruction(HloOpcode::kHostCompute, shape)); + for (auto operand : operands) { + instruction->AppendOperand(operand); + } + instruction->channel_name_ = channel_name.ToString(); + instruction->cost_estimate_ns_ = cost_estimate_ns; + return instruction; +} + /* static */ std::unique_ptr HloInstruction::CreateTuple( tensorflow::gtl::ArraySlice elements) { std::vector element_shapes; @@ -1131,6 +1155,40 @@ bool HloInstruction::HasSideEffect() const { return CreateVariadic(tuple_shape, HloOpcode::kTuple, elements); } +/* static */ std::unique_ptr HloInstruction::CreateGather( + const Shape& shape, HloInstruction* operand, HloInstruction* gather_indices, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds) { + std::unique_ptr instruction = + WrapUnique(new HloInstruction(HloOpcode::kGather, shape)); + instruction->AppendOperand(operand); + instruction->AppendOperand(gather_indices); + instruction->gather_dimension_numbers_ = + MakeUnique(gather_dim_numbers); + c_copy(window_bounds, std::back_inserter(instruction->gather_window_bounds_)); + return instruction; +} + +/* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers( + tensorflow::gtl::ArraySlice output_window_dims, + tensorflow::gtl::ArraySlice elided_window_dims, + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim) { + GatherDimensionNumbers gather_dim_numbers; + for (int64 output_window_dim : output_window_dims) { + gather_dim_numbers.add_output_window_dims(output_window_dim); + } + for (int64 elided_window_dim : elided_window_dims) { + gather_dim_numbers.add_elided_window_dims(elided_window_dim); + } + for (int64 gather_dim_to_input_dim : gather_dims_to_operand_dims) { + gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim); + } + + gather_dim_numbers.set_index_vector_dim(index_vector_dim); + return gather_dim_numbers; +} + std::unique_ptr HloInstruction::CloneWithNewOperands( const Shape& shape, tensorflow::gtl::ArraySlice new_operands, @@ -1212,6 +1270,10 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kCustomCall: clone = CreateCustomCall(shape, new_operands, custom_call_target_); break; + case HloOpcode::kHostCompute: + clone = CreateHostCompute(shape, new_operands, channel_name_, + cost_estimate_ns_); + break; case HloOpcode::kConcatenate: clone = CreateConcatenate(shape, new_operands, dimensions(0)); break; @@ -1361,12 +1423,19 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( break; case HloOpcode::kRecv: CHECK_EQ(new_operands.size(), 0); - clone = CreateRecv(shape, channel_id()); + // The shape is a tuple, but CreateRecv() wants the raw data shape. + clone = + CreateRecv(ShapeUtil::GetTupleElementShape(shape, 0), channel_id()); break; case HloOpcode::kRecvDone: CHECK_EQ(new_operands.size(), 1); clone = CreateRecvDone(new_operands[0]); break; + case HloOpcode::kGather: + CHECK_EQ(new_operands.size(), 2); + clone = CreateGather(shape, new_operands[0], new_operands[1], + *gather_dimension_numbers_, gather_window_bounds_); + break; case HloOpcode::kTrace: LOG(FATAL) << "Not yet implemented, clone: " << HloOpcodeString(opcode_); } @@ -1661,8 +1730,12 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kTuple: return true; - // These opcodes have complex or special behavior so just return false. case HloOpcode::kFusion: + return fusion_kind() == other.fusion_kind() && + eq_computations(fused_instructions_computation(), + other.fused_instructions_computation()); + + // These opcodes have complex or special behavior so just return false. case HloOpcode::kRng: case HloOpcode::kTrace: case HloOpcode::kWhile: @@ -1706,6 +1779,11 @@ bool HloInstruction::IdenticalSlowPath( return protobuf_util::ProtobufEquals(dot_dimension_numbers(), other.dot_dimension_numbers()); + case HloOpcode::kGather: + return protobuf_util::ProtobufEquals(gather_dimension_numbers(), + other.gather_dimension_numbers()) && + gather_window_bounds() == other.gather_window_bounds(); + // FFT has various types & lengths. case HloOpcode::kFft: return fft_type() == other.fft_type() && @@ -1776,6 +1854,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kRecvDone: case HloOpcode::kSend: case HloOpcode::kSendDone: + case HloOpcode::kHostCompute: return false; } } @@ -1801,7 +1880,8 @@ void HloInstruction::RemoveUser(HloInstruction* user) { Status HloInstruction::ReplaceUseWith(HloInstruction* user, HloInstruction* new_producer) { - TF_RET_CHECK(ShapeUtil::Compatible(shape(), new_producer->shape())) + TF_RET_CHECK( + ShapeUtil::CompatibleIgnoringFpPrecision(shape(), new_producer->shape())) << "this shape: " << ShapeUtil::HumanString(shape()) << ", replacement shape: " << ShapeUtil::HumanString(new_producer->shape()); @@ -1824,8 +1904,8 @@ Status HloInstruction::ReplaceOperandWith(int64 operand_num, TF_RET_CHECK(operand_num >= 0); TF_RET_CHECK(operand_num < operand_count()); HloInstruction* old_operand = mutable_operand(operand_num); - TF_RET_CHECK( - ShapeUtil::Compatible(old_operand->shape(), new_operand->shape())) + TF_RET_CHECK(ShapeUtil::CompatibleIgnoringFpPrecision(old_operand->shape(), + new_operand->shape())) << old_operand->shape().ShortDebugString() << " is not compatible with " << new_operand->shape().ShortDebugString(); operands_[operand_num] = new_operand; @@ -2135,6 +2215,11 @@ std::vector HloInstruction::ExtraAttributesToString( if (dot_dimension_numbers_ != nullptr) { extra.push_back(DotDimensionNumbersToString()); } + if (gather_dimension_numbers_ != nullptr) { + extra.push_back(GatherDimensionNumbersToString()); + extra.push_back( + StrCat("window_bounds={", Join(gather_window_bounds(), ","), "}")); + } if (opcode() == HloOpcode::kFft) { extra.push_back(StrCat("fft_type=", FftType_Name(fft_type()))); extra.push_back(StrCat("fft_length={", Join(fft_length(), ","), "}")); @@ -2266,6 +2351,14 @@ HloInstructionProto HloInstruction::ToProto() const { if (dot_dimension_numbers_ != nullptr) { *proto.mutable_dot_dimension_numbers() = *dot_dimension_numbers_; } + if (gather_dimension_numbers_ != nullptr) { + *proto.mutable_gather_dimension_numbers() = *gather_dimension_numbers_; + } + if (opcode() == HloOpcode::kGather) { + for (int64 bound : gather_window_bounds()) { + proto.add_gather_window_bounds(bound); + } + } for (int i = 0; i < slice_starts_.size(); ++i) { auto* slice_dimension = proto.add_slice_dimensions(); slice_dimension->set_start(slice_starts_[i]); @@ -2560,6 +2653,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleInfeed(this); case HloOpcode::kOutfeed: return visitor->HandleOutfeed(this); + case HloOpcode::kHostCompute: + return visitor->HandleHostCompute(this); case HloOpcode::kRng: return visitor->HandleRng(this); case HloOpcode::kWhile: @@ -2580,13 +2675,17 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleSend(this); case HloOpcode::kSendDone: return visitor->HandleSendDone(this); + case HloOpcode::kGather: + return visitor->HandleGather(this); // These opcodes are not handled here. case HloOpcode::kTrace: break; } - return Unimplemented("unhandled HloOpcode for DfsHloVisitor: %s", - HloOpcodeString(opcode_).c_str()); + return InternalError( + "Unhandled HloOpcode for DfsHloVisitor: %s. This should not happen - " + "please file a bug for XLA.", + HloOpcodeString(opcode_).c_str()); } // Explicit instantiations. @@ -3263,6 +3362,26 @@ string HloInstruction::DotDimensionNumbersToString() const { return Join(result, ", "); } +string HloInstruction::GatherDimensionNumbersToString() const { + CHECK_NE(gather_dimension_numbers_.get(), nullptr); + string output_window_dims = + StrCat("output_window_dims={", + Join(gather_dimension_numbers_->output_window_dims(), ","), "}"); + string elided_window_dims = + StrCat("elided_window_dims={", + Join(gather_dimension_numbers_->elided_window_dims(), ","), "}"); + string gather_dims_to_operand_dims = StrCat( + "gather_dims_to_operand_dims={", + Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}"); + string index_vector_dim = StrCat( + "index_vector_dim=", gather_dimension_numbers_->index_vector_dim()); + + return Join>( + {output_window_dims, elided_window_dims, gather_dims_to_operand_dims, + index_vector_dim}, + ", "); +} + bool HloInstruction::CouldBeBitcast() const { switch (opcode_) { case HloOpcode::kTranspose: diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 50931c563a5319ecdf8493ee5507eb2551b34673..e4c86214c2014095b2e171ff10691e1221574cb7 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -451,6 +451,12 @@ class HloInstruction { HloInstruction* true_computation_arg, HloComputation* true_computation, HloInstruction* false_computation_arg, HloComputation* false_computation); + static std::unique_ptr CreateGather( + const Shape& shape, HloInstruction* operand, + HloInstruction* gather_indices, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds); + // Creates a fusion instruction. A fusion instruction contains one or more // fused instructions forming an expression with a single root // "fused_root". Additional instructions can be added to the fusion @@ -475,6 +481,12 @@ class HloInstruction { const Shape& shape, tensorflow::gtl::ArraySlice operands, tensorflow::StringPiece custom_call_target); + // Creates a HostCompute instruction, which records host-side control and + // data dependencies for use in instruction scheduling. + static std::unique_ptr CreateHostCompute( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns); + // Creates a tuple instruction with the given elements. This is a convenience // wrapper around CreateVariadic. static std::unique_ptr CreateTuple( @@ -486,6 +498,13 @@ class HloInstruction { const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions); + // Creates an instance of GatherDimensionNumbers. + static GatherDimensionNumbers MakeGatherDimNumbers( + tensorflow::gtl::ArraySlice output_window_dims, + tensorflow::gtl::ArraySlice elided_window_dims, + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim); + // Returns the opcode for this instruction. HloOpcode opcode() const { return opcode_; } @@ -571,12 +590,9 @@ class HloInstruction { if (opcode() != other.opcode()) { return false; } - auto eq_shapes = layout_sensitive - ? [](const Shape& a, - const Shape& b) { return ShapeUtil::Equal(a, b); } - : [](const Shape& a, const Shape& b) { - return ShapeUtil::Compatible(a, b); - }; + using EqShapeFuncType = bool (*)(const Shape&, const Shape&); + EqShapeFuncType eq_shapes = + layout_sensitive ? ShapeUtil::Equal : ShapeUtil::Compatible; if (!eq_shapes(shape(), other.shape())) { return false; } @@ -770,6 +786,10 @@ class HloInstruction { // // (We express the default options using an overload rather than a default // param because gdb ignores default params, but does resolve overloads.) + // + // TODO(b/73348663): Make ToString() adaptive to the size of the string by + // default, backing off on providing full information for very large strings, + // or provide a different name for a ToString-like function that does that. string ToString() const { return ToString(HloPrintOptions()); } string ToString(const HloPrintOptions& options) const; @@ -805,6 +825,12 @@ class HloInstruction { // Precondition: opcode() == HloOpcode::kSend or HloOpcode::kRecv int64 channel_id() const { return channel_id_; } + // Returns the channel name associated with the instruction. The name is + // used to identify host Send/Recv operations. + // + // Precondition: opcode() == HloOpcode::kHostCompute + string channel_name() const { return channel_name_; } + // Returns feature_index field associated with the instruction. The index // represents the index of the feature dimension. // @@ -917,6 +943,9 @@ class HloInstruction { // Return true if this operator has a sharding assigned. bool has_sharding() const { return sharding_ != nullptr; } + // Adds a new operand the fusion instruction. + HloInstruction* AddFusionOperand(HloInstruction* new_operand); + // Merges the fused instructions from 'instruction_to_merge' into the // fused instruction set of 'this', updating operands as necessary. // @@ -1089,6 +1118,19 @@ class HloInstruction { // Returns the dump string of the dot dimension numbers. string DotDimensionNumbersToString() const; + const GatherDimensionNumbers& gather_dimension_numbers() const { + CHECK(gather_dimension_numbers_ != nullptr); + return *gather_dimension_numbers_; + } + + tensorflow::gtl::ArraySlice gather_window_bounds() const { + CHECK_EQ(opcode(), HloOpcode::kGather); + return gather_window_bounds_; + } + + // Returns the dump string of the gather dimension numbers. + string GatherDimensionNumbersToString() const; + // Returns the random distribution for this rng node. // // Precondition: opcode() == HloOpcode::kRng @@ -1353,6 +1395,9 @@ class HloInstruction { // Describes the dimension numbers used for a dot. std::unique_ptr dot_dimension_numbers_; + std::unique_ptr gather_dimension_numbers_; + std::vector gather_window_bounds_; + // Describes FFT type for an FFT instruction. FftType fft_type_ = FftType::FFT; @@ -1391,6 +1436,12 @@ class HloInstruction { // Name of a global symbol to call, only present for kCustomCall. string custom_call_target_; + // Name to use for host send/recv channels, only present for kHostCompute. + string channel_name_; + + // Estimate of the duration of a host computation in nanoseconds. + int64 cost_estimate_ns_; + // Computations called by this instruction. std::vector called_computations_; diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index 1038ab555567aa654342d59e02efaf844f2b95ba..f2980d309d01fdf3b3e601bc260a0ad0895b3064 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -825,17 +825,42 @@ TEST_F(HloInstructionTest, ComplexFusionOp) { EXPECT_THAT(c1->users(), ElementsAre(fusion)); } -// Convenience function for comparing two HloInstructions inside of -// std::unique_ptrs. -static bool Identical(std::unique_ptr instruction1, - std::unique_ptr instruction2) { +// Convenience function for comparing two HloInstructions. +static bool Identical(const HloInstruction& instruction1, + const HloInstruction& instruction2) { // Verify Identical is reflexive for both instructions. - EXPECT_TRUE(instruction1->Identical(*instruction1)); - EXPECT_TRUE(instruction2->Identical(*instruction2)); + EXPECT_TRUE(instruction1.Identical(instruction1)); + EXPECT_TRUE(instruction2.Identical(instruction2)); - bool is_equal = instruction1->Identical(*instruction2); + bool is_equal = instruction1.Identical(instruction2); // Verify Identical is symmetric. - EXPECT_EQ(is_equal, instruction2->Identical(*instruction1)); + EXPECT_EQ(is_equal, instruction2.Identical(instruction1)); + return is_equal; +} + +// Convenience function for comparing two HloInstructions for structural +// equality. +static bool StructuralEqual(const HloInstruction& instruction1, + const HloInstruction& instruction2) { + auto eq_operand_shapes = [](const HloInstruction* a, + const HloInstruction* b) { + return ShapeUtil::Equal(a->shape(), b->shape()); + }; + auto eq_computations = [](const HloComputation* a, const HloComputation* b) { + return *a == *b; + }; + + // Verify Identical is reflexive for both instructions. + EXPECT_TRUE( + instruction1.Identical(instruction1, eq_operand_shapes, eq_computations)); + EXPECT_TRUE( + instruction2.Identical(instruction2, eq_operand_shapes, eq_computations)); + + bool is_equal = + instruction1.Identical(instruction2, eq_operand_shapes, eq_computations); + // Verify Identical is symmetric. + EXPECT_EQ(is_equal, instruction2.Identical(instruction1, eq_operand_shapes, + eq_computations)); return is_equal; } @@ -858,42 +883,42 @@ TEST_F(HloInstructionTest, IdenticalInstructions) { // Operations which only depend on their operands and opcode. EXPECT_TRUE( - Identical(HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), - HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1))); + Identical(*HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), + *HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1))); EXPECT_FALSE( - Identical(HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), - HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op2))); + Identical(*HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), + *HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op2))); EXPECT_FALSE( - Identical(HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), - HloInstruction::CreateUnary(shape, HloOpcode::kNegate, op1))); + Identical(*HloInstruction::CreateUnary(shape, HloOpcode::kCopy, op1), + *HloInstruction::CreateUnary(shape, HloOpcode::kNegate, op1))); // Tuples. - EXPECT_TRUE(Identical(HloInstruction::CreateTuple({op1, op2}), - HloInstruction::CreateTuple({op1, op2}))); - EXPECT_FALSE(Identical(HloInstruction::CreateTuple({op1, op2}), - HloInstruction::CreateTuple({op2, op1}))); + EXPECT_TRUE(Identical(*HloInstruction::CreateTuple({op1, op2}), + *HloInstruction::CreateTuple({op1, op2}))); + EXPECT_FALSE(Identical(*HloInstruction::CreateTuple({op1, op2}), + *HloInstruction::CreateTuple({op2, op1}))); // Broadcasts. - EXPECT_TRUE(Identical(HloInstruction::CreateBroadcast(shape, op1, {0, 1}), - HloInstruction::CreateBroadcast(shape, op1, {0, 1}))); - EXPECT_FALSE(Identical(HloInstruction::CreateBroadcast(shape, op1, {0, 1}), - HloInstruction::CreateBroadcast(shape, op1, {1, 0}))); + EXPECT_TRUE(Identical(*HloInstruction::CreateBroadcast(shape, op1, {0, 1}), + *HloInstruction::CreateBroadcast(shape, op1, {0, 1}))); + EXPECT_FALSE(Identical(*HloInstruction::CreateBroadcast(shape, op1, {0, 1}), + *HloInstruction::CreateBroadcast(shape, op1, {1, 0}))); Shape bcast_shape1 = ShapeUtil::MakeShape(F32, {2, 2, 42}); Shape bcast_shape2 = ShapeUtil::MakeShape(F32, {2, 2, 123}); EXPECT_FALSE( - Identical(HloInstruction::CreateBroadcast(bcast_shape1, op1, {0, 1}), - HloInstruction::CreateBroadcast(bcast_shape2, op1, {0, 1}))); + Identical(*HloInstruction::CreateBroadcast(bcast_shape1, op1, {0, 1}), + *HloInstruction::CreateBroadcast(bcast_shape2, op1, {0, 1}))); // Binary operands. EXPECT_TRUE(Identical( - HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), - HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2))); + *HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), + *HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2))); EXPECT_FALSE(Identical( - HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), - HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op2, op1))); + *HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), + *HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op2, op1))); EXPECT_FALSE(Identical( - HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), - HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op1, op2))); + *HloInstruction::CreateBinary(shape, HloOpcode::kAdd, op1, op2), + *HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op1, op2))); } TEST_F(HloInstructionTest, FunctionVisitor) { @@ -1089,6 +1114,70 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) { ShapeUtil::Equal(root->operand(1)->shape(), root2->operand(1)->shape())); EXPECT_TRUE(ShapeUtil::Equal(root->operand(1)->operand(0)->shape(), root2->operand(1)->operand(0)->shape())); + EXPECT_TRUE(StructuralEqual(*fusion, *fusion2)); +} + +TEST_F(HloInstructionTest, FusionEquality) { + HloModule module(TestName()); + HloComputation::Builder builder(TestName()); + + // Create two fusion instructions containing a single unary operation. + auto parameter = + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "x")); + auto exp = builder.AddInstruction( + HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, parameter)); + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, parameter)); + auto* computation = module.AddEntryComputation(builder.Build()); + auto* fusion = computation->CreateFusionInstruction( + {exp}, HloInstruction::FusionKind::kLoop); + auto* fusion2 = computation->CreateFusionInstruction( + {neg}, HloInstruction::FusionKind::kLoop); + EXPECT_FALSE(StructuralEqual(*fusion, *fusion2)); + + auto clone = fusion->Clone(); + EXPECT_TRUE(StructuralEqual(*fusion, *clone)); +} + +TEST_F(HloInstructionTest, NestedFusionEquality) { + HloModule module(TestName()); + HloComputation::Builder builder(TestName()); + + // Build a nested fusion computation. + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + auto a = builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); + auto b = builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + auto b_t = builder.AddInstruction( + HloInstruction::CreateTranspose(data_shape, b, {1, 0})); + DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(1); + dot_dnums.add_rhs_contracting_dimensions(0); + auto dot = builder.AddInstruction( + HloInstruction::CreateDot(data_shape, a, b_t, dot_dnums)); + auto one = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + auto add_operand = builder.AddInstruction( + HloInstruction::CreateBroadcast(data_shape, one, {1})); + auto add = builder.AddInstruction(HloInstruction::CreateBinary( + data_shape, HloOpcode::kAdd, dot, add_operand)); + auto sub = builder.AddInstruction(HloInstruction::CreateBinary( + data_shape, HloOpcode::kSubtract, dot, add_operand)); + builder.AddInstruction( + HloInstruction::CreateBinary(data_shape, HloOpcode::kMultiply, add, sub)); + auto computation = module.AddEntryComputation(builder.Build()); + + auto nested_fusion = computation->CreateFusionInstruction( + {dot, b_t}, HloInstruction::FusionKind::kTransposeDot); + + auto fusion = computation->CreateFusionInstruction( + {add, nested_fusion}, HloInstruction::FusionKind::kOutput); + auto fusion2 = computation->CreateFusionInstruction( + {sub, nested_fusion}, HloInstruction::FusionKind::kOutput); + auto clone = fusion->Clone(); + EXPECT_TRUE(StructuralEqual(*fusion, *clone)); + EXPECT_FALSE(StructuralEqual(*fusion, *fusion2)); } TEST_F(HloInstructionTest, CloneSuffixNames) { @@ -1182,5 +1271,77 @@ TEST_F(HloInstructionTest, Stringification) { "true_computation=%TransposeDot, false_computation=%TransposeDot"); } +TEST_F(HloInstructionTest, StringifyGather_0) { + Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); + Shape gather_indices_tensor_shape = + ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5}); + Shape gather_result_shape = + ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 30, 29, 28, 27, 26}); + + HloComputation::Builder builder("Gather"); + HloInstruction* input = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor")); + HloInstruction* gather_indices = + builder.AddInstruction(HloInstruction::CreateParameter( + 1, gather_indices_tensor_shape, "gather_indices")); + + HloInstruction* gather_instruction = + builder.AddInstruction(HloInstruction::CreateGather( + gather_result_shape, input, gather_indices, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + HloModule module(TestName()); + module.AddEntryComputation(builder.Build()); + + EXPECT_EQ(gather_instruction->ToString(), + "%gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} " + "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, " + "s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), " + "output_window_dims={4,5,6,7,8}, elided_window_dims={}, " + "gather_dims_to_operand_dims={0,1,2,3,4}, " + "index_vector_dim=4, window_bounds={30,29,28,27,26}"); +} + +TEST_F(HloInstructionTest, StringifyGather_1) { + Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); + Shape gather_indices_tensor_shape = + ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6}); + Shape gather_result_shape = + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}); + + HloComputation::Builder builder("Gather"); + HloInstruction* input = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor")); + HloInstruction* gather_indices = + builder.AddInstruction(HloInstruction::CreateParameter( + 1, gather_indices_tensor_shape, "gather_indices")); + + HloInstruction* gather_instruction = + builder.AddInstruction(HloInstruction::CreateGather( + gather_result_shape, input, gather_indices, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/2), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + HloModule module(TestName()); + module.AddEntryComputation(builder.Build()); + + EXPECT_EQ(gather_instruction->ToString(), + "%gather = f32[10,9,7,6,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} " + "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, " + "s64[10,9,5,7,6]{4,3,2,1,0} %gather_indices), " + "output_window_dims={4,5,6,7,8}, elided_window_dims={}, " + "gather_dims_to_operand_dims={0,1,2,3,4}, " + "index_vector_dim=2, window_bounds={30,29,28,27,26}"); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 60270b0595dcfca8f1fcea5ab0914428880f35b5..cdea3d597824d155241a544d226aa18d3b0b0274 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -145,6 +145,21 @@ void HloModule::ReplaceComputations( } break; } + case HloOpcode::kConditional: { + HloComputation* new_true_computation = + tensorflow::gtl::FindWithDefault( + replacements, instruction->true_computation(), nullptr); + if (new_true_computation != nullptr) { + instruction->set_true_computation(new_true_computation); + } + HloComputation* new_false_computation = + tensorflow::gtl::FindWithDefault( + replacements, instruction->false_computation(), nullptr); + if (new_false_computation != nullptr) { + instruction->set_false_computation(new_false_computation); + } + break; + } case HloOpcode::kSelectAndScatter: { HloComputation* new_select = tensorflow::gtl::FindWithDefault( replacements, instruction->select(), nullptr); @@ -198,74 +213,23 @@ HloModuleProto HloModule::ToProto() const { continue; } HloComputationProto computation_proto = computation->ToProto(); + if (computation->name() == entry_computation_->name()) { + *proto.mutable_program_shape() = computation_proto.program_shape(); + } proto.add_computations()->Swap(&computation_proto); } return proto; } -namespace { - -// Construct a ProgramShape matching the shape of the parameters and root of the -// given module's entry computation. -StatusOr ProgramShapeFromProto(const HloModuleProto& module) { - const HloComputationProto* entry_computation = nullptr; - for (const HloComputationProto& computation : module.computations()) { - if (computation.name() == module.entry_computation_name()) { - entry_computation = &computation; - break; - } - } - TF_RET_CHECK(entry_computation != nullptr) - << "No computation with entry computation name" - << module.entry_computation_name(); - - tensorflow::gtl::FlatMap> parameters; - const HloInstructionProto* root = nullptr; - for (const HloInstructionProto& instruction : - entry_computation->instructions()) { - if (instruction.name() == entry_computation->root_name()) { - TF_RET_CHECK(root == nullptr) << "Entry computation has more than " - "one instruction with (root) name " - << instruction.name(); - root = &instruction; - } - if (instruction.opcode() == HloOpcodeString(HloOpcode::kParameter)) { - TF_RET_CHECK(!ContainsKey(parameters, instruction.parameter_number())) - << "Entry computation has more than one parameter instruction " - "with parameter number " - << instruction.parameter_number(); - parameters[instruction.parameter_number()] = {instruction.name(), - &instruction.shape()}; - } - } - TF_RET_CHECK(root != nullptr) - << "Entry computation is missing root instruction named " - << entry_computation->root_name(); - - ProgramShape program_shape; - *program_shape.mutable_result() = root->shape(); - for (int64 i = 0; i < parameters.size(); ++i) { - TF_RET_CHECK(ContainsKey(parameters, i)) - << "Entry computation missing parameter number " << i; - const string& name = parameters.at(i).first; - const Shape& shape = *parameters.at(i).second; - *program_shape.add_parameters() = shape; - program_shape.add_parameter_names(name); - } - - return std::move(program_shape); -} - -} // namespace - /* static */ StatusOr> HloModule::CreateFromProto( const HloModuleProto& proto, const HloModuleConfig& module_config, const VersionedComputationHandle& entry_computation_handle) { // The ProgramShape in the passed in module config must match the shapes of // the entry parameters and root. - TF_ASSIGN_OR_RETURN(ProgramShape expected_program_shape, - ProgramShapeFromProto(proto)); + TF_RET_CHECK(proto.has_program_shape()) + << "No program shape found in the proto"; + const auto& expected_program_shape = proto.program_shape(); TF_RET_CHECK(expected_program_shape.parameters_size() == module_config.entry_computation_layout().parameter_count()); for (int i = 0; i < expected_program_shape.parameters_size(); ++i) { @@ -339,8 +303,9 @@ StatusOr> HloModule::CreateFromProto( /* static */ StatusOr HloModule::CreateModuleConfigFromProto( const HloModuleProto& module) { - TF_ASSIGN_OR_RETURN(ProgramShape program_shape, - ProgramShapeFromProto(module)); + TF_RET_CHECK(module.has_program_shape()) + << "No program shape found in the proto"; + const auto& program_shape = module.program_shape(); HloModuleConfig module_config(program_shape); @@ -563,6 +528,18 @@ std::unique_ptr HloModule::Clone(const string& suffix) const { return module; } +HloComputation* HloModule::DeepCloneComputation(HloComputation* computation) { + HloComputation* clone = AddEmbeddedComputation(computation->Clone("", this)); + TF_CHECK_OK( + clone->root_instruction()->Accept([this](HloInstruction* instruction) { + instruction->ReplaceCalledComputations([this](HloComputation* callee) { + return DeepCloneComputation(callee); + }); + return Status::OK(); + })); + return clone; +} + uint64 HloModule::RandomNew64() const { tensorflow::mutex_lock l(rng_mutex_); return rng_(); diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index 4bfe8d89ce0a285de6d05d4867aaa6b266d78d12..755bbd359f7b95e7f3f3cbee1b46df85908202c6 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -85,6 +85,10 @@ class HloModule { // Returns a deep copy of this module including all computations. std::unique_ptr Clone(const string& suffix = "clone") const; + // Performs a deep clone of the computation, by recursively cloning all + // the called computations as well. + HloComputation* DeepCloneComputation(HloComputation* computation); + // Return a pointer to the entry computation of the module.. const HloComputation* entry_computation() const { CHECK_NE(nullptr, entry_computation_); @@ -99,7 +103,7 @@ class HloModule { return config_.mutable_entry_computation_layout(); } - ComputationLayout entry_computation_layout() const { + const ComputationLayout& entry_computation_layout() const { return config_.entry_computation_layout(); } @@ -183,11 +187,6 @@ class HloModule { // Returns a randomly generated uint64. uint64 RandomNew64() const; - // Returns the unique name for a computation in this module. - string GetUniqueCompuationName(const string& prefix) { - return computation_name_uniquer_.GetUniqueName(prefix); - } - // Returns the NameUniquer for uniquing instruction names in this module. NameUniquer& instruction_name_uniquer() { return instruction_name_uniquer_; } diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h index a5ee895e48448fbb8fa3879dc1b6764c1f9f6966..d3c1fae592bb465609ffbde2d0262e2600912e63 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.h +++ b/tensorflow/compiler/xla/service/hlo_module_config.h @@ -67,6 +67,15 @@ class HloModuleConfig { bool hlo_profiling_enabled() const { return hlo_profiling_enabled_; } void enable_hlo_profiling(bool enabled) { hlo_profiling_enabled_ = enabled; } + // Sets/returns whether this is a "host module". Host modules are used to + // record the data- and control-flow dependencies of host side computation + // that communicates with compiled code. They are used for analysis and + // scheduling purposes, but no code is generated. + bool is_host_module() const { return is_host_module_; } + void set_is_host_module(bool is_host_module) { + is_host_module_ = is_host_module; + } + // Sets/returns the module seed set during execution. void set_seed(uint64 seed) { seed_ = seed; } uint64 seed() const { return seed_; } @@ -104,6 +113,9 @@ class HloModuleConfig { // Whether to enable HLO-level profiling. bool hlo_profiling_enabled_ = false; + // Whether this is a 'host module'. + bool is_host_module_ = false; + // Module/graph-level seed handle. uint64 seed_ = 0; diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc new file mode 100644 index 0000000000000000000000000000000000000000..eed0112f625d73f331edd36d6fdd6be068c9804a --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -0,0 +1,349 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_module_group_metadata.h" + +#include +#include + +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +string HloModuleGroupMetadata::TrackedInstruction::ToString() const { + string repr = + (instruction_ != nullptr) ? instruction_->ToShortString() : "NULL"; + switch (kind_) { + case ComputationKind::kInvalid: + repr += ":INVALID"; + break; + case ComputationKind::kWhileCondition: + repr += ":WHILE_CONDITION"; + break; + case ComputationKind::kWhileBody: + repr += ":WHILE_BODY"; + break; + case ComputationKind::kConditionalTrue: + repr += ":CONDITIONAL_TRUE"; + break; + case ComputationKind::kConditionalFalse: + repr += ":CONDITIONAL_FALSE"; + break; + } + return repr; +} + +/* static */ StatusOr> +HloModuleGroupMetadata::Build(const std::vector& modules) { + auto metadata = absl::make_unique(modules); + TF_RETURN_IF_ERROR(metadata->Build()); + return std::move(metadata); +} + +Status HloModuleGroupMetadata::Build() { + TF_RETURN_IF_ERROR(RecordInstructions()); + TF_RETURN_IF_ERROR(VerifyChannelInstructions()); + + // Record all companion while instructions. + const auto visitor = [this](HloInstruction* hlo) -> Status { + // We only need to process if the instruction is within the computation + // of a companion instruction, like in the condition or body computation + // of a While. + const TrackedInstruction* tracked = GetTrackedInstruction(hlo->parent()); + if (tracked == nullptr) { + return Status::OK(); + } + // Add the parent computation of this channel instruction and its peer + // computation (both must be while computations) as companions. + if (IsChannelInstruction(hlo)) { + HloComputation* peer_computation = PeerComputation(hlo); + const TrackedInstruction* peer_tracked = + GetTrackedInstruction(peer_computation); + TF_RET_CHECK(peer_tracked != nullptr) + << "Peer instruction is not a possible companion"; + TF_RET_CHECK(*tracked == *peer_tracked) + << "Peer instruction does not match the computation kind"; + TF_RETURN_IF_ERROR( + AddCompanion(tracked->instruction(), peer_tracked->instruction())); + } + + // Add the parents of companion instructions (they must be all of the same + // kind of instructions, opcode wise) as companions. + if (IsCompanionInstruction(hlo)) { + for (HloInstruction* companion : Companions(hlo)) { + const TrackedInstruction* companion_tracked = + GetTrackedInstruction(companion->parent()); + TF_RET_CHECK(companion_tracked != nullptr); + TF_RET_CHECK(*tracked == *companion_tracked); + TF_RETURN_IF_ERROR(AddCompanion(tracked->instruction(), + companion_tracked->instruction())); + } + } + return Status::OK(); + }; + + // Visit the computations in postorder so that the companion information grows + // from inner computations to outer ones. + for (HloModule* module : modules_) { + for (HloComputation* computation : module->MakeComputationPostOrder()) { + TF_RETURN_IF_ERROR(computation->Accept(visitor)); + } + } + return Status::OK(); +} + +bool HloModuleGroupMetadata::IsChannelInstruction( + const HloInstruction* instruction) const { + switch (instruction->opcode()) { + case HloOpcode::kSend: + case HloOpcode::kRecv: + case HloOpcode::kSendDone: + case HloOpcode::kRecvDone: + return true; + default: + return false; + } +} + +bool HloModuleGroupMetadata::IsCompanionInstruction(HloInstruction* hlo) const { + return companion_set_index_.count(hlo) > 0; +} + +bool HloModuleGroupMetadata::InstructionCommunicates( + HloInstruction* hlo) const { + return IsChannelInstruction(hlo) || IsCompanionInstruction(hlo); +} + +const HloModuleGroupMetadata::Channel& HloModuleGroupMetadata::GetChannel( + int64 channel_id) const { + CHECK(channel_id_map_.find(channel_id) != channel_id_map_.end()); + return channels_[channel_id_map_.at(channel_id)]; +} + +HloComputation* HloModuleGroupMetadata::PeerComputation( + const HloInstruction* instruction) const { + CHECK(IsChannelInstruction(instruction)); + const Channel& channel = GetChannel(instruction->channel_id()); + switch (instruction->opcode()) { + case HloOpcode::kSend: + case HloOpcode::kSendDone: + return channel.recv->parent(); + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + return channel.send->parent(); + default: + LOG(FATAL) << "opcode not supported"; + } +} + +std::vector +HloModuleGroupMetadata::GetCompanionsPath(const HloInstruction* hlo) const { + std::vector path; + const HloComputation* parent = hlo->parent(); + const TrackedInstruction* companion; + while ((companion = GetTrackedInstruction(parent)) != nullptr) { + parent = companion->instruction()->parent(); + path.push_back(*companion); + } + return path; +} + +bool HloModuleGroupMetadata::CheckCompanionPathsCompatibility( + const std::vector& path0, + const std::vector& path1) const { + if (path0.size() != path1.size()) { + VLOG(5) << "Companion path size do not match: " << path0.size() + << " != " << path1.size(); + return false; + } + for (int64 i = 0; i < path0.size(); ++i) { + if (path0[i] != path1[i]) { + VLOG(5) << "Companion instructions at path index " << i + << " do not have the same opcode: " << path0[i].ToString() + << " vs " << path1[i].ToString(); + return false; + } + } + return true; +} + +int64 HloModuleGroupMetadata::GetModuleId(const HloModule* module) const { + for (int64 i = 0; i < modules_.size(); ++i) { + if (modules_[i] == module) { + return i; + } + } + LOG(FATAL) << "unknown module"; +} + +Status HloModuleGroupMetadata::RecordInstructions() { + const auto visitor = [this](HloInstruction* hlo) -> Status { + if (hlo->opcode() == HloOpcode::kWhile) { + tracked_instructions_[hlo->while_condition()] = + TrackedInstruction(hlo, ComputationKind::kWhileCondition); + tracked_instructions_[hlo->while_body()] = + TrackedInstruction(hlo, ComputationKind::kWhileBody); + } else if (hlo->opcode() == HloOpcode::kConditional) { + tracked_instructions_[hlo->true_computation()] = + TrackedInstruction(hlo, ComputationKind::kConditionalTrue); + tracked_instructions_[hlo->false_computation()] = + TrackedInstruction(hlo, ComputationKind::kConditionalFalse); + } + if (!IsChannelInstruction(hlo)) { + return Status::OK(); + } + + // Add a new channel if needed. + if (channel_id_map_.find(hlo->channel_id()) == channel_id_map_.end()) { + channels_.emplace_back(); + channels_.back().id = hlo->channel_id(); + channel_id_map_[hlo->channel_id()] = channels_.size() - 1; + } + Channel& channel = channels_[channel_id_map_[hlo->channel_id()]]; + + if (hlo->opcode() == HloOpcode::kSend) { + TF_RET_CHECK(channel.send == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple send instructions"; + channel.send = hlo; + } + if (hlo->opcode() == HloOpcode::kRecv) { + TF_RET_CHECK(channel.recv == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple recv instructions"; + channel.recv = hlo; + } + if (hlo->opcode() == HloOpcode::kSendDone) { + TF_RET_CHECK(channel.send_done == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple send-done instructions"; + channel.send_done = hlo; + } + if (hlo->opcode() == HloOpcode::kRecvDone) { + TF_RET_CHECK(channel.recv_done == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple recv-done instructions"; + channel.recv_done = hlo; + } + return Status::OK(); + }; + + for (HloModule* module : modules_) { + for (auto* computation : module->computations()) { + TF_RETURN_IF_ERROR(computation->Accept(visitor)); + } + } + return Status::OK(); +} + +Status HloModuleGroupMetadata::AddCompanion(HloInstruction* instruction1, + HloInstruction* instruction2) { + TF_RET_CHECK(instruction1->opcode() == HloOpcode::kWhile || + instruction1->opcode() == HloOpcode::kConditional); + VLOG(2) << "adding as companions:" << instruction1->ToString() << " and " + << instruction2->ToString(); + + if (!ContainsKey(companion_set_index_, instruction1) && + !ContainsKey(companion_set_index_, instruction2)) { + companion_sets_.push_back( + absl::make_unique>()); + auto companion_set = companion_sets_.back().get(); + companion_set->insert(instruction1); + companion_set->insert(instruction2); + companion_set_index_[instruction1] = companion_sets_.size() - 1; + companion_set_index_[instruction2] = companion_sets_.size() - 1; + } else if (!ContainsKey(companion_set_index_, instruction1)) { + companion_sets_[companion_set_index_[instruction2]]->insert(instruction1); + companion_set_index_[instruction1] = companion_set_index_[instruction2]; + } else if (!ContainsKey(companion_set_index_, instruction2)) { + companion_sets_[companion_set_index_[instruction1]]->insert(instruction2); + companion_set_index_[instruction2] = companion_set_index_[instruction1]; + } else if (companion_set_index_[instruction1] != + companion_set_index_[instruction2]) { + companion_sets_[companion_set_index_[instruction1]]->insert( + Companions(instruction2).begin(), Companions(instruction2).end()); + int64 index_to_remove = companion_set_index_[instruction2]; + for (HloInstruction* hlo : Companions(instruction2)) { + companion_set_index_[hlo] = companion_set_index_[instruction1]; + } + companion_sets_.erase(companion_sets_.begin() + index_to_remove); + } + return Status::OK(); +} + +Status HloModuleGroupMetadata::VerifyChannelInstructions() { + for (const Channel& channel : channels_) { + if (channel.send == nullptr) { + return FailedPrecondition("missing send for id : %lld", channel.id); + } + if (channel.recv == nullptr) { + return FailedPrecondition("missing recv for id : %lld", channel.id); + } + if (channel.send_done == nullptr) { + return FailedPrecondition("missing send-done for id : %lld", channel.id); + } + if (channel.recv_done == nullptr) { + return FailedPrecondition("missing recv-done for id : %lld", channel.id); + } + } + + // Check if the shapes match for each channel. + for (const Channel& channel : channels_) { + const Shape& send_shape = channel.send->operand(0)->shape(); + const Shape& recv_shape = channel.recv_done->shape(); + if (!ShapeUtil::Compatible(send_shape, recv_shape)) { + return FailedPrecondition("send/recv shapes do not match"); + } + } + + // Check if channel instructions are used only in allowed computations. + const auto allowed = [this](HloInstruction* hlo) { + HloComputation* computation = hlo->parent(); + const HloModule* module = computation->parent(); + if (module->entry_computation() == computation || + tracked_instructions_.count(computation) > 0) { + return true; + } + return false; + }; + for (const Channel& channel : channels_) { + if (!allowed(channel.send) || !allowed(channel.send_done) || + !allowed(channel.recv) || !allowed(channel.recv_done)) { + return FailedPrecondition("channel is used in disallowed computation"); + } + } + // Check if the nest levels match for each channel. + for (const Channel& channel : channels_) { + std::vector path = GetCompanionsPath(channel.send); + if (!CheckCompanionPathsCompatibility( + path, GetCompanionsPath(channel.send_done)) || + !CheckCompanionPathsCompatibility(path, + GetCompanionsPath(channel.recv)) || + !CheckCompanionPathsCompatibility( + path, GetCompanionsPath(channel.recv_done))) { + return FailedPrecondition( + "Nest companion paths do not match for channel %lld", channel.id); + } + } + return Status::OK(); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h new file mode 100644 index 0000000000000000000000000000000000000000..15cdbdaade57d4da69d7daab59693273bdfde8de --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h @@ -0,0 +1,230 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ + +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/status.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +// Class for bookkeeping the information on the given modules, in particular on +// the interaction between computations. +// +// Companion instructions are one of the information collected as we build the +// metadata. For example, for each While instruction, companion instructions +// refer to a set of While instructions in other computations that communicate +// with each other. +// In the example below with 3 modules, {While_0, While_2, While_5}, {While_1, +// While_4}, {While_3, While_6} are companion sets. +// +// +// While_0() { While_2() { While_5() { +// While_1() { Send(0) } While_3() { Send(1) } While_6() { Recv(1) } +// } While_4() { Recv(0) } +// } +// +// Companion instructions are used to detect cycles in the graph and also for +// global scheduling. +class HloModuleGroupMetadata { + public: + // The kind of companion computation a given instruction can be within. + enum class ComputationKind { + kInvalid, + kWhileCondition, + kWhileBody, + kConditionalTrue, + kConditionalFalse, + }; + + // Tracks the instruction mapped to a given computation, and the computation + // kind. + // For example, a body computation of a while instruction, will generate a + // TrackedInstruction with instruction being the while instruction, and + // kind being ComputationKind::kWhileBody. + class TrackedInstruction { + public: + TrackedInstruction() = default; + TrackedInstruction(HloInstruction* instruction, ComputationKind kind) + : instruction_(instruction), kind_(kind) {} + + bool operator==(const TrackedInstruction& rhs) const { + return instruction_->opcode() == rhs.instruction_->opcode() && + kind_ == rhs.kind_; + } + bool operator!=(const TrackedInstruction& rhs) const { + return !operator==(rhs); + } + + HloInstruction* instruction() const { return instruction_; } + + string ToString() const; + + private: + HloInstruction* instruction_ = nullptr; + ComputationKind kind_ = ComputationKind::kInvalid; + }; + + // Represents a channel and the 4 instructions that form the channel. + struct Channel { + int64 id = -1; + HloInstruction* send = nullptr; + HloInstruction* recv = nullptr; + HloInstruction* send_done = nullptr; + HloInstruction* recv_done = nullptr; + }; + + explicit HloModuleGroupMetadata(const std::vector& modules) + : modules_(modules) {} + + ~HloModuleGroupMetadata() = default; + + // Build and return the metadata for the given modules. + static StatusOr> Build( + const std::vector& modules); + + // Returns true if the instruction is one of the 4 channel instructions (Send, + // Recv, SendDone, RecvDone). + bool IsChannelInstruction(const HloInstruction* instruction) const; + + // Returns true if the instruction is a companion instruction. See the class + // comment above on companion instructions. + bool IsCompanionInstruction(HloInstruction* hlo) const; + + // Returns true if the instruction is either a channel instruction or a + // companion instruction. + bool InstructionCommunicates(HloInstruction* hlo) const; + + // Returns the Channel instance for the given channel id. + const Channel& GetChannel(int64 channel_id) const; + + // Returns the computation that contains the peer channel instructions for + // the given instruction. + // + // Precondition: IsChannelInstruction(instruction) is true. + HloComputation* PeerComputation(const HloInstruction* instruction) const; + + // Returns the path of the nested companion instructions, in terms of HLO + // instructions. The path goes from inner to outer companions. + // The returned path does not include the input hlo instruction, in case it + // is a companion instruction. + std::vector GetCompanionsPath( + const HloInstruction* hlo) const; + + // Checks whether two companion paths (as returned by the GetCompanionsPath() + // API) are compatible. The two paths are compatible if the sequence of + // opcodes, and the companion kinds, of the two paths matches. + bool CheckCompanionPathsCompatibility( + const std::vector& path0, + const std::vector& path1) const; + + // Returns the unique integer for each module. The returned id is the index of + // the module in the module vector. + int64 GetModuleId(const HloModule* module) const; + + // Returns the companion instructions for the given instruction. + // + // Precondition: IsCompanionWhile(instruction) is true. + const std::unordered_set& Companions( + HloInstruction* instruction) const { + CHECK_EQ(companion_set_index_.count(instruction), 1); + return companion_set(companion_set_index_.at(instruction)); + } + + // Returns the companion set at the given index. + const std::unordered_set& companion_set(int64 index) const { + CHECK_LT(index, companion_sets_.size()); + return *companion_sets_[index]; + } + + // Returns the companion set index of the given instruction. + int64 companion_set_index(HloInstruction* instruction) const { + return companion_set_index_.at(instruction); + } + + // Returns the list of all companion sets in the HLO module group. + const std::vector>>& + companion_sets() const { + return companion_sets_; + } + + private: + Status Build(); + + // Record all channel instructions and While instructions. + Status RecordInstructions(); + + // Verifies the given HloModules are well-formed and follow the specification, + // in particular with respect to using channel instructions. + // + // * Each channel has all 4 instructions (Send, Recv, SendDone, RecvDone). + // * The shape of channel instructions match. + // * The nest level of channel instructions match. + // * Channel instructions are used in allowed computations; i.e., in the + // entry computation of the module or condition/body of While computations. + // + // TODO(b/62064342): Currently, HloModuleGroupScheduler checks if there is a + // cycle in the graph, but it would be good to verify here. + Status VerifyChannelInstructions(); + + // Adds metadata that the given two instructions are companions. + Status AddCompanion(HloInstruction* instruction1, + HloInstruction* instruction2); + + // Retrieves a pointer to the stored TrackedInstruction associated with a + // tracked computation, or nullptr in case such computation is not tracked. + const TrackedInstruction* GetTrackedInstruction( + const HloComputation* computation) const { + auto it = tracked_instructions_.find(computation); + return it != tracked_instructions_.end() ? &it->second : nullptr; + } + + // List of all companion instructions sets in the module. + std::vector>> + companion_sets_; + + // Map from each companion while instruction to the index into companion_set_. + tensorflow::gtl::FlatMap companion_set_index_; + + // Map from computation to the instruction using it (a kWhile, kConditional). + tensorflow::gtl::FlatMap + tracked_instructions_; + + // All channels in the module. + std::vector channels_; + + // Map from channel ids to the index in channels_. + tensorflow::gtl::FlatMap channel_id_map_; + + // The modules that this metadata was built from. + const std::vector& modules_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..289c96b0a7b90c5f8a122cd3fc327a5762099106 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -0,0 +1,316 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_module_group_util.h" + +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_reachability.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +std::vector HloModuleGroupUtil::GlobalPredecessors( + HloInstruction* instruction) { + std::vector predecessors; + + // Adds to the unique predecessors list and also add companion instructions + // if the given predecessor has those. + auto add_unique_predecessor = [&](HloInstruction* predecessor) { + if (std::find(predecessors.begin(), predecessors.end(), predecessor) != + predecessors.end()) { + return; + } + if (!metadata_.IsCompanionInstruction(predecessor)) { + predecessors.push_back(predecessor); + return; + } + for (HloInstruction* companion : metadata_.Companions(predecessor)) { + predecessors.push_back(companion); + } + }; + + // If the given instruction is a companion instruction, we need to find the + // predecessors of all of its companion instructions. + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(instruction)) { + for (HloInstruction* companion : metadata_.Companions(instruction)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(instruction); + } + + for (HloInstruction* hlo : instruction_group) { + for (HloInstruction* operand : hlo->operands()) { + add_unique_predecessor(operand); + } + for (HloInstruction* control_predecessor : hlo->control_predecessors()) { + add_unique_predecessor(control_predecessor); + } + } + if (instruction->opcode() == HloOpcode::kRecvDone) { + // Send is a remote predecessor of RecvDone. + HloInstruction* send = metadata_.GetChannel(instruction->channel_id()).send; + add_unique_predecessor(send); + } + if (instruction->opcode() == HloOpcode::kSend) { + // Recv is a remote predecessor of Send. + HloInstruction* recv_done = + metadata_.GetChannel(instruction->channel_id()).recv_done; + CHECK(recv_done->opcode() == HloOpcode::kRecvDone); + CHECK_EQ(recv_done->operand_count(), 1); + HloInstruction* recv = recv_done->mutable_operand(0); + add_unique_predecessor(recv); + } + return predecessors; +} + +std::vector HloModuleGroupUtil::GlobalSuccessors( + HloInstruction* instruction) { + std::vector successors; + + // Adds to the unique successors list and also add companion instructions + // if the given successor has those. + auto add_unique_successor = [&](HloInstruction* successor) { + if (std::find(successors.begin(), successors.end(), successor) != + successors.end()) { + return; + } + if (!metadata_.IsCompanionInstruction(successor)) { + successors.push_back(successor); + return; + } + for (HloInstruction* companion : metadata_.Companions(successor)) { + successors.push_back(companion); + } + }; + + // If the given instruction is a companion instruction, we need to find the + // successors of all of its companion instructions. + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(instruction)) { + for (HloInstruction* companion : metadata_.Companions(instruction)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(instruction); + } + + for (HloInstruction* hlo : instruction_group) { + for (HloInstruction* user : hlo->users()) { + add_unique_successor(user); + } + for (HloInstruction* control_successor : hlo->control_successors()) { + add_unique_successor(control_successor); + } + } + if (instruction->opcode() == HloOpcode::kRecv) { + // Send is a remote successor of Recv. + const HloInstruction* recv_done = instruction->users().front(); + CHECK(recv_done->opcode() == HloOpcode::kRecvDone); + HloInstruction* send = metadata_.GetChannel(instruction->channel_id()).send; + add_unique_successor(send); + } + if (instruction->opcode() == HloOpcode::kSend) { + // RecvDone is a remote successor of Send. + HloInstruction* recv_done = + metadata_.GetChannel(instruction->channel_id()).recv_done; + add_unique_successor(recv_done); + } + return successors; +} + +std::vector HloModuleGroupUtil::RootInstructions( + tensorflow::gtl::ArraySlice computations) { + std::vector roots; + for (HloComputation* computation : computations) { + for (HloInstruction* instruction : computation->instructions()) { + if (GlobalSuccessors(instruction).empty()) { + roots.push_back(instruction); + } + } + } + return roots; +} + +Status HloModuleGroupUtil::VisitTopologicalOrder( + VisitStates* visit_state, const VisitFunction& visit_function, + HloInstruction* root) { + // Stack of HLO instructions visited in DFS order. + std::stack stack; + stack.push(root); + + while (!stack.empty()) { + HloInstruction* hlo = stack.top(); + + // Find the instruction group of the currently visited instruction. The + // instruction group represents all companion instructions of the + // current instruction, and are considered to be a single entity for the + // purpose of the traversal (i.e., they must always be in the same visit + // state). + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(hlo)) { + for (HloInstruction* companion : metadata_.Companions(hlo)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(hlo); + } + + if ((*visit_state)[hlo] == VisitState::kVisited) { + // All instructions in the group must be in the same state. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kVisited); + } + stack.pop(); + continue; + } + + if ((*visit_state)[hlo] == VisitState::kVisiting) { + TF_RETURN_IF_ERROR(visit_function(hlo, instruction_group)); + + // Set the visit state of all instructions in the group to kVisited. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kVisiting); + (*visit_state)[instruction] = VisitState::kVisited; + } + stack.pop(); + continue; + } + + // Set the visit state of all instructions in the group to kVisiting. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kNotVisited) + << instruction->ToString(); + (*visit_state)[instruction] = VisitState::kVisiting; + } + + // For each instruction in the group, visit its predecessors (operands, + // control predecessors and remote predecessors). + for (HloInstruction* instruction : instruction_group) { + for (HloInstruction* predecessor : GlobalPredecessors(instruction)) { + // Visiting a node that is already being visited implies that there is + // a cycle. Generate an error with the list of instructions in the + // cycle. + if ((*visit_state)[predecessor] == VisitState::kVisiting) { + string cyclic_instructions; + for (const auto& state : *visit_state) { + if (state.second == VisitState::kVisiting) { + tensorflow::strings::StrAppend(&cyclic_instructions, + state.first->ToString(), "\n"); + } + } + // TODO(b/64305524): Improve the error message to print out the + // instructions in a deterministic order that forms the cycle. + return FailedPrecondition( + "Cross-computation cycle detected via communicating nodes. The " + "cycle contains the node %s. The cycle is found among the " + "following nodes. Note that the order of the nodes is arbitrary " + "and that the list may include nodes that are not part of the " + "cycle.\n%s", + predecessor->ToString().c_str(), cyclic_instructions.c_str()); + } + stack.push(predecessor); + } + } + } + + return Status::OK(); +} + +Status HloModuleGroupUtil::VerifyComputations( + tensorflow::gtl::ArraySlice computations) { + auto visit_function = + [&](HloInstruction* instruction, + const std::vector& instruction_group) { + return Status::OK(); + }; + int64 instructions_count = 0; + VisitStates visit_states; + for (HloComputation* computation : computations) { + // Visit all instructions, and not just from the root instruction of the + // computation. This allows us to detect dead cycles (i.e., cycles that + // are not reachable from the root) or to enforce an order for the + // communication instructions that are not reachable from any roots. + for (HloInstruction* instruction : computation->instructions()) { + TF_RETURN_IF_ERROR( + VisitTopologicalOrder(&visit_states, visit_function, instruction)); + } + instructions_count += computation->instruction_count(); + } + + // Check if all instructions are visited and are in the visited state. + TF_RET_CHECK(visit_states.size() == instructions_count); + for (auto& state : visit_states) { + TF_RET_CHECK(state.second == VisitState::kVisited); + } + + return Status::OK(); +} + +StatusOr> +HloModuleGroupUtil::ComputeReachability( + tensorflow::gtl::ArraySlice computations) { + std::list post_order; + auto visit_function = + [&](HloInstruction* instruction, + const std::vector& instruction_group) { + post_order.insert(post_order.end(), instruction_group.begin(), + instruction_group.end()); + return Status::OK(); + }; + HloModuleGroupUtil::VisitStates visit_states; + for (HloInstruction* root : RootInstructions(computations)) { + TF_RETURN_IF_ERROR( + VisitTopologicalOrder(&visit_states, visit_function, root)); + } + auto reachability = absl::make_unique(post_order); + for (HloInstruction* hlo : post_order) { + reachability->SetReachabilityToUnion(GlobalPredecessors(hlo), hlo); + } + return std::move(reachability); +} + +void HloModuleGroupUtil::UpdateReachabilityThroughInstruction( + HloInstruction* instruction, HloReachabilityMap* reachability_map) { + std::queue worklist; + worklist.push(instruction); + + while (!worklist.empty()) { + HloInstruction* item = worklist.front(); + worklist.pop(); + if (reachability_map->SetReachabilityToUnion(GlobalPredecessors(item), + item)) { + for (HloInstruction* successor : GlobalSuccessors(item)) { + worklist.push(successor); + } + } + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.h b/tensorflow/compiler/xla/service/hlo_module_group_util.h new file mode 100644 index 0000000000000000000000000000000000000000..c25ca1aff50b288f3ac3885cbed53e7ba9768430 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.h @@ -0,0 +1,117 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module_group_metadata.h" +#include "tensorflow/compiler/xla/service/hlo_reachability.h" +#include "tensorflow/compiler/xla/status.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" + +namespace xla { + +// Collection of utilities for handling HloModuleGroups. +class HloModuleGroupUtil { + public: + explicit HloModuleGroupUtil(const HloModuleGroupMetadata& metadata) + : metadata_(metadata) {} + + // Returns all unique predecessors of the instruction. This includes: + // * predecessors in the same computation: operands and control predecessors + // * Recv is a predecessor of Send + // * Send is a predecessor of RecvDone + // * predecessors of companions (if the instruction is a companion while) + // * predecessors' companions (for any predecessor that is a companion while) + std::vector GlobalPredecessors(HloInstruction* instruction); + + // Returns all unique successors of the instruction. This includes: + // * successors in the same computation: users and control successors + // * Send is a successor of Recv + // * RecvDone is a predecessor of Send + // * successors of companions (if the instruction is a companion while) + // * successors' companions (for any successor that is a companion while) + std::vector GlobalSuccessors(HloInstruction* instruction); + + // Returns the root instructions of the computations. + std::vector RootInstructions( + tensorflow::gtl::ArraySlice computations); + + // Visit state of each instruction during DFS traversal. + enum VisitState { + kNotVisited = 0, + kVisiting, + kVisited, + }; + + // Function called on each instruction group during the DFS traversal. See the + // comment for VisitTopologicalOrder()). + using VisitFunction = std::function& instruction_group)>; + + // Given the hlo instruction as the root, recursively visits all its + // predecessor instructions in DFS order to visit nodes in topological order. + // + // Note that the DFS traversal does not only visit nodes in the same + // computation (parent of the root instruction), but also visits nodes in + // different computations connected via communication instructions. During the + // traversal, companion While instructions (see the class comment in + // HloModuleGroupMetadata) are treated as a single instruction (called + // instruction group, which contains only a single instruction if the visiting + // node is not a companion while) -- visiting one of the instructions in the + // group effectively visits all other instructions in the group, and then all + // predecessor instructions of the group are visited. + // + // * visit_state: map from each instruction to its visit state. + // * visit_function: function called when each instruction group. + // * root: the root instruction of the traversal. + using VisitStates = tensorflow::gtl::FlatMap; + Status VisitTopologicalOrder(VisitStates* visit_state, + const VisitFunction& visit_function, + HloInstruction* root); + + // Verifies that the computations are well-formed (e.g., no cycles). + Status VerifyComputations( + tensorflow::gtl::ArraySlice computations); + + // Below Reachability utils resemble those in HloComputation, except that + // they can handle instructions across multiple computations. + // + // Creates the reachability map for the instructions in the computations. + StatusOr> ComputeReachability( + tensorflow::gtl::ArraySlice computations); + + // Updates the reachability of the given instruction, taking the global + // predeccessorss and successors into account. + void UpdateReachabilityThroughInstruction( + HloInstruction* instruction, HloReachabilityMap* reachability_map); + + private: + const HloModuleGroupMetadata& metadata_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h index 3d64523a79fc50638fdf378b5d521a5cd4482b90..af24604c39b554f146793594958f373999844b4c 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.h +++ b/tensorflow/compiler/xla/service/hlo_opcode.h @@ -76,9 +76,11 @@ namespace xla { V(kFft, "fft") \ V(kFloor, "floor") \ V(kFusion, "fusion", kHloOpcodeIsVariadic) \ + V(kGather, "gather") \ V(kGe, "greater-than-or-equal-to", kHloOpcodeIsComparison) \ V(kGetTupleElement, "get-tuple-element") \ V(kGt, "greater-than", kHloOpcodeIsComparison) \ + V(kHostCompute, "host-compute") \ V(kImag, "imag") \ V(kInfeed, "infeed") \ V(kIsFinite, "is-finite") \ diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc index 68e3c9618c1fe9daacb0aee3ee98862c8b9e4bc4..1b24d8da9e832e6847cb6f405e15af3c455f695a 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering.cc @@ -186,6 +186,22 @@ bool HloOrdering::UseIsBeforeValueDefinition( } } + if (use.instruction->opcode() == HloOpcode::kConditional) { + const HloInstruction* conditional = use.instruction; + if (call_graph_->InstructionIsNestedIn(value.defining_instruction(), + conditional->true_computation())) { + VLOG(4) << " use is conditional " << use.instruction->name() + << " and def is in TRUE computation"; + return true; + } + if (call_graph_->InstructionIsNestedIn(value.defining_instruction(), + conditional->false_computation())) { + VLOG(4) << " use is conditional " << use.instruction->name() + << " and def is in FALSE computation"; + return true; + } + } + VLOG(4) << " use is not before value"; return false; } diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc index aba66114de649ce7667ae77174e9c4073b010b90..a989fce63234cb860d08c48b02462e96bec879bc 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc @@ -262,8 +262,8 @@ TEST_F(HloOrderingTest, ValuesInWhileComputations) { scalar_shape, HloOpcode::kAdd, constant, xla_while)); module->AddEntryComputation(builder.Build()); - TF_ASSERT_OK_AND_ASSIGN( - auto dataflow, HloDataflowAnalysis::Run(module.get(), /*ssa_form=*/true)); + TF_ASSERT_OK_AND_ASSIGN(auto dataflow, + HloDataflowAnalysis::Run(*module, /*ssa_form=*/true)); DependencyHloOrdering ordering(module.get()); // Init value is defined before the while, but live range is not before the diff --git a/tensorflow/compiler/xla/service/hlo_proto_util.cc b/tensorflow/compiler/xla/service/hlo_proto_util.cc index 78e6a101c10a1e812e3e2631d520139fd0bc425c..3460679558d185d1e022660d9a1d23176d0d96bf 100644 --- a/tensorflow/compiler/xla/service/hlo_proto_util.cc +++ b/tensorflow/compiler/xla/service/hlo_proto_util.cc @@ -15,6 +15,10 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_proto_util.h" +#include + +#include "tensorflow/compiler/xla/util.h" + namespace xla { HloProto MakeHloProto(const HloModule& module, @@ -35,4 +39,35 @@ HloProto MakeHloProto(const HloModule& module) { return proto; } +StatusOr> EntryComputationParameterShapes( + const HloProto& hlo_proto) { + if (!hlo_proto.has_hlo_module()) { + return NotFound("HloProto missing HloModuleProto."); + } + if (!hlo_proto.hlo_module().has_program_shape()) { + return NotFound("HloProto missing program shape."); + } + + std::vector parameter_shapes; + const auto& program_shape = hlo_proto.hlo_module().program_shape(); + for (const Shape& shape : program_shape.parameters()) { + parameter_shapes.push_back(&shape); + } + return parameter_shapes; +} + +StatusOr EntryComputationOutputShape(const HloProto& hlo_proto) { + if (!hlo_proto.has_hlo_module()) { + return NotFound("HloProto missing HloModuleProto."); + } + if (!hlo_proto.hlo_module().has_program_shape()) { + return NotFound("HloProto missing program shape."); + } + if (!hlo_proto.hlo_module().program_shape().has_result()) { + return NotFound("HloProto missing result in its program shape"); + } + + return &hlo_proto.hlo_module().program_shape().result(); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_proto_util.h b/tensorflow/compiler/xla/service/hlo_proto_util.h index 320288fdb9aa0810b306b1d78bd1ff4cfc366ed2..3d9c375cd5d26f92cf8316f78789daf4fc08c927 100644 --- a/tensorflow/compiler/xla/service/hlo_proto_util.h +++ b/tensorflow/compiler/xla/service/hlo_proto_util.h @@ -35,6 +35,15 @@ HloProto MakeHloProto(const HloModule& module, // will not be included in the output. HloProto MakeHloProto(const HloModule& module); +// Returns the shapes of the parameters of the entry computation. Shape pointers +// refer to shapes inside of the given HloProto. +StatusOr> EntryComputationParameterShapes( + const HloProto& hlo_proto); + +// Returns the shape of the output of the entry computation. The shape pointer +// refers to the output shape inside of the given HloProto. +StatusOr EntryComputationOutputShape(const HloProto& hlo_proto); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PROTO_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/hlo_proto_util_test.cc b/tensorflow/compiler/xla/service/hlo_proto_util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..b9cca138703c8fa61aadf69dd7304a215a9f4be2 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_proto_util_test.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_proto_util.h" + +#include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/lib/strings/str_util.h" + +namespace xla { +namespace { + +class HloProtoUtilTest : public ::testing::Test {}; + +TEST_F(HloProtoUtilTest, ParamsAndOutputShapeMissingModule) { + HloProto hlo_proto; + + auto status = EntryComputationParameterShapes(hlo_proto).status(); + ASSERT_FALSE(status.ok()); + ASSERT_THAT(status.error_message(), + ::testing::HasSubstr("missing HloModuleProto")); +} + +TEST_F(HloProtoUtilTest, MissingProgramShape) { + HloProto hlo_proto; + HloModuleProto* module = hlo_proto.mutable_hlo_module(); + module->set_name("entry"); + + auto status = EntryComputationParameterShapes(hlo_proto).status(); + ASSERT_FALSE(status.ok()); + ASSERT_THAT(status.error_message(), + ::testing::HasSubstr("missing program shape")); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index c6b4dc0368d92fd477decdfb38045f74f8696803..98b8d34be1f331aaeac94e952deeae1e76379861 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -60,6 +60,7 @@ bool IsRematerializable(const HloInstruction* instruction) { switch (instruction->opcode()) { case HloOpcode::kCall: case HloOpcode::kConstant: + case HloOpcode::kConditional: case HloOpcode::kCrossReplicaSum: case HloOpcode::kCustomCall: case HloOpcode::kParameter: diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index 2594c29efd717b3bead34d326c28c7efdf093c50..da448ed71ab470e0c4d72e234bf1f1087d3ea7b4 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_scheduling.h" +#include #include #include @@ -100,7 +101,7 @@ class ListScheduler { // LogicalBuffer is in an operand of the instruction as indicated by // points-to analysis. for (auto* instruction : computation.instructions()) { - std::unordered_set instr_uses; + tensorflow::gtl::FlatSet instr_uses; for (auto* operand : instruction->operands()) { for (const LogicalBuffer* buffer : points_to_analysis.GetBuffersDefinedByInstruction(operand)) { @@ -150,7 +151,7 @@ class ListScheduler { int64 bytes_defined; // For each buffer B used by this instruction, we keep a pair (B, U), where - // U is the number of uses of B that have not yet been scheduled. This pair + // U is the number of uses of B that have not yet been scheduled. This pair // is a pointer into the unscheduled_use_count_ map, so it gets updated for // free when we update counts in the map. std::vector*> @@ -205,7 +206,8 @@ class ListScheduler { // Populate the ready list with instructions which have no operands or // control predecessors. - std::unordered_map unscheduled_pred_count; + tensorflow::gtl::FlatMap + unscheduled_pred_count; for (auto* instruction : computation_.instructions()) { // TODO(b/34466113): Replace this and above with successors() or // predecessors() when these methods are added to HloInstruction. @@ -217,39 +219,48 @@ class ListScheduler { } } - std::list ready_list; + // Use a multimap to sort ReadyListEntry according to their priority. + std::multimap ready_queue; + + // Map of ready instructions to their iterators in ready_queue. + tensorflow::gtl::FlatMap::iterator> + ready_instructions; + + auto add_to_ready_queue = [&](HloInstruction* inst) { + auto entry = MakeReadyListEntry(inst); + auto it = ready_queue.emplace(GetPriority(entry), std::move(entry)); + ready_instructions[inst] = it; + }; + for (auto* instruction : computation_.instructions()) { // Instruction with no operands or control predecessors will // not be in the map. if (unscheduled_pred_count.count(instruction) == 0) { - ready_list.push_back(MakeReadyListEntry(instruction)); + add_to_ready_queue(instruction); } } - while (!ready_list.empty()) { - // Select the highest priority HLO instruction from the ready list. - auto best_it = ready_list.begin(); - Priority best_priority = GetPriority(*best_it); - for (auto ready_it = std::next(ready_list.begin()); - ready_it != ready_list.end(); ++ready_it) { - Priority priority = GetPriority(*ready_it); - if (priority > best_priority) { - best_it = ready_it; - best_priority = priority; - } - } - + while (!ready_queue.empty()) { // Remove the selected instruction from the ready list and add it to the // schedule. - const HloInstruction* best = best_it->instruction; - ready_list.erase(best_it); + auto best_it = ready_queue.end(); + --best_it; + const HloInstruction* best = best_it->second.instruction; + ready_queue.erase(best_it); + ready_instructions.erase(best); schedule.push_back(best); scheduled_instructions_.insert(best); + bool adjust_ready_queue = false; // Update the unscheduled uses of the logical buffers. for (const LogicalBuffer* buffer : buffer_uses_.at(best)) { - CHECK_GT(unscheduled_use_count_.at(buffer), 0); - --unscheduled_use_count_[buffer]; + int64& count = unscheduled_use_count_[buffer]; + CHECK_GT(count, 0); + --count; + if (count == 1) { + adjust_ready_queue = true; + } } // Add new instructions to ready list. @@ -257,7 +268,7 @@ class ListScheduler { int64 pred_count = --unscheduled_pred_count.at(inst); CHECK_GE(pred_count, 0); if (pred_count == 0) { - ready_list.push_back(MakeReadyListEntry(inst)); + add_to_ready_queue(inst); } }; // TODO(b/34466113): Replace this and above with successors() or @@ -268,6 +279,31 @@ class ListScheduler { for (HloInstruction* succ : best->control_successors()) { update_pred_count(succ); } + // The unscheduled use count for a buffer has changed to 1, so the + // priorities of some ready instructions may go up. We update them in the + // ready queue, so that they can appear earlier. + if (adjust_ready_queue) { + for (HloInstruction* operand : best->operands()) { + for (HloInstruction* operand_user : operand->users()) { + auto ready_instructions_it = ready_instructions.find(operand_user); + if (ready_instructions_it == ready_instructions.end()) { + continue; + } + auto ready_queue_it = ready_instructions_it->second; + auto& entry = ready_queue_it->second; + Priority new_priority = GetPriority(entry); + if (new_priority == ready_queue_it->first) { + continue; + } + // Create a new entry in ready_queue, then update + // ready_instructions[operand_user] to refer to the new entry. + ready_instructions_it->second = + ready_queue.emplace(new_priority, std::move(entry)); + // Remove the old entry in ready_queue. + ready_queue.erase(ready_queue_it); + } + } + } } CHECK_EQ(schedule.size(), computation_.instruction_count()); CHECK_EQ(scheduled_instructions_.size(), computation_.instruction_count()); @@ -280,15 +316,17 @@ class ListScheduler { const LogicalBuffer::SizeFunction& size_function_; // A map containing the LogicalBuffers that each instruction uses. - std::unordered_map> + tensorflow::gtl::FlatMap> buffer_uses_; // A map containing the count of unscheduled HLOs which using a particular - // LogicalBuffer. We rely on iterator stability in this map. + // LogicalBuffer. We rely on iterator stability in this map, and that the map + // entries are std::pair's. std::unordered_map unscheduled_use_count_; // Set of instructions which have been scheduled. - std::unordered_set scheduled_instructions_; + tensorflow::gtl::FlatSet scheduled_instructions_; }; int64 SumLogicalBufferSizes( @@ -310,6 +348,7 @@ StatusOr> RunDFSMemoryScheduler( // simply users-1 for each instruction. By subtracting 1, we're saying that // instructions with no users or a single user don't count; instructions with // lots of fan-out will be visited earlier. + int64 cumulative_total_size = 0; tensorflow::gtl::FlatMap extra_users; tensorflow::gtl::FlatMap total_sizes; for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) { @@ -319,14 +358,17 @@ StatusOr> RunDFSMemoryScheduler( continue; } extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1; - total_sizes[hlo] = SumLogicalBufferSizes( + int64 logical_buffer_size = SumLogicalBufferSizes( points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function); + total_sizes[hlo] = logical_buffer_size; + cumulative_total_size += logical_buffer_size; tensorflow::gtl::FlatSet unique_operands( hlo->operands().begin(), hlo->operands().end()); for (const HloInstruction* operand : unique_operands) { extra_users[hlo] += extra_users[operand]; total_sizes[hlo] += total_sizes[operand]; } + total_sizes[hlo] = std::min(total_sizes[hlo], cumulative_total_size); } CHECK_EQ(extra_users.size(), computation.instruction_count()); CHECK_EQ(total_sizes.size(), computation.instruction_count()); diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index 447c2446668253c932b44b51b2db22bfd47f9957..afe79c9f17befdcb2812c0a08b205f21b0715b19 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -183,6 +183,10 @@ Status HloSharding::ValidateTuple(const Shape& shape, int64 num_devices) const { // shape tree. ShapeTree shape_tree = GetAsShapeTree(shape); for (const auto& index_to_sharding : shape_tree.leaves()) { + if (index_to_sharding.first.empty()) { + // An empty tuple has a ShapeTree with a single leaf at the empty index. + continue; + } Status status = index_to_sharding.second.ValidateNonTuple( ShapeUtil::GetSubshape(shape, index_to_sharding.first), num_devices); if (!status.ok()) { @@ -222,7 +226,7 @@ Status HloSharding::ValidateNonTuple(const Shape& shape, Status status = Status::OK(); std::set seen_cores; tile_assignment_.Each( - [&](tensorflow::gtl::ArraySlice indices, uint32 core) { + [&](tensorflow::gtl::ArraySlice indices, int32 core) { // Don't overwrite a bad status, so we report the first error. if (status.ok()) { if (core >= num_devices) { diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 04d4656546684063d3d6532e443ad7995c6ea8db..b1fd068115e1d104a11d880675ef84e07d6d5602 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/compiler/xla/service/hlo_verifier.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/lib/core/errors.h" @@ -123,6 +125,10 @@ Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) { return CheckShape(outfeed, ShapeUtil::MakeNil()); } +Status ShapeVerifier::HandleHostCompute(HloInstruction*) { + return tensorflow::Status::OK(); +} + Status ShapeVerifier::HandleRng(HloInstruction*) { return tensorflow::Status::OK(); } @@ -164,6 +170,8 @@ Status ShapeVerifier::HandleBroadcast(HloInstruction* broadcast) { // HLO broadcast has no exact analog at the proto level so there is no // ShapeInference method. Check the output shape explicitly. const Shape& operand_shape = broadcast->operand(0)->shape(); + // Check for mixed precision. + TF_RETURN_IF_ERROR(CheckShape(broadcast, broadcast->shape())); TF_RET_CHECK(ShapeUtil::Rank(operand_shape) == broadcast->dimensions().size()); for (int64 operand_dimension = 0; @@ -178,6 +186,8 @@ Status ShapeVerifier::HandleBroadcast(HloInstruction* broadcast) { } Status ShapeVerifier::HandleReshape(HloInstruction* reshape) { + // Check for mixed precision. + TF_RETURN_IF_ERROR(CheckShape(reshape, reshape->shape())); TF_RET_CHECK(ShapeUtil::ElementsIn(reshape->shape()) == ShapeUtil::ElementsIn(reshape->operand(0)->shape())); return tensorflow::Status::OK(); @@ -359,13 +369,130 @@ Status ShapeVerifier::HandleBatchNormGrad(HloInstruction* batch_norm_grad) { batch_norm_grad->feature_index())); } +namespace { + +// Checks that the instruction does not have mixed precision floating point +// inputs. +Status CheckMixedPrecisionOperands(const HloInstruction* instruction) { + switch (instruction->opcode()) { + // White list the following opcodes for mixed-precision check, because they + // involve data pass through or grouping via tuples, where the precisions + // of buffers can be different. + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kConstant: + case HloOpcode::kCrossReplicaSum: + case HloOpcode::kCustomCall: + case HloOpcode::kFusion: + case HloOpcode::kGetTupleElement: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: + case HloOpcode::kParameter: + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + case HloOpcode::kReducePrecision: + case HloOpcode::kSelect: + case HloOpcode::kSend: + case HloOpcode::kSendDone: + case HloOpcode::kTuple: + case HloOpcode::kWhile: + break; + default: { + PrimitiveType fp_type = PRIMITIVE_TYPE_INVALID; + for (auto operand : instruction->operands()) { + TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( + operand->shape(), + [&](const Shape& subshape, const ShapeIndex& index) { + if (!ShapeUtil::ElementIsFloating(subshape)) { + return Status::OK(); + } + if (fp_type == PRIMITIVE_TYPE_INVALID) { + fp_type = subshape.element_type(); + } else if (fp_type != subshape.element_type()) { + return FailedPrecondition( + "Seen floating point types of different precisions in " + "%s, but mixed precision is disallowed.", + instruction->ToString().c_str()); + } + return Status::OK(); + })); + } + } + } + return Status::OK(); +} + +} // namespace + +Status ShapeVerifier::HandleGather(HloInstruction* gather) { + return CheckShape( + gather, + ShapeInference::InferGatherShape( + gather->operand(0)->shape(), gather->operand(1)->shape(), + gather->gather_dimension_numbers(), gather->gather_window_bounds())); +} + Status ShapeVerifier::CheckShape(const HloInstruction* instruction, - const Shape& expected_shape) { - if (!ShapeUtil::Compatible(instruction->shape(), expected_shape)) { + const Shape& inferred_shape) { + // If allow_mixed_precision_ is false, check if there are operands with + // different precisions. We need this check because ShapeInference allows + // mixed precision inputs. + if (!allow_mixed_precision_) { + TF_RETURN_IF_ERROR(CheckMixedPrecisionOperands(instruction)); + } + + // Check if the output shape matches the expected shape. + bool compatible; + // We treat BF16 and F32 as compatible types if mixed precision is allowed, + // but only when the instruction defines the BF16/F32 buffer. + switch (instruction->opcode()) { + case HloOpcode::kSelect: + if (ShapeUtil::IsTuple(inferred_shape) || !allow_mixed_precision_) { + // Select only defines the top-level buffer, which in this case is the + // tuple, so we cannot allow mixed precision. + compatible = + ShapeUtil::Compatible(instruction->shape(), inferred_shape); + } else { + compatible = ShapeUtil::CompatibleIgnoringFpPrecision( + instruction->shape(), inferred_shape); + } + break; + case HloOpcode::kGetTupleElement: + case HloOpcode::kTuple: + // Tuple and GetTupleElement do not define BF16/F32 buffers, so mixed + // precision is disallowed. + case HloOpcode::kConstant: + case HloOpcode::kBitcast: + case HloOpcode::kBitcastConvert: + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kConvert: + case HloOpcode::kCustomCall: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: + case HloOpcode::kParameter: + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + case HloOpcode::kSend: + case HloOpcode::kSendDone: + case HloOpcode::kWhile: + // The above opcodes should match the expected shapes exactly. + compatible = ShapeUtil::Compatible(instruction->shape(), inferred_shape); + break; + default: + if (allow_mixed_precision_) { + compatible = ShapeUtil::CompatibleIgnoringFpPrecision( + instruction->shape(), inferred_shape); + } else { + compatible = + ShapeUtil::Compatible(instruction->shape(), inferred_shape); + } + } + if (!compatible) { return InvalidArgument( "Expected instruction to have shape compatible with %s, actual " "shape is %s:\n%s", - ShapeUtil::HumanString(expected_shape).c_str(), + ShapeUtil::HumanString(inferred_shape).c_str(), ShapeUtil::HumanString(instruction->shape()).c_str(), instruction->ToString().c_str()); } @@ -373,14 +500,14 @@ Status ShapeVerifier::CheckShape(const HloInstruction* instruction, } Status ShapeVerifier::CheckShape(const HloInstruction* instruction, - const StatusOr& expected_shape_status) { - if (!expected_shape_status.ok()) { - Status s = expected_shape_status.status(); + const StatusOr& inferred_shape_status) { + if (!inferred_shape_status.ok()) { + Status s = inferred_shape_status.status(); tensorflow::errors::AppendToMessage(&s, ", for instruction ", instruction->ToString()); return s; } - return CheckShape(instruction, expected_shape_status.ValueOrDie()); + return CheckShape(instruction, inferred_shape_status.ValueOrDie()); } Status ShapeVerifier::CheckUnaryShape(const HloInstruction* instruction) { diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 26d53dec1e52f0bf19d6a8af998c56db8a850518..1dd7ec3c51e18dcfe89bd478de87798ba3858119 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -27,6 +27,10 @@ namespace xla { // TODO(b/26024837): Check output shape for all instruction types. class ShapeVerifier : public DfsHloVisitor { public: + explicit ShapeVerifier() : allow_mixed_precision_(false) {} + explicit ShapeVerifier(bool allow_mixed_precision) + : allow_mixed_precision_(allow_mixed_precision) {} + Status HandleElementwiseUnary(HloInstruction* hlo) override; Status HandleElementwiseBinary(HloInstruction* hlo) override; Status HandleClamp(HloInstruction* clamp) override; @@ -56,6 +60,7 @@ class ShapeVerifier : public DfsHloVisitor { Status HandleFusion(HloInstruction*) override; Status HandleCall(HloInstruction* call) override; Status HandleCustomCall(HloInstruction*) override; + Status HandleHostCompute(HloInstruction*) override; Status HandleSlice(HloInstruction* slice) override; Status HandleDynamicSlice(HloInstruction* dynamic_slice) override; Status HandleDynamicUpdateSlice( @@ -75,20 +80,21 @@ class ShapeVerifier : public DfsHloVisitor { Status HandleBatchNormInference( HloInstruction* batch_norm_inference) override; Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override; + Status HandleGather(HloInstruction* gather) override; Status FinishVisit(HloInstruction*) override { return tensorflow::Status::OK(); } protected: - // Check the instruction's shape against the given expected shape and return - // an appropriate error if there is a mismatch. + // Check the instruction's shape against the shape given by ShapeInference + // and return an appropriate error if there is a mismatch. Status CheckShape(const HloInstruction* instruction, - const Shape& expected_shape); + const Shape& inferred_shape); // Overload which takes a StatusOr to reduce boilerplate in the caller. Status CheckShape(const HloInstruction* instruction, - const StatusOr& expected_shape_status); + const StatusOr& inferred_shape_status); // Check a unary (binary, etc) instruction's shape against the inferred shape. Status CheckUnaryShape(const HloInstruction* instruction); @@ -99,19 +105,32 @@ class ShapeVerifier : public DfsHloVisitor { // Checks if the given two instructions shares the same channel id. Status CheckSameChannel(const HloInstruction* instr1, const HloInstruction* instr2); + + private: + // Whether the inputs and output of an instruction can contain both F32s and + // BF16s. Tuples that include both F32s and BF16s are allowed regardless of + // this flag. + bool allow_mixed_precision_; }; // HLO pass that verifies invariants of HLO instructions for each computation in // the module. class HloVerifier : public HloPassInterface { public: + using ShapeVerifierFactory = std::function()>; + // Uses standard shape inference. explicit HloVerifier() - : shape_verifier_factory_([] { return MakeUnique(); }) {} + : shape_verifier_factory_( + [] { return MakeUnique(false); }) {} + + explicit HloVerifier(bool allow_mixed_precision) + : shape_verifier_factory_([allow_mixed_precision] { + return MakeUnique(allow_mixed_precision); + }) {} // Uses custom shape verification. - explicit HloVerifier( - std::function()> shape_verifier_factory) + explicit HloVerifier(ShapeVerifierFactory shape_verifier_factory) : shape_verifier_factory_(std::move(shape_verifier_factory)) {} ~HloVerifier() override = default; @@ -129,7 +148,7 @@ class HloVerifier : public HloPassInterface { // expectations. This is a factory function because ShapeVerifier, Note that // ShapeVerifier, being a DfsHloVisitor, is stateful. We want a clean object // for each run of the verifier. - std::function()> shape_verifier_factory_; + ShapeVerifierFactory shape_verifier_factory_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index 90e1f0acdc4cdeda280dabaab2df66b181d0f407..d69ad80bdb4d2eab2d34228be026d7bc0b76efc0 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -102,6 +102,8 @@ namespace xla { case HloOpcode::kExp: case HloOpcode::kFft: case HloOpcode::kFusion: + case HloOpcode::kGather: + case HloOpcode::kHostCompute: case HloOpcode::kLog: case HloOpcode::kMap: case HloOpcode::kParameter: @@ -300,7 +302,7 @@ StatusOr InstructionFusion::Run(HloModule* module) { // Consider each operand of this instruction for fusion into this // instruction. We want to consider the operands in a particular order to - // avoid created duplicate instruction clones in the fusion instruction. + // avoid creating duplicate instruction clones in the fusion instruction. // For example, consider the following expression: // // A = ... @@ -375,7 +377,7 @@ StatusOr InstructionFusion::Run(HloModule* module) { changed = true; if (operand->user_count() == 0) { - // Operand is now dead. Remove from post order by setting it's + // Operand is now dead. Remove from post order by setting its // location to nullptr. post_order[FindOrDie(post_order_index, operand)] = nullptr; post_order_index.erase(operand); diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.cc b/tensorflow/compiler/xla/service/interpreter/compiler.cc index c83880e03090bc316b029ae9c902e27f3d8d826e..9171e859c6f84ceef9664aa1eb90a07c87dfab40 100644 --- a/tensorflow/compiler/xla/service/interpreter/compiler.cc +++ b/tensorflow/compiler/xla/service/interpreter/compiler.cc @@ -44,28 +44,12 @@ namespace interpreter { namespace se = ::perftools::gputools; namespace sep = ::perftools::gputools::interpreter; -/* - * Run optimization passes on the module. The graph is transformed by - * each pass in the optimization pipeline. The service subdirectory - * contains useful optimization passes. - */ Status InterpreterCompiler::RunHloOptimization(HloModule* hlo_module) { HloPassPipeline pipeline("Interpreter"); - pipeline.AddPass(); - pipeline.AddPass(); - pipeline.AddPass(false); - - pipeline.AddPass>( - false, [](const Shape&, const Shape&) { return false; }); - pipeline.AddPass(); - pipeline.AddPass(); - pipeline.AddPass(); - pipeline.AddPass(true); + pipeline.AddPass( hlo_module->mutable_entry_computation_layout()); - pipeline.AddPass(); - pipeline.AddPass(); return pipeline.Run(hlo_module).status(); } diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc index 0cb9b5d8107cd8bf468b07d5fe2a22930d9e8b8c..883063d0f075f5b0d79edc01bcd27a7c579272f4 100644 --- a/tensorflow/compiler/xla/service/interpreter/executable.cc +++ b/tensorflow/compiler/xla/service/interpreter/executable.cc @@ -93,7 +93,7 @@ StatusOr> InterpreterExecutable::ExecuteOnStream( TF_ASSIGN_OR_RETURN(std::unique_ptr result, transfer_manager->AllocateShapedBuffer( result_literal->shape(), run_options->allocator(), - run_options->device_ordinal())); + executor->device_ordinal())); TF_RETURN_IF_ERROR(transfer_manager->TransferLiteralToDevice( executor, *result_literal, *result)); diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index fce135ef61a7868386b869def1a79167c428d928..39f9120e552f014dd2759bff2892157402d9c47a 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -53,6 +53,83 @@ limitations under the License. namespace xla { +// For now moving only one API here, but we should have a single top level +// anonymous namespace, instead of three or four spread all over this file. +namespace { + +// Creates and returns a copy of the given instruction with a different +// layout. Tuple-shaped instructions will be deep-copied, and the last Tuple +// instruction producing the copy is returned. +StatusOr CreateCopyWithNewLayout( + const Shape& shape_with_layout, HloInstruction* instruction) { + TF_RET_CHECK(LayoutUtil::HasLayout(shape_with_layout)); + DCHECK(ShapeUtil::Compatible(shape_with_layout, instruction->shape())) + << ShapeUtil::HumanString(shape_with_layout) << " " + << ShapeUtil::HumanString(instruction->shape()) + << " instruction: " << instruction->ToString(); + + if (ShapeUtil::IsTuple(instruction->shape())) { + // Deep-copy tuples. + std::vector element_copies; + for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape()); + ++i) { + HloInstruction* gte = instruction->parent()->AddInstruction( + HloInstruction::CreateGetTupleElement( + ShapeUtil::GetSubshape(instruction->shape(), {i}), instruction, + i)); + + // Recurse to copy each elements. + TF_ASSIGN_OR_RETURN( + HloInstruction * element_copy, + CreateCopyWithNewLayout( + ShapeUtil::GetSubshape(shape_with_layout, {i}), gte)); + element_copies.push_back(element_copy); + } + // Gather element copies into a tuple with a new Tuple instruction. + HloInstruction* tuple_copy = instruction->parent()->AddInstruction( + HloInstruction::CreateTuple(element_copies)); + LayoutUtil::ClearLayout(tuple_copy->mutable_shape()); + TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( + shape_with_layout, tuple_copy->mutable_shape())); + return tuple_copy; + } else if (ShapeUtil::IsArray(instruction->shape())) { + HloInstruction* copy = + instruction->parent()->AddInstruction(HloInstruction::CreateUnary( + instruction->shape(), HloOpcode::kCopy, instruction)); + LayoutUtil::ClearLayout(copy->mutable_shape()); + TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( + shape_with_layout, copy->mutable_shape())); + + return copy; + } else { + return FailedPrecondition( + "Can only copy array and tuple shaped instructions"); + } +} + +// Creates a copy of the given operand if the operand's layout does not match +// the given layout. This copy replaces the use in the given instruction. Tuple +// operands will be deep-copied. +Status CopyOperandIfLayoutsDiffer(const ShapeLayout& operand_layout, + HloInstruction* instruction, + int64 operand_no) { + HloInstruction* operand = instruction->mutable_operand(operand_no); + TF_RET_CHECK(operand_layout.LayoutIsSet()); + TF_RET_CHECK(LayoutUtil::HasLayout(operand->shape())); + + if (ShapeUtil::Equal(operand_layout.shape(), operand->shape())) { + // Operand layout already matches our constraint. Nothing to do. + return Status::OK(); + } + + TF_ASSIGN_OR_RETURN(HloInstruction * operand_copy, + CreateCopyWithNewLayout(operand_layout.shape(), operand)); + + return instruction->ReplaceOperandWith(operand_no, operand_copy); +} + +} // namespace + std::ostream& operator<<(std::ostream& out, const LayoutConstraint& constraint) { out << constraint.ToString(); @@ -115,17 +192,34 @@ LayoutConstraints::LayoutConstraints( } } +PointsToSet::BufferSet* LayoutConstraints::GetBufferSet( + const HloInstruction* instruction) const { + auto it = buffer_sets_cache_.find(instruction); + if (it != buffer_sets_cache_.end()) { + return it->second.get(); + } + auto& buffer_set = + buffer_sets_cache_ + .emplace(instruction, MakeUnique()) + .first->second; + const auto& points_to_set = points_to_analysis_.GetPointsToSet(instruction); + points_to_set.ForEachElement( + [&buffer_set](const ShapeIndex& /*index*/, + const PointsToSet::BufferList& buffers) { + buffer_set->insert(buffers.begin(), buffers.end()); + }); + return buffer_set.get(); +} + bool LayoutConstraints::OperandBufferForwarded( const HloInstruction* instruction, int64 operand_no) const { // The operand is potentially forwarded if the intersection of points-to sets // of the operand and the instruction is non-empty. - auto output_buffers = - points_to_analysis_.GetPointsToSet(instruction).CreateFlattenedSet(); - auto operand_buffers = - points_to_analysis_.GetPointsToSet(instruction->operand(operand_no)) - .CreateFlattenedSet(); - for (const LogicalBuffer* output_buffer : output_buffers) { - if (operand_buffers.count(output_buffer) > 0) { + PointsToSet::BufferSet* output_buffers = GetBufferSet(instruction); + PointsToSet::BufferSet* operand_buffers = + GetBufferSet(instruction->operand(operand_no)); + for (const LogicalBuffer* output_buffer : *output_buffers) { + if (operand_buffers->count(output_buffer) > 0) { return true; } } @@ -512,6 +606,36 @@ Status LayoutAssignment::AddMandatoryConstraints( body_layout.result_shape(), instruction)); TF_RETURN_IF_ERROR(constraints->SetOperandLayout( body_layout.result_shape(), instruction, 0)); + } else if (instruction->opcode() == HloOpcode::kConditional) { + // The layout of the true and false computations must match, and must + // be the layout of the kConditional instruction. + TF_RET_CHECK(instruction->operand_count() == 3); + + HloComputation* true_computation = instruction->true_computation(); + HloComputation* false_computation = instruction->false_computation(); + const HloInstruction* true_operand = instruction->operand(1); + const HloInstruction* false_operand = instruction->operand(2); + + TF_RET_CHECK(true_computation->num_parameters() == 1); + TF_RET_CHECK(false_computation->num_parameters() == 1); + ComputationLayout& true_computation_layout = + FindOrDie(computation_layouts_, true_computation); + ComputationLayout& false_computation_layout = + FindOrDie(computation_layouts_, false_computation); + + DCHECK(ShapeUtil::Compatible(true_operand->shape(), + true_computation_layout.parameter_shape(0))); + DCHECK(ShapeUtil::Compatible( + false_operand->shape(), false_computation_layout.parameter_shape(0))); + + TF_RETURN_IF_ERROR(constraints->SetInstructionLayout( + true_computation_layout.result_shape(), instruction)); + TF_RETURN_IF_ERROR(constraints->SetOperandLayout( + true_computation_layout.parameter_shape(0), instruction, 1, + /*mandatory=*/true)); + TF_RETURN_IF_ERROR(constraints->SetOperandLayout( + false_computation_layout.parameter_shape(0), instruction, 2, + /*mandatory=*/true)); } else if (instruction->opcode() == HloOpcode::kCustomCall) { if (!CustomCallRequiresMajorFirstLayout(instruction)) { continue; @@ -598,6 +722,33 @@ Status CheckWhileLayout(HloInstruction* while_inst, return Status::OK(); } +Status CheckConditionalLayout( + HloInstruction* instruction, + const ComputationLayout& true_computation_layout, + const ComputationLayout& false_computation_layout) { + HloComputation* true_computation = instruction->true_computation(); + HloComputation* false_computation = instruction->false_computation(); + const HloInstruction* true_operand = instruction->operand(1); + const HloInstruction* false_operand = instruction->operand(2); + + TF_RET_CHECK(true_computation_layout.result_layout() == + false_computation_layout.result_layout()); + TF_RET_CHECK(true_computation_layout.result_layout().MatchesLayoutInShape( + instruction->shape())); + TF_RET_CHECK(true_computation_layout.result_layout().MatchesLayoutInShape( + true_computation->root_instruction()->shape())); + TF_RET_CHECK(false_computation_layout.result_layout().MatchesLayoutInShape( + instruction->shape())); + TF_RET_CHECK(false_computation_layout.result_layout().MatchesLayoutInShape( + false_computation->root_instruction()->shape())); + TF_RET_CHECK(true_computation_layout.parameter_layout(0).MatchesLayoutInShape( + true_operand->shape())); + TF_RET_CHECK( + false_computation_layout.parameter_layout(0).MatchesLayoutInShape( + false_operand->shape())); + return Status::OK(); +} + // Fusion parameters must match the layout of the fusion instructions operands, // and the root of the fusion expression must match the layout of the fusion // instruction. @@ -710,6 +861,13 @@ Status LayoutAssignment::CheckLayouts(HloModule* module) { FindOrDie(computation_layouts_, instruction->while_condition()), FindOrDie(computation_layouts_, instruction->while_body()))); break; + case HloOpcode::kConditional: + TF_RETURN_IF_ERROR(CheckConditionalLayout( + instruction, + FindOrDie(computation_layouts_, instruction->true_computation()), + FindOrDie(computation_layouts_, + instruction->false_computation()))); + break; default: break; } @@ -1165,77 +1323,6 @@ StatusOr InferArrayLayout( return *first_buffer_layout; } -// Creates and returns a copy of the given instruction with a different -// layout. Tuple-shaped instructions will be deep-copied, and the last Tuple -// instruction producing the copy is returned. -StatusOr CreateCopyWithNewLayout( - const Shape& shape_with_layout, HloInstruction* instruction) { - TF_RET_CHECK(LayoutUtil::HasLayout(shape_with_layout)); - DCHECK(ShapeUtil::Compatible(shape_with_layout, instruction->shape())) - << ShapeUtil::HumanString(shape_with_layout) << " " - << ShapeUtil::HumanString(instruction->shape()) - << " instruction: " << instruction->ToString(); - - if (ShapeUtil::IsTuple(instruction->shape())) { - // Deep-copy tuples. - std::vector element_copies; - for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape()); - ++i) { - HloInstruction* gte = instruction->parent()->AddInstruction( - HloInstruction::CreateGetTupleElement( - ShapeUtil::GetSubshape(instruction->shape(), {i}), instruction, - i)); - - // Recurse to copy each elements. - TF_ASSIGN_OR_RETURN( - HloInstruction * element_copy, - CreateCopyWithNewLayout( - ShapeUtil::GetSubshape(shape_with_layout, {i}), gte)); - element_copies.push_back(element_copy); - } - // Gather element copies into a tuple with a new Tuple instruction. - HloInstruction* tuple_copy = instruction->parent()->AddInstruction( - HloInstruction::CreateTuple(element_copies)); - LayoutUtil::ClearLayout(tuple_copy->mutable_shape()); - TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( - shape_with_layout, tuple_copy->mutable_shape())); - return tuple_copy; - } else if (ShapeUtil::IsArray(instruction->shape())) { - HloInstruction* copy = - instruction->parent()->AddInstruction(HloInstruction::CreateUnary( - instruction->shape(), HloOpcode::kCopy, instruction)); - LayoutUtil::ClearLayout(copy->mutable_shape()); - TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( - shape_with_layout, copy->mutable_shape())); - - return copy; - } else { - return FailedPrecondition( - "Can only copy array and tuple shaped instructions"); - } -} - -// Creates a copy of the given operand if the operand's layout does not match -// the given layout. This copy replaces the use in the given instruction. Tuple -// operands will be deep-copied. -Status CopyOperandIfLayoutsDiffer(const ShapeLayout& operand_layout, - HloInstruction* instruction, - int64 operand_no) { - HloInstruction* operand = instruction->mutable_operand(operand_no); - TF_RET_CHECK(operand_layout.LayoutIsSet()); - TF_RET_CHECK(LayoutUtil::HasLayout(operand->shape())); - - if (ShapeUtil::Equal(operand_layout.shape(), operand->shape())) { - // Operand layout already matches our constraint. Nothing to do. - return Status::OK(); - } - - TF_ASSIGN_OR_RETURN(HloInstruction * operand_copy, - CreateCopyWithNewLayout(operand_layout.shape(), operand)); - - return instruction->ReplaceOperandWith(operand_no, operand_copy); -} - // For fusion instructions, set the layout of each fused parameter instruction // to match the layout of its corresponding fusion instruction operand. Also, // set the layout of the fused root to match the layout of the fusion @@ -1474,6 +1561,13 @@ StatusOr LayoutAssignment::Run(HloModule* module) { // infeeds. Clearing the layouts here avoids hiding potential bugs in the // layout assignment pass that may accidently use the existing layout. for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kBitcast) { + // bitcasts are inherently layout sensitive and so a bitcast instruction + // present in the IR before layout assignment is a bug. + return InternalError( + "Unexpected bitcast operation seen during layout assignment: %s.", + instruction->ToString().c_str()); + } if (instruction->opcode() != HloOpcode::kInfeed) { LayoutUtil::ClearLayout(instruction->mutable_shape()); } diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index 29018584487cabfd740d7914625c2a50f552d6ff..7126cb50cf168241979178c9e1077051cc935e53 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -38,6 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -199,6 +200,11 @@ class LayoutConstraints { string ToString() const; private: + // Find a bufferset in the bufferset cache. This is useful since we can + // currently create the flattened buffer set for the same instruction many + // times, which is often slow. + PointsToSet::BufferSet* GetBufferSet(const HloInstruction* instruction) const; + // The set of BufferLayoutConstraints applied to the computation. std::unordered_map buffer_constraints_; @@ -221,6 +227,10 @@ class LayoutConstraints { // Array-shaped buffers which have not yet been constrained. std::set unconstrained_buffer_ids_; + mutable tensorflow::gtl::FlatMap> + buffer_sets_cache_; + HloComputation* computation_; }; diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index e269a13459f1146f1d2952870399827d9e705e38..4b1c9bad41de8030cf14bc6d1c0db21b9c56c3bf 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -590,6 +590,85 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) { transpose->shape(), {2, 3, 0, 1})); } +// TransposeIsBitcast shouldn't be called without layout information. +TEST_F(LayoutAssignmentTest, TransposeIsBitcastFail) { + auto builder = HloComputation::Builder(TestName()); + Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2}); + Shape input_shape_with_layout(input_shape); + *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0}); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_shape_with_layout, "param")); + auto hlo = builder.AddInstruction( + HloInstruction::CreateTranspose(input_shape, param, {0, 2, 1})); + // Clear the default layout assigned to the instruction. + LayoutUtil::ClearLayout(hlo->mutable_shape()); + EXPECT_DEATH(ShapeUtil::TransposeIsBitcast(hlo->operand(0)->shape(), + hlo->shape(), hlo->dimensions()), + "LayoutUtil::HasLayout"); +} + +// ReshapeIsBitcast shouldn't be called without layout information. +TEST_F(LayoutAssignmentTest, ReshapeIsBitcastFail) { + auto builder = HloComputation::Builder(TestName()); + Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2}); + Shape input_shape_with_layout(input_shape); + *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0}); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_shape_with_layout, "param")); + auto hlo = + builder.AddInstruction(HloInstruction::CreateReshape(input_shape, param)); + // Clear the default layout assigned to the instruction. + LayoutUtil::ClearLayout(hlo->mutable_shape()); + EXPECT_DEATH( + ShapeUtil::ReshapeIsBitcast(hlo->operand(0)->shape(), hlo->shape()), + "LayoutUtil::HasLayout"); +} + +// Check that the computation below doesn't crash the compiler. +// +// Within a fusion computation, only the parameters and result get assigned a +// layout. When we run the algebraic simplifier on this computation post layout +// assignment, it should not call TransposeIsBitcast on the `transpose` node +// inside the fusion computation as TransposeIsBitcast checks both input_shape +// and output_shape have layouts. +TEST_F(LayoutAssignmentTest, TransposeWithinFusionDoesNotCrash) { + const char* module_str = R"( + HloModule test_module + + fused_computation { + param_1 = f32[2,2,2]{2,1,0} parameter(1) + transpose = f32[2,2,2]{2,1,0} transpose(param_1), dimensions={0,2,1} + reduce_1 = f32[] parameter(0) + broadcast_1 = f32[2,2,2]{2,1,0} broadcast(reduce_1), dimensions={} + ROOT divide_1 = f32[2,2,2]{2,1,0} divide(transpose, broadcast_1) + } + + ENTRY entry_computation { + fusion.1 = f32[2,2,2]{2,1,0} parameter(1) + reduce.1 = f32[] parameter(0) + fusion.2 = f32[2,2,2]{2,1,0} fusion(reduce.1, fusion.1), kind=kLoop, calls=fused_computation + ROOT tuple.1 = (f32[2,2,2]{2,1,0}) tuple(fusion.2) + } + )"; + + auto module = tools::Parse(module_str).ValueOrDie(); + + module = + backend() + .compiler() + ->RunHloPasses(std::move(module), backend().default_stream_executor(), + /*device_allocator=*/nullptr) + .ConsumeValueOrDie(); + + EXPECT_EQ( + ::tensorflow::Status::OK(), + backend() + .compiler() + ->RunBackend(std::move(module), backend().default_stream_executor(), + /*device_allocator=*/nullptr) + .status()); +} + // A GTE inside of a fusion node inherits the layout of its operand (which // should, if we keep following operands, eventually be a parameter). TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { @@ -629,33 +708,113 @@ TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { LayoutUtil::MakeLayout({2, 1, 0})); AssignLayouts(module.get(), &computation_layout); - HloComputation* fused_computation = *std::find_if( - module->computations().begin(), module->computations().end(), - [](const HloComputation* c) { return c->name() == "fused_computation"; }); - - auto fused_instr = [&](const string& name) { - auto it = std::find_if( - fused_computation->instructions().begin(), - fused_computation->instructions().end(), - [&](const HloInstruction* i) { return i->name() == name; }); - CHECK(it != fused_computation->instructions().end()); - return *it; + auto layout_of = [&](tensorflow::StringPiece name) { + return FindInstruction(module.get(), name) + ->shape() + .layout() + .minor_to_major(); }; - EXPECT_THAT(fused_instr("gte0")->shape().layout().minor_to_major(), - ElementsAre(0, 1, 2)); - EXPECT_THAT( - fused_instr("gte1")->shape().tuple_shapes(0).layout().minor_to_major(), - ElementsAre(1, 2, 0)); - EXPECT_THAT( - fused_instr("gte1")->shape().tuple_shapes(1).layout().minor_to_major(), - ElementsAre(2, 0, 1)); - EXPECT_THAT(fused_instr("gte1a")->shape().layout().minor_to_major(), + EXPECT_THAT(layout_of("gte0"), ElementsAre(0, 1, 2)); + EXPECT_THAT(layout_of("gte1a"), ElementsAre(1, 2, 0)); + EXPECT_THAT(layout_of("gte1b"), ElementsAre(2, 0, 1)); + EXPECT_THAT(layout_of("fresult"), ElementsAre(2, 1, 0)); + EXPECT_THAT(FindInstruction(module.get(), "gte1") + ->shape() + .tuple_shapes(0) + .layout() + .minor_to_major(), ElementsAre(1, 2, 0)); - EXPECT_THAT(fused_instr("gte1b")->shape().layout().minor_to_major(), + EXPECT_THAT(FindInstruction(module.get(), "gte1") + ->shape() + .tuple_shapes(1) + .layout() + .minor_to_major(), ElementsAre(2, 0, 1)); - EXPECT_THAT(fused_instr("fresult")->shape().layout().minor_to_major(), - ElementsAre(2, 1, 0)); +} + +TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) { + auto builder = HloComputation::Builder(TestName()); + auto module = CreateNewModule(); + Shape shape = ShapeUtil::MakeShape(F32, {128, 8}); + Shape tshape = ShapeUtil::MakeTupleShape({shape, shape}); + Shape result_tshape = ShapeUtil::MakeTupleShape({shape}); + + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + auto param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + auto pred = builder.AddInstruction(HloInstruction::CreateParameter( + 2, ShapeUtil::MakeShape(PRED, {}), "param2")); + auto tuple = + builder.AddInstruction(HloInstruction::CreateTuple({param0, param1})); + + auto true_builder = HloComputation::Builder(TestName() + "_TrueBranch"); + { + auto param = true_builder.AddInstruction( + HloInstruction::CreateParameter(0, tshape, "param")); + auto gte0 = true_builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, param, 0)); + auto gte1 = true_builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, param, 1)); + auto add = true_builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, gte0, gte1)); + true_builder.AddInstruction(HloInstruction::CreateTuple({add})); + } + HloComputation* true_computation = + module->AddEmbeddedComputation(true_builder.Build()); + + auto false_builder = HloComputation::Builder(TestName() + "_FalseBranch"); + { + Shape xshape = ShapeUtil::MakeShapeWithLayout(F32, {128, 8}, {0, 1}); + false_builder.AddInstruction( + HloInstruction::CreateParameter(0, tshape, "param")); + // Using infeed as layout assignment does not mess up with it. + auto infeed = + false_builder.AddInstruction(HloInstruction::CreateInfeed(xshape, "")); + false_builder.AddInstruction(HloInstruction::CreateTuple({infeed})); + } + HloComputation* false_computation = + module->AddEmbeddedComputation(false_builder.Build()); + builder.AddInstruction(HloInstruction::CreateConditional( + result_tshape, pred, tuple, true_computation, tuple, false_computation)); + + HloComputation* computation = module->AddEntryComputation(builder.Build()); + ComputationLayout computation_layout(computation->ComputeProgramShape()); + + AssignLayouts(module.get(), &computation_layout); + + const HloInstruction* true_root = true_computation->root_instruction(); + const HloInstruction* false_root = false_computation->root_instruction(); + EXPECT_THAT(true_root->opcode(), HloOpcode::kTuple); + EXPECT_THAT(false_root->opcode(), HloOpcode::kTuple); + + const HloInstruction* true_result = true_root->operand(0); + const HloInstruction* false_result = false_root->operand(0); + EXPECT_TRUE(LayoutUtil::Equal(true_result->shape().layout(), + false_result->shape().layout())); + EXPECT_THAT(false_result->opcode(), HloOpcode::kCopy); +} + +TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) { + auto builder = HloComputation::Builder(TestName()); + auto constant0 = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); + builder.AddInstruction(HloInstruction::CreateUnary( + constant0->shape(), HloOpcode::kBitcast, constant0)); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + ComputationLayout computation_layout( + module->entry_computation()->ComputeProgramShape()); + LayoutAssignment layout_assignment(&computation_layout); + Status error_status = layout_assignment.Run(module.get()).status(); + EXPECT_FALSE(error_status.ok()); + EXPECT_THAT( + error_status.error_message(), + ::testing::HasSubstr( + "Unexpected bitcast operation seen during layout assignment")); } } // namespace diff --git a/tensorflow/compiler/xla/service/liveness_util_test.cc b/tensorflow/compiler/xla/service/liveness_util_test.cc index 2c2a02f6375343d67dfb155bbb03729ff6e490d2..f8b309488eeb5391b1cad5db760934ec1f7e3521 100644 --- a/tensorflow/compiler/xla/service/liveness_util_test.cc +++ b/tensorflow/compiler/xla/service/liveness_util_test.cc @@ -35,8 +35,7 @@ class PointsToAnalysisTestBase : public HloTestBase { CHECK_NOTNULL(module_.get()); points_to_analysis_ = TuplePointsToAnalysis::Run(module_.get()).ConsumeValueOrDie(); - dataflow_analysis_ = - HloDataflowAnalysis::Run(module_.get()).ConsumeValueOrDie(); + dataflow_analysis_ = HloDataflowAnalysis::Run(*module_).ConsumeValueOrDie(); } void BuildModuleAndRunAnalysis(std::unique_ptr computation) { diff --git a/tensorflow/compiler/xla/service/llvm_compiler.cc b/tensorflow/compiler/xla/service/llvm_compiler.cc index f98fc0400a7d827a29dcddc5eecf9a4a01e76590..911b243fe28a5baf8a4b8ed752b892265f5388ac 100644 --- a/tensorflow/compiler/xla/service/llvm_compiler.cc +++ b/tensorflow/compiler/xla/service/llvm_compiler.cc @@ -14,12 +14,29 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/llvm_compiler.h" +#include "tensorflow/core/platform/denormal.h" + +#ifdef __FAST_MATH__ +#error "Don't build XLA with -ffast-math" +#endif namespace xla { StatusOr>> LLVMCompiler::Compile( std::vector> modules, std::vector> stream_execs, DeviceMemoryAllocator* device_allocator) { + // Tensorflow tries to enable the following behaviors in all its threads: + // + // - Denormals are zero (DAZ): roughly, operations treat denormal floats as + // zero. + // - Flush denormals to zero (FTZ): roughly, operations produce zero instead + // of denormal floats. + // + // In theory enabling these shouldn't matter since the compiler should ideally + // not leak its environment into generated code, but we turn off DAZ and FTZ + // to get some defense-in-depth. + tensorflow::port::ScopedDontFlushDenormal dont_flush_denormals; + std::vector> result; for (size_t i = 0; i < modules.size(); i++) { if (stream_execs[i].size() != 1) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index ffc78bd5cfac3df1001d8125327607c85169ae92..37261ed1e665ebed9685751161a412ad114a9e96 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -54,6 +54,7 @@ cc_library( "@llvm//:core", "@llvm//:support", "@llvm//:target", + "@llvm//:transform_utils", ], ) diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index 6384c7f46f5ebbedaeda232b40095611a5d738a4..f7821adc74c70f8ba0b4a23637803c52b827287c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -29,18 +29,13 @@ limitations under the License. namespace xla { namespace llvm_ir { -IrArray::Index::Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) - : multidim_(ShapeUtil::Rank(shape)), - linear_(linear), - layout_(shape.layout()), - dims_(shape.dimensions().begin(), shape.dimensions().end()) { - CHECK(LayoutUtil::HasLayout(shape)) - << "Shape " << ShapeUtil::HumanStringWithLayout(shape) - << " should have a layout."; +static void Delinearize(std::vector* multidim, + llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) { int64 divisor = 1; - for (int64 i = 0; i < layout_.minor_to_major_size(); ++i) { - int64 dimension = layout_.minor_to_major(i); + const Layout& layout = shape.layout(); + for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { + int64 dimension = layout.minor_to_major(i); int64 size_of_current_dimension = shape.dimensions(dimension); // If i is not the last dimension, compute @@ -54,16 +49,28 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. auto* quot = ir_builder->CreateUDiv(linear, ir_builder->getInt64(divisor)); - if (i < layout_.minor_to_major_size() - 1) { - multidim_[dimension] = ir_builder->CreateURem( + if (i < layout.minor_to_major_size() - 1) { + (*multidim)[dimension] = ir_builder->CreateURem( quot, ir_builder->getInt64(size_of_current_dimension)); } else { - multidim_[dimension] = quot; + (*multidim)[dimension] = quot; } divisor *= size_of_current_dimension; } } +IrArray::Index::Index(llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) + : multidim_(ShapeUtil::Rank(shape)), + linear_(linear), + layout_(shape.layout()), + dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK(LayoutUtil::HasLayout(shape)) + << "Shape " << ShapeUtil::HumanStringWithLayout(shape) + << " should have a layout."; + Delinearize(&multidim_, linear, shape, ir_builder); +} + IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, llvm::Value* linear, const Shape& shape) : multidim_(multidim.begin(), multidim.end()), @@ -83,7 +90,6 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, dims_(shape.dimensions().begin(), shape.dimensions().end()) { CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)); - linear_ = Linearize(AsInt64Slice(shape.dimensions()), ir_builder); } IrArray::IrArray(llvm::Value* base_ptr, const Shape& shape) @@ -106,16 +112,13 @@ IrArray::IrArray(llvm::Value* base_ptr, const Shape& shape) } } -// Returns whether given linear index valid on given shape. +// Returns whether the given linear index is valid on the given shape. bool IrArray::Index::LinearValidOnShape(const Shape& a) const { - auto b = ShapeUtil::MakeShape(PRED /* irrelevant */, dims_); + auto b = ShapeUtil::MakeShape(a.element_type(), dims_); *b.mutable_layout() = layout_; return linear_ != nullptr && - ContainersEqual( - ShapeUtil::StripDegenerateDimensions(a).dimensions(), - ShapeUtil::StripDegenerateDimensions(b).dimensions()) && - LayoutUtil::Equal(ShapeUtil::StripDegenerateDimensions(a).layout(), - ShapeUtil::StripDegenerateDimensions(b).layout()); + ShapeUtil::ElementsIn(a) == ShapeUtil::ElementsIn(b) && + ShapeUtil::ReshapeIsBitcast(a, b); } IrArray::Index IrArray::Index::SourceIndexOfReshape( @@ -160,7 +163,8 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( } } - if (linear() != nullptr && + if (linear() != nullptr && LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape) && ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) { return Index(source_multidim_index, linear(), input_shape); } @@ -195,13 +199,42 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose( llvm::IRBuilder<>* builder) const { std::vector operand_multidim_index = Permute(dimension_mapping, multidim()); - if (linear() != nullptr && + + if (linear() != nullptr && LayoutUtil::HasLayout(operand_shape) && + LayoutUtil::HasLayout(shape) && ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) { return Index(operand_multidim_index, linear(), operand_shape); } + return Index(operand_multidim_index); } +IrArray::Index IrArray::Index::SourceIndexOfBitcast( + const Shape& shape, const Shape& operand_shape, + llvm::IRBuilder<>* builder) const { + CHECK(LayoutUtil::HasLayout(shape) && LayoutUtil::HasLayout(operand_shape)); + + // First linearize the index coming from the output of the bitcast. We want + // the physical index of the element in the buffer. This is like Linearize, + // but takes the layout into account. + int64 scale = 1; + llvm::Value* linear_index = builder->getInt64(0); + for (auto dimension : LayoutUtil::MinorToMajor(shape)) { + linear_index = builder->CreateAdd( + linear_index, + builder->CreateMul(multidim_[dimension], builder->getInt64(scale), "", + /*HasNUW=*/true, /*HasNSW=*/true), + "", /*HasNUW=*/true, /*HasNSW=*/true); + scale *= shape.dimensions(dimension); + } + + // Now delinearize it for the input of the bitcast. + std::vector multi_index(operand_shape.dimensions_size()); + Delinearize(&multi_index, linear_index, operand_shape, builder); + + return Index(multi_index, linear_index, operand_shape); +} + llvm::Value* IrArray::Index::Linearize( tensorflow::gtl::ArraySlice dimensions, llvm::IRBuilder<>* builder) const { diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 387d4629125cbb791840e943013188d14159908a..b942717512086a474fc8baa534193e41fb1728b5 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -134,6 +134,11 @@ class IrArray { tensorflow::gtl::ArraySlice dimension_mapping, llvm::IRBuilder<>* builder) const; + // Given that "this" is the target index of a bitcast from `operand_shape` + // to `shape` with the given dimension mapping, returns the source index. + Index SourceIndexOfBitcast(const Shape& shape, const Shape& operand_shape, + llvm::IRBuilder<>* builder) const; + // Linearizes the index into the given shape, i.e. reshapes it to rank-1 and // returns the index into the sole dimension 0 of the new shape. llvm::Value* Linearize(tensorflow::gtl::ArraySlice dimensions, diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index 8d1e6338e189a055ac20f09961a783b52600866d..2a282f3be79f847a6569416794d1a2a3fcd69148 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -20,9 +20,11 @@ limitations under the License. #include #include "llvm/IR/DerivedTypes.h" +#include "llvm/IR/GlobalValue.h" #include "llvm/IR/MDBuilder.h" #include "llvm/IR/Operator.h" #include "llvm/Target/TargetOptions.h" +#include "llvm/Transforms/Utils/Cloning.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" @@ -61,6 +63,16 @@ llvm::StringRef AsStringRef(tensorflow::StringPiece str) { return llvm::StringRef(str.data(), str.size()); } +std::unique_ptr DropConstantInitializers( + const llvm::Module& module) { + std::unique_ptr cloned_module = CloneModule(module); + for (llvm::GlobalVariable& global_var : cloned_module->globals()) { + global_var.setInitializer(nullptr); + global_var.setLinkage(llvm::GlobalValue::LinkageTypes::ExternalLinkage); + } + return cloned_module; +} + string DumpModuleToString(const llvm::Module& module) { std::string buffer_string; llvm::raw_string_ostream ostream(buffer_string); @@ -94,8 +106,10 @@ llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, auto cmp = ir_builder->CreateFCmpUGE(lhs_value, rhs_value); return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); } else { - return EmitCallToIntrinsic(llvm::Intrinsic::maxnum, {lhs_value, rhs_value}, - {lhs_value->getType()}, ir_builder); + auto cmp_ge = ir_builder->CreateFCmpOGE(lhs_value, rhs_value); + auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = ir_builder->CreateOr(cmp_ge, lhs_is_nan); + return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); } } @@ -105,8 +119,10 @@ llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, auto cmp = ir_builder->CreateFCmpULE(lhs_value, rhs_value); return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); } else { - return EmitCallToIntrinsic(llvm::Intrinsic::minnum, {lhs_value, rhs_value}, - {lhs_value->getType()}, ir_builder); + auto cmp_le = ir_builder->CreateFCmpOLE(lhs_value, rhs_value); + auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = ir_builder->CreateOr(cmp_le, lhs_is_nan); + return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); } } @@ -672,6 +688,19 @@ static string GetProcessUniqueIrFileName(tensorflow::StringPiece prefix) { return uniquer->GetUniqueName(prefix); } +static Status CreateAndWriteStringToFile(const string& directory_name, + const string& file_name, + const string& text) { + std::unique_ptr f; + TF_RETURN_IF_ERROR( + tensorflow::Env::Default()->RecursivelyCreateDir(directory_name)); + TF_RETURN_IF_ERROR( + tensorflow::Env::Default()->NewWritableFile(file_name, &f)); + TF_RETURN_IF_ERROR(f->Append(text)); + TF_RETURN_IF_ERROR(f->Close()); + return Status::OK(); +} + Status DumpIRToDirectory(const string& directory_name, const string& hlo_module_name, const llvm::Module& llvm_module, bool optimized) { @@ -686,13 +715,17 @@ Status DumpIRToDirectory(const string& directory_name, directory_name, tensorflow::strings::StrCat(unique_and_safe_file_name, ".ll")); - std::unique_ptr f; - TF_RETURN_IF_ERROR( - tensorflow::Env::Default()->RecursivelyCreateDir(directory_name)); - TF_RETURN_IF_ERROR( - tensorflow::Env::Default()->NewWritableFile(ir_file_name, &f)); - TF_RETURN_IF_ERROR(f->Append(DumpModuleToString(llvm_module))); - return f->Close(); + // For some models the embedded constants can be huge, so also dump the module + // with the constants stripped to get IR that is easier to manipulate. + string ir_no_constant_initializers_file_name = tensorflow::io::JoinPath( + directory_name, + tensorflow::strings::StrCat(unique_and_safe_file_name, "-noconst.ll")); + + TF_RETURN_IF_ERROR(CreateAndWriteStringToFile( + directory_name, ir_file_name, DumpModuleToString(llvm_module))); + return CreateAndWriteStringToFile( + directory_name, ir_no_constant_initializers_file_name, + DumpModuleToString(*DropConstantInitializers(llvm_module))); } llvm::Function* CreateFunction(llvm::FunctionType* function_type, diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 98dfc89867ab33788c4cc837a66d6751a1ef2507..25c2fe97e430847243d7ca972e16f5351168bc64 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -44,6 +44,7 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" @@ -231,10 +232,14 @@ tensorflow::Status Service::ValidateResultShapeWithLayout( return ShapeUtil::ValidateShape(shape_with_layout); } -StatusOr> Service::ResolveAndValidateArguments( +StatusOr>> +Service::ResolveAndValidateArguments( tensorflow::gtl::ArraySlice arguments, - int device_ordinal) { - std::vector shaped_buffers; + tensorflow::gtl::ArraySlice + stream_executors) { + CHECK_EQ(options_.number_of_replicas(), stream_executors.size()); + std::vector> replicated_arguments; + replicated_arguments.resize(options_.number_of_replicas()); for (size_t i = 0; i < arguments.size(); ++i) { auto buffer_status = allocation_tracker_.Resolve(*arguments[i]); if (!buffer_status.ok()) { @@ -242,22 +247,25 @@ StatusOr> Service::ResolveAndValidateArguments( StrCat(buffer_status.status().error_message(), ", ", "failed to resolve allocation for parameter ", i)); } - const ShapedBuffer* shaped_buffer = buffer_status.ValueOrDie(); - - // Verify allocation is same platform and device as the execution. - if (shaped_buffer->platform() != execute_backend_->platform() || - shaped_buffer->device_ordinal() != device_ordinal) { - return InvalidArgument( - "argument %lu is on device %s:%d but computation will be executed " - "on device %s", - i, shaped_buffer->platform()->Name().c_str(), - shaped_buffer->device_ordinal(), - execute_backend_->device_name(device_ordinal).c_str()); + auto replicated_buffers = buffer_status.ValueOrDie(); + CHECK_EQ(options_.number_of_replicas(), replicated_buffers.size()); + for (int replica = 0; replica < options_.number_of_replicas(); ++replica) { + const ShapedBuffer* shaped_buffer = replicated_buffers[replica]; + int replica_device_ordinal = stream_executors[replica]->device_ordinal(); + // Verify allocation is same platform and device as the execution. + if (shaped_buffer->platform() != execute_backend_->platform() || + shaped_buffer->device_ordinal() != replica_device_ordinal) { + return InvalidArgument( + "argument %lu is on device %s:%d but computation will be executed " + "on device %s", + i, shaped_buffer->platform()->Name().c_str(), + shaped_buffer->device_ordinal(), + execute_backend_->device_name(replica_device_ordinal).c_str()); + } + replicated_arguments[replica].push_back(shaped_buffer); } - - shaped_buffers.push_back(shaped_buffer); } - return shaped_buffers; + return replicated_arguments; } StatusOr> Service::CreateModuleConfig( @@ -489,7 +497,8 @@ StatusOr> Service::BuildAndCacheExecutable( StatusOr> Service::ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, - tensorflow::gtl::ArraySlice> arguments, + tensorflow::gtl::ArraySlice>> + arguments, Backend* backend, tensorflow::gtl::ArraySlice device_handles, tensorflow::gtl::ArraySlice result_tags, ExecutionProfile* profile) { @@ -512,6 +521,8 @@ Service::ExecuteParallelAndRegisterResult( for (int64 i = 0; i < executables.size(); i++) { // Stream executors for the replicas of the current computation. TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i])); + CHECK_EQ(replicas.size(), arguments[i].size()); + std::vector> result_buffers; for (int64 replica = 0; replica < replicas.size(); ++replica) { TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, backend->BorrowStream(replicas[replica])); @@ -544,23 +555,20 @@ Service::ExecuteParallelAndRegisterResult( backend->StreamBorrower()); // Asynchronously launch the computation. - TF_ASSIGN_OR_RETURN( - std::unique_ptr result, - executables[i]->ExecuteAsyncOnStream(&run_options, arguments[i])); + TF_ASSIGN_OR_RETURN(std::unique_ptr result, + executables[i]->ExecuteAsyncOnStream( + &run_options, arguments[i][replica])); if (replica == 0 && profile != nullptr) { streams.back()->ThenStopTimer(timers.back().get()); } - // All replicas share the same device address for the result allocation, - // so only one of the replicas need to register the result handle. - if (replica == 0) { - TF_ASSIGN_OR_RETURN( - GlobalDataHandle handle, - allocation_tracker_.Register(std::move(result), result_tags[i])); - result_handles.push_back(handle); - } + result_buffers.emplace_back(std::move(result)); } + TF_ASSIGN_OR_RETURN(GlobalDataHandle handle, + allocation_tracker_.RegisterReplicatedBuffers( + std::move(result_buffers), result_tags[i])); + result_handles.push_back(handle); } // Wait for all executions to complete. @@ -626,9 +634,9 @@ Service::ExecuteParallelAndRegisterResult( StatusOr Service::ExecuteAndRegisterResult( Executable* executable, - const tensorflow::gtl::ArraySlice arguments, - Backend* backend, perftools::gputools::StreamExecutor* executor, - const string& result_tag, ExecutionProfile* profile) { + const tensorflow::gtl::ArraySlice> + arguments, + Backend* backend, const string& result_tag, ExecutionProfile* profile) { // Set up streams. std::vector::SmartPtr> streams; @@ -661,21 +669,26 @@ StatusOr Service::ExecuteAndRegisterResult( backend->inter_op_thread_pool()); } - std::unique_ptr result; if (options_.number_of_replicas() == 1) { - TF_ASSIGN_OR_RETURN(result, executable->ExecuteOnStreamWrapper( - &run_options[0], profile, arguments)); - } else { - // TODO(b/69985541): Support profiling also on this path. - std::vector> - repeated_arguments(options_.number_of_replicas(), arguments); - - TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams( - run_options, repeated_arguments)); - TF_RET_CHECK(!results.empty()); - result = std::move(results[0]); + TF_ASSIGN_OR_RETURN( + auto result, executable->ExecuteOnStreamWrapper(&run_options[0], + profile, arguments[0])); + return allocation_tracker_.Register(std::move(result), result_tag); + } + + // TODO(b/69985541): Support profiling also on this path. + + std::vector> + replicated_arguments; + for (const auto& arg : arguments) { + replicated_arguments.emplace_back(arg); } - return allocation_tracker_.Register(std::move(result), result_tag); + + TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams( + run_options, replicated_arguments)); + TF_RET_CHECK(!results.empty()); + return allocation_tracker_.RegisterReplicatedBuffers(std::move(results), + result_tag); } tensorflow::Status Service::SetReturnValue(const SetReturnValueRequest* arg, @@ -689,7 +702,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, ExecuteParallelResponse* result) { VLOG(1) << "running execute-parallel request: " << arg->ShortDebugString(); - std::vector> all_arguments; + std::vector>> all_arguments; std::vector> all_executors; std::vector versioned_handles; std::vector> module_configs; @@ -717,6 +730,14 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, return FailedPrecondition( "device handles must be given to execute parallel computations"); } + if (arg->requests_size() > 1 && + execution_options.device_handles_size() > 1) { + return InvalidArgument( + "Parallel requests with multiple device handles is not supported. " + "Found %d parallel requests, with request %lld containing %d device " + "handles.", + arg->requests_size(), i, execution_options.device_handles_size()); + } std::vector executors; for (const auto& device_handle : execution_options.device_handles()) { TF_ASSIGN_OR_RETURN(auto replicas, @@ -746,22 +767,26 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, // In the case of partitioned computations, assume all arguments go on the // zeroth core. TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(request.arguments(), - executors[0]->device_ordinal())); + auto replicas, + Replicas(*execute_backend_, execution_options.device_handles(0))); + TF_ASSIGN_OR_RETURN( + std::vector> replicated_arguments, + ResolveAndValidateArguments(request.arguments(), replicas)); // Create an HloModuleConfig object for the computation, given the shape of - // the program and the argument allocations. + // the program and the argument allocations. Here, we care only about the + // shapes of the arguments, so, it is sufficient to use the arguments of + // replica 0. TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, + CreateModuleConfig(*program_shape, replicated_arguments.front(), request.execution_options(), *user_computation)); VLOG(3) << "ExecuteParallel created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); // Adds to the vectors to build and execute the computations after the loop. - all_arguments.push_back(arguments); - all_arguments.insert(all_arguments.end(), executors.size() - 1, {}); + all_arguments.push_back(replicated_arguments); + all_arguments.insert(all_arguments.end(), executors.size() - 1, {{}}); versioned_handles.push_back(versioned_handle); module_configs.push_back(std::move(module_config)); computation_names.insert(computation_names.end(), executors.size(), @@ -860,15 +885,18 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, std::shared_ptr program_shape, user_computation->ComputeProgramShape(versioned_handle.version)); + TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, + SingleComputationDeviceHandle())); TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(arg->arguments(), - execute_backend_->default_device_ordinal())); + std::vector> replicated_arguments, + ResolveAndValidateArguments(arg->arguments(), replicas)); + // Since we care only about the shapes of the arguments, it is sufficient to + // use the arguments of replica 0. TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options(), - *user_computation)); + CreateModuleConfig(*program_shape, replicated_arguments.front(), + arg->execution_options(), *user_computation)); VLOG(3) << "Execute created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -884,20 +912,21 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, executable->session_module()->set_execution_platform( execute_backend_->platform()->Name()); TF_RETURN_IF_ERROR(RecordArguments( - arguments, execute_backend_->default_stream_executor(), + replicated_arguments.front(), + execute_backend_->default_stream_executor(), execute_backend_->transfer_manager(), executable->session_module())); } TF_ASSIGN_OR_RETURN( *result->mutable_output(), ExecuteAndRegisterResult( - executable.get(), arguments, execute_backend_.get(), - execute_backend_->default_stream_executor(), + executable.get(), replicated_arguments, execute_backend_.get(), "result of " + user_computation->name(), result->mutable_profile())); if (executable->dumping()) { - TF_ASSIGN_OR_RETURN(const ShapedBuffer* result_buffer, - allocation_tracker_.Resolve(result->output())); + TF_ASSIGN_OR_RETURN( + const ShapedBuffer* result_buffer, + allocation_tracker_.ResolveForReplica(result->output(), 0)); TF_RETURN_IF_ERROR(RecordResult( *result_buffer, execute_backend_->default_stream_executor(), execute_backend_->transfer_manager(), executable->session_module())); @@ -925,15 +954,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, std::shared_ptr program_shape, user_computation->ComputeProgramShape(versioned_handle.version)); + TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, + SingleComputationDeviceHandle())); + TF_RET_CHECK(!replicas.empty()); TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(arg->arguments(), - execute_backend_->default_device_ordinal())); + std::vector> replicated_arguments, + ResolveAndValidateArguments(arg->arguments(), replicas)); TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options(), - *user_computation)); + CreateModuleConfig(*program_shape, replicated_arguments.front(), + arg->execution_options(), *user_computation)); VLOG(3) << "ExecuteAsync created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -946,21 +977,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, versioned_handle, std::move(module_config), execute_backend_.get(), execute_backend_->default_stream_executor(), &profile)); - TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, - SingleComputationDeviceHandle())); - TF_RET_CHECK(!replicas.empty()); - // Set up streams. std::vector::SmartPtr> streams; - for (se::StreamExecutor* executor : replicas) { TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, execute_backend_->BorrowStream(executor)); streams.push_back(std::move(stream)); } - std::unique_ptr result_buffer; - for (const Pool::SmartPtr& stream : streams) { + std::vector> result_buffers; + for (size_t i = 0; i < streams.size(); ++i) { + const auto& stream = streams[i]; ExecutableRunOptions options; options.set_stream(stream.get()); options.set_allocator(execute_backend_->memory_allocator()); @@ -971,20 +998,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, ServiceExecutableRunOptions service_options( options, execute_backend_->StreamBorrower()); - TF_ASSIGN_OR_RETURN( - std::unique_ptr this_result_buffer, - executable->ExecuteAsyncOnStream(&service_options, arguments)); + TF_ASSIGN_OR_RETURN(std::unique_ptr this_result_buffer, + executable->ExecuteAsyncOnStream( + &service_options, replicated_arguments[i])); - // Take the first result. - if (result_buffer == nullptr) { - result_buffer = std::move(this_result_buffer); - } + result_buffers.emplace_back(std::move(this_result_buffer)); } TF_ASSIGN_OR_RETURN( GlobalDataHandle output, - allocation_tracker_.Register(std::move(result_buffer), - "result of " + user_computation->name())); + allocation_tracker_.RegisterReplicatedBuffers( + std::move(result_buffers), "result of " + user_computation->name())); *result->mutable_execution() = execution_tracker_.Register( execute_backend_.get(), std::move(streams), profile, output); @@ -1012,7 +1036,7 @@ tensorflow::Status Service::WaitForExecution(const WaitForExecutionRequest* arg, tensorflow::Status Service::TransferToClient(const TransferToClientRequest* arg, TransferToClientResponse* result) { TF_ASSIGN_OR_RETURN(const ShapedBuffer* shaped_buffer, - allocation_tracker_.Resolve(arg->data())); + allocation_tracker_.ResolveForReplica(arg->data(), 0)); const Shape* return_shape; if (arg->has_shape_with_layout()) { @@ -1073,37 +1097,24 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg, replicas, Replicas(*execute_backend_, SingleComputationDeviceHandle())); } - // All memory allocation is done on the first replica. The allocations in all - // other replicas mirror the firsts'. - int master_device_ordinal = replicas[0]->device_ordinal(); - TF_ASSIGN_OR_RETURN( - std::unique_ptr shaped_buffer, - execute_backend_->transfer_manager()->AllocateShapedBuffer( - shape, execute_backend_->memory_allocator(), master_device_ordinal)); - - // Transfer the data to the replicas. + // Allocate memory in each replica and transfer the data to all replicas. + std::vector> replicated_buffers; for (se::StreamExecutor* executor : replicas) { - if (executor->device_ordinal() == master_device_ordinal) { - TF_RETURN_IF_ERROR( - execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, *shaped_buffer)); - } else { - // The replica is not the master. Create an cloned shaped buffer with - // the replica's device ordinal. This is required because - // TransferLiteralToDevice verifies that the device ordinal of the shaped - // buffer matches that of the executor. - std::unique_ptr clone = - CloneShapedBufferOnDevice(*shaped_buffer, executor->device_ordinal()); - TF_RETURN_IF_ERROR( - execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, *clone)); - } + TF_ASSIGN_OR_RETURN( + std::unique_ptr shaped_buffer, + execute_backend_->transfer_manager()->AllocateShapedBuffer( + shape, execute_backend_->memory_allocator(), + executor->device_ordinal())); + TF_RETURN_IF_ERROR( + execute_backend_->transfer_manager()->TransferLiteralToDevice( + executor, *literal, *shaped_buffer)); + replicated_buffers.emplace_back(std::move(shaped_buffer)); } - TF_ASSIGN_OR_RETURN( - *result->mutable_data(), - allocation_tracker_.Register(std::move(shaped_buffer), - StrCat("TransferToServer literal of shape ", - ShapeUtil::HumanString(shape)))); + TF_ASSIGN_OR_RETURN(*result->mutable_data(), + allocation_tracker_.RegisterReplicatedBuffers( + std::move(replicated_buffers), + StrCat("TransferToServer literal of shape ", + ShapeUtil::HumanString(shape)))); return tensorflow::Status::OK(); } @@ -1286,7 +1297,7 @@ tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg, tensorflow::Status Service::GetShape(const GetShapeRequest* arg, GetShapeResponse* result) { TF_ASSIGN_OR_RETURN(const ShapedBuffer* buffer, - allocation_tracker_.Resolve(arg->data())); + allocation_tracker_.ResolveForReplica(arg->data(), 0)); *result->mutable_shape() = buffer->on_host_shape(); return tensorflow::Status::OK(); } @@ -1445,6 +1456,9 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) { case OpRequest::kFftRequest: handle_status = computation->AddFftInstruction(arg->fft_request()); break; + case OpRequest::kGatherRequest: + handle_status = computation->AddGatherInstruction(arg->gather_request()); + break; case OpRequest::kGetTupleElementRequest: handle_status = computation->AddGetTupleElementInstruction( arg->get_tuple_element_request()); @@ -1456,6 +1470,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) { handle_status = computation->AddOutfeedInstruction(arg->outfeed_request()); break; + case OpRequest::kHostComputeRequest: + handle_status = + computation->AddHostComputeInstruction(arg->host_compute_request()); + break; case OpRequest::kMapRequest: { TF_ASSIGN_OR_RETURN( UserComputation * to_apply, @@ -1548,8 +1566,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) { case OpRequest::kSendRequest: { TF_RETURN_IF_ERROR( channel_tracker_.RegisterSend(arg->send_request().channel_handle())); - TF_RETURN_IF_ERROR(computation->AddSendInstruction(arg->send_request())); - return tensorflow::Status::OK(); + // Send does not return a value, but we need a handle to be able to + // set OpMetadata and OpSharding (device assignment). + handle_status = computation->AddSendInstruction(arg->send_request()); + break; } case OpRequest::kRecvRequest: { TF_RETURN_IF_ERROR( diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 6ce241971156599aaa25aea1b0caac0e1bd5379c..e047df26484c5b82a11c906aeb3ec16a63c1b4f8 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -265,11 +265,14 @@ class Service : public ServiceInterface { static StatusOr> CreateComputeConstantBackend(); // Resolves the given argument handles in the allocation tracker and returns - // the corresponding allocations. The function also verifies that each - // allocation matches the execution platform and device ordinal. - StatusOr> ResolveAndValidateArguments( + // the corresponding allocations for every replica. The function also verifies + // that each allocation matches the execution platform and device ordinal of + // the corresponding replica. + StatusOr>> + ResolveAndValidateArguments( tensorflow::gtl::ArraySlice arguments, - int device_ordinal); + tensorflow::gtl::ArraySlice + stream_executors); // Create a Hlo module config for the given program shape and arguments. // execution_options is optional; if not given a default is used. @@ -314,16 +317,17 @@ class Service : public ServiceInterface { // ExecutionProfile object which will be filled in with profile data. StatusOr ExecuteAndRegisterResult( Executable* executable, - const tensorflow::gtl::ArraySlice arguments, - Backend* backend, perftools::gputools::StreamExecutor* executor, - const string& result_tag, ExecutionProfile* profile); + const tensorflow::gtl::ArraySlice> + arguments, + Backend* backend, const string& result_tag, ExecutionProfile* profile); // Runs the given executables with the given arguments and register the result // from each executable in the allocation tracker. The handles of the result // from the tracker are returned. StatusOr> ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, - tensorflow::gtl::ArraySlice> arguments, + tensorflow::gtl::ArraySlice>> + arguments, Backend* backend, tensorflow::gtl::ArraySlice device_handles, tensorflow::gtl::ArraySlice result_tags, diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index 4ba6da6ccc44be8f3c70d2af80b30f0b2e388c2a..915baecc56272c57643fe6fc658b1e76a96527ff 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -169,11 +169,11 @@ bool AllUnique(tensorflow::gtl::ArraySlice slice) { tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape, tensorflow::StringPiece op_type) { if (ShapeUtil::IsTuple(shape)) { - return InvalidArgument("Expected non-tuple argument for %s. Got: %s", + return InvalidArgument("Expected non-tuple argument for %s, but got %s.", op_type.ToString().c_str(), ShapeUtil::HumanString(shape).c_str()); } else if (ShapeUtil::IsOpaque(shape)) { - return InvalidArgument("Expected non-opaque argument for %s. Got: %s", + return InvalidArgument("Expected non-opaque argument for %s, but got %s.", op_type.ToString().c_str(), ShapeUtil::HumanString(shape).c_str()); } else { @@ -193,8 +193,7 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, const Shape& accumulator_shape = reducer_shape.result(); if (ShapeUtil::Rank(accumulator_shape) != 0) { - return Unimplemented( - "Reduction function currently must have rank-0 result."); + return InvalidArgument("Reduction function must have rank 0."); } // Check that the accumulator can be passed in as the first argument. @@ -209,7 +208,8 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, } // Check that init_value's shape is suitable for reducer_shape. - if (!ShapeUtil::Compatible(accumulator_shape, init_value_shape)) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(accumulator_shape, + init_value_shape)) { return InvalidArgument( "Reduction function's accumulator shape differs from the " "init_value shape: %s vs %s", @@ -220,8 +220,8 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, // Check that the inputs can be passed in as the second argument. const Shape& input_element_shape = ShapeUtil::MakeShape(input_element_type, {}); - if (!ShapeUtil::Compatible(input_element_shape, - reducer_shape.parameters(1))) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(input_element_shape, + reducer_shape.parameters(1))) { return InvalidArgument( "Reduction function's second parameter shape differs from the " "input type element type: %s vs %s", @@ -231,10 +231,11 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, // Currently the accumulator and inputs must be the same type, // though that restriction could be relaxed. - if (!ShapeUtil::Compatible(accumulator_shape, reducer_shape.parameters(1))) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(accumulator_shape, + reducer_shape.parameters(1))) { return InvalidArgument( - "Reduction function's second parameter shape currently must " - "match the result shape. Got %s vs %s", + "Reduction function's second parameter shape must " + "match the result shape, but got %s vs %s.", ShapeUtil::HumanString(reducer_shape.parameters(1)).c_str(), ShapeUtil::HumanString(accumulator_shape).c_str()); } @@ -256,29 +257,29 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, for (int64 i = 0; i < window.dimensions_size(); ++i) { const auto& dim = window.dimensions(i); if (dim.size() <= 0) { - return InvalidArgument("Window has a non-positive dimension. Window: %s", + return InvalidArgument("Window %s has a non-positive dimension.", window.DebugString().c_str()); } if (dim.stride() <= 0) { - return InvalidArgument("Window has a non-positive stride. Window: %s", + return InvalidArgument("Window %s has a non-positive stride.", window.DebugString().c_str()); } if (!allow_negative_padding && dim.padding_low() < 0) { - return InvalidArgument("Window has a negative low padding. Window: %s", + return InvalidArgument("Window %s has a negative low padding.", window.DebugString().c_str()); } if (!allow_negative_padding && dim.padding_high() < 0) { - return InvalidArgument("Window has a negative high padding. Window: %s", + return InvalidArgument("Window %s has a negative high padding.", window.DebugString().c_str()); } if (dim.base_dilation() < 1) { return InvalidArgument( - "Window has a non-positive base area dilation factor. Window: %s", + "Window %s has a non-positive base area dilation factor.", window.DebugString().c_str()); } if (dim.window_dilation() < 1) { return InvalidArgument( - "Window has a non-positive window dilation factor. Window: %s", + "Window %s has a non-positive window dilation factor.", window.DebugString().c_str()); } @@ -318,8 +319,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_CEIL: if (!ShapeUtil::ElementIsFloating(arg)) { return InvalidArgument( - "expected element type in shape to be floating for floor/ceil " - "operation; got %s", + "Expected element type in shape to be floating for floor/ceil " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -331,8 +332,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (!ShapeUtil::ElementIsFloating(arg) && !ShapeUtil::ElementIsComplex(arg)) { return InvalidArgument( - "expected element type in shape to be floating or complex for " - "sin/cos/exp/log/tanh operation; got %s", + "Expected element type in shape to be floating or complex for " + "sin/cos/exp/log/tanh operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -340,8 +341,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_IMAG: if (!ShapeUtil::ElementIsComplex(arg)) { return InvalidArgument( - "expected element type in shape to be complex for real/imag " - "operation; got %s", + "Expected element type in shape to be complex for real/imag " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return ShapeUtil::ChangeElementType(arg, F32); @@ -361,8 +362,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (arg.element_type() != PRED && !primitive_util::IsIntegralType(arg.element_type())) { return InvalidArgument( - "expected pred or an integral element type in argument to not " - "operation; got %s", + "Expected pred or an integral element type in argument to Not " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -370,8 +371,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_IS_FINITE: if (!ShapeUtil::ElementIsFloating(arg)) { return InvalidArgument( - "expected element type in shape to be floating point for IsFinite " - "operation; got %s", + "Expected element type in shape to be floating point for IsFinite " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return ShapeUtil::ChangeElementType(arg, PRED); @@ -387,31 +388,33 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, tensorflow::gtl::ArraySlice arg_shapes, const int64 dimension) { if (arg_shapes.empty()) { - return InvalidArgument("Concatenate expects at least one argument"); + return InvalidArgument("Concatenate expects at least one argument."); } if (dimension < 0 || dimension >= ShapeUtil::Rank(*arg_shapes[0])) { - return InvalidArgument("dimension to concatenate along out of bounds: %lld", + return InvalidArgument("Concatenate dimension out of bounds: %lld.", dimension); } const Shape* arg_shape = nullptr; + PrimitiveType element_type = PRIMITIVE_TYPE_INVALID; for (const Shape* shape : arg_shapes) { TF_RETURN_IF_ERROR( ExpectNotTupleOrOpaque(*shape, "operand of concatenation")); if (!arg_shape) { arg_shape = shape; + element_type = arg_shape->element_type(); continue; } if (ShapeUtil::Rank(*arg_shape) != ShapeUtil::Rank(*shape)) { return InvalidArgument( "Cannot concatenate arrays with different ranks: %lld (%s) vs %lld " - "(%s)", + "(%s).", ShapeUtil::Rank(*arg_shape), ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::Rank(*shape), ShapeUtil::HumanString(*shape).c_str()); } - if (arg_shape->element_type() != shape->element_type()) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shape, *shape)) { return InvalidArgument( - "cannot concatenate arrays with different element types: %s vs %s", + "Cannot concatenate arrays with different element types: %s vs %s.", PrimitiveType_Name(arg_shape->element_type()).c_str(), PrimitiveType_Name(shape->element_type()).c_str()); } @@ -424,13 +427,14 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, // concatenating. } return InvalidArgument( - "cannot concatenate arrays that differ in dimensions other than " + "Cannot concatenate arrays that differ in dimensions other than " "the one being concatenated (the other array dimensions must be " - "the same): %s vs %s in dimension %lld", + "the same): %s vs %s in dimension %lld.", ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::HumanString(*shape).c_str(), dimension); } } + element_type = ShapeUtil::HigherPrecisionElementType(*shape, *arg_shape); } std::vector new_dimensions(arg_shape->dimensions().begin(), @@ -438,7 +442,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, for (size_t i = 1; i < arg_shapes.size(); ++i) { new_dimensions[dimension] += arg_shapes[i]->dimensions(dimension); } - return ShapeUtil::MakeShape(arg_shape->element_type(), new_dimensions); + return ShapeUtil::MakeShape(element_type, new_dimensions); } /* static */ StatusOr ShapeInference::InferConvertShape( @@ -447,7 +451,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (primitive_util::IsComplexType(old_element_type) && !primitive_util::IsComplexType(new_element_type)) { return Unimplemented( - "Unsupported conversion from complex to real type: %s => %s", + "Conversion from complex to real type %s => %s is not implemented.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -456,7 +460,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "cannot convert from or to tuple type; requested conversion: %s => %s", + "Convert does not allow tuples, so cannot convert from %s to %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -469,24 +473,23 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, auto old_element_type = operand_shape.element_type(); if (primitive_util::IsComplexType(old_element_type) != primitive_util::IsComplexType(new_element_type)) { - return Unimplemented( - "Unsupported conversion between real and complex types: %s => %s", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + return InvalidArgument("Conversion from complex to real type %s => %s.", + ShapeUtil::HumanString(operand_shape).c_str(), + PrimitiveType_Name(new_element_type).c_str()); } if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "cannot convert from or to tuple type; requested conversion: %s => %s", + "Cannot convert from or to tuple type; requested conversion: %s => %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } if (primitive_util::BitWidth(old_element_type) != primitive_util::BitWidth(new_element_type)) { return InvalidArgument( - "cannot bitcast types with different bit-widths: %s => %s", + "Cannot bitcast types with different bit-widths: %s => %s.", PrimitiveType_Name(old_element_type).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -499,20 +502,20 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const int mantissa_bits) { if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( - "expected element type in shape to be floating point for " - "ReducePrecision operation; got %s", + "Expected element type in shape to be floating point for " + "ReducePrecision operation; got %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } if (exponent_bits < 1) { // One exponent bit is necessary to distinguish 0 from infinity. Having // no exponent bits doesn't produce a sensible number, so we require at // least one. - return InvalidArgument("expected exponent_bits >= 1; got %d", + return InvalidArgument("Expected exponent_bits >= 1; got %d.", exponent_bits); } if (mantissa_bits < 0) { // A number with no mantissa bits is still meaningful, however. - return InvalidArgument("expected non-negative mantissa_bits; got %d", + return InvalidArgument("Expected non-negative mantissa_bits; got %d.", mantissa_bits); } return operand_shape; @@ -523,22 +526,23 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const PaddingConfig& padding_config) { if (ShapeUtil::IsTuple(operand_shape)) { return InvalidArgument( - "pad operation does not support tuple-shape operands"); + "Pad operation does not support tuple-shape operands."); } if (!ShapeUtil::IsScalar(padding_value_shape)) { return InvalidArgument( - "pad operation does not support non-scalar padding values"); + "Pad operation does not support non-scalar padding values."); } if (ShapeUtil::Rank(operand_shape) != padding_config.dimensions_size()) { return InvalidArgument( "The rank of the operand and the padding configuration do not match: " - "%s vs %s", + "%s vs %s.", ShapeUtil::HumanString(operand_shape).c_str(), padding_config.ShortDebugString().c_str()); } - if (operand_shape.element_type() != padding_value_shape.element_type()) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape, + padding_value_shape)) { return InvalidArgument( - "the element types of the operands to pad do not match"); + "The element types of the operands to Pad do not match."); } std::vector dimensions(ShapeUtil::Rank(operand_shape)); for (int64 i = 0; i < operand_shape.dimensions_size(); ++i) { @@ -548,7 +552,9 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, std::max(operand_shape.dimensions(i) - 1, 0LL) * padding_config.dimensions(i).interior_padding(); } - return ShapeUtil::MakeShape(operand_shape.element_type(), dimensions); + return ShapeUtil::MakeShape( + ShapeUtil::HigherPrecisionElementType(operand_shape, padding_value_shape), + dimensions); } // Current DotDimensionNumbers Requirements: @@ -597,7 +603,7 @@ Status ValidateDotDimensionNumbers( lhs_batch_dimensions) || !dims_in_range(ShapeUtil::Rank(rhs), rhs_contracting_dimensions, rhs_batch_dimensions)) { - return InvalidArgument("A dimension number is out of range in dot: %s", + return InvalidArgument("A dimension number is out of range in Dot: %s.", dimension_numbers.DebugString().c_str()); } @@ -615,7 +621,7 @@ Status ValidateDotDimensionNumbers( if (!dims_unique(lhs_contracting_dimensions, lhs_batch_dimensions) || !dims_unique(rhs_contracting_dimensions, rhs_batch_dimensions)) { - return InvalidArgument("A dimension number is not unique in dot: %s", + return InvalidArgument("A dimension number is not unique in Dot: %s.", dimension_numbers.DebugString().c_str()); } @@ -633,8 +639,7 @@ Status ValidateDotDimensionNumbers( rhs_non_contracting_non_batch_dims < 0 || rhs_non_contracting_non_batch_dims > 1) { return InvalidArgument( - "batch and contracting dimension number mismatch " - "with rank "); + "Batch and contracting dimension number mismatch with rank."); } // Check that batch dimension numbers are ordered before all others, and @@ -646,7 +651,7 @@ Status ValidateDotDimensionNumbers( !std::equal(batch_dim_numbers.begin(), batch_dim_numbers.end(), rhs_batch_dimensions.begin())) { return InvalidArgument( - "batch dimension numbers must precede non-batch dimensions and be" + "Batch dimension numbers must precede non-batch dimensions and be" "monotonically increasing."); } @@ -663,22 +668,22 @@ Status ValidateDotDimensionNumbers( auto fail = [lhs, rhs](const string& addendum) -> Status { string message = tensorflow::strings::Printf( - "cannot infer shape for dot operation: %s %s", + "Cannot infer shape for dot operation: %s %s.", ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); if (!addendum.empty()) { - message += ": " + addendum; + message += " " + addendum; } return InvalidArgument("%s", message.c_str()); }; // Check if both element types are the same. - if (lhs.element_type() != rhs.element_type()) { - return fail("element types do not match"); + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { + return fail("Element types do not match."); } if ((ShapeUtil::Rank(lhs) < 1) || (ShapeUtil::Rank(rhs) < 1)) { - return fail("dot only supports rank 1 or above."); + return fail("Dot only supports rank 1 or above."); } // Validate basic properties of dot dimension numbers. @@ -688,7 +693,7 @@ Status ValidateDotDimensionNumbers( if (dimension_numbers.lhs_contracting_dimensions_size() != dimension_numbers.rhs_contracting_dimensions_size() || dimension_numbers.lhs_contracting_dimensions_size() != 1) { - return fail("must specify one contracting dimension for both lhs and rhs."); + return fail("Must specify one contracting dimension for both lhs and rhs."); } // Check that contracting dimension sizes match. @@ -698,13 +703,13 @@ Status ValidateDotDimensionNumbers( dimension_numbers.rhs_contracting_dimensions(0); if (lhs.dimensions(lhs_contracting_dimension) != rhs.dimensions(rhs_contracting_dimension)) { - return fail("contracting dimension sizes do not match."); + return fail("Contracting dimension sizes do not match."); } // Check that number of batch dimensions match. if (dimension_numbers.lhs_batch_dimensions_size() != dimension_numbers.rhs_batch_dimensions_size()) { - return fail("must the same number of batch dimensions for lhs and rhs."); + return fail("Must the same number of batch dimensions for lhs and rhs."); } // Check that batch dimension numbers and sizes match. @@ -713,7 +718,7 @@ Status ValidateDotDimensionNumbers( dimension_numbers.rhs_batch_dimensions(i) || lhs.dimensions(dimension_numbers.lhs_batch_dimensions(i)) != rhs.dimensions(dimension_numbers.rhs_batch_dimensions(i))) { - return fail("batch dimension numbers and sizes must match for lhs/rhs."); + return fail("Batch dimension numbers and sizes must match for lhs/rhs."); } } @@ -736,7 +741,8 @@ Status ValidateDotDimensionNumbers( dimensions.push_back(rhs.dimensions(i)); } } - Shape result = ShapeUtil::MakeShape(lhs.element_type(), dimensions); + Shape result = ShapeUtil::MakeShape( + ShapeUtil::HigherPrecisionElementType(lhs, rhs), dimensions); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(result)); VLOG(2) << "inferred dot shape: " << ShapeUtil::HumanString(result); @@ -761,13 +767,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } else if (rhs.dimensions(i) == 1) { output_dimensions[i] = lhs.dimensions(i); } else { - return InvalidArgument("binary op %s with incompatible shapes: %s and %s", - BinaryOperation_Name(operation).c_str(), - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + return InvalidArgument( + "Binary op %s with incompatible shapes: %s and %s.", + BinaryOperation_Name(operation).c_str(), + ShapeUtil::HumanString(lhs).c_str(), + ShapeUtil::HumanString(rhs).c_str()); } } - return ShapeUtil::MakeShape(lhs.element_type(), output_dimensions); + return ShapeUtil::MakeShape(ShapeUtil::HigherPrecisionElementType(lhs, rhs), + output_dimensions); } /* static */ StatusOr ShapeInference::InferInDimBroadcastShape( @@ -778,15 +786,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Reject "magic" inference for binops on different shapes, requiring // the user to provide an explicit broadcast dimension in this case. // See b/25177275 for more details. - return InvalidArgument("automatic shape inference not supported: %s and %s", + return InvalidArgument("Automatic shape inference not supported: %s and %s", ShapeUtil::HumanString(smaller_shape).c_str(), ShapeUtil::HumanString(larger_shape).c_str()); } else if (broadcast_dimensions.size() != ShapeUtil::Rank(smaller_shape)) { return InvalidArgument( - "size of broadcast_dimensions has to match lower-rank operand's " + "Size of broadcast_dimensions has to match lower-rank operand's " "rank; " " lower-rank operand's rank is %lld, size of broadcast_dimensions is " - "%zu", + "%zu.", ShapeUtil::Rank(smaller_shape), broadcast_dimensions.size()); } @@ -829,18 +837,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // specified in broadcast_dimensions are then changed to match the // corresponding dimension size in smaller_shape. Shape output_shape(larger_shape); + output_shape.set_element_type( + ShapeUtil::HigherPrecisionElementType(larger_shape, smaller_shape)); for (int i = 0; i < smaller_shape.dimensions_size(); ++i) { int64 dimension_to_match = broadcast_dimensions.at(i); if (dimension_to_match < 0) { return InvalidArgument( - "broadcast dimension number (%lld) cannot be negative", + "Broadcast dimension number (%lld) cannot be negative.", dimension_to_match); } if (dimension_to_match >= larger_shape.dimensions_size()) { return InvalidArgument( - "broadcast dimension number (%lld) too large; higher-rank " - "operand has rank %d", + "Broadcast dimension number (%lld) too large; higher-rank " + "operand has rank %d.", dimension_to_match, larger_shape.dimensions_size()); } int64 small_dimension_size = smaller_shape.dimensions(i); @@ -851,7 +861,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (small_dimension_size != large_dimension_size && small_dimension_size != 1 && large_dimension_size != 1) { return InvalidArgument( - "broadcast dimension %d mismatch: %lld != %lld; %s and %s", i, + "Broadcast dimension %d mismatch: %lld != %lld; %s and %s.", i, small_dimension_size, large_dimension_size, ShapeUtil::HumanString(smaller_shape).c_str(), ShapeUtil::HumanString(larger_shape).c_str()); @@ -860,7 +870,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // order. if (i > 0 && broadcast_dimensions.at(i - 1) >= dimension_to_match) { return InvalidArgument( - "broadcast dimensions order is wrong: %lld comes after %lld", + "Broadcast dimensions order is wrong: %lld comes after %lld.", dimension_to_match, broadcast_dimensions.at(i - 1)); } @@ -878,9 +888,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( TF_RETURN_IF_ERROR( ExpectNotTupleOrOpaque(rhs, "rhs of elementwise binary operation")); - if (!ShapeUtil::SameElementType(lhs, rhs)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( - "binary op %s with different element types: %s and %s", + "Binary op %s with different element types: %s and %s.", BinaryOperation_Name(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); @@ -892,15 +902,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!broadcast_dimensions.empty() && broadcast_dimensions != identity_dims) { return InvalidArgument( - "broadcast dimensions field must either be not set or be the " - "identity on binary operations with operands of the same rank"); + "Broadcast dimensions field must either be not set or be the " + "identity on binary operations with operands of the same rank."); } } - if (ShapeUtil::Compatible(lhs, rhs)) { + if (ShapeUtil::CompatibleIgnoringFpPrecision(lhs, rhs)) { // If the shapes are the same other than layout, the output shape is the // same (elementwise op). - return lhs; + return ShapeUtil::ChangeElementType( + lhs, ShapeUtil::HigherPrecisionElementType(lhs, rhs)); } if (ShapeUtil::Rank(lhs) == ShapeUtil::Rank(rhs)) { @@ -966,17 +977,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case BINOP_COMPLEX: { if (!ShapeUtil::ElementIsFloating(lhs)) { return InvalidArgument( - "expected element type in shape to be floating for complex compose " - "operation; got %s", + "Expected element type in shape to be floating for complex compose " + "operation; got %s.", PrimitiveType_Name(lhs.element_type()).c_str()); } TF_ASSIGN_OR_RETURN(const Shape& shape, InferElementwiseBinaryOpShape(operation, lhs, rhs, broadcast_dimensions)); - if (lhs.element_type() == F32) { + if (lhs.element_type() == F32 && rhs.element_type() == F32) { return ShapeUtil::ChangeElementType(shape, C64); } else { - return Unimplemented("complex component type not supported"); + return Unimplemented("Complex component type is not implemented."); } } case BINOP_AND: @@ -984,8 +995,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (lhs.element_type() != PRED && !primitive_util::IsIntegralType(lhs.element_type())) { return InvalidArgument( - "expected pred or integral type in argument to and/or operation; " - "got %s", + "Expected pred or integral type in argument to and/or operation; " + "got %s.", PrimitiveType_Name(lhs.element_type()).c_str()); } return InferElementwiseBinaryOpShape(operation, lhs, rhs, @@ -1003,7 +1014,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } default: return Unimplemented( - "not yet implemented; infer binary op shape: %s; lhs: %s; rhs: %s", + "Binary op shape inference: %s; lhs: %s; rhs: %s is not implemented.", BinaryOperation_Name(operation).c_str(), lhs.ShortDebugString().c_str(), rhs.ShortDebugString().c_str()); } @@ -1028,7 +1039,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case TRIOP_SELECT: return InferSelectShape(lhs, rhs, ehs); default: - return InvalidArgument("unknown operation %s", + return InvalidArgument("Unknown operation %s.", TernaryOperation_Name(operation).c_str()); } } @@ -1059,7 +1070,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return result; } default: - return InvalidArgument("unknown operation %s", + return InvalidArgument("Unknown operation %s.", VariadicOperation_Name(operation).c_str()); } } @@ -1069,7 +1080,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const ProgramShape& to_apply, tensorflow::gtl::ArraySlice dimensions) { if (arg_shapes.empty()) { - return InvalidArgument("Map expects at least one argument"); + return InvalidArgument("Map expects at least one argument."); } // All arguments must have the same shape. @@ -1078,12 +1089,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( TF_RETURN_IF_ERROR( ExpectNotTupleOrOpaque(*arg_shapes[i], "operand of map")); - if (ShapeUtil::Compatible(*arg_shapes[i], *arg_shape)) { + if (ShapeUtil::CompatibleIgnoringFpPrecision(*arg_shapes[i], *arg_shape)) { continue; } if (!ShapeUtil::IsTuple(*arg_shapes[i]) && !ShapeUtil::IsTuple(*arg_shape) && - ShapeUtil::SameElementType(*arg_shapes[i], *arg_shape)) { + ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shapes[i], + *arg_shape)) { if (ShapeUtil::IsScalar(*arg_shapes[i])) { continue; } @@ -1099,7 +1111,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } return InvalidArgument( "Map operation requires all operands to have the same shape; got: " - "%s", + "%s.", Join(pieces, ", ").c_str()); } @@ -1108,7 +1120,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (dimensions.size() != arg_shape->dimensions_size()) { return InvalidArgument( "Map applied to a subset of dimensions currently not supported: " - "arg_dimension_size: %d, requested_map_dimensions_size: %zu", + "arg_dimension_size: %d, requested_map_dimensions_size: %zu.", arg_shape->dimensions_size(), dimensions.size()); } @@ -1116,7 +1128,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int i = 0; i < dimensions.size(); ++i) { if (dimensions[i] != i) { return InvalidArgument( - "Map requires monotonically increasing dimension numbers, found: %s ", + "Map requires monotonically increasing dimension numbers; got: %s.", Join(dimensions, ", ").c_str()); } } @@ -1125,7 +1137,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (arg_shapes.size() != to_apply.parameters_size()) { return InvalidArgument( "Map applied function arity must match number of arguments; got: " - "arity: %d, arguments: %zu", + "arity: %d, arguments: %zu.", to_apply.parameters_size(), arg_shapes.size()); } @@ -1133,8 +1145,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& output_shape = to_apply.result(); if (!ShapeUtil::IsScalar(output_shape)) { return InvalidArgument( - "mapped computation's result has to be a scalar; " - "got: %s", + "Mapped computation's result has to be a scalar; got: %s.", ShapeUtil::HumanString(output_shape).c_str()); } @@ -1143,15 +1154,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::IsScalar(parameter_shape)) { return InvalidArgument( - "mapped computation's parameter has to be a scalar; " - "got parameter %d shape: %s", + "Mapped computation's parameter has to be a scalar; " + "got parameter %d shape: %s.", i, ShapeUtil::HumanString(parameter_shape).c_str()); } - if (parameter_shape.element_type() != arg_shape->element_type()) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(parameter_shape, + *arg_shape)) { return InvalidArgument( - "mapped computation's parameter type has to match argument element " - "type; got parameter %d shape: %s, argument shape: %s", + "Mapped computation's parameter type has to match argument element " + "type; got parameter %d shape: %s, argument shape: %s.", i, ShapeUtil::HumanString(parameter_shape).c_str(), ShapeUtil::HumanString(*arg_shape).c_str()); } @@ -1182,21 +1194,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-training to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-training to " - "be a non-negative number, got %lld", + "be a non-negative number, got %lld.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-training to be at least 1; got %lld", + "batch-norm-training to be at least 1; got %lld.", ShapeUtil::Rank(operand_shape)); } @@ -1217,24 +1229,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-training must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(offset_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(offset_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-training, " "but the shape of offset factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(offset_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(scale_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-training, " "but the shape of scale factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1247,7 +1261,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of offset factor should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } @@ -1255,7 +1269,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1290,21 +1304,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-inference to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-inference to " - "be a non-negative number, got %lld", + "be a non-negative number, got %lld.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-inference to be at least 1; got %lld", + "batch-norm-inference to be at least 1; got %lld.", ShapeUtil::Rank(operand_shape)); } @@ -1325,46 +1339,50 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-inference must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(offset_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(offset_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of offset factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(offset_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(scale_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of scale factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(mean_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(mean_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of mean is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(variance_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(variance_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of variance is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(variance_shape.element_type()).c_str()); } @@ -1377,7 +1395,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of offset factor should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } @@ -1385,7 +1403,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1393,7 +1411,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of mean should be the same as feature count," "but the size of mean is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } @@ -1401,7 +1419,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of variance should be the same as feature count," "but the size of variance is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(variance_shape, 0), feature_count); } @@ -1434,7 +1452,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-grad to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } @@ -1442,7 +1460,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected operand_shape of batch-norm-grad to have the same rank as" " output_grad_shape; got rank(oprand_shape) %lld, and" - " rank(output_grad_shape) %lld", + " rank(output_grad_shape) %lld.", ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(output_grad_shape)); } @@ -1470,49 +1488,53 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-grad must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } if (!ShapeUtil::ElementIsFloating(output_grad_shape)) { return InvalidArgument( "The output_grad to batch-norm-grad must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(output_grad_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(output_grad_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(output_grad_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of output_grad is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(output_grad_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(scale_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of scale factor is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(mean_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(mean_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } - if (!ShapeUtil::SameElementType(var_shape, operand_shape)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(var_shape, + operand_shape)) { return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1526,7 +1548,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of mean should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } @@ -1534,7 +1556,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1542,7 +1564,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of variance should be the same as feature count," "but the size of variance is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(var_shape, 0), feature_count); } @@ -1553,7 +1575,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The bounds of operand shape should be the same as output_grad's," "but the bound of operand_shape at dimension %lld is %lld " - "and the bound of output_grad_shape is %lld", + "and the bound of output_grad_shape is %lld.", i, ShapeUtil::GetDimension(operand_shape, i), ShapeUtil::GetDimension(output_grad_shape, i)); } @@ -1569,9 +1591,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(lhs, "lhs of convolution")); TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(rhs, "rhs of convolution")); - if (!ShapeUtil::SameElementType(lhs, rhs)) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( - "Convolution with different element types: %s and %s", + "Convolution with different element types: %s and %s.", ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); } @@ -1587,21 +1609,19 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (window.dimensions_size() != num_spatial_dims) { return InvalidArgument( "Window must have same number of dimensions as dimension numbers.\n" - "Window: %s\nDimension numbers: %s", + "Window: %s\nDimension numbers: %s.", window.DebugString().c_str(), dnums.DebugString().c_str()); } const int num_dims = num_spatial_dims + 2; if (ShapeUtil::Rank(lhs) != num_dims) { return InvalidArgument( - "The LHS argument to a convolution should have rank %d.\n" - "lhs: %s", + "The LHS argument to a convolution should have rank %d; lhs: %s.", num_dims, ShapeUtil::HumanString(lhs).c_str()); } if (ShapeUtil::Rank(rhs) != num_dims) { return InvalidArgument( - "The RHS argument to a convolution should have rank %d.\n" - "lhs: %s", + "The RHS argument to a convolution should have rank %d; lhs: %s.", num_dims, ShapeUtil::HumanString(lhs).c_str()); } TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); @@ -1638,26 +1658,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( !std::all_of(window_dnums.begin(), window_dnums.end(), in_range) || !std::all_of(output_dnums.begin(), output_dnums.end(), in_range)) { return InvalidArgument( - "A dimension number is out of range in convolution: %s", + "A dimension number is out of range in convolution: %s.", dnums.DebugString().c_str()); } if (input_dnums != expected_dnums) { return InvalidArgument( "Input dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } if (window_dnums != expected_dnums) { return InvalidArgument( "Window dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } if (output_dnums != expected_dnums) { return InvalidArgument( "Output dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } @@ -1681,7 +1701,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected LHS feature dimension (value %lld) to match RHS " "input feature dimension (value %lld); got (%s, %s)\n" - "Dimension numbers: {%s}", + "Dimension numbers: {%s}.", input_features, kernel_input_features, ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str(), dnums.DebugString().c_str()); @@ -1695,7 +1715,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "Window dimensions do not match RHS shape:\n\t" "RHS shape: %s\n\t" "Window: {%s}\n\t" - "Dimension numbers: {%s}", + "Dimension numbers: {%s}.", ShapeUtil::HumanString(rhs).c_str(), window.ShortDebugString().c_str(), dnums.ShortDebugString().c_str()); } @@ -1714,8 +1734,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( dimensions[dnums.output_spatial_dimensions(i)] = window_output_shape.dimensions(i); } - - return ShapeUtil::MakeShape(lhs.element_type(), dimensions); + return ShapeUtil::MakeShape(ShapeUtil::HigherPrecisionElementType(lhs, rhs), + dimensions); } /* static */ StatusOr ShapeInference::InferFftShape( @@ -1723,8 +1743,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const tensorflow::gtl::ArraySlice fft_length) { const int64 fft_rank = fft_length.size(); if (fft_rank < 1 || fft_rank > 3) { - return InvalidArgument("FFT only supports ranks 1-3, but got %lld", - fft_rank); + return InvalidArgument("FFT only supports ranks 1-3; got %lld.", fft_rank); } #define RET_CHECK_RANK(x) \ if (x.dimensions_size() < fft_rank) { \ @@ -1737,7 +1756,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case FFT: case IFFT: if (in.element_type() != C64) { - return InvalidArgument("%s requires C64 input type, found %s", + return InvalidArgument("%s requires C64 input type, found %s.", FftType_Name(fft_type).c_str(), PrimitiveType_Name(in.element_type()).c_str()); } @@ -1745,7 +1764,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return in; case RFFT: { if (in.element_type() != F32) { - return InvalidArgument("RFFT requires F32 input type, found %s", + return InvalidArgument("RFFT requires F32 input type, found %s.", PrimitiveType_Name(in.element_type()).c_str()); } RET_CHECK_RANK(in); @@ -1754,7 +1773,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[i]) { return InvalidArgument( "RFFT requires innermost dimensions match fft_length but " - "dimension %lld is %lld and should be %lld", + "dimension %lld is %lld and should be %lld.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1767,7 +1786,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } case IRFFT: { if (in.element_type() != C64) { - return InvalidArgument("IRFFT requires C64 input type, found %s", + return InvalidArgument("IRFFT requires C64 input type, found %s.", PrimitiveType_Name(in.element_type()).c_str()); } RET_CHECK_RANK(in); @@ -1777,7 +1796,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[i]) { return InvalidArgument( "IRFFT requires all but one innermost dimensions match " - "fft_length, but dimension %lld is %lld and should be %lld", + "fft_length, but dimension %lld is %lld and should be %lld.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1787,7 +1806,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[fft_rank - 1] / 2 + 1) { return InvalidArgument( "IRFFT requires innermost dimension matches fft_length/2+1, but " - "dimension %d is %lld and should be %lld", + "dimension %d is %lld and should be %lld.", in.dimensions_size() - 1, in.dimensions(in.dimensions_size() - 1), fft_length[fft_rank - 1] / 2 + 1); } @@ -1825,8 +1844,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int64 dimension : dimensions_to_reduce) { if (dimension >= ShapeUtil::Rank(arg) || dimension < 0) { return InvalidArgument( - "attempting to reduce out-of-bounds dimension %lld in shape %s", - dimension, ShapeUtil::HumanString(arg).c_str()); + "Reducing out-of-bounds dimension %lld in shape %s.", dimension, + ShapeUtil::HumanString(arg).c_str()); } } TF_RETURN_IF_ERROR( @@ -1866,30 +1885,30 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Check if the select function has a proper shape of (T,T) -> PRED. if (select_shape.parameters_size() != 2) { return InvalidArgument( - "select function must take 2 parameters, but " + "Select function must take 2 parameters, but " "takes %d parameter(s).", select_shape.parameters_size()); } const Shape& select_result_shape = select_shape.result(); if (!ShapeUtil::Compatible(select_result_shape, ShapeUtil::MakeShape(PRED, {}))) { - return Unimplemented("select function must have rank-0 PRED result."); + return InvalidArgument("Select function must have rank-0 PRED result."); } const Shape& operand_element_shape = ShapeUtil::MakeShape(operand_shape.element_type(), {}); - if (!ShapeUtil::Compatible(operand_element_shape, - select_shape.parameters(0))) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape, + select_shape.parameters(0))) { return InvalidArgument( - "select function's first parameter shape currently must " - "match the operand element shape. Got %s vs %s", + "Select function's first parameter shape currently must " + "match the operand element shape, but got %s vs %s.", ShapeUtil::HumanString(select_shape.parameters(0)).c_str(), ShapeUtil::HumanString(operand_element_shape).c_str()); } - if (!ShapeUtil::Compatible(operand_element_shape, - select_shape.parameters(1))) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape, + select_shape.parameters(1))) { return InvalidArgument( - "select function's second parameter shape currently must " - "match the operand element shape. Got %s vs %s", + "Select function's second parameter shape currently must " + "match the operand element shape, but got %s vs %s.", ShapeUtil::HumanString(select_shape.parameters(1)).c_str(), ShapeUtil::HumanString(operand_element_shape).c_str()); } @@ -1903,10 +1922,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( InferWindowOutputShape(operand_shape, window, operand_shape.element_type(), /*allow_negative_padding=*/false)); - if (!ShapeUtil::Compatible(source_shape, window_result_shape)) { + if (!ShapeUtil::CompatibleIgnoringFpPrecision(source_shape, + window_result_shape)) { return InvalidArgument( - "source shape does not match the shape of window-reduced operand: " - "source(%s), window-reduced operand(%s)", + "Source shape does not match the shape of window-reduced operand: " + "source(%s), window-reduced operand(%s).", ShapeUtil::HumanString(source_shape).c_str(), ShapeUtil::HumanString(window_result_shape).c_str()); } @@ -1920,7 +1940,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( auto error = [&](const string& message) { return InvalidArgument( "%s in slice operation; argument shape: %s; starts: {%s}; limits: " - "{%s}; strides: {%s}", + "{%s}; strides: {%s}.", message.c_str(), ShapeUtil::HumanString(arg).c_str(), Join(starts, ",").c_str(), Join(limits, ",").c_str(), Join(strides, ",").c_str()); @@ -1943,7 +1963,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (starts.size() != ShapeUtil::Rank(arg)) { return InvalidArgument( - "slice index count does not match argument rank: %zu vs %lld", + "Slice index count does not match argument rank: %zu vs %lld.", starts.size(), ShapeUtil::Rank(arg)); } @@ -1953,7 +1973,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( int64 limit_index = limits[dimension]; int64 stride = strides[dimension]; if (start_index < 0) { - return InvalidArgument("negative start index to slice: %lld", + return InvalidArgument("Negative start index to slice: %lld.", start_index); } if (limit_index > arg.dimensions(dimension)) { @@ -1973,7 +1993,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( limit_index, start_index)); } if (stride <= 0) { - return InvalidArgument("stride (%lld) must be positive", stride); + return InvalidArgument("Stride (%lld) must be positive.", stride); } sizes.push_back((limit_index - start_index + stride - 1) / stride); } @@ -1997,20 +2017,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "dynamic slice start indices of rank %lld must be rank1.", + "Dynamic slice start indices of rank %lld must be rank1.", ShapeUtil::Rank(start_indices_shape)); } if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) { return InvalidArgument( - "dynamic slice start indices must be of integral type."); + "Dynamic slice start indices must be of integral type."); } const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "dynamic slice start number of dimensions %lld (%s) must match rank " - "%lld of slice input (%s)", + "Dynamic slice start number of dimensions %lld (%s) must match rank " + "%lld of slice input (%s).", start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape).c_str()); @@ -2018,7 +2038,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (slice_sizes.size() != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( - "dynamic slice index count does not match argument rank: %zu vs %lld", + "Dynamic slice index count does not match argument rank: %zu vs %lld.", slice_sizes.size(), ShapeUtil::Rank(operand_shape)); } @@ -2026,12 +2046,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const int64 input_dim_size = operand_shape.dimensions(dim); const int64 slice_dim_size = slice_sizes[dim]; if (slice_dim_size < 0) { - return InvalidArgument("negative size index to dynamic slice: %lld", + return InvalidArgument("Negative size index to dynamic slice: %lld.", slice_dim_size); } if (slice_dim_size > input_dim_size) { return InvalidArgument( - "slice dim size %lld greater than dynamic slice dimension: %lld", + "Slice dim size %lld greater than dynamic slice dimension: %lld.", slice_dim_size, input_dim_size); } VLOG(2) << tensorflow::strings::Printf("slice_sizes[%lld] = %lld", dim, @@ -2060,20 +2080,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "dynamic update slice start indices of rank %lld must be rank1.", + "Dynamic update slice start indices of rank %lld must be rank1.", ShapeUtil::Rank(start_indices_shape)); } if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) { return InvalidArgument( - "dynamic update slice start indices must be of integral type."); + "Dynamic update slice start indices must be of integral type."); } const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "dynamic slice start number of dimensions %lld (%s) must match rank " - "%lld of slice input (%s)", + "Dynamic slice start number of dimensions %lld (%s) must match rank " + "%lld of slice input (%s).", start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape).c_str()); @@ -2081,15 +2101,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(update_shape) != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( - "dynamic update slice update rank does not match argument rank: " - "%lld vs %lld", + "Dynamic update slice update rank does not match argument rank: " + "%lld vs %lld.", ShapeUtil::Rank(update_shape), ShapeUtil::Rank(operand_shape)); } - if (operand_shape.element_type() != update_shape.element_type()) { + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape, + update_shape)) { return InvalidArgument( - "dynamic update slice update element type does not match argument. " - "operand.element_type: %s vs update.element_type: %s", + "Dynamic update slice update element type does not match argument. " + "operand.element_type: %s vs update.element_type: %s.", PrimitiveType_Name(operand_shape.element_type()).c_str(), PrimitiveType_Name(update_shape.element_type()).c_str()); } @@ -2099,12 +2120,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const int64 update_dim_size = update_shape.dimensions(dim); if (update_dim_size < 0) { return InvalidArgument( - "size index %lld to dynamic update slice must be >= 0", + "Size index %lld to dynamic update slice must be >= 0.", update_dim_size); } if (update_dim_size > input_dim_size) { return InvalidArgument( - "update dim size %lld greater than dynamic slice dimension: %lld", + "Update dim size %lld greater than dynamic slice dimension: %lld.", update_dim_size, input_dim_size); } VLOG(2) << tensorflow::strings::Printf("update_sizes[%lld] = %lld", dim, @@ -2124,7 +2145,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int64 dimension : dimensions) { if (dimension >= ShapeUtil::Rank(operand_shape) || dimension < 0) { return InvalidArgument( - "one of the reverse dimensions (%lld) is out-of-bounds in shape %s", + "One of the reverse dimensions (%lld) is out-of-bounds in shape %s.", dimension, ShapeUtil::HumanString(operand_shape).c_str()); } } @@ -2135,14 +2156,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& arg, int64 index) { if (!ShapeUtil::IsTuple(arg)) { return InvalidArgument( - "cannot infer shape: attempting to index into non-tuple: %s", + "Cannot infer shape: attempting to index into non-tuple: %s.", ShapeUtil::HumanString(arg).c_str()); } if (index >= arg.tuple_shapes_size()) { return InvalidArgument( - "cannot infer shape: attempt to index out of tuple bounds: %lld " - ">= %d in shape %s", + "Cannot infer shape: attempt to index out of tuple bounds: %lld " + ">= %d in shape %s.", index, arg.tuple_shapes_size(), ShapeUtil::HumanString(arg).c_str()); } @@ -2154,17 +2175,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& init) { // Check the number of parameters for given computations. if (condition.parameters_size() != 1) { - return InvalidArgument("condition must take 1 arguments; got %d", + return InvalidArgument("Condition must take 1 arguments; got %d.", condition.parameters_size()); } if (body.parameters_size() != 1) { - return InvalidArgument("body must take 1 arguments; got %d", + return InvalidArgument("Body must take 1 arguments; got %d.", body.parameters_size()); } auto shape_string = [&]() { return tensorflow::strings::Printf( - "condition: %s; body: %s; init: %s", + "Condition: %s; body: %s; init: %s.", ShapeUtil::HumanString(condition).c_str(), ShapeUtil::HumanString(body).c_str(), ShapeUtil::HumanString(init).c_str()); @@ -2172,15 +2193,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Check the shapes of computation parameters and return types. if (!ShapeUtil::ShapeIs(condition.result(), PRED, {})) { - return InvalidArgument("condition must return a boolean; got %s", + return InvalidArgument("Condition must return a boolean; got %s.", shape_string().c_str()); } if (!ShapeUtil::Compatible(body.result(), condition.parameters(0)) || !ShapeUtil::Compatible(body.result(), body.parameters(0)) || !ShapeUtil::Compatible(body.result(), init)) { return InvalidArgument( - "the parameter of condition and body, the result of the body, and init " - "must all have the same shape; got %s", + "The parameter of condition and body, the result of the body, and init " + "must all have the same shape; got %s.", shape_string().c_str()); } @@ -2192,7 +2213,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& false_operand, const ProgramShape& true_computation, const ProgramShape& false_computation) { if (!ShapeUtil::ShapeIs(predicate, PRED, {})) { - return InvalidArgument("predicate must be a boolean; got %s.", + return InvalidArgument("Predicate must be a boolean; got %s.", ShapeUtil::HumanString(predicate).c_str()); } @@ -2275,8 +2296,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::ElementsIn(operand) != ShapeUtil::ElementsIn(inferred_shape)) { return InvalidArgument( - "reshape operation has mismatched element counts: from=%lld (%s) " - "to=%lld (%s)", + "Reshape operation has mismatched element counts: from=%lld (%s) " + "to=%lld (%s).", ShapeUtil::ElementsIn(operand), ShapeUtil::HumanString(operand).c_str(), ShapeUtil::ElementsIn(inferred_shape), ShapeUtil::HumanString(inferred_shape).c_str()); @@ -2322,28 +2343,30 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(min, "clamp min")); TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "clamp operand")); TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(max, "clamp max")); - if (!ShapeUtil::SameElementType(min, operand) || - !ShapeUtil::SameElementType(max, operand)) { - return InvalidArgument("clamp op with different operand types: %s, %s, %s", + if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) || + !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) { + return InvalidArgument("Clamp with different operand types: %s, %s, %s.", ShapeUtil::HumanString(min).c_str(), ShapeUtil::HumanString(operand).c_str(), ShapeUtil::HumanString(max).c_str()); } - if (((ShapeUtil::Compatible(min, operand) || ShapeUtil::IsScalar(min)) && - (ShapeUtil::Compatible(max, operand) || ShapeUtil::IsScalar(max)))) { + if (((ShapeUtil::CompatibleIgnoringFpPrecision(min, operand) || + ShapeUtil::IsScalar(min)) && + (ShapeUtil::CompatibleIgnoringFpPrecision(max, operand) || + ShapeUtil::IsScalar(max)))) { return operand; } if (ShapeUtil::IsScalar(operand)) { - if (ShapeUtil::Compatible(min, max)) { - return min; + if (ShapeUtil::CompatibleIgnoringFpPrecision(min, max)) { + return ShapeUtil::ChangeElementType(min, operand.element_type()); } else if (ShapeUtil::IsScalar(min)) { - return max; + return ShapeUtil::ChangeElementType(max, operand.element_type()); } else if (ShapeUtil::IsScalar(max)) { - return min; + return ShapeUtil::ChangeElementType(min, operand.element_type()); } } return Unimplemented( - "not yet implemented: %s, %s %s", min.ShortDebugString().c_str(), + "%s, %s %s is not implemented.", min.ShortDebugString().c_str(), max.ShortDebugString().c_str(), operand.ShortDebugString().c_str()); } @@ -2352,26 +2375,36 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // broadcast from all operands, not just the predicate. /* static */ StatusOr ShapeInference::InferSelectShape( const Shape& pred, const Shape& on_true, const Shape& on_false) { - if (!ShapeUtil::Compatible(on_true, on_false)) { + bool compatible; + if (ShapeUtil::IsTuple(on_true)) { + // Select only defines the top-level buffer, so if it's a tuple, the two + // input must match exactly. + compatible = ShapeUtil::Compatible(on_true, on_false); + } else { + compatible = ShapeUtil::CompatibleIgnoringFpPrecision(on_true, on_false); + } + if (!compatible) { return InvalidArgument( - "operands to select must be the same shape; got %s and %s", + "Operands to select must be the same shape; got %s and %s.", ShapeUtil::HumanString(on_true).c_str(), ShapeUtil::HumanString(on_false).c_str()); } if (pred.element_type() != PRED) { return InvalidArgument( - "select's pred operand must have PRED element type; got %s", + "Select's pred operand must have PRED element type; got %s.", ShapeUtil::HumanString(pred).c_str()); } - if (ShapeUtil::SameDimensions(pred, on_true) || ShapeUtil::Rank(pred) == 0) { + if (ShapeUtil::CompatibleIgnoringElementType(pred, on_true) || + ShapeUtil::Rank(pred) == 0) { // By this stage we know that pred's element type is PRED. Therefore, this // check restricts pred to be a PRED scalar, or a PRED array with the same // dimensions as on_true and on_false. - return on_true; + return ShapeUtil::ChangeElementType( + on_true, ShapeUtil::HigherPrecisionElementType(on_true, on_false)); } else { - return Unimplemented( - "select operation with non-scalar predicate with dimensionality " - " different from the other operands: %s", + return InvalidArgument( + "Select operation with non-scalar predicate with dimensionality " + " different from the other operands: %s.", ShapeUtil::HumanString(pred).c_str()); } } @@ -2389,7 +2422,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Call applied function arity must match number of arguments; got: " "arity: %d, arguments: %zu; computation signature: %s; argument " - "shapes: [%s]", + "shapes: [%s].", to_apply.parameters_size(), arg_shapes.size(), computation_signature.c_str(), argument_shapes.c_str()); } @@ -2401,7 +2434,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::Compatible(arg_shape, param_shape)) { return InvalidArgument( "Call parameter must match argument; got parameter %d shape: %s, " - "argument shape: %s", + "argument shape: %s.", i, ShapeUtil::HumanString(param_shape).c_str(), ShapeUtil::HumanString(arg_shape).c_str()); } @@ -2410,4 +2443,209 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return to_apply.result(); } +static Status ValidateGatherDimensionNumbers( + const Shape& input_shape, + tensorflow::gtl::ArraySlice gather_indices_shape, + const GatherDimensionNumbers& dim_numbers) { + if (!c_is_sorted(dim_numbers.output_window_dims())) { + return InvalidArgument( + "Output window dimensions in gather op must be ascending; got: %s.", + Join(dim_numbers.output_window_dims(), ", ").c_str()); + } + + if (c_adjacent_find(dim_numbers.output_window_dims()) != + dim_numbers.output_window_dims().end()) { + return InvalidArgument( + "Output window dimensions in gather op must not repeat; got: %s.", + Join(dim_numbers.output_window_dims(), ", ").c_str()); + } + + const int64 output_window_dim_count = dim_numbers.output_window_dims_size(); + const int64 output_shape_rank = + output_window_dim_count + gather_indices_shape.size() - 1; + + for (int i = 0; i < dim_numbers.output_window_dims_size(); ++i) { + int64 window_index = dim_numbers.output_window_dims(i); + if (window_index < 0 || window_index >= output_shape_rank) { + return InvalidArgument( + "Window index %d in gather op is out of bounds; got %lld, but should " + "have been in [0,%lld).", + i, window_index, output_shape_rank); + } + } + + if (dim_numbers.gather_dims_to_operand_dims_size() != + gather_indices_shape[dim_numbers.index_vector_dim()]) { + return InvalidArgument( + "Gather op has %d elements in gather_dims_to_operand_dims and the " + "bound of dimension index_vector_dim=%lld of gather_indices is " + "%lld. These two numbers must be equal.", + dim_numbers.gather_dims_to_operand_dims_size(), + dim_numbers.index_vector_dim(), + gather_indices_shape[dim_numbers.index_vector_dim()]); + } + + for (int i = 0; i < dim_numbers.gather_dims_to_operand_dims_size(); i++) { + int64 gather_dim_to_input_dim = dim_numbers.gather_dims_to_operand_dims(i); + if (gather_dim_to_input_dim < 0 || + gather_dim_to_input_dim >= input_shape.dimensions_size()) { + return InvalidArgument( + "Invalid gather_dims_to_operand_dims mapping; domain is [0, %d), " + "got: %d->%lld.", + input_shape.dimensions_size(), i, gather_dim_to_input_dim); + } + } + + std::vector sorted_gather_dims_to_operand_dims( + dim_numbers.gather_dims_to_operand_dims().begin(), + dim_numbers.gather_dims_to_operand_dims().end()); + + c_sort(sorted_gather_dims_to_operand_dims); + + if (c_adjacent_find(sorted_gather_dims_to_operand_dims) != + sorted_gather_dims_to_operand_dims.end()) { + return InvalidArgument( + "Repeated dimensions are not allowed in gather_dims_to_operand_dims; " + "got: %s.", + Join(dim_numbers.gather_dims_to_operand_dims(), ", ").c_str()); + } + + for (int64 elided_dim : dim_numbers.elided_window_dims()) { + if (elided_dim < 0 || elided_dim >= input_shape.dimensions_size()) { + return InvalidArgument( + "Invalid elided_window_dims set in gather op; valid range is [0, " + "%d), got: %lld.", + input_shape.dimensions_size(), elided_dim); + } + } + + if (!c_is_sorted(dim_numbers.elided_window_dims())) { + return InvalidArgument( + "elided_window_dims in gather op must be sorted; got: %s", + Join(dim_numbers.elided_window_dims(), ", ").c_str()); + } + + if (c_adjacent_find(dim_numbers.elided_window_dims()) != + dim_numbers.elided_window_dims().end()) { + return InvalidArgument( + "Repeated dimensions not allowed in elided_window_dims in gather op; " + "got: %s.", + Join(dim_numbers.elided_window_dims(), ", ").c_str()); + } + + return Status::OK(); +} + +/*static*/ StatusOr ShapeInference::InferGatherShape( + const Shape& input_shape, const Shape& gather_indices_shape, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds) { + TF_RETURN_IF_ERROR( + ExpectNotTupleOrOpaque(input_shape, "input tensor operand gather op")); + TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( + gather_indices_shape, "gather indices operand of gather op")); + + if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) { + return InvalidArgument( + "Gather indices parameter must be an integral tensor; got %s.", + ShapeUtil::HumanString(gather_indices_shape).c_str()); + } + + // We implicitly reshape gather indices of shape P[A,B,C] to P[A,B,C,1] if + // index_vector_dim is rank(P). The bounds of this expanded shape is + // stored in expanded_gather_indices_shape. + + if (gather_indices_shape.dimensions_size() < + gather_dim_numbers.index_vector_dim() || + gather_dim_numbers.index_vector_dim() < 0) { + return InvalidArgument( + "Gather index leaf dimension must be within [0, rank(gather_indices) + " + "1). rank(gather_indices) is %d and gather index leaf dimension is " + "%lld.", + gather_indices_shape.dimensions_size(), + gather_dim_numbers.index_vector_dim()); + } + + std::vector expanded_gather_indices_shape; + expanded_gather_indices_shape.reserve(gather_indices_shape.dimensions_size()); + c_copy(gather_indices_shape.dimensions(), + std::back_inserter(expanded_gather_indices_shape)); + if (expanded_gather_indices_shape.size() == + gather_dim_numbers.index_vector_dim()) { + expanded_gather_indices_shape.push_back(1); + } + + TF_RETURN_IF_ERROR(ValidateGatherDimensionNumbers( + input_shape, expanded_gather_indices_shape, gather_dim_numbers)); + + if (window_bounds.size() != input_shape.dimensions_size()) { + return InvalidArgument( + "Gather op must have one window bound for every input dimension; got: " + "len(window_bounds)=%lu, input_shape.rank=%d.", + window_bounds.size(), input_shape.dimensions_size()); + } + + if (window_bounds.size() != + gather_dim_numbers.output_window_dims_size() + + gather_dim_numbers.elided_window_dims_size()) { + return InvalidArgument( + "All components of the window index in a gather op must either be a " + "output window index or explicitly elided; got len(window_bounds)=%lu, " + "output_window_bounds=%s, elided_window_bounds=%s.", + window_bounds.size(), + Join(gather_dim_numbers.output_window_dims(), ",").c_str(), + Join(gather_dim_numbers.elided_window_dims(), ",").c_str()); + } + + for (int i = 0; i < window_bounds.size(); i++) { + int64 window_bound = window_bounds[i]; + int64 corresponding_input_bound = input_shape.dimensions(i); + if (window_bound < 0 || window_bound > corresponding_input_bound) { + return InvalidArgument( + "Window bound at index %d in gather op is out of range, must be " + "within " + "[0, %lld), got %lld.", + i, corresponding_input_bound + 1, window_bound); + } + } + + for (int i = 0; i < gather_dim_numbers.elided_window_dims_size(); i++) { + if (window_bounds[gather_dim_numbers.elided_window_dims(i)] != 1) { + return InvalidArgument( + "Gather op can only elide window indices with bound 1, but bound is " + "%lld for index %lld at position %d.", + window_bounds[gather_dim_numbers.elided_window_dims(i)], + gather_dim_numbers.elided_window_dims(i), i); + } + } + + int64 result_rank = gather_dim_numbers.output_window_dims_size() + + (expanded_gather_indices_shape.size() - 1); + int64 window_dims_seen = 0; + int64 gather_dims_seen = 0; + std::vector output_dim_bounds; + output_dim_bounds.reserve(result_rank); + for (int64 i = 0; i < result_rank; i++) { + int64 current_bound; + bool is_window_index = + c_binary_search(gather_dim_numbers.output_window_dims(), i); + if (is_window_index) { + while (c_binary_search(gather_dim_numbers.elided_window_dims(), + window_dims_seen)) { + window_dims_seen++; + } + current_bound = window_bounds[window_dims_seen++]; + } else { + if (gather_dims_seen == gather_dim_numbers.index_vector_dim()) { + gather_dims_seen++; + } + current_bound = expanded_gather_indices_shape[gather_dims_seen++]; + } + + output_dim_bounds.push_back(current_bound); + } + + return ShapeUtil::MakeShape(input_shape.element_type(), output_dim_bounds); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h index b39151ebbc19f5d0b702a80da5069f58c8dfb07d..0d3045213db2230da3e18ffcb1a9923250560b64 100644 --- a/tensorflow/compiler/xla/service/shape_inference.h +++ b/tensorflow/compiler/xla/service/shape_inference.h @@ -37,6 +37,11 @@ namespace xla { // the expected result type for computations that are built up via the API -- // the shape that results from an operation is inferred. Some methods have // overloads for inferring shape at the HLO level. +// +// TODO(b/73352135): Shape inference does not issue very good error messages, in +// part because HloInstruction::ToString() is not available since shape +// inference runs before the HloInstruction object is created. We need a +// solution for this. class ShapeInference { public: // Infers the shape produced by applying the given unary operation to the @@ -248,6 +253,14 @@ class ShapeInference { const Shape& lhs, const Shape& rhs, const DotDimensionNumbers& dimension_numbers); + // Helper that infers the shape of the tensor produced by a gather operation + // with the given input shape, gather indices shape and gather dimension + // numbers. + static StatusOr InferGatherShape( + const Shape& input_shape, const Shape& gather_indices_shape, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds); + private: // Helper that infers the shape produced by performing an element-wise binary // operation with the given LHS and RHS shapes. diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 026c021165785bd3945d6a846dae446ad45da9b7..0e61994a786b53a295ef9c9c2287b28fbf754d9b 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -18,15 +18,16 @@ limitations under the License. #include #include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" - #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/gtl/array_slice.h" namespace xla { namespace { +using ::tensorflow::gtl::ArraySlice; using ::testing::ContainsRegex; using ::testing::HasSubstr; @@ -134,7 +135,7 @@ TEST_F(ShapeInferenceTest, SelectBadShapes) { TernaryOperation::TRIOP_SELECT, pred_, matrix_64_48_, matrix_32_64_); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("operands to select must be the same shape")); + HasSubstr("Operands to select must be the same shape")); auto inferred_status_error2 = ShapeInference::InferTernaryOpShape( TernaryOperation::TRIOP_SELECT, s32_, matrix_64_48_, matrix_64_48_); @@ -339,7 +340,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSourceShape) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("source shape does not match")); + HasSubstr("Source shape does not match")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) { @@ -350,7 +351,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function must take 2 parameters")); + HasSubstr("Select function must take 2 parameters")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) { @@ -361,7 +362,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function must have rank-0 PRED")); + HasSubstr("Select function must have rank-0 PRED")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) { @@ -372,7 +373,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function's first parameter")); + HasSubstr("Select function's first parameter")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) { @@ -383,7 +384,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function's second parameter")); + HasSubstr("Select function's second parameter")); } TEST_F(ShapeInferenceTest, Convolve) { @@ -905,7 +906,7 @@ TEST_F(ShapeInferenceTest, ScalarDotVector) { ShapeInference::InferDotOpShape(f32_, vector_32_, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("dot only supports rank")); + HasSubstr("Dot only supports rank")); } // 3D 2D: error @@ -917,7 +918,7 @@ TEST_F(ShapeInferenceTest, DotWithRankHigherThanTwo) { ShapeUtil::MakeShape(F32, {32, 32, 32}), matrix_32_64_, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch and contracting dimension number mismatch")); + HasSubstr("Batch and contracting dimension number mismatch")); } // vector vector -> scalar @@ -1023,7 +1024,7 @@ TEST_F(ShapeInferenceTest, DotWithTwoContractingDimsFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("must specify one contracting dimension for both " + HasSubstr("Must specify one contracting dimension for both " "lhs and rhs")); } @@ -1043,7 +1044,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimSizesFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch dimension numbers and sizes must match")); + HasSubstr("Batch dimension numbers and sizes must match")); } // BatchMatMul with different batch dimension numbers fails. @@ -1062,7 +1063,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimNumbersFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch dimension numbers must precede non-batch")); + HasSubstr("Batch dimension numbers must precede non-batch")); } // BatchMatMul with out-of-range dimension numbers fails. @@ -1165,42 +1166,42 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { BinaryOperation::BINOP_ADD, tensor, vec8, {}); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("automatic")); + HasSubstr("Automatic")); // broadcast_dimension out of bounds for tensor's rank auto inferred_status_error2 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, vec8, {3}); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - ContainsRegex("broadcast dimension number .* too large")); + ContainsRegex("Broadcast dimension number .* too large")); // broadcast_dimension doesn't match corresponding dimension auto inferred_status_error3 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, vec8, {0}); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("Broadcast dimension 0 mismatch")); // broadcast_dimensions list too long auto inferred_status_error4 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {0, 1, 2}); ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT(inferred_status_error4.status().error_message(), - HasSubstr("size of broadcast_dimensions has to match")); + HasSubstr("broadcast_dimensions has to match")); // there's a dimension above the rank of the tensor auto inferred_status_error5 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {3, 0}); ASSERT_FALSE(inferred_status_error5.ok()); ASSERT_THAT(inferred_status_error5.status().error_message(), - ContainsRegex("broadcast dimension number .* too large")); + ContainsRegex("dimension number .* too large")); // broadcasting dimensions don't match in this order auto inferred_status_error6 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {2, 1}); ASSERT_FALSE(inferred_status_error6.ok()); ASSERT_THAT(inferred_status_error6.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("dimension 0 mismatch")); // The following two tests make sure that broadcasting dimensions are listed // in a proper (strictly increasing) order, even if the lower-rank array @@ -1209,13 +1210,13 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {0, 0}); ASSERT_FALSE(inferred_status_error7.ok()); ASSERT_THAT(inferred_status_error7.status().error_message(), - HasSubstr("broadcast dimensions order is wrong")); + HasSubstr("dimensions order is wrong")); auto inferred_status_error8 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {1, 0}); ASSERT_FALSE(inferred_status_error8.ok()); ASSERT_THAT(inferred_status_error8.status().error_message(), - HasSubstr("broadcast dimensions order is wrong")); + HasSubstr("dimensions order is wrong")); } // Tests for the while instruction with proper shapes. @@ -1241,7 +1242,7 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) { ShapeInference::InferWhileShape(bad_shape_1, body, result_shape); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("condition must take 1 arguments")); + HasSubstr("Condition must take 1 arguments")); auto bad_shape_2 = ShapeUtil::MakeProgramShape({s32_, result_shape}, result_shape); @@ -1249,14 +1250,14 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) { ShapeInference::InferWhileShape(cond, bad_shape_2, result_shape); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - HasSubstr("body must take 1 arguments")); + HasSubstr("Body must take 1 arguments")); auto bad_shape_3 = ShapeUtil::MakeProgramShape({result_shape}, s32_); auto inferred_status_error3 = ShapeInference::InferWhileShape(bad_shape_3, body, result_shape); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("condition must return a boolean")); + HasSubstr("Condition must return a boolean")); auto bad_shape_4 = ShapeUtil::MakeProgramShape({result_shape}, vector_32_); auto inferred_status_error4 = @@ -1300,13 +1301,13 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/-1); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - HasSubstr("dimension to concatenate along out of bounds: -1")); + HasSubstr("dimension out of bounds: -1")); auto inferred_status_error3 = ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/1); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("dimension to concatenate along out of bounds: 1")); + HasSubstr("dimension out of bounds: 1")); Shape tuple = ShapeUtil::MakeTupleShape({vector_32_}); auto inferred_status_error4 = ShapeInference::InferConcatOpShape( @@ -1314,21 +1315,20 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT( inferred_status_error4.status().error_message(), - HasSubstr("Expected non-tuple argument for operand of concatenation.")); + HasSubstr("Expected non-tuple argument for operand of concatenation")); const Shape vector_s32 = ShapeUtil::MakeShape(S32, {32}); auto inferred_status_error5 = ShapeInference::InferConcatOpShape( {&vector_32_, &vector_s32}, /*dimension=*/0); ASSERT_FALSE(inferred_status_error5.ok()); - ASSERT_THAT( - inferred_status_error5.status().error_message(), - HasSubstr("cannot concatenate arrays with different element types")); + ASSERT_THAT(inferred_status_error5.status().error_message(), + HasSubstr("concatenate arrays with different element types")); auto inferred_status_error6 = ShapeInference::InferConcatOpShape( {&matrix_32_48_, &matrix_32_64_}, /*dimension=*/0); ASSERT_FALSE(inferred_status_error6.ok()); ASSERT_THAT(inferred_status_error6.status().error_message(), - HasSubstr("cannot concatenate arrays that differ in " + HasSubstr("concatenate arrays that differ in " "dimensions other than the one being " "concatenated")); } @@ -1466,7 +1466,7 @@ TEST_F(ShapeInferenceTest, Conditional) { ShapeUtil::MakeProgramShape({vector_64_}, f32_)); EXPECT_FALSE(inferred_status_error0.ok()); EXPECT_THAT(inferred_status_error0.status().error_message(), - HasSubstr("predicate must be a boolean")); + HasSubstr("Predicate must be a boolean")); auto inferred_status_error1 = ShapeInference::InferConditionalShape( pred_, ShapeUtil::MakeTupleShape({f32_, vector_32_}), matrix_32_48_, @@ -1527,5 +1527,458 @@ TEST_F(ShapeInferenceTest, BadSlice) { << statusor.status(); } +class GatherShapeInferenceTest : public ShapeInferenceTest { + protected: + const Shape s64_scalar_ = ShapeUtil::MakeShape(S64, {}); + const Shape s64_vector_5_ = ShapeUtil::MakeShape(S64, {5}); + const Shape s64_vector_32_ = ShapeUtil::MakeShape(S64, {32}); + const Shape s64_4d_tensor_10_9_8_7_1_ = + ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 1}); + const Shape s64_4d_tensor_10_9_8_7_5_ = + ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5}); + const Shape s64_4d_tensor_5_10_9_7_6_ = + ShapeUtil::MakeShape(S64, {5, 10, 9, 7, 6}); + const Shape s64_4d_tensor_10_9_5_7_6_ = + ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6}); + const Shape f32_5d_tensor_50_49_48_47_46_ = + ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); + const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( + {s64_4d_tensor_10_9_8_7_1_, s64_4d_tensor_10_9_8_7_1_}); +}; + +TEST_F(GatherShapeInferenceTest, TensorFlowGather) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), + /*window_bounds=*/{64, 1})); + EXPECT_TRUE( + ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{1}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/1), + /*window_bounds=*/{1, 48})); + EXPECT_TRUE( + ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape(matrix_64_48_, s64_4d_tensor_10_9_8_7_1_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/4), + /*window_bounds=*/{1, 48})); + EXPECT_TRUE(ShapeUtil::Equal(gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26})); + EXPECT_TRUE(ShapeUtil::Equal( + gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_A) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/2), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal( + gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_B) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_5_10_9_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/0), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal( + gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, NoOutputGatherDims) { + // This is equivalent to a dynamic slice. + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_vector_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0, 1, 2, 3, 4}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/0), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal(gather_shape, + ShapeUtil::MakeShape(F32, {30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) { + // The gather indices "tensor" is a scalar S here that's used to slice out + // [S,0,0,0,0]..[S,30,29,28,27] into a [30,29,28,27] shaped result. + TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_scalar_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0, 1, 2, 3}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/0), + /*window_bounds=*/{1, 30, 29, 28, 27})); + + EXPECT_TRUE(ShapeUtil::Equal(gather_shape, + ShapeUtil::MakeShape(F32, {30, 29, 28, 27}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { + StatusOr statusor = ShapeInference::InferGatherShape( + tuple_shape_, s64_vector_32_, + HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), + /*window_bounds=*/{64, 1}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Expected non-tuple argument for input")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { + StatusOr statusor = ShapeInference::InferGatherShape( + s64_vector_32_, tuple_shape_, + HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), + /*window_bounds=*/{64, 1}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Expected non-tuple argument for gather indices")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) { + StatusOr statusor = ShapeInference::InferGatherShape( + s64_vector_32_, vector_32_, + HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), + /*window_bounds=*/{64, 1}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Gather indices parameter must be an integral tensor")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_NonAscendingWindowIndices) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 8, 7}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("Output window dimensions in gather op must be ascending")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_RepeatedWindowIndices) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 7}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("Output window dimensions in gather op must not repeat")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_WindowIndexOutOfBounds) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 99, 100, 101}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Window index 2 in gather op is out of bounds")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_WindowIndexBarelyOutOfBounds) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 9}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Window index 4 in gather op is out of bounds")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_MismatchingElidedWindowDims) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{4}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("All components of the window index in a gather op must either " + "be a output window index or explicitly elided")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_OutOfBoundsWindowToInputMapping) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{0, 1, 2, 3, 19}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Invalid elided_window_dims set in gather op; valid " + "range is [0, 5), got: 19")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_RepeatedWindowToInputMapping) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{0, 1, 2, 3, 3}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr( + "Repeated dimensions not allowed in elided_window_dims in gather op")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_MismatchingGatherToInputMapping) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("Gather op has 4 elements in gather_dims_to_operand_dims and " + "the bound of dimension index_vector_dim=4 of " + "gather_indices is 5. These two numbers must be equal.")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_OutOfBoundsGatherToInputMapping) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("Invalid gather_dims_to_operand_dims mapping; domain is " + "[0, 5), got: 4->7")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_RepeatedGatherToInputMapping) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr( + "Repeated dimensions are not allowed in gather_dims_to_operand_dims")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_NonAscendingElidedWindowDims) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{2, 1}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{1, 1, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("elided_window_dims in gather op must be sorted")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7}, + /*elided_window_dims=*/{2}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 1, 300, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Window bound at index 3 in gather op is out of range, " + "must be within [0, 48), got 300")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_MismatchingNumberOfWindowBounds) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr( + "Gather op must have one window bound for every input dimension")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_WindowBoundsNot1ForElidedDim) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 26, 20}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Gather op can only elide window indices with bound 1, " + "but bound is 29 for index 1 at position 0")) + << statusor.status(); +} + +TEST_F(GatherShapeInferenceTest, OutOfBoundsGatherIndicesLeafDim) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/32), + /*window_bounds=*/{30, 29, 28, 27, 26}); + + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Gather index leaf dimension must be within [0, " + "rank(gather_indices) + 1)")) + << statusor.status(); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/shaped_buffer.cc b/tensorflow/compiler/xla/service/shaped_buffer.cc index c679d401c3691b14a43ce77cbe953cd4c64a9e92..6e9986165f7eaf71a964b42b734a5ae5db5e45d7 100644 --- a/tensorflow/compiler/xla/service/shaped_buffer.cc +++ b/tensorflow/compiler/xla/service/shaped_buffer.cc @@ -41,7 +41,32 @@ ShapedBuffer::ShapedBuffer(const Shape& on_host_shape, on_device_shape_(on_device_shape), platform_(platform), device_ordinal_(device_ordinal), - buffers_(on_device_shape) {} + buffers_(&on_device_shape_) {} + +ShapedBuffer::ShapedBuffer(ShapedBuffer&& s) + : on_host_shape_(std::move(s.on_host_shape_)), + on_device_shape_(std::move(s.on_device_shape_)), + platform_(s.platform_), + device_ordinal_(s.device_ordinal_), + buffers_(std::move(s.buffers_)) { + // s.buffers_ has a pointer to s.on_device_shape_. When we move s.buffers_ + // into buffers_, we also need to update this pointer so that buffers_ doesn't + // point into s. + buffers_.replace_shape_ptr(&on_device_shape_); +} + +ShapedBuffer& ShapedBuffer::operator=(ShapedBuffer&& s) { + on_host_shape_ = std::move(s.on_host_shape_); + on_device_shape_ = std::move(s.on_device_shape_); + platform_ = s.platform_; + device_ordinal_ = s.device_ordinal_; + buffers_ = std::move(s.buffers_); + // buffers_ has a pointer to its on_device_shape_. When we move s.buffers_ + // into buffers_, we also need to update this pointer so that buffers_ doesn't + // point into s. + buffers_.replace_shape_ptr(&on_device_shape_); + return *this; +} void ShapedBuffer::clear() { for (auto& pair : buffers_) { @@ -99,6 +124,10 @@ ScopedShapedBuffer::ScopedShapedBuffer(const Shape& on_host_shape, device_ordinal), allocator_(allocator) {} +ScopedShapedBuffer::ScopedShapedBuffer(ShapedBuffer shaped_buffer, + DeviceMemoryAllocator* allocator) + : ShapedBuffer(std::move(shaped_buffer)), allocator_(allocator) {} + ScopedShapedBuffer::~ScopedShapedBuffer() { // Deallocate all non-null buffers. A buffer may appear in more than one spot // in the shape (eg, a tuple with a repeated element) so keep track of what @@ -116,12 +145,8 @@ ScopedShapedBuffer::~ScopedShapedBuffer() { } std::unique_ptr ScopedShapedBuffer::release() { - auto shaped_buffer = MakeUnique( - on_host_shape(), on_device_shape(), platform(), device_ordinal()); - - shaped_buffer->buffers() = buffers(); - clear(); - + auto shaped_buffer = MakeUnique(std::move(*this)); + buffers_ = ShapeTree(); return shaped_buffer; } diff --git a/tensorflow/compiler/xla/service/shaped_buffer.h b/tensorflow/compiler/xla/service/shaped_buffer.h index d397e47d2ca734458c7dc99baa5c81b16d0fd72b..b816df8385ef65b0b69ede1d6e65a1991b4bd7c6 100644 --- a/tensorflow/compiler/xla/service/shaped_buffer.h +++ b/tensorflow/compiler/xla/service/shaped_buffer.h @@ -87,18 +87,24 @@ class ShapedBuffer { string ToString() const; + ShapedBuffer(ShapedBuffer&& s); + ShapedBuffer& operator=(ShapedBuffer&&); + protected: + ShapedBuffer(const ShapedBuffer&) = delete; + ShapedBuffer& operator=(const ShapedBuffer&) = delete; + // The shape of the data when represented on the host. - const Shape on_host_shape_; + Shape on_host_shape_; // The shape of the data on the device. - const Shape on_device_shape_; + Shape on_device_shape_; // The platform the memory is allocated on. const perftools::gputools::Platform* platform_; // The device the memory is allocated on. - const int device_ordinal_; + int device_ordinal_; // The tree of device buffers. Its shape is on_device_shape(). ShapeTree buffers_; @@ -121,14 +127,20 @@ class ScopedShapedBuffer : public ShapedBuffer { ScopedShapedBuffer(const Shape& on_host_shape, const Shape& on_device_shape, DeviceMemoryAllocator* allocator, int device_ordinal); + // Create a ScopedShapedBuffer by taking over the memory from the incoming + // ShapedBuffer. + ScopedShapedBuffer(ShapedBuffer shaped_buffer, + DeviceMemoryAllocator* allocator); + // Return the allocator used to allocate the device memory held in this // ScopedShapedBuffer. DeviceMemoryAllocator* memory_allocator() const { return allocator_; } - // Release all device memory owned by this ScopedShapedBuffer and return the - // device memory pointers in the form of a ShapedBuffer. Device memory - // pointers in this ScopedShapedBuffer object are set to null. This method is - // analogous to std::unique_ptr::release(). + // Release all device memory owned by this ScopedShapedBuffer and + // return the device memory pointers in the form of a + // ShapedBuffer. The returned ShapedBuffer takes over the memory + // from the ScopedShapedBuffer. The resulting ScopedShapedBuffer can + // only be destroyed. std::unique_ptr release(); // All buffers in the shape are deallocated on destruction. diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc index ef9c80b0431cb7dbf813732724d24d113dfbc4ab..0dca30a804005c6f536aca5b54af24eb08d4560b 100644 --- a/tensorflow/compiler/xla/service/user_computation.cc +++ b/tensorflow/compiler/xla/service/user_computation.cc @@ -226,7 +226,8 @@ StatusOr UserComputation::AddParameterInstruction( return handle; } -Status UserComputation::AddSendInstruction(const SendRequest& send_request) { +StatusOr UserComputation::AddSendInstruction( + const SendRequest& send_request) { tensorflow::mutex_lock lock(mutex_); // Check if the operand of the instruction is valid. @@ -244,7 +245,7 @@ Status UserComputation::AddSendInstruction(const SendRequest& send_request) { VLOG(1) << "AddSendInstruction (" << GetVersionedHandleInternal() << "), data handle " << handle.handle() << ": " << send_request.ShortDebugString(); - return Status::OK(); + return handle; } StatusOr UserComputation::AddRecvInstruction( @@ -315,6 +316,36 @@ StatusOr UserComputation::AddConstantInstruction( return handle; } +StatusOr UserComputation::AddGatherInstruction( + const GatherRequest& gather_request) { + tensorflow::mutex_lock lock(mutex_); + + TF_ASSIGN_OR_RETURN(const OperationRequest* input_request, + LookUpRequest(gather_request.input())); + TF_ASSIGN_OR_RETURN(const OperationRequest* gather_indices_request, + LookUpRequest(gather_request.gather_indices())); + + TF_ASSIGN_OR_RETURN( + Shape shape, + ShapeInference::InferGatherShape( + input_request->output_shape(), gather_indices_request->output_shape(), + gather_request.dimension_numbers(), + AsInt64Slice(gather_request.window_bounds()))); + + const ComputationDataHandle handle = CreateComputationDataHandle(); + + OperationRequest& request = + (*session_computation_.mutable_requests())[handle.handle()]; + *request.mutable_output_handle() = handle; + *request.mutable_output_shape() = shape; + *request.mutable_request()->mutable_gather_request() = gather_request; + + VLOG(1) << "AddGatherInstruction (" << GetVersionedHandleInternal() + << "), data handle " << handle.handle() << ": " + << gather_request.ShortDebugString(); + return handle; +} + StatusOr UserComputation::AddGetTupleElementInstruction( const GetTupleElementRequest& get_tuple_element_request) { tensorflow::mutex_lock lock(mutex_); @@ -1276,6 +1307,28 @@ StatusOr UserComputation::AddCustomCallInstruction( return handle; } +StatusOr UserComputation::AddHostComputeInstruction( + const HostComputeRequest& host_compute_request) { + tensorflow::mutex_lock lock(mutex_); + + for (const ComputationDataHandle& handle : host_compute_request.operands()) { + TF_RETURN_IF_ERROR(LookUpRequest(handle).status()); + } + + ComputationDataHandle handle = CreateComputationDataHandle(); + OperationRequest& request = + (*session_computation_.mutable_requests())[handle.handle()]; + *request.mutable_output_handle() = handle; + *request.mutable_output_shape() = host_compute_request.shape(); + *request.mutable_request()->mutable_host_compute_request() = + host_compute_request; + + VLOG(1) << "AddHostComputeInstruction (" << GetVersionedHandleInternal() + << "), data handle " << handle.handle() << ": " + << host_compute_request.ShortDebugString(); + return handle; +} + StatusOr UserComputation::AddDotInstruction( const DotRequest& dot_request) { tensorflow::mutex_lock lock(mutex_); @@ -1713,6 +1766,11 @@ void PureFunctionalVisitor(const SessionComputation& session_computation, break; } + case OpRequest::kHostComputeRequest: { + *is_functional = false; + break; + } + case OpRequest::kCallRequest: { const CallRequest& call_request = request.request().call_request(); for (const ComputationDataHandle& handle : call_request.operands()) { @@ -1991,12 +2049,25 @@ void PureFunctionalVisitor(const SessionComputation& session_computation, break; } + case OpRequest::kGatherRequest: { + PureFunctionalVisitor(session_computation, + request.request().gather_request().input(), + num_parameters, visited, is_functional); + PureFunctionalVisitor(session_computation, + request.request().gather_request().gather_indices(), + num_parameters, visited, is_functional); + break; + } + case OpRequest::OP_NOT_SET: LOG(FATAL) << "OperationRequest doesn't contain a request"; default: LOG(FATAL) << "Unexpected request type: " << request.request().op_case(); } + if (!*is_functional) { + VLOG(1) << "Non-functional: " << request.request().DebugString(); + } visited->insert(handle.handle()); } @@ -2640,6 +2711,15 @@ static void ForEachOperand( break; } + case OpRequest::kHostComputeRequest: { + const HostComputeRequest& hc_request = + request.request().host_compute_request(); + for (const ComputationDataHandle& operand : hc_request.operands()) { + apply(operand); + } + break; + } + case OpRequest::kDotRequest: { const DotRequest& dot_request = request.request().dot_request(); apply(dot_request.rhs()); @@ -2681,6 +2761,13 @@ static void ForEachOperand( break; } + case OpRequest::kGatherRequest: { + const GatherRequest& gather_request = request.request().gather_request(); + apply(gather_request.input()); + apply(gather_request.gather_indices()); + break; + } + case OpRequest::OP_NOT_SET: LOG(FATAL) << "OperationRequest doesn't contain a request"; @@ -3228,20 +3315,23 @@ void ComputationLowerer::Visit( HloInstruction* rhs = lookup_instruction(ternary_op_request.rhs()); HloInstruction* ehs = lookup_instruction(ternary_op_request.ehs()); auto hlo_opcode = TernaryOperationToHloOpcode(ternary_op_request.triop()); - - if (debug_options_.xla_eliminate_hlo_implicit_broadcast()) { - if (!ShapeUtil::SameDimensions(request.output_shape(), lhs->shape())) { + if (debug_options_.xla_eliminate_hlo_implicit_broadcast() && + !ShapeUtil::IsTuple(request.output_shape())) { + if (!ShapeUtil::IsTuple(lhs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), lhs->shape())) { // lhs side is being implicitly broadcast. Change to explicit. lhs = ImplicitBroadcastToExplicitBroadcast(lhs, request.output_shape()); } - if (!ShapeUtil::SameDimensions(request.output_shape(), rhs->shape())) { + if (!ShapeUtil::IsTuple(rhs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), rhs->shape())) { rhs = ImplicitBroadcastToExplicitBroadcast(rhs, request.output_shape()); } - if (!ShapeUtil::SameDimensions(request.output_shape(), ehs->shape())) { + if (!ShapeUtil::IsTuple(ehs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), ehs->shape())) { ehs = ImplicitBroadcastToExplicitBroadcast(ehs, request.output_shape()); } @@ -3296,6 +3386,22 @@ void ComputationLowerer::Visit( break; } + case OpRequest::kHostComputeRequest: { + const HostComputeRequest& host_compute_request = + request.request().host_compute_request(); + std::vector operands; + for (const ComputationDataHandle& operand : + host_compute_request.operands()) { + operands.push_back(lookup_instruction(operand)); + } + auto output_shape = host_compute_request.shape(); + auto channel_name = host_compute_request.channel_name(); + auto cost_estimate_ns = host_compute_request.cost_estimate_ns(); + hlo_instruction = add_instruction(HloInstruction::CreateHostCompute( + output_shape, operands, channel_name, cost_estimate_ns)); + break; + } + case OpRequest::kUnaryOpRequest: { const UnaryOpRequest& unary_op_request = request.request().unary_op_request(); @@ -3398,6 +3504,20 @@ void ComputationLowerer::Visit( break; } + case OpRequest::kGatherRequest: { + const GatherRequest& gather_request = request.request().gather_request(); + HloInstruction* input_operand = + lookup_instruction(gather_request.input()); + HloInstruction* gather_indices_operand = + lookup_instruction(gather_request.gather_indices()); + std::vector window_bounds; + c_copy(gather_request.window_bounds(), std::back_inserter(window_bounds)); + hlo_instruction = add_instruction(HloInstruction::CreateGather( + request.output_shape(), input_operand, gather_indices_operand, + gather_request.dimension_numbers(), window_bounds)); + break; + } + case OpRequest::OP_NOT_SET: LOG(FATAL) << "OperationRequest doesn't contain a request"; diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h index 54bb24d6d7fe7aa8cc7c684795e40464e4eb6614..5544c868fe905c1ca7e6cab32738440add2e3b4f 100644 --- a/tensorflow/compiler/xla/service/user_computation.h +++ b/tensorflow/compiler/xla/service/user_computation.h @@ -149,6 +149,10 @@ class UserComputation { StatusOr AddOutfeedInstruction( const OutfeedRequest& outfeed_request); + // Enqueues a host compute instruction onto this user computation. + StatusOr AddHostComputeInstruction( + const HostComputeRequest& host_compute_request); + // Enqueues a call instruction onto this user computation. StatusOr AddCallInstruction( const CallRequest& call_request, @@ -232,12 +236,17 @@ class UserComputation { const UserComputation& false_computation); // Enqueues a Send instruction onto this user computation. - Status AddSendInstruction(const SendRequest& send_request); + StatusOr AddSendInstruction( + const SendRequest& send_request); // Enqueues a Recv instruction onto this user computation. StatusOr AddRecvInstruction( const RecvRequest& recv_request); + // Enqueues a Gather instruction onto this user computation. + StatusOr AddGatherInstruction( + const GatherRequest& gather_request); + // Returns the user-provided name of this user computation, which is provided // via the XLA computation-building API. const string& name() const { return name_; } diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc index a5f9b01f011ce04f1114c74391a967c62f015221..3ef0cdff6751258e4489ce350deb0931fdf69ef9 100644 --- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc +++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc @@ -106,20 +106,12 @@ static bool NotWorthHoistingIndividually(const HloInstruction& instruction) { case HloOpcode::kBitcast: case HloOpcode::kBroadcast: case HloOpcode::kConstant: + case HloOpcode::kReshape: case HloOpcode::kReverse: case HloOpcode::kSlice: + case HloOpcode::kTranspose: case HloOpcode::kTuple: return true; - - case HloOpcode::kTranspose: - return ShapeUtil::TransposeIsBitcast( - /*input_shape=*/instruction.operand(0)->shape(), - /*output_shape=*/instruction.shape(), instruction.dimensions()); - - case HloOpcode::kReshape: - return ShapeUtil::ReshapeIsBitcast( - /*input_shape=*/instruction.operand(0)->shape(), - /*output_shape=*/instruction.shape()); } } diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier.cc b/tensorflow/compiler/xla/service/while_loop_simplifier.cc index 87a7f86f4ec9844de3e350d7774093dd6248dd83..c9d77c9376ffa5e992c97e77fbd632e5e62e18cd 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier.cc @@ -212,7 +212,7 @@ static optional GetLoopTripCount(HloInstruction* while_op) { // Now that we know the index of the induction variable, we can we can try to // compute how many times the loop executes. Start by computing the induction // variable's initial value. - HloEvaluator evaluator; + HloEvaluator evaluator(/*max_loop_iterations=*/0); auto* while_init = while_op->mutable_operand(0); auto* indvar_init = while_init->mutable_operand(*indvar_tuple_idx); StatusOr> indvar_init_result = @@ -564,9 +564,11 @@ static StatusOr TryRemoveWhileLoop(HloInstruction* while_op) { // // This is not a fundamental limitation. The control operands can be moved // onto the new HLOs after simplification, and any side-effecting ops inside - // the loop aren't removed, just cloned and added back to the loop. - // Nevertheless our infrastructure sees loop simplification as removal of - // these nodes and currently doesn't allow it. + // the loop aren't removed, just cloned and added back to the loop. But + // moving an op out of the loop also removes implicit control dependencies + // between the op and the ops outside the loop, so we'd have to add those back + // for things like infeed/outfeed. It gets complicated. So for now we just + // avoid it. if (!while_op->parent()->IsRemovable(while_op) || while_op->HasSideEffect()) { VLOG(2) << "Not attempting to remove while loop it is not removable: " << while_op->ToShortString(); diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h index d752619bd65751779c24f061e44e206d66b01465..ffaa40c2d673a2365342371ed8dab59565d1d08f 100644 --- a/tensorflow/compiler/xla/shape_tree.h +++ b/tensorflow/compiler/xla/shape_tree.h @@ -53,7 +53,7 @@ struct ShapeTreeNode { ShapeTreeNode(const ShapeTreeNode& other) : data(other.data), children(other.children.size()) { for (size_t i = 0; i < children.size(); ++i) { - children[i] = MakeUnique(*other.children[i]); + children[i] = ::xla::MakeUnique(*other.children[i]); } } @@ -62,7 +62,7 @@ struct ShapeTreeNode { data = other.data; children.resize(other.children.size()); for (size_t i = 0; i < children.size(); ++i) { - children[i] = MakeUnique(*other.children[i]); + children[i] = ::xla::MakeUnique(*other.children[i]); } } return *this; @@ -143,6 +143,18 @@ class ShapeTree { // Return the shape represented with this ShapeTree. const Shape& shape() const { return *shape_; } + // Replaces *only* the underlying shape of this ShapeTree. The caller must own + // the Shape object and hence shape_storage_ is not updated. + // + // Only safe to use this if the ShapeTree was constructed with 'explicit + // ShapeTree(const Shape* shape)' or is moved from one such ShapeTree. The + // caller must ensure that the input shape is consistent with the underlying + // tree. + void replace_shape_ptr(const Shape* shape) { + CHECK(shape_storage_.get() == nullptr); + shape_ = shape; + } + // Returns true if the node at the given index is a leaf node (an array // shape). bool IsLeaf(const ShapeIndex& index) const { @@ -433,7 +445,7 @@ class ShapeTreeIterator : public std::iterator(index, node_->data); + current_ = ::xla::MakeUnique(index, node_->data); return *current_; } @@ -480,7 +492,7 @@ void ShapeTree::InitChildren(const Shape& shape, Node* node) { template ShapeTree::ShapeTree(Shape shape) : root_(), - shape_storage_(MakeUnique(std::move(shape))), + shape_storage_(::xla::MakeUnique(std::move(shape))), shape_(shape_storage_.get()) { // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. @@ -496,7 +508,7 @@ ShapeTree::ShapeTree(const Shape* shape) : root_(), shape_(shape) { template ShapeTree::ShapeTree(Shape shape, const T& init_value) : root_(init_value), - shape_storage_(MakeUnique(std::move(shape))), + shape_storage_(::xla::MakeUnique(std::move(shape))), shape_(shape_storage_.get()) { // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index d63e16ce2bf51cff0d113640d31ec6e70bfaf421..4f604e6f7cb18c1aaf844967d54e3b0e07e54b34 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -609,6 +609,8 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { /* static */ bool ShapeUtil::SameDimensions(const Shape& lhs, const Shape& rhs) { + CHECK(ShapeUtil::IsArray(lhs)); + CHECK(ShapeUtil::IsArray(rhs)); return ContainersEqual(lhs.dimensions(), rhs.dimensions()); } @@ -617,7 +619,10 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { return rhs.element_type() == TUPLE && ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), Compatible); } - return SameDimensions(lhs, rhs) && SameElementType(lhs, rhs); + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } + return SameElementType(lhs, rhs) && SameDimensions(lhs, rhs); } /* static */ bool ShapeUtil::CompatibleIgnoringElementType(const Shape& lhs, @@ -627,7 +632,26 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), CompatibleIgnoringElementType); } - return SameDimensions(lhs, rhs); + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } + return ShapeUtil::IsArray(rhs) && SameDimensions(lhs, rhs); +} + +/* static */ bool ShapeUtil::CompatibleIgnoringFpPrecision(const Shape& lhs, + const Shape& rhs) { + if (lhs.element_type() == TUPLE) { + return rhs.element_type() == TUPLE && + ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), + CompatibleIgnoringFpPrecision); + } + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } + if (SameElementTypeIgnoringFpPrecision(lhs, rhs)) { + return CompatibleIgnoringElementType(lhs, rhs); + } + return false; } /* static */ int64 ShapeUtil::GetDimension(const Shape& shape, @@ -1060,9 +1084,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape, /* static */ bool ShapeUtil::TransposeIsBitcast( const Shape& input_shape, const Shape& output_shape, tensorflow::gtl::ArraySlice dimension_mapping) { - // Can't insert bitcasts without layout information. - if (!LayoutUtil::HasLayout(input_shape) && - !LayoutUtil::HasLayout(output_shape)) { + CHECK(LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape)); + + if (!SameElementType(input_shape, output_shape)) { return false; } @@ -1093,9 +1118,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape, /* static */ bool ShapeUtil::ReshapeIsBitcast(const Shape& input_shape, const Shape& output_shape) { - // Can't convert reshapes into bitcasts without layout information. - if (!LayoutUtil::HasLayout(input_shape) || - !LayoutUtil::HasLayout(output_shape)) { + CHECK(LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape)); + + if (!SameElementType(input_shape, output_shape)) { return false; } diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 453d4ec04726a4dd3851b8becb439bb7506e4ca9..3e130a02e2ce853ee157e46afb9760f5ff5a5026 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -23,6 +23,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -62,6 +64,9 @@ class ShapeIndex { void push_back(int64 value) { indices_.push_back(value); } void pop_back() { indices_.pop_back(); } + // push_front is O(n^2), but shapes don't usually have a ton of dimensions. + void push_front(int64 value) { indices_.insert(indices_.begin(), value); } + std::vector::const_iterator begin() const { return indices_.begin(); } std::vector::const_iterator end() const { return indices_.end(); } std::vector::iterator begin() { return indices_.begin(); } @@ -204,6 +209,7 @@ class ShapeUtil { // Returns whether the LHS and RHS shapes have the same dimensions; note: does // not check element type. + // Precondition: IsArray(lhs) && IsArray(rhs) static bool SameDimensions(const Shape& lhs, const Shape& rhs); // Returns whether the lhs and rhs shapes have the same element type. @@ -211,6 +217,31 @@ class ShapeUtil { return lhs.element_type() == rhs.element_type(); } + // As SameElementType, but allows floating point types to have different + // precisions. + static bool SameElementTypeIgnoringFpPrecision(const Shape& a, + const Shape& b) { + if (ElementIsFloating(a) && ElementIsFloating(b)) { + return true; + } + return ShapeUtil::SameElementType(a, b); + } + + // Returns the higher-precision element type if a and b are both floating + // point types; otherwise, checks that that they have the same element type + // and returns it. + static PrimitiveType HigherPrecisionElementType(const Shape& a, + const Shape& b) { + if (SameElementType(a, b)) { + return a.element_type(); + } + CHECK(SameElementTypeIgnoringFpPrecision(a, b)); + return primitive_util::BitWidth(a.element_type()) < + primitive_util::BitWidth(b.element_type()) + ? b.element_type() + : a.element_type(); + } + // Returns true if the rank, dimension sizes, and element type are // identical. Layout is ignored. Tuple elements are compared recursively for // compatibility. @@ -221,6 +252,10 @@ class ShapeUtil { // compatibility. static bool CompatibleIgnoringElementType(const Shape& lhs, const Shape& rhs); + // As Compatible, but allow one of lhs and rhs to be BF16 while the other + // being F32. Tuple elements are compared recursively for compatibility. + static bool CompatibleIgnoringFpPrecision(const Shape& lhs, const Shape& rhs); + // Returns whether the lhs and rhs shapes are identical protobufs. static bool Equal(const Shape& lhs, const Shape& rhs); @@ -287,6 +322,15 @@ class ShapeUtil { static Shape MakeShape(PrimitiveType element_type, tensorflow::gtl::ArraySlice dimensions); + // Creates a Shape with element type corresponding to T and the given + // dimensions + template + static Shape MakeShapeWithType( + tensorflow::gtl::ArraySlice dimensions) { + return ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType(), + dimensions); + } + // Constructs a new shape with the given minor_to_major order in its Layout. // Returns a value shape such that shape.has_layout(). static Shape MakeShapeWithLayout( @@ -489,12 +533,16 @@ class ShapeUtil { // Returns whether a transpose from input_shape to output_shape with dimension // mapping "dimension_mapping" produces a result which is bit-wise identical // to its input and thus may be replaced with a bitcast. + // + // Precondition: Both input_shape and output_shape have explicit layouts. static bool TransposeIsBitcast( const Shape& input_shape, const Shape& output_shape, tensorflow::gtl::ArraySlice dimension_mapping); // Returns whether a reshape from "input_shape" to "output_shape" is a // bitcast. + // + // Precondition: Both input_shape and output_shape have explicit layouts. static bool ReshapeIsBitcast(const Shape& input_shape, const Shape& output_shape); @@ -527,16 +575,16 @@ class ShapeUtil { // The visitor_function visitor function should return true if it wants to // continue, or false otherwise. // - // visitor_function must be a callable of type bool(const std::vector&) - // or compatible. + // visitor_function must be a callable of type + // StatusOr(ArraySlice) or compatible. template - static void ForEachIndex(const Shape& shape, - tensorflow::gtl::ArraySlice base, - tensorflow::gtl::ArraySlice count, - tensorflow::gtl::ArraySlice incr, - const FnType& visitor_function) { + static Status ForEachIndexWithStatus(const Shape& shape, + tensorflow::gtl::ArraySlice base, + tensorflow::gtl::ArraySlice count, + tensorflow::gtl::ArraySlice incr, + const FnType& visitor_function) { if (ShapeUtil::HasZeroElements(shape)) { - return; + return Status::OK(); } CHECK_EQ(Rank(shape), base.size()); CHECK_EQ(incr.size(), base.size()); @@ -546,7 +594,11 @@ class ShapeUtil { // once with the proper empty indexes. int64 n = -1; std::vector indexes(base.begin(), base.end()); - while (n < rank && visitor_function(indexes)) { + while (n < rank) { + TF_ASSIGN_OR_RETURN(bool should_continue, visitor_function(indexes)); + if (!should_continue) { + break; + } // Increments dimensions in minor to major order. for (n = 0; n < rank; ++n) { int64 dim = LayoutUtil::Minor(shape.layout(), n); @@ -557,6 +609,37 @@ class ShapeUtil { indexes[dim] = base[dim]; } } + + return Status::OK(); + } + + // Simple ergonomic wrapper around ShapeUtil::ForEachIndexWithStatus. + struct IndexIterationSpace { + std::vector index_base; + std::vector index_count; + std::vector index_incr; + }; + + template + static Status ForEachIndexWithStatus( + const Shape& shape, const IndexIterationSpace& iteration_space, + FnTy&& function) { + return ShapeUtil::ForEachIndexWithStatus( + shape, iteration_space.index_base, iteration_space.index_count, + iteration_space.index_incr, std::forward(function)); + } + + template + static void ForEachIndex(const Shape& shape, + tensorflow::gtl::ArraySlice base, + tensorflow::gtl::ArraySlice count, + tensorflow::gtl::ArraySlice incr, + const FnType& visitor_function) { + ForEachIndexWithStatus(shape, base, count, incr, + [&](tensorflow::gtl::ArraySlice indices) { + return StatusOr(visitor_function(indices)); + }) + .IgnoreError(); } private: diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index 81ba7afb95265398e830e26122cd0056a32daee3..424cfe37ea44d64884e08695fd1f49ca1970ca62 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -170,6 +170,18 @@ TEST(ShapeUtilTest, CompatibleNotIdenticalShapes) { EXPECT_TRUE(ShapeUtil::Compatible(shape_1, shape_2)); } +TEST(ShapeUtilTest, CompatibleIgnoringFpPrecision) { + Shape shape1 = ShapeUtil::MakeShape(BF16, {3, 2}); + Shape shape2 = ShapeUtil::MakeShape(F32, {3, 2}); + ASSERT_TRUE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); +} + +TEST(ShapeUtilTest, IncompatibleIgnoringFpPrecision) { + Shape shape1 = ShapeUtil::MakeShape(BF16, {3, 2}); + Shape shape2 = ShapeUtil::MakeShape(F32, {2, 2}); + ASSERT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); +} + TEST(ShapeUtilTest, IncompatibleDifferentElementShapes) { Shape shape_1 = ShapeUtil::MakeShape(F32, {3, 2}); Shape shape_2 = ShapeUtil::MakeShape(PRED, {3, 2}); @@ -184,6 +196,14 @@ TEST(ShapeUtilTest, CompatibleTuples) { EXPECT_TRUE(ShapeUtil::Compatible(tuple1, tuple2)); } +TEST(ShapeUtilTest, CompatibleTuplesIgnoringFpPrecision) { + Shape tuple1 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(BF16, {3, 2}), ShapeUtil::MakeShape(F32, {4, 5})}); + Shape tuple2 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F64, {3, 2}), ShapeUtil::MakeShape(BF16, {4, 5})}); + EXPECT_TRUE(ShapeUtil::CompatibleIgnoringFpPrecision(tuple1, tuple2)); +} + TEST(ShapeUtilTest, IncompatibleTuplesWithSwappedElements) { Shape tuple1 = ShapeUtil::MakeTupleShape( {ShapeUtil::MakeShape(PRED, {4, 5}), ShapeUtil::MakeShape(F32, {3, 2})}); @@ -193,6 +213,14 @@ TEST(ShapeUtilTest, IncompatibleTuplesWithSwappedElements) { EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(tuple1, tuple2)); } +TEST(ShapeUtilTest, IncompatibleTuplesIgnoringFpPrecision) { + Shape tuple1 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(BF16, {4, 5}), ShapeUtil::MakeShape(F32, {3, 2})}); + Shape tuple2 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {3, 2}), ShapeUtil::MakeShape(BF16, {4, 5})}); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(tuple1, tuple2)); +} + TEST(ShapeUtilTest, IncompatibleTuplesWithDifferentPrimitiveType) { Shape tuple1 = ShapeUtil::MakeTupleShape( {ShapeUtil::MakeShape(PRED, {4, 5}), ShapeUtil::MakeShape(F32, {3, 2})}); @@ -210,6 +238,18 @@ TEST(ShapeUtilTest, IncompatibleTuplesWithDifferentDimensions) { EXPECT_FALSE(ShapeUtil::Compatible(tuple1, tuple2)); } +TEST(ShapeUtilTest, IncompatibleScalarVsTuple) { + Shape shape1 = ShapeUtil::MakeShape(F32, {}); + Shape shape2 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {3, 2}), ShapeUtil::MakeShape(U32, {})}); + EXPECT_FALSE(ShapeUtil::Compatible(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::Compatible(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); +} + TEST(ShapeUtilTest, CompareShapesWithPaddedDimensionsMismatch) { Shape shape1 = ShapeUtil::MakeShape(F32, {20, 30}); shape1.mutable_layout()->add_padded_dimensions(10); @@ -545,10 +585,11 @@ TEST(ShapeUtilTest, ForEachIndex) { Shape shape = ShapeUtil::MakeShape(F32, data.dimensions); // Increments at every invocation. int invocations = 0; - auto increment_func = [&invocations](const std::vector& indexes) { - invocations++; - return true; - }; + auto increment_func = + [&invocations](tensorflow::gtl::ArraySlice indexes) { + invocations++; + return true; + }; std::vector zero_base(data.dimensions.size(), 0); std::vector step(data.dimensions.size(), 1); @@ -560,6 +601,29 @@ TEST(ShapeUtilTest, ForEachIndex) { } } +TEST(ShapeUtilTest, ForEachIndexWithStatus) { + Shape shape = ShapeUtil::MakeShape(F32, {10, 10}); + // Increments at every invocation. + int invocations = 0; + auto increment_func = + [&invocations]( + tensorflow::gtl::ArraySlice indexes) -> StatusOr { + if (++invocations == 5) { + return Unimplemented("Cannot increment beyond 5."); + } + return true; + }; + + Status error_status = ShapeUtil::ForEachIndexWithStatus( + shape, /*base=*/{0, 0}, /*count=*/{10, 10}, /*incr=*/{0, 1}, + increment_func); + + EXPECT_FALSE(error_status.ok()); + EXPECT_THAT(error_status.error_message(), + ::testing::HasSubstr("Cannot increment beyond 5.")); + EXPECT_EQ(invocations, 5); +} + TEST(ShapeUtilTest, DimensionsUnmodifiedByReshape_1x1x1x1_to_1x1x1) { // All output dimensions should be unmodified. One of the input dimensions is // modified because the input rank is larger by one. diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index d4820d1b6d6d8c848794b120a9064609ba72e5a9..5fb38d65f1cc15f95ac12d05229ede7c5343ebfa 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -44,6 +44,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:test", ], + alwayslink = True, ) cc_library( @@ -138,6 +139,7 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_verifier", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", "//tensorflow/core:test", ], @@ -271,6 +273,9 @@ cc_library( xla_test( name = "bad_rng_shape_validation_test", srcs = ["bad_rng_shape_validation_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -290,6 +295,9 @@ xla_test( xla_test( name = "check_execution_arity_test", srcs = ["check_execution_arity_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -309,6 +317,9 @@ xla_test( xla_test( name = "query_inferred_shape_test", srcs = ["query_inferred_shape_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -325,6 +336,9 @@ xla_test( xla_test( name = "while_test", srcs = ["while_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -366,6 +380,9 @@ xla_test( xla_test( name = "axpy_simple_test", srcs = ["axpy_simple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", @@ -430,6 +447,9 @@ xla_test( xla_test( name = "pred_test", srcs = ["pred_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla/client:computation_builder", @@ -444,6 +464,9 @@ xla_test( xla_test( name = "select_test", srcs = ["select_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", @@ -460,6 +483,7 @@ xla_test( xla_test( name = "conditional_test", srcs = ["conditional_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", @@ -476,6 +500,7 @@ xla_test( xla_test( name = "unary_op_test", srcs = ["unary_op_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", @@ -575,6 +600,27 @@ xla_test( ], ) +xla_test( + name = "exhaustive_f32_elementwise_op_test", + srcs = ["exhaustive_f32_elementwise_op_test.cc"], + backends = [ + "cpu", + "gpu", + ], + shard_count = 48, + tags = [ + "enormous", + "manual", + "notap", + ], + deps = [ + ":client_library_test_base", + ":literal_test_util", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", + ], +) + xla_test( name = "reduce_precision_test", srcs = ["reduce_precision_test.cc"], @@ -601,28 +647,11 @@ xla_test( xla_test( name = "dot_operation_test", srcs = ["dot_operation_test.cc"], - deps = [ - "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:array3d", - "//tensorflow/compiler/xla:reference_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla/client:computation_builder", - "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/tests:client_library_test_base", - "//tensorflow/compiler/xla/tests:literal_test_util", - "//tensorflow/compiler/xla/tests:test_utils", - "//tensorflow/compiler/xla/tests:xla_internal_test_main", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:test", + shard_count = 20, + tags = [ + "enable_for_xla_interpreter", + "optonly", ], -) - -# Tests the dot operation in some cases that can be performed via a -# runtime call on some backends - e.g. a runtime call to Eigen. -xla_test( - name = "dot_operation_runtime_test", - srcs = ["dot_operation_test.cc"], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -640,7 +669,7 @@ xla_test( ], ) -# Repeat dot_operation_runtime_test with single-threded eigen. +# Repeat dot_operation_runtime_test with single-threaded eigen. xla_test( name = "dot_operation_single_threaded_runtime_test", srcs = ["dot_operation_test.cc"], @@ -652,6 +681,8 @@ xla_test( "--xla_cpu_multi_thread_eigen=false", ], }, + shard_count = 20, + tags = ["optonly"], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -672,6 +703,9 @@ xla_test( xla_test( name = "transpose_test", srcs = ["transpose_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", @@ -690,6 +724,9 @@ xla_test( xla_test( name = "constants_test", srcs = ["constants_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -850,8 +887,7 @@ xla_test( name = "half_test", srcs = ["half_test.cc"], backends = [ - # TODO(b/72509305): Flaky (fails with SEGV) as of 2018-01-25 - # "cpu", + "cpu", "gpu", ], deps = [ @@ -875,6 +911,9 @@ xla_test( name = "slice_test", srcs = ["slice_test.cc"], shard_count = 40, + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", @@ -891,6 +930,9 @@ xla_test( xla_test( name = "multidimensional_slice_test", srcs = ["multidimensional_slice_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -907,6 +949,9 @@ xla_test( name = "dynamic_ops_test", timeout = "moderate", srcs = ["dynamic_ops_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", @@ -953,6 +998,9 @@ xla_test( xla_test( name = "vector_ops_reduce_test", srcs = ["vector_ops_reduce_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -971,6 +1019,10 @@ xla_test( name = "reduce_test", srcs = ["reduce_test.cc"], shard_count = 40, + tags = [ + "enable_for_xla_interpreter", + "optonly", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1064,6 +1116,9 @@ xla_test( xla_test( name = "copy_test", srcs = ["copy_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ ":client_library_test_base", "//tensorflow/compiler/xla:array2d", @@ -1082,6 +1137,9 @@ xla_test( xla_test( name = "reduce_hlo_test", srcs = ["reduce_hlo_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -1095,6 +1153,9 @@ xla_test( xla_test( name = "call_test", srcs = ["call_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -1132,6 +1193,9 @@ xla_test( xla_test( name = "binop_scaling_test", srcs = ["binop_scaling_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1148,6 +1212,9 @@ xla_test( xla_test( name = "broadcast_simple_test", srcs = ["broadcast_simple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1165,6 +1232,9 @@ xla_test( xla_test( name = "pad_test", srcs = ["pad_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1185,6 +1255,9 @@ xla_test( xla_test( name = "fmax_test", srcs = ["fmax_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", @@ -1198,6 +1271,9 @@ xla_test( xla_test( name = "log_test", srcs = ["log_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", @@ -1211,6 +1287,9 @@ xla_test( xla_test( name = "matrix_ops_simple_test", srcs = ["matrix_ops_simple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", @@ -1225,6 +1304,7 @@ xla_test( "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", "//tensorflow/core:test", @@ -1253,6 +1333,9 @@ xla_test( name = "reshape_test", srcs = ["reshape_test.cc"], shard_count = 30, + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1279,6 +1362,9 @@ xla_test( xla_test( name = "reverse_test", srcs = ["reverse_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", @@ -1295,6 +1381,9 @@ xla_test( xla_test( name = "vector_ops_simple_test", srcs = ["vector_ops_simple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:shape_util", @@ -1318,6 +1407,9 @@ xla_test( xla_test( name = "concat_test", srcs = ["concat_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", @@ -1338,8 +1430,12 @@ xla_test( xla_test( name = "convert_test", srcs = ["convert_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", @@ -1394,6 +1490,9 @@ xla_test( xla_test( name = "floor_ceil_test", srcs = ["floor_ceil_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", @@ -1477,6 +1576,9 @@ xla_test( xla_test( name = "replay_test", srcs = ["replay_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:protobuf_util", @@ -1499,6 +1601,9 @@ xla_test( xla_test( name = "broadcast_test", srcs = ["broadcast_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -1566,6 +1671,7 @@ xla_test( "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_runner", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -1592,6 +1698,7 @@ xla_test( "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_runner", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index b788631fa3e7ae45f4d72e8d6fb006c2332d2d1e..6e21dda25d8e5151b31b8c2328253260595a94c4 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -101,6 +101,33 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) { {}, error_spec_); } +XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) { + ComputationBuilder builder(client_, TestName()); + auto a = builder.ConstantR1({ + -1, + 1, + 0, + 0x12345678, + static_cast(0xffffffff12345678l), + static_cast(0x8000000000000000LL), + static_cast(0x8000000000000001LL), + }); + auto result = builder.Neg(a); + LOG(INFO) << -static_cast(0x7FFFFFFFFFFFFFFFLL); + + ComputeAndCompareR1(&builder, + { + 1, + -1, + 0, + -0x12345678, + 0xedcba988, + static_cast(0x8000000000000000LL), + -static_cast(0x8000000000000001LL), + }, + {}); +} + XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteZeroElementF32s) { ComputationBuilder builder(client_, TestName()); auto a = builder.ConstantR1({}); @@ -186,6 +213,86 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementC64s) { ComputeAndCompareR1(&builder, {}, {}, error_spec_); } +XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) { + ComputationBuilder b(client_, TestName()); + + std::vector lhs{0xFFFFFFFF, + static_cast(-1), + 0, + 0, + 0x7FFFFFFFFFFFFFFFLL, + 0x7FFFFFFFFFFFFFFLL, + 0x8000000000000000LL, + 0x8000000000000000LL, + 1}; + std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); + auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param"); + std::unique_ptr lhs_data = + client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); + + std::vector rhs{1, + 0x7FFFFFFFFFFFFFFLL, + 0x7FFFFFFFFFFFFFFFLL, + 0x8000000000000000LL, + 0, + static_cast(-1), + 0, + 1, + 0x8000000000000000LL}; + std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); + auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param"); + std::unique_ptr rhs_data = + client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); + + auto add = b.Add(lhs_param, rhs_param); + + std::vector expected(lhs.size()); + for (int64 i = 0; i < lhs.size(); ++i) { + expected[i] = lhs[i] + rhs[i]; + } + + ComputeAndCompareR1(&b, expected, {lhs_data.get(), rhs_data.get()}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) { + ComputationBuilder b(client_, TestName()); + + std::vector lhs{static_cast(0x8000000000000000LL), + static_cast(0x8000000000000000LL), + -1, + 0x7FFFFFFFFFFFFFFLL, + 0x7FFFFFFFFFFFFFFFLL, + 1, + 0, + -1}; + std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); + auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param"); + std::unique_ptr lhs_data = + client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); + + std::vector rhs{-1, + 0, + static_cast(0x8000000000000000LL), + 1, + 0, + 0x7FFFFFFFFFFFFFFLL, + 0x7FFFFFFFFFFFFFFFLL, + 0x7FFFFFFFFFFFFFFFLL}; + std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); + auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param"); + std::unique_ptr rhs_data = + client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); + + auto sub = b.Sub(lhs_param, rhs_param); + + std::vector expected(lhs.size()); + for (int64 i = 0; i < lhs.size(); ++i) { + expected[i] = lhs[i] - rhs[i]; + } + + ComputeAndCompareR1(&b, expected, {lhs_data.get(), rhs_data.get()}); +} + TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) { const int count = GetParam(); ComputationBuilder builder(client_, TestName()); @@ -847,68 +954,76 @@ XLA_TEST_F(ArrayElementwiseOpTest, NotZeroElementU32R1) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftS32) { ComputationBuilder builder(client_, TestName()); - auto a = - builder.ConstantR1({static_cast(0x12345678), - static_cast(0xF0001000), 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 15}); + auto a = builder.ConstantR1({static_cast(0x12345678), + static_cast(0xF0001000), 1, 3, 77, + 1, -3, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 15, 32, 100, -1}); auto out = builder.ShiftLeft(a, b); - ComputeAndCompareR1( - &builder, - {static_cast(0x23456780), 0x00100000, 0x4, 0x180, 2523136}, {}); + ComputeAndCompareR1(&builder, + {static_cast(0x23456780), 0x00100000, 0x4, + 0x180, 2523136, 0, 0, 0}, + {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticS32) { ComputationBuilder builder(client_, TestName()); - auto a = - builder.ConstantR1({static_cast(0x92345678), - static_cast(0x10001000), 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 2}); + auto a = builder.ConstantR1({static_cast(0x92345678), + static_cast(0x10001000), 1, 3, 77, + 1, -3, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 2, 32, 100, -1}); auto out = builder.ShiftRightArithmetic(a, b); - ComputeAndCompareR1(&builder, - {static_cast(0xF9234567), - static_cast(0x00100010), 0, 0, 19}, - {}); + ComputeAndCompareR1( + &builder, + {static_cast(0xF9234567), static_cast(0x00100010), 0, 0, 19, + 0, -1, 0}, + {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalS32) { ComputationBuilder builder(client_, TestName()); - auto a = - builder.ConstantR1({static_cast(0x92345678), - static_cast(0x10001000), 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 5}); + auto a = builder.ConstantR1({static_cast(0x92345678), + static_cast(0x10001000), 1, 3, 77, + 1, -3, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 5, 32, 100, -1}); auto out = builder.ShiftRightLogical(a, b); - ComputeAndCompareR1(&builder, {0x09234567, 0x00100010, 0, 0, 2}, {}); + ComputeAndCompareR1(&builder, + {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftU32) { ComputationBuilder builder(client_, TestName()); - auto a = builder.ConstantR1({0x12345678, 0xF0001000, 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 15}); + auto a = builder.ConstantR1( + {0x12345678, 0xF0001000, 1, 3, 77, 1, ~3u, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 15, 32, 100, ~0u}); auto out = builder.ShiftLeft(a, b); ComputeAndCompareR1( - &builder, {0x23456780, 0x00100000, 0x4, 0x180, 2523136}, {}); + &builder, {0x23456780, 0x00100000, 0x4, 0x180, 2523136, 0, 0, 0}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticU32) { ComputationBuilder builder(client_, TestName()); - auto a = builder.ConstantR1({0x92345678, 0x10001000, 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 2}); + auto a = builder.ConstantR1( + {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 2, 32, 100, ~0u}); auto out = builder.ShiftRightArithmetic(a, b); - ComputeAndCompareR1(&builder, {0xF9234567, 0x00100010, 0, 0, 19}, {}); + ComputeAndCompareR1( + &builder, {0xF9234567, 0x00100010, 0, 0, 19, 0, ~0u, 0}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalU32) { ComputationBuilder builder(client_, TestName()); - auto a = builder.ConstantR1({0x92345678, 0x10001000, 1, 3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 5}); + auto a = builder.ConstantR1( + {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); + auto b = builder.ConstantR1({4, 8, 2, 7, 5, 32, 100, ~0u}); auto out = builder.ShiftRightLogical(a, b); - ComputeAndCompareR1(&builder, {0x09234567, 0x00100010, 0, 0, 2}, {}); + ComputeAndCompareR1(&builder, + {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, CompareEqF32s) { @@ -1533,33 +1648,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4DZeroElements) { ComputeAndCompareR4(&builder, expected, {}, error_spec_); } -// GPU backend emits nvvm intrinsic for fmin and fmax, whose semantics is NOT -// such -// * fmin(NaN, x) = x -// * fmax(NaN, x) = x -// so we only test NAN on CPU. -// -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. XLA_TEST_F(ArrayElementwiseOpTest, MinF32s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); -#endif auto minimum = builder.Min(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {1.0f, -5.0f, 1.0f}, -#else - {1.0f, -5.0f, 1.0f, 10.0f, 6.0f}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {1.0f, -5.0f, 1.0f, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) { @@ -1570,50 +1667,26 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) { ComputeAndCompareR1(&builder, {}, {}, error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MinF64s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); -#endif auto minimum = builder.Min(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {1.0, -5.0, 1.0}, -#else - {1.0, -5.0, 1.0, 10.0, 6.0}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {1.0, -5.0, 1.0, NAN, NAN}, {}, + error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MaxF32s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); -#endif auto maximum = builder.Max(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {2.0f, 1.0f, 2.25f}, -#else - {2.0f, 1.0f, 2.25f, 10.0f, 6.0f}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {2.0f, 1.0f, 2.25f, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) { @@ -1624,27 +1697,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) { ComputeAndCompareR1(&builder, {}, {}, error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MaxF64s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); -#endif auto maximum = builder.Max(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {2.0, 1.0, 2.25}, -#else - {2.0, 1.0, 2.25, 10.0, 6.0}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {2.0, 1.0, 2.25, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MaxS32s) { @@ -2048,47 +2109,117 @@ XLA_TEST_F(ArrayElementwiseOpTest, TanhF32s) { XLA_TEST_F(ArrayElementwiseOpTest, TanhF32sVector) { // This is like the test ArrayElementwiseOpTest.TanhF32s above, except that // the input tensor is large enough to exercise the vectorized tanh - // implementation. - ComputationBuilder builder(client_, TestName()); - auto input_literal = Literal::CreateR2( - {{1.02, -0.32, 0.85, 0.90, 1.23, -0.91, -0.49, 0.80}, - {-0.67, 0.16, -0.07, 0.39, -0.41, 0.04, 1.36, 1.25}, - {0.41, 0.65, -1.08, 0.32, -1.45, -0.77, -1.09, 0.91}, - {-1.03, -0.30, -1.11, -1.17, 1.50, -0.85, 0.04, 1.02}, - {0.34, -0.61, 0.41, 0.07, -0.02, 1.42, -0.62, 0.81}, - {0.08, 0.81, -0.30, 1.17, -0.65, -0.44, 0.92, 1.26}, - {-1.29, 1.35, 0.08, -1.24, -0.92, 0.49, 1.17, -0.45}, - {-1.31, -1.44, -0.13, -1.31, -0.79, 1.41, 1.21, 1.05}}); - auto input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); + // implementation on XLA CPU. + ComputationBuilder builder(client_, TestName()); + auto input_literal = Literal::CreateR1( + {1.02, -0.32, 0.85, 0.90, 1.23, -0.91, -0.49, 0.80, -0.67, 0.16, + -0.07, 0.39, -0.41, 0.04, 1.36, 1.25, 0.41, 0.65, -1.08, 0.32, + -1.45, -0.77, -1.09, 0.91, -1.03, -0.30, -1.11, -1.17, 1.50, -0.85, + 0.04, 1.02, 0.34, -0.61, 0.41, 0.07, -0.02, 1.42, -0.62, 0.81, + 0.08, 0.81, -0.30, 1.17, -0.65, -0.44, 0.92, 1.26, -1.29, 1.35, + 0.08, -1.24, -0.92, 0.49, 1.17, -0.45, -1.31, -1.44, -0.13, -1.31, + -0.79, 1.41, 1.21, 1.05}); + TF_ASSERT_OK_AND_ASSIGN(auto input_data, + client_->TransferToServer(*input_literal)); auto input = builder.Parameter(0, input_literal->shape(), "input"); builder.Tanh(input); - ComputeAndCompareR2( + ComputeAndCompareR1( &builder, - {{0.77009583, -0.30665702, 0.69070244, 0.71401149, 0.84400684, - -0.71985596, -0.45764771, 0.66664988}, - {-0.58278900, 0.16050975, -0.06770509, 0.36843640, -0.38476998, - 0.04018109, 0.87562293, 0.84788644}, - {0.38603750, 0.57294142, -0.79140943, 0.31032649, -0.89590985, - -0.64770776, -0.79625875, 0.72234446}, - {-0.77389336, -0.28871772, -0.80428445, -0.82541436, 0.90456349, - -0.68856895, 0.03877772, 0.76877952}, - {0.32561871, -0.54546672, 0.39072621, 0.07273290, -0.01924866, - 0.88924897, -0.55283129, 0.67183107}, - {0.08006320, 0.66944766, -0.29068485, 0.82573754, -0.57170743, - -0.41581789, 0.72739530, 0.85025692}, - {-0.85931867, 0.87357593, 0.07782833, -0.84597743, -0.72748238, - 0.45396307, 0.82449573, -0.42462519}, - {-0.86363792, -0.89368379, -0.12621804, -0.86445558, -0.65565848, - 0.88789743, 0.83566397, 0.78287679}}, + {0.77009583, -0.30665702, 0.69070244, 0.71401149, 0.84400684, + -0.71985596, -0.45764771, 0.66664988, -0.58278900, 0.16050975, + -0.06770509, 0.36843640, -0.38476998, 0.04018109, 0.87562293, + 0.84788644, 0.38603750, 0.57294142, -0.79140943, 0.31032649, + -0.89590985, -0.64770776, -0.79625875, 0.72234446, -0.77389336, + -0.28871772, -0.80428445, -0.82541436, 0.90456349, -0.68856895, + 0.03877772, 0.76877952, 0.32561871, -0.54546672, 0.39072621, + 0.07273290, -0.01924866, 0.88924897, -0.55283129, 0.67183107, + 0.08006320, 0.66944766, -0.29068485, 0.82573754, -0.57170743, + -0.41581789, 0.72739530, 0.85025692, -0.85931867, 0.87357593, + 0.07782833, -0.84597743, -0.72748238, 0.45396307, 0.82449573, + -0.42462519, -0.86363792, -0.89368379, -0.12621804, -0.86445558, + -0.65565848, 0.88789743, 0.83566397, 0.78287679}, {input_data.get()}, // The error spec is unusually high here to account for the fact that we // use a rational interpolant to approximate tanh. ErrorSpec(0.004, 0.004)); } +XLA_TEST_F(ArrayElementwiseOpTest, ExpF32sVector) { + // The input tensor is large enough to exercise the vectorized exp + // implementation on XLA CPU. + ComputationBuilder builder(client_, TestName()); + + // Just to help make sense of the scales here -- exp(89) saturates float32 and + // exp(-10) is smaller than our error spec. + std::unique_ptr input_literal = Literal::CreateR1( + {1.02, -0.32, 0.85, 0.9, 1.23, -0.91, -0.49, 0.8, -1.31, + -1.44, -0.13, -1.31, -0.79, 1.41, 1.21, 1.05, -195.6, -194.5, + -193.4, -192.3, -191.2, -190.1, -189.0, -187.9, -19.6, -18.5, -17.4, + -16.3, -15.2, -14.1, -13.0, -11.9, -10.8, -9.7, -8.6, -7.5, + -6.4, -5.3, -4.2, -3.1, -2.0, -0.9, 0.2, 1.3, 2.4, + 3.5, 4.6, 5.7, 6.8, 7.9, 9.0, 10.1, 11.2, 12.3, + 13.4, 14.5, 15.6, 16.7, 17.8, 18.9, 20.0, 21.1, 22.2, + 23.3, 24.4, 25.5, 26.6, 27.7, 28.8, 29.9, 31.0, 32.1, + 68.4, 69.5, 70.6, 71.7, 72.8, 73.9, 75.0, 76.1, 77.2, + 78.3, 79.4, 80.5, 81.6, 82.7, 83.8, 84.9, 85.2, 86.3, + 86.4, 86.5, 87.6, 87.7, 87.8, 87.9}); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, + client_->TransferToServer(*input_literal)); + + auto input = builder.Parameter(0, input_literal->shape(), "input"); + builder.Exp(input); + + std::vector expected_result; + int64 input_size = input_literal->shape().dimensions(0); + expected_result.reserve(input_size); + for (int64 i = 0; i < input_size; i++) { + expected_result.push_back(std::exp(input_literal->Get({i}))); + } + + ComputeAndCompareR1(&builder, expected_result, {input_data.get()}, + error_spec_); +} + +XLA_TEST_F(ArrayElementwiseOpTest, LogF32sVector) { + // The input tensor is large enough to exercise the vectorized exp + // implementation on XLA CPU. + ComputationBuilder builder(client_, TestName()); + + std::unique_ptr input_literal = Literal::CreateR1( + {-1.29, -1.41, -1.25, -13.5, -11.7, -17.9, -198, + -167, 1.29, 1.41, 1.25, 13.5, 11.7, 17.9, + 198, 167, 1.27e+03, 1.33e+03, 1.74e+03, 1.6e+04, 1.84e+04, + 1.74e+04, 1.89e+05, 1.9e+05, 1.93e+06, 1.98e+06, 1.65e+06, 1.97e+07, + 1.66e+07, 1e+07, 1.98e+08, 1.96e+08, 1.64e+09, 1.58e+09, 1.64e+09, + 1.44e+10, 1.5e+10, 1.99e+10, 1.17e+11, 1.08e+11, 1.08e+12, 1.38e+12, + 1.4e+12, 1.03e+13, 1.6e+13, 1.99e+13, 1.26e+14, 1.51e+14, 1.33e+15, + 1.41e+15, 1.63e+15, 1.39e+16, 1.21e+16, 1.27e+16, 1.28e+17, 1.62e+17, + 2e+18, 1.96e+18, 1.81e+18, 1.99e+19, 1.86e+19, 1.61e+19, 1.71e+20, + 1.47e+20, 1.83e+21, 1.33e+21, 1.3e+21, 1.35e+22, 1.84e+22, 1.02e+22, + 1.81e+23, 1.02e+23, 1.89e+24, 1.49e+24, 1.08e+24, 1.95e+25, 1.1e+25, + 1.62e+25, 1.2e+26, 1.41e+26, 1.93e+27, 1.66e+27, 1.62e+27, 1.05e+28, + 1.5e+28, 1.79e+28, 1.36e+29, 1.95e+29, 1.5e+30, 1.81e+30, 1.34e+30, + 1.7e+31, 1.44e+31, 1.1e+31, 1.4e+32, 1.67e+32, 1.96e+33, 1.11e+33, + 1.19e+33, 1.61e+34, 1.05e+34, 1.88e+34, 1.67e+35, 1.7e+35}); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, + client_->TransferToServer(*input_literal)); + + auto input = builder.Parameter(0, input_literal->shape(), "input"); + builder.Log(input); + + std::vector expected_result; + int64 input_size = input_literal->shape().dimensions(0); + expected_result.reserve(input_size); + for (int64 i = 0; i < input_size; i++) { + expected_result.push_back(std::log(input_literal->Get({i}))); + } + + ComputeAndCompareR1(&builder, expected_result, {input_data.get()}, + error_spec_); +} + XLA_TEST_F(ArrayElementwiseOpTest, AddChainFoldLeft) { // a ------ (add) --------- (add) // / / diff --git a/tensorflow/compiler/xla/tests/axpy_simple_test.cc b/tensorflow/compiler/xla/tests/axpy_simple_test.cc index 627a9c3e7d9f6eb8d360228362ea5adf12c6c798..3f6fd7c65d3360a622dbf754833009fb20410535 100644 --- a/tensorflow/compiler/xla/tests/axpy_simple_test.cc +++ b/tensorflow/compiler/xla/tests/axpy_simple_test.cc @@ -62,6 +62,10 @@ TEST_F(AxpySimpleTest, AxpyTenValues) { auto ax = builder.Mul(alpha, x); auto axpy = builder.Add(ax, y); + TF_ASSERT_OK_AND_ASSIGN(ProgramShape shape, builder.GetProgramShape()); + + EXPECT_EQ("() -> f32[10]", ShapeUtil::HumanString(shape)); + std::vector expected = { 1.85840735, -1.85840735, 2.28318531, -2.28318531, -6.42477796, 6.42477796, 10.56637061, -10.56637061, -14.70796327, 14.70796327}; diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc index 03f5e08315bfed2bcb43ebb7098aaa0b97228605..97095f1cc427789845051a8fea24c95475286fe2 100644 --- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc @@ -662,7 +662,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("dimension 0 mismatch")); } XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) { @@ -675,7 +675,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("binary op BINOP_ADD with incompatible shapes")); + HasSubstr("op BINOP_ADD with incompatible shapes")); } XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) { @@ -688,7 +688,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("binary op BINOP_ADD with incompatible shapes")); + HasSubstr("op BINOP_ADD with incompatible shapes")); } } // namespace diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index 1bcad5a3f37a37c9d482f3a5a899ac527666cca3..fb0e9c724a69b61801e6e0c2d07ef75b63a00465 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -75,7 +75,7 @@ XLA_TEST_F(ConcatTest, CannotConcatR0WithR0) { StatusOr computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), - HasSubstr("dimension to concatenate along out of bounds: 0")); + HasSubstr("out of bounds: 0")); } XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L0) { diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index f66e3b57bf45fbc9f8ea786146d6fffe5d55a262..59d6d7a4153be1b76ed8195a12a90cb103baa422 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" @@ -106,11 +107,108 @@ TEST_F(ConvertTest, ConvertR1F32ToR1S32) { XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) { ComputationBuilder builder(client_, TestName()); - auto a = builder.ConstantR1({32, 64}); - builder.ConvertElementType(a, F32); + std::vector arg{ + -9223371216516022272, + -2, + -1, + -0x7FFFFFFF, + -0x80000000, + 0, + 1, + 2, + 1073742145, + 1073742656, + 0x7FFFFFFF, + 0x80000000, + 826720496944058148, + 4296062029846194332, + 0x0007FB72E4000000LL, + 0x0007FB72E4000001LL, + 0x0007FB72E6000000LL, + 0x0007FB72E7000000LL, + 0x0007FB72E7FFFFFFLL, + 0x0007FB72E8000000LL, + 0x0007FB72E8000001LL, + 0x0007FB72EA000000LL, + 0x0007FB72EB000000LL, + 0x0007FB72EBFFFFFFLL, + 0x0007FB72EC000000LL, + 0x7FFFFF0000000000LL, + 0x7FFFFF8000000000LL, + 0x7FFFFFFFFFFFFF00, + static_cast(0xFFFFFFFFFFFFFFFF), + static_cast(0x0000f234e67e0001LL), + static_cast(0x8000000000000000), + static_cast(0x8000000000000000LL), + static_cast(0x8000000000000001LL), + static_cast(0x8000008000000000LL), + static_cast(0x8000010000000000LL), + }; + std::unique_ptr arg_literal = Literal::CreateR1({arg}); + auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + std::unique_ptr arg_data = + client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); + + builder.ConvertElementType(arg_param, F32); + + std::vector expected(arg.size()); + for (int64 i = 0; i < arg.size(); ++i) { + expected[i] = static_cast(arg[i]); + } + ComputeAndCompareR1(&builder, expected, {arg_data.get()}); +} - std::vector expected = {32.0, 64.0}; - ComputeAndCompareR1(&builder, expected, {}); +XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) { + ComputationBuilder builder(client_, TestName()); + std::vector arg{0, 1, 0x1000, 0x7fffffff, + 0x80000000, 0x80000001, 0x80000002, 0x80000003, + 0x80000080, 0x80000081, 0x80000082, 0xFFFFFFFF}; + std::unique_ptr arg_literal = Literal::CreateR1({arg}); + auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + std::unique_ptr arg_data = + client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); + + builder.ConvertElementType(arg_param, F32); + + std::vector expected(arg.size()); + for (int64 i = 0; i < arg.size(); ++i) { + expected[i] = static_cast(arg[i]); + } + ComputeAndCompareR1(&builder, expected, {arg_data.get()}); +} + +XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { + ComputationBuilder builder(client_, TestName()); + std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000082, 0xFFFFFFFF}; + std::unique_ptr arg_literal = Literal::CreateR1({arg}); + auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + std::unique_ptr arg_data = + client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); + + builder.ConvertElementType(arg_param, S64); + + std::vector expected(arg.size()); + for (int64 i = 0; i < arg.size(); ++i) { + expected[i] = static_cast(arg[i]); + } + ComputeAndCompareR1(&builder, expected, {arg_data.get()}); +} + +XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) { + ComputationBuilder builder(client_, TestName()); + std::vector arg{0, 1, 0x1000, -1, -0x1000}; + std::unique_ptr arg_literal = Literal::CreateR1({arg}); + auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + std::unique_ptr arg_data = + client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); + + builder.ConvertElementType(arg_param, S64); + + std::vector expected(arg.size()); + for (int64 i = 0; i < arg.size(); ++i) { + expected[i] = static_cast(arg[i]); + } + ComputeAndCompareR1(&builder, expected, {arg_data.get()}); } XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) { @@ -208,5 +306,65 @@ TEST_F(ConvertTest, ConvertReshape) { ComputeAndCompareR0(&builder, 42.0f, {}, ErrorSpec(0.0001)); } +std::vector GetInterestingF16ConversionTestCases() { + float infinity = std::numeric_limits::infinity(); + float half_min_positive_normal = + tensorflow::bit_cast(0x38800000); + float half_max_subnormal = tensorflow::bit_cast(0x387fc000); + float half_min_positive_subnormal = + tensorflow::bit_cast(0x33800000); + float half_max = 65504.0f; + + std::vector test_cases( + {-infinity, -(half_max * 2 + 1), -half_max, -42.0f, -1.0f, + -half_min_positive_subnormal, -half_max_subnormal, + -half_min_positive_normal, -0.0f, 0.0f, half_min_positive_subnormal, + half_max_subnormal, half_min_positive_normal, 1.0f, 42.0f, half_max, + (half_max * 2 + 1), infinity}); + return test_cases; +} + +XLA_TEST_F(ConvertTest, ConvertR1F16ToR1F32) { + std::vector test_cases = GetInterestingF16ConversionTestCases(); + std::vector input; + c_transform(test_cases, std::back_inserter(input), + [](float f) { return Eigen::half(f); }); + std::vector expected_output; + c_transform(input, std::back_inserter(expected_output), + [](Eigen::half h) { return static_cast(h); }); + + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr dot_lhs_handle, + client_->TransferToServer(*Literal::CreateR1(input))); + + ComputationBuilder builder(client_, TestName()); + builder.ConvertElementType( + builder.Parameter( + 0, ShapeUtil::MakeShape(F16, {static_cast(input.size())}), + "param"), + F32); + + ComputeAndCompareR1(&builder, expected_output, {dot_lhs_handle.get()}); +} + +XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { + std::vector input = GetInterestingF16ConversionTestCases(); + std::vector expected_output; + c_transform(input, std::back_inserter(expected_output), + [](float f) { return Eigen::half(f); }); + + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr dot_lhs_handle, + client_->TransferToServer(*Literal::CreateR1(input))); + + ComputationBuilder builder(client_, TestName()); + builder.ConvertElementType( + builder.Parameter( + 0, ShapeUtil::MakeShape(F32, {static_cast(input.size())}), + "param"), + F16); + + ComputeAndCompareR1(&builder, expected_output, {dot_lhs_handle.get()}); +} } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc index 0ceb9aff378ae8aa8098be9360310b1d78d31ab2..72715398dea468d0000144759454c5f8d8673516 100644 --- a/tensorflow/compiler/xla/tests/convolution_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_test.cc @@ -53,157 +53,185 @@ class ConvolutionTest : public ClientLibraryTestBase { #endif }; -XLA_TEST_F(ConvolutionTest, ForwardPassConvolution_3x3x256_256_OutputZ_Iota) { - const int kInputActivationSizeY = 3; - const int kInputActivationSizeX = 3; - const int kInputActivationSizeZ = 256; - const int kKernelSizeX = 2; - const int kKernelSizeY = 2; - const int kOutputActivationSizeZ = 256; - const int kMiniBatchSize = 4; - auto alhs = - MakeUnique>(kMiniBatchSize, kInputActivationSizeZ, - kInputActivationSizeY, kInputActivationSizeX); - alhs->FillWithMultiples(1.0f); - ASSERT_EQ(3, alhs->width()); - ASSERT_EQ(3, alhs->height()); - - auto arhs = - MakeUnique>(kOutputActivationSizeZ, kInputActivationSizeZ, - kKernelSizeY, kKernelSizeX); - Array2D rhs_raster({ - {1.0f, 0.0f}, // row 0 - {0.0f, 0.0f}, // row 1 - }); - arhs->FillWithYX(rhs_raster); - ASSERT_EQ(2, arhs->width()); - ASSERT_EQ(2, arhs->height()); +#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +using TestTypes = ::testing::Types; +#else +using TestTypes = ::testing::Types; +#endif - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR4FromArray4D(*alhs); - auto rhs = builder.ConstantR4FromArray4D(*arhs); - auto conv = builder.Conv(lhs, rhs, {1, 1}, Padding::kValid); +template +class ForwardPassConvolution_3x3x256_256_OutputZ_Iota : public ConvolutionTest { + public: + void RunTest() { + const int kInputActivationSizeY = 3; + const int kInputActivationSizeX = 3; + const int kInputActivationSizeZ = 256; + const int kKernelSizeX = 2; + const int kKernelSizeY = 2; + const int kOutputActivationSizeZ = 256; + const int kMiniBatchSize = 4; + auto alhs = + MakeUnique>(kMiniBatchSize, kInputActivationSizeZ, + kInputActivationSizeY, kInputActivationSizeX); + alhs->FillWithMultiples(static_cast(1.0f)); + ASSERT_EQ(3, alhs->width()); + ASSERT_EQ(3, alhs->height()); + + auto arhs = + MakeUnique>(kOutputActivationSizeZ, kInputActivationSizeZ, + kKernelSizeY, kKernelSizeX); + Array2D rhs_raster({ + {1.0f, 0.0f}, // row 0 + {0.0f, 0.0f}, // row 1 + }); + arhs->FillWithYX(rhs_raster); + ASSERT_EQ(2, arhs->width()); + ASSERT_EQ(2, arhs->height()); + + ComputationBuilder builder(client_, TestName()); + auto lhs = builder.ConstantR4FromArray4D(*alhs); + auto rhs = builder.ConstantR4FromArray4D(*arhs); + auto conv = builder.Conv(lhs, rhs, {1, 1}, Padding::kValid); + + ComputeAndCompare(&builder, conv, {}, error_spec_); + } +}; - ComputeAndCompare(&builder, conv, {}, error_spec_); +TYPED_TEST_CASE(ForwardPassConvolution_3x3x256_256_OutputZ_Iota, TestTypes); +XLA_TYPED_TEST(ForwardPassConvolution_3x3x256_256_OutputZ_Iota, Types) { + this->RunTest(); } -TEST_F(ConvolutionTest, Convolve_1x1x1x2_1x1x1x2_Valid) { - ComputationBuilder builder(client_, TestName()); - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 1, 1, 2}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 1, 1, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); - - Array4D input_data(1, 1, 1, 2); - input_data.FillWithYX(Array2D({ - {1, 2}, - })); - Array4D filter_data(1, 1, 1, 2); - filter_data.FillWithYX(Array2D({ - {5, 6}, - })); +template +class Convolve_1x1x1x2_1x1x1x2_Valid : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); + + Array4D input_data(1, 1, 1, 2); + input_data.FillWithYX(Array2D({ + {1.0f, 2.0f}, + })); + Array4D filter_data(1, 1, 1, 2); + filter_data.FillWithYX(Array2D({ + {5.0f, 6.0f}, + })); + + ComputeAndCompare(&builder, conv, + {std::move(*Literal::CreateFromArray(input_data)), + std::move(*Literal::CreateFromArray(filter_data))}, + error_spec_); + } +}; - ComputeAndCompare(&builder, conv, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, - error_spec_); -} +TYPED_TEST_CASE(Convolve_1x1x1x2_1x1x1x2_Valid, TestTypes); +TYPED_TEST(Convolve_1x1x1x2_1x1x1x2_Valid, Types) { this->RunTest(); } // Tests valid padding for 2D convolution in raster space. -TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x2x2_Valid) { - ComputationBuilder builder(client_, TestName()); - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 1, 4, 4}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); +template +class Convolve_1x1x4x4_1x1x2x2_Valid : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); + + Array4D input_data(1, 1, 4, 4); + input_data.FillWithYX(Array2D({ + {1.0f, 2.0f, 3.0f, 4.0f}, + {5.0f, 6.0f, 7.0f, 8.0f}, + {9.0f, 10.0f, 11.0f, 12.0f}, + {13.0f, 14.0f, 15.0f, 16.0f}, + })); + Array4D filter_data(1, 1, 2, 2); + filter_data.FillWithYX(Array2D({ + {5.0f, 6.0f}, + {7.0f, 8.0f}, + })); + ComputeAndCompare(&builder, conv, + {std::move(*Literal::CreateFromArray(input_data)), + std::move(*Literal::CreateFromArray(filter_data))}, + error_spec_); + } +}; - Array4D input_data(1, 1, 4, 4); - // clang-format off - input_data.FillWithYX(Array2D({ - {1, 2, 3, 4 }, - {5, 6, 7, 8 }, - {9, 10, 11, 12}, - {13, 14, 15, 16}, - })); - // clang-format on - Array4D filter_data(1, 1, 2, 2); - // clang-format off - filter_data.FillWithYX(Array2D({ - {5, 6}, - {7, 8}, - })); - // clang-format on - ComputeAndCompare(&builder, conv, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, - error_spec_); -} +TYPED_TEST_CASE(Convolve_1x1x4x4_1x1x2x2_Valid, TestTypes); +TYPED_TEST(Convolve_1x1x4x4_1x1x2x2_Valid, Types) { this->RunTest(); } // Tests same padding for 2D convolution in raster space. -TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x2x2_Same) { - ComputationBuilder builder(client_, TestName()); - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 1, 4, 4}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); - - Array4D input_data(1, 1, 4, 4); - // clang-format off - input_data.FillWithYX(Array2D({ - {1, 2, 3, 4 }, - {5, 6, 7, 8 }, - {9, 10, 11, 12}, - {13, 14, 15, 16}, - })); - // clang-format on - Array4D filter_data(1, 1, 2, 2); - // clang-format off - filter_data.FillWithYX(Array2D({ - {5, 6}, - {7, 8}, - })); - // clang-format on - ComputeAndCompare(&builder, conv, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, - error_spec_); -} +template +class Convolve_1x1x4x4_1x1x2x2_Same : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); + + Array4D input_data(1, 1, 4, 4); + input_data.FillWithYX(Array2D({ + {1.0f, 2.0f, 3.0f, 4.0f}, + {5.0f, 6.0f, 7.0f, 8.0f}, + {9.0f, 10.0f, 11.0f, 12.0f}, + {13.0f, 14.0f, 15.0f, 16.0f}, + })); + Array4D filter_data(1, 1, 2, 2); + filter_data.FillWithYX(Array2D({ + {5.0f, 6.0f}, + {7.0f, 8.0f}, + })); + + ComputeAndCompare(&builder, conv, + {std::move(*Literal::CreateFromArray(input_data)), + std::move(*Literal::CreateFromArray(filter_data))}, + error_spec_); + } +}; + +TYPED_TEST_CASE(Convolve_1x1x4x4_1x1x2x2_Same, TestTypes); +TYPED_TEST(Convolve_1x1x4x4_1x1x2x2_Same, Types) { this->RunTest(); } // Tests same padding for 2D convolution in raster space with an odd sized // kernel. -TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x3x3_Same) { - ComputationBuilder builder(client_, TestName()); - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 1, 4, 4}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 1, 3, 3}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); - - Array4D input_data(1, 1, 4, 4); - // clang-format off - input_data.FillWithYX(Array2D({ - {1, 2, 3, 4 }, - {5, 6, 7, 8 }, - {9, 10, 11, 12}, - {13, 14, 15, 16}, - })); - // clang-format on - Array4D filter_data(1, 1, 3, 3); - // clang-format off - filter_data.FillWithYX(Array2D({ - { 5, 6, 7}, - { 8, 9, 10}, - {11, 12, 13}, - })); - // clang-format on - ComputeAndCompare(&builder, conv, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, - error_spec_); -} +template +class Convolve_1x1x4x4_1x1x3x3_Same : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 3, 3}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); + + Array4D input_data(1, 1, 4, 4); + input_data.FillWithYX(Array2D({{1.0f, 2.0f, 3.0f, 4.0f}, + {5.0f, 6.0f, 7.0f, 8.0f}, + {9.0f, 10.0f, 11.0f, 12.0f}, + {13.0f, 14.0f, 15.0f, 16.0f}})); + Array4D filter_data(1, 1, 3, 3); + filter_data.FillWithYX(Array2D( + {{5.0f, 6.0f, 7.0f}, {8.0f, 9.0f, 10.0f}, {11.0f, 12.0f, 13.0f}})); + // clang-format on + ComputeAndCompare(&builder, conv, + {std::move(*Literal::CreateFromArray(input_data)), + std::move(*Literal::CreateFromArray(filter_data))}, + error_spec_); + } +}; + +TYPED_TEST_CASE(Convolve_1x1x4x4_1x1x3x3_Same, TestTypes); +TYPED_TEST(Convolve_1x1x4x4_1x1x3x3_Same, Types) { this->RunTest(); } XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_Valid) { ComputationBuilder builder(client_, TestName()); @@ -232,36 +260,44 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_Valid) { error_spec_); } -XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithRHSDilation) { - ComputationBuilder builder(client_, TestName()); - { - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 2, 5}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( - input, filter, /*window_strides=*/{1}, /*padding=*/{{0, 0}}, - /*lhs_dilation=*/{1}, /*rhs_dilation=*/{2}, - /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); +template +class Convolve1D_1x2x5_1x2x2_WithRHSDilation : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + { + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + // Convolution dimensions are bf0_oi0->bo0. + builder.ConvGeneralDilated( + input, filter, /*window_strides=*/{1}, /*padding=*/{{0, 0}}, + /*lhs_dilation=*/{1}, /*rhs_dilation=*/{2}, + /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); + } + + Array3D input( + {{{1.0f, 2.0f, 3.0f, 4.0f, 5.0f}, {6.0f, 7.0f, 8.0f, 9.0f, 10.0f}}}); + Array3D filter({{{10.0f, 20.0f}, {30.0f, 40.0f}}}); + + Array3D expected({{{570.0f, 670.0f, 770.0f}}}); + + auto input_literal = + client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + .ConsumeValueOrDie(); + auto filter_literal = + client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + .ConsumeValueOrDie(); + + ComputeAndCompareR3(&builder, expected, + {input_literal.get(), filter_literal.get()}, + error_spec_); } +}; // namespace - Array3D input({{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}}}); - Array3D filter({{{10, 20}, {30, 40}}}); - - Array3D expected({{{570, 670, 770}}}); - - auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) - .ConsumeValueOrDie(); - auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) - .ConsumeValueOrDie(); - - ComputeAndCompareR3(&builder, expected, - {input_literal.get(), filter_literal.get()}, - error_spec_); -} +TYPED_TEST_CASE(Convolve1D_1x2x5_1x2x2_WithRHSDilation, TestTypes); +TYPED_TEST(Convolve1D_1x2x5_1x2x2_WithRHSDilation, Types) { this->RunTest(); } XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSDilation) { ComputationBuilder builder(client_, TestName()); @@ -325,36 +361,45 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSAndRHSDilation) { error_spec_); } -XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithPadding) { - ComputationBuilder builder(client_, TestName()); - { - Shape input_shape = ShapeUtil::MakeShape(F32, {1, 2, 5}); - Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( - input, filter, /*window_strides=*/{1}, /*padding=*/{{2, 2}}, - /*lhs_dilation=*/{1}, /*rhs_dilation=*/{1}, - /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); +template +class Convolve1D_1x2x5_1x2x2_WithPadding : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + { + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + // Convolution dimensions are bf0_oi0->bo0. + builder.ConvGeneralDilated( + input, filter, /*window_strides=*/{1}, /*padding=*/{{2, 2}}, + /*lhs_dilation=*/{1}, /*rhs_dilation=*/{1}, + /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); + } + + Array3D input( + {{{1.0f, 2.0f, 3.0f, 4.0f, 5.0f}, {6.0f, 7.0f, 8.0f, 9.0f, 10.0f}}}); + Array3D filter({{{10.0f, 20.0f}, {30.0f, 40.0f}}}); + + Array3D expected( + {{{0.0f, 260.0f, 510.0f, 610.0f, 710.0f, 810.0f, 350.0f, 0.0f}}}); + + auto input_literal = + client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + .ConsumeValueOrDie(); + auto filter_literal = + client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + .ConsumeValueOrDie(); + + ComputeAndCompareR3(&builder, expected, + {input_literal.get(), filter_literal.get()}, + error_spec_); } +}; - Array3D input({{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}}}); - Array3D filter({{{10, 20}, {30, 40}}}); - - Array3D expected({{{0, 260, 510, 610, 710, 810, 350, 0}}}); - - auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) - .ConsumeValueOrDie(); - auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) - .ConsumeValueOrDie(); - - ComputeAndCompareR3(&builder, expected, - {input_literal.get(), filter_literal.get()}, - error_spec_); -} +TYPED_TEST_CASE(Convolve1D_1x2x5_1x2x2_WithPadding, TestTypes); +TYPED_TEST(Convolve1D_1x2x5_1x2x2_WithPadding, Types) { this->RunTest(); } XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { ComputationBuilder builder(client_, TestName()); @@ -389,12 +434,12 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { } std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); - std::iota(input_elems.begin(), input_elems.end(), 1.0f); + iota(input_elems.begin(), input_elems.end(), 1.0f); auto input_r1 = Literal::CreateR1(input_elems); auto input_r5 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape)); - std::iota(filter_elems.begin(), filter_elems.end(), 1.0f); + iota(filter_elems.begin(), filter_elems.end(), 1.0f); auto filter_r1 = Literal::CreateR1(filter_elems); auto filter_r5 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); @@ -412,56 +457,73 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { error_spec_); } -XLA_TEST_F(ConvolutionTest, Convolve2D_1x3x3x5_3x3x5x5_Valid) { - ComputationBuilder builder(client_, TestName()); - std::vector input_dims = {1, 3, 3, 5}; - std::vector filter_dims = {3, 3, 5, 3}; - Shape input_shape = ShapeUtil::MakeShape(F32, input_dims); - Shape filter_shape = ShapeUtil::MakeShape(F32, filter_dims); - { - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - - // Tensorflow dimension numbers for 2D convolution. - ConvolutionDimensionNumbers dnums; - dnums.set_input_batch_dimension(0); - dnums.set_output_batch_dimension(0); - dnums.add_input_spatial_dimensions(1); - dnums.add_output_spatial_dimensions(1); - dnums.add_input_spatial_dimensions(2); - dnums.add_output_spatial_dimensions(2); - dnums.set_input_feature_dimension(3); - dnums.set_output_feature_dimension(3); - dnums.add_kernel_spatial_dimensions(0); - dnums.add_kernel_spatial_dimensions(1); - dnums.set_kernel_input_feature_dimension(2); - dnums.set_kernel_output_feature_dimension(3); +// std::iota doesn't work when init_value has a type Eigen::half in some build +// servers. The error message is missing the operator ++. +template +void iota_int_init_value(std::vector& values, int init_value) { + std::for_each(values.begin(), values.end(), + [&](T& value) { value = static_cast(init_value++); }); +} - builder.ConvWithGeneralDimensions(input, filter, {1, 1}, Padding::kValid, - dnums); +template +class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest { + public: + void RunTest() { + ComputationBuilder builder(client_, TestName()); + std::vector input_dims = {1, 3, 3, 5}; + std::vector filter_dims = {3, 3, 5, 3}; + Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); + Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); + { + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + + // Tensorflow dimension numbers for 2D convolution. + ConvolutionDimensionNumbers dnums; + dnums.set_input_batch_dimension(0); + dnums.set_output_batch_dimension(0); + dnums.add_input_spatial_dimensions(1); + dnums.add_output_spatial_dimensions(1); + dnums.add_input_spatial_dimensions(2); + dnums.add_output_spatial_dimensions(2); + dnums.set_input_feature_dimension(3); + dnums.set_output_feature_dimension(3); + dnums.add_kernel_spatial_dimensions(0); + dnums.add_kernel_spatial_dimensions(1); + dnums.set_kernel_input_feature_dimension(2); + dnums.set_kernel_output_feature_dimension(3); + + builder.ConvWithGeneralDimensions(input, filter, {1, 1}, Padding::kValid, + dnums); + } + + std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); + iota_int_init_value(input_elems, 1); + auto input_r1 = Literal::CreateR1(input_elems); + auto input_r4 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); + + std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape)); + iota_int_init_value(filter_elems, 1); + auto filter_r1 = Literal::CreateR1(filter_elems); + auto filter_r4 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); + + auto expected_r1 = Literal::CreateR1( + {static_cast(92115), static_cast(93150), static_cast(94185)}); + auto expected_r4 = expected_r1->Reshape({1, 1, 1, 3}).ConsumeValueOrDie(); + + auto input_literal = + client_->TransferToServer(*input_r4).ConsumeValueOrDie(); + auto filter_literal = + client_->TransferToServer(*filter_r4).ConsumeValueOrDie(); + + ComputeAndCompareLiteral(&builder, *expected_r4, + {input_literal.get(), filter_literal.get()}, + error_spec_); } +}; - std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); - std::iota(input_elems.begin(), input_elems.end(), 1.0f); - auto input_r1 = Literal::CreateR1(input_elems); - auto input_r4 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); - - std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape)); - std::iota(filter_elems.begin(), filter_elems.end(), 1.0f); - auto filter_r1 = Literal::CreateR1(filter_elems); - auto filter_r4 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); - - auto expected_r1 = Literal::CreateR1({92115, 93150, 94185}); - auto expected_r4 = expected_r1->Reshape({1, 1, 1, 3}).ConsumeValueOrDie(); - - auto input_literal = client_->TransferToServer(*input_r4).ConsumeValueOrDie(); - auto filter_literal = - client_->TransferToServer(*filter_r4).ConsumeValueOrDie(); - - ComputeAndCompareLiteral(&builder, *expected_r4, - {input_literal.get(), filter_literal.get()}, - error_spec_); -} +TYPED_TEST_CASE(Convolve2D_1x3x3x5_3x3x5x5_Valid, TestTypes); +TYPED_TEST(Convolve2D_1x3x3x5_3x3x5x5_Valid, Types) { this->RunTest(); } // Test fixture to run convolution tests with and without convolution // canonicalization enabled. @@ -519,67 +581,78 @@ struct Convolve1DTestParam { int64 num_windows; }; -class Convolve1D1WindowTest +class Convolve1D1WindowTestBase : public ConvolutionTest, - public ::testing::WithParamInterface {}; - -XLA_TEST_P(Convolve1D1WindowTest, Convolve1D1Window) { - ComputationBuilder builder(client_, TestName()); - int64 input_feature = GetParam().input_feature; - int64 output_feature = GetParam().output_feature; - int64 batch = GetParam().batch; - int64 num_windows = GetParam().num_windows; - int64 window_size = GetParam().window_size; - std::vector input_dims = {batch, window_size + num_windows - 1, - input_feature}; - std::vector filter_dims = {window_size, input_feature, output_feature}; - Shape input_shape = ShapeUtil::MakeShape(F32, input_dims); - Shape filter_shape = ShapeUtil::MakeShape(F32, filter_dims); - { - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - - // Tensorflow dimension numbers for 1D convolution. - ConvolutionDimensionNumbers dnums; - dnums.set_input_batch_dimension(0); - dnums.set_output_batch_dimension(0); - dnums.add_input_spatial_dimensions(1); - dnums.add_output_spatial_dimensions(1); - dnums.set_input_feature_dimension(2); - dnums.set_output_feature_dimension(2); - dnums.add_kernel_spatial_dimensions(0); - dnums.set_kernel_input_feature_dimension(1); - dnums.set_kernel_output_feature_dimension(2); - - builder.ConvWithGeneralDimensions(input, filter, {1}, Padding::kValid, - dnums); + public ::testing::WithParamInterface { + protected: + template + void TestImpl() { + ComputationBuilder builder(client_, TestName()); + int64 input_feature = GetParam().input_feature; + int64 output_feature = GetParam().output_feature; + int64 batch = GetParam().batch; + int64 num_windows = GetParam().num_windows; + int64 window_size = GetParam().window_size; + std::vector input_dims = {batch, window_size + num_windows - 1, + input_feature}; + std::vector filter_dims = {window_size, input_feature, + output_feature}; + Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); + Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); + { + auto input = builder.Parameter(0, input_shape, "input"); + auto filter = builder.Parameter(1, filter_shape, "filter"); + + // Tensorflow dimension numbers for 1D convolution. + ConvolutionDimensionNumbers dnums; + dnums.set_input_batch_dimension(0); + dnums.set_output_batch_dimension(0); + dnums.add_input_spatial_dimensions(1); + dnums.add_output_spatial_dimensions(1); + dnums.set_input_feature_dimension(2); + dnums.set_output_feature_dimension(2); + dnums.add_kernel_spatial_dimensions(0); + dnums.set_kernel_input_feature_dimension(1); + dnums.set_kernel_output_feature_dimension(2); + + builder.ConvWithGeneralDimensions(input, filter, {1}, Padding::kValid, + dnums); + } + + std::vector input_elems(ShapeUtil::ElementsIn(input_shape), + static_cast(1.0f)); + auto input_r1 = Literal::CreateR1(input_elems); + auto input_r3 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); + + std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape), + static_cast(1.0f)); + + auto filter_r1 = Literal::CreateR1(filter_elems); + auto filter_r3 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); + + std::vector expect_elems(batch * output_feature * num_windows, + static_cast(window_size * input_feature)); + auto expected_r1 = Literal::CreateR1(expect_elems); + auto expected_r3 = + expected_r1->Reshape({batch, num_windows, output_feature}) + .ConsumeValueOrDie(); + + auto input_literal = + client_->TransferToServer(*input_r3).ConsumeValueOrDie(); + auto filter_literal = + client_->TransferToServer(*filter_r3).ConsumeValueOrDie(); + ComputeAndCompareLiteral(&builder, *expected_r3, + {input_literal.get(), filter_literal.get()}, + error_spec_); } +}; - std::vector input_elems(ShapeUtil::ElementsIn(input_shape), 1.0); - auto input_r1 = Literal::CreateR1(input_elems); - auto input_r3 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); - - std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape), 1.0); - - auto filter_r1 = Literal::CreateR1(filter_elems); - auto filter_r3 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); - - std::vector expect_elems(batch * output_feature * num_windows, - window_size * input_feature); - auto expected_r1 = Literal::CreateR1(expect_elems); - auto expected_r3 = expected_r1->Reshape({batch, num_windows, output_feature}) - .ConsumeValueOrDie(); +class Convolve1D1WindowTestFloat : public Convolve1D1WindowTestBase {}; - auto input_literal = client_->TransferToServer(*input_r3).ConsumeValueOrDie(); - auto filter_literal = - client_->TransferToServer(*filter_r3).ConsumeValueOrDie(); - ComputeAndCompareLiteral(&builder, *expected_r3, - {input_literal.get(), filter_literal.get()}, - error_spec_); -} +XLA_TEST_P(Convolve1D1WindowTestFloat, Convolve1D1Window) { TestImpl(); } INSTANTIATE_TEST_CASE_P( - Convolve1D1WindowTest_Instantiation, Convolve1D1WindowTest, + Convolve1D1WindowTest_Instantiation, Convolve1D1WindowTestFloat, ::testing::Values(Convolve1DTestParam{1, 1, 1, 1, 2}, Convolve1DTestParam{160, 1, 1, 5, 1}, Convolve1DTestParam{24, 1, 1, 20, 1}, @@ -608,7 +681,49 @@ INSTANTIATE_TEST_CASE_P( ); -TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { +#if (XLA_TEST_BACKEND_GPU || XLA_TEST_BACKEND_CPU) +class Convolve1D1WindowTestHalf : public Convolve1D1WindowTestBase {}; + +XLA_TEST_P(Convolve1D1WindowTestHalf, Convolve1D1Window) { + TestImpl(); +} + +INSTANTIATE_TEST_CASE_P( + Convolve1D1WindowTest_Instantiation, Convolve1D1WindowTestHalf, + ::testing::Values(Convolve1DTestParam{1, 1, 1, 1, 2}, + Convolve1DTestParam{160, 1, 1, 5, 1}, + Convolve1DTestParam{24, 1, 1, 20, 1}, + Convolve1DTestParam{30, 1, 1, 20, 1}, + Convolve1DTestParam{23, 1, 1, 20, 20}, + Convolve1DTestParam{25, 1, 1, 20, 1}, + Convolve1DTestParam{24, 1, 1, 10, 5}, + Convolve1DTestParam{160, 1, 1, 10, 1}, + Convolve1DTestParam{255, 1, 1, 3, 1}, + Convolve1DTestParam{130, 1, 1, 1, 3}, + Convolve1DTestParam{64, 1, 1, 1, 1}, + Convolve1DTestParam{128, 1, 1, 1, 1}, +// TODO(b/72566306): The following five tests failed on CPU with unreasonable +// relative errors. Last ran on 2018-02-22. +#if XLA_TEST_BACKEND_GPU + Convolve1DTestParam{139, 1, 1, 128, 1}, + Convolve1DTestParam{640, 3, 3, 128, 1}, + Convolve1DTestParam{900, 1, 1, 10, 1}, + Convolve1DTestParam{1, 10, 10, 1, 10}, + Convolve1DTestParam{1, 10, 130, 1, 1}, +#endif + Convolve1DTestParam{1, 10, 130, 1, 2}, + Convolve1DTestParam{1, 64, 64, 1, 10}, + Convolve1DTestParam{1, 65, 65, 1, 1}, + Convolve1DTestParam{1, 128, 128, 1, 1}, + Convolve1DTestParam{128, 128, 128, 128, 1}, + Convolve1DTestParam{1, 128, 128, 1, 1}, + Convolve1DTestParam{2, 2, 2, 2, 1}, + Convolve1DTestParam{161, 1, 1, 10, 1}) + +); +#endif + +XLA_TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { ComputationBuilder builder(client_, TestName()); Shape input_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); Shape filter_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index 032c06cd3c9f872f57674d3d7b5adc201c91ea77..3ab0ea4ad48c00724d48e7d285ec024e10d5db31 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -195,7 +195,7 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) { auto result_status = client_->DeconstructTuple(*global_data); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("deconstructing nested tuples not yet supported")); + HasSubstr("Deconstructing nested tuples is not implemented")); } } // namespace diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index cc683701e6305510d202721fe645310f1009081c..09b1dd283e4d026a2f0007240d88cd9ac38acb19 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -34,169 +34,194 @@ limitations under the License. namespace xla { namespace { -// TODO(b/34468543): use GUnit typed tests when we can do all tests on all -// backends. class DotOperationTest : public ClientLibraryTestBase { public: ErrorSpec error_spec_{0.0001, 1e-5}; - - protected: - template - void TestOneElementVectorDot(); - template - void TestVectorDot(); - template - void TestSquareMatrixDot(bool lhs_row_major = false, - bool rhs_row_major = false); - template - void TestNonsquareMatrixDot(bool lhs_row_major = false, - bool rhs_row_major = false); }; -XLA_TEST_F(DotOperationTest, ZeroElementVectorDotF32) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); +#if defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \ + defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) +using TypesF16F32 = ::testing::Types; +using TypesF16F32F64 = ::testing::Types; +using TypesF16F32F64CF64 = ::testing::Types; +#elif !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \ + !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) +using TypesF16F32 = ::testing::Types; +using TypesF16F32F64 = ::testing::Types; +using TypesF16F32F64CF64 = + ::testing::Types; +#else +#error "Situation not handled yet" +#endif + +template +class DotOperationTest_F16F32F64CF64 : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTest_F16F32F64CF64, TypesF16F32F64CF64); + +XLA_TYPED_TEST(DotOperationTest_F16F32F64CF64, ZeroElementVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + + auto lhs = builder.ConstantR1({}); + auto rhs = builder.ConstantR1({}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 0.0, {}, error_spec_); + this->template ComputeAndCompareR0(&builder, static_cast(0.0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, TrivialMatrixVectorDotF32) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2({{3.0, 4.0}}); - auto rhs = builder.ConstantR1({3.0, 4.0}); - auto result = builder.Dot(lhs, rhs); +template +class DotOperationTest_F16F32F64 : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTest_F16F32F64, TypesF16F32F64); - ComputeAndCompareR1(&builder, {25.0}, {}, error_spec_); -} - -template -void DotOperationTest::TestOneElementVectorDot() { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({2.0}); - auto rhs = builder.ConstantR1({3.0}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D({{3.0f, 4.0f}}); + auto rhs = builder.ConstantFromArray({3.0f, 4.0f}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 6.0, {}, error_spec_); + this->template ComputeAndCompareR1(&builder, {static_cast(25.0f)}, {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, OneElementVectorDotF32) { - TestOneElementVectorDot(); -} +XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR1({static_cast(2.0f)}); + auto rhs = builder.ConstantR1({static_cast(3.0f)}); + auto result = builder.Dot(lhs, rhs); -XLA_TEST_F(DotOperationTest, OneElementVectorDotF64) { - TestOneElementVectorDot(); + this->template ComputeAndCompareR0(&builder, static_cast(6.0f), {}, + this->error_spec_); } -template -void DotOperationTest::TestVectorDot() { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({1.0, 2.5, 42.0}); - auto rhs = builder.ConstantR1({11.0, -1.0, 0.5}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, VectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantFromArray({1.0f, 2.5f, 42.0f}); + auto rhs = builder.ConstantFromArray({11.0f, -1.0f, 0.5f}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 29.5, {}, error_spec_); + this->template ComputeAndCompareR0(&builder, static_cast(29.5f), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, VectorDotF32) { TestVectorDot(); } - -XLA_TEST_F(DotOperationTest, VectorDotF64) { TestVectorDot(); } - -namespace { - std::vector MinorToMajorForIsRowMajor(bool row_major) { return {row_major ? 1 : 0, row_major ? 0 : 1}; } -} // namespace - -XLA_TEST_F(DotOperationTest, Dot_0x2_2x0) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); + auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(0, 0), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(0, 0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_0x2_2x3) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2({{7.0, 8.0, 9.0}, {42.0, 77.0, 101.0}}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); + auto rhs = builder.ConstantR2FromArray2D( + {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(0, 3), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(0, 3), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_3x2_2x0) { - ComputationBuilder builder(client_, TestName()); - auto lhs = - builder.ConstantR2({{7.0, 8.0}, {9.0, 42.0}, {77.0, 101.0}}); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D( + {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}}); + auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(3, 0), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(3, 0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_2x0_0x2) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); + auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(2, 2, 0.0f), {}, - error_spec_); + this->template ComputeAndCompareR2( + &builder, Array2D(2, 2, static_cast(0.0f)), {}, this->error_spec_); } -XLA_TEST_F(DotOperationTest, FusedDot) { - ComputationBuilder builder(client_, TestName()); - auto param0 = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 4}), "arg0"); - auto param1 = builder.Parameter(1, ShapeUtil::MakeShape(F32, {4, 1}), "arg1"); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto param0 = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 4}), "arg0"); + auto param1 = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({4, 1}), "arg1"); auto exp0 = builder.Exp(param0); auto result = builder.Dot(exp0, param1); - auto lhs_handle = client_ - ->TransferToServer(*Literal::CreateR2( - {{1.0, 2.0, 3.0, 4.0}, {-1.0, -2.0, -3.0, -4.0}})) - .ConsumeValueOrDie(); - auto rhs_handle = client_ - ->TransferToServer(*Literal::CreateR2( - {{1.0}, {2.0}, {3.0}, {4.0}})) - .ConsumeValueOrDie(); - - ComputeAndCompareR2( - &builder, Array2D({{296.14560492846033}, {0.8611737683031964}}), - {lhs_handle.get(), rhs_handle.get()}, error_spec_); -} - -template -void DotOperationTest::TestSquareMatrixDot(bool lhs_row_major, - bool rhs_row_major) { auto lhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 2.0}, {3.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major)))) - .ConsumeValueOrDie(); - auto rhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 6.0}, {7.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2D( + {{1.0f, 2.0f, 3.0f, 4.0f}, {-1.0f, -2.0f, -3.0f, -4.0f}})) .ConsumeValueOrDie(); + auto rhs_handle = this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2D( + {{1.0f}, {2.0f}, {3.0f}, {4.0f}})) + .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); + if (std::is_same::value) { + this->error_spec_ = ErrorSpec{0.0001, 1e-3}; + } - Array2D expected({{15.0, -2.0}, {-25.0, 34.0}}); - ComputeAndCompareR2( - &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); + this->template ComputeAndCompareR2( + &builder, Array2D({{296.14560492846033f}, {0.8611737683031964f}}), + {lhs_handle.get(), rhs_handle.get()}, this->error_spec_); } +template +class SquareMatrixDot : public DotOperationTest { + public: + void TestImpl(bool lhs_row_major, bool rhs_row_major) { + auto lhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 2.0f}, {3.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(lhs_row_major)))) + .ConsumeValueOrDie(); + auto rhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 6.0f}, {7.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(rhs_row_major)))) + .ConsumeValueOrDie(); + ComputationBuilder builder(client_, TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); + auto result = builder.Dot( + builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), + builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); + + Array2D expected({{15.0f, -2.0f}, {-25.0f, 34.0f}}); + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, error_spec_); + } +}; + +TYPED_TEST_CASE(SquareMatrixDot, TypesF16F32F64CF64); +XLA_TYPED_TEST(SquareMatrixDot, TypesFF) { this->TestImpl(false, false); } +XLA_TYPED_TEST(SquareMatrixDot, TypesFT) { this->TestImpl(false, true); } +XLA_TYPED_TEST(SquareMatrixDot, TypesTF) { this->TestImpl(true, false); } +XLA_TYPED_TEST(SquareMatrixDot, TypesTT) { this->TestImpl(true, true); } + struct DotTestParam { int m; int k; @@ -225,33 +250,39 @@ string PrintDotTestParam( } class ParametricDotTest : public DotOperationTest, - public ::testing::WithParamInterface {}; + public ::testing::WithParamInterface { + protected: + template + void TestImpl(); +}; -XLA_TEST_P(ParametricDotTest, TestF32) { +template +void ParametricDotTest::TestImpl() { DotTestParam param = GetParam(); - std::unique_ptr> dot_lhs_data = - MakeLinspaceArray2D(0.0, 1.0, param.m, param.k); + std::unique_ptr> dot_lhs_data = + MakeLinspaceArray2D(0.0, 1.0, param.m, param.k); std::unique_ptr dot_lhs_lit = Literal::CreateR2FromArray2DWithLayout( *dot_lhs_data, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(param.dot_lhs_row_major))); std::unique_ptr dot_lhs_handle = client_->TransferToServer(*dot_lhs_lit).ConsumeValueOrDie(); - std::unique_ptr> dot_rhs_data = - MakeLinspaceArray2D(0.0, 1.0, param.k, param.n); - std::unique_ptr dot_rhs_lit = Literal::CreateR2FromArray2DWithLayout( - *dot_rhs_data, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(param.dot_rhs_row_major))); + std::unique_ptr> dot_rhs_data = + MakeLinspaceArray2D(0.0, 1.0, param.k, param.n); + Layout rhs_layout = LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(param.dot_rhs_row_major)); + std::unique_ptr dot_rhs_lit = + Literal::CreateR2FromArray2DWithLayout(*dot_rhs_data, rhs_layout); std::unique_ptr dot_rhs_handle = client_->TransferToServer(*dot_rhs_lit).ConsumeValueOrDie(); - std::unique_ptr> addend_data; + std::unique_ptr> addend_data; std::unique_ptr addend_lit; std::unique_ptr addend_handle; if (param.has_addend) { - addend_data = MakeLinspaceArray2D(0.0, 1.0, param.m, param.n); + addend_data = MakeLinspaceArray2D(0.0, 1.0, param.m, param.n); addend_lit = Literal::CreateR2FromArray2DWithLayout( *addend_data, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(param.addend_row_major))); @@ -259,24 +290,33 @@ XLA_TEST_P(ParametricDotTest, TestF32) { } ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); + auto prim_type = primitive_util::NativeToPrimitiveType(); auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {param.m, param.k}), + builder.Parameter(0, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.m, param.k}, + MinorToMajorForIsRowMajor(param.dot_lhs_row_major)), "dot_lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {param.k, param.n}), + builder.Parameter(1, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.k, param.n}, + MinorToMajorForIsRowMajor(param.dot_rhs_row_major)), "dot_rhs")); if (param.has_addend) { result = builder.Add( - result, - builder.Parameter( - 2, ShapeUtil::MakeShape(prim_type, {param.m, param.n}), "addend")); + result, builder.Parameter( + 2, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.m, param.n}, + MinorToMajorForIsRowMajor(param.addend_row_major)), + "addend")); } - std::unique_ptr> expected; + std::unique_ptr> expected; if (param.has_addend) { expected = ReferenceUtil::ApplyElementwise2D( - std::plus(), + std::plus(), *ReferenceUtil::MatmulArray2D(*dot_lhs_data, *dot_rhs_data), *addend_data); } else { @@ -287,8 +327,11 @@ XLA_TEST_P(ParametricDotTest, TestF32) { if (param.has_addend) { args.push_back(addend_handle.get()); } - - ComputeAndCompareR2(&builder, *expected, args, ErrorSpec(0.3, 3e-3)); + ErrorSpec error_spec(0.3, 3e-3); + if (std::is_same::value) { + error_spec = ErrorSpec(0.3, 5e-3); + } + ComputeAndCompareR2(&builder, *expected, args, error_spec); } std::vector CreateDotTestParameters() { @@ -305,30 +348,77 @@ std::vector CreateDotTestParameters() { } }; + add_matrix_matrix_dot_test(/*m=*/12, /*k=*/117, /*n=*/7); + add_matrix_matrix_dot_test(/*m=*/270, /*k=*/270, /*n=*/520); + add_matrix_matrix_dot_test(/*m=*/260, /*k=*/3, /*n=*/520); + + return params; +} + +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(ParametricDotTest, TestF16) { TestImpl(); } +#endif +XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl(); } +XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl(); } + +INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest, + ::testing::ValuesIn(CreateDotTestParameters()), + PrintDotTestParam); + +class ParametricDotTestWithoutLayoutAssignment : public ParametricDotTest { + public: + ParametricDotTestWithoutLayoutAssignment() { + execution_options_.mutable_debug_options()->add_xla_disable_hlo_passes( + "layout-assignment"); + } +}; + +std::vector CreateNoLayoutAssignmentDotTestParameters() { + std::vector params; + auto add_matrix_vector_dot_test = [&](int k, int n) { - for (bool has_addend : {false, true}) { - params.push_back({/*m=*/1, /*k=*/k, /*n=*/n, - /*dot_lhs_row_major=*/true, /*dot_rhs_row_major=*/true, - /*has_addend=*/has_addend, /*addend_row_major=*/true}); - if (n != 1) { - params.push_back( - {/*m=*/n, /*k=*/k, /*n=*/1, - /*dot_lhs_row_major=*/true, /*dot_rhs_row_major=*/true, - /*has_addend=*/has_addend, /*addend_row_major=*/true}); + for (bool lhs_row_major : {true, false}) { + for (bool rhs_row_major : {true, false}) { + for (bool has_addend : {true, false}) { + params.push_back({/*m=*/1, /*k=*/k, /*n=*/n, + /*dot_lhs_row_major=*/lhs_row_major, + /*dot_rhs_row_major=*/rhs_row_major, + /*has_addend=*/has_addend, + /*addend_row_major=*/true}); + if (has_addend) { + params.push_back({/*m=*/1, /*k=*/k, /*n=*/n, + /*dot_lhs_row_major=*/lhs_row_major, + /*dot_rhs_row_major=*/rhs_row_major, + /*has_addend=*/has_addend, + /*addend_row_major=*/false}); + } + if (n != 1) { + params.push_back({/*m=*/n, /*k=*/k, /*n=*/1, + /*dot_lhs_row_major=*/lhs_row_major, + /*dot_rhs_row_major=*/rhs_row_major, + /*has_addend=*/has_addend, + /*addend_row_major=*/true}); + if (has_addend) { + params.push_back({/*m=*/n, /*k=*/k, /*n=*/1, + /*dot_lhs_row_major=*/lhs_row_major, + /*dot_rhs_row_major=*/rhs_row_major, + /*has_addend=*/has_addend, + /*addend_row_major=*/false}); + } + } + } } } }; - add_matrix_matrix_dot_test(/*m=*/12, /*k=*/117, /*n=*/7); - add_matrix_matrix_dot_test(/*m=*/270, /*k=*/270, /*n=*/520); - add_matrix_matrix_dot_test(/*m=*/260, /*k=*/3, /*n=*/520); - add_matrix_vector_dot_test(/*k=*/8, /*n=*/8); add_matrix_vector_dot_test(/*k=*/130, /*n=*/8); add_matrix_vector_dot_test(/*k=*/8, /*n=*/130); add_matrix_vector_dot_test(/*k=*/290, /*n=*/130); add_matrix_vector_dot_test(/*k=*/1, /*n=*/1); add_matrix_vector_dot_test(/*k=*/1, /*n=*/16); + add_matrix_vector_dot_test(/*k=*/1, /*n=*/4); + add_matrix_vector_dot_test(/*k=*/1, /*n=*/3); add_matrix_vector_dot_test(/*k=*/3, /*n=*/16); add_matrix_vector_dot_test(/*k=*/3, /*n=*/3); add_matrix_vector_dot_test(/*k=*/29, /*n=*/29); @@ -339,109 +429,60 @@ std::vector CreateDotTestParameters() { return params; } -INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest, - ::testing::ValuesIn(CreateDotTestParameters()), - PrintDotTestParam); - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFF) { - TestSquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFT) { - TestSquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTF) { - TestSquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTT) { - TestSquareMatrixDot(true, true); +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF16) { + TestImpl(); } - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFF) { - TestSquareMatrixDot(false, false); +#endif +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) { + TestImpl(); } - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFT) { - TestSquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTF) { - TestSquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTT) { - TestSquareMatrixDot(true, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF64) { - TestSquareMatrixDot(); +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) { + TestImpl(); } -template -void DotOperationTest::TestNonsquareMatrixDot(bool lhs_row_major, - bool rhs_row_major) { - auto lhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major)))) - .ConsumeValueOrDie(); - auto rhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major)))) - .ConsumeValueOrDie(); - - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); - - Array2D expected({{26.0, 0.0}, {-12.0, 10.0}}); - - ComputeAndCompareR2( - &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFF) { - TestNonsquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFT) { - TestNonsquareMatrixDot(false, true); -} +INSTANTIATE_TEST_CASE_P( + DotTests, ParametricDotTestWithoutLayoutAssignment, + ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()), + PrintDotTestParam); -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTF) { - TestNonsquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTT) { - TestNonsquareMatrixDot(true, true); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF64) { - TestNonsquareMatrixDot(); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFF) { - TestNonsquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFT) { - TestNonsquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTF) { - TestNonsquareMatrixDot(true, false); -} +template +class NonsquareMatrixDot : public DotOperationTest { + public: + void TestImpl(bool lhs_row_major, bool rhs_row_major) { + auto lhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(lhs_row_major)))) + .ConsumeValueOrDie(); + auto rhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(rhs_row_major)))) + .ConsumeValueOrDie(); + + ComputationBuilder builder(client_, TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); + auto result = builder.Dot( + builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), + builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); + + Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); + + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, error_spec_); + } +}; -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTT) { - TestNonsquareMatrixDot(true, true); -} +TYPED_TEST_CASE(NonsquareMatrixDot, TypesF16F32F64CF64); +XLA_TYPED_TEST(NonsquareMatrixDot, TestFF) { this->TestImpl(false, false); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestFT) { this->TestImpl(false, true); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestTF) { this->TestImpl(true, false); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestTT) { this->TestImpl(true, true); } XLA_TEST_F(DotOperationTest, MatrixVectorC64) { auto lhs_handle = @@ -468,25 +509,35 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) { &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); } -XLA_TEST_F(DotOperationTest, ConcurrentMatMul) { - ComputationBuilder builder(client_, TestName()); - auto matrix1 = builder.ConstantR2({{1.0, 2.0}, {3.0, 4.0}}); - auto matrix2 = builder.ConstantR2({{5.0, 6.0}, {7.0, 8.0}}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, ConcurrentMatMult) { + using T = TypeParam; + + ComputationBuilder builder(this->client_, this->TestName()); + auto matrix1 = builder.ConstantR2FromArray2D({{1.0f, 2.0f}, {3.0f, 4.0f}}); + auto matrix2 = builder.ConstantR2FromArray2D({{5.0f, 6.0f}, {7.0f, 8.0f}}); auto matrix12 = builder.Dot(matrix1, matrix2); auto matrix21 = builder.Dot(matrix2, matrix1); builder.Add(matrix12, matrix21); - Array2D expected({{42.0, 56.0}, {74.0, 96.0}}); - ComputeAndCompareR2(&builder, expected, {}, error_spec_); + Array2D expected({{42.0f, 56.0f}, {74.0f, 96.0f}}); + this->template ComputeAndCompareR2(&builder, expected, {}, + this->error_spec_); } +template +class DotOperationTestForBatchMatMul : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTestForBatchMatMul, TypesF16F32F64); + // Regression test for b/32055648. The root of the graph is a kFusion of 4 // bitcasts. Although bitcasts don't map to thunks, the root should still be // sync-dependent on bitcasts' operands. -XLA_TEST_F(DotOperationTest, BatchMatMul) { - ComputationBuilder builder(client_, TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "y"); +XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto x = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "x"); + auto y = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "y"); auto x_flat = builder.Reshape(x, {0, 1, 2, 3}, {4, 2, 2}); auto y_flat = builder.Reshape(y, {0, 1, 2, 3}, {4, 2, 2}); @@ -507,33 +558,79 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) { auto out_flat = builder.ConcatInDim(out_slices, 0); builder.Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2}); - auto x_data = client_ - ->TransferToServer(*Literal::CreateR4( - {{{{1000, 100}, {10, 1}}, {{2000, 200}, {20, 2}}}, - {{{3000, 300}, {30, 3}}, {{4000, 400}, {40, 4}}}})) - .ConsumeValueOrDie(); - auto y_data = client_ - ->TransferToServer(*Literal::CreateR4( - {{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}, - {{{11, 22}, {33, 44}}, {{55, 66}, {77, 88}}}})) + auto x_data = this->client_ + ->TransferToServer(*Literal::CreateR4FromArray4D( + {{{{1000.0f, 100.0f}, {10.0f, 1.0f}}, + {{2000.0f, 200.0f}, {20.0f, 2.0f}}}, + {{{3000.0f, 300.0f}, {30.0f, 3.0f}}, + {{4000.0f, 400.0f}, {40.0f, 4.0f}}}})) .ConsumeValueOrDie(); + auto y_data = + this->client_ + ->TransferToServer(*Literal::CreateR4FromArray4D( + {{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}, + {{{11.0f, 22.0f}, {33.0f, 44.0f}}, + {{55.0f, 66.0f}, {77.0f, 88.0f}}}})) + .ConsumeValueOrDie(); - ComputeAndCompareR4( + if (std::is_same::value) { + this->error_spec_ = ErrorSpec{0.0001, 1e-3}; + } + this->template ComputeAndCompareR4( &builder, - /*expected=*/{{{{1300, 2400}, {13, 24}}, {{11400, 13600}, {114, 136}}}, - {{{42900, 79200}, {429, 792}}, - {{250800, 299200}, {2508, 2992}}}}, - {x_data.get(), y_data.get()}, error_spec_); + /*expected=*/ + {{{{1300.0f, 2400.0f}, {13.0f, 24.0f}}, + {{11400.0f, 13600.0f}, {114.0f, 136.0f}}}, + {{{42900.0f, 79200.0f}, {429.0f, 792.0f}}, + {{250800.0f, 299200.0f}, {2508.0f, 2992.0f}}}}, + {x_data.get(), y_data.get()}, this->error_spec_); +} + +XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { + using T = TypeParam; + + ComputationBuilder builder(this->client_, this->TestName()); + auto x = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2}), "x"); + auto y = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2}), "y"); + + DotDimensionNumbers dnums; + dnums.add_lhs_contracting_dimensions(2); + dnums.add_rhs_contracting_dimensions(1); + dnums.add_lhs_batch_dimensions(0); + dnums.add_rhs_batch_dimensions(0); + + auto out = builder.DotGeneral(x, y, dnums); + + auto x_data = + this->client_ + ->TransferToServer(*Literal::CreateR3FromArray3D( + {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}})) + .ConsumeValueOrDie(); + + auto y_data = + this->client_ + ->TransferToServer(*Literal::CreateR3FromArray3D( + {{{1.0f, 0.0f}, {0.0f, 1.0f}}, {{1.0f, 0.0f}, {0.0f, 1.0f}}})) + .ConsumeValueOrDie(); + + this->template ComputeAndCompareR3( + &builder, + /*expected=*/ + {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}, + {x_data.get(), y_data.get()}, this->error_spec_); } -TEST_F(DotOperationTest, TransposeFolding) { +XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) { + using T = TypeParam; for (bool transpose_lhs : {false, true}) { for (bool transpose_rhs : {false, true}) { for (bool row_major : {false, true}) { - std::unique_ptr> lhs( - new Array2D({{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}})); - std::unique_ptr> rhs( - new Array2D({{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}})); + std::unique_ptr> lhs( + new Array2D({{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}})); + std::unique_ptr> rhs( + new Array2D({{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}})); if (transpose_lhs) { lhs = ReferenceUtil::TransposeArray2D(*lhs); @@ -542,22 +639,20 @@ TEST_F(DotOperationTest, TransposeFolding) { rhs = ReferenceUtil::TransposeArray2D(*rhs); } auto lhs_handle = - client_ - ->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - *lhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + *lhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); auto rhs_handle = - client_ - ->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - *rhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + *rhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); + ComputationBuilder builder(this->client_, this->TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); auto lhs_arg = builder.Parameter( 0, ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}), "lhs"); @@ -572,24 +667,27 @@ TEST_F(DotOperationTest, TransposeFolding) { } auto result = builder.Dot(lhs_arg, rhs_arg); - Array2D expected({{26.0, 0.0}, {-12.0, 10.0}}); + Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); VLOG(1) << "TestTransposeFolding " << transpose_lhs << " " << transpose_rhs << " " << row_major; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - error_spec_); + this->template ComputeAndCompareR2( + &builder, expected, {lhs_handle.get(), rhs_handle.get()}, + this->error_spec_); } } } } -TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) { - auto prim_type = primitive_util::NativeToPrimitiveType(); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, + DotOfConcatOptimizationWithConstLHS) { + using T = TypeParam; + auto prim_type = primitive_util::NativeToPrimitiveType(); - std::unique_ptr> constant_lhs_array(new Array2D( - {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}})); + std::unique_ptr> constant_lhs_array( + new Array2D({{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, + {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}})); - ComputationBuilder builder(client_, TestName()); + ComputationBuilder builder(this->client_, this->TestName()); auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); auto rhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs_arg_0"); @@ -600,78 +698,80 @@ TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) { auto result = builder.Dot( lhs_constant, builder.ConcatInDim({rhs_arg_0, rhs_arg_1, rhs_arg_2}, 0)); - std::unique_ptr> arg_0_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}})); - std::unique_ptr> arg_1_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}})); - std::unique_ptr> arg_2_value_array( - new Array2D({{1.0, 2.0}})); + std::unique_ptr> arg_0_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); + std::unique_ptr> arg_1_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}})); + std::unique_ptr> arg_2_value_array(new Array2D({{1.0f, 2.0f}})); TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_2_value_array))); - Array2D expected({{53.0, 74.0}, {45.0, 66.0}}); - ComputeAndCompareR2( + Array2D expected({{53.0f, 74.0f}, {45.0f, 66.0f}}); + this->template ComputeAndCompareR2( &builder, expected, - {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_); -} - -TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstRHS) { - auto prim_type = primitive_util::NativeToPrimitiveType(); - - std::unique_ptr> constant_rhs_array( - new Array2D({{1.0, 2.0}, - {3.0, 4.0}, - {5.0, 6.0}, - {6.0, 5.0}, - {4.0, 3.0}, - {2.0, 1.0}})); - - ComputationBuilder builder(client_, TestName()); + {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, + this->error_spec_); +} + +XLA_TYPED_TEST(DotOperationTest_F16F32F64, + DotOfConcatOptimizationWithConstRHS) { + using T = TypeParam; + std::unique_ptr> constant_rhs_array( + new Array2D({{1.0f, 2.0f}, + {3.0f, 4.0f}, + {5.0f, 6.0f}, + {6.0f, 5.0f}, + {4.0f, 3.0f}, + {2.0f, 1.0f}})); + + ComputationBuilder builder(this->client_, this->TestName()); auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), + auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2}), "lhs_arg_0"); - auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 3}), + auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 3}), "lhs_arg_1"); - auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShape(prim_type, {2, 1}), + auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShapeWithType({2, 1}), "lhs_arg_2"); auto result = builder.Dot( builder.ConcatInDim({lhs_arg_0, lhs_arg_1, lhs_arg_2}, 1), rhs_constant); - std::unique_ptr> arg_0_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}})); - std::unique_ptr> arg_1_value_array( - new Array2D({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); - std::unique_ptr> arg_2_value_array( - new Array2D({{1.0}, {2.0}})); + std::unique_ptr> arg_0_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); + std::unique_ptr> arg_1_value_array( + new Array2D({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}})); + std::unique_ptr> arg_2_value_array( + new Array2D({{1.0f}, {2.0f}})); TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_2_value_array))); - Array2D expected({{38.0, 36.0}, {93.0, 91.0}}); - ComputeAndCompareR2( + Array2D expected({{38.0f, 36.0f}, {93.0f, 91.0f}}); + this->template ComputeAndCompareR2( &builder, expected, - {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_); + {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, + this->error_spec_); } + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..6fe7737de7af349dca2931b52d62dbc03b14e0b3 --- /dev/null +++ b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc @@ -0,0 +1,128 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/core/lib/core/casts.h" + +namespace xla { +namespace { +class ExhaustiveF32ElementwiseOpTest + : public ClientLibraryTestBase, + public ::testing::WithParamInterface> { + protected: + ErrorSpec error_spec_{0.0001, 0.0001, /*relaxed_nans=*/true}; + + template + void ExhaustivelyTestF32Op(EnqueueOpTy enqueue_op, + float (*evaluate_op)(float), + std::pair known_incorrect_range) { + int64 begin, end; + std::tie(begin, end) = GetParam(); + int64 input_size = end - begin; + LOG(INFO) << "Checking range [" << begin << ", " << end << ")"; + + ComputationBuilder builder(client_, TestName()); + + std::unique_ptr input_literal = + Literal::CreateFromDimensions(F32, {input_size}); + for (int64 i = begin; i < end; i++) { + if (i >= known_incorrect_range.first && + i < known_incorrect_range.second) { + // If the operation is known to be buggy on a specific input clamp that + // input to 0 under the assumption that the op is at least correct on 0. + input_literal->Set({i - begin}, 0.0f); + } else { + input_literal->Set({i - begin}, tensorflow::bit_cast(i)); + } + } + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, + client_->TransferToServer(*input_literal)); + + auto input = builder.Parameter(0, input_literal->shape(), "input"); + enqueue_op(&builder, input); + + std::vector expected_result; + expected_result.reserve(input_size); + for (int64 i = 0; i < input_size; i++) { + expected_result.push_back(evaluate_op(input_literal->Get({i}))); + } + + ComputeAndCompareR1(&builder, expected_result, {input_data.get()}, + error_spec_); + } +}; + +XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, LogF32) { +#ifdef XLA_TEST_BACKEND_CPU + // TODO(b/73141998): The vectorized Log implementation gives results outside + // our error spec in this range (these numbers are bitwise representations of + // floats expressed as a zero extended int64): + std::pair known_incorrect_range = {1, 8315654}; +#else + std::pair known_incorrect_range = {0, 0}; +#endif + + ExhaustivelyTestF32Op( + [](ComputationBuilder* builder, const ComputationDataHandle& input) { + builder->Log(input); + }, + std::log, known_incorrect_range); +} + +XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, ExpF32) { +#ifdef XLA_TEST_BACKEND_CPU + // TODO(b/73142289): The vectorized Exp implementation gives results outside + // our error spec in this range (these numbers are bitwise representations of + // floats expressed as a zero extended int64): + std::pair known_incorrect_range = {1107296256 + 11583654, + 1107296256 + 11629080}; +#else + std::pair known_incorrect_range = {0, 0}; +#endif + + ExhaustivelyTestF32Op( + [](ComputationBuilder* builder, const ComputationDataHandle& input) { + builder->Exp(input); + }, + std::exp, known_incorrect_range); +} + +XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, TanhF32) { + ExhaustivelyTestF32Op( + [](ComputationBuilder* builder, const ComputationDataHandle& input) { + builder->Tanh(input); + }, + std::tanh, /*known_incorrect_range=*/{0, 0}); +} + +std::vector> CreateExhaustiveParameters() { + // We break up the 2^32-element space into small'ish chunks to keep peak + // memory usage low. + std::vector> result; + const int64 step = 1 << 25; + for (int64 i = 0; i < (1l << 32); i += step) { + result.push_back({i, i + step}); + } + return result; +} + +INSTANTIATE_TEST_CASE_P(ExhaustiveF32ElementwiseOpTestInstance, + ExhaustiveF32ElementwiseOpTest, + ::testing::ValuesIn(CreateExhaustiveParameters())); +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 9f5806c5e16c30cf198027cffab5f78c315cb957..6723c99edb945492abfbac159bed1959d551ec57 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -267,6 +267,28 @@ template reference_preprocessor); } +HloComputation* HloTestBase::FindComputation(HloModule* module, + tensorflow::StringPiece name) { + auto it = c_find_if(module->computations(), + [&](HloComputation* c) { return c->name() == name; }); + if (it == module->computations().end()) { + return nullptr; + } + return *it; +} + +HloInstruction* HloTestBase::FindInstruction(HloModule* module, + tensorflow::StringPiece name) { + for (const HloComputation* c : module->computations()) { + auto it = c_find_if(c->instructions(), + [&](HloInstruction* i) { return i->name() == name; }); + if (it != c->instructions().end()) { + return *it; + } + } + return nullptr; +} + Backend& HloTestBase::backend() { return test_runner_.backend(); } /* static */ diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 4aea9fc9fd027231106e529eb16bcd43f23fbe1c..413bb213fdcb1303f396308d13d9d0b96b47b71f 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -197,6 +197,15 @@ class HloTestBase : public ::testing::Test { ->Clear(); } + // Gets the computation/instruction from the given module with the given name. + // + // This is useful for tests which create HLOs from a string and then want to + // inspect a particular computation or instruction. + HloComputation* FindComputation(HloModule* module, + tensorflow::StringPiece name); + HloInstruction* FindInstruction(HloModule* module, + tensorflow::StringPiece name); + // Return an HLO verifier constructed for the test backend. HloVerifier& verifier() const { return *hlo_verifier_; } diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc index 506091ddd8d1d8e6519525bb7031f4e8b296b5fb..641907acf260c099a5ac885c362d92a0b6d78a42 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_verifier.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/test.h" @@ -40,18 +41,22 @@ void HloVerifiedTestBase::TearDown() { << "TearDown called more than once; it should be called exactly once."; tear_down_called_ = true; if (module_) { - HloVerifier verifier; - xla::StatusOr mutated = verifier.Run(module_.get()); - if (!mutated.ok()) { - ADD_FAILURE() << "HloVerifier failed: " << mutated.status(); - } else { - EXPECT_FALSE(mutated.ValueOrDie()) - << "HloVerifier should never mutate the HloModule"; - } + VerifyModule(); } HloTestBase::TearDown(); } +void HloVerifiedTestBase::VerifyModule() { + HloVerifier verifier; + xla::StatusOr mutated = verifier.Run(module_.get()); + if (!mutated.ok()) { + ADD_FAILURE() << "HloVerifier failed: " << mutated.status(); + } else { + EXPECT_FALSE(mutated.ValueOrDie()) + << "HloVerifier should never mutate the HloModule"; + } +} + HloModule& HloVerifiedTestBase::module() { if (!module_) { module_ = CreateNewModule(); @@ -59,4 +64,9 @@ HloModule& HloVerifiedTestBase::module() { return *module_; } +void HloVerifiedTestBase::ParseAndVerifyModule(const char* hlo_text) { + CHECK(!module_) << "Called ParseModule when test already has a module."; + TF_ASSERT_OK_AND_ASSIGN(module_, tools::Parse(hlo_text)); + VerifyModule(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h index 492688bf7d682cf991cb8c09399492a0437f651b..c0cb12bc93f56a5cb5ebdac94488369331f0cea6 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h @@ -44,6 +44,7 @@ class HloVerifiedTestBase : public HloTestBase { // Returns the default HloModule, lazily creating it if necessary via // HloTestBase::CreateNewModule(). HloModule& module(); + void ParseAndVerifyModule(const char* hlo_text); // Sets the shape-size function used during hlo verification. If this isn't // called, a default ShapeVerifier is used instead. @@ -55,6 +56,7 @@ class HloVerifiedTestBase : public HloTestBase { std::unique_ptr module_; // Lazily populated. Access via module(). std::unique_ptr shape_verifier_; bool tear_down_called_ = false; + void VerifyModule(); }; } // namespace xla diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc index 474d2547aeba1ec478eb3aa0cacfc04d9dee142e..81630df34c58526b6d41492b2b4b3892a02a21c2 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util.cc +++ b/tensorflow/compiler/xla/tests/literal_test_util.cc @@ -209,6 +209,11 @@ template <> return CompareFloatsBitwiseEqual(lhs, rhs); } template <> +::testing::AssertionResult CompareEqual(Eigen::half lhs, + Eigen::half rhs) { + return CompareFloatsBitwiseEqual(lhs, rhs); +} +template <> ::testing::AssertionResult CompareEqual(float lhs, float rhs) { return CompareFloatsBitwiseEqual(lhs, rhs); } @@ -449,8 +454,12 @@ class NearComparator { private: template - bool NanMismatch(NativeT lhs, NativeT rhs) { - return std::isnan(lhs) != std::isnan(rhs); + bool NanMismatch(NativeT expected, NativeT actual, bool relaxed_nans) { + if (relaxed_nans) { + return !std::isnan(expected) && std::isnan(actual); + } else { + return std::isnan(expected) != std::isnan(actual); + } } template @@ -472,7 +481,8 @@ class NearComparator { const float abs_diff = std::abs(actual - expected); const float rel_err = abs_diff / std::abs(expected); - const bool nan_mismatch = NanMismatch(expected, actual); + const bool nan_mismatch = + NanMismatch(expected, actual, error_.relaxed_nans); const bool mismatch = (nan_mismatch || (abs_diff >= error_.abs && rel_err >= error_.rel)); return !mismatch; @@ -630,9 +640,11 @@ class NearComparator { }; template <> -bool NearComparator::NanMismatch(complex64 lhs, complex64 rhs) { - return std::isnan(lhs.real()) != std::isnan(rhs.real()) || - std::isnan(lhs.imag()) != std::isnan(rhs.imag()); +bool NearComparator::NanMismatch(complex64 expected, + complex64 actual, + bool relaxed_nans) { + return NanMismatch(expected.real(), actual.real(), relaxed_nans) || + NanMismatch(expected.imag(), actual.imag(), relaxed_nans); } template <> diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h index 9b0724262d51ec7964a918bb8eb8716308662b96..7b757a4bd7e7592583b7596b4305ddb7e6c52d75 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util.h +++ b/tensorflow/compiler/xla/tests/literal_test_util.h @@ -40,10 +40,16 @@ namespace xla { // Structure describing permissible absolute and relative error bounds. struct ErrorSpec { - explicit ErrorSpec(float aabs, float arel = 0) : abs(aabs), rel(arel) {} + explicit ErrorSpec(float aabs, float arel = 0, bool relaxed_nans = false) + : abs(aabs), rel(arel), relaxed_nans(relaxed_nans) {} float abs; // Absolute error bound. float rel; // Relative error bound. + + // If relaxed_nans is true then any result is valid if we are expecting NaNs. + // In effect, this allows the tested operation to produce incorrect results + // for inputs outside its mathematical domain. + bool relaxed_nans; }; // Utility class for making expectations/assertions related to XLA literals. diff --git a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc index 99514baf23cafe61adc28a30dfdfe2691ab82d32..3023df47cda33f5d11abc921fd0355d48f761107 100644 --- a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc +++ b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" namespace xla { @@ -49,11 +50,11 @@ void LLVMIRGenTestBase::CompileAndVerifyIr( std::unique_ptr hlo_module, const string& pattern, bool match_optimized_ir) { SetIrHook(match_optimized_ir); - ASSERT_TRUE(CompileToExecutable(std::move(hlo_module)).ok()); + TF_ASSERT_OK(CompileToExecutable(std::move(hlo_module)).status()); ResetIrHook(); StatusOr filecheck_result = RunFileCheck(ir_, pattern); - ASSERT_TRUE(filecheck_result.ok()); + TF_ASSERT_OK(filecheck_result.status()); EXPECT_TRUE(filecheck_result.ValueOrDie()); } diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 2b0f7e6e80c48435ca55432a2afa3b6d69162625..0cd812fd1b4bc69c34b70d3ca0fd0aa6cf57fa4c 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -531,7 +531,7 @@ TEST_F(MapTest, MapOperantionWithBuildError) { ASSERT_TRUE(!computation_status.ok()); EXPECT_THAT( computation_status.status().ToString(), - ::testing::HasSubstr("error from: ErrorAdd: binary op BINOP_ADD with " + ::testing::HasSubstr("error from: ErrorAdd: Binary op BINOP_ADD with " "different element types: f32[] and u16[]")); } diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 6c86dd5b9ef673c9facffafa37e00a859ce82010..c42f71388baba73e08a361d817e41b03e03bf133 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -29,6 +29,8 @@ limitations under the License. #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" @@ -38,258 +40,223 @@ limitations under the License. namespace xla { namespace { -class MatOpsSimpleTest : public ClientLibraryTestBase { - protected: - Computation BuildSum() { - // sum(x, y) = x + y - ComputationBuilder builder(client_, "sum"); - auto x_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value"); - auto y_value = - builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y_value"); - builder.Add(x_value, y_value); - auto computation_status = builder.Build(); - TF_CHECK_OK(computation_status.status()); - return computation_status.ConsumeValueOrDie(); - } - - void TestLinspaceMax(int64 rows, int64 cols) { - float from = -128.0, to = 256.0; - std::unique_ptr> alhs = - MakeLinspaceArray2D(from, to, rows, cols); - auto arhs = MakeUnique>(rows, cols, 1.0); - - ComputationBuilder builder( - client_, - tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols)); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - auto max = builder.Max(lhs, rhs); - - Array2D aexpected(rows, cols); - for (int row = 0; row < rows; ++row) { - for (int col = 0; col < cols; ++col) { - aexpected(row, col) = std::max((*alhs)(row, col), (*arhs)(row, col)); - } - } - - ComputeAndCompareR2(&builder, aexpected, {}, ErrorSpec(1e-6)); - } -}; - -TEST_F(MatOpsSimpleTest, ExpTwoByTwoValues) { - ComputationBuilder builder(client_, "exp_2x2"); - auto data = builder.ConstantR2({ - {1.0, 0.0}, // row 0 - {-1.0, 0.5}, // row 1 +#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +using TypesF16F32 = ::testing::Types; +#else +using TypesF16F32 = ::testing::Types; +#endif + +class MatOpsSimpleTest : public ClientLibraryTestBase {}; + +template +class MatOpsSimpleTest_F16F32 : public MatOpsSimpleTest {}; + +// TODO(bixia): This test for F16 failed on GPU 02-25-2018. +#ifdef XLA_TEST_BACKEND_GPU +TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, ::testing::Types); +#else +TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32); +#endif + +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) { + using T = TypeParam; + ComputationBuilder builder(this->client_, "exp_2x2"); + auto data = builder.ConstantR2FromArray2D({ + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 }); builder.Exp(data); std::unique_ptr expected = - Literal::CreateR2({{2.71828, 1.00000}, // row 0 - {0.36788, 1.64872}}); // row 1 + Literal::CreateR2FromArray2D({{2.71828f, 1.00000f}, // row 0 + {0.36788f, 1.64872f}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-5)); } -TEST_F(MatOpsSimpleTest, MapTwoByTwo) { +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) { + using T = TypeParam; Computation add_half; { // add_half(x) = x + 0.5 - ComputationBuilder builder(client_, "add_half"); + ComputationBuilder builder(this->client_, "add_half"); auto x_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value"); - auto half = builder.ConstantR0(0.5); + builder.Parameter(0, ShapeUtil::MakeShapeWithType({}), "x_value"); + auto half = builder.ConstantR0(static_cast(0.5)); builder.Add(x_value, half); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); add_half = computation_status.ConsumeValueOrDie(); } - ComputationBuilder builder(client_, "map_2x2"); - auto data = builder.ConstantR2({ - {1.0, 0.0}, // row 0 - {-1.0, 0.5}, // row 1 + ComputationBuilder builder(this->client_, "map_2x2"); + auto data = builder.ConstantR2FromArray2D({ + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 }); auto map = builder.Map({data}, add_half, {0, 1}); std::unique_ptr expected = - Literal::CreateR2({{1.5, 0.5}, // row 0 - {-0.5, 1.0}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); + Literal::CreateR2FromArray2D({{1.5f, 0.5f}, // row 0 + {-0.5f, 1.0f}}); // row 1 + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-5)); } -TEST_F(MatOpsSimpleTest, MaxTwoByTwoValues) { - ComputationBuilder builder(client_, "max_2x2"); - auto lhs = builder.ConstantR2({ - {7.0, 2.0}, // row 0 - {3.0, -4.0}, // row 1 +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) { + using T = TypeParam; + ComputationBuilder builder(this->client_, "max_2x2"); + auto lhs = builder.ConstantR2FromArray2D({ + {7.0f, 2.0f}, // row 0 + {3.0f, -4.0f}, // row 1 }); - auto rhs = builder.ConstantR2({ - {5.0, 6.0}, // row 0 - {1.0, -8.0}, // row 1 + auto rhs = builder.ConstantR2FromArray2D({ + {5.0f, 6.0f}, // row 0 + {1.0f, -8.0f}, // row 1 }); auto max = builder.Max(lhs, rhs); std::unique_ptr expected = - Literal::CreateR2({{7.0, 6.0}, // row 0 - {3.0, -4.0}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6)); + Literal::CreateR2FromArray2D({{7.0f, 6.0f}, // row 0 + {3.0f, -4.0f}}); // row 1 + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-6)); } -TEST_F(MatOpsSimpleTest, Max1x1Linspace) { TestLinspaceMax(1, 1); } - -TEST_F(MatOpsSimpleTest, Max2x2Linspace) { TestLinspaceMax(2, 2); } - -TEST_F(MatOpsSimpleTest, Max3x3Linspace) { TestLinspaceMax(3, 3); } - -TEST_F(MatOpsSimpleTest, Max4x4Linspace) { TestLinspaceMax(4, 4); } - -TEST_F(MatOpsSimpleTest, Max6x6Linspace) { TestLinspaceMax(6, 6); } - -TEST_F(MatOpsSimpleTest, Max8x8Linspace) { TestLinspaceMax(8, 8); } - -TEST_F(MatOpsSimpleTest, Max12x12Linspace) { TestLinspaceMax(12, 12); } - -TEST_F(MatOpsSimpleTest, Max16x16Linspace) { TestLinspaceMax(16, 16); } +struct TestLinspaceMaxParam { + int64 rows; + int64 cols; +}; -TEST_F(MatOpsSimpleTest, Max32x8Linspace) { TestLinspaceMax(32, 8); } +class TestLinspaceMaxParametric + : public MatOpsSimpleTest, + public ::testing::WithParamInterface { + public: + template + void TestImpl() { + TestLinspaceMaxParam param = GetParam(); + int64 rows = param.rows; + int64 cols = param.cols; + float from = -128.0, to = 256.0; + std::unique_ptr> alhs = + MakeLinspaceArray2D(from, to, rows, cols); + auto arhs = MakeUnique>(rows, cols, static_cast(1.0f)); -TEST_F(MatOpsSimpleTest, Max64x8Linspace) { TestLinspaceMax(64, 8); } + ComputationBuilder builder( + client_, + tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols)); + auto lhs = builder.ConstantR2FromArray2D(*alhs); + auto rhs = builder.ConstantR2FromArray2D(*arhs); + auto max = builder.Max(lhs, rhs); -class MatOpsDotAddTest - : public ClientLibraryTestBase, - public ::testing::WithParamInterface> {}; - -TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2) { - bool row_major = std::get<0>(GetParam()); - bool add_lhs = std::get<1>(GetParam()); - bool transpose = std::get<2>(GetParam()); - Array2D lhs({{1.0, 2.0}, {3.0, 4.0}}); - Array2D rhs({{10.0, 11.0}, {12.0, 13.0}}); - - auto minor_to_major = [](bool row_major) -> std::vector { - return {row_major ? 1 : 0, row_major ? 0 : 1}; - }; - - auto prim_type = primitive_util::NativeToPrimitiveType(); - Shape lhs_shape = - ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); - Shape rhs_shape = - ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); - - TF_ASSERT_OK_AND_ASSIGN( - auto lhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - TF_ASSERT_OK_AND_ASSIGN( - auto rhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - - ComputationBuilder builder(client_, TestName()); - auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); - auto lhs_mat_arg = lhs_arg; - if (transpose) { - lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); - } - auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); - auto result = builder.Dot(lhs_mat_arg, rhs_arg); - Array2D expected; - if (add_lhs) { - result = builder.Add(result, lhs_arg); - if (transpose) { - expected = Array2D({{47, 52}, {71, 78}}); - } else { - expected = Array2D({{35, 39}, {81, 89}}); + Array2D expected(rows, cols); + for (int row = 0; row < rows; ++row) { + for (int col = 0; col < cols; ++col) { + expected(row, col) = std::max((*alhs)(row, col), (*arhs)(row, col)); + } } - } else { - result = builder.Add(result, rhs_arg); - if (transpose) { - expected = Array2D({{56, 61}, {80, 87}}); - } else { - expected = Array2D({{44, 48}, {90, 98}}); + ErrorSpec error_spec(1e-6); + if (std::is_same::value) { + error_spec = ErrorSpec(1e-6, 2e-4); } + ComputeAndCompareR2(&builder, expected, {}, error_spec); } +}; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - ErrorSpec(1e-6)); +string PrintTestLinspaceMaxParam( + const ::testing::TestParamInfo& test_param) { + const TestLinspaceMaxParam& param = test_param.param; + return tensorflow::strings::StrCat(param.rows, "r", param.cols, "c"); } -INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest, - ::testing::Combine(::testing::Bool(), ::testing::Bool(), - ::testing::Bool())); +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +// TODO(bixia): This test failed on GPU 02-25-2018 +#ifdef XLA_TEST_BACKEND_CPU +XLA_TEST_P(TestLinspaceMaxParametric, TestF16) { TestImpl(); } +#endif +#endif +XLA_TEST_P(TestLinspaceMaxParametric, TestF32) { TestImpl(); } + +INSTANTIATE_TEST_CASE_P( + TestLinspaceMax, TestLinspaceMaxParametric, + ::testing::Values(TestLinspaceMaxParam{1, 1}, TestLinspaceMaxParam{2, 2}, + TestLinspaceMaxParam{3, 3}, TestLinspaceMaxParam{4, 4}, + TestLinspaceMaxParam{6, 6}, TestLinspaceMaxParam{8, 8}, + TestLinspaceMaxParam{12, 12}, + TestLinspaceMaxParam{16, 16}, TestLinspaceMaxParam{32, 8}, + TestLinspaceMaxParam{64, 8}), + PrintTestLinspaceMaxParam); -class MatOpsDotAddTest_bf16 +class MatOpsDotAddTest : public ClientLibraryTestBase, - public ::testing::WithParamInterface> {}; - -TEST_P(MatOpsDotAddTest_bf16, Dot_Add_2x2_2x2) { - bool row_major = std::get<0>(GetParam()); - bool add_lhs = std::get<1>(GetParam()); - bool transpose = std::get<2>(GetParam()); - Array2D lhs( - {{bfloat16(1.0f), bfloat16(2.0f)}, {bfloat16(3.0), bfloat16(4.0)}}); - Array2D rhs( - {{bfloat16(10.0f), bfloat16(11.0f)}, {bfloat16(12.0f), bfloat16(13.0f)}}); - - auto minor_to_major = [](bool row_major) -> std::vector { - return {row_major ? 1 : 0, row_major ? 0 : 1}; - }; - - auto prim_type = primitive_util::NativeToPrimitiveType(); - Shape lhs_shape = - ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); - Shape rhs_shape = - ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); - - TF_ASSERT_OK_AND_ASSIGN( - auto lhs_handle, - client_->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - TF_ASSERT_OK_AND_ASSIGN( - auto rhs_handle, - client_->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - - ComputationBuilder builder(client_, TestName()); - auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); - auto lhs_mat_arg = lhs_arg; - if (transpose) { - lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); - } - auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); - auto result = builder.Dot(lhs_mat_arg, rhs_arg); - Array2D expected; - if (add_lhs) { - result = builder.Add(result, lhs_arg); + public ::testing::WithParamInterface> { + public: + template + void TestImpl() { + bool row_major = std::get<0>(GetParam()); + bool add_lhs = std::get<1>(GetParam()); + bool transpose = std::get<2>(GetParam()); + Array2D lhs({{1.0f, 2.0f}, {3.0f, 4.0f}}); + Array2D rhs({{10.0f, 11.0f}, {12.0f, 13.0f}}); + + auto minor_to_major = [](bool row_major) -> std::vector { + return {row_major ? 1 : 0, row_major ? 0 : 1}; + }; + + auto prim_type = primitive_util::NativeToPrimitiveType(); + Shape lhs_shape = + ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); + Shape rhs_shape = + ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); + + TF_ASSERT_OK_AND_ASSIGN( + auto lhs_handle, + client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + TF_ASSERT_OK_AND_ASSIGN( + auto rhs_handle, + client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + + ComputationBuilder builder(client_, TestName()); + auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); + auto lhs_mat_arg = lhs_arg; if (transpose) { - expected = Array2D( - {{bfloat16(47), bfloat16(52)}, {bfloat16(71), bfloat16(78)}}); - } else { - expected = Array2D( - {{bfloat16(35), bfloat16(39)}, {bfloat16(81), bfloat16(89)}}); + lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); } - } else { - result = builder.Add(result, rhs_arg); - if (transpose) { - expected = Array2D( - {{bfloat16(56), bfloat16(61)}, {bfloat16(80), bfloat16(87)}}); + auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); + auto result = builder.Dot(lhs_mat_arg, rhs_arg); + Array2D expected; + if (add_lhs) { + result = builder.Add(result, lhs_arg); + if (transpose) { + expected = Array2D({{47.0f, 52.0f}, {71.0f, 78.0f}}); + } else { + expected = Array2D({{35.0f, 39.0f}, {81.0f, 89.0f}}); + } } else { - expected = Array2D( - {{bfloat16(44), bfloat16(48)}, {bfloat16(90), bfloat16(98)}}); + result = builder.Add(result, rhs_arg); + if (transpose) { + expected = Array2D({{56.0f, 61.0f}, {80.0f, 87.0f}}); + } else { + expected = Array2D({{44.0f, 48.0f}, {90.0f, 98.0f}}); + } } + + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, + ErrorSpec(1e-6)); } +}; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - ErrorSpec(1e-6)); -} +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2BF16) { TestImpl(); } +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F16) { TestImpl(); } +#endif +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F32) { TestImpl(); } -INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest_bf16, +INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest, ::testing::Combine(::testing::Bool(), ::testing::Bool(), ::testing::Bool())); diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 6e6cb7ff1e2ac74dc54f14d8811c9a5d3662bbd2..0a603f4954badd12adf3144320789a5edd0d9c6c 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_runner.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" @@ -35,6 +36,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" @@ -176,5 +178,38 @@ XLA_TEST_F(MultiOutputFusionTest, 2DFusionSize129) { RunTest2D(true, 129); } XLA_TEST_F(MultiOutputFusionTest, DiffentTypesNoFusion) { RunTest1D(false, 8); } XLA_TEST_F(MultiOutputFusionTest, DiffentTypesFusion) { RunTest1D(true, 8); } +XLA_TEST_F(MultiOutputFusionTest, FusionNodeIsRoot) { + const char* testcase = R"( + HloModule m + + fused_computation { + x.param_0 = (((s32[]), f32[]), (f32[], s32[])) parameter(0) + gte.3 = ((s32[]), f32[]) get-tuple-element(x.param_0), index=0 + gte.2 = (s32[]) get-tuple-element(gte.3), index=0 + gte.4 = s32[] get-tuple-element(gte.2), index=0 + copy = s32[] copy(gte.4) + ROOT tuple = (s32[]) tuple(copy) + } + + ENTRY thing.v3 { + x = (((s32[]), f32[]), (f32[], s32[])) parameter(0) + ROOT fusion = (s32[]) fusion(x), kind=kLoop, calls=fused_computation + } + )"; + auto module = + HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) + .ValueOrDie(); + auto param = Literal::MakeTupleOwned( + Literal::MakeTupleOwned( + Literal::MakeTupleOwned(Literal::CreateR0(42)), + Literal::CreateR0(1.0)), + Literal::MakeTupleOwned(Literal::CreateR0(3.0), + Literal::CreateR0(4))); + TF_ASSERT_OK_AND_ASSIGN(auto result, + Execute(std::move(module), {param.get()})); + EXPECT_TRUE(LiteralTestUtil::Equal( + *result, *Literal::MakeTupleOwned(Literal::CreateR0(42)))); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 7f3c72671d51bcdfba89b050371626d672c6d945..8e976e8a3162b2f8c7782d1d92f383f907ab339e 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -41,7 +41,9 @@ limitations under the License. namespace xla { namespace { -#ifdef XLA_BACKEND_SUPPORTS_BFLOAT16 +// TODO(b/74260408): This test is timing out if bfloat16 is enabled on +// GPU. Last timed out on 2018-03-06. +#if defined(XLA_BACKEND_SUPPORTS_BFLOAT16) && !defined(XLA_TEST_BACKEND_GPU) // Tests both F32 and BF16. static std::array use_bfloat16_params{false, true}; #else @@ -272,7 +274,7 @@ XLA_TEST_P(ReduceWindowTest, NonstandardReduceFunction) { builder_.ReduceWindow( input, - CreateConstantFromLiteral(*Literal::CreateR0(3.0f), &builder_), + CreateConstantFromLiteral(*Literal::CreateR0(0.0f), &builder_), reduce_fn, /*window_dimensions=*/{1, 1, 2, 1}, /*window_strides=*/{1, 1, 1, 1}, padding); @@ -282,7 +284,7 @@ XLA_TEST_P(ReduceWindowTest, NonstandardReduceFunction) { }; auto expected = - ReferenceUtil::ReduceWindow4DGeneric(input_array, 3.0f, reduce_func, + ReferenceUtil::ReduceWindow4DGeneric(input_array, 0.0f, reduce_func, /*window=*/{1, 1, 2, 1}, /*stride=*/{1, 1, 1, 1}, padding); @@ -800,6 +802,14 @@ const R4ReduceWindowTestData kR4ReduceWindowLargeTestValues[] = { /*pad_high=*/{1, 1, 0, 0}, /*layout=*/{3, 2, 1, 0}, /*reducer=*/kAdd}, + + R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 32768 - 3, 2}, + /*window_bounds=*/{1, 1, 4, 1}, + /*strides=*/{1, 1, 4, 1}, + /*pad_low=*/{0, 0, 1, 0}, + /*pad_high=*/{0, 0, 2, 0}, + /*layout=*/{3, 2, 1, 0}, + /*reducer=*/kMax}, }; INSTANTIATE_TEST_CASE_P( diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 4da6ee91607941b395b00befc98a10e7c17746ed..0c88bef69dfc522fef52422b0bd3a825fa173d44 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -163,7 +163,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a"); builder.ConvertElementType(a, F32); - int64 value = 3LL << 32; + int64 value = 3LL << 35; std::unique_ptr a_literal = Literal::CreateR0(value); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); @@ -860,6 +860,12 @@ XLA_TEST_F(ScalarComputationsTest, MinF32Below) { TestMinMax(-100.1f, 3.1f, -100.1f, &ComputationBuilder::Min); } +XLA_TEST_F(ScalarComputationsTest, MinPropagatesNan) { + SetFastMathDisabled(true); + TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Min); + TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Min); +} + XLA_TEST_F(ScalarComputationsTest, MaxF32Above) { TestMinMax(10.1f, 3.1f, 10.1f, &ComputationBuilder::Max); } @@ -868,6 +874,12 @@ XLA_TEST_F(ScalarComputationsTest, MaxF32Below) { TestMinMax(-100.1f, 3.1f, 3.1f, &ComputationBuilder::Max); } +XLA_TEST_F(ScalarComputationsTest, MaxPropagatesNan) { + SetFastMathDisabled(true); + TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Max); + TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Max); +} + XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { // Compute the expression (1 * (3 - 1) * (7 + 0) - 4) / 20. ComputationBuilder b(client_, TestName()); diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc index ac163df127e0087c02777fa3d5ce7970c51b97b9..fe36df160daacc4fdfbdb0b75f8304f91e1a4245 100644 --- a/tensorflow/compiler/xla/tests/slice_test.cc +++ b/tensorflow/compiler/xla/tests/slice_test.cc @@ -237,6 +237,12 @@ INSTANTIATE_TEST_CASE_P( SliceR1TestInstantiation, SliceR1Test, ::testing::Values( +// TODO(b/69425338): This uses too much memory on GPU. +#ifndef XLA_TEST_BACKEND_GPU + R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024, 12 * 1024 * 1024, 1}, + R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 + 1, 12 * 1024 * 1024 - 1, 1}, + R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 - 1, 12 * 1024 * 1024 + 1, 1}, +#endif R1Spec{10, 0, 0, 1}, R1Spec{10, 7, 7, 1}, R1Spec{10, 0, 5, 1}, @@ -267,13 +273,15 @@ INSTANTIATE_TEST_CASE_P( R1Spec{64 * 1024, 1024 + 1, 63 * 1024 - 1, 1}, R1Spec{64 * 1024, 32 * 1024, 33 * 1024, 1}, R1Spec{64 * 1024, 32 * 1024 + 1, 33 * 1024 - 1, 1}, - R1Spec{64 * 1024, 32 * 1024 - 17, 36 * 1024 - 18, 1}, -// TODO(b/69425338): This uses too much memory on GPU. -#ifndef XLA_TEST_BACKEND_GPU - R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024, 12 * 1024 * 1024, 1}, - R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 + 1, 12 * 1024 * 1024 - 1, 1}, - R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 - 1, 12 * 1024 * 1024 + 1, 1}, -#endif + R1Spec{64 * 1024, 32 * 1024 - 17, 36 * 1024 - 18, 1} + ), + SliceR1TestDataToString +); + +INSTANTIATE_TEST_CASE_P( + SliceStridedR1TestInstantiation, + SliceR1Test, + ::testing::Values( R1Spec{10, 2, 4, 2}, R1Spec{10, 0, 10, 2}, R1Spec{10, 0, 10, 3}, @@ -285,8 +293,24 @@ INSTANTIATE_TEST_CASE_P( R1Spec{2047, 1024 - 24, 1024 + 160, 31}, R1Spec{2047, 1, 2046, 3 * 128}, R1Spec{4096, 1024 + 3, 4095, 500}, - R1Spec{8192, 0, 8192, 1024 * 3 + 400} - ), + R1Spec{8192, 0, 8192, 1024 * 3 + 400}, + R1Spec{1024 * 1024, 0, 1024 * 1024, 2}, + R1Spec{1024 * 1024, 0, 1024 * 1024, 8}, + R1Spec{1024 * 1024, 0, 1024 * 1024, 7}, + R1Spec{1024 * 1024, 0, 1024 * 1024, 125}, + R1Spec{1024 * 1024, 3, 1024 - 9, 2}, + R1Spec{1024 * 1024, 3, 1024 - 9, 8}, + R1Spec{1024 * 1024, 3, 1024 - 9, 7}, + R1Spec{1024 * 1024, 3, 1024 - 9, 125}, + R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 2}, + R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 8}, + R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 7}, + R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 125}, + R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 2}, + R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 8}, + R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 7}, + R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 125} + ), SliceR1TestDataToString ); // clang-format on diff --git a/tensorflow/compiler/xla/tests/test_macros.h b/tensorflow/compiler/xla/tests/test_macros.h index cc4eaf62f50d1fa622c705fab810fe1e1b0fbf08..e2d406f66d94f8ec76faa5b7d2d2e84dcaf6db57 100644 --- a/tensorflow/compiler/xla/tests/test_macros.h +++ b/tensorflow/compiler/xla/tests/test_macros.h @@ -161,4 +161,31 @@ string PrependDisabledIfIndicated(const string& test_case_name, #define XLA_TEST_P(test_case_name, test_name) \ XLA_TEST_P_IMPL_(test_case_name, test_name) + +// This is identical to the TEST_F macro from "gtest", but it potentially +// disables the test based on an external manifest file, DISABLED_MANIFEST. +#define XLA_TYPED_TEST(CaseName, TestName) \ + template \ + class GTEST_TEST_CLASS_NAME_(CaseName, TestName) \ + : public CaseName { \ + private: \ + typedef CaseName TestFixture; \ + typedef gtest_TypeParam_ TypeParam; \ + virtual void TestBody(); \ + }; \ + bool gtest_##CaseName##_##TestName##_registered_ GTEST_ATTRIBUTE_UNUSED_ = \ + ::testing::internal::TypeParameterizedTest< \ + CaseName, \ + ::testing::internal::TemplateSel, \ + GTEST_TYPE_PARAMS_(CaseName)>:: \ + Register( \ + "", ::testing::internal::CodeLocation(__FILE__, __LINE__), \ + #CaseName, \ + ::xla::PrependDisabledIfIndicated(#CaseName, #TestName).c_str(), \ + 0); \ + template \ + void GTEST_TEST_CLASS_NAME_(CaseName, \ + TestName)::TestBody() + #endif // TENSORFLOW_COMPILER_XLA_TESTS_TEST_MACROS_H_ diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc index b060fb13b1451aab30cfca73bea0a4a598a9fa3a..0bc7df2a65b44a76f877b6513e6bf93b99fbc1a3 100644 --- a/tensorflow/compiler/xla/tests/test_utils.cc +++ b/tensorflow/compiler/xla/tests/test_utils.cc @@ -287,7 +287,7 @@ StatusOr> MakeFakeLiteral(const Shape& shape) { StatusOr>> MakeFakeArguments( HloModule* const module) { - TF_ASSIGN_OR_RETURN(auto dataflow, HloDataflowAnalysis::Run(module)); + TF_ASSIGN_OR_RETURN(auto dataflow, HloDataflowAnalysis::Run(*module)); const auto params = module->entry_computation()->parameter_instructions(); std::minstd_rand0 engine; std::vector> arguments(params.size()); diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index a8bca70d85ddf168bc441231d6f43bead019b10a..2029312f94a14bc81706368b9ecfc2727fd9fe4c 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -194,8 +194,7 @@ XLA_TEST_F(TupleTest, TupleGTEToTuple) { ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } -// TODO(b/68395210): GPU does not tolerate ambiguous top-level buffers. -XLA_TEST_F(TupleTest, DISABLED_ON_GPU(SelectBetweenPredTuples)) { +XLA_TEST_F(TupleTest, SelectBetweenPredTuples) { ComputationBuilder b(client_, TestName()); ComputationDataHandle v1, v2; diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index 52157b837c383205f77a030ef98b2fd03a41aff5..33d457c70bac84c2da10e3cf9302c2c952cf1bc2 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -910,7 +910,7 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) { // Per backend the values generated can be different as the different backends // use different random number generators. // TODO(b/32240857): Extend test to verify outputs. -TEST_F(WhileTest, WhileWithPrngScalarResult) { +TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) { auto v6s32 = ShapeUtil::MakeShape(S32, {6}); // Create a computation for the condition: repeat for count iterations. @@ -1166,7 +1166,7 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { // while (f(result).get<0>()) { // result = result + 1; // } -TEST_F(WhileTest, WhileWithCallInsideCondition) { +TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) { auto result_shape = ShapeUtil::MakeShape(S32, {}); // Create a computation for the condition: repeat for 5 iterations. diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index d9c4d094b8ea3b4de9164f35d670fc686c921a27..e60a5a4919f2207939821e787c3c59a08ff3ba4e 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -220,10 +220,13 @@ class HloParser { bool AddComputation(const string& name, HloComputation* computation, LocTy name_loc); - // The map from the instruction name to the instruction. This does not own the - // instructions. - std::unordered_map instruction_pool_; - std::unordered_map computation_pool_; + // The map from the instruction/computation name to the + // instruction/computation itself and it's location. This does not own the + // pointers. + std::unordered_map> + instruction_pool_; + std::unordered_map> + computation_pool_; HloLexer lexer_; std::unique_ptr module_; @@ -340,15 +343,16 @@ bool HloParser::ParseComputation(HloComputation** entry_computation) { return false; } - HloInstruction* root = - tensorflow::gtl::FindPtrOrNull(instruction_pool_, root_name); + std::pair* root_node = + tensorflow::gtl::FindOrNull(instruction_pool_, root_name); // This means some instruction was marked as ROOT but we didn't find it in the // pool, which should not happen. - if (!root_name.empty() && root == nullptr) { + if (!root_name.empty() && root_node == nullptr) { LOG(FATAL) << "instruction " << root_name << " was marked as ROOT but the parser has not seen it before"; } + HloInstruction* root = root_node == nullptr ? nullptr : root_node->first; // Now root can be either an existing instruction or a nullptr. If it's a // nullptr, the implementation of Builder will set the last instruction as // root instruction. @@ -990,6 +994,20 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, shape, operands, *custom_call_target)); break; } + case HloOpcode::kHostCompute: { + optional channel_name; + optional cost_estimate_ns; + attrs["channel_name"] = {/*required=*/true, AttrTy::kString, + &channel_name}; + attrs["cost_estimate_ns"] = {/*required=*/true, AttrTy::kInt64, + &cost_estimate_ns}; + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { + return false; + } + instruction = builder->AddInstruction(HloInstruction::CreateHostCompute( + shape, operands, *channel_name, *cost_estimate_ns)); + break; + } case HloOpcode::kDot: { optional> lhs_contracting_dims; attrs["lhs_contracting_dims"] = { @@ -1031,6 +1049,40 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloInstruction::CreateDot(shape, operands[0], operands[1], dnum)); break; } + case HloOpcode::kGather: { + optional> output_window_dims; + attrs["output_window_dims"] = { + /*required=*/true, AttrTy::kBracedInt64List, &output_window_dims}; + optional> elided_window_dims; + attrs["elided_window_dims"] = { + /*required=*/true, AttrTy::kBracedInt64List, &elided_window_dims}; + optional> gather_dims_to_operand_dims; + attrs["gather_dims_to_operand_dims"] = {/*required=*/true, + AttrTy::kBracedInt64List, + &gather_dims_to_operand_dims}; + optional index_vector_dim; + attrs["index_vector_dim"] = {/*required=*/true, AttrTy::kInt64, + &index_vector_dim}; + optional> window_bounds; + attrs["window_bounds"] = {/*required=*/true, AttrTy::kBracedInt64List, + &window_bounds}; + + if (!ParseOperands(&operands, /*expected_size=*/2) || + !ParseAttributes(attrs)) { + return false; + } + + GatherDimensionNumbers dim_numbers = HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/*output_window_dims, + /*elided_window_dims=*/*elided_window_dims, + /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims, + /*index_vector_dim=*/*index_vector_dim); + + instruction = builder->AddInstruction(HloInstruction::CreateGather( + shape, /*operand=*/operands[0], /*gather_indices=*/operands[1], + dim_numbers, *window_bounds)); + break; + } case HloOpcode::kTrace: return TokenError(StrCat("parsing not yet implemented for op: ", HloOpcodeString(opcode))); @@ -1229,13 +1281,13 @@ bool HloParser::ParseInstructionNames( if (!ParseName(&name)) { return Error(loc, "expects a instruction name"); } - HloInstruction* instr = - tensorflow::gtl::FindPtrOrNull(instruction_pool_, name); + std::pair* instr = + tensorflow::gtl::FindOrNull(instruction_pool_, name); if (!instr) { return TokenError( Printf("instruction '%s' is not defined", name.c_str())); } - instructions->push_back(instr); + instructions->push_back(instr->first); } while (EatIfPresent(TokKind::kComma)); return ParseToken(TokKind::kRbrace, @@ -1705,12 +1757,12 @@ bool HloParser::ParseOperands(std::vector* operands) { if (!ParseName(&name)) { return false; } - HloInstruction* instruction = - tensorflow::gtl::FindPtrOrNull(instruction_pool_, name); + std::pair* instruction = + tensorflow::gtl::FindOrNull(instruction_pool_, name); if (!instruction) { return Error(loc, StrCat("instruction does not exist: ", name)); } - operands->push_back(instruction); + operands->push_back(instruction->first); } while (EatIfPresent(TokKind::kComma)); } return ParseToken(TokKind::kRparen, "expects ')' at the end of operands"); @@ -1957,10 +2009,12 @@ bool HloParser::ParseComputationName(HloComputation** value) { if (!ParseName(&name)) { return Error(loc, "expects computation name"); } - *value = tensorflow::gtl::FindPtrOrNull(computation_pool_, name); - if (*value == nullptr) { + std::pair* computation = + tensorflow::gtl::FindOrNull(computation_pool_, name); + if (computation == nullptr) { return Error(loc, StrCat("computation does not exist: ", name)); } + *value = computation->first; return true; } @@ -2576,18 +2630,22 @@ bool HloParser::EatIfPresent(TokKind kind) { bool HloParser::AddInstruction(const string& name, HloInstruction* instruction, LocTy name_loc) { - auto result = instruction_pool_.insert({name, instruction}); + auto result = instruction_pool_.insert({name, {instruction, name_loc}}); if (!result.second) { - return Error(name_loc, StrCat("instruction already exists: ", name)); + Error(name_loc, StrCat("instruction already exists: ", name)); + return Error(/*loc=*/result.first->second.second, + "instruction previously defined here"); } return true; } bool HloParser::AddComputation(const string& name, HloComputation* computation, LocTy name_loc) { - auto result = computation_pool_.insert({name, computation}); + auto result = computation_pool_.insert({name, {computation, name_loc}}); if (!result.second) { - return Error(name_loc, StrCat("computation already exists: ", name)); + Error(name_loc, StrCat("computation already exists: ", name)); + return Error(/*loc=*/result.first->second.second, + "computation previously defined here"); } return true; } diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc index dd76d8d0fee7cdfa22829fe92ff889e44157216e..863081d654390440aa6506bab4576b3cc5c1cbd1 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc @@ -716,6 +716,18 @@ ENTRY %sparse_f32_r1 () -> f32[9] { ROOT %foo = f32[9]sparse{10} constant(f32[9]{1: 2, 3: 4, 5: 6}) } +)" +}, +{ +"gather", +R"(HloModule StringifyGather + +ENTRY %Gather (input_tensor: f32[50,49,48,47,46], gather_indices: s64[10,9,8,7,5]) -> f32[10,9,8,7,30,29,28,27,26] { + %input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0) + %gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1) + ROOT %gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26} +} + )" }, }); @@ -860,6 +872,18 @@ ENTRY dot { ROOT dot = f32[2,3]{1,0} dot(a, b), lhs_batch_dims={0}, lhs_contracting_dims={1}, rhs_contracting_dims={0} } +)" +}, +{ +"gather", +R"(HloModule gather + +ENTRY Gather { + input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0) + gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1) + ROOT gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(input_tensor, gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26} +} + )" }, }); @@ -1275,6 +1299,35 @@ ENTRY consts { "one computation should have only one ROOT"); } +TEST_F(HloParserTest, InstructionExists) { + const string original = R"(HloModule comp_exists +c1 { + instr = f32[1]{0} constant({12345}) +} +c2 { + instr = f32[1]{0} constant({67890}) +})"; + + ExpectHasSubstr(Parse(original).status().error_message(), + R"(was parsing 3:3: error: instruction previously defined here + instr = f32[1]{0} constant({12345}) + ^)"); +} + +TEST_F(HloParserTest, ComputationExists) { + const string original = R"(HloModule comp_exists +comp { + const1 = f32[1]{0} constant({12345}) +} +comp { + const2 = f32[1]{0} constant({67890}) +})"; + ExpectHasSubstr(Parse(original).status().error_message(), + R"(was parsing 2:1: error: computation previously defined here +comp { +^)"); +} + } // namespace } // namespace tools } // namespace xla diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index 08df5b12b3a53a138f56705531baa3333b23c5d8..98467cd6509b07e88616c3698e11075308cba37a 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -427,32 +427,78 @@ std::vector> CommonFactors( string SanitizeFileName(string file_name); template -bool c_all_of(Container container, Predicate predicate) { - return std::all_of(std::begin(container), std::end(container), predicate); +bool c_all_of(const Container& container, Predicate&& predicate) { + return std::all_of(std::begin(container), std::end(container), + std::forward(predicate)); +} + +template +bool c_any_of(const Container& container, Predicate&& predicate) { + return std::any_of(std::begin(container), std::end(container), + std::forward(predicate)); } template -OutputIterator c_transform(InputContainer input_container, +OutputIterator c_transform(const InputContainer& input_container, OutputIterator output_iterator, - UnaryOperation unary_op) { + UnaryOperation&& unary_op) { return std::transform(std::begin(input_container), std::end(input_container), - output_iterator, unary_op); + output_iterator, + std::forward(unary_op)); } template -OutputIterator c_copy_if(InputContainer input_container, +OutputIterator c_copy_if(const InputContainer& input_container, OutputIterator output_iterator, - UnaryPredicate predicate) { + UnaryPredicate&& predicate) { return std::copy_if(std::begin(input_container), std::end(input_container), - output_iterator, predicate); + output_iterator, std::forward(predicate)); +} + +template +OutputIterator c_copy(const InputContainer& input_container, + OutputIterator output_iterator) { + return std::copy(std::begin(input_container), std::end(input_container), + output_iterator); +} + +template +void c_sort(InputContainer& input_container) { + std::sort(std::begin(input_container), std::end(input_container)); } template -void c_sort(InputContainer& input_container, Comparator comparator) { - std::sort(input_container.begin(), input_container.end(), comparator); +void c_sort(InputContainer& input_container, Comparator&& comparator) { + std::sort(std::begin(input_container), std::end(input_container), + std::forward(comparator)); +} + +template +bool c_binary_search(const Sequence& sequence, T&& value) { + return std::binary_search(std::begin(sequence), std::end(sequence), + std::forward(value)); +} + +template +bool c_is_sorted(const C& c) { + return std::is_sorted(std::begin(c), std::end(c)); +} + +template +auto c_adjacent_find(const C& c) -> decltype(std::begin(c)) { + return std::adjacent_find(std::begin(c), std::end(c)); +} + +template +auto c_find_if(const C& c, Pred&& pred) -> decltype(std::begin(c)) { + return std::find_if(std::begin(c), std::end(c), std::forward(pred)); } +template +auto c_find(const C& c, Value&& value) -> decltype(std::begin(c)) { + return std::find(std::begin(c), std::end(c), std::forward(value)); +} } // namespace xla #define XLA_LOG_LINES(SEV, STRING) \ diff --git a/tensorflow/compiler/xla/window_util.cc b/tensorflow/compiler/xla/window_util.cc index 55f42ed3a454baa3f8b6adf60a78582488733e9b..93284b80f9e1f82c4b18dc7388754d5c01a7740c 100644 --- a/tensorflow/compiler/xla/window_util.cc +++ b/tensorflow/compiler/xla/window_util.cc @@ -32,6 +32,8 @@ Window MakeWindow(tensorflow::gtl::ArraySlice sizes) { auto* dimension = window.add_dimensions(); dimension->set_size(size); dimension->set_stride(1); + dimension->set_base_dilation(1); + dimension->set_window_dilation(1); } return window; } diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 3aea0217539b89b5d60ecfaf2605eee4b69af728..1f16e6d25178fd9c10a30b0c500e090ee2e08117 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -393,6 +393,37 @@ message Window { repeated WindowDimension dimensions = 1; } +// Describes the dimension numbers for a gather operation. +// +// See https://www.tensorflow.org/performance/xla/operation_semantics#gather for +// more details. +message GatherDimensionNumbers { + // "Window indices" is a term for a set of indices that index into the + // interior of a dynamic-slice from the input tensor, the starting indices for + // which were computed from output_gather_dims (see the operation semantic for + // how this is defined) and the gather_indices tensor. + // + // The window indices for a specific output index Out is computed as: + // + // i = 0 + // for (k : [0, input_tensor_shape.rank)) + // window_indices[k] = + // if k in elided_window_dims + // then 0 + // else Out[output_window_dims[i++]] + repeated int64 output_window_dims = 1; + repeated int64 elided_window_dims = 2; + + // This is interpreted as a map from i to gather_dims_to_operand_dims[i]. It + // transforms the gather index looked up from the gather_indices tensor into + // the starting index in the input space. + repeated int64 gather_dims_to_operand_dims = 3; + + // The dimension in the gather_indices input that contains the starting + // indices. + int64 index_vector_dim = 4; +} + // Operation requests that are all collected as a tagged union with a oneof // field in OpRequest. @@ -519,6 +550,20 @@ message CustomCallRequest { Shape shape = 4; } +message HostComputeRequest { + // Operand to the HostCompute. Supports tuple. + repeated ComputationDataHandle operands = 1; + + // Name used to identify HostSend/Recv channels. + string channel_name = 2; + + // Cost estimate in nanoseconds. + int64 cost_estimate_ns = 3; + + // The shape of any data returned by host. + Shape shape = 4; +} + message DotDimensionNumbers { // The dimension numbers that represent the 'lhs' contracting dimensions. repeated int64 lhs_contracting_dimensions = 1; @@ -880,6 +925,13 @@ message RecvRequest { ChannelHandle channel_handle = 2; } +message GatherRequest { + ComputationDataHandle input = 1; + ComputationDataHandle gather_indices = 2; + GatherDimensionNumbers dimension_numbers = 3; + repeated int64 window_bounds = 4; +} + message OpSharding { enum Type { // This sharding is replicated across all devices (implies maximal, @@ -957,7 +1009,9 @@ message OpRequest { FftRequest fft_request = 41; ConvertRequest bitcast_convert_request = 42; ConditionalRequest conditional_request = 44; - // Next: 45 + HostComputeRequest host_compute_request = 45; + GatherRequest gather_request = 46; + // Next: 47 } } diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 1c497c666bd73e0ae39b750307c25e3b28bfaf2d..c2663c5e83352a1088166dc7581a0346c7b104a4 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -7,6 +7,8 @@ package(default_visibility = ["//tensorflow:__subpackages__"]) load("//third_party/mpi:mpi.bzl", "if_mpi") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") +load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") +load("//tensorflow:tensorflow.bzl", "if_not_windows") py_library( name = "contrib_py", @@ -37,7 +39,7 @@ py_library( "//tensorflow/contrib/eager/python:tfe", "//tensorflow/contrib/estimator:estimator_py", "//tensorflow/contrib/factorization:factorization_py", - "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", + "//tensorflow/contrib/feature_column:feature_column_py", "//tensorflow/contrib/framework:framework_py", "//tensorflow/contrib/fused_conv:fused_conv_py", "//tensorflow/contrib/gan", @@ -49,7 +51,6 @@ py_library( "//tensorflow/contrib/image:single_image_random_dot_stereograms_py", "//tensorflow/contrib/input_pipeline:input_pipeline_py", "//tensorflow/contrib/integrate:integrate_py", - "//tensorflow/contrib/kafka", "//tensorflow/contrib/keras", "//tensorflow/contrib/kernel_methods", "//tensorflow/contrib/kfac", @@ -61,7 +62,6 @@ py_library( "//tensorflow/contrib/linalg:linalg_py", "//tensorflow/contrib/linear_optimizer:sdca_estimator_py", "//tensorflow/contrib/linear_optimizer:sdca_ops_py", - "//tensorflow/contrib/lite/python:lite", "//tensorflow/contrib/lookup:lookup_py", "//tensorflow/contrib/losses:losses_py", "//tensorflow/contrib/losses:metric_learning_py", @@ -70,7 +70,6 @@ py_library( "//tensorflow/contrib/metrics:metrics_py", "//tensorflow/contrib/model_pruning", "//tensorflow/contrib/nccl:nccl_py", - "//tensorflow/contrib/ndlstm", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_py", "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/opt:opt_py", @@ -107,7 +106,13 @@ py_library( "//tensorflow/contrib/training:training_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:util", - ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]), + ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([ + "//tensorflow/contrib/tensorrt:init_py", + ]) + if_not_windows([ + "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", # unix dependency, need to fix code + "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code + "//tensorflow/contrib/kafka", # has some linking issue on opensssl. + ]), ) cc_library( @@ -117,6 +122,8 @@ cc_library( "//tensorflow/contrib/boosted_trees:boosted_trees_kernels", "//tensorflow/contrib/coder:all_kernels", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_kernels", + "//tensorflow/contrib/data/kernels:dataset_kernels", + "//tensorflow/contrib/kafka:dataset_kernels", "//tensorflow/contrib/factorization/kernels:all_kernels", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_kernels", "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel", @@ -139,10 +146,11 @@ cc_library( "//tensorflow/contrib/boosted_trees:boosted_trees_ops_op_lib", "//tensorflow/contrib/coder:all_ops", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_ops_op_lib", + "//tensorflow/contrib/data:dataset_ops_op_lib", "//tensorflow/contrib/factorization:all_ops", "//tensorflow/contrib/framework:all_ops", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib", - "//tensorflow/contrib/kafka:kafka_ops_op_lib", + "//tensorflow/contrib/kafka:dataset_ops_op_lib", "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib", "//tensorflow/contrib/nccl:nccl_ops_op_lib", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_ops_op_lib", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 8f6a3cb1ca4544cae6f42fd1727d509af9fc0233..669d611b01b585d91ab48921b7ba17703dd6bc98 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + # Add projects here, they will show up under tf.contrib. from tensorflow.contrib import batching from tensorflow.contrib import bayesflow @@ -33,6 +35,7 @@ from tensorflow.contrib import deprecated from tensorflow.contrib import distributions from tensorflow.contrib import estimator from tensorflow.contrib import factorization +from tensorflow.contrib import feature_column from tensorflow.contrib import framework from tensorflow.contrib import gan from tensorflow.contrib import graph_editor @@ -82,8 +85,8 @@ from tensorflow.contrib import tpu from tensorflow.contrib import training from tensorflow.contrib import util from tensorflow.contrib.eager.python import tfe as eager -from tensorflow.contrib.lite.python import lite -from tensorflow.contrib.ndlstm import python as ndlstm +if os.name != 'nt': + from tensorflow.contrib.lite.python import lite from tensorflow.contrib.receptive_field import receptive_field_api as receptive_field from tensorflow.contrib.remote_fused_graph import pylib as remote_fused_graph from tensorflow.contrib.specs import python as specs @@ -92,6 +95,7 @@ from tensorflow.contrib.summary import summary from tensorflow.python.util.lazy_loader import LazyLoader ffmpeg = LazyLoader("ffmpeg", globals(), "tensorflow.contrib.ffmpeg") +del os del LazyLoader del absolute_import diff --git a/tensorflow/contrib/all_reduce/python/all_reduce.py b/tensorflow/contrib/all_reduce/python/all_reduce.py index 28f60b34996945d573facc665c01d0bc10cf5cd1..6658f0d9c13f6db17b25354cde2593d57f104f17 100644 --- a/tensorflow/contrib/all_reduce/python/all_reduce.py +++ b/tensorflow/contrib/all_reduce/python/all_reduce.py @@ -48,7 +48,7 @@ def _flatten_tensors(tensors): if shape.ndims is None: raise ValueError("At least one of the tensors in 'tensors' must have " "statically known rank.") - if len(shape) > 1: + if len(shape) != 1: reshaped = [] for t in tensors: with ops.colocate_with(t): @@ -289,7 +289,7 @@ def build_ring_all_reduce(input_tensors, num_workers, num_subchunks, chunks_by_dev) if pad_len > 0: output_tensors = _strip_padding(output_tensors, pad_len) - if len(shape) > 1: + if len(shape) != 1: output_tensors = _reshape_tensors(output_tensors, shape) return output_tensors @@ -466,7 +466,7 @@ def build_recursive_hd_all_reduce(input_tensors, red_op, un_op=None): if un_op: reduced_shards = [un_op(t) for t in reduced_shards] output_tensors = _build_recursive_hd_scatter(reduced_shards, devices) - if len(shape) > 1: + if len(shape) != 1: output_tensors = _reshape_tensors(output_tensors, shape) return output_tensors @@ -578,7 +578,7 @@ def build_shuffle_all_reduce(input_tensors, gather_devices, red_op, un_op=None): reduced_shards = _build_shuffle_gather(input_tensors, gather_devices, red_op, un_op) output_tensors = _build_shuffle_scatter(reduced_shards, dst_devices) - if len(shape) > 1: + if len(shape) != 1: output_tensors = _reshape_tensors(output_tensors, shape) return output_tensors @@ -752,13 +752,13 @@ def _build_nccl_hybrid(input_tensors, red_op, upper_level_f): dst_tensors.append(array_ops.identity(broadcast_src)) down_values[w] = dst_tensors output_tensors = [v for sublist in down_values for v in sublist] - if len(shape) > 1: + if len(shape) != 1: output_tensors = _reshape_tensors(output_tensors, shape) return output_tensors def _reduce_non_singleton(input_tensors, red_f, un_op): - """If input_tenors has more than one element apply red_f, else apply un_op.""" + """If input_tensors has more than one element apply red_f, else apply un_op.""" if len(input_tensors) > 1: return red_f(input_tensors) else: @@ -831,7 +831,7 @@ def _build_shuffle_hybrid(input_tensors, gather_devices, red_op, upper_level_f): for w in range(0, num_workers): output_tensors += _build_shuffle_scatter( [level_2_output[w]], per_worker_devices[w]) - if len(shape) > 1: + if len(shape) != 1: output_tensors = _reshape_tensors(output_tensors, shape) return output_tensors diff --git a/tensorflow/contrib/all_reduce/python/all_reduce_test.py b/tensorflow/contrib/all_reduce/python/all_reduce_test.py index 0802b2736909c2a6f075ea2eac6d4dd3ab2918d8..47bab0a3670a90644972b2c961954a3036b8ecba 100644 --- a/tensorflow/contrib/all_reduce/python/all_reduce_test.py +++ b/tensorflow/contrib/all_reduce/python/all_reduce_test.py @@ -119,7 +119,7 @@ class AllReduceTest(test_util.TensorFlowTestCase): def _buildInitialVars(self, shape, dev_list): values = [] num_devices = len(dev_list) - dim = np.prod(shape) + dim = np.prod(shape) if shape else 1 for d in range(0, num_devices): with ops.device(dev_list[d]): npt = np.zeros(shape).astype(np.float32) @@ -164,6 +164,7 @@ class AllReduceTest(test_util.TensorFlowTestCase): (num_workers, num_gpus, shape, subdiv, elapsed)) def testRingAllReduce(self): + self._testRingAllReduce(1, 2, [], 1) self._testRingAllReduce(1, 2, [8], 1) self._testRingAllReduce(1, 2, [4, 4], 1) self._testRingAllReduce(6, 1, [8], 1) @@ -192,6 +193,7 @@ class AllReduceTest(test_util.TensorFlowTestCase): "elapsed=%f" % (num_workers, num_gpus, shape, elapsed)) def testShuffleAllReduce(self): + self._testShuffleAllReduce(1, 2, [], 1) self._testShuffleAllReduce(1, 2, [8], 1) self._testShuffleAllReduce(1, 2, [4, 4], 1) self._testShuffleAllReduce(1, 8, [32], 1) diff --git a/tensorflow/contrib/android/README.md b/tensorflow/contrib/android/README.md index b8d73bf24ce60e0b3850d4f39ac9e6d6c2194a02..db37bcf73d144eb81c32a461a276d10be7e2d193 100644 --- a/tensorflow/contrib/android/README.md +++ b/tensorflow/contrib/android/README.md @@ -81,6 +81,11 @@ For documentation on building a self-contained AAR file with cmake, see [tensorflow/contrib/android/cmake](cmake). +### Makefile + +For documentation on building native TF libraries with make, including a CUDA-enabled variant for devices like the Nvidia Shield TV, see [tensorflow/contrib/makefile/README.md](../makefile/README.md) + + ## AssetManagerFileSystem This directory also contains a TensorFlow filesystem supporting the Android diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java index e51e3f747b57cf1c9fd79ee5cc9fdb8acee349c9..abddadac5bcace9b1f992b69bdcc69c24b29cd13 100644 --- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java +++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java @@ -197,9 +197,7 @@ public class TensorFlowInferenceInterface { run(outputNames, enableStats, new String[] {}); } - /** - * An overloaded version of runInference that allows supplying targetNodeNames as well - */ + /** An overloaded version of runInference that allows supplying targetNodeNames as well */ public void run(String[] outputNames, boolean enableStats, String[] targetNodeNames) { // Release any Tensors from the previous run calls. closeFetches(); @@ -211,7 +209,7 @@ public class TensorFlowInferenceInterface { runner.fetch(tid.name, tid.outputIndex); } - // Add targets. + // Add targets. for (String t : targetNodeNames) { runner.addTarget(t); } diff --git a/tensorflow/contrib/batching/python/ops/batch_ops.py b/tensorflow/contrib/batching/python/ops/batch_ops.py index 4e0b3f9af989c414ad88c510c1bfd180dbadd5ea..921d6917a4e478c3e60771fdc3ae99febc33d2e3 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops.py @@ -53,10 +53,13 @@ def _UnbatchGrad(op, grad): # pylint: disable=invalid-name ] -def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, +def batch_function(num_batch_threads, + max_batch_size, + batch_timeout_micros, allowed_batch_sizes=None, grad_timeout_micros=60 * 1000 * 1000, - unbatch_timeout_micros=60 * 1000 * 1000): + unbatch_timeout_micros=60 * 1000 * 1000, + max_enqueued_batches=10): """Batches the computation done by the decorated function. So, for example, in the following code @@ -94,6 +97,7 @@ def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, documentation of the unbatch op for more details. Defaults to 60s. unbatch_timeout_micros: The timeout to use for unbatching. See the documentation of the unbatch op for more details. Defaults to 60s. + max_enqueued_batches: The maximum depth of the batch queue. Defaults to 10. Returns: The decorated function will return the unbatched computation output Tensors. @@ -111,6 +115,7 @@ def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, num_batch_threads=num_batch_threads, max_batch_size=max_batch_size, batch_timeout_micros=batch_timeout_micros, + max_enqueued_batches=max_enqueued_batches, allowed_batch_sizes=allowed_batch_sizes, grad_timeout_micros=grad_timeout_micros, shared_name=name) diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD index 6e0f0a05726a46b513a4270fd5843ff20fc95a18..e1b34d6debf730dcb4de426481eed15832a7f388 100644 --- a/tensorflow/contrib/bayesflow/BUILD +++ b/tensorflow/contrib/bayesflow/BUILD @@ -39,7 +39,7 @@ py_library( cuda_py_test( name = "metropolis_hastings_test", - size = "medium", + size = "large", srcs = ["python/kernel_tests/metropolis_hastings_test.py"], additional_deps = [ ":bayesflow_py", @@ -56,29 +56,6 @@ cuda_py_test( ], ) -cuda_py_test( - name = "csiszar_divergence_test", - size = "medium", - srcs = ["python/kernel_tests/csiszar_divergence_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], - tags = [ - "manual", # b/64490288 - "notap", - ], -) - cuda_py_test( name = "custom_grad_test", size = "small", @@ -99,44 +76,6 @@ cuda_py_test( ], ) -cuda_py_test( - name = "layers_conv_variational_test", - size = "small", - srcs = ["python/kernel_tests/layers_conv_variational_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], -) - -cuda_py_test( - name = "layers_dense_variational_test", - size = "small", - srcs = ["python/kernel_tests/layers_dense_variational_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], -) - cuda_py_test( name = "monte_carlo_test", size = "small", @@ -158,28 +97,9 @@ cuda_py_test( ], ) -cuda_py_test( - name = "halton_sequence_test", - size = "small", - srcs = ["python/kernel_tests/halton_sequence_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/python:array_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:platform_test", - "//tensorflow/python:random_ops", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - ], -) - cuda_py_test( name = "hmc_test", - size = "medium", + size = "large", srcs = ["python/kernel_tests/hmc_test.py"], additional_deps = [ ":bayesflow_py", @@ -196,6 +116,7 @@ cuda_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:random_seed", ], + tags = ["nomsan"], ) cuda_py_test( diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py index 95b9452b1ada60c44672f37800ced2133d2bd8b2..bff8ac2cf62128b478c1e9af9376e3cca496d7eb 100644 --- a/tensorflow/contrib/bayesflow/__init__.py +++ b/tensorflow/contrib/bayesflow/__init__.py @@ -21,11 +21,8 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import,line-too-long -from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence from tensorflow.contrib.bayesflow.python.ops import custom_grad -from tensorflow.contrib.bayesflow.python.ops import halton_sequence from tensorflow.contrib.bayesflow.python.ops import hmc -from tensorflow.contrib.bayesflow.python.ops import layers from tensorflow.contrib.bayesflow.python.ops import metropolis_hastings from tensorflow.contrib.bayesflow.python.ops import monte_carlo from tensorflow.contrib.bayesflow.python.ops import optimizers @@ -35,12 +32,9 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'csiszar_divergence', 'custom_grad', 'entropy', - 'halton_sequence', 'hmc', - 'layers', 'metropolis_hastings', 'monte_carlo', 'optimizers', diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py deleted file mode 100644 index 2e94b7206de4f7c40c89f083f3bfa2a22bb7b917..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py +++ /dev/null @@ -1,1004 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for Csiszar Divergence Ops.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence_impl -from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib -from tensorflow.contrib.distributions.python.ops import mvn_full_covariance as mvn_full_lib -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import linalg_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops.distributions import kullback_leibler -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.platform import test - - -cd = csiszar_divergence_impl - - -def tridiag(d, diag_value, offdiag_value): - """d x d matrix with given value on diag, and one super/sub diag.""" - diag_mat = linalg_ops.eye(d) * (diag_value - offdiag_value) - three_bands = array_ops.matrix_band_part( - array_ops.fill([d, d], offdiag_value), 1, 1) - return diag_mat + three_bands - - -class AmariAlphaTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for alpha in [-1., 0., 1., 2.]: - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.amari_alpha(0., alpha=alpha, - self_normalized=normalized).eval(), - 0.) - - def test_correct_when_alpha0(self): - with self.test_session(): - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=0.).eval(), - -self._logu) - - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=0., self_normalized=True).eval(), - -self._logu + (self._u - 1.)) - - def test_correct_when_alpha1(self): - with self.test_session(): - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=1.).eval(), - self._u * self._logu) - - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=1., self_normalized=True).eval(), - self._u * self._logu - (self._u - 1.)) - - def test_correct_when_alpha_not_01(self): - for alpha in [-2, -1., -0.5, 0.5, 2.]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.amari_alpha(self._logu, - alpha=alpha, - self_normalized=False).eval(), - ((self._u**alpha - 1)) / (alpha * (alpha - 1.))) - - self.assertAllClose( - cd.amari_alpha(self._logu, - alpha=alpha, - self_normalized=True).eval(), - ((self._u**alpha - 1.) - - alpha * (self._u - 1)) / (alpha * (alpha - 1.))) - - -class KLReverseTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.kl_reverse(0., self_normalized=normalized).eval(), - 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.kl_reverse(self._logu).eval(), - -self._logu) - - self.assertAllClose( - cd.kl_reverse(self._logu, self_normalized=True).eval(), - -self._logu + (self._u - 1.)) - - -class KLForwardTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.kl_forward(0., self_normalized=normalized).eval(), - 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.kl_forward(self._logu).eval(), - self._u * self._logu) - - self.assertAllClose( - cd.kl_forward(self._logu, self_normalized=True).eval(), - self._u * self._logu - (self._u - 1.)) - - -class JensenShannonTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.jensen_shannon(0.).eval(), np.log(0.25)) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.jensen_shannon(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.jensen_shannon).eval()) - - self.assertAllClose( - cd.jensen_shannon(self._logu, self_normalized=True).eval(), - cd.symmetrized_csiszar_function( - self._logu, - lambda x: cd.jensen_shannon(x, self_normalized=True)).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.jensen_shannon(self._logu).eval(), - (self._u * self._logu - - (1 + self._u) * np.log1p(self._u))) - - self.assertAllClose( - cd.jensen_shannon(self._logu, self_normalized=True).eval(), - (self._u * self._logu - - (1 + self._u) * np.log((1 + self._u) / 2))) - - -class ArithmeticGeometricMeanTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.arithmetic_geometric(0.).eval(), np.log(4)) - self.assertAllClose( - cd.arithmetic_geometric(0., self_normalized=True).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.arithmetic_geometric(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.arithmetic_geometric).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.arithmetic_geometric(self._logu).eval(), - (1. + self._u) * np.log((1. + self._u) / np.sqrt(self._u))) - - self.assertAllClose( - cd.arithmetic_geometric(self._logu, self_normalized=True).eval(), - (1. + self._u) * np.log(0.5 * (1. + self._u) / np.sqrt(self._u))) - - -class TotalVariationTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.total_variation(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.total_variation(self._logu).eval(), - 0.5 * np.abs(self._u - 1)) - - -class PearsonTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.pearson(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.pearson(self._logu).eval(), - np.square(self._u - 1)) - - -class SquaredHellingerTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.squared_hellinger(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.squared_hellinger(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.squared_hellinger).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.squared_hellinger(self._logu).eval(), - np.square(np.sqrt(self._u) - 1)) - - -class TriangularTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.triangular(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.triangular(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.triangular).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.triangular(self._logu).eval(), - np.square(self._u - 1) / (1 + self._u)) - - -class TPowerTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.t_power(0., t=-0.1).eval(), 0.) - self.assertAllClose(cd.t_power(0., t=0.5).eval(), 0.) - self.assertAllClose(cd.t_power(0., t=1.1).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=-0.1, self_normalized=True).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=0.5, self_normalized=True).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=1.1, self_normalized=True).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(-0.1)).eval(), - self._u ** -0.1 - 1.) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(0.5)).eval(), - -self._u ** 0.5 + 1.) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(1.1)).eval(), - self._u ** 1.1 - 1.) - - def test_correct_self_normalized(self): - with self.test_session(): - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(-0.1), - self_normalized=True).eval(), - self._u ** -0.1 - 1. + 0.1 * (self._u - 1.)) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(0.5), - self_normalized=True).eval(), - -self._u ** 0.5 + 1. + 0.5 * (self._u - 1.)) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(1.1), - self_normalized=True).eval(), - self._u ** 1.1 - 1. - 1.1 * (self._u - 1.)) - - -class Log1pAbsTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.log1p_abs(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.log1p_abs(self._logu).eval(), - self._u**(np.sign(self._u - 1)) - 1) - - -class JeffreysTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.jeffreys(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.jeffreys(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.jeffreys).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.jeffreys(self._logu).eval(), - 0.5 * (self._u * self._logu - self._logu)) - - -class ChiSquareTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.chi_square(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.chi_square(self._logu).eval(), - self._u**2 - 1) - - -class ModifiedGanTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose( - cd.modified_gan(0.).eval(), np.log(2)) - self.assertAllClose( - cd.modified_gan(0., self_normalized=True).eval(), np.log(2)) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.modified_gan(self._logu).eval(), - np.log1p(self._u) - self._logu) - - self.assertAllClose( - cd.modified_gan(self._logu, self_normalized=True).eval(), - np.log1p(self._u) - self._logu + 0.5 * (self._u - 1)) - - -class SymmetrizedCsiszarFunctionTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10., 100) - self._u = np.exp(self._logu) - - def test_jensen_shannon(self): - with self.test_session(): - - # The following functions come from the claim made in the - # symmetrized_csiszar_function docstring. - def js1(logu): - return (-logu - - (1. + math_ops.exp(logu)) * ( - nn_ops.softplus(logu))) - - def js2(logu): - return 2. * (math_ops.exp(logu) * ( - logu - nn_ops.softplus(logu))) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, js1).eval(), - cd.jensen_shannon(self._logu).eval()) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, js2).eval(), - cd.jensen_shannon(self._logu).eval()) - - def test_jeffreys(self): - with self.test_session(): - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, cd.kl_reverse).eval(), - cd.jeffreys(self._logu).eval()) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, cd.kl_forward).eval(), - cd.jeffreys(self._logu).eval()) - - -class DualCsiszarFunctionTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10., 100) - self._u = np.exp(self._logu) - - def test_kl_forward(self): - with self.test_session(): - self.assertAllClose( - cd.dual_csiszar_function(self._logu, cd.kl_forward).eval(), - cd.kl_reverse(self._logu).eval()) - - def test_kl_reverse(self): - with self.test_session(): - self.assertAllClose( - cd.dual_csiszar_function(self._logu, cd.kl_reverse).eval(), - cd.kl_forward(self._logu).eval()) - - -class MonteCarloCsiszarFDivergenceTest(test.TestCase): - - def test_kl_forward(self): - with self.test_session() as sess: - q = normal_lib.Normal( - loc=np.ones(6), - scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])) - - p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_forward, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_forward(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(p, q) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.08, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.02, atol=0.) - - def test_kl_reverse(self): - with self.test_session() as sess: - - q = normal_lib.Normal( - loc=np.ones(6), - scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])) - - p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.07, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.02, atol=0.) - - def test_kl_reverse_multidim(self): - - with self.test_session() as sess: - d = 5 # Dimension - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[0.5]*d) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.08, atol=0.) - - def test_kl_forward_multidim(self): - - with self.test_session() as sess: - d = 5 # Dimension - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[1.]*d) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_forward, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_forward(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(p, q) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.06, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.05, atol=0.) - - def test_score_trick(self): - - with self.test_session() as sess: - d = 5 # Dimension - num_draws = int(1e5) - seed = 1 - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - s = array_ops.constant(1.) - q = mvn_diag_lib.MultivariateNormalDiag( - scale_diag=array_ops.tile([s], [d])) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - seed=seed) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - seed=seed) - - approx_kl_score_trick = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - use_reparametrization=False, - seed=seed) - - approx_kl_self_normalized_score_trick = ( - cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - use_reparametrization=False, - seed=seed)) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0] - - [ - approx_kl_grad_, - approx_kl_self_normalized_grad_, - approx_kl_score_trick_grad_, - approx_kl_self_normalized_score_trick_grad_, - exact_kl_grad_, - approx_kl_, - approx_kl_self_normalized_, - approx_kl_score_trick_, - approx_kl_self_normalized_score_trick_, - exact_kl_, - ] = sess.run([ - grad_sum(approx_kl), - grad_sum(approx_kl_self_normalized), - grad_sum(approx_kl_score_trick), - grad_sum(approx_kl_self_normalized_score_trick), - grad_sum(exact_kl), - approx_kl, - approx_kl_self_normalized, - approx_kl_score_trick, - approx_kl_self_normalized_score_trick, - exact_kl, - ]) - - # Test average divergence. - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.08, atol=0.) - - self.assertAllClose(approx_kl_score_trick_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_, - rtol=0.08, atol=0.) - - # Test average gradient-divergence. - self.assertAllClose(approx_kl_grad_, exact_kl_grad_, - rtol=0.007, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_, - rtol=0.011, atol=0.) - - self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_, - rtol=0.018, atol=0.) - - self.assertAllClose( - approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_, - rtol=0.017, atol=0.) - - -class CsiszarVIMCOTest(test.TestCase): - - def _csiszar_vimco_helper(self, logu): - """Numpy implementation of `csiszar_vimco_helper`.""" - - # Since this is a naive/intuitive implementation, we compensate by using the - # highest precision we can. - logu = np.float128(logu) - n = logu.shape[0] - u = np.exp(logu) - loogeoavg_u = [] # Leave-one-out geometric-average of exp(logu). - for j in range(n): - loogeoavg_u.append(np.exp(np.mean( - [logu[i, ...] for i in range(n) if i != j], - axis=0))) - loogeoavg_u = np.array(loogeoavg_u) - - loosum_u = [] # Leave-one-out sum of exp(logu). - for j in range(n): - loosum_u.append(np.sum( - [u[i, ...] for i in range(n) if i != j], - axis=0)) - loosum_u = np.array(loosum_u) - - # Natural log of the average u except each is swapped-out for its - # leave-`i`-th-out Geometric average. - log_sooavg_u = np.log(loosum_u + loogeoavg_u) - np.log(n) - - log_avg_u = np.log(np.mean(u, axis=0)) - return log_avg_u, log_sooavg_u - - def _csiszar_vimco_helper_grad(self, logu, delta): - """Finite difference approximation of `grad(csiszar_vimco_helper, logu)`.""" - - # This code actually estimates the sum of the Jacobiab because that's what - # TF's `gradients` does. - np_log_avg_u1, np_log_sooavg_u1 = self._csiszar_vimco_helper( - logu[..., None] + np.diag([delta]*len(logu))) - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper( - logu[..., None]) - return [ - (np_log_avg_u1 - np_log_avg_u) / delta, - np.sum(np_log_sooavg_u1 - np_log_sooavg_u, axis=0) / delta, - ] - - def test_vimco_helper_1(self): - """Tests that function calculation correctly handles batches.""" - - logu = np.linspace(-100., 100., 100).reshape([10, 2, 5]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-8, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-8, atol=0.) - - def test_vimco_helper_2(self): - """Tests that function calculation correctly handles overflow.""" - - # Using 700 (rather than 1e3) since naive numpy version can't handle higher. - logu = np.float32([0., 700, -1, 1]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-6, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-5, atol=0.) - - def test_vimco_helper_3(self): - """Tests that function calculation correctly handles underlow.""" - - logu = np.float32([0., -1000, -1, 1]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-5, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-4, atol=1e-15) - - def test_vimco_helper_gradient_using_finite_difference_1(self): - """Tests that gradient calculation correctly handles batches.""" - - logu_ = np.linspace(-100., 100., 100).reshape([10, 2, 5]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - # We skip checking against finite-difference approximation since it - # doesn't support batches. - - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_helper_gradient_using_finite_difference_2(self): - """Tests that gradient calculation correctly handles overflow.""" - - delta = 1e-3 - logu_ = np.float32([0., 1000, -1, 1]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - [ - np_grad_log_avg_u, - np_grad_log_sooavg_u, - ] = self._csiszar_vimco_helper_grad(logu_, delta) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - self.assertAllClose(np_grad_log_avg_u, grad_log_avg_u, - rtol=delta, atol=0.) - self.assertAllClose(np_grad_log_sooavg_u, grad_log_sooavg_u, - rtol=delta, atol=0.) - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_helper_gradient_using_finite_difference_3(self): - """Tests that gradient calculation correctly handles underlow.""" - - delta = 1e-3 - logu_ = np.float32([0., -1000, -1, 1]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - [ - np_grad_log_avg_u, - np_grad_log_sooavg_u, - ] = self._csiszar_vimco_helper_grad(logu_, delta) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - self.assertAllClose(np_grad_log_avg_u, grad_log_avg_u, - rtol=delta, atol=0.) - self.assertAllClose(np_grad_log_sooavg_u, grad_log_sooavg_u, - rtol=delta, atol=0.) - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_and_gradient(self): - - with self.test_session() as sess: - dims = 5 # Dimension - num_draws = int(20) - num_batch_draws = int(3) - seed = 1 - - f = lambda logu: cd.kl_reverse(logu, self_normalized=False) - np_f = lambda logu: -logu - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(dims, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - s = array_ops.constant(1.) - q = mvn_diag_lib.MultivariateNormalDiag( - scale_diag=array_ops.tile([s], [dims])) - - vimco = cd.csiszar_vimco( - f=f, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - num_batch_draws=num_batch_draws, - seed=seed) - - x = q.sample(sample_shape=[num_draws, num_batch_draws], - seed=seed) - x = array_ops.stop_gradient(x) - logu = p.log_prob(x) - q.log_prob(x) - f_log_sum_u = f(cd.csiszar_vimco_helper(logu)[0]) - - grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0] - - def jacobian(x): - # Warning: this function is slow and may not even finish if prod(shape) - # is larger than, say, 100. - shape = x.shape.as_list() - assert all(s is not None for s in shape) - x = array_ops.reshape(x, shape=[-1]) - r = [grad_sum(x[i]) for i in range(np.prod(shape))] - return array_ops.reshape(array_ops.stack(r), shape=shape) - - [ - logu_, - jacobian_logqx_, - vimco_, - grad_vimco_, - f_log_sum_u_, - grad_mean_f_log_sum_u_, - ] = sess.run([ - logu, - jacobian(q.log_prob(x)), - vimco, - grad_sum(vimco), - f_log_sum_u, - grad_sum(f_log_sum_u) / num_batch_draws, - ]) - - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu_) - - # Test VIMCO loss is correct. - self.assertAllClose(np_f(np_log_avg_u).mean(axis=0), vimco_, - rtol=1e-5, atol=0.) - - # Test gradient of VIMCO loss is correct. - # - # To make this computation we'll inject two gradients from TF: - # - grad[mean(f(log(sum(p(x)/q(x)))))] - # - jacobian[log(q(x))]. - # - # We now justify why using these (and only these) TF values for - # ground-truth does not undermine the completeness of this test. - # - # Regarding `grad_mean_f_log_sum_u_`, note that we validate the - # correctness of the zero-th order derivative (for each batch member). - # Since `cd.csiszar_vimco_helper` itself does not manipulate any gradient - # information, we can safely rely on TF. - self.assertAllClose(np_f(np_log_avg_u), f_log_sum_u_, rtol=1e-4, atol=0.) - # - # Regarding `jacobian_logqx_`, note that testing the gradient of - # `q.log_prob` is outside the scope of this unit-test thus we may safely - # use TF to find it. - - # The `mean` is across batches and the `sum` is across iid samples. - np_grad_vimco = ( - grad_mean_f_log_sum_u_ - + np.mean( - np.sum( - jacobian_logqx_ * (np_f(np_log_avg_u) - - np_f(np_log_sooavg_u)), - axis=0), - axis=0)) - - self.assertAllClose(np_grad_vimco, grad_vimco_, - rtol=1e-5, atol=0.) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py deleted file mode 100644 index 0a85862abfd744a86b9a38e10dbb5b985d0a0e94..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for halton_sequence.py.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import halton_sequence as halton -from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.platform import test - - -mc = monte_carlo_lib - - -class HaltonSequenceTest(test.TestCase): - - def test_known_values_small_bases(self): - with self.test_session(): - # The first five elements of the Halton sequence with base 2 and 3 - expected = np.array(((1. / 2, 1. / 3), - (1. / 4, 2. / 3), - (3. / 4, 1. / 9), - (1. / 8, 4. / 9), - (5. / 8, 7. / 9)), dtype=np.float32) - sample = halton.sample(2, num_samples=5) - self.assertAllClose(expected, sample.eval(), rtol=1e-6) - - def test_sample_indices(self): - with self.test_session(): - dim = 5 - indices = math_ops.range(10, dtype=dtypes.int32) - sample_direct = halton.sample(dim, num_samples=10) - sample_from_indices = halton.sample(dim, sample_indices=indices) - self.assertAllClose(sample_direct.eval(), sample_from_indices.eval(), - rtol=1e-6) - - def test_dtypes_works_correctly(self): - with self.test_session(): - dim = 3 - sample_float32 = halton.sample(dim, num_samples=10, dtype=dtypes.float32) - sample_float64 = halton.sample(dim, num_samples=10, dtype=dtypes.float64) - self.assertEqual(sample_float32.eval().dtype, np.float32) - self.assertEqual(sample_float64.eval().dtype, np.float64) - - def test_normal_integral_mean_and_var_correctly_estimated(self): - n = int(1000) - # This test is almost identical to the similarly named test in - # monte_carlo_test.py. The only difference is that we use the Halton - # samples instead of the random samples to evaluate the expectations. - # MC with pseudo random numbers converges at the rate of 1/ Sqrt(N) - # (N=number of samples). For QMC in low dimensions, the expected convergence - # rate is ~ 1/N. Hence we should only need 1e3 samples as compared to the - # 1e6 samples used in the pseudo-random monte carlo. - with self.test_session(): - mu_p = array_ops.constant([-1.0, 1.0], dtype=dtypes.float64) - mu_q = array_ops.constant([0.0, 0.0], dtype=dtypes.float64) - sigma_p = array_ops.constant([0.5, 0.5], dtype=dtypes.float64) - sigma_q = array_ops.constant([1.0, 1.0], dtype=dtypes.float64) - p = normal_lib.Normal(loc=mu_p, scale=sigma_p) - q = normal_lib.Normal(loc=mu_q, scale=sigma_q) - - cdf_sample = halton.sample(2, num_samples=n, dtype=dtypes.float64) - q_sample = q.quantile(cdf_sample) - - # Compute E_p[X]. - e_x = mc.expectation_importance_sampler( - f=lambda x: x, log_p=p.log_prob, sampling_dist_q=q, z=q_sample, - seed=42) - - # Compute E_p[X^2]. - e_x2 = mc.expectation_importance_sampler( - f=math_ops.square, log_p=p.log_prob, sampling_dist_q=q, z=q_sample, - seed=42) - - stddev = math_ops.sqrt(e_x2 - math_ops.square(e_x)) - # Keep the tolerance levels the same as in monte_carlo_test.py. - self.assertEqual(p.batch_shape, e_x.get_shape()) - self.assertAllClose(p.mean().eval(), e_x.eval(), rtol=0.01) - self.assertAllClose(p.stddev().eval(), stddev.eval(), rtol=0.02) - - def test_docstring_example(self): - # Produce the first 1000 members of the Halton sequence in 3 dimensions. - num_samples = 1000 - dim = 3 - with self.test_session(): - sample = halton.sample(dim, num_samples=num_samples) - - # Evaluate the integral of x_1 * x_2^2 * x_3^3 over the three dimensional - # hypercube. - powers = math_ops.range(1.0, limit=dim + 1) - integral = math_ops.reduce_mean( - math_ops.reduce_prod(sample ** powers, axis=-1)) - true_value = 1.0 / math_ops.reduce_prod(powers + 1.0) - - # Produces a relative absolute error of 1.7%. - self.assertAllClose(integral.eval(), true_value.eval(), rtol=0.02) - - # Now skip the first 1000 samples and recompute the integral with the next - # thousand samples. The sample_indices argument can be used to do this. - - sample_indices = math_ops.range(start=1000, limit=1000 + num_samples, - dtype=dtypes.int32) - sample_leaped = halton.sample(dim, sample_indices=sample_indices) - - integral_leaped = math_ops.reduce_mean( - math_ops.reduce_prod(sample_leaped ** powers, axis=-1)) - self.assertAllClose(integral_leaped.eval(), true_value.eval(), rtol=0.001) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py index cbc66b6dc13db62c25952de6b6c13b2fdfe27f12..dabadfc7b6a3da8786e88d559fe2d05b44599ca0 100644 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py +++ b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py @@ -18,30 +18,40 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + import numpy as np -from scipy import special from scipy import stats from tensorflow.contrib.bayesflow.python.ops import hmc +from tensorflow.contrib.bayesflow.python.ops.hmc_impl import _compute_energy_change +from tensorflow.contrib.bayesflow.python.ops.hmc_impl import _leapfrog_integrator +from tensorflow.contrib.distributions.python.ops import independent as independent_lib +from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import gen_linalg_ops +from tensorflow.python.ops import gradients_impl as gradients_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops.distributions import gamma as gamma_lib +from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.platform import test -from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.platform import tf_logging as logging_ops + + +def _reduce_variance(x, axis=None, keepdims=False): + sample_mean = math_ops.reduce_mean(x, axis, keepdims=True) + return math_ops.reduce_mean( + math_ops.squared_difference(x, sample_mean), axis, keepdims) -# TODO(b/66964210): Test float16. class HMCTest(test.TestCase): def setUp(self): self._shape_param = 5. self._rate_param = 10. - self._expected_x = (special.digamma(self._shape_param) - - np.log(self._rate_param)) - self._expected_exp_x = self._shape_param / self._rate_param random_seed.set_random_seed(10003) np.random.seed(10003) @@ -63,63 +73,46 @@ class HMCTest(test.TestCase): self._rate_param * math_ops.exp(x), event_dims) - def _log_gamma_log_prob_grad(self, x, event_dims=()): - """Computes log-pdf and gradient of a log-gamma random variable. - - Args: - x: Value of the random variable. - event_dims: Dimensions not to treat as independent. Default is (), - i.e., all dimensions are independent. - - Returns: - log_prob: The log-pdf up to a normalizing constant. - grad: The gradient of the log-pdf with respect to x. - """ - return (math_ops.reduce_sum(self._shape_param * x - - self._rate_param * math_ops.exp(x), - event_dims), - self._shape_param - self._rate_param * math_ops.exp(x)) - - def _n_event_dims(self, x_shape, event_dims): - return np.prod([int(x_shape[i]) for i in event_dims]) - - def _integrator_conserves_energy(self, x, event_dims, sess, + def _integrator_conserves_energy(self, x, independent_chain_ndims, sess, feed_dict=None): - def potential_and_grad(x): - log_prob, grad = self._log_gamma_log_prob_grad(x, event_dims) - return -log_prob, -grad - - step_size = array_ops.placeholder(np.float32, [], name='step_size') - hmc_lf_steps = array_ops.placeholder(np.int32, [], name='hmc_lf_steps') + step_size = array_ops.placeholder(np.float32, [], name="step_size") + hmc_lf_steps = array_ops.placeholder(np.int32, [], name="hmc_lf_steps") if feed_dict is None: feed_dict = {} feed_dict[hmc_lf_steps] = 1000 - m = random_ops.random_normal(array_ops.shape(x)) - potential_0, grad_0 = potential_and_grad(x) - old_energy = potential_0 + 0.5 * math_ops.reduce_sum(m * m, - event_dims) + event_dims = math_ops.range(independent_chain_ndims, + array_ops.rank(x)) - _, new_m, potential_1, _ = ( - hmc.leapfrog_integrator(step_size, hmc_lf_steps, x, - m, potential_and_grad, grad_0)) - - new_energy = potential_1 + 0.5 * math_ops.reduce_sum(new_m * new_m, + m = random_ops.random_normal(array_ops.shape(x)) + log_prob_0 = self._log_gamma_log_prob(x, event_dims) + grad_0 = gradients_ops.gradients(log_prob_0, x) + old_energy = -log_prob_0 + 0.5 * math_ops.reduce_sum(m**2., event_dims) + + new_m, _, log_prob_1, _ = _leapfrog_integrator( + current_momentums=[m], + target_log_prob_fn=lambda x: self._log_gamma_log_prob(x, event_dims), + current_state_parts=[x], + step_sizes=[step_size], + num_leapfrog_steps=hmc_lf_steps, + current_target_log_prob=log_prob_0, + current_grads_target_log_prob=grad_0) + new_m = new_m[0] + + new_energy = -log_prob_1 + 0.5 * math_ops.reduce_sum(new_m * new_m, event_dims) x_shape = sess.run(x, feed_dict).shape - n_event_dims = self._n_event_dims(x_shape, event_dims) - feed_dict[step_size] = 0.1 / n_event_dims - old_energy_val, new_energy_val = sess.run([old_energy, new_energy], - feed_dict) - logging.vlog(1, 'average energy change: {}'.format( - abs(old_energy_val - new_energy_val).mean())) - - self.assertAllEqual(np.ones_like(new_energy_val, dtype=np.bool), - abs(old_energy_val - new_energy_val) < 1.) - - def _integrator_conserves_energy_wrapper(self, event_dims): + event_size = np.prod(x_shape[independent_chain_ndims:]) + feed_dict[step_size] = 0.1 / event_size + old_energy_, new_energy_ = sess.run([old_energy, new_energy], + feed_dict) + logging_ops.vlog(1, "average energy relative change: {}".format( + (1. - new_energy_ / old_energy_).mean())) + self.assertAllClose(old_energy_, new_energy_, atol=0., rtol=0.02) + + def _integrator_conserves_energy_wrapper(self, independent_chain_ndims): """Tests the long-term energy conservation of the leapfrog integrator. The leapfrog integrator is symplectic, so for sufficiently small step @@ -127,135 +120,314 @@ class HMCTest(test.TestCase): the energy of the system blowing up or collapsing. Args: - event_dims: A tuple of dimensions that should not be treated as - independent. This allows for multiple chains to be run independently - in parallel. Default is (), i.e., all dimensions are independent. + independent_chain_ndims: Python `int` scalar representing the number of + dims associated with independent chains. """ - with self.test_session() as sess: - x_ph = array_ops.placeholder(np.float32, name='x_ph') - - feed_dict = {x_ph: np.zeros([50, 10, 2])} - self._integrator_conserves_energy(x_ph, event_dims, sess, feed_dict) + with self.test_session(graph=ops.Graph()) as sess: + x_ph = array_ops.placeholder(np.float32, name="x_ph") + feed_dict = {x_ph: np.random.rand(50, 10, 2)} + self._integrator_conserves_energy(x_ph, independent_chain_ndims, + sess, feed_dict) def testIntegratorEnergyConservationNullShape(self): - self._integrator_conserves_energy_wrapper([]) + self._integrator_conserves_energy_wrapper(0) def testIntegratorEnergyConservation1(self): - self._integrator_conserves_energy_wrapper([1]) + self._integrator_conserves_energy_wrapper(1) def testIntegratorEnergyConservation2(self): - self._integrator_conserves_energy_wrapper([2]) - - def testIntegratorEnergyConservation12(self): - self._integrator_conserves_energy_wrapper([1, 2]) - - def testIntegratorEnergyConservation012(self): - self._integrator_conserves_energy_wrapper([0, 1, 2]) - - def _chain_gets_correct_expectations(self, x, event_dims, sess, - feed_dict=None): + self._integrator_conserves_energy_wrapper(2) + + def testIntegratorEnergyConservation3(self): + self._integrator_conserves_energy_wrapper(3) + + def testSampleChainSeedReproducibleWorksCorrectly(self): + with self.test_session(graph=ops.Graph()) as sess: + num_results = 10 + independent_chain_ndims = 1 + + def log_gamma_log_prob(x): + event_dims = math_ops.range(independent_chain_ndims, + array_ops.rank(x)) + return self._log_gamma_log_prob(x, event_dims) + + kwargs = dict( + target_log_prob_fn=log_gamma_log_prob, + current_state=np.random.rand(4, 3, 2), + step_size=0.1, + num_leapfrog_steps=2, + num_burnin_steps=150, + seed=52, + ) + + samples0, kernel_results0 = hmc.sample_chain( + **dict(list(kwargs.items()) + list(dict( + num_results=2 * num_results, + num_steps_between_results=0).items()))) + + samples1, kernel_results1 = hmc.sample_chain( + **dict(list(kwargs.items()) + list(dict( + num_results=num_results, + num_steps_between_results=1).items()))) + + [ + samples0_, + samples1_, + target_log_prob0_, + target_log_prob1_, + ] = sess.run([ + samples0, + samples1, + kernel_results0.current_target_log_prob, + kernel_results1.current_target_log_prob, + ]) + self.assertAllClose(samples0_[::2], samples1_, + atol=1e-5, rtol=1e-5) + self.assertAllClose(target_log_prob0_[::2], target_log_prob1_, + atol=1e-5, rtol=1e-5) + + def _chain_gets_correct_expectations(self, x, independent_chain_ndims, + sess, feed_dict=None): + counter = collections.Counter() def log_gamma_log_prob(x): + counter["target_calls"] += 1 + event_dims = math_ops.range(independent_chain_ndims, + array_ops.rank(x)) return self._log_gamma_log_prob(x, event_dims) - step_size = array_ops.placeholder(np.float32, [], name='step_size') - hmc_lf_steps = array_ops.placeholder(np.int32, [], name='hmc_lf_steps') - hmc_n_steps = array_ops.placeholder(np.int32, [], name='hmc_n_steps') + num_results = array_ops.placeholder( + np.int32, [], name="num_results") + step_size = array_ops.placeholder( + np.float32, [], name="step_size") + num_leapfrog_steps = array_ops.placeholder( + np.int32, [], name="num_leapfrog_steps") if feed_dict is None: feed_dict = {} - feed_dict.update({step_size: 0.1, - hmc_lf_steps: 2, - hmc_n_steps: 300}) - - sample_chain, acceptance_prob_chain = hmc.chain([hmc_n_steps], - step_size, - hmc_lf_steps, - x, log_gamma_log_prob, - event_dims) - - acceptance_probs, samples = sess.run([acceptance_prob_chain, sample_chain], - feed_dict) - samples = samples[feed_dict[hmc_n_steps] // 2:] - expected_x_est = samples.mean() - expected_exp_x_est = np.exp(samples).mean() - - logging.vlog(1, 'True E[x, exp(x)]: {}\t{}'.format( - self._expected_x, self._expected_exp_x)) - logging.vlog(1, 'Estimated E[x, exp(x)]: {}\t{}'.format( - expected_x_est, expected_exp_x_est)) - self.assertNear(expected_x_est, self._expected_x, 2e-2) - self.assertNear(expected_exp_x_est, self._expected_exp_x, 2e-2) - self.assertTrue((acceptance_probs > 0.5).all()) - self.assertTrue((acceptance_probs <= 1.0).all()) - - def _chain_gets_correct_expectations_wrapper(self, event_dims): - with self.test_session() as sess: - x_ph = array_ops.placeholder(np.float32, name='x_ph') - - feed_dict = {x_ph: np.zeros([50, 10, 2])} - self._chain_gets_correct_expectations(x_ph, event_dims, sess, - feed_dict) + feed_dict.update({num_results: 150, + step_size: 0.05, + num_leapfrog_steps: 2}) + + samples, kernel_results = hmc.sample_chain( + num_results=num_results, + target_log_prob_fn=log_gamma_log_prob, + current_state=x, + step_size=step_size, + num_leapfrog_steps=num_leapfrog_steps, + num_burnin_steps=150, + seed=42) + + self.assertAllEqual(dict(target_calls=2), counter) + + expected_x = (math_ops.digamma(self._shape_param) + - np.log(self._rate_param)) + + expected_exp_x = self._shape_param / self._rate_param + + log_accept_ratio_, samples_, expected_x_ = sess.run( + [kernel_results.log_accept_ratio, samples, expected_x], + feed_dict) + + actual_x = samples_.mean() + actual_exp_x = np.exp(samples_).mean() + acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.)) + + logging_ops.vlog(1, "True E[x, exp(x)]: {}\t{}".format( + expected_x_, expected_exp_x)) + logging_ops.vlog(1, "Estimated E[x, exp(x)]: {}\t{}".format( + actual_x, actual_exp_x)) + self.assertNear(actual_x, expected_x_, 2e-2) + self.assertNear(actual_exp_x, expected_exp_x, 2e-2) + self.assertAllEqual(np.ones_like(acceptance_probs, np.bool), + acceptance_probs > 0.5) + self.assertAllEqual(np.ones_like(acceptance_probs, np.bool), + acceptance_probs <= 1.) + + def _chain_gets_correct_expectations_wrapper(self, independent_chain_ndims): + with self.test_session(graph=ops.Graph()) as sess: + x_ph = array_ops.placeholder(np.float32, name="x_ph") + feed_dict = {x_ph: np.random.rand(50, 10, 2)} + self._chain_gets_correct_expectations(x_ph, independent_chain_ndims, + sess, feed_dict) def testHMCChainExpectationsNullShape(self): - self._chain_gets_correct_expectations_wrapper([]) + self._chain_gets_correct_expectations_wrapper(0) def testHMCChainExpectations1(self): - self._chain_gets_correct_expectations_wrapper([1]) + self._chain_gets_correct_expectations_wrapper(1) def testHMCChainExpectations2(self): - self._chain_gets_correct_expectations_wrapper([2]) + self._chain_gets_correct_expectations_wrapper(2) - def testHMCChainExpectations12(self): - self._chain_gets_correct_expectations_wrapper([1, 2]) - - def _kernel_leaves_target_invariant(self, initial_draws, event_dims, + def testKernelResultsUsingTruncatedDistribution(self): + def log_prob(x): + return array_ops.where( + x >= 0., + -x - x**2, # Non-constant gradient. + array_ops.fill(x.shape, math_ops.cast(-np.inf, x.dtype))) + # This log_prob has the property that it is likely to attract + # the flow toward, and below, zero...but for x <=0, + # log_prob(x) = -inf, which should result in rejection, as well + # as a non-finite log_prob. Thus, this distribution gives us an opportunity + # to test out the kernel results ability to correctly capture rejections due + # to finite AND non-finite reasons. + # Why use a non-constant gradient? This ensures the leapfrog integrator + # will not be exact. + + num_results = 1000 + # Large step size, will give rejections due to integration error in addition + # to rejection due to going into a region of log_prob = -inf. + step_size = 0.1 + num_leapfrog_steps = 5 + num_chains = 2 + + with self.test_session(graph=ops.Graph()) as sess: + + # Start multiple independent chains. + initial_state = ops.convert_to_tensor([0.1] * num_chains) + + states, kernel_results = hmc.sample_chain( + num_results=num_results, + target_log_prob_fn=log_prob, + current_state=initial_state, + step_size=step_size, + num_leapfrog_steps=num_leapfrog_steps, + seed=42) + + states_, kernel_results_ = sess.run([states, kernel_results]) + pstates_ = kernel_results_.proposed_state + + neg_inf_mask = np.isneginf(kernel_results_.proposed_target_log_prob) + + # First: Test that the mathematical properties of the above log prob + # function in conjunction with HMC show up as expected in kernel_results_. + + # We better have log_prob = -inf some of the time. + self.assertLess(0, neg_inf_mask.sum()) + # We better have some rejections due to something other than -inf. + self.assertLess(neg_inf_mask.sum(), (~kernel_results_.is_accepted).sum()) + # We better have accepted a decent amount, even near end of the chain. + self.assertLess( + 0.1, kernel_results_.is_accepted[int(0.9 * num_results):].mean()) + # We better not have any NaNs in states or log_prob. + # We may have some NaN in grads, which involve multiplication/addition due + # to gradient rules. This is the known "NaN grad issue with tf.where." + self.assertAllEqual(np.zeros_like(states_), + np.isnan(kernel_results_.proposed_target_log_prob)) + self.assertAllEqual(np.zeros_like(states_), + np.isnan(states_)) + # We better not have any +inf in states, grads, or log_prob. + self.assertAllEqual(np.zeros_like(states_), + np.isposinf(kernel_results_.proposed_target_log_prob)) + self.assertAllEqual( + np.zeros_like(states_), + np.isposinf(kernel_results_.proposed_grads_target_log_prob[0])) + self.assertAllEqual(np.zeros_like(states_), + np.isposinf(states_)) + + # Second: Test that kernel_results is congruent with itself and + # acceptance/rejection of states. + + # Proposed state is negative iff proposed target log prob is -inf. + np.testing.assert_array_less(pstates_[neg_inf_mask], 0.) + np.testing.assert_array_less(0., pstates_[~neg_inf_mask]) + + # Acceptance probs are zero whenever proposed state is negative. + acceptance_probs = np.exp(np.minimum( + kernel_results_.log_accept_ratio, 0.)) + self.assertAllEqual( + np.zeros_like(pstates_[neg_inf_mask]), + acceptance_probs[neg_inf_mask]) + + # The move is accepted ==> state = proposed state. + self.assertAllEqual( + states_[kernel_results_.is_accepted], + pstates_[kernel_results_.is_accepted], + ) + # The move was rejected <==> state[t] == state[t - 1]. + for t in range(1, num_results): + for i in range(num_chains): + if kernel_results_.is_accepted[t, i]: + self.assertNotEqual(states_[t, i], states_[t - 1, i]) + else: + self.assertEqual(states_[t, i], states_[t - 1, i]) + + def _kernel_leaves_target_invariant(self, initial_draws, + independent_chain_ndims, sess, feed_dict=None): def log_gamma_log_prob(x): + event_dims = math_ops.range(independent_chain_ndims, array_ops.rank(x)) return self._log_gamma_log_prob(x, event_dims) def fake_log_prob(x): """Cooled version of the target distribution.""" return 1.1 * log_gamma_log_prob(x) - step_size = array_ops.placeholder(np.float32, [], name='step_size') + step_size = array_ops.placeholder(np.float32, [], name="step_size") if feed_dict is None: feed_dict = {} feed_dict[step_size] = 0.4 - sample, acceptance_probs, _, _ = hmc.kernel(step_size, 5, initial_draws, - log_gamma_log_prob, event_dims) - bad_sample, bad_acceptance_probs, _, _ = hmc.kernel( - step_size, 5, initial_draws, fake_log_prob, event_dims) - (acceptance_probs_val, bad_acceptance_probs_val, initial_draws_val, - updated_draws_val, fake_draws_val) = sess.run([acceptance_probs, - bad_acceptance_probs, - initial_draws, sample, - bad_sample], feed_dict) + sample, kernel_results = hmc.kernel( + target_log_prob_fn=log_gamma_log_prob, + current_state=initial_draws, + step_size=step_size, + num_leapfrog_steps=5, + seed=43) + + bad_sample, bad_kernel_results = hmc.kernel( + target_log_prob_fn=fake_log_prob, + current_state=initial_draws, + step_size=step_size, + num_leapfrog_steps=5, + seed=44) + + [ + log_accept_ratio_, + bad_log_accept_ratio_, + initial_draws_, + updated_draws_, + fake_draws_, + ] = sess.run([ + kernel_results.log_accept_ratio, + bad_kernel_results.log_accept_ratio, + initial_draws, + sample, + bad_sample, + ], feed_dict) + # Confirm step size is small enough that we usually accept. - self.assertGreater(acceptance_probs_val.mean(), 0.5) - self.assertGreater(bad_acceptance_probs_val.mean(), 0.5) + acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.)) + bad_acceptance_probs = np.exp(np.minimum(bad_log_accept_ratio_, 0.)) + self.assertGreater(acceptance_probs.mean(), 0.5) + self.assertGreater(bad_acceptance_probs.mean(), 0.5) + # Confirm step size is large enough that we sometimes reject. - self.assertLess(acceptance_probs_val.mean(), 0.99) - self.assertLess(bad_acceptance_probs_val.mean(), 0.99) - _, ks_p_value_true = stats.ks_2samp(initial_draws_val.flatten(), - updated_draws_val.flatten()) - _, ks_p_value_fake = stats.ks_2samp(initial_draws_val.flatten(), - fake_draws_val.flatten()) - logging.vlog(1, 'acceptance rate for true target: {}'.format( - acceptance_probs_val.mean())) - logging.vlog(1, 'acceptance rate for fake target: {}'.format( - bad_acceptance_probs_val.mean())) - logging.vlog(1, 'K-S p-value for true target: {}'.format(ks_p_value_true)) - logging.vlog(1, 'K-S p-value for fake target: {}'.format(ks_p_value_fake)) + self.assertLess(acceptance_probs.mean(), 0.99) + self.assertLess(bad_acceptance_probs.mean(), 0.99) + + _, ks_p_value_true = stats.ks_2samp(initial_draws_.flatten(), + updated_draws_.flatten()) + _, ks_p_value_fake = stats.ks_2samp(initial_draws_.flatten(), + fake_draws_.flatten()) + + logging_ops.vlog(1, "acceptance rate for true target: {}".format( + acceptance_probs.mean())) + logging_ops.vlog(1, "acceptance rate for fake target: {}".format( + bad_acceptance_probs.mean())) + logging_ops.vlog(1, "K-S p-value for true target: {}".format( + ks_p_value_true)) + logging_ops.vlog(1, "K-S p-value for fake target: {}".format( + ks_p_value_fake)) # Make sure that the MCMC update hasn't changed the empirical CDF much. self.assertGreater(ks_p_value_true, 1e-3) # Confirm that targeting the wrong distribution does # significantly change the empirical CDF. self.assertLess(ks_p_value_fake, 1e-6) - def _kernel_leaves_target_invariant_wrapper(self, event_dims): + def _kernel_leaves_target_invariant_wrapper(self, independent_chain_ndims): """Tests that the kernel leaves the target distribution invariant. Draws some independent samples from the target distribution, @@ -267,86 +439,28 @@ class HMCTest(test.TestCase): does change the target distribution. (And that we can detect that.) Args: - event_dims: A tuple of dimensions that should not be treated as - independent. This allows for multiple chains to be run independently - in parallel. Default is (), i.e., all dimensions are independent. + independent_chain_ndims: Python `int` scalar representing the number of + dims associated with independent chains. """ - with self.test_session() as sess: + with self.test_session(graph=ops.Graph()) as sess: initial_draws = np.log(np.random.gamma(self._shape_param, size=[50000, 2, 2])) initial_draws -= np.log(self._rate_param) - x_ph = array_ops.placeholder(np.float32, name='x_ph') + x_ph = array_ops.placeholder(np.float32, name="x_ph") feed_dict = {x_ph: initial_draws} - self._kernel_leaves_target_invariant(x_ph, event_dims, sess, - feed_dict) - - def testKernelLeavesTargetInvariantNullShape(self): - self._kernel_leaves_target_invariant_wrapper([]) + self._kernel_leaves_target_invariant(x_ph, independent_chain_ndims, + sess, feed_dict) def testKernelLeavesTargetInvariant1(self): - self._kernel_leaves_target_invariant_wrapper([1]) + self._kernel_leaves_target_invariant_wrapper(1) def testKernelLeavesTargetInvariant2(self): - self._kernel_leaves_target_invariant_wrapper([2]) - - def testKernelLeavesTargetInvariant12(self): - self._kernel_leaves_target_invariant_wrapper([1, 2]) - - def _ais_gets_correct_log_normalizer(self, init, event_dims, sess, - feed_dict=None): - def proposal_log_prob(x): - return math_ops.reduce_sum(-0.5 * x * x - 0.5 * np.log(2*np.pi), - event_dims) - - def target_log_prob(x): - return self._log_gamma_log_prob(x, event_dims) - - if feed_dict is None: - feed_dict = {} - - w, _, _ = hmc.ais_chain(200, 0.5, 2, init, target_log_prob, - proposal_log_prob, event_dims) - - w_val = sess.run(w, feed_dict) - init_shape = sess.run(init, feed_dict).shape - normalizer_multiplier = np.prod([init_shape[i] for i in event_dims]) - - true_normalizer = -self._shape_param * np.log(self._rate_param) - true_normalizer += special.gammaln(self._shape_param) - true_normalizer *= normalizer_multiplier - - n_weights = np.prod(w_val.shape) - normalized_w = np.exp(w_val - true_normalizer) - standard_error = np.std(normalized_w) / np.sqrt(n_weights) - logging.vlog(1, 'True normalizer {}, estimated {}, n_weights {}'.format( - true_normalizer, np.log(normalized_w.mean()) + true_normalizer, - n_weights)) - self.assertNear(normalized_w.mean(), 1.0, 4.0 * standard_error) + self._kernel_leaves_target_invariant_wrapper(2) - def _ais_gets_correct_log_normalizer_wrapper(self, event_dims): - """Tests that AIS yields reasonable estimates of normalizers.""" - with self.test_session() as sess: - x_ph = array_ops.placeholder(np.float32, name='x_ph') - - initial_draws = np.random.normal(size=[30, 2, 1]) - feed_dict = {x_ph: initial_draws} - - self._ais_gets_correct_log_normalizer(x_ph, event_dims, sess, - feed_dict) - - def testAISNullShape(self): - self._ais_gets_correct_log_normalizer_wrapper([]) - - def testAIS1(self): - self._ais_gets_correct_log_normalizer_wrapper([1]) - - def testAIS2(self): - self._ais_gets_correct_log_normalizer_wrapper([2]) - - def testAIS12(self): - self._ais_gets_correct_log_normalizer_wrapper([1, 2]) + def testKernelLeavesTargetInvariant3(self): + self._kernel_leaves_target_invariant_wrapper(3) def testNanRejection(self): """Tests that an update that yields NaN potentials gets rejected. @@ -359,86 +473,265 @@ class HMCTest(test.TestCase): """ def _unbounded_exponential_log_prob(x): """An exponential distribution with log-likelihood NaN for x < 0.""" - per_element_potentials = array_ops.where(x < 0, - np.nan * array_ops.ones_like(x), - -x) + per_element_potentials = array_ops.where( + x < 0., + array_ops.fill(array_ops.shape(x), x.dtype.as_numpy_dtype(np.nan)), + -x) return math_ops.reduce_sum(per_element_potentials) - with self.test_session() as sess: + with self.test_session(graph=ops.Graph()) as sess: initial_x = math_ops.linspace(0.01, 5, 10) - updated_x, acceptance_probs, _, _ = hmc.kernel( - 2., 5, initial_x, _unbounded_exponential_log_prob, [0]) - initial_x_val, updated_x_val, acceptance_probs_val = sess.run( - [initial_x, updated_x, acceptance_probs]) - - logging.vlog(1, 'initial_x = {}'.format(initial_x_val)) - logging.vlog(1, 'updated_x = {}'.format(updated_x_val)) - logging.vlog(1, 'acceptance_probs = {}'.format(acceptance_probs_val)) - - self.assertAllEqual(initial_x_val, updated_x_val) - self.assertEqual(acceptance_probs_val, 0.) + updated_x, kernel_results = hmc.kernel( + target_log_prob_fn=_unbounded_exponential_log_prob, + current_state=initial_x, + step_size=2., + num_leapfrog_steps=5, + seed=46) + initial_x_, updated_x_, log_accept_ratio_ = sess.run( + [initial_x, updated_x, kernel_results.log_accept_ratio]) + acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.)) + + logging_ops.vlog(1, "initial_x = {}".format(initial_x_)) + logging_ops.vlog(1, "updated_x = {}".format(updated_x_)) + logging_ops.vlog(1, "log_accept_ratio = {}".format(log_accept_ratio_)) + + self.assertAllEqual(initial_x_, updated_x_) + self.assertEqual(acceptance_probs, 0.) def testNanFromGradsDontPropagate(self): """Test that update with NaN gradients does not cause NaN in results.""" def _nan_log_prob_with_nan_gradient(x): return np.nan * math_ops.reduce_sum(x) - with self.test_session() as sess: + with self.test_session(graph=ops.Graph()) as sess: initial_x = math_ops.linspace(0.01, 5, 10) - updated_x, acceptance_probs, new_log_prob, new_grad = hmc.kernel( - 2., 5, initial_x, _nan_log_prob_with_nan_gradient, [0]) - initial_x_val, updated_x_val, acceptance_probs_val = sess.run( - [initial_x, updated_x, acceptance_probs]) - - logging.vlog(1, 'initial_x = {}'.format(initial_x_val)) - logging.vlog(1, 'updated_x = {}'.format(updated_x_val)) - logging.vlog(1, 'acceptance_probs = {}'.format(acceptance_probs_val)) - - self.assertAllEqual(initial_x_val, updated_x_val) - self.assertEqual(acceptance_probs_val, 0.) + updated_x, kernel_results = hmc.kernel( + target_log_prob_fn=_nan_log_prob_with_nan_gradient, + current_state=initial_x, + step_size=2., + num_leapfrog_steps=5, + seed=47) + initial_x_, updated_x_, log_accept_ratio_ = sess.run( + [initial_x, updated_x, kernel_results.log_accept_ratio]) + acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.)) + + logging_ops.vlog(1, "initial_x = {}".format(initial_x_)) + logging_ops.vlog(1, "updated_x = {}".format(updated_x_)) + logging_ops.vlog(1, "log_accept_ratio = {}".format(log_accept_ratio_)) + + self.assertAllEqual(initial_x_, updated_x_) + self.assertEqual(acceptance_probs, 0.) self.assertAllFinite( - gradients_impl.gradients(updated_x, initial_x)[0].eval()) - self.assertTrue( - gradients_impl.gradients(new_grad, initial_x)[0] is None) + gradients_ops.gradients(updated_x, initial_x)[0].eval()) + self.assertAllEqual([True], [g is None for g in gradients_ops.gradients( + kernel_results.proposed_grads_target_log_prob, initial_x)]) + self.assertAllEqual([False], [g is None for g in gradients_ops.gradients( + kernel_results.proposed_grads_target_log_prob, + kernel_results.proposed_state)]) # Gradients of the acceptance probs and new log prob are not finite. - _ = new_log_prob # Prevent unused arg error. # self.assertAllFinite( - # gradients_impl.gradients(acceptance_probs, initial_x)[0].eval()) + # gradients_ops.gradients(acceptance_probs, initial_x)[0].eval()) # self.assertAllFinite( - # gradients_impl.gradients(new_log_prob, initial_x)[0].eval()) + # gradients_ops.gradients(new_log_prob, initial_x)[0].eval()) + + def _testChainWorksDtype(self, dtype): + with self.test_session(graph=ops.Graph()) as sess: + states, kernel_results = hmc.sample_chain( + num_results=10, + target_log_prob_fn=lambda x: -math_ops.reduce_sum(x**2., axis=-1), + current_state=np.zeros(5).astype(dtype), + step_size=0.01, + num_leapfrog_steps=10, + seed=48) + states_, log_accept_ratio_ = sess.run( + [states, kernel_results.log_accept_ratio]) + self.assertEqual(dtype, states_.dtype) + self.assertEqual(dtype, log_accept_ratio_.dtype) def testChainWorksIn64Bit(self): - def log_prob(x): - return - math_ops.reduce_sum(x * x, axis=-1) - states, acceptance_probs = hmc.chain( - n_iterations=10, - step_size=np.float64(0.01), - n_leapfrog_steps=10, - initial_x=np.zeros(5).astype(np.float64), - target_log_prob_fn=log_prob, - event_dims=[-1]) - with self.test_session() as sess: - states_, acceptance_probs_ = sess.run([states, acceptance_probs]) - self.assertEqual(np.float64, states_.dtype) - self.assertEqual(np.float64, acceptance_probs_.dtype) + self._testChainWorksDtype(np.float64) def testChainWorksIn16Bit(self): - def log_prob(x): - return - math_ops.reduce_sum(x * x, axis=-1) - states, acceptance_probs = hmc.chain( - n_iterations=10, - step_size=np.float16(0.01), - n_leapfrog_steps=10, - initial_x=np.zeros(5).astype(np.float16), - target_log_prob_fn=log_prob, - event_dims=[-1]) - with self.test_session() as sess: - states_, acceptance_probs_ = sess.run([states, acceptance_probs]) - self.assertEqual(np.float16, states_.dtype) - self.assertEqual(np.float16, acceptance_probs_.dtype) - - -if __name__ == '__main__': + self._testChainWorksDtype(np.float16) + + def testChainWorksCorrelatedMultivariate(self): + dtype = np.float32 + true_mean = dtype([0, 0]) + true_cov = dtype([[1, 0.5], + [0.5, 1]]) + num_results = 2000 + counter = collections.Counter() + with self.test_session(graph=ops.Graph()) as sess: + def target_log_prob(x, y): + counter["target_calls"] += 1 + # Corresponds to unnormalized MVN. + # z = matmul(inv(chol(true_cov)), [x, y] - true_mean) + z = array_ops.stack([x, y], axis=-1) - true_mean + z = array_ops.squeeze( + gen_linalg_ops.matrix_triangular_solve( + np.linalg.cholesky(true_cov), + z[..., array_ops.newaxis]), + axis=-1) + return -0.5 * math_ops.reduce_sum(z**2., axis=-1) + states, _ = hmc.sample_chain( + num_results=num_results, + target_log_prob_fn=target_log_prob, + current_state=[dtype(-2), dtype(2)], + step_size=[0.5, 0.5], + num_leapfrog_steps=2, + num_burnin_steps=200, + num_steps_between_results=1, + seed=54) + self.assertAllEqual(dict(target_calls=2), counter) + states = array_ops.stack(states, axis=-1) + self.assertEqual(num_results, states.shape[0].value) + sample_mean = math_ops.reduce_mean(states, axis=0) + x = states - sample_mean + sample_cov = math_ops.matmul(x, x, transpose_a=True) / dtype(num_results) + [sample_mean_, sample_cov_] = sess.run([ + sample_mean, sample_cov]) + self.assertAllClose(true_mean, sample_mean_, + atol=0.05, rtol=0.) + self.assertAllClose(true_cov, sample_cov_, + atol=0., rtol=0.1) + + +class _EnergyComputationTest(object): + + def testHandlesNanFromPotential(self): + with self.test_session(graph=ops.Graph()) as sess: + x = [1, np.inf, -np.inf, np.nan] + target_log_prob, proposed_target_log_prob = [ + self.dtype(x.flatten()) for x in np.meshgrid(x, x)] + num_chains = len(target_log_prob) + dummy_momentums = [-1, 1] + momentums = [self.dtype([dummy_momentums] * num_chains)] + proposed_momentums = [self.dtype([dummy_momentums] * num_chains)] + + target_log_prob = ops.convert_to_tensor(target_log_prob) + momentums = [ops.convert_to_tensor(momentums[0])] + proposed_target_log_prob = ops.convert_to_tensor(proposed_target_log_prob) + proposed_momentums = [ops.convert_to_tensor(proposed_momentums[0])] + + energy = _compute_energy_change( + target_log_prob, + momentums, + proposed_target_log_prob, + proposed_momentums, + independent_chain_ndims=1) + grads = gradients_ops.gradients(energy, momentums) + + [actual_energy, grads_] = sess.run([energy, grads]) + + # Ensure energy is `inf` (note: that's positive inf) in weird cases and + # finite otherwise. + expected_energy = self.dtype([0] + [np.inf]*(num_chains - 1)) + self.assertAllEqual(expected_energy, actual_energy) + + # Ensure gradient is finite. + self.assertAllEqual(np.ones_like(grads_).astype(np.bool), + np.isfinite(grads_)) + + def testHandlesNanFromKinetic(self): + with self.test_session(graph=ops.Graph()) as sess: + x = [1, np.inf, -np.inf, np.nan] + momentums, proposed_momentums = [ + [np.reshape(self.dtype(x), [-1, 1])] + for x in np.meshgrid(x, x)] + num_chains = len(momentums[0]) + target_log_prob = np.ones(num_chains, self.dtype) + proposed_target_log_prob = np.ones(num_chains, self.dtype) + + target_log_prob = ops.convert_to_tensor(target_log_prob) + momentums = [ops.convert_to_tensor(momentums[0])] + proposed_target_log_prob = ops.convert_to_tensor(proposed_target_log_prob) + proposed_momentums = [ops.convert_to_tensor(proposed_momentums[0])] + + energy = _compute_energy_change( + target_log_prob, + momentums, + proposed_target_log_prob, + proposed_momentums, + independent_chain_ndims=1) + grads = gradients_ops.gradients(energy, momentums) + + [actual_energy, grads_] = sess.run([energy, grads]) + + # Ensure energy is `inf` (note: that's positive inf) in weird cases and + # finite otherwise. + expected_energy = self.dtype([0] + [np.inf]*(num_chains - 1)) + self.assertAllEqual(expected_energy, actual_energy) + + # Ensure gradient is finite. + g = grads_[0].reshape([len(x), len(x)])[:, 0] + self.assertAllEqual(np.ones_like(g).astype(np.bool), np.isfinite(g)) + + # The remaining gradients are nan because the momentum was itself nan or + # inf. + g = grads_[0].reshape([len(x), len(x)])[:, 1:] + self.assertAllEqual(np.ones_like(g).astype(np.bool), np.isnan(g)) + + +class EnergyComputationTest16(test.TestCase, _EnergyComputationTest): + dtype = np.float16 + + +class EnergyComputationTest32(test.TestCase, _EnergyComputationTest): + dtype = np.float32 + + +class EnergyComputationTest64(test.TestCase, _EnergyComputationTest): + dtype = np.float64 + + +class _HMCHandlesLists(object): + + def testStateParts(self): + with self.test_session(graph=ops.Graph()) as sess: + dist_x = normal_lib.Normal(loc=self.dtype(0), scale=self.dtype(1)) + dist_y = independent_lib.Independent( + gamma_lib.Gamma(concentration=self.dtype([1, 2]), + rate=self.dtype([0.5, 0.75])), + reinterpreted_batch_ndims=1) + def target_log_prob(x, y): + return dist_x.log_prob(x) + dist_y.log_prob(y) + x0 = [dist_x.sample(seed=1), dist_y.sample(seed=2)] + samples, _ = hmc.sample_chain( + num_results=int(2e3), + target_log_prob_fn=target_log_prob, + current_state=x0, + step_size=0.85, + num_leapfrog_steps=3, + num_burnin_steps=int(250), + seed=49) + actual_means = [math_ops.reduce_mean(s, axis=0) for s in samples] + actual_vars = [_reduce_variance(s, axis=0) for s in samples] + expected_means = [dist_x.mean(), dist_y.mean()] + expected_vars = [dist_x.variance(), dist_y.variance()] + [ + actual_means_, + actual_vars_, + expected_means_, + expected_vars_, + ] = sess.run([ + actual_means, + actual_vars, + expected_means, + expected_vars, + ]) + self.assertAllClose(expected_means_, actual_means_, atol=0.05, rtol=0.16) + self.assertAllClose(expected_vars_, actual_vars_, atol=0., rtol=0.25) + + +class HMCHandlesLists32(_HMCHandlesLists, test.TestCase): + dtype = np.float32 + + +class HMCHandlesLists64(_HMCHandlesLists, test.TestCase): + dtype = np.float64 + + +if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py deleted file mode 100644 index 750afb6654311fea30a1dc6b31b20aa3b4160ae2..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py +++ /dev/null @@ -1,521 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for convolutional Bayesian layers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import layers_conv_variational as prob_layers_lib -from tensorflow.contrib.bayesflow.python.ops import layers_util as prob_layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util -from tensorflow.python.platform import test - - -class Counter(object): - """Helper class to manage incrementing a counting `int`.""" - - def __init__(self): - self._value = -1 - - @property - def value(self): - return self._value - - def __call__(self): - self._value += 1 - return self._value - - -class MockDistribution(independent_lib.Independent): - """Monitors layer calls to the underlying distribution.""" - - def __init__(self, result_sample, result_log_prob, loc=None, scale=None): - self.result_sample = result_sample - self.result_log_prob = result_log_prob - self.result_loc = loc - self.result_scale = scale - self.result_distribution = normal_lib.Normal(loc=0.0, scale=1.0) - if loc is not None and scale is not None: - self.result_distribution = normal_lib.Normal(loc=self.result_loc, - scale=self.result_scale) - self.called_log_prob = Counter() - self.called_sample = Counter() - self.called_loc = Counter() - self.called_scale = Counter() - - def log_prob(self, *args, **kwargs): - self.called_log_prob() - return self.result_log_prob - - def sample(self, *args, **kwargs): - self.called_sample() - return self.result_sample - - @property - def distribution(self): # for dummy check on Independent(Normal) - return self.result_distribution - - @property - def loc(self): - self.called_loc() - return self.result_loc - - @property - def scale(self): - self.called_scale() - return self.result_scale - - -class MockKLDivergence(object): - """Monitors layer calls to the divergence implementation.""" - - def __init__(self, result): - self.result = result - self.args = [] - self.called = Counter() - - def __call__(self, *args, **kwargs): - self.called() - self.args.append(args) - return self.result - - -class ConvVariational(test.TestCase): - - def _testKLPenaltyKernel(self, layer_class): - with self.test_session(): - layer = layer_class(filters=2, kernel_size=3) - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform([2, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 3, 1], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 1) - self.assertListEqual(layer.losses, losses) - - def _testKLPenaltyBoth(self, layer_class): - def _make_normal(dtype, *args): # pylint: disable=unused-argument - return normal_lib.Normal( - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)) - with self.test_session(): - layer = layer_class( - filters=2, - kernel_size=3, - bias_posterior_fn=prob_layers_util.default_mean_field_normal_fn(), - bias_prior_fn=_make_normal) - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform([2, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 3, 1], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 2) - self.assertListEqual(layer.losses, losses) - - def _testConvSetUp(self, layer_class, batch_size, depth=None, - height=None, width=None, channels=None, filters=None, - **kwargs): - seed = Counter() - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform( - [batch_size, width, channels], seed=seed()) - kernel_size = (2,) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform( - [batch_size, height, width, channels], seed=seed()) - kernel_size = (2, 2) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform( - [batch_size, depth, height, width, channels], seed=seed()) - kernel_size = (2, 2, 2) - - kernel_shape = kernel_size + (channels, filters) - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform(kernel_shape, seed=seed()), - scale=random_ops.random_uniform(kernel_shape, seed=seed()), - result_log_prob=random_ops.random_uniform(kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform(kernel_shape, seed=seed())) - kernel_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform(kernel_shape, seed=seed())) - kernel_divergence = MockKLDivergence( - result=random_ops.random_uniform(kernel_shape, seed=seed())) - - bias_size = (filters,) - bias_posterior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_divergence = MockKLDivergence( - result=random_ops.random_uniform(bias_size, seed=seed())) - - layer = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - kernel_prior_fn=lambda *args: kernel_prior, - kernel_divergence_fn=kernel_divergence, - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - bias_prior_fn=lambda *args: bias_prior, - bias_divergence_fn=bias_divergence, - **kwargs) - - outputs = layer(inputs) - - kl_penalty = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - return (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, - layer, inputs, outputs, kl_penalty, kernel_shape) - - def _testConvReparameterization(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty, kernel_shape) = self._testConvSetUp( - layer_class, batch_size, - depth=depth, height=height, width=width, channels=channels, - filters=filters) - - convolution_op = nn_ops.Convolution( - tensor_shape.TensorShape(inputs.shape), - filter_shape=tensor_shape.TensorShape(kernel_shape), - padding="SAME") - expected_outputs = convolution_op(inputs, kernel_posterior.result_sample) - expected_outputs = nn.bias_add(expected_outputs, - bias_posterior.result_sample, - data_format="NHWC") - - [ - expected_outputs_, actual_outputs_, - expected_kernel_, actual_kernel_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_posterior.result_sample, layer.kernel_posterior_tensor, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_kernel_, actual_kernel_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - kernel_posterior.result_sample]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def _testConvFlipout(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty, kernel_shape) = self._testConvSetUp( - layer_class, batch_size, - depth=depth, height=height, width=width, channels=channels, - filters=filters, seed=44) - - convolution_op = nn_ops.Convolution( - tensor_shape.TensorShape(inputs.shape), - filter_shape=tensor_shape.TensorShape(kernel_shape), - padding="SAME") - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(kernel_posterior.result_loc), - scale=kernel_posterior.result_scale) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - - expected_outputs = convolution_op( - inputs, kernel_posterior.distribution.loc) - - input_shape = array_ops.shape(inputs) - output_shape = array_ops.shape(expected_outputs) - batch_shape = array_ops.expand_dims(input_shape[0], 0) - channels = input_shape[-1] - rank = len(inputs.get_shape()) - 2 - - sign_input = random_ops.random_uniform( - array_ops.concat([batch_shape, - array_ops.expand_dims(channels, 0)], 0), - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=layer.seed) - sign_input = math_ops.cast(2 * sign_input - 1, inputs.dtype) - sign_output = random_ops.random_uniform( - array_ops.concat([batch_shape, - array_ops.expand_dims(filters, 0)], 0), - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=distribution_util.gen_new_seed( - layer.seed, salt="conv_flipout")) - sign_output = math_ops.cast(2 * sign_output - 1, inputs.dtype) - for _ in range(rank): - sign_input = array_ops.expand_dims(sign_input, 1) # 2D ex: (B, 1, 1, C) - sign_output = array_ops.expand_dims(sign_output, 1) - - sign_input = array_ops.tile( # tile for element-wise op broadcasting - sign_input, - [1] + [input_shape[i + 1] for i in range(rank)] + [1]) - sign_output = array_ops.tile( - sign_output, - [1] + [output_shape[i + 1] for i in range(rank)] + [1]) - - perturbed_inputs = convolution_op( - inputs * sign_input, expected_kernel_posterior_affine_tensor) - perturbed_inputs *= sign_output - - expected_outputs += perturbed_inputs - expected_outputs = nn.bias_add(expected_outputs, - bias_posterior.result_sample, - data_format="NHWC") - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, kernel_prior.distribution, None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def _testRandomConvFlipout(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - seed = Counter() - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform( - [batch_size, width, channels], seed=seed()) - kernel_size = (2,) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform( - [batch_size, height, width, channels], seed=seed()) - kernel_size = (2, 2) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform( - [batch_size, depth, height, width, channels], seed=seed()) - kernel_size = (2, 2, 2) - - kernel_shape = kernel_size + (channels, filters) - bias_size = (filters,) - - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform( - kernel_shape, seed=seed()), - scale=random_ops.random_uniform( - kernel_shape, seed=seed()), - result_log_prob=random_ops.random_uniform( - kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform( - kernel_shape, seed=seed())) - bias_posterior = MockDistribution( - loc=random_ops.random_uniform( - bias_size, seed=seed()), - scale=random_ops.random_uniform( - bias_size, seed=seed()), - result_log_prob=random_ops.random_uniform( - bias_size, seed=seed()), - result_sample=random_ops.random_uniform( - bias_size, seed=seed())) - layer_one = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=44) - layer_two = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=45) - - outputs_one = layer_one(inputs) - outputs_two = layer_two(inputs) - - outputs_one_, outputs_two_ = sess.run([ - outputs_one, outputs_two]) - - self.assertLess(np.sum(np.isclose(outputs_one_, outputs_two_)), - np.prod(outputs_one_.shape)) - - def testKLPenaltyKernelConv1DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv1DReparameterization) - - def testKLPenaltyKernelConv2DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv2DReparameterization) - - def testKLPenaltyKernelConv3DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv3DReparameterization) - - def testKLPenaltyKernelConv1DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv1DFlipout) - - def testKLPenaltyKernelConv2DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv2DFlipout) - - def testKLPenaltyKernelConv3DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv3DFlipout) - - def testKLPenaltyBothConv1DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv1DReparameterization) - - def testKLPenaltyBothConv2DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv2DReparameterization) - - def testKLPenaltyBothConv3DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv3DReparameterization) - - def testKLPenaltyBothConv1DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv1DFlipout) - - def testKLPenaltyBothConv2DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv2DFlipout) - - def testKLPenaltyBothConv3DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv3DFlipout) - - def testConv1DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv1DReparameterization) - - def testConv2DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv2DReparameterization) - - def testConv3DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv3DReparameterization) - - def testConv1DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv1DFlipout) - - def testConv2DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv2DFlipout) - - def testConv3DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv3DFlipout) - - def testRandomConv1DFlipout(self): - self._testRandomConvFlipout(prob_layers_lib.Conv1DFlipout) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py deleted file mode 100644 index 342f38ccec7ec74db1b393d6cdc22300205cc547..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py +++ /dev/null @@ -1,443 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for dense Bayesian layers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import layers_dense_variational as prob_layers_lib -from tensorflow.contrib.bayesflow.python.ops import layers_util as prob_layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util -from tensorflow.python.platform import test - - -class Counter(object): - """Helper class to manage incrementing a counting `int`.""" - - def __init__(self): - self._value = -1 - - @property - def value(self): - return self._value - - def __call__(self): - self._value += 1 - return self._value - - -class MockDistribution(independent_lib.Independent): - """Monitors layer calls to the underlying distribution.""" - - def __init__(self, result_sample, result_log_prob, loc=None, scale=None): - self.result_sample = result_sample - self.result_log_prob = result_log_prob - self.result_loc = loc - self.result_scale = scale - self.result_distribution = normal_lib.Normal(loc=0.0, scale=1.0) - if loc is not None and scale is not None: - self.result_distribution = normal_lib.Normal(loc=self.result_loc, - scale=self.result_scale) - self.called_log_prob = Counter() - self.called_sample = Counter() - self.called_loc = Counter() - self.called_scale = Counter() - - def log_prob(self, *args, **kwargs): - self.called_log_prob() - return self.result_log_prob - - def sample(self, *args, **kwargs): - self.called_sample() - return self.result_sample - - @property - def distribution(self): # for dummy check on Independent(Normal) - return self.result_distribution - - @property - def loc(self): - self.called_loc() - return self.result_loc - - @property - def scale(self): - self.called_scale() - return self.result_scale - - -class MockKLDivergence(object): - """Monitors layer calls to the divergence implementation.""" - - def __init__(self, result): - self.result = result - self.args = [] - self.called = Counter() - - def __call__(self, *args, **kwargs): - self.called() - self.args.append(args) - return self.result - - -class DenseVariational(test.TestCase): - - def _testKLPenaltyKernel(self, layer_class): - with self.test_session(): - layer = layer_class(units=2) - inputs = random_ops.random_uniform([2, 3], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 1) - self.assertListEqual(layer.losses, losses) - - def _testKLPenaltyBoth(self, layer_class): - def _make_normal(dtype, *args): # pylint: disable=unused-argument - return normal_lib.Normal( - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)) - with self.test_session(): - layer = layer_class( - units=2, - bias_posterior_fn=prob_layers_util.default_mean_field_normal_fn(), - bias_prior_fn=_make_normal) - inputs = random_ops.random_uniform([2, 3], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 2) - self.assertListEqual(layer.losses, losses) - - def _testDenseSetUp(self, layer_class, batch_size, in_size, out_size, - **kwargs): - seed = Counter() - inputs = random_ops.random_uniform([batch_size, in_size], seed=seed()) - - kernel_size = [in_size, out_size] - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform(kernel_size, seed=seed()), - scale=random_ops.random_uniform(kernel_size, seed=seed()), - result_log_prob=random_ops.random_uniform(kernel_size, seed=seed()), - result_sample=random_ops.random_uniform(kernel_size, seed=seed())) - kernel_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(kernel_size, seed=seed()), - result_sample=random_ops.random_uniform(kernel_size, seed=seed())) - kernel_divergence = MockKLDivergence( - result=random_ops.random_uniform(kernel_size, seed=seed())) - - bias_size = [out_size] - bias_posterior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_divergence = MockKLDivergence( - result=random_ops.random_uniform(bias_size, seed=seed())) - - layer = layer_class( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - kernel_prior_fn=lambda *args: kernel_prior, - kernel_divergence_fn=kernel_divergence, - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - bias_prior_fn=lambda *args: bias_prior, - bias_divergence_fn=bias_divergence, - **kwargs) - - outputs = layer(inputs) - - kl_penalty = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - return (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, - layer, inputs, outputs, kl_penalty) - - def testKLPenaltyKernelReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseReparameterization) - - def testKLPenaltyKernelLocalReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseLocalReparameterization) - - def testKLPenaltyKernelFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseFlipout) - - def testKLPenaltyBothReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseReparameterization) - - def testKLPenaltyBothLocalReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseLocalReparameterization) - - def testKLPenaltyBothFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseFlipout) - - def testDenseReparameterization(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseReparameterization, - batch_size, in_size, out_size) - - expected_outputs = ( - math_ops.matmul(inputs, kernel_posterior.result_sample) + - bias_posterior.result_sample) - - [ - expected_outputs_, actual_outputs_, - expected_kernel_, actual_kernel_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_posterior.result_sample, layer.kernel_posterior_tensor, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_kernel_, actual_kernel_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - kernel_posterior.result_sample]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testDenseLocalReparameterization(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseLocalReparameterization, - batch_size, in_size, out_size) - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=math_ops.matmul(inputs, kernel_posterior.result_loc), - scale=math_ops.matmul( - inputs**2., kernel_posterior.result_scale**2)**0.5) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - expected_outputs = (expected_kernel_posterior_affine_tensor + - bias_posterior.result_sample) - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testDenseFlipout(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseFlipout, - batch_size, in_size, out_size, seed=44) - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(kernel_posterior.result_loc), - scale=kernel_posterior.result_scale) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - - sign_input = random_ops.random_uniform( - [batch_size, in_size], - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=layer.seed) - sign_input = math_ops.cast(2 * sign_input - 1, inputs.dtype) - sign_output = random_ops.random_uniform( - [batch_size, out_size], - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=distribution_util.gen_new_seed( - layer.seed, salt="dense_flipout")) - sign_output = math_ops.cast(2 * sign_output - 1, inputs.dtype) - perturbed_inputs = math_ops.matmul( - inputs * sign_input, expected_kernel_posterior_affine_tensor) - perturbed_inputs *= sign_output - - expected_outputs = math_ops.matmul(inputs, kernel_posterior.result_loc) - expected_outputs += perturbed_inputs - expected_outputs += bias_posterior.result_sample - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, kernel_prior.distribution, None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testRandomDenseFlipout(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - seed = Counter() - inputs = random_ops.random_uniform([batch_size, in_size], seed=seed()) - - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - scale=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - result_log_prob=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - result_sample=random_ops.random_uniform( - [in_size, out_size], seed=seed())) - bias_posterior = MockDistribution( - loc=random_ops.random_uniform( - [out_size], seed=seed()), - scale=random_ops.random_uniform( - [out_size], seed=seed()), - result_log_prob=random_ops.random_uniform( - [out_size], seed=seed()), - result_sample=random_ops.random_uniform( - [out_size], seed=seed())) - layer_one = prob_layers_lib.DenseFlipout( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=44) - layer_two = prob_layers_lib.DenseFlipout( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=45) - - outputs_one = layer_one(inputs) - outputs_two = layer_two(inputs) - - outputs_one_, outputs_two_ = sess.run([ - outputs_one, outputs_two]) - - self.assertLess(np.sum(np.isclose(outputs_one_, outputs_two_)), out_size) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py index 63d93fad64d077aa385b72428665e841b6784b90..f508e5b114a55fc1aeb07212595fda45fc308c7b 100644 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py +++ b/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py @@ -12,34 +12,195 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for metropolis_hastings.py.""" +"""Tests for Metropolis-Hastings.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np + from tensorflow.contrib.bayesflow.python.ops import metropolis_hastings_impl as mh +from tensorflow.contrib.distributions.python.ops import mvn_tril as mvn_tril_lib +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables +from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.platform import test -class McmcStepTest(test.TestCase): +class MetropolisHastingsTest(test.TestCase): + + def testKernelStateTensor(self): + """Test that transition kernel works with tensor input to `state`.""" + loc = variable_scope.get_variable("loc", initializer=0.) + + def target_log_prob_fn(loc): + return normal_lib.Normal(loc=0.0, scale=0.1).log_prob(loc) + + new_state, _ = mh.kernel( + target_log_prob_fn=target_log_prob_fn, + proposal_fn=mh.proposal_normal(scale=0.05), + current_state=loc, + seed=231251) + loc_update = loc.assign(new_state) + + init = variables.initialize_all_variables() + with self.test_session() as sess: + sess.run(init) + loc_samples = [] + for _ in range(2500): + loc_sample = sess.run(loc_update) + loc_samples.append(loc_sample) + loc_samples = loc_samples[500:] # drop samples for burn-in + + self.assertAllClose(np.mean(loc_samples), 0.0, rtol=1e-5, atol=1e-1) + self.assertAllClose(np.std(loc_samples), 0.1, rtol=1e-5, atol=1e-1) + + def testKernelStateList(self): + """Test that transition kernel works with list input to `state`.""" + num_chains = 2 + loc_one = variable_scope.get_variable( + "loc_one", [num_chains], + initializer=init_ops.zeros_initializer()) + loc_two = variable_scope.get_variable( + "loc_two", [num_chains], initializer=init_ops.zeros_initializer()) + + def target_log_prob_fn(loc_one, loc_two): + loc = array_ops.stack([loc_one, loc_two]) + log_prob = mvn_tril_lib.MultivariateNormalTriL( + loc=constant_op.constant([0., 0.]), + scale_tril=constant_op.constant([[0.1, 0.1], [0.0, 0.1]])).log_prob( + loc) + return math_ops.reduce_sum(log_prob, 0) + + def proposal_fn(loc_one, loc_two): + loc_one_proposal = mh.proposal_normal(scale=0.05) + loc_two_proposal = mh.proposal_normal(scale=0.05) + loc_one_sample, _ = loc_one_proposal(loc_one) + loc_two_sample, _ = loc_two_proposal(loc_two) + return [loc_one_sample, loc_two_sample], None + + new_state, _ = mh.kernel( + target_log_prob_fn=target_log_prob_fn, + proposal_fn=proposal_fn, + current_state=[loc_one, loc_two], + seed=12415) + loc_one_update = loc_one.assign(new_state[0]) + loc_two_update = loc_two.assign(new_state[1]) + + init = variables.initialize_all_variables() + with self.test_session() as sess: + sess.run(init) + loc_one_samples = [] + loc_two_samples = [] + for _ in range(10000): + loc_one_sample, loc_two_sample = sess.run( + [loc_one_update, loc_two_update]) + loc_one_samples.append(loc_one_sample) + loc_two_samples.append(loc_two_sample) + + loc_one_samples = np.array(loc_one_samples) + loc_two_samples = np.array(loc_two_samples) + loc_one_samples = loc_one_samples[1000:] # drop samples for burn-in + loc_two_samples = loc_two_samples[1000:] # drop samples for burn-in + + self.assertAllClose(np.mean(loc_one_samples, 0), + np.array([0.] * num_chains), + rtol=1e-5, atol=1e-1) + self.assertAllClose(np.mean(loc_two_samples, 0), + np.array([0.] * num_chains), + rtol=1e-5, atol=1e-1) + self.assertAllClose(np.std(loc_one_samples, 0), + np.array([0.1] * num_chains), + rtol=1e-5, atol=1e-1) + self.assertAllClose(np.std(loc_two_samples, 0), + np.array([0.1] * num_chains), + rtol=1e-5, atol=1e-1) + + def testKernelResultsUsingTruncatedDistribution(self): + def log_prob(x): + return array_ops.where( + x >= 0., + -x - x**2, + array_ops.fill(x.shape, math_ops.cast(-np.inf, x.dtype))) + # The truncated distribution has the property that it is likely to attract + # the flow toward, and below, zero...but for x <=0, + # log_prob(x) = -inf, which should result in rejection, as well + # as a non-finite log_prob. Thus, this distribution gives us an opportunity + # to test out the kernel results ability to correctly capture rejections due + # to finite AND non-finite reasons. + + num_results = 1000 + # Large step size, will give rejections due to going into a region of + # log_prob = -inf. + step_size = 0.3 + num_chains = 2 + + with self.test_session(graph=ops.Graph()) as sess: + + # Start multiple independent chains. + initial_state = ops.convert_to_tensor([0.1] * num_chains) - def test_density_increasing_step_accepted(self): + states = [] + is_accepted = [] + proposed_states = [] + current_state = initial_state + for _ in range(num_results): + current_state, kernel_results = mh.kernel( + target_log_prob_fn=log_prob, + proposal_fn=mh.proposal_uniform(step_size=step_size), + current_state=current_state, + seed=42) + states.append(current_state) + proposed_states.append(kernel_results.proposed_state) + is_accepted.append(kernel_results.is_accepted) + + states = array_ops.stack(states) + proposed_states = array_ops.stack(proposed_states) + is_accepted = array_ops.stack(is_accepted) + states_, pstates_, is_accepted_ = sess.run( + [states, proposed_states, is_accepted]) + + # We better have accepted a decent amount, even near end of the chain. + self.assertLess( + 0.1, is_accepted_[int(0.9 * num_results):].mean()) + # We better not have any NaNs in states. + self.assertAllEqual(np.zeros_like(states_), + np.isnan(states_)) + # We better not have any +inf in states. + self.assertAllEqual(np.zeros_like(states_), + np.isposinf(states_)) + + # The move is accepted ==> state = proposed state. + self.assertAllEqual( + states_[is_accepted_], + pstates_[is_accepted_], + ) + + # The move was rejected <==> state[t] == state[t - 1]. + for t in range(1, num_results): + for i in range(num_chains): + if is_accepted_[t, i]: + self.assertNotEqual(states_[t, i], states_[t - 1, i]) + else: + self.assertEqual(states_[t, i], states_[t - 1, i]) + + def testDensityIncreasingStepAccepted(self): """Tests that if a transition increases density, it is always accepted.""" target_log_density = lambda x: - x * x - state = variable_scope.get_variable('state', initializer=10.) + state = variable_scope.get_variable("state", initializer=10.) state_log_density = variable_scope.get_variable( - 'state_log_density', + "state_log_density", initializer=target_log_density(state.initialized_value())) log_accept_ratio = variable_scope.get_variable( - 'log_accept_ratio', initializer=0.) + "log_accept_ratio", initializer=0.) get_next_proposal = lambda x: (x - 1., None) step = mh.evolve(state, state_log_density, log_accept_ratio, @@ -54,7 +215,7 @@ class McmcStepTest(test.TestCase): self.assertAlmostEqual(sample, 9 - j) self.assertAlmostEqual(sample_log_density, - (9 - j) * (9 - j)) - def test_sample_properties(self): + def testSampleProperties(self): """Tests that the samples converge to the target distribution.""" def target_log_density(x): @@ -62,16 +223,16 @@ class McmcStepTest(test.TestCase): return - (x - 2.0) * (x - 2.0) * 0.5 # Use the uniform random walker to generate proposals. - proposal_fn = mh.uniform_random_proposal( + proposal_fn = mh.proposal_uniform( step_size=1.0, seed=1234) - state = variable_scope.get_variable('state', initializer=0.0) + state = variable_scope.get_variable("state", initializer=0.0) state_log_density = variable_scope.get_variable( - 'state_log_density', + "state_log_density", initializer=target_log_density(state.initialized_value())) - log_accept_ratio = variable_scope.get_variable( - 'log_accept_ratio', initializer=0.) + "log_accept_ratio", initializer=0.) + # Random walk MCMC converges slowly so need to put in enough iterations. num_iterations = 5000 step = mh.evolve(state, state_log_density, log_accept_ratio, @@ -98,11 +259,11 @@ class McmcStepTest(test.TestCase): self.assertAlmostEqual(sample_mean, 2.0, delta=0.1) self.assertAlmostEqual(sample_variance, 1.0, delta=0.1) - def test_normal_proposals(self): + def testProposalNormal(self): """Tests that the normal proposals are correctly distributed.""" initial_points = array_ops.ones([10000], dtype=dtypes.float32) - proposal_fn = mh.normal_random_proposal( + proposal_fn = mh.proposal_normal( scale=2.0, seed=1234) proposal_points, _ = proposal_fn(initial_points) @@ -115,7 +276,7 @@ class McmcStepTest(test.TestCase): self.assertAlmostEqual(np.mean(sample), 1.0, delta=0.1) self.assertAlmostEqual(np.std(sample), 2.0, delta=0.1) - def test_docstring_example(self): + def testDocstringExample(self): """Tests the simplified docstring example with multiple chains.""" n = 2 # dimension of the problem @@ -123,7 +284,7 @@ class McmcStepTest(test.TestCase): # Generate 300 initial values randomly. Each of these would be an # independent starting point for a Markov chain. state = variable_scope.get_variable( - 'state', initializer=random_ops.random_normal( + "state", initializer=random_ops.random_normal( [300, n], mean=3.0, dtype=dtypes.float32, seed=42)) # Computes the log(p(x)) for the unit normal density and ignores the @@ -133,12 +294,12 @@ class McmcStepTest(test.TestCase): # Initial log-density value state_log_density = variable_scope.get_variable( - 'state_log_density', + "state_log_density", initializer=log_density(state.initialized_value())) # A variable to store the log_acceptance_ratio: log_acceptance_ratio = variable_scope.get_variable( - 'log_acceptance_ratio', + "log_acceptance_ratio", initializer=array_ops.zeros([300], dtype=dtypes.float32)) # Generates random proposals by moving each coordinate uniformly and @@ -175,5 +336,5 @@ class McmcStepTest(test.TestCase): - np.reshape(covariance, [n**2]))), 0, delta=0.2) -if __name__ == '__main__': +if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py deleted file mode 100644 index 8efd59d6516924bea538717d45bb4ae303583421..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py +++ /dev/null @@ -1,1105 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Csiszar f-Divergence and helpers. - -@@amari_alpha -@@arithmetic_geometric -@@chi_square -@@csiszar_vimco -@@dual_csiszar_function -@@jeffreys -@@jensen_shannon -@@kl_forward -@@kl_reverse -@@log1p_abs -@@modified_gan -@@monte_carlo_csiszar_f_divergence -@@pearson -@@squared_hellinger -@@symmetrized_csiszar_function -@@total_variation -@@triangular - -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib import framework as contrib_framework -from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops.distributions import distribution -from tensorflow.python.ops.distributions import util as distribution_util - - -def amari_alpha(logu, alpha=1., self_normalized=False, name=None): - """The Amari-alpha Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the Amari-alpha Csiszar-function is: - - ```none - f(u) = { -log(u) + (u - 1), alpha = 0 - { u log(u) - (u - 1), alpha = 1 - { [(u**alpha - 1) - alpha (u - 1)] / (alpha (alpha - 1)), otherwise - ``` - - When `self_normalized = False` the `(u - 1)` terms are omitted. - - Warning: when `alpha != 0` and/or `self_normalized = True` this function makes - non-log-space calculations and may therefore be numerically unstable for - `|logu| >> 0`. - - For more information, see: - A. Cichocki and S. Amari. "Families of Alpha-Beta-and GammaDivergences: - Flexible and Robust Measures of Similarities." Entropy, vol. 12, no. 6, pp. - 1532-1568, 2010. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - alpha: `float`-like Python scalar. (See Mathematical Details for meaning.) - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - amari_alpha_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - - Raises: - TypeError: if `alpha` is `None` or a `Tensor`. - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - with ops.name_scope(name, "amari_alpha", [logu]): - if alpha is None or contrib_framework.is_tensor(alpha): - raise TypeError("`alpha` cannot be `None` or `Tensor` type.") - if self_normalized is None or contrib_framework.is_tensor(self_normalized): - raise TypeError("`self_normalized` cannot be `None` or `Tensor` type.") - - logu = ops.convert_to_tensor(logu, name="logu") - - if alpha == 0.: - f = -logu - elif alpha == 1.: - f = math_ops.exp(logu) * logu - else: - f = math_ops.expm1(alpha * logu) / (alpha * (alpha - 1.)) - - if not self_normalized: - return f - - if alpha == 0.: - return f + math_ops.expm1(logu) - elif alpha == 1.: - return f - math_ops.expm1(logu) - else: - return f - math_ops.expm1(logu) / (alpha - 1.) - - -def kl_reverse(logu, self_normalized=False, name=None): - """The reverse Kullback-Leibler Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the KL-reverse Csiszar-function is: - - ```none - f(u) = -log(u) + (u - 1) - ``` - - When `self_normalized = False` the `(u - 1)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[q, p] - ``` - - The KL is "reverse" because in maximum likelihood we think of minimizing `q` - as in `KL[p, q]`. - - Warning: when self_normalized = True` this function makes non-log-space - calculations and may therefore be numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - kl_reverse_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - - Raises: - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - - with ops.name_scope(name, "kl_reverse", [logu]): - return amari_alpha(logu, alpha=0., self_normalized=self_normalized) - - -def kl_forward(logu, self_normalized=False, name=None): - """The forward Kullback-Leibler Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the KL-forward Csiszar-function is: - - ```none - f(u) = u log(u) - (u - 1) - ``` - - When `self_normalized = False` the `(u - 1)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[p, q] - ``` - - The KL is "forward" because in maximum likelihood we think of minimizing `q` - as in `KL[p, q]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - kl_forward_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - - Raises: - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - - with ops.name_scope(name, "kl_forward", [logu]): - return amari_alpha(logu, alpha=1., self_normalized=self_normalized) - - -def jensen_shannon(logu, self_normalized=False, name=None): - """The Jensen-Shannon Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the Jensen-Shannon Csiszar-function is: - - ```none - f(u) = u log(u) - (1 + u) log(1 + u) + (u + 1) log(2) - ``` - - When `self_normalized = False` the `(u + 1) log(2)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[p, m] + KL[q, m] - m(x) = 0.5 p(x) + 0.5 q(x) - ``` - - In a sense, this divergence is the "reverse" of the Arithmetic-Geometric - f-Divergence. - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - For more information, see: - Lin, J. "Divergence measures based on the Shannon entropy." IEEE Trans. - Inf. Th., 37, 145-151, 1991. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - jensen_shannon_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "jensen_shannon", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - npdt = logu.dtype.as_numpy_dtype - y = nn_ops.softplus(logu) - if self_normalized: - y -= np.log(2).astype(npdt) - return math_ops.exp(logu) * logu - (1. + math_ops.exp(logu)) * y - - -def arithmetic_geometric(logu, self_normalized=False, name=None): - """The Arithmetic-Geometric Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the Arithmetic-Geometric Csiszar-function is: - - ```none - f(u) = (1 + u) log( (1 + u) / sqrt(u) ) - (1 + u) log(2) - ``` - - When `self_normalized = False` the `(1 + u) log(2)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[m, p] + KL[m, q] - m(x) = 0.5 p(x) + 0.5 q(x) - ``` - - In a sense, this divergence is the "reverse" of the Jensen-Shannon - f-Divergence. - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - arithmetic_geometric_of_u: `float`-like `Tensor` of the - Csiszar-function evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "arithmetic_geometric", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - y = nn_ops.softplus(logu) - 0.5 * logu - if self_normalized: - y -= np.log(2.).astype(logu.dtype.as_numpy_dtype) - return (1. + math_ops.exp(logu)) * y - - -def total_variation(logu, name=None): - """The Total Variation Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Total-Variation Csiszar-function is: - - ```none - f(u) = 0.5 |u - 1| - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - total_variation_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "total_variation", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * math_ops.abs(math_ops.expm1(logu)) - - -def pearson(logu, name=None): - """The Pearson Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Pearson Csiszar-function is: - - ```none - f(u) = (u - 1)**2 - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - pearson_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - """ - - with ops.name_scope(name, "pearson", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.square(math_ops.expm1(logu)) - - -def squared_hellinger(logu, name=None): - """The Squared-Hellinger Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Squared-Hellinger Csiszar-function is: - - ```none - f(u) = (sqrt(u) - 1)**2 - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - squared_hellinger_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "squared_hellinger", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return pearson(0.5 * logu) - - -def triangular(logu, name=None): - """The Triangular Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Triangular Csiszar-function is: - - ```none - f(u) = (u - 1)**2 / (1 + u) - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - triangular_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "triangular", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return pearson(logu) / (1. + math_ops.exp(logu)) - - -def t_power(logu, t, self_normalized=False, name=None): - """The T-Power Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the T-Power Csiszar-function is: - - ```none - f(u) = s [ u**t - 1 - t(u - 1) ] - s = { -1 0 < t < 1 - { +1 otherwise - ``` - - When `self_normalized = False` the `- t(u - 1)` term is omitted. - - This is similar to the `amari_alpha` Csiszar-function, with the associated - divergence being the same up to factors depending only on `t`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - t: `Tensor` of same `dtype` as `logu` and broadcastable shape. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - with ops.name_scope(name, "t_power", [logu, t]): - logu = ops.convert_to_tensor(logu, name="logu") - t = ops.convert_to_tensor(t, dtype=logu.dtype.base_dtype, name="t") - fu = math_ops.expm1(t * logu) - if self_normalized: - fu -= t * math_ops.expm1(logu) - fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.), - -array_ops.ones_like(t), - array_ops.ones_like(t)) - return fu - - -def log1p_abs(logu, name=None): - """The log1p-abs Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Log1p-Abs Csiszar-function is: - - ```none - f(u) = u**(sign(u-1)) - 1 - ``` - - This function is so-named because it was invented from the following recipe. - Choose a convex function g such that g(0)=0 and solve for f: - - ```none - log(1 + f(u)) = g(log(u)). - <=> - f(u) = exp(g(log(u))) - 1 - ``` - - That is, the graph is identically `g` when y-axis is `log1p`-domain and x-axis - is `log`-domain. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - log1p_abs_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "log1p_abs", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.expm1(math_ops.abs(logu)) - - -def jeffreys(logu, name=None): - """The Jeffreys Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Jeffreys Csiszar-function is: - - ```none - f(u) = 0.5 ( u log(u) - log(u) ) - = 0.5 kl_forward + 0.5 kl_reverse - = symmetrized_csiszar_function(kl_reverse) - = symmetrized_csiszar_function(kl_forward) - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - jeffreys_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "jeffreys", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * math_ops.expm1(logu) * logu - - -def chi_square(logu, name=None): - """The chi-Square Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Chi-square Csiszar-function is: - - ```none - f(u) = u**2 - 1 - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "chi_square", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.expm1(2. * logu) - - -def modified_gan(logu, self_normalized=False, name=None): - """The Modified-GAN Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the modified-GAN (Generative/Adversarial - Network) Csiszar-function is: - - ```none - f(u) = log(1 + u) - log(u) + 0.5 (u - 1) - ``` - - When `self_normalized = False` the `0.5 (u - 1)` is omitted. - - The unmodified GAN Csiszar-function is identical to Jensen-Shannon (with - `self_normalized = False`). - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "chi_square", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - y = nn_ops.softplus(logu) - logu - if self_normalized: - y += 0.5 * math_ops.expm1(logu) - return y - - -def dual_csiszar_function(logu, csiszar_function, name=None): - """Calculates the dual Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Csiszar-dual is defined as: - - ```none - f^*(u) = u f(1 / u) - ``` - - where `f` is some other Csiszar-function. - - For example, the dual of `kl_reverse` is `kl_forward`, i.e., - - ```none - f(u) = -log(u) - f^*(u) = u f(1 / u) = -u log(1 / u) = u log(u) - ``` - - The dual of the dual is the original function: - - ```none - f^**(u) = {u f(1/u)}^*(u) = u (1/u) f(1/(1/u)) = f(u) - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - csiszar_function: Python `callable` representing a Csiszar-function over - log-domain. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - dual_f_of_u: `float`-like `Tensor` of the result of calculating the dual of - `f` at `u = exp(logu)`. - """ - - with ops.name_scope(name, "dual_csiszar_function", [logu]): - return math_ops.exp(logu) * csiszar_function(-logu) - - -def symmetrized_csiszar_function(logu, csiszar_function, name=None): - """Symmetrizes a Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The symmetrized Csiszar-function is defined as: - - ```none - f_g(u) = 0.5 g(u) + 0.5 u g (1 / u) - ``` - - where `g` is some other Csiszar-function. - - We say the function is "symmetrized" because: - - ```none - D_{f_g}[p, q] = D_{f_g}[q, p] - ``` - - for all `p << >> q` (i.e., `support(p) = support(q)`). - - There exists alternatives for symmetrizing a Csiszar-function. For example, - - ```none - f_g(u) = max(f(u), f^*(u)), - ``` - - where `f^*` is the dual Csiszar-function, also implies a symmetric - f-Divergence. - - Example: - - When either of the following functions are symmetrized, we obtain the - Jensen-Shannon Csiszar-function, i.e., - - ```none - g(u) = -log(u) - (1 + u) log((1 + u) / 2) + u - 1 - h(u) = log(4) + 2 u log(u / (1 + u)) - ``` - - implies, - - ```none - f_g(u) = f_h(u) = u log(u) - (1 + u) log((1 + u) / 2) - = jensen_shannon(log(u)). - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - csiszar_function: Python `callable` representing a Csiszar-function over - log-domain. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - symmetrized_g_of_u: `float`-like `Tensor` of the result of applying the - symmetrization of `g` evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "symmetrized_csiszar_function", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * (csiszar_function(logu) - + dual_csiszar_function(logu, csiszar_function)) - - -def monte_carlo_csiszar_f_divergence( - f, - p_log_prob, - q, - num_draws, - use_reparametrization=None, - seed=None, - name=None): - """Monte-Carlo approximation of the Csiszar f-Divergence. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Csiszar f-Divergence for Csiszar-function f is given by: - - ```none - D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ] - ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ), - where x_j ~iid q(X) - ``` - - Tricks: Reparameterization and Score-Gradient - - When q is "reparameterized", i.e., a diffeomorphic transformation of a - parameterless distribution (e.g., - `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and - expectation, i.e., - `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}` - and `s_i = f(x_i), x_i ~iid q(X)`. - - However, if q is not reparameterized, TensorFlow's gradient will be incorrect - since the chain-rule stops at samples of unreparameterized distributions. In - this circumstance using the Score-Gradient trick results in an unbiased - gradient, i.e., - - ```none - grad[ E_q[f(X)] ] - = grad[ int dx q(x) f(x) ] - = int dx grad[ q(x) f(x) ] - = int dx [ q'(x) f(x) + q(x) f'(x) ] - = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ] - = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ] - = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ] - ``` - - Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is - usually preferable to set `use_reparametrization = True`. - - Example Application: - - The Csiszar f-Divergence is a useful framework for variational inference. - I.e., observe that, - - ```none - f(p(x)) = f( E_{q(Z | x)}[ p(x, Z) / q(Z | x) ] ) - <= E_{q(Z | x)}[ f( p(x, Z) / q(Z | x) ) ] - := D_f[p(x, Z), q(Z | x)] - ``` - - The inequality follows from the fact that the "perspective" of `f`, i.e., - `(s, t) |-> t f(s / t))`, is convex in `(s, t)` when `s/t in domain(f)` and - `t` is a real. Since the above framework includes the popular Evidence Lower - BOund (ELBO) as a special case, i.e., `f(u) = -log(u)`, we call this framework - "Evidence Divergence Bound Optimization" (EDBO). - - Args: - f: Python `callable` representing a Csiszar-function in log-space, i.e., - takes `p_log_prob(q_samples) - q.log_prob(q_samples)`. - p_log_prob: Python `callable` taking (a batch of) samples from `q` and - returning the natural-log of the probability under distribution `p`. - (In variational inference `p` is the joint distribution.) - q: `tf.Distribution`-like instance; must implement: - `reparameterization_type`, `sample(n, seed)`, and `log_prob(x)`. - (In variational inference `q` is the approximate posterior distribution.) - num_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - use_reparametrization: Python `bool`. When `None` (the default), - automatically set to: - `q.reparameterization_type == distribution.FULLY_REPARAMETERIZED`. - When `True` uses the standard Monte-Carlo average. When `False` uses the - score-gradient trick. (See above for details.) When `False`, consider - using `csiszar_vimco`. - seed: Python `int` seed for `q.sample`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - monte_carlo_csiszar_f_divergence: `float`-like `Tensor` Monte Carlo - approximation of the Csiszar f-Divergence. - - Raises: - ValueError: if `q` is not a reparameterized distribution and - `use_reparametrization = True`. A distribution `q` is said to be - "reparameterized" when its samples are generated by transforming the - samples of another distribution which does not depend on the - parameterization of `q`. This property ensures the gradient (with respect - to parameters) is valid. - TypeError: if `p_log_prob` is not a Python `callable`. - """ - with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]): - if use_reparametrization is None: - use_reparametrization = (q.reparameterization_type - == distribution.FULLY_REPARAMETERIZED) - elif (use_reparametrization and - q.reparameterization_type != distribution.FULLY_REPARAMETERIZED): - # TODO(jvdillon): Consider only raising an exception if the gradient is - # requested. - raise ValueError( - "Distribution `q` must be reparameterized, i.e., a diffeomorphic " - "transformation of a parameterless distribution. (Otherwise this " - "function has a biased gradient.)") - if not callable(p_log_prob): - raise TypeError("`p_log_prob` must be a Python `callable` function.") - return monte_carlo.expectation( - f=lambda q_samples: f(p_log_prob(q_samples) - q.log_prob(q_samples)), - samples=q.sample(num_draws, seed=seed), - log_prob=q.log_prob, # Only used if use_reparametrization=False. - use_reparametrization=use_reparametrization) - - -def csiszar_vimco(f, - p_log_prob, - q, - num_draws, - num_batch_draws=1, - seed=None, - name=None): - """Use VIMCO to lower the variance of gradient[csiszar_function(Avg(logu))]. - - This function generalizes "Variational Inference for Monte Carlo Objectives" - (VIMCO), i.e., https://arxiv.org/abs/1602.06725, to Csiszar f-Divergences. - - Note: if `q.reparameterization_type = distribution.FULLY_REPARAMETERIZED`, - consider using `monte_carlo_csiszar_f_divergence`. - - The VIMCO loss is: - - ```none - vimco = f(Avg{logu[i] : i=0,...,m-1}) - where, - logu[i] = log( p(x, h[i]) / q(h[i] | x) ) - h[i] iid~ q(H | x) - ``` - - Interestingly, the VIMCO gradient is not the naive gradient of `vimco`. - Rather, it is characterized by: - - ```none - grad[vimco] - variance_reducing_term - where, - variance_reducing_term = Sum{ grad[log q(h[i] | x)] * - (vimco - f(log Avg{h[j;i] : j=0,...,m-1})) - : i=0, ..., m-1 } - h[j;i] = { u[j] j!=i - { GeometricAverage{ u[k] : k!=i} j==i - ``` - - (We omitted `stop_gradient` for brevity. See implementation for more details.) - - The `Avg{h[j;i] : j}` term is a kind of "swap-out average" where the `i`-th - element has been replaced by the leave-`i`-out Geometric-average. - - This implementation prefers numerical precision over efficiency, i.e., - `O(num_draws * num_batch_draws * prod(batch_shape) * prod(event_shape))`. - (The constant may be fairly large, perhaps around 12.) - - Args: - f: Python `callable` representing a Csiszar-function in log-space. - p_log_prob: Python `callable` representing the natural-log of the - probability under distribution `p`. (In variational inference `p` is the - joint distribution.) - q: `tf.Distribution`-like instance; must implement: `sample(n, seed)`, and - `log_prob(x)`. (In variational inference `q` is the approximate posterior - distribution.) - num_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - num_batch_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - seed: Python `int` seed for `q.sample`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - vimco: The Csiszar f-Divergence generalized VIMCO objective. - - Raises: - ValueError: if `num_draws < 2`. - """ - with ops.name_scope(name, "csiszar_vimco", [num_draws, num_batch_draws]): - if num_draws < 2: - raise ValueError("Must specify num_draws > 1.") - stop = array_ops.stop_gradient # For readability. - x = stop(q.sample(sample_shape=[num_draws, num_batch_draws], - seed=seed)) - logqx = q.log_prob(x) - logu = p_log_prob(x) - logqx - f_log_avg_u, f_log_sooavg_u = [f(r) for r in csiszar_vimco_helper(logu)] - dotprod = math_ops.reduce_sum( - logqx * stop(f_log_avg_u - f_log_sooavg_u), - axis=0) # Sum over iid samples. - # We now rewrite f_log_avg_u so that: - # `grad[f_log_avg_u] := grad[f_log_avg_u + dotprod]`. - # To achieve this, we use a trick that - # `f(x) - stop(f(x)) == zeros_like(f(x))` - # but its gradient is grad[f(x)]. - # Note that IEEE754 specifies that `x - x == 0.` and `x + 0. == x`, hence - # this trick loses no precision. For more discussion regarding the relevant - # portions of the IEEE754 standard, see the StackOverflow question, - # "Is there a floating point value of x, for which x-x == 0 is false?" - # http://stackoverflow.com/q/2686644 - f_log_avg_u += dotprod - stop(dotprod) # Add zeros_like(dot_prod). - return math_ops.reduce_mean(f_log_avg_u, axis=0) # Avg over batches. - - -def csiszar_vimco_helper(logu, name=None): - """Helper to `csiszar_vimco`; computes `log_avg_u`, `log_sooavg_u`. - - `axis = 0` of `logu` is presumed to correspond to iid samples from `q`, i.e., - - ```none - logu[j] = log(u[j]) - u[j] = p(x, h[j]) / q(h[j] | x) - h[j] iid~ q(H | x) - ``` - - Args: - logu: Floating-type `Tensor` representing `log(p(x, h) / q(h | x))`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - log_avg_u: `logu.dtype` `Tensor` corresponding to the natural-log of the - average of `u`. The sum of the gradient of `log_avg_u` is `1`. - log_sooavg_u: `logu.dtype` `Tensor` characterized by the natural-log of the - average of `u`` except that the average swaps-out `u[i]` for the - leave-`i`-out Geometric-average. The mean of the gradient of - `log_sooavg_u` is `1`. Mathematically `log_sooavg_u` is, - ```none - log_sooavg_u[i] = log(Avg{h[j ; i] : j=0, ..., m-1}) - h[j ; i] = { u[j] j!=i - { GeometricAverage{u[k] : k != i} j==i - ``` - - """ - with ops.name_scope(name, "csiszar_vimco_helper", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - - n = logu.shape.with_rank_at_least(1)[0].value - if n is None: - n = array_ops.shape(logu)[0] - log_n = math_ops.log(math_ops.cast(n, dtype=logu.dtype)) - nm1 = math_ops.cast(n - 1, dtype=logu.dtype) - else: - log_n = np.log(n).astype(logu.dtype.as_numpy_dtype) - nm1 = np.asarray(n - 1, dtype=logu.dtype.as_numpy_dtype) - - # Throughout we reduce across axis=0 since this is presumed to be iid - # samples. - - log_max_u = math_ops.reduce_max(logu, axis=0) - log_sum_u_minus_log_max_u = math_ops.reduce_logsumexp( - logu - log_max_u, axis=0) - - # log_loosum_u[i] = - # = logsumexp(logu[j] : j != i) - # = log( exp(logsumexp(logu)) - exp(logu[i]) ) - # = log( exp(logsumexp(logu - logu[i])) exp(logu[i]) - exp(logu[i])) - # = logu[i] + log(exp(logsumexp(logu - logu[i])) - 1) - # = logu[i] + log(exp(logsumexp(logu) - logu[i]) - 1) - # = logu[i] + softplus_inverse(logsumexp(logu) - logu[i]) - d = log_sum_u_minus_log_max_u + (log_max_u - logu) - # We use `d != 0` rather than `d > 0.` because `d < 0.` should never - # happens; if it does we want to complain loudly (which `softplus_inverse` - # will). - d_ok = math_ops.not_equal(d, 0.) - safe_d = array_ops.where(d_ok, d, array_ops.ones_like(d)) - d_ok_result = logu + distribution_util.softplus_inverse(safe_d) - - inf = np.array(np.inf, dtype=logu.dtype.as_numpy_dtype) - - # When not(d_ok) and is_positive_and_largest then we manually compute the - # log_loosum_u. (We can efficiently do this for any one point but not all, - # hence we still need the above calculation.) This is good because when - # this condition is met, we cannot use the above calculation; its -inf. - # We now compute the log-leave-out-max-sum, replicate it to every - # point and make sure to select it only when we need to. - is_positive_and_largest = math_ops.logical_and( - logu > 0., - math_ops.equal(logu, log_max_u[array_ops.newaxis, ...])) - log_lomsum_u = math_ops.reduce_logsumexp( - array_ops.where(is_positive_and_largest, - array_ops.fill(array_ops.shape(logu), -inf), - logu), - axis=0, keep_dims=True) - log_lomsum_u = array_ops.tile( - log_lomsum_u, - multiples=1 + array_ops.pad([n-1], [[0, array_ops.rank(logu)-1]])) - - d_not_ok_result = array_ops.where( - is_positive_and_largest, - log_lomsum_u, - array_ops.fill(array_ops.shape(d), -inf)) - - log_loosum_u = array_ops.where(d_ok, d_ok_result, d_not_ok_result) - - # The swap-one-out-sum ("soosum") is n different sums, each of which - # replaces the i-th item with the i-th-left-out average, i.e., - # soo_sum_u[i] = [exp(logu) - exp(logu[i])] + exp(mean(logu[!=i])) - # = exp(log_loosum_u[i]) + exp(looavg_logu[i]) - looavg_logu = (math_ops.reduce_sum(logu, axis=0) - logu) / nm1 - log_soosum_u = math_ops.reduce_logsumexp( - array_ops.stack([log_loosum_u, looavg_logu]), - axis=0) - - log_avg_u = log_sum_u_minus_log_max_u + log_max_u - log_n - log_sooavg_u = log_soosum_u - log_n - - log_avg_u.set_shape(logu.shape.with_rank_at_least(1)[1:]) - log_sooavg_u.set_shape(logu.shape) - - return log_avg_u, log_sooavg_u diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad.py b/tensorflow/contrib/bayesflow/python/ops/custom_grad.py index ca1ecb9c40204c3c723fa3423cfe148e823adc28..c8218c57cc487392716510c6c0c1a55e2de88f44 100644 --- a/tensorflow/contrib/bayesflow/python/ops/custom_grad.py +++ b/tensorflow/contrib/bayesflow/python/ops/custom_grad.py @@ -14,7 +14,7 @@ # ============================================================================== """Functions for specifying custom gradients. -See ${python/contrib.bayesflow.custom_gradient}. +See @{tf.contrib.bayesflow.custom_grad.custom_gradient}. """ from __future__ import absolute_import diff --git a/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py b/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py deleted file mode 100644 index 8cabf18903b5f15002470acdfb8fdd3ec31a7413..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Quasi Monte Carlo support: Halton sequence. - -@@sample -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops - - -__all__ = [ - 'sample', -] - - -# The maximum dimension we support. This is limited by the number of primes -# in the _PRIMES array. -_MAX_DIMENSION = 1000 - - -def sample(dim, num_samples=None, sample_indices=None, dtype=None, name=None): - r"""Returns a sample from the `m` dimensional Halton sequence. - - Warning: The sequence elements take values only between 0 and 1. Care must be - taken to appropriately transform the domain of a function if it differs from - the unit cube before evaluating integrals using Halton samples. It is also - important to remember that quasi-random numbers are not a replacement for - pseudo-random numbers in every context. Quasi random numbers are completely - deterministic and typically have significant negative autocorrelation (unless - randomized). - - Computes the members of the low discrepancy Halton sequence in dimension - `dim`. The d-dimensional sequence takes values in the unit hypercube in d - dimensions. Currently, only dimensions up to 1000 are supported. The prime - base for the `k`-th axes is the k-th prime starting from 2. For example, - if dim = 3, then the bases will be [2, 3, 5] respectively and the first - element of the sequence will be: [0.5, 0.333, 0.2]. For a more complete - description of the Halton sequences see: - https://en.wikipedia.org/wiki/Halton_sequence. For low discrepancy sequences - and their applications see: - https://en.wikipedia.org/wiki/Low-discrepancy_sequence. - - The user must supply either `num_samples` or `sample_indices` but not both. - The former is the number of samples to produce starting from the first - element. If `sample_indices` is given instead, the specified elements of - the sequence are generated. For example, sample_indices=tf.range(10) is - equivalent to specifying n=10. - - Example Use: - - ```python - bf = tf.contrib.bayesflow - - # Produce the first 1000 members of the Halton sequence in 3 dimensions. - num_samples = 1000 - dim = 3 - sample = bf.halton_sequence.sample(dim, num_samples=num_samples) - - # Evaluate the integral of x_1 * x_2^2 * x_3^3 over the three dimensional - # hypercube. - powers = tf.range(1.0, limit=dim + 1) - integral = tf.reduce_mean(tf.reduce_prod(sample ** powers, axis=-1)) - true_value = 1.0 / tf.reduce_prod(powers + 1.0) - with tf.Session() as session: - values = session.run((integral, true_value)) - - # Produces a relative absolute error of 1.7%. - print ("Estimated: %f, True Value: %f" % values) - - # Now skip the first 1000 samples and recompute the integral with the next - # thousand samples. The sample_indices argument can be used to do this. - - - sample_indices = tf.range(start=1000, limit=1000 + num_samples, - dtype=tf.int32) - sample_leaped = halton.sample(dim, sample_indices=sample_indices) - - integral_leaped = tf.reduce_mean(tf.reduce_prod(sample_leaped ** powers, - axis=-1)) - with tf.Session() as session: - values = session.run((integral_leaped, true_value)) - # Now produces a relative absolute error of 0.05%. - print ("Leaped Estimated: %f, True Value: %f" % values) - ``` - - Args: - dim: Positive Python `int` representing each sample's `event_size.` Must - not be greater than 1000. - num_samples: (Optional) positive Python `int`. The number of samples to - generate. Either this parameter or sample_indices must be specified but - not both. If this parameter is None, then the behaviour is determined by - the `sample_indices`. - sample_indices: (Optional) `Tensor` of dtype int32 and rank 1. The elements - of the sequence to compute specified by their position in the sequence. - The entries index into the Halton sequence starting with 0 and hence, - must be whole numbers. For example, sample_indices=[0, 5, 6] will produce - the first, sixth and seventh elements of the sequence. If this parameter - is None, then the `num_samples` parameter must be specified which gives - the number of desired samples starting from the first sample. - dtype: (Optional) The dtype of the sample. One of `float32` or `float64`. - Default is `float32`. - name: (Optional) Python `str` describing ops managed by this function. If - not supplied the name of this function is used. - - Returns: - halton_elements: Elements of the Halton sequence. `Tensor` of supplied dtype - and `shape` `[num_samples, dim]` if `num_samples` was specified or shape - `[s, dim]` where s is the size of `sample_indices` if `sample_indices` - were specified. - - Raises: - ValueError: if both `sample_indices` and `num_samples` were specified or - if dimension `dim` is less than 1 or greater than 1000. - """ - if dim < 1 or dim > _MAX_DIMENSION: - raise ValueError( - 'Dimension must be between 1 and {}. Supplied {}'.format(_MAX_DIMENSION, - dim)) - if (num_samples is None) == (sample_indices is None): - raise ValueError('Either `num_samples` or `sample_indices` must be' - ' specified but not both.') - - dtype = dtype or dtypes.float32 - if not dtype.is_floating: - raise ValueError('dtype must be of `float`-type') - - with ops.name_scope(name, 'sample', values=[sample_indices]): - # Here and in the following, the shape layout is as follows: - # [sample dimension, event dimension, coefficient dimension]. - # The coefficient dimension is an intermediate axes which will hold the - # weights of the starting integer when expressed in the (prime) base for - # an event dimension. - indices = _get_indices(num_samples, sample_indices, dtype) - radixes = array_ops.constant(_PRIMES[0:dim], dtype=dtype, shape=[dim, 1]) - - max_sizes_by_axes = _base_expansion_size(math_ops.reduce_max(indices), - radixes) - - max_size = math_ops.reduce_max(max_sizes_by_axes) - - # The powers of the radixes that we will need. Note that there is a bit - # of an excess here. Suppose we need the place value coefficients of 7 - # in base 2 and 3. For 2, we will have 3 digits but we only need 2 digits - # for base 3. However, we can only create rectangular tensors so we - # store both expansions in a [2, 3] tensor. This leads to the problem that - # we might end up attempting to raise large numbers to large powers. For - # example, base 2 expansion of 1024 has 10 digits. If we were in 10 - # dimensions, then the 10th prime (29) we will end up computing 29^10 even - # though we don't need it. We avoid this by setting the exponents for each - # axes to 0 beyond the maximum value needed for that dimension. - exponents_by_axes = array_ops.tile([math_ops.range(max_size)], [dim, 1]) - weight_mask = exponents_by_axes > max_sizes_by_axes - capped_exponents = array_ops.where( - weight_mask, array_ops.zeros_like(exponents_by_axes), exponents_by_axes) - weights = radixes ** capped_exponents - coeffs = math_ops.floor_div(indices, weights) - coeffs *= 1 - math_ops.cast(weight_mask, dtype) - coeffs = (coeffs % radixes) / radixes - return math_ops.reduce_sum(coeffs / weights, axis=-1) - - -def _get_indices(n, sample_indices, dtype, name=None): - """Generates starting points for the Halton sequence procedure. - - The k'th element of the sequence is generated starting from a positive integer - which must be distinct for each `k`. It is conventional to choose the starting - point as `k` itself (or `k+1` if k is zero based). This function generates - the starting integers for the required elements and reshapes the result for - later use. - - Args: - n: Positive `int`. The number of samples to generate. If this - parameter is supplied, then `sample_indices` should be None. - sample_indices: `Tensor` of dtype int32 and rank 1. The entries - index into the Halton sequence starting with 0 and hence, must be whole - numbers. For example, sample_indices=[0, 5, 6] will produce the first, - sixth and seventh elements of the sequence. If this parameter is not None - then `n` must be None. - dtype: The dtype of the sample. One of `float32` or `float64`. - Default is `float32`. - name: Python `str` name which describes ops created by this function. - - Returns: - indices: `Tensor` of dtype `dtype` and shape = `[n, 1, 1]`. - """ - with ops.name_scope(name, 'get_indices', [n, sample_indices]): - if sample_indices is None: - sample_indices = math_ops.range(n, dtype=dtype) - else: - sample_indices = math_ops.cast(sample_indices, dtype) - - # Shift the indices so they are 1 based. - indices = sample_indices + 1 - - # Reshape to make space for the event dimension and the place value - # coefficients. - return array_ops.reshape(indices, [-1, 1, 1]) - - -def _base_expansion_size(num, bases): - """Computes the number of terms in the place value expansion. - - Let num = a0 + a1 b + a2 b^2 + ... ak b^k be the place value expansion of - `num` in base b (ak <> 0). This function computes and returns `k` for each - base `b` specified in `bases`. - - This can be inferred from the base `b` logarithm of `num` as follows: - $$k = Floor(log_b (num)) + 1 = Floor( log(num) / log(b)) + 1$$ - - Args: - num: Scalar `Tensor` of dtype either `float32` or `float64`. The number to - compute the base expansion size of. - bases: `Tensor` of the same dtype as num. The bases to compute the size - against. - - Returns: - Tensor of same dtype and shape as `bases` containing the size of num when - written in that base. - """ - return math_ops.floor(math_ops.log(num) / math_ops.log(bases)) + 1 - - -def _primes_less_than(n): - # Based on - # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188 - """Returns sorted array of primes such that `2 <= prime < n`.""" - small_primes = np.array((2, 3, 5)) - if n <= 6: - return small_primes[small_primes < n] - sieve = np.ones(n // 3 + (n % 6 == 2), dtype=np.bool) - sieve[0] = False - m = int(n ** 0.5) // 3 + 1 - for i in range(m): - if not sieve[i]: - continue - k = 3 * i + 1 | 1 - sieve[k ** 2 // 3::2 * k] = False - sieve[(k ** 2 + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = False - return np.r_[2, 3, 3 * np.nonzero(sieve)[0] + 1 | 1] - -_PRIMES = _primes_less_than(7919+1) - -assert len(_PRIMES) == _MAX_DIMENSION diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc.py b/tensorflow/contrib/bayesflow/python/ops/hmc.py index 977d42fc16bb91777a76c45ac24f3c5dc587f5fe..c8a5a195d3d709ded7afd09287255deab2ac2f3c 100644 --- a/tensorflow/contrib/bayesflow/python/ops/hmc.py +++ b/tensorflow/contrib/bayesflow/python/ops/hmc.py @@ -12,8 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Hamiltonian Monte Carlo, a gradient-based MCMC algorithm. -""" +"""Hamiltonian Monte Carlo, a gradient-based MCMC algorithm.""" from __future__ import absolute_import from __future__ import division @@ -24,11 +23,8 @@ from tensorflow.contrib.bayesflow.python.ops.hmc_impl import * # pylint: disabl from tensorflow.python.util import all_util _allowed_symbols = [ - 'chain', - 'kernel', - 'leapfrog_integrator', - 'leapfrog_step', - 'ais_chain' + "sample_chain", + "kernel", ] all_util.remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py index 5685a942e98800a39ec718adc67bcfd43aeafd52..66afcc749746ab5c04114e585c5f93a3f3354d86 100644 --- a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py +++ b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py @@ -14,17 +14,15 @@ # ============================================================================== """Hamiltonian Monte Carlo, a gradient-based MCMC algorithm. -@@chain -@@update -@@leapfrog_integrator -@@leapfrog_step -@@ais_chain +@@sample_chain +@@kernel """ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import numpy as np from tensorflow.python.framework import dtypes @@ -32,283 +30,312 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import functional_ops -from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import gradients_impl as gradients_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.ops.distributions import util as distributions_util __all__ = [ - 'chain', - 'kernel', - 'leapfrog_integrator', - 'leapfrog_step', - 'ais_chain' + "sample_chain", + "kernel", ] -def _make_potential_and_grad(target_log_prob_fn): - def potential_and_grad(x): - log_prob_result = -target_log_prob_fn(x) - grad_result = gradients_impl.gradients(math_ops.reduce_sum(log_prob_result), - x)[0] - return log_prob_result, grad_result - return potential_and_grad - - -def chain(n_iterations, step_size, n_leapfrog_steps, initial_x, - target_log_prob_fn, event_dims=(), name=None): +KernelResults = collections.namedtuple( + "KernelResults", + [ + "log_accept_ratio", + "current_grads_target_log_prob", # "Current result" means "accepted". + "current_target_log_prob", # "Current result" means "accepted". + "is_accepted", + "proposed_grads_target_log_prob", + "proposed_state", + "proposed_target_log_prob", + ]) + + +def _make_dummy_kernel_results( + dummy_state, + dummy_target_log_prob, + dummy_grads_target_log_prob): + return KernelResults( + log_accept_ratio=dummy_target_log_prob, + current_grads_target_log_prob=dummy_grads_target_log_prob, + current_target_log_prob=dummy_target_log_prob, + is_accepted=array_ops.ones_like(dummy_target_log_prob, dtypes.bool), + proposed_grads_target_log_prob=dummy_grads_target_log_prob, + proposed_state=dummy_state, + proposed_target_log_prob=dummy_target_log_prob, + ) + + +def sample_chain( + num_results, + target_log_prob_fn, + current_state, + step_size, + num_leapfrog_steps, + num_burnin_steps=0, + num_steps_between_results=0, + seed=None, + current_target_log_prob=None, + current_grads_target_log_prob=None, + name=None): """Runs multiple iterations of one or more Hamiltonian Monte Carlo chains. - Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) - algorithm that takes a series of gradient-informed steps to produce - a Metropolis proposal. This function samples from an HMC Markov - chain whose initial state is `initial_x` and whose stationary - distribution has log-density `target_log_prob_fn()`. - - This function can update multiple chains in parallel. It assumes - that all dimensions of `initial_x` not specified in `event_dims` are - independent, and should therefore be updated independently. The - output of `target_log_prob_fn()` should sum log-probabilities across - all event dimensions. Slices along dimensions not in `event_dims` - may have different target distributions; this is up to + Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm + that takes a series of gradient-informed steps to produce a Metropolis + proposal. This function samples from an HMC Markov chain at `current_state` + and whose stationary distribution has log-unnormalized-density `target_log_prob_fn()`. - This function basically just wraps `hmc.kernel()` in a tf.scan() loop. + This function samples from multiple chains in parallel. It assumes that the + the leftmost dimensions of (each) `current_state` (part) index an independent + chain. The function `target_log_prob_fn()` sums log-probabilities across + event dimensions (i.e., current state (part) rightmost dimensions). Each + element of the output of `target_log_prob_fn()` represents the (possibly + unnormalized) log-probability of the joint distribution over (all) the current + state (parts). - Args: - n_iterations: Integer number of Markov chain updates to run. - step_size: Scalar step size or array of step sizes for the - leapfrog integrator. Broadcasts to the shape of - `initial_x`. Larger step sizes lead to faster progress, but - too-large step sizes make rejection exponentially more likely. - When possible, it's often helpful to match per-variable step - sizes to the standard deviations of the target distribution in - each variable. - n_leapfrog_steps: Integer number of steps to run the leapfrog - integrator for. Total progress per HMC step is roughly - proportional to step_size * n_leapfrog_steps. - initial_x: Tensor of initial state(s) of the Markov chain(s). - target_log_prob_fn: Python callable which takes an argument like `initial_x` - and returns its (possibly unnormalized) log-density under the target - distribution. - event_dims: List of dimensions that should not be treated as - independent. This allows for multiple chains to be run independently - in parallel. Default is (), i.e., all dimensions are independent. - name: Python `str` name prefixed to Ops created by this function. + The `current_state` can be represented as a single `Tensor` or a `list` of + `Tensors` which collectively represent the current state. When specifying a + `list`, one must also specify a list of `step_size`s. - Returns: - acceptance_probs: Tensor with the acceptance probabilities for each - iteration. Has shape matching `target_log_prob_fn(initial_x)`. - chain_states: Tensor with the state of the Markov chain at each iteration. - Has shape `[n_iterations, initial_x.shape[0],...,initial_x.shape[-1]`. + Note: `target_log_prob_fn` is called exactly twice. - #### Examples: + Since HMC states are correlated, it is sometimes desirable to produce + additional intermediate states, and then discard them, ending up with a set of + states with decreased autocorrelation. See [1]. Such "thinning" is made + possible by setting `num_steps_between_results > 0`. The chain then takes + `num_steps_between_results` extra steps between the steps that make it into + the results. The extra steps are never materialized (in calls to `sess.run`), + and thus do not increase memory requirements. - ```python - # Sampling from a standard normal (note `log_joint()` is unnormalized): - def log_joint(x): - return tf.reduce_sum(-0.5 * tf.square(x)) - chain, acceptance_probs = hmc.chain(1000, 0.5, 2, tf.zeros(10), log_joint, - event_dims=[0]) - # Discard first half of chain as warmup/burn-in - warmed_up = chain[500:] - mean_est = tf.reduce_mean(warmed_up, 0) - var_est = tf.reduce_mean(tf.square(warmed_up), 0) - tf.square(mean_est) - ``` + [1]: "Statistically efficient thinning of a Markov chain sampler." + Art B. Owen. April 2017. + http://statweb.stanford.edu/~owen/reports/bestthinning.pdf - ```python - # Sampling from a diagonal-variance Gaussian: - variances = tf.linspace(1., 3., 10) - def log_joint(x): - return tf.reduce_sum(-0.5 / variances * tf.square(x)) - chain, acceptance_probs = hmc.chain(1000, 0.5, 2, tf.zeros(10), log_joint, - event_dims=[0]) - # Discard first half of chain as warmup/burn-in - warmed_up = chain[500:] - mean_est = tf.reduce_mean(warmed_up, 0) - var_est = tf.reduce_mean(tf.square(warmed_up), 0) - tf.square(mean_est) - ``` + #### Examples: - ```python - # Sampling from factor-analysis posteriors with known factors W: - # mu[i, j] ~ Normal(0, 1) - # x[i] ~ Normal(matmul(mu[i], W), I) - def log_joint(mu, x, W): - prior = -0.5 * tf.reduce_sum(tf.square(mu), 1) - x_mean = tf.matmul(mu, W) - likelihood = -0.5 * tf.reduce_sum(tf.square(x - x_mean), 1) - return prior + likelihood - chain, acceptance_probs = hmc.chain(1000, 0.1, 2, - tf.zeros([x.shape[0], W.shape[0]]), - lambda mu: log_joint(mu, x, W), - event_dims=[1]) - # Discard first half of chain as warmup/burn-in - warmed_up = chain[500:] - mean_est = tf.reduce_mean(warmed_up, 0) - var_est = tf.reduce_mean(tf.square(warmed_up), 0) - tf.square(mean_est) - ``` + ##### Sample from a diagonal-variance Gaussian. ```python - # Sampling from the posterior of a Bayesian regression model.: - - # Run 100 chains in parallel, each with a different initialization. - initial_beta = tf.random_normal([100, x.shape[1]]) - chain, acceptance_probs = hmc.chain(1000, 0.1, 10, initial_beta, - log_joint_partial, event_dims=[1]) - # Discard first halves of chains as warmup/burn-in - warmed_up = chain[500:] - # Averaging across samples within a chain and across chains - mean_est = tf.reduce_mean(warmed_up, [0, 1]) - var_est = tf.reduce_mean(tf.square(warmed_up), [0, 1]) - tf.square(mean_est) + tfd = tf.contrib.distributions + + def make_likelihood(true_variances): + return tfd.MultivariateNormalDiag( + scale_diag=tf.sqrt(true_variances)) + + dims = 10 + dtype = np.float32 + true_variances = tf.linspace(dtype(1), dtype(3), dims) + likelihood = make_likelihood(true_variances) + + states, kernel_results = hmc.sample_chain( + num_results=1000, + target_log_prob_fn=likelihood.log_prob, + current_state=tf.zeros(dims), + step_size=0.5, + num_leapfrog_steps=2, + num_burnin_steps=500) + + # Compute sample stats. + sample_mean = tf.reduce_mean(states, axis=0) + sample_var = tf.reduce_mean( + tf.squared_difference(states, sample_mean), + axis=0) ``` - """ - with ops.name_scope(name, 'hmc_chain', [n_iterations, step_size, - n_leapfrog_steps, initial_x]): - initial_x = ops.convert_to_tensor(initial_x, name='initial_x') - non_event_shape = array_ops.shape(target_log_prob_fn(initial_x)) - - def body(a, _): - updated_x, acceptance_probs, log_prob, grad = kernel( - step_size, n_leapfrog_steps, a[0], target_log_prob_fn, event_dims, - a[2], a[3]) - return updated_x, acceptance_probs, log_prob, grad - - potential_and_grad = _make_potential_and_grad(target_log_prob_fn) - potential, grad = potential_and_grad(initial_x) - return functional_ops.scan( - body, array_ops.zeros(n_iterations, dtype=initial_x.dtype), - (initial_x, - array_ops.zeros(non_event_shape, dtype=initial_x.dtype), - -potential, -grad))[:2] - - -def ais_chain(n_iterations, step_size, n_leapfrog_steps, initial_x, - target_log_prob_fn, proposal_log_prob_fn, event_dims=(), - name=None): - """Runs annealed importance sampling (AIS) to estimate normalizing constants. - This routine uses Hamiltonian Monte Carlo to sample from a series of - distributions that slowly interpolates between an initial "proposal" - distribution + ##### Sampling from factor-analysis posteriors with known factors. - `exp(proposal_log_prob_fn(x) - proposal_log_normalizer)` + I.e., - and the target distribution - - `exp(target_log_prob_fn(x) - target_log_normalizer)`, + ```none + for i=1..n: + w[i] ~ Normal(0, eye(d)) # prior + x[i] ~ Normal(loc=matmul(w[i], F)) # likelihood + ``` - accumulating importance weights along the way. The product of these - importance weights gives an unbiased estimate of the ratio of the - normalizing constants of the initial distribution and the target - distribution: + where `F` denotes factors. - E[exp(w)] = exp(target_log_normalizer - proposal_log_normalizer). + ```python + tfd = tf.contrib.distributions + + def make_prior(dims, dtype): + return tfd.MultivariateNormalDiag( + loc=tf.zeros(dims, dtype)) + + def make_likelihood(weights, factors): + return tfd.MultivariateNormalDiag( + loc=tf.tensordot(weights, factors, axes=[[0], [-1]])) + + # Setup data. + num_weights = 10 + num_factors = 4 + num_chains = 100 + dtype = np.float32 + + prior = make_prior(num_weights, dtype) + weights = prior.sample(num_chains) + factors = np.random.randn(num_factors, num_weights).astype(dtype) + x = make_likelihood(weights, factors).sample(num_chains) + + def target_log_prob(w): + # Target joint is: `f(w) = p(w, x | factors)`. + return prior.log_prob(w) + make_likelihood(w, factors).log_prob(x) + + # Get `num_results` samples from `num_chains` independent chains. + chains_states, kernels_results = hmc.sample_chain( + num_results=1000, + target_log_prob_fn=target_log_prob, + current_state=tf.zeros([num_chains, dims], dtype), + step_size=0.1, + num_leapfrog_steps=2, + num_burnin_steps=500) + + # Compute sample stats. + sample_mean = tf.reduce_mean(chains_states, axis=[0, 1]) + sample_var = tf.reduce_mean( + tf.squared_difference(chains_states, sample_mean), + axis=[0, 1]) + ``` Args: - n_iterations: Integer number of Markov chain updates to run. More - iterations means more expense, but smoother annealing between q - and p, which in turn means exponentially lower variance for the - normalizing constant estimator. - step_size: Scalar step size or array of step sizes for the - leapfrog integrator. Broadcasts to the shape of - `initial_x`. Larger step sizes lead to faster progress, but - too-large step sizes make rejection exponentially more likely. - When possible, it's often helpful to match per-variable step - sizes to the standard deviations of the target distribution in - each variable. - n_leapfrog_steps: Integer number of steps to run the leapfrog - integrator for. Total progress per HMC step is roughly - proportional to step_size * n_leapfrog_steps. - initial_x: Tensor of initial state(s) of the Markov chain(s). Must - be a sample from q, or results will be incorrect. - target_log_prob_fn: Python callable which takes an argument like `initial_x` - and returns its (possibly unnormalized) log-density under the target - distribution. - proposal_log_prob_fn: Python callable that returns the log density of the - initial distribution. - event_dims: List of dimensions that should not be treated as - independent. This allows for multiple chains to be run independently - in parallel. Default is (), i.e., all dimensions are independent. + num_results: Integer number of Markov chain draws. + target_log_prob_fn: Python callable which takes an argument like + `current_state` (or `*current_state` if it's a list) and returns its + (possibly unnormalized) log-density under the target distribution. + current_state: `Tensor` or Python `list` of `Tensor`s representing the + current state(s) of the Markov chain(s). The first `r` dimensions index + independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`. + step_size: `Tensor` or Python `list` of `Tensor`s representing the step size + for the leapfrog integrator. Must broadcast with the shape of + `current_state`. Larger step sizes lead to faster progress, but too-large + step sizes make rejection exponentially more likely. When possible, it's + often helpful to match per-variable step sizes to the standard deviations + of the target distribution in each variable. + num_leapfrog_steps: Integer number of steps to run the leapfrog integrator + for. Total progress per HMC step is roughly proportional to `step_size * + num_leapfrog_steps`. + num_burnin_steps: Integer number of chain steps to take before starting to + collect results. + Default value: 0 (i.e., no burn-in). + num_steps_between_results: Integer number of chain steps between collecting + a result. Only one out of every `num_steps_between_samples + 1` steps is + included in the returned results. The number of returned chain states is + still equal to `num_results`. Default value: 0 (i.e., no thinning). + seed: Python integer to seed the random number generator. + current_target_log_prob: (Optional) `Tensor` representing the value of + `target_log_prob_fn` at the `current_state`. The only reason to specify + this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). + current_grads_target_log_prob: (Optional) Python list of `Tensor`s + representing gradient of `target_log_prob` at the `current_state` and wrt + the `current_state`. Must have same shape as `current_state`. The only + reason to specify this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). name: Python `str` name prefixed to Ops created by this function. + Default value: `None` (i.e., "hmc_sample_chain"). Returns: - ais_weights: Tensor with the estimated weight(s). Has shape matching - `target_log_prob_fn(initial_x)`. - chain_states: Tensor with the state(s) of the Markov chain(s) the final - iteration. Has shape matching `initial_x`. - acceptance_probs: Tensor with the acceptance probabilities for the final - iteration. Has shape matching `target_log_prob_fn(initial_x)`. - - #### Examples: - - ```python - # Estimating the normalizing constant of a log-gamma distribution: - def proposal_log_prob(x): - # Standard normal log-probability. This is properly normalized. - return tf.reduce_sum(-0.5 * tf.square(x) - 0.5 * np.log(2 * np.pi), 1) - def target_log_prob(x): - # Unnormalized log-gamma(2, 3) distribution. - # True normalizer is (lgamma(2) - 2 * log(3)) * x.shape[1] - return tf.reduce_sum(2. * x - 3. * tf.exp(x), 1) - # Run 100 AIS chains in parallel - initial_x = tf.random_normal([100, 20]) - w, _, _ = hmc.ais_chain(1000, 0.2, 2, initial_x, target_log_prob, - proposal_log_prob, event_dims=[1]) - log_normalizer_estimate = tf.reduce_logsumexp(w) - np.log(100) - ``` - - ```python - # Estimating the marginal likelihood of a Bayesian regression model: - base_measure = -0.5 * np.log(2 * np.pi) - def proposal_log_prob(x): - # Standard normal log-probability. This is properly normalized. - return tf.reduce_sum(-0.5 * tf.square(x) + base_measure, 1) - def regression_log_joint(beta, x, y): - # This function returns a vector whose ith element is log p(beta[i], y | x). - # Each row of beta corresponds to the state of an independent Markov chain. - log_prior = tf.reduce_sum(-0.5 * tf.square(beta) + base_measure, 1) - means = tf.matmul(beta, x, transpose_b=True) - log_likelihood = tf.reduce_sum(-0.5 * tf.square(y - means) + - base_measure, 1) - return log_prior + log_likelihood - def log_joint_partial(beta): - return regression_log_joint(beta, x, y) - # Run 100 AIS chains in parallel - initial_beta = tf.random_normal([100, x.shape[1]]) - w, beta_samples, _ = hmc.ais_chain(1000, 0.1, 2, initial_beta, - log_joint_partial, proposal_log_prob, - event_dims=[1]) - log_normalizer_estimate = tf.reduce_logsumexp(w) - np.log(100) - ``` + next_states: Tensor or Python list of `Tensor`s representing the + state(s) of the Markov chain(s) at each result step. Has same shape as + input `current_state` but with a prepended `num_results`-size dimension. + kernel_results: `collections.namedtuple` of internal calculations used to + advance the chain. """ - with ops.name_scope(name, 'hmc_ais_chain', - [n_iterations, step_size, n_leapfrog_steps, initial_x]): - non_event_shape = array_ops.shape(target_log_prob_fn(initial_x)) - - beta_series = math_ops.linspace(0., 1., n_iterations+1)[1:] - def _body(a, beta): # pylint: disable=missing-docstring - def log_prob_beta(x): - return ((1 - beta) * proposal_log_prob_fn(x) + - beta * target_log_prob_fn(x)) - last_x = a[0] - w = a[2] - w += (1. / n_iterations) * (target_log_prob_fn(last_x) - - proposal_log_prob_fn(last_x)) - # TODO(b/66917083): There's an opportunity for gradient reuse here. - updated_x, acceptance_probs, _, _ = kernel(step_size, n_leapfrog_steps, - last_x, log_prob_beta, - event_dims) - return updated_x, acceptance_probs, w - - x, acceptance_probs, w = functional_ops.scan( - _body, beta_series, - (initial_x, array_ops.zeros(non_event_shape, dtype=initial_x.dtype), - array_ops.zeros(non_event_shape, dtype=initial_x.dtype))) - return w[-1], x[-1], acceptance_probs[-1] - - -def kernel(step_size, n_leapfrog_steps, x, target_log_prob_fn, event_dims=(), - x_log_prob=None, x_grad=None, name=None): + with ops.name_scope( + name, "hmc_sample_chain", + [num_results, current_state, step_size, num_leapfrog_steps, + num_burnin_steps, num_steps_between_results, seed, + current_target_log_prob, current_grads_target_log_prob]): + with ops.name_scope("initialize"): + [ + current_state, + step_size, + current_target_log_prob, + current_grads_target_log_prob, + ] = _prepare_args( + target_log_prob_fn, + current_state, + step_size, + current_target_log_prob, + current_grads_target_log_prob) + num_results = ops.convert_to_tensor( + num_results, + dtype=dtypes.int32, + name="num_results") + num_leapfrog_steps = ops.convert_to_tensor( + num_leapfrog_steps, + dtype=dtypes.int32, + name="num_leapfrog_steps") + num_burnin_steps = ops.convert_to_tensor( + num_burnin_steps, + dtype=dtypes.int32, + name="num_burnin_steps") + num_steps_between_results = ops.convert_to_tensor( + num_steps_between_results, + dtype=dtypes.int32, + name="num_steps_between_results") + + def _run_chain(num_steps, current_state, kernel_results): + """Runs the chain(s) for `num_steps`.""" + def _loop_body(iter_, current_state, kernel_results): + return [iter_ + 1] + list(kernel( + target_log_prob_fn, + current_state, + step_size, + num_leapfrog_steps, + seed, + kernel_results.current_target_log_prob, + kernel_results.current_grads_target_log_prob)) + while_loop_kwargs = dict( + cond=lambda iter_, *args: iter_ < num_steps, + body=_loop_body, + loop_vars=[ + np.int32(0), + current_state, + kernel_results, + ], + ) + if seed is not None: + while_loop_kwargs["parallel_iterations"] = 1 + return control_flow_ops.while_loop( + **while_loop_kwargs)[1:] # Lop-off "iter_". + + def _scan_body(args_list, iter_): + """Closure which implements `tf.scan` body.""" + current_state, kernel_results = args_list + return _run_chain( + 1 + array_ops.where(math_ops.equal(iter_, 0), + num_burnin_steps, + num_steps_between_results), + current_state, + kernel_results) + + scan_kwargs = dict( + fn=_scan_body, + elems=math_ops.range(num_results), # iter_: used to choose burnin. + initializer=[ + current_state, + _make_dummy_kernel_results( + current_state, + current_target_log_prob, + current_grads_target_log_prob), + ]) + if seed is not None: + scan_kwargs["parallel_iterations"] = 1 + return functional_ops.scan(**scan_kwargs) + + +def kernel(target_log_prob_fn, + current_state, + step_size, + num_leapfrog_steps, + seed=None, + current_target_log_prob=None, + current_grads_target_log_prob=None, + name=None): """Runs one iteration of Hamiltonian Monte Carlo. Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) @@ -316,334 +343,619 @@ def kernel(step_size, n_leapfrog_steps, x, target_log_prob_fn, event_dims=(), a Metropolis proposal. This function applies one step of HMC to randomly update the variable `x`. - This function can update multiple chains in parallel. It assumes - that all dimensions of `x` not specified in `event_dims` are - independent, and should therefore be updated independently. The - output of `target_log_prob_fn()` should sum log-probabilities across - all event dimensions. Slices along dimensions not in `event_dims` - may have different target distributions; for example, if - `event_dims == (1,)`, then `x[0, :]` could have a different target - distribution from x[1, :]. This is up to `target_log_prob_fn()`. - - Args: - step_size: Scalar step size or array of step sizes for the - leapfrog integrator. Broadcasts to the shape of - `x`. Larger step sizes lead to faster progress, but - too-large step sizes make rejection exponentially more likely. - When possible, it's often helpful to match per-variable step - sizes to the standard deviations of the target distribution in - each variable. - n_leapfrog_steps: Integer number of steps to run the leapfrog - integrator for. Total progress per HMC step is roughly - proportional to step_size * n_leapfrog_steps. - x: Tensor containing the value(s) of the random variable(s) to update. - target_log_prob_fn: Python callable which takes an argument like `initial_x` - and returns its (possibly unnormalized) log-density under the target - distribution. - event_dims: List of dimensions that should not be treated as - independent. This allows for multiple chains to be run independently - in parallel. Default is (), i.e., all dimensions are independent. - x_log_prob (optional): Tensor containing the cached output of a previous - call to `target_log_prob_fn()` evaluated at `x` (such as that provided by - a previous call to `kernel()`). Providing `x_log_prob` and - `x_grad` saves one gradient computation per call to `kernel()`. - x_grad (optional): Tensor containing the cached gradient of - `target_log_prob_fn()` evaluated at `x` (such as that provided by - a previous call to `kernel()`). Providing `x_log_prob` and - `x_grad` saves one gradient computation per call to `kernel()`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - updated_x: The updated variable(s) x. Has shape matching `initial_x`. - acceptance_probs: Tensor with the acceptance probabilities for the final - iteration. This is useful for diagnosing step size problems etc. Has - shape matching `target_log_prob_fn(initial_x)`. - new_log_prob: The value of `target_log_prob_fn()` evaluated at `updated_x`. - new_grad: The value of the gradient of `target_log_prob_fn()` evaluated at - `updated_x`. + This function can update multiple chains in parallel. It assumes that all + leftmost dimensions of `current_state` index independent chain states (and are + therefore updated independently). The output of `target_log_prob_fn()` should + sum log-probabilities across all event dimensions. Slices along the rightmost + dimensions may have different target distributions; for example, + `current_state[0, :]` could have a different target distribution from + `current_state[1, :]`. This is up to `target_log_prob_fn()`. (The number of + independent chains is `tf.size(target_log_prob_fn(*current_state))`.) #### Examples: + ##### Simple chain with warm-up. + ```python + tfd = tf.contrib.distributions + # Tuning acceptance rates: + dtype = np.float32 target_accept_rate = 0.631 - def target_log_prob(x): - # Standard normal - return tf.reduce_sum(-0.5 * tf.square(x)) - initial_x = tf.zeros([10]) - initial_log_prob = target_log_prob(initial_x) - initial_grad = tf.gradients(initial_log_prob, initial_x)[0] - # Algorithm state - x = tf.Variable(initial_x, name='x') - step_size = tf.Variable(1., name='step_size') - last_log_prob = tf.Variable(initial_log_prob, name='last_log_prob') - last_grad = tf.Variable(initial_grad, name='last_grad') - # Compute updates - new_x, acceptance_prob, log_prob, grad = hmc.kernel(step_size, 3, x, - target_log_prob, - event_dims=[0], - x_log_prob=last_log_prob) - x_update = tf.assign(x, new_x) - log_prob_update = tf.assign(last_log_prob, log_prob) - grad_update = tf.assign(last_grad, grad) - step_size_update = tf.assign(step_size, - tf.where(acceptance_prob > target_accept_rate, - step_size * 1.01, step_size / 1.01)) - adaptive_updates = [x_update, log_prob_update, grad_update, step_size_update] - sampling_updates = [x_update, log_prob_update, grad_update] - - sess = tf.Session() - sess.run(tf.global_variables_initializer()) + num_warmup_iter = 500 + num_chain_iter = 500 + + x = tf.get_variable(name="x", initializer=dtype(1)) + step_size = tf.get_variable(name="step_size", initializer=dtype(1)) + + target = tfd.Normal(loc=dtype(0), scale=dtype(1)) + + next_x, other_results = hmc.kernel( + target_log_prob_fn=target.log_prob, + current_state=x, + step_size=step_size, + num_leapfrog_steps=3)[:4] + + x_update = x.assign(next_x) + + step_size_update = step_size.assign_add( + step_size * tf.where( + tf.exp(tf.minimum(other_results.log_accept_ratio), 0.) > + target_accept_rate, + 0.01, -0.01)) + + warmup = tf.group([x_update, step_size_update]) + + tf.global_variables_initializer().run() + + sess.graph.finalize() # No more graph building. + # Warm up the sampler and adapt the step size - for i in xrange(500): - sess.run(adaptive_updates) - # Collect samples without adapting step size - samples = np.zeros([500, 10]) - for i in xrange(500): - x_val, _ = sess.run([new_x, sampling_updates]) - samples[i] = x_val - ``` + for _ in xrange(num_warmup_iter): + sess.run(warmup) - ```python - # Empirical-Bayes estimation of a hyperparameter by MCMC-EM: - - # Problem setup - N = 150 - D = 10 - x = np.random.randn(N, D).astype(np.float32) - true_sigma = 0.5 - true_beta = true_sigma * np.random.randn(D).astype(np.float32) - y = x.dot(true_beta) + np.random.randn(N).astype(np.float32) - - def log_prior(beta, log_sigma): - return tf.reduce_sum(-0.5 / tf.exp(2 * log_sigma) * tf.square(beta) - - log_sigma) - def regression_log_joint(beta, log_sigma, x, y): - # This function returns log p(beta | log_sigma) + log p(y | x, beta). - means = tf.matmul(tf.expand_dims(beta, 0), x, transpose_b=True) - means = tf.squeeze(means) - log_likelihood = tf.reduce_sum(-0.5 * tf.square(y - means)) - return log_prior(beta, log_sigma) + log_likelihood - def log_joint_partial(beta): - return regression_log_joint(beta, log_sigma, x, y) - # Our estimate of log(sigma) - log_sigma = tf.Variable(0., name='log_sigma') - # The state of the Markov chain - beta = tf.Variable(tf.random_normal([x.shape[1]]), name='beta') - new_beta, _, _, _ = hmc.kernel(0.1, 5, beta, log_joint_partial, - event_dims=[0]) - beta_update = tf.assign(beta, new_beta) - optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) - with tf.control_dependencies([beta_update]): - log_sigma_update = optimizer.minimize(-log_prior(beta, log_sigma), - var_list=[log_sigma]) - - sess = tf.Session() - sess.run(tf.global_variables_initializer()) - log_sigma_history = np.zeros(1000) - for i in xrange(1000): - log_sigma_val, _ = sess.run([log_sigma, log_sigma_update]) - log_sigma_history[i] = log_sigma_val - # Should converge to something close to true_sigma - plt.plot(np.exp(log_sigma_history)) + # Collect samples without adapting step size + samples = np.zeros([num_chain_iter]) + for i in xrange(num_chain_iter): + _, x_, target_log_prob_, grad_ = sess.run([ + x_update, + x, + other_results.target_log_prob, + other_results.grads_target_log_prob]) + samples[i] = x_ + + print(samples.mean(), samples.std()) ``` - """ - with ops.name_scope(name, 'hmc_kernel', [step_size, n_leapfrog_steps, x]): - potential_and_grad = _make_potential_and_grad(target_log_prob_fn) - x = ops.convert_to_tensor(x, name='x') - - x_shape = array_ops.shape(x) - m = random_ops.random_normal(x_shape, dtype=x.dtype) - - kinetic_0 = 0.5 * math_ops.reduce_sum(math_ops.square(m), event_dims) - - if (x_log_prob is not None) and (x_grad is not None): - log_potential_0, grad_0 = -x_log_prob, -x_grad # pylint: disable=invalid-unary-operand-type - else: - if x_log_prob is not None: - logging.warn('x_log_prob was provided, but x_grad was not,' - ' so x_log_prob was not used.') - if x_grad is not None: - logging.warn('x_grad was provided, but x_log_prob was not,' - ' so x_grad was not used.') - log_potential_0, grad_0 = potential_and_grad(x) - - new_x, new_m, log_potential_1, grad_1 = leapfrog_integrator( - step_size, n_leapfrog_steps, x, m, potential_and_grad, grad_0) - - kinetic_1 = 0.5 * math_ops.reduce_sum(math_ops.square(new_m), event_dims) - - energy_change = log_potential_1 - log_potential_0 + kinetic_1 - kinetic_0 - # Treat NaN as infinite energy (and therefore guaranteed rejection). - energy_change = array_ops.where( - math_ops.is_nan(energy_change), - array_ops.fill(array_ops.shape(energy_change), - energy_change.dtype.as_numpy_dtype(np.inf)), - energy_change) - acceptance_probs = math_ops.exp(math_ops.minimum(-energy_change, 0.)) - accepted = ( - random_ops.random_uniform( - array_ops.shape(acceptance_probs), dtype=x.dtype) - < acceptance_probs) - new_log_prob = -array_ops.where(accepted, log_potential_1, log_potential_0) - - # TODO(b/65738010): This should work, but it doesn't for now. - # reduced_shape = math_ops.reduced_shape(x_shape, event_dims) - reduced_shape = array_ops.shape(math_ops.reduce_sum(x, event_dims, - keep_dims=True)) - accepted = array_ops.reshape(accepted, reduced_shape) - accepted = math_ops.logical_or( - accepted, math_ops.cast(array_ops.zeros_like(x), dtypes.bool)) - new_x = array_ops.where(accepted, new_x, x) - new_grad = -array_ops.where(accepted, grad_1, grad_0) - - # TODO(langmore) Gradients of acceptance_probs and new_log_prob with respect - # to initial_x will propagate NaNs (see testNanFromGradsDontPropagate). This - # should be fixed. - return new_x, acceptance_probs, new_log_prob, new_grad - - -def leapfrog_integrator(step_size, n_steps, initial_position, initial_momentum, - potential_and_grad, initial_grad, name=None): - """Applies `n_steps` steps of the leapfrog integrator. - - This just wraps `leapfrog_step()` in a `tf.while_loop()`, reusing - gradient computations where possible. - Args: - step_size: Scalar step size or array of step sizes for the - leapfrog integrator. Broadcasts to the shape of - `initial_position`. Larger step sizes lead to faster progress, but - too-large step sizes lead to larger discretization error and - worse energy conservation. - n_steps: Number of steps to run the leapfrog integrator. - initial_position: Tensor containing the value(s) of the position variable(s) - to update. - initial_momentum: Tensor containing the value(s) of the momentum variable(s) - to update. - potential_and_grad: Python callable that takes a position tensor like - `initial_position` and returns the potential energy and its gradient at - that position. - initial_grad: Tensor with the value of the gradient of the potential energy - at `initial_position`. - name: Python `str` name prefixed to Ops created by this function. + ##### Sample from more complicated posterior. - Returns: - updated_position: Updated value of the position. - updated_momentum: Updated value of the momentum. - new_potential: Potential energy of the new position. Has shape matching - `potential_and_grad(initial_position)`. - new_grad: Gradient from potential_and_grad() evaluated at the new position. - Has shape matching `initial_position`. + I.e., - Example: Simple quadratic potential. + ```none + W ~ MVN(loc=0, scale=sigma * eye(dims)) + for i=1...num_samples: + X[i] ~ MVN(loc=0, scale=eye(dims)) + eps[i] ~ Normal(loc=0, scale=1) + Y[i] = X[i].T * W + eps[i] + ``` ```python - def potential_and_grad(position): - return tf.reduce_sum(0.5 * tf.square(position)), position - position = tf.placeholder(np.float32) - momentum = tf.placeholder(np.float32) - potential, grad = potential_and_grad(position) - new_position, new_momentum, new_potential, new_grad = hmc.leapfrog_integrator( - 0.1, 3, position, momentum, potential_and_grad, grad) - - sess = tf.Session() - position_val = np.random.randn(10) - momentum_val = np.random.randn(10) - potential_val, grad_val = sess.run([potential, grad], - {position: position_val}) - positions = np.zeros([100, 10]) - for i in xrange(100): - position_val, momentum_val, potential_val, grad_val = sess.run( - [new_position, new_momentum, new_potential, new_grad], - {position: position_val, momentum: momentum_val}) - positions[i] = position_val - # Should trace out sinusoidal dynamics. - plt.plot(positions[:, 0]) - ``` - """ - def leapfrog_wrapper(step_size, x, m, grad, l): - x, m, _, grad = leapfrog_step(step_size, x, m, potential_and_grad, grad) - return step_size, x, m, grad, l + 1 + tfd = tf.contrib.distributions + + def make_training_data(num_samples, dims, sigma): + dt = np.asarray(sigma).dtype + zeros = tf.zeros(dims, dtype=dt) + x = tfd.MultivariateNormalDiag( + loc=zeros).sample(num_samples, seed=1) + w = tfd.MultivariateNormalDiag( + loc=zeros, + scale_identity_multiplier=sigma).sample(seed=2) + noise = tfd.Normal( + loc=dt(0), + scale=dt(1)).sample(num_samples, seed=3) + y = tf.tensordot(x, w, axes=[[1], [0]]) + noise + return y, x, w + + def make_prior(sigma, dims): + # p(w | sigma) + return tfd.MultivariateNormalDiag( + loc=tf.zeros([dims], dtype=sigma.dtype), + scale_identity_multiplier=sigma) + + def make_likelihood(x, w): + # p(y | x, w) + return tfd.MultivariateNormalDiag( + loc=tf.tensordot(x, w, axes=[[1], [0]])) + + # Setup assumptions. + dtype = np.float32 + num_samples = 150 + dims = 10 + num_iters = int(5e3) + + true_sigma = dtype(0.5) + y, x, true_weights = make_training_data(num_samples, dims, true_sigma) + + # Estimate of `log(true_sigma)`. + log_sigma = tf.get_variable(name="log_sigma", initializer=dtype(0)) + sigma = tf.exp(log_sigma) + + # State of the Markov chain. + weights = tf.get_variable( + name="weights", + initializer=np.random.randn(dims).astype(dtype)) + + prior = make_prior(sigma, dims) + + def joint_log_prob_fn(w): + # f(w) = log p(w, y | x) + return prior.log_prob(w) + make_likelihood(x, w).log_prob(y) + + weights_update = weights.assign( + hmc.kernel(target_log_prob_fn=joint_log_prob, + current_state=weights, + step_size=0.1, + num_leapfrog_steps=5)[0]) + + with tf.control_dependencies([weights_update]): + loss = -prior.log_prob(weights) + + optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) + log_sigma_update = optimizer.minimize(loss, var_list=[log_sigma]) + + sess.graph.finalize() # No more graph building. - def counter_fn(a, b, c, d, counter): # pylint: disable=unused-argument - return counter < n_steps + tf.global_variables_initializer().run() - with ops.name_scope(name, 'leapfrog_integrator', - [step_size, n_steps, initial_position, initial_momentum, - initial_grad]): - _, new_x, new_m, new_grad, _ = control_flow_ops.while_loop( - counter_fn, leapfrog_wrapper, [step_size, initial_position, - initial_momentum, initial_grad, - array_ops.constant(0)], back_prop=False) - # We're counting on the runtime to eliminate this redundant computation. - new_potential, new_grad = potential_and_grad(new_x) - return new_x, new_m, new_potential, new_grad + sigma_history = np.zeros(num_iters, dtype) + weights_history = np.zeros([num_iters, dims], dtype) + for i in xrange(num_iters): + _, sigma_, weights_, _ = sess.run([log_sigma_update, sigma, weights]) + weights_history[i, :] = weights_ + sigma_history[i] = sigma_ -def leapfrog_step(step_size, position, momentum, potential_and_grad, grad, - name=None): - """Applies one step of the leapfrog integrator. + true_weights_ = sess.run(true_weights) - Assumes a simple quadratic kinetic energy function: 0.5 * ||momentum||^2. + # Should converge to something close to true_sigma. + plt.plot(sigma_history); + plt.ylabel("sigma"); + plt.xlabel("iteration"); + ``` Args: - step_size: Scalar step size or array of step sizes for the - leapfrog integrator. Broadcasts to the shape of - `position`. Larger step sizes lead to faster progress, but - too-large step sizes lead to larger discretization error and - worse energy conservation. - position: Tensor containing the value(s) of the position variable(s) - to update. - momentum: Tensor containing the value(s) of the momentum variable(s) - to update. - potential_and_grad: Python callable that takes a position tensor like - `position` and returns the potential energy and its gradient at that - position. - grad: Tensor with the value of the gradient of the potential energy - at `position`. + target_log_prob_fn: Python callable which takes an argument like + `current_state` (or `*current_state` if it's a list) and returns its + (possibly unnormalized) log-density under the target distribution. + current_state: `Tensor` or Python `list` of `Tensor`s representing the + current state(s) of the Markov chain(s). The first `r` dimensions index + independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`. + step_size: `Tensor` or Python `list` of `Tensor`s representing the step size + for the leapfrog integrator. Must broadcast with the shape of + `current_state`. Larger step sizes lead to faster progress, but too-large + step sizes make rejection exponentially more likely. When possible, it's + often helpful to match per-variable step sizes to the standard deviations + of the target distribution in each variable. + num_leapfrog_steps: Integer number of steps to run the leapfrog integrator + for. Total progress per HMC step is roughly proportional to `step_size * + num_leapfrog_steps`. + seed: Python integer to seed the random number generator. + current_target_log_prob: (Optional) `Tensor` representing the value of + `target_log_prob_fn` at the `current_state`. The only reason to + specify this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). + current_grads_target_log_prob: (Optional) Python list of `Tensor`s + representing gradient of `current_target_log_prob` at the `current_state` + and wrt the `current_state`. Must have same shape as `current_state`. The + only reason to specify this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). name: Python `str` name prefixed to Ops created by this function. + Default value: `None` (i.e., "hmc_kernel"). Returns: - updated_position: Updated value of the position. - updated_momentum: Updated value of the momentum. - new_potential: Potential energy of the new position. Has shape matching - `potential_and_grad(position)`. - new_grad: Gradient from potential_and_grad() evaluated at the new position. - Has shape matching `position`. + next_state: Tensor or Python list of `Tensor`s representing the state(s) + of the Markov chain(s) at each result step. Has same shape as + `current_state`. + kernel_results: `collections.namedtuple` of internal calculations used to + advance the chain. + + Raises: + ValueError: if there isn't one `step_size` or a list with same length as + `current_state`. + """ + with ops.name_scope( + name, "hmc_kernel", + [current_state, step_size, num_leapfrog_steps, seed, + current_target_log_prob, current_grads_target_log_prob]): + with ops.name_scope("initialize"): + [current_state_parts, step_sizes, current_target_log_prob, + current_grads_target_log_prob] = _prepare_args( + target_log_prob_fn, current_state, step_size, + current_target_log_prob, current_grads_target_log_prob, + maybe_expand=True) + independent_chain_ndims = distributions_util.prefer_static_rank( + current_target_log_prob) + current_momentums = [] + for s in current_state_parts: + current_momentums.append(random_ops.random_normal( + shape=array_ops.shape(s), + dtype=s.dtype.base_dtype, + seed=seed)) + seed = distributions_util.gen_new_seed( + seed, salt="hmc_kernel_momentums") + + num_leapfrog_steps = ops.convert_to_tensor( + num_leapfrog_steps, + dtype=dtypes.int32, + name="num_leapfrog_steps") + [ + proposed_momentums, + proposed_state_parts, + proposed_target_log_prob, + proposed_grads_target_log_prob, + ] = _leapfrog_integrator(current_momentums, + target_log_prob_fn, + current_state_parts, + step_sizes, + num_leapfrog_steps, + current_target_log_prob, + current_grads_target_log_prob) + + energy_change = _compute_energy_change(current_target_log_prob, + current_momentums, + proposed_target_log_prob, + proposed_momentums, + independent_chain_ndims) + log_accept_ratio = -energy_change + + # u < exp(log_accept_ratio), where u~Uniform[0,1) + # ==> log(u) < log_accept_ratio + random_value = random_ops.random_uniform( + shape=array_ops.shape(energy_change), + dtype=energy_change.dtype, + seed=seed) + random_negative = math_ops.log(random_value) + is_accepted = random_negative < log_accept_ratio + + accepted_target_log_prob = array_ops.where(is_accepted, + proposed_target_log_prob, + current_target_log_prob) + + next_state_parts = [_choose(is_accepted, + proposed_state_part, + current_state_part, + independent_chain_ndims) + for current_state_part, proposed_state_part + in zip(current_state_parts, proposed_state_parts)] + + accepted_grads_target_log_prob = [ + _choose(is_accepted, + proposed_grad, + grad, + independent_chain_ndims) + for proposed_grad, grad + in zip(proposed_grads_target_log_prob, current_grads_target_log_prob)] + + maybe_flatten = lambda x: x if _is_list_like(current_state) else x[0] + return [ + maybe_flatten(next_state_parts), + KernelResults( + log_accept_ratio=log_accept_ratio, + current_grads_target_log_prob=accepted_grads_target_log_prob, + current_target_log_prob=accepted_target_log_prob, + is_accepted=is_accepted, + proposed_grads_target_log_prob=proposed_grads_target_log_prob, + proposed_state=maybe_flatten(proposed_state_parts), + proposed_target_log_prob=proposed_target_log_prob, + ), + ] + + +def _leapfrog_integrator(current_momentums, + target_log_prob_fn, + current_state_parts, + step_sizes, + num_leapfrog_steps, + current_target_log_prob=None, + current_grads_target_log_prob=None, + name=None): + """Applies `num_leapfrog_steps` of the leapfrog integrator. + + Assumes a simple quadratic kinetic energy function: `0.5 ||momentum||**2`. - Example: Simple quadratic potential. + #### Examples: + + ##### Simple quadratic potential. ```python - def potential_and_grad(position): - # Simple quadratic potential - return tf.reduce_sum(0.5 * tf.square(position)), position + tfd = tf.contrib.distributions + + dims = 10 + num_iter = int(1e3) + dtype = np.float32 + position = tf.placeholder(np.float32) momentum = tf.placeholder(np.float32) - potential, grad = potential_and_grad(position) - new_position, new_momentum, new_potential, new_grad = hmc.leapfrog_step( - 0.1, position, momentum, potential_and_grad, grad) - - sess = tf.Session() - position_val = np.random.randn(10) - momentum_val = np.random.randn(10) - potential_val, grad_val = sess.run([potential, grad], - {position: position_val}) - positions = np.zeros([100, 10]) - for i in xrange(100): - position_val, momentum_val, potential_val, grad_val = sess.run( - [new_position, new_momentum, new_potential, new_grad], - {position: position_val, momentum: momentum_val}) - positions[i] = position_val - # Should trace out sinusoidal dynamics. - plt.plot(positions[:, 0]) + + [ + next_momentums, + next_positions, + ] = hmc._leapfrog_integrator( + current_momentums=[momentum], + target_log_prob_fn=tfd.MultivariateNormalDiag( + loc=tf.zeros(dims, dtype)).log_prob, + current_state_parts=[position], + step_sizes=0.1, + num_leapfrog_steps=3)[:2] + + sess.graph.finalize() # No more graph building. + + momentum_ = np.random.randn(dims).astype(dtype) + position_ = np.random.randn(dims).astype(dtype) + + positions = np.zeros([num_iter, dims], dtype) + for i in xrange(num_iter): + position_, momentum_ = sess.run( + [next_momentums[0], next_position[0]], + feed_dict={position: position_, momentum: momentum_}) + positions[i] = position_ + + plt.plot(positions[:, 0]); # Sinusoidal. ``` + + Args: + current_momentums: Tensor containing the value(s) of the momentum + variable(s) to update. + target_log_prob_fn: Python callable which takes an argument like + `*current_state_parts` and returns its (possibly unnormalized) log-density + under the target distribution. + current_state_parts: Python `list` of `Tensor`s representing the current + state(s) of the Markov chain(s). The first `independent_chain_ndims` of + the `Tensor`(s) index different chains. + step_sizes: Python `list` of `Tensor`s representing the step size for the + leapfrog integrator. Must broadcast with the shape of + `current_state_parts`. Larger step sizes lead to faster progress, but + too-large step sizes make rejection exponentially more likely. When + possible, it's often helpful to match per-variable step sizes to the + standard deviations of the target distribution in each variable. + num_leapfrog_steps: Integer number of steps to run the leapfrog integrator + for. Total progress per HMC step is roughly proportional to `step_size * + num_leapfrog_steps`. + current_target_log_prob: (Optional) `Tensor` representing the value of + `target_log_prob_fn(*current_state_parts)`. The only reason to specify + this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). + current_grads_target_log_prob: (Optional) Python list of `Tensor`s + representing gradient of `target_log_prob_fn(*current_state_parts`) wrt + `current_state_parts`. Must have same shape as `current_state_parts`. The + only reason to specify this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). + name: Python `str` name prefixed to Ops created by this function. + Default value: `None` (i.e., "hmc_leapfrog_integrator"). + + Returns: + proposed_momentums: Updated value of the momentum. + proposed_state_parts: Tensor or Python list of `Tensor`s representing the + state(s) of the Markov chain(s) at each result step. Has same shape as + input `current_state_parts`. + proposed_target_log_prob: `Tensor` representing the value of + `target_log_prob_fn` at `next_state`. + proposed_grads_target_log_prob: Gradient of `proposed_target_log_prob` wrt + `next_state`. + + Raises: + ValueError: if `len(momentums) != len(state_parts)`. + ValueError: if `len(state_parts) != len(step_sizes)`. + ValueError: if `len(state_parts) != len(grads_target_log_prob)`. + TypeError: if `not target_log_prob.dtype.is_floating`. """ - with ops.name_scope(name, 'leapfrog_step', [step_size, position, momentum, - grad]): - momentum -= 0.5 * step_size * grad - position += step_size * momentum - potential, grad = potential_and_grad(position) - momentum -= 0.5 * step_size * grad - - return position, momentum, potential, grad + def _loop_body(step, + current_momentums, + current_state_parts, + ignore_current_target_log_prob, # pylint: disable=unused-argument + current_grads_target_log_prob): + return [step + 1] + list(_leapfrog_step(current_momentums, + target_log_prob_fn, + current_state_parts, + step_sizes, + current_grads_target_log_prob)) + + with ops.name_scope( + name, "hmc_leapfrog_integrator", + [current_momentums, current_state_parts, step_sizes, num_leapfrog_steps, + current_target_log_prob, current_grads_target_log_prob]): + if len(current_momentums) != len(current_state_parts): + raise ValueError("`momentums` must be in one-to-one correspondence " + "with `state_parts`") + num_leapfrog_steps = ops.convert_to_tensor(num_leapfrog_steps, + name="num_leapfrog_steps") + current_target_log_prob, current_grads_target_log_prob = ( + _maybe_call_fn_and_grads( + target_log_prob_fn, + current_state_parts, + current_target_log_prob, + current_grads_target_log_prob)) + return control_flow_ops.while_loop( + cond=lambda iter_, *args: iter_ < num_leapfrog_steps, + body=_loop_body, + loop_vars=[ + np.int32(0), # iter_ + current_momentums, + current_state_parts, + current_target_log_prob, + current_grads_target_log_prob, + ], + back_prop=False)[1:] # Lop-off "iter_". + + +def _leapfrog_step(current_momentums, + target_log_prob_fn, + current_state_parts, + step_sizes, + current_grads_target_log_prob, + name=None): + """Applies one step of the leapfrog integrator.""" + with ops.name_scope( + name, "_leapfrog_step", + [current_momentums, current_state_parts, step_sizes, + current_grads_target_log_prob]): + proposed_momentums = [m + 0.5 * ss * g for m, ss, g + in zip(current_momentums, + step_sizes, + current_grads_target_log_prob)] + proposed_state_parts = [x + ss * m for x, ss, m + in zip(current_state_parts, + step_sizes, + proposed_momentums)] + proposed_target_log_prob = target_log_prob_fn(*proposed_state_parts) + if not proposed_target_log_prob.dtype.is_floating: + raise TypeError("`target_log_prob_fn` must produce a `Tensor` " + "with `float` `dtype`.") + proposed_grads_target_log_prob = gradients_ops.gradients( + proposed_target_log_prob, proposed_state_parts) + if any(g is None for g in proposed_grads_target_log_prob): + raise ValueError( + "Encountered `None` gradient. Does your target `target_log_prob_fn` " + "access all `tf.Variable`s via `tf.get_variable`?\n" + " current_state_parts: {}\n" + " proposed_state_parts: {}\n" + " proposed_grads_target_log_prob: {}".format( + current_state_parts, + proposed_state_parts, + proposed_grads_target_log_prob)) + proposed_momentums = [m + 0.5 * ss * g for m, ss, g + in zip(proposed_momentums, + step_sizes, + proposed_grads_target_log_prob)] + return [ + proposed_momentums, + proposed_state_parts, + proposed_target_log_prob, + proposed_grads_target_log_prob, + ] + + +def _compute_energy_change(current_target_log_prob, + current_momentums, + proposed_target_log_prob, + proposed_momentums, + independent_chain_ndims, + name=None): + """Helper to `kernel` which computes the energy change.""" + with ops.name_scope( + name, "compute_energy_change", + ([current_target_log_prob, proposed_target_log_prob, + independent_chain_ndims] + + current_momentums + proposed_momentums)): + # Abbreviate lk0=log_kinetic_energy and lk1=proposed_log_kinetic_energy + # since they're a mouthful and lets us inline more. + lk0, lk1 = [], [] + for current_momentum, proposed_momentum in zip(current_momentums, + proposed_momentums): + axis = math_ops.range(independent_chain_ndims, + array_ops.rank(current_momentum)) + lk0.append(_log_sum_sq(current_momentum, axis)) + lk1.append(_log_sum_sq(proposed_momentum, axis)) + + lk0 = -np.log(2.) + math_ops.reduce_logsumexp(array_ops.stack(lk0, axis=-1), + axis=-1) + lk1 = -np.log(2.) + math_ops.reduce_logsumexp(array_ops.stack(lk1, axis=-1), + axis=-1) + lp0 = -current_target_log_prob # potential + lp1 = -proposed_target_log_prob # proposed_potential + x = array_ops.stack([lp1, math_ops.exp(lk1), -lp0, -math_ops.exp(lk0)], + axis=-1) + + # The sum is NaN if any element is NaN or we see both +Inf and -Inf. + # Thus we will replace such rows with infinite energy change which implies + # rejection. Recall that float-comparisons with NaN are always False. + is_sum_determinate = ( + math_ops.reduce_all(math_ops.is_finite(x) | (x >= 0.), axis=-1) & + math_ops.reduce_all(math_ops.is_finite(x) | (x <= 0.), axis=-1)) + is_sum_determinate = array_ops.tile( + is_sum_determinate[..., array_ops.newaxis], + multiples=array_ops.concat([ + array_ops.ones(array_ops.rank(is_sum_determinate), + dtype=dtypes.int32), + [4], + ], axis=0)) + x = array_ops.where(is_sum_determinate, + x, + array_ops.fill(array_ops.shape(x), + value=x.dtype.as_numpy_dtype(np.inf))) + + return math_ops.reduce_sum(x, axis=-1) + + +def _choose(is_accepted, + accepted, + rejected, + independent_chain_ndims, + name=None): + """Helper to `kernel` which expand_dims `is_accepted` to apply tf.where.""" + def _expand_is_accepted_like(x): + with ops.name_scope("_choose"): + expand_shape = array_ops.concat([ + array_ops.shape(is_accepted), + array_ops.ones([array_ops.rank(x) - array_ops.rank(is_accepted)], + dtype=dtypes.int32), + ], axis=0) + multiples = array_ops.concat([ + array_ops.ones([array_ops.rank(is_accepted)], dtype=dtypes.int32), + array_ops.shape(x)[independent_chain_ndims:], + ], axis=0) + m = array_ops.tile(array_ops.reshape(is_accepted, expand_shape), + multiples) + m.set_shape(x.shape) + return m + with ops.name_scope(name, "_choose", values=[ + is_accepted, accepted, rejected, independent_chain_ndims]): + return array_ops.where(_expand_is_accepted_like(accepted), + accepted, + rejected) + + +def _maybe_call_fn_and_grads(fn, + fn_arg_list, + fn_result=None, + grads_fn_result=None, + description="target_log_prob"): + """Helper which computes `fn_result` and `grads` if needed.""" + fn_arg_list = (list(fn_arg_list) if _is_list_like(fn_arg_list) + else [fn_arg_list]) + if fn_result is None: + fn_result = fn(*fn_arg_list) + if not fn_result.dtype.is_floating: + raise TypeError("`{}` must be a `Tensor` with `float` `dtype`.".format( + description)) + if grads_fn_result is None: + grads_fn_result = gradients_ops.gradients( + fn_result, fn_arg_list) + if len(fn_arg_list) != len(grads_fn_result): + raise ValueError("`{}` must be in one-to-one correspondence with " + "`grads_{}`".format(*[description]*2)) + if any(g is None for g in grads_fn_result): + raise ValueError("Encountered `None` gradient.") + return fn_result, grads_fn_result + + +def _prepare_args(target_log_prob_fn, state, step_size, + target_log_prob=None, grads_target_log_prob=None, + maybe_expand=False, description="target_log_prob"): + """Helper which processes input args to meet list-like assumptions.""" + state_parts = list(state) if _is_list_like(state) else [state] + state_parts = [ops.convert_to_tensor(s, name="state") + for s in state_parts] + target_log_prob, grads_target_log_prob = _maybe_call_fn_and_grads( + target_log_prob_fn, + state_parts, + target_log_prob, + grads_target_log_prob, + description) + step_sizes = list(step_size) if _is_list_like(step_size) else [step_size] + step_sizes = [ + ops.convert_to_tensor( + s, name="step_size", dtype=target_log_prob.dtype) + for s in step_sizes] + if len(step_sizes) == 1: + step_sizes *= len(state_parts) + if len(state_parts) != len(step_sizes): + raise ValueError("There should be exactly one `step_size` or it should " + "have same length as `current_state`.") + maybe_flatten = lambda x: x if maybe_expand or _is_list_like(state) else x[0] + return [ + maybe_flatten(state_parts), + maybe_flatten(step_sizes), + target_log_prob, + grads_target_log_prob, + ] + + +def _is_list_like(x): + """Helper which returns `True` if input is `list`-like.""" + return isinstance(x, (tuple, list)) + + +def _log_sum_sq(x, axis=None): + """Computes log(sum(x**2)).""" + return math_ops.reduce_logsumexp(2. * math_ops.log(math_ops.abs(x)), axis) diff --git a/tensorflow/contrib/bayesflow/python/ops/layers.py b/tensorflow/contrib/bayesflow/python/ops/layers.py deleted file mode 100644 index a742b7c1aa593d6c08bf9d8d597c99c9fc4e7aed..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Probabilistic neural layers. - -See ${python/contrib.bayesflow.layers}. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.layers_conv_variational import * -from tensorflow.contrib.bayesflow.python.ops.layers_dense_variational import * -from tensorflow.contrib.bayesflow.python.ops.layers_util import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = [ - 'Convolution1DReparameterization', - 'Convolution2DReparameterization', - 'Convolution3DReparameterization', - 'Convolution1DFlipout', - 'Convolution2DFlipout', - 'Convolution3DFlipout', - 'Conv1DReparameterization', - 'Conv2DReparameterization', - 'Conv3DReparameterization', - 'Conv1DFlipout', - 'Conv2DFlipout', - 'Conv3DFlipout', - 'convolution1d_reparameterization', - 'convolution2d_reparameterization', - 'convolution3d_reparameterization', - 'convolution1d_flipout', - 'convolution2d_flipout', - 'convolution3d_flipout', - 'conv1d_reparameterization', - 'conv2d_reparameterization', - 'conv3d_reparameterization', - 'conv1d_flipout', - 'conv2d_flipout', - 'conv3d_flipout', - 'DenseReparameterization', - 'DenseLocalReparameterization', - 'DenseFlipout', - 'dense_reparameterization', - 'dense_local_reparameterization', - 'dense_flipout', - 'default_loc_scale_fn', - 'default_mean_field_normal_fn', -] - -remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py deleted file mode 100644 index 7723cfb442712626ff415f1412e3362f2392ce9f..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py +++ /dev/null @@ -1,2943 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Convolutional variational layer classes and their functional aliases. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.bayesflow.python.ops import layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.layers import base as layers_lib -from tensorflow.python.layers import utils -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import standard_ops -from tensorflow.python.ops.distributions import kullback_leibler as kl_lib -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util - - -class _ConvVariational(layers_lib.Layer): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, ..., channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: A string, the name of the layer. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - """ - - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(_ConvVariational, self).__init__( - trainable=trainable, - name=name, - activity_regularizer=activity_regularizer, - **kwargs) - self.rank = rank - self.filters = filters - self.kernel_size = utils.normalize_tuple(kernel_size, rank, "kernel_size") - self.strides = utils.normalize_tuple(strides, rank, "strides") - self.padding = utils.normalize_padding(padding) - self.data_format = utils.normalize_data_format(data_format) - self.dilation_rate = utils.normalize_tuple( - dilation_rate, rank, "dilation_rate") - self.activation = activation - self.input_spec = layers_lib.InputSpec(ndim=self.rank + 2) - self.kernel_posterior_fn = kernel_posterior_fn - self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn - self.kernel_prior_fn = kernel_prior_fn - self.kernel_divergence_fn = kernel_divergence_fn - self.bias_posterior_fn = bias_posterior_fn - self.bias_posterior_tensor_fn = bias_posterior_tensor_fn - self.bias_prior_fn = bias_prior_fn - self.bias_divergence_fn = bias_divergence_fn - - def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape) - if self.data_format == "channels_first": - channel_axis = 1 - else: - channel_axis = -1 - if input_shape[channel_axis].value is None: - raise ValueError("The channel dimension of the inputs " - "should be defined. Found `None`.") - input_dim = input_shape[channel_axis].value - kernel_shape = self.kernel_size + (input_dim, self.filters) - dtype = dtypes.as_dtype(self.dtype) - - # Must have a posterior kernel. - self.kernel_posterior = self.kernel_posterior_fn( - dtype, kernel_shape, "kernel_posterior", - self.trainable, self.add_variable) - - if self.kernel_prior_fn is None: - self.kernel_prior = None - else: - self.kernel_prior = self.kernel_prior_fn( - dtype, kernel_shape, "kernel_prior", - self.trainable, self.add_variable) - self._built_kernel_divergence = False - - if self.bias_posterior_fn is None: - self.bias_posterior = None - else: - self.bias_posterior = self.bias_posterior_fn( - dtype, (self.filters,), "bias_posterior", - self.trainable, self.add_variable) - - if self.bias_prior_fn is None: - self.bias_prior = None - else: - self.bias_prior = self.bias_prior_fn( - dtype, (self.filters,), "bias_prior", - self.trainable, self.add_variable) - self._built_bias_divergence = False - - self.input_spec = layers_lib.InputSpec(ndim=self.rank + 2, - axes={channel_axis: input_dim}) - self._convolution_op = nn_ops.Convolution( - input_shape, - filter_shape=tensor_shape.TensorShape(kernel_shape), - dilation_rate=self.dilation_rate, - strides=self.strides, - padding=self.padding.upper(), - data_format=utils.convert_data_format(self.data_format, - self.rank + 2)) - - self.built = True - - def call(self, inputs): - inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) - - outputs = self._apply_variational_kernel(inputs) - outputs = self._apply_variational_bias(outputs) - if self.activation is not None: - outputs = self.activation(outputs) - if not self._built_kernel_divergence: - kernel_posterior = self.kernel_posterior - kernel_prior = self.kernel_prior - if isinstance(self.kernel_posterior, independent_lib.Independent): - kernel_posterior = kernel_posterior.distribution - if isinstance(self.kernel_prior, independent_lib.Independent): - kernel_prior = kernel_prior.distribution - self._apply_divergence(self.kernel_divergence_fn, - kernel_posterior, - kernel_prior, - self.kernel_posterior_tensor, - name="divergence_kernel") - self._built_kernel_divergence = True - if not self._built_bias_divergence: - bias_posterior = self.bias_posterior - bias_prior = self.bias_prior - if isinstance(self.bias_posterior, independent_lib.Independent): - bias_posterior = bias_posterior.distribution - if isinstance(self.bias_prior, independent_lib.Independent): - bias_prior = bias_prior.distribution - self._apply_divergence(self.bias_divergence_fn, - bias_posterior, - bias_prior, - self.bias_posterior_tensor, - name="divergence_bias") - self._built_bias_divergence = True - return outputs - - def _apply_variational_bias(self, inputs): - if self.bias_posterior is None: - self.bias_posterior_tensor = None - return inputs - self.bias_posterior_tensor = self.bias_posterior_tensor_fn( - self.bias_posterior) - outputs = inputs - if self.data_format == "channels_first": - if self.rank == 1: - # nn.bias_add does not accept a 1D input tensor. - bias = array_ops.reshape(self.bias_posterior_tensor, - (1, self.filters, 1)) - outputs += bias - if self.rank == 2: - outputs = nn.bias_add(outputs, - self.bias_posterior_tensor, - data_format="NCHW") - if self.rank == 3: - # As of Mar 2017, direct addition is significantly slower than - # bias_add when computing gradients. To use bias_add, we collapse Z - # and Y into a single dimension to obtain a 4D input tensor. - outputs_shape = outputs.shape.as_list() - outputs_4d = array_ops.reshape(outputs, - [outputs_shape[0], outputs_shape[1], - outputs_shape[2] * outputs_shape[3], - outputs_shape[4]]) - outputs_4d = nn.bias_add(outputs_4d, - self.bias_posterior_tensor, - data_format="NCHW") - outputs = array_ops.reshape(outputs_4d, outputs_shape) - else: - outputs = nn.bias_add(outputs, - self.bias_posterior_tensor, - data_format="NHWC") - return outputs - - def _apply_divergence(self, divergence_fn, posterior, prior, - posterior_tensor, name): - if (divergence_fn is None or - posterior is None or - prior is None): - divergence = None - return - divergence = standard_ops.identity( - divergence_fn( - posterior, prior, posterior_tensor), - name=name) - self.add_loss(divergence) - - def _compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() - if self.data_format == "channels_last": - space = input_shape[1:-1] - new_space = [] - for i in range(len(space)): - new_dim = utils.conv_output_length( - space[i], - self.kernel_size[i], - padding=self.padding, - stride=self.strides[i], - dilation=self.dilation_rate[i]) - new_space.append(new_dim) - return tensor_shape.TensorShape([input_shape[0]] + new_space + - [self.filters]) - else: - space = input_shape[2:] - new_space = [] - for i in range(len(space)): - new_dim = utils.conv_output_length( - space[i], - self.kernel_size[i], - padding=self.padding, - stride=self.strides[i], - dilation=self.dilation_rate[i]) - new_space.append(new_dim) - return tensor_shape.TensorShape([input_shape[0], self.filters] + - new_space) - - -class _ConvReparameterization(_ConvVariational): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, ..., channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: A string, the name of the layer. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(_ConvReparameterization, self).__init__( - rank=rank, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - def _apply_variational_kernel(self, inputs): - self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn( - self.kernel_posterior) - self.kernel_posterior_affine = None - self.kernel_posterior_affine_tensor = None - outputs = self._convolution_op(inputs, self.kernel_posterior_tensor) - return outputs - - -class Conv1DReparameterization(_ConvReparameterization): - """1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.Conv1DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - def __init__( - self, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(Conv1DReparameterization, self).__init__( - rank=1, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -def conv1d_reparameterization( - inputs, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - """Functional interface for 1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.conv1d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - layer = Conv1DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv2DReparameterization(_ConvReparameterization): - """2D convolution layer (e.g. spatial convolution over images). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.Conv2DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - def __init__( - self, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(Conv2DReparameterization, self).__init__( - rank=2, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -def conv2d_reparameterization( - inputs, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - """Functional interface for the 2D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.conv2d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - layer = Conv2DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv3DReparameterization(_ConvReparameterization): - """3D convolution layer (e.g. spatial convolution over volumes). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.Conv3DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - def __init__( - self, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(Conv3DReparameterization, self).__init__( - rank=3, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -def conv3d_reparameterization( - inputs, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - """Functional interface for the 3D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.conv3d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - layer = Conv3DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class _ConvFlipout(_ConvVariational): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, ..., channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - super(_ConvFlipout, self).__init__( - rank=rank, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - self.seed = seed - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`{}` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format( - type(self).__name__, self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(self.kernel_posterior.distribution.loc), - scale=self.kernel_posterior.distribution.scale) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - - outputs = self._convolution_op( - inputs, self.kernel_posterior.distribution.loc) - - input_shape = array_ops.shape(inputs) - output_shape = array_ops.shape(outputs) - batch_shape = array_ops.expand_dims(input_shape[0], 0) - channels = input_shape[-1] - - sign_input = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(channels, 0)], 0), - dtype=inputs.dtype, - seed=self.seed) - sign_output = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(self.filters, 0)], 0), - dtype=inputs.dtype, - seed=distribution_util.gen_new_seed( - self.seed, salt="conv_flipout")) - for _ in range(self.rank): - sign_input = array_ops.expand_dims(sign_input, 1) # 2D ex: (B, 1, 1, C) - sign_output = array_ops.expand_dims(sign_output, 1) - - sign_input = array_ops.tile( # tile for element-wise op broadcasting - sign_input, - [1] + [input_shape[i + 1] for i in range(self.rank)] + [1]) - sign_output = array_ops.tile( - sign_output, - [1] + [output_shape[i + 1] for i in range(self.rank)] + [1]) - - perturbed_inputs = self._convolution_op( - inputs * sign_input, self.kernel_posterior_affine_tensor) * sign_output - - outputs += perturbed_inputs - return outputs - - -class Conv1DFlipout(_ConvFlipout): - """1D convolution layer (e.g. temporal convolution) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.Conv1DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - def __init__( - self, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - super(Conv1DFlipout, self).__init__( - rank=1, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -def conv1d_flipout( - inputs, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - """Functional interface for 1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.conv1d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - layer = Conv1DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv2DFlipout(_ConvFlipout): - """2D convolution layer (e.g. spatial convolution over images) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.Conv2DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - def __init__( - self, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - super(Conv2DFlipout, self).__init__( - rank=2, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -def conv2d_flipout( - inputs, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - """Functional interface for the 2D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.conv2d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - layer = Conv2DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv3DFlipout(_ConvFlipout): - """3D convolution layer (e.g. spatial convolution over volumes) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.Conv3DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - def __init__( - self, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - super(Conv3DFlipout, self).__init__( - rank=3, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -def conv3d_flipout( - inputs, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - """Functional interface for the 3D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Arguments: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: A string, the name of the layer. - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.conv3d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - layer = Conv3DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -# Aliases - -Convolution1DReparameterization = Conv1DReparameterization -Convolution2DReparameterization = Conv2DReparameterization -Convolution3DReparameterization = Conv3DReparameterization -convolution1d_reparameterization = conv1d_reparameterization -convolution2d_reparameterization = conv2d_reparameterization -convolution3d_reparameterization = conv3d_reparameterization -Convolution1DFlipout = Conv1DFlipout -Convolution2DFlipout = Conv2DFlipout -Convolution3DFlipout = Conv3DFlipout -convolution1d_flipout = conv1d_flipout -convolution2d_flipout = conv2d_flipout -convolution3d_flipout = conv3d_flipout diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py deleted file mode 100644 index 591a8e553de0c194786c7ee8693665f762711b2d..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py +++ /dev/null @@ -1,1176 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Dense Bayesian layer using KL-divergence based variational inference. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.bayesflow.python.ops import layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.layers import base as layers_lib -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import standard_ops -from tensorflow.python.ops.distributions import kullback_leibler as kl_lib -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util - - -class _DenseVariational(layers_lib.Layer): - """Abstract densely-connected class (private, used as implementation base). - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - """ - - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(_DenseVariational, self).__init__( - trainable=trainable, - name=name, - activity_regularizer=activity_regularizer, - **kwargs) - self.units = units - self.activation = activation - self.input_spec = layers_lib.InputSpec(min_ndim=2) - self.kernel_posterior_fn = kernel_posterior_fn - self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn - self.kernel_prior_fn = kernel_prior_fn - self.kernel_divergence_fn = kernel_divergence_fn - self.bias_posterior_fn = bias_posterior_fn - self.bias_posterior_tensor_fn = bias_posterior_tensor_fn - self.bias_prior_fn = bias_prior_fn - self.bias_divergence_fn = bias_divergence_fn - - def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape) - in_size = input_shape.with_rank_at_least(2)[-1].value - if in_size is None: - raise ValueError("The last dimension of the inputs to `Dense` " - "should be defined. Found `None`.") - self._input_spec = layers_lib.InputSpec(min_ndim=2, axes={-1: in_size}) - dtype = dtypes.as_dtype(self.dtype) - - # Must have a posterior kernel. - self.kernel_posterior = self.kernel_posterior_fn( - dtype, [in_size, self.units], "kernel_posterior", - self.trainable, self.add_variable) - - if self.kernel_prior_fn is None: - self.kernel_prior = None - else: - self.kernel_prior = self.kernel_prior_fn( - dtype, [in_size, self.units], "kernel_prior", - self.trainable, self.add_variable) - self._built_kernel_divergence = False - - if self.bias_posterior_fn is None: - self.bias_posterior = None - else: - self.bias_posterior = self.bias_posterior_fn( - dtype, [self.units], "bias_posterior", - self.trainable, self.add_variable) - - if self.bias_prior_fn is None: - self.bias_prior = None - else: - self.bias_prior = self.bias_prior_fn( - dtype, [self.units], "bias_prior", - self.trainable, self.add_variable) - self._built_bias_divergence = False - - self.built = True - - def call(self, inputs): - inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) - - outputs = self._apply_variational_kernel(inputs) - outputs = self._apply_variational_bias(outputs) - if self.activation is not None: - outputs = self.activation(outputs) # pylint: disable=not-callable - if not self._built_kernel_divergence: - kernel_posterior = self.kernel_posterior - kernel_prior = self.kernel_prior - if isinstance(self.kernel_posterior, independent_lib.Independent): - kernel_posterior = kernel_posterior.distribution - if isinstance(self.kernel_prior, independent_lib.Independent): - kernel_prior = kernel_prior.distribution - self._apply_divergence(self.kernel_divergence_fn, - kernel_posterior, - kernel_prior, - self.kernel_posterior_tensor, - name="divergence_kernel") - self._built_kernel_divergence = True - if not self._built_bias_divergence: - bias_posterior = self.bias_posterior - bias_prior = self.bias_prior - if isinstance(self.bias_posterior, independent_lib.Independent): - bias_posterior = bias_posterior.distribution - if isinstance(self.bias_prior, independent_lib.Independent): - bias_prior = bias_prior.distribution - self._apply_divergence(self.bias_divergence_fn, - bias_posterior, - bias_prior, - self.bias_posterior_tensor, - name="divergence_bias") - self._built_bias_divergence = True - return outputs - - def _apply_variational_bias(self, inputs): - if self.bias_posterior is None: - self.bias_posterior_tensor = None - return inputs - self.bias_posterior_tensor = self.bias_posterior_tensor_fn( - self.bias_posterior) - return nn.bias_add(inputs, self.bias_posterior_tensor) - - def _apply_divergence(self, divergence_fn, posterior, prior, - posterior_tensor, name): - if (divergence_fn is None or - posterior is None or - prior is None): - divergence = None - return - divergence = standard_ops.identity( - divergence_fn( - posterior, prior, posterior_tensor), - name=name) - self.add_loss(divergence) - - def _matmul(self, inputs, kernel): - if inputs.shape.ndims <= 2: - return standard_ops.matmul(inputs, kernel) - # To handle broadcasting, we must use `tensordot`. - return standard_ops.tensordot(inputs, kernel, axes=[[-1], [0]]) - - def _compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).with_rank_at_least(2) - if input_shape[-1].value is None: - raise ValueError( - "The innermost dimension of input_shape must be defined, " - "but saw: {}".format(input_shape)) - return input_shape[:-1].concatenate(self.units) - - -class DenseReparameterization(_DenseVariational): - """Densely-connected layer class with reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the reparameterization estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseReparameterization( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(DenseReparameterization, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - - def _apply_variational_kernel(self, inputs): - self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn( - self.kernel_posterior) - self.kernel_posterior_affine = None - self.kernel_posterior_affine_tensor = None - return self._matmul(inputs, self.kernel_posterior_tensor) - - -def dense_reparameterization( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - """Densely-connected layer with reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the reparameterization estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_reparameterization( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - layer = DenseReparameterization( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class DenseLocalReparameterization(_DenseVariational): - """Densely-connected layer class with local reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the local reparameterization estimator [1], which performs a - Monte Carlo approximation of the distribution on the hidden units - induced by the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseLocalReparameterization( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseLocalReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses local reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Variational Dropout and the Local Reparameterization Trick." - Diederik P. Kingma, Tim Salimans, Max Welling. - Neural Information Processing Systems, 2015. - """ - - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - super(DenseLocalReparameterization, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`DenseLocalReparameterization` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format(self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=self._matmul(inputs, self.kernel_posterior.distribution.loc), - scale=standard_ops.sqrt(self._matmul( - standard_ops.square(inputs), - standard_ops.square(self.kernel_posterior.distribution.scale)))) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - return self.kernel_posterior_affine_tensor - - -def dense_local_reparameterization( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - """Densely-connected layer with local reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the local reparameterization estimator [1], which performs a - Monte Carlo approximation of the distribution on the hidden units - induced by the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_local_reparameterization( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_local_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses local reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Variational Dropout and the Local Reparameterization Trick." - Diederik P. Kingma, Tim Salimans, Max Welling. - Neural Information Processing Systems, 2015. - """ - layer = DenseLocalReparameterization( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class DenseFlipout(_DenseVariational): - """Densely-connected layer class with Flipout estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the Flipout estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. Flipout uses roughly twice as many floating point operations - as the reparameterization estimator but has the advantage of - significantly lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseFlipout( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - super(DenseFlipout, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - self.seed = seed - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`DenseFlipout` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format(self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(self.kernel_posterior.distribution.loc), - scale=self.kernel_posterior.distribution.scale) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - - input_shape = array_ops.shape(inputs) - batch_shape = input_shape[:-1] - - sign_input = layers_util.random_sign( - input_shape, - dtype=inputs.dtype, - seed=self.seed) - sign_output = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(self.units, 0)], 0), - dtype=inputs.dtype, - seed=distribution_util.gen_new_seed( - self.seed, salt="dense_flipout")) - perturbed_inputs = self._matmul( - inputs * sign_input, self.kernel_posterior_affine_tensor) * sign_output - - outputs = self._matmul(inputs, self.kernel_posterior.distribution.loc) - outputs += perturbed_inputs - return outputs - - -def dense_flipout( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - """Densely-connected layer with Flipout estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the Flipout estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. Flipout uses roughly twice as many floating point operations - as the reparameterization estimator but has the advantage of - significantly lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name. - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_flipout( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - layer = DenseFlipout( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_util.py b/tensorflow/contrib/bayesflow/python/ops/layers_util.py deleted file mode 100644 index 8c1fb203f7328e8260e49b4326d813fbe133613e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_util.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for probabilistic layers. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.distributions.python.ops import deterministic as deterministic_lib -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops.distributions import normal as normal_lib - - -def default_loc_scale_fn( - is_singular=False, - loc_initializer=init_ops.random_normal_initializer(stddev=0.1), - untransformed_scale_initializer=init_ops.random_normal_initializer( - mean=-3., stddev=0.1), - loc_regularizer=None, - untransformed_scale_regularizer=None, - loc_constraint=None, - untransformed_scale_constraint=None): - """Makes closure which creates `loc`, `scale` params from `tf.get_variable`. - - This function produces a closure which produces `loc`, `scale` using - `tf.get_variable`. The closure accepts the following arguments: - - dtype: Type of parameter's event. - shape: Python `list`-like representing the parameter's event shape. - name: Python `str` name prepended to any created (or existing) - `tf.Variable`s. - trainable: Python `bool` indicating all created `tf.Variable`s should be - added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. - add_variable_fn: `tf.get_variable`-like `callable` used to create (or - access existing) `tf.Variable`s. - - Args: - is_singular: Python `bool` indicating if `scale is None`. Default: `False`. - loc_initializer: Initializer function for the `loc` parameters. - The default is `tf.random_normal_initializer(mean=0., stddev=0.1)`. - untransformed_scale_initializer: Initializer function for the `scale` - parameters. Default value: `tf.random_normal_initializer(mean=-3., - stddev=0.1)`. This implies the softplus transformed result has mean - approximately `0.05` and std. deviation approximately `0.005`. - loc_regularizer: Regularizer function for the `loc` parameters. - The default (`None`) is to use the `tf.get_variable` default. - untransformed_scale_regularizer: Regularizer function for the `scale` - parameters. The default (`None`) is to use the `tf.get_variable` default. - loc_constraint: An optional projection function to be applied to the - loc after being updated by an `Optimizer`. The function must take as input - the unprojected variable and must return the projected variable (which - must have the same shape). Constraints are not safe to use when doing - asynchronous distributed training. - The default (`None`) is to use the `tf.get_variable` default. - untransformed_scale_constraint: An optional projection function to be - applied to the `scale` parameters after being updated by an `Optimizer` - (e.g. used to implement norm constraints or value constraints). The - function must take as input the unprojected variable and must return the - projected variable (which must have the same shape). Constraints are not - safe to use when doing asynchronous distributed training. The default - (`None`) is to use the `tf.get_variable` default. - - Returns: - default_loc_scale_fn: Python `callable` which instantiates `loc`, `scale` - parameters from args: `dtype, shape, name, trainable, add_variable_fn`. - """ - def _fn(dtype, shape, name, trainable, add_variable_fn): - """Creates `loc`, `scale` parameters.""" - loc = add_variable_fn( - name=name + "_loc", - shape=shape, - initializer=loc_initializer, - regularizer=loc_regularizer, - constraint=loc_constraint, - dtype=dtype, - trainable=trainable) - if is_singular: - return loc, None - untransformed_scale = add_variable_fn( - name=name + "_untransformed_scale", - shape=shape, - initializer=untransformed_scale_initializer, - regularizer=untransformed_scale_regularizer, - constraint=untransformed_scale_constraint, - dtype=dtype, - trainable=trainable) - scale = (np.finfo(dtype.as_numpy_dtype).eps + - nn_ops.softplus(untransformed_scale)) - return loc, scale - return _fn - - -def default_mean_field_normal_fn( - is_singular=False, - loc_initializer=None, - untransformed_scale_initializer=None, - loc_regularizer=None, - untransformed_scale_regularizer=None, - loc_constraint=None, - untransformed_scale_constraint=None): - """Creates a function to build Normal distributions with trainable params. - - This function produces a closure which produces `tf.distributions.Normal` - parameterized by a loc` and `scale` each created using `tf.get_variable`. The - produced closure accepts the following arguments: - - name: Python `str` name prepended to any created (or existing) - `tf.Variable`s. - shape: Python `list`-like representing the parameter's event shape. - dtype: Type of parameter's event. - trainable: Python `bool` indicating all created `tf.Variable`s should be - added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. - add_variable_fn: `tf.get_variable`-like `callable` used to create (or - access existing) `tf.Variable`s. - - Args: - is_singular: Python `bool` if `True`, forces the special case limit of - `scale->0`, i.e., a `Deterministic` distribution. - loc_initializer: Initializer function for the `loc` parameters. - If `None` (default), values are initialized using the default - initializer used by `tf.get_variable`. - untransformed_scale_initializer: Initializer function for the `scale` - parameters. If `None` (default), values are initialized using the default - initializer used by `tf.get_variable`. - loc_regularizer: Regularizer function for the `loc` parameters. - untransformed_scale_regularizer: Regularizer function for the `scale` - parameters. - loc_constraint: An optional projection function to be applied to the - loc after being updated by an `Optimizer`. The function must take as input - the unprojected variable and must return the projected variable (which - must have the same shape). Constraints are not safe to use when doing - asynchronous distributed training. - untransformed_scale_constraint: An optional projection function to be - applied to the `scale` parameters after being updated by an `Optimizer` - (e.g. used to implement norm constraints or value constraints). The - function must take as input the unprojected variable and must return the - projected variable (which must have the same shape). Constraints are not - safe to use when doing asynchronous distributed training. - - Returns: - make_normal_fn: Python `callable` which creates a `tf.distributions.Normal` - using from args: `dtype, shape, name, trainable, add_variable_fn`. - """ - loc_scale_fn_ = default_loc_scale_fn( - is_singular, - loc_initializer, - untransformed_scale_initializer, - loc_regularizer, - untransformed_scale_regularizer, - loc_constraint, - untransformed_scale_constraint) - def _fn(dtype, shape, name, trainable, add_variable_fn): - """Creates multivariate `Deterministic` or `Normal` distribution.""" - loc, scale = loc_scale_fn_(dtype, shape, name, trainable, add_variable_fn) - if scale is None: - dist = deterministic_lib.Deterministic(loc=loc) - else: - dist = normal_lib.Normal(loc=loc, scale=scale) - reinterpreted_batch_ndims = array_ops.shape(dist.batch_shape_tensor())[0] - return independent_lib.Independent( - dist, reinterpreted_batch_ndims=reinterpreted_batch_ndims) - return _fn - - -def random_sign(shape, dtype=dtypes.float32, seed=None): - """Draw values from {-1, 1} uniformly, i.e., Rademacher distribution.""" - random_bernoulli = random_ops.random_uniform(shape, minval=0, maxval=2, - dtype=dtypes.int32, - seed=seed) - return math_ops.cast(2 * random_bernoulli - 1, dtype) diff --git a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py index 7bdeaa862d5bb64fa8940df453c7aa2b66023eda..e7fcbc65ef379e84a140a06e020549f74f905a99 100644 --- a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py +++ b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py @@ -25,9 +25,10 @@ from tensorflow.contrib.bayesflow.python.ops.metropolis_hastings_impl import * from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ + 'kernel', 'evolve', - 'uniform_random_proposal', - 'normal_random_proposal', + 'proposal_uniform', + 'proposal_normal', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py index dc1ac68ce009fa46d6c05a3200a29d9fdf245707..05aa134ed5c11092316af5f3e45ba07fdb491e90 100644 --- a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py +++ b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py @@ -12,17 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Functions to create a Markov Chain Monte Carlo Metropolis step. +"""Metropolis-Hastings and proposal distributions. +@@kernel @@evolve -@@uniform_random_proposal -@@normal_random_proposal +@@proposal_uniform +@@proposal_normal """ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -31,123 +34,198 @@ from tensorflow.python.ops import random_ops from tensorflow.python.ops import state_ops __all__ = [ - 'evolve', - 'uniform_random_proposal', - 'normal_random_proposal', + "kernel", + "evolve", + "proposal_uniform", + "proposal_normal", ] -def _single_iteration(current_state, current_log_density, - log_unnormalized_prob_fn, proposal_fn, seed=None, - name='None'): - """Performs a single Metropolis-Hastings step. +KernelResults = collections.namedtuple( + "KernelResults", + [ + "log_accept_ratio", + "current_target_log_prob", # "Current result" means "accepted". + "is_accepted", + "proposed_state", + ]) + + +def kernel(target_log_prob_fn, + proposal_fn, + current_state, + seed=None, + current_target_log_prob=None, + name=None): + """Runs the Metropolis-Hastings transition kernel. + + This function can update multiple chains in parallel. It assumes that all + leftmost dimensions of `current_state` index independent chain states (and are + therefore updated independently). The output of `target_log_prob_fn()` should + sum log-probabilities across all event dimensions. Slices along the rightmost + dimensions may have different target distributions; for example, + `current_state[0, :]` could have a different target distribution from + `current_state[1, :]`. This is up to `target_log_prob_fn()`. (The number of + independent chains is `tf.size(target_log_prob_fn(*current_state))`.) Args: - current_state: Float-like `Tensor` (i.e., `dtype` is either - `tf.float16`, `tf.float32` or `tf.float64`) of any shape that can - be consumed by the `log_unnormalized_prob_fn` and `proposal_fn` - callables. - current_log_density: Float-like `Tensor` with `dtype` and shape equivalent - to `log_unnormalized_prob_fn(current_state)`, i.e., matching the result of - `log_unnormalized_prob_fn` invoked at `current_state`. - log_unnormalized_prob_fn: A Python callable evaluated at - `current_state` and returning a float-like `Tensor` of log target-density - up to a normalizing constant. In other words, - `log_unnormalized_prob_fn(x) = log(g(x))`, where - `target_density = g(x)/Z` for some constant `A`. The shape of the input - tensor is the same as the shape of the `current_state`. The shape of the - output tensor is either - (a). Same as the input shape if the density being sampled is one - dimensional, or - (b). If the density is defined for `events` of shape - `event_shape = [E1, E2, ... Ee]`, then the input tensor should be of - shape `batch_shape + event_shape`, where `batch_shape = [B1, ..., Bb]` - and the result must be of shape [B1, ..., Bb]. For example, if the - distribution that is being sampled is a 10 dimensional normal, - then the input tensor may be of shape [100, 10] or [30, 20, 10]. The - last dimension will then be 'consumed' by `log_unnormalized_prob_fn` - and it should return tensors of shape [100] and [30, 20] respectively. - proposal_fn: A callable accepting a real valued `Tensor` of current sample - points and returning a tuple of two `Tensors`. The first element of the - pair is a `Tensor` containing the proposal state and should have - the same shape as the input `Tensor`. The second element of the pair gives - the log of the ratio of the probability of transitioning from the - proposal points to the input points and the probability of transitioning - from the input points to the proposal points. If the proposal is - symmetric (e.g., random walk, where the proposal is either - normal or uniform centered at `current_state`), i.e., - Probability(Proposal -> Current) = Probability(Current -> Proposal) - the second value should be set to `None` instead of explicitly supplying a - tensor of zeros. In addition to being convenient, this also leads to a - more efficient graph. - seed: `int` or None. The random seed for this `Op`. If `None`, no seed is - applied. - name: Python `str` name prefix for ops managed by this function. + target_log_prob_fn: Python callable which takes an argument like + `current_state` (or `*current_state` if it's a list) and returns its + (possibly unnormalized) log-density under the target distribution. + proposal_fn: Python callable which takes an argument like `current_state` + (or `*current_state` if it's a list) and returns a tuple of proposed + states of same shape as `state`, and a log ratio `Tensor` of same shape + as `current_target_log_prob`. The log ratio is the log-probability of + `state` given proposed states minus the log-probability of proposed + states given `state`. If the proposal is symmetric, set the second value + to `None`: this enables more efficient computation than explicitly + supplying a tensor of zeros. + current_state: `Tensor` or Python `list` of `Tensor`s representing the + current state(s) of the Markov chain(s). The first `r` dimensions index + independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`. + seed: Python integer to seed the random number generator. + current_target_log_prob: (Optional) `Tensor` representing the value of + `target_log_prob_fn` at the `current_state`. The only reason to + specify this argument is to reduce TF graph size. + Default value: `None` (i.e., compute as needed). + name: A name of the operation (optional). Returns: - next_state: `Tensor` with `dtype` and shape matching `current_state`. - Created by propagating the chain by one step, starting from + next_state: Tensor or Python list of `Tensor`s representing the state(s) + of the Markov chain(s) at each result step. Has same shape as `current_state`. - next_log_density: `Tensor` with `dtype` and shape matching - `current_log_density`, which is equal to the value of the unnormalized - `log_unnormalized_prob_fn` computed at `next_state`. - log_accept_ratio: `Tensor` with `dtype` and shape matching - `current_log_density`. Stands for the log of Metropolis-Hastings - acceptance ratio used in generating the `next_state`. - """ + kernel_results: `collections.namedtuple` of internal calculations used to + advance the chain. - with ops.name_scope(name, 'single_iteration', [current_state]): - # The proposed state and the log of the corresponding Hastings ratio. - proposal_state, log_transit_ratio = proposal_fn(current_state) - - # If the log ratio is None, assume that the transitions are symmetric, - # i.e., Prob(Current -> Proposed) = Prob(Proposed -> Current). - if log_transit_ratio is None: - log_transit_ratio = 0. - - # Log-density of the proposal state. - proposal_log_density = log_unnormalized_prob_fn(proposal_state) - - # Ops to compute the log of the acceptance ratio. Recall that the - # acceptance ratio is: [Prob(Proposed) / Prob(Current)] * - # [Prob(Proposed -> Current) / Prob(Current -> Proposed)]. The log of the - # second term is the log_transit_ratio. - with ops.name_scope('accept_reject'): - # The log of the acceptance ratio. - log_accept_ratio = (proposal_log_density - current_log_density - + log_transit_ratio) - - # A proposal is accepted or rejected depending on the acceptance ratio. - # If the acceptance ratio is greater than 1 then it is always accepted. - # If the acceptance ratio is less than 1 then the proposal is accepted - # with probability = acceptance ratio. As we are working in log space to - # prevent over/underflows, this logic is expressed in log terms below. - # If a proposal is accepted we place a True in the acceptance state - # tensor and if it is to be rejected we place a False. - # The log_draws below have to be compared to the log_accept_ratio so we - # make sure that they have the same data type. - log_draws = math_ops.log(random_ops.random_uniform( - array_ops.shape(current_log_density), seed=seed, - dtype=log_accept_ratio.dtype)) - is_proposal_accepted = log_draws < log_accept_ratio - - # The acceptance state decides which elements of the current state are to - # be replaced with the corresponding elements in the proposal state. - with ops.name_scope(name, 'metropolis_single_step', - [current_state, current_log_density]): - next_log_density = array_ops.where(is_proposal_accepted, - proposal_log_density, - current_log_density) - next_state = array_ops.where(is_proposal_accepted, proposal_state, - current_state) - - return next_state, next_log_density, log_accept_ratio + #### Examples + + We illustrate Metropolis-Hastings on a Normal likelihood with + unknown mean. + + ```python + tfd = tf.contrib.distributions + tfp = tf.contrib.bayesflow + + loc = tf.get_variable("loc", initializer=1.) + x = tf.constant([0.0] * 50) + + def make_target_log_prob_fn(x): + def target_log_prob_fn(loc): + prior = tfd.Normal(loc=0., scale=1.) + likelihood = tfd.Independent( + tfd.Normal(loc=loc, scale=0.1), + reinterpreted_batch_ndims=1) + return prior.log_prob(loc) + likelihood.log_prob(x) + return target_log_prob_fn + + next_state, kernel_results = tfp.metropolis_hastings.kernel( + target_log_prob_fn=make_target_log_prob_fn(x), + proposal_fn=tfp.metropolis_hastings.proposal_normal(), + current_state=loc) + loc_update = loc.assign(next_state) + ``` + + We illustrate Metropolis-Hastings on a Normal likelihood with + unknown mean and variance. We apply 4 chains. + + ```python + tfd = tf.contrib.distributions + tfp = tf.contrib.bayesflow + + num_chains = 4 + loc = tf.get_variable("loc", shape=[num_chains], + initializer=tf.random_normal_initializer()) + scale = tf.get_variable("scale", shape=[num_chains], + initializer=tf.ones_initializer()) + x = tf.constant([0.0] * 50) + + def make_target_log_prob_fn(x): + data = tf.reshape(x, shape=[-1, 1]) + def target_log_prob_fn(loc, scale): + prior_loc = tfd.Normal(loc=0., scale=1.) + prior_scale = tfd.InverseGamma(concentration=1., rate=1.) + likelihood = tfd.Independent( + tfd.Normal(loc=loc, scale=scale), + reinterpreted_batch_ndims=1) + return (prior_loc.log_prob(loc) + + prior_scale.log_prob(scale) + + likelihood.log_prob(data)) + return target_log_prob_fn + + def proposal_fn(loc, scale): + loc_proposal = tfp.metropolis_hastings.proposal_normal() + scale_proposal = tfp.metropolis_hastings.proposal_uniform(minval=-1.) + proposed_loc, _ = loc_proposal(loc) + proposed_scale, _ = scale_proposal(scale) + proposed_scale = tf.maximum(proposed_scale, 0.01) + return [proposed_loc, proposed_scale], None + + next_state, kernel_results = tfp.metropolis_hastings.kernel( + target_log_prob_fn=make_target_log_prob_fn(x), + proposal_fn=proposal_fn, + current_state=[loc, scale]) + train_op = tf.group(loc.assign(next_state[0]), + scale.assign(next_state[1])) + ``` + + """ + with ops.name_scope( + name, "metropolis_hastings_kernel", + [current_state, seed, current_target_log_prob]): + with ops.name_scope("initialize"): + maybe_expand = lambda x: list(x) if _is_list_like(x) else [x] + current_state_parts = maybe_expand(current_state) + if current_target_log_prob is None: + current_target_log_prob = target_log_prob_fn(*current_state_parts) + + proposed_state, log_transit_ratio = proposal_fn(*current_state_parts) + proposed_state_parts = maybe_expand(proposed_state) + + proposed_target_log_prob = target_log_prob_fn(*proposed_state_parts) + + with ops.name_scope( + "accept_reject", + [current_state_parts, proposed_state_parts, + current_target_log_prob, proposed_target_log_prob]): + log_accept_ratio = proposed_target_log_prob - current_target_log_prob + if log_transit_ratio is not None: + # If the log_transit_ratio is None, then assume the proposal is + # symmetric, i.e., + # log p(old | new) - log p(new | old) = 0. + log_accept_ratio += log_transit_ratio + + # u < exp(log_accept_ratio), where u~Uniform[0,1) + # ==> log(u) < log_accept_ratio + random_value = random_ops.random_uniform( + array_ops.shape(log_accept_ratio), + dtype=log_accept_ratio.dtype, + seed=seed) + random_negative = math_ops.log(random_value) + is_accepted = random_negative < log_accept_ratio + next_state_parts = [array_ops.where(is_accepted, + proposed_state_part, + current_state_part) + for proposed_state_part, current_state_part in + zip(proposed_state_parts, current_state_parts)] + accepted_log_prob = array_ops.where(is_accepted, + proposed_target_log_prob, + current_target_log_prob) + maybe_flatten = lambda x: x if _is_list_like(current_state) else x[0] + return [ + maybe_flatten(next_state_parts), + KernelResults( + log_accept_ratio=log_accept_ratio, + current_target_log_prob=accepted_log_prob, + is_accepted=is_accepted, + proposed_state=maybe_flatten(proposed_state_parts), + ), + ] def evolve(initial_sample, initial_log_density, initial_log_accept_ratio, - log_unnormalized_prob_fn, + target_log_prob_fn, proposal_fn, n_steps=1, seed=None, @@ -162,9 +240,11 @@ def evolve(initial_sample, The probability distribution may have an unknown normalization constan. We parameterize the probability density as follows: - ``` - f(x) = exp(L(x) + constant) - ``` + + ```none + f(x) = exp(L(x) + constant) + ``` + Here `L(x)` is any continuous function with an (possibly unknown but finite) upper bound, i.e. there exists a number beta such that `L(x)< beta < infinity` for all x. The constant is the normalization needed @@ -188,72 +268,77 @@ def evolve(initial_sample, The following example, demonstrates the use to generate a 1000 uniform random walk Metropolis samplers run in parallel for the normal target distribution. + ```python - n = 3 # dimension of the problem - - # Generate 1000 initial values randomly. Each of these would be an - # independent starting point for a Markov chain. - state = tf.get_variable( - 'state',initializer=tf.random_normal([1000, n], mean=3.0, - dtype=tf.float64, seed=42)) - - # Computes the log(p(x)) for the unit normal density and ignores the - # normalization constant. - def log_density(x): - return - tf.reduce_sum(x * x, reduction_indices=-1) / 2.0 - - # Initial log-density value - state_log_density = tf.get_variable( - 'state_log_density', initializer=log_density(state.initialized_value())) - - # A variable to store the log_acceptance_ratio: - log_acceptance_ratio = tf.get_variable( - 'log_acceptance_ratio', initializer=tf.zeros([1000], dtype=tf.float64)) - - # Generates random proposals by moving each coordinate uniformly and - # independently in a box of size 2 centered around the current value. - # Returns the new point and also the log of the Hastings ratio (the - # ratio of the probability of going from the proposal to origin and the - # probability of the reverse transition). When this ratio is 1, the value - # may be omitted and replaced by None. - def random_proposal(x): - return (x + tf.random_uniform(tf.shape(x), minval=-1, maxval=1, - dtype=x.dtype, seed=12)), None - - # Create the op to propagate the chain for 100 steps. - stepper = mh.evolve( - state, state_log_density, log_acceptance_ratio, - log_density, random_proposal, n_steps=100, seed=123) - init = tf.initialize_all_variables() - with tf.Session() as sess: - sess.run(init) - # Run the chains for a total of 1000 steps and print out the mean across - # the chains every 100 iterations. - for n_iter in range(10): - # Executing the stepper advances the chain to the next state. - sess.run(stepper) - # Print out the current value of the mean(sample) for every dimension. - print(np.mean(sess.run(state), 0)) - # Estimated covariance matrix - samples = sess.run(state) - print('') - print(np.cov(samples, rowvar=False)) + n = 3 # dimension of the problem + + # Generate 1000 initial values randomly. Each of these would be an + # independent starting point for a Markov chain. + state = tf.get_variable( + "state", + initializer=tf.random_normal([1000, n], + mean=3.0, + dtype=tf.float64, + seed=42)) + + # Computes the log(p(x)) for the unit normal density and ignores the + # normalization constant. + def log_density(x): + return -tf.reduce_sum(x * x, reduction_indices=-1) / 2.0 + + # Initial log-density value + state_log_density = tf.get_variable( + "state_log_density", + initializer=log_density(state.initialized_value())) + + # A variable to store the log_acceptance_ratio: + log_acceptance_ratio = tf.get_variable( + "log_acceptance_ratio", + initializer=tf.zeros([1000], dtype=tf.float64)) + + # Generates random proposals by moving each coordinate uniformly and + # independently in a box of size 2 centered around the current value. + # Returns the new point and also the log of the Hastings ratio (the + # ratio of the probability of going from the proposal to origin and the + # probability of the reverse transition). When this ratio is 1, the value + # may be omitted and replaced by None. + def random_proposal(x): + return (x + tf.random_uniform(tf.shape(x), minval=-1, maxval=1, + dtype=x.dtype, seed=12)), None + + # Create the op to propagate the chain for 100 steps. + stepper = mh.evolve( + state, state_log_density, log_acceptance_ratio, + log_density, random_proposal, n_steps=100, seed=123) + init = tf.initialize_all_variables() + with tf.Session() as sess: + sess.run(init) + # Run the chains for a total of 1000 steps and print out the mean across + # the chains every 100 iterations. + for n_iter in range(10): + # Executing the stepper advances the chain to the next state. + sess.run(stepper) + # Print out the current value of the mean(sample) for every dimension. + print(np.mean(sess.run(state), 0)) + # Estimated covariance matrix + samples = sess.run(state) + print(np.cov(samples, rowvar=False)) ``` Args: initial_sample: A float-like `tf.Variable` of any shape that can - be consumed by the `log_unnormalized_prob_fn` and `proposal_fn` + be consumed by the `target_log_prob_fn` and `proposal_fn` callables. initial_log_density: Float-like `tf.Variable` with `dtype` and shape - equivalent to `log_unnormalized_prob_fn(initial_sample)`, i.e., matching - the result of `log_unnormalized_prob_fn` invoked at `current_state`. + equivalent to `target_log_prob_fn(initial_sample)`, i.e., matching + the result of `target_log_prob_fn` invoked at `current_state`. initial_log_accept_ratio: A `tf.Variable` with `dtype` and shape matching `initial_log_density`. Stands for the log of Metropolis-Hastings acceptance ratio after propagating the chain for `n_steps`. - log_unnormalized_prob_fn: A Python callable evaluated at + target_log_prob_fn: A Python callable evaluated at `current_state` and returning a float-like `Tensor` of log target-density up to a normalizing constant. In other words, - `log_unnormalized_prob_fn(x) = log(g(x))`, where + `target_log_prob_fn(x) = log(g(x))`, where `target_density = g(x)/Z` for some constant `A`. The shape of the input tensor is the same as the shape of the `current_state`. The shape of the output tensor is either @@ -265,7 +350,7 @@ def evolve(initial_sample, and the result must be of shape [B1, ..., Bb]. For example, if the distribution that is being sampled is a 10 dimensional normal, then the input tensor may be of shape [100, 10] or [30, 20, 10]. The - last dimension will then be 'consumed' by `log_unnormalized_prob_fn` + last dimension will then be 'consumed' by `target_log_prob_fn` and it should return tensors of shape [100] and [30, 20] respectively. proposal_fn: A callable accepting a real valued `Tensor` of current sample points and returning a tuple of two `Tensors`. The first element of the @@ -289,42 +374,48 @@ def evolve(initial_sample, forward_step: an `Op` to step the Markov chain forward for `n_steps`. """ - with ops.name_scope(name, 'metropolis_hastings', [initial_sample]): + with ops.name_scope(name, "metropolis_hastings", [initial_sample]): current_state = initial_sample - current_log_density = initial_log_density + current_target_log_prob = initial_log_density log_accept_ratio = initial_log_accept_ratio - # Stop condition for the while_loop - def stop_condition(i, _): - return i < n_steps - - def step(i, loop_vars): - """Wrap `_single_iteration` for `while_loop`.""" - state = loop_vars[0] - state_log_density = loop_vars[1] - return i + 1, list(_single_iteration(state, state_log_density, - log_unnormalized_prob_fn, - proposal_fn, seed=seed)) - - loop_vars = [current_state, current_log_density, log_accept_ratio] - # Build an `Op` to evolve the Markov chain for `n_steps` - (_, [end_state, end_log_density, end_log_acceptance]) = ( + def step(i, current_state, current_target_log_prob, log_accept_ratio): + """Wrap single Markov chain iteration in `while_loop`.""" + next_state, kernel_results = kernel( + target_log_prob_fn=target_log_prob_fn, + proposal_fn=proposal_fn, + current_state=current_state, + current_target_log_prob=current_target_log_prob, + seed=seed) + accepted_log_prob = kernel_results.current_target_log_prob + log_accept_ratio = kernel_results.log_accept_ratio + return i + 1, next_state, accepted_log_prob, log_accept_ratio + + (_, accepted_state, accepted_target_log_prob, accepted_log_accept_ratio) = ( control_flow_ops.while_loop( - stop_condition, step, - (0, loop_vars), - parallel_iterations=1, swap_memory=1)) + cond=lambda i, *ignored_args: i < n_steps, + body=step, + loop_vars=[ + 0, # i + current_state, + current_target_log_prob, + log_accept_ratio, + ], + parallel_iterations=1 if seed is not None else 10, + # TODO(b/73775595): Confirm optimal setting of swap_memory. + swap_memory=1)) forward_step = control_flow_ops.group( - state_ops.assign(current_log_density, end_log_density), - state_ops.assign(current_state, end_state), - state_ops.assign(log_accept_ratio, end_log_acceptance)) + state_ops.assign(current_target_log_prob, accepted_target_log_prob), + state_ops.assign(current_state, accepted_state), + state_ops.assign(log_accept_ratio, accepted_log_accept_ratio)) return forward_step -def uniform_random_proposal(step_size=1., - seed=None, - name=None): +def proposal_uniform(step_size=1., + seed=None, + name=None): """Returns a callable that adds a random uniform tensor to the input. This function returns a callable that accepts one `Tensor` argument of any @@ -346,11 +437,13 @@ def uniform_random_proposal(step_size=1., Returns: proposal_fn: A callable accepting one float-like `Tensor` and returning a - 2-tuple. The first value in the tuple is a `Tensor` of the same shape and - dtype as the input argument and the second element of the tuple is None. + 2-tuple. The first value in the tuple is a `Tensor` of the same shape and + dtype as the input argument and the second element of the tuple is None. """ - with ops.name_scope(name, 'uniform_random_proposal', [step_size]): + with ops.name_scope(name, "proposal_uniform", [step_size]): + step_size = ops.convert_to_tensor(step_size, name="step_size") + def proposal_fn(input_state, name=None): """Adds a uniform perturbation to the input state. @@ -359,12 +452,12 @@ def uniform_random_proposal(step_size=1., name: A string that sets the name for this `Op`. Returns: - proposal_state: A float-like `Tensot` with `dtype` and shape matching + proposal_state: A float-like `Tensor` with `dtype` and shape matching `input_state`. log_transit_ratio: `None`. Proposal is symmetric. """ - with ops.name_scope(name, 'proposer', [input_state]): - input_state = ops.convert_to_tensor(input_state, name='input_state') + with ops.name_scope(name, "proposer", [input_state]): + input_state = ops.convert_to_tensor(input_state, name="input_state") return input_state + random_ops.random_uniform( array_ops.shape(input_state), minval=-step_size, @@ -373,9 +466,9 @@ def uniform_random_proposal(step_size=1., return proposal_fn -def normal_random_proposal(scale=1., - seed=None, - name=None): +def proposal_normal(scale=1., + seed=None, + name=None): """Returns a callable that adds a random normal tensor to the input. This function returns a callable that accepts one `Tensor` argument of any @@ -398,11 +491,13 @@ def normal_random_proposal(scale=1., Returns: proposal_fn: A callable accepting one float-like `Tensor` and returning a - 2-tuple. The first value in the tuple is a `Tensor` of the same shape and - dtype as the input argument and the second element of the tuple is None. + 2-tuple. The first value in the tuple is a `Tensor` of the same shape and + dtype as the input argument and the second element of the tuple is None. """ - with ops.name_scope(name, 'normal_random_proposal', [scale]): + with ops.name_scope(name, "proposal_normal", [scale]): + scale = ops.convert_to_tensor(scale, name="scale") + def proposal_fn(input_state, name=None): """Adds a normal perturbation to the input state. @@ -411,16 +506,22 @@ def normal_random_proposal(scale=1., name: A string that sets the name for this `Op`. Returns: - proposal_state: A float-like `Tensot` with `dtype` and shape matching + proposal_state: A float-like `Tensor` with `dtype` and shape matching `input_state`. log_transit_ratio: `None`. Proposal is symmetric. """ - with ops.name_scope(name, 'proposer', [input_state]): - input_state = ops.convert_to_tensor(input_state, name='input_state') + with ops.name_scope(name, "proposer", [input_state]): + input_state = ops.convert_to_tensor(input_state, name="input_state") return input_state + random_ops.random_normal( array_ops.shape(input_state), mean=0., stddev=scale, + dtype=scale.dtype, seed=seed), None return proposal_fn + + +def _is_list_like(x): + """Helper which returns `True` if input is `list`-like.""" + return isinstance(x, (tuple, list)) diff --git a/tensorflow/contrib/bayesflow/python/ops/optimizers.py b/tensorflow/contrib/bayesflow/python/ops/optimizers.py index fb70628d1083836281e9327e83e109493276c64f..bff6bb7948ecb7487349a5de6743624416a652eb 100644 --- a/tensorflow/contrib/bayesflow/python/ops/optimizers.py +++ b/tensorflow/contrib/bayesflow/python/ops/optimizers.py @@ -14,7 +14,7 @@ # ============================================================================== """Probabilistic optimizer modules. -See ${python/contrib.bayesflow.optimizers}. +See @{tf.contrib.bayesflow.optimizers}. """ from __future__ import absolute_import diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py index 31f5c444817b9b82723c86bea3504d4934e57eb8..23ba76210b3b68d0d0b2eef9d4040882654bdad9 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py @@ -93,7 +93,9 @@ def make_custom_export_strategy(name, "w") as f: f.write("\n".join("%s, %f" % (k, v) for k, v in sorted_by_importance)) return result_dir - return export_strategy.ExportStrategy(name, export_fn) + + return export_strategy.ExportStrategy( + name, export_fn, strip_default_attrs=True) def convert_to_universal_format(dtec, sorted_feature_names, diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc index 754b7bc3270d647fc381033b769eadd7b791771e..3bf33186ec13f5ff991db938d59849c0124a30a0 100644 --- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc @@ -137,6 +137,61 @@ class TreeEnsembleDeserializeOp : public OpKernel { } }; +class TreeEnsembleUsedHandlersOp : public OpKernel { + public: + explicit TreeEnsembleUsedHandlersOp(OpKernelConstruction* context) + : OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("num_all_handlers", &num_handlers_)); + } + + void Compute(OpKernelContext* context) override { + boosted_trees::models::DecisionTreeEnsembleResource* ensemble_resource; + + OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), + &ensemble_resource)); + tf_shared_lock l(*ensemble_resource->get_mutex()); + core::ScopedUnref unref_me(ensemble_resource); + + // Get the stamp token. + const Tensor* stamp_token_t; + OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t)); + int64 stamp_token = stamp_token_t->scalar()(); + + // Only the Chief should run this Op and it is guaranteed to be in + // a consistent state so the stamps must always match. + CHECK(ensemble_resource->is_stamp_valid(stamp_token)); + + Tensor* output_used_handlers_t = nullptr; + OP_REQUIRES_OK( + context, context->allocate_output("used_handlers_mask", {num_handlers_}, + &output_used_handlers_t)); + auto output_used_handlers = output_used_handlers_t->vec(); + + Tensor* output_num_used_handlers_t = nullptr; + OP_REQUIRES_OK(context, + context->allocate_output("num_used_handlers", {}, + &output_num_used_handlers_t)); + int handler_idx = 0; + std::vector used_handlers = ensemble_resource->GetUsedHandlers(); + output_num_used_handlers_t->scalar()() = used_handlers.size(); + for (int64 i = 0; i < num_handlers_; ++i) { + if (handler_idx >= used_handlers.size() || + used_handlers[handler_idx] > i) { + output_used_handlers(i) = false; + } else { + OP_REQUIRES(context, used_handlers[handler_idx] == i, + errors::InvalidArgument("Handler IDs should be sorted.")); + ++handler_idx; + output_used_handlers(i) = true; + } + } + } + + private: + int64 num_handlers_; +}; + REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeEnsembleResource); REGISTER_KERNEL_BUILDER( @@ -155,5 +210,7 @@ REGISTER_KERNEL_BUILDER(Name("TreeEnsembleSerialize").Device(DEVICE_CPU), REGISTER_KERNEL_BUILDER(Name("TreeEnsembleDeserialize").Device(DEVICE_CPU), TreeEnsembleDeserializeOp); +REGISTER_KERNEL_BUILDER(Name("TreeEnsembleUsedHandlers").Device(DEVICE_CPU), + TreeEnsembleUsedHandlersOp); } // namespace boosted_trees } // namespace tensorflow diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc index 7f8dea1d3c2a04b725843f6e2932a0cdfbc7733c..1bfeed306641111718984b2097512e5ec3fa8630 100644 --- a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc @@ -361,27 +361,10 @@ class GrowTreeEnsembleOp : public OpKernel { // Increment attempt stats. ensemble_resource->IncrementAttempts(); - // In case we want to do feature selection and we have reached the limit, - // build a list of handlers used so far to avoid adding new features. - std::vector allowed_handlers; - if (learner_config_.constraints().max_number_of_unique_feature_columns() > - 0) { - allowed_handlers = ensemble_resource->GetUsedHandlers(); - // TODO(soroush): We can disable handlers that are not going to be used to - // avoid unnecessary computations. - if (allowed_handlers.size() < - learner_config_.constraints() - .max_number_of_unique_feature_columns()) { - // We have not reached the limit yet. Empty the list of allow features - // which means we can keep adding new features. - allowed_handlers.clear(); - } - } - // Find best splits for each active partition. std::map best_splits; - FindBestSplitsPerPartition(context, allowed_handlers, partition_ids_list, - gains_list, splits_list, &best_splits); + FindBestSplitsPerPartition(context, partition_ids_list, gains_list, + splits_list, &best_splits); // No-op if no new splits can be considered. if (best_splits.empty()) { @@ -422,19 +405,12 @@ class GrowTreeEnsembleOp : public OpKernel { // and finds the best split for each partition. void FindBestSplitsPerPartition( OpKernelContext* const context, - const std::vector& allowed_handlers, // Empty means all handlers. const OpInputList& partition_ids_list, const OpInputList& gains_list, const OpInputList& splits_list, std::map* best_splits) { // Find best split per partition going through every feature candidate. // TODO(salehay): Is this worth parallelizing? for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) { - if (!allowed_handlers.empty()) { - if (!std::binary_search(allowed_handlers.begin(), - allowed_handlers.end(), handler_id)) { - continue; - } - } const auto& partition_ids = partition_ids_list[handler_id].vec(); const auto& gains = gains_list[handler_id].vec(); const auto& splits = splits_list[handler_id].vec(); diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h index cd925f6b65e569538212e9c26aef0abc8482960b..794ba2bcb0aafa26c5e1c90fcd66caf9dd5bf7d5 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h +++ b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h @@ -137,7 +137,7 @@ struct NodeStats { Eigen::MatrixXf hessian = TensorToEigenMatrix(grad_stats.second.t, grad_dim, grad_dim); // I is an identity matrix. - // The gain in general form is -g^T (H+l2 I)^-1 g. + // The gain in general form is g^T (H+l2 I)^-1 g. // The node weights are -(H+l2 I)^-1 g. Eigen::MatrixXf identity; identity.setIdentity(grad_dim, grad_dim); @@ -240,7 +240,7 @@ struct NodeStats { // given regularized Hessian and gradient vector g. void CalculateWeightAndGain(const Eigen::MatrixXf& hessian_and_reg, const Eigen::VectorXf& g) { - // The gain in general form is -g^T (Hessian_and_regularization)^-1 g. + // The gain in general form is g^T (Hessian_and_regularization)^-1 g. // The node weights are -(Hessian_and_regularization)^-1 g. Eigen::VectorXf weight; // If we want to calculate x = K^-1 v, instead of explicitly calculating diff --git a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h index da5e7448519cb7f4092f7bbbe1b526271008ec22..7815fa049aa165a944c45872c762b7a5bf91b316 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h +++ b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h @@ -48,9 +48,9 @@ class BatchFeatures { Status GetFeatureColumnSizes(int64* const num_dense_float_features, int64* const num_sparse_float_features, int64* const num_sparse_int_features) const { - QCHECK_NE(num_dense_float_features, nullptr); - QCHECK_NE(num_sparse_float_features, nullptr); - QCHECK_NE(num_sparse_int_features, nullptr); + QCHECK_NE(num_dense_float_features, (int64*) nullptr); + QCHECK_NE(num_sparse_float_features, (int64*) nullptr); + QCHECK_NE(num_sparse_int_features, (int64*) nullptr); *num_dense_float_features = dense_float_feature_columns_.size(); *num_sparse_float_features = sparse_float_feature_columns_.size(); *num_sparse_int_features = sparse_int_feature_columns_.size(); diff --git a/tensorflow/contrib/boosted_trees/ops/model_ops.cc b/tensorflow/contrib/boosted_trees/ops/model_ops.cc index 0786c4166410720e8d4d70960e5747ff111076d8..9d6343c7e80f369bf6a5465821c5f4bacb984cd0 100644 --- a/tensorflow/contrib/boosted_trees/ops/model_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/model_ops.cc @@ -110,5 +110,32 @@ stamp_token: Token to use as the new value of the resource stamp. tree_ensemble_config: Serialized proto of the ensemble. )doc"); +REGISTER_OP("TreeEnsembleUsedHandlers") + .Attr("num_all_handlers: int >= 0") + .Input("tree_ensemble_handle: resource") + .Input("stamp_token: int64") + .Output("num_used_handlers: int64") + .Output("used_handlers_mask: bool") + .SetShapeFn([](shape_inference::InferenceContext* c) { + shape_inference::ShapeHandle unused_input; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input)); + c->set_output(0, c->Scalar()); + int num_all_handlers; + c->GetAttr("num_all_handlers", &num_all_handlers).IgnoreError(); + c->set_output(1, {c->Vector(num_all_handlers)}); + + return Status::OK(); + }) + .Doc(R"doc( +Returns the mask of used handlers along with the number of non-zero elements in +this mask. Used in feature selection. + +tree_ensemble_handle: Handle to the tree ensemble. +stamp_token: Token to use as the new value of the resource stamp. +num_used_handlers: number of feature column handlers used in the model. +used_handlers_mask: A boolean vector of showing which handlers are used in the + model. +)doc"); + } // namespace boosted_trees } // namespace tensorflow diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py index 27c288bbf78b3b593d0807e92ac7fd9afc4d2725..63b9c5fddf0d9967d53077608664b59d9ae00481 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py @@ -310,6 +310,22 @@ class ModelOpsTest(test_util.TensorFlowTestCase): # The third tree was added after the save. self.assertAllClose(result.eval(), [[-1.1], [-1.1]]) + def testUsedHandlers(self): + with self.test_session(): + tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() + tree_ensemble_config.growing_metadata.used_handler_ids.append(1) + tree_ensemble_config.growing_metadata.used_handler_ids.append(5) + stamp_token = 3 + tree_ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=stamp_token, + tree_ensemble_config=tree_ensemble_config.SerializeToString(), + name="create_tree") + resources.initialize_resources(resources.shared_resources()).run() + result = model_ops.tree_ensemble_used_handlers( + tree_ensemble_handle, stamp_token, num_all_handlers=6) + self.assertAllEqual([0, 1, 0, 0, 0, 1], result.used_handlers_mask.eval()) + self.assertEqual(2, result.num_used_handlers.eval()) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py index 8ca1aabacaf53b66aaba184962922294427d6803..3e524efbeac74ff754d63cae92b3e194411cb2de 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py @@ -1588,7 +1588,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): self.assertEqual( 2, tree_ensemble_config.tree_metadata[2].num_tree_weight_updates) - def testGrowExistingEnsembleTreeWithFeatureSelectionCanStillGrow(self): + def testGrowExistingEnsembleTreeWithFeatureSelectionUsedHandlers(self): """Test growing a tree with feature selection.""" with self.test_session() as session: # Create existing ensemble with one root split and one bias tree. @@ -1649,7 +1649,6 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): num_trees_attempted: 2 num_layers_attempted: 2 used_handler_ids: 2 - used_handler_ids: 5 } """, tree_ensemble_config) tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1668,183 +1667,8 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - # There are 2 handler_ids in used_handler_ids already but one of them - # is handler 2, so we can still grow trees. - learner_config.constraints.max_number_of_unique_feature_columns = 2 - learner_config = learner_config.SerializeToString() - # Prepare handler inputs. - handler1_partitions = np.array([0], dtype=np.int32) - handler1_gains = np.array([7.62], dtype=np.float32) - handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)] - handler2_partitions = np.array([0], dtype=np.int32) - handler2_gains = np.array([0.63], dtype=np.float32) - handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)] - handler3_partitions = np.array([0], dtype=np.int32) - handler3_gains = np.array([7.62], dtype=np.float32) - handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)] - - # Grow tree ensemble. - grow_op = training_ops.grow_tree_ensemble( - tree_ensemble_handle, - stamp_token=0, - next_stamp_token=1, - learning_rate=1, - partition_ids=[ - handler1_partitions, handler2_partitions, handler3_partitions - ], - gains=[handler1_gains, handler2_gains, handler3_gains], - splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, - dropout_seed=123, - center_bias=True) - session.run(grow_op) - - # Expect a new tree to be added with the split from handler 1. - _, serialized = session.run( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)) - tree_ensemble_config.ParseFromString(serialized) - self.assertEqual(3, len(tree_ensemble_config.trees)) - self.assertEqual( - 2, len(tree_ensemble_config.growing_metadata.used_handler_ids)) - - def testGrowExistingEnsembleTreeWithFeatureSelectionEmptyEnsemble(self): - """Test growing a tree with feature selection with empty ensemble.""" - with self.test_session() as session: - # Create existing ensemble with one root split and one bias tree. - tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble_config.SerializeToString(), - name="tree_ensemble") - resources.initialize_resources(resources.shared_resources()).run() - - # Prepare learner config. - learner_config = _gen_learner_config( - num_classes=2, - l1_reg=0, - l2_reg=0, - tree_complexity=0, - max_depth=1, - min_node_weight=0, - pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - learner_config.constraints.max_number_of_unique_feature_columns = 2 - learner_config = learner_config.SerializeToString() - # Prepare handler inputs. - handler1_partitions = np.array([0], dtype=np.int32) - handler1_gains = np.array([7.62], dtype=np.float32) - handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)] - handler2_partitions = np.array([0], dtype=np.int32) - handler2_gains = np.array([0.63], dtype=np.float32) - handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)] - handler3_partitions = np.array([0], dtype=np.int32) - handler3_gains = np.array([7.62], dtype=np.float32) - handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)] - - # Grow tree ensemble. - grow_op = training_ops.grow_tree_ensemble( - tree_ensemble_handle, - stamp_token=0, - next_stamp_token=1, - learning_rate=1, - partition_ids=[ - handler1_partitions, handler2_partitions, handler3_partitions - ], - gains=[handler1_gains, handler2_gains, handler3_gains], - splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, - dropout_seed=123, - center_bias=True) - session.run(grow_op) - - _, serialized = session.run( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)) - tree_ensemble_config.ParseFromString(serialized) - self.assertEqual(1, len(tree_ensemble_config.trees)) - self.assertEqual( - 1, len(tree_ensemble_config.growing_metadata.used_handler_ids)) - - def testGrowExistingEnsembleTreeWithFeatureSelectionCantGrow(self): - """Test growing a tree with feature selection with empty ensemble.""" - with self.test_session() as session: - # Create existing ensemble with one root split and one bias tree. - tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() - text_format.Merge(""" - trees { - nodes { - leaf { - vector { - value: -0.32 - value: 0.28 - } - } - } - } - trees { - nodes { - categorical_id_binary_split { - feature_column: 3 - feature_id: 7 - left_id: 1 - right_id: 2 - } - node_metadata { - gain: 1.3 - } - } - nodes { - leaf { - sparse_vector { - index: 0 - value: 2.3 - } - } - } - nodes { - leaf { - sparse_vector { - index: 0 - value: -0.9 - } - } - } - } - tree_weights: 0.7 - tree_weights: 1 - tree_metadata { - num_tree_weight_updates: 1 - num_layers_grown: 1 - is_finalized: true - } - tree_metadata { - num_tree_weight_updates: 5 - num_layers_grown: 1 - is_finalized: true - } - growing_metadata { - num_trees_attempted: 2 - num_layers_attempted: 2 - used_handler_ids: 4 - used_handler_ids: 5 - } - """, tree_ensemble_config) - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble_config.SerializeToString(), - name="tree_ensemble") - resources.initialize_resources(resources.shared_resources()).run() - # Prepare learner config. - learner_config = _gen_learner_config( - num_classes=2, - l1_reg=0, - l2_reg=0, - tree_complexity=0, - max_depth=1, - min_node_weight=0, - pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - learner_config.constraints.max_number_of_unique_feature_columns = 2 + learner_config.constraints.max_number_of_unique_feature_columns = 3 learner_config = learner_config.SerializeToString() # Prepare handler inputs. handler1_partitions = np.array([0], dtype=np.int32) @@ -1876,12 +1700,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): _, serialized = session.run( model_ops.tree_ensemble_serialize(tree_ensemble_handle)) tree_ensemble_config.ParseFromString(serialized) - # We can't grow a tree since we have reached the limit of 2 unique - # features [4, 5] and the only available splits are from - # handlers [0, 1, 2]. - self.assertEqual(2, len(tree_ensemble_config.trees)) - self.assertEqual( - 2, len(tree_ensemble_config.growing_metadata.used_handler_ids)) + self.assertEqual(3, len(tree_ensemble_config.trees)) + # 2 was already used. handler 0 is being added in this tree. + self.assertAllEqual( + [0, 2], tree_ensemble_config.growing_metadata.used_handler_ids) if __name__ == "__main__": diff --git a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py index 7a5f509047d46549ba81039a23d29ec987ca7920..25b2c9e2fd72bd018717e8a87fce726f26bad968 100644 --- a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py +++ b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py @@ -25,6 +25,7 @@ from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensem from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_serialize # pylint: disable=unused-import from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_stamp_token +from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_used_handlers # pylint: enable=unused-import from tensorflow.python.framework import ops diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index f0b66dcbbe1c5167b9993e66b30b1dc8a839c380..233e21f1cf286a51c27810f3b42511e698e23281 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -57,6 +57,8 @@ PREDICTIONS = "predictions" PARTITION_IDS = "partition_ids" NUM_LAYERS_ATTEMPTED = "num_layers" NUM_TREES_ATTEMPTED = "num_trees" +NUM_USED_HANDLERS = "num_used_handlers" +USED_HANDLERS_MASK = "used_handlers_mask" _FEATURE_NAME_TEMPLATE = "%s_%d" @@ -70,7 +72,8 @@ def _get_column_by_index(tensor, indices): return array_ops.reshape(array_ops.gather(p_flat, i_flat), [shape[0], -1]) -def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): +def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats, + used_handlers): """Returns predictions for the given logits and n_classes. Args: @@ -79,6 +82,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): that contains predictions when no dropout was applied. partition_ids: A rank 1 `Tensor` with shape [batch_size]. ensemble_stats: A TreeEnsembleStatsOp result tuple. + used_handlers: A TreeEnsembleUsedHandlerOp result tuple of an int and a + boolean mask.. Returns: A dict of predictions. @@ -89,6 +94,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): result[PARTITION_IDS] = partition_ids result[NUM_LAYERS_ATTEMPTED] = ensemble_stats.attempted_layers result[NUM_TREES_ATTEMPTED] = ensemble_stats.attempted_trees + result[NUM_USED_HANDLERS] = used_handlers.num_used_handlers + result[USED_HANDLERS_MASK] = used_handlers.used_handlers_mask return result @@ -361,6 +368,13 @@ class GradientBoostedDecisionTreeModel(object): """ ensemble_stats = training_ops.tree_ensemble_stats(ensemble_handle, ensemble_stamp) + num_handlers = ( + len(self._dense_floats) + len(self._sparse_float_shapes) + + len(self._sparse_int_shapes)) + # Used during feature selection. + used_handlers = model_ops.tree_ensemble_used_handlers( + ensemble_handle, ensemble_stamp, num_all_handlers=num_handlers) + # We don't need dropout info - we can always restore it based on the # seed. apply_dropout, seed = _dropout_params(mode, ensemble_stats) @@ -395,7 +409,7 @@ class GradientBoostedDecisionTreeModel(object): use_locking=True) return _make_predictions_dict(ensemble_stamp, predictions, partition_ids, - ensemble_stats) + ensemble_stats, used_handlers) def predict(self, mode): """Returns predictions given the features and mode. @@ -716,6 +730,22 @@ class GradientBoostedDecisionTreeModel(object): else: active_handlers = array_ops.ones([len(handlers), 2], dtype=dtypes.bool) + if self._learner_config.constraints.max_number_of_unique_feature_columns: + target = ( + self._learner_config.constraints.max_number_of_unique_feature_columns) + + def _feature_selection_active_handlers(): + # The active list for current and the next iteration. + used_handlers = array_ops.reshape(predictions_dict[USED_HANDLERS_MASK], + [-1, 1]) + used_handlers = array_ops.concat([used_handlers, used_handlers], axis=1) + return math_ops.logical_and(used_handlers, active_handlers) + + active_handlers = ( + control_flow_ops.cond(predictions_dict[NUM_USED_HANDLERS] >= target, + _feature_selection_active_handlers, + lambda: active_handlers)) + # Prepare empty gradients and hessians when handlers are not ready. empty_hess_shape = [1] + hessian_shape.as_list() empty_grad_shape = [1] + gradient_shape.as_list() diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py index dba51d4f527792d2a8dedc693f74c07119fd231d..6411f57a5419123e799af9231a04fce8ae7724d4 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py @@ -47,6 +47,38 @@ def _squared_loss(label, unused_weights, predictions): return loss +def _append_to_leaf(leaf, c_id, w): + """Helper method for building tree leaves. + + Appends weight contributions for the given class index to a leaf node. + + Args: + leaf: leaf node to append to. + c_id: class Id for the weight update. + w: weight contribution value. + """ + leaf.sparse_vector.index.append(c_id) + leaf.sparse_vector.value.append(w) + + +def _set_float_split(split, feat_col, thresh, l_id, r_id): + """Helper method for building tree float splits. + + Sets split feature column, threshold and children. + + Args: + split: split node to update. + feat_col: feature column for the split. + thresh: threshold to split on forming rule x <= thresh. + l_id: left child Id. + r_id: right child Id. + """ + split.feature_column = feat_col + split.threshold = thresh + split.left_id = l_id + split.right_id = r_id + + class GbdtTest(test_util.TensorFlowTestCase): def setUp(self): @@ -917,6 +949,350 @@ class GbdtTest(test_util.TensorFlowTestCase): output.trees[0].nodes[2].leaf.sparse_vector.value[0], atol=1e-4, rtol=1e-4) + def testTrainFnChiefFeatureSelectionReachedLimitNoGoodSplit(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + # Feature 1 is predictive but it won't be used because we have reached the + # limit of num_used_handlers >= max_number_of_unique_feature_columns + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([True, False], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 0) + self.assertEquals(len(output.tree_weights), 0) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + # On second run, expect a trivial split to be chosen to basically + # predict the average. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 1) + self.assertAllClose(output.tree_weights, [0.1]) + self.assertEquals(stamp_token.eval(), 2) + expected_tree = """ + nodes { + dense_float_binary_split { + feature_column: 0 + threshold: 1.0 + left_id: 1 + right_id: 2 + } + node_metadata { + gain: 0 + } + } + nodes { + leaf { + vector { + value: -0.25 + } + } + } + nodes { + leaf { + vector { + value: 0.0 + } + } + }""" + self.assertProtoEquals(expected_tree, output.trees[0]) + + def testTrainFnChiefFeatureSelectionWithGoodSplits(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + # Feature 1 is predictive and is in our selected features so it will be + # used even when we're at the limit. + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([False, True], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 0) + self.assertEquals(len(output.tree_weights), 0) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + + self.assertEquals(len(output.trees), 1) + self.assertAllClose(output.tree_weights, [0.1]) + self.assertEquals(stamp_token.eval(), 2) + expected_tree = """ + nodes { + dense_float_binary_split { + feature_column: 1 + left_id: 1 + right_id: 2 + } + node_metadata { + gain: 0.5 + } + } + nodes { + leaf { + vector { + value: 0.0 + } + } + } + nodes { + leaf { + vector { + value: -0.5 + } + } + }""" + self.assertProtoEquals(expected_tree, output.trees[0]) + + def testTrainFnChiefFeatureSelectionReachedLimitIncrementAttemptedLayer(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() + tree = tree_ensemble_config.trees.add() + + _set_float_split(tree.nodes.add() + .sparse_float_binary_split_default_right.split, 2, 4.0, + 1, 2) + _append_to_leaf(tree.nodes.add().leaf, 0, 0.5) + _append_to_leaf(tree.nodes.add().leaf, 1, 1.2) + tree_ensemble_config.tree_weights.append(1.0) + metadata = tree_ensemble_config.tree_metadata.add() + metadata.is_finalized = False + metadata.num_layers_grown = 1 + tree_ensemble_config = tree_ensemble_config.SerializeToString() + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config=tree_ensemble_config, + name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + # Both features will be disabled since the feature selection limit is + # already reached. + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + # We have somehow reached our limit 1. Both of the handlers will be + # disabled. + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([False, False], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 1) + self.assertEquals(output.growing_metadata.num_layers_attempted, 1) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + # Make sure the trees are not modified, but the num_layers_attempted is + # incremented so that eventually the training stops. + self.assertEquals(len(output.trees), 1) + self.assertEquals(len(output.trees[0].nodes), 3) + + self.assertEquals(output.growing_metadata.num_layers_attempted, 2) if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/python/utils/losses.py b/tensorflow/contrib/boosted_trees/python/utils/losses.py index 1e8b3ac08a74a94a0e5729e42ace91398a7b5c94..ab7ac2aba605db22a8ed370049b27d55cf1d413a 100644 --- a/tensorflow/contrib/boosted_trees/python/utils/losses.py +++ b/tensorflow/contrib/boosted_trees/python/utils/losses.py @@ -78,7 +78,7 @@ def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15): # Calculate softmax probabilities for each class. unnormalized_probs = math_ops.exp(logits) - normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keep_dims=True) + normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keepdims=True) softmax_predictions = math_ops.divide(unnormalized_probs, math_ops.add(normalizers, eps)) @@ -120,7 +120,7 @@ def per_example_squared_loss(labels, weights, predictions): update_op: An update operation to update the loss's internal state. """ unweighted_loss = math_ops.reduce_sum( - math_ops.square(predictions - labels), 1, keep_dims=True) + math_ops.square(predictions - labels), 1, keepdims=True) return unweighted_loss * weights, control_flow_ops.no_op() diff --git a/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h b/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h index 3ebf28ea442edf87815c39971ae9e01a2a8aae9a..94aeb2c7bb48c6eddb6c7894f8bf6f1567470113 100644 --- a/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h +++ b/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h @@ -126,7 +126,8 @@ class DecisionTreeEnsembleResource : public StampedResource { return; } used_ids->Add(handler_id); - std::rotate(first, used_ids->end() - 1, used_ids->end()); + // Keep the list of used handlers sorted. + std::sort(used_ids->begin(), used_ids->end()); } std::vector GetUsedHandlers() const { diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD index 80e18a43a71cc9d6c9e2ccf5836e50c6427a30f6..1a124eca364424b651de86bfaac6f33ad131804b 100644 --- a/tensorflow/contrib/cluster_resolver/BUILD +++ b/tensorflow/contrib/cluster_resolver/BUILD @@ -30,6 +30,7 @@ py_library( "python/training/__init__.py", ], srcs_version = "PY2AND3", + visibility = ["//visibility:public"], deps = [ ":cluster_resolver_py", ":gce_cluster_resolver_py", @@ -109,5 +110,6 @@ tf_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:training", ], + grpc_enabled = True, main = "python/training/tpu_cluster_resolver_test.py", ) diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py index b04822fa9d66465e34a545d3b00c399bbb196514..1c480b25134b1e54200e0ddb780bd7bb0f122341 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py @@ -53,11 +53,16 @@ class ClusterResolver(object): raise NotImplementedError( 'cluster_spec is not implemented for {}.'.format(self)) + @abc.abstractmethod + def master(self): + """...""" + raise NotImplementedError('master is not implemented for {}.'.format(self)) + class SimpleClusterResolver(ClusterResolver): """Simple implementation of ClusterResolver that accepts a ClusterSpec.""" - def __init__(self, cluster_spec): + def __init__(self, cluster_spec, master=''): """Creates a SimpleClusterResolver from a ClusterSpec.""" super(SimpleClusterResolver, self).__init__() @@ -65,10 +70,18 @@ class SimpleClusterResolver(ClusterResolver): raise TypeError('cluster_spec must be a ClusterSpec.') self._cluster_spec = cluster_spec + if not isinstance(master, str): + raise TypeError('master must be a string.') + self._master = master + def cluster_spec(self): """Returns the ClusterSpec passed into the constructor.""" return self._cluster_spec + def master(self): + """Returns the master address to use when creating a session.""" + return self._master + class UnionClusterResolver(ClusterResolver): """Performs a union on underlying ClusterResolvers. @@ -87,9 +100,13 @@ class UnionClusterResolver(ClusterResolver): Raises: TypeError: If any argument is not a subclass of `ClusterResolvers`. + ValueError: If there are no arguments passed. """ super(UnionClusterResolver, self).__init__() + if not args: + raise ValueError('At least one ClusterResolver is required.') + for cluster_resolver in args: if not isinstance(cluster_resolver, ClusterResolver): raise TypeError('All arguments must be a sub-class of ' @@ -169,3 +186,7 @@ class UnionClusterResolver(ClusterResolver): merged_cluster[job_name].update(task_dict) return ClusterSpec(merged_cluster) + + def master(self): + """master returns the master address from the first cluster resolver.""" + return self._cluster_resolvers[0].master() diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py index dbfb77723cdaab66e29bb41b764593bb5fd61b35..d9c97d53eb3663f6ab2f7b40395592dc7638b896 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py +++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py @@ -234,5 +234,7 @@ class UnionClusterResolverTest(test.TestCase): self._verifyClusterSpecEquality(cluster_spec, expected_proto) +# TODO(saeta): Include tests for master resolution + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py index d6f2eced93ba4fda5ac27f9412b6f729981f4f40..3f5824128948453634bc5e5a7d6fdeedae60f5bd 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py @@ -134,3 +134,6 @@ class GceClusterResolver(ClusterResolver): worker_list.sort() return ClusterSpec({self._job_name: worker_list}) + + def master(self): + return '' diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py index a6a6e642e4e4c721b94821a70d55d6fe931347d6..300b19733e2b4d1b912f966e94ae0286ed9c694d 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -18,12 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os from six.moves.urllib.request import Request from six.moves.urllib.request import urlopen from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver -from tensorflow.python.training.server_lib import ClusterSpec +from tensorflow.python.training import server_lib +from tensorflow.python.util import compat _GOOGLE_API_CLIENT_INSTALLED = True try: @@ -33,6 +35,9 @@ except ImportError: _GOOGLE_API_CLIENT_INSTALLED = False +_GKE_ENV_VARIABLE = 'KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' + + class TPUClusterResolver(ClusterResolver): """Cluster Resolver for Google Cloud TPUs. @@ -46,13 +51,30 @@ class TPUClusterResolver(ClusterResolver): req = Request('http://metadata/computeMetadata/v1/%s' % path, headers={'Metadata-Flavor': 'Google'}) resp = urlopen(req) - return resp.read() + return compat.as_bytes(resp.read()) + + def _shouldResolve(self): + if (self._tpu == compat.as_bytes('') or + self._tpu == compat.as_bytes('local') or + self._tpu.startswith(compat.as_bytes('/bns')) or + self._tpu.startswith(compat.as_bytes('grpc://'))): + return False + return True + + def _inGke(self): + """When running in GKE, the environment variable will be set.""" + return _GKE_ENV_VARIABLE in os.environ + + def _gkeMaster(self): + return os.environ[_GKE_ENV_VARIABLE].split(',')[0] def __init__(self, - tpu_names, + tpu=None, zone=None, project=None, - job_name='tpu_worker', + job_name='worker', + coordinator_name='coordinator', + coordinator_address=None, credentials='default', service=None): """Creates a new TPUClusterResolver object. @@ -61,7 +83,11 @@ class TPUClusterResolver(ClusterResolver): for the IP addresses and ports of each Cloud TPU listed. Args: - tpu_names: A list of names of the target Cloud TPUs. + tpu: Either a string, or a list of strings corresponding to the TPUs to + use. If the single string is the empty string, the string 'local', or a + string that begins with 'grpc://' or '/bns', then it is assumed to not + correspond with a Cloud TPU and will instead be passed as the session + master and no ClusterSpec propagation will be done. zone: Zone where the TPUs are located. If omitted or empty, we will assume that the zone of the TPU is the same as the zone of the GCE VM, which we will try to discover from the GCE metadata service. @@ -69,6 +95,12 @@ class TPUClusterResolver(ClusterResolver): empty, we will try to discover the project name of the GCE VM from the GCE metadata service. job_name: Name of the TensorFlow job the TPUs belong to. + coordinator_name: The name to use for the coordinator. Set to None if the + coordinator should not be included in the computed ClusterSpec. + coordinator_address: The address of the coordinator (typically an ip:port + pair). If set to None, a TF server will be started. If coordinator_name + is None, a TF server will not be started even if coordinator_address is + None. credentials: GCE Credentials. If None, then we use default credentials from the oauth2client service: The GCE API object returned by the googleapiclient.discovery @@ -77,29 +109,47 @@ class TPUClusterResolver(ClusterResolver): Raises: ImportError: If the googleapiclient is not installed. + ValueError: If no TPUs are specified. """ + if isinstance(tpu, list): + if not tpu: + raise ValueError('At least one TPU must be specified.') + if len(tpu) != 1: + raise NotImplementedError( + 'Using multiple TPUs in a single session is not yet implemented') + tpu = tpu[0] + + # When using GKE with Cloud TPUs, the env variable will be set. + if tpu is None and self._inGke(): + tpu = self._gkeMaster() + + self._tpu = compat.as_bytes(tpu) # self._tpu is always bytes + self._job_name = job_name + self._credentials = credentials + + should_resolve = self._shouldResolve() - if not project: - project = self._requestComputeMetadata('/project/project-id') + if not project and should_resolve: + project = compat.as_str( + self._requestComputeMetadata('project/project-id')) - if not zone: - zone_path = self._requestComputeMetadata('/instance/zone') + if not zone and should_resolve: + zone_path = compat.as_str(self._requestComputeMetadata('instance/zone')) zone = zone_path.split('/')[-1] self._project = project self._zone = zone - self._tpu_names = tpu_names - self._job_name = job_name - self._credentials = credentials - if credentials == 'default': + if credentials == 'default' and should_resolve: if _GOOGLE_API_CLIENT_INSTALLED: self._credentials = GoogleCredentials.get_application_default() - if service is None: + if service is None and should_resolve: if not _GOOGLE_API_CLIENT_INSTALLED: raise ImportError('googleapiclient must be installed before using the ' - 'TPU cluster resolver') + 'TPU cluster resolver. Execute: `pip install ' + '--upgrade google-api-python-client` to install with ' + 'pip.') self._service = discovery.build( 'tpu', 'v1alpha1', @@ -107,25 +157,41 @@ class TPUClusterResolver(ClusterResolver): else: self._service = service - def get_master(self): - """Get the ClusterSpec grpc master path. + self._coordinator_name = coordinator_name + if coordinator_name and not coordinator_address and should_resolve: + self._start_local_server() + else: + self._coordinator_address = coordinator_address - This returns the grpc path (grpc://1.2.3.4:8470) of first instance in the - ClusterSpec returned by the cluster_spec function. This is suitable for use - for the `master` argument in tf.Session() when you are using one TPU. + def master(self): + """Get the Master string to be used for the session. + + In the normal case, this returns the grpc path (grpc://1.2.3.4:8470) of + first instance in the ClusterSpec returned by the cluster_spec function. + + If a non-TPU name is used when constructing a TPUClusterResolver, that will + be returned instead (e.g. If the tpus argument's value when constructing + this TPUClusterResolver was 'grpc://10.240.1.2:8470', + 'grpc://10.240.1.2:8470' will be returned). Returns: - string, the grpc path of the first instance in the ClusterSpec. + string, the connection string to use when creating a session. Raises: ValueError: If none of the TPUs specified exists. """ + if not self._shouldResolve(): + return self._tpu + job_tasks = self.cluster_spec().job_tasks(self._job_name) if not job_tasks: raise ValueError('No TPUs exists with the specified names exist.') return 'grpc://' + job_tasks[0] + def get_master(self): + return self.master() + def cluster_spec(self): """Returns a ClusterSpec object based on the latest TPU information. @@ -134,17 +200,54 @@ class TPUClusterResolver(ClusterResolver): Returns: A ClusterSpec containing host information returned from Cloud TPUs. - """ - worker_list = [] - for tpu_name in self._tpu_names: - full_name = 'projects/%s/locations/%s/nodes/%s' % ( - self._project, self._zone, tpu_name) - request = self._service.projects().locations().nodes().get(name=full_name) - response = request.execute() - - if 'health' in response and response['health'] == 'HEALTHY': - instance_url = '%s:%s' % (response['ipAddress'], response['port']) - worker_list.append(instance_url) - - return ClusterSpec({self._job_name: worker_list}) + Raises: + RuntimeError: If the provided TPU is not healthy. + """ + if not self._shouldResolve(): + return server_lib.ClusterSpec({}) + + full_name = 'projects/%s/locations/%s/nodes/%s' % ( + self._project, self._zone, compat.as_text(self._tpu)) + request = self._service.projects().locations().nodes().get(name=full_name) + response = request.execute() + + if 'health' in response and response['health'] != 'HEALTHY': + raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, + response['health'])) + + if 'networkEndpoints' in response: + worker_list = [ + '%s:%s' % (endpoint['ipAddress'], endpoint['port']) + for endpoint in response['networkEndpoints'] + ] + else: + # Fall back to the deprecated response format + instance_url = '%s:%s' % (response['ipAddress'], response['port']) + worker_list = [instance_url] + + cluster_spec = {self._job_name: worker_list} + + if self._coordinator_address: + cluster_spec[self._coordinator_name] = [self._coordinator_address] + + return server_lib.ClusterSpec(cluster_spec) + + def _start_local_server(self): + address = self._requestComputeMetadata('instance/network-interfaces/0/ip') + self._server = server_lib.Server( + { + 'local': ['0.0.0.0:0'] + }, protocol='grpc', config=None, start=True) + # self._server.target is of the form: grpc://ipaddress:port + target = compat.as_bytes(self._server.target) + splits = target.split(compat.as_bytes(':')) + assert len(splits) == 3, self._server.target + assert splits[0] == compat.as_bytes('grpc'), self._server.target + self._coordinator_port = compat.as_text(splits[2]) + self._coordinator_address = '%s:%s' % ( + address, compat.as_text(self._coordinator_port)) + + def __deepcopy__(self, memo): + # TODO(b/73668574): Remove this once RunConfig avoids performing deepcopy. + return self diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py index 4fd34629cf74f90869c77b8cb098d3c585a49404..48c3f6bb4f2d1643982e03d9ed68db14c10c184a 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py @@ -18,10 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + from tensorflow.contrib.cluster_resolver.python.training.tpu_cluster_resolver import TPUClusterResolver from tensorflow.python.platform import test from tensorflow.python.training import server_lib - +from tensorflow.python.util import compat mock = test.mock @@ -50,10 +52,12 @@ class MockNodeClass(object): def mock_request_compute_metadata(cls, *args, **kwargs): del cls, kwargs # Unused. - if args[0] == '/project/project-id': + if args[0] == 'project/project-id': return 'test-project' - elif args[0] == '/instance/zone': + elif args[0] == 'instance/zone': return 'projects/test-project/locations/us-central1-c' + elif args[0] == 'instance/network-interfaces/0/ip': + return '10.128.1.2' return '' @@ -71,18 +75,17 @@ class TPUClusterResolverTest(test.TestCase): expected_proto: Expected protobuf """ self.assertProtoEquals(expected_proto, cluster_spec.as_cluster_def()) - self.assertProtoEquals( - expected_proto, server_lib.ClusterSpec(cluster_spec).as_cluster_def()) - self.assertProtoEquals( - expected_proto, - server_lib.ClusterSpec(cluster_spec.as_cluster_def()).as_cluster_def()) self.assertProtoEquals( expected_proto, - server_lib.ClusterSpec(cluster_spec.as_dict()).as_cluster_def()) + server_lib.ClusterSpec(cluster_spec).as_cluster_def()) + self.assertProtoEquals(expected_proto, + server_lib.ClusterSpec( + cluster_spec.as_cluster_def()).as_cluster_def()) + self.assertProtoEquals(expected_proto, + server_lib.ClusterSpec( + cluster_spec.as_dict()).as_cluster_def()) - def mock_service_client( - self, - tpu_map=None): + def mock_service_client(self, tpu_map=None): if tpu_map is None: tpu_map = {} @@ -98,8 +101,7 @@ class TPUClusterResolverTest(test.TestCase): return mock_client - @mock.patch.object(TPUClusterResolver, - '_requestComputeMetadata', + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', mock_request_compute_metadata) def testRetrieveProjectAndZoneFromMetadata(self): tpu_map = { @@ -113,17 +115,26 @@ class TPUClusterResolverTest(test.TestCase): tpu_cluster_resolver = TPUClusterResolver( project=None, zone=None, - tpu_names=['test-tpu-1'], + tpu=['test-tpu-1'], credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } } - """ - self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + job { + name: 'coordinator' + tasks { key: 0 value: '10.128.1.2:%s' } + } + job { + name: 'worker' + tasks { key: 0 value: '10.1.2.3:8470' } + } + """ % tpu_cluster_resolver._coordinator_port + self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) - def testSimpleSuccessfulRetrieval(self): + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testRetrieveProjectAndZoneFromMetadataNoCoordinator(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { 'ipAddress': '10.1.2.3', @@ -133,116 +144,230 @@ class TPUClusterResolverTest(test.TestCase): } tpu_cluster_resolver = TPUClusterResolver( - project='test-project', - zone='us-central1-c', - tpu_names=['test-tpu-1'], + project=None, + zone=None, + tpu=['test-tpu-1'], + coordinator_name=None, credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } } + job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - def testMultipleSuccessfulRetrieval(self): + def testSimpleSuccessfulRetrieval(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { 'ipAddress': '10.1.2.3', 'port': '8470', 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - 'health': 'HEALTHY' } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1'], + tpu=['test-tpu-1'], + coordinator_address='10.128.1.5:10203', credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.4.5.6:8470' } - tasks { key: 1 value: '10.1.2.3:8470' } } + job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } } + job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - def testHealthyTpuNodeRetrieval(self): + def testNewNetworkEndpointFormat(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { - 'ipAddress': '10.1.2.3', - 'port': '8470', - 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-3': { - 'ipAddress': '10.7.8.9', - 'port': '8470', - 'health': 'UNHEALTHY' + 'health': 'HEALTHY', + 'networkEndpoints': [{ + 'ipAddress': '10.2.3.4', + 'port': 8470, + }] } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1', 'test-tpu-3'], + tpu='test-tpu-1', + coordinator_address='10.128.1.5:10203', credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { - name: 'tpu_worker' - tasks { - key: 0 - value: '10.1.2.3:8470' - } - } + job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } } + job { name: 'worker' tasks { key: 0 value: '10.2.3.4:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + self.assertEqual('grpc://10.2.3.4:8470', tpu_cluster_resolver.master()) - def testGetMasterMultipleEntries(self): + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testPodResolution(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { - 'ipAddress': '10.1.2.3', - 'port': '8470', - 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - 'health': 'HEALTHY' + 'health': + 'HEALTHY', + 'networkEndpoints': [ + { + 'ipAddress': '10.2.3.4', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.5', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.6', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.7', + 'port': 8470, + }, + ] + } + } + + tpu_cluster_resolver = TPUClusterResolver( + tpu='test-tpu-1', + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'coordinator', + tasks { key: 0 value: '10.128.1.2:%s'} + } + job { + name: 'worker' + tasks { key: 0 value: '10.2.3.4:8470' } + tasks { key: 1 value: '10.2.3.5:8470' } + tasks { key: 2 value: '10.2.3.6:8470' } + tasks { key: 3 value: '10.2.3.7:8470' } + } + """ % tpu_cluster_resolver._coordinator_port + self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) + + def testPodResolutionNoCoordinator(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'health': + 'HEALTHY', + 'networkEndpoints': [ + { + 'ipAddress': '10.2.3.4', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.5', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.6', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.7', + 'port': 8470, + }, + ] } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1'], + tpu='test-tpu-1', + coordinator_name=None, credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - self.assertEqual('grpc://10.4.5.6:8470', tpu_cluster_resolver.get_master()) + + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'worker' + tasks { key: 0 value: '10.2.3.4:8470' } + tasks { key: 1 value: '10.2.3.5:8470' } + tasks { key: 2 value: '10.2.3.6:8470' } + tasks { key: 3 value: '10.2.3.7:8470' } + } + """ + self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) def testGetMasterNoEntries(self): tpu_map = {} + with self.assertRaises(ValueError): + TPUClusterResolver( + project='test-project', + zone='us-central1-c', + tpu=[], + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + # TODO(saeta): Convert to parameterized test when included in OSS TF. + def verifyShouldResolve(self, tpu, should_resolve): tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=[], + tpu=tpu, + coordinator_name=None, credentials=None, - service=self.mock_service_client(tpu_map=tpu_map)) - with self.assertRaises(ValueError): - tpu_cluster_resolver.get_master() + service=self.mock_service_client(tpu_map={})) + self.assertEqual(should_resolve, tpu_cluster_resolver._shouldResolve(), + "TPU: '%s'" % tpu) + + def testShouldResolveNoName(self): + self.verifyShouldResolve('', False) + + def testShouldResolveLocal(self): + self.verifyShouldResolve('local', False) + + def testShouldResolveGrpc(self): + self.verifyShouldResolve('grpc://10.1.2.3:8470', False) + + def testShouldResolveBns(self): + self.verifyShouldResolve('/bns/foo/bar', False) + + def testShouldResolveName(self): + self.verifyShouldResolve('mytpu', True) + + def testShouldResolveList(self): + self.verifyShouldResolve(['myothertpu'], True) + + def testShouldResolveGrpcPrefix(self): + self.verifyShouldResolve('grpctpu', True) + + def testNoCallComputeMetadata(self): + tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/foo/bar') + self.assertEqual( + compat.as_bytes('/bns/foo/bar'), tpu_cluster_resolver.master()) + self.assertEqual( + server_lib.ClusterSpec({}), tpu_cluster_resolver.cluster_spec()) + + def testGkeEnvironment(self): + os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] = 'grpc://10.120.27.5:8470' + self.assertTrue('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' in os.environ) + tpu_cluster_resolver = TPUClusterResolver() + self.assertTrue(tpu_cluster_resolver._inGke()) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(tpu_cluster_resolver._gkeMaster())) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(tpu_cluster_resolver.get_master())) + del os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 12bfd3c62b49c7f9ef40c84abf25268ceafcdbdc..23b31ae1dcc83d8a7152354ac147de9ada320429 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -52,6 +52,7 @@ if (NOT WIN32) # for targets that link ${CMAKE_THREAD_LIBS_INIT}. find_package (Threads) + # Options for linking CUDA/CUDNN libraries option(tensorflow_PATH_STATIC_LIB "Additional library search path for libcudnn_static.a, libnccl_static.a, libculibos.a" /usr/local/cuda/lib64/) option(tensorflow_CUDNN_INCLUDE "cudnn.h header install path" /usr/include/) if (NOT tensorflow_CUDNN_INCLUDE) @@ -73,6 +74,14 @@ if (NOT WIN32) # option's default value is OFF. Fill it with real default values set(tensorflow_CUDA_LIBRARY_PATH /usr/local/cuda/lib64) endif (NOT tensorflow_CUDA_LIBRARY_PATH) + + # Options for linking other libraries + option(systemlib_ZLIB "Use the system installed library as shared objects instead of downloading ZLIB and statically linking to it: ZLIB" OFF) + + option(systemlib_ALL "Turn on every possible systemlib_* options" OFF) + if (systemlib_ALL) + set (systmelib_ZLIB ON) + endif (systemlib_ALL) endif() if (WIN32) @@ -188,8 +197,10 @@ if (tensorflow_BUILD_CC_TESTS) include(googletest) endif() +add_definitions(${ADD_CFLAGS}) +link_directories(${ADD_LINK_DIRECTORY}) + set(tensorflow_EXTERNAL_LIBRARIES - ${zlib_STATIC_LIBRARIES} ${gif_STATIC_LIBRARIES} ${png_STATIC_LIBRARIES} ${jpeg_STATIC_LIBRARIES} @@ -203,6 +214,15 @@ set(tensorflow_EXTERNAL_LIBRARIES ${re2_STATIC_LIBRARIES} ${sqlite_STATIC_LIBRARIES} ) + +if (systemlib_ZLIB) + set(tensorflow_EXTERNAL_LIBRARIES ${tensorflow_EXTERNAL_LIBRARIES} + ${ZLIB_LIBRARIES}) +else (systemlib_ZLIB) + set(tensorflow_EXTERNAL_LIBRARIES ${tensorflow_EXTERNAL_LIBRARIES} + ${zlib_STATIC_LIBRARIES}) +endif (systemlib_ZLIB) + set(tensorflow_EXTERNAL_DEPENDENCIES zlib_copy_headers_to_destination gif_copy_headers_to_destination @@ -286,7 +306,21 @@ if (tensorflow_ENABLE_GPU) list(APPEND CMAKE_LIBRARY_PATH "${tensorflow_CUDA_LIBRARY_PATH}/stubs") endif (NOT WIN32) - find_package(CUDA ${tensorflow_CUDA_VERSION} REQUIRED) + # later command will make use of the value in tensorflow_CUDA_VERSION + find_package(CUDA ${tensorflow_CUDA_VERSION} REQUIRED EXACT) + + # Test compatibility of compiler on CUDA + try_compile(CUDA_TEST_COMPILE_C + ${CMAKE_CURRENT_BINARY_DIR}/tests/cuda + ${CMAKE_CURRENT_SOURCE_DIR}/tests/cuda/compatibility_test.c + CMAKE_FLAGS -DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}) + try_compile(CUDA_TEST_COMPILE_CXX + ${CMAKE_CURRENT_BINARY_DIR}/tests/cuda + ${CMAKE_CURRENT_SOURCE_DIR}/tests/cuda/compatibility_test.cc + CMAKE_FLAGS -DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}) + if(NOT (CUDA_TEST_COMPILE_C AND CUDA_TEST_COMPILE_CXX)) + message(FATAL_ERROR "Selected compiler (or version) is not supported for CUDA") + endif() # by default we assume compute cabability 3.5 and 5.2. If you change this change it in # CUDA_NVCC_FLAGS and cuda_config.h below @@ -307,7 +341,8 @@ if (tensorflow_ENABLE_GPU) if(NOT CUDNN_HOME) set(CUDNN_HOME ${CUDA_TOOLKIT_TARGET_DIR}) endif(NOT CUDNN_HOME) - include_directories(${CUDNN_HOME}) + set(CUDNN_INCLUDE "${CUDNN_HOME}/include") + set(CUDA_LIBRARIES ${CUDA_LIBRARIES} ${CUDA_CUDA_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_CUFFT_LIBRARIES} ${CUDA_curand_LIBRARY} ${CUDA_cupti_LIBRARY} ${CUDA_cusolver_LIBRARY} ${CUDNN_HOME}/lib/x64/cudnn.lib) else (WIN32) @@ -335,10 +370,10 @@ if (tensorflow_ENABLE_GPU) message("culibos-static: ${culibos_STATIC_LIBRARY}") endif (NOT culibos_STATIC_LIBRARY) - include_directories(${CUDNN_INCLUDE}) set(CUDA_LIBRARIES ${CUDA_LIBRARIES} ${CUDA_CUDA_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_CUFFT_LIBRARIES} ${CUDA_curand_LIBRARY} ${CUDA_cupti_LIBRARY} ${CUDA_cusolver_LIBRARY} ${cudnn_STATIC_LIBRARY} ${culibos_STATIC_LIBRARY} ${nccl_STATIC_LIBRARY}) endif (WIN32) + include_directories(${CUDNN_INCLUDE}) # Remove "." from CUDA version variable. string(REPLACE "." "" short_CUDA_VER ${tensorflow_CUDA_VERSION}) @@ -354,29 +389,22 @@ if (tensorflow_ENABLE_GPU) "#endif // CUDA_CUDA_CONFIG_H_\n" ) - if (WIN32) - # tf assumes in various places header files to be in cuda/include. On windows the cuda sdk - # installs them under cuda/version/include and to avoid that we need to change tf we copy a - # few files to cuda/include - FILE(COPY - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h ${CUDNN_HOME}/include/cudnn.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_runtime_api.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cusolverDn.h - DESTINATION ${tensorflow_source_dir}/third_party/gpus/cuda/include - ) - else(WIN32) - # Linux has slightly differnt install paths than Windows - FILE(COPY - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h ${CUDNN_INCLUDE}/cudnn.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_runtime_api.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cusolverDn.h - DESTINATION ${tensorflow_source_dir}/third_party/gpus/cuda/include - ) - endif(WIN32) + # tf assumes in various places header files to be in cuda/include. On windows the cuda sdk + # installs them under cuda/version/include and to avoid that we need to change tf we copy a + # few files to cuda/include + FILE(COPY + ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cusolverDn.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_fp16.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/device_functions.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h + ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_runtime_api.h + ${CUDNN_INCLUDE}/cudnn.h + DESTINATION ${tensorflow_source_dir}/third_party/gpus/cuda/include + ) include_directories(${tensorflow_source_dir}/third_party/gpus) # add cuda libraries to tensorflow_EXTERNAL_LIBRARIES diff --git a/tensorflow/contrib/cmake/external/boringssl.cmake b/tensorflow/contrib/cmake/external/boringssl.cmake index 5ad477fdff68feab4adf0c0072c68c8e55390ab8..3c4bb01e24fd121c9d0fc3594cc25de37af0e8a1 100644 --- a/tensorflow/contrib/cmake/external/boringssl.cmake +++ b/tensorflow/contrib/cmake/external/boringssl.cmake @@ -37,6 +37,7 @@ ExternalProject_Add(boringssl GIT_TAG ${boringssl_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" # BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${boringssl_STATIC_LIBRARIES} INSTALL_COMMAND "" CMAKE_CACHE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake index 836889895567f679d9960e29ece1600d1a7a58eb..98a8c7e736e5c8c407b90e8eac440cdc7ab21579 100644 --- a/tensorflow/contrib/cmake/external/cub.cmake +++ b/tensorflow/contrib/cmake/external/cub.cmake @@ -14,8 +14,8 @@ # ============================================================================== include (ExternalProject) -set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip) -set(cub_HASH SHA256=20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31) +set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip) +set(cub_HASH SHA256=6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3) set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub) set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub) set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive) diff --git a/tensorflow/contrib/cmake/external/farmhash.cmake b/tensorflow/contrib/cmake/external/farmhash.cmake index 0cd0c1030c73d5218411f281d2b077af217e8275..d51569bc213f2bd354571a00910714e787120951 100644 --- a/tensorflow/contrib/cmake/external/farmhash.cmake +++ b/tensorflow/contrib/cmake/external/farmhash.cmake @@ -33,6 +33,7 @@ if(WIN32) URL_HASH ${farmhash_HASH} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${farmhash_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/farmhash/CMakeLists.txt ${farmhash_BUILD} INSTALL_DIR ${farmhash_INSTALL} CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/fft2d.cmake b/tensorflow/contrib/cmake/external/fft2d.cmake index d3af2a46761c0f7f0b5db134af8400fc93f2f095..a7bc50d5bcd4384d5c943d681fd7cd6fa1ffa796 100644 --- a/tensorflow/contrib/cmake/external/fft2d.cmake +++ b/tensorflow/contrib/cmake/external/fft2d.cmake @@ -29,6 +29,7 @@ if(WIN32) URL_HASH ${fft2d_HASH} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${fft2d_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/fft2d/CMakeLists.txt ${fft2d_BUILD}/src/fft2d/CMakeLists.txt INSTALL_DIR ${fft2d_INSTALL} CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/gif.cmake b/tensorflow/contrib/cmake/external/gif.cmake index 3d53c51fffcec1602a3b5553cdf3b225e3b0ae46..e1f8d13f8ea47b83e4a1840afac7398ef226eb45 100644 --- a/tensorflow/contrib/cmake/external/gif.cmake +++ b/tensorflow/contrib/cmake/external/gif.cmake @@ -33,6 +33,7 @@ if(WIN32) PREFIX gif URL ${gif_URL} URL_HASH ${gif_HASH} + BUILD_BYPRODUCTS ${gif_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_SOURCE_DIR}/patches/gif/CMakeLists.txt ${gif_BUILD} INSTALL_DIR ${gif_INSTALL} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" diff --git a/tensorflow/contrib/cmake/external/googletest.cmake b/tensorflow/contrib/cmake/external/googletest.cmake index d09bb02890f25a0312e62c876c1729e57a059e82..7cc5ae6390934773635cf7a4dff77a3cbfb41ba1 100644 --- a/tensorflow/contrib/cmake/external/googletest.cmake +++ b/tensorflow/contrib/cmake/external/googletest.cmake @@ -20,8 +20,13 @@ set(googletest_BUILD ${CMAKE_CURRENT_BINARY_DIR}/googletest/) set(googletest_TAG ec44c6c1675c25b9827aacd08c02433cccde7780) if(WIN32) - set(googletest_STATIC_LIBRARIES - ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/$(Configuration)/gtest.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(googletest_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/$(Configuration)/gtest.lib) + else() + set(googletest_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/gtest.lib) + endif() else() set(googletest_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/${CMAKE_BUILD_TYPE}/gtest.a) @@ -33,6 +38,7 @@ ExternalProject_Add(googletest GIT_TAG ${googletest_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${googletest_STATIC_LIBRARIES} #PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_SOURCE_DIR}/patches/grpc/CMakeLists.txt ${GRPC_BUILD} INSTALL_COMMAND "" CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/grpc.cmake b/tensorflow/contrib/cmake/external/grpc.cmake index 28adb4fe84423bb5a21c78dac4e757505ce87d1d..17f65999faaf5c0ca39bfbc968a9140dbff49c2e 100644 --- a/tensorflow/contrib/cmake/external/grpc.cmake +++ b/tensorflow/contrib/cmake/external/grpc.cmake @@ -20,14 +20,22 @@ set(GRPC_BUILD ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc) set(GRPC_TAG 730b778632e79cc3c96ad237f282d687ee325ce7) if(WIN32) - set(grpc_STATIC_LIBRARIES - ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc++_unsecure.lib - ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc_unsecure.lib - ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/gpr.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(grpc_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc++_unsecure.lib + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc_unsecure.lib + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/gpr.lib) + else() + set(grpc_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/grpc++_unsecure.lib + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/grpc_unsecure.lib + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/gpr.lib) + endif() else() set(grpc_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc_unsecure.a + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/third_party/cares/cares/lib/libcares.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgpr.a) endif() @@ -40,6 +48,7 @@ ExternalProject_Add(grpc GIT_TAG ${GRPC_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${grpc_STATIC_LIBRARIES} BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc++_unsecure COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc_cpp_plugin INSTALL_COMMAND "" diff --git a/tensorflow/contrib/cmake/external/highwayhash.cmake b/tensorflow/contrib/cmake/external/highwayhash.cmake index 2c23bef8a331de356c93dbf9d0e91d8bb13bd6c8..a6e8a38d8c2ee3deb5453c264e0c5eb23248301f 100644 --- a/tensorflow/contrib/cmake/external/highwayhash.cmake +++ b/tensorflow/contrib/cmake/external/highwayhash.cmake @@ -42,6 +42,7 @@ ExternalProject_Add(highwayhash GIT_TAG ${highwayhash_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${highwayhash_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/highwayhash/CMakeLists.txt ${highwayhash_BUILD} INSTALL_DIR ${highwayhash_INSTALL} CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/jemalloc.cmake b/tensorflow/contrib/cmake/external/jemalloc.cmake index 198ba13e64e4b6df57c4325a0104b1a6745d173a..afadcc007d66414be3306e91e7186a00b6e587ce 100644 --- a/tensorflow/contrib/cmake/external/jemalloc.cmake +++ b/tensorflow/contrib/cmake/external/jemalloc.cmake @@ -24,8 +24,11 @@ if (WIN32) ${jemalloc_INCLUDE_DIRS} ${CMAKE_CURRENT_BINARY_DIR}/jemalloc/src/jemalloc/include/msvc_compat ) - set(jemalloc_ADDITIONAL_CMAKE_OPTIONS -A x64) - set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.lib) + else() + set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/jemalloc.lib) + endif() else() set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.a) endif() @@ -36,12 +39,12 @@ ExternalProject_Add(jemalloc URL_HASH ${jemalloc_HASH} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 - CONFIGURE_COMMAND ${CMAKE_COMMAND} + BUILD_BYPRODUCTS ${jemalloc_STATIC_LIBRARIES} + BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target jemalloc + INSTALL_COMMAND ${CMAKE_COMMAND} -E echo "Skipping install step." + CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF -Dwith-jemalloc-prefix:STRING=jemalloc_ -Dwithout-export:BOOL=ON - ${jemalloc_ADDITIONAL_CMAKE_OPTIONS} - BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target jemalloc - INSTALL_COMMAND ${CMAKE_COMMAND} -E echo "Skipping install step." ) diff --git a/tensorflow/contrib/cmake/external/jpeg.cmake b/tensorflow/contrib/cmake/external/jpeg.cmake index d9a165e856c588880ebdf996666d70c9e7f53da8..c1c5842aa4454f1c95ec284392194a89d47ee8d5 100644 --- a/tensorflow/contrib/cmake/external/jpeg.cmake +++ b/tensorflow/contrib/cmake/external/jpeg.cmake @@ -46,6 +46,7 @@ if (WIN32) PREFIX jpeg URL ${jpeg_URL} URL_HASH ${jpeg_HASH} + BUILD_BYPRODUCTS ${jpeg_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/jpeg/CMakeLists.txt ${jpeg_BUILD} INSTALL_DIR ${jpeg_INSTALL} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" diff --git a/tensorflow/contrib/cmake/external/jsoncpp.cmake b/tensorflow/contrib/cmake/external/jsoncpp.cmake index 861201f97edbce2d9d70a833ce5a8cad46f2470a..84c52e3652ff935c287d32c0c80fd407e1213f29 100644 --- a/tensorflow/contrib/cmake/external/jsoncpp.cmake +++ b/tensorflow/contrib/cmake/external/jsoncpp.cmake @@ -23,7 +23,11 @@ set(jsoncpp_LIBRARIES ${jsoncpp_BUILD}/obj/so/libjsoncpp.so) set(jsoncpp_INCLUDES ${jsoncpp_BUILD}) if(WIN32) - set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/$(Configuration)/jsoncpp.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/$(Configuration)/jsoncpp.lib) + else() + set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/jsoncpp.lib) + endif() else() set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/libjsoncpp.a) endif() @@ -40,6 +44,7 @@ ExternalProject_Add(jsoncpp GIT_TAG ${jsoncpp_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${jsoncpp_STATIC_LIBRARIES} INSTALL_COMMAND "" CMAKE_CACHE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} diff --git a/tensorflow/contrib/cmake/external/lmdb.cmake b/tensorflow/contrib/cmake/external/lmdb.cmake index 41b314e2857577581eb27eb6c6480b757d0b436c..ed5ab788acc5625b9c8020fce15f027d98433096 100644 --- a/tensorflow/contrib/cmake/external/lmdb.cmake +++ b/tensorflow/contrib/cmake/external/lmdb.cmake @@ -20,10 +20,17 @@ set(lmdb_HASH SHA256=108532fb94c6f227558d45be3f3347b52539f0f58290a7bb31ec06c462d set(lmdb_BUILD ${CMAKE_BINARY_DIR}/lmdb/src/lmdb) set(lmdb_INSTALL ${CMAKE_BINARY_DIR}/lmdb/install) +if(WIN32) + set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/lmdb.lib) +else() + set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/liblmdb.a) +endif() + ExternalProject_Add(lmdb PREFIX lmdb URL ${lmdb_URL} URL_HASH ${lmdb_HASH} + BUILD_BYPRODUCTS ${lmdb_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/lmdb/CMakeLists.txt ${lmdb_BUILD} INSTALL_DIR ${lmdb_INSTALL} @@ -35,12 +42,6 @@ ExternalProject_Add(lmdb -DCMAKE_INSTALL_PREFIX:STRING=${lmdb_INSTALL} ) -if(WIN32) - set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/lmdb.lib) -else() - set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/liblmdb.a) -endif() - set(lmdb_HEADERS "${lmdb_INSTALL}/include/lmdb.h" "${lmdb_INSTALL}/include/midl.h" diff --git a/tensorflow/contrib/cmake/external/nsync.cmake b/tensorflow/contrib/cmake/external/nsync.cmake index 05080060479b6240edb8ab9f65160b3dd182feb9..f3a37ff5088e3f9e54e38c0edb5777c27b26969f 100644 --- a/tensorflow/contrib/cmake/external/nsync.cmake +++ b/tensorflow/contrib/cmake/external/nsync.cmake @@ -42,6 +42,7 @@ ExternalProject_Add(nsync GIT_TAG ${nsync_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${nsync_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/nsync/CMakeLists.txt ${nsync_BUILD} INSTALL_DIR ${nsync_INSTALL} CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/png.cmake b/tensorflow/contrib/cmake/external/png.cmake index b277be5690387b06876ca89eb88becbf885486a4..6cd66a65990e7a2b963b52b310061b551752cd4d 100644 --- a/tensorflow/contrib/cmake/external/png.cmake +++ b/tensorflow/contrib/cmake/external/png.cmake @@ -21,9 +21,19 @@ set(png_BUILD ${CMAKE_BINARY_DIR}/png/src/png) set(png_INSTALL ${CMAKE_BINARY_DIR}/png/install) if(WIN32) - set(png_STATIC_LIBRARIES - debug ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib - optimized ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(png_STATIC_LIBRARIES + debug ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib + optimized ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib) + else() + if(CMAKE_BUILD_TYPE EQUAL Debug) + set(png_STATIC_LIBRARIES + ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib) + else() + set(png_STATIC_LIBRARIES + ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib) + endif() + endif() else() set(png_STATIC_LIBRARIES ${CMAKE_BINARY_DIR}/png/install/lib/libpng12.a) endif() @@ -38,6 +48,7 @@ ExternalProject_Add(png DEPENDS zlib URL ${png_URL} URL_HASH ${png_HASH} + BUILD_BYPRODUCTS ${png_STATIC_LIBRARIES} INSTALL_DIR ${png_INSTALL} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" CMAKE_CACHE_ARGS diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake index fd05fa6d47209edd825b6a97aa0b77b3f9cb8ee1..ab464bc99a43138130bb2758ae28ecef29805c31 100644 --- a/tensorflow/contrib/cmake/external/protobuf.cmake +++ b/tensorflow/contrib/cmake/external/protobuf.cmake @@ -16,14 +16,37 @@ include (ExternalProject) set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src) set(PROTOBUF_URL https://github.com/google/protobuf.git) -set(PROTOBUF_TAG 396336eb961b75f03b25824fe86cf6490fb75e3a) +set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9) if(WIN32) - set(protobuf_STATIC_LIBRARIES - debug ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobufd.lib - optimized ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobuf.lib) - set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/protoc.exe) - set(PROTOBUF_ADDITIONAL_CMAKE_OPTIONS -Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF -A x64) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(protobuf_STATIC_LIBRARIES + debug ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobufd.lib + optimized ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobuf.lib) + set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/protoc.exe) + else() + if(CMAKE_BUILD_TYPE EQUAL Debug) + set(protobuf_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobufd.lib) + else() + set(protobuf_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.lib) + endif() + set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc.exe) + endif() + + # This section is to make sure CONFIGURE_COMMAND use the same generator settings + set(PROTOBUF_GENERATOR_PLATFORM) + if (CMAKE_GENERATOR_PLATFORM) + set(PROTOBUF_GENERATOR_PLATFORM -A ${CMAKE_GENERATOR_PLATFORM}) + endif() + set(PROTOBUF_GENERATOR_TOOLSET) + if (CMAKE_GENERATOR_TOOLSET) + set(PROTOBUF_GENERATOR_TOOLSET -T ${CMAKE_GENERATOR_TOOLSET}) + endif() + set(PROTOBUF_ADDITIONAL_CMAKE_OPTIONS -Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF + -G${CMAKE_GENERATOR} ${PROTOBUF_GENERATOR_PLATFORM} ${PROTOBUF_GENERATOR_TOOLSET}) + # End of section else() set(protobuf_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.a) set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc) @@ -36,10 +59,15 @@ ExternalProject_Add(protobuf GIT_TAG ${PROTOBUF_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${PROTOBUF_PROTOC_EXECUTABLE} ${protobuf_STATIC_LIBRARIES} SOURCE_DIR ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf + # SOURCE_SUBDIR cmake/ # Requires CMake 3.7, this will allow removal of CONFIGURE_COMMAND + # CONFIGURE_COMMAND resets some settings made in CMAKE_CACHE_ARGS and the generator used CONFIGURE_COMMAND ${CMAKE_COMMAND} cmake/ - -Dprotobuf_BUILD_TESTS=OFF - -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} + -DCMAKE_BUILD_TYPE:STRING=Release + -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF + -Dprotobuf_BUILD_TESTS:BOOL=OFF -DZLIB_ROOT=${ZLIB_INSTALL} ${PROTOBUF_ADDITIONAL_CMAKE_OPTIONS} INSTALL_COMMAND "" @@ -47,5 +75,7 @@ ExternalProject_Add(protobuf -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} -DCMAKE_BUILD_TYPE:STRING=Release -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF + -Dprotobuf_BUILD_TESTS:BOOL=OFF + -Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF -DZLIB_ROOT:STRING=${ZLIB_INSTALL} ) diff --git a/tensorflow/contrib/cmake/external/re2.cmake b/tensorflow/contrib/cmake/external/re2.cmake index 371d8447f93735e7af2a5a2b16f128a47b5a082a..c4bc0b1707bf9e86ea41234c8155fd6321c4c33b 100644 --- a/tensorflow/contrib/cmake/external/re2.cmake +++ b/tensorflow/contrib/cmake/external/re2.cmake @@ -21,7 +21,11 @@ set(re2_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/re2/install) set(re2_TAG e7efc48) if(WIN32) - set(re2_STATIC_LIBRARIES ${re2_BUILD}/$(Configuration)/re2.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(re2_STATIC_LIBRARIES ${re2_BUILD}/$(Configuration)/re2.lib) + else() + set(re2_STATIC_LIBRARIES ${re2_BUILD}/re2.lib) + endif() else() set(re2_STATIC_LIBRARIES ${re2_BUILD}/libre2.a) endif() @@ -36,6 +40,7 @@ ExternalProject_Add(re2 GIT_TAG ${re2_TAG} INSTALL_DIR ${re2_INSTALL} BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${re2_STATIC_LIBRARIES} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" CMAKE_CACHE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} diff --git a/tensorflow/contrib/cmake/external/snappy.cmake b/tensorflow/contrib/cmake/external/snappy.cmake index fd57734298affda13fa90f4cff560eeeb08e59ab..f54197643b06781dad35b40f526f28d301047299 100644 --- a/tensorflow/contrib/cmake/external/snappy.cmake +++ b/tensorflow/contrib/cmake/external/snappy.cmake @@ -20,7 +20,11 @@ set(snappy_BUILD ${CMAKE_CURRENT_BINARY_DIR}/snappy/src/snappy) set(snappy_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/snappy/src/snappy) if(WIN32) - set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/$(Configuration)/snappy.lib) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/$(Configuration)/snappy.lib) + else() + set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/snappy.lib) + endif() else() set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/libsnappy.a) endif() @@ -35,6 +39,7 @@ ExternalProject_Add(snappy GIT_TAG ${snappy_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${snappy_STATIC_LIBRARIES} INSTALL_COMMAND "" LOG_DOWNLOAD ON LOG_CONFIGURE ON diff --git a/tensorflow/contrib/cmake/external/sqlite.cmake b/tensorflow/contrib/cmake/external/sqlite.cmake index 8297c60712c49ed6f47a9750691eee1325a5b55e..57c4ae76517e4d7247093edd5e5bd95a83258d87 100644 --- a/tensorflow/contrib/cmake/external/sqlite.cmake +++ b/tensorflow/contrib/cmake/external/sqlite.cmake @@ -36,6 +36,7 @@ if (WIN32) PREFIX sqlite URL ${sqlite_URL} URL_HASH ${sqlite_HASH} + BUILD_BYPRODUCTS ${sqlite_STATIC_LIBRARIES} PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/sqlite/CMakeLists.txt ${sqlite_BUILD} INSTALL_DIR ${sqlite_INSTALL} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" diff --git a/tensorflow/contrib/cmake/external/zlib.cmake b/tensorflow/contrib/cmake/external/zlib.cmake index 5bec14fb00a50f6e6e8c7d8b703bde681e9d02ae..116d42309394b92407cef79c9d3a975f494bc3ff 100644 --- a/tensorflow/contrib/cmake/external/zlib.cmake +++ b/tensorflow/contrib/cmake/external/zlib.cmake @@ -12,50 +12,75 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -include (ExternalProject) - -set(zlib_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/zlib_archive) -set(ZLIB_URL https://github.com/madler/zlib) -set(ZLIB_BUILD ${CMAKE_CURRENT_BINARY_DIR}/zlib/src/zlib) -set(ZLIB_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/zlib/install) -set(ZLIB_TAG 50893291621658f355bc5b4d450a8d06a563053d) - -if(WIN32) - set(zlib_STATIC_LIBRARIES - debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib - optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib) -else() - set(zlib_STATIC_LIBRARIES - ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/libz.a) -endif() - -set(ZLIB_HEADERS - "${ZLIB_INSTALL}/include/zconf.h" - "${ZLIB_INSTALL}/include/zlib.h" -) - -ExternalProject_Add(zlib - PREFIX zlib - GIT_REPOSITORY ${ZLIB_URL} - GIT_TAG ${ZLIB_TAG} - INSTALL_DIR ${ZLIB_INSTALL} - BUILD_IN_SOURCE 1 - DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" - CMAKE_CACHE_ARGS - -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} - -DCMAKE_BUILD_TYPE:STRING=Release - -DCMAKE_INSTALL_PREFIX:STRING=${ZLIB_INSTALL} -) - -# put zlib includes in the directory where they are expected -add_custom_target(zlib_create_destination_dir - COMMAND ${CMAKE_COMMAND} -E make_directory ${zlib_INCLUDE_DIR} - DEPENDS zlib) - -add_custom_target(zlib_copy_headers_to_destination - DEPENDS zlib_create_destination_dir) - -foreach(header_file ${ZLIB_HEADERS}) - add_custom_command(TARGET zlib_copy_headers_to_destination PRE_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different ${header_file} ${zlib_INCLUDE_DIR}) -endforeach() +if (systemlib_ZLIB) + find_package(PkgConfig) + pkg_search_module(ZLIB REQUIRED zlib) + set(zlib_INCLUDE_DIR ${ZLIB_INCLUDE_DIRS}) + set(ADD_LINK_DIRECTORY ${ADD_LINK_DIRECTORY} ${ZLIB_LIBRARY_DIRS}) + set(ADD_CFLAGS ${ADD_CFLAGS} ${ZLIB_CFLAGS_OTHER}) + + # To meet DEPENDS zlib from other projects. + # If we hit this line, zlib is already built and installed to the system. + add_custom_target(zlib) + add_custom_target(zlib_copy_headers_to_destination) + +else (systemlib_ZLIB) + include (ExternalProject) + + set(zlib_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/zlib_archive) + set(ZLIB_URL https://github.com/madler/zlib) + set(ZLIB_BUILD ${CMAKE_CURRENT_BINARY_DIR}/zlib/src/zlib) + set(ZLIB_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/zlib/install) + set(ZLIB_TAG 50893291621658f355bc5b4d450a8d06a563053d) + + if(WIN32) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(zlib_STATIC_LIBRARIES + debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib + optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib) + else() + if(CMAKE_BUILD_TYPE EQUAL Debug) + set(zlib_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib) + else() + set(zlib_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib) + endif() + endif() + else() + set(zlib_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/libz.a) + endif() + + set(ZLIB_HEADERS + "${ZLIB_INSTALL}/include/zconf.h" + "${ZLIB_INSTALL}/include/zlib.h" + ) + + ExternalProject_Add(zlib + PREFIX zlib + GIT_REPOSITORY ${ZLIB_URL} + GIT_TAG ${ZLIB_TAG} + INSTALL_DIR ${ZLIB_INSTALL} + BUILD_IN_SOURCE 1 + BUILD_BYPRODUCTS ${zlib_STATIC_LIBRARIES} + DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" + CMAKE_CACHE_ARGS + -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE} + -DCMAKE_BUILD_TYPE:STRING=Release + -DCMAKE_INSTALL_PREFIX:STRING=${ZLIB_INSTALL} + ) + + # put zlib includes in the directory where they are expected + add_custom_target(zlib_create_destination_dir + COMMAND ${CMAKE_COMMAND} -E make_directory ${zlib_INCLUDE_DIR} + DEPENDS zlib) + + add_custom_target(zlib_copy_headers_to_destination + DEPENDS zlib_create_destination_dir) + + foreach(header_file ${ZLIB_HEADERS}) + add_custom_command(TARGET zlib_copy_headers_to_destination PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${header_file} ${zlib_INCLUDE_DIR}) + endforeach() +endif (systemlib_ZLIB) diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index a7938f1f0752a3e50ebdb18fbd81ed797bb037d7..0d2a6a23db26af2fb9498849aa93e74379915fe3 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -165,6 +165,7 @@ tensorflow/contrib/distributions/python tensorflow/contrib/distributions/python/ops tensorflow/contrib/distributions/python/ops/bijectors tensorflow/contrib/eager +tensorflow/contrib/eager/proto tensorflow/contrib/eager/python tensorflow/contrib/estimator tensorflow/contrib/estimator/python @@ -175,6 +176,9 @@ tensorflow/contrib/factorization/kernels tensorflow/contrib/factorization/ops tensorflow/contrib/factorization/python tensorflow/contrib/factorization/python/ops +tensorflow/contrib/feature_column +tensorflow/contrib/feature_column/python +tensorflow/contrib/feature_column/python/feature_column tensorflow/contrib/ffmpeg tensorflow/contrib/ffmpeg/default tensorflow/contrib/framework @@ -296,7 +300,9 @@ tensorflow/contrib/linear_optimizer/kernels/g3doc tensorflow/contrib/linear_optimizer/python tensorflow/contrib/linear_optimizer/python/ops # TODO(drpngx): Fix failing imports +# tensorflow/contrib/lite # tensorflow/contrib/lite/python +# tensorflow/contrib/lite/toco # tensorflow/contrib/lite/toco/python tensorflow/contrib/lookup tensorflow/contrib/losses @@ -326,8 +332,6 @@ tensorflow/contrib/nccl/kernels tensorflow/contrib/nccl/ops tensorflow/contrib/nccl/python tensorflow/contrib/nccl/python/ops -tensorflow/contrib/ndlstm -tensorflow/contrib/ndlstm/python tensorflow/contrib/nearest_neighbor/kernels tensorflow/contrib/nearest_neighbor/ops tensorflow/contrib/nearest_neighbor/python @@ -359,6 +363,7 @@ tensorflow/contrib/reduce_slice_ops/kernels tensorflow/contrib/reduce_slice_ops/ops tensorflow/contrib/reduce_slice_ops/python tensorflow/contrib/reduce_slice_ops/python/ops +tensorflow/contrib/remote_fused_graph tensorflow/contrib/remote_fused_graph/pylib tensorflow/contrib/remote_fused_graph/pylib/python tensorflow/contrib/remote_fused_graph/pylib/python/ops @@ -408,6 +413,10 @@ tensorflow/contrib/summary tensorflow/contrib/tensorboard tensorflow/contrib/tensorboard/plugins tensorflow/contrib/tensorboard/plugins/projector +tensorflow/contrib/tensorboard/plugins/trace +# TODO(sami): Add cmake implementations. +# tensorflow/contrib/tensorrt/python +# tensorflow/contrib/tensorrt/python/ops tensorflow/contrib/tensor_forest tensorflow/contrib/tensor_forest/client tensorflow/contrib/tensor_forest/hybrid @@ -418,6 +427,7 @@ tensorflow/contrib/tensor_forest/hybrid/python/layers tensorflow/contrib/tensor_forest/hybrid/python/models tensorflow/contrib/tensor_forest/hybrid/python/ops tensorflow/contrib/tensor_forest/kernels +tensorflow/contrib/tensor_forest/proto tensorflow/contrib/tensor_forest/python tensorflow/contrib/tensor_forest/python/ops tensorflow/contrib/testing @@ -438,6 +448,7 @@ tensorflow/contrib/timeseries/python/timeseries/state_space_models tensorflow/contrib/tpu tensorflow/contrib/tpu/ops tensorflow/contrib/tpu/profiler +tensorflow/contrib/tpu/proto tensorflow/contrib/tpu/python tensorflow/contrib/tpu/python/ops tensorflow/contrib/tpu/python/profiler diff --git a/tensorflow/contrib/cmake/python_protos.txt b/tensorflow/contrib/cmake/python_protos.txt index 8a9c406d8b118c10ddcaafb0e4fc242aa79cdb57..c03c0c80fe62a4f95d0fcf240ee25725a19d86f0 100644 --- a/tensorflow/contrib/cmake/python_protos.txt +++ b/tensorflow/contrib/cmake/python_protos.txt @@ -4,6 +4,7 @@ tensorflow/python tensorflow/contrib/boosted_trees/proto tensorflow/contrib/cloud/kernels tensorflow/contrib/decision_trees/proto +tensorflow/contrib/eager/proto tensorflow/contrib/gdr tensorflow/contrib/lite/toco tensorflow/contrib/mpi diff --git a/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c new file mode 100644 index 0000000000000000000000000000000000000000..9e355da33a7258119b6086216f5487d7ea94716c --- /dev/null +++ b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c @@ -0,0 +1,22 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This is a program to test if compiler is compatible with CUDA. +#define __CUDACC__ +#include "crt/host_config.h" + +int main(void) { + return 0; +} diff --git a/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..beb574061bea8d04af8386223749677ae36a5d9b --- /dev/null +++ b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc @@ -0,0 +1,22 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +============================================================================*/ + +// This is a program to test if compiler is compatible with CUDA. +#define __CUDACC__ +#include "crt/host_config.h" + +int main(void) { + return 0; +} diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake index f3cf3e70441de67ef79bc9cedf85549315170c29..f73da0b8ab18af1eca4c2bd577604595f8b8ec6d 100644 --- a/tensorflow/contrib/cmake/tf_cc_ops.cmake +++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake @@ -149,7 +149,11 @@ add_library(tf_cc OBJECT ${tf_cc_srcs}) add_dependencies(tf_cc tf_cc_framework tf_cc_ops) if (WIN32) - set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow_internal.lib") + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow_internal.lib") + else() + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib") + endif() else (WIN32) set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so") endif (WIN32) diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake index e4213ea2a47da2a7381cccd0504235ad62018d4e..a54cbff33b66d63d7229fa2f50b8a4ca962111ed 100644 --- a/tensorflow/contrib/cmake/tf_core_cpu.cmake +++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake @@ -50,6 +50,12 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc" "${tensorflow_source_dir}/tensorflow/core/graph/graph.h" "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/graph_def_builder.h" + "${tensorflow_source_dir}/tensorflow/core/graph/graph_def_builder.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/node_builder.h" + "${tensorflow_source_dir}/tensorflow/core/graph/node_builder.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/tensor_id.h" + "${tensorflow_source_dir}/tensorflow/core/graph/tensor_id.cc" "${tensorflow_source_dir}/tensorflow/core/graph/while_context.h" "${tensorflow_source_dir}/tensorflow/core/graph/while_context.cc" "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.h" @@ -57,6 +63,12 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h" "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc" ) +file(GLOB_RECURSE tf_core_cpu_whitelisted_srcs + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.h" + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.cc" + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc" +) +list(REMOVE_ITEM tf_core_cpu_exclude_srcs ${tf_core_cpu_whitelisted_srcs}) list(REMOVE_ITEM tf_core_cpu_srcs ${tf_core_cpu_exclude_srcs}) if (tensorflow_ENABLE_GPU) @@ -73,6 +85,7 @@ if (tensorflow_ENABLE_GPU) "${tensorflow_source_dir}/tensorflow/core/*test*.cc" ) list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_gpu_exclude_srcs}) + list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_cpu_whitelisted_srcs}) list(APPEND tf_core_cpu_srcs ${tf_core_gpu_srcs}) endif() diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake index 129c208ecd6b574ed63c2fe378e1a6ebb92de558..a1c320347fe60f87806736befc677541a93e7e93 100644 --- a/tensorflow/contrib/cmake/tf_core_framework.cmake +++ b/tensorflow/contrib/cmake/tf_core_framework.cmake @@ -292,6 +292,12 @@ file(GLOB_RECURSE tf_core_framework_srcs "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc" "${tensorflow_source_dir}/tensorflow/core/graph/graph.h" "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/graph_def_builder.h" + "${tensorflow_source_dir}/tensorflow/core/graph/graph_def_builder.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/node_builder.h" + "${tensorflow_source_dir}/tensorflow/core/graph/node_builder.cc" + "${tensorflow_source_dir}/tensorflow/core/graph/tensor_id.h" + "${tensorflow_source_dir}/tensorflow/core/graph/tensor_id.cc" "${tensorflow_source_dir}/tensorflow/core/graph/while_context.h" "${tensorflow_source_dir}/tensorflow/core/graph/while_context.cc" "${tensorflow_source_dir}/tensorflow/core/util/*.h" diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake index 6927bf03f08b68a1f13f6a0978af629af45575e8..998f99ecc19f88921dce14fde892912fb699ad08 100644 --- a/tensorflow/contrib/cmake/tf_core_kernels.cmake +++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake @@ -69,8 +69,11 @@ if(tensorflow_BUILD_CONTRIB_KERNELS) "${tensorflow_source_dir}/tensorflow/contrib/coder/ops/coder_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/kernels/cudnn_rnn_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc" + "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc" "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/prefetching_kernels.cc" - "${tensorflow_source_dir}/tensorflow/contrib/data/ops/prefetching_ops.cc" + "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc" + "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/unique_dataset_op.cc" + "${tensorflow_source_dir}/tensorflow/contrib/data/ops/dataset_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/clustering_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc" diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake index c42bc35ce7e75c72a8719f6730f2f8b63b35febb..59e094812aaf4da2549d96314fc550e5635f9de8 100644 --- a/tensorflow/contrib/cmake/tf_core_ops.cmake +++ b/tensorflow/contrib/cmake/tf_core_ops.cmake @@ -85,7 +85,7 @@ GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_quantiles "${tensorflow_source_dir}/te GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_stats_accumulator "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(coder "${tensorflow_source_dir}/tensorflow/contrib/coder/ops/coder_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(cudnn_rnn "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc") -GENERATE_CONTRIB_OP_LIBRARY(data_prefetching "${tensorflow_source_dir}/tensorflow/contrib/data/ops/prefetching_ops.cc") +GENERATE_CONTRIB_OP_LIBRARY(data_dataset "${tensorflow_source_dir}/tensorflow/contrib/data/ops/dataset_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(factorization_clustering "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/clustering_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(factorization_factorization "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/factorization_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(framework_variable "${tensorflow_source_dir}/tensorflow/contrib/framework/ops/variable_ops.cc") diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 34c466fa01e5795fc477403b6fd9704dc0afa1e2..b730ebd3baacafe8ae401e8987104f3062372954 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -368,8 +368,8 @@ GENERATE_PYTHON_OP_LIB("contrib_coder_ops" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/coder/python/ops/gen_coder_ops.py) GENERATE_PYTHON_OP_LIB("contrib_cudnn_rnn_ops" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/cudnn_rnn/ops/gen_cudnn_rnn_ops.py) -GENERATE_PYTHON_OP_LIB("contrib_data_prefetching_ops" - DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/data/python/ops/gen_prefetching_ops.py) +GENERATE_PYTHON_OP_LIB("contrib_data_dataset_ops" + DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/data/python/ops/gen_dataset_ops.py) GENERATE_PYTHON_OP_LIB("contrib_factorization_clustering_ops" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/factorization/python/ops/gen_clustering_ops.py) GENERATE_PYTHON_OP_LIB("contrib_factorization_factorization_ops" @@ -541,7 +541,11 @@ if(WIN32) ${nsync_STATIC_LIBRARIES} ) - set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow.def") + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow.def") + else() + set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow.def") + endif() set_source_files_properties(${pywrap_tensorflow_deffile} PROPERTIES GENERATED TRUE) add_custom_command(TARGET pywrap_tensorflow_internal_static POST_BUILD @@ -549,6 +553,7 @@ if(WIN32) --input "${pywrap_tensorflow_internal_static_dependencies}" --output "${pywrap_tensorflow_deffile}" --target _pywrap_tensorflow_internal.pyd + BYPRODUCTS ${pywrap_tensorflow_deffile} # Required for Ninja ) endif(WIN32) @@ -702,11 +707,19 @@ add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/) if(WIN32) - add_custom_command(TARGET tf_python_build_pip_package POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.dll - ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.lib - ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/) + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + add_custom_command(TARGET tf_python_build_pip_package POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.dll + ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.lib + ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/) + else() + add_custom_command(TARGET tf_python_build_pip_package POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.dll + ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib + ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/) + endif() else() add_custom_command(TARGET tf_python_build_pip_package POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so diff --git a/tensorflow/contrib/cmake/tf_shared_lib.cmake b/tensorflow/contrib/cmake/tf_shared_lib.cmake index 571d2b0decb5e9afcec2314f9837546f0974e90d..6d36d5fc5c2854b2d7d2542a3cb12e033e193b88 100644 --- a/tensorflow/contrib/cmake/tf_shared_lib.cmake +++ b/tensorflow/contrib/cmake/tf_shared_lib.cmake @@ -46,7 +46,11 @@ if(WIN32) $ ) - set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/tensorflow.def") + if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") + set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/tensorflow.def") + else() + set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/tensorflow.def") + endif() set_source_files_properties(${tensorflow_deffile} PROPERTIES GENERATED TRUE) add_custom_command(TARGET tensorflow_static POST_BUILD diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index 73edd616ea43510d0b34196cbf9c4caba2c8219f..1c4ebd7f0c1113bcd0857fb0858df2248499f920 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -156,6 +156,7 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/contrib/coder/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/data/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/factorization/*_test.py" + "${tensorflow_source_dir}/tensorflow/contrib/feature_column/python/feature_column/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/image/*_test.py" "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/periodic_resample/python/kernel_tests/*_test.py" @@ -275,8 +276,7 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py" # Segfaults on windows "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py" # Segfaults on Windows. "${tensorflow_source_dir}/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py" - # Broken tensorboard test due to cmake issues. - "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/iterator_ops_cluster_test.py" # Needs portpicker + "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py" # Deadlocks "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/sloppy_transformation_dataset_op_test.py" # b/65430561 # tensor_forest tests (also note that we exclude the hybrid tests for now) "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py" # Results in wrong order. diff --git a/tensorflow/contrib/cmake/tf_tools.cmake b/tensorflow/contrib/cmake/tf_tools.cmake index cb58a2e7df85b2f214654eff5547c5788592f208..58c7df95c821b4d1aa2cc63c8aaf4039518b83ca 100644 --- a/tensorflow/contrib/cmake/tf_tools.cmake +++ b/tensorflow/contrib/cmake/tf_tools.cmake @@ -48,9 +48,6 @@ file(GLOB_RECURSE tf_tools_transform_graph_lib_exclude_srcs "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/compare_graphs.cc" "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/summarize_graph_main.cc" "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/transform_graph_main.cc" - "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/quantize_nodes.cc" - "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/quantize_weights.cc" - "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/round_weights.cc" ) list(REMOVE_ITEM tf_tools_transform_graph_lib_srcs ${tf_tools_transform_graph_lib_exclude_srcs}) diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index 77ea914380dfa4f3ec903e9fc7062d429f8c0d6f..53c2285699a6ca94e1e6b147080338b507f4d768 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -32,7 +32,6 @@ from __future__ import print_function import argparse import codecs -import io import os import re import subprocess diff --git a/tensorflow/contrib/copy_graph/python/util/copy_test.py b/tensorflow/contrib/copy_graph/python/util/copy_test.py index 2798d31229d048561f8ebd9b63d3df94a44c45c7..05744bec4e05405c04b5ec442e72e4495737ab5b 100644 --- a/tensorflow/contrib/copy_graph/python/util/copy_test.py +++ b/tensorflow/contrib/copy_graph/python/util/copy_test.py @@ -17,9 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np from tensorflow.contrib.copy_graph.python.util import copy_elements -from tensorflow.contrib.framework.python.framework import tensor_util from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index 62708636c6181ca63cddf2b2e7c84d3da740282a..1233c8f251c404c57d9e2b38993e7a386b1e6ceb 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -105,8 +105,8 @@ def crf_sequence_score(inputs, tag_indices, sequence_lengths, return utils.smart_cond( pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1], 1), - fn1=_single_seq_fn, - fn2=_multi_seq_fn) + true_fn=_single_seq_fn, + false_fn=_multi_seq_fn) def crf_log_norm(inputs, sequence_lengths, transition_params): @@ -166,8 +166,8 @@ def crf_log_likelihood(inputs, sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix, if available. Returns: - log_likelihood: A scalar containing the log-likelihood of the given sequence - of tag indices. + log_likelihood: A [batch_size] `Tensor` containing the log-likelihood of + each example, given the sequence of tag indices. transition_params: A [num_tags, num_tags] transition matrix. This is either provided by the caller or created in this function. """ @@ -182,7 +182,7 @@ def crf_log_likelihood(inputs, transition_params) log_norm = crf_log_norm(inputs, sequence_lengths, transition_params) - # Normalize the scores to get the log-likelihood. + # Normalize the scores to get the log-likelihood per example. log_likelihood = sequence_scores - log_norm return log_likelihood, transition_params @@ -511,7 +511,7 @@ def crf_decode(potentials, transition_params, sequence_length): return decode_tags, best_score return utils.smart_cond( - pred=math_ops.equal( - potentials.shape[1].value or array_ops.shape(potentials)[1], 1), - fn1=_single_seq_fn, - fn2=_multi_seq_fn) + pred=math_ops.equal(potentials.shape[1].value or + array_ops.shape(potentials)[1], 1), + true_fn=_single_seq_fn, + false_fn=_multi_seq_fn) diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py index 56c562a3bad1f9e8a55a25eb3273fb2c9fbdd4b5..933df6d71dd7c972efe63d54fa7344ecfc39b0a7 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py @@ -20,7 +20,7 @@ from __future__ import print_function import time -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import rnn as contrib_rnn from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops from tensorflow.contrib.rnn.python.ops import lstm_ops diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py index 49d305cb0dd0387c34b7feb79ef631eac9e935cd..9897c31a98e0b335c18a84825fc518ed1fc310a2 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py @@ -51,7 +51,11 @@ from tensorflow.python.ops.losses import losses from tensorflow.python.platform import googletest from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import adagrad +from tensorflow.python.training import adam from tensorflow.python.training import gradient_descent +from tensorflow.python.training import momentum +from tensorflow.python.training import rmsprop from tensorflow.python.training import saver as saver_lib @@ -316,6 +320,55 @@ class CudnnRNNTestBasic(TensorFlowTestCase): self.assertEqual(0, total_sum2_v) self.assertEqual(0, total_sum3_v) + @unittest.skipUnless(test.is_built_with_cuda(), + "Test only applicable when running on GPUs") + def testOptimizersSupport(self): + for opt in ("adagrad", "adam", "rmsprop", "momentum", "sgd"): + self._TestOptimizerSupportHelper(opt) + + def _GetOptimizer(self, opt): + if opt == "adagrad": + return adagrad.AdagradOptimizer(learning_rate=1e-2) + elif opt == "adam": + return adam.AdamOptimizer(learning_rate=1e-2) + elif opt == "rmsprop": + return rmsprop.RMSPropOptimizer(learning_rate=1e-2) + elif opt == "momentum": + return momentum.MomentumOptimizer(learning_rate=1e-2, momentum=0.9) + elif opt == "sgd": + return gradient_descent.GradientDescentOptimizer(learning_rate=1e-2) + else: + raise ValueError("Unsupported optimizer: %s" % opt) + + def _TestOptimizerSupportHelper(self, opt): + num_layers = 4 + num_units = 2 + batch_size = 8 + direction = CUDNN_RNN_UNIDIRECTION + dir_count = 1 + + with ops.Graph().as_default() as g: + kernel_initializer = init_ops.constant_initializer(0.) + bias_initializer = init_ops.constant_initializer(0.) + inputs = random_ops.random_uniform([ + num_layers * dir_count, batch_size, num_units], dtype=dtypes.float32) + + lstm = cudnn_rnn.CudnnLSTM(num_layers, num_units, + direction=direction, + kernel_initializer=kernel_initializer, + bias_initializer=bias_initializer, + name="awesome_lstm") + outputs, _ = lstm(inputs) + loss = math_ops.reduce_sum(outputs) + optimizer = self._GetOptimizer(opt) + train_op = optimizer.minimize(loss) + + with self.test_session(use_gpu=True, graph=g) as sess: + sess.run(variables.global_variables_initializer()) + sess.run(train_op) + + @unittest.skipUnless(test.is_built_with_cuda(), + "Test only applicable when running on GPUs") def testSaveableGraphDeviceAssignment(self): num_layers = 4 num_units = 2 diff --git a/tensorflow/contrib/data/BUILD b/tensorflow/contrib/data/BUILD index 8ecc003348d70379ee48d050e63e93d0dd38efaa..0458199ff771bc45603106411550a39448e515b8 100644 --- a/tensorflow/contrib/data/BUILD +++ b/tensorflow/contrib/data/BUILD @@ -27,13 +27,13 @@ py_library( ) tf_custom_op_library( - name = "_prefetching_ops.so", - srcs = ["ops/prefetching_ops.cc"], - deps = ["//tensorflow/contrib/data/kernels:prefetching_kernels"], + name = "_dataset_ops.so", + srcs = ["ops/dataset_ops.cc"], + deps = ["//tensorflow/contrib/data/kernels:dataset_kernels"], ) tf_gen_op_libs( - op_lib_names = ["prefetching_ops"], + op_lib_names = ["dataset_ops"], ) filegroup( diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index daeb6a610533404044d42033709d644deb481024..9212b69700941c190df1d44ed308147105c56fba 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -12,26 +12,26 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""`tf.contrib.data` API for input pipelines. +"""Experimental API for building input pipelines. -This module contains the experimental (less stable) counterpart to the -`tf.data` API. See @{tf.data.Dataset} and @{tf.data.Iterator} for the -stable classes. +This module contains experimental `Dataset` sources and transformations that can +be used in conjunction with the @{tf.data.Dataset} API. Note that the +`tf.contrib.data` API is not subject to the same backwards compatibility +guarantees as `tf.data`, but we will provide deprecation advice in advance of +removing existing functionality. See the @{$datasets$Importing Data} Programmer's Guide for an overview. -@@Dataset @@Counter -@@Iterator -@@TFRecordDataset -@@FixedLengthRecordDataset -@@TextLineDataset +@@SqlDataset @@batch_and_drop_remainder +@@bucket_by_sequence_length @@dense_to_sparse_batch @@enumerate_dataset @@group_by_window @@ignore_errors +@@make_batched_features_dataset @@make_saveable_from_iterator @@map_and_batch @@padded_batch_and_drop_remainder @@ -40,6 +40,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@rejection_resample @@scan @@shuffle_and_repeat +@@sliding_window_batch @@sloppy_interleave @@unbatch @@ -58,23 +59,23 @@ from tensorflow.contrib.data.python.ops.batching import map_and_batch from tensorflow.contrib.data.python.ops.batching import padded_batch_and_drop_remainder from tensorflow.contrib.data.python.ops.batching import unbatch from tensorflow.contrib.data.python.ops.counter import Counter -from tensorflow.contrib.data.python.ops.dataset_ops import Dataset -from tensorflow.contrib.data.python.ops.dataset_ops import get_single_element from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset from tensorflow.contrib.data.python.ops.error_ops import ignore_errors +from tensorflow.contrib.data.python.ops.get_single_element import get_single_element +from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_window from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator -from tensorflow.contrib.data.python.ops.readers import FixedLengthRecordDataset +from tensorflow.contrib.data.python.ops.readers import make_batched_features_dataset from tensorflow.contrib.data.python.ops.readers import read_batch_features from tensorflow.contrib.data.python.ops.readers import SqlDataset -from tensorflow.contrib.data.python.ops.readers import TextLineDataset -from tensorflow.contrib.data.python.ops.readers import TFRecordDataset from tensorflow.contrib.data.python.ops.resampling import rejection_resample from tensorflow.contrib.data.python.ops.scan_ops import scan from tensorflow.contrib.data.python.ops.shuffle_ops import shuffle_and_repeat +from tensorflow.contrib.data.python.ops.sliding import sliding_window_batch from tensorflow.python.data.ops.iterator_ops import Iterator +from tensorflow.python.ops.parsing_ops import parse_single_example_v2 as parse_single_example # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD index 4cb53741ebf8cd0db41b382c878bd2ccd1dcf7f1..c87da7dfaa5943f7918c370f63362673844c7f0e 100644 --- a/tensorflow/contrib/data/kernels/BUILD +++ b/tensorflow/contrib/data/kernels/BUILD @@ -10,6 +10,7 @@ cc_library( name = "prefetching_kernels", srcs = ["prefetching_kernels.cc"], deps = [ + "//tensorflow/core:core_cpu_headers_lib", "//tensorflow/core:framework_headers_lib", "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", @@ -17,6 +18,50 @@ cc_library( alwayslink = 1, ) +cc_library( + name = "ignore_errors_dataset_op", + srcs = ["ignore_errors_dataset_op.cc"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], + alwayslink = 1, +) + +cc_library( + name = "threadpool_dataset_op", + srcs = ["threadpool_dataset_op.cc"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], +) + +cc_library( + name = "unique_dataset_op", + srcs = ["unique_dataset_op.cc"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], +) + +cc_library( + name = "dataset_kernels", + deps = [ + ":ignore_errors_dataset_op", + ":prefetching_kernels", + ":threadpool_dataset_op", + ":unique_dataset_op", + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/core/kernels/data/ignore_errors_dataset_op.cc b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc similarity index 98% rename from tensorflow/core/kernels/data/ignore_errors_dataset_op.cc rename to tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc index 99df699d719b896df37515fc4147cd48db52a113..bb29df60e8f114aaa50f578c43e73874f72ab0a3 100644 --- a/tensorflow/core/kernels/data/ignore_errors_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc @@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/kernels/data/dataset.h" #include "tensorflow/core/lib/random/random.h" namespace tensorflow { diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index d3df14bdd03476e9ee4015b374512e5bb9893a63..1baac3ea5239659e65881e5b2dea4fe1a8c49d1b 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include +#include "tensorflow/core/common_runtime/process_function_library_runtime.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_op_kernel.h" @@ -35,12 +36,14 @@ using FunctionBufferCallback = std::function; class FunctionBufferingResource : public ResourceBase { public: FunctionBufferingResource(FunctionLibraryRuntime* lib, + std::unique_ptr pflr, const NameAttrList& func, int64 buffer_size, const string& source_device, const string& target_device, const std::vector& func_args, int64 thread_pool_size) : lib_(lib), + pflr_(std::move(pflr)), func_(func), buffer_size_(buffer_size), source_device_(source_device), @@ -222,6 +225,7 @@ class FunctionBufferingResource : public ResourceBase { mutex mu_; FunctionLibraryRuntime* lib_; + std::unique_ptr pflr_; NameAttrList func_; const int64 buffer_size_; const string source_device_; @@ -241,7 +245,7 @@ class FunctionBufferingResource : public ResourceBase { class FunctionBufferResourceHandleOp : public OpKernel { public: explicit FunctionBufferResourceHandleOp(OpKernelConstruction* ctx) - : OpKernel(ctx) { + : OpKernel(ctx), flib_def_(nullptr) { OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("buffer_size", &buffer_size_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("container", &container_)); @@ -249,6 +253,17 @@ class FunctionBufferResourceHandleOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("thread_pool_size", &thread_pool_size_)); } + ~FunctionBufferResourceHandleOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + void Compute(OpKernelContext* ctx) override { const Tensor* string_arg; OP_REQUIRES_OK(ctx, ctx->input("string_arg", &string_arg)); @@ -267,28 +282,39 @@ class FunctionBufferResourceHandleOp : public OpKernel { const string& source_device = ctx->device()->name(); - ContainerInfo cinfo; - OP_REQUIRES_OK(ctx, cinfo.Init(ctx->resource_manager(), def())); - // Create the resource. - FunctionBufferingResource* buffer; - OP_REQUIRES_OK( - ctx, ctx->resource_manager()->LookupOrCreate( - cinfo.container(), cinfo.name(), &buffer, - [lib, &source_device, &target_device, func_args, - this](FunctionBufferingResource** ptr) { - *ptr = new FunctionBufferingResource( - lib, func_, buffer_size_, source_device, target_device, - func_args, thread_pool_size_); - return Status::OK(); - })); - OP_REQUIRES_OK(ctx, buffer->Instantiate()); + mutex_lock l(mu_); + if (!initialized_) { + OP_REQUIRES_OK(ctx, cinfo_.Init(ctx->resource_manager(), def())); + FunctionLibraryRuntime* clone_lib; + std::unique_ptr pflr; + OP_REQUIRES_OK(ctx, lib->Clone(&flib_def_, &pflr, &clone_lib)); + // Create the resource. + FunctionBufferingResource* buffer; + OP_REQUIRES_OK( + ctx, + ctx->resource_manager()->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &buffer, + [clone_lib, &pflr, &source_device, &target_device, func_args, + this](FunctionBufferingResource** ptr) { + *ptr = new FunctionBufferingResource( + clone_lib, std::move(pflr), func_, buffer_size_, + source_device, target_device, func_args, thread_pool_size_); + return Status::OK(); + })); + OP_REQUIRES_OK(ctx, buffer->Instantiate()); + initialized_ = true; + } OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( - ctx, 0, cinfo.container(), cinfo.name(), + ctx, 0, cinfo_.container(), cinfo_.name(), MakeTypeIndex())); } private: + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; + std::unique_ptr flib_def_; NameAttrList func_; int64 buffer_size_; string container_; diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..63e19ae3f837c9d3cfb1221df64360ee74117f13 --- /dev/null +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -0,0 +1,193 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/dataset.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/resource_mgr.h" +#include "tensorflow/core/lib/core/threadpool.h" + +namespace tensorflow { +namespace { + +class ThreadPoolResource : public ResourceBase { + public: + ThreadPoolResource(Env* env, const ThreadOptions& thread_options, + const string& name, int num_threads, bool low_latency_hint) + : thread_pool_(env, thread_options, name, num_threads, low_latency_hint) { + } + + // Schedules fn() for execution in the pool of threads. + void Schedule(std::function fn) { + thread_pool_.Schedule(std::move(fn)); + } + + string DebugString() override { return "ThreadPoolResource"; } + + private: + thread::ThreadPool thread_pool_; +}; + +// Creates a handle to a ThreadPool resource. Note that we don't use +// ResourceOpKernel here because the ThreadPoolResource constructor requires +// access to `OpKernelContext::env()`, which isn't provided by +// `ResourceOpKernel::CreateResource()`. +class ThreadPoolHandleOp : public OpKernel { + public: + explicit ThreadPoolHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("display_name", &display_name_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_threads", &num_threads_)); + OP_REQUIRES( + ctx, num_threads_ > 0, + errors::InvalidArgument("`num_threads` must be greater than zero.")); + } + + // The resource is deleted from the resource manager only when it is private + // to kernel. Ideally the resource should be deleted when it is no longer held + // by anyone, but it would break backward compatibility. + ~ThreadPoolHandleOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + + void Compute(OpKernelContext* ctx) override LOCKS_EXCLUDED(mu_) { + mutex_lock l(mu_); + if (!initialized_) { + ResourceMgr* mgr = ctx->resource_manager(); + OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def())); + ThreadPoolResource* resource; + OP_REQUIRES_OK(ctx, mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, ctx](ThreadPoolResource** ret) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + *ret = new ThreadPoolResource( + ctx->env(), {}, display_name_, + num_threads_, + false /* low_latency_hint */); + return Status::OK(); + })); + initialized_ = true; + } + OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( + ctx, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; + string display_name_; + int num_threads_; +}; + +class ThreadPoolDatasetOp : public UnaryDatasetOpKernel { + public: + explicit ThreadPoolDatasetOp(OpKernelConstruction* ctx) + : UnaryDatasetOpKernel(ctx) {} + + void MakeDataset(OpKernelContext* ctx, DatasetBase* input, + DatasetBase** output) override { + ThreadPoolResource* threadpool_resource; + OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 1), + &threadpool_resource)); + core::ScopedUnref unref_iterator(threadpool_resource); + + *output = new Dataset(ctx, input, threadpool_resource); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, const DatasetBase* input, + ThreadPoolResource* threadpool) + : GraphDatasetBase(ctx), input_(input), threadpool_(threadpool) { + input_->Ref(); + threadpool_->Ref(); + } + + ~Dataset() override { + input_->Unref(); + threadpool_->Unref(); + } + + std::unique_ptr MakeIterator( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::ThreadPool")})); + } + + const DataTypeVector& output_dtypes() const override { + return input_->output_dtypes(); + } + const std::vector& output_shapes() const override { + return input_->output_shapes(); + } + + string DebugString() override { return "ThreadPoolDatasetOp::Dataset"; } + + protected: + Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Node** output) const override { + return errors::Unimplemented( + "Cannot currently serialize the thread pool for a " + "ThreadPoolDataset."); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params), + input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + ThreadPoolResource* pool = dataset()->threadpool_; + IteratorContext::Params params; + params.env = ctx->env(); + params.runner = [pool](std::function c) { + pool->Schedule(std::move(c)); + }; + params.stats_aggregator_getter = ctx->stats_aggregator_getter(); + params.lib = ctx->lib(); + params.function_library = ctx->function_library(); + params.allocator_getter = ctx->allocator_getter(); + IteratorContext threadpool_ctx(params); + return input_impl_->GetNext(&threadpool_ctx, out_tensors, + end_of_sequence); + } + + private: + std::unique_ptr input_impl_; + }; + + const DatasetBase* const input_; + ThreadPoolResource* const threadpool_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("ThreadPoolHandle").Device(DEVICE_CPU), + ThreadPoolHandleOp); +REGISTER_KERNEL_BUILDER(Name("ThreadPoolDataset").Device(DEVICE_CPU), + ThreadPoolDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/kernels/data/unique_dataset_op.cc b/tensorflow/contrib/data/kernels/unique_dataset_op.cc similarity index 99% rename from tensorflow/core/kernels/data/unique_dataset_op.cc rename to tensorflow/contrib/data/kernels/unique_dataset_op.cc index 7726ee0edf71b34cb65fe5fceb2b60dd30bb58e2..69fbb0fcdcce87951d2c9b84210fda378081b103 100644 --- a/tensorflow/core/kernels/data/unique_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/unique_dataset_op.cc @@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/kernels/data/dataset.h" #include "tensorflow/core/lib/hash/hash.h" namespace tensorflow { diff --git a/tensorflow/contrib/data/ops/prefetching_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc similarity index 56% rename from tensorflow/contrib/data/ops/prefetching_ops.cc rename to tensorflow/contrib/data/ops/dataset_ops.cc index 23cb62b6f0dbfed15667dd00ae0039b33aa944d4..a4c1212da11a2410461a120ed5f7116e80e4b903 100644 --- a/tensorflow/contrib/data/ops/prefetching_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -17,6 +17,26 @@ limitations under the License. namespace tensorflow { +REGISTER_OP("IgnoreErrorsDataset") + .Input("input_dataset: variant") + .Output("handle: variant") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that contains the elements of `input_dataset` ignoring errors. +)doc"); + +REGISTER_OP("UniqueDataset") + .Input("input_dataset: variant") + .Output("handle: variant") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that contains the unique elements of `input_dataset`. +)doc"); + REGISTER_OP("FunctionBufferingResource") .Input("string_arg: string") .Input("target_device: string") @@ -55,4 +75,33 @@ output: A list of return values. output_types: The type list for the return values. )doc"); +REGISTER_OP("ThreadPoolDataset") + .Input("input_dataset: variant") + .Input("thread_pool: resource") + .Output("handle: variant") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that uses a custom thread pool to compute `input_dataset`. + +handle: A resource produced by the ThreadPoolHandle op. +)doc"); + +REGISTER_OP("ThreadPoolHandle") + .Output("handle: resource") + .SetShapeFn(shape_inference::ScalarShape) + .Attr("num_threads: int") + .Attr("display_name: string") + .Attr("container: string = ''") + .Attr("shared_name: string = ''") + .Doc(R"doc( +Creates a custom thread pool with the given number of threads. + +handle: A resource that can be consumed by one or more ThreadPoolDataset ops. +num_threads: The number of threads in the thread pool. +display_name: A human-readable name for the threads that may be visible in + some visualizations. +)doc"); + } // namespace tensorflow diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 04a21f2b0f5b508fcd2e6bafa6a6ebdb555329b4..107625ab75b59ca5a004d5f6e65bffafe85ad313 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -52,24 +52,6 @@ py_test( ], ) -py_test( - name = "cache_dataset_op_test", - size = "small", - srcs = ["cache_dataset_op_test.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/contrib/data/python/ops:dataset_ops", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:iterator_ops", - "//third_party/py/numpy", - ], -) - py_test( name = "concatenate_dataset_op_test", size = "small", @@ -186,8 +168,10 @@ py_test( srcs = ["interleave_dataset_op_test.py"], srcs_version = "PY2AND3", tags = [ + "no_cuda_on_cpu_tap", "no_oss", "no_pip", + "nomsan", ], deps = [ ":dataset_serialization_test", @@ -208,73 +192,18 @@ py_test( ) tf_py_test( - name = "iterator_ops_cluster_test", - size = "small", - srcs = ["iterator_ops_cluster_test.py"], - additional_deps = [ - "//tensorflow/contrib/data/python/ops:dataset_ops", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:function", - "//tensorflow/python:functional_ops", - "//tensorflow/python:session", - "//tensorflow/python/data/ops:iterator_ops", - ], - grpc_enabled = True, - tags = [ - "no_windows", - "oss_serial", - ], -) - -tf_py_test( - name = "iterator_ops_test", + name = "get_single_element_test", size = "small", - srcs = ["iterator_ops_test.py"], + srcs = ["get_single_element_test.py"], additional_deps = [ "//third_party/py/numpy", "//tensorflow/contrib/data/python/ops:dataset_ops", - "//tensorflow/contrib/data/python/ops:readers", - "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", - "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", - "//tensorflow/python:function", - "//tensorflow/python:functional_ops", - "//tensorflow/python:gradients", - "//tensorflow/python:io_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:script_ops", - "//tensorflow/python:session", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:iterator_ops", - ], - grpc_enabled = True, -) - -py_test( - name = "list_files_dataset_op_test", - size = "small", - srcs = ["list_files_dataset_op_test.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/contrib/data/python/ops:dataset_ops", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:util", ], ) @@ -368,6 +297,7 @@ py_test( "//tensorflow/python:parsing_ops", "//tensorflow/python:util", "//tensorflow/python/data/ops:iterator_ops", + "//third_party/py/numpy", ], ) @@ -426,14 +356,17 @@ py_test( ) py_test( - name = "shard_dataset_op_test", + name = "serialization_integration_test", size = "small", - srcs = ["shard_dataset_op_test.py"], + srcs = ["serialization_integration_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - "//tensorflow/contrib/data/python/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:iterator_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", ], ) @@ -489,6 +422,20 @@ py_test( ], ) +py_test( + name = "threadpool_dataset_ops_test", + size = "small", + srcs = ["threadpool_dataset_ops_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + "//tensorflow/contrib/data/python/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:transformation_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", + ], +) + py_test( name = "unique_dataset_op_test", size = "small", @@ -535,7 +482,7 @@ py_test( "no_oss", # b/68785503 ], deps = [ - "//tensorflow/contrib/data/python/ops:prefetching_py", + "//tensorflow/contrib/data/python/ops:prefetching_ops", "//tensorflow/core:protos_all_py", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", @@ -549,6 +496,23 @@ py_test( ], ) +tf_py_test( + name = "slide_dataset_op_test", + size = "small", + srcs = ["slide_dataset_op_test.py"], + additional_deps = [ + "//tensorflow/contrib/data/python/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:transformation_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", + "//third_party/py/numpy", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py index 0c2827b1e49919d236aeb922645236251f1344e0..71dc1c1172c9d515d4c85f85257c952135098329 100644 --- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py @@ -23,283 +23,24 @@ import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching -from tensorflow.contrib.data.python.ops import dataset_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import sparse_tensor -from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import string_ops from tensorflow.python.platform import test -from tensorflow.python.util import compat class BatchDatasetTest(test.TestCase): - def testBatchDataset(self): - """Test an dataset that maps a TF function across its input elements.""" - # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> - # RepeatDataset(count) -> BatchDataset(batch_size). - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - - count = array_ops.placeholder(dtypes.int64, shape=[]) - batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(count).batch(batch_size).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([[None] + list(c.shape[1:]) for c in components], - [t.shape.as_list() for t in get_next]) - - with self.test_session() as sess: - # Batch of a finite input, where the batch_size divides the - # total number of elements. - sess.run(init_op, feed_dict={count: 28, batch_size: 14}) - num_batches = (28 * 7) // 14 - for i in range(num_batches): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(14): - self.assertAllEqual(component[(i * 14 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Batch of a finite input, where the batch_size does not - # divide the total number of elements. - sess.run(init_op, feed_dict={count: 14, batch_size: 8}) - - # We expect (num_batches - 1) full-sized batches. - num_batches = int(math.ceil((14 * 7) / 8)) - for i in range(num_batches - 1): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(8): - self.assertAllEqual(component[(i * 8 + j) % 7]**2, - result_component[j]) - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range((14 * 7) % 8): - self.assertAllEqual(component[((num_batches - 1) * 8 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Batch of an empty input should fail straight away. - sess.run(init_op, feed_dict={count: 0, batch_size: 8}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Empty batch should be an initialization time error. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, batch_size: 0}) - def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) self.assertAllEqual(a.values, b.values) self.assertAllEqual(a.dense_shape, b.dense_shape) - def testBatchSparse(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=[[0]], values=(i * [1]), dense_shape=[1]) - - iterator = dataset_ops.Dataset.range(10).map(_sparse).batch( - 5).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(2): - actual = sess.run(get_next) - expected = sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]], - values=[i * 5, i * 5 + 1, i * 5 + 2, i * 5 + 3, i * 5 + 4], - dense_shape=[5, 1]) - self.assertTrue(sparse_tensor.is_sparse(actual)) - self.assertSparseValuesEqual(actual, expected) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testNestedBatchSparse(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=[[0]], values=(i * [1]), dense_shape=[1]) - - iterator = dataset_ops.Dataset.range(10).map(_sparse).batch(5).batch( - 2).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - actual = sess.run(get_next) - expected = sparse_tensor.SparseTensorValue( - indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0], - [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], [1, 4, 0]], - values=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], - dense_shape=[2, 5, 1]) - self.assertTrue(sparse_tensor.is_sparse(actual)) - self.assertSparseValuesEqual(actual, expected) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testPaddedBatchDataset(self): - seq_lens = array_ops.placeholder(dtypes.int32, shape=[None]) - padded_shape = array_ops.placeholder(dtypes.int64, shape=[1]) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(seq_lens) - .map(lambda x: array_ops.fill([x], x)).padded_batch( - 4, padded_shapes=padded_shape).make_initializable_iterator()) - - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - # Test with random sequence lengths, and max padding. - random_seq_lens = np.random.randint(20, size=(32,)).astype(np.int32) - sess.run( - init_op, feed_dict={ - padded_shape: [-1], - seq_lens: random_seq_lens - }) - for i in range(8): - result = sess.run(get_next) - padded_len = np.max(result) - self.assertEqual((4, padded_len), result.shape) - for j in range(4): - seq_len = random_seq_lens[(i * 4) + j] - self.assertAllEqual(result[j, :seq_len], [seq_len] * seq_len) - self.assertAllEqual(result[j, seq_len:], [0] * (padded_len - seq_len)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test with random sequence lengths, and constant padding. - sess.run( - init_op, feed_dict={ - padded_shape: [25], - seq_lens: random_seq_lens - }) - for i in range(8): - result = sess.run(get_next) - self.assertEqual((4, 25), result.shape) - for j in range(4): - seq_len = random_seq_lens[(i * 4) + j] - self.assertAllEqual(result[j, :seq_len], [seq_len] * seq_len) - self.assertAllEqual(result[j, seq_len:], [0] * (25 - seq_len)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test correct handling of empty tensors. - sess.run(init_op, feed_dict={padded_shape: [-1], seq_lens: [0, 0, 0, 0]}) - result = sess.run(get_next) - self.assertAllEqual([[], [], [], []], result) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test error handling with constant sequence lengths, and - # too-short padding. - sess.run(init_op, feed_dict={padded_shape: [5], seq_lens: [6, 5, 5, 5]}) - with self.assertRaises(errors.DataLossError): - result = sess.run(get_next) - - def testPaddedBatchDatasetNonDefaultPadding(self): - seq_lens = array_ops.placeholder(dtypes.int32, shape=[None]) - padded_shape = array_ops.placeholder(dtypes.int64, shape=[1]) - - def fill_tuple(x): - filled = array_ops.fill([x], x) - return (filled, string_ops.as_string(filled)) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(seq_lens).map(fill_tuple) - .padded_batch( - 4, - padded_shapes=(padded_shape, padded_shape), - padding_values=(-1, "")).make_initializable_iterator()) - - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - # Test with random sequence lengths, and max padding. - random_seq_lens = np.random.randint(20, size=(32,)).astype(np.int32) - sess.run( - init_op, feed_dict={ - padded_shape: [-1], - seq_lens: random_seq_lens - }) - for i in range(8): - result = sess.run(get_next) - padded_len = np.max(result[0]) - self.assertEqual((4, padded_len), result[0].shape) - self.assertEqual((4, padded_len), result[1].shape) - for j in range(4): - seq_len = random_seq_lens[(i * 4) + j] - self.assertAllEqual(result[0][j, :seq_len], [seq_len] * seq_len) - self.assertAllEqual(result[0][j, seq_len:], - [-1] * (padded_len - seq_len)) - self.assertAllEqual(result[1][j, :seq_len], - [compat.as_bytes(str(seq_len))] * seq_len) - self.assertAllEqual(result[1][j, seq_len:], - [b""] * (padded_len - seq_len)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testPaddedBatchDatasetShapeSpecifications(self): - int_placeholder = array_ops.placeholder(dtypes.int32) - float_placeholder = array_ops.placeholder(dtypes.float32) - string_placeholder = array_ops.placeholder(dtypes.string) - input_dataset = dataset_ops.Dataset.from_tensors( - (int_placeholder, float_placeholder, string_placeholder)) - - # Test different ways of specifying the `padded_shapes` argument. - dynamic_padding_from_tensor_shapes = input_dataset.padded_batch( - 32, - padded_shapes=(tensor_shape.TensorShape([None]), - tensor_shape.TensorShape([None, None]), - tensor_shape.TensorShape([37]))) - dynamic_padding_from_lists = input_dataset.padded_batch( - 32, padded_shapes=([None], [None, None], [37])) - dynamic_padding_from_lists_with_minus_one = input_dataset.padded_batch( - 32, padded_shapes=([-1], [-1, -1], [37])) - dynamic_padding_from_tensors = input_dataset.padded_batch( - 32, - padded_shapes=(constant_op.constant([-1], dtype=dtypes.int64), - constant_op.constant([-1, -1], dtype=dtypes.int64), - constant_op.constant([37], dtype=dtypes.int64))) - - for dataset in [ - dynamic_padding_from_tensor_shapes, dynamic_padding_from_lists, - dynamic_padding_from_lists_with_minus_one, dynamic_padding_from_tensors - ]: - self.assertEqual([None, None], dataset.output_shapes[0].as_list()) - self.assertEqual([None, None, None], dataset.output_shapes[1].as_list()) - self.assertEqual([None, 37], dataset.output_shapes[2].as_list()) - - def testPaddedBatchSparseError(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0]], values=(i * [1]), dense_shape=[1, 1]), i - - with self.assertRaises(TypeError): - _ = dataset_ops.Dataset.range(10).map(_map_fn).padded_batch(10) - def testDenseToSparseBatchDataset(self): components = np.random.randint(12, size=(100,)).astype(np.int32) iterator = ( diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index 6de93059d8cfbcf43c4e359b6973c33f22fff5fa..94f800e8a58bc34eef3034cd976b931528c01940 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -17,11 +17,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import random + import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.data.python.ops import grouping +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -379,5 +381,93 @@ class BucketTest(test.TestCase): self.assertEqual(batches, 15) +class BucketBySequenceLength(test.TestCase): + + def testBucket(self): + + boundaries = [10, 20, 30] + batch_sizes = [10, 8, 4, 2] + lengths = [8, 13, 25, 35] + + def element_gen(): + # Produce 1 batch for each bucket + elements = [] + for batch_size, length in zip(batch_sizes, lengths): + for _ in range(batch_size): + elements.append([1] * length) + random.shuffle(elements) + for el in elements: + yield (el,) + + element_len = lambda el: array_ops.shape(el)[0] + dataset = dataset_ops.Dataset.from_generator( + element_gen, (dtypes.int64,), ([None],)).apply( + grouping.bucket_by_sequence_length( + element_len, boundaries, batch_sizes)) + batch, = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + batches = [] + for _ in range(4): + batches.append(sess.run(batch)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(batch) + batch_sizes_val = [] + lengths_val = [] + for batch in batches: + batch_size = batch.shape[0] + length = batch.shape[1] + batch_sizes_val.append(batch_size) + lengths_val.append(length) + self.assertEqual(sum(batch_sizes_val), sum(batch_sizes)) + self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val)) + self.assertEqual(sorted(lengths), sorted(lengths_val)) + + def testPadToBoundary(self): + + boundaries = [10, 20, 30] + batch_sizes = [10, 8, 4, 2] + lengths = [8, 13, 25] + + def element_gen(): + # Produce 1 batch for each bucket + elements = [] + for batch_size, length in zip(batch_sizes[:-1], lengths): + for _ in range(batch_size): + elements.append([1] * length) + random.shuffle(elements) + for el in elements: + yield (el,) + for _ in range(batch_sizes[-1]): + el = [1] * (boundaries[-1] + 5) + yield (el,) + + element_len = lambda el: array_ops.shape(el)[0] + dataset = dataset_ops.Dataset.from_generator( + element_gen, (dtypes.int64,), ([None],)).apply( + grouping.bucket_by_sequence_length( + element_len, boundaries, batch_sizes, + pad_to_bucket_boundary=True)) + batch, = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + batches = [] + for _ in range(3): + batches.append(sess.run(batch)) + with self.assertRaisesOpError("bucket_boundaries"): + sess.run(batch) + batch_sizes_val = [] + lengths_val = [] + for batch in batches: + batch_size = batch.shape[0] + length = batch.shape[1] + batch_sizes_val.append(batch_size) + lengths_val.append(length) + batch_sizes = batch_sizes[:-1] + self.assertEqual(sum(batch_sizes_val), sum(batch_sizes)) + self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val)) + self.assertEqual(sorted(boundaries), sorted(lengths_val)) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py deleted file mode 100644 index 9818020680afb9d0f0197d272ec5339c6358db36..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py +++ /dev/null @@ -1,300 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for the experimental input pipeline ops.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from os import path -import shutil -import tempfile - -import numpy as np - -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - - -class FilesystemCacheDatasetTest(test.TestCase): - - def setUp(self): - self.tmp_dir = tempfile.mkdtemp() - self.cache_prefix = path.join(self.tmp_dir, "cache") - - def tearDown(self): - if self.tmp_dir: - shutil.rmtree(self.tmp_dir, ignore_errors=True) - - def testCacheDatasetPassthrough(self): - components = (np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), - np.array([9.0, 10.0, 11.0, 12.0])) - count_placeholder = array_ops.placeholder_with_default( - constant_op.constant(5, dtypes.int64), shape=[]) - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - - repeat_dataset = (dataset_ops.Dataset.from_tensor_slices(components) - .repeat(count_placeholder)) - - cache_dataset = repeat_dataset.cache(filename_placeholder) - - self.assertEqual( - tuple([c.shape[1:] for c in components]), cache_dataset.output_shapes) - - # Create initialization ops for iterators without and with - # caching, respectively. - iterator = iterator_ops.Iterator.from_structure(cache_dataset.output_types, - cache_dataset.output_shapes) - init_fifo_op = iterator.make_initializer(repeat_dataset) - init_cache_op = iterator.make_initializer(cache_dataset) - - get_next = iterator.get_next() - - with self.test_session() as sess: - # First run without caching to collect the "ground truth". - sess.run(init_fifo_op) - elements = [] - for _ in range(20): - elements.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Assert that the cached dataset has the same elements as the - # "ground truth". - sess.run( - init_cache_op, feed_dict={filename_placeholder: self.cache_prefix}) - cached_elements = [] - for _ in range(20): - cached_elements.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertAllEqual(elements, cached_elements) - - # Re-initialize with an empty upstream (to throw errors.OutOfRangeError - # if we didn't use the cache). - sess.run( - init_cache_op, - feed_dict={ - count_placeholder: 0, - filename_placeholder: self.cache_prefix - }) - replayed_elements = [] - for _ in range(20): - replayed_elements.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertEqual(cached_elements, replayed_elements) - - # Re-initialize with an empty upstream and a missing cache file (should - # throw errors.OutOfRangeError immediately). - sess.run( - init_cache_op, - feed_dict={ - count_placeholder: 0, - filename_placeholder: self.cache_prefix + "nonsense" - }) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testConcurrentWriters(self): - components = (np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), - np.array([9.0, 10.0, 11.0, 12.0])) - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - - cache_dataset1 = (dataset_ops.Dataset.from_tensor_slices(components) - .cache(filename_placeholder)) - cache_dataset2 = (dataset_ops.Dataset.from_tensor_slices(components) - .cache(filename_placeholder)) - - iterator1 = cache_dataset1.make_initializable_iterator() - iterator2 = cache_dataset2.make_initializable_iterator() - init_cache_op1 = iterator1.initializer - init_cache_op2 = iterator2.initializer - - get_next1 = iterator1.get_next() - get_next2 = iterator2.get_next() - - with self.test_session() as sess: - sess.run( - init_cache_op1, feed_dict={filename_placeholder: self.cache_prefix}) - sess.run(get_next1) # this should succeed - - sess.run( - init_cache_op2, feed_dict={filename_placeholder: self.cache_prefix}) - with self.assertRaises(errors.AlreadyExistsError): - sess.run(get_next2) - - sess.run(get_next1) # this should continue to succeed - - def testConcurrentReaders(self): - components = (np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), - np.array([9.0, 10.0, 11.0, 12.0])) - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - - cache_dataset1 = (dataset_ops.Dataset.from_tensor_slices(components) - .cache(filename_placeholder)) - cache_dataset2 = (dataset_ops.Dataset.from_tensor_slices(components) - .cache(filename_placeholder)) - - iterator1 = cache_dataset1.make_initializable_iterator() - iterator2 = cache_dataset2.make_initializable_iterator() - init_cache_op1 = iterator1.initializer - init_cache_op2 = iterator2.initializer - - get_next1 = iterator1.get_next() - get_next2 = iterator2.get_next() - - with self.test_session() as sess: - sess.run( - init_cache_op1, feed_dict={filename_placeholder: self.cache_prefix}) - elements = [] - for _ in range(4): - elements.append(sess.run(get_next1)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next1) - - # Re-initialize - sess.run( - init_cache_op1, feed_dict={filename_placeholder: self.cache_prefix}) - sess.run( - init_cache_op2, feed_dict={filename_placeholder: self.cache_prefix}) - - # Reading concurrently should succeed. - elements_itr1 = [] - elements_itr2 = [] - elements_itr2.append(sess.run(get_next2)) - elements_itr1.append(sess.run(get_next1)) - elements_itr2.append(sess.run(get_next2)) - elements_itr1.append(sess.run(get_next1)) - # Intentionally reversing the order - elements_itr1.append(sess.run(get_next1)) - elements_itr2.append(sess.run(get_next2)) - elements_itr1.append(sess.run(get_next1)) - elements_itr2.append(sess.run(get_next2)) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next2) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next1) - - self.assertAllEqual(elements, elements_itr1) - self.assertAllEqual(elements, elements_itr2) - - -class MemoryCacheDatasetTest(test.TestCase): - - def testCacheDatasetPassthrough(self): - repeat_count = variables.Variable(constant_op.constant(10, dtypes.int64)) - dataset = dataset_ops.Dataset.range(3).flat_map( - lambda x: dataset_ops.Dataset.from_tensors(x).repeat(repeat_count)) - - cached_dataset = dataset.cache().repeat(2) - uncached_dataset = dataset.repeat(2) - - # Needs to be initializable to capture the variable. - cached_iterator = cached_dataset.make_initializable_iterator() - cached_next = cached_iterator.get_next() - uncached_iterator = uncached_dataset.make_initializable_iterator() - uncached_next = uncached_iterator.get_next() - - with self.test_session() as sess: - - sess.run(repeat_count.initializer) - sess.run(cached_iterator.initializer) - sess.run(uncached_iterator.initializer) - - for i in range(3): - for _ in range(10): - self.assertEqual(sess.run(cached_next), i) - self.assertEqual(sess.run(uncached_next), i) - - sess.run(repeat_count.assign(0)) - - # The uncached iterator should now be empty. - with self.assertRaises(errors.OutOfRangeError): - sess.run(uncached_next) - - # The cached iterator replays from cache. - for i in range(3): - for _ in range(10): - self.assertEqual(sess.run(cached_next), i) - - # The cached iterator should now be empty. - with self.assertRaises(errors.OutOfRangeError): - sess.run(cached_next) - - def testEmptyCacheReading(self): - components = (np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), - np.array([9.0, 10.0, 11.0, 12.0])) - count_placeholder = array_ops.placeholder_with_default( - constant_op.constant(5, dtypes.int64), shape=[]) - - repeat_dataset = (dataset_ops.Dataset.from_tensor_slices(components) - .repeat(count_placeholder)) - - cache_dataset = repeat_dataset.cache() - - # Create initialization ops for iterators without and with - # caching, respectively. - iterator = cache_dataset.make_initializable_iterator() - init_cache_op = iterator.initializer - - get_next = iterator.get_next() - - with self.test_session() as sess: - # Initialize with an empty upstream and a missing cache file (should - # throw errors.OutOfRangeError immediately). - sess.run(init_cache_op, feed_dict={count_placeholder: 0}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testConcurrentReaders(self): - count_placeholder = array_ops.placeholder_with_default( - constant_op.constant(5, dtypes.int64), shape=[]) - dataset = dataset_ops.Dataset.range(count_placeholder).cache() - d1 = dataset.map(lambda x: x + 1) - d2 = dataset.map(lambda x: x + 6) - - i1 = d1.make_initializable_iterator() - i2 = d2.make_initializable_iterator() - - with self.test_session() as sess: - sess.run(i1.initializer) - - self.assertEqual(1, sess.run(i1.get_next())) - self.assertEqual(2, sess.run(i1.get_next())) - self.assertEqual(3, sess.run(i1.get_next())) - - sess.run(i2.initializer, feed_dict={count_placeholder: 3}) - - self.assertEqual(6, sess.run(i2.get_next())) - self.assertEqual(7, sess.run(i2.get_next())) - self.assertEqual(4, sess.run(i1.get_next())) # interleave execution - self.assertEqual([8, 5], sess.run([i2.get_next(), i1.get_next()])) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(i1.get_next()) - with self.assertRaises(errors.OutOfRangeError): - sess.run(i2.get_next()) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py index 063c71063601002af8168c4facf4057433061ab7..17f2980157ddd0350dafd1d745cbb9b64e65f7c5 100644 --- a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py @@ -20,117 +20,10 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.framework import errors -from tensorflow.python.framework import tensor_shape +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test -class ConcatenateDatasetTest(test.TestCase): - - def testConcatenateDataset(self): - input_components = ( - np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 15), - np.array([37.0, 38.0, 39.0, 40.0])) - to_concatenate_components = ( - np.tile(np.array([[1], [2], [3], [4], [5]]), 20), - np.tile(np.array([[12], [13], [14], [15], [16]]), 15), - np.array([37.0, 38.0, 39.0, 40.0, 41.0])) - - input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components) - dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices( - to_concatenate_components) - concatenated = input_dataset.concatenate(dataset_to_concatenate) - self.assertEqual(concatenated.output_shapes, (tensor_shape.TensorShape( - [20]), tensor_shape.TensorShape([15]), tensor_shape.TensorShape([]))) - - iterator = concatenated.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(9): - result = sess.run(get_next) - if i < 4: - for component, result_component in zip(input_components, result): - self.assertAllEqual(component[i], result_component) - else: - for component, result_component in zip(to_concatenate_components, - result): - self.assertAllEqual(component[i - 4], result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testConcatenateDatasetDifferentShape(self): - input_components = ( - np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 4)) - to_concatenate_components = ( - np.tile(np.array([[1], [2], [3], [4], [5]]), 20), - np.tile(np.array([[12], [13], [14], [15], [16]]), 15)) - - input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components) - dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices( - to_concatenate_components) - concatenated = input_dataset.concatenate(dataset_to_concatenate) - self.assertEqual( - [ts.as_list() - for ts in nest.flatten(concatenated.output_shapes)], [[20], [None]]) - - iterator = concatenated.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(9): - result = sess.run(get_next) - if i < 4: - for component, result_component in zip(input_components, result): - self.assertAllEqual(component[i], result_component) - else: - for component, result_component in zip(to_concatenate_components, - result): - self.assertAllEqual(component[i - 4], result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testConcatenateDatasetDifferentStructure(self): - input_components = ( - np.tile(np.array([[1], [2], [3], [4]]), 5), - np.tile(np.array([[12], [13], [14], [15]]), 4)) - to_concatenate_components = ( - np.tile(np.array([[1], [2], [3], [4], [5]]), 20), - np.tile(np.array([[12], [13], [14], [15], [16]]), 15), - np.array([37.0, 38.0, 39.0, 40.0, 41.0])) - - input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components) - dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices( - to_concatenate_components) - - with self.assertRaisesRegexp(ValueError, - "don't have the same number of elements"): - input_dataset.concatenate(dataset_to_concatenate) - - def testConcatenateDatasetDifferentType(self): - input_components = ( - np.tile(np.array([[1], [2], [3], [4]]), 5), - np.tile(np.array([[12], [13], [14], [15]]), 4)) - to_concatenate_components = ( - np.tile(np.array([[1.0], [2.0], [3.0], [4.0]]), 5), - np.tile(np.array([[12], [13], [14], [15]]), 15)) - - input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components) - dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices( - to_concatenate_components) - - with self.assertRaisesRegexp(TypeError, "have different types"): - input_dataset.concatenate(dataset_to_concatenate) - - class ConcatenateDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py index a90ba30e60cef13156719bba24fb553c0acec391..a842502cc6fe3605dde0be5f50cf46e3e37d7ed4 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py @@ -17,713 +17,20 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import threading - import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test class DatasetConstructorTest(test.TestCase): - def testFromTensors(self): - """Test an dataset that represents a single tuple of tensors.""" - components = (np.array(1), np.array([1, 2, 3]), np.array(37.0)) - - iterator = (dataset_ops.Dataset.from_tensors(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - sess.run(init_op) - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def assertSparseValuesEqual(self, a, b): - self.assertAllEqual(a.indices, b.indices) - self.assertAllEqual(a.values, b.values) - self.assertAllEqual(a.dense_shape, b.dense_shape) - - def testFromTensorsSparse(self): - """Test an dataset that represents a single tuple of tensors.""" - components = (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 1]]), - values=np.array([-1, 1]), - dense_shape=np.array([2, 2]))) - - iterator = ( - dataset_ops.Dataset.from_tensors(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual( - [tensor_shape.TensorShape(c.dense_shape) for c in components], - [shape for shape in iterator.output_shapes]) - - with self.test_session() as sess: - sess.run(init_op) - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertSparseValuesEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromTensorsMixed(self): - """Test an dataset that represents a single tuple of tensors.""" - components = (np.array(1), np.array([1, 2, 3]), np.array(37.0), - sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 1]]), - values=np.array([-1, 1]), - dense_shape=np.array([2, 2]))) - - iterator = ( - dataset_ops.Dataset.from_tensors(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([ - tensor_shape.TensorShape(c.dense_shape) - if sparse_tensor.is_sparse(c) else c.shape for c in components - ], [shape for shape in iterator.output_shapes]) - - with self.test_session() as sess: - sess.run(init_op) - results = sess.run(get_next) - for component, result_component in zip(components, results): - if sparse_tensor.is_sparse(component): - self.assertSparseValuesEqual(component, result_component) - else: - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromTensorSlices(self): - """Test an dataset that represents the slices from a tuple of tensors.""" - components = ( - np.tile(np.array([[1], [2], [3], [4]]), 20), np.tile( - np.array([[12], [13], [14], [15]]), 22), - np.array([37.0, 38.0, 39.0, 40.0]) - ) - - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - sess.run(init_op) - for i in range(4): - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component[i], result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromTensorSlicesSparse(self): - """Test an dataset that represents the slices from a tuple of tensors.""" - components = (sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 0], [2, 0]]), - values=np.array([0, 0, 0]), - dense_shape=np.array([3, 1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 1], [2, 2]]), - values=np.array([1, 2, 3]), - dense_shape=np.array([3, 3]))) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual( - [tensor_shape.TensorShape(c.dense_shape[1:]) for c in components], - [shape for shape in iterator.output_shapes]) - - with self.test_session() as sess: - sess.run(init_op) - expected = [ - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([1]), - dense_shape=np.array([3]))), - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[1]]), - values=np.array([2]), - dense_shape=np.array([3]))), - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[2]]), - values=np.array([3]), - dense_shape=np.array([3]))), - ] - for i in range(3): - results = sess.run(get_next) - for component, result_component in zip(expected[i], results): - self.assertSparseValuesEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromTensorSlicesMixed(self): - """Test an dataset that represents the slices from a tuple of tensors.""" - components = (np.tile(np.array([[1], [2], [3]]), 20), - np.tile(np.array([[12], [13], [14]]), 22), - np.array([37.0, 38.0, 39.0]), - sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 0], [2, 0]]), - values=np.array([0, 0, 0]), - dense_shape=np.array([3, 1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0], [1, 1], [2, 2]]), - values=np.array([1, 2, 3]), - dense_shape=np.array([3, 3]))) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([ - tensor_shape.TensorShape(c.dense_shape[1:]) - if sparse_tensor.is_sparse(c) else c.shape[1:] for c in components - ], [shape for shape in iterator.output_shapes]) - - with self.test_session() as sess: - sess.run(init_op) - expected = [ - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([1]), - dense_shape=np.array([3]))), - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[1]]), - values=np.array([2]), - dense_shape=np.array([3]))), - (sparse_tensor.SparseTensorValue( - indices=np.array([[0]]), - values=np.array([0]), - dense_shape=np.array([1])), - sparse_tensor.SparseTensorValue( - indices=np.array([[2]]), - values=np.array([3]), - dense_shape=np.array([3]))), - ] - for i in range(3): - results = sess.run(get_next) - for component, result_component in zip( - (zip(*components[:3])[i] + expected[i]), results): - if sparse_tensor.is_sparse(component): - self.assertSparseValuesEqual(component, result_component) - else: - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromTensorSlicesWithDict(self): - components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]} - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual(dtypes.int32, iterator.output_types["foo"]) - self.assertEqual(dtypes.float32, iterator.output_types["bar"]) - self.assertEqual((), iterator.output_shapes["foo"]) - self.assertEqual((1,), iterator.output_shapes["bar"]) - - with self.test_session() as sess: - sess.run(init_op) - for i in range(3): - results = sess.run(get_next) - self.assertEqual(components["foo"][i], results["foo"]) - self.assertEqual(components["bar"][i], results["bar"]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromSparseTensorSlices(self): - """Test a dataset based on slices of a `tf.SparseTensor`.""" - st = array_ops.sparse_placeholder(dtypes.float64) - iterator = (dataset_ops.Dataset.from_sparse_tensor_slices(st) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = sparse_tensor.SparseTensor(*iterator.get_next()) - - with self.test_session() as sess: - slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []] - - # Test with sparse tensor in the appropriate order. - indices = np.array( - [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))]) - values = np.array([val for s in slices for val in s]) - dense_shape = np.array([len(slices), max(len(s) for s in slices) + 1]) - sparse_feed = sparse_tensor.SparseTensorValue(indices, values, - dense_shape) - sess.run(init_op, feed_dict={st: sparse_feed}) - for i, s in enumerate(slices): - results = sess.run(get_next) - self.assertAllEqual(s, results.values) - expected_indices = np.array( - [[j] for j in range(len(slices[i]))]).reshape([-1, 1]) - self.assertAllEqual(expected_indices, results.indices) - self.assertAllEqual(dense_shape[1:], results.dense_shape) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test with sparse tensor in the reverse order, which is not - # currently supported. - reverse_order_indices = indices[::-1, :] - reverse_order_values = values[::-1] - sparse_feed = sparse_tensor.SparseTensorValue( - reverse_order_indices, reverse_order_values, dense_shape) - with self.assertRaises(errors.UnimplementedError): - sess.run(init_op, feed_dict={st: sparse_feed}) - - # Test with an empty sparse tensor. - empty_indices = np.empty((0, 4), dtype=np.int64) - empty_values = np.empty((0,), dtype=np.float64) - empty_dense_shape = [0, 4, 37, 9] - sparse_feed = sparse_tensor.SparseTensorValue(empty_indices, empty_values, - empty_dense_shape) - sess.run(init_op, feed_dict={st: sparse_feed}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # pylint: disable=g-long-lambda,unnecessary-lambda - def testNestedStructure(self): - components = (np.array([1, 2, 3]), (np.array([4., 5.]), np.array([6., 7.])), - np.array([8, 9, 10])) - - dataset = dataset_ops.Dataset.from_tensors(components) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes) - - dataset = dataset.shuffle(10, 10) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes) - - dataset = dataset.repeat(-1) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes) - - dataset = dataset.filter(lambda x, y, z: True) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes) - - dataset = dataset.take(5) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes) - - dataset = dataset.map(lambda x, y, z: ((x, z), (y[0], y[1]))) - self.assertEquals(((dtypes.int64, dtypes.int64), - (dtypes.float64, dtypes.float64)), dataset.output_types) - self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes) - - dataset = dataset.flat_map( - lambda x, y: dataset_ops.Dataset.from_tensors(((x[0], x[1]), - (y[0], y[1]))) - ) - self.assertEquals(((dtypes.int64, dtypes.int64), - (dtypes.float64, dtypes.float64)), dataset.output_types) - self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes) - - dataset = dataset.batch(32) - self.assertEquals(((dtypes.int64, dtypes.int64), - (dtypes.float64, dtypes.float64)), dataset.output_types) - self.assertEquals((([None, 3], [None, 3]), ([None, 2], [None, 2])), - nest.pack_sequence_as(dataset.output_shapes, [ - s.as_list() - for s in nest.flatten(dataset.output_shapes) - ])) - - iterator = dataset.make_one_shot_iterator() - (w, x), (y, z) = iterator.get_next() - self.assertEquals(dtypes.int64, w.dtype) - self.assertEquals(dtypes.int64, x.dtype) - self.assertEquals(dtypes.float64, y.dtype) - self.assertEquals(dtypes.float64, z.dtype) - self.assertEquals([None, 3], w.shape.as_list()) - self.assertEquals([None, 3], x.shape.as_list()) - self.assertEquals([None, 2], y.shape.as_list()) - self.assertEquals([None, 2], z.shape.as_list()) - - iterator = dataset.make_initializable_iterator() - (w, x), (y, z) = iterator.get_next() - self.assertEquals(dtypes.int64, w.dtype) - self.assertEquals(dtypes.int64, x.dtype) - self.assertEquals(dtypes.float64, y.dtype) - self.assertEquals(dtypes.float64, z.dtype) - self.assertEquals([None, 3], w.shape.as_list()) - self.assertEquals([None, 3], x.shape.as_list()) - self.assertEquals([None, 2], y.shape.as_list()) - self.assertEquals([None, 2], z.shape.as_list()) - - # Define a separate set of components with matching leading - # dimension for the from-slices constructor. - components_for_slices = (np.array([1, 2, 3]), (np.array( - [4., 5., 6.]), np.array([7., 8., 9.])), np.array([10, 11, 12])) - - dataset = dataset_ops.Dataset.from_tensor_slices(components_for_slices) - self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64), - dtypes.int64), dataset.output_types) - self.assertEquals(([], ([], []), []), dataset.output_shapes) - - def testNestedDict(self): - components = {"a": {"aa": 1, "ab": [2.0, 2.0]}, "b": [3, 3, 3]} - dataset = dataset_ops.Dataset.from_tensors(components) - self.assertEquals(dtypes.int32, dataset.output_types["a"]["aa"]) - self.assertEquals(dtypes.float32, dataset.output_types["a"]["ab"]) - self.assertEquals(dtypes.int32, dataset.output_types["b"]) - self.assertEquals([], dataset.output_shapes["a"]["aa"]) - self.assertEquals([2], dataset.output_shapes["a"]["ab"]) - self.assertEquals([3], dataset.output_shapes["b"]) - - def testNonSequenceNestedStructure(self): - components = np.array([1, 2, 3]) - - dataset = dataset_ops.Dataset.from_tensors(components) - self.assertEquals(dtypes.int64, dataset.output_types) - self.assertEquals([3], dataset.output_shapes) - - dataset = dataset.filter( - lambda x: math_ops.reduce_all(math_ops.equal(x, components))) - self.assertEquals(dtypes.int64, dataset.output_types) - self.assertEquals([3], dataset.output_shapes) - - dataset = dataset.map(lambda x: array_ops.stack([x, x])) - self.assertEquals(dtypes.int64, dataset.output_types) - self.assertEquals([2, 3], dataset.output_shapes) - - dataset = dataset.flat_map( - lambda x: dataset_ops.Dataset.from_tensor_slices(x)) - self.assertEquals(dtypes.int64, dataset.output_types) - self.assertEquals([3], dataset.output_shapes) - - iterator = dataset.make_one_shot_iterator() - get_next = iterator.get_next() - self.assertEquals(dtypes.int64, get_next.dtype) - self.assertEquals([3], get_next.shape) - - def _testFromGenerator(self, generator, elem_sequence, num_repeats): - iterator = ( - dataset_ops.Dataset.from_generator(generator, output_types=dtypes.int64) - .repeat(num_repeats) - .prefetch(5) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - for _ in range(2): # Run twice to test reinitialization. - sess.run(init_op) - for _ in range(num_repeats): - for elem in elem_sequence: - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def _testFromGeneratorOneShot(self, generator, elem_sequence, num_repeats): - iterator = ( - dataset_ops.Dataset.from_generator(generator, output_types=dtypes.int64) - .repeat(num_repeats) - .prefetch(5) - .make_one_shot_iterator()) - get_next = iterator.get_next() - - with self.test_session() as sess: - for _ in range(num_repeats): - for elem in elem_sequence: - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromGeneratorUsingFunction(self): - def generator(): - for i in range(1, 100): - yield [i] * i - elem_sequence = list(generator()) - self._testFromGenerator(generator, elem_sequence, 1) - self._testFromGenerator(generator, elem_sequence, 5) - self._testFromGeneratorOneShot(generator, elem_sequence, 1) - self._testFromGeneratorOneShot(generator, elem_sequence, 5) - - def testFromGeneratorUsingList(self): - generator = lambda: [[i] * i for i in range(1, 100)] - elem_sequence = list(generator()) - self._testFromGenerator(generator, elem_sequence, 1) - self._testFromGenerator(generator, elem_sequence, 5) - - def testFromGeneratorUsingNdarray(self): - generator = lambda: np.arange(100, dtype=np.int64) - elem_sequence = list(generator()) - self._testFromGenerator(generator, elem_sequence, 1) - self._testFromGenerator(generator, elem_sequence, 5) - - def testFromGeneratorUsingGeneratorExpression(self): - # NOTE(mrry): Generator *expressions* are not repeatable (or in - # general reusable), because they eagerly evaluate the `for` - # expression as `iter(range(1, 100))` and discard the means of - # reconstructing `range(1, 100)`. Wrapping the generator - # expression in a `lambda` makes it repeatable. - generator = lambda: ([i] * i for i in range(1, 100)) - elem_sequence = list(generator()) - self._testFromGenerator(generator, elem_sequence, 1) - self._testFromGenerator(generator, elem_sequence, 5) - - def testFromMultipleConcurrentGenerators(self): - num_inner_repeats = 5 - num_outer_repeats = 100 - - def generator(): - for i in range(1, 10): - yield ([i] * i, [i, i ** 2, i ** 3]) - input_list = list(generator()) - - # The interleave transformation is essentially a flat map that - # draws from multiple input datasets concurrently (in a cyclic - # fashion). By placing `Datsaet.from_generator()` inside an - # interleave, we test its behavior when multiple iterators are - # active at the same time; by additionally prefetching inside the - # interleave, we create the possibility of parallel (modulo GIL) - # invocations to several iterators created by the same dataset. - def interleave_fn(_): - return (dataset_ops.Dataset.from_generator( - generator, output_types=(dtypes.int64, dtypes.int64), - output_shapes=([None], [3])) - .repeat(num_inner_repeats).prefetch(5)) - - iterator = ( - dataset_ops.Dataset.range(num_outer_repeats) - .interleave(interleave_fn, cycle_length=10, - block_length=len(input_list)) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for _ in range(num_inner_repeats * num_outer_repeats): - for elem in input_list: - val0, val1 = sess.run(get_next) - self.assertAllEqual(elem[0], val0) - self.assertAllEqual(elem[1], val1) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromGeneratorsRunningInParallel(self): - num_parallel_iterators = 3 - - # Define shared state that multiple iterator instances will access to - # demonstrate their concurrent activity. - lock = threading.Lock() - condition = threading.Condition(lock) - next_ticket = [0] # GUARDED_BY(lock) - - def generator(): - # NOTE(mrry): We yield one element before the barrier, because - # the current implementation of `Dataset.interleave()` must - # fetch one element from each incoming dataset to start the - # prefetching. - yield 0 - - # Define a barrier that `num_parallel_iterators` iterators must enter - # before any can proceed. Demonstrates that multiple iterators may be - # active at the same time. - condition.acquire() - ticket = next_ticket[0] - next_ticket[0] += 1 - if ticket == num_parallel_iterators - 1: - # The last iterator to join the barrier notifies the others. - condition.notify_all() - else: - # Wait until the last iterator enters the barrier. - while next_ticket[0] < num_parallel_iterators: - condition.wait() - condition.release() - - yield 1 - - # As in `testFromMultipleConcurrentGenerators()`, we use a combination of - # `Dataset.interleave()` and `Dataset.prefetch()` to cause multiple - # iterators to be active concurrently. - def interleave_fn(_): - return dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64, output_shapes=[]).prefetch(2) - - iterator = ( - dataset_ops.Dataset.range(num_parallel_iterators) - .interleave( - interleave_fn, cycle_length=num_parallel_iterators, block_length=1) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for elem in [0, 1]: - for _ in range(num_parallel_iterators): - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromGeneratorImplicitConversion(self): - def generator(): - yield [1] - yield [2] - yield [3] - - for dtype in [dtypes.int8, dtypes.int32, dtypes.int64]: - iterator = (dataset_ops.Dataset.from_generator( - generator, output_types=dtype, output_shapes=[1]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual(dtype, get_next.dtype) - - with self.test_session() as sess: - sess.run(init_op) - for expected in [[1], [2], [3]]: - next_val = sess.run(get_next) - self.assertEqual(dtype.as_numpy_dtype, next_val.dtype) - self.assertAllEqual(expected, next_val) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromGeneratorTypeError(self): - def generator(): - yield np.array([1, 2, 3], dtype=np.int64) - yield np.array([4, 5, 6], dtype=np.int64) - yield "ERROR" - yield np.array([7, 8, 9], dtype=np.int64) - - iterator = (dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64, output_shapes=[3]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - self.assertAllEqual([4, 5, 6], sess.run(get_next)) - with self.assertRaisesOpError(r"invalid literal for long\(\)"): - sess.run(get_next) - self.assertAllEqual([7, 8, 9], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFromGeneratorShapeError(self): - def generator(): - yield np.array([1, 2, 3], dtype=np.int64) - yield np.array([4, 5, 6], dtype=np.int64) - yield np.array([7, 8, 9, 10], dtype=np.int64) - yield np.array([11, 12, 13], dtype=np.int64) - - iterator = (dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64, output_shapes=[3]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - self.assertAllEqual([4, 5, 6], sess.run(get_next)) - with self.assertRaisesOpError(r"element of shape \(3,\) was expected"): - sess.run(get_next) - self.assertAllEqual([11, 12, 13], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testSplitPipelineFailsWithPlacementError(self): - with session.Session( - target="", - config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess: - - dataset = dataset_ops.Dataset.from_tensors(0) - - # Define a pipeline that attempts to use variables on two - # different devices. - # - # Initialize the variables before creating to iterator, to avoid the - # placement algorithm overriding the DT_RESOURCE colocation constraints. - with ops.device("/cpu:0"): - var_0 = resource_variable_ops.ResourceVariable(initial_value=0) - dataset = dataset.map(lambda x: x + var_0.read_value()) - sess.run(var_0.initializer) - - with ops.device("/cpu:1"): - var_1 = resource_variable_ops.ResourceVariable(initial_value=0) - dataset = dataset.map(lambda x: x + var_1.read_value()) - sess.run(var_1.initializer) - - iterator = dataset.make_initializable_iterator() - sess.run(iterator.initializer) - - with self.assertRaisesRegexp( - errors.FailedPreconditionError, - "Error while reading resource variable Variable"): - sess.run(iterator.get_next()) - def testRestructureDataset(self): components = (array_ops.placeholder(dtypes.int32), (array_ops.placeholder(dtypes.int32, shape=[None]), diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py index 06883934d044c2c5faf467dd1708b858a2f8f9ab..b572d6ed770fc0fe0f852359baf343c55966eddd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py @@ -20,144 +20,12 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import functional_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class FilterDatasetTest(test.TestCase): - - def testFilterDataset(self): - components = ( - np.arange(7, dtype=np.int64), - np.array([[1, 2, 3]], dtype=np.int64) * np.arange( - 7, dtype=np.int64)[:, np.newaxis], - np.array(37.0, dtype=np.float64) * np.arange(7) - ) - count = array_ops.placeholder(dtypes.int64, shape=[]) - modulus = array_ops.placeholder(dtypes.int64) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(count) - .filter(lambda x, _y, _z: math_ops.equal(math_ops.mod(x, modulus), 0)) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - # Test that we can dynamically feed a different modulus value for each - # iterator. - def do_test(count_val, modulus_val): - sess.run(init_op, feed_dict={count: count_val, modulus: modulus_val}) - for _ in range(count_val): - for i in [x for x in range(7) if x**2 % modulus_val == 0]: - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - do_test(14, 2) - do_test(4, 18) - - # Test an empty dataset. - do_test(0, 1) - - def testFilterRange(self): - dataset = dataset_ops.Dataset.range(100).filter( - lambda x: math_ops.not_equal(math_ops.mod(x, 3), 2)) - iterator = dataset.make_one_shot_iterator() - get_next = iterator.get_next() - - with self.test_session() as sess: - self.assertEqual(0, sess.run(get_next)) - self.assertEqual(1, sess.run(get_next)) - self.assertEqual(3, sess.run(get_next)) - - def testFilterDict(self): - iterator = (dataset_ops.Dataset.range(10) - .map(lambda x: {"foo": x * 2, "bar": x ** 2}) - .filter(lambda d: math_ops.equal(d["bar"] % 2, 0)) - .map(lambda d: d["foo"] + d["bar"]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - if (i ** 2) % 2 == 0: - self.assertEqual(i * 2 + i ** 2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testUseStepContainerInFilter(self): - input_data = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64) - - # Define a predicate that returns true for the first element of - # the sequence and not the second, and uses `tf.map_fn()`. - def _predicate(xs): - squared_xs = functional_ops.map_fn(lambda x: x * x, xs) - summed = math_ops.reduce_sum(squared_xs) - return math_ops.equal(summed, 1 + 4 + 9) - - iterator = ( - dataset_ops.Dataset.from_tensor_slices([[1, 2, 3], [4, 5, 6]]) - .filter(_predicate) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - self.assertAllEqual(input_data[0], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def assertSparseValuesEqual(self, a, b): - self.assertAllEqual(a.indices, b.indices) - self.assertAllEqual(a.values, b.values) - self.assertAllEqual(a.dense_shape, b.dense_shape) - - def testSparse(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0]]), - values=(i * np.array([1])), - dense_shape=np.array([1, 1])), i - - def _filter_fn(_, i): - return math_ops.equal(i % 2, 0) - - iterator = ( - dataset_ops.Dataset.range(10).map(_map_fn).filter(_filter_fn).map( - lambda x, i: x).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(5): - actual = sess.run(get_next) - self.assertTrue(isinstance(actual, sparse_tensor.SparseTensorValue)) - self.assertSparseValuesEqual(actual, _map_fn(i * 2)[0]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - class FilterDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py index 86d69495ef47da0bc93b8d9b1299e552fc676ee1..f3feecef32e587045be25056815315136a883ca7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py @@ -17,13 +17,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import random - -import numpy as np - from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.client import session +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -34,124 +29,6 @@ from tensorflow.python.ops import random_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test -from tensorflow.python.training import server_lib - - -class FlatMapDatasetTest(test.TestCase): - - # pylint: disable=g-long-lambda - def testFlatMapDataset(self): - repeats = [1, 2, 3, 4, 5, 0, 1] - components = np.array(repeats, dtype=np.int64) - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components) - .flat_map(lambda x: dataset_ops.Dataset.from_tensors([x]).repeat(x)) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in repeats: - for _ in range(i): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testNestedFlatMapDataset(self): - repeats = [[1, 2], [3, 4], [5, 0], [1, 7]] - components = np.array(repeats, dtype=np.int64) - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components) - .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x) - .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y) - .repeat(y))).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for row in repeats: - for i in row: - for _ in range(i): - self.assertEqual(i, sess.run(get_next)) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testSharedResourceNestedFlatMapDataset(self): - repeats = [[1, 2], [3, 4], [5, 0], [1, 7]] - components = np.array(repeats, dtype=np.int64) - iterator = ( - dataset_ops.Dataset.from_tensor_slices(components) - .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x) - .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y) - .repeat(y))).make_initializable_iterator( - shared_name="shared_flat_map_iterator")) - init_op = iterator.initializer - get_next = iterator.get_next() - - # Create two concurrent sessions that share the same iterator - # resource on the same server, and verify that a random - # interleaving of `Session.run(get_next)` calls on the two - # sessions yields the expected result. - server = server_lib.Server.create_local_server() - with session.Session(server.target) as sess1: - with session.Session(server.target) as sess2: - for _ in range(3): - sess = random.choice([sess1, sess2]) - sess.run(init_op) - for row in repeats: - for i in row: - for _ in range(i): - sess = random.choice([sess1, sess2]) - self.assertEqual(i, sess.run(get_next)) - - with self.assertRaises(errors.OutOfRangeError): - sess = random.choice([sess1, sess2]) - sess.run(get_next) - - def testMapDict(self): - iterator = (dataset_ops.Dataset.range(10) - .map(lambda x: {"foo": x * 2, "bar": x ** 2}) - .flat_map(lambda d: dataset_ops.Dataset.from_tensors(d["foo"]) - .repeat(d["bar"])) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - for _ in range(i ** 2): - self.assertEqual(i * 2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - # pylint: enable=g-long-lambda - - def testSparse(self): - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _flat_map_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - iterator = ( - dataset_ops.Dataset.range(10).map(_map_fn).flat_map(_flat_map_fn) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - for j in range(2): - expected = [i, 0] if j % 2 == 0 else [0, -i] - self.assertAllEqual(expected, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) class FlatMapDatasetSerializationTest( diff --git a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py new file mode 100644 index 0000000000000000000000000000000000000000..32ea44f7c7ba329dc253bb9fbbcac0a1ed16aec7 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py @@ -0,0 +1,58 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the experimental input pipeline ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.ops import get_single_element +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class GetSingleElementTest(test.TestCase): + + def testGetSingleElement(self): + skip_value = array_ops.placeholder(dtypes.int64, shape=[]) + take_value = array_ops.placeholder_with_default( + constant_op.constant(1, dtype=dtypes.int64), shape=[]) + + dataset = (dataset_ops.Dataset.range(100) + .skip(skip_value) + .map(lambda x: x * x) + .take(take_value)) + + element = get_single_element.get_single_element(dataset) + + with self.test_session() as sess: + self.assertEqual(0, sess.run(element, feed_dict={skip_value: 0})) + self.assertEqual(25, sess.run(element, feed_dict={skip_value: 5})) + self.assertEqual(100, sess.run(element, feed_dict={skip_value: 10})) + + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "Dataset was empty."): + sess.run(element, feed_dict={skip_value: 100}) + + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "Dataset had more than one element."): + sess.run(element, feed_dict={skip_value: 0, take_value: 2}) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py index db8429512bf2bf944e67b65d185aca99477c86d3..256ad8d94dc1a7c2b26df3f1ebf8e8e321882c15 100644 --- a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py @@ -26,8 +26,8 @@ import numpy as np from six.moves import zip_longest from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import sparse_tensor @@ -38,182 +38,7 @@ from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test -class InterleaveDatasetTest(test.TestCase): - - def _interleave(self, lists, cycle_length, block_length): - # TODO(b/69678297): Consolidate python interleave implementations. - num_open = 0 - - # `all_iterators` acts as a queue of iterators over each element of `lists`. - all_iterators = [iter(l) for l in lists] - - # `open_iterators` are the iterators whose elements are currently being - # interleaved. - open_iterators = [] - for i in range(cycle_length): - if all_iterators: - open_iterators.append(all_iterators.pop(0)) - num_open += 1 - else: - open_iterators.append(None) - - while num_open or all_iterators: - for i in range(cycle_length): - if open_iterators[i] is None: - if all_iterators: - open_iterators[i] = all_iterators.pop(0) - num_open += 1 - else: - continue - for _ in range(block_length): - try: - yield next(open_iterators[i]) - except StopIteration: - open_iterators[i] = None - num_open -= 1 - break - - def testPythonImplementation(self): - input_lists = [[4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6], - [4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]] - - # Cycle length 1 acts like `Dataset.flat_map()`. - expected_elements = itertools.chain(*input_lists) - for expected, produced in zip( - expected_elements, self._interleave(input_lists, 1, 1)): - self.assertEqual(expected, produced) - - # Cycle length > 1. - expected_elements = [4, 5, 4, 5, 4, 5, 4, - 5, 5, 6, 6, # NOTE(mrry): When we cycle back - # to a list and are already at - # the end of that list, we move - # on to the next element. - 4, 6, 4, 6, 4, 6, 4, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5] - for expected, produced in zip( - expected_elements, self._interleave(input_lists, 2, 1)): - self.assertEqual(expected, produced) - - # Cycle length > 1 and block length > 1. - expected_elements = [4, 4, 4, 5, 5, 5, 4, 5, 5, 6, 6, 6, 4, 4, 4, 6, 6, 6, - 4, 5, 5, 5, 6, 6, 6, 5, 5, 6, 6, 6] - for expected, produced in zip( - expected_elements, self._interleave(input_lists, 2, 3)): - self.assertEqual(expected, produced) - - # Cycle length > len(input_values). - expected_elements = [4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, - 4, 4, 5, 5, 6, 6, 5, 6, 6, 5, 6, 6] - for expected, produced in zip( - expected_elements, self._interleave(input_lists, 7, 2)): - self.assertEqual(expected, produced) - - def testInterleaveDataset(self): - input_values = array_ops.placeholder(dtypes.int64, shape=[None]) - cycle_length = array_ops.placeholder(dtypes.int64, shape=[]) - block_length = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_count = 2 - - dataset = ( - dataset_ops.Dataset.from_tensor_slices(input_values) - .repeat(repeat_count) - .interleave(lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x), - cycle_length, block_length)) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - next_element = iterator.get_next() - - with self.test_session() as sess: - # Cycle length 1 acts like `Dataset.flat_map()`. - sess.run(init_op, feed_dict={input_values: [4, 5, 6], - cycle_length: 1, block_length: 3}) - - for expected_element in self._interleave( - [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 1, 3): - self.assertEqual(expected_element, sess.run(next_element)) - - # Cycle length > 1. - # expected: [4, 5, 4, 5, 4, 5, 4, 5, 5, 6, 6, 4, 6, 4, 6, 4, 6, 4, 6, 5, - # 6, 5, 6, 5, 6, 5, 6, 5] - sess.run(init_op, feed_dict={input_values: [4, 5, 6], - cycle_length: 2, block_length: 1}) - for expected_element in self._interleave( - [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 2, 1): - self.assertEqual(expected_element, sess.run(next_element)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Cycle length > 1 and block length > 1. - # expected: [4, 4, 4, 5, 5, 5, 4, 5, 5, 6, 6, 6, 4, 4, 4, 6, 6, 6, 4, 5, - # 5, 5, 6, 6, 6, 5, 5, 6, 6, 6] - sess.run(init_op, feed_dict={input_values: [4, 5, 6], - cycle_length: 2, block_length: 3}) - for expected_element in self._interleave( - [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 2, 3): - self.assertEqual(expected_element, sess.run(next_element)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Cycle length > len(input_values) * repeat_count. - # expected: [4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4, - # 5, 5, 6, 6, 5, 6, 6, 5, 6, 6] - sess.run(init_op, feed_dict={input_values: [4, 5, 6], - cycle_length: 7, block_length: 2}) - for expected_element in self._interleave( - [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 7, 2): - self.assertEqual(expected_element, sess.run(next_element)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Empty input. - sess.run(init_op, feed_dict={input_values: [], - cycle_length: 2, block_length: 3}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Non-empty input leading to empty output. - sess.run(init_op, feed_dict={input_values: [0, 0, 0], - cycle_length: 2, block_length: 3}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Mixture of non-empty and empty interleaved datasets. - sess.run(init_op, feed_dict={input_values: [4, 0, 6], - cycle_length: 2, block_length: 3}) - for expected_element in self._interleave( - [[4] * 4, [], [6] * 6] * repeat_count, 2, 3): - self.assertEqual(expected_element, sess.run(next_element)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - def testSparse(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _interleave_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - iterator = ( - dataset_ops.Dataset.range(10).map(_map_fn).interleave( - _interleave_fn, cycle_length=1).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - for j in range(2): - expected = [i, 0] if j % 2 == 0 else [0, -i] - self.assertAllEqual(expected, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - -class InterleaveDatasetSeriazationTest( +class InterleaveDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def _build_iterator_graph(self, input_values, cycle_length, block_length): diff --git a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_cluster_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_cluster_test.py deleted file mode 100644 index 02379d064d4ab857ce9c7d13881a3ae37eea0980..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_cluster_test.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for the experimental input pipeline ops that need test_util.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session -from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.framework import function -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import functional_ops -from tensorflow.python.platform import test - - -class IteratorClusterTest(test.TestCase): - - def testRemoteIteratorWithoutRemoteCallFail(self): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 - worker, _ = test_util.create_local_cluster( - 1, 1, worker_config=worker_config) - - with ops.device("/job:worker/replica:0/task:0/cpu:1"): - dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) - iterator_3 = dataset_3.make_one_shot_iterator() - iterator_3_handle = iterator_3.string_handle() - - with ops.device("/job:worker/replica:0/task:0/cpu:0"): - remote_it = iterator_ops.Iterator.from_string_handle( - iterator_3_handle, dataset_3.output_types, dataset_3.output_shapes) - get_next_op = remote_it.get_next() - - with session.Session(worker[0].target) as sess: - with self.assertRaises(errors.InvalidArgumentError): - sess.run(get_next_op) - - def _testRemoteIteratorHelper(self, device0, device1, target): - with ops.device(device1): - dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) - iterator_3 = dataset_3.make_one_shot_iterator() - iterator_3_handle = iterator_3.string_handle() - - @function.Defun(dtypes.string) - def _remote_fn(h): - remote_iterator = iterator_ops.Iterator.from_string_handle( - h, dataset_3.output_types, dataset_3.output_shapes) - return remote_iterator.get_next() - - with ops.device(device0): - target_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - remote_op = functional_ops.remote_call( - args=[iterator_3_handle], - Tout=[dtypes.int32], - f=_remote_fn, - target=target_placeholder) - - with session.Session(target) as sess: - elem = sess.run(remote_op, feed_dict={target_placeholder: device1}) - self.assertEqual(elem, [1]) - # Fails when target is cpu:0 where the resource is not located. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(remote_op, feed_dict={target_placeholder: device0}) - elem = sess.run(iterator_3.get_next()) - self.assertEqual(elem, [2]) - elem = sess.run(remote_op, feed_dict={target_placeholder: device1}) - self.assertEqual(elem, [3]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(remote_op, feed_dict={target_placeholder: device1}) - - def testRemoteIteratorUsingRemoteCallOp(self): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 - worker, _ = test_util.create_local_cluster( - 1, 1, worker_config=worker_config) - - self._testRemoteIteratorHelper("/job:worker/replica:0/task:0/cpu:0", - "/job:worker/replica:0/task:0/cpu:1", - worker[0].target) - - def testRemoteIteratorUsingRemoteCallOpCrossProcess(self): - workers, _ = test_util.create_local_cluster(2, 1) - - self._testRemoteIteratorHelper("/job:worker/replica:0/task:0/cpu:0", - "/job:worker/replica:0/task:1/cpu:0", - workers[0].target) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py deleted file mode 100644 index bda9a2a4a37e9c3d35ff99041d1150ffc43f4c43..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py +++ /dev/null @@ -1,625 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for the experimental input pipeline ops.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import numpy as np - -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.contrib.data.python.ops import readers -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session -from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.framework import function -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import functional_ops -from tensorflow.python.ops import gen_dataset_ops -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import io_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import parsing_ops -from tensorflow.python.ops import script_ops -from tensorflow.python.platform import test -from tensorflow.python.training import server_lib - - -class IteratorTest(test.TestCase): - - def testAttemptingGradientsRaiseExceptions(self): - component = constant_op.constant([1]) - side = constant_op.constant(0) - add = lambda x: x + side - dataset = dataset_ops.Dataset.from_tensor_slices(component).map(add) - value = dataset.make_one_shot_iterator().get_next() - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, component) - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, side) - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, [component, side]) - - def testOneShotIterator(self): - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - iterator = (dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(14).make_one_shot_iterator()) - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testOneShotIteratorCaptureByValue(self): - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - tensor_components = tuple([ops.convert_to_tensor(c) for c in components]) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - iterator = (dataset_ops.Dataset.from_tensor_slices(tensor_components) - .map(_map_fn).repeat(14).make_one_shot_iterator()) - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testOneShotIteratorInsideContainer(self): - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - - def within_container(): - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .map(_map_fn).repeat(14).make_one_shot_iterator()) - return iterator.get_next() - - server = server_lib.Server.create_local_server() - - # Create two iterators within unique containers, and run them to - # make sure that the resources aren't shared. - # - # The test below would fail if cname were the same across both - # sessions. - for i in range(2): - with session.Session(server.target) as sess: - cname = "iteration%d" % i - with ops.container(cname): - get_next = within_container() - - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testOneShotIteratorNonBlocking(self): - dataset = dataset_ops.Dataset.from_tensors([1, 2, 3]).map(lambda x: x * x) - iterator = dataset.make_one_shot_iterator() - next_element = iterator.get_next() - - # Create a session with a single thread to ensure that the - # one-shot iterator initializer does not deadlock. - config = config_pb2.ConfigProto(inter_op_parallelism_threads=1, - use_per_session_threads=True) - with session.Session(config=config) as sess: - self.assertAllEqual([1, 4, 9], sess.run(next_element)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element) - - # Test with multiple threads invoking the one-shot iterator concurrently. - with session.Session(config=config) as sess: - results = [] - def consumer_thread(): - try: - results.append(sess.run(next_element)) - except errors.OutOfRangeError: - results.append(None) - - num_threads = 8 - threads = [ - self.checkedThread(consumer_thread) for _ in range(num_threads)] - for t in threads: - t.start() - for t in threads: - t.join() - - self.assertEqual(num_threads, len(results)) - self.assertEqual(num_threads - 1, - len([None for r in results if r is None])) - self.assertAllEqual([[1, 4, 9]], [r for r in results if r is not None]) - - def testOneShotIteratorInitializerFails(self): - # Define a dataset whose initialization will always fail. - dataset = dataset_ops.Dataset.from_tensors( - array_ops.check_numerics( - constant_op.constant(1.0) / constant_op.constant(0.0), "oops")) - iterator = dataset.make_one_shot_iterator() - next_element = iterator.get_next() - - with self.test_session() as sess: - with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): - sess.run(next_element) - - # Test that subsequent attempts to use the iterator also fail. - with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): - sess.run(next_element) - - with self.test_session() as sess: - def consumer_thread(): - with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): - sess.run(next_element) - - num_threads = 8 - threads = [ - self.checkedThread(consumer_thread) for _ in range(num_threads)] - for t in threads: - t.start() - for t in threads: - t.join() - - def testSimpleSharedResource(self): - components = ( - np.array(1, dtype=np.int64), - np.array([1, 2, 3], dtype=np.int64), - np.array(37.0, dtype=np.float64) - ) - - server = server_lib.Server.create_local_server() - - # Create two non-overlapping sessions that share the same iterator - # resource on the same server, and verify that an action of the - # first session (initializing the iterator) is visible in the - # second session. - with ops.Graph().as_default(): - iterator = (dataset_ops.Dataset.from_tensors(components) - .map(lambda x, y, z: (x, y, z)).make_initializable_iterator( - shared_name="shared_iterator")) - init_op = iterator.initializer - get_next = iterator.get_next() - - with session.Session(server.target) as sess: - sess.run(init_op) - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Re-initialize the iterator in the first session. - sess.run(init_op) - - with ops.Graph().as_default(): - # Re-define the iterator manually, without defining any of the - # functions in this graph, to ensure that we are not - # accidentally redefining functions with the same names in the - # new graph. - iterator = iterator_ops.Iterator.from_structure( - shared_name="shared_iterator", - output_types=(dtypes.int64, dtypes.int64, dtypes.float64), - output_shapes=([], [3], [])) - get_next = iterator.get_next() - - with session.Session(server.target) as sess: - # Use the iterator without re-initializing in the second session. - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testNotInitializedError(self): - components = (np.array(1), np.array([1, 2, 3]), np.array(37.0)) - iterator = (dataset_ops.Dataset.from_tensors(components) - .make_initializable_iterator()) - get_next = iterator.get_next() - - with self.test_session() as sess: - with self.assertRaisesRegexp(errors.FailedPreconditionError, - "iterator has not been initialized"): - sess.run(get_next) - - def testReinitializableIterator(self): - dataset_3 = dataset_ops.Dataset.from_tensors( - constant_op.constant([1, 2, 3])) - dataset_4 = dataset_ops.Dataset.from_tensors( - constant_op.constant([4, 5, 6, 7])) - iterator = iterator_ops.Iterator.from_structure(dataset_3.output_types, - [None]) - - dataset_3_init_op = iterator.make_initializer(dataset_3) - dataset_4_init_op = iterator.make_initializer(dataset_4) - get_next = iterator.get_next() - - self.assertEqual(dataset_3.output_types, iterator.output_types) - self.assertEqual(dataset_4.output_types, iterator.output_types) - self.assertEqual([None], iterator.output_shapes.as_list()) - - with self.test_session() as sess: - # The iterator is initially uninitialized. - with self.assertRaises(errors.FailedPreconditionError): - sess.run(get_next) - - # Initialize with one dataset. - sess.run(dataset_3_init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Initialize with a different dataset. - sess.run(dataset_4_init_op) - self.assertAllEqual([4, 5, 6, 7], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Reinitialize with the first dataset. - sess.run(dataset_3_init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testReinitializableIteratorStaticErrors(self): - # Non-matching structure for types and shapes. - with self.assertRaises(TypeError): - iterator = iterator_ops.Iterator.from_structure((dtypes.int64, - dtypes.float64), [None]) - - # Test validation of dataset argument. - iterator = iterator_ops.Iterator.from_structure((dtypes.int64, - dtypes.float64)) - - # Incompatible structure. - with self.assertRaises(ValueError): - iterator.make_initializer( - dataset_ops.Dataset.from_tensors(((constant_op.constant( - [1, 2, 3], dtype=dtypes.int64),), (constant_op.constant( - [4., 5., 6., 7.], dtype=dtypes.float64),)))) - - # Incompatible types. - with self.assertRaises(TypeError): - iterator.make_initializer( - dataset_ops.Dataset.from_tensors((constant_op.constant( - [1, 2, 3], dtype=dtypes.int32), constant_op.constant( - [4., 5., 6., 7.], dtype=dtypes.float32)))) - - # Incompatible shapes. - iterator = iterator_ops.Iterator.from_structure( - (dtypes.int64, dtypes.float64), ([None], [])) - with self.assertRaises(TypeError): - iterator.make_initializer( - dataset_ops.Dataset.from_tensors((constant_op.constant( - [1, 2, 3], dtype=dtypes.int64), constant_op.constant( - [4., 5., 6., 7.], dtype=dtypes.float64)))) - - def testIteratorStringHandle(self): - dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) - dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40]) - - iterator_3 = dataset_3.make_one_shot_iterator() - iterator_4 = dataset_4.make_one_shot_iterator() - - handle_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - feedable_iterator = iterator_ops.Iterator.from_string_handle( - handle_placeholder, dataset_3.output_types, dataset_3.output_shapes) - next_element = feedable_iterator.get_next() - - self.assertEqual(dataset_3.output_types, feedable_iterator.output_types) - self.assertEqual(dataset_4.output_types, feedable_iterator.output_types) - self.assertEqual([], feedable_iterator.output_shapes) - - with self.test_session() as sess: - iterator_3_handle = sess.run(iterator_3.string_handle()) - iterator_4_handle = sess.run(iterator_4.string_handle()) - - self.assertEqual( - 10, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 1, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 20, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 2, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 30, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 3, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 40, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle}) - - def testIteratorStringHandleError(self): - dataset_int_scalar = (dataset_ops.Dataset.from_tensor_slices([1, 2, - 3]).repeat()) - dataset_float_vector = (dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0])) - - handle_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - - feedable_int_scalar = iterator_ops.Iterator.from_string_handle( - handle_placeholder, dtypes.int32, []) - feedable_int_vector = iterator_ops.Iterator.from_string_handle( - handle_placeholder, dtypes.int32, [None]) - feedable_int_any = iterator_ops.Iterator.from_string_handle( - handle_placeholder, dtypes.int32) - - with self.test_session() as sess: - handle_int_scalar = sess.run( - dataset_int_scalar.make_one_shot_iterator().string_handle()) - handle_float_vector = sess.run( - dataset_float_vector.make_one_shot_iterator().string_handle()) - - self.assertEqual(1, - sess.run( - feedable_int_scalar.get_next(), - feed_dict={handle_placeholder: handle_int_scalar})) - - self.assertEqual(2, - sess.run( - feedable_int_any.get_next(), - feed_dict={handle_placeholder: handle_int_scalar})) - - with self.assertRaises(errors.InvalidArgumentError): - print(sess.run( - feedable_int_vector.get_next(), - feed_dict={handle_placeholder: handle_int_scalar})) - - with self.assertRaises(errors.InvalidArgumentError): - print(sess.run( - feedable_int_vector.get_next(), - feed_dict={handle_placeholder: handle_float_vector})) - - def testRemoteIteratorUsingRemoteCallOpDirectSession(self): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 3 - - with ops.device("/job:localhost/replica:0/task:0/cpu:1"): - dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) - iterator_3 = dataset_3.make_one_shot_iterator() - iterator_3_handle = iterator_3.string_handle() - - @function.Defun(dtypes.string) - def _remote_fn(h): - remote_iterator = iterator_ops.Iterator.from_string_handle( - h, dataset_3.output_types, dataset_3.output_shapes) - return remote_iterator.get_next() - - with ops.device("/job:localhost/replica:0/task:0/cpu:0"): - target_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - remote_op = functional_ops.remote_call( - args=[iterator_3_handle], - Tout=[dtypes.int32], - f=_remote_fn, - target=target_placeholder) - - with self.test_session(config=worker_config) as sess: - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:1" - }) - self.assertEqual(elem, [1]) - # Fails when target is cpu:2 where the resource is not located. - with self.assertRaises(errors.InvalidArgumentError): - sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:2" - }) - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:1" - }) - self.assertEqual(elem, [2]) - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:1" - }) - self.assertEqual(elem, [3]) - with self.assertRaises(errors.OutOfRangeError): - sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:1" - }) - - def testRemoteIteratorUsingRemoteCallOpDirectSessionGPUCPU(self): - if not test_util.is_gpu_available(): - self.skipTest("No GPU available") - - with ops.device("/job:localhost/replica:0/task:0/cpu:0"): - dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) - iterator_3 = dataset_3.make_one_shot_iterator() - iterator_3_handle = iterator_3.string_handle() - - def _encode_raw(byte_array): - return bytes(bytearray(byte_array)) - - @function.Defun(dtypes.uint8) - def _remote_fn(h): - handle = script_ops.py_func(_encode_raw, [h], dtypes.string) - remote_iterator = iterator_ops.Iterator.from_string_handle( - handle, dataset_3.output_types, dataset_3.output_shapes) - return remote_iterator.get_next() - - with ops.device("/job:localhost/replica:0/task:0/device:GPU:0"): - target_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - iterator_3_handle_uint8 = parsing_ops.decode_raw( - bytes=iterator_3_handle, out_type=dtypes.uint8) - remote_op = functional_ops.remote_call( - args=[iterator_3_handle_uint8], - Tout=[dtypes.int32], - f=_remote_fn, - target=target_placeholder) - - with self.test_session() as sess: - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:0" - }) - self.assertEqual(elem, [1]) - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:0" - }) - self.assertEqual(elem, [2]) - elem = sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:0" - }) - self.assertEqual(elem, [3]) - with self.assertRaises(errors.OutOfRangeError): - sess.run( - remote_op, - feed_dict={ - target_placeholder: "/job:localhost/replica:0/task:0/cpu:0" - }) - - def testIncorrectIteratorRestore(self): - - def _path(): - return os.path.join(self.get_temp_dir(), "iterator") - - def _save_op(iterator_resource): - iterator_state_variant = gen_dataset_ops.serialize_iterator( - iterator_resource) - save_op = io_ops.write_file( - _path(), parsing_ops.serialize_tensor(iterator_state_variant)) - return save_op - - def _restore_op(iterator_resource): - iterator_state_variant = parsing_ops.parse_tensor( - io_ops.read_file(_path()), dtypes.variant) - restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource, - iterator_state_variant) - return restore_op - - def _build_range_dataset_graph(): - start = 1 - stop = 10 - iterator = dataset_ops.Dataset.range(start, - stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - save_op = _save_op(iterator._iterator_resource) - restore_op = _restore_op(iterator._iterator_resource) - return init_op, get_next, save_op, restore_op - - def _build_reader_dataset_graph(): - filenames = ["test"] # Does not exist but we don't care in this test. - iterator = readers.FixedLengthRecordDataset( - filenames, 1, 0, 0).make_initializable_iterator() - init_op = iterator.initializer - get_next_op = iterator.get_next() - save_op = _save_op(iterator._iterator_resource) - restore_op = _restore_op(iterator._iterator_resource) - return init_op, get_next_op, save_op, restore_op - - # Saving iterator for RangeDataset graph. - with ops.Graph().as_default() as g: - init_op, _, save_op, _ = _build_range_dataset_graph() - with self.test_session(graph=g) as sess: - sess.run(init_op) - sess.run(save_op) - - # Attempt to restore the saved iterator into an IteratorResource of - # incompatible type. An iterator of RangeDataset has output type int64, - # while an iterator of FixedLengthRecordDataset has output type string. - # So an InvalidArgumentError should be raised by - # IteratorResource::set_iterator. - with ops.Graph().as_default() as g: - _, _, _, restore_op = _build_reader_dataset_graph() - with self.test_session(graph=g) as sess: - with self.assertRaises(errors.InvalidArgumentError): - sess.run(restore_op) - - def testToSingleElement(self): - skip_value = array_ops.placeholder(dtypes.int64, shape=[]) - take_value = array_ops.placeholder_with_default( - constant_op.constant(1, dtype=dtypes.int64), shape=[]) - - dataset = (dataset_ops.Dataset.range(100) - .skip(skip_value) - .map(lambda x: x * x) - .take(take_value)) - - element = dataset_ops.get_single_element(dataset) - - with self.test_session() as sess: - self.assertEqual(0, sess.run(element, feed_dict={skip_value: 0})) - self.assertEqual(25, sess.run(element, feed_dict={skip_value: 5})) - self.assertEqual(100, sess.run(element, feed_dict={skip_value: 10})) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset was empty."): - sess.run(element, feed_dict={skip_value: 100}) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset had more than one element."): - sess.run(element, feed_dict={skip_value: 0, take_value: 2}) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py deleted file mode 100644 index 27298de65f90c627e5eb638385bfe0478ef74fca..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py +++ /dev/null @@ -1,159 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for the experimental input pipeline ops.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from os import path -import shutil -import tempfile - -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops -from tensorflow.python.platform import test -from tensorflow.python.util import compat - - -class ListFilesDatasetOpTest(test.TestCase): - - def setUp(self): - self.tmp_dir = tempfile.mkdtemp() - - def tearDown(self): - shutil.rmtree(self.tmp_dir, ignore_errors=True) - - def _touchTempFiles(self, filenames): - for filename in filenames: - open(path.join(self.tmp_dir, filename), 'a').close() - - def testEmptyDirectory(self): - dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) - with self.test_session() as sess: - itr = dataset.make_one_shot_iterator() - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - def testSimpleDirectory(self): - filenames = ['a', 'b', 'c'] - self._touchTempFiles(filenames) - - dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) - with self.test_session() as sess: - itr = dataset.make_one_shot_iterator() - - full_filenames = [] - produced_filenames = [] - for filename in filenames: - full_filenames.append( - compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) - self.assertItemsEqual(full_filenames, produced_filenames) - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - def testEmptyDirectoryInitializer(self): - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - dataset = dataset_ops.Dataset.list_files(filename_placeholder) - - with self.test_session() as sess: - itr = dataset.make_initializable_iterator() - sess.run( - itr.initializer, - feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - def testSimpleDirectoryInitializer(self): - filenames = ['a', 'b', 'c'] - self._touchTempFiles(filenames) - - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - dataset = dataset_ops.Dataset.list_files(filename_placeholder) - - with self.test_session() as sess: - itr = dataset.make_initializable_iterator() - sess.run( - itr.initializer, - feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) - - full_filenames = [] - produced_filenames = [] - for filename in filenames: - full_filenames.append( - compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) - - self.assertItemsEqual(full_filenames, produced_filenames) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - def testFileSuffixes(self): - filenames = ['a.txt', 'b.py', 'c.py', 'd.pyc'] - self._touchTempFiles(filenames) - - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - dataset = dataset_ops.Dataset.list_files(filename_placeholder) - - with self.test_session() as sess: - itr = dataset.make_initializable_iterator() - sess.run( - itr.initializer, - feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py')}) - - full_filenames = [] - produced_filenames = [] - for filename in filenames[1:-1]: - full_filenames.append( - compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) - self.assertItemsEqual(full_filenames, produced_filenames) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - def testFileMiddles(self): - filenames = ['a.txt', 'b.py', 'c.pyc'] - self._touchTempFiles(filenames) - - filename_placeholder = array_ops.placeholder(dtypes.string, shape=[]) - dataset = dataset_ops.Dataset.list_files(filename_placeholder) - - with self.test_session() as sess: - itr = dataset.make_initializable_iterator() - sess.run( - itr.initializer, - feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py*')}) - - full_filenames = [] - produced_filenames = [] - for filename in filenames[1:]: - full_filenames.append( - compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) - - self.assertItemsEqual(full_filenames, produced_filenames) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py index d3ce89298be342e22f12c46e8e8213ef636d0dc6..8d4042927970cab2f5a518fc0da49b38444dbcdf 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py @@ -16,15 +16,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from collections import namedtuple import os -import threading import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops as contrib_dataset_ops from tensorflow.contrib.data.python.ops import error_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -34,15 +31,9 @@ from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops -from tensorflow.python.ops import data_flow_ops -from tensorflow.python.ops import functional_ops from tensorflow.python.ops import io_ops -from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from tensorflow.python.ops import script_ops -from tensorflow.python.ops import sparse_ops -from tensorflow.python.ops import string_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -50,231 +41,11 @@ from tensorflow.python.util import compat class MapDatasetTest(test.TestCase): - def _buildMapDataset(self, components, count): - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(count)) - - def testMapDataset(self): - """Test an dataset that maps a TF function across its input elements.""" - # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> - # RepeatDataset(count). - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - - dataset = self._buildMapDataset(components, count) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - # Test single-threaded access to the iterator. - sess.run(init_op, feed_dict={count: 14}) - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test multi-threaded access to the same iterator. - sess.run(init_op, feed_dict={count: 18}) - results = [] - def iterator_thread(): - while True: - try: - results.append(sess.run(get_next)) - except errors.OutOfRangeError: - return - threads = [self.checkedThread(target=iterator_thread) for _ in range(8)] - for t in threads: - t.start() - for t in threads: - t.join() - - # `results` will contain the same elements components**2 - # repeated 18 times, but in a non-deterministic order. Sort the - # results, and assert that each element of components**2 is - # produced 18 times. - results.sort(key=lambda x: x[0]) - for i in range(7): - for j in range(18): - for component, result_component in zip(components, - results[i * 18 + j]): - self.assertAllEqual(component[i]**2, result_component) - - def _buildParallelMapDataset(self, components, count, num_threads, - output_buffer_size): - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return (contrib_dataset_ops.Dataset.from_tensor_slices(components).map( - _map_fn, num_threads=num_threads, output_buffer_size=output_buffer_size) - .repeat(count)) - - def testParallelMapDataset(self): - """Test an dataset that maps a TF function across its input elements.""" - # The pipeline is TensorSliceDataset -> ParallelMapDataset(square_3) -> - # RepeatDataset(count). - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - num_threads = array_ops.placeholder(dtypes.int32, shape=[]) - output_buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) - - dataset = self._buildParallelMapDataset(components, count, num_threads, - output_buffer_size) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - def do_test(num_threads_val, output_buffer_size_val): - # Test single-threaded access to the iterator. - sess.run(init_op, feed_dict={ - count: 14, - num_threads: num_threads_val, - output_buffer_size: output_buffer_size_val}) - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test multi-threaded access to the same iterator. - sess.run(init_op, feed_dict={ - count: 18, - num_threads: num_threads_val, - output_buffer_size: output_buffer_size_val}) - results = [] - def iterator_thread(): - while True: - try: - results.append(sess.run(get_next)) - except errors.OutOfRangeError: - return - threads = [self.checkedThread(target=iterator_thread) - for _ in range(64)] - for t in threads: - t.start() - for t in threads: - t.join() - - # `results` will contain the same elements components**2 - # repeated 18 times, but in a non-deterministic order. Sort the - # results, and assert that each element of components**2 is - # produced 18 times. - results.sort(key=lambda x: x[0]) - for i in range(7): - for j in range(18): - for component, result_component in zip(components, - results[i * 18 + j]): - self.assertAllEqual(component[i]**2, result_component) - - for num_threads_val, output_buffer_size_val in [ - (1, 1), (1, 2), (2, 2), (2, 4), (8, 8), (8, 16)]: - do_test(num_threads_val, output_buffer_size_val) - - def testImplicitDisposeParallelMapDataset(self): - # Tests whether a parallel map dataset will be cleaned up correctly when - # the pipeline does not run it until exhaustion. - # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> - # RepeatDataset(1000). - components = (np.arange(1000), - np.array([[1, 2, 3]]) * np.arange(1000)[:, np.newaxis], - np.array(37.0) * np.arange(1000)) - - dataset = self._buildParallelMapDataset(components, 1000, 100, 100) - # NOTE(mrry): Also test that the prefetching thread is cancelled correctly. - dataset = dataset.prefetch(100) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - - def testParallelMapUnspecifiedOutputSize(self): - components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) - - dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.check_numerics(x, "message"), num_threads=2)) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - - def testParallelMapError(self): - components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) - - dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.check_numerics(x, "message"), - num_threads=2, - output_buffer_size=2)) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - # The 4th element is NaN, so `array_ops.check_numerics()` should fail. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(get_next) - sess.run(get_next) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testPrefetchError(self): - components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) - - dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components) - .map(lambda x: array_ops.check_numerics(x, "message")).prefetch(2)) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - # The 4th element is NaN, so `array_ops.check_numerics()` should fail. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(get_next) - sess.run(get_next) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - def testMapIgnoreError(self): components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components) + dataset_ops.Dataset.from_tensor_slices(components) .map(lambda x: array_ops.check_numerics(x, "message")).apply( error_ops.ignore_errors())) iterator = dataset.make_initializable_iterator() @@ -292,10 +63,9 @@ class MapDatasetTest(test.TestCase): components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).map( + dataset_ops.Dataset.from_tensor_slices(components).map( lambda x: array_ops.check_numerics(x, "message"), - num_threads=2, - output_buffer_size=2).apply(error_ops.ignore_errors())) + num_parallel_calls=2).prefetch(2).apply(error_ops.ignore_errors())) iterator = dataset.make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() @@ -317,8 +87,8 @@ class MapDatasetTest(test.TestCase): write_string_to_file(filename, filename) dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(filenames).map( - io_ops.read_file, num_threads=2, output_buffer_size=2).apply( + dataset_ops.Dataset.from_tensor_slices(filenames).map( + io_ops.read_file, num_parallel_calls=2).prefetch(2).apply( error_ops.ignore_errors())) iterator = dataset.make_initializable_iterator() init_op = iterator.initializer @@ -343,350 +113,6 @@ class MapDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - def testCaptureHashTable(self): - # NOTE(mrry): We must use the V2 variants of `HashTable` - # etc. because these produce a `tf.resource`-typed output that is - # compatible with the in-graph function implementation. - default_val = -1 - keys = constant_op.constant(["brain", "salad", "surgery"]) - values = constant_op.constant([0, 1, 2], dtypes.int64) - table = lookup_ops.HashTable( - lookup_ops.KeyValueTensorInitializer(keys, values), default_val) - - input_sentences = contrib_dataset_ops.Dataset.from_tensor_slices( - ["brain brain tank salad surgery", "surgery brain"]) - - iterator = (input_sentences - .map(lambda x: string_ops.string_split([x]).values) - .map(table.lookup) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(table.init) - sess.run(init_op) - - print(sess.run(get_next)) - print(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testCaptureQueue(self): - elements = np.random.randint(100, size=[200]) - queue = data_flow_ops.FIFOQueue(200, dtypes.int64, shapes=[]) - enqueue_op = queue.enqueue_many(elements) - close_op = queue.close() - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(0).repeat(-1) - .map(lambda _: queue.dequeue()).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(enqueue_op) - sess.run(close_op) - sess.run(init_op) - for element in elements: - self.assertEqual(element, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testCaptureSameResourceMultipleTimes(self): - elements = np.random.randint(100, size=[200]) - queue = data_flow_ops.FIFOQueue( - 200, dtypes.int64, shapes=[], shared_name="shared_queue") - queue_2 = data_flow_ops.FIFOQueue( - 200, dtypes.int64, shapes=[], shared_name="shared_queue") - - enqueue_op = queue.enqueue_many(elements) - close_op = queue.close() - - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(0).repeat(-1) - .map(lambda _: (queue.dequeue(), queue_2.dequeue())) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(enqueue_op) - sess.run(close_op) - sess.run(init_op) - for i in range(100): - self.assertEqual(sorted([elements[i * 2], elements[i * 2 + 1]]), - sorted(sess.run(get_next))) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testCaptureVariable(self): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: counter_var.assign_add(1)).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(counter_var.initializer) - sess.run(init_op) - for i in range(10): - self.assertEqual(i, sess.run(counter_var)) - self.assertEqual(i + 1, sess.run(get_next)) - self.assertEqual(10, sess.run(counter_var)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertEqual(10, sess.run(counter_var)) - - def testCaptureUninitializedVariableError(self): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: counter_var.assign_add(1)).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - with self.assertRaises(errors.NotFoundError): - sess.run(get_next) - - def testSeededStatefulOperatorIsProperlyStateful(self): - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: random_ops.random_uniform((), seed=11)).batch(2) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - random_values = [] - with self.assertRaises(errors.OutOfRangeError): - while True: - random_values.extend(sess.run(get_next)) - self.assertEqual(10, len(random_values)) - self.assertGreater(np.abs(np.diff(random_values)).max(), 1e-6) - sess.run(init_op) - random_values_2 = [] - with self.assertRaises(errors.OutOfRangeError): - while True: - random_values_2.extend(sess.run(get_next)) - - # Randomness is repeatable given same seed - self.assertAllClose(random_values, random_values_2) - - def testMapDict(self): - iterator = (contrib_dataset_ops.Dataset.range(10) - .map(lambda x: {"foo": x * 2, "bar": x ** 2}) - .map(lambda d: d["foo"] + d["bar"]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual(i * 2 + i ** 2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testMapNamedtuple(self, count=10): - # construct dataset of tuples - labels = contrib_dataset_ops.Dataset.range(count) - images = labels.map(lambda l: -l) - dataset_tuple = contrib_dataset_ops.Dataset.zip((labels, images)) - - # convert dataset of tuples to dataset of namedtuples - example = namedtuple("Example", ["label", "image"]) - dataset_namedtuple = dataset_tuple.map(example) - - def preprocess_tuple(label, image): - image = 2 * image - return label, image - - def preprocess_namedtuple(example): - return example._replace(image=2 * example.image) - - # preprocess both datasets - dataset_tuple = dataset_tuple.map(preprocess_tuple) - dataset_namedtuple = dataset_namedtuple.map(preprocess_namedtuple) - - next_tuple = dataset_tuple.make_one_shot_iterator().get_next() - next_namedtuple = dataset_namedtuple.make_one_shot_iterator().get_next() - - # make sure both datasets contain the same data - with self.test_session() as sess: - for i in range(count): - tuple_, namedtuple_ = sess.run([next_tuple, next_namedtuple]) - self.assertEqual(tuple_, namedtuple_) - self.assertEqual(tuple_, (i, -2 * i)) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_namedtuple) - - def testUseStepContainerInMap(self): - row = np.arange(6) - iterator = ( - contrib_dataset_ops.Dataset.from_tensors(row) - .map(lambda elems: functional_ops.map_fn(lambda x: x * x, elems)) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - self.assertAllEqual(row ** 2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testPrefetch(self): - # We will use this event to test that `_map_py_func()` has been - # invoked a certain number of times (6 times, to be exact) after - # consuming fewer elements from the iterator. - ev = threading.Event() - - set_event_during_invocation = 5 - - def _map_py_func(x): - if x == set_event_during_invocation: - ev.set() - return x * x - - def _map_fn(x): - return script_ops.py_func(_map_py_func, [x], x.dtype) - - buffer_size_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = ( - contrib_dataset_ops.Dataset.range(100).map(_map_fn) - .prefetch(buffer_size_placeholder).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - # Simple test that prefetch yields the expected values in the - # expected order. - for buffer_size in [1, 10, 100, 1000]: - sess.run(init_op, feed_dict={buffer_size_placeholder: buffer_size}) - for i in range(100): - self.assertEqual(i * i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # We can indirectly observe that varying the buffer size has the - # intended effect by observing when `ev` is set (on the 6th - # invocation of `_map_py_func()`). - # NOTE(mrry): We do not test with `buffer_size == - # set_event_during_invocation`, because we must consume at least - # one element to start the prefetching. - for buffer_size in range(1, set_event_during_invocation): - event_will_be_set_after_consuming = ( - set_event_during_invocation - buffer_size + 1) - - ev.clear() - sess.run(init_op, feed_dict={buffer_size_placeholder: buffer_size}) - for i in range(event_will_be_set_after_consuming): - self.assertFalse(ev.is_set()) - self.assertEqual(i * i, sess.run(get_next)) - ev.wait() - for i in range(event_will_be_set_after_consuming, 100): - self.assertEqual(i * i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testReturnList(self): - iterator = ( - contrib_dataset_ops.Dataset.range(10) - .map(lambda x: [x, constant_op.constant(37.0)]) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual((i, 37.0), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testMultiOutputPyFunc(self): - # The `tf.py_func()` op returns a list of tensors for its outputs. - def _map_fn(x_tensor): - def _map_py_func(x): - return x, np.array(37.0, dtype=np.float64) - return script_ops.py_func( - _map_py_func, [x_tensor], [dtypes.int64, dtypes.float64]) - - iterator = ( - contrib_dataset_ops.Dataset.range(10).map(_map_fn) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual((i, 37.0), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def assertSparseValuesEqual(self, a, b): - self.assertAllEqual(a.indices, b.indices) - self.assertAllEqual(a.values, b.values) - self.assertAllEqual(a.dense_shape, b.dense_shape) - - def testSparse(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0]]), - values=(i * np.array([1])), - dense_shape=np.array([1, 1])) - - iterator = ( - contrib_dataset_ops.Dataset.range(10).map(_sparse) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - actual = sess.run(get_next) - self.assertTrue(isinstance(actual, sparse_tensor.SparseTensorValue)) - self.assertSparseValuesEqual(actual, _sparse(i)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testSparseChain(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0]]), - values=(i * np.array([1])), - dense_shape=np.array([1, 1])) - - def _check(i): - self.assertTrue(sparse_tensor.is_sparse(i)) - return sparse_ops.sparse_concat(0, [i, i]) - - iterator = ( - contrib_dataset_ops.Dataset.range(10).map(_sparse).map(_check) - .make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - for i in range(10): - actual = sess.run(get_next) - self.assertTrue(isinstance(actual, sparse_tensor.SparseTensorValue)) - self.assertSparseValuesEqual(actual, _check(_sparse(i)).eval()) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - def testCaptureResourceInMapFn(self): def _build_ds(iterator): @@ -695,10 +121,10 @@ class MapDatasetTest(test.TestCase): get_next = iterator.get_next() return x * get_next - return contrib_dataset_ops.Dataset.range(10).map(_map_fn) + return dataset_ops.Dataset.range(10).map(_map_fn) def _build_graph(): - captured_iterator = contrib_dataset_ops.Dataset.range( + captured_iterator = dataset_ops.Dataset.range( 10).make_initializable_iterator() ds = _build_ds(captured_iterator) iterator = ds.make_initializable_iterator() @@ -734,7 +160,7 @@ class MapDatasetSerializationTest( return math_ops.square(x), math_ops.square(y), math_ops.square(z) return ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) .repeat(self._num_epochs)) def testSaveRestoreCore(self): @@ -751,7 +177,7 @@ class MapDatasetSerializationTest( return random_ops.random_uniform( (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(100).map(_map_fn) + return dataset_ops.Dataset.range(100).map(_map_fn) self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) @@ -760,7 +186,7 @@ class MapDatasetSerializationTest( def _build_ds(): counter_var = variable_scope.get_variable( "counter", (), dtypes.int32, use_resource=True) - return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( lambda _: counter_var.assign_add(1))) self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) @@ -769,7 +195,7 @@ class MapDatasetSerializationTest( def _build_ds(): constant_var = constant_op.constant(5) - return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( lambda x: x + constant_var)) self.run_core_tests(_build_ds, None, 10) @@ -783,7 +209,7 @@ class MapDatasetSerializationTest( def defun_fn(x): return constant_op.constant(1000) + math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(num_outputs).map(defun_fn) + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) self.run_core_tests(_build_ds, None, num_outputs) @@ -801,7 +227,7 @@ class MapDatasetSerializationTest( return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - return contrib_dataset_ops.Dataset.range(num_outputs).map(defun_fn) + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) self.run_core_tests(_build_ds, None, num_outputs) @@ -814,7 +240,7 @@ class MapDatasetSerializationTest( dense_shape=np.array([1, 1])) def _build_ds(num_outputs): - return contrib_dataset_ops.Dataset.range(num_outputs).map(_sparse) + return dataset_ops.Dataset.range(num_outputs).map(_sparse) num_outputs = 10 self.run_core_tests(lambda: _build_ds(num_outputs), @@ -866,7 +292,7 @@ class ParallelMapDatasetSerializationTest( return random_ops.random_uniform( (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(100).map( + return dataset_ops.Dataset.range(100).map( _map_fn, num_parallel_calls=2).prefetch(2) self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) @@ -876,7 +302,7 @@ class ParallelMapDatasetSerializationTest( def _build_ds(): counter_var = variable_scope.get_variable( "counter", (), dtypes.int32, use_resource=True) - return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( lambda _: counter_var.assign_add(1), num_parallel_calls=2).prefetch(2)) @@ -886,7 +312,7 @@ class ParallelMapDatasetSerializationTest( def _build_ds(): constant_var = constant_op.constant(5) - return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) self.run_core_tests(_build_ds, None, 10) @@ -900,7 +326,7 @@ class ParallelMapDatasetSerializationTest( def defun_fn(x): return constant_op.constant(1000) + math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(num_outputs).map( + return dataset_ops.Dataset.range(num_outputs).map( defun_fn, num_parallel_calls=2).prefetch(2) self.run_core_tests(_build_ds, None, num_outputs) @@ -919,7 +345,7 @@ class ParallelMapDatasetSerializationTest( return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - return contrib_dataset_ops.Dataset.range(num_outputs).map( + return dataset_ops.Dataset.range(num_outputs).map( defun_fn, num_parallel_calls=2).prefetch(2) self.run_core_tests(_build_ds, None, num_outputs) @@ -929,7 +355,7 @@ class IgnoreErrorsSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def _build_ds(self, components): - return contrib_dataset_ops.Dataset.from_tensor_slices(components).map( + return dataset_ops.Dataset.from_tensor_slices(components).map( lambda x: array_ops.check_numerics(x, "message")).apply( error_ops.ignore_errors()) diff --git a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py index a431670829ed1d66f1719985af73eafa1fe45982..80e1cb0041024b68bd5268b5de5d69c88c839896 100644 --- a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py @@ -21,14 +21,13 @@ import os from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import counter -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.data.python.ops import enumerate_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import io_ops from tensorflow.python.ops import parsing_ops @@ -38,131 +37,6 @@ from tensorflow.python.platform import test class RangeDatasetTest(test.TestCase): - def testStop(self): - stop = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={stop: 5}) - for i in range(5): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testStartStop(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, - stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 2, stop: 5}) - for i in range(2, 5): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testStartStopStep(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - step = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, stop, - step).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 2, stop: 10, step: 2}) - for i in range(2, 10, 2): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testZeroStep(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - step = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, stop, - step).make_initializable_iterator() - init_op = iterator.initializer - - with self.test_session() as sess: - with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={start: 2, stop: 10, step: 0}) - - def testNegativeStep(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - step = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, stop, - step).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 2, stop: 10, step: -1}) - # This for loop is a no-op but will ensure that the implementation is - # consistent with range if it ever changes. - for i in range(2, 10, -1): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testStopLessThanStart(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, - stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 10, stop: 2}) - # This for loop is a no-op but will ensure that the implementation is - # consistent with range if it ever changes. - for i in range(10, 2): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testStopLessThanStartWithPositiveStep(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - step = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, stop, - step).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 10, stop: 2, step: 2}) - # This for loop is a no-op but will ensure that the implementation is - # consistent with range if it ever changes. - for i in range(10, 2, 2): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testStopLessThanStartWithNegativeStep(self): - start = array_ops.placeholder(dtypes.int64, shape=[]) - stop = array_ops.placeholder(dtypes.int64, shape=[]) - step = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.Dataset.range(start, stop, - step).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op, feed_dict={start: 10, stop: 2, step: -1}) - for i in range(10, 2, -1): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - def testEnumerateDataset(self): components = (["a", "b"], [1, 2], [37.0, 38]) start = constant_op.constant(20, dtype=dtypes.int64) diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py index 1c42a3d855bc16c21e385d7108c3106884ae4f5e..15bd55bf6410c23b83c684922e9a12d2d969e00d 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py @@ -21,11 +21,14 @@ import gzip import os import zlib +import numpy as np + from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import readers from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -76,101 +79,12 @@ class TextLineDatasetTestBase(test.TestCase): return filenames -class TextLineDatasetTest(TextLineDatasetTestBase): - - def _testTextLineDataset(self, compression_type=None): - test_filenames = self._createFiles( - 2, 5, crlf=True, compression_type=compression_type) - filenames = array_ops.placeholder(dtypes.string, shape=[None]) - num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) - batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_dataset = readers.TextLineDataset( - filenames, compression_type=compression_type).repeat(num_epochs) - batch_dataset = repeat_dataset.batch(batch_size) - - iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) - init_op = iterator.make_initializer(repeat_dataset) - init_batch_op = iterator.make_initializer(batch_dataset) - get_next = iterator.get_next() - - with self.test_session() as sess: - # Basic test: read from file 0. - sess.run( - init_op, feed_dict={filenames: [test_filenames[0]], - num_epochs: 1}) - for i in range(5): - self.assertEqual(self._lineText(0, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Basic test: read from file 1. - sess.run( - init_op, feed_dict={filenames: [test_filenames[1]], - num_epochs: 1}) - for i in range(5): - self.assertEqual(self._lineText(1, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Basic test: read from both files. - sess.run(init_op, feed_dict={filenames: test_filenames, num_epochs: 1}) - for j in range(2): - for i in range(5): - self.assertEqual(self._lineText(j, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test repeated iteration through both files. - sess.run(init_op, feed_dict={filenames: test_filenames, num_epochs: 10}) - for _ in range(10): - for j in range(2): - for i in range(5): - self.assertEqual(self._lineText(j, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test batched and repeated iteration through both files. - sess.run( - init_batch_op, - feed_dict={filenames: test_filenames, - num_epochs: 10, - batch_size: 5}) - for _ in range(10): - self.assertAllEqual([self._lineText(0, i) for i in range(5)], - sess.run(get_next)) - self.assertAllEqual([self._lineText(1, i) for i in range(5)], - sess.run(get_next)) - - def testTextLineDatasetNoCompression(self): - self._testTextLineDataset() - - def testTextLineDatasetGzipCompression(self): - self._testTextLineDataset(compression_type="GZIP") - - def testTextLineDatasetZlibCompression(self): - self._testTextLineDataset(compression_type="ZLIB") - - def testTextLineDatasetBuffering(self): - test_filenames = self._createFiles(2, 5, crlf=True) - - repeat_dataset = readers.TextLineDataset(test_filenames, buffer_size=10) - iterator = repeat_dataset.make_one_shot_iterator() - - with self.test_session() as sess: - for j in range(2): - for i in range(5): - self.assertEqual(self._lineText(j, i), sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - class TextLineDatasetSerializationTest( TextLineDatasetTestBase, dataset_serialization_test_base.DatasetSerializationTestBase): def _build_iterator_graph(self, test_filenames, compression_type=None): - return readers.TextLineDataset( + return core_readers.TextLineDataset( test_filenames, compression_type=compression_type, buffer_size=10) def testTextLineCore(self): @@ -217,101 +131,13 @@ class FixedLengthRecordReaderTestBase(test.TestCase): return filenames -class FixedLengthRecordReaderTest(FixedLengthRecordReaderTestBase): - - def testFixedLengthRecordDataset(self): - test_filenames = self._createFiles() - filenames = array_ops.placeholder(dtypes.string, shape=[None]) - num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) - batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_dataset = (readers.FixedLengthRecordDataset( - filenames, self._record_bytes, self._header_bytes, self._footer_bytes) - .repeat(num_epochs)) - batch_dataset = repeat_dataset.batch(batch_size) - - iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) - init_op = iterator.make_initializer(repeat_dataset) - init_batch_op = iterator.make_initializer(batch_dataset) - get_next = iterator.get_next() - - with self.test_session() as sess: - # Basic test: read from file 0. - sess.run( - init_op, feed_dict={filenames: [test_filenames[0]], - num_epochs: 1}) - for i in range(self._num_records): - self.assertEqual(self._record(0, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Basic test: read from file 1. - sess.run( - init_op, feed_dict={filenames: [test_filenames[1]], - num_epochs: 1}) - for i in range(self._num_records): - self.assertEqual(self._record(1, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Basic test: read from both files. - sess.run(init_op, feed_dict={filenames: test_filenames, num_epochs: 1}) - for j in range(self._num_files): - for i in range(self._num_records): - self.assertEqual(self._record(j, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test repeated iteration through both files. - sess.run(init_op, feed_dict={filenames: test_filenames, num_epochs: 10}) - for _ in range(10): - for j in range(self._num_files): - for i in range(self._num_records): - self.assertEqual(self._record(j, i), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test batched and repeated iteration through both files. - sess.run( - init_batch_op, - feed_dict={ - filenames: test_filenames, - num_epochs: 10, - batch_size: self._num_records - }) - for _ in range(10): - for j in range(self._num_files): - self.assertAllEqual( - [self._record(j, i) for i in range(self._num_records)], - sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testFixedLengthRecordDatasetBuffering(self): - test_filenames = self._createFiles() - dataset = readers.FixedLengthRecordDataset( - test_filenames, - self._record_bytes, - self._header_bytes, - self._footer_bytes, - buffer_size=10) - iterator = dataset.make_one_shot_iterator() - - with self.test_session() as sess: - for j in range(self._num_files): - for i in range(self._num_records): - self.assertEqual(self._record(j, i), sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - class FixedLengthRecordDatasetSerializationTest( FixedLengthRecordReaderTestBase, dataset_serialization_test_base.DatasetSerializationTestBase): def _build_iterator_graph(self, num_epochs, compression_type=None): filenames = self._createFiles() - return readers.FixedLengthRecordDataset( + return core_readers.FixedLengthRecordDataset( filenames, self._record_bytes, self._header_bytes, self._footer_bytes).repeat(num_epochs) @@ -338,9 +164,8 @@ class TFRecordDatasetTestBase(test.TestCase): self.compression_type = array_ops.placeholder_with_default("", shape=[]) self.batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - repeat_dataset = readers.TFRecordDataset(self.filenames, - self.compression_type).repeat( - self.num_epochs) + repeat_dataset = core_readers.TFRecordDataset( + self.filenames, self.compression_type).repeat(self.num_epochs) batch_dataset = repeat_dataset.batch(self.batch_size) iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) @@ -363,129 +188,6 @@ class TFRecordDatasetTestBase(test.TestCase): return filenames -class TFRecordDatasetTest(TFRecordDatasetTestBase): - - def testReadOneEpoch(self): - with self.test_session() as sess: - # Basic test: read from file 0. - sess.run( - self.init_op, - feed_dict={ - self.filenames: [self.test_filenames[0]], - self.num_epochs: 1 - }) - for i in range(self._num_records): - self.assertAllEqual(self._record(0, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - # Basic test: read from file 1. - sess.run( - self.init_op, - feed_dict={ - self.filenames: [self.test_filenames[1]], - self.num_epochs: 1 - }) - for i in range(self._num_records): - self.assertAllEqual(self._record(1, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - # Basic test: read from both files. - sess.run( - self.init_op, - feed_dict={self.filenames: self.test_filenames, - self.num_epochs: 1}) - for j in range(self._num_files): - for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - def testReadTenEpochs(self): - with self.test_session() as sess: - sess.run( - self.init_op, - feed_dict={self.filenames: self.test_filenames, - self.num_epochs: 10}) - for _ in range(10): - for j in range(self._num_files): - for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - def testReadTenEpochsOfBatches(self): - with self.test_session() as sess: - sess.run( - self.init_batch_op, - feed_dict={ - self.filenames: self.test_filenames, - self.num_epochs: 10, - self.batch_size: self._num_records - }) - for _ in range(10): - for j in range(self._num_files): - values = sess.run(self.get_next) - self.assertAllEqual( - [self._record(j, i) for i in range(self._num_records)], values) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - def testReadZlibFiles(self): - zlib_files = [] - for i, fn in enumerate(self.test_filenames): - with open(fn, "rb") as f: - cdata = zlib.compress(f.read()) - - zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) - with open(zfn, "wb") as f: - f.write(cdata) - zlib_files.append(zfn) - - with self.test_session() as sess: - sess.run( - self.init_op, - feed_dict={self.filenames: zlib_files, - self.compression_type: "ZLIB"}) - for j in range(self._num_files): - for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - def testReadGzipFiles(self): - gzip_files = [] - for i, fn in enumerate(self.test_filenames): - with open(fn, "rb") as f: - gzfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) - with gzip.GzipFile(gzfn, "wb") as gzf: - gzf.write(f.read()) - gzip_files.append(gzfn) - - with self.test_session() as sess: - sess.run( - self.init_op, - feed_dict={self.filenames: gzip_files, - self.compression_type: "GZIP"}) - for j in range(self._num_files): - for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(self.get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(self.get_next) - - def testReadWithBuffer(self): - one_mebibyte = 2**20 - d = readers.TFRecordDataset(self.test_filenames, buffer_size=one_mebibyte) - iterator = d.make_one_shot_iterator() - with self.test_session() as sess: - for j in range(self._num_files): - for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - class TFRecordDatasetSerializationTest( TFRecordDatasetTestBase, dataset_serialization_test_base.DatasetSerializationTestBase): @@ -517,7 +219,7 @@ class TFRecordDatasetSerializationTest( gzip_files.append(gzfn) filenames = gzip_files - return readers.TFRecordDataset( + return core_readers.TFRecordDataset( filenames, compression_type, buffer_size=buffer_size).repeat(num_epochs).batch(batch_size) @@ -562,12 +264,19 @@ class ReadBatchFeaturesTest(test.TestCase): self._num_records = 7 self.test_filenames = self._createFiles() - def _read_batch_features(self, filenames, num_epochs, batch_size): + def _read_batch_features(self, + filenames, + num_epochs, + batch_size, + reader_num_threads=1, + parser_num_threads=1, + shuffle=False, + shuffle_seed=None): self.filenames = filenames self.num_epochs = num_epochs self.batch_size = batch_size - return readers.read_batch_features( + return readers.make_batched_features_dataset( file_pattern=self.filenames, batch_size=self.batch_size, features={ @@ -575,9 +284,13 @@ class ReadBatchFeaturesTest(test.TestCase): "record": parsing_ops.FixedLenFeature([], dtypes.int64), "keywords": parsing_ops.VarLenFeature(dtypes.string) }, - reader=readers.TFRecordDataset, - randomize_input=False, - num_epochs=self.num_epochs) + reader=core_readers.TFRecordDataset, + num_epochs=self.num_epochs, + shuffle=shuffle, + shuffle_seed=shuffle_seed, + reader_num_threads=reader_num_threads, + parser_num_threads=parser_num_threads).make_one_shot_iterator( + ).get_next() def _record(self, f, r): example = example_pb2.Example(features=feature_pb2.Features( @@ -612,24 +325,35 @@ class ReadBatchFeaturesTest(test.TestCase): writer.close() return filenames - def _next_actual_batch(self, sess): - file_op = self.outputs["file"] - keywords_indices_op = self.outputs["keywords"].indices - keywords_values_op = self.outputs["keywords"].values - keywords_dense_shape_op = self.outputs["keywords"].dense_shape - record_op = self.outputs["record"] + def _run_actual_batch(self, outputs, sess): + file_op = outputs["file"] + keywords_indices_op = outputs["keywords"].indices + keywords_values_op = outputs["keywords"].values + keywords_dense_shape_op = outputs["keywords"].dense_shape + record_op = outputs["record"] return sess.run([ file_op, keywords_indices_op, keywords_values_op, keywords_dense_shape_op, record_op ]) - def _next_expected_batch(self, file_indices, batch_size, num_epochs): + def _next_actual_batch(self, sess): + return self._run_actual_batch(self.outputs, sess) + + def _next_expected_batch(self, + file_indices, + batch_size, + num_epochs, + cycle_length=1): def _next_record(file_indices): for j in file_indices: for i in range(self._num_records): yield j, i + def _next_record_interleaved(file_indices, cycle_length): + return self._interleave([_next_record([i]) for i in file_indices], + cycle_length) + file_batch = [] keywords_batch_indices = [] keywords_batch_values = [] @@ -637,7 +361,11 @@ class ReadBatchFeaturesTest(test.TestCase): record_batch = [] batch_index = 0 for _ in range(num_epochs): - for record in _next_record(file_indices): + if cycle_length == 1: + next_records = _next_record(file_indices) + else: + next_records = _next_record_interleaved(file_indices, cycle_length) + for record in next_records: f = record[0] r = record[1] file_batch.append(f) @@ -665,14 +393,41 @@ class ReadBatchFeaturesTest(test.TestCase): [len(file_batch), keywords_batch_max_len], record_batch ] - def _verify_records(self, sess, batch_size, file_index=None, num_epochs=1): + def _interleave(self, iterators, cycle_length): + pending_iterators = iterators + open_iterators = [] + num_open = 0 + for i in range(cycle_length): + if pending_iterators: + open_iterators.append(pending_iterators.pop(0)) + num_open += 1 + + while num_open: + for i in range(min(cycle_length, len(open_iterators))): + if open_iterators[i] is None: + continue + try: + yield next(open_iterators[i]) + except StopIteration: + if pending_iterators: + open_iterators[i] = pending_iterators.pop(0) + else: + open_iterators[i] = None + num_open -= 1 + + def _verify_records(self, + sess, + batch_size, + file_index=None, + num_epochs=1, + interleave_cycle_length=1): if file_index is not None: file_indices = [file_index] else: file_indices = range(self._num_files) - for expected_batch in self._next_expected_batch(file_indices, batch_size, - num_epochs): + for expected_batch in self._next_expected_batch( + file_indices, batch_size, num_epochs, interleave_cycle_length): actual_batch = self._next_actual_batch(sess) for i in range(len(expected_batch)): self.assertAllEqual(expected_batch[i], actual_batch[i]) @@ -714,12 +469,11 @@ class ReadBatchFeaturesTest(test.TestCase): self._next_actual_batch(sess) def testReadWithEquivalentDataset(self): - # TODO(mrry): Add support for tf.SparseTensor as a Dataset component. features = { "file": parsing_ops.FixedLenFeature([], dtypes.int64), "record": parsing_ops.FixedLenFeature([], dtypes.int64), } - dataset = (readers.TFRecordDataset(self.test_filenames) + dataset = (core_readers.TFRecordDataset(self.test_filenames) .map(lambda x: parsing_ops.parse_single_example(x, features)) .repeat(10).batch(2)) iterator = dataset.make_initializable_iterator() @@ -736,6 +490,75 @@ class ReadBatchFeaturesTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) + def testReadWithFusedShuffleRepeatDataset(self): + num_epochs = 5 + total_records = num_epochs * self._num_records + for batch_size in [1, 2]: + # Test that shuffling with same seed produces the same result. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + outputs1 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + outputs2 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + self.assertAllEqual(batch1[i], batch2[i]) + + # Test that shuffling with different seeds produces a different order. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + outputs1 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + outputs2 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=15) + all_equal = True + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + all_equal = all_equal and np.array_equal(batch1[i], batch2[i]) + self.assertFalse(all_equal) + + def testParallelReadersAndParsers(self): + num_epochs = 5 + for batch_size in [1, 2]: + for reader_num_threads in [2, 4]: + for parser_num_threads in [2, 4]: + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + self.outputs = self._read_batch_features( + filenames=self.test_filenames, + num_epochs=num_epochs, + batch_size=batch_size, + reader_num_threads=reader_num_threads, + parser_num_threads=parser_num_threads) + self._verify_records( + sess, + batch_size, + num_epochs=num_epochs, + interleave_cycle_length=reader_num_threads) + with self.assertRaises(errors.OutOfRangeError): + self._next_actual_batch(sess) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 0ac8d7359f7234d98167277724780bf31555e6fb..3c7b46629edb13459766b5ef3f392e8d00ad4db8 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -19,8 +19,8 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.data.python.ops import resampling +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors from tensorflow.python.ops import string_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py index 1a26da82e533ec01106ea10525c1cd96627c34fb..36ddf3004237ed042f21d691d83eafbaa20621e6 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py @@ -20,194 +20,10 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test -class SequenceDatasetTest(test.TestCase): - - def testRepeatTensorDataset(self): - """Test a dataset that repeats its input multiple times.""" - components = (np.array(1), np.array([1, 2, 3]), np.array(37.0)) - # This placeholder can be fed when dataset-definition subgraph - # runs (i.e. `init_op` below) to configure the number of - # repetitions used in a particular iterator. - count_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - - iterator = (dataset_ops.Dataset.from_tensors(components) - .repeat(count_placeholder).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - # Test a finite repetition. - sess.run(init_op, feed_dict={count_placeholder: 3}) - for _ in range(3): - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test a different finite repetition. - sess.run(init_op, feed_dict={count_placeholder: 7}) - for _ in range(7): - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test an empty repetition. - sess.run(init_op, feed_dict={count_placeholder: 0}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test an infinite repetition. - # NOTE(mrry): There's not a good way to test that the sequence - # actually is infinite. - sess.run(init_op, feed_dict={count_placeholder: -1}) - for _ in range(17): - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - - def testTakeTensorDataset(self): - components = (np.arange(10),) - count_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .take(count_placeholder).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - # Take fewer than input size - sess.run(init_op, feed_dict={count_placeholder: 4}) - for i in range(4): - results = sess.run(get_next) - self.assertAllEqual(results, components[0][i:i+1]) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Take more than input size - sess.run(init_op, feed_dict={count_placeholder: 25}) - for i in range(10): - results = sess.run(get_next) - self.assertAllEqual(results, components[0][i:i+1]) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Take all of input - sess.run(init_op, feed_dict={count_placeholder: -1}) - for i in range(10): - results = sess.run(get_next) - self.assertAllEqual(results, components[0][i:i+1]) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Take nothing - sess.run(init_op, feed_dict={count_placeholder: 0}) - - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testSkipTensorDataset(self): - components = (np.arange(10),) - count_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .skip(count_placeholder).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - # Skip fewer than input size, we should skip - # the first 4 elements and then read the rest. - sess.run(init_op, feed_dict={count_placeholder: 4}) - for i in range(4, 10): - results = sess.run(get_next) - self.assertAllEqual(results, components[0][i:i+1]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Skip more than input size: get nothing. - sess.run(init_op, feed_dict={count_placeholder: 25}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Skip exactly input size. - sess.run(init_op, feed_dict={count_placeholder: 10}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Set -1 for 'count': skip the entire dataset. - sess.run(init_op, feed_dict={count_placeholder: -1}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Skip nothing - sess.run(init_op, feed_dict={count_placeholder: 0}) - for i in range(0, 10): - results = sess.run(get_next) - self.assertAllEqual(results, components[0][i:i+1]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testRepeatRepeatTensorDataset(self): - """Test the composition of repeat datasets.""" - components = (np.array(1), np.array([1, 2, 3]), np.array(37.0)) - inner_count = array_ops.placeholder(dtypes.int64, shape=[]) - outer_count = array_ops.placeholder(dtypes.int64, shape=[]) - - iterator = (dataset_ops.Dataset.from_tensors(components).repeat(inner_count) - .repeat(outer_count).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape for c in components], - [t.shape for t in get_next]) - - with self.test_session() as sess: - sess.run(init_op, feed_dict={inner_count: 7, outer_count: 14}) - for _ in range(7 * 14): - results = sess.run(get_next) - for component, result_component in zip(components, results): - self.assertAllEqual(component, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testRepeatEmptyDataset(self): - """Test that repeating an empty dataset does not hang.""" - iterator = (dataset_ops.Dataset.from_tensors(0).repeat(10).skip(10) - .repeat(-1).make_initializable_iterator()) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - sess.run(init_op) - with self.assertRaisesRegexp( - errors.OutOfRangeError, - "Attempted to repeat an empty dataset infinitely."): - sess.run(get_next) - - class SequenceDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py new file mode 100644 index 0000000000000000000000000000000000000000..0a6b74dc3eb80a6168117beed06935737198cecb --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py @@ -0,0 +1,85 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Integration test for input pipeline serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.ops import iterator_ops as contrib_iterator_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.training import saver as saver_lib + + +class MultipleInputPipelinesTest(test.TestCase): + + def _build_input_pipeline(self, name, num_outputs): + with ops.name_scope(name): + ds = dataset_ops.Dataset.range(num_outputs).shuffle( + 10, reshuffle_each_iteration=False).prefetch(10) + iterator = ds.make_initializable_iterator() + saveable = contrib_iterator_ops.make_saveable_from_iterator(iterator) + ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable) + return iterator.initializer, iterator.get_next() + + def _build_graph(self, num_pipelines, num_outputs): + init_ops = [] + get_next_ops = [] + for i in range(num_pipelines): + name = "input_pipeline_%d" % i + init_op, get_next_op = self._build_input_pipeline(name, num_outputs) + init_ops.append(init_op) + get_next_ops.append(get_next_op) + saver = saver_lib.Saver() + return init_ops, get_next_ops, saver + + def _ckpt_path(self): + return os.path.join(self.get_temp_dir(), "iterator") + + def testConcurrentSaves(self): + num_pipelines = 100 + num_outputs = 100 + break_point = 10 + all_outputs = [[] for _ in range(num_pipelines)] + with ops.Graph().as_default() as g: + init_ops, get_next_ops, saver = self._build_graph(num_pipelines, + num_outputs) + with self.test_session(graph=g) as sess: + sess.run(init_ops) + for _ in range(break_point): + output = sess.run(get_next_ops) + for i in range(num_pipelines): + all_outputs[i].append(output[i]) + saver.save(sess, self._ckpt_path()) + + with ops.Graph().as_default() as g: + init_ops, get_next_ops, saver = self._build_graph(num_pipelines, + num_outputs) + with self.test_session(graph=g) as sess: + saver.restore(sess, self._ckpt_path()) + for _ in range(num_outputs - break_point): + output = sess.run(get_next_ops) + for i in range(num_pipelines): + all_outputs[i].append(output[i]) + + for output in all_outputs: + self.assertSequenceEqual(sorted(output), range(num_outputs)) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/shard_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/shard_dataset_op_test.py deleted file mode 100644 index 0b3c32c06eb1d69244c9a02ca4ba571769f13f40..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/kernel_tests/shard_dataset_op_test.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for the experimental input pipeline ops.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.framework import errors -from tensorflow.python.platform import test - - -class ShardDatasetOpTest(test.TestCase): - - def testSimpleCase(self): - dataset = dataset_ops.Dataset.range(10).shard(5, 2) - iterator = dataset.make_one_shot_iterator() - - with self.test_session() as sess: - self.assertEqual(2, sess.run(iterator.get_next())) - self.assertEqual(7, sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testNestedData(self): - dataset_a = dataset_ops.Dataset.range(10) - dataset_b = dataset_ops.Dataset.range(10, 0, -1) - dataset = dataset_ops.Dataset.zip((dataset_a, dataset_b)).shard(5, 2) - iterator = dataset.make_one_shot_iterator() - - with self.test_session() as sess: - self.assertEqual((2, 8), sess.run(iterator.get_next())) - self.assertEqual((7, 3), sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testOffsetZero(self): - dataset = dataset_ops.Dataset.range(10).shard(5, 0) - iterator = dataset.make_one_shot_iterator() - - with self.test_session() as sess: - self.assertEqual(0, sess.run(iterator.get_next())) - self.assertEqual(5, sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testOffsetGreaterNumShards(self): - with self.assertRaises(ValueError): - dataset_ops.Dataset.range(10).shard(5, 7) - - def testNegativeOffset(self): - with self.assertRaises(ValueError): - dataset_ops.Dataset.range(10).shard(5, -3) - - def testNegativeNumShards(self): - with self.assertRaises(ValueError): - dataset_ops.Dataset.range(10).shard(-3, 1) - - def testZeroNumShards(self): - with self.assertRaises(ValueError): - dataset_ops.Dataset.range(10).shard(0, 1) - - def testIteratorEndsBeforeFirstElem(self): - dataset = dataset_ops.Dataset.range(1).shard(5, 2) - iterator = dataset.make_one_shot_iterator() - - with self.test_session() as sess: - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testLargerWorkerPool(self): - dataset = dataset_ops.Dataset.range(10).shard(7, 5) - iterator = dataset.make_one_shot_iterator() - with self.test_session() as sess: - self.assertEqual(5, sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testIndexEqualsNumShards(self): - dataset = dataset_ops.Dataset.range(10).shard(5, 4) - iterator = dataset.make_one_shot_iterator() - with self.test_session() as sess: - self.assertEqual(4, sess.run(iterator.get_next())) - self.assertEqual(9, sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - def testIndexEqualsNumShards2(self): - dataset = dataset_ops.Dataset.range(10).shard(4, 3) - iterator = dataset.make_one_shot_iterator() - with self.test_session() as sess: - self.assertEqual(3, sess.run(iterator.get_next())) - self.assertEqual(7, sess.run(iterator.get_next())) - with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py index 45943d56ecb4bc18a6221157d0eeeae4efdf23cc..bcc644c0971854d948025009dc7add2fea214048 100644 --- a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py @@ -17,144 +17,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import collections - import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops as contrib_dataset_ops from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class ShuffleDatasetTest(test.TestCase): - - def testShuffleDataset(self): - components = ( - np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), - np.array([9.0, 10.0, 11.0, 12.0]) - ) - count_placeholder = array_ops.placeholder_with_default( - constant_op.constant(5, dtypes.int64), shape=[]) - buffer_size_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - seed_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_dataset = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components) - .repeat(count_placeholder)) - - shuffle_dataset = repeat_dataset.shuffle(buffer_size_placeholder, - seed_placeholder) - - self.assertEqual(tuple([c.shape[1:] for c in components]), - shuffle_dataset.output_shapes) - - # Create initialization ops for iterators without and with - # shuffling, respectively. - iterator = iterator_ops.Iterator.from_structure( - shuffle_dataset.output_types, shuffle_dataset.output_shapes) - init_fifo_op = iterator.make_initializer(repeat_dataset) - init_shuffle_op = iterator.make_initializer(shuffle_dataset) - - get_next = iterator.get_next() - - with self.test_session() as sess: - # First run without shuffling to collect the "ground truth". - sess.run(init_fifo_op) - unshuffled_elements = [] - for _ in range(20): - unshuffled_elements.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Assert that the shuffled dataset has the same elements as the - # "ground truth". - sess.run( - init_shuffle_op, - feed_dict={buffer_size_placeholder: 100, - seed_placeholder: 37}) - shuffled_elements = [] - for _ in range(20): - shuffled_elements.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertAllEqual( - sorted(unshuffled_elements), sorted(shuffled_elements)) - - # Assert that shuffling twice with the same seeds gives the same sequence. - sess.run( - init_shuffle_op, - feed_dict={buffer_size_placeholder: 100, - seed_placeholder: 37}) - reshuffled_elements_same_seed = [] - for _ in range(20): - reshuffled_elements_same_seed.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertEqual(shuffled_elements, reshuffled_elements_same_seed) - - # Assert that shuffling twice with a different seed gives a different - # permutation of the same elements. - sess.run( - init_shuffle_op, - feed_dict={buffer_size_placeholder: 100, - seed_placeholder: 1037}) - reshuffled_elements_different_seed = [] - for _ in range(20): - reshuffled_elements_different_seed.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertNotEqual(shuffled_elements, reshuffled_elements_different_seed) - self.assertAllEqual( - sorted(shuffled_elements), sorted(reshuffled_elements_different_seed)) - - # Assert that the shuffled dataset has the same elements as the - # "ground truth" when the buffer size is smaller than the input - # dataset. - sess.run( - init_shuffle_op, - feed_dict={buffer_size_placeholder: 2, - seed_placeholder: 37}) - reshuffled_elements_small_buffer = [] - for _ in range(20): - reshuffled_elements_small_buffer.append(sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertAllEqual( - sorted(unshuffled_elements), sorted(reshuffled_elements_small_buffer)) - - # Test the case of shuffling an empty dataset. - sess.run(init_shuffle_op, feed_dict={buffer_size_placeholder: 2, - seed_placeholder: 37, - count_placeholder: 0}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testDefaultArguments(self): - components = [0, 1, 2, 3, 4] - iterator = ( - contrib_dataset_ops.Dataset.from_tensor_slices(components).shuffle(5) - .repeat().make_one_shot_iterator()) - - get_next = iterator.get_next() - - with self.test_session() as sess: - counts = collections.defaultdict(lambda: 0) - for _ in range(10): - for _ in range(5): - counts[sess.run(get_next)] += 1 - - for i in range(5): - self.assertEqual(10, counts[i]) - - class ShuffleDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..33c48e20bea53b88d69a59e715af38b22dd2cbd4 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py @@ -0,0 +1,242 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the experimental input pipeline ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.ops import sliding +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class SlideDatasetTest(test.TestCase): + + def testSlideDataset(self): + """Test an dataset that maps a TF function across its input elements.""" + components = (np.arange(7), + np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], + np.array(37.0) * np.arange(7)) + + count = array_ops.placeholder(dtypes.int64, shape=[]) + window_size = array_ops.placeholder(dtypes.int64, shape=[]) + stride = array_ops.placeholder(dtypes.int64, shape=[]) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> + # RepeatDataset(count) -> _SlideDataset(window_size, stride). + iterator = (dataset_ops.Dataset.from_tensor_slices(components) + .map(_map_fn) + .repeat(count) + .apply(sliding.sliding_window_batch(window_size, stride)) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + self.assertEqual([[None] + list(c.shape[1:]) for c in components], + [t.shape.as_list() for t in get_next]) + + with self.test_session() as sess: + # Slide over a finite input, where the window_size divides the + # total number of elements. + sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7}) + # Same formula with convolution layer. + num_batches = (20 * 7 - 14) // 7 + 1 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(14): + self.assertAllEqual(component[(i*7 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over a finite input, where the window_size does not + # divide the total number of elements. + sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9}) + + num_batches = (20 * 7 - 17) // 9 + 1 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(17): + self.assertAllEqual(component[(i*9 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over a finite input, which is less than window_size, + # should fail straight away. + sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 8}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over an empty input should fail straight away. + sess.run(init_op, feed_dict={count: 0, window_size: 8, stride: 4}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Empty window_size should be an initialization time error. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 0, stride: 0}) + + # Invalid stride should be an initialization time error. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0}) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 3}) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 5}) + + def assertSparseValuesEqual(self, a, b): + self.assertAllEqual(a.indices, b.indices) + self.assertAllEqual(a.values, b.values) + self.assertAllEqual(a.dense_shape, b.dense_shape) + + def testSlideSparse(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( + sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + num_batches = (10 - 5) // 3 + 1 + for i in range(num_batches): + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]], + values=[i * 3, i * 3 + 1, i * 3 + 2, i * 3 + 3, i * 3 + 4], + dense_shape=[5, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testSlideSparseWithDifferentDenseShapes(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=array_ops.expand_dims( + math_ops.range(i, dtype=dtypes.int64), 1), + values=array_ops.fill([math_ops.to_int32(i)], i), + dense_shape=[i]) + + iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( + sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + num_batches = (10 - 5) // 3 + 1 + for i in range(num_batches): + actual = sess.run(get_next) + expected_indices = [] + expected_values = [] + for j in range(5): + for k in range(i * 3 + j): + expected_indices.append([j, k]) + expected_values.append(i * 3 + j) + expected = sparse_tensor.SparseTensorValue( + indices=expected_indices, + values=expected_values, + dense_shape=[5, i * 3 + 5 - 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testNestedSlideSparse(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + iterator = (dataset_ops.Dataset.range(10) + .map(_sparse) + .apply(sliding.sliding_window_batch(4, 2)) + .apply(sliding.sliding_window_batch(3, 1)) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + # Slide: 1st batch. + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], + [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], + [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + values=[0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7], + dense_shape=[3, 4, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + # Slide: 2nd batch. + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], + [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], + [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + values=[2, 3, 4, 5, 4, 5, 6, 7, 6, 7, 8, 9], + dense_shape=[3, 4, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testSlideShapeError(self): + + def generator(): + yield [1.0, 2.0, 3.0] + yield [4.0, 5.0, 6.0] + yield [7.0, 8.0, 9.0, 10.0] + + iterator = (dataset_ops.Dataset.from_generator(generator, dtypes.float32, + output_shapes=[None]) + .apply(sliding.sliding_window_batch(3, 1)) + .make_initializable_iterator()) + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r"Cannot batch tensors with different shapes in component 0. " + r"First element had shape \[3\] and element 2 had shape \[4\]."): + sess.run(next_element) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py index efd864f866611bfd3bac1edcf98d84be852410fd..e26cef8ec522c7e69a0c19b2b30a969bbfc0ad78 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import os + import sqlite3 from tensorflow.contrib.data.python.ops import readers diff --git a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9167cb3379bba5cb1ba76a96549395c45dca9e35 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py @@ -0,0 +1,77 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the experimental input pipeline statistics gathering ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import threading + +import numpy as np + +from tensorflow.contrib.data.python.ops import threadpool +from tensorflow.contrib.data.python.ops import unique +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.ops import script_ops +from tensorflow.python.platform import test + + +class OverrideThreadpoolDatasetTest(test.TestCase): + + def testNumThreads(self): + + def get_thread_id(_): + # Python creates a dummy thread object to represent the current + # thread when called from an "alien" thread (such as a + # `PrivateThreadPool` thread in this case). It does not include + # the TensorFlow-given display name, but it has a unique + # identifier that maps one-to-one with the underlying OS thread. + return np.array(threading.current_thread().ident).astype(np.int64) + + for num_threads in [1, 2, 4, 8, 16]: + + dataset = ( + dataset_ops.Dataset.range(1000).map( + lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), + num_parallel_calls=32).apply(unique.unique())) + + dataset = threadpool.override_threadpool( + dataset, + threadpool.PrivateThreadPool( + num_threads, display_name="private_thread_pool_%d" % num_threads)) + + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + thread_ids = [] + try: + while True: + thread_ids.append(sess.run(next_element)) + except errors.OutOfRangeError: + pass + self.assertEqual(len(thread_ids), len(set(thread_ids))) + self.assertGreater(len(thread_ids), 0) + # NOTE(mrry): We don't control the thread pool scheduling, and + # so cannot guarantee that all of the threads in the pool will + # perform work. + self.assertLessEqual(len(thread_ids), num_threads) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py index 55296d5710e7f66408bb7464cf790149d6df9fa1..3c436f7a0b45a13109960e87dd97ca56b10bb871 100644 --- a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py @@ -18,8 +18,8 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.data.python.ops import unique +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py index 5d34b0024c472d0393544ff3dad8acea7964345f..e39fa957f0bbb9d3671274d5f58b993e8399814b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py @@ -20,97 +20,10 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import dataset_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test -class ZipDatasetTest(test.TestCase): - - def testZipDataset(self): - component_placeholders = [ - array_ops.placeholder(dtypes.int64), - array_ops.placeholder(dtypes.int64), - array_ops.placeholder(dtypes.float64) - ] - - datasets = tuple([ - dataset_ops.Dataset.from_tensor_slices(component_placeholder) - for component_placeholder in component_placeholders - ]) - zipped = dataset_ops.Dataset.zip(datasets) - - iterator = zipped.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.test_session() as sess: - equal_length_components = [ - np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 22), - np.array([37.0, 38.0, 39.0, 40.0]) - ] - sess.run(init_op, feed_dict={ph: value for ph, value in zip( - component_placeholders, equal_length_components)}) - for i in range(4): - results = sess.run(get_next) - for component, result_component in zip( - equal_length_components, results): - self.assertAllEqual(component[i], result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - variable_length_components = [[1, 2, 3, 4], [1, 2, 3, 4, 5], [1.0, 2.0]] - sess.run(init_op, feed_dict={ph: value for ph, value in zip( - component_placeholders, variable_length_components)}) - for i in range(2): - results = sess.run(get_next) - for component, result_component in zip( - variable_length_components, results): - self.assertAllEqual(component[i], result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testNestedZipDataset(self): - component_placeholders = [ - array_ops.placeholder(dtypes.int64, shape=[4, 20]), - array_ops.placeholder(dtypes.int64, shape=[4, 22]), - array_ops.placeholder(dtypes.float64, shape=[4]) - ] - - datasets = [ - dataset_ops.Dataset.from_tensor_slices(component_placeholder) - for component_placeholder in component_placeholders - ] - zipped = dataset_ops.Dataset.zip((datasets[0], (datasets[1], datasets[2]))) - - iterator = zipped.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([20], get_next[0].shape) - self.assertEqual([22], get_next[1][0].shape) - self.assertEqual([], get_next[1][1].shape) - - with self.test_session() as sess: - equal_length_components = [ - np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 22), - np.array([37.0, 38.0, 39.0, 40.0]) - ] - sess.run(init_op, feed_dict={ph: value for ph, value in zip( - component_placeholders, equal_length_components)}) - for i in range(4): - result1, (result2, result3) = sess.run(get_next) - self.assertAllEqual(equal_length_components[0][i], result1) - self.assertAllEqual(equal_length_components[1][i], result2) - self.assertAllEqual(equal_length_components[2][i], result3) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - class ZipDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 4349085a10135b4dee842a29916aeb5febe9ddd4..fd871ef5ce098310cadcff34b629d93e40e4843d 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -15,7 +15,7 @@ py_library( name = "dataset_ops", srcs = [ "counter.py", - "dataset_ops.py", + "get_single_element.py", ], srcs_version = "PY2AND3", deps = [ @@ -67,6 +67,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":dataset_ops", + ":shuffle_ops", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", @@ -104,11 +105,15 @@ py_library( "interleave_ops.py", "resampling.py", "scan_ops.py", + "sliding.py", "stats_ops.py", + "threadpool.py", "unique.py", ], srcs_version = "PY2AND3", deps = [ + ":contrib_op_loader", + ":gen_dataset_ops", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", "//tensorflow/python:dataset_ops_gen", @@ -118,10 +123,12 @@ py_library( "//tensorflow/python:logging_ops", "//tensorflow/python:math_ops", "//tensorflow/python:random_ops", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python:tensor_util", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:readers", "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", @@ -130,36 +137,44 @@ py_library( ) tf_gen_op_wrapper_py( - name = "prefetching_ops", - out = "gen_prefetching_ops.py", - deps = ["//tensorflow/contrib/data:prefetching_ops_op_lib"], + name = "gen_dataset_ops", + out = "gen_dataset_ops.py", + deps = ["//tensorflow/contrib/data:dataset_ops_op_lib"], ) tf_kernel_library( - name = "prefetching_ops_kernels", + name = "dataset_ops_kernels", deps = [ - "//tensorflow/contrib/data/kernels:prefetching_kernels", + "//tensorflow/contrib/data/kernels:dataset_kernels", "//tensorflow/core:framework", ], alwayslink = 1, ) tf_custom_op_py_library( - name = "prefetching_py", - srcs = ["prefetching_ops.py"], - dso = ["//tensorflow/contrib/data:_prefetching_ops.so"], + name = "contrib_op_loader", + srcs = ["contrib_op_loader.py"], + dso = ["//tensorflow/contrib/data:_dataset_ops.so"], kernels = [ - ":prefetching_ops_kernels", - "//tensorflow/contrib/data:prefetching_ops_op_lib", + ":dataset_ops_kernels", + "//tensorflow/contrib/data:dataset_ops_op_lib", ], srcs_version = "PY2AND3", deps = [ - ":prefetching_ops", + ":gen_dataset_ops", "//tensorflow/contrib/util:util_py", "//tensorflow/python:platform", ], ) +py_library( + name = "prefetching_ops", + srcs = ["prefetching_ops.py"], + deps = [ + ":contrib_op_loader", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/data/python/ops/contrib_op_loader.py b/tensorflow/contrib/data/python/ops/contrib_op_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..8f495a9dc9c82311435e71d2ac9ed35fd9aea794 --- /dev/null +++ b/tensorflow/contrib/data/python/ops/contrib_op_loader.py @@ -0,0 +1,24 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python helper for loading contrib ops and kernels.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader + +_dataset_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("../../_dataset_ops.so")) diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py deleted file mode 100644 index fafd231061a9108b2585f4fc9256b6f069b7c37a..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/data/python/ops/dataset_ops.py +++ /dev/null @@ -1,691 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Python wrappers for Datasets and Iterators.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.data.python.ops import batching -from tensorflow.contrib.data.python.ops import enumerate_ops -from tensorflow.contrib.data.python.ops import error_ops -from tensorflow.contrib.data.python.ops import grouping -from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.ops import gen_dataset_ops -from tensorflow.python.ops import gen_io_ops -from tensorflow.python.util import deprecation - - -class Dataset(dataset_ops.Dataset): - """Represents a potentially large set of elements. - - A `Dataset` can be used to represent an input pipeline as a - collection of elements (nested structures of tensors) and a "logical - plan" of transformations that act on those elements. - """ - - def __init__(self, dataset): - super(Dataset, self).__init__() - self._dataset = dataset - - @deprecation.deprecated(None, "Use `ds._as_variant_tensor()`.") - def make_dataset_resource(self): - return self._as_variant_tensor() - - def _as_variant_tensor(self): - return self._dataset._as_variant_tensor() # pylint: disable=protected-access - - @property - def output_classes(self): - return self._dataset.output_classes - - @property - def output_shapes(self): - return self._dataset.output_shapes - - @property - def output_types(self): - return self._dataset.output_types - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensors()`.") - def from_tensors(tensors): - """Creates a `Dataset` with a single element, comprising the given tensors. - - Args: - tensors: A nested structure of tensors. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.TensorDataset(tensors)) - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.") - def from_tensor_slices(tensors): - """Creates a `Dataset` whose elements are slices of the given tensors. - - Args: - tensors: A nested structure of tensors, each having the same size in the - 0th dimension. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.TensorSliceDataset(tensors)) - - @staticmethod - @deprecation.deprecated(None, - "Use `tf.data.Dataset.from_sparse_tensor_slices()`.") - def from_sparse_tensor_slices(sparse_tensor): - """Splits each rank-N `tf.SparseTensor` in this dataset row-wise. - - Args: - sparse_tensor: A `tf.SparseTensor`. - - Returns: - A `Dataset` of rank-(N-1) sparse tensors. - """ - return Dataset(dataset_ops.SparseTensorSliceDataset(sparse_tensor)) - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.from_generator()`.") - def from_generator(generator, output_types, output_shapes=None): - """Creates a `Dataset` whose elements are generated by `generator`. - - The `generator` argument must be a callable object that returns - an object that support the `iter()` protocol (e.g. a generator function). - The elements generated by `generator` must be compatible with the given - `output_types` and (optional) `output_shapes` arguments. - - For example: - - ```python - import itertools - - def gen(): - for i in itertools.count(1): - yield (i, [1] * i) - - ds = Dataset.from_generator( - gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None]))) - value = ds.make_one_shot_iterator().get_next() - - sess.run(value) # (1, array([1])) - sess.run(value) # (2, array([1, 1])) - ``` - - Args: - generator: A callable object that takes no arguments and returns an - object that supports the `iter()` protocol. - output_types: A nested structure of `tf.DType` objects corresponding to - each component of an element yielded by `generator`. - output_shapes: (Optional.) A nested structure of `tf.TensorShape` - objects corresponding to each component of an element yielded by - `generator`. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.Dataset.from_generator( - generator, output_types, output_shapes)) - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.range()`.") - def range(*args): - """Creates a `Dataset` of a step-separated range of values. - - For example: - - ```python - Dataset.range(5) == [0, 1, 2, 3, 4] - Dataset.range(2, 5) == [2, 3, 4] - Dataset.range(1, 5, 2) == [1, 3] - Dataset.range(1, 5, -2) == [] - Dataset.range(5, 1) == [] - Dataset.range(5, 1, -2) == [5, 3] - ``` - - Args: - *args: follow same semantics as python's xrange. - len(args) == 1 -> start = 0, stop = args[0], step = 1 - len(args) == 2 -> start = args[0], stop = args[1], step = 1 - len(args) == 3 -> start = args[0], stop = args[1, stop = args[2] - - Returns: - A `RangeDataset`. - - Raises: - ValueError: if len(args) == 0. - """ - return Dataset(dataset_ops.RangeDataset(*args)) - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.zip()`.") - def zip(datasets): - """Creates a `Dataset` by zipping together the given datasets. - - This method has similar semantics to the built-in `zip()` function - in Python, with the main difference being that the `datasets` - argument can be an arbitrary nested structure of `Dataset` objects. - For example: - - ```python - # NOTE: The following examples use `{ ... }` to represent the - # contents of a dataset. - a = { 1, 2, 3 } - b = { 4, 5, 6 } - c = { (7, 8), (9, 10), (11, 12) } - d = { 13, 14 } - - # The nested structure of the `datasets` argument determines the - # structure of elements in the resulting dataset. - Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) } - Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) } - - # The `datasets` argument may contain an arbitrary number of - # datasets. - Dataset.zip((a, b, c)) == { (1, 4, (7, 8)), - (2, 5, (9, 10)), - (3, 6, (11, 12)) } - - # The number of elements in the resulting dataset is the same as - # the size of the smallest dataset in `datasets`. - Dataset.zip((a, d)) == { (1, 13), (2, 14) } - ``` - - Args: - datasets: A nested structure of datasets. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.ZipDataset(datasets)) - - def concatenate(self, dataset): - """Creates a `Dataset` by concatenating given dataset with this dataset. - - ```python - # NOTE: The following examples use `{ ... }` to represent the - # contents of a dataset. - a = { 1, 2, 3 } - b = { 4, 5, 6, 7 } - - # Input dataset and dataset to be concatenated should have same - # nested structures and output types. - # c = { (8, 9), (10, 11), (12, 13) } - # d = { 14.0, 15.0, 16.0 } - # a.concatenate(c) and a.concatenate(d) would result in error. - - a.concatenate(b) == { 1, 2, 3, 4, 5, 6, 7 } - ``` - - Args: - dataset: `Dataset` to be concatenated. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.ConcatenateDataset(self._dataset, dataset)) - - def prefetch(self, buffer_size): - """Creates a `Dataset` that prefetches elements from this dataset. - - Args: - buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the - maximum number elements that will be buffered when prefetching. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.PrefetchDataset(self._dataset, buffer_size)) - - @staticmethod - @deprecation.deprecated(None, "Use `tf.data.Dataset.list_files()`.") - def list_files(file_pattern): - """A dataset of all files matching a pattern. - - Example: - If we had the following files on our filesystem: - - /path/to/dir/a.txt - - /path/to/dir/b.py - - /path/to/dir/c.py - If we pass "/path/to/dir/*.py" as the directory, the dataset would - produce: - - /path/to/dir/b.py - - /path/to/dir/c.py - - Args: - file_pattern: A string or scalar string `tf.Tensor`, representing - the filename pattern that will be matched. - - Returns: - A `Dataset` of strings corresponding to file names. - """ - return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern)) - - def repeat(self, count=None): - """Repeats this dataset `count` times. - - Args: - count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the - number of times the elements of this dataset should be repeated. The - default behavior (if `count` is `None` or `-1`) is for the elements to - be repeated indefinitely. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.RepeatDataset(self._dataset, count)) - - @deprecation.deprecated( - None, "Use `ds.apply(tf.contrib.data.enumerate_dataset())`.") - def enumerate(self, start=0): - """Deprecated: Use `Dataset.apply(tf.contrib.data.enumerate_dataset(..)`.""" - - return self.apply(enumerate_ops.enumerate_dataset(start)) - - def shuffle(self, buffer_size, seed=None): - """Randomly shuffles the elements of this dataset. - - Args: - buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the - number of elements from this dataset from which the new - dataset will sample. - seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the - random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.ShuffleDataset(self._dataset, buffer_size, seed)) - - def cache(self, filename=""): - """Caches the elements in this dataset. - - Args: - filename: A `tf.string` scalar `tf.Tensor`, representing the name of a - directory on the filesystem to use for caching tensors in this Dataset. - If a filename is not provided, the dataset will be cached in memory. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.CacheDataset(self._dataset, filename)) - - def take(self, count): - """Creates a `Dataset` with at most `count` elements from this dataset. - - Args: - count: A `tf.int64` scalar `tf.Tensor`, representing the number of - elements of this dataset that should be taken to form the new dataset. - If `count` is -1, or if `count` is greater than the size of this - dataset, the new dataset will contain all elements of this dataset. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.TakeDataset(self._dataset, count)) - - def skip(self, count): - """Creates a `Dataset` that skips `count` elements from this dataset. - - Args: - count: A `tf.int64` scalar `tf.Tensor`, representing the number - of elements of this dataset that should be skipped to form the - new dataset. If `count` is greater than the size of this - dataset, the new dataset will contain no elements. If `count` - is -1, skips the entire dataset. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.SkipDataset(self._dataset, count)) - - def shard(self, num_shards, index): - """Creates a `Dataset` that includes only 1/`num_shards` of this dataset. - - This dataset operator is very useful when running distributed training, as - it allows each worker to read a unique subset. - - When reading a single input file, you can skip elements as follows: - - ```python - d = tf.data.TFRecordDataset(FLAGS.input_file) - d = d.shard(FLAGS.num_workers, FLAGS.worker_index) - d = d.repeat(FLAGS.num_epochs) - d = d.shuffle(FLAGS.shuffle_buffer_size) - d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads) - ``` - - Important caveats: - - - Be sure to shard before you use any randomizing operator (such as - shuffle). - - Generally it is best if the shard operator is used early in the dataset - pipeline. For example, when reading from a set of TFRecord files, shard - before converting the dataset to input samples. This avoids reading every - file on every worker. The following is an example of an efficient - sharding strategy within a complete pipeline: - - ```python - d = tf.data.Dataset.list_files(FLAGS.pattern) - d = d.shard(FLAGS.num_workers, FLAGS.worker_index) - d = d.repeat(FLAGS.num_epochs) - d = d.shuffle(FLAGS.shuffle_buffer_size) - d = d.interleave(tf.data.TFRecordDataset, - cycle_length=FLAGS.num_readers, block_length=1) - d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads) - ``` - - Args: - num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of - shards operating in parallel. - index: A `tf.int64` scalar `tf.Tensor`, representing the worker index. - - Returns: - A `Dataset`. - - Raises: - ValueError: if `num_shards` or `index` are illegal values. Note: error - checking is done on a best-effort basis, and aren't guaranteed to be - caught upon dataset creation. (e.g. providing in a placeholder tensor - bypasses the early checking, and will instead result in an error during - a session.run call.) - """ - return Dataset(self._dataset.shard(num_shards, index)) - - @deprecation.deprecated( - None, "Use `ds.apply(tf.contrib.data.ignore_errors())`.") - def ignore_errors(self): - """Deprecated: Use `Dataset.apply(tf.contrib.data.ignore_errors())`.""" - - return self.apply(error_ops.ignore_errors()) - - def batch(self, batch_size): - """Combines consecutive elements of this dataset into batches. - - Args: - batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of - consecutive elements of this dataset to combine in a single batch. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.BatchDataset(self._dataset, batch_size)) - - def padded_batch(self, batch_size, padded_shapes, padding_values=None): - """Combines consecutive elements of this dataset into padded batches. - - Like `Dataset.dense_to_sparse_batch()`, this method combines - multiple consecutive elements of this dataset, which might have - different shapes, into a single element. The tensors in the - resulting element have an additional outer dimension, and are - padded to the respective shape in `padded_shapes`. - - Args: - batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of - consecutive elements of this dataset to combine in a single batch. - padded_shapes: A nested structure of `tf.TensorShape` or - `tf.int64` vector tensor-like objects representing the shape - to which the respective component of each input element should - be padded prior to batching. Any unknown dimensions - (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a - tensor-like object) will be padded to the maximum size of that - dimension in each batch. - padding_values: (Optional.) A nested structure of scalar-shaped - `tf.Tensor`, representing the padding values to use for the - respective components. Defaults are `0` for numeric types and - the empty string for string types. - - Returns: - A `Dataset`. - """ - return Dataset( - dataset_ops.PaddedBatchDataset(self._dataset, batch_size, padded_shapes, - padding_values)) - - @deprecation.deprecated( - None, "Use `ds.apply(tf.contrib.data.dense_to_sparse_batch())`.") - def dense_to_sparse_batch(self, batch_size, row_shape): - """Use: `Dataset.apply(tf.contrib.data.dense_to_sparse_batch(...))`.""" - - return self.apply(batching.dense_to_sparse_batch(batch_size, row_shape)) - - @deprecation.deprecated( - None, "Use `ds.apply(tf.contrib.data.group_by_window())`.") - def group_by_window(self, key_func, reduce_func, window_size): - """Deprecated: Use `Dataset.apply(tf.contrib.data.group_by_window(...))`.""" - - return self.apply( - grouping.group_by_window(key_func, reduce_func, window_size)) - - @deprecation.deprecated_args( - None, - "Replace `num_threads=T` with `num_parallel_calls=T`. Replace " - "`output_buffer_size=N` with `ds.prefetch(N)` on the returned dataset.", - "num_threads", "output_buffer_size") - def map(self, - map_func, - num_threads=None, - output_buffer_size=None, - num_parallel_calls=None): - """Maps `map_func` across this datset. - - Args: - map_func: A function mapping a nested structure of tensors (having - shapes and types defined by `self.output_shapes` and - `self.output_types`) to another nested structure of tensors. - num_threads: (Optional.) Deprecated, use `num_parallel_calls` instead. - output_buffer_size: (Optional.) A `tf.int64` scalar `tf.Tensor`, - representing the maximum number of processed elements that will be - buffered. - num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, - representing the number elements to process in parallel. If not - specified, elements will be processed sequentially. - - Returns: - A `Dataset`. - """ - if num_threads is None and num_parallel_calls is None: - ret = Dataset(dataset_ops.MapDataset(self._dataset, map_func)) - else: - if num_threads is None: - ret = Dataset( - dataset_ops.ParallelMapDataset(self._dataset, map_func, - num_parallel_calls)) - else: - ret = Dataset( - dataset_ops.ParallelMapDataset(self._dataset, map_func, - num_threads)) - if output_buffer_size is not None: - ret = ret.prefetch(output_buffer_size) - return ret - - def flat_map(self, map_func): - """Maps `map_func` across this dataset and flattens the result. - - Args: - map_func: A function mapping a nested structure of tensors (having shapes - and types defined by `self.output_shapes` and `self.output_types`) to a - `Dataset`. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.FlatMapDataset(self._dataset, map_func)) - - def interleave(self, map_func, cycle_length, block_length=1): - """Maps `map_func` across this dataset, and interleaves the results. - - For example, you can use `Dataset.interleave()` to process many input files - concurrently: - - ```python - # Preprocess 4 files concurrently, and interleave blocks of 16 records from - # each file. - filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ...] - dataset = (Dataset.from_tensor_slices(filenames) - .interleave(lambda x: - TextLineDataset(x).map(parse_fn, num_parallel_calls=1), - cycle_length=4, block_length=16)) - ``` - - The `cycle_length` and `block_length` arguments control the order in which - elements are produced. `cycle_length` controls the number of input elements - that are processed concurrently. If you set `cycle_length` to 1, this - transformation will handle one input element at a time, and will produce - identical results = to @{tf.data.Dataset.flat_map}. In general, - this transformation will apply `map_func` to `cycle_length` input elements, - open iterators on the returned `Dataset` objects, and cycle through them - producing `block_length` consecutive elements from each iterator, and - consuming the next input element each time it reaches the end of an - iterator. - - For example: - - ```python - # NOTE: The following examples use `{ ... }` to represent the - # contents of a dataset. - a = { 1, 2, 3, 4, 5 } - - # NOTE: New lines indicate "block" boundaries. - a.interleave(lambda x: Dataset.from_tensors(x).repeat(6), - cycle_length=2, block_length=4) == { - 1, 1, 1, 1, - 2, 2, 2, 2, - 1, 1, - 2, 2, - 3, 3, 3, 3, - 4, 4, 4, 4, - 3, 3, - 4, 4, - 5, 5, 5, 5, - 5, 5, - } - ``` - - NOTE: The order of elements yielded by this transformation is - deterministic, as long as `map_func` is a pure function. If - `map_func` contains any stateful operations, the order in which - that state is accessed is undefined. - - Args: - map_func: A function mapping a nested structure of tensors (having shapes - and types defined by `self.output_shapes` and `self.output_types`) to a - `Dataset`. - cycle_length: The number of elements from this dataset that will be - processed concurrently. - block_length: The number of consecutive elements to produce from each - input element before cycling to another input element. - - Returns: - A `Dataset`. - """ - return Dataset( - dataset_ops.InterleaveDataset(self._dataset, map_func, cycle_length, - block_length)) - - @deprecation.deprecated(None, "Use `ds.apply(tf.contrib.data.unbatch())`.") - def unbatch(self): - """Deprecated: Use `Dataset.apply(tf.contrib.data.unbatch()`.""" - - return self.apply(batching.unbatch()) - - def filter(self, predicate): - """Filters this dataset according to `predicate`. - - Args: - predicate: A function mapping a nested structure of tensors (having shapes - and types defined by `self.output_shapes` and `self.output_types`) to a - scalar `tf.bool` tensor. - - Returns: - A `Dataset`. - """ - return Dataset(dataset_ops.FilterDataset(self._dataset, predicate)) - - def apply(self, transformation_func): - """Apply a transformation function to this dataset. - - `apply` enables chaining of custom `Dataset` transformations, which are - represented as functions that take one `Dataset` argument and return a - transformed `Dataset`. - - For example: - - ``` - dataset = (dataset.map(lambda x: x ** 2) - .(group_by_window(key_func, reduce_func, window_size)) - .map(lambda x: x ** 3)) - ``` - - Args: - transformation_func: A function that takes one `Dataset` argument and - returns a `Dataset`. - - Returns: - The `Dataset` returned by applying `transformation_func` to this dataset. - """ - dataset = transformation_func(self) - if not isinstance(dataset, dataset_ops.Dataset): - raise TypeError("`transformation_func` must return a Dataset.") - return Dataset(dataset) - - -def get_single_element(dataset): - """Returns the single element in `dataset` as a nested structure of tensors. - - This function enables you to use a @{tf.data.Dataset} in a stateless - "tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}. - This can be useful when your preprocessing transformations are expressed - as a `Dataset`, and you want to use the transformation at serving time. - For example: - - ```python - input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE]) - - def preprocessing_fn(input_str): - # ... - return image, label - - dataset = (tf.data.Dataset.from_tensor_slices(input_batch) - .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) - .batch(BATCH_SIZE)) - - image_batch, label_batch = tf.contrib.data.get_single_element(dataset) - ``` - - Args: - dataset: A @{tf.data.Dataset} object containing a single element. - - Returns: - A nested structure of @{tf.Tensor} objects, corresponding to the single - element of `dataset`. - - Raises: - TypeError: if `dataset` is not a `tf.data.Dataset` object. - InvalidArgumentError (at runtime): if `dataset` does not contain exactly - one element. - """ - if not isinstance(dataset, dataset_ops.Dataset): - raise TypeError("`dataset` must be a `tf.data.Dataset` object.") - return nest.pack_sequence_as( - dataset.output_types, - gen_dataset_ops.dataset_to_single_element( - dataset._as_variant_tensor(), # pylint: disable=protected-access - output_types=nest.flatten(dataset.output_types), - output_shapes=nest.flatten(dataset.output_shapes))) diff --git a/tensorflow/contrib/data/python/ops/error_ops.py b/tensorflow/contrib/data/python/ops/error_ops.py index aa629cba479102ee4244884e7c546615b28cf4e5..6c21e489f7c35484ebacd465e3b46d6920df5933 100644 --- a/tensorflow/contrib/data/python/ops/error_ops.py +++ b/tensorflow/contrib/data/python/ops/error_ops.py @@ -17,10 +17,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import +from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse -from tensorflow.python.ops import gen_dataset_ops def ignore_errors(): diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py new file mode 100644 index 0000000000000000000000000000000000000000..a817b45b71b608810a9d7536ec123ab84f7cdc3b --- /dev/null +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -0,0 +1,67 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python wrappers for Datasets and Iterators.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.ops import gen_dataset_ops + + +def get_single_element(dataset): + """Returns the single element in `dataset` as a nested structure of tensors. + + This function enables you to use a @{tf.data.Dataset} in a stateless + "tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}. + This can be useful when your preprocessing transformations are expressed + as a `Dataset`, and you want to use the transformation at serving time. + For example: + + ```python + input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE]) + + def preprocessing_fn(input_str): + # ... + return image, label + + dataset = (tf.data.Dataset.from_tensor_slices(input_batch) + .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) + .batch(BATCH_SIZE)) + + image_batch, label_batch = tf.contrib.data.get_single_element(dataset) + ``` + + Args: + dataset: A @{tf.data.Dataset} object containing a single element. + + Returns: + A nested structure of @{tf.Tensor} objects, corresponding to the single + element of `dataset`. + + Raises: + TypeError: if `dataset` is not a `tf.data.Dataset` object. + InvalidArgumentError (at runtime): if `dataset` does not contain exactly + one element. + """ + if not isinstance(dataset, dataset_ops.Dataset): + raise TypeError("`dataset` must be a `tf.data.Dataset` object.") + return nest.pack_sequence_as( + dataset.output_types, + gen_dataset_ops.dataset_to_single_element( + dataset._as_variant_tensor(), # pylint: disable=protected-access + output_types=nest.flatten(dataset.output_types), + output_shapes=nest.flatten(dataset.output_shapes))) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index ef91c56726e969053fdad667dda3e89430045652..a19be222545ef0242502ec07badbdae5c7634a0c 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -17,13 +17,20 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.ops import math_ops def group_by_window(key_func, @@ -45,7 +52,7 @@ def group_by_window(key_func, key_func: A function mapping a nested structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to a scalar `tf.int64` tensor. - reduce_func: A function mapping a key and a dataset of up to `batch_size` + reduce_func: A function mapping a key and a dataset of up to `window_size` consecutive elements matching that key to another dataset. window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements matching the same key to combine in a single @@ -85,6 +92,114 @@ def group_by_window(key_func, return _apply_fn +def bucket_by_sequence_length(element_length_func, + bucket_boundaries, + bucket_batch_sizes, + padded_shapes=None, + padding_values=None, + pad_to_bucket_boundary=False): + """A transformation that buckets elements in a `Dataset` by length. + + Elements of the `Dataset` are grouped together by length and then are padded + and batched. + + This is useful for sequence tasks in which the elements have variable length. + Grouping together elements that have similar lengths reduces the total + fraction of padding in a batch which increases training step efficiency. + + Args: + element_length_func: function from element in `Dataset` to `tf.int64`, + determines the length of the element, which will determine the bucket it + goes into. + bucket_boundaries: `list`, upper length boundaries of the buckets. + bucket_batch_sizes: `list`, batch size per bucket. Length should be + `len(bucket_boundaries) + 1`. + padded_shapes: Nested structure of `tf.TensorShape` to pass to + @{tf.data.Dataset.padded_batch}. If not provided, will use + `dataset.output_shapes`, which will result in variable length dimensions + being padded out to the maximum length in each batch. + padding_values: Values to pad with, passed to + @{tf.data.Dataset.padded_batch}. Defaults to padding with 0. + pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown + size to maximum length in batch. If `True`, will pad dimensions with + unknown size to bucket boundary, and caller must ensure that the source + `Dataset` does not contain any elements with length longer than + `max(bucket_boundaries)`. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + + Raises: + ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`. + """ + with ops.name_scope("bucket_by_seq_length"): + if len(bucket_batch_sizes) != (len(bucket_boundaries) + 1): + raise ValueError( + "len(bucket_batch_sizes) must equal len(bucket_boundaries) + 1") + + batch_sizes = constant_op.constant(bucket_batch_sizes, dtype=dtypes.int64) + + def element_to_bucket_id(element): + """Return int64 id of the length bucket for this element.""" + seq_length = element_length_func(element) + + boundaries = list(bucket_boundaries) + buckets_min = [np.iinfo(np.int32).min] + boundaries + buckets_max = boundaries + [np.iinfo(np.int32).max] + conditions_c = math_ops.logical_and( + math_ops.less_equal(buckets_min, seq_length), + math_ops.less(seq_length, buckets_max)) + bucket_id = math_ops.reduce_min(array_ops.where(conditions_c)) + + return bucket_id + + def window_size_fn(bucket_id): + # The window size is set to the batch size for this bucket + window_size = batch_sizes[bucket_id] + return window_size + + def make_padded_shapes(shapes, none_filler=None): + padded = [] + for shape in nest.flatten(shapes): + shape = tensor_shape.TensorShape(shape) + shape = [ + none_filler if d.value is None else d + for d in shape + ] + padded.append(shape) + return nest.pack_sequence_as(shapes, padded) + + def batching_fn(bucket_id, grouped_dataset): + """Batch elements in dataset.""" + batch_size = batch_sizes[bucket_id] + none_filler = None + if pad_to_bucket_boundary: + err_msg = ("When pad_to_bucket_boundary=True, elements must have " + "length <= max(bucket_boundaries).") + check = check_ops.assert_less( + bucket_id, + constant_op.constant(len(bucket_batch_sizes) - 1, + dtype=dtypes.int64), + message=err_msg) + with ops.control_dependencies([check]): + boundaries = constant_op.constant(bucket_boundaries, + dtype=dtypes.int64) + bucket_boundary = boundaries[bucket_id] + none_filler = bucket_boundary + shapes = make_padded_shapes( + padded_shapes or grouped_dataset.output_shapes, + none_filler=none_filler) + return grouped_dataset.padded_batch(batch_size, shapes, padding_values) + + def _apply_fn(dataset): + return dataset.apply( + group_by_window(element_to_bucket_id, batching_fn, + window_size_func=window_size_fn)) + + return _apply_fn + + class _VariantDataset(dataset_ops.Dataset): """A Dataset wrapper for a tf.variant-typed function argument.""" diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index 3124ca1d1540e12d949dded88ce1c66181be3595..91f19da02d4a479820782822475d9121125fc38e 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -17,101 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import convert -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function -from tensorflow.python.framework import ops -from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.data.ops import readers from tensorflow.python.util import deprecation -class ParallelInterleaveDataset(dataset_ops.Dataset): - """A `Dataset` that maps a function over its input and flattens the result.""" - - def __init__(self, input_dataset, map_func, cycle_length, block_length, - sloppy, buffer_output_elements, prefetch_input_elements): - """See `tf.contrib.data.parallel_interleave()` for details.""" - super(ParallelInterleaveDataset, self).__init__() - self._input_dataset = input_dataset - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if dataset_ops._should_unpack_args(nested_args): # pylint: disable=protected-access - dataset = map_func(*nested_args) - else: - dataset = map_func(nested_args) - - if not isinstance(dataset, dataset_ops.Dataset): - raise TypeError("`map_func` must return a `Dataset` object.") - - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - - return dataset._as_variant_tensor() # pylint: disable=protected-access - - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) - - self._cycle_length = ops.convert_to_tensor( - cycle_length, dtype=dtypes.int64, name="cycle_length") - self._block_length = ops.convert_to_tensor( - block_length, dtype=dtypes.int64, name="block_length") - self._sloppy = ops.convert_to_tensor( - sloppy, dtype=dtypes.bool, name="sloppy") - self._buffer_output_elements = convert.optional_param_to_tensor( - "buffer_output_elements", - buffer_output_elements, - argument_default=2 * block_length) - self._prefetch_input_elements = convert.optional_param_to_tensor( - "prefetch_input_elements", - prefetch_input_elements, - argument_default=2 * cycle_length) - - def _as_variant_tensor(self): - return gen_dataset_ops.parallel_interleave_dataset( - self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - self._map_func.captured_inputs, - self._cycle_length, - self._block_length, - self._sloppy, - self._buffer_output_elements, - self._prefetch_input_elements, - f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) - - @property - def output_classes(self): - return self._output_classes - - @property - def output_shapes(self): - return self._output_shapes - - @property - def output_types(self): - return self._output_types - - def parallel_interleave(map_func, cycle_length, block_length=1, @@ -162,7 +71,7 @@ def parallel_interleave(map_func, @{tf.data.Dataset.apply}. """ def _apply_fn(dataset): - return ParallelInterleaveDataset( + return readers.ParallelInterleaveDataset( dataset, map_func, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements) @@ -221,7 +130,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1): @{tf.data.Dataset.apply}. """ def _apply_fn(dataset): - return ParallelInterleaveDataset( + return readers.ParallelInterleaveDataset( dataset, map_func, cycle_length, diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index cfe8012b5657995b78d701528ea35cbb3748adb9..7059b358f349e0ec847e85c37652012d48ed910a 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -17,25 +17,23 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.ops import gen_prefetching_ops -from tensorflow.contrib.util import loader -from tensorflow.python.platform import resource_loader - -_prefetching_ops = loader.load_op_library( - resource_loader.get_path_to_datafile("../../_prefetching_ops.so")) +from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import +from tensorflow.contrib.data.python.ops import gen_dataset_ops # TODO(rohanj): Add a python class that constructs resource in the __init__ # method and provides a get_next() that calls the prefetch op. def function_buffering_resource(string_arg, target_device, - shared_name, f, buffer_size, thread_pool_size=1, container="", + shared_name=None, name=None): - return gen_prefetching_ops.function_buffering_resource( + if shared_name is None: + shared_name = "" + return gen_dataset_ops.function_buffering_resource( string_arg=string_arg, target_device=target_device, shared_name=shared_name, @@ -49,7 +47,7 @@ def function_buffering_resource(string_arg, def function_buffering_resource_get_next(function_buffer_resource, output_types, name=None): - return gen_prefetching_ops.function_buffering_resource_get_next( + return gen_dataset_ops.function_buffering_resource_get_next( function_buffer_resource=function_buffer_resource, output_types=output_types, name=name) diff --git a/tensorflow/contrib/data/python/ops/random_ops.py b/tensorflow/contrib/data/python/ops/random_ops.py index 7d727165feabb101549567f28a2dfa07083de244..28ef5e50f39dd7d1b6f124e58e068fc968ddd6dc 100644 --- a/tensorflow/contrib/data/python/ops/random_ops.py +++ b/tensorflow/contrib/data/python/ops/random_ops.py @@ -19,11 +19,10 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops @@ -34,16 +33,7 @@ class RandomDataset(dataset_ops.Dataset): def __init__(self, seed=None): """A `Dataset` of pseudorandom values.""" super(RandomDataset, self).__init__() - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) def _as_variant_tensor(self): return gen_dataset_ops.random_dataset( diff --git a/tensorflow/contrib/data/python/ops/readers.py b/tensorflow/contrib/data/python/ops/readers.py index 347e5edc7b0d479dfa260e8cec500ffaaba375be..b346bed3e6533503e65c775295e29c1d106cbc7b 100644 --- a/tensorflow/contrib/data/python/ops/readers.py +++ b/tensorflow/contrib/data/python/ops/readers.py @@ -17,9 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.ops import dataset_ops as contrib_dataset_ops +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.ops import readers +from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.data.util import nest from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -30,77 +31,147 @@ from tensorflow.python.platform import gfile from tensorflow.python.util import deprecation -class TextLineDataset(contrib_dataset_ops.Dataset): - """A `Dataset` comprising lines from one or more text files.""" +def make_batched_features_dataset(file_pattern, + batch_size, + features, + reader=core_readers.TFRecordDataset, + reader_args=None, + num_epochs=None, + shuffle=True, + shuffle_buffer_size=10000, + shuffle_seed=None, + prefetch_buffer_size=1, + reader_num_threads=1, + parser_num_threads=2, + sloppy_ordering=False): + """Returns a `Dataset` of feature dictionaries from `Example` protos. - @deprecation.deprecated(None, "Use `tf.data.TextLineDataset`.") - def __init__(self, filenames, compression_type=None, buffer_size=None): - """Creates a `TextLineDataset`. + Example: - Args: - filenames: A `tf.string` tensor containing one or more filenames. - compression_type: (Optional.) A `tf.string` scalar evaluating to one of - `""` (no compression), `"ZLIB"`, or `"GZIP"`. - buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes - to buffer. A value of 0 results in the default buffering values chosen - based on the compression type. - """ - dataset = readers.TextLineDataset(filenames, compression_type, - buffer_size) - super(TextLineDataset, self).__init__(dataset) + ``` + serialized_examples = [ + features { + feature { key: "age" value { int64_list { value: [ 0 ] } } } + feature { key: "gender" value { bytes_list { value: [ "f" ] } } } + feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } } + }, + features { + feature { key: "age" value { int64_list { value: [] } } } + feature { key: "gender" value { bytes_list { value: [ "f" ] } } } + feature { key: "kws" value { bytes_list { value: [ "sports" ] } } } + } + ] + ``` + We can use arguments: -class TFRecordDataset(contrib_dataset_ops.Dataset): - """A `Dataset` comprising records from one or more TFRecord files.""" + ``` + features: { + "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), + "gender": FixedLenFeature([], dtype=tf.string), + "kws": VarLenFeature(dtype=tf.string), + } + ``` - @deprecation.deprecated(None, "Use `tf.data.TFRecordDataset`.") - def __init__(self, filenames, compression_type=None, buffer_size=None): - """Creates a `TFRecordDataset`. + And the expected output is: - Args: - filenames: A `tf.string` tensor containing one or more filenames. - compression_type: (Optional.) A `tf.string` scalar evaluating to one of - `""` (no compression), `"ZLIB"`, or `"GZIP"`. - buffer_size: (Optional.) A `tf.int64` scalar representing the number of - bytes in the read buffer. 0 means no buffering. - """ - dataset = readers.TFRecordDataset(filenames, compression_type, - buffer_size) - super(TFRecordDataset, self).__init__(dataset) + ```python + { + "age": [[0], [-1]], + "gender": [["f"], ["f"]], + "kws": SparseTensor( + indices=[[0, 0], [0, 1], [1, 0]], + values=["code", "art", "sports"] + dense_shape=[2, 2]), + } + ``` + Args: + file_pattern: List of files or patterns of file paths containing + `Example` records. See `tf.gfile.Glob` for pattern rules. + batch_size: An int representing the number of consecutive elements of this + dataset to combine in a single batch. + features: A `dict` mapping feature keys to `FixedLenFeature` or + `VarLenFeature` values. See `tf.parse_example`. + reader: A function or class that can be + called with a `filenames` tensor and (optional) `reader_args` and returns + a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. + reader_args: Additional arguments to pass to the reader class. + num_epochs: Integer specifying the number of times to read through the + dataset. If None, cycles through the dataset forever. Defaults to `None`. + shuffle: A boolean, indicates whether the input should be shuffled. Defaults + to `True`. + shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity + ensures better shuffling but would increase memory usage and startup time. + shuffle_seed: Randomization seed to use for shuffling. + prefetch_buffer_size: Number of feature batches to prefetch in order to + improve performance. Recommended value is the number of batches consumed + per training step (default is 1). + reader_num_threads: Number of threads used to read `Example` records. If >1, + the results will be interleaved. + parser_num_threads: Number of threads to use for parsing `Example` tensors + into a dictionary of `Feature` tensors. + sloppy_ordering: If `True`, reading performance will be improved at + the cost of non-deterministic ordering. If `False`, the order of elements + produced is deterministic prior to shuffling (elements are still + randomized if `shuffle=True`. Note that if the seed is set, then order + of elements after shuffling is deterministic). Defaults to `False`. -class FixedLengthRecordDataset(contrib_dataset_ops.Dataset): - """A `Dataset` of fixed-length records from one or more binary files.""" + Returns: + A dataset of `dict` elements. Each `dict` maps feature keys to + `Tensor` or `SparseTensor` objects. + """ + # Create dataset of all matching filenames + if shuffle: + dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=True) + else: + # TODO(b/73959787): Use Dataset.list_files() once ordering is deterministic. + filenames = _get_file_names(file_pattern, shuffle) + dataset = dataset_ops.Dataset.from_tensor_slices(filenames) + + # Read `Example` records from files as tensor objects. + if reader_args is None: + reader_args = [] + + # Read files sequentially (if reader_num_threads=1) or in parallel + dataset = dataset.apply( + interleave_ops.parallel_interleave( + lambda filename: reader(filename, *reader_args), + cycle_length=reader_num_threads, + sloppy=sloppy_ordering)) + + # Extract values if the `Example` tensors are stored as key-value tuples. + if dataset.output_types == (dtypes.string, dtypes.string): + dataset = dataset.map(lambda _, v: v) - @deprecation.deprecated(None, "Use `tf.data.FixedLengthRecordDataset`.") - def __init__(self, - filenames, - record_bytes, - header_bytes=None, - footer_bytes=None, - buffer_size=None): - """Creates a `FixedLengthRecordDataset`. + # Apply dataset repeat and shuffle transformations. + repeat_dataset = (num_epochs != 1) + if repeat_dataset and shuffle: + # Used fused shuffle_and_repeat operation for better performance + dataset = dataset.apply( + shuffle_ops.shuffle_and_repeat(shuffle_buffer_size, num_epochs, + shuffle_seed)) + elif repeat_dataset: + dataset = dataset.repeat(num_epochs) + elif shuffle: + dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) - Args: - filenames: A `tf.string` tensor containing one or more filenames. - record_bytes: A `tf.int64` scalar representing the number of bytes in - each record. - header_bytes: (Optional.) A `tf.int64` scalar representing the number of - bytes to skip at the start of a file. - footer_bytes: (Optional.) A `tf.int64` scalar representing the number of - bytes to ignore at the end of a file. - buffer_size: (Optional.) A `tf.int64` scalar representing the number of - bytes to buffer when reading. - """ - dataset = readers.FixedLengthRecordDataset( - filenames, record_bytes, header_bytes, footer_bytes, buffer_size) - super(FixedLengthRecordDataset, self).__init__(dataset) + dataset = dataset.batch(batch_size) + + # Parse `Example` tensors to a dictionary of `Feature` tensors. + dataset = dataset.map( + lambda x: parsing_ops.parse_example(x, features), + num_parallel_calls=parser_num_threads) + dataset = dataset.prefetch(prefetch_buffer_size) + return dataset +@deprecation.deprecated(None, + "Use `tf.contrib.data.make_batched_features_dataset`") def read_batch_features(file_pattern, batch_size, features, - reader, + reader=core_readers.TFRecordDataset, reader_args=None, randomize_input=True, num_epochs=None, @@ -154,43 +225,38 @@ def read_batch_features(file_pattern, dataset to combine in a single batch. features: A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.parse_example`. - reader: A function or class that can be called with a `filenames` tensor - and (optional) `reader_args` and returns a `Dataset` of Examples. + reader: A function or class that can be + called with a `filenames` tensor and (optional) `reader_args` and returns + a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. reader_args: Additional arguments to pass to the reader class. randomize_input: Whether the input should be randomized. num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. - capacity: Capacity of the ShuffleDataset. A large capacity ensures better + capacity: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time. - Returns: A dict from keys in features to `Tensor` or `SparseTensor` objects. """ - filenames = _get_file_names(file_pattern, randomize_input) - if reader_args: - dataset = reader(filenames, *reader_args) - else: - dataset = reader(filenames) - if dataset.output_types == (dtypes.string, dtypes.string): - dataset = dataset.map(lambda _, v: v) - if num_epochs != 1: - dataset = dataset.repeat(num_epochs) - if randomize_input: - dataset = dataset.shuffle(capacity) - dataset = dataset.batch(batch_size) - dataset = dataset.map(lambda x: parsing_ops.parse_example(x, features)) - dataset = dataset.prefetch(1) + dataset = make_batched_features_dataset( + file_pattern, + batch_size, + features, + reader=reader, + reader_args=reader_args, + shuffle=randomize_input, + num_epochs=num_epochs, + shuffle_buffer_size=capacity) iterator = dataset.make_one_shot_iterator() outputs = iterator.get_next() return outputs -def _get_file_names(file_pattern, randomize_input): +def _get_file_names(file_pattern, shuffle): """Parse list of file names from pattern, optionally shuffled. Args: file_pattern: File glob pattern, or list of glob patterns. - randomize_input: Whether to shuffle the order of file names. + shuffle: Whether to shuffle the order of file names. Returns: List of file names matching `file_pattern`. @@ -211,19 +277,12 @@ def _get_file_names(file_pattern, randomize_input): raise ValueError("No files match %s." % file_pattern) # Sort files so it will be deterministic for unit tests. - if not randomize_input: + if not shuffle: file_names = sorted(file_names) return file_names -class SqlDataset(contrib_dataset_ops.Dataset): - - def __init__(self, driver_name, data_source_name, query, output_types): - dataset = _SqlDataset(driver_name, data_source_name, query, output_types) - super(SqlDataset, self).__init__(dataset) - - -class _SqlDataset(dataset_ops.Dataset): +class SqlDataset(dataset_ops.Dataset): """A `Dataset` consisting of the results from a SQL query.""" def __init__(self, driver_name, data_source_name, query, output_types): @@ -255,7 +314,7 @@ class _SqlDataset(dataset_ops.Dataset): output_types: A tuple of `tf.DType` objects representing the types of the columns returned by `query`. """ - super(_SqlDataset, self).__init__() + super(SqlDataset, self).__init__() self._driver_name = ops.convert_to_tensor( driver_name, dtype=dtypes.string, name="driver_name") self._data_source_name = ops.convert_to_tensor( diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py index 99bb79bc06a421f811869ca9169aaa11deaca2f3..f35795abd38000b13cec0f08596e2ff66e86286c 100644 --- a/tensorflow/contrib/data/python/ops/shuffle_ops.py +++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py @@ -19,11 +19,11 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed from tensorflow.python.ops import gen_dataset_ops @@ -45,17 +45,7 @@ class _ShuffleAndRepeatDataset(dataset_ops.Dataset): else: self._count = ops.convert_to_tensor( count, dtype=dtypes.int64, name="count") - - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) def _as_variant_tensor(self): # pylint: disable=protected-access diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py new file mode 100644 index 0000000000000000000000000000000000000000..19cc3cb89fc5c494f79ce1d25ed57c92099c8bd2 --- /dev/null +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -0,0 +1,102 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Sliding dataset transformations.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import gen_dataset_ops + + +class _SlideDataset(dataset_ops.Dataset): + """A `Dataset` that passes a sliding window over its input.""" + + def __init__(self, input_dataset, window_size, stride=1): + """See `sliding_window_batch` for details.""" + super(_SlideDataset, self).__init__() + self._input_dataset = input_dataset + self._window_size = ops.convert_to_tensor( + window_size, dtype=dtypes.int64, name="window_size") + self._stride = ops.convert_to_tensor( + stride, dtype=dtypes.int64, name="stride") + + def _as_variant_tensor(self): + return gen_dataset_ops.slide_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + window_size=self._window_size, + stride=self._stride, + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes)), + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes))) + + @property + def output_classes(self): + return self._input_dataset.output_classes + + @property + def output_shapes(self): + input_shapes = self._input_dataset.output_shapes + return nest.pack_sequence_as(input_shapes, [ + tensor_shape.vector(None).concatenate(s) + for s in nest.flatten(self._input_dataset.output_shapes) + ]) + + @property + def output_types(self): + return self._input_dataset.output_types + + +def sliding_window_batch(window_size, stride=1): + """A sliding window with size of `window_size` and step of `stride`. + + This transformation passes a sliding window over this dataset. The + window size is `window_size` and step size is `stride`. If the left + elements cannot fill up the sliding window, this transformation will + drop the final smaller element. For example: + + ```python + # NOTE: The following examples use `{ ... }` to represent the + # contents of a dataset. + a = { [1], [2], [3], [4], [5], [6] } + + a.apply(tf.contrib.data.sliding_window_batch(window_size=3, stride=2)) == + { + [[1], [2], [3]], + [[3], [4], [5]], + } + ``` + + Args: + window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of + elements in the sliding window. + stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the + steps moving the sliding window forward for one iteration. The default + is `1`. It must be in `[1, window_size)`. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + def _apply_fn(dataset): + return _SlideDataset(dataset, window_size, stride) + + return _apply_fn diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 9cd1701c397b5a0bf5cc47c1bcab033704794d80..b5cf0fcfe91ebc22444302fca5d488a278ef2994 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -47,7 +47,7 @@ class StatsAggregator(object): dataset = ... iterator = dataset.make_one_shot_iterator() stats_aggregator = stats_ops.StatsAggregator() - set_op = stats_op.set_stats_aggregator_op(iterator, stats_aggregator) + set_op = stats_aggregator.subscribe(iterator) with tf.Session() as sess: # Running `set_op` will associate `iterator` with `stats_aggregator`. diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py new file mode 100644 index 0000000000000000000000000000000000000000..56f67e1766bbaff680bdff6b939df0c3ba68c679 --- /dev/null +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -0,0 +1,102 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Experimental API for controlling threading in `tf.data` pipelines.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import threading + +from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import +from tensorflow.contrib.data.python.ops import gen_dataset_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse +from tensorflow.python.eager import context +from tensorflow.python.ops import resource_variable_ops + +_uid_counter = 0 +_uid_lock = threading.Lock() + + +def _generate_shared_name(prefix): + with _uid_lock: + global _uid_counter + uid = _uid_counter + _uid_counter += 1 + return "{}{}".format(prefix, uid) + + +class PrivateThreadPool(object): + """A stateful resource that represents a private thread pool.""" + + def __init__(self, num_threads, display_name=None): + """Creates a `PrivateThreadPool` with the given number of threads.""" + if context.executing_eagerly(): + shared_name = _generate_shared_name("privatethreadpool") + self._resource = gen_dataset_ops.thread_pool_handle( + num_threads=num_threads, + display_name=display_name, + shared_name=shared_name) + self._resource_deleter = resource_variable_ops.EagerResourceDeleter( + handle=self._resource, handle_device=context.context().device_name) + else: + self._resource = gen_dataset_ops.thread_pool_handle( + num_threads=num_threads, display_name=display_name) + + +class _ThreadPoolDataset(dataset_ops.Dataset): + """A `Dataset` that acts as an identity, and sets a custom threadpool.""" + + def __init__(self, input_dataset, thread_pool): + super(_ThreadPoolDataset, self).__init__() + self._input_dataset = input_dataset + self._thread_pool = thread_pool + + def _as_variant_tensor(self): + return gen_dataset_ops.thread_pool_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + self._thread_pool._resource, # pylint: disable=protected-access + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes)), + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes))) + + @property + def output_shapes(self): + return self._input_dataset.output_shapes + + @property + def output_types(self): + return self._input_dataset.output_types + + @property + def output_classes(self): + return self._input_dataset.output_classes + + +def override_threadpool(dataset, thread_pool): + """Returns a new dataset that uses the given thread pool for its operations. + + Args: + dataset: A `tf.data.Dataset` object. + thread_pool: A `PrivateThreadPool` object. + + Returns: + A dataset containing the same values as `dataset`, but which uses + `thread_pool` to compute any of its parallel operations (such as + @{tf.data.Dataset.map}). + """ + return _ThreadPoolDataset(dataset, thread_pool) diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py index 133e17d20d0fc4c8d52cef3c95c132374e927a0b..765ef3f9b6d42c9d7af3ce4916731d37d65c9260 100644 --- a/tensorflow/contrib/data/python/ops/unique.py +++ b/tensorflow/contrib/data/python/ops/unique.py @@ -17,11 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import +from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes -from tensorflow.python.ops import gen_dataset_ops def unique(): diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD index 7f510c42215f48a9e795eb81bd9f66b0a2108335..96d2763f7f4a20d7e0f2a9c0f17609103ed19aa8 100644 --- a/tensorflow/contrib/distributions/BUILD +++ b/tensorflow/contrib/distributions/BUILD @@ -251,6 +251,21 @@ cuda_py_test( ], ) +cuda_py_test( + name = "kumaraswamy_test", + srcs = ["python/kernel_tests/kumaraswamy_test.py"], + additional_deps = [ + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:nn_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "moving_stats_test", size = "small", @@ -335,6 +350,7 @@ cuda_py_test( "//tensorflow/python:nn_ops", "//tensorflow/python:platform_test", ], + tags = ["nomsan"], ) cuda_py_test( @@ -403,7 +419,7 @@ cuda_py_test( cuda_py_test( name = "poisson_lognormal_test", - size = "small", + size = "medium", srcs = ["python/kernel_tests/poisson_lognormal_test.py"], additional_deps = [ ":distributions_py", @@ -438,6 +454,7 @@ cuda_py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( @@ -459,6 +476,20 @@ cuda_py_test( tags = ["nomsan"], # disable to avoid false positives from scipy. ) +cuda_py_test( + name = "statistical_testing_test", + size = "medium", + srcs = [ + "python/kernel_tests/statistical_testing_test.py", + ], + additional_deps = [ + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + ], + tags = ["noasan"], +) + cuda_py_test( name = "vector_sinh_arcsinh_diag_test", size = "medium", @@ -801,6 +832,22 @@ cuda_py_test( ], ) +cuda_py_test( + name = "batch_normalization_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/batch_normalization_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "chain_test", size = "small", @@ -915,6 +962,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "kumaraswamy_bijector_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/kumaraswamy_bijector_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "masked_autoregressive_test", size = "small", @@ -984,7 +1050,7 @@ cuda_py_test( cuda_py_test( name = "reshape_test", - size = "small", + size = "medium", srcs = ["python/kernel_tests/bijectors/reshape_test.py"], additional_deps = [ ":bijectors_py", @@ -1055,6 +1121,7 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:platform_test", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py index 60a187e541df4a794ae3944c30c427944915f7d0..61c411271d0bb8d7b4cc3b14992b82ec1e5674ed 100644 --- a/tensorflow/contrib/distributions/__init__.py +++ b/tensorflow/contrib/distributions/__init__.py @@ -40,6 +40,7 @@ from tensorflow.contrib.distributions.python.ops.geometric import * from tensorflow.contrib.distributions.python.ops.half_normal import * from tensorflow.contrib.distributions.python.ops.independent import * from tensorflow.contrib.distributions.python.ops.inverse_gamma import * +from tensorflow.contrib.distributions.python.ops.kumaraswamy import * from tensorflow.contrib.distributions.python.ops.logistic import * from tensorflow.contrib.distributions.python.ops.mixture import * from tensorflow.contrib.distributions.python.ops.mixture_same_family import * @@ -97,7 +98,6 @@ _allowed_symbols = [ 'Autoregressive', 'Binomial', 'Bernoulli', - 'BernoulliWithSigmoidProbs', 'Beta', 'BetaWithSoftplusConcentration', 'Categorical', @@ -115,6 +115,7 @@ _allowed_symbols = [ 'Independent', 'InverseGamma', 'InverseGammaWithSoftplusConcentrationRate', + 'Kumaraswamy', 'Laplace', 'LaplaceWithSoftplusScale', 'Logistic', diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a215a4a2b1ffbea7951bdb9b4352ed567e0b1e41 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py @@ -0,0 +1,236 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for BatchNorm Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib import distributions +from tensorflow.contrib.distributions.python.ops import test_util +from tensorflow.contrib.distributions.python.ops.bijectors.batch_normalization import BatchNormalization +from tensorflow.contrib.distributions.python.ops.bijectors.invert import Invert +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.layers import normalization +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables +from tensorflow.python.ops.distributions import normal as normal_lib +from tensorflow.python.ops.distributions import transformed_distribution as transformed_distribution_lib +from tensorflow.python.platform import test +from tensorflow.python.training import adam + + +class BatchNormTest(test_util.VectorDistributionTestHelpers, + test.TestCase): + + def _reduction_axes(self, input_shape, event_dims): + if isinstance(event_dims, int): + event_dims = [event_dims] + ndims = len(input_shape) + # Convert event_dims to non-negative indexing. + event_dims = list(event_dims) + for idx, x in enumerate(event_dims): + if x < 0: + event_dims[idx] = ndims + x + return tuple(i for i in range(ndims) if i not in event_dims) + + def testForwardInverse(self): + """Tests forward and backward passes with different event shapes. + + input_shape: Tuple of shapes for input tensor. + event_dims: Tuple of dimension indices that will be normalized. + training: Boolean of whether bijector runs in training or inference mode. + """ + params = [ + ((5*2, 4), [-1], False), + ((5, 2, 4), [-1], False), + ((5, 2, 4), [1, 2], False), + ((5, 2, 4), [0, 1], False), + ((5*2, 4), [-1], True), + ((5, 2, 4), [-1], True), + ((5, 2, 4), [1, 2], True), + ((5, 2, 4), [0, 1], True) + ] + for input_shape, event_dims, training in params: + x_ = np.arange(5 * 4 * 2).astype(np.float32).reshape(input_shape) + with self.test_session() as sess: + x = constant_op.constant(x_) + # When training, memorize the exact mean of the last + # minibatch that it normalized (instead of moving average assignment). + layer = normalization.BatchNormalization( + axis=event_dims, momentum=0., epsilon=0.) + batch_norm = BatchNormalization( + batchnorm_layer=layer, training=training) + # Minibatch statistics are saved only after norm_x has been computed. + norm_x = batch_norm.inverse(x) + with ops.control_dependencies(batch_norm.batchnorm.updates): + moving_mean = array_ops.identity(batch_norm.batchnorm.moving_mean) + moving_var = array_ops.identity(batch_norm.batchnorm.moving_variance) + denorm_x = batch_norm.forward(array_ops.identity(norm_x)) + fldj = batch_norm.forward_log_det_jacobian(x) + # Use identity to invalidate cache. + ildj = batch_norm.inverse_log_det_jacobian( + array_ops.identity(denorm_x)) + variables.global_variables_initializer().run() + # Update variables. + norm_x_ = sess.run(norm_x) + [ + norm_x_, + moving_mean_, + moving_var_, + denorm_x_, + ildj_, + fldj_, + ] = sess.run([ + norm_x, + moving_mean, + moving_var, + denorm_x, + ildj, + fldj, + ]) + self.assertEqual("batch_normalization", batch_norm.name) + + reduction_axes = self._reduction_axes(input_shape, event_dims) + keepdims = len(event_dims) > 1 + + expected_batch_mean = np.mean( + x_, axis=reduction_axes, keepdims=keepdims) + expected_batch_var = np.var(x_, axis=reduction_axes, keepdims=keepdims) + + if training: + # When training=True, values become normalized across batch dim and + # original values are recovered after de-normalizing. + zeros = np.zeros_like(norm_x_) + self.assertAllClose(np.mean(zeros, axis=reduction_axes), + np.mean(norm_x_, axis=reduction_axes)) + + self.assertAllClose(expected_batch_mean, moving_mean_) + self.assertAllClose(expected_batch_var, moving_var_) + self.assertAllClose(x_, denorm_x_, atol=1e-5) + # Since moving statistics are set to batch statistics after + # normalization, ildj and -fldj should match. + self.assertAllClose(ildj_, -fldj_) + # ildj is computed with minibatch statistics. + expected_ildj = np.sum(np.log(1.) - .5 * np.log( + expected_batch_var + batch_norm.batchnorm.epsilon)) + self.assertAllClose(expected_ildj, ildj_) + else: + # When training=False, moving_mean, moving_var remain at their + # initialized values (0., 1.), resulting in no scale/shift (a small + # shift occurs if epsilon > 0.) + self.assertAllClose(x_, norm_x_) + self.assertAllClose(x_, denorm_x_, atol=1e-5) + # ildj is computed with saved statistics. + expected_ildj = np.sum( + np.log(1.) - .5 * np.log(1. + batch_norm.batchnorm.epsilon)) + self.assertAllClose(expected_ildj, ildj_) + + def testMaximumLikelihoodTraining(self): + # Test Maximum Likelihood training with default bijector. + with self.test_session() as sess: + base_dist = distributions.MultivariateNormalDiag(loc=[0., 0.]) + batch_norm = BatchNormalization(training=True) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=base_dist, + bijector=batch_norm) + target_dist = distributions.MultivariateNormalDiag(loc=[1., 2.]) + target_samples = target_dist.sample(100) + dist_samples = dist.sample(3000) + loss = -math_ops.reduce_mean(dist.log_prob(target_samples)) + with ops.control_dependencies(batch_norm.batchnorm.updates): + train_op = adam.AdamOptimizer(1e-2).minimize(loss) + moving_mean = array_ops.identity(batch_norm.batchnorm.moving_mean) + moving_var = array_ops.identity(batch_norm.batchnorm.moving_variance) + variables.global_variables_initializer().run() + for _ in range(3000): + sess.run(train_op) + [ + dist_samples_, + moving_mean_, + moving_var_ + ] = sess.run([ + dist_samples, + moving_mean, + moving_var + ]) + self.assertAllClose([1., 2.], np.mean(dist_samples_, axis=0), atol=5e-2) + self.assertAllClose([1., 2.], moving_mean_, atol=5e-2) + self.assertAllClose([1., 1.], moving_var_, atol=5e-2) + + def testLogProb(self): + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = BatchNormalization(batchnorm_layer=layer, training=False) + base_dist = distributions.MultivariateNormalDiag(loc=[0., 0.]) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=base_dist, + bijector=batch_norm, + validate_args=True) + samples = dist.sample(int(1e5)) + # No volume distortion since training=False, bijector is initialized + # to the identity transformation. + base_log_prob = base_dist.log_prob(samples) + dist_log_prob = dist.log_prob(samples) + variables.global_variables_initializer().run() + base_log_prob_, dist_log_prob_ = sess.run([base_log_prob, dist_log_prob]) + self.assertAllClose(base_log_prob_, dist_log_prob_) + + def testMutuallyConsistent(self): + # BatchNorm bijector is only mutually consistent when training=False. + dims = 4 + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = BatchNormalization(batchnorm_layer=layer, training=False) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=normal_lib.Normal(loc=0., scale=1.), + bijector=batch_norm, + event_shape=[dims], + validate_args=True) + self.run_test_sample_consistent_log_prob( + sess_run_fn=sess.run, + dist=dist, + num_samples=int(1e5), + radius=2., + center=0., + rtol=0.02) + + def testInvertMutuallyConsistent(self): + # BatchNorm bijector is only mutually consistent when training=False. + dims = 4 + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = Invert( + BatchNormalization(batchnorm_layer=layer, training=False)) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=normal_lib.Normal(loc=0., scale=1.), + bijector=batch_norm, + event_shape=[dims], + validate_args=True) + self.run_test_sample_consistent_log_prob( + sess_run_fn=sess.run, + dist=dist, + num_samples=int(1e5), + radius=2., + center=0., + rtol=0.02) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ad11d9f2484c4b08c67c5f82aec1320475d1d983 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py @@ -0,0 +1,80 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Kumaraswamy Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import Kumaraswamy +from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite +from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency +from tensorflow.python.platform import test + + +class KumaraswamyBijectorTest(test.TestCase): + """Tests correctness of the Kumaraswamy bijector.""" + + def testBijector(self): + with self.test_session(): + a = 2. + b = 0.3 + bijector = Kumaraswamy( + concentration1=a, concentration0=b, + event_ndims=0, validate_args=True) + self.assertEqual("kumaraswamy", bijector.name) + x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32) + # Kumaraswamy cdf. This is the same as inverse(x). + y = 1. - (1. - x ** a) ** b + self.assertAllClose(y, bijector.inverse(x).eval()) + self.assertAllClose(x, bijector.forward(y).eval()) + kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) + + (b - 1) * np.log1p(-x ** a)) + + self.assertAllClose( + # We should lose a dimension from calculating the determinant of the + # jacobian. + kumaraswamy_log_pdf, + bijector.inverse_log_det_jacobian(x).eval()) + self.assertAllClose( + -bijector.inverse_log_det_jacobian(x).eval(), + bijector.forward_log_det_jacobian(y).eval(), + rtol=1e-4, + atol=0.) + + def testScalarCongruency(self): + with self.test_session(): + assert_scalar_congruency( + Kumaraswamy(concentration1=0.5, concentration0=1.1), + lower_x=0., upper_x=1., n=int(10e3), rtol=0.02) + + def testBijectiveAndFinite(self): + with self.test_session(): + concentration1 = 1.2 + concentration0 = 2. + bijector = Kumaraswamy( + concentration1=concentration1, + concentration0=concentration0, validate_args=True) + # Omitting the endpoints 0 and 1, since idlj will be inifinity at these + # endpoints. + y = np.linspace(.01, 0.99, num=10).astype(np.float32) + x = 1 - (1 - y ** concentration1) ** concentration0 + assert_bijective_and_finite(bijector, x, y, rtol=1e-3) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py index 62e3869db090e9c9327bc552d10234ff76ba28fd..4a7679daad6f6acc632eb9133078499dda89e43d 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py @@ -21,7 +21,9 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import SoftmaxCentered +from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite from tensorflow.python.platform import test @@ -76,6 +78,32 @@ class SoftmaxCenteredBijectorTest(test.TestCase): atol=0., rtol=1e-7) + def testBijectorUnknownShape(self): + with self.test_session(): + softmax = SoftmaxCentered(event_ndims=1) + self.assertEqual("softmax_centered", softmax.name) + x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_x = np.log([[2., 3, 4], [4., 8, 12]]) + y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]] + self.assertAllClose(real_y, softmax.forward(x).eval( + feed_dict={x: real_x})) + self.assertAllClose(real_x, softmax.inverse(y).eval( + feed_dict={y: real_y})) + self.assertAllClose( + -np.sum(np.log(real_y), axis=1), + softmax.inverse_log_det_jacobian(y).eval( + feed_dict={y: real_y}), + atol=0., + rtol=1e-7) + self.assertAllClose( + -softmax.inverse_log_det_jacobian(y).eval( + feed_dict={y: real_y}), + softmax.forward_log_det_jacobian(x).eval( + feed_dict={x: real_x}), + atol=0., + rtol=1e-7) + def testShapeGetters(self): with self.test_session(): for x, y, b in ((tensor_shape.TensorShape([]), diff --git a/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py b/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py index 06318ca09dec851cf025fa35c83732b85824cbee..6a69f9e60b99a17c657f074597a075890265a93b 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bernoulli as bernoulli_lib +from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging @@ -126,6 +127,100 @@ class ProductDistributionTest(test.TestCase): self.assertAllClose(sample_entropy_, actual_entropy_, rtol=0.01, atol=0.) self.assertAllClose(loc, actual_mode_, rtol=1e-6, atol=0.) + def testKLRaises(self): + ind1 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=1) + ind2 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32(-1), + scale=np.float32(0.5)), + reinterpreted_batch_ndims=0) + + with self.assertRaisesRegexp( + ValueError, "Event shapes do not match"): + kullback_leibler.kl_divergence(ind1, ind2) + + ind1 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=1) + ind2 = independent_lib.Independent( + distribution=mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([-1., 1]), + scale_diag=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=0) + + with self.assertRaisesRegexp( + NotImplementedError, "different event shapes"): + kullback_leibler.kl_divergence(ind1, ind2) + + def testKLScalarToMultivariate(self): + normal1 = normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])) + ind1 = independent_lib.Independent( + distribution=normal1, reinterpreted_batch_ndims=1) + + normal2 = normal_lib.Normal( + loc=np.float32([-3., 3]), + scale=np.float32([0.3, 0.3])) + ind2 = independent_lib.Independent( + distribution=normal2, reinterpreted_batch_ndims=1) + + normal_kl = kullback_leibler.kl_divergence(normal1, normal2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(math_ops.reduce_sum(normal_kl, axis=-1)), + self.evaluate(ind_kl)) + + def testKLIdentity(self): + normal1 = normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])) + # This is functionally just a wrapper around normal1, + # and doesn't change any outputs. + ind1 = independent_lib.Independent( + distribution=normal1, reinterpreted_batch_ndims=0) + + normal2 = normal_lib.Normal( + loc=np.float32([-3., 3]), + scale=np.float32([0.3, 0.3])) + # This is functionally just a wrapper around normal2, + # and doesn't change any outputs. + ind2 = independent_lib.Independent( + distribution=normal2, reinterpreted_batch_ndims=0) + + normal_kl = kullback_leibler.kl_divergence(normal1, normal2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(normal_kl), self.evaluate(ind_kl)) + + def testKLMultivariateToMultivariate(self): + # (1, 1, 2) batch of MVNDiag + mvn1 = mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([[[[-1., 1, 3.], [2., 4., 3.]]]]), + scale_diag=np.float32([[[0.2, 0.1, 5.], [2., 3., 4.]]])) + ind1 = independent_lib.Independent( + distribution=mvn1, reinterpreted_batch_ndims=2) + + # (1, 1, 2) batch of MVNDiag + mvn2 = mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([[[[-2., 3, 2.], [1., 3., 2.]]]]), + scale_diag=np.float32([[[0.1, 0.5, 3.], [1., 2., 1.]]])) + + ind2 = independent_lib.Independent( + distribution=mvn2, reinterpreted_batch_ndims=2) + + mvn_kl = kullback_leibler.kl_divergence(mvn1, mvn2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(math_ops.reduce_sum(mvn_kl, axis=[-1, -2])), + self.evaluate(ind_kl)) + def _testMnistLike(self, static_shape): sample_shape = [4, 5] batch_shape = [10] diff --git a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py index ea3c86b5c0f42b64fc6e4e362cbcc162bccf74a2..2980e2bfe93b2e2aa01d38fc9fa4650a015efc06 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py @@ -130,10 +130,8 @@ class KumaraswamyTest(test.TestCase): dist.prob([.1, .3, .6]).eval() dist.prob([.2, .3, .5]).eval() # Either condition can trigger. - with self.assertRaisesOpError("sample must be positive"): + with self.assertRaisesOpError("sample must be non-negative"): dist.prob([-1., 0.1, 0.5]).eval() - with self.assertRaisesOpError("sample must be positive"): - dist.prob([0., 0.1, 0.5]).eval() with self.assertRaisesOpError("sample must be no larger than `1`"): dist.prob([.1, .2, 1.2]).eval() @@ -249,13 +247,13 @@ class KumaraswamyTest(test.TestCase): a = np.array([1., 2, 3]) b = np.array([2., 4, 1.2]) dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) - with self.assertRaisesOpError("Condition x < y.*"): + with self.assertRaisesOpError("Mode undefined for concentration1 <= 1."): dist.mode().eval() a = np.array([2., 2, 3]) b = np.array([1., 4, 1.2]) dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) - with self.assertRaisesOpError("Condition x < y.*"): + with self.assertRaisesOpError("Mode undefined for concentration0 <= 1."): dist.mode().eval() def testKumaraswamyModeEnableAllowNanStats(self): diff --git a/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py b/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py index d9c9008417cdb20b62390630cf887d3bd888a0d3..19a7472d91758a2dbd00c4d918853d7bae33685d 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function import numpy as np +from scipy import special from scipy import stats from tensorflow.contrib.distributions.python.ops import poisson as poisson_lib from tensorflow.python.framework import constant_op @@ -110,7 +111,7 @@ class PoissonTest(test.TestCase): batch_size = 6 lam = constant_op.constant([3.0] * batch_size) lam_v = 3.0 - x = [2.2, 3.1, 4., 5.5, 6., 7.] + x = [2., 3., 4., 5., 6., 7.] poisson = self._make_poisson(rate=lam) log_cdf = poisson.log_cdf(x) @@ -121,12 +122,31 @@ class PoissonTest(test.TestCase): self.assertEqual(cdf.get_shape(), (6,)) self.assertAllClose(cdf.eval(), stats.poisson.cdf(x, lam_v)) + def testPoissonCDFNonIntegerValues(self): + with self.test_session(): + batch_size = 6 + lam = constant_op.constant([3.0] * batch_size) + lam_v = 3.0 + x = np.array([2.2, 3.1, 4., 5.5, 6., 7.], dtype=np.float32) + + poisson = self._make_poisson(rate=lam) + cdf = poisson.cdf(x) + self.assertEqual(cdf.get_shape(), (6,)) + + # The Poisson CDF should be valid on these non-integer values, and + # equal to igammac(1 + x, rate). + self.assertAllClose(cdf.eval(), special.gammaincc(1. + x, lam_v)) + + with self.assertRaisesOpError("cannot contain fractional components"): + poisson_validate = self._make_poisson(rate=lam, validate_args=True) + poisson_validate.cdf(x).eval() + def testPoissonCdfMultidimensional(self): with self.test_session(): batch_size = 6 lam = constant_op.constant([[2.0, 4.0, 5.0]] * batch_size) lam_v = [2.0, 4.0, 5.0] - x = np.array([[2.2, 3.1, 4., 5.5, 6., 7.]], dtype=np.float32).T + x = np.array([[2., 3., 4., 5., 6., 7.]], dtype=np.float32).T poisson = self._make_poisson(rate=lam) log_cdf = poisson.log_cdf(x) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3548ac18078a0b40f117c2bf9e2b34d20cee163b --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py @@ -0,0 +1,166 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the statistical testing library.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import statistical_testing as st +from tensorflow.python.framework import errors +from tensorflow.python.ops import check_ops +from tensorflow.python.platform import test + + +class StatisticalTestingTest(test.TestCase): + + def test_dkwm_design_mean_one_sample_soundness(self): + numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10] + rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.] + with self.test_session() as sess: + for ff in rates: + for fp in rates: + sufficient_n = st.min_num_samples_for_dkwm_mean_test( + numbers, 0., 1., false_fail_rate=ff, false_pass_rate=fp) + detectable_d = st.min_discrepancy_of_true_means_detectable_by_dkwm( + sufficient_n, 0., 1., false_fail_rate=ff, false_pass_rate=fp) + sess.run(check_ops.assert_less_equal(detectable_d, numbers)) + + def test_dkwm_design_mean_two_sample_soundness(self): + numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10] + rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.] + with self.test_session() as sess: + for ff in rates: + for fp in rates: + (sufficient_n1, + sufficient_n2) = st.min_num_samples_for_dkwm_mean_two_sample_test( + numbers, 0., 1., 0., 1., + false_fail_rate=ff, false_pass_rate=fp) + d_fn = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample + detectable_d = d_fn( + sufficient_n1, 0., 1., sufficient_n2, 0., 1., + false_fail_rate=ff, false_pass_rate=fp) + sess.run(check_ops.assert_less_equal(detectable_d, numbers)) + + def test_true_mean_confidence_interval_by_dkwm_one_sample(self): + rng = np.random.RandomState(seed=0) + + num_samples = 5000 + # 5000 samples is chosen to be enough to find discrepancies of + # size 0.1 or more with assurance 1e-6, as confirmed here: + with self.test_session() as sess: + d = st.min_discrepancy_of_true_means_detectable_by_dkwm( + num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6) + d = sess.run(d) + self.assertLess(d, 0.1) + + # Test that the confidence interval computed for the mean includes + # 0.5 and excludes 0.4 and 0.6. + with self.test_session() as sess: + samples = rng.uniform(size=num_samples).astype(np.float32) + (low, high) = st.true_mean_confidence_interval_by_dkwm( + samples, 0., 1., error_rate=1e-6) + low, high = sess.run([low, high]) + self.assertGreater(low, 0.4) + self.assertLess(low, 0.5) + self.assertGreater(high, 0.5) + self.assertLess(high, 0.6) + + def test_dkwm_mean_one_sample_assertion(self): + rng = np.random.RandomState(seed=0) + num_samples = 5000 + + # Test that the test assertion agrees that the mean of the standard + # uniform distribution is 0.5. + samples = rng.uniform(size=num_samples).astype(np.float32) + with self.test_session() as sess: + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.5, false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is not 0.4. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.4, false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is not 0.6. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.6, false_fail_rate=1e-6)) + + def test_dkwm_mean_two_sample_assertion(self): + rng = np.random.RandomState(seed=0) + num_samples = 15000 + + # 15000 samples is chosen to be enough to find discrepancies of + # size 0.1 or more with assurance 1e-6, as confirmed here: + with self.test_session() as sess: + d = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample( + num_samples, 0., 1., num_samples, 0., 1., + false_fail_rate=1e-6, false_pass_rate=1e-6) + d = sess.run(d) + self.assertLess(d, 0.1) + + # Test that the test assertion agrees that the standard + # uniform distribution has the same mean as itself. + samples1 = rng.uniform(size=num_samples).astype(np.float32) + samples2 = rng.uniform(size=num_samples).astype(np.float32) + with self.test_session() as sess: + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., samples2, 0., 1., false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is different from the mean of beta(2, 1). + beta_high_samples = rng.beta(2, 1, size=num_samples).astype(np.float32) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., + beta_high_samples, 0., 1., + false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is different from the mean of beta(1, 2). + beta_low_samples = rng.beta(1, 2, size=num_samples).astype(np.float32) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., + beta_low_samples, 0., 1., + false_fail_rate=1e-6)) + + def test_dkwm_argument_validity_checking(self): + rng = np.random.RandomState(seed=0) + samples = rng.uniform(size=5000).astype(np.float32) + + # Test that the test library complains if the given samples fall + # outside the purported bounds. + with self.test_session() as sess: + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.true_mean_confidence_interval_by_dkwm( + samples, 0., 0.5, error_rate=0.5)) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.true_mean_confidence_interval_by_dkwm( + samples, 0.5, 1., error_rate=0.5)) + + # But doesn't complain if they don't. + op = st.true_mean_confidence_interval_by_dkwm( + samples, 0., 1., error_rate=0.5) + _ = sess.run(op) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py index 93923c3f083c7f5136b55e9021cbd6323684b976..46ec49754a24cc063bcf3660cde672200f4e29f5 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py @@ -18,6 +18,7 @@ @@Affine @@AffineLinearOperator @@Bijector +@@BatchNormalization @@Chain @@CholeskyOuterProduct @@ConditionalBijector @@ -26,6 +27,7 @@ @@Identity @@Inline @@Invert +@@Kumaraswamy @@MaskedAutoregressiveFlow @@Permute @@PowerTransform @@ -52,6 +54,7 @@ from __future__ import print_function from tensorflow.contrib.distributions.python.ops.bijectors.absolute_value import * from tensorflow.contrib.distributions.python.ops.bijectors.affine import * from tensorflow.contrib.distributions.python.ops.bijectors.affine_linear_operator import * +from tensorflow.contrib.distributions.python.ops.bijectors.batch_normalization import * from tensorflow.contrib.distributions.python.ops.bijectors.chain import * from tensorflow.contrib.distributions.python.ops.bijectors.cholesky_outer_product import * from tensorflow.contrib.distributions.python.ops.bijectors.conditional_bijector import * @@ -59,6 +62,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.exp import * from tensorflow.contrib.distributions.python.ops.bijectors.gumbel import * from tensorflow.contrib.distributions.python.ops.bijectors.inline import * from tensorflow.contrib.distributions.python.ops.bijectors.invert import * +from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import * from tensorflow.contrib.distributions.python.ops.bijectors.masked_autoregressive import * from tensorflow.contrib.distributions.python.ops.bijectors.permute import * from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import * diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..be72ff3081225b9f9fdb6541322b7fc3d4aaa41e --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py @@ -0,0 +1,258 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Batch Norm bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import numpy as np + +from tensorflow.python.framework import ops +from tensorflow.python.layers import normalization +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops.distributions import bijector + + +__all__ = [ + "BatchNormalization", +] + + +def _undo_batch_normalization(x, + mean, + variance, + offset, + scale, + variance_epsilon, + name=None): + r"""Inverse of tf.nn.batch_normalization. + + Args: + x: Input `Tensor` of arbitrary dimensionality. + mean: A mean `Tensor`. + variance: A variance `Tensor`. + offset: An offset `Tensor`, often denoted `beta` in equations, or + None. If present, will be added to the normalized tensor. + scale: A scale `Tensor`, often denoted `gamma` in equations, or + `None`. If present, the scale is applied to the normalized tensor. + variance_epsilon: A small `float` added to the minibatch `variance` to + prevent dividing by zero. + name: A name for this operation (optional). + + Returns: + batch_unnormalized: The de-normalized, de-scaled, de-offset `Tensor`. + """ + with ops.name_scope( + name, "undo_batchnorm", [x, mean, variance, scale, offset]): + # inv = math_ops.rsqrt(variance + variance_epsilon) + # if scale is not None: + # inv *= scale + # return x * inv + ( + # offset - mean * inv if offset is not None else -mean * inv) + rescale = math_ops.sqrt(variance + variance_epsilon) + if scale is not None: + rescale /= scale + batch_unnormalized = x * rescale + ( + mean - offset * rescale if offset is not None else mean) + return batch_unnormalized + + +class BatchNormalization(bijector.Bijector): + """Compute `Y = g(X) s.t. X = g^-1(Y) = (Y - mean(Y)) / std(Y)`. + + Applies Batch Normalization [1] to samples from a data distribution. This can + be used to stabilize training of normalizing flows [2, 3]. + + When training Deep Neural Networks (DNNs), it is common practice to + normalize or whiten features by shifting them to have zero mean and + scaling them to have unit variance. + + The `inverse()` method of the BatchNorm bijector, which is used in the + log-likelihood computation of data samples, implements the normalization + procedure (shift-and-scale) using the mean and standard deviation of the + current minibatch. + + Conversely, the `forward()` method of the bijector de-normalizes samples (e.g. + `X*std(Y) + mean(Y)` with the running-average mean and standard deviation + computed at training-time. De-normalization is useful for sampling. + + + ```python + + dist = tfd.TransformedDistribution( + distribution=tfd.Normal()), + bijector=tfb.BatchNorm()) + + y = tfd.MultivariateNormalDiag(loc=1., scale=2.).sample(100) # ~ N(1, 2) + x = dist.bijector.inverse(y) # ~ N(0, 1) + y = dist.sample() # ~ N(1, 2) + ``` + + During training time, `BatchNorm.inverse` and `BatchNorm.forward` are not + guaranteed to be inverses of each other because `inverse(y)` uses statistics + of the current minibatch, while `forward(x)` uses running-average statistics + accumulated from training. In other words, + `BatchNorm.inverse(BatchNorm.forward(...))` and + `BatchNorm.forward(BatchNorm.inverse(...))` will be identical when + `training=False` but may be different when `training=True`. + + [1]: "Batch Normalization: Accelerating Deep Network Training by Reducing + Internal Covariate Shift." + Sergey Ioffe, Christian Szegedy. Arxiv. 2015. + https://arxiv.org/abs/1502.03167 + + [2]: "Density Estimation using Real NVP." + Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio. ICLR. 2017. + https://arxiv.org/abs/1605.08803 + + [3]: "Masked Autoregressive Flow for Density Estimation." + George Papamakarios, Theo Pavlakou, Iain Murray. Arxiv. 2017. + https://arxiv.org/abs/1705.07057 + + """ + + def __init__(self, + batchnorm_layer=None, + training=True, + validate_args=False, + name="batch_normalization"): + """Instantiates the `BatchNorm` bijector. + + Args: + batchnorm_layer: `tf.layers.BatchNormalization` layer object. If `None`, + defaults to + `tf.layers.BatchNormalization(gamma_constraint=nn_ops.relu(x) + 1e-6)`. + This ensures positivity of the scale variable. + + training: If True, updates running-average statistics during call to + `inverse()`. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + Raises: + ValueError: If bn_layer is not an instance of + `tf.layers.BatchNormalization`, or if it is specified with `renorm=True` + or a virtual batch size. + """ + # Scale must be positive. + g_constraint = lambda x: nn.relu(x) + 1e-6 + self.batchnorm = batchnorm_layer or normalization.BatchNormalization( + gamma_constraint=g_constraint) + self._validate_bn_layer(self.batchnorm) + self._training = training + super(BatchNormalization, self).__init__( + validate_args=validate_args, name=name) + + def _validate_bn_layer(self, layer): + """Check for valid BatchNormalization layer. + + Args: + layer: Instance of `tf.layers.BatchNormalization`. + Raises: + ValueError: If batchnorm_layer argument is not an instance of + `tf.layers.BatchNormalization`, or if `batchnorm_layer.renorm=True` or + if `batchnorm_layer.virtual_batch_size` is specified. + """ + if not isinstance(layer, normalization.BatchNormalization): + raise ValueError( + "batchnorm_layer must be an instance of BatchNormalization layer.") + if layer.renorm: + raise ValueError("BatchNorm Bijector does not support renormalization.") + if layer.virtual_batch_size: + raise ValueError( + "BatchNorm Bijector does not support virtual batch sizes.") + + def _get_broadcast_fn(self, x): + # Compute shape to broadcast scale/shift parameters to. + if not x.shape.is_fully_defined(): + raise ValueError("Input must have shape known at graph construction.") + input_shape = np.int32(x.shape.as_list()) + + ndims = len(input_shape) + # event_dims = self._compute_event_dims(x) + reduction_axes = [i for i in range(ndims) if i not in self.batchnorm.axis] + # Broadcasting only necessary for single-axis batch norm where the axis is + # not the last dimension + broadcast_shape = [1] * ndims + broadcast_shape[self.batchnorm.axis[0]] = ( + input_shape[self.batchnorm.axis[0]]) + def _broadcast(v): + if (v is not None and + len(v.get_shape()) != ndims and + reduction_axes != list(range(ndims - 1))): + return array_ops.reshape(v, broadcast_shape) + return v + return _broadcast + + def _normalize(self, y): + return self.batchnorm.apply(y, training=self._training) + + def _de_normalize(self, x): + # Uses the saved statistics. + if not self.batchnorm.built: + input_shape = x.get_shape() + self.batchnorm.build(input_shape) + broadcast_fn = self._get_broadcast_fn(x) + mean = broadcast_fn(self.batchnorm.moving_mean) + variance = broadcast_fn(self.batchnorm.moving_variance) + beta = broadcast_fn(self.batchnorm.beta) if self.batchnorm.center else None + gamma = broadcast_fn(self.batchnorm.gamma) if self.batchnorm.scale else None + return _undo_batch_normalization( + x, mean, variance, beta, gamma, self.batchnorm.epsilon) + + def _forward(self, x): + return self._de_normalize(x) + + def _inverse(self, y): + return self._normalize(y) + + def _forward_log_det_jacobian(self, x): + # Uses saved statistics to compute volume distortion. + return -self._inverse_log_det_jacobian(x, use_saved_statistics=True) + + def _inverse_log_det_jacobian(self, y, use_saved_statistics=False): + if not y.shape.is_fully_defined(): + raise ValueError("Input must have shape known at graph construction.") + input_shape = np.int32(y.shape.as_list()) + + if not self.batchnorm.built: + # Create variables. + self.batchnorm.build(input_shape) + + event_dims = self.batchnorm.axis + reduction_axes = [i for i in range(len(input_shape)) if i not in event_dims] + + if use_saved_statistics or not self._training: + log_variance = math_ops.log( + self.batchnorm.moving_variance + self.batchnorm.epsilon) + else: + # At training-time, ildj is computed from the mean and log-variance across + # the current minibatch. + _, v = nn.moments(y, axes=reduction_axes, keep_dims=True) + log_variance = math_ops.log(v + self.batchnorm.epsilon) + + # `gamma` and `log Var(y)` reductions over event_dims. + # Log(total change in area from gamma term). + log_total_gamma = math_ops.reduce_sum(math_ops.log(self.batchnorm.gamma)) + + # Log(total change in area from log-variance term). + log_total_variance = math_ops.reduce_sum(log_variance) + # The ildj is scalar, as it does not depend on the values of x and are + # constant across minibatch elements. + return log_total_gamma - 0.5 * log_total_variance diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py new file mode 100644 index 0000000000000000000000000000000000000000..f5de052c9ed18b1ebf4c174aeea3a951b1ddcd9d --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py @@ -0,0 +1,153 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Kumaraswamy bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.distributions import bijector + +__all__ = [ + "Kumaraswamy", +] + + +class Kumaraswamy(bijector.Bijector): + """Compute `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), X in [0, 1]`. + + This bijector maps inputs from `[0, 1]` to [0, 1]`. The inverse of the + bijector applied to a uniform random variable `X ~ U(0, 1) gives back a + random variable with the [Kumaraswamy distribution]( + https://en.wikipedia.org/wiki/Kumaraswamy_distribution): + + ```none + Y ~ Kumaraswamy(a, b) + pdf(y; a, b, 0 <= y <= 1) = a * b * y ** (a - 1) * (1 - y**a) ** (b - 1) + ``` + """ + + def __init__(self, + concentration1=None, + concentration0=None, + event_ndims=0, + validate_args=False, + name="kumaraswamy"): + """Instantiates the `Kumaraswamy` bijector. + + Args: + concentration1: Python `float` scalar indicating the transform power, + i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is + `concentration1`. + concentration0: Python `float` scalar indicating the transform power, + i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is + `concentration0`. + event_ndims: Python scalar indicating the number of dimensions associated + with a particular draw from the distribution. Currently only zero is + supported. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + + Raises: + ValueError: If `event_ndims` is not zero. + """ + self._graph_parents = [] + self._name = name + self._validate_args = validate_args + + event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims") + event_ndims_const = tensor_util.constant_value(event_ndims) + if event_ndims_const is not None and event_ndims_const not in (0,): + raise ValueError("event_ndims(%s) was not 0" % event_ndims_const) + else: + if validate_args: + event_ndims = control_flow_ops.with_dependencies( + [check_ops.assert_equal( + event_ndims, 0, message="event_ndims was not 0")], + event_ndims) + + with self._name_scope("init", values=[concentration1, concentration0]): + concentration1 = self._maybe_assert_valid_concentration( + ops.convert_to_tensor(concentration1, name="concentration1"), + validate_args=validate_args) + concentration0 = self._maybe_assert_valid_concentration( + ops.convert_to_tensor(concentration0, name="concentration0"), + validate_args=validate_args) + + self._concentration1 = concentration1 + self._concentration0 = concentration0 + super(Kumaraswamy, self).__init__( + event_ndims=0, + validate_args=validate_args, + name=name) + + @property + def concentration1(self): + """The `a` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`.""" + return self._concentration1 + + @property + def concentration0(self): + """The `b` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`.""" + return self._concentration0 + + def _forward(self, x): + x = self._maybe_assert_valid(x) + return math_ops.exp( + math_ops.log1p(-math_ops.exp(math_ops.log1p(-x) / self.concentration0)) + / self.concentration1) + + def _inverse(self, y): + y = self._maybe_assert_valid(y) + return math_ops.exp(math_ops.log1p( + -(1 - y**self.concentration1)**self.concentration0)) + + def _inverse_log_det_jacobian(self, y): + y = self._maybe_assert_valid(y) + event_dims = self._event_dims_tensor(y) + return math_ops.reduce_sum( + math_ops.log(self.concentration1) + math_ops.log(self.concentration0) + + (self.concentration1 - 1) * math_ops.log(y) + + (self.concentration0 - 1) * math_ops.log1p(-y**self.concentration1), + axis=event_dims) + + def _maybe_assert_valid_concentration(self, concentration, validate_args): + """Checks the validity of a concentration parameter.""" + if not validate_args: + return concentration + return control_flow_ops.with_dependencies([ + check_ops.assert_positive( + concentration, + message="Concentration parameter must be positive."), + ], concentration) + + def _maybe_assert_valid(self, x): + if not self.validate_args: + return x + return control_flow_ops.with_dependencies([ + check_ops.assert_non_negative( + x, + message="sample must be non-negative"), + check_ops.assert_less_equal( + x, array_ops.ones([], self.concentration0.dtype), + message="sample must be no larger than `1`."), + ], x) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py index a9dcce6c526600f3b26c6bceb730417000917ce7..24add40445c60db533aac6d0c8eb537774895c65 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -161,33 +159,16 @@ class SoftmaxCentered(bijector.Bijector): # x[i] = log(exp(x[i])) - log(y[end]) - log(normalization) # = log(exp(x[i])/normalization) - log(y[end]) # = log(y[i]) - log(y[end]) - shape = (np.asarray(y.shape.as_list(), dtype=np.int32) - if y.shape.is_fully_defined() - else array_ops.shape(y, name="shape")) - ndims = distribution_util.prefer_static_rank(y) # Do this first to make sure CSE catches that it'll happen again in # _inverse_log_det_jacobian. x = math_ops.log(y) - # We now extract the last coordinate of the rightmost dimension. - # Our trick is to slice from [0,0,...,shape[-1]-1] to shape[:-1]+[1]. - begin = array_ops.one_hot(indices=ndims-1, - depth=ndims, - on_value=shape[-1]-np.array(1, dtype=shape.dtype), - dtype=shape.dtype) - size = array_ops.concat([shape[:-1], np.asarray([1], dtype=shape.dtype)], 0) - log_normalization = -array_ops.strided_slice(x, begin, begin + size) - - # Here we slice out all but the last coordinate; see above for idea. - begin = array_ops.zeros_like(shape) - size = array_ops.concat([shape[:-1], [shape[-1] - 1]], 0) - x = array_ops.strided_slice(x, begin, begin + size) - - x += log_normalization + log_normalization = (-x[..., -1])[..., array_ops.newaxis] + x = x[..., :-1] + log_normalization if self._static_event_ndims == 0: - x = array_ops.squeeze(x, squeeze_dims=[ndims-1]) + x = array_ops.squeeze(x, squeeze_dims=-1) # Set shape hints. if y.shape.ndims is not None: diff --git a/tensorflow/contrib/distributions/python/ops/gumbel.py b/tensorflow/contrib/distributions/python/ops/gumbel.py index d0efaefb8e78ddf4436e9e5a112d2c1cdddaf3b5..8d05ad6b8032fb8bada99389959091fb1c28beda 100644 --- a/tensorflow/contrib/distributions/python/ops/gumbel.py +++ b/tensorflow/contrib/distributions/python/ops/gumbel.py @@ -190,9 +190,6 @@ class _Gumbel(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return -math_ops.exp(-self._z(x)) diff --git a/tensorflow/contrib/distributions/python/ops/independent.py b/tensorflow/contrib/distributions/python/ops/independent.py index cbce005013281ff3c58c94d525d5ce7a865d725a..7dcb3e3ac4db1855adacb7ec0fa8554c45d9c859 100644 --- a/tensorflow/contrib/distributions/python/ops/independent.py +++ b/tensorflow/contrib/distributions/python/ops/independent.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib +from tensorflow.python.ops.distributions import kullback_leibler class Independent(distribution_lib.Distribution): @@ -254,3 +255,58 @@ class Independent(distribution_lib.Distribution): else: which_maximum = np.maximum return which_maximum(0, ndims - 1) + + +@kullback_leibler.RegisterKL(Independent, Independent) +def _kl_independent(a, b, name="kl_independent"): + """Batched KL divergence `KL(a || b)` for Independent distributions. + + We can leverage the fact that + ``` + KL(Independent(a) || Independent(b)) = sum(KL(a || b)) + ``` + where the sum is over the `reinterpreted_batch_ndims`. + + Args: + a: Instance of `Independent`. + b: Instance of `Independent`. + name: (optional) name to use for created ops. Default "kl_independent". + + Returns: + Batchwise `KL(a || b)`. + + Raises: + ValueError: If the event space for `a` and `b`, or their underlying + distributions don't match. + """ + p = a.distribution + q = b.distribution + + # The KL between any two (non)-batched distributions is a scalar. + # Given that the KL between two factored distributions is the sum, i.e. + # KL(p1(x)p2(y) || q1(x)q2(y)) = KL(p1 || q1) + KL(q1 || q2), we compute + # KL(p || q) and do a `reduce_sum` on the reinterpreted batch dimensions. + if a.event_shape.is_fully_defined() and b.event_shape.is_fully_defined(): + if a.event_shape == b.event_shape: + if p.event_shape == q.event_shape: + num_reduce_dims = a.event_shape.ndims - p.event_shape.ndims + reduce_dims = [-i - 1 for i in range(0, num_reduce_dims)] + + return math_ops.reduce_sum( + kullback_leibler.kl_divergence(p, q, name=name), axis=reduce_dims) + else: + raise NotImplementedError("KL between Independents with different " + "event shapes not supported.") + else: + raise ValueError("Event shapes do not match.") + else: + with ops.control_dependencies([ + check_ops.assert_equal(a.event_shape_tensor(), b.event_shape_tensor()), + check_ops.assert_equal(p.event_shape_tensor(), q.event_shape_tensor()) + ]): + num_reduce_dims = ( + array_ops.shape(a.event_shape_tensor()[0]) - + array_ops.shape(p.event_shape_tensor()[0])) + reduce_dims = math_ops.range(-num_reduce_dims - 1, -1, 1) + return math_ops.reduce_sum( + kullback_leibler.kl_divergence(p, q, name=name), axis=reduce_dims) diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py index ee4d86867d48b20e97757bcec57d452085814b80..51ac61dcf640ca89f22c47127bda71316a179ca4 100644 --- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py +++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py @@ -192,12 +192,6 @@ class InverseGamma(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - - def _log_cdf(self, x): - return math_ops.log(self._cdf(x)) - def _cdf(self, x): x = self._maybe_assert_valid_sample(x) # Note that igammac returns the upper regularized incomplete gamma diff --git a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py index 74d5d8773cf3e69a52554c87d656fea2835c8354..120b38db3cf72e8fce56a7e9293cdf25e75784e2 100644 --- a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py +++ b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py @@ -20,15 +20,17 @@ from __future__ import print_function import numpy as np +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.contrib.distributions.python.ops import distribution_util +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops from tensorflow.python.ops import special_math_ops -from tensorflow.python.ops.distributions import beta from tensorflow.python.ops.distributions import distribution -from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.ops.distributions import transformed_distribution +from tensorflow.python.ops.distributions import uniform from tensorflow.python.util.tf_export import tf_export __all__ = [ @@ -60,7 +62,7 @@ def _harmonic_number(x): @tf_export("distributions.Kumaraswamy") -class Kumaraswamy(beta.Beta): +class Kumaraswamy(transformed_distribution.TransformedDistribution): """Kumaraswamy distribution. The Kumaraswamy distribution is defined over the `(0, 1)` interval using @@ -151,59 +153,32 @@ class Kumaraswamy(beta.Beta): more of the statistic's batch members are undefined. name: Python `str` name prefixed to Ops created by this class. """ + concentration1 = ops.convert_to_tensor( + concentration1, name="concentration1") + concentration0 = ops.convert_to_tensor( + concentration0, name="concentration0") super(Kumaraswamy, self).__init__( - concentration1=concentration1, - concentration0=concentration0, - validate_args=validate_args, - allow_nan_stats=allow_nan_stats, + distribution=uniform.Uniform( + low=array_ops.zeros([], dtype=concentration1.dtype), + high=array_ops.ones([], dtype=concentration1.dtype), + allow_nan_stats=allow_nan_stats), + bijector=bijectors.Kumaraswamy( + concentration1=concentration1, concentration0=concentration0, + validate_args=validate_args), + batch_shape=distribution_util.get_broadcast_shape( + concentration1, concentration0), name=name) self._reparameterization_type = distribution.FULLY_REPARAMETERIZED - def _sample_n(self, n, seed=None): - expanded_concentration1 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration1 - expanded_concentration0 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration0 - shape = array_ops.concat([[n], self.batch_shape_tensor()], 0) - uniform_sample = random_ops.random_uniform( - shape=shape, minval=0.0, maxval=1.0, dtype=self.dtype, seed=seed) - - kumaraswamy_sample = (1 - uniform_sample**(1. / expanded_concentration0))**( - 1. / expanded_concentration1) - return kumaraswamy_sample - - @distribution_util.AppendDocstring(_kumaraswamy_sample_note) - def _log_cdf(self, x): - a = self.concentration1 - b = self.concentration0 - return math_ops.log1p(-(1 - x**a)**b) + @property + def concentration1(self): + """Concentration parameter associated with a `1` outcome.""" + return self.bijector.concentration1 - @distribution_util.AppendDocstring(_kumaraswamy_sample_note) - def _cdf(self, x): - a = self.concentration1 - b = self.concentration0 - return 1 - (1 - x**a)**b - - def _survival_function(self, x): - a = self.concentration1 - b = self.concentration0 - return (1 - x**a)**b - - def _log_survival_function(self, x): - a = self.concentration1 - b = self.concentration0 - return b * math_ops.log1p(-x**a) - - def _log_unnormalized_prob(self, x): - x = self._maybe_assert_valid_sample(x) - a = self.concentration1 - b = self.concentration0 - return (a - 1) * math_ops.log(x) + (b - 1) * math_ops.log1p(-x**a) - - def _log_normalization(self): - a = self.concentration1 - b = self.concentration0 - return -(math_ops.log(a) + math_ops.log(b)) + @property + def concentration0(self): + """Concentration parameter associated with a `0` outcome.""" + return self.bijector.concentration0 def _entropy(self): a = self.concentration1 @@ -213,10 +188,11 @@ class Kumaraswamy(beta.Beta): def _moment(self, n): """Compute the n'th (uncentered) moment.""" + total_concentration = self.concentration1 + self.concentration0 expanded_concentration1 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration1 + total_concentration, dtype=self.dtype) * self.concentration1 expanded_concentration0 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration0 + total_concentration, dtype=self.dtype) * self.concentration0 beta_arg0 = 1 + n / expanded_concentration1 beta_arg = array_ops.stack([beta_arg0, expanded_concentration0], -1) log_moment = math_ops.log(expanded_concentration0) + special_math_ops.lbeta( @@ -246,13 +222,14 @@ class Kumaraswamy(beta.Beta): name="nan") is_defined = (self.concentration1 > 1.) & (self.concentration0 > 1.) return array_ops.where(is_defined, mode, nan) + return control_flow_ops.with_dependencies([ check_ops.assert_less( - array_ops.ones([], dtype=self.dtype), + array_ops.ones([], dtype=self.concentration1.dtype), self.concentration1, message="Mode undefined for concentration1 <= 1."), check_ops.assert_less( - array_ops.ones([], dtype=self.dtype), + array_ops.ones([], dtype=self.concentration0.dtype), self.concentration0, message="Mode undefined for concentration0 <= 1.") ], mode) diff --git a/tensorflow/contrib/distributions/python/ops/logistic.py b/tensorflow/contrib/distributions/python/ops/logistic.py index 473677f8d91b184e029f345bb05f5c5d63df7a40..68e6bca5a554b29a450911073eb5c4fe55f313c6 100644 --- a/tensorflow/contrib/distributions/python/ops/logistic.py +++ b/tensorflow/contrib/distributions/python/ops/logistic.py @@ -185,9 +185,6 @@ class Logistic(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return -nn_ops.softplus(-self._z(x)) diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py index b76cebf79fad09ebec68f2459c6fe80794ea81c0..46c2cc8b7a8c536a90176fbb2b2d52fed61e4705 100644 --- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py @@ -203,9 +203,6 @@ class OneHotCategorical(distribution.Distribution): ret = array_ops.reshape(ret, logits_shape) return ret - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _entropy(self): return -math_ops.reduce_sum( nn_ops.log_softmax(self.logits) * self.probs, axis=-1) diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py index e967dcc90d0712ffc346fb61ee67c44a6d9207cb..02e97c0a2fd004c4fa9382d5367af9f5b034a869 100644 --- a/tensorflow/contrib/distributions/python/ops/poisson.py +++ b/tensorflow/contrib/distributions/python/ops/poisson.py @@ -35,9 +35,15 @@ __all__ = [ _poisson_sample_note = """ -Note that the input value must be a non-negative floating point tensor with -dtype `dtype` and whose shape can be broadcast with `self.rate`. `x` is only -legal if it is non-negative and its components are equal to integer values. +The Poisson distribution is technically only defined for non-negative integer +values. When `validate_args=False`, non-integral inputs trigger an assertion. + +When `validate_args=False` calculations are otherwise unchanged despite +integral or non-integral inputs. + +When `validate_args=False`, evaluating the pmf at non-integral values, +corresponds to evaluations of an unnormalized distribution, that does not +correspond to evaluations of the cdf. """ @@ -150,10 +156,6 @@ class Poisson(distribution.Distribution): def _cdf(self, x): if self.validate_args: x = distribution_util.embed_check_nonnegative_integer_form(x) - else: - # Whether or not x is integer-form, the following is well-defined. - # However, scipy takes the floor, so we do too. - x = math_ops.floor(x) return math_ops.igammac(1. + x, self.rate) def _log_normalization(self): @@ -162,9 +164,6 @@ class Poisson(distribution.Distribution): def _log_unnormalized_prob(self, x): if self.validate_args: x = distribution_util.embed_check_nonnegative_integer_form(x) - else: - # For consistency with cdf, we take the floor. - x = math_ops.floor(x) return x * self.log_rate - math_ops.lgamma(1. + x) def _mean(self): diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py index b6becfa9fc93f189a1a7bf7b2a7af8dc1f2e9720..ff33f327c7a77597e516208cacad8c4aed65d1c9 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py @@ -278,16 +278,13 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution): * math_ops.log(self.temperature)) # compute the unnormalized density log_softmax = nn_ops.log_softmax(logits_2d - x_2d * self._temperature_2d) - log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keep_dims=False) + log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keepdims=False) # combine unnormalized density with normalization constant log_prob = log_norm_const + log_unnorm_prob # Reshapes log_prob to be consistent with shape of user-supplied logits ret = array_ops.reshape(log_prob, logits_shape) return ret - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _assert_valid_sample(self, x): if not self.validate_args: return x diff --git a/tensorflow/contrib/distributions/python/ops/statistical_testing.py b/tensorflow/contrib/distributions/python/ops/statistical_testing.py new file mode 100644 index 0000000000000000000000000000000000000000..d66c34cc1a45cc09da5138a5f72ae3817690db49 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/statistical_testing.py @@ -0,0 +1,728 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Statistical test assertions calibrated for their error rates.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools + +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops + +__all__ = [ + "true_mean_confidence_interval_by_dkwm", + "assert_true_mean_equal_by_dkwm", + "min_discrepancy_of_true_means_detectable_by_dkwm", + "min_num_samples_for_dkwm_mean_test", + "assert_true_mean_equal_by_dkwm_two_sample", + "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample", + "min_num_samples_for_dkwm_mean_two_sample_test", +] + + +def _batch_sort_vector(x, ascending=True, name=None): + with ops.name_scope(name, "sort_each_row", [x]): + x = ops.convert_to_tensor(x, name="x") + n = array_ops.shape(x)[-1] + if ascending: + y, _ = nn_ops.top_k(-x, k=n, sorted=True) + y = -y + else: + y, _ = nn_ops.top_k(x, k=n, sorted=True) + y.set_shape(x.shape) + return y + + +def _do_maximum_mean(samples, envelope, high, name=None): + """Common code between maximum_mean and minimum_mean.""" + with ops.name_scope(name, "do_maximum_mean", [samples, envelope, high]): + n = array_ops.rank(samples) + # Move the batch dimension of `samples` to the rightmost position, + # where the _batch_sort_vector function wants it. + perm = array_ops.concat([math_ops.range(1, n), [0]], axis=0) + samples = array_ops.transpose(samples, perm) + + samples = _batch_sort_vector(samples) + batch_shape = array_ops.shape(samples)[:-1] + n = array_ops.shape(samples)[-1] + step = 1. / math_ops.cast(n, dtype=samples.dtype.base_dtype) + + def _loop_body(iter_, total, to_skip): + total = array_ops.where( + step <= to_skip, + total, + array_ops.where( + to_skip > 0., + total + (step - to_skip) * samples[..., iter_], + total + step * samples[..., iter_])) + to_skip = array_ops.where(step <= to_skip, to_skip - step, 0.) + return [iter_ + 1, total, to_skip] + + _, total, _ = control_flow_ops.while_loop( + cond=lambda iter_, *args: iter_ < n, + body=_loop_body, + loop_vars=[ + 0, + array_ops.zeros(batch_shape, dtype=samples.dtype.base_dtype), + envelope, # to_skip + ]) + + return total + envelope * high + + +def _maximum_mean(samples, envelope, high, name=None): + """Returns a stochastic upper bound on the mean of a scalar distribution. + + The idea is that if the true CDF is within an `eps`-envelope of the + empirical CDF of the samples, and the support is bounded above, then + the mean is bounded above as well. In symbols, + + ```none + sup_x(|F_n(x) - F(x)|) < eps + ``` + + The 0th dimension of `samples` is interpreted as independent and + identically distributed samples. The remaining dimensions are + broadcast together with `envelope` and `high`, and operated on + separately. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `envelope` and `high`. + envelope: Floating-point tensor of sizes of admissible CDF + envelopes (i.e., the `eps` above). + high: Floating-point tensor of upper bounds on the distributions' + supports. + name: A name for this operation (optional). + + Returns: + bound: Floating-point tensor of upper bounds on the true means. + + Raises: + InvalidArgumentError: If some `sample` is found to be larger than + the corresponding `high`. + """ + with ops.name_scope(name, "maximum_mean", [samples, envelope, high]): + samples = ops.convert_to_tensor(samples, name="samples") + envelope = ops.convert_to_tensor(envelope, name="envelope") + high = ops.convert_to_tensor(high, name="high") + + xmax = math_ops.reduce_max(samples, axis=[-1]) + msg = "Given sample maximum value exceeds expectations" + check_op = check_ops.assert_less_equal(xmax, high, message=msg) + with ops.control_dependencies([check_op]): + return array_ops.identity(_do_maximum_mean(samples, envelope, high)) + + +def _minimum_mean(samples, envelope, low, name=None): + """Returns a stochastic lower bound on the mean of a scalar distribution. + + The idea is that if the true CDF is within an `eps`-envelope of the + empirical CDF of the samples, and the support is bounded below, then + the mean is bounded below as well. In symbols, + + ```none + sup_x(|F_n(x) - F(x)|) < eps + ``` + + The 0th dimension of `samples` is interpreted as independent and + identically distributed samples. The remaining dimensions are + broadcast together with `envelope` and `low`, and operated on + separately. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `envelope` and `low`. + envelope: Floating-point tensor of sizes of admissible CDF + envelopes (i.e., the `eps` above). + low: Floating-point tensor of lower bounds on the distributions' + supports. + name: A name for this operation (optional). + + Returns: + bound: Floating-point tensor of lower bounds on the true means. + + Raises: + InvalidArgumentError: If some `sample` is found to be smaller than + the corresponding `low`. + """ + with ops.name_scope(name, "minimum_mean", [samples, envelope, low]): + samples = ops.convert_to_tensor(samples, name="samples") + envelope = ops.convert_to_tensor(envelope, name="envelope") + low = ops.convert_to_tensor(low, name="low") + + xmin = math_ops.reduce_min(samples, axis=[-1]) + msg = "Given sample minimum value falls below expectations" + check_op = check_ops.assert_greater_equal(xmin, low, message=msg) + with ops.control_dependencies([check_op]): + return - _do_maximum_mean(-samples, envelope, -low) + + +def _dkwm_cdf_envelope(n, error_rate, name=None): + """Computes the CDF envelope that the DKWM inequality licenses. + + The [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval) + gives a stochastic bound on the distance between the true cumulative + distribution function (CDF) of any distribution and its empirical + CDF. To wit, for `n` iid samples from any distribution with CDF F, + + ```none + P(sup_x |F_n(x) - F(x)| > eps) < 2exp(-2n eps^2) + ``` + + This function computes the envelope size `eps` as a function of the + number of samples `n` and the desired limit on the left-hand + probability above. + + Args: + n: Tensor of numbers of samples drawn. + error_rate: Floating-point tensor of admissible rates of mistakes. + name: A name for this operation (optional). + + Returns: + eps: Tensor of maximum distances the true CDF can be from the + empirical CDF. This scales as `O(sqrt(-log(error_rate)))` and + as `O(1 / sqrt(n))`. The shape is the broadcast of `n` and + `error_rate`. + """ + with ops.name_scope(name, "dkwm_cdf_envelope", [n, error_rate]): + n = math_ops.cast(n, dtype=error_rate.dtype) + return math_ops.sqrt(-gen_math_ops.log(error_rate / 2.) / (2. * n)) + + +def _check_shape_dominates(tensor, tensors): + """Check that broadcasting `tensor` against `tensors` does not expand it. + + Why? Because I want to be very sure that the samples tensor is not + accidentally enlarged by broadcasting against tensors that are + supposed to be describing the distribution(s) sampled from, lest the + sample counts end up inflated. + + Args: + tensor: A Tensor whose shape is to be protected against broadcasting. + tensors: A list of Tensors to check + + Returns: + tensor: `tf.identity(tensor)` with control dependencies attached; + be sure to use that downstream. + """ + def check(t): + target = array_ops.shape(tensor)[1:] + result = array_ops.broadcast_dynamic_shape(target, array_ops.shape(t)) + # This rank check ensures that I don't get a wrong answer from the + # _shapes_ broadcasting against each other. + gt = check_ops.assert_greater(array_ops.rank(target), array_ops.rank(t)) + eq = check_ops.assert_equal(target, result) + return gt, eq + checks = list(itertools.chain(*[check(t) for t in tensors])) + with ops.control_dependencies(checks): + return array_ops.identity(array_ops.identity(tensor)) + + +def true_mean_confidence_interval_by_dkwm( + samples, low, high, error_rate=1e-6, name=None): + """Computes a confidence interval for the mean of a scalar distribution. + + In batch mode, computes confidence intervals for all distributions + in the batch (which need not be identically distributed). + + Relies on the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + The probability (over the randomness of drawing the given samples) + that any true mean is outside the corresponding returned interval is + no more than the given `error_rate`. The size of the intervals + scale as + `O(1 / sqrt(#samples))`, as `O(high - low)`, and as `O(-log(error_rate))`. + + Note that `error_rate` is a total error rate for all the confidence + intervals in the batch. As such, if the batch is nontrivial, the + error rate is not broadcast but divided (evenly) among the batch + members. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `low` and `high`. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + error_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + low: A floating-point tensor of stochastic lower bounds on the true means. + high: A floating-point tensor of stochastic upper bounds on the true means. + """ + with ops.name_scope( + name, "true_mean_confidence_interval_by_dkwm", + [samples, low, high, error_rate]): + samples = ops.convert_to_tensor(samples, name="samples") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + error_rate = ops.convert_to_tensor(error_rate, name="error_rate") + samples = _check_shape_dominates(samples, [low, high]) + check_ops.assert_scalar(error_rate) # Static shape + error_rate = _itemwise_error_rate(error_rate, [low, high], samples) + n = array_ops.shape(samples)[0] + envelope = _dkwm_cdf_envelope(n, error_rate) + min_mean = _minimum_mean(samples, envelope, low) + max_mean = _maximum_mean(samples, envelope, high) + return min_mean, max_mean + + +def _itemwise_error_rate( + total_error_rate, param_tensors, sample_tensor=None, name=None): + with ops.name_scope( + name, "itemwise_error_rate", + [total_error_rate, param_tensors, sample_tensor]): + result_shape = [1] + for p_tensor in param_tensors: + result_shape = array_ops.broadcast_dynamic_shape( + array_ops.shape(p_tensor), result_shape) + if sample_tensor is not None: + result_shape = array_ops.broadcast_dynamic_shape( + array_ops.shape(sample_tensor)[1:], result_shape) + num_items = math_ops.reduce_prod(result_shape) + return total_error_rate / math_ops.cast( + num_items, dtype=total_error_rate.dtype) + + +def assert_true_mean_equal_by_dkwm( + samples, low, high, expected, false_fail_rate=1e-6, name=None): + """Asserts the mean of the given distribution is as expected. + + More precisely, fails if there is enough evidence (using the + [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)) + that the true mean of some distribution from which the given samples are + drawn is _not_ the given expected mean with statistical significance + `false_fail_rate` or stronger, otherwise passes. If you also want to + check that you are gathering enough evidence that a pass is not + spurious, see `min_num_samples_for_dkwm_mean_test` and + `min_discrepancy_of_true_means_detectable_by_dkwm`. + + Note that `false_fail_rate` is a total false failure rate for all + the assertions in the batch. As such, if the batch is nontrivial, + the assertion will insist on stronger evidence to fail any one member. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `low` and `high`. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + expected: Floating-point tensor of expected true means. + false_fail_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + check: Op that raises `InvalidArgumentError` if any expected mean is + outside the corresponding confidence interval. + """ + with ops.name_scope( + name, "assert_true_mean_equal_by_dkwm", + [samples, low, high, expected, false_fail_rate]): + samples = ops.convert_to_tensor(samples, name="samples") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + expected = ops.convert_to_tensor(expected, name="expected") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + samples = _check_shape_dominates(samples, [low, high, expected]) + min_mean, max_mean = true_mean_confidence_interval_by_dkwm( + samples, low, high, error_rate=false_fail_rate) + less_op = check_ops.assert_less( + min_mean, expected, message="Mean confidence interval too high") + with ops.control_dependencies([less_op]): + return check_ops.assert_greater( + max_mean, expected, message="Mean confidence interval too low") + + +def min_discrepancy_of_true_means_detectable_by_dkwm( + n, low, high, false_fail_rate, false_pass_rate, name=None): + """Returns the minimum mean discrepancy that a DKWM-based test can detect. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Note that `false_fail_rate` is a total false failure rate for all + the tests in the batch. As such, if the batch is nontrivial, each + member will demand more samples. The `false_pass_rate` is also + interpreted as a total, but is treated asymmetrically: If each test + in the batch detects its corresponding discrepancy with probability + at least `1 - false_pass_rate`, then running all those tests and + failing if any one fails will jointly detect all those discrepancies + with the same `false_pass_rate`. + + Args: + n: Tensor of numbers of samples to be drawn from the distributions + of interest. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + discr: Tensor of lower bounds on the distances between true + means detectable by a DKWM-based test. + + For each batch member `i`, of `K` total, drawing `n[i]` samples from + some scalar distribution supported on `[low[i], high[i]]` is enough + to detect a difference in means of size `discr[i]` or more. + Specifically, we guarantee that (a) if the true mean is the expected + mean, `assert_true_mean_equal_by_dkwm` will fail with probability at + most `false_fail_rate / K` (which amounts to `false_fail_rate` if + applied to the whole batch at once), and (b) if the true mean + differs from the expected mean by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm` will pass with probability at most + `false_pass_rate`. + + The detectable discrepancy scales as + + - `O(high[i] - low[i])`, + - `O(1 / sqrt(n[i]))`, + - `O(-log(false_fail_rate/K))`, and + - `O(-log(false_pass_rate))`. + """ + with ops.name_scope( + name, "min_discrepancy_of_true_means_detectable_by_dkwm", + [n, low, high, false_fail_rate, false_pass_rate]): + n = ops.convert_to_tensor(n, name="n") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Algorithm: Assume a true CDF F. The DKWM inequality gives a + # stochastic bound on how far the observed empirical CDF F_n can be. + # Then, using the DKWM inequality again gives a stochastic bound on + # the farthest candidate true CDF F' that + # true_mean_confidence_interval_by_dkwm might consider. At worst, these + # errors may go in the same direction, so the distance between F and + # F' is bounded by the sum. + # On batching: false fail rates sum, so I need to reduce + # the input to account for the batching. False pass rates + # max, so I don't. + sampling_envelope = _dkwm_cdf_envelope(n, false_pass_rate) + false_fail_rate = _itemwise_error_rate(false_fail_rate, [n, low, high]) + analysis_envelope = _dkwm_cdf_envelope(n, false_fail_rate) + return (high - low) * (sampling_envelope + analysis_envelope) + + +def min_num_samples_for_dkwm_mean_test( + discrepancy, low, high, + false_fail_rate=1e-6, false_pass_rate=1e-6, name=None): + """Returns how many samples suffice for a one-sample DKWM mean test. + + To wit, returns an upper bound on the number of samples necessary to + guarantee detecting a mean difference of at least the given + `discrepancy`, with the given `false_fail_rate` and `false_pass_rate`, + using the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval) + on a scalar distribution supported on `[low, high]`. + + Args: + discrepancy: Floating-point tensor of desired upper limits on mean + differences that may go undetected with probability higher than + `1 - false_pass_rate`. + low: Tensor of lower bounds on the distributions' support. + high: Tensor of upper bounds on the distributions' support. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + n: Tensor of numbers of samples to be drawn from the distributions + of interest. + + The `discrepancy`, `low`, and `high` tensors must have + broadcast-compatible shapes. + + For each batch member `i`, of `K` total, drawing `n[i]` samples from + some scalar distribution supported on `[low[i], high[i]]` is enough + to detect a difference in means of size `discrepancy[i]` or more. + Specifically, we guarantee that (a) if the true mean is the expected + mean, `assert_true_mean_equal_by_dkwm` will fail with probability at + most `false_fail_rate / K` (which amounts to `false_fail_rate` if + applied to the whole batch at once), and (b) if the true mean + differs from the expected mean by at least `discrepancy[i]`, + `assert_true_mean_equal_by_dkwm` will pass with probability at most + `false_pass_rate`. + + The required number of samples scales + as `O((high[i] - low[i])**2)`, `O(-log(false_fail_rate/K))`, + `O(-log(false_pass_rate))`, and `O(1 / discrepancy[i]**2)`. + """ + with ops.name_scope( + name, "min_num_samples_for_dkwm_mean_test", + [low, high, false_fail_rate, false_pass_rate, discrepancy]): + discrepancy = ops.convert_to_tensor( + discrepancy, name="discrepancy") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Could choose to cleverly allocate envelopes, but this is sound. + envelope1 = discrepancy / (2. * (high - low)) + envelope2 = envelope1 + false_fail_rate = _itemwise_error_rate( + false_fail_rate, [low, high, discrepancy]) + n1 = -math_ops.log(false_fail_rate / 2.) / (2. * envelope1**2) + n2 = -math_ops.log(false_pass_rate / 2.) / (2. * envelope2**2) + return math_ops.maximum(n1, n2) + + +def assert_true_mean_equal_by_dkwm_two_sample( + samples1, low1, high1, samples2, low2, high2, + false_fail_rate=1e-6, name=None): + """Asserts the means of the given distributions are equal. + + More precisely, fails if there is enough evidence (using the + [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)) + that the means of the distributions from which the given samples are + drawn are _not_ equal with statistical significance `false_fail_rate` + or stronger, otherwise passes. If you also want to check that you + are gathering enough evidence that a pass is not spurious, see + `min_num_samples_for_dkwm_mean_two_sample_test` and + `min_discrepancy_of_true_means_detectable_by_dkwm_two_sample`. + + Note that `false_fail_rate` is a total false failure rate for all + the assertions in the batch. As such, if the batch is nontrivial, + the assertion will insist on stronger evidence to fail any one member. + + Args: + samples1: Floating-point tensor of samples from the + distribution(s) A. Entries are assumed IID across the 0th + dimension. The other dimensions must broadcast with `low1`, + `high1`, `low2`, and `high2`. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + samples2: Floating-point tensor of samples from the + distribution(s) B. Entries are assumed IID across the 0th + dimension. The other dimensions must broadcast with `low1`, + `high1`, `low2`, and `high2`. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + check: Op that raises `InvalidArgumentError` if any pair of confidence + intervals true for corresponding true means do not overlap. + """ + with ops.name_scope( + name, "assert_true_mean_equal_by_dkwm_two_sample", + [samples1, low1, high1, samples2, low2, high2, false_fail_rate]): + samples1 = ops.convert_to_tensor(samples1, name="samples1") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + samples2 = ops.convert_to_tensor(samples2, name="samples2") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + samples1 = _check_shape_dominates(samples1, [low1, high1]) + samples2 = _check_shape_dominates(samples2, [low2, high2]) + compatible_samples = check_ops.assert_equal( + array_ops.shape(samples1)[1:], array_ops.shape(samples2)[1:]) + with ops.control_dependencies([compatible_samples]): + # Could in principle play games with cleverly allocating + # significance instead of the even split below. It may be possible + # to get tighter intervals, in order to obtain a higher power test. + # Any allocation strategy that depends only on the support bounds + # and sample counts should be valid; however, because the intervals + # scale as O(-log(false_fail_rate)), there doesn't seem to be much + # room to win. + min_mean_1, max_mean_1 = true_mean_confidence_interval_by_dkwm( + samples1, low1, high1, false_fail_rate / 2.) + min_mean_2, max_mean_2 = true_mean_confidence_interval_by_dkwm( + samples2, low2, high2, false_fail_rate / 2.) + # I want to assert + # not (max_mean_1 < min_mean_2 or min_mean_1 > max_mean_2), + # but I think I only have and-combination of asserts, so use DeMorgan. + clause1_op = check_ops.assert_greater_equal(max_mean_1, min_mean_2) + with ops.control_dependencies([clause1_op]): + return check_ops.assert_less_equal(min_mean_1, max_mean_2) + + +def min_discrepancy_of_true_means_detectable_by_dkwm_two_sample( + n1, low1, high1, n2, low2, high2, + false_fail_rate, false_pass_rate, name=None): + """Returns the minimum mean discrepancy for a two-sample DKWM-based test. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Note that `false_fail_rate` is a total false failure rate for all + the tests in the batch. As such, if the batch is nontrivial, each + member will demand more samples. The `false_pass_rate` is also + interpreted as a total, but is treated asymmetrically: If each test + in the batch detects its corresponding discrepancy with probability + at least `1 - false_pass_rate`, then running all those tests and + failing if any one fails will jointly detect all those discrepancies + with the same `false_pass_rate`. + + Args: + n1: Tensor of numbers of samples to be drawn from the distributions A. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + n2: Tensor of numbers of samples to be drawn from the distributions B. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + discr: Tensor of lower bounds on the distances between true means + detectable by a two-sample DKWM-based test. + + For each batch member `i`, of `K` total, drawing `n1[i]` samples + from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]` + samples from scalar distribution B supported on `[low2[i], high2[i]]` + is enough to detect a difference in their true means of size + `discr[i]` or more. Specifically, we guarantee that (a) if their + true means are equal, `assert_true_mean_equal_by_dkwm_two_sample` + will fail with probability at most `false_fail_rate/K` (which + amounts to `false_fail_rate` if applied to the whole batch at once), + and (b) if their true means differ by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm_two_sample` will pass with + probability at most `false_pass_rate`. + + The detectable distribution scales as + + - `O(high1[i] - low1[i])`, `O(high2[i] - low2[i])`, + - `O(1 / sqrt(n1[i]))`, `O(1 / sqrt(n2[i]))`, + - `O(-log(false_fail_rate/K))`, and + - `O(-log(false_pass_rate))`. + """ + with ops.name_scope( + name, "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample", + [n1, low1, high1, n2, low2, high2, false_fail_rate, false_pass_rate]): + n1 = ops.convert_to_tensor(n1, name="n1") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + n2 = ops.convert_to_tensor(n2, name="n2") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + det_disc1 = min_discrepancy_of_true_means_detectable_by_dkwm( + n1, low1, high1, false_fail_rate / 2., false_pass_rate / 2.) + det_disc2 = min_discrepancy_of_true_means_detectable_by_dkwm( + n2, low2, high2, false_fail_rate / 2., false_pass_rate / 2.) + return det_disc1 + det_disc2 + + +def min_num_samples_for_dkwm_mean_two_sample_test( + discrepancy, low1, high1, low2, high2, + false_fail_rate=1e-6, false_pass_rate=1e-6, name=None): + """Returns how many samples suffice for a two-sample DKWM mean test. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Args: + discrepancy: Floating-point tensor of desired upper limits on mean + differences that may go undetected with probability higher than + `1 - false_pass_rate`. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + n1: Tensor of numbers of samples to be drawn from the distributions A. + n2: Tensor of numbers of samples to be drawn from the distributions B. + + For each batch member `i`, of `K` total, drawing `n1[i]` samples + from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]` + samples from scalar distribution B supported on `[low2[i], high2[i]]` + is enough to detect a difference in their true means of size + `discr[i]` or more. Specifically, we guarantee that (a) if their + true means are equal, `assert_true_mean_equal_by_dkwm_two_sample` + will fail with probability at most `false_fail_rate/K` (which + amounts to `false_fail_rate` if applied to the whole batch at once), + and (b) if their true means differ by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm_two_sample` will pass with + probability at most `false_pass_rate`. + + The required number of samples scales as + + - `O((high1[i] - low1[i])**2)`, `O((high2[i] - low2[i])**2)`, + - `O(-log(false_fail_rate/K))`, + - `O(-log(false_pass_rate))`, and + - `O(1 / discrepancy[i]**2)`. + """ + with ops.name_scope( + name, "min_num_samples_for_dkwm_mean_two_sample_test", + [low1, high1, low2, high2, + false_fail_rate, false_pass_rate, discrepancy]): + discrepancy = ops.convert_to_tensor(discrepancy, name="discrepancy") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Could choose to cleverly allocate discrepancy tolerances and + # failure probabilities, but this is sound. + n1 = min_num_samples_for_dkwm_mean_test( + discrepancy / 2., low1, high1, + false_fail_rate / 2., false_pass_rate / 2.) + n2 = min_num_samples_for_dkwm_mean_test( + discrepancy / 2., low2, high2, + false_fail_rate / 2., false_pass_rate / 2.) + return n1, n2 diff --git a/tensorflow/contrib/eager/proto/checkpointable_object_graph.proto b/tensorflow/contrib/eager/proto/checkpointable_object_graph.proto index c962638aa11c06dcd5be6a794314e029ae84e572..024765acb28726fd102dfbf167f4e780072ce6e7 100644 --- a/tensorflow/contrib/eager/proto/checkpointable_object_graph.proto +++ b/tensorflow/contrib/eager/proto/checkpointable_object_graph.proto @@ -4,9 +4,9 @@ option cc_enable_arenas = true; package tensorflow.contrib.eager; -// Prototype for an addition to BundleHeaderProto which saves extra information -// about the objects which own variables, allowing for more robust checkpoint -// loading into modified programs. +// Prototype format which saves extra information about the objects which own +// variables, allowing for more robust checkpoint loading into modified +// programs. Currently stored in its own entry in a TensorBundle. message CheckpointableObjectGraph { message Object { @@ -14,40 +14,39 @@ message CheckpointableObjectGraph { // An index into `CheckpointableObjectGraph.nodes`, indicating the object // being referenced. int32 node_id = 1; - // A numeric identifier for this object within its parent. - int32 local_uid = 2; - // A user-provided name for the edge. May be blank/omitted, in which case - // there is no explicitly provided local name; fall back on local_uid. - string local_name = 3; + // A user-provided name for the edge. + string local_name = 2; } - message VariableReference { - // A name for the variable which is unique within the object which owns - // it. Does not include a name_scope or variable_scope prefix. - string local_name = 1; - // The full name of the variable. Used to allow name-based loading of - // checkpoints which were saved using an object-based API. + message SerializedTensor { + // A name for the Tensor. Simple variables have only one + // `SerializedTensor` named "VARIABLE_VALUE" by convention. This value may + // be restored on object creation as an optimization. + string name = 1; + // The full name of the variable/tensor, if applicable. Used to allow + // name-based loading of checkpoints which were saved using an + // object-based API. Should match the checkpoint key which would have been + // assigned by tf.train.Saver. string full_name = 2; + // The generated name of the Tensor in the checkpoint. + string checkpoint_key = 3; } message SlotVariableReference { - // An index into `CheckpointableObjectGraph.nodes`, indicating the object - // which created the variable that this variable is slotting for. + // An index into `CheckpointableObjectGraph.nodes`, indicating the + // variable object this slot was created for. int32 original_variable_node_id = 1; - // The local name of the variable being slotted for within the object that - // owns it. - string original_variable_local_name = 2; // The name of the slot (e.g. "m"/"v"). - string slot_name = 3; - // The full name of the slot variable. Used to allow name-based loading of - // checkpoints which were saved using an object-based API. - string full_name = 4; + string slot_name = 2; + // An index into `CheckpointableObjectGraph.nodes`, indicating the + // `Object` with the value of the slot variable. + int32 slot_variable_node_id = 3; } // Objects which this object depends on. repeated ObjectReference children = 1; - // Non-slot variables owned by this object. - repeated VariableReference variables = 2; + // Serialized data specific to this object. + repeated SerializedTensor attributes = 2; // Slot variables owned by this object. repeated SlotVariableReference slot_variables = 3; } diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD index e984c63af7ce2b32ab30121bf34bb2de4dfeb218..5a6251b871430e7cf69444b2062c1cb677877c28 100644 --- a/tensorflow/contrib/eager/python/BUILD +++ b/tensorflow/contrib/eager/python/BUILD @@ -11,12 +11,14 @@ py_library( srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ + ":checkpointable_utils", ":datasets", ":metrics", ":network", ":saver", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", "//tensorflow/python:numerics", "//tensorflow/python:resource_variable_ops", "//tensorflow/python:script_ops", @@ -26,7 +28,6 @@ py_library( "//tensorflow/python/eager:backprop", "//tensorflow/python/eager:context", "//tensorflow/python/eager:core", - "//tensorflow/python/eager:custom_gradient", "//tensorflow/python/eager:execution_callbacks", "//tensorflow/python/eager:function", ], @@ -52,7 +53,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//tensorflow:internal"], deps = [ - "//tensorflow/contrib/data/python/ops:prefetching_py", + "//tensorflow/contrib/data/python/ops:prefetching_ops", "//tensorflow/python:array_ops", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:errors", @@ -69,6 +70,7 @@ cuda_py_test( srcs = ["datasets_test.py"], additional_deps = [ ":datasets", + "//tensorflow/contrib/data/python/ops:transformation_ops", "//tensorflow/contrib/lookup:lookup_py", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", @@ -115,6 +117,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//tensorflow:internal"], deps = [ + "//tensorflow/contrib/eager/python:checkpointable_utils", "//tensorflow/contrib/summary:summary_ops", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", @@ -220,36 +223,50 @@ py_test( ) py_library( - name = "checkpointable", - srcs = ["checkpointable.py"], + name = "checkpointable_utils", + srcs = ["checkpointable_utils.py"], srcs_version = "PY2AND3", visibility = ["//tensorflow:internal"], deps = [ "//tensorflow/contrib/eager/proto:checkpointable_object_graph_proto_py", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:init_ops", + "//tensorflow/python:io_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:tensor_shape", "//tensorflow/python:training", "//tensorflow/python:variable_scope", + "//tensorflow/python/eager:context", ], ) -py_test( - name = "checkpointable_test", - srcs = ["checkpointable_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":checkpointable", +cuda_py_test( + name = "checkpointable_utils_test", + srcs = ["checkpointable_utils_test.py"], + additional_deps = [ + ":checkpointable_utils", ":network", + "@six_archive//:six", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", + "//tensorflow/python:init_ops", "//tensorflow/python:layers", + "//tensorflow/python:layers_base", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:state_ops", "//tensorflow/python:training", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/eager:context", "//tensorflow/python/eager:test", - "@six_archive//:six", + "//tensorflow/python/keras", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) filegroup( diff --git a/tensorflow/contrib/eager/python/checkpointable.py b/tensorflow/contrib/eager/python/checkpointable.py deleted file mode 100644 index b141ffb2bc03b8e38f8481bc044c3aae7e156c15..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/checkpointable.py +++ /dev/null @@ -1,392 +0,0 @@ -"""An object-local variable management scheme.""" -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import re - -from tensorflow.contrib.eager.proto import checkpointable_object_graph_pb2 -from tensorflow.python.ops import variable_scope -from tensorflow.python.training import optimizer as optimizer_lib -from tensorflow.python.training import saver as saver_lib - -_CheckpointableReference = collections.namedtuple( - "_CheckpointableReference", - [ - "name", # The local name if explicitly specified, else None. - "local_uid", # 0 for the first dependency, 1 for the next, ... Used for - # routing checkpointed variables to their correct - # Checkpointables when "name" is not set (see docstring of - # `track_checkpointable`). - "ref" # The Checkpointable object being referenced. - ]) - -_OwnedVariable = collections.namedtuple( - "_OwnedVariable", - [ - "name", # The variable's (local) name. - "variable" # The owned variable object. - ]) - -# Validation regular expression for the local names of Checkpointable -# objects. In particular, disallows "/" in names, and reserves -# underscore-prefixed names. -_VALID_LOCAL_NAME = re.compile(r"^[A-Za-z0-9.][A-Za-z0-9_.-]*$") - -# Keyword for identifying that the next bit of a checkpoint variable name is a -# slot name. May not be the local name of a checkpointable. Checkpoint names for -# slot variables look like: -# -# /<_OPTIMIZER_SLOTS_NAME>// -# -# Where is a full path from the checkpoint root to the -# variable being slotted for. -_OPTIMIZER_SLOTS_NAME = "_OPTIMIZER_SLOT" - - -class Checkpointable(object): - """Manages variables and dependencies on other objects. - - To make reliable checkpoints, all `Checkpointable`s on which this object - depends must be registered in the constructor using `track_checkpointable` in - a deterministic order, and if possible they should be named. Variables may be - created using `add_variable` outside of the constructor and in any order, but - only these variables will be saved. - """ - - def __init__(self): - # Basically less useful OrderedDicts but without the reference cycles. - # TODO(allenl): Switch these to OrderedDict once TensorFlow supports only - # Python 3.6+. - self._checkpoint_dependencies = [] # A list of _CheckpointableReference - # objects. - self._dependency_names = set() - self._owned_variables = [] # A list of _OwnedVariable objects. - self._owned_variable_names = set() - - def add_variable(self, name, shape, dtype=None, initializer=None, **kwargs): - """Create a new variable object to be saved with this `Checkpointable`. - - If the user has requested that this object or another `Checkpointable` which - depends on this object be restored from a checkpoint (deferred loading - before variable object creation), `initializer` may be ignored and the value - from the checkpoint used instead. - - Args: - name: A name for the variable. Must be unique within this object. - shape: The shape of the variable. - dtype: The data type of the variable. - initializer: The initializer to use. Ignored if deferred loading has been - requested. - **kwargs: Passed to get_variable. - - Returns: - The new variable object. - - Raises: - ValueError: If the variable name is not unique. - """ - if name in self._owned_variable_names: - raise ValueError( - ("A variable named '%s' already exists in this Checkpointable, but " - "Checkpointable.add_variable called to create another with " - "that name. Variable names must be unique within a Checkpointable " - "object.") % (name,)) - if "getter" in kwargs: - # Allow the getter to be overridden, typically because there is a need for - # compatibility with some other variable creation mechanism. This should - # be relatively uncommon in user code. - getter = kwargs.pop("getter") - else: - getter = variable_scope.get_variable - # TODO(allenl): handle deferred loading - new_variable = getter( - name=name, shape=shape, dtype=dtype, initializer=initializer, **kwargs) - self._owned_variables.append( - _OwnedVariable(name=name, variable=new_variable)) - self._owned_variable_names.add(name) - return new_variable - - def track_checkpointable(self, checkpointable, name=None): - """Declare a dependency on another `Checkpointable` object. - - Indicates that checkpoints for this object should include variables from - `checkpointable`. - - Variables in a checkpoint are mapped to `Checkpointable`s based on names if - provided when the checkpoint was written, but otherwise use the order those - `Checkpointable`s were declared as dependencies. Both `name` arguments and - the dependency declaration order should be deterministic. - - There are two sufficient conditions to avoid breaking existing checkpoints - when modifying a class: (1) New dependencies must be declared after existing - dependencies, and (2) dependencies which were previously declared may never - be removed (a trivial placeholder with the same name may be used instead). - - Args: - checkpointable: A `Checkpointable` which this object depends on. - name: A local name for `checkpointable`, used for loading checkpoints into - the correct objects. If provided, it must be unique within this - `Checkpointable`. If None, dependency declaration order is used instead. - - Returns: - `checkpointable`, for convenience when declaring a dependency and - assigning to a member variable in one statement. - - Raises: - RuntimeError: If __init__ was not called. - TypeError: If `checkpointable` does not inherit from `Checkpointable`. - ValueError: For invalid names. - """ - if not hasattr(self, "_checkpoint_dependencies"): - raise RuntimeError("Need to call Checkpointable.__init__ before calling " - "Checkpointable.track_checkpointable().") - if not isinstance(checkpointable, Checkpointable): - raise TypeError( - ("Checkpointable.track_checkpointable() passed type %s, not a " - "Checkpointable.") % (type(checkpointable),)) - if name is not None: - if not _VALID_LOCAL_NAME.match(name): - raise ValueError( - ("Checkpointable names must match the regular expression '%s', but " - "got an invalid name '%s' instead.") % (_VALID_LOCAL_NAME.pattern, - name)) - if name in self._dependency_names: - raise ValueError( - ("Called Checkpointable.track_checkpointable() with name='%s', but " - "a Checkpointable with this name is already declared as a " - "dependency. If provided, names must be unique.") % (name,)) - self._dependency_names.add(name) - self._checkpoint_dependencies.append( - _CheckpointableReference( - name=name, - ref=checkpointable, - # TODO(allenl): Should this be exposed to allow users to stop - # depending on things and still load checkpoints when not using - # names? - local_uid=len(self._checkpoint_dependencies))) - return checkpointable - - @property - def checkpoint_dependencies(self): - """Other `Checkpointable` objects on which this object depends.""" - return self._checkpoint_dependencies - - -def _breadth_first_checkpointable_traversal(root_checkpointable): - """Find shortest paths to all variables owned by dependencies of root.""" - bfs_sorted = [] - root_checkpointable_reference = _CheckpointableReference( - name=None, local_uid=0, ref=root_checkpointable) - to_visit = collections.deque([root_checkpointable_reference]) - path_to_root = {root_checkpointable_reference: ()} - while to_visit: - current_checkpointable = to_visit.popleft() - bfs_sorted.append(current_checkpointable) - for child_checkpointable in ( - current_checkpointable.ref.checkpoint_dependencies): - if child_checkpointable not in path_to_root: - path_to_root[child_checkpointable] = ( - path_to_root[current_checkpointable] + (child_checkpointable,)) - to_visit.append(child_checkpointable) - return bfs_sorted, path_to_root - - -def _object_prefix_from_path(path_to_root): - return "/".join((checkpointable.name if checkpointable.name else "_%d" % ( - checkpointable.local_uid,)) for checkpointable in path_to_root) - - -def _escape_variable_name(variable_name): - # We need to support slashes in variable names for compatibility, since this - # naming scheme is being patched in to things like Layer.add_variable where - # slashes were previously accepted. We also want to use slashes to indicate - # edges traversed to reach the variable, so we escape forward slashes in - # variable names. - return variable_name.replace("_S_", "_S_.").replace(r"/", r"_S__") - - -def _variable_naming_for_object(path_to_root): - """Make a function for naming variables in an object.""" - # Name non-slot variables: - # - # / - # - # is not necessarily unique, but this is fine since we also - # save the graph of `Checkpointable`s with the checkpoint. Even if this path - # no longer exists because of a change in the Python program, we can look up - # the `Checkpointable` which owns the variable in the checkpoint's graph and - # use another path if one still exists. - - object_prefix = _object_prefix_from_path(path_to_root) - if object_prefix: - object_prefix += "/" - - def _name_single_variable(owned_variable): - """Names a variable within an object.""" - return object_prefix + _escape_variable_name(owned_variable.name) - - return _name_single_variable - - -def _slot_variable_naming_for_optimizer(optimizer, path_to_root): - """Make a function for naming slot variables in an optimizer.""" - # Name slot variables: - # - # /<_OPTIMIZER_SLOTS_NAME>// - # - # where is exactly the checkpoint name used for the original - # variable, including the path from the checkpoint root and the local name in - # the object which owns it. Note that we only save slot variables if the - # variable it's slotting for is also being saved. - - optimizer_identifier = "/%s/%s/" % (_OPTIMIZER_SLOTS_NAME, - _object_prefix_from_path(path_to_root)) - - def _name_slot_variable(variable_path, slot_name): - """With an optimizer specified, name a slot variable.""" - - if not _VALID_LOCAL_NAME.match(slot_name): - # Slot variable names include the name of the slot. We need to - # validate that part of the name to be sure that the checkpoint name - # is a valid name scope name. - raise ValueError( - ("Could not save slot variables for optimizer %s, because its " - "slot name has invalid characters (got '%s', was expecting it " - "to match the regular expression '%s').") % - (optimizer, slot_name, _VALID_LOCAL_NAME.pattern)) - - return variable_path + optimizer_identifier + slot_name - - return _name_slot_variable - - -def _serialize_non_slot_variables(checkpointable_objects, path_to_root, - object_graph_proto): - """Name non-slot variables and add them to `object_graph_proto`.""" - named_variables = {} - non_slot_variables = [] - checkpoint_node_ids = {} - - for checkpoint_id, checkpointable in enumerate(checkpointable_objects): - checkpoint_node_ids[checkpointable] = checkpoint_id - - for checkpoint_id, checkpointable in enumerate(checkpointable_objects): - naming_scheme = _variable_naming_for_object(path_to_root[checkpointable]) - object_proto = object_graph_proto.nodes.add() - for owned_variable in checkpointable.ref._owned_variables: # pylint: disable=protected-access - variable_name = naming_scheme(owned_variable) - named_variables[variable_name] = owned_variable.variable - non_slot_variables.append(( - variable_name, # The variable's full checkpoint name - owned_variable, # The variable's _OwnedVariable object - checkpoint_id)) # The checkpoint ID of the node which owns this - # variable. - variable_proto = object_proto.variables.add() - variable_proto.local_name = owned_variable.name - # Figure out the name-based Saver's name for this variable. - saver_dict = saver_lib.BaseSaverBuilder.OpListToDict( - [owned_variable.variable], convert_variable_to_tensor=False) - variable_full_name, = saver_dict.keys() - variable_proto.full_name = variable_full_name - - for child in checkpointable.ref.checkpoint_dependencies: - child_proto = object_proto.children.add() - child_proto.node_id = checkpoint_node_ids[child] - child_proto.local_uid = child.local_uid - if child.name is not None: - child_proto.local_name = child.name - return named_variables, non_slot_variables - - -def _serialize_slot_variables(checkpointable_objects, path_to_root, - non_slot_variables, object_graph_proto): - """Name slot variables and add them to `object_graph_proto`.""" - named_slot_variables = {} - for optimizer_checkpoint_id, checkpointable_ref in enumerate( - checkpointable_objects): - if isinstance(checkpointable_ref.ref, optimizer_lib.Optimizer): - optimizer_object_proto = object_graph_proto.nodes[optimizer_checkpoint_id] - naming_scheme = _slot_variable_naming_for_optimizer( - optimizer=checkpointable_ref.ref, - path_to_root=path_to_root[checkpointable_ref]) - slot_names = checkpointable_ref.ref.get_slot_names() - for (variable_path, owned_variable, - original_node_checkpoint_id) in non_slot_variables: - for slot_name in slot_names: - slot_variable = checkpointable_ref.ref.get_slot( - owned_variable.variable, slot_name) - if slot_variable is not None: - checkpoint_name = naming_scheme( - variable_path=variable_path, slot_name=slot_name) - named_slot_variables[checkpoint_name] = slot_variable - slot_variable_proto = optimizer_object_proto.slot_variables.add() - slot_variable_proto.slot_name = slot_name - # Figure out the name-based Saver's name for this variable. - saver_dict = saver_lib.BaseSaverBuilder.OpListToDict( - [slot_variable], convert_variable_to_tensor=False) - slot_variable_full_name, = saver_dict.keys() - slot_variable_proto.full_name = slot_variable_full_name - slot_variable_proto.original_variable_local_name = ( - owned_variable.name) - slot_variable_proto.original_variable_node_id = ( - original_node_checkpoint_id) - return named_slot_variables - - -# TODO(allenl): Convenience utility for saving multiple objects (i.e. construct -# a root Checkpointable if passed a list of Checkpointables). -def _serialize_object_graph(root_checkpointable): - """Determine checkpoint keys for variables and build a serialized graph. - - Non-slot variables are keyed based on a shortest path from the root saveable - to the object which owns the variable (i.e. the one which called - `Checkpointable.add_variable` to create it). - - Slot variables are keyed based on a shortest path to the variable being - slotted for, a shortest path to their optimizer, and the slot name. - - Args: - root_checkpointable: A `Checkpointable` object whose variables (including - the variables of dependencies, recursively) should be saved. - - Returns: - A tuple of (named_variables, object_graph_proto): - named_variables: A dictionary mapping names to variable objects. - object_graph_proto: A CheckpointableObjectGraph protocol buffer containing - the serialized object graph and variable references. - - Raises: - ValueError: If there are invalid characters in an optimizer's slot names. - """ - checkpointable_objects, path_to_root = ( - _breadth_first_checkpointable_traversal(root_checkpointable)) - object_graph_proto = ( - checkpointable_object_graph_pb2.CheckpointableObjectGraph()) - - # Gather non-slot variables. - named_variables, non_slot_variables = _serialize_non_slot_variables( - checkpointable_objects, path_to_root, object_graph_proto) - - # Gather slot variables which are associated with variables gathered above. - named_slot_variables = _serialize_slot_variables( - checkpointable_objects, path_to_root, non_slot_variables, - object_graph_proto) - - named_variables.update(named_slot_variables) - return named_variables, object_graph_proto diff --git a/tensorflow/contrib/eager/python/checkpointable_test.py b/tensorflow/contrib/eager/python/checkpointable_test.py deleted file mode 100644 index ff419614f580d3bace9d99648478cc2204d7801d..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/checkpointable_test.py +++ /dev/null @@ -1,271 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import functools -import six - -from tensorflow.contrib.eager.python import checkpointable -from tensorflow.contrib.eager.python import network as network_lib -from tensorflow.python.eager import context -from tensorflow.python.eager import test -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.layers import core -from tensorflow.python.ops import variable_scope -from tensorflow.python.ops import variables -from tensorflow.python.training import adam -from tensorflow.python.training import training_util - - -class CheckpointableDenseLayer(core.Dense, checkpointable.Checkpointable): - - def __init__(self, *args, **kwargs): - checkpointable.Checkpointable.__init__(self) - core.Dense.__init__(self, *args, **kwargs) - - def add_variable(self, name, shape, **kwargs): - # Calls both Checkpointable.add_variable and Layer.add_variable. Eventually - # Layer.add_variable should inherit from Checkpointable and simply call - # super and then do post-processing. - return checkpointable.Checkpointable.add_variable( - self, - name=name, - shape=shape, - getter=functools.partial(core.Dense.add_variable, self), - **kwargs) - - -# pylint: disable=not-callable -class CheckpointableNetwork(network_lib.Network, checkpointable.Checkpointable): - - def __init__(self): - network_lib.Network.__init__(self) - checkpointable.Checkpointable.__init__(self) - - def track_layer(self, layer, name=None): - self.track_checkpointable(layer, name=name) - return super(CheckpointableNetwork, self).track_layer(layer) - - -class CheckpointableAdam(adam.AdamOptimizer, checkpointable.Checkpointable): - - def __init__(self, *args, **kwargs): - checkpointable.Checkpointable.__init__(self) - adam.AdamOptimizer.__init__(self, *args, **kwargs) - - # NOTE: Copied from Optimizer with modifications to use add_variable - # for non-slot variables. These contortions are necessary to maintain - # checkpoint compatibility with variable.name based saving. - # TODO(allenl): Make this cleaner. - def _create_non_slot_variable(self, initial_value, name, colocate_with): - """Add an extra variable, not associated with a slot.""" - if context.in_graph_mode(): - graph = colocate_with.graph - else: - graph = None - - key = (name, graph) - v = self._non_slot_dict.get(key, None) - if v is None: - with ops.colocate_with(colocate_with): - def _variable_getter(name, shape, dtype, initializer): - del shape, dtype # not used, but there for compatibility - return variable_scope.variable( - name=name, initial_value=initializer, trainable=False) - - initial_value = ops.convert_to_tensor(initial_value) - v = self.add_variable( - name=name, - shape=initial_value.get_shape(), - initializer=initial_value, - getter=_variable_getter) - - self._non_slot_dict[key] = v - - return v - - # TODO(allenl): Override slot variable creation (_get_or_make_slot, - # _get_or_make_slot_with_initializer, _zeros_slot) to allow deferred - # loading. Likely no need to run this through add_variable, since gathering - # slot variables is special cased anyway. - - -class MyNetwork(CheckpointableNetwork): - """A concrete Network for testing.""" - - def __init__(self): - super(MyNetwork, self).__init__() - self._named = self.track_layer( - CheckpointableDenseLayer(1, use_bias=True), name="named_dense") - self._unnamed = self.track_layer( - CheckpointableDenseLayer(1, use_bias=False)) - - def call(self, values): - return self._unnamed(self._named(values)) - - -class Root(checkpointable.Checkpointable): - """A stand-in for a Trainer class.""" - - def __init__(self, optimizer, network): - super(Root, self).__init__() - self.track_checkpointable(optimizer, name="optimizer") - self.track_checkpointable(network, name="network") - self._global_step = None - - @property - def global_step(self): - if self._global_step is None: - # Get the default create_global_step utility to actually call - # self.add_variable, by setting a custom getter. - def _owned_variable_as_custom_getter(getter, *args, **kwargs): - return self.add_variable(*args, getter=getter, **kwargs) - - with variable_scope.variable_scope( - "", custom_getter=_owned_variable_as_custom_getter): - self._global_step = training_util.create_global_step() - return self._global_step - - -class CheckpointNamingTests(test.TestCase): - - @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) - def testNamingWithOptimizer(self): - input_value = constant_op.constant([[3.]]) - network = MyNetwork() - # A nuisance Network using the same optimizer. Its slot variables should not - # go in the checkpoint, since it is never depended on. - other_network = MyNetwork() - optimizer = CheckpointableAdam(0.001) - root_checkpointable = Root(optimizer=optimizer, network=network) - if context.in_eager_mode(): - optimizer.minimize( - lambda: network(input_value), - global_step=root_checkpointable.global_step) - optimizer.minimize( - lambda: other_network(input_value), - global_step=root_checkpointable.global_step) - else: - train_op = optimizer.minimize( - network(input_value), global_step=root_checkpointable.global_step) - optimizer.minimize( - other_network(input_value), - global_step=root_checkpointable.global_step) - self.evaluate(variables.global_variables_initializer()) - self.evaluate(train_op) - named_variables, serialized_graph = checkpointable._serialize_object_graph( - root_checkpointable) - expected_checkpoint_names = ( - # Created in the root node, so no prefix. - "global_step", - # No name provided to track_checkpointable(), so the position (1, after - # the named track_checkpointable() which is 0) is used instead. - "network/_1/kernel", - # track_checkpointable() with a name provided, so that's used - "network/named_dense/kernel", - "network/named_dense/bias", - # The optimizer creates two non-slot variables - "optimizer/beta1_power", - "optimizer/beta2_power", - # Slot variables - "network/_1/kernel/_OPTIMIZER_SLOT/optimizer/m", - "network/_1/kernel/_OPTIMIZER_SLOT/optimizer/v", - "network/named_dense/kernel/_OPTIMIZER_SLOT/optimizer/m", - "network/named_dense/kernel/_OPTIMIZER_SLOT/optimizer/v", - "network/named_dense/bias/_OPTIMIZER_SLOT/optimizer/m", - "network/named_dense/bias/_OPTIMIZER_SLOT/optimizer/v", - ) - six.assertCountEqual(self, expected_checkpoint_names, - named_variables.keys()) - # Check that we've mapped to the right variable objects (not exhaustive) - self.assertEqual("global_step:0", named_variables["global_step"].name) - self.assertEqual("my_network/checkpointable_dense_layer_1/kernel:0", - named_variables["network/_1/kernel"].name) - self.assertEqual("my_network/checkpointable_dense_layer/kernel:0", - named_variables["network/named_dense/kernel"].name) - self.assertEqual("beta1_power:0", - named_variables["optimizer/beta1_power"].name) - self.assertEqual("beta2_power:0", - named_variables["optimizer/beta2_power"].name) - # Spot check the generated protocol buffers. - self.assertEqual(0, serialized_graph.nodes[0].children[0].local_uid) - self.assertEqual("optimizer", - serialized_graph.nodes[0].children[0].local_name) - optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[ - 0].node_id] - self.assertEqual("beta1_power", optimizer_node.variables[0].local_name) - self.assertEqual("beta1_power", optimizer_node.variables[0].full_name) - self.assertEqual( - "kernel", optimizer_node.slot_variables[0].original_variable_local_name) - original_variable_owner = serialized_graph.nodes[ - optimizer_node.slot_variables[0].original_variable_node_id] - self.assertEqual("kernel", original_variable_owner.variables[0].local_name) - self.assertEqual("m", optimizer_node.slot_variables[0].slot_name) - # We strip off the :0 suffix, as variable.name-based saving does. - self.assertEqual("my_network/checkpointable_dense_layer/kernel/Adam", - optimizer_node.slot_variables[0].full_name) - self.assertEqual("my_network/checkpointable_dense_layer/kernel/Adam:0", - optimizer.get_slot( - var=named_variables["network/named_dense/kernel"], - name="m").name) - - def _get_checkpoint_name(self, name): - root = checkpointable.Checkpointable() - with variable_scope.variable_scope("get_checkpoint_name"): - # Create the variable in a variable scope so that we get more relaxed - # naming rules (variables outside a scope may not start with "_", "/" or - # "-"). Since we don't use the scope part of the name, these cases are - # somewhat annoying. - root.add_variable(name=name, shape=[1, 2], dtype=dtypes.float64) - named_variables, _ = checkpointable._serialize_object_graph(root) - checkpoint_name, = named_variables.keys() - with ops.name_scope("root/" + checkpoint_name): - pass # Make sure we can use this as an op name if we prefix it. - return checkpoint_name - - @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) - def testVariableNameEscaping(self): - self.assertEqual(r"a_S__b_S__c", self._get_checkpoint_name(r"a/b/c")) - self.assertEqual(r"", self._get_checkpoint_name(r"")) - self.assertEqual(r"_S__", self._get_checkpoint_name(r"/")) - self.assertEqual(r"_S___S_._", self._get_checkpoint_name(r"/_S__")) - - @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) - def testNumberedPath(self): - root = checkpointable.Checkpointable() - leaf = checkpointable.Checkpointable() - root.track_checkpointable(leaf) - leaf.add_variable(name="v", shape=[]) - named_variables, _ = checkpointable._serialize_object_graph(root) - variable_name, = named_variables.keys() - self.assertEqual(r"_0/v", variable_name) - - @test_util.run_in_graph_and_eager_modes() - def testLocalNameValidation(self): - root = checkpointable.Checkpointable() - leaf = checkpointable.Checkpointable() - with self.assertRaisesRegexp(ValueError, "invalid name"): - # Leading underscores are reserved, which avoids conflicts with - # un-named edges in paths and the optimizer slots identifier. - root.track_checkpointable(leaf, name="_12") - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d07121df635cc95402a4811f810007807dfa0c37 --- /dev/null +++ b/tensorflow/contrib/eager/python/checkpointable_utils.py @@ -0,0 +1,879 @@ +"""Utilities for working with Checkpointable objects.""" +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import collections +import weakref + +from tensorflow.contrib.eager.proto import checkpointable_object_graph_pb2 +from tensorflow.python import pywrap_tensorflow +from tensorflow.python.client import session as session_lib +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import io_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.training import checkpointable as core_checkpointable +from tensorflow.python.training import checkpointable_utils as core_checkpointable_utils +from tensorflow.python.training import optimizer as optimizer_lib +from tensorflow.python.training import saver as saver_lib +from tensorflow.python.util import deprecation + + +_ESCAPE_CHAR = "." # For avoiding conflicts with user-specified names. + +# Keyword for identifying that the next bit of a checkpoint variable name is a +# slot name. Checkpoint names for slot variables look like: +# +# /<_OPTIMIZER_SLOTS_NAME>// +# +# Where is a full path from the checkpoint root to the +# variable being slotted for. +_OPTIMIZER_SLOTS_NAME = _ESCAPE_CHAR + "OPTIMIZER_SLOT" +# Keyword for separating the path to an object from the name of an +# attribute in checkpoint names. Used like: +# /<_OBJECT_ATTRIBUTES_NAME>/ +_OBJECT_ATTRIBUTES_NAME = _ESCAPE_CHAR + "ATTRIBUTES" +# Key where the object graph proto is saved in a TensorBundle +_OBJECT_GRAPH_PROTO_KEY = "_CHECKPOINTABLE_OBJECT_GRAPH" + + +# TODO(allenl): If this ends up in a public API, consider adding LINT.IfChange +# or consolidating the implementation with get_variable. +def _default_getter(name, shape, dtype, initializer=None, + partition_info=None, **kwargs): + """A pared-down version of get_variable which does not reuse variables.""" + dtype = dtypes.as_dtype(dtype) + shape_object = tensor_shape.as_shape(shape) + with ops.init_scope(): + if initializer is None: + initializer, initializing_from_value = ( + variable_scope._get_default_variable_store()._get_default_initializer( # pylint: disable=protected-access + name=name, shape=shape_object, dtype=dtype)) + else: + initializing_from_value = not callable(initializer) + # Same logic as get_variable + variable_dtype = dtype.base_dtype + if initializing_from_value: + if shape is not None: + raise ValueError("If initializer is a constant, do not specify shape.") + initial_value = initializer + else: + # Instantiate initializer if provided initializer is a type object. + if isinstance(initializer, type(init_ops.Initializer)): + initializer = initializer(dtype=dtype) + def initial_value(): + return initializer( + shape_object.as_list(), dtype=dtype, partition_info=partition_info) + return resource_variable_ops.ResourceVariable( + initial_value=initial_value, + name=name, + dtype=variable_dtype, + **kwargs + ) + + +def add_variable(checkpointable, name, shape=None, dtype=dtypes.float32, + initializer=None): + """Add a variable to a Checkpointable with no scope influence.""" + return checkpointable._add_variable_with_custom_getter( # pylint: disable=protected-access + name=name, shape=shape, dtype=dtype, + initializer=initializer, getter=_default_getter) + + +def _breadth_first_checkpointable_traversal(root_checkpointable): + """Find shortest paths to all variables owned by dependencies of root.""" + bfs_sorted = [] + to_visit = collections.deque([root_checkpointable]) + path_to_root = {root_checkpointable: ()} + while to_visit: + current_checkpointable = to_visit.popleft() + current_checkpointable._maybe_initialize_checkpointable() # pylint: disable=protected-access + bfs_sorted.append(current_checkpointable) + for child_checkpointable in ( + current_checkpointable._checkpoint_dependencies): # pylint: disable=protected-access + if child_checkpointable.ref not in path_to_root: + path_to_root[child_checkpointable.ref] = ( + path_to_root[current_checkpointable] + (child_checkpointable,)) + to_visit.append(child_checkpointable.ref) + return bfs_sorted, path_to_root + + +def _escape_local_name(name): + # We need to support slashes in local names for compatibility, since this + # naming scheme is being patched in to things like Layer.add_variable where + # slashes were previously accepted. We also want to use slashes to indicate + # edges traversed to reach the variable, so we escape forward slashes in + # names. + return (name.replace(_ESCAPE_CHAR, _ESCAPE_CHAR + _ESCAPE_CHAR) + .replace(r"/", _ESCAPE_CHAR + "S")) + + +def _object_prefix_from_path(path_to_root): + return "/".join( + (_escape_local_name(checkpointable.name) + for checkpointable in path_to_root)) + + +def _slot_variable_naming_for_optimizer(optimizer_path): + """Make a function for naming slot variables in an optimizer.""" + # Name slot variables: + # + # /<_OPTIMIZER_SLOTS_NAME>// + # + # where is exactly the checkpoint name used for the original + # variable, including the path from the checkpoint root and the local name in + # the object which owns it. Note that we only save slot variables if the + # variable it's slotting for is also being saved. + + optimizer_identifier = "/%s/%s/" % (_OPTIMIZER_SLOTS_NAME, optimizer_path) + + def _name_slot_variable(variable_path, slot_name): + """With an optimizer specified, name a slot variable.""" + return (variable_path + + optimizer_identifier + + _escape_local_name(slot_name)) + + return _name_slot_variable + + +def _serialize_slot_variables(checkpointable_objects, node_ids, object_names): + """Gather and name slot variables.""" + non_slot_objects = list(checkpointable_objects) + slot_variables = {} + for checkpointable in non_slot_objects: + if isinstance(checkpointable, optimizer_lib.Optimizer): + naming_scheme = _slot_variable_naming_for_optimizer( + optimizer_path=object_names[checkpointable]) + slot_names = checkpointable.get_slot_names() + for slot_name in slot_names: + for original_variable_node_id, original_variable in enumerate( + non_slot_objects): + try: + slot_variable = checkpointable.get_slot( + original_variable, slot_name) + except AttributeError: + slot_variable = None + if slot_variable is None: + continue + slot_variable._maybe_initialize_checkpointable() # pylint: disable=protected-access + if slot_variable._checkpoint_dependencies: # pylint: disable=protected-access + # TODO(allenl): Gather dependencies of slot variables. + raise NotImplementedError( + "Currently only variables with no dependencies can be saved as " + "slot variables. File a feature request if this limitation " + "bothers you.") + if slot_variable in node_ids: + raise NotImplementedError( + "A slot variable was re-used as a dependency of a " + "Checkpointable object. This is not currently allowed. File a " + "feature request if this limitation bothers you.") + checkpoint_name = naming_scheme( + variable_path=object_names[original_variable], + slot_name=slot_name) + object_names[slot_variable] = checkpoint_name + slot_variable_node_id = len(checkpointable_objects) + node_ids[slot_variable] = slot_variable_node_id + checkpointable_objects.append(slot_variable) + slot_variable_proto = ( + checkpointable_object_graph_pb2.CheckpointableObjectGraph + .Object.SlotVariableReference( + slot_name=slot_name, + original_variable_node_id=original_variable_node_id, + slot_variable_node_id=slot_variable_node_id)) + slot_variables.setdefault(checkpointable, []).append( + slot_variable_proto) + return slot_variables + + +def _serialize_checkpointables( + checkpointable_objects, node_ids, object_names, slot_variables): + """Name non-slot `Checkpointable`s and add them to `object_graph_proto`.""" + object_graph_proto = ( + checkpointable_object_graph_pb2.CheckpointableObjectGraph()) + named_saveables = {} + + for checkpoint_id, checkpointable in enumerate(checkpointable_objects): + assert node_ids[checkpointable] == checkpoint_id + object_proto = object_graph_proto.nodes.add() + object_proto.slot_variables.extend(slot_variables.get(checkpointable, ())) + object_name = object_names[checkpointable] + for name, saveable in ( + checkpointable._gather_saveables_for_checkpoint().items()): # pylint: disable=protected-access + attribute = object_proto.attributes.add() + attribute.name = name + attribute.checkpoint_key = "%s/%s/%s" % ( + object_name, _OBJECT_ATTRIBUTES_NAME, _escape_local_name(name)) + # Figure out the name-based Saver's name for this variable. + saver_dict = saver_lib.BaseSaverBuilder.OpListToDict( + [saveable], convert_variable_to_tensor=False) + attribute.full_name, = saver_dict.keys() + named_saveables[attribute.checkpoint_key] = saveable + + for child in checkpointable._checkpoint_dependencies: # pylint: disable=protected-access + child_proto = object_proto.children.add() + child_proto.node_id = node_ids[child.ref] + child_proto.local_name = child.name + + return named_saveables, object_graph_proto + + +def _serialize_object_graph(root_checkpointable): + """Determine checkpoint keys for variables and build a serialized graph. + + Non-slot variables are keyed based on a shortest path from the root saveable + to the object which owns the variable (i.e. the one which called + `Checkpointable._add_variable` to create it). + + Slot variables are keyed based on a shortest path to the variable being + slotted for, a shortest path to their optimizer, and the slot name. + + Args: + root_checkpointable: A `Checkpointable` object whose variables (including + the variables of dependencies, recursively) should be saved. + + Returns: + A tuple of (named_variables, object_graph_proto): + named_variables: A dictionary mapping names to variable objects. + object_graph_proto: A CheckpointableObjectGraph protocol buffer containing + the serialized object graph and variable references. + + Raises: + ValueError: If there are invalid characters in an optimizer's slot names. + """ + checkpointable_objects, path_to_root = ( + _breadth_first_checkpointable_traversal(root_checkpointable)) + object_names = { + obj: _object_prefix_from_path(path) + for obj, path in path_to_root.items()} + node_ids = {node: node_id for node_id, node + in enumerate(checkpointable_objects)} + slot_variables = _serialize_slot_variables( + checkpointable_objects=checkpointable_objects, + node_ids=node_ids, + object_names=object_names) + return _serialize_checkpointables( + checkpointable_objects=checkpointable_objects, + node_ids=node_ids, + object_names=object_names, + slot_variables=slot_variables) + + +def gather_initializers(root_checkpointable): + """Traverse the object graph and find initialization ops. + + Looks for `Checkpointable` objects which are dependencies of + `root_checkpointable` and which have an `initializer` property. Includes + initializers for slot variables only if the variable they are slotting for and + the optimizer are dependencies of `root_checkpointable` (i.e. if they would be + saved with a checkpoint). + + Args: + root_checkpointable: A `Checkpointable` object to gather initializers for. + Returns: + A list of initialization ops. + """ + # TODO(allenl): Extract out gathering logic so the naming logic doesn't have + # to run. + checkpointable_objects, path_to_root = ( + _breadth_first_checkpointable_traversal(root_checkpointable)) + object_names = { + obj: _object_prefix_from_path(path) + for obj, path in path_to_root.items()} + node_ids = {node: node_id for node_id, node + in enumerate(checkpointable_objects)} + _serialize_slot_variables( + checkpointable_objects=checkpointable_objects, + node_ids=node_ids, + object_names=object_names) + return [c.initializer for c in checkpointable_objects + if hasattr(c, "initializer") and c.initializer is not None] + + +class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject): + + def __init__(self, tensor, name): + spec = saver_lib.BaseSaverBuilder.SaveSpec(tensor, "", name) + super(_NoRestoreSaveable, self).__init__(tensor, [spec], name) + + def restore(self, restored_tensors, restored_shapes): + return control_flow_ops.no_op() + + +class _LoadStatus(object): + """Abstract base for load status callbacks.""" + + @abc.abstractmethod + def assert_consumed(self): + """Raises an exception unless a non-trivial restoration has completed.""" + pass + + @abc.abstractmethod + def run_restore_ops(self, session=None): + """Runs restore ops from the checkpoint. Requires a valid checkpoint.""" + pass + + @abc.abstractmethod + def initialize_or_restore(self, session=None): + """Runs restore ops from the checkpoint, or initializes variables.""" + pass + + +class CheckpointLoadStatus(_LoadStatus): + """Checks the status of checkpoint loading and manages restore ops. + + Returned from `Saver.restore`. Since `restore` may defer the loading of values + in the checkpoint which don't yet have corresponding Python objects, + `CheckpointLoadStatus` provides a callback to verify that checkpoint loading + is complete (`assert_consumed`). + + When graph building, `restore` does not run restore ops itself since their + creation may be deferred. The `run_restore_ops` method must be called once all + Python objects with values to restore have been created and added to the + dependency graph (this does not necessarily have to be the whole checkpoint; + calling `run_restore_ops` while `assert_consumed` fails is supported and will + partially restore the checkpoint). + + See `Saver.restore` for usage examples. + """ + + def __init__(self, checkpoint, feed_dict): + self._checkpoint = checkpoint + self._feed_dict = feed_dict + + def assert_consumed(self): + """Asserts that all objects in the checkpoint have been created/matched. + + Returns: + `self` for chaining. + Raises: + AssertionError: If there are any Python objects in the dependency graph + which have not been restored from this checkpoint or a later `restore`, + or if there are any checkpointed values which have not been matched to + Python objects. + """ + for node_id, node in enumerate(self._checkpoint.object_graph_proto.nodes): + checkpointable = self._checkpoint.object_by_proto_id.get(node_id, None) + if checkpointable is None: + raise AssertionError("Unresolved object in checkpoint: %s" % (node,)) + if checkpointable._update_uid < self._checkpoint.restore_uid: # pylint: disable=protected-access + raise AssertionError( + "Object not assigned a value from checkpoint: %s" % (node,)) + if self._checkpoint.slot_restorations: + # Sanity check; this collection should be clear if everything has been + # restored. + raise AssertionError("Unresolved slot restorations: %s" % ( + self._checkpoint.slot_restorations,)) + if self._checkpoint.unused_attributes: + raise AssertionError( + ("Unused attributes in these objects (the attributes exist in the " + "checkpoint but not in the objects): %s") % ( + self._checkpoint.unused_attributes.items(),)) + return self + + def run_restore_ops(self, session=None): + """Run operations to restore objects in the dependency graph.""" + if context.executing_eagerly(): + return # Run eagerly + if session is None: + session = ops.get_default_session() + session.run(self._checkpoint.restore_ops, feed_dict=self._feed_dict) + + def initialize_or_restore(self, session=None): + """Alias for `run_restore_ops`. + + This method has a sibling in `InitializationOnlyStatus` which instead + initializes variables. That type is returned if no checkpoint is specified + in `Saver.restore`. + + Args: + session: The session to run restore ops in. If `None`, uses the default + session. + """ + self.run_restore_ops(session=session) + + +class InitializationOnlyStatus(_LoadStatus): + """Returned from `Saver.restore` when no checkpoint has been specified. + + Objects of this type have the same `assert_consumed` method as + `CheckpointLoadStatus`, but it always fails. However, + `initialize_or_restore` works on objects of both types, and will + initialize variables in `InitializationOnlyStatus` objects or restore them + otherwise. + """ + + def __init__(self, root_checkpointable): + self._root_checkpointable = root_checkpointable + + def assert_consumed(self): + """Assertion for consistency with `CheckpointLoadStatus`. Always fails.""" + raise AssertionError( + "No checkpoint specified (save_path=None); nothing is being restored.") + + def run_restore_ops(self, session=None): + """For consistency with `CheckpointLoadStatus`. + + Use `initialize_or_restore` for initializing if no checkpoint was passed + to `Saver.restore` and restoring otherwise. + + Args: + session: Not used. + """ + raise AssertionError( + "No checkpoint specified, so no restore ops are available " + "(save_path=None to Saver.restore).") + + def initialize_or_restore(self, session=None): + """Runs initialization ops for variables. + + Only objects which would be saved by `Saver.save` will be initialized. See + `gather_initializers` for details. + + This method does nothing when executing eagerly (initializers get run + eagerly). + + Args: + session: The session to run initialization ops in. If `None`, uses the + default session. + """ + if context.executing_eagerly(): + return # run eagerly + if session is None: + session = ops.get_default_session() + session.run(gather_initializers(self._root_checkpointable)) + + +_DEPRECATED_RESTORE_INSTRUCTIONS = ( + "Restoring a name-based tf.train.Saver checkpoint using the object-based " + "restore API. This mode uses global names to match variables, and so is " + "somewhat fragile. It also adds new restore ops to the graph each time it " + "is called. Prefer re-encoding training checkpoints in the object-based " + "format: run save() on the object-based saver (the same one this message " + "is coming from) and use that checkpoint in the future.") + + +class NameBasedSaverStatus(_LoadStatus): + """Status for loading a name-based training checkpoint.""" + + def __init__(self, object_saver, save_path): + self._object_saver = object_saver + self._save_path = save_path + + def assert_consumed(self): + """Assertion for consistency with `CheckpointLoadStatus`. Always fails.""" + raise AssertionError( + "Restoring a name-based checkpoint. No load status is available.") + + @deprecation.deprecated( + date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS) + def run_restore_ops(self, session=None): + """Load the name-based training checkpoint using a new `tf.train.Saver`.""" + if session is None and not context.executing_eagerly(): + session = ops.get_default_session() + with ops.device("/cpu:0"): + saver_lib.Saver(self._object_saver._global_variable_names()).restore( # pylint: disable=protected-access + sess=session, save_path=self._save_path) + + def initialize_or_restore(self, session=None): + """Alias for `run_restore_ops`.""" + self.run_restore_ops(session=session) + + +class _SessionWithFeedDictAdditions(session_lib.SessionInterface): + """Pretends to be a session, inserts extra feeds on run().""" + + def __init__(self, session, feed_additions): + self._wrapped_session = session + self._feed_additions = feed_additions + + def run(self, fetches, feed_dict=None, **kwargs): + if feed_dict is None: + feed_dict = {} + else: + feed_dict = feed_dict.copy() + feed_dict.update(self._feed_additions) + return self._wrapped_session.run( + fetches=fetches, feed_dict=feed_dict, **kwargs) + + +class CheckpointableSaver(object): + """Saves and restores a `Checkpointable` object and its dependencies. + + See `Checkpointable` for details of dependency management. `Saver` wraps + `tf.train.Saver` for saving, including extra information about the graph of + dependencies between Python objects. When restoring, it uses this information + about the save-time dependency graph to more robustly match objects with their + checkpointed values. When executing eagerly, it supports restoring variables + on object creation (see `Saver.restore`). + + Values in a checkpoint are mapped to `Checkpointable` Python objects + (`Variable`s, `Optimizer`s, `Layer`s) based on the names provided when the + checkpoint was written. To avoid breaking existing checkpoints when modifying + a class, dependency names (the names of attributes to which `Checkpointable` + objects are assigned) may not change. These names are local to objects, in + contrast to the `Variable.name`-based save/restore from `tf.train.Saver`, and + so allow additional program transformations. + """ + + def __init__(self, root_checkpointable): + """Configure saving. + + Args: + root_checkpointable: The root of the object graph to save/restore. This + object and all of its dependencies are saved in the checkpoint. When + restoring, objects are matched and restored starting from this root. + """ + # Allow passing in a weak reference to avoid reference cycles when + # `Checkpointable` objects save themselves. + self._root_checkpointable_ref = root_checkpointable + if not context.executing_eagerly(): + with ops.device("/cpu:0"): + self._file_prefix_placeholder = constant_op.constant("model") + else: + self._file_prefix_placeholder = None + + # Op caching for save + self._object_graph_feed_tensor = None + self._last_save_object_graph = None + self._last_save_saver = None + + # Op caching for restore + self._object_graph_restore_tensor = None + self._last_restore_object_graph = None + self._last_restore_checkpoint = None + + @property + def _root_checkpointable(self): + if isinstance(self._root_checkpointable_ref, weakref.ref): + derefed = self._root_checkpointable_ref() + assert derefed is not None + return derefed + else: + return self._root_checkpointable_ref + + def save(self, file_prefix, checkpoint_number=None, session=None): + """Save a training checkpoint. + + The saved checkpoint includes variables created by this object and any + Checkpointable objects it depends on at the time `Saver.save()` is called. + + Args: + file_prefix: A prefix to use for the checkpoint filenames + (/path/to/directory/and_a_prefix). Names are generated based on this + prefix and `checkpoint_number`, if provided. + checkpoint_number: An integer variable or Tensor, used to number + checkpoints. Typically this value is saved along with other variables in + training checkpoints, which will happen automatically if it was created + by `root_checkpointable` or one of its dependencies (via + `Checkpointable._add_variable`). + session: The session to evaluate variables in. Ignored when executing + eagerly. If not provided when graph building, the default session is + used. + + Returns: + The full path to the checkpoint. + """ + named_variables, graph_proto = _serialize_object_graph( + self._root_checkpointable) + in_graph_mode = not context.executing_eagerly() + if in_graph_mode: + if session is None: + session = ops.get_default_session() + if self._object_graph_feed_tensor is None: + with ops.device("/cpu:0"): + self._object_graph_feed_tensor = constant_op.constant( + "", dtype=dtypes.string) + object_graph_tensor = self._object_graph_feed_tensor + feed_additions = {object_graph_tensor: graph_proto.SerializeToString()} + else: + session = None + with ops.device("/cpu:0"): + object_graph_tensor = constant_op.constant( + graph_proto.SerializeToString(), dtype=dtypes.string) + feed_additions = None + assert _OBJECT_GRAPH_PROTO_KEY not in named_variables + named_variables[_OBJECT_GRAPH_PROTO_KEY] = _NoRestoreSaveable( + tensor=object_graph_tensor, + name=_OBJECT_GRAPH_PROTO_KEY) + if not in_graph_mode or self._last_save_object_graph != graph_proto: + if self._last_save_object_graph is not None and in_graph_mode: + raise NotImplementedError( + "Using a single Saver to save a mutated object graph is not " + "currently supported when graph building. Use a different Saver " + "when the object graph changes (save ops will be duplicated), or " + "file a feature request if this limitation bothers you.") + saver = saver_lib.Saver(var_list=named_variables) + if in_graph_mode: + self._last_save_saver = saver + self._last_save_object_graph = graph_proto + else: + saver = self._last_save_saver + with ops.device("/cpu:0"): + save_path = saver.save( + sess=_SessionWithFeedDictAdditions( + session=session, feed_additions=feed_additions), + save_path=file_prefix, + write_meta_graph=False, + global_step=checkpoint_number) + return save_path + + def _global_variable_names(self): + """Generate a `tf.train.Saver`-style `var_list` using `variable.name`s.""" + named_saveables, graph_proto = _serialize_object_graph( + self._root_checkpointable) + saver_names = {} + for object_proto in graph_proto.nodes: + for attribute_proto in object_proto.attributes: + saver_names[attribute_proto.full_name] = named_saveables[ + attribute_proto.checkpoint_key] + return saver_names + + def restore(self, save_path, session=None): + """Restore a training checkpoint. + + Restores `root_checkpointable` and any objects that it tracks + (transitive). Either assigns values immediately if variables to restore have + been created already, or defers restoration until the variables are + created. Dependencies added to the `root_checkpointable` passed to the + constructor after this call will be matched if they have a corresponding + object in the checkpoint. + + When building a graph, restorations are added to the graph but not run. A + session is required to retrieve checkpoint metadata. + + To disallow deferred loading, assert immediately that all checkpointed + variables have been matched to variable objects: + + ```python + saver = Saver(root) + saver.restore(path).assert_consumed() + ``` + + An exception will be raised unless every object was matched and its + variables already exist. + + When graph building, `assert_consumed()` indicates that all of the restore + ops which will be created for this checkpoint have been created. They can be + run via the `run_restore_ops()` function of the status object: + + ```python + saver.restore(path).assert_consumed().run_restore_ops() + ``` + + If the checkpoint has not been consumed completely, then the list of restore + ops will grow as more objects are added to the dependency graph. + + Name-based `tf.train.Saver` checkpoints can be loaded using this + method. There is no deferred loading, and names are used to match + variables. No restore ops are created/run until `run_restore_ops()` or + `initialize_or_restore()` are called on the returned status object, even + when executing eagerly. Re-encode name-based checkpoints using this + object-based `Saver.save` as soon as possible. + + Args: + save_path: The path to the checkpoint, as returned by `save` or + `tf.train.latest_checkpoint`. If None (as when there is no latest + checkpoint for `tf.train.latest_checkpoint` to return), returns an + object which may run initializers for objects in the dependency + graph. If the checkpoint was written by the name-based `tf.train.Saver`, + names are used to match variables. + session: The session to retrieve metadata with. Ignored when executing + eagerly. If not provided when graph building, the default session is + used. + + Returns: + A load status object, which can be used to make assertions about the + status of checkpoint restoration and run initialization/restore ops + (of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if + `save_path` is `None`). + + If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus` + object is returned which runs restore ops from a name-based saver. + """ + if save_path is None: + return InitializationOnlyStatus(self._root_checkpointable) + in_graph_mode = not context.executing_eagerly() + if in_graph_mode: + if session is None: + session = ops.get_default_session() + file_prefix_tensor = self._file_prefix_placeholder + file_prefix_feed_dict = {self._file_prefix_placeholder: save_path} + else: + session = None + with ops.device("/cpu:0"): + file_prefix_tensor = constant_op.constant(save_path) + file_prefix_feed_dict = None + try: + if not in_graph_mode or self._object_graph_restore_tensor is None: + with ops.device("/cpu:0"): + object_graph_string, = io_ops.restore_v2( + prefix=file_prefix_tensor, + tensor_names=[_OBJECT_GRAPH_PROTO_KEY], + shape_and_slices=[""], + dtypes=[dtypes.string], + name="object_graph_proto_read") + if in_graph_mode: + self._object_graph_restore_tensor = object_graph_string + if in_graph_mode: + object_graph_string = session.run( + self._object_graph_restore_tensor, + feed_dict=file_prefix_feed_dict) + else: + object_graph_string = object_graph_string.numpy() + except errors_impl.NotFoundError: + # The object graph proto does not exist in this checkpoint. Try again with + # name-based saving. + return NameBasedSaverStatus(self, save_path) + + object_graph_proto = ( + checkpointable_object_graph_pb2.CheckpointableObjectGraph()) + object_graph_proto.ParseFromString(object_graph_string) + if in_graph_mode and object_graph_proto == self._last_restore_object_graph: + checkpoint = self._last_restore_checkpoint + else: + if in_graph_mode: + dtype_map = None + else: + reader = pywrap_tensorflow.NewCheckpointReader(save_path) + dtype_map = reader.get_variable_to_dtype_map() + checkpoint = core_checkpointable_utils._Checkpoint( # pylint: disable=protected-access + object_graph_proto=object_graph_proto, + save_path=file_prefix_tensor, + dtype_map=dtype_map) + if in_graph_mode: + if self._last_restore_object_graph is not None: + raise NotImplementedError( + "Using a single Saver to restore different object graphs is not " + "currently supported when graph building. Use a different Saver " + "for each object graph (restore ops will be duplicated), or " + "file a feature request if this limitation bothers you.") + self._last_restore_checkpoint = checkpoint + self._last_restore_object_graph = object_graph_proto + core_checkpointable._CheckpointPosition( # pylint: disable=protected-access + checkpoint=checkpoint, proto_id=0).restore(self._root_checkpointable) + load_status = CheckpointLoadStatus( + checkpoint, feed_dict=file_prefix_feed_dict) + return load_status + + +class Checkpoint(core_checkpointable.Checkpointable): + """A utility class which groups `Checkpointable` objects. + + Accepts arbitrary keyword arguments to its constructor and saves those values + with a checkpoint. Maintains a `save_counter` for numbering checkpoints. + + Example usage: + + ```python + import tensorflow as tf + import tensorflow.contrib.eager as tfe + import os + + checkpoint_directory = "/tmp/training_checkpoints" + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + + root = tfe.Checkpoint(optimizer=optimizer, model=model) + root.restore(tf.train.latest_checkpoint(checkpoint_directory)) + for _ in range(num_training_steps): + optimizer.minimize( ... ) + root.save(file_prefix=checkpoint_prefix) + ``` + + For more manual control over saving, use `tfe.CheckpointableSaver` directly. + + Attributes: + save_counter: Incremented when `save()` is called. Used to number + checkpoints. + """ + + def __init__(self, **kwargs): + """Group objects into a training checkpoint. + + Args: + **kwargs: Keyword arguments are set as attributes of this object, and are + saved with the checkpoint. Attribute values must derive from + `CheckpointableBase`. + Raises: + ValueError: If objects in `kwargs` are not Checkpointable. + """ + super(Checkpoint, self).__init__() + for k, v in sorted(kwargs.items(), key=lambda item: item[0]): + if not isinstance(v, core_checkpointable.CheckpointableBase): + raise ValueError( + ("`Checkpoint` was expecting an object derived from " + "`CheckpointableBase`, got %s.") % (v,)) + setattr(self, k, v) + self._save_counter = None # Created lazily for restore-on-create. + self._saver = CheckpointableSaver(weakref.ref(self)) + + def _maybe_create_save_counter(self): + """Create a save counter if it does not yet exist.""" + if self._save_counter is None: + # Initialized to 0 and incremented before saving. + with ops.device("/cpu:0"): + self._save_counter = add_variable( + self, name="save_counter", initializer=0, dtype=dtypes.int64) + + @property + def save_counter(self): + """An integer variable which starts at zero and is incremented on save. + + Used to number checkpoints. + + Returns: + The save counter variable. + """ + self._maybe_create_save_counter() + return self._save_counter + + def save(self, file_prefix, session=None): + """Save a checkpoint. Wraps `tfe.CheckpointableSaver.save`.""" + in_graph_mode = not context.executing_eagerly() + if in_graph_mode: + if session is None: + session = ops.get_default_session() + if self._save_counter is None: + # When graph building, if this is a new save counter variable then it + # needs to be initialized before assign_add. This is only an issue if + # restore() has not been called first. + session.run(self.save_counter.initializer) + with ops.colocate_with(self.save_counter): + assign_op = self.save_counter.assign_add(1) + if in_graph_mode: + session.run(assign_op) + return self._saver.save( + file_prefix=file_prefix, + checkpoint_number=self.save_counter, + session=session) + + def restore(self, save_path): + """Restore a checkpoint. Wraps `tfe.CheckpointableSaver.restore`.""" + status = self._saver.restore(save_path=save_path) + # Create the save counter now so it gets initialized with other variables + # when graph building. Creating it earlier would lead to double + # initialization when executing eagerly. + self._maybe_create_save_counter() + return status diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2054878bf861553bb6cfa8d3730fa2070cf6b8bb --- /dev/null +++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py @@ -0,0 +1,1049 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import six + +from tensorflow.contrib.eager.python import checkpointable_utils +from tensorflow.python.client import session as session_lib +from tensorflow.python.eager import context +from tensorflow.python.eager import test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.keras._impl.keras.engine import training +from tensorflow.python.layers import core +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import template +from tensorflow.python.ops import variable_scope +from tensorflow.python.training import adam +from tensorflow.python.training import checkpointable +from tensorflow.python.training import saver as core_saver +from tensorflow.python.training import training_util + + +class NonLayerCheckpointable(checkpointable.Checkpointable): + + def __init__(self): + super(NonLayerCheckpointable, self).__init__() + self.a_variable = checkpointable_utils.add_variable( + self, name="a_variable", shape=[]) + + +# pylint: disable=not-callable +class MyModel(training.Model): + """A concrete Model for testing.""" + + def __init__(self): + super(MyModel, self).__init__() + self._named_dense = core.Dense(1, use_bias=True) + self._second = core.Dense(1, use_bias=False) + # We can still track Checkpointables which aren't Layers. + self._non_layer = NonLayerCheckpointable() + + def call(self, values): + ret = self._second(self._named_dense(values)) + return ret + + +class InterfaceTests(test.TestCase): + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testAddVariable(self): + obj = NonLayerCheckpointable() + with self.assertRaisesRegexp(ValueError, "do not specify shape"): + checkpointable_utils.add_variable( + obj, name="shape_specified_twice", shape=[], initializer=1) + constant_initializer = checkpointable_utils.add_variable( + obj, name="constant_initializer", initializer=1) + with variable_scope.variable_scope("some_variable_scope"): + ones_initializer = checkpointable_utils.add_variable( + obj, + name="ones_initializer", + shape=[2], + initializer=init_ops.ones_initializer(dtype=dtypes.float32)) + bare_initializer = checkpointable_utils.add_variable( + obj, + name="bare_initializer", + shape=[2, 2], + dtype=dtypes.float64, + initializer=init_ops.zeros_initializer) + + # Even in graph mode, there are no naming conflicts between objects, only + # naming conflicts within an object. + other_duplicate = resource_variable_ops.ResourceVariable( + name="duplicate", initial_value=1.) + duplicate = checkpointable_utils.add_variable( + obj, name="duplicate", shape=[]) + with self.assertRaisesRegexp(ValueError, "'duplicate' already exists"): + checkpointable_utils.add_variable(obj, name="duplicate", shape=[]) + + self.evaluate(checkpointable_utils.gather_initializers(obj)) + self.assertEqual("constant_initializer:0", constant_initializer.name) + self.assertEqual(1, self.evaluate(constant_initializer)) + self.assertEqual("some_variable_scope/ones_initializer:0", + ones_initializer.name) + self.assertAllEqual([1, 1], self.evaluate(ones_initializer)) + self.assertAllEqual([[0., 0.], + [0., 0.]], self.evaluate(bare_initializer)) + self.assertEqual("a_variable:0", obj.a_variable.name) + self.assertEqual("duplicate:0", other_duplicate.name) + if context.executing_eagerly(): + # When executing eagerly, there's no uniquification of variable names. The + # checkpoint name will be the same. + self.assertEqual("duplicate:0", duplicate.name) + else: + # The .name attribute may be globally influenced, but the checkpoint name + # won't be (tested below). + self.assertEqual("duplicate_1:0", duplicate.name) + named_variables, _ = checkpointable_utils._serialize_object_graph(obj) + expected_checkpoint_names = ( + "a_variable/.ATTRIBUTES/VARIABLE_VALUE", + "bare_initializer/.ATTRIBUTES/VARIABLE_VALUE", + "constant_initializer/.ATTRIBUTES/VARIABLE_VALUE", + "duplicate/.ATTRIBUTES/VARIABLE_VALUE", + "ones_initializer/.ATTRIBUTES/VARIABLE_VALUE", + ) + six.assertCountEqual( + self, expected_checkpoint_names, named_variables.keys()) + + def testInitNotCalled(self): + + class NoInit(checkpointable.Checkpointable): + + def __init__(self): + pass + + # __init__ for Checkpointable will be called implicitly. + checkpointable_utils.add_variable(NoInit(), "var", shape=[]) + + def testShapeDtype(self): + root = checkpointable.Checkpointable() + v1 = checkpointable_utils.add_variable( + root, name="v1", initializer=3., dtype=dtypes.float64) + self.assertEqual(dtypes.float64, v1.dtype) + v2 = checkpointable_utils.add_variable( + root, + name="v2", + shape=[3], + initializer=init_ops.ones_initializer, + dtype=dtypes.float64) + self.assertEqual(dtypes.float64, v2.dtype) + self.assertAllEqual([1., 1., 1.], self.evaluate(v2)) + + +class CheckpointingTests(test.TestCase): + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testNamingWithOptimizer(self): + input_value = constant_op.constant([[3.]]) + model = MyModel() + # A nuisance Model using the same optimizer. Its slot variables should not + # go in the checkpoint, since it is never depended on. + other_model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + optimizer_step = training_util.get_or_create_global_step() + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=optimizer_step) + if context.executing_eagerly(): + optimizer.minimize( + lambda: model(input_value), + global_step=optimizer_step) + optimizer.minimize( + lambda: other_model(input_value), + global_step=optimizer_step) + else: + train_op = optimizer.minimize( + model(input_value), global_step=optimizer_step) + optimizer.minimize( + other_model(input_value), + global_step=optimizer_step) + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + named_variables, serialized_graph = ( + checkpointable_utils._serialize_object_graph(root_checkpointable)) + expected_checkpoint_names = ( + # Created in the root node, so no prefix. + "optimizer_step", + "model/_second/kernel", + "model/_named_dense/kernel", + "model/_named_dense/bias", + # non-Layer dependency of the model + "model/_non_layer/a_variable", + # The optimizer creates two non-slot variables + "optimizer/beta1_power", + "optimizer/beta2_power", + # Slot variables + "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/m", + "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/v", + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m", + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v", + "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m", + "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v", + ) + suffix = "/.ATTRIBUTES/VARIABLE_VALUE" + expected_checkpoint_names = [ + name + suffix for name in expected_checkpoint_names] + six.assertCountEqual(self, expected_checkpoint_names, + named_variables.keys()) + # Check that we've mapped to the right variable objects (not exhaustive) + self.assertEqual( + "global_step:0", + named_variables["optimizer_step" + suffix].name) + self.assertEqual( + "my_model/dense_1/kernel:0", + named_variables["model/_second/kernel" + suffix].name) + self.assertEqual( + "my_model/dense/kernel:0", + named_variables["model/_named_dense/kernel" + suffix].name) + self.assertEqual( + "beta1_power:0", + named_variables["optimizer/beta1_power" + suffix].name) + self.assertEqual( + "beta2_power:0", + named_variables["optimizer/beta2_power" + suffix].name) + # Spot check the generated protocol buffers. + self.assertEqual("optimizer", + serialized_graph.nodes[0].children[1].local_name) + optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[ + 1].node_id] + self.assertEqual("beta1_power", + optimizer_node.children[0].local_name) + self.assertEqual("beta1_power", + serialized_graph.nodes[optimizer_node.children[0].node_id] + .attributes[0].full_name) + self.assertEqual( + "my_model/dense/kernel", + serialized_graph.nodes[optimizer_node.slot_variables[0] + .original_variable_node_id] + .attributes[0].full_name) + # We strip off the :0 suffix, as variable.name-based saving does. + self.assertEqual( + "my_model/dense/kernel/Adam", + serialized_graph.nodes[optimizer_node.slot_variables[0] + .slot_variable_node_id] + .attributes[0].full_name) + self.assertEqual( + "my_model/dense/kernel/Adam:0", + optimizer.get_slot( + var=named_variables["model/_named_dense/kernel" + suffix], + name="m").name) + self.assertEqual( + "model/_named_dense/kernel" + suffix, + serialized_graph.nodes[ + optimizer_node.slot_variables[0] + .original_variable_node_id].attributes[0].checkpoint_key) + self.assertEqual("m", optimizer_node.slot_variables[0].slot_name) + self.assertEqual( + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix, + serialized_graph.nodes[ + optimizer_node.slot_variables[0] + .slot_variable_node_id].attributes[0].checkpoint_key) + + @test_util.run_in_graph_and_eager_modes() + def testSaveRestore(self): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model) + input_value = constant_op.constant([[3.]]) + if context.executing_eagerly(): + optimizer.minimize( + lambda: model(input_value)) + else: + train_op = optimizer.minimize(model(input_value)) + # TODO(allenl): Make initialization more pleasant when graph building. + root_checkpointable.save_counter # pylint: disable=pointless-statement + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + prefix = os.path.join(self.get_temp_dir(), "ckpt") + self.evaluate(state_ops.assign(model._named_dense.variables[1], [42.])) + m_bias_slot = optimizer.get_slot(model._named_dense.variables[1], "m") + self.evaluate(state_ops.assign(m_bias_slot, [1.5])) + save_path = root_checkpointable.save(file_prefix=prefix) + self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.])) + self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3)) + optimizer_variables = self.evaluate(optimizer.variables()) + self.evaluate(state_ops.assign(m_bias_slot, [-2.])) + # Immediate restoration + status = root_checkpointable.restore(save_path=save_path).assert_consumed() + status.run_restore_ops() + self.assertAllEqual([42.], self.evaluate(model._named_dense.variables[1])) + self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter)) + self.assertAllEqual([1.5], self.evaluate(m_bias_slot)) + if not context.executing_eagerly(): + return # Restore-on-create is only supported when executing eagerly + on_create_model = MyModel() + on_create_optimizer = adam.AdamOptimizer(0.001) + on_create_root = checkpointable_utils.Checkpoint( + optimizer=on_create_optimizer, model=on_create_model) + # Deferred restoration + status = on_create_root.restore(save_path=save_path) + on_create_model(constant_op.constant([[3.]])) # create variables + self.assertAllEqual(1, self.evaluate(on_create_root.save_counter)) + self.assertAllEqual([42.], + self.evaluate( + on_create_model._named_dense.variables[1])) + on_create_m_bias_slot = on_create_optimizer.get_slot( + on_create_model._named_dense.variables[1], "m") + # Optimizer slot variables are created when the original variable is + # restored. + self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot)) + self.assertAllEqual(optimizer_variables[2:], + self.evaluate(on_create_optimizer.variables())) + on_create_optimizer._create_slots( + [resource_variable_ops.ResourceVariable([1.])]) + status.assert_consumed() + beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators() + self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power)) + self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power)) + + # TODO(allenl): Debug garbage created by this test in python3. + def testDeferredRestorationUsageEager(self): + """An idiomatic eager execution example.""" + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + optimizer_step=training_util.get_or_create_global_step()) + root.restore(core_saver.latest_checkpoint(checkpoint_directory)) + for _ in range(num_training_steps): + # TODO(allenl): Use a Dataset and serialize/checkpoint it. + input_value = constant_op.constant([[3.]]) + optimizer.minimize( + lambda: model(input_value), # pylint: disable=cell-var-from-loop + global_step=root.optimizer_step) + root.save(file_prefix=checkpoint_prefix) + self.assertEqual((training_continuation + 1) * num_training_steps, + root.optimizer_step.numpy()) + + def testUsageGraph(self): + """Expected usage when graph building.""" + with context.graph_mode(): + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + with ops.Graph().as_default(): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + input_value = constant_op.constant([[3.]]) + train_op = optimizer.minimize( + model(input_value), + global_step=root.global_step) + checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory) + with self.test_session(graph=ops.get_default_graph()) as session: + status = root.restore(save_path=checkpoint_path) + status.initialize_or_restore(session=session) + if checkpoint_path is None: + self.assertEqual(0, training_continuation) + with self.assertRaises(AssertionError): + status.assert_consumed() + else: + status.assert_consumed() + for _ in range(num_training_steps): + session.run(train_op) + root.save(file_prefix=checkpoint_prefix, session=session) + self.assertEqual((training_continuation + 1) * num_training_steps, + session.run(root.global_step)) + self.assertEqual(training_continuation + 1, + session.run(root.save_counter)) + + @test_util.run_in_graph_and_eager_modes() + def testAgnosticUsage(self): + """Graph/eager agnostic usage.""" + # Does create garbage when executing eagerly due to ops.Graph() creation. + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + with ops.Graph().as_default(), self.test_session( + graph=ops.get_default_graph()), test_util.device(use_gpu=True): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory) + status = root.restore(save_path=checkpoint_path) + input_value = constant_op.constant([[3.]]) + train_fn = functools.partial( + optimizer.minimize, + functools.partial(model, input_value), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial(self.evaluate, train_fn()) + status.initialize_or_restore() + for _ in range(num_training_steps): + train_fn() + root.save(file_prefix=checkpoint_prefix) + self.assertEqual((training_continuation + 1) * num_training_steps, + self.evaluate(root.global_step)) + self.assertEqual(training_continuation + 1, + self.evaluate(root.save_counter)) + + def _get_checkpoint_name(self, name): + root = checkpointable.Checkpointable() + checkpointable_utils.add_variable( + root, name=name, shape=[1, 2], dtype=dtypes.float64) + named_variables, _ = checkpointable_utils._serialize_object_graph(root) + checkpoint_name, = named_variables.keys() + with ops.name_scope("root/" + checkpoint_name): + pass # Make sure we can use this as an op name if we prefix it. + return checkpoint_name + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testVariableNameEscaping(self): + suffix = "/.ATTRIBUTES/VARIABLE_VALUE" + self.assertEqual(r"a.Sb.Sc" + suffix, self._get_checkpoint_name(r"a/b/c")) + self.assertEqual(r"b" + suffix, self._get_checkpoint_name(r"b")) + self.assertEqual(r"c.S" + suffix, self._get_checkpoint_name(r"c/")) + self.assertEqual(r"d.S..S" + suffix, self._get_checkpoint_name(r"d/.S")) + self.assertEqual(r"d.S..ATTRIBUTES.Sf" + suffix, + self._get_checkpoint_name(r"d/.ATTRIBUTES/f")) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testNumberedPath(self): + root = checkpointable.Checkpointable() + leaf = checkpointable.Checkpointable() + root.leaf = leaf + checkpointable_utils.add_variable(leaf, name="v", shape=[]) + named_variables, _ = checkpointable_utils._serialize_object_graph(root) + variable_name, = named_variables.keys() + self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", variable_name) + + @test_util.run_in_graph_and_eager_modes() + def testLocalNameValidation(self): + root = checkpointable.Checkpointable() + leaf = checkpointable.Checkpointable() + # Dots are escaped, which avoids conflicts with reserved names. + root._track_checkpointable(leaf, name=".ATTRIBUTES") + checkpointable_utils.add_variable(checkpointable=leaf, name="a", shape=[]) + named_variables, _ = checkpointable_utils._serialize_object_graph(root) + name, = named_variables.keys() + self.assertEqual(name, "..ATTRIBUTES/a/.ATTRIBUTES/VARIABLE_VALUE") + + @test_util.run_in_graph_and_eager_modes() + def testLateDependencyTracking(self): + + class Dependency(checkpointable.Checkpointable): + + def build(self): + self.var = checkpointable_utils.add_variable( + self, "var", initializer=0.) + + class LateDependencies(checkpointable.Checkpointable): + + def add_dep(self): + self.dep = Dependency() + self.dep.build() + + original = LateDependencies() + original.add_dep() + self.evaluate(state_ops.assign(original.dep.var, 123.)) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_path = checkpointable_utils.CheckpointableSaver( + original).save(checkpoint_prefix) + load_into = LateDependencies() + status = checkpointable_utils.CheckpointableSaver( + load_into).restore(save_path) + with self.assertRaises(AssertionError): + status.assert_consumed() + load_into.add_dep() + status.assert_consumed() + status.run_restore_ops() + self.assertEqual(123., self.evaluate(load_into.dep.var)) + + @test_util.run_in_graph_and_eager_modes() + def testDepAfterVar(self): + + class Dependency(checkpointable.Checkpointable): + + def build(self): + self.var = checkpointable_utils.add_variable( + self, "var", initializer=0.) + + class DepAfterVar(checkpointable.Checkpointable): + + def add_dep(self): + dep = Dependency() + dep.build() + self.dep = dep + + dep_after_var = DepAfterVar() + dep_after_var.add_dep() + self.evaluate(state_ops.assign(dep_after_var.dep.var, -14.)) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_path = checkpointable_utils.CheckpointableSaver(dep_after_var).save( + checkpoint_prefix) + + loaded_dep_after_var = DepAfterVar() + status = checkpointable_utils.CheckpointableSaver( + loaded_dep_after_var).restore(save_path) + loaded_dep_after_var.add_dep() + status.assert_consumed() + status.run_restore_ops() + self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var)) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testDeferredSlotRestoration(self): + checkpoint_directory = self.get_temp_dir() + + root = checkpointable.Checkpointable() + root.var = checkpointable_utils.add_variable( + root, name="var", initializer=0.) + optimizer = adam.AdamOptimizer(0.1) + if context.executing_eagerly(): + optimizer.minimize(root.var.read_value) + else: + train_op = optimizer.minimize(root.var) + # Note that `optimizer` has not been added as a dependency of + # `root`. Create a one-off grouping so that slot variables for `root.var` + # get initialized too. + self.evaluate(checkpointable_utils.gather_initializers( + checkpointable_utils.Checkpoint(root=root, optimizer=optimizer))) + self.evaluate(train_op) + self.evaluate(state_ops.assign(root.var, 12.)) + no_slots_path = checkpointable_utils.CheckpointableSaver(root).save( + os.path.join(checkpoint_directory, "no_slots")) + root.optimizer = optimizer + self.evaluate(state_ops.assign(root.var, 13.)) + self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var), + 14.)) + slots_path = checkpointable_utils.CheckpointableSaver(root).save( + os.path.join(checkpoint_directory, "with_slots")) + new_root = checkpointable.Checkpointable() + # Load the slot-containing checkpoint (deferred), then immediately overwrite + # the non-slot variable (also deferred). + slot_status = checkpointable_utils.CheckpointableSaver( + new_root).restore(slots_path) + no_slot_status = checkpointable_utils.CheckpointableSaver( + new_root).restore(no_slots_path) + with self.assertRaises(AssertionError): + no_slot_status.assert_consumed() + new_root.var = checkpointable_utils.add_variable( + new_root, name="var", shape=[]) + no_slot_status.assert_consumed() + no_slot_status.run_restore_ops() + self.assertEqual(12., self.evaluate(new_root.var)) + new_root.optimizer = adam.AdamOptimizer(0.1) + with self.assertRaisesRegexp(AssertionError, "beta1_power"): + slot_status.assert_consumed() + self.assertEqual(12., self.evaluate(new_root.var)) + if context.executing_eagerly(): + # Slot variables are only created with restoring initializers when + # executing eagerly. + self.assertEqual(14., self.evaluate( + new_root.optimizer.get_slot(name="m", var=new_root.var))) + else: + self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var), + None) + if context.executing_eagerly(): + new_root.optimizer.minimize(new_root.var.read_value) + else: + train_op = new_root.optimizer.minimize(new_root.var) + # The slot variable now exists; restore() didn't create it, but we should + # now have a restore op for it. + slot_status.run_restore_ops() + self.assertEqual(14., self.evaluate( + new_root.optimizer.get_slot(name="m", var=new_root.var))) + self.evaluate(train_op) + slot_status.assert_consumed() + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testOverlappingRestores(self): + checkpoint_directory = self.get_temp_dir() + save_root = checkpointable.Checkpointable() + save_root.dep = checkpointable.Checkpointable() + save_root.dep.var = checkpointable_utils.add_variable( + save_root.dep, name="var", initializer=0.) + self.evaluate(state_ops.assign(save_root.dep.var, 12.)) + saver = checkpointable_utils.CheckpointableSaver(save_root) + first_path = saver.save(os.path.join(checkpoint_directory, "first")) + self.evaluate(state_ops.assign(save_root.dep.var, 13.)) + second_path = saver.save(os.path.join(checkpoint_directory, "second")) + + first_root = checkpointable.Checkpointable() + second_root = checkpointable.Checkpointable() + first_status = checkpointable_utils.CheckpointableSaver( + first_root).restore(first_path) + second_status = checkpointable_utils.CheckpointableSaver( + second_root).restore(second_path) + load_dep = checkpointable.Checkpointable() + load_dep.var = checkpointable_utils.add_variable( + load_dep, name="var", shape=[]) + first_root.dep = load_dep + first_status.assert_consumed() + first_status.run_restore_ops() + self.assertEqual(12., self.evaluate(load_dep.var)) + second_root.dep = load_dep + second_status.assert_consumed() + second_status.run_restore_ops() + self.assertEqual(13., self.evaluate(load_dep.var)) + + # Try again with the order of the restore() reversed. The last restore + # determines the final value. + first_root = checkpointable.Checkpointable() + second_root = checkpointable.Checkpointable() + second_status = checkpointable_utils.CheckpointableSaver( + second_root).restore(second_path) + first_status = checkpointable_utils.CheckpointableSaver( + first_root).restore(first_path) + load_dep = checkpointable.Checkpointable() + load_dep.var = checkpointable_utils.add_variable( + load_dep, name="var", shape=[]) + first_root.dep = load_dep + first_status.assert_consumed() + first_status.run_restore_ops() + self.assertEqual(12., self.evaluate(load_dep.var)) + second_root.dep = load_dep + second_status.assert_consumed() + second_status.run_restore_ops() + self.assertEqual(12., self.evaluate(load_dep.var)) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testAmbiguousLoad(self): + # Not OK to split one checkpoint object into two + checkpoint_directory = self.get_temp_dir() + save_root = checkpointable.Checkpointable() + save_root.dep_one = checkpointable.Checkpointable() + save_root.dep_two = checkpointable.Checkpointable() + dep_three = checkpointable.Checkpointable() + save_root.dep_one.dep_three = dep_three + save_root.dep_two.dep_three = dep_three + checkpointable_utils.add_variable(dep_three, name="var", initializer=0.) + self.evaluate(checkpointable_utils.gather_initializers(save_root)) + save_path = checkpointable_utils.CheckpointableSaver(save_root).save( + os.path.join(checkpoint_directory, "ckpt")) + load_root = checkpointable.Checkpointable() + checkpointable_utils.CheckpointableSaver(load_root).restore(save_path) + load_root.dep_one = checkpointable.Checkpointable() + load_root.dep_two = checkpointable.Checkpointable() + load_root.dep_one.dep_three = checkpointable.Checkpointable() + with self.assertRaisesRegexp(AssertionError, + "resolved to different objects"): + load_root.dep_two.dep_three = checkpointable.Checkpointable() + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testObjectsCombined(self): + # Currently fine to load two checkpoint objects into one Python object + checkpoint_directory = self.get_temp_dir() + save_root = checkpointable.Checkpointable() + save_root.dep_one = checkpointable.Checkpointable() + save_root.dep_two = checkpointable.Checkpointable() + checkpointable_utils.add_variable( + save_root.dep_one, name="var1", initializer=32., dtype=dtypes.float64) + checkpointable_utils.add_variable( + save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64) + self.evaluate(checkpointable_utils.gather_initializers(save_root)) + save_path = checkpointable_utils.CheckpointableSaver(save_root).save( + os.path.join(checkpoint_directory, "ckpt")) + load_root = checkpointable.Checkpointable() + load_root.dep_one = checkpointable.Checkpointable() + load_root.dep_two = load_root.dep_one + v1 = checkpointable_utils.add_variable( + load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64) + v2 = checkpointable_utils.add_variable( + load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64) + status = checkpointable_utils.CheckpointableSaver(load_root).restore( + save_path).assert_consumed() + status.run_restore_ops() + self.assertEqual(32., self.evaluate(v1)) + self.assertEqual(64., self.evaluate(v2)) + + @test_util.run_in_graph_and_eager_modes() + def testDependencyLoop(self): + # Note: this test creates garbage during eager execution because it + # purposefully creates a reference cycle. + first = checkpointable.Checkpointable() + second = checkpointable.Checkpointable() + first.second = second + second.first = first + first.v = checkpointable_utils.add_variable( + first, "v1", initializer=[3., 1., 4.]) + second.v = checkpointable_utils.add_variable( + second, "v2", initializer=[1., 1., 2., 3.]) + self.evaluate(checkpointable_utils.gather_initializers(first)) + checkpoint_directory = self.get_temp_dir() + save_path = checkpointable_utils.CheckpointableSaver(first).save( + os.path.join(checkpoint_directory, "ckpt")) + + # Test deferred loading + first_load = checkpointable.Checkpointable() + status = checkpointable_utils.CheckpointableSaver( + first_load).restore(save_path) + second_load = checkpointable.Checkpointable() + first_load.second = second_load + second_load.first = first_load + with self.assertRaises(AssertionError): + status.assert_consumed() + first_load.v = checkpointable_utils.add_variable( + first_load, "v1", shape=[3]) + second_load.v = checkpointable_utils.add_variable( + second_load, "v2", shape=[4]) + status.assert_consumed() + status.run_restore_ops() + self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v)) + self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v)) + + # Test loading when variables have already been created + self.evaluate(first_load.v.assign([2., 7., 1.])) + self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v)) + self.evaluate(second_load.v.assign([2., 7., 1., 8.])) + self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v)) + status = checkpointable_utils.CheckpointableSaver(first_load).restore( + save_path).assert_consumed() + status.run_restore_ops() + self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v)) + self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v)) + + @test_util.run_in_graph_and_eager_modes() + def testRestoreOnAssign(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_graph = ops.Graph() + with save_graph.as_default(), self.test_session(save_graph): + first = checkpointable.Checkpointable() + first.var1 = variable_scope.get_variable( + name="outside_var", initializer=0.) + first.var2 = variable_scope.get_variable( + name="blah", initializer=0.) + self.evaluate(first.var1.assign(4.)) + self.evaluate(first.var2.assign(8.)) + save_path = checkpointable_utils.CheckpointableSaver(first).save( + checkpoint_prefix) + restore_graph = ops.Graph() + with restore_graph.as_default(), self.test_session(restore_graph): + second = checkpointable.Checkpointable() + second.var2 = variable_scope.get_variable( + name="blah", initializer=0.) + status = checkpointable_utils.CheckpointableSaver( + second).restore(save_path) + recreated_var1 = variable_scope.get_variable( + name="outside_var", initializer=0.) + status.run_restore_ops() + self.assertEqual(8., self.evaluate(second.var2)) + self.evaluate(recreated_var1.assign(-2.)) + self.assertEqual(-2., self.evaluate(recreated_var1)) + second.var1 = recreated_var1 + status.run_restore_ops() + self.assertEqual(4., self.evaluate(recreated_var1)) + + def testManySavesGraph(self): + """Saves after the first should not modify the graph.""" + with context.graph_mode(): + graph = ops.Graph() + with graph.as_default(), self.test_session(graph): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + obj = checkpointable.Checkpointable() + obj.var = variable_scope.get_variable(name="v", initializer=0.) + obj.opt = adam.AdamOptimizer(0.1) + obj.opt.minimize(obj.var.read_value()) + self.evaluate(checkpointable_utils.gather_initializers(obj)) + saver = checkpointable_utils.CheckpointableSaver(obj) + saver.save(checkpoint_prefix) + before_ops = graph.get_operations() + saver.save(checkpoint_prefix) + self.assertEqual(before_ops, graph.get_operations()) + + def testManyRestoresGraph(self): + """Restores after the first should not modify the graph.""" + with context.graph_mode(): + graph = ops.Graph() + with graph.as_default(), self.test_session(graph): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + obj = checkpointable.Checkpointable() + obj.var = variable_scope.get_variable(name="v", initializer=0.) + obj.opt = adam.AdamOptimizer(0.1) + obj.opt.minimize(obj.var.read_value()) + self.evaluate(checkpointable_utils.gather_initializers(obj)) + saver = checkpointable_utils.CheckpointableSaver(obj) + save_path = saver.save(checkpoint_prefix) + saver.restore(save_path) + before_ops = graph.get_operations() + saver.restore(save_path) + self.assertEqual(before_ops, graph.get_operations()) + + def testMultipleGraphsNonSlotVariables(self): + with context.graph_mode(): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + optimizer = adam.AdamOptimizer(0.001) + # Construct a model in one graph + first_graph = ops.Graph() + first_session = session_lib.Session(graph=first_graph) + with first_graph.as_default(), first_session.as_default(): + first_variable = resource_variable_ops.ResourceVariable([1.]) + first_root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, variable=first_variable) + train_op = optimizer.minimize(first_variable.read_value) + self.evaluate(checkpointable_utils.gather_initializers( + first_root_checkpointable)) + self.evaluate(train_op) + self.evaluate(first_variable.assign([1.])) + self.evaluate(optimizer.get_slot( + var=first_variable, name="m").assign([2.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(3.)) + + # Save and load in a second graph + second_graph = ops.Graph() + with second_graph.as_default(), session_lib.Session(graph=second_graph): + second_variable = resource_variable_ops.ResourceVariable([1.]) + second_root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, variable=second_variable) + train_op = optimizer.minimize(second_variable.read_value) + second_root_checkpointable.restore(None).initialize_or_restore() + self.evaluate(train_op) + self.evaluate(second_variable.assign([4.])) + self.evaluate(optimizer.get_slot( + var=second_variable, name="m").assign([5.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(6.)) + save_path = second_root_checkpointable.save(checkpoint_prefix) + self.evaluate(second_variable.assign([7.])) + self.evaluate(optimizer.get_slot( + var=second_variable, name="m").assign([8.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(6., self.evaluate(beta1_power)) + status = second_root_checkpointable.restore(save_path) + status.assert_consumed().run_restore_ops() + self.assertAllEqual([4.], self.evaluate(second_variable)) + self.assertAllEqual([5.], self.evaluate(optimizer.get_slot( + var=second_variable, name="m"))) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(6., self.evaluate(beta1_power)) + + # Check that the first graph is unmolested + with first_graph.as_default(), first_session.as_default(): + self.assertAllEqual([1.], self.evaluate(first_variable)) + self.assertAllEqual([2.], self.evaluate(optimizer.get_slot( + var=first_variable, name="m"))) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(3., self.evaluate(beta1_power)) + + +class TemplateTests(test.TestCase): + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_checkpointable_save_restore(self): + + def _templated(): + v = variable_scope.get_variable( + "v", shape=[1], initializer=init_ops.zeros_initializer()) + v2 = variable_scope.get_variable( + "v2", shape=[1], initializer=init_ops.zeros_initializer()) + return v, v + 1., v2 + + save_template = template.make_template("s1", _templated) + save_root = checkpointable_utils.Checkpoint(my_template=save_template) + v1_save, _, v2_save = save_template() + self.evaluate(v1_save.assign([12.])) + self.evaluate(v2_save.assign([14.])) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_path = save_root.save(checkpoint_prefix) + + load_template = template.make_template("s2", _templated) + load_root = checkpointable_utils.Checkpoint(my_template=load_template) + status = load_root.restore(save_path) + var, var_plus_one, var2 = load_template() + self.assertEqual(2, len(load_template._checkpoint_dependencies)) + self.assertEqual("v", load_template._checkpoint_dependencies[0].name) + self.assertEqual("v2", load_template._checkpoint_dependencies[1].name) + status.assert_consumed().run_restore_ops() + self.assertAllEqual([12.], self.evaluate(var)) + self.assertAllEqual([13.], self.evaluate(var_plus_one)) + self.assertAllEqual([14.], self.evaluate(var2)) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_checkpointable_save_restore_nested(self): + + def _inner_template(): + v = variable_scope.get_variable( + "v", shape=[1], initializer=init_ops.zeros_initializer()) + return v + + def _outer_template(): + first_inner = template.make_template("i1", _inner_template) + second_inner = template.make_template("i2", _inner_template) + v1 = first_inner() + v2 = second_inner() + v3 = second_inner() + return (first_inner, second_inner), (v1, v2, v3) + + with variable_scope.variable_scope("ignored"): + save_template = template.make_template("s1", _outer_template) + save_root = checkpointable_utils.Checkpoint(my_template=save_template) + (inner_template_one, inner_template_two), _ = save_template() + self.evaluate(inner_template_one.variables[0].assign([20.])) + self.evaluate(inner_template_two.variables[0].assign([25.])) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_path = save_root.save(checkpoint_prefix) + + load_template = template.make_template("s2", _outer_template) + load_root = checkpointable_utils.Checkpoint(my_template=load_template) + status = load_root.restore(save_path) + (inner_template_one, inner_template_two), (v1, v2, v3) = load_template() + outer_template_dependencies = load_root.my_template._checkpoint_dependencies + self.assertEqual(2, len(outer_template_dependencies)) + self.assertEqual("i1", outer_template_dependencies[0].name) + self.assertIs(inner_template_one, outer_template_dependencies[0].ref) + self.assertEqual("i2", outer_template_dependencies[1].name) + self.assertIs(inner_template_two, outer_template_dependencies[1].ref) + self.assertEqual(1, len(inner_template_one._checkpoint_dependencies)) + self.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name) + self.assertEqual(1, len(inner_template_two._checkpoint_dependencies)) + self.assertEqual("v", inner_template_two._checkpoint_dependencies[0].name) + status.assert_consumed().run_restore_ops() + self.assertAllEqual([20.], self.evaluate(v1)) + self.assertAllEqual([25.], self.evaluate(v2)) + self.assertAllEqual([25.], self.evaluate(v3)) + + +class CheckpointCompatibilityTests(test.TestCase): + + def _initialized_model(self): + input_value = constant_op.constant([[3.]]) + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + optimizer_step = training_util.get_or_create_global_step() + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=optimizer_step) + train_op = optimizer.minimize( + functools.partial(model, input_value), + global_step=optimizer_step) + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + # A regular variable, a slot variable, and a non-slot Optimizer variable + # with known values to check when loading. + self.evaluate(model._named_dense.bias.assign([1.])) + self.evaluate(optimizer.get_slot( + var=model._named_dense.bias, name="m").assign([2.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(3.)) + return root_checkpointable + + def _set_sentinels(self, root_checkpointable): + self.evaluate(root_checkpointable.model._named_dense.bias.assign([101.])) + self.evaluate( + root_checkpointable.optimizer.get_slot( + var=root_checkpointable.model._named_dense.bias, name="m") + .assign([102.])) + beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(103.)) + + def _check_sentinels(self, root_checkpointable): + self.assertAllEqual( + [1.], self.evaluate(root_checkpointable.model._named_dense.bias)) + self.assertAllEqual([2.], self.evaluate( + root_checkpointable.optimizer.get_slot( + var=root_checkpointable.model._named_dense.bias, name="m"))) + beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() + self.assertAllEqual(3., self.evaluate(beta1_power)) + + def _write_name_based_checkpoint(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.test_session( + graph=save_graph) as session: + root = self._initialized_model() + name_saver = core_saver.Saver() + return name_saver.save( + sess=session, save_path=checkpoint_prefix, + global_step=root.optimizer_step) + + @test_util.run_in_graph_and_eager_modes() + def testLoadFromNameBasedSaver(self): + """Save a name-based checkpoint, load it using the object-based API.""" + with test_util.device(use_gpu=True): + save_path = self._write_name_based_checkpoint() + root = self._initialized_model() + self._set_sentinels(root) + with self.assertRaises(AssertionError): + self._check_sentinels(root) + object_saver = checkpointable_utils.CheckpointableSaver(root) + status = object_saver.restore(save_path) + with self.assertRaises(AssertionError): + status.assert_consumed() + status.run_restore_ops() + self._check_sentinels(root) + self._set_sentinels(root) + status.initialize_or_restore() + self._check_sentinels(root) + + # TODO(allenl): Test for the core name-based saver loading object-based + # checkpoints once object-based checkpointing is in core. + + def testSaveGraphLoadEager(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.test_session( + graph=save_graph) as session: + root = self._initialized_model() + object_saver = checkpointable_utils.CheckpointableSaver(root) + save_path = object_saver.save( + session=session, file_prefix=checkpoint_prefix) + with context.eager_mode(): + root = self._initialized_model() + self._set_sentinels(root) + root.restore(save_path).assert_consumed() + self._check_sentinels(root) + + def testSaveEagerLoadGraph(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.eager_mode(): + root = self._initialized_model() + object_saver = checkpointable_utils.CheckpointableSaver(root) + save_path = object_saver.save(file_prefix=checkpoint_prefix) + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.test_session( + graph=save_graph): + root = self._initialized_model() + self._set_sentinels(root) + root.restore(save_path).assert_consumed().run_restore_ops() + self._check_sentinels(root) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index d177bfeab2d1fdc05d7ced54df8723fae2c77fdb..30a7642dd3ce6125ddc84be2f2f15c0f00f587f2 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -68,10 +68,10 @@ class Iterator(object): RuntimeError: When invoked without eager execution enabled. """ - if not context.in_eager_mode(): + if not context.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " - "tf.data.Dataset.make_iterator or " + "tf.data.Dataset.make_initializable_iterator or " "tf.data.Dataset.make_one_shot_iterator for graph construction". format(type(self))) with ops.device("/device:CPU:0"): diff --git a/tensorflow/contrib/eager/python/datasets_test.py b/tensorflow/contrib/eager/python/datasets_test.py index a1611e92b113839c2dd2a3b2560b0ba90c0a7ef0..35c3c5d3fad0a84bbe4d24c7bb17878583bded4b 100644 --- a/tensorflow/contrib/eager/python/datasets_test.py +++ b/tensorflow/contrib/eager/python/datasets_test.py @@ -16,11 +16,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import threading import time import numpy as np from tensorflow.contrib import lookup +from tensorflow.contrib.data.python.ops import threadpool +from tensorflow.contrib.data.python.ops import unique from tensorflow.contrib.eager.python import datasets from tensorflow.python.data import Dataset from tensorflow.python.eager import test @@ -165,6 +168,38 @@ class IteratorTest(test.TestCase): x = math_ops.add(x, x) self.assertAllEqual([0., 2.], x.numpy()) + def testOverrideThreadPool(self): + + def get_thread_id(_): + # Python creates a dummy thread object to represent the current + # thread when called from an "alien" thread (such as a + # `PrivateThreadPool` thread in this case). It does not include + # the TensorFlow-given display name, but it has a unique + # identifier that maps one-to-one with the underlying OS thread. + return np.array(threading.current_thread().ident).astype(np.int64) + + for num_threads in [1, 2, 4, 8, 16]: + + dataset = ( + Dataset.range(1000).map( + lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), + num_parallel_calls=32).apply(unique.unique())) + + dataset = threadpool.override_threadpool( + dataset, + threadpool.PrivateThreadPool( + num_threads, display_name='private_thread_pool_%d' % num_threads)) + + thread_ids = [] + for next_element in datasets.Iterator(dataset): + thread_ids.append(next_element) + self.assertEqual(len(thread_ids), len(set(thread_ids))) + self.assertGreater(len(thread_ids), 0) + # NOTE(mrry): We don't control the thread pool scheduling, and + # so cannot guarantee that all of the threads in the pool will + # perform work. + self.assertLessEqual(len(thread_ids), num_threads) + class DatasetConstructorBenchmark(test.Benchmark): diff --git a/tensorflow/contrib/eager/python/evaluator.py b/tensorflow/contrib/eager/python/evaluator.py index 68e7b5421fec7f73f10e381ca45f9d900de299d7..37c8f0d47adbde6932bf409cdcae9a1845d700b5 100644 --- a/tensorflow/contrib/eager/python/evaluator.py +++ b/tensorflow/contrib/eager/python/evaluator.py @@ -57,7 +57,7 @@ class Evaluator(object): self._model = model self._metrics = {} self._evaluators = {} - if context.in_graph_mode(): + if not context.executing_eagerly(): self.call = function.defun(self.call) # ---- API for users ---- @@ -90,7 +90,7 @@ class Evaluator(object): Only for graph execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Evaluator.init_variables() not needed when " "eager execution is enabled.") return control_flow_ops.group([m.init_variables() for _, m in self.metrics]) @@ -113,7 +113,8 @@ class Evaluator(object): with summary_ops.create_file_writer( summary_logdir).as_default(), summary_ops.always_record_summaries(): return self._all_metric_results() - if context.in_eager_mode(): + + if context.executing_eagerly(): return f() else: return function.defun(f)() @@ -158,16 +159,16 @@ class Evaluator(object): @end_compatibility """ summary_logdir = kwargs.pop("summary_logdir", None) - if context.in_graph_mode(): - call_op = self.__call__(dataset.make_one_shot_iterator().get_next(), - *args, **kwargs) - init_op = self.init_variables() - results_op = self.all_metric_results(summary_logdir) - return (init_op, call_op, results_op) - # Eager case - for example in datasets.Iterator(dataset): - self.__call__(example, *args, **kwargs) - return self.all_metric_results(summary_logdir) + if context.executing_eagerly(): + for example in datasets.Iterator(dataset): + self.__call__(example, *args, **kwargs) + return self.all_metric_results(summary_logdir) + # Graph construction + call_op = self.__call__(dataset.make_one_shot_iterator().get_next(), *args, + **kwargs) + init_op = self.init_variables() + results_op = self.all_metric_results(summary_logdir) + return (init_op, call_op, results_op) @staticmethod def run_evaluation(init_op, call_op, results_op, sess=None): @@ -192,7 +193,7 @@ class Evaluator(object): Only for graph execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Evaluator.run_evaluation() not supported when " "eager execution is enabled.") sess = sess or ops.get_default_session() diff --git a/tensorflow/contrib/eager/python/examples/BUILD b/tensorflow/contrib/eager/python/examples/BUILD index 15a21885f66eface291a39fa0ee1ff28bc297548..c1fd9e0ed020beeb722204edf1adfe1dfcf8ff03 100644 --- a/tensorflow/contrib/eager/python/examples/BUILD +++ b/tensorflow/contrib/eager/python/examples/BUILD @@ -8,7 +8,6 @@ py_library( deps = [ "//tensorflow/contrib/eager/python/examples/gan:mnist", "//tensorflow/contrib/eager/python/examples/linear_regression", - "//tensorflow/contrib/eager/python/examples/mnist", "//tensorflow/contrib/eager/python/examples/resnet50", "//tensorflow/contrib/eager/python/examples/rnn_colorbot", "//tensorflow/contrib/eager/python/examples/rnn_ptb", diff --git a/tensorflow/contrib/eager/python/examples/gan/README.md b/tensorflow/contrib/eager/python/examples/gan/README.md index e8c9db1a1e2eb5881b08a4d3866c82b24d64be12..208a64b05d47eea10b49a1bf967a5453677bfd21 100644 --- a/tensorflow/contrib/eager/python/examples/gan/README.md +++ b/tensorflow/contrib/eager/python/examples/gan/README.md @@ -11,7 +11,7 @@ Other eager execution examples can be found under the parent directory. - `mnist.py`: Model definitions and training routines. - `mnist_test.py`: Benchmarks for training and using the models using eager execution. -- `mnist_graph_test.py`: Benchmarks for trainig and using the models using +- `mnist_graph_test.py`: Benchmarks for training and using the models using graph execution. The same model definitions and loss functions are used in all benchmarks. diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py index b9ac79f46c83bb709918e3b72830b90ddcfd71b4..2b7e199fad08c9a5e320b51b3a4de92c2d7dbb1a 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py @@ -35,7 +35,7 @@ from tensorflow.examples.tutorials.mnist import input_data FLAGS = None -class Discriminator(tfe.Network): +class Discriminator(tf.keras.Model): """GAN Discriminator. A network to differentiate between generated and real handwritten digits. @@ -56,19 +56,15 @@ class Discriminator(tfe.Network): else: assert data_format == 'channels_last' self._input_shape = [-1, 28, 28, 1] - self.conv1 = self.track_layer(tf.layers.Conv2D(64, 5, padding='SAME', - data_format=data_format, - activation=tf.tanh)) - self.pool1 = self.track_layer( - tf.layers.AveragePooling2D(2, 2, data_format=data_format)) - self.conv2 = self.track_layer(tf.layers.Conv2D(128, 5, - data_format=data_format, - activation=tf.tanh)) - self.pool2 = self.track_layer( - tf.layers.AveragePooling2D(2, 2, data_format=data_format)) - self.flatten = self.track_layer(tf.layers.Flatten()) - self.fc1 = self.track_layer(tf.layers.Dense(1024, activation=tf.tanh)) - self.fc2 = self.track_layer(tf.layers.Dense(1, activation=None)) + self.conv1 = tf.layers.Conv2D( + 64, 5, padding='SAME', data_format=data_format, activation=tf.tanh) + self.pool1 = tf.layers.AveragePooling2D(2, 2, data_format=data_format) + self.conv2 = tf.layers.Conv2D( + 128, 5, data_format=data_format, activation=tf.tanh) + self.pool2 = tf.layers.AveragePooling2D(2, 2, data_format=data_format) + self.flatten = tf.layers.Flatten() + self.fc1 = tf.layers.Dense(1024, activation=tf.tanh) + self.fc2 = tf.layers.Dense(1, activation=None) def call(self, inputs): """Return two logits per image estimating input authenticity. @@ -95,7 +91,7 @@ class Discriminator(tfe.Network): return x -class Generator(tfe.Network): +class Generator(tf.keras.Model): """Generator of handwritten digits similar to the ones in the MNIST dataset. """ @@ -116,18 +112,17 @@ class Generator(tfe.Network): else: assert data_format == 'channels_last' self._pre_conv_shape = [-1, 6, 6, 128] - self.fc1 = self.track_layer(tf.layers.Dense(6 * 6 * 128, - activation=tf.tanh)) + self.fc1 = tf.layers.Dense(6 * 6 * 128, activation=tf.tanh) # In call(), we reshape the output of fc1 to _pre_conv_shape # Deconvolution layer. Resulting image shape: (batch, 14, 14, 64) - self.conv1 = self.track_layer(tf.layers.Conv2DTranspose( - 64, 4, strides=2, activation=None, data_format=data_format)) + self.conv1 = tf.layers.Conv2DTranspose( + 64, 4, strides=2, activation=None, data_format=data_format) # Deconvolution layer. Resulting image shape: (batch, 28, 28, 1) - self.conv2 = self.track_layer(tf.layers.Conv2DTranspose( - 1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format)) + self.conv2 = tf.layers.Conv2DTranspose( + 1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format) def call(self, inputs): """Return a batch of generated images. @@ -168,7 +163,8 @@ def discriminator_loss(discriminator_real_outputs, discriminator_gen_outputs): """ loss_on_real = tf.losses.sigmoid_cross_entropy( - tf.ones_like(discriminator_real_outputs), discriminator_real_outputs, + tf.ones_like(discriminator_real_outputs), + discriminator_real_outputs, label_smoothing=0.25) loss_on_generated = tf.losses.sigmoid_cross_entropy( tf.zeros_like(discriminator_gen_outputs), discriminator_gen_outputs) @@ -198,9 +194,9 @@ def generator_loss(discriminator_gen_outputs): return loss -def train_one_epoch(generator, discriminator, - generator_optimizer, discriminator_optimizer, - dataset, log_interval, noise_dim): +def train_one_epoch(generator, discriminator, generator_optimizer, + discriminator_optimizer, dataset, step_counter, + log_interval, noise_dim): """Trains `generator` and `discriminator` models on `dataset`. Args: @@ -209,7 +205,8 @@ def train_one_epoch(generator, discriminator, generator_optimizer: Optimizer to use for generator. discriminator_optimizer: Optimizer to use for discriminator. dataset: Dataset of images to train on. - log_interval: How many global steps to wait between logging and collecting + step_counter: An integer variable, used to write summaries regularly. + log_interval: How many steps to wait between logging and collecting summaries. noise_dim: Dimension of noise vector to use. """ @@ -218,18 +215,23 @@ def train_one_epoch(generator, discriminator, total_discriminator_loss = 0.0 for (batch_index, images) in enumerate(tfe.Iterator(dataset)): with tf.device('/cpu:0'): - tf.assign_add(tf.train.get_global_step(), 1) + tf.assign_add(step_counter, 1) - with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval): + with tf.contrib.summary.record_summaries_every_n_global_steps( + log_interval, global_step=step_counter): current_batch_size = images.shape[0] - noise = tf.random_uniform(shape=[current_batch_size, noise_dim], - minval=-1., maxval=1., seed=batch_index) + noise = tf.random_uniform( + shape=[current_batch_size, noise_dim], + minval=-1., + maxval=1., + seed=batch_index) with tfe.GradientTape(persistent=True) as g: generated_images = generator(noise) - tf.contrib.summary.image('generated_images', - tf.reshape(generated_images, [-1, 28, 28, 1]), - max_images=10) + tf.contrib.summary.image( + 'generated_images', + tf.reshape(generated_images, [-1, 28, 28, 1]), + max_images=10) discriminator_gen_outputs = discriminator(generated_images) discriminator_real_outputs = discriminator(images) @@ -244,18 +246,16 @@ def train_one_epoch(generator, discriminator, discriminator_grad = g.gradient(discriminator_loss_val, discriminator.variables) - with tf.variable_scope('generator'): - generator_optimizer.apply_gradients(zip(generator_grad, - generator.variables)) - with tf.variable_scope('discriminator'): - discriminator_optimizer.apply_gradients(zip(discriminator_grad, - discriminator.variables)) + generator_optimizer.apply_gradients( + zip(generator_grad, generator.variables)) + discriminator_optimizer.apply_gradients( + zip(discriminator_grad, discriminator.variables)) if log_interval and batch_index > 0 and batch_index % log_interval == 0: print('Batch #%d\tAverage Generator Loss: %.6f\t' - 'Average Discriminator Loss: %.6f' % ( - batch_index, total_generator_loss/batch_index, - total_discriminator_loss/batch_index)) + 'Average Discriminator Loss: %.6f' % + (batch_index, total_generator_loss / batch_index, + total_discriminator_loss / batch_index)) def main(_): @@ -266,18 +266,18 @@ def main(_): # Load the datasets data = input_data.read_data_sets(FLAGS.data_dir) - dataset = (tf.data.Dataset - .from_tensor_slices(data.train.images) - .shuffle(60000) - .batch(FLAGS.batch_size)) - - # Create the models and optimizers - generator = Generator(data_format) - discriminator = Discriminator(data_format) - with tf.variable_scope('generator'): - generator_optimizer = tf.train.AdamOptimizer(FLAGS.lr) - with tf.variable_scope('discriminator'): - discriminator_optimizer = tf.train.AdamOptimizer(FLAGS.lr) + dataset = ( + tf.data.Dataset.from_tensor_slices(data.train.images).shuffle(60000) + .batch(FLAGS.batch_size)) + + # Create the models and optimizers. + model_objects = { + 'generator': Generator(data_format), + 'discriminator': Discriminator(data_format), + 'generator_optimizer': tf.train.AdamOptimizer(FLAGS.lr), + 'discriminator_optimizer': tf.train.AdamOptimizer(FLAGS.lr), + 'step_counter': tf.train.get_or_create_global_step(), + } # Prepare summary writer and checkpoint info summary_writer = tf.contrib.summary.create_summary_file_writer( @@ -286,28 +286,22 @@ def main(_): latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) if latest_cpkt: print('Using latest checkpoint at ' + latest_cpkt) + checkpoint = tfe.Checkpoint(**model_objects) + # Restore variables on creation if a checkpoint exists. + checkpoint.restore(latest_cpkt) with tf.device(device): - for epoch in range(1, 101): - with tfe.restore_variables_on_create(latest_cpkt): - global_step = tf.train.get_or_create_global_step() - start = time.time() - with summary_writer.as_default(): - train_one_epoch(generator, discriminator, generator_optimizer, - discriminator_optimizer, - dataset, FLAGS.log_interval, FLAGS.noise) - end = time.time() - print('\nTrain time for epoch #%d (global step %d): %f' % ( - epoch, global_step.numpy(), end - start)) - - all_variables = ( - generator.variables - + discriminator.variables - + generator_optimizer.variables() - + discriminator_optimizer.variables() - + [global_step]) - tfe.Saver(all_variables).save( - checkpoint_prefix, global_step=global_step) + for _ in range(100): + start = time.time() + with summary_writer.as_default(): + train_one_epoch(dataset=dataset, log_interval=FLAGS.log_interval, + noise_dim=FLAGS.noise, **model_objects) + end = time.time() + checkpoint.save(checkpoint_prefix) + print('\nTrain time for epoch #%d (step %d): %f' % + (checkpoint.save_counter.numpy(), + checkpoint.step_counter.numpy(), + end - start)) if __name__ == '__main__': diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist_test.py b/tensorflow/contrib/eager/python/examples/gan/mnist_test.py index 4a3ca8d82bc2619b05a734f6d2e58431c1a45995..bd35e50c1f434d167c5a8c5aa7d224912523ce28 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist_test.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist_test.py @@ -62,7 +62,7 @@ class MnistEagerGanBenchmark(tf.test.Benchmark): for _ in range(measure_batches)] measure_dataset = tf.data.Dataset.from_tensor_slices(measure_images) - tf.train.get_or_create_global_step() + step_counter = tf.train.get_or_create_global_step() with tf.device(device()): # Create the models and optimizers generator = mnist.Generator(data_format()) @@ -78,13 +78,15 @@ class MnistEagerGanBenchmark(tf.test.Benchmark): # warm up mnist.train_one_epoch(generator, discriminator, generator_optimizer, discriminator_optimizer, - burn_dataset, log_interval=SUMMARY_INTERVAL, + burn_dataset, step_counter, + log_interval=SUMMARY_INTERVAL, noise_dim=NOISE_DIM) # measure start = time.time() mnist.train_one_epoch(generator, discriminator, generator_optimizer, discriminator_optimizer, - measure_dataset, log_interval=SUMMARY_INTERVAL, + measure_dataset, step_counter, + log_interval=SUMMARY_INTERVAL, noise_dim=NOISE_DIM) self._report('train', start, measure_batches, batch_size) diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/BUILD b/tensorflow/contrib/eager/python/examples/linear_regression/BUILD index f86331af6f7928f0f86c888e22706c6e0a5978b2..2f6cfdf31e852d5d69a7a87980c9a441da504cf2 100644 --- a/tensorflow/contrib/eager/python/examples/linear_regression/BUILD +++ b/tensorflow/contrib/eager/python/examples/linear_regression/BUILD @@ -22,6 +22,7 @@ cuda_py_test( ":linear_regression", "//tensorflow:tensorflow_py", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py index 6ce4de6ee0bf50400eff339ac04e132252a2b53e..6ab847cb78a09ab0a38beefff56f87d8314c0713 100644 --- a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py +++ b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py @@ -33,23 +33,13 @@ import tensorflow as tf import tensorflow.contrib.eager as tfe -class LinearModel(tfe.Network): - """A TensorFlow linear regression model. - - Uses TensorFlow's eager execution. - - For those familiar with TensorFlow graphs, notice the absence of - `tf.Session`. The `forward()` method here immediately executes and - returns output values. The `loss()` method immediately compares the - output of `forward()` with the target and returns the MSE loss value. - The `fit()` performs gradient-descent training on the model's weights - and bias. - """ +class LinearModel(tf.keras.Model): + """A TensorFlow linear regression model.""" def __init__(self): """Constructs a LinearModel object.""" super(LinearModel, self).__init__() - self._hidden_layer = self.track_layer(tf.layers.Dense(1)) + self._hidden_layer = tf.layers.Dense(1) def call(self, xs): """Invoke the linear model. @@ -64,7 +54,7 @@ class LinearModel(tfe.Network): def mean_square_loss(model, xs, ys): - return tf.reduce_mean(tf.square(model(xs) - ys)) + return tf.reduce_mean(tf.square(tf.subtract(model(xs), ys))) def fit(model, dataset, optimizer, verbose=False, logdir=None): diff --git a/tensorflow/contrib/eager/python/examples/mnist/BUILD b/tensorflow/contrib/eager/python/examples/mnist/BUILD deleted file mode 100644 index c61ec2dbae60a782c0e6589701554b045dcb92ae..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/examples/mnist/BUILD +++ /dev/null @@ -1,36 +0,0 @@ -licenses(["notice"]) # Apache 2.0 - -package(default_visibility = ["//tensorflow:internal"]) - -load("//tensorflow:tensorflow.bzl", "cuda_py_test") - -py_binary( - name = "mnist", - srcs = ["mnist.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow:tensorflow_py", - "//tensorflow/contrib/eager/python:tfe", - "//tensorflow/examples/tutorials/mnist:input_data", - ], -) - -cuda_py_test( - name = "mnist_test", - srcs = ["mnist_test.py"], - additional_deps = [ - ":mnist", - "//tensorflow/contrib/eager/python:tfe", - "//tensorflow:tensorflow_py", - ], -) - -cuda_py_test( - name = "mnist_graph_test", - srcs = ["mnist_graph_test.py"], - additional_deps = [ - ":mnist", - "//third_party/py/numpy", - "//tensorflow:tensorflow_py", - ], -) diff --git a/tensorflow/contrib/eager/python/examples/mnist/README.md b/tensorflow/contrib/eager/python/examples/mnist/README.md index e987996b88ccf54a322749aadec4f9840760a90f..d1c079ff6b5cb187bbcfe2742293982b1bedd2d4 100644 --- a/tensorflow/contrib/eager/python/examples/mnist/README.md +++ b/tensorflow/contrib/eager/python/examples/mnist/README.md @@ -1,10 +1 @@ -Classification model for the MNIST dataset using eager execution. - -To run: - -``` -python mnist.py -``` - -`mnist_graph_test.py` demonstrates that the same code that is executed eagerly -in `mnist.py` is used to construct a TensorFlow graph. +See https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist.py b/tensorflow/contrib/eager/python/examples/mnist/mnist.py deleted file mode 100644 index 772f59562ba27cce510c82681f491d005298f44c..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/examples/mnist/mnist.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""A deep MNIST classifier using convolutional layers. - -Sample usage: - python mnist.py --help -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import os -import sys -import time - -import tensorflow as tf - -import tensorflow.contrib.eager as tfe -from tensorflow.examples.tutorials.mnist import input_data - -FLAGS = None - - -class MNISTModel(tfe.Network): - """MNIST Network. - - Network structure is equivalent to: - https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/examples/tutorials/mnist/mnist_deep.py - and - https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py - - But written using the tf.layers API. - """ - - def __init__(self, data_format): - """Creates a model for classifying a hand-written digit. - - Args: - data_format: Either 'channels_first' or 'channels_last'. - 'channels_first' is typically faster on GPUs while 'channels_last' is - typically faster on CPUs. See - https://www.tensorflow.org/performance/performance_guide#data_formats - """ - super(MNISTModel, self).__init__(name='') - if data_format == 'channels_first': - self._input_shape = [-1, 1, 28, 28] - else: - assert data_format == 'channels_last' - self._input_shape = [-1, 28, 28, 1] - self.conv1 = self.track_layer( - tf.layers.Conv2D(32, 5, data_format=data_format, activation=tf.nn.relu)) - self.conv2 = self.track_layer( - tf.layers.Conv2D(64, 5, data_format=data_format, activation=tf.nn.relu)) - self.fc1 = self.track_layer(tf.layers.Dense(1024, activation=tf.nn.relu)) - self.fc2 = self.track_layer(tf.layers.Dense(10)) - self.dropout = self.track_layer(tf.layers.Dropout(0.5)) - self.max_pool2d = self.track_layer( - tf.layers.MaxPooling2D( - (2, 2), (2, 2), padding='SAME', data_format=data_format)) - - def call(self, inputs, training): - """Computes labels from inputs. - - Users should invoke __call__ to run the network, which delegates to this - method (and not call this method directly). - - Args: - inputs: A batch of images as a Tensor with shape [batch_size, 784]. - training: True if invoked in the context of training (causing dropout to - be applied). False otherwise. - - Returns: - A Tensor with shape [batch_size, 10] containing the predicted logits - for each image in the batch, for each of the 10 classes. - """ - - x = tf.reshape(inputs, self._input_shape) - x = self.conv1(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.max_pool2d(x) - x = tf.layers.flatten(x) - x = self.fc1(x) - x = self.dropout(x, training=training) - x = self.fc2(x) - return x - - -def loss(predictions, labels): - return tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits( - logits=predictions, labels=labels)) - - -def compute_accuracy(predictions, labels): - return tf.reduce_sum( - tf.cast( - tf.equal( - tf.argmax(predictions, axis=1, - output_type=tf.int64), - tf.argmax(labels, axis=1, - output_type=tf.int64)), - dtype=tf.float32)) / float(predictions.shape[0].value) - - -def train_one_epoch(model, optimizer, dataset, log_interval=None): - """Trains model on `dataset` using `optimizer`.""" - - tf.train.get_or_create_global_step() - - for (batch, (images, labels)) in enumerate(tfe.Iterator(dataset)): - with tf.contrib.summary.record_summaries_every_n_global_steps(10): - with tfe.GradientTape() as tape: - prediction = model(images, training=True) - loss_value = loss(prediction, labels) - tf.contrib.summary.scalar('loss', loss_value) - tf.contrib.summary.scalar('accuracy', - compute_accuracy(prediction, labels)) - grads = tape.gradient(loss_value, model.variables) - optimizer.apply_gradients(zip(grads, model.variables)) - if log_interval and batch % log_interval == 0: - print('Batch #%d\tLoss: %.6f' % (batch, loss_value)) - - -def test(model, dataset): - """Perform an evaluation of `model` on the examples from `dataset`.""" - avg_loss = tfe.metrics.Mean('loss') - accuracy = tfe.metrics.Accuracy('accuracy') - - for (images, labels) in tfe.Iterator(dataset): - predictions = model(images, training=False) - avg_loss(loss(predictions, labels)) - accuracy(tf.argmax(predictions, axis=1, output_type=tf.int64), - tf.argmax(labels, axis=1, output_type=tf.int64)) - print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' % - (avg_loss.result(), 100 * accuracy.result())) - with tf.contrib.summary.always_record_summaries(): - tf.contrib.summary.scalar('loss', avg_loss.result()) - tf.contrib.summary.scalar('accuracy', accuracy.result()) - - -def load_data(data_dir): - """Returns training and test tf.data.Dataset objects.""" - data = input_data.read_data_sets(data_dir, one_hot=True) - train_ds = tf.data.Dataset.from_tensor_slices((data.train.images, - data.train.labels)) - test_ds = tf.data.Dataset.from_tensors((data.test.images, data.test.labels)) - return (train_ds, test_ds) - - -def main(_): - tfe.enable_eager_execution() - - (device, data_format) = ('/gpu:0', 'channels_first') - if FLAGS.no_gpu or tfe.num_gpus() <= 0: - (device, data_format) = ('/cpu:0', 'channels_last') - print('Using device %s, and data format %s.' % (device, data_format)) - - # Load the datasets - (train_ds, test_ds) = load_data(FLAGS.data_dir) - train_ds = train_ds.shuffle(60000).batch(FLAGS.batch_size) - - # Create the model and optimizer - model = MNISTModel(data_format) - optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum) - - if FLAGS.output_dir: - train_dir = os.path.join(FLAGS.output_dir, 'train') - test_dir = os.path.join(FLAGS.output_dir, 'eval') - tf.gfile.MakeDirs(FLAGS.output_dir) - else: - train_dir = None - test_dir = None - summary_writer = tf.contrib.summary.create_file_writer( - train_dir, flush_millis=10000) - test_summary_writer = tf.contrib.summary.create_file_writer( - test_dir, flush_millis=10000, name='test') - checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt') - - with tf.device(device): - for epoch in range(1, 11): - with tfe.restore_variables_on_create( - tf.train.latest_checkpoint(FLAGS.checkpoint_dir)): - global_step = tf.train.get_or_create_global_step() - start = time.time() - with summary_writer.as_default(): - train_one_epoch(model, optimizer, train_ds, FLAGS.log_interval) - end = time.time() - print('\nTrain time for epoch #%d (global step %d): %f' % ( - epoch, global_step.numpy(), end - start)) - with test_summary_writer.as_default(): - test(model, test_ds) - all_variables = ( - model.variables - + optimizer.variables() - + [global_step]) - tfe.Saver(all_variables).save( - checkpoint_prefix, global_step=global_step) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument( - '--data-dir', - type=str, - default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - parser.add_argument( - '--batch-size', - type=int, - default=64, - metavar='N', - help='input batch size for training (default: 64)') - parser.add_argument( - '--log-interval', - type=int, - default=10, - metavar='N', - help='how many batches to wait before logging training status') - parser.add_argument( - '--output_dir', - type=str, - default=None, - metavar='N', - help='Directory to write TensorBoard summaries') - parser.add_argument( - '--checkpoint_dir', - type=str, - default='/tmp/tensorflow/mnist/checkpoints/', - metavar='N', - help='Directory to save checkpoints in (once per epoch)') - parser.add_argument( - '--lr', - type=float, - default=0.01, - metavar='LR', - help='learning rate (default: 0.01)') - parser.add_argument( - '--momentum', - type=float, - default=0.5, - metavar='M', - help='SGD momentum (default: 0.5)') - parser.add_argument( - '--no-gpu', - action='store_true', - default=False, - help='disables GPU usage even if a GPU is available') - - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py b/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py deleted file mode 100644 index 1af26553120b34d4682b17b1c29c81dc65e421d4..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf -from tensorflow.contrib.eager.python.examples.mnist import mnist - - -def data_format(): - return "channels_first" if tf.test.is_gpu_available() else "channels_last" - - -class MNISTGraphTest(tf.test.TestCase): - - def testTrainGraph(self): - # The MNISTModel class can be executed eagerly (as in mnist.py and - # mnist_test.py) and also be used to construct a TensorFlow graph, which is - # then trained in a session. - with tf.Graph().as_default(): - # Generate some random data. - batch_size = 64 - images = np.random.randn(batch_size, 784).astype(np.float32) - digits = np.random.randint(low=0, high=10, size=batch_size) - labels = np.zeros((batch_size, 10)) - labels[np.arange(batch_size), digits] = 1. - - # Create a model, optimizer, and dataset as would be done - # for eager execution as well. - model = mnist.MNISTModel(data_format()) - optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0) - dataset = tf.data.Dataset.from_tensors((images, labels)) - - # Define the loss tensor (as opposed to a loss function when - # using eager execution). - (images, labels) = dataset.make_one_shot_iterator().get_next() - predictions = model(images, training=True) - loss = mnist.loss(predictions, labels) - - train_op = optimizer.minimize(loss) - init = tf.global_variables_initializer() - with tf.Session() as sess: - # Variables have to be initialized in the session. - sess.run(init) - # Train using the optimizer. - sess.run(train_op) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py b/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py deleted file mode 100644 index 136085eba21284a42282395e54f32c33bf63b5c3..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -import tensorflow.contrib.eager as tfe -from tensorflow.contrib.eager.python.examples.mnist import mnist - - -def device(): - return "/device:GPU:0" if tfe.num_gpus() else "/device:CPU:0" - - -def data_format(): - return "channels_first" if tfe.num_gpus() else "channels_last" - - -def random_dataset(): - batch_size = 64 - images = tf.random_normal([batch_size, 784]) - digits = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32) - labels = tf.one_hot(digits, 10) - return tf.data.Dataset.from_tensors((images, labels)) - - -def train_one_epoch(defun=False): - model = mnist.MNISTModel(data_format()) - if defun: - model.call = tfe.defun(model.call) - optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) - dataset = random_dataset() - with tf.device(device()): - tf.train.get_or_create_global_step() - mnist.train_one_epoch(model, optimizer, dataset) - - -def evaluate(defun=False): - model = mnist.MNISTModel(data_format()) - dataset = random_dataset() - if defun: - model.call = tfe.defun(model.call) - with tf.device(device()): - tf.train.get_or_create_global_step() - mnist.test(model, dataset) - - -class MNISTTest(tf.test.TestCase): - - def testTrainOneEpoch(self): - train_one_epoch(defun=False) - - def testTest(self): - evaluate(defun=False) - - def testTrainOneEpochWithDefunCall(self): - train_one_epoch(defun=True) - - def testTestWithDefunCall(self): - evaluate(defun=True) - - -if __name__ == "__main__": - tfe.enable_eager_execution() - tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py index 9982fdb07eefa665379e7be095f4f8017d92cf97..6b59413141f78fc85474850e109454ecdeb68cd3 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py @@ -27,10 +27,9 @@ from __future__ import print_function import functools import tensorflow as tf -import tensorflow.contrib.eager as tfe -class _IdentityBlock(tfe.Network): +class _IdentityBlock(tf.keras.Model): """_IdentityBlock is the block that has no conv layer at shortcut. Args: @@ -50,31 +49,24 @@ class _IdentityBlock(tfe.Network): bn_name_base = 'bn' + str(stage) + block + '_branch' bn_axis = 1 if data_format == 'channels_first' else 3 - self.conv2a = self.track_layer( - tf.layers.Conv2D( - filters1, (1, 1), - name=conv_name_base + '2a', - data_format=data_format)) - self.bn2a = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')) - - self.conv2b = self.track_layer( - tf.layers.Conv2D( - filters2, - kernel_size, - padding='same', - data_format=data_format, - name=conv_name_base + '2b')) - self.bn2b = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')) - - self.conv2c = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - name=conv_name_base + '2c', - data_format=data_format)) - self.bn2c = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')) + self.conv2a = tf.layers.Conv2D( + filters1, (1, 1), name=conv_name_base + '2a', data_format=data_format) + self.bn2a = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2a') + + self.conv2b = tf.layers.Conv2D( + filters2, + kernel_size, + padding='same', + data_format=data_format, + name=conv_name_base + '2b') + self.bn2b = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2b') + + self.conv2c = tf.layers.Conv2D( + filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format) + self.bn2c = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2c') def call(self, input_tensor, training=False): x = self.conv2a(input_tensor) @@ -92,7 +84,7 @@ class _IdentityBlock(tfe.Network): return tf.nn.relu(x) -class _ConvBlock(tfe.Network): +class _ConvBlock(tf.keras.Model): """_ConvBlock is the block that has a conv layer at shortcut. Args: @@ -121,41 +113,35 @@ class _ConvBlock(tfe.Network): bn_name_base = 'bn' + str(stage) + block + '_branch' bn_axis = 1 if data_format == 'channels_first' else 3 - self.conv2a = self.track_layer( - tf.layers.Conv2D( - filters1, (1, 1), - strides=strides, - name=conv_name_base + '2a', - data_format=data_format)) - self.bn2a = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')) - - self.conv2b = self.track_layer( - tf.layers.Conv2D( - filters2, - kernel_size, - padding='same', - name=conv_name_base + '2b', - data_format=data_format)) - self.bn2b = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')) - - self.conv2c = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - name=conv_name_base + '2c', - data_format=data_format)) - self.bn2c = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')) - - self.conv_shortcut = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - strides=strides, - name=conv_name_base + '1', - data_format=data_format)) - self.bn_shortcut = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '1')) + self.conv2a = tf.layers.Conv2D( + filters1, (1, 1), + strides=strides, + name=conv_name_base + '2a', + data_format=data_format) + self.bn2a = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2a') + + self.conv2b = tf.layers.Conv2D( + filters2, + kernel_size, + padding='same', + name=conv_name_base + '2b', + data_format=data_format) + self.bn2b = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2b') + + self.conv2c = tf.layers.Conv2D( + filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format) + self.bn2c = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2c') + + self.conv_shortcut = tf.layers.Conv2D( + filters3, (1, 1), + strides=strides, + name=conv_name_base + '1', + data_format=data_format) + self.bn_shortcut = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '1') def call(self, input_tensor, training=False): x = self.conv2a(input_tensor) @@ -176,7 +162,8 @@ class _ConvBlock(tfe.Network): return tf.nn.relu(x) -class ResNet50(tfe.Network): +# pylint: disable=not-callable +class ResNet50(tf.keras.Model): """Instantiates the ResNet50 architecture. Args: @@ -220,32 +207,28 @@ class ResNet50(tfe.Network): self.include_top = include_top def conv_block(filters, stage, block, strides=(2, 2)): - l = _ConvBlock( + return _ConvBlock( 3, filters, stage=stage, block=block, data_format=data_format, strides=strides) - return self.track_layer(l) def id_block(filters, stage, block): - l = _IdentityBlock( + return _IdentityBlock( 3, filters, stage=stage, block=block, data_format=data_format) - return self.track_layer(l) - - self.conv1 = self.track_layer( - tf.layers.Conv2D( - 64, (7, 7), - strides=(2, 2), - data_format=data_format, - padding='same', - name='conv1')) + + self.conv1 = tf.layers.Conv2D( + 64, (7, 7), + strides=(2, 2), + data_format=data_format, + padding='same', + name='conv1') bn_axis = 1 if data_format == 'channels_first' else 3 - self.bn_conv1 = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1')) - self.max_pool = self.track_layer( - tf.layers.MaxPooling2D((3, 3), strides=(2, 2), data_format=data_format)) + self.bn_conv1 = tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1') + self.max_pool = tf.layers.MaxPooling2D( + (3, 3), strides=(2, 2), data_format=data_format) self.l2a = conv_block([64, 64, 256], stage=2, block='a', strides=(1, 1)) self.l2b = id_block([64, 64, 256], stage=2, block='b') @@ -267,13 +250,11 @@ class ResNet50(tfe.Network): self.l5b = id_block([512, 512, 2048], stage=5, block='b') self.l5c = id_block([512, 512, 2048], stage=5, block='c') - self.avg_pool = self.track_layer( - tf.layers.AveragePooling2D( - (7, 7), strides=(7, 7), data_format=data_format)) + self.avg_pool = tf.layers.AveragePooling2D( + (7, 7), strides=(7, 7), data_format=data_format) if self.include_top: - self.fc1000 = self.track_layer( - tf.layers.Dense(classes, name='fc1000')) + self.fc1000 = tf.layers.Dense(classes, name='fc1000') else: reduction_indices = [1, 2] if data_format == 'channels_last' else [2, 3] reduction_indices = tf.constant(reduction_indices) @@ -288,7 +269,7 @@ class ResNet50(tfe.Network): else: self.global_pooling = None - def call(self, input_tensor, training=False): + def call(self, input_tensor, training): x = self.conv1(input_tensor) x = self.bn_conv1(x, training=training) x = tf.nn.relu(x) diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py index 23317886e712323f4b520000e0fd372734fc53a1..551c76b0df71c88919df9cd6d81b4176b23b0ba3 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py @@ -55,7 +55,7 @@ class ResNet50GraphTest(tf.test.TestCase): with tf.Graph().as_default(): images = tf.placeholder(tf.float32, image_shape(None)) model = resnet50.ResNet50(data_format()) - predictions = model(images) + predictions = model(images, training=False) init = tf.global_variables_initializer() @@ -114,7 +114,7 @@ class ResNet50Benchmarks(tf.test.Benchmark): with tf.Graph().as_default(): images = tf.placeholder(tf.float32, image_shape(None)) model = resnet50.ResNet50(data_format()) - predictions = model(images) + predictions = model(images, training=False) init = tf.global_variables_initializer() diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py index 1f7beee68525e9cf338847caa0bb2dcc8bd60f62..65dcc53aab39670cae10846b6996c17d7b4c5ba8 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py @@ -22,7 +22,7 @@ import gc import tempfile import time -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf import tensorflow.contrib.eager as tfe @@ -71,7 +71,7 @@ class ResNet50Test(tf.test.TestCase): model.call = tfe.defun(model.call) with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) self.assertEqual((2, 1000), output.shape) def test_apply(self): @@ -85,7 +85,7 @@ class ResNet50Test(tf.test.TestCase): model = resnet50.ResNet50(data_format, include_top=False) with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) output_shape = ((2, 2048, 1, 1) if data_format == 'channels_first' else (2, 1, 1, 2048)) self.assertEqual(output_shape, output.shape) @@ -95,7 +95,7 @@ class ResNet50Test(tf.test.TestCase): model = resnet50.ResNet50(data_format, include_top=False, pooling='avg') with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) self.assertEqual((2, 2048), output.shape) def test_train(self): @@ -194,11 +194,11 @@ class ResNet50Benchmarks(tf.test.Benchmark): with tf.device(device): images, _ = random_batch(batch_size) for _ in xrange(num_burn): - model(images).cpu() + model(images, training=False).cpu() gc.collect() start = time.time() for _ in xrange(num_iters): - model(images).cpu() + model(images, training=False).cpu() self._report(label, start, num_iters, device, batch_size, data_format) def benchmark_eager_apply(self): diff --git a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py index 40919f2d4cf511eb35fac954719286366aef6c7c..29f02324544ede172500f799cd84068984d7d87b 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py +++ b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py @@ -65,7 +65,6 @@ import six import tensorflow as tf from tensorflow.contrib.eager.python import tfe -from tensorflow.python.eager import context try: import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top @@ -110,7 +109,7 @@ def load_dataset(data_dir, url, batch_size): # pylint: disable=not-callable -class RNNColorbot(tfe.Network): +class RNNColorbot(tf.keras.Model): """Multi-layer (LSTM) RNN that regresses on real-valued vector labels. """ @@ -128,23 +127,20 @@ class RNNColorbot(tfe.Network): self.label_dimension = label_dimension self.keep_prob = keep_prob - # Note the calls to `track_layer` below; these calls register the layers as - # network components that house trainable variables. - self.cells = [ - self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(size)) - for size in rnn_cell_sizes - ] - self.relu = self.track_layer( - tf.layers.Dense(label_dimension, activation=tf.nn.relu, name="relu")) + self.cells = self._add_cells( + [tf.nn.rnn_cell.BasicLSTMCell(size) for size in rnn_cell_sizes]) + self.relu = tf.layers.Dense( + label_dimension, activation=tf.nn.relu, name="relu") - def call(self, chars, sequence_length, training=False): + def call(self, inputs, training=False): """Implements the RNN logic and prediction generation. Args: - chars: a Tensor of dimension [batch_size, time_steps, 256] holding a - batch of one-hot encoded color names - sequence_length: a Tensor of dimension [batch_size] holding the length - of each character sequence (i.e., color name) + inputs: A tuple (chars, sequence_length), where chars is a batch of + one-hot encoded color names represented as a Tensor with dimensions + [batch_size, time_steps, 256] and sequence_length holds the length + of each character sequence (color name) as a Tensor with dimension + [batch_size]. training: whether the invocation is happening during training Returns: @@ -152,6 +148,7 @@ class RNNColorbot(tfe.Network): passing chars through a multi-layer RNN and applying a ReLU to the final hidden state. """ + (chars, sequence_length) = inputs # Transpose the first and second dimensions so that chars is of shape # [time_steps, batch_size, dimension]. chars = tf.transpose(chars, [1, 0, 2]) @@ -182,6 +179,14 @@ class RNNColorbot(tfe.Network): hidden_states = tf.gather_nd(chars, indices) return self.relu(hidden_states) + def _add_cells(self, cells): + # "Magic" required for keras.Model classes to track all the variables in + # a list of tf.layers.Layer objects. + # TODO(ashankar): Figure out API so user code doesn't have to do this. + for i, c in enumerate(cells): + setattr(self, "cell-%d" % i, c) + return cells + def loss(labels, predictions): """Computes mean squared loss.""" @@ -192,7 +197,7 @@ def test(model, eval_data): """Computes the average loss on eval_data, which should be a Dataset.""" avg_loss = tfe.metrics.Mean("loss") for (labels, chars, sequence_length) in tfe.Iterator(eval_data): - predictions = model(chars, sequence_length, training=False) + predictions = model((chars, sequence_length), training=False) avg_loss(loss(labels, predictions)) print("eval/loss: %.6f\n" % avg_loss.result()) with tf.contrib.summary.always_record_summaries(): @@ -205,7 +210,7 @@ def train_one_epoch(model, optimizer, train_data, log_interval=10): tf.train.get_or_create_global_step() def model_loss(labels, chars, sequence_length): - predictions = model(chars, sequence_length, training=True) + predictions = model((chars, sequence_length), training=True) loss_value = loss(labels, predictions) tf.contrib.summary.scalar("loss", loss_value) return loss_value @@ -278,7 +283,7 @@ def main(_): (chars, length) = (tf.identity(chars), tf.identity(length)) chars = tf.expand_dims(chars, 0) length = tf.expand_dims(length, 0) - preds = tf.unstack(model(chars, length, training=False)[0]) + preds = tf.unstack(model((chars, length), training=False)[0]) # Predictions cannot be negative, as they are generated by a ReLU layer; # they may, however, be greater than 1. diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py index d34e9ea68b76373d4b5a9ee9e3852c60a7c81525..69cd16d12c32c8c7c4744d8f0b4b1feedf946aa1 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py @@ -39,21 +39,23 @@ from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn from tensorflow.contrib.eager.python import tfe -class RNN(tfe.Network): +class RNN(tf.keras.Model): """A static RNN. - Similar to tf.nn.static_rnn, implemented as a tf.layer.Layer. + Similar to tf.nn.static_rnn, implemented as a class. """ def __init__(self, hidden_dim, num_layers, keep_ratio): super(RNN, self).__init__() self.keep_ratio = keep_ratio - for _ in range(num_layers): - self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim)) + self.cells = self._add_cells([ + tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim) + for _ in range(num_layers) + ]) def call(self, input_seq, training): batch_size = int(input_seq.shape[1]) - for c in self.layers: + for c in self.cells: state = c.zero_state(batch_size, tf.float32) outputs = [] input_seq = tf.unstack(input_seq, num=int(input_seq.shape[0]), axis=0) @@ -64,7 +66,19 @@ class RNN(tfe.Network): input_seq = tf.stack(outputs, axis=0) if training: input_seq = tf.nn.dropout(input_seq, self.keep_ratio) - return input_seq, None + # Returning a list instead of a single tensor so that the line: + # y = self.rnn(y, ...)[0] + # in PTBModel.call works for both this RNN and CudnnLSTM (which returns a + # tuple (output, output_states). + return [input_seq] + + def _add_cells(self, cells): + # "Magic" required for keras.Model classes to track all the variables in + # a list of tf.layers.Layer objects. + # TODO(ashankar): Figure out API so user code doesn't have to do this. + for i, c in enumerate(cells): + setattr(self, "cell-%d" % i, c) + return cells class Embedding(tf.layers.Layer): @@ -87,7 +101,8 @@ class Embedding(tf.layers.Layer): return tf.nn.embedding_lookup(self.embedding, x) -class PTBModel(tfe.Network): +# pylint: disable=not-callable +class PTBModel(tf.keras.Model): """LSTM for word language modeling. Model described in: @@ -109,19 +124,16 @@ class PTBModel(tfe.Network): self.keep_ratio = 1 - dropout_ratio self.use_cudnn_rnn = use_cudnn_rnn - self.embedding = self.track_layer(Embedding(vocab_size, embedding_dim)) + self.embedding = Embedding(vocab_size, embedding_dim) if self.use_cudnn_rnn: self.rnn = cudnn_rnn.CudnnLSTM( num_layers, hidden_dim, dropout=dropout_ratio) else: self.rnn = RNN(hidden_dim, num_layers, self.keep_ratio) - self.track_layer(self.rnn) - self.linear = self.track_layer( - tf.layers.Dense( - vocab_size, - kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1))) + self.linear = tf.layers.Dense( + vocab_size, kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1)) self._output_shape = [-1, embedding_dim] def call(self, input_seq, training): @@ -136,7 +148,7 @@ class PTBModel(tfe.Network): y = self.embedding(input_seq) if training: y = tf.nn.dropout(y, self.keep_ratio) - y, _ = self.rnn(y, training=training) + y = self.rnn(y, training=training)[0] return self.linear(tf.reshape(y, self._output_shape)) @@ -148,7 +160,7 @@ def clip_gradients(grads_and_vars, clip_ratio): def loss_fn(model, inputs, targets, training): labels = tf.reshape(targets, [-1]) - outputs = model(inputs, training) + outputs = model(inputs, training=training) return tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=outputs)) @@ -339,8 +351,7 @@ if __name__ == "__main__": "http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz") parser.add_argument( "--logdir", type=str, default="", help="Directory for checkpoint.") - parser.add_argument( - "--epoch", type=int, default=20, help="Number of epochs.") + parser.add_argument("--epoch", type=int, default=20, help="Number of epochs.") parser.add_argument("--batch-size", type=int, default=20, help="Batch size.") parser.add_argument( "--seq-len", type=int, default=35, help="Sequence length.") diff --git a/tensorflow/contrib/eager/python/examples/spinn/BUILD b/tensorflow/contrib/eager/python/examples/spinn/BUILD index a1f8a759e2a556bc219f0aa13942f293c4f34cfa..98d01ad1d5a70788d2d4cb07031a8d76a6bf628f 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/BUILD +++ b/tensorflow/contrib/eager/python/examples/spinn/BUILD @@ -38,5 +38,8 @@ cuda_py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", ], - tags = ["no_pip"], # because spinn.py is under third_party/. + tags = [ + "no_cuda_on_cpu_tap", + "no_pip", # because spinn.py is under third_party/. + ], ) diff --git a/tensorflow/contrib/eager/python/examples/spinn/data.py b/tensorflow/contrib/eager/python/examples/spinn/data.py index fcaae0a4f8c0bad916d74bd9b80fcfa55a63d84a..3bc3bb49bcbbc26f7a3134a8bfc385ec080dde1e 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/data.py +++ b/tensorflow/contrib/eager/python/examples/spinn/data.py @@ -227,6 +227,29 @@ def calculate_bins(length2count, min_bin_size): return bounds +def encode_sentence(sentence, word2index): + """Encode a single sentence as word indices and shift-reduce code. + + Args: + sentence: The sentence with added binary parse information, represented as + a string, with all the word items and parentheses separated by spaces. + E.g., '( ( The dog ) ( ( is ( playing toys ) ) . ) )'. + word2index: A `dict` mapping words to their word indices. + + Returns: + 1. Word indices as a numpy array, with shape `(sequence_len, 1)`. + 2. Shift-reduce sequence as a numpy array, with shape + `(sequence_len * 2 - 3, 1)`. + """ + items = [w for w in sentence.split(" ") if w] + words = get_non_parenthesis_words(items) + shift_reduce = get_shift_reduce(items) + word_indices = pad_and_reverse_word_ids( + [[word2index.get(word, UNK_CODE) for word in words]]).T + return (word_indices, + np.expand_dims(np.array(shift_reduce, dtype=np.int64), -1)) + + class SnliData(object): """A split of SNLI data.""" diff --git a/tensorflow/contrib/eager/python/examples/spinn/data_test.py b/tensorflow/contrib/eager/python/examples/spinn/data_test.py index e4f0b37c5099e45b7e3b258b258c0a203c36b3b7..54fef2c3fe4111cd2d93ac109a5b8fffad0c2fad 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/data_test.py +++ b/tensorflow/contrib/eager/python/examples/spinn/data_test.py @@ -22,6 +22,7 @@ import os import shutil import tempfile +import numpy as np import tensorflow as tf from tensorflow.contrib.eager.python.examples.spinn import data @@ -173,14 +174,9 @@ class DataTest(tf.test.TestCase): ValueError, "Cannot find GloVe embedding file at"): data.load_word_vectors(self._temp_data_dir, vocab) - def testSnliData(self): - """Unit test for SnliData objects.""" - snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") - fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt") - os.makedirs(snli_1_0_dir) - + def _createFakeSnliData(self, fake_snli_file): # Four sentences in total. - with open(fake_train_file, "wt") as f: + with open(fake_snli_file, "wt") as f: f.write("gold_label\tsentence1_binary_parse\tsentence2_binary_parse\t" "sentence1_parse\tsentence2_parse\tsentence1\tsentence2\t" "captionID\tpairID\tlabel1\tlabel2\tlabel3\tlabel4\tlabel5\n") @@ -205,10 +201,7 @@ class DataTest(tf.test.TestCase): "4705552913.jpg#2\t4705552913.jpg#2r1n\t" "neutral\tentailment\tneutral\tneutral\tneutral\n") - glove_dir = os.path.join(self._temp_data_dir, "glove") - os.makedirs(glove_dir) - glove_file = os.path.join(glove_dir, "glove.42B.300d.txt") - + def _createFakeGloveData(self, glove_file): words = [".", "foo", "bar", "baz", "quux", "quuz", "grault", "garply"] with open(glove_file, "wt") as f: for i, word in enumerate(words): @@ -220,6 +213,40 @@ class DataTest(tf.test.TestCase): else: f.write("\n") + def testEncodeSingleSentence(self): + snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt") + os.makedirs(snli_1_0_dir) + self._createFakeSnliData(fake_train_file) + vocab = data.load_vocabulary(self._temp_data_dir) + glove_dir = os.path.join(self._temp_data_dir, "glove") + os.makedirs(glove_dir) + glove_file = os.path.join(glove_dir, "glove.42B.300d.txt") + self._createFakeGloveData(glove_file) + word2index, _ = data.load_word_vectors(self._temp_data_dir, vocab) + + sentence_variants = [ + "( Foo ( ( bar baz ) . ) )", + " ( Foo ( ( bar baz ) . ) ) ", + "( Foo ( ( bar baz ) . ) )"] + for sentence in sentence_variants: + word_indices, shift_reduce = data.encode_sentence(sentence, word2index) + self.assertEqual(np.int64, word_indices.dtype) + self.assertEqual((5, 1), word_indices.shape) + self.assertAllClose( + np.array([[3, 3, 3, 2, 3, 2, 2]], dtype=np.int64).T, shift_reduce) + + def testSnliData(self): + snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt") + os.makedirs(snli_1_0_dir) + self._createFakeSnliData(fake_train_file) + + glove_dir = os.path.join(self._temp_data_dir, "glove") + os.makedirs(glove_dir) + glove_file = os.path.join(glove_dir, "glove.42B.300d.txt") + self._createFakeGloveData(glove_file) + vocab = data.load_vocabulary(self._temp_data_dir) word2index, _ = data.load_word_vectors(self._temp_data_dir, vocab) @@ -230,7 +257,7 @@ class DataTest(tf.test.TestCase): self.assertEqual(1, train_data.num_batches(4)) generator = train_data.get_generator(2)() - for i in range(2): + for _ in range(2): label, prem, prem_trans, hypo, hypo_trans = next(generator) self.assertEqual(2, len(label)) self.assertEqual((4, 2), prem.shape) diff --git a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py index 19b0104c807cb43b16c3cd47dbacee6e890021db..081b0af14fcc983a3f85d2a50e2bb04d2f2493b3 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py +++ b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py @@ -26,7 +26,7 @@ import tempfile import time import numpy as np -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf # pylint: disable=g-bad-import-order @@ -36,6 +36,7 @@ from third_party.examples.eager.spinn import spinn from tensorflow.contrib.summary import summary_test_util from tensorflow.python.eager import test from tensorflow.python.framework import test_util +from tensorflow.python.training import checkpoint_utils # pylint: enable=g-bad-import-order @@ -66,13 +67,30 @@ def _generate_synthetic_snli_data_batch(sequence_length, return labels, prem, prem_trans, hypo, hypo_trans -def _test_spinn_config(d_embed, d_out, logdir=None): +def _test_spinn_config(d_embed, d_out, logdir=None, inference_sentences=None): + """Generate a config tuple for testing. + + Args: + d_embed: Embedding dimensions. + d_out: Model output dimensions. + logdir: Optional logdir. + inference_sentences: A 2-tuple of strings representing the sentences (with + binary parsing result), e.g., + ("( ( The dog ) ( ( is running ) . ) )", "( ( The dog ) ( moves . ) )"). + + Returns: + A config tuple. + """ config_tuple = collections.namedtuple( "Config", ["d_hidden", "d_proj", "d_tracker", "predict", "embed_dropout", "mlp_dropout", "n_mlp_layers", "d_mlp", "d_out", "projection", "lr", "batch_size", "epochs", "force_cpu", "logdir", "log_every", "dev_every", "save_every", - "lr_decay_every", "lr_decay_by"]) + "lr_decay_every", "lr_decay_by", "inference_premise", + "inference_hypothesis"]) + + inference_premise = inference_sentences[0] if inference_sentences else None + inference_hypothesis = inference_sentences[1] if inference_sentences else None return config_tuple( d_hidden=d_embed, d_proj=d_embed * 2, @@ -86,14 +104,16 @@ def _test_spinn_config(d_embed, d_out, logdir=None): projection=True, lr=2e-2, batch_size=2, - epochs=10, + epochs=20, force_cpu=False, logdir=logdir, log_every=1, dev_every=2, save_every=2, lr_decay_every=1, - lr_decay_by=0.75) + lr_decay_by=0.75, + inference_premise=inference_premise, + inference_hypothesis=inference_hypothesis) class SpinnTest(test_util.TensorFlowTestCase): @@ -288,11 +308,7 @@ class SpinnTest(test_util.TensorFlowTestCase): # Training on the batch should have led to a change in the loss value. self.assertNotEqual(loss1.numpy(), loss2.numpy()) - def testTrainSpinn(self): - """Test with fake toy SNLI data and GloVe vectors.""" - - # 1. Create and load a fake SNLI data file and a fake GloVe embedding file. - snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + def _create_test_data(self, snli_1_0_dir): fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt") os.makedirs(snli_1_0_dir) @@ -337,13 +353,52 @@ class SpinnTest(test_util.TensorFlowTestCase): else: f.write("\n") + return fake_train_file + + def testInferSpinnWorks(self): + """Test inference with the spinn model.""" + snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + self._create_test_data(snli_1_0_dir) + + vocab = data.load_vocabulary(self._temp_data_dir) + word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab) + + config = _test_spinn_config( + data.WORD_VECTOR_LEN, 4, + logdir=os.path.join(self._temp_data_dir, "logdir"), + inference_sentences=("( foo ( bar . ) )", "( bar ( foo . ) )")) + logits = spinn.train_or_infer_spinn( + embed, word2index, None, None, None, config) + self.assertEqual(tf.float32, logits.dtype) + self.assertEqual((3,), logits.shape) + + def testInferSpinnThrowsErrorIfOnlyOneSentenceIsSpecified(self): + snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + self._create_test_data(snli_1_0_dir) + + vocab = data.load_vocabulary(self._temp_data_dir) + word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab) + + config = _test_spinn_config( + data.WORD_VECTOR_LEN, 4, + logdir=os.path.join(self._temp_data_dir, "logdir"), + inference_sentences=("( foo ( bar . ) )", None)) + with self.assertRaises(ValueError): + spinn.train_or_infer_spinn(embed, word2index, None, None, None, config) + + def testTrainSpinn(self): + """Test with fake toy SNLI data and GloVe vectors.""" + + # 1. Create and load a fake SNLI data file and a fake GloVe embedding file. + snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") + fake_train_file = self._create_test_data(snli_1_0_dir) + vocab = data.load_vocabulary(self._temp_data_dir) word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab) train_data = data.SnliData(fake_train_file, word2index) dev_data = data.SnliData(fake_train_file, word2index) test_data = data.SnliData(fake_train_file, word2index) - print(embed) # 2. Create a fake config. config = _test_spinn_config( @@ -351,7 +406,8 @@ class SpinnTest(test_util.TensorFlowTestCase): logdir=os.path.join(self._temp_data_dir, "logdir")) # 3. Test training of a SPINN model. - spinn.train_spinn(embed, train_data, dev_data, test_data, config) + trainer = spinn.train_or_infer_spinn( + embed, word2index, train_data, dev_data, test_data, config) # 4. Load train loss values from the summary files and verify that they # decrease with training. @@ -363,6 +419,15 @@ class SpinnTest(test_util.TensorFlowTestCase): self.assertEqual(config.epochs, len(train_losses)) self.assertLess(train_losses[-1], train_losses[0]) + # 5. Verify that checkpoints exist and contains all the expected variables. + self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*"))) + ckpt_variable_names = [ + item[0] for item in checkpoint_utils.list_variables(config.logdir)] + self.assertIn("global_step", ckpt_variable_names) + for v in trainer.variables: + variable_name = v.name[:v.name.index(":")] if ":" in v.name else v.name + self.assertIn(variable_name, ckpt_variable_names) + class EagerSpinnSNLIClassifierBenchmark(test.Benchmark): diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md index ffc1d0332eae605ce0444a225e53baa68954cae0..ebb05051f27841f1cd3d21b6218986e774ed4c9f 100644 --- a/tensorflow/contrib/eager/python/g3doc/guide.md +++ b/tensorflow/contrib/eager/python/g3doc/guide.md @@ -22,11 +22,10 @@ to models defined without using eager execution. Eager execution is included in TensorFlow versions 1.5 and above. Installation instructions at https://www.tensorflow.org/install/ -The contents of this guide are compatible with TensorFlow 1.5. -However, if you run into bugs that are fixed in source but not the -release, you may want to either either [building from -source](https://www.tensorflow.org/install/install_sources) -or the try latest nightly builds. The nightly builds are available as: +The contents of this guide are compatible with TensorFlow 1.5. However, if you +run into bugs that are fixed in source but not the release, you may want to +either [build from source](https://www.tensorflow.org/install/install_sources) +or try a nightly build. The nightly builds are available as: - [`pip` packages](https://github.com/tensorflow/tensorflow/blob/master/README.md#installation) and @@ -570,8 +569,8 @@ for i in range(20001): print("Loss on test set: %f" % loss(model, data.test.images, data.test.labels).numpy()) ``` -For a more complete example, see -[`tensorflow/contrib/eager/python/examples/mnist.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist.py) +For a more complete example, see [the example in the tensorflow/models +repository](https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py). ### Checkpointing trained variables @@ -860,11 +859,9 @@ eagerly or constructing graphs. This means that you can iteratively develop your model with eager execution enabled and later, if needed, use the same code to reap the benefits of representing models as computational graphs. -For example, -[`mnist.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist.py) -defines a model that is eagerly executed. That same code is used to construct -and execute a graph in -[`mnist_graph_test.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py). +For example, the same model definition used to construct a graph in +[mnist.py`](https://github.com/tensorflow/models/tree/master/official/mnist/mnist.py) +can be trained with eager execution enabled as in [`mnist_eager.py`](https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py). Other models in the [examples directory](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/) diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index ea8dbf2b46ea4bd0e33645ae3c590c4dd13f7a52..1490c2ccacd55156bcc1cf8c07d9941336e18e1b 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -30,12 +30,12 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope - +from tensorflow.python.training import checkpointable _to_replace = re.compile("[^A-Za-z0-9.]") -class Metric(object): +class Metric(checkpointable.CheckpointableBase): """A metric holds state for aggregating statistics over an evaluation run. Example use with eager execution: @@ -93,11 +93,12 @@ class Metric(object): `aggregate()`, it is for use by TensorFlow infrastructure. """ - def __init__(self, name=None): + def __init__(self, name=None, use_global_variables=False): self._built = False self._vars = [] self._initial_values = {} self._updates = [] + self._use_global_variables = use_global_variables name = name or self.__class__.__name__ # Replace things like spaces in name to create a valid scope name. scope_name = _to_replace.sub("_", name) @@ -108,13 +109,13 @@ class Metric(object): pos = scope.name.rfind(scope_name) self._name = name + scope.name[pos + len(scope_name):] self._scope = scope - if context.in_graph_mode(): + if context.executing_eagerly(): + self._construction_scope = context.eager_mode + else: # We make self.call() into a graph callable here, so that we can # return a single op that performs all of the variable updates. self._construction_scope = ops.get_default_graph().as_default self.call = function.defun(self.call) - else: - self._construction_scope = context.eager_mode # ---- API for users ---- def __call__(self, *args, **kwargs): @@ -155,10 +156,11 @@ class Metric(object): initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None. """ - if context.in_graph_mode(): + if context.executing_eagerly(): + for v in self._vars: + v.assign(self._initial_values[v]) + else: return control_flow_ops.group([v.initializer for v in self._vars]) - for v in self._vars: - v.assign(self._initial_values[v]) # ---- To be implemented by descendants --- def build(self, *args, **kwargs): @@ -200,10 +202,10 @@ class Metric(object): def value(self): """In graph mode returns the result Tensor while in eager the callable.""" - if context.in_graph_mode(): - return self.result() - else: + if context.executing_eagerly(): return self.result + else: + return self.result() # We can support two different strategies of for doing data-parallel # distributed metric computations: @@ -245,19 +247,31 @@ class Metric(object): """***Only for use by descendants of Metric***.""" if self._built: raise RuntimeError("Can't call add_variable() except in build().") - collections = None if context.in_eager_mode() else [ - ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES - ] - v = variable_scope.get_variable( - name, - shape, - dtype, - initializer, + if context.executing_eagerly(): + collections = None + else: + if self._use_global_variables: + collections = [ops.GraphKeys.GLOBAL_VARIABLES] + else: + collections = [ops.GraphKeys.LOCAL_VARIABLES] + collections += [ops.GraphKeys.METRIC_VARIABLES] + # Variables are Checkpointable dependencies of Metrics regardless of the + # global/local distinction. Users can avoid saving variables by not adding a + # dependency on the Metric. + v = self._add_variable_with_custom_getter( + name=name, + shape=shape, + dtype=dtype, + initializer=initializer, trainable=False, collections=collections, - use_resource=True) + use_resource=True, + getter=variable_scope.get_variable, + # Raise duplicate variable exceptions from get_variable rather than + # Checkpointable. + overwrite=True) self._vars.append(v) - if context.in_eager_mode(): + if context.executing_eagerly(): self._initial_values[v] = v.value() return v @@ -267,8 +281,10 @@ class Mean(Metric): # TODO(josh11b): Maybe have a dtype argument that defaults to tf.float64? # Or defaults to type of the input if it is tf.float32, else tf.float64? - def __init__(self, name=None, dtype=dtypes.float64): - super(Mean, self).__init__(name=name) + def __init__(self, name=None, dtype=dtypes.float64, + use_global_variables=False): + super(Mean, self).__init__(name=name, + use_global_variables=use_global_variables) self.dtype = dtype def build(self, *args, **kwargs): diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index a9ecaa3f8bced3043ea0eb0ac3aa8bfa65e9e1ff..6b5450ba89bdfa6e0195f488b75f596b58c463d5 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -18,8 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import tempfile +from tensorflow.contrib.eager.python import checkpointable_utils from tensorflow.contrib.eager.python import metrics from tensorflow.contrib.summary import summary_ops from tensorflow.contrib.summary import summary_test_util @@ -50,6 +52,19 @@ class MetricsTest(test.TestCase): self.assertEqual( set(m.variables), set(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES))) + self.assertEqual(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES), []) + self.assertEqual( + set(m.variables), + set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES))) + + def testUseGlobalVariablesCollections(self): + with context.graph_mode(), ops.Graph().as_default(): + m = metrics.Mean(use_global_variables=True) + m(1000) + self.assertEqual( + set(m.variables), + set(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))) + self.assertEqual(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES), []) self.assertEqual( set(m.variables), set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES))) @@ -193,6 +208,31 @@ class MetricsTest(test.TestCase): self.assertAllEqual(m2.result().eval(), 2.0) self.assertAllEqual(m1.result().eval(), 1.0) + @test_util.run_in_graph_and_eager_modes() + def testSaveRestore(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + mean = metrics.Mean() + checkpoint = checkpointable_utils.Checkpoint(mean=mean) + mean.build() + mean._built = True + self.evaluate(mean.init_variables()) + self.evaluate(mean(100.)) + self.evaluate(mean(200.)) + save_path = checkpoint.save(checkpoint_prefix) + self.evaluate(mean(1000.)) + checkpoint.restore(save_path).assert_consumed().run_restore_ops() + self.evaluate(mean(300.)) + self.assertAllEqual(200., self.evaluate(mean.value())) + + restore_mean = metrics.Mean() + restore_checkpoint = checkpointable_utils.Checkpoint(mean=restore_mean) + status = restore_checkpoint.restore(save_path) + restore_update = restore_mean(300.) + status.assert_consumed().run_restore_ops() + self.evaluate(restore_update) + self.assertAllEqual(200., self.evaluate(restore_mean.value())) + self.assertEqual(3, self.evaluate(restore_mean.denom)) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/eager/python/network.py b/tensorflow/contrib/eager/python/network.py index e3c13cbd2e8ccd2ab79da74e0e97905c6ed5c02d..4c937716e8df7c8cda26d6431885ce33346b77fb 100644 --- a/tensorflow/contrib/eager/python/network.py +++ b/tensorflow/contrib/eager/python/network.py @@ -639,7 +639,7 @@ def _make_custom_getter_for_deferred_restorations(): # Mark as already restored from this checkpoint. delayed_restoration.checkpointed_variables_to_restore[ checkpoint_name] = None - if context.in_graph_mode(): + if not context.executing_eagerly(): delayed_restoration.session.run(variable.initializer) if found_value: # Error checking should run even if we've already restored a value. @@ -772,7 +772,7 @@ def save_network_checkpoint( variable_map[mapped_name]._shared_name, variable._shared_name, network.scope_name)) - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() @@ -853,7 +853,7 @@ def _restore_existing_variables(network, save_path, map_func, user_map_func): network_name=network.name, network_scope_name=network.scope_name)) if existing_variables_by_checkpoint_name: - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() @@ -880,7 +880,7 @@ def _set_restore_on_create(network, save_path, map_func, user_map_func, # _DeferredRestoration objects once a Network has been built (so that # restoring in a loop does not take increasing amounts of memory). if checkpointed_variables_to_restore: - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py index 81c77e41acf420fa84857ccb366aa2fbd6055f42..3329fc6c513265deff41a368f5688dd605209c14 100644 --- a/tensorflow/contrib/eager/python/network_test.py +++ b/tensorflow/contrib/eager/python/network_test.py @@ -539,7 +539,7 @@ class NetworkTest(test.TestCase): # No issue here since the name is unique within its scope. name_conflict3 = MyNetwork(name="name_conflict") net2 = MyNetwork() # name=outside_scope/my_network_2 to avoid the - # variable_scope my_network_1 below. + # variable_scope my_network_1 below. vs_name_conflict = MyNetwork(name="vs_name_conflict") # conflict below with variable_scope.variable_scope("intervening_scope"): with variable_scope.variable_scope(captured_scope): diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index 62421849c766a1124c726812428985c913c653a3..fdaca90fd13576e6ca8a3408aaf528dbc2384b0c 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -73,7 +73,7 @@ def restore_variables_on_create(save_path, map_func=None): NotFoundError: If the variable is not found in checkpoint. ValueError: If not used in eager mode or map_func is not callable. """ - if context.in_graph_mode(): + if not context.executing_eagerly(): raise ValueError( "Currently, restore_variables_on_create can only be used with " "eager execution enabled.") @@ -131,7 +131,7 @@ class Saver(object): Raises: RuntimeError: if invoked when eager execution has not been enabled. """ - if context.in_graph_mode(): + if not context.executing_eagerly(): raise RuntimeError("tfe.Saver can only be used when eager " "execution is enabled. Use tf.train.Saver when " "building graphs.") diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index d32bebf90c1e768d1efec26b3b78bf1a522a8f00..5aabc9aae868021284e83a4c4d80d65c2ee63fca 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -56,8 +56,12 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@save_network_checkpoint @@restore_network_checkpoint +@@Checkpoint +@@Checkpointable +@@CheckpointableSaver + +@@executing_eagerly @@in_eager_mode -@@in_graph_mode @@run_test_in_graph_and_eager_modes @@ -74,6 +78,8 @@ from __future__ import print_function # pylint:disable=g-bad-import-order,g-import-not-at-top,unused-import # from tensorflow.contrib.eager.python import metrics +from tensorflow.contrib.eager.python.checkpointable_utils import CheckpointableSaver +from tensorflow.contrib.eager.python.checkpointable_utils import Checkpoint from tensorflow.contrib.eager.python.datasets import Iterator from tensorflow.contrib.eager.python.network import Network from tensorflow.contrib.eager.python.network import Sequential @@ -87,11 +93,9 @@ from tensorflow.python.eager import function from tensorflow.python.eager.context import DEVICE_PLACEMENT_EXPLICIT from tensorflow.python.eager.context import DEVICE_PLACEMENT_WARN from tensorflow.python.eager.context import DEVICE_PLACEMENT_SILENT -from tensorflow.python.eager.context import in_eager_mode -from tensorflow.python.eager.context import in_graph_mode +from tensorflow.python.eager.context import executing_eagerly from tensorflow.python.eager.context import list_devices from tensorflow.python.eager.context import num_gpus -from tensorflow.python.eager.custom_gradient import custom_gradient from tensorflow.python.eager.execution_callbacks import add_execution_callback from tensorflow.python.eager.execution_callbacks import clear_execution_callbacks from tensorflow.python.eager.execution_callbacks import inf_callback @@ -101,10 +105,12 @@ from tensorflow.python.eager.execution_callbacks import seterr from tensorflow.python.framework.ops import enable_eager_execution from tensorflow.python.framework.ops import eager_run as run from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes +from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable from tensorflow.python.ops.variable_scope import EagerVariableStore from tensorflow.python.ops import script_ops from tensorflow.python.ops import template +from tensorflow.python.training.checkpointable import Checkpointable from tensorflow.python.util.all_util import remove_undocumented py_func = script_ops.eager_py_func @@ -115,5 +121,6 @@ implicit_value_and_gradients = backprop.implicit_val_and_grad gradients_function = backprop.gradients_function value_and_gradients_function = backprop.val_and_grad_function GradientTape = backprop.GradientTape # pylint: disable=invalid-name +in_eager_mode = executing_eagerly remove_undocumented(__name__) diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py index b6659c2a1797feab261d756e78b45231dbea5a02..e80ccbb74d8623e977a98cb7fa5eb41f3c9bf250 100644 --- a/tensorflow/contrib/eager/python/tfe_test.py +++ b/tensorflow/contrib/eager/python/tfe_test.py @@ -47,7 +47,8 @@ class TFETest(test_util.TensorFlowTestCase): def testVariableError(self): with self.assertRaisesRegexp( - RuntimeError, r'Variable not supported in Eager mode'): + RuntimeError, + r'Variable not supported when eager execution is enabled'): variables.Variable(initial_value=1.0) def testGradients(self): diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 6cdbed5b896577f5622b1bd0123c289c798bc0a5..773c6ab6c79217698c7c598a133082e2553f28f6 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -138,6 +138,7 @@ py_test( size = "medium", srcs = ["python/estimator/extenders_test.py"], srcs_version = "PY2AND3", + tags = ["notsan"], # b/62863147 deps = [ ":extenders", "//tensorflow/contrib/data/python/ops:dataset_ops", @@ -169,6 +170,7 @@ py_library( "//tensorflow/python:lookup_ops", "//tensorflow/python:math_ops", "//tensorflow/python:metrics", + "//tensorflow/python:nn", "//tensorflow/python:sparse_ops", "//tensorflow/python:sparse_tensor", "//tensorflow/python:summary", @@ -191,6 +193,7 @@ py_test( ":head", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:control_flow_ops", diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index 0f75b77050b0ba4c752a6a74fdc7024170b6f318..6b9f9575b606f1822d760e8597c55994dd8af04c 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -39,6 +39,7 @@ _allowed_symbols = [ 'multi_class_head', 'multi_head', 'multi_label_head', + 'poisson_regression_head', 'regression_head', 'DNNEstimator', 'DNNLinearCombinedEstimator', diff --git a/tensorflow/contrib/estimator/python/estimator/extenders.py b/tensorflow/contrib/estimator/python/estimator/extenders.py index c99bf8badb35e6fffb7cae8761db9d402b8b3a8f..2b6881b81487dfdb682d5d6261a0318c59d461f6 100644 --- a/tensorflow/contrib/estimator/python/estimator/extenders.py +++ b/tensorflow/contrib/estimator/python/estimator/extenders.py @@ -33,7 +33,7 @@ _VALID_METRIC_FN_ARGS = set(['features', 'labels', 'predictions', 'config']) def add_metrics(estimator, metric_fn): - """Creates a new ${tf.estimator.Estimator} which has given metrics. + """Creates a new @{tf.estimator.Estimator} which has given metrics. Example: @@ -60,7 +60,7 @@ def add_metrics(estimator, metric_fn): ``` Args: - estimator: A ${tf.estimator.Estimator} object. + estimator: A @{tf.estimator.Estimator} object. metric_fn: A function which should obey the following signature: - Args: can only have following four arguments in any order: * predictions: Predictions `Tensor` or dict of `Tensor` created by given @@ -78,7 +78,7 @@ def add_metrics(estimator, metric_fn): function, namely a `(metric_tensor, update_op)` tuple. Returns: - A new ${tf.estimator.Estimator} which has a union of original metrics with + A new @{tf.estimator.Estimator} which has a union of original metrics with given ones. """ _verify_metric_fn_args(metric_fn) @@ -161,14 +161,14 @@ def forward_features(estimator, keys=None): ``` Args: - estimator: A ${tf.estimator.Estimator} object. + estimator: A @{tf.estimator.Estimator} object. keys: a `string` or a `list` of `string`. If it is `None`, all of the `features` in `dict` is forwarded to the `predictions`. If it is a `string`, only given key is forwarded. If it is a `list` of strings, all the given `keys` are forwarded. Returns: - A new ${tf.estimator.Estimator} which forwards features to predictions. + A new @{tf.estimator.Estimator} which forwards features to predictions. Raises: ValueError: diff --git a/tensorflow/contrib/estimator/python/estimator/extenders_test.py b/tensorflow/contrib/estimator/python/estimator/extenders_test.py index 5f4a3cc902c9cc07c0688ad41dab7391a641c133..ad1a8ef152b07ecbab33d9eb3184a2ae89def27d 100644 --- a/tensorflow/contrib/estimator/python/estimator/extenders_test.py +++ b/tensorflow/contrib/estimator/python/estimator/extenders_test.py @@ -20,8 +20,8 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.contrib.estimator.python.estimator import extenders +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator_lib from tensorflow.python.estimator.canned import linear from tensorflow.python.feature_column import feature_column as fc diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index 238cf287b768eee28b20202084eb244c085c8b75..f95fcc8039cb54c26543781b31013a7676168b0b 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import nn from tensorflow.python.ops import sparse_ops from tensorflow.python.ops.losses import losses from tensorflow.python.saved_model import signature_constants @@ -177,6 +178,7 @@ def regression_head(weight_column=None, label_dimension=1, loss_reduction=losses.Reduction.SUM, loss_fn=None, + inverse_link_fn=None, name=None): """Creates a `_Head` for regression using the `mean_squared_error` loss. @@ -195,10 +197,16 @@ def regression_head(weight_column=None, `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or `[D0, D1, ... DN, label_dimension]`. - Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + Supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or `(labels, logits, features)` as arguments and returns unreduced loss with shape `[D0, D1, ... DN, label_dimension]`. + Also supports custom `inverse_link_fn`, also known as 'mean function'. + `inverse_link_fn` takes `logits` as argument and returns predicted values. + This function is the inverse of the link function defined in + https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function + Namely, for poisson regression, set `inverse_link_fn=tf.exp`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -209,7 +217,9 @@ def regression_head(weight_column=None, `[batch_size, label_dimension]`). loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. - loss_fn: Optional loss function. + loss_fn: Optional loss function. Defaults to `mean_squared_error`. + inverse_link_fn: Optional inverse link function, also known as 'mean + function'. Defaults to identity. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -224,6 +234,67 @@ def regression_head(weight_column=None, label_dimension=label_dimension, loss_reduction=loss_reduction, loss_fn=loss_fn, + inverse_link_fn=inverse_link_fn, + name=name) + + +def poisson_regression_head( + weight_column=None, + label_dimension=1, + loss_reduction=losses.Reduction.SUM, + compute_full_loss=True, + name=None): + """Creates a `_Head` for poisson regression using `tf.nn.log_poisson_loss`. + + The loss is the weighted sum over all input dimensions. Namely, if the input + labels have shape `[batch_size, label_dimension]`, the loss is the weighted + sum over both `batch_size` and `label_dimension`. + + The head expects `logits` with shape `[D0, D1, ... DN, label_dimension]`. + In many applications, the shape is `[batch_size, label_dimension]`. + + The `labels` shape must match `logits`, namely + `[D0, D1, ... DN, label_dimension]`. If `label_dimension=1`, shape + `[D0, D1, ... DN]` is also supported. + + If `weight_column` is specified, weights must be of shape + `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or + `[D0, D1, ... DN, label_dimension]`. + + This is implemented as a generalized linear model, see + https://en.wikipedia.org/wiki/Generalized_linear_model. + + Args: + weight_column: A string or a `_NumericColumn` created by + `tf.feature_column.numeric_column` defining feature column representing + weights. It is used to down weight or boost examples during training. It + will be multiplied by the loss of the example. + label_dimension: Number of regression labels per example. This is the size + of the last dimension of the labels `Tensor` (typically, this has shape + `[batch_size, label_dimension]`). + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to + reduce training loss over batch. Defaults to `SUM`. + compute_full_loss: Whether to include the constant `log(z!)` term in + computing the poisson loss. See `tf.nn.log_poisson_loss` for the full + documentation. + name: name of the head. If provided, summary and metrics keys will be + suffixed by `"/" + name`. Also used as `name_scope` when creating ops. + + Returns: + An instance of `_Head` for poisson regression. + + Raises: + ValueError: If `label_dimension` or `loss_reduction` is invalid. + """ + def _poisson_loss(labels, logits): + return nn.log_poisson_loss( + targets=labels, log_input=logits, compute_full_loss=compute_full_loss) + return head_lib._regression_head_with_mean_squared_error_loss( # pylint:disable=protected-access + weight_column=weight_column, + label_dimension=label_dimension, + loss_reduction=loss_reduction, + loss_fn=_poisson_loss, + inverse_link_fn=math_ops.exp, name=name) diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index 43cdfec9689879201305385499b3b784e1593d60..dc30dde877ab5f912e3f6a724d481b151a3ed044 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import string_ops @@ -1106,5 +1107,75 @@ class MultiLabelHead(test.TestCase): expected_metrics=expected_metrics) +class PoissonRegressionHead(test.TestCase): + + def setUp(self): + ops.reset_default_graph() + + def test_train(self): + head = head_lib.poisson_regression_head() + + # Create estimator spec. + logits = np.array([[0], [-1], [1]], dtype=np.float32) + labels = np.array([[1], [2], [3]], dtype=np.int32) + # With x = exp(logits), z = labels. + # loss = -ln(exp(-x) * (x^z) / z!) + # = x - z * ln(x) + ln(z!) + # = exp(logits) - labels * logits - ln(labels!) + # But for ln(z!) and z > 1, the Stirling approximation is used + # ln(z!) = z*ln(z) - z + 0.5*ln(2*pi*z) + # loss = [exp(0) - 1 * 0 + ln(1!), + # exp(-1) - 2 * (-1) + 2*ln(2) - 2 + 0.5*ln(2*pi*2), + # exp(1) - 3 * 1 + 3*ln(3) - 3 + 0.5*ln(2*pi*3)] + # = [1.0, 3.020, 1.482] + # sum_loss = 5.502 + expected_loss = 5.502 + atol = 0.001 + expected_train_result = b'my_train_op' + def _train_op_fn(loss): + with ops.control_dependencies((check_ops.assert_near( + math_ops.to_float(expected_loss), math_ops.to_float(loss), + atol=atol, name='assert_loss'),)): + return constant_op.constant(expected_train_result) + + spec = head.create_estimator_spec( + features={'x': np.array(((42.,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.TRAIN, + logits=logits, + labels=labels, + train_op_fn=_train_op_fn) + + with self.test_session() as sess: + _initialize_variables(self, spec.scaffold) + loss, train_result = sess.run([spec.loss, spec.train_op]) + self.assertAlmostEqual(expected_loss, loss, delta=atol) + self.assertEqual(expected_train_result, train_result) + + def test_predict(self): + head = head_lib.poisson_regression_head() + + # Create estimator spec. + logits = np.array([[0], [-1], [1]], dtype=np.float32) + expected_predictions = np.exp(logits) + spec = head.create_estimator_spec( + features={'x': np.array(((42.,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.PREDICT, + logits=logits) + + # Assert spec contains expected tensors. + keys = prediction_keys.PredictionKeys + self.assertItemsEqual( + (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys()) + self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype) + self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype) + + # Assert predictions. + with self.test_session(): + _initialize_variables(self, spec.scaffold) + self.assertAllClose( + expected_predictions, spec.predictions[keys.PREDICTIONS].eval()) + self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval()) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py index dfae034afc9a115dcc97e401e8a6d9c66a9b46e9..e0fae2c99292385c6dd32cc6002cee2076a2bb20 100644 --- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py +++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py @@ -110,7 +110,8 @@ def replicate_model_fn(model_fn, Certain algorithms were chosen for aggregating results of computations on multiple towers: - Losses from all towers are reduced according to `loss_reduction`. - - Gradients are reduced using sum for each trainable variable. + - Gradients from all towers are reduced according to `loss_reduction` + for each trainable variable. - `eval_metrics_ops` are reduced per metric using `reduce_mean`. - `EstimatorSpec.predictions` and `EstimatorSpec.export_outputs` are reduced using concatenation. @@ -790,7 +791,7 @@ def _extract_tensors(tensors_and_vars): tensor, _ = tensor_and_var if isinstance(tensor, ops_lib.IndexedSlices): tensors.append(tensor.values) - else: + elif tensor is not None: tensors.append(tensor) return tensors diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py index ab117e61a7059a224ebf6ff0355ae10363b758f5..d46a18aacfcd911c56a9f22dc9581060c7b458a6 100644 --- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py @@ -240,6 +240,13 @@ class ReplicateModelTest(test_util.TensorFlowTestCase): labels = np.array([[1.0], [2.0]]) with self.test_session() as session: + # Add another trainable variable that doesn't produce a gradient to + # verify that None gradients are supported. + _ = variable_scope.get_variable( + 'another_variable', + initializer=constant_op.constant(1, dtype=dtypes.float64), + dtype=dtypes.float64) + replicated_model_fn = replicate_model_fn.replicate_model_fn( self.model_fn, losses.Reduction.MEAN, devices=['/gpu:0', '/gpu:1']) estimator_spec = replicated_model_fn( @@ -1119,8 +1126,6 @@ class SplitBatchTest(test_util.TensorFlowTestCase): feature_shards, label_shards = replicate_model_fn._split_batch( features, labels, 2, device='/gpu:0') - print(feature_shards[0]['x'].eval()) - print(feature_shards[1]['x'].eval()) self.assertSparseValuesEqual( sparse_tensor.SparseTensorValue( indices=[[0, 0], [1, 0], [1, 1]], diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD index 180f1b68f3b56113dfbbfc100bd04efc3bb8b31f..c56c92a0a4a01218d1da5a6b366df3272d14b861 100644 --- a/tensorflow/contrib/factorization/BUILD +++ b/tensorflow/contrib/factorization/BUILD @@ -223,7 +223,10 @@ py_test( srcs = ["python/ops/kmeans_test.py"], shard_count = 4, srcs_version = "PY2AND3", - tags = ["notsan"], # b/67512932 + tags = [ + "nomac", # b/73741358 + "notsan", # b/67512932 + ], deps = [ ":factorization_py", ":factorization_py_CYCLIC_DEPENDENCIES_THAT_NEED_TO_GO", diff --git a/tensorflow/contrib/factorization/python/ops/clustering_ops.py b/tensorflow/contrib/factorization/python/ops/clustering_ops.py index 6d3acb2750743318aad83991bc1e89d64c329423..23137e0a973c0bdd2cdbd97159f7fd310178bf54 100644 --- a/tensorflow/contrib/factorization/python/ops/clustering_ops.py +++ b/tensorflow/contrib/factorization/python/ops/clustering_ops.py @@ -192,11 +192,11 @@ class KMeans(object): # Computes Euclidean distance. Note the first and third terms are # broadcast additions. squared_distance = ( - math_ops.reduce_sum(math_ops.square(inp), 1, keep_dims=True) - + math_ops.reduce_sum(math_ops.square(inp), 1, keepdims=True) - 2 * math_ops.matmul(inp, clusters, transpose_b=True) + array_ops.transpose( math_ops.reduce_sum( - math_ops.square(clusters), 1, keep_dims=True))) + math_ops.square(clusters), 1, keepdims=True))) output.append(squared_distance) return output diff --git a/tensorflow/contrib/factorization/python/ops/gmm.py b/tensorflow/contrib/factorization/python/ops/gmm.py index f72280c4ecf19e33278ffe74061f44bbb7b21709..b2dfe48b2dbe0ec0975f865bba95a7ceba0f590c 100644 --- a/tensorflow/contrib/factorization/python/ops/gmm.py +++ b/tensorflow/contrib/factorization/python/ops/gmm.py @@ -24,17 +24,16 @@ import numpy as np from tensorflow.contrib import framework from tensorflow.contrib.factorization.python.ops import gmm_ops from tensorflow.contrib.framework.python.framework import checkpoint_utils -from tensorflow.python.training import training_util from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import logging_ops as logging -from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops.control_flow_ops import with_dependencies from tensorflow.python.training import session_run_hook +from tensorflow.python.training import training_util def _streaming_sum(scalar_tensor): @@ -70,8 +69,8 @@ class _InitializeClustersHook(session_run_hook.SessionRunHook): class GMM(estimator.Estimator): """An estimator for GMM clustering.""" SCORES = 'scores' + LOG_LIKELIHOOD = 'loss' ASSIGNMENTS = 'assignments' - ALL_SCORES = 'all_scores' def __init__(self, num_clusters, @@ -113,10 +112,7 @@ class GMM(estimator.Estimator): yield result[GMM.ASSIGNMENTS] def score(self, input_fn=None, batch_size=None, steps=None): - """Predict total sum of distances to nearest clusters. - - Note that this function is different from the corresponding one in sklearn - which returns the negative of the sum of distances. + """Predict total log-likelihood. Args: input_fn: see predict. @@ -124,11 +120,11 @@ class GMM(estimator.Estimator): steps: see predict. Returns: - Total sum of distances to nearest clusters. + Total log-likelihood. """ results = self.evaluate(input_fn=input_fn, batch_size=batch_size, steps=steps) - return np.sum(results[GMM.SCORES]) + return np.log(np.sum(np.exp(results[GMM.SCORES]))) def weights(self): """Returns the cluster weights.""" @@ -158,9 +154,10 @@ class GMM(estimator.Estimator): def _model_fn(features, labels, mode, config): """Model function.""" assert labels is None, labels - (all_scores, + (loss, + scores, model_predictions, - losses, training_op, + training_op, init_op, is_initialized) = gmm_ops.gmm(self._parse_tensor_or_dict(features), self._training_initial_clusters, @@ -168,16 +165,15 @@ class GMM(estimator.Estimator): self._covariance_type, self._params) incr_step = state_ops.assign_add(training_util.get_global_step(), 1) - loss = math_ops.reduce_sum(losses) training_op = with_dependencies([training_op, incr_step], loss) training_hooks = [_InitializeClustersHook( init_op, is_initialized, config.is_chief)] predictions = { - GMM.ALL_SCORES: all_scores[0], GMM.ASSIGNMENTS: model_predictions[0][0], } eval_metric_ops = { - GMM.SCORES: _streaming_sum(loss), + GMM.SCORES: scores, + GMM.LOG_LIKELIHOOD: _streaming_sum(loss), } return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions, eval_metric_ops=eval_metric_ops, diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops.py b/tensorflow/contrib/factorization/python/ops/gmm_ops.py index a61681c7f5a69a0fff1089404fc80b95c1c3106e..98d6434f4752b224201e38bed05ccd14428a758b 100644 --- a/tensorflow/contrib/factorization/python/ops/gmm_ops.py +++ b/tensorflow/contrib/factorization/python/ops/gmm_ops.py @@ -21,7 +21,6 @@ from __future__ import division from __future__ import print_function import numpy as np -from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -36,7 +35,6 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.embedding_ops import embedding_lookup -from tensorflow.python.summary import summary # Machine epsilon. MEPS = np.finfo(float).eps @@ -253,14 +251,16 @@ class GmmAlgorithm(object): return ret def scores(self): - """Returns the distances to each class. + """Returns the per-sample likelihood fo the data. Returns: - A tuple with two Tensors. The first contains the distance to - each class. The second contains the distance to the assigned - class. + Log probabilities of each data point. """ - return (self._all_scores, self._scores) + return self._scores + + def log_likelihood_op(self): + """Returns the log-likelihood operation.""" + return self._log_likelihood_op def _define_graph(self, data): """Define graph for a single iteration. @@ -276,7 +276,8 @@ class GmmAlgorithm(object): self._define_expectation_operation(shard_id) self._define_partial_maximization_operation(shard_id, shard) self._define_maximization_operation(len(data)) - self._define_distance_to_clusters(data) + self._define_loglikelihood_operation() + self._define_score_samples() def _define_full_covariance_probs(self, shard_id, shard): """Defines the full covariance probabilties per example in a class. @@ -440,50 +441,20 @@ class GmmAlgorithm(object): state_ops.assign( self._covs, new_covs, validate_shape=False)) - def _define_distance_to_clusters(self, data): - """Defines the Mahalanobis distance to the assigned Gaussian.""" - # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input - - # mean) from log probability function. - self._all_scores = [] - for shard in data: - all_scores = [] - shard = array_ops.expand_dims(shard, 0) - for c in xrange(self._num_classes): - if self._covariance_type == FULL_COVARIANCE: - cov = self._covs[c, :, :] - elif self._covariance_type == DIAG_COVARIANCE: - cov = array_ops.diag(self._covs[c, :]) - inverse = linalg_ops.matrix_inverse(cov + self._min_var) - inv_cov = array_ops.tile( - array_ops.expand_dims(inverse, 0), - array_ops.stack([self._num_examples, 1, 1])) - diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2]) - m_left = math_ops.matmul(diff, inv_cov) - all_scores.append( - math_ops.sqrt( - math_ops.matmul( - m_left, array_ops.transpose( - diff, perm=[0, 2, 1])))) - self._all_scores.append( - array_ops.reshape( - array_ops.concat(all_scores, 1), - array_ops.stack([self._num_examples, self._num_classes]))) - - # Distance to the associated class. - self._all_scores = array_ops.concat(self._all_scores, 0) - assignments = array_ops.concat(self.assignments(), 0) - rows = math_ops.to_int64(math_ops.range(0, self._num_examples)) - indices = array_ops.concat( - [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)], - 1) - self._scores = array_ops.gather_nd(self._all_scores, indices) - def _define_loglikelihood_operation(self): """Defines the total log-likelihood of current iteration.""" - self._ll_op = [] + op = [] for prior_probs in self._prior_probs: - self._ll_op.append(math_ops.reduce_sum(math_ops.log(prior_probs))) - summary.scalar('ll', math_ops.reduce_sum(self._ll_op)) + op.append(math_ops.reduce_logsumexp(prior_probs)) + self._log_likelihood_op = math_ops.reduce_logsumexp(op) + + def _define_score_samples(self): + """Defines the likelihood of each data sample.""" + op = [] + for shard_id, prior_probs in enumerate(self._prior_probs): + op.append(prior_probs + math_ops.log(self._w[shard_id])) + self._scores = array_ops.squeeze( + math_ops.reduce_logsumexp(op, axis=2, keep_dims=True), axis=0) def gmm(inp, @@ -511,14 +482,9 @@ def gmm(inp, Returns: Note: tuple of lists returned to be consistent with skflow A tuple consisting of: - all_scores: A matrix (or list of matrices) of dimensions (num_input, - num_clusters) where the value is the distance of an input vector and a - cluster center. assignments: A vector (or list of vectors). Each element in the vector corresponds to an input row in 'inp' and specifies the cluster id corresponding to the input. - scores: Similar to assignments but specifies the distance to the - assigned cluster instead. training_op: an op that runs an iteration of training. init_op: an op that runs the initialization. """ @@ -532,6 +498,7 @@ def gmm(inp, gmm_tool = GmmAlgorithm(inp, num_clusters, initial_means, params, covariance_type, random_seed) assignments = gmm_tool.assignments() - all_scores, scores = gmm_tool.scores() - return ([all_scores], [assignments], [scores], gmm_tool.training_ops(), + scores = gmm_tool.scores() + loss = gmm_tool.log_likelihood_op() + return (loss, scores, [assignments], gmm_tool.training_ops(), gmm_tool.init_ops(), gmm_tool.is_initialized()) diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py index c50e82db8a230012ba13c1d7ad7e28c23bd27355..888c3c238c2654ea11ea3bf8270d6c3fcd951a03 100644 --- a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py +++ b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py @@ -122,17 +122,23 @@ class GmmOpsTest(test.TestCase): g.seed = 5 with self.test_session() as sess: data = constant_op.constant(self.data, dtype=dtypes.float32) - _, assignments, _, training_op, init_op, _ = gmm_ops.gmm( + loss_op, scores, assignments, training_op, init_op, _ = gmm_ops.gmm( data, 'random', num_classes, random_seed=self.seed) variables.global_variables_initializer().run() sess.run(init_op) + first_loss = sess.run(loss_op) for _ in xrange(self.iterations): sess.run(training_op) assignments = sess.run(assignments) + end_loss = sess.run(loss_op) + scores = sess.run(scores) + self.assertEqual((self.num_examples, 1), scores.shape) accuracy = np.mean( np.asarray(self.true_assignments) == np.squeeze(assignments)) logging.info('Accuracy: %f', accuracy) + logging.info('First loss: %f, end loss: %f', first_loss, end_loss) + self.assertGreater(end_loss, first_loss) self.assertGreater(accuracy, 0.98) def testParams(self): diff --git a/tensorflow/contrib/factorization/python/ops/gmm_test.py b/tensorflow/contrib/factorization/python/ops/gmm_test.py index 7717b47daefce9ff65b1f1e84f671a463cf2e826..00a4734eb6d89cd02484f1c5161366377cc71208 100644 --- a/tensorflow/contrib/factorization/python/ops/gmm_test.py +++ b/tensorflow/contrib/factorization/python/ops/gmm_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import numpy as np -from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.factorization.python.ops import gmm as gmm_lib from tensorflow.contrib.learn.python.learn.estimators import kmeans @@ -30,12 +29,9 @@ from tensorflow.python.framework import random_seed as random_seed_lib from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import random_ops -from tensorflow.python.platform import flags from tensorflow.python.platform import test from tensorflow.python.training import queue_runner -FLAGS = flags.FLAGS - class GMMTest(test.TestCase): @@ -64,9 +60,8 @@ class GMMTest(test.TestCase): self.batch_size = self.num_points self.true_centers = self.make_random_centers(self.num_centers, self.num_dims) - self.points, self.assignments, self.scores = self.make_random_points( + self.points, self.assignments = self.make_random_points( self.true_centers, self.num_points) - self.true_score = np.add.reduce(self.scores) # Use initial means from kmeans (just like scikit-learn does). clusterer = kmeans.KMeansClustering(num_clusters=self.num_centers) @@ -86,24 +81,7 @@ class GMMTest(test.TestCase): offsets = np.round( np.random.randn(num_points, num_dims).astype(np.float32) * 20) points = centers[assignments] + offsets - means = [ - np.mean( - points[assignments == center], axis=0) - for center in xrange(num_centers) - ] - covs = [ - np.cov(points[assignments == center].T) - for center in xrange(num_centers) - ] - scores = [] - for r in xrange(num_points): - scores.append( - np.sqrt( - np.dot( - np.dot(points[r, :] - means[assignments[r]], - np.linalg.inv(covs[assignments[r]])), points[r, :] - - means[assignments[r]]))) - return (points, assignments, scores) + return (points, assignments) def test_weights(self): """Tests the shape of the weights.""" @@ -136,8 +114,7 @@ class GMMTest(test.TestCase): gmm.fit(input_fn=self.input_fn(), steps=10) score2 = gmm.score(input_fn=self.input_fn(batch_size=self.num_points), steps=1) - self.assertGreater(score1, score2) - self.assertNear(self.true_score, score2, self.true_score * 0.15) + self.assertLess(score1, score2) def test_infer(self): gmm = gmm_lib.GMM(self.num_centers, @@ -149,8 +126,7 @@ class GMMTest(test.TestCase): # Make a small test set num_points = 40 - points, true_assignments, true_offsets = ( - self.make_random_points(clusters, num_points)) + points, true_assignments = self.make_random_points(clusters, num_points) assignments = [] for item in gmm.predict_assignments( @@ -159,11 +135,6 @@ class GMMTest(test.TestCase): assignments = np.ravel(assignments) self.assertAllEqual(true_assignments, assignments) - # Test score - score = gmm.score(input_fn=self.input_fn(points=points, - batch_size=num_points), steps=1) - self.assertNear(score, np.sum(true_offsets), 4.05) - def _compare_with_sklearn(self, cov_type): # sklearn version. iterations = 40 diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index c861cfff544a78617aa1ace730b50c094cf16330..7319eaa7de8db8e4677bdf64af3b0a72c1007a90 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -61,8 +61,8 @@ class _LossRelativeChangeHook(session_run_hook.SessionRunHook): loss = run_values.results assert loss is not None if self._prev_loss: - relative_change = (abs(loss - self._prev_loss) / - (1 + abs(self._prev_loss))) + relative_change = ( + abs(loss - self._prev_loss) / (1 + abs(self._prev_loss))) if relative_change < self._tolerance: run_context.request_stop() self._prev_loss = loss @@ -233,7 +233,57 @@ class _ModelFn(object): # TODO(agarwal,ands): support sharded input. class KMeansClustering(estimator.Estimator): - """An Estimator for K-Means clustering.""" + """An Estimator for K-Means clustering. + + Example: + ``` + import numpy as np + import tensorflow as tf + + num_points = 100 + dimensions = 2 + points = np.random.uniform(0, 1000, [num_points, dimensions]) + + def input_fn(): + return tf.train.limit_epochs( + tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1) + + num_clusters = 5 + kmeans = tf.contrib.factorization.KMeansClustering( + num_clusters=num_clusters, use_mini_batch=False) + + # train + num_iterations = 10 + previous_centers = None + for _ in xrange(num_iterations): + kmeans.train(input_fn) + cluster_centers = kmeans.cluster_centers() + if previous_centers is not None: + print 'delta:', cluster_centers - previous_centers + previous_centers = cluster_centers + print 'score:', kmeans.score(input_fn) + print 'cluster centers:', cluster_centers + + # map the input points to their clusters + cluster_indices = list(kmeans.predict_cluster_index(input_fn)) + for i, point in enumerate(points): + cluster_index = cluster_indices[i] + center = cluster_centers[cluster_index] + print 'point:', point, 'is in cluster', cluster_index, 'centered at', center + ``` + + The `SavedModel` saved by the `export_savedmodel` method does not include the + cluster centers. However, the cluster centers may be retrieved by the + latest checkpoint saved during training. Specifically, + ``` + kmeans.cluster_centers() + ``` + is equivalent to + ``` + tf.train.load_variable( + kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME) + ``` + """ # Valid values for the distance_metric constructor argument. SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE @@ -253,6 +303,9 @@ class KMeansClustering(estimator.Estimator): CLUSTER_INDEX = 'cluster_index' ALL_DISTANCES = 'all_distances' + # Variable name used by cluster_centers(). + CLUSTER_CENTERS_VAR_NAME = clustering_ops.CLUSTERS_VAR_NAME + def __init__(self, num_clusters, model_dir=None, @@ -406,4 +459,4 @@ class KMeansClustering(estimator.Estimator): def cluster_centers(self): """Returns the cluster centers.""" - return self.get_variable_value(clustering_ops.CLUSTERS_VAR_NAME) + return self.get_variable_value(KMeansClustering.CLUSTER_CENTERS_VAR_NAME) diff --git a/tensorflow/contrib/feature_column/BUILD b/tensorflow/contrib/feature_column/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..8ba0823a71a5aa05ea276bdd7e7117658bee4351 --- /dev/null +++ b/tensorflow/contrib/feature_column/BUILD @@ -0,0 +1,66 @@ +package( + default_visibility = [ + "//tensorflow:internal", + ], +) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "feature_column_py", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":sequence_feature_column", + ], +) + +py_library( + name = "sequence_feature_column", + srcs = ["python/feature_column/sequence_feature_column.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:tensor_shape", + "//tensorflow/python:variable_scope", + "//tensorflow/python/feature_column", + ], +) + +py_test( + name = "sequence_feature_column_test", + srcs = ["python/feature_column/sequence_feature_column_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":sequence_feature_column", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:training", + "//tensorflow/python/feature_column", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/bayesflow/python/ops/halton_sequence.py b/tensorflow/contrib/feature_column/__init__.py similarity index 67% rename from tensorflow/contrib/bayesflow/python/ops/halton_sequence.py rename to tensorflow/contrib/feature_column/__init__.py index 49d747d538f5a4aa3134d28ba00a651cb509fa41..650a80144f2e2445d189bfd28a619aad1cfb13a7 100644 --- a/tensorflow/contrib/bayesflow/python/ops/halton_sequence.py +++ b/tensorflow/contrib/feature_column/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,22 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Support for low discrepancy Halton sequences. - -""" +"""Experimental utilities for tf.feature_column.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.halton_sequence_impl import * -# pylint: enable=wildcard-import +# pylint: disable=unused-import,line-too-long,wildcard-import +from tensorflow.contrib.feature_column.python.feature_column.sequence_feature_column import * + from tensorflow.python.util.all_util import remove_undocumented +# pylint: enable=unused-import,line-too-long,wildcard-import _allowed_symbols = [ - 'sample', ] -remove_undocumented(__name__, _allowed_symbols) +remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py new file mode 100644 index 0000000000000000000000000000000000000000..f57557c1ccdddb5a868611141cad3b12959e2f29 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -0,0 +1,624 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Experimental methods for tf.feature_column sequence input.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import abc +import collections + + +from tensorflow.python.feature_column import feature_column as fc +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.ops import variable_scope + +# pylint: disable=protected-access +# TODO(b/73827486): Support SequenceExample. + + +def sequence_input_layer( + features, + feature_columns, + weight_collections=None, + trainable=True): + """"Builds input layer for sequence input. + + All `feature_columns` must be sequence dense columns with the same + `sequence_length`. The output of this method can be fed into sequence + networks, such as RNN. + + The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ from + batch to batch. + + If multiple `feature_columns` are given with `Di` `num_elements` each, their + outputs are concatenated. So, the final `Tensor` has shape + `[batch_size, T, D0 + D1 + ... + Dn]`. + + Example: + + ```python + rating = sequence_numeric_column('rating') + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [rating, watches] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + features: A dict mapping keys to tensors. + feature_columns: An iterable of dense sequence columns. Valid columns are + - `embedding_column` that wraps a `sequence_categorical_column_with_*` + - `sequence_numeric_column`. + weight_collections: A list of collection names to which the Variable will be + added. Note that variables will also be added to collections + `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`. + trainable: If `True` also add the variable to the graph collection + `GraphKeys.TRAINABLE_VARIABLES`. + + Returns: + An `(input_layer, sequence_length)` tuple where: + - input_layer: A float `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ + from batch to batch. `D` is the sum of `num_elements` for all + `feature_columns`. + - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence + length for each example. + + Raises: + ValueError: If any of the `feature_columns` is the wrong type. + """ + feature_columns = fc._clean_feature_columns(feature_columns) + for c in feature_columns: + if not isinstance(c, _SequenceDenseColumn): + raise ValueError( + 'All feature_columns must be of type _SequenceDenseColumn. ' + 'Given (type {}): {}'.format(type(c), c)) + + with variable_scope.variable_scope( + None, default_name='sequence_input_layer', values=features.values()): + builder = fc._LazyBuilder(features) + output_tensors = [] + sequence_lengths = [] + ordered_columns = [] + for column in sorted(feature_columns, key=lambda x: x.name): + ordered_columns.append(column) + with variable_scope.variable_scope( + None, default_name=column._var_scope_name): + dense_tensor, sequence_length = column._get_sequence_dense_tensor( + builder, + weight_collections=weight_collections, + trainable=trainable) + # Flattens the final dimension to produce a 3D Tensor. + num_elements = column._variable_shape.num_elements() + shape = array_ops.shape(dense_tensor) + output_tensors.append( + array_ops.reshape( + dense_tensor, + shape=array_ops.concat([shape[:2], [num_elements]], axis=0))) + sequence_lengths.append(sequence_length) + fc._verify_static_batch_size_equality(output_tensors, ordered_columns) + fc._verify_static_batch_size_equality(sequence_lengths, ordered_columns) + sequence_length = _assert_all_equal_and_return(sequence_lengths) + return array_ops.concat(output_tensors, -1), sequence_length + + +def sequence_categorical_column_with_identity( + key, num_buckets, default_value=None): + """Returns a feature column that represents sequences of integers. + + Example: + + ```python + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [watches_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + num_buckets: Range of inputs. Namely, inputs are expected to be in the + range `[0, num_buckets)`. + default_value: If `None`, this column's graph operations will fail for + out-of-range inputs. Otherwise, this value must be in the range + `[0, num_buckets)`, and will replace out-of-range inputs. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return _SequenceCategoricalColumn( + fc.categorical_column_with_identity( + key=key, + num_buckets=num_buckets, + default_value=default_value)) + + +def sequence_categorical_column_with_hash_bucket( + key, hash_bucket_size, dtype=dtypes.string): + """A sequence of categorical terms where ids are set by hashing. + + Example: + + ```python + tokens = sequence_categorical_column_with_hash_bucket( + 'tokens', hash_bucket_size=1000) + tokens_embedding = embedding_column(tokens, dimension=10) + columns = [tokens_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + hash_bucket_size: An int > 1. The number of buckets. + dtype: The type of features. Only string and integer types are supported. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return _SequenceCategoricalColumn( + fc.categorical_column_with_hash_bucket( + key=key, + hash_bucket_size=hash_bucket_size, + dtype=dtype)) + + +def sequence_categorical_column_with_vocabulary_file( + key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0, + default_value=None, dtype=dtypes.string): + """A sequence of categorical terms where ids use a vocabulary file. + + Example: + + ```python + states = sequence_categorical_column_with_vocabulary_file( + key='states', vocabulary_file='/us/states.txt', vocabulary_size=50, + num_oov_buckets=5) + states_embedding = embedding_column(states, dimension=10) + columns = [states_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + vocabulary_file: The vocabulary file name. + vocabulary_size: Number of the elements in the vocabulary. This must be no + greater than length of `vocabulary_file`, if less than length, later + values are ignored. If None, it is set to the length of `vocabulary_file`. + num_oov_buckets: Non-negative integer, the number of out-of-vocabulary + buckets. All out-of-vocabulary inputs will be assigned IDs in the range + `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of + the input value. A positive `num_oov_buckets` can not be specified with + `default_value`. + default_value: The integer ID value to return for out-of-vocabulary feature + values, defaults to `-1`. This can not be specified with a positive + `num_oov_buckets`. + dtype: The type of features. Only string and integer types are supported. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return _SequenceCategoricalColumn( + fc.categorical_column_with_vocabulary_file( + key=key, + vocabulary_file=vocabulary_file, + vocabulary_size=vocabulary_size, + num_oov_buckets=num_oov_buckets, + default_value=default_value, + dtype=dtype)) + + +def sequence_categorical_column_with_vocabulary_list( + key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0): + """A sequence of categorical terms where ids use an in-memory list. + + Example: + + ```python + colors = sequence_categorical_column_with_vocabulary_list( + key='colors', vocabulary_list=('R', 'G', 'B', 'Y'), + num_oov_buckets=2) + colors_embedding = embedding_column(colors, dimension=3) + columns = [colors_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + vocabulary_list: An ordered iterable defining the vocabulary. Each feature + is mapped to the index of its value (if present) in `vocabulary_list`. + Must be castable to `dtype`. + dtype: The type of features. Only string and integer types are supported. + If `None`, it will be inferred from `vocabulary_list`. + default_value: The integer ID value to return for out-of-vocabulary feature + values, defaults to `-1`. This can not be specified with a positive + `num_oov_buckets`. + num_oov_buckets: Non-negative integer, the number of out-of-vocabulary + buckets. All out-of-vocabulary inputs will be assigned IDs in the range + `[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets)` based on a + hash of the input value. A positive `num_oov_buckets` can not be specified + with `default_value`. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return _SequenceCategoricalColumn( + fc.categorical_column_with_vocabulary_list( + key=key, + vocabulary_list=vocabulary_list, + dtype=dtype, + default_value=default_value, + num_oov_buckets=num_oov_buckets)) + + +# TODO(b/73160931): Merge with embedding_column +def _sequence_embedding_column( + categorical_column, dimension, initializer=None, ckpt_to_load_from=None, + tensor_name_in_ckpt=None, max_norm=None, trainable=True): + """Returns a feature column that represents sequences of embeddings. + + Use this to convert sequence categorical data into dense representation for + input to sequence NN, such as RNN. + + Example: + + ```python + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = _sequence_embedding_column(watches, dimension=10) + columns = [watches] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + categorical_column: A `_SequenceCategoricalColumn` created with a + `sequence_cateogrical_column_with_*` function. + dimension: Integer dimension of the embedding. + initializer: Initializer function used to initialize the embeddings. + ckpt_to_load_from: String representing checkpoint name/pattern from which to + restore column weights. Required if `tensor_name_in_ckpt` is not `None`. + tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from + which to restore the column weights. Required if `ckpt_to_load_from` is + not `None`. + max_norm: If not `None`, embedding values are l2-normalized to this value. + trainable: Whether or not the embedding is trainable. Default is True. + + Returns: + A `_SequenceCategoricalToDenseColumn`. + + Raises: + ValueError: If `categorical_column` is not the right type. + """ + if not isinstance(categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'categorical_column must be of type _SequenceCategoricalColumn. ' + 'Given (type {}): {}'.format( + type(categorical_column), categorical_column)) + return _SequenceCategoricalToDenseColumn( + fc.embedding_column( + categorical_column, + dimension=dimension, + initializer=initializer, + ckpt_to_load_from=ckpt_to_load_from, + tensor_name_in_ckpt=tensor_name_in_ckpt, + max_norm=max_norm, + trainable=trainable)) + + +# TODO(b/73160931): Merge with indicator_column +def _sequence_indicator_column(categorical_column): + """Returns a feature column that represents sequences of multi-hot tensors. + + Use this to convert sequence categorical data into dense representation for + input to sequence NN, such as RNN. + + Example: + + ```python + colors = sequence_categorical_column_with_vocabulary_list( + key='colors', vocabulary_list=('R', 'G', 'B', 'Y')) + colors_indicator = _sequence_indicator_column(colors) + columns = [colors] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + categorical_column: A `_SequenceCategoricalColumn` created with a + `sequence_cateogrical_column_with_*` function. + + Returns: + A `_SequenceCategoricalToDenseColumn`. + + Raises: + ValueError: If `categorical_column` is not the right type. + """ + if not isinstance(categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'categorical_column must be of type _SequenceCategoricalColumn. ' + 'Given (type {}): {}'.format( + type(categorical_column), categorical_column)) + return _SequenceCategoricalToDenseColumn( + fc.indicator_column(categorical_column)) + + +def sequence_numeric_column( + key, + shape=(1,), + default_value=0., + dtype=dtypes.float32): + """Returns a feature column that represents sequences of numeric data. + + Example: + + ```python + temperature = sequence_numeric_column('temperature') + columns = [temperature] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input features. + shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`, + each example must contain `2 * sequence_length` values. + default_value: A single value compatible with `dtype` that is used for + padding the sparse data into a dense `Tensor`. + dtype: The type of values. + + Returns: + A `_SequenceNumericColumn`. + """ + # TODO(b/73160931): Add validations. + return _SequenceNumericColumn( + key, + shape=shape, + default_value=default_value, + dtype=dtype) + + +def _assert_all_equal_and_return(tensors, name=None): + """Asserts that all tensors are equal and returns the first one.""" + with ops.name_scope(name, 'assert_all_equal', values=tensors): + if len(tensors) == 1: + return tensors[0] + assert_equal_ops = [] + for t in tensors[1:]: + assert_equal_ops.append(check_ops.assert_equal(tensors[0], t)) + with ops.control_dependencies(assert_equal_ops): + return array_ops.identity(tensors[0]) + + +class _SequenceDenseColumn(fc._FeatureColumn): + """Represents dense sequence data.""" + + __metaclass__ = abc.ABCMeta + + TensorSequenceLengthPair = collections.namedtuple( # pylint: disable=invalid-name + 'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length']) + + @abc.abstractproperty + def _variable_shape(self): + """`TensorShape` without batch and sequence dimensions.""" + pass + + @abc.abstractmethod + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + """Returns a `TensorSequenceLengthPair`.""" + pass + + +def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1): + with ops.name_scope(None, 'sequence_length') as name_scope: + row_ids = sp_tensor.indices[:, 0] + column_ids = sp_tensor.indices[:, 1] + column_ids += array_ops.ones_like(column_ids) + seq_length = math_ops.to_int64( + math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements) + # If the last n rows do not have ids, seq_length will have shape + # [batch_size - n]. Pad the remaining values with zeros. + n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1] + padding = array_ops.zeros(n_pad, dtype=seq_length.dtype) + return array_ops.concat([seq_length, padding], axis=0, name=name_scope) + + +class _SequenceCategoricalColumn( + fc._CategoricalColumn, + collections.namedtuple( + '_SequenceCategoricalColumn', ['categorical_column'])): + """Represents sequences of categorical data.""" + + @property + def name(self): + return self.categorical_column.name + + @property + def _parse_example_spec(self): + return self.categorical_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.categorical_column._transform_feature(inputs) + + @property + def _num_buckets(self): + return self.categorical_column._num_buckets + + def _get_sparse_tensors(self, inputs, weight_collections=None, + trainable=None): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + id_tensor = sparse_tensors.id_tensor + weight_tensor = sparse_tensors.weight_tensor + # Expands final dimension, so that embeddings are not combined during + # embedding lookup. + check_id_rank = check_ops.assert_equal( + array_ops.rank(id_tensor), 2, + data=[ + 'Column {} expected ID tensor of rank 2. '.format(self.name), + 'id_tensor shape: ', array_ops.shape(id_tensor)]) + with ops.control_dependencies([check_id_rank]): + id_tensor = sparse_ops.sparse_reshape( + id_tensor, + shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0)) + if weight_tensor is not None: + check_weight_rank = check_ops.assert_equal( + array_ops.rank(weight_tensor), 2, + data=[ + 'Column {} expected weight tensor of rank 2.'.format(self.name), + 'weight_tensor shape:', array_ops.shape(weight_tensor)]) + with ops.control_dependencies([check_weight_rank]): + weight_tensor = sparse_ops.sparse_reshape( + weight_tensor, + shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) + return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) + + def _sequence_length(self, inputs): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + return _sequence_length_from_sparse_tensor(sparse_tensors.id_tensor) + + +class _SequenceCategoricalToDenseColumn( + _SequenceDenseColumn, + collections.namedtuple( + '_SequenceCategoricalToDenseColumn', ['dense_column'])): + """Densifies a _SequenceCategoricalColumn using the specified column.""" + + @property + def name(self): + return self.dense_column.name + + @property + def _parse_example_spec(self): + return self.dense_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.dense_column._transform_feature(inputs) + + @property + def _variable_shape(self): + return self.dense_column._variable_shape + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + dense_tensor = self.dense_column._get_dense_tensor( + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sequence_length = self.dense_column.categorical_column._sequence_length( + inputs) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + + +class _SequenceNumericColumn( + _SequenceDenseColumn, + collections.namedtuple( + '_SequenceNumericColumn', + ['key', 'shape', 'default_value', 'dtype'])): + """Represents sequences of numeric data.""" + + @property + def name(self): + return self.key + + @property + def _parse_example_spec(self): + return {self.key: parsing_ops.VarLenFeature(self.dtype)} + + def _transform_feature(self, inputs): + return inputs.get(self.key) + + @property + def _variable_shape(self): + return tensor_shape.TensorShape(self.shape) + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + # Do nothing with weight_collections and trainable since no variables are + # created in this function. + del weight_collections + del trainable + sp_tensor = inputs.get(self) + dense_tensor = sparse_ops.sparse_tensor_to_dense( + sp_tensor, default_value=self.default_value) + # Reshape into [batch_size, T, variable_shape]. + dense_shape = array_ops.concat( + [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape], + axis=0) + dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape) + sequence_length = _sequence_length_from_sparse_tensor( + sp_tensor, num_elements=self._variable_shape.num_elements()) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + +# pylint: enable=protected-access diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c077f03291b811be8b715bb71f25850863389590 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -0,0 +1,774 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for sequential_feature_column.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import numpy as np + +from tensorflow.contrib.feature_column.python.feature_column import sequence_feature_column as sfc +from tensorflow.python.feature_column.feature_column import _LazyBuilder +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session + + +class SequenceInputLayerTest(test.TestCase): + + def test_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension_a = 2 + embedding_values_a = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + embedding_dimension_b = 3 + embedding_values_b = ( + (11., 12., 13.), # id 0 + (14., 15., 16.), # id 1 + (17., 18., 19.) # id 2 + ) + def _get_initializer(embedding_dimension, embedding_values): + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = sfc._sequence_embedding_column( + categorical_column_a, dimension=embedding_dimension_a, + initializer=_get_initializer(embedding_dimension_a, embedding_values_a)) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + embedding_column_b = sfc._sequence_embedding_column( + categorical_column_b, dimension=embedding_dimension_b, + initializer=_get_initializer(embedding_dimension_b, embedding_values_b)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[embedding_column_b, embedding_column_a]) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_embedding/embedding_weights:0', + 'sequence_input_layer/bbb_embedding/embedding_weights:0'), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess)) + self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_indicator_column(self): + vocabulary_size_a = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + vocabulary_size_b = 2 + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [1, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 1, 0), + dense_shape=(2, 2)) + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[0., 0., 1., 0., 1.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [1, 0] + [[1., 0., 0., 0., 1.], [0., 1., 0., 1., 0.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size_a) + indicator_column_a = sfc._sequence_indicator_column(categorical_column_a) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size_b) + indicator_column_b = sfc._sequence_indicator_column(categorical_column_b) + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[indicator_column_b, indicator_column_a]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_input_layer = [ + [[0.], [1.]], + [[10.], [0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column_multi_dim(self): + """Tests sequence_input_layer for multi-dimensional numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + # The output of numeric_column._get_dense_tensor should be flattened. + expected_input_layer = [ + [[0., 1., 2., 3.], [4., 5., 6., 7.]], + [[10., 11., 12., 13.], [0., 0., 0., 0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_not_equal(self): + """Tests that an error is raised when sequence lengths are not equal.""" + # Input a with sequence_length = [2, 1] + sparse_input_a = sparse_tensor.SparseTensorValue( + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + # Input b with sequence_length = [1, 1] + sparse_input_b = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0)), + values=(1., 10.), + dense_shape=(2, 2)) + numeric_column_a = sfc.sequence_numeric_column('aaa') + numeric_column_b = sfc.sequence_numeric_column('bbb') + + _, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + feature_columns=[numeric_column_a, numeric_column_b]) + + with monitored_session.MonitoredSession() as sess: + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'\[Condition x == y did not hold element-wise:\] ' + r'\[x \(sequence_input_layer/aaa/sequence_length:0\) = \] \[2 1\] ' + r'\[y \(sequence_input_layer/bbb/sequence_length:0\) = \] \[1 1\]'): + sess.run(sequence_length) + + +def _assert_sparse_tensor_value(test_case, expected, actual): + _assert_sparse_tensor_indices_shape(test_case, expected, actual) + + test_case.assertEqual( + np.array(expected.values).dtype, np.array(actual.values).dtype) + test_case.assertAllEqual(expected.values, actual.values) + + +def _assert_sparse_tensor_indices_shape(test_case, expected, actual): + test_case.assertEqual(np.int64, np.array(actual.indices).dtype) + test_case.assertAllEqual(expected.indices, actual.indices) + + test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype) + test_case.assertAllEqual(expected.dense_shape, actual.dense_shape) + + +class SequenceCategoricalColumnWithIdentityTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((1, 2, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_get_sparse_tensors_inputs3d(self): + """Tests _get_sparse_tensors when the input is already 3D Tensor.""" + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=(1, 2, 0), + dense_shape=(2, 2, 1)) + + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'Column aaa expected ID tensor of rank 2\.\s*' + r'id_tensor shape:\s*\[2 2 1\]'): + id_weight_pair = column._get_sparse_tensors( + _LazyBuilder({'aaa': inputs})) + with monitored_session.MonitoredSession() as sess: + id_weight_pair.id_tensor.eval(session=sess) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_zeros(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((1, 0), (3, 0), (3, 1)), + values=(1, 2, 0), + dense_shape=(5, 2)) + expected_sequence_length = [0, 1, 0, 2, 0] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceCategoricalColumnWithHashBucketTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_hash_bucket( + 'aaa', hash_bucket_size=10) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('omar', 'stringer', 'marlo'), + dense_shape=(2, 2)) + + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + # Ignored to avoid hash dependence in test. + values=np.array((0, 0, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_indices_shape( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_hash_bucket( + 'aaa', hash_bucket_size=10) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('omar', 'stringer', 'marlo'), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceCategoricalColumnWithVocabularyFileTest(test.TestCase): + + def _write_vocab(self, vocab_strings, file_name): + vocab_file = os.path.join(self.get_temp_dir(), file_name) + with open(vocab_file, 'w') as f: + f.write('\n'.join(vocab_strings)) + return vocab_file + + def setUp(self): + super(SequenceCategoricalColumnWithVocabularyFileTest, self).setUp() + + vocab_strings = ['omar', 'stringer', 'marlo'] + self._wire_vocabulary_file_name = self._write_vocab(vocab_strings, + 'wire_vocabulary.txt') + self._wire_vocabulary_size = 3 + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_vocabulary_file( + key='aaa', + vocabulary_file=self._wire_vocabulary_file_name, + vocabulary_size=self._wire_vocabulary_size) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((2, -1, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_vocabulary_file( + key='aaa', + vocabulary_file=self._wire_vocabulary_file_name, + vocabulary_size=self._wire_vocabulary_size) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceCategoricalColumnWithVocabularyListTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_vocabulary_list( + key='aaa', + vocabulary_list=('omar', 'stringer', 'marlo')) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((2, -1, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_vocabulary_list( + key='aaa', + vocabulary_list=('omar', 'stringer', 'marlo')) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + expected_lookups = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup, _ = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('embedding_weights:0',), tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceIndicatorColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + expected_lookups = [ + # example 0, ids [2] + [[0., 0., 1.], [0., 0., 0.]], + # example 1, ids [0, 1] + [[1., 0., 0.], [0., 1., 0.]], + # example 2, ids [] + [[0., 0., 0.], [0., 0., 0.]], + # example 3, ids [1] + [[0., 1., 0.], [0., 0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = sfc._sequence_indicator_column(categorical_column) + + indicator_tensor, _ = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_lookups, indicator_tensor.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = sfc._sequence_indicator_column(categorical_column) + + _, sequence_length = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = sfc._sequence_indicator_column(categorical_column) + + _, sequence_length = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceNumericColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_dense_tensor = [ + [[0.], [1.]], + [[10.], [0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa') + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_sequence_dense_tensor_with_shape(self): + """Tests get_sequence_dense_tensor with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_dense_tensor = [ + [[0., 1., 2.], [3., 4., 5.]], + [[10., 11., 12.], [0., 0., 0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_dense_tensor_multi_dim(self): + """Tests get_sequence_dense_tensor for multi-dim numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + expected_dense_tensor = [ + [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]], + [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_sequence_length(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_shape(self): + """Tests _sequence_length with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [] + # example 1, values [[0.], [1.]] + # example 2, [[2.]] + # example 3, values [] + # example 4, [[3.]] + # example 5, values [] + indices=((1, 0), (1, 1), (2, 0), (4, 0)), + values=(0., 1., 2., 3.), + dense_shape=(6, 2)) + expected_sequence_length = [0, 2, 1, 0, 1, 0] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py new file mode 100644 index 0000000000000000000000000000000000000000..4ed7268e7a921284eed7767d870e56ecac39a3b1 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py @@ -0,0 +1,325 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Experimental methods for tf.feature_column sequence input.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import abc +import collections + + +from tensorflow.python.feature_column import feature_column as fc +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.ops import variable_scope + +# TODO(b/73160931): Fix pydoc. +# pylint: disable=g-doc-args,missing-docstring,protected-access +# TODO(b/73827486): Support SequenceExample. + + +def sequence_input_layer( + features, + feature_columns, + weight_collections=None, + trainable=True, + scope=None): + """"Builds input layer for sequence input. + + All `feature_columns` must be sequence dense columns with the same + `sequence_length`. The output of this method can be fed into sequence + networks, such as RNN. + + The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ from + batch to batch. + + If multiple `feature_columns` are given with `Di` `num_elements` each, their + outputs are concatenated. So, the final `Tensor` has shape + `[batch_size, T, D0 + D1 + ... + Dn]`. + + Example: + + ```python + rating = sequence_numeric_column('rating') + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [rating, watches] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Returns: + An `(input_layer, sequence_length)` tuple where: + - input_layer: A float `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ + from batch to batch. `D` is the sum of `num_elements` for all + `feature_columns`. + - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence + length for each example. + Raises: + ValueError: If any of the `feature_columns` is the wrong type. + """ + feature_columns = fc._clean_feature_columns(feature_columns) + for c in feature_columns: + if not isinstance(c, _SequenceDenseColumn): + raise ValueError( + 'All feature_columns must be of type _SequenceDenseColumn. ' + 'Given (type {}): {}'.format(type(c), c)) + + with variable_scope.variable_scope( + scope, default_name='sequence_input_layer', values=features.values()): + builder = fc._LazyBuilder(features) + output_tensors = [] + sequence_lengths = [] + ordered_columns = [] + for column in sorted(feature_columns, key=lambda x: x.name): + ordered_columns.append(column) + with variable_scope.variable_scope( + None, default_name=column._var_scope_name): + dense_tensor, sequence_length = column._get_sequence_dense_tensor( + builder, + weight_collections=weight_collections, + trainable=trainable) + # Flattens the final dimension to produce a 3D Tensor. + num_elements = column._variable_shape.num_elements() + shape = array_ops.shape(dense_tensor) + output_tensors.append( + array_ops.reshape( + dense_tensor, + shape=array_ops.concat([shape[:2], [num_elements]], axis=0))) + sequence_lengths.append(sequence_length) + fc._verify_static_batch_size_equality(output_tensors, ordered_columns) + # TODO(b/73160931): Verify sequence_length equality. + return array_ops.concat(output_tensors, -1), sequence_lengths[0] + + +# TODO(b/73160931): Add remaining categorical columns. +def sequence_categorical_column_with_identity( + key, num_buckets, default_value=None): + return _SequenceCategoricalColumn( + fc.categorical_column_with_identity( + key=key, + num_buckets=num_buckets, + default_value=default_value)) + + +# TODO(b/73160931): Merge with embedding_column +def _sequence_embedding_column( + categorical_column, dimension, initializer=None, ckpt_to_load_from=None, + tensor_name_in_ckpt=None, max_norm=None, trainable=True): + if not isinstance(categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'categorical_column must be of type _SequenceCategoricalColumn. ' + 'Given (type {}): {}'.format( + type(categorical_column), categorical_column)) + return _SequenceEmbeddingColumn( + fc.embedding_column( + categorical_column, + dimension=dimension, + initializer=initializer, + ckpt_to_load_from=ckpt_to_load_from, + tensor_name_in_ckpt=tensor_name_in_ckpt, + max_norm=max_norm, + trainable=trainable)) + + +def sequence_numeric_column( + key, + shape=(1,), + default_value=0., + dtype=dtypes.float32): + # TODO(b/73160931): Add validations. + return _SequenceNumericColumn( + key, + shape=shape, + default_value=default_value, + dtype=dtype) + + +class _SequenceDenseColumn(fc._FeatureColumn): + """Represents dense sequence data.""" + + __metaclass__ = abc.ABCMeta + + TensorSequenceLengthPair = collections.namedtuple( # pylint: disable=invalid-name + 'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length']) + + @abc.abstractproperty + def _variable_shape(self): + """`TensorShape` without batch and sequence dimensions.""" + pass + + @abc.abstractmethod + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + """Returns a `TensorSequenceLengthPair`.""" + pass + + +def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1): + with ops.name_scope(None, 'sequence_length') as name_scope: + row_ids = sp_tensor.indices[:, 0] + column_ids = sp_tensor.indices[:, 1] + column_ids += array_ops.ones_like(column_ids) + seq_length = ( + math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements) + # If the last n rows do not have ids, seq_length will have shape + # [batch_size - n]. Pad the remaining values with zeros. + n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1] + padding = array_ops.zeros(n_pad, dtype=seq_length.dtype) + return array_ops.concat([seq_length, padding], axis=0, name=name_scope) + + +class _SequenceCategoricalColumn( + fc._CategoricalColumn, + collections.namedtuple( + '_SequenceCategoricalColumn', ['categorical_column'])): + + @property + def name(self): + return self.categorical_column.name + + @property + def _parse_example_spec(self): + return self.categorical_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.categorical_column._transform_feature(inputs) + + @property + def _num_buckets(self): + return self.categorical_column._num_buckets + + def _get_sparse_tensors(self, inputs, weight_collections=None, + trainable=None): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + id_tensor = sparse_tensors.id_tensor + weight_tensor = sparse_tensors.weight_tensor + # Expands final dimension, so that embeddings are not combined during + # embedding lookup. + check_id_rank = check_ops.assert_equal( + array_ops.rank(id_tensor), 2, + data=[ + 'Column {} expected ID tensor of rank 2. '.format(self.name), + 'id_tensor shape: ', array_ops.shape(id_tensor)]) + with ops.control_dependencies([check_id_rank]): + id_tensor = sparse_ops.sparse_reshape( + id_tensor, + shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0)) + if weight_tensor is not None: + check_weight_rank = check_ops.assert_equal( + array_ops.rank(weight_tensor), 2, + data=[ + 'Column {} expected weight tensor of rank 2.'.format(self.name), + 'weight_tensor shape:', array_ops.shape(weight_tensor)]) + with ops.control_dependencies([check_weight_rank]): + weight_tensor = sparse_ops.sparse_reshape( + weight_tensor, + shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) + return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) + + def _sequence_length(self, inputs): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + return _sequence_length_from_sparse_tensor(sparse_tensors.id_tensor) + + +class _SequenceEmbeddingColumn( + _SequenceDenseColumn, + collections.namedtuple('_SequenceEmbeddingColumn', ['embedding_column'])): + + @property + def name(self): + return self.embedding_column.name + + @property + def _parse_example_spec(self): + return self.embedding_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.embedding_column._transform_feature(inputs) + + @property + def _variable_shape(self): + return self.embedding_column._variable_shape + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + dense_tensor = self.embedding_column._get_dense_tensor( + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sequence_length = self.embedding_column.categorical_column._sequence_length( + inputs) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + + +class _SequenceNumericColumn( + _SequenceDenseColumn, + collections.namedtuple( + '_SequenceNumericColumn', + ['key', 'shape', 'default_value', 'dtype'])): + + @property + def name(self): + return self.key + + @property + def _parse_example_spec(self): + return {self.key: parsing_ops.VarLenFeature(self.dtype)} + + def _transform_feature(self, inputs): + return inputs.get(self.key) + + @property + def _variable_shape(self): + return tensor_shape.TensorShape(self.shape) + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + # Do nothing with weight_collections and trainable since no variables are + # created in this function. + del weight_collections + del trainable + sp_tensor = inputs.get(self) + dense_tensor = sparse_ops.sparse_tensor_to_dense( + sp_tensor, default_value=self.default_value) + # Reshape into [batch_size, T, variable_shape]. + dense_shape = array_ops.concat( + [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape], + axis=0) + dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape) + sequence_length = _sequence_length_from_sparse_tensor( + sp_tensor, num_elements=self._variable_shape.num_elements()) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + +# pylint: enable=g-doc-args,missing-docstring,protected-access diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py new file mode 100644 index 0000000000000000000000000000000000000000..59674869a27c3a40ab9cb3dcede384d1cda7ce27 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py @@ -0,0 +1,471 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for sequential_feature_column.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.feature_column.python.feature_column import sequential_feature_column as sfc +from tensorflow.python.feature_column.feature_column import _LazyBuilder +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session + + +class SequenceInputLayerTest(test.TestCase): + + def test_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension_a = 2 + embedding_values_a = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + embedding_dimension_b = 3 + embedding_values_b = ( + (11., 12., 13.), # id 0 + (14., 15., 16.), # id 1 + (17., 18., 19.) # id 2 + ) + def _get_initializer(embedding_dimension, embedding_values): + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = sfc._sequence_embedding_column( + categorical_column_a, dimension=embedding_dimension_a, + initializer=_get_initializer(embedding_dimension_a, embedding_values_a)) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + embedding_column_b = sfc._sequence_embedding_column( + categorical_column_b, dimension=embedding_dimension_b, + initializer=_get_initializer(embedding_dimension_b, embedding_values_b)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[embedding_column_b, embedding_column_a]) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_embedding/embedding_weights:0', + 'sequence_input_layer/bbb_embedding/embedding_weights:0'), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess)) + self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_input_layer = [ + [[0.], [1.]], + [[10.], [0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column_multi_dim(self): + """Tests sequence_input_layer for multi-dimensional numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + # The output of numeric_column._get_dense_tensor should be flattened. + expected_input_layer = [ + [[0., 1., 2., 3.], [4., 5., 6., 7.]], + [[10., 11., 12., 13.], [0., 0., 0., 0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +def _assert_sparse_tensor_value(test_case, expected, actual): + test_case.assertEqual(np.int64, np.array(actual.indices).dtype) + test_case.assertAllEqual(expected.indices, actual.indices) + + test_case.assertEqual( + np.array(expected.values).dtype, np.array(actual.values).dtype) + test_case.assertAllEqual(expected.values, actual.values) + + test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype) + test_case.assertAllEqual(expected.dense_shape, actual.dense_shape) + + +class SequenceCategoricalColumnWithIdentityTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((1, 2, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_get_sparse_tensors_inputs3d(self): + """Tests _get_sparse_tensors when the input is already 3D Tensor.""" + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=(1, 2, 0), + dense_shape=(2, 2, 1)) + + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'Column aaa expected ID tensor of rank 2\.\s*' + r'id_tensor shape:\s*\[2 2 1\]'): + id_weight_pair = column._get_sparse_tensors( + _LazyBuilder({'aaa': inputs})) + with monitored_session.MonitoredSession() as sess: + id_weight_pair.id_tensor.eval(session=sess) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_zeros(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((1, 0), (3, 0), (3, 1)), + values=(1, 2, 0), + dense_shape=(5, 2)) + expected_sequence_length = [0, 1, 0, 2, 0] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + expected_lookups = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup, _ = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('embedding_weights:0',), tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceNumericColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_dense_tensor = [ + [[0.], [1.]], + [[10.], [0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa') + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_sequence_dense_tensor_with_shape(self): + """Tests get_sequence_dense_tensor with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_dense_tensor = [ + [[0., 1., 2.], [3., 4., 5.]], + [[10., 11., 12.], [0., 0., 0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_dense_tensor_multi_dim(self): + """Tests get_sequence_dense_tensor for multi-dim numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + expected_dense_tensor = [ + [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]], + [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_sequence_length(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_shape(self): + """Tests _sequence_length with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [] + # example 1, values [[0.], [1.]] + # example 2, [[2.]] + # example 3, values [] + # example 4, [[3.]] + # example 5, values [] + indices=((1, 0), (1, 1), (2, 0), (4, 0)), + values=(0., 1., 2., 3.), + dense_shape=(6, 2)) + expected_sequence_length = [0, 2, 1, 0, 1, 0] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD index 9e5f54f0973eae899ca65e4098358107053cb7d4..ac043fda0638e61f422e769ab3047a53a1b377bd 100644 --- a/tensorflow/contrib/framework/BUILD +++ b/tensorflow/contrib/framework/BUILD @@ -28,7 +28,6 @@ tf_custom_op_py_library( "python/framework/graph_util.py", "python/framework/tensor_util.py", "python/ops/__init__.py", - "python/ops/accumulate_n_v2.py", "python/ops/arg_scope.py", "python/ops/audio_ops.py", "python/ops/checkpoint_ops.py", @@ -63,7 +62,9 @@ tf_custom_op_py_library( "//tensorflow/python:math_ops", "//tensorflow/python:platform", "//tensorflow/python:pywrap_tensorflow", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python:script_ops", + "//tensorflow/python:smart_cond", "//tensorflow/python:sparse_tensor", "//tensorflow/python:state_ops", "//tensorflow/python:state_ops_gen", @@ -161,23 +162,6 @@ py_test( ], ) -py_test( - name = "accumulate_n_v2_test", - size = "small", - srcs = ["python/ops/accumulate_n_v2_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":framework_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:platform_test", - "//tensorflow/python:variables", - "//third_party/py/numpy", - ], -) - cuda_py_test( name = "critical_section_test", size = "medium", @@ -185,31 +169,14 @@ cuda_py_test( additional_deps = [ "//tensorflow/python:client_testlib", ":framework_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:control_flow_ops", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:framework_test_lib", "//tensorflow/python:gradients", "//tensorflow/python:platform_test", "//tensorflow/python:resource_variable_ops", - ], -) - -py_test( - name = "accumulate_n_v2_eager_test", - size = "small", - srcs = ["python/ops/accumulate_n_v2_eager_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":framework_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:math_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python/eager:backprop", - "//tensorflow/python/eager:context", - "//tensorflow/python/eager:tape", - "//third_party/py/numpy", + "//tensorflow/python:tensor_array_ops", ], ) diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index fb101c36538f72d0665c41a625824eb0d66f48ce..21f96513182c79a22827eff3aaf26b6b308c329a 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -71,6 +71,7 @@ See the @{$python/contrib.framework} guide. @@model_variable @@variable @@VariableDeviceChooser +@@convolutional_delta_orthogonal @@zero_initializer @@load_checkpoint @@ -85,6 +86,11 @@ See the @{$python/contrib.framework} guide. @@py_func @@sort +@@get_placeholders + +@@smart_cond +@@smart_constant_value + @@CriticalSection @@BoundedTensorSpec @@ -102,10 +108,11 @@ from tensorflow.contrib.framework.python.ops import * from tensorflow.python.framework.ops import prepend_name_scope from tensorflow.python.framework.ops import strip_name_scope - +from tensorflow.python.framework.smart_cond import smart_cond +from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec - +from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = ['nest'] diff --git a/tensorflow/contrib/framework/python/framework/experimental_test.py b/tensorflow/contrib/framework/python/framework/experimental_test.py index 8e54e09e04ee3c0ddbd4fa84cc0912cb70c93e62..cfdc7df7d8fd4c1406bf447a79038ac33b11e047 100644 --- a/tensorflow/contrib/framework/python/framework/experimental_test.py +++ b/tensorflow/contrib/framework/python/framework/experimental_test.py @@ -49,7 +49,6 @@ class ExperimentalTest(test.TestCase): "\nTHIS FUNCTION IS EXPERIMENTAL. It may change or " "be removed at any time, and without warning." "\n" - "\n" "\nArgs:" "\n arg0: Arg 0." "\n arg1: Arg 1." diff --git a/tensorflow/contrib/framework/python/framework/graph_util.py b/tensorflow/contrib/framework/python/framework/graph_util.py index a18ff2320d99726bb355ff6179fc97a070c2fec7..2703224b1bf62831b6088558d4f93950fe938c10 100644 --- a/tensorflow/contrib/framework/python/framework/graph_util.py +++ b/tensorflow/contrib/framework/python/framework/graph_util.py @@ -85,14 +85,19 @@ def fuse_op(graph_def, input_nodes, output_nodes, output_dtypes, if n not in reachable_by_input and n not in output_nodes_set: # n is between input and output, i.e., part of the fused op next_to_visit = [n] + visited = set() while next_to_visit: cur_node = next_to_visit[0] + visited.add(cur_node) del next_to_visit[0] if cur_node in reachable_by_input and cur_node not in input_nodes_set: raise TypeError("Node %s uses input %s not in input_nodes." % (n, cur_node)) if cur_node not in input_nodes_set: - next_to_visit += name_to_input_name[cur_node] + next_to_visit += [ + input_node for input_node in name_to_input_name[cur_node] + if input_node not in visited + ] elif n not in reachable_by_input: nodes_post_output.append(n) @@ -133,6 +138,18 @@ def fuse_op(graph_def, input_nodes, output_nodes, output_dtypes, def get_placeholders(graph): """Get placeholders of a graph. + For example: + + ```python + a = tf.placeholder(dtype=tf.float32, shape=[2, 2], name='a') + a = tf.placeholder(dtype=tf.int32, shape=[3, 2], name='b') + + tf.contrib.framework.get_placeholders(tf.get_default_graph()) + # Returns: + # [, + # ] + ``` + Args: graph: A tf.Graph. Returns: diff --git a/tensorflow/contrib/framework/python/framework/graph_util_test.py b/tensorflow/contrib/framework/python/framework/graph_util_test.py index b8a6d109e19211d271c2b15bac66ddacd38fe395..812c5fbd8cb759aef6eb1aad532c03794b2ceaf4 100644 --- a/tensorflow/contrib/framework/python/framework/graph_util_test.py +++ b/tensorflow/contrib/framework/python/framework/graph_util_test.py @@ -42,7 +42,8 @@ class GraphUtilTest(test.TestCase): graph_def = graph_pb2.GraphDef() node_a = GetNewNode('A', 'Placeholder', []) node_b = GetNewNode('B', 'Op1', ['A']) - node_c = GetNewNode('C', 'Op1', ['B']) + # A loop in the part that will be fused. + node_c = GetNewNode('C', 'Op1', ['B', 'C']) node_d = GetNewNode('D', 'Op1', ['C']) node_e = GetNewNode('E', 'Op1', ['D']) graph_def.node.extend([node_a, node_b, node_c, node_d, node_e]) diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py deleted file mode 100644 index 2375ee4f550616ff60d20b87b5773704d8fbbe1e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Ops that will eventually be folded into tensorflow/python/ops/math_ops.py -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -from tensorflow.python.eager import context -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import gen_math_ops -from tensorflow.python.ops import math_ops - - - -def accumulate_n_v2(inputs, shape=None, tensor_dtype=None, name=None): - """Returns the element-wise sum of a list of tensors. - - Optionally, pass `shape` and `tensor_dtype` for shape and type checking, - otherwise, these are inferred. - - `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not - wait for all of its inputs to be ready before beginning to sum. This can - save memory if inputs are ready at different times, since minimum temporary - storage is proportional to the output size rather than the inputs size. - - Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable. - - For example: - - ```python - a = tf.constant([[1, 2], [3, 4]]) - b = tf.constant([[5, 0], [0, 6]]) - tf.accumulate_n_v2([a, b, a]) # [[7, 4], [6, 14]] - - # Explicitly pass shape and type - tf.accumulate_n_v2([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) - # [[7, 4], - # [6, 14]] - ``` - - Args: - inputs: A list of `Tensor` objects, each with same shape and type. - shape: Shape of elements of `inputs`. - tensor_dtype: The type of `inputs`. - name: A name for the operation (optional). - - Returns: - A `Tensor` of same shape and type as the elements of `inputs`. - - Raises: - ValueError: If `inputs` don't all have same shape and dtype or the shape - cannot be inferred. - """ - _INPUTS_ERR_MSG = ValueError("inputs must be a list of at least one Tensor" - "with the same dtype and shape") - if not inputs or not isinstance(inputs, (list, tuple)): - raise _INPUTS_ERR_MSG - inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs) - if not all(isinstance(x, ops.Tensor) for x in inputs): - raise _INPUTS_ERR_MSG - if not all(x.dtype == inputs[0].dtype for x in inputs): - raise _INPUTS_ERR_MSG - if shape is not None: - shape = tensor_shape.as_shape(shape) - else: - shape = tensor_shape.unknown_shape() - for input_tensor in inputs: - if isinstance(input_tensor, ops.Tensor): - shape = shape.merge_with(input_tensor.get_shape()) - - # tensor_dtype is for safety only; operator's output type computed in C++ - if tensor_dtype is not None and tensor_dtype != inputs[0].dtype: - raise TypeError("tensor_dtype is {}, but input is of type {}" - .format(tensor_dtype, inputs[0].dtype)) - - if len(inputs) == 1 and name is None: - return inputs[0] - elif len(inputs) == 1 and name is not None: - return array_ops.identity(inputs[0], name=name) - elif context.in_eager_mode(): - # TemporaryVariable not currently supported in eager mode; fall back - # onto AddN for now. - # TODO(frreiss) remove this once the lifetime of eager variables gets - # addressed - return math_ops.add_n(inputs, name=name) - else: - return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape) - -# The following code should eventually be merged into -# tensorflow/python/ops/math_grad.py -@ops.RegisterGradient("AccumulateNV2") -def _AddNGrad(op, grad): - """Same as gradient for AddN. Copies the gradient to all inputs.""" - # Not broadcasting. - return [grad] * len(op.inputs) - diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py index 182fec924febb74a23b82b1664d137f033f3b1b4..cc19372acf956371c2d029c7b8eb5534c3789413 100644 --- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py +++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py @@ -27,7 +27,11 @@ from tensorflow.python.eager import context from tensorflow.python.eager import function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_resource_variable_ops +from tensorflow.python.ops import tensor_array_ops from tensorflow.python.util import nest @@ -38,7 +42,8 @@ CRITICAL_SECTION_EXECUTIONS = "critical_section_executions" class _ExecutionSignature( collections.namedtuple("_ExecutionSignature", - ("op", "exclusive_resource_access"))): + ("op", "handle", + "resources", "exclusive_resource_access"))): """A class storing an `ExecuteInCriticalResource` op and associated attrs.""" pass @@ -112,16 +117,18 @@ class CriticalSection(object): ``` """ - def __init__(self, name=None, critical_section_def=None, import_scope=None): + def __init__(self, name=None, shared_name=None, + critical_section_def=None, import_scope=None): """Creates a critical section.""" if critical_section_def and name is not None: - raise ValueError("critical_section_def and name are mutually exclusive.") + raise ValueError("critical_section_def and shared_name are " + "mutually exclusive.") if critical_section_def: self._init_from_proto(critical_section_def, import_scope=import_scope) else: - self._init_from_args(name) + self._init_from_args(name, shared_name) - def _init_from_proto(self, critical_section_def, import_scope): + def _init_from_proto(self, critical_section_def, import_scope): # pylint: disable=invalid-name raise NotImplementedError("Not yet implemented") # TODO(ebrevdo): Re-enable once CriticalSection is in core. # assert isinstance( @@ -133,19 +140,21 @@ class CriticalSection(object): # critical_section_def.critical_section_name, # import_scope=import_scope)) - def _init_from_args(self, name): + def _init_from_args(self, name, shared_name): # pylint: disable=invalid-name """Initialize the CriticalSection from constructor arguments.""" with ops.name_scope(name, "CriticalSection", []) as name: - with ops.control_dependencies(None): + with ops.init_scope(): # pylint: disable=protected-access - handle_name = ops._name_from_scope_name(name) container = ops.get_default_graph()._container # pylint: enable=protected-access + if shared_name is None: + shared_name = name if container is None: container = "" - self._handle = gen_resource_variable_ops.critical_section_op( - shared_name=handle_name, name=name) - if context.in_graph_mode(): + self._handle = gen_resource_variable_ops.mutex_v2( + shared_name=shared_name, container=container, name=name) + + if not context.executing_eagerly(): ops.add_to_collections(CRITICAL_SECTIONS, self) @property @@ -183,68 +192,98 @@ class CriticalSection(object): name = kwargs.pop("name", None) exclusive_resource_access = kwargs.pop("exclusive_resource_access", True) - args = nest.map_structure(ops.convert_to_tensor, args) with ops.name_scope(name, "critical_section_execute", []): - fn_op = function.make_defun_op(fn, *args, **kwargs) - flat_dtypes = nest.flatten(fn_op.output_dtypes) - flat_shapes = nest.flatten(fn_op.output_shapes) - all_inputs = nest.flatten(args) + fn_op.captured_inputs - if self._handle in all_inputs: + lock = gen_resource_variable_ops.mutex_lock(self._handle) + + with ops.control_dependencies([lock]): + c_known_ops = set() + c_captured_tensors = set() + + def add_op_internal(op): + c_known_ops.add(op) + for i in op.inputs: + if i.op not in c_known_ops: + c_captured_tensors.add(i) + + c = function.HelperContext(add_op_internal) + with c: + r = fn(*args, **kwargs) + + resource_inputs = set([ + x for x in + list(nest.flatten(args)) + nest.flatten(kwargs.values()) + + list(c_captured_tensors) + if tensor_util.is_tensor(x) and x.dtype == dtypes.resource]) + + if self._handle in resource_inputs: raise ValueError("The function fn attempts to access the " - "CriticalSection in which it would be running. This " - "is illegal and would cause deadlocks. " + "CriticalSection in which it would be running. " + "This is illegal and would cause deadlocks. " "CriticalSection: %s." % self._handle) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Collections and op introspection does not work in eager # mode. This is generally ok; since eager mode (as of # writing) executes sequentially anyway. - all_input_resources = [ - x for x in all_inputs if x.dtype == dtypes.resource] for sg in ops.get_collection(CRITICAL_SECTION_EXECUTIONS): - if sg.op.inputs[0].name == self._handle.name: + sg_handle_name = ops.convert_to_tensor(sg.handle).name + self_handle_name = ops.convert_to_tensor(self._handle).name + if sg_handle_name == self_handle_name: # Other executions in the same critical section are allowed. continue if not (exclusive_resource_access or sg.exclusive_resource_access): # Neither execution requested exclusive access. continue - sg_input_names = [y.name for y in sg.op.inputs[1:]] - for res in all_input_resources: - if res.name in sg_input_names: - raise ValueError( - "This execution would access resource %s; but either this " - "execution (CriticalSection: %s) or Execution '%s' " - "(CriticalSection: %s) requested exclusive resource access " - "of this resource for their critical section. Did you mean " - "to call execute with keyword argument " - "exclusive_resource_access=False?" - % (res.name, - self.name, - sg.op.name, - sg.op.inputs[0].op.name)) - - flat_outputs = gen_resource_variable_ops.execute_in_critical_section( - critical_section=self._handle, - arguments=all_inputs, - f=fn_op, - output_types=flat_dtypes, - output_shapes=flat_shapes) - - if context.in_graph_mode(): - if isinstance(flat_outputs, ops.Operation): - flat_outputs = [flat_outputs] - op = (flat_outputs[0].op if isinstance(flat_outputs[0], ops.Tensor) - else flat_outputs[0]) + resource_intersection = resource_inputs.intersection(sg.resources) + if resource_intersection: + raise ValueError( + "This execution would access resources: %s. Either this " + "lock (CriticalSection: %s) or lock '%s' " + "(CriticalSection: %s) requested exclusive resource access " + "of this resource. Did you mean to call execute with keyword " + "argument exclusive_resource_access=False?" % + (list(resource_intersection), self._handle.name, + sg.op.name, sg.handle.name)) + + def identity(x): # pylint: disable=invalid-name + if isinstance(x, tensor_array_ops.TensorArray): + return x.identity() + elif isinstance(x, ops.Operation): + return control_flow_ops.group(x) + elif context.executing_eagerly() and x is None: + return None + else: + return array_ops.identity(x) + + r_flat = [identity(x) for x in nest.flatten(r)] + + with ops.control_dependencies(r_flat): + # The identity must run on the same machine as self._handle + with ops.colocate_with(self._handle): + # Do not use array_ops.identity as there are special + # optimizations within TensorFlow which seem to elide it + # even when optimizations are disabled(!). + ensure_lock_exists = gen_resource_variable_ops.consume_mutex_lock( + lock) + + # Make sure that if any element of r is accessed, all of + # them are executed together. + r = nest.pack_sequence_as( + r, control_flow_ops.tuple(nest.flatten(r))) + + with ops.control_dependencies([ensure_lock_exists]): + outputs = nest.map_structure(identity, r) + + if not context.executing_eagerly(): signature = _ExecutionSignature( - op=op, + op=lock.op, + handle=self._handle, + resources=list(resource_inputs), exclusive_resource_access=exclusive_resource_access) ops.add_to_collections( CRITICAL_SECTION_EXECUTIONS, signature) - return (flat_outputs[0] - if (len(flat_outputs) == 1 - and isinstance(flat_outputs[0], ops.Operation)) - else nest.pack_sequence_as(fn_op.output_dtypes, flat_outputs)) + return outputs # TODO(ebrevdo): Re-enable once CriticalSection is in core. @@ -276,6 +315,7 @@ class CriticalSection(object): # def _execution_to_proto_fn(execution_signature, export_scope=None): # """Converts `_ExecutionSignature` to a `CriticalSectionExecutionDef`. +# # TODO(ebrevdo): Update for _ExecutionSignature storing resource list. # Args: # execution_signature: Instance of `_ExecutionSignature`. @@ -298,6 +338,7 @@ class CriticalSection(object): # def _execution_from_proto_fn(op_def, import_scope=None): # """Converts a `CriticalSectionExecutionDef` to a `_ExecutionSignature`.""" +# # TODO(ebrevdo): Update for _ExecutionSignature storing resource list. # assert isinstance( # op_def, critical_section_pb2.CriticalSectionExecutionDef) diff --git a/tensorflow/contrib/framework/python/ops/critical_section_test.py b/tensorflow/contrib/framework/python/ops/critical_section_test.py index a416724d3ba1719471d70667e140f9cd2daf86c7..c916592ce1979fe3a79cf28ad4bdac44284cce97 100644 --- a/tensorflow/contrib/framework/python/ops/critical_section_test.py +++ b/tensorflow/contrib/framework/python/ops/critical_section_test.py @@ -19,12 +19,10 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.framework.python.ops import critical_section_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test # TODO(ebrevdo): Re-enable once CriticalSection is in core. @@ -35,7 +33,7 @@ class CriticalSectionTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testCreateCriticalSection(self): - cs = critical_section_ops.CriticalSection(name="cs") + cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") def fn(a, b): @@ -45,16 +43,72 @@ class CriticalSectionTest(test.TestCase): with ops.control_dependencies([nv]): return array_ops.identity(c) - num_concurrent = 1000 + num_concurrent = 100 r = [cs.execute(fn, 1.0, 2.0) for _ in range(num_concurrent)] self.evaluate(v.initializer) r_value = self.evaluate(r) self.assertAllClose([2.0 * i for i in range(num_concurrent)], sorted(r_value)) + @test_util.run_in_graph_and_eager_modes() + def testCriticalSectionWithControlFlow(self): + for outer_cond in [False, True]: + for inner_cond in [False, True]: + cs = critical_section_ops.CriticalSection(shared_name="cs") + v = resource_variable_ops.ResourceVariable(0.0, name="v") + num_concurrent = 100 + + # pylint: disable=cell-var-from-loop + def fn(a, b): + c = v.read_value() + def true_fn(): + with ops.control_dependencies([c]): + nv = v.assign_add(a * b) + with ops.control_dependencies([nv]): + return array_ops.identity(c) + return control_flow_ops.cond( + array_ops.identity(inner_cond), true_fn, lambda: c) + + def execute(): + return cs.execute(fn, 1.0, 2.0) + + r = [ + control_flow_ops.cond(array_ops.identity(outer_cond), + execute, + v.read_value) + for _ in range(num_concurrent) + ] + # pylint: enable=cell-var-from-loop + + self.evaluate(v.initializer) + r_value = self.evaluate(r) + if inner_cond and outer_cond: + self.assertAllClose([2.0 * i for i in range(num_concurrent)], + sorted(r_value)) + else: + self.assertAllClose([0] * num_concurrent, r_value) + + def testCriticalSectionInParallelDoesntDeadlockOnError(self): + # No eager mode execution of this test because eager does not + # run fn() in parallel, which is where the deadlock could + # potentially occur (in graph mode). + cs = critical_section_ops.CriticalSection(shared_name="cs") + v = resource_variable_ops.ResourceVariable(0.0, name="v") + + def fn(i): + error = control_flow_ops.Assert((i % 2) == 1, ["Error"]) + with ops.control_dependencies([error]): + return v.read_value() + num_concurrent = 2 + r = [cs.execute(fn, i) for i in range(num_concurrent)] + self.evaluate(v.initializer) + for _ in range(100): + with self.assertRaisesOpError("Error"): + self.evaluate(r) + @test_util.run_in_graph_and_eager_modes() def testCreateCriticalSectionFnReturnsOp(self): - cs = critical_section_ops.CriticalSection(name="cs") + cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") def fn_return_op(a, b): @@ -62,7 +116,7 @@ class CriticalSectionTest(test.TestCase): with ops.control_dependencies([c]): nv = v.assign_add(a * b) with ops.control_dependencies([nv]): - return () + return control_flow_ops.no_op() num_concurrent = 100 r = [cs.execute(fn_return_op, 1.0, 2.0) for _ in range(num_concurrent)] @@ -71,47 +125,25 @@ class CriticalSectionTest(test.TestCase): final_v = self.evaluate(v) self.assertAllClose(2.0 * num_concurrent, final_v) - def testCreateCriticalSectionRaw(self): - cs = critical_section_ops.CriticalSection(name="cs") - v = resource_variable_ops.ResourceVariable(0.0, name="v") - - @function.Defun(dtypes.float32, dtypes.float32) - def fn(a, b): - c = v.read_value() - with ops.control_dependencies([c]): - nv = v.assign_add(a * b) - with ops.control_dependencies([nv]): - return array_ops.identity(c) - - def execute(fn, *args): - output_args = fn.definition.signature.output_arg - return resource_variable_ops.execute_in_critical_section( - critical_section=cs._handle, - arguments=list(args) + fn.captured_inputs, - f=fn, - output_types=[out.type for out in output_args], - output_shapes=[tensor_shape.TensorShape(None) for _ in output_args]) - - num_concurrent = 1000 - r = [execute(fn, 1.0, 2.0)[0] for _ in range(num_concurrent)] - self.evaluate(v.initializer) - r_value = self.evaluate(r) - self.assertAllClose([2.0 * i for i in range(num_concurrent)], - sorted(r_value)) - def testCollection(self): - cs = critical_section_ops.CriticalSection(name="cs") + cs = critical_section_ops.CriticalSection(shared_name="cs") self.assertIn( cs, ops.get_collection(critical_section_ops.CRITICAL_SECTIONS)) - execute_op = cs.execute(lambda x: x + 1, 1.0).op + execute = cs.execute(lambda x: x + 1, 1.0, name="my_execute") + execute_op = [ + x for x in execute.graph.get_operations() + if "my_execute" in x.name and "MutexLock" in x.type + ][0] self.assertIn( execute_op, [signature.op for signature in ops.get_collection(critical_section_ops.CRITICAL_SECTION_EXECUTIONS)]) - @test_util.run_in_graph_and_eager_modes() def testRecursiveCriticalSectionAccessIsIllegal(self): - cs = critical_section_ops.CriticalSection(name="cs") + # This does not work properly in eager mode. Eager users will + # just hit a deadlock if they do this. But at least it'll be easier + # to debug. + cs = critical_section_ops.CriticalSection(shared_name="cs") def fn(x): return cs.execute(lambda x: x+1, x) with self.assertRaisesRegexp( @@ -167,7 +199,7 @@ class CriticalSectionTest(test.TestCase): # self.assertEqual(restored_exec[0].op.name, "imported/%s" % r.op.name) # def testToProto(self): - # cs = critical_section_ops.CriticalSection(name="cs") + # cs = critical_section_ops.CriticalSection(shared_name="cs") # proto = cs.to_proto() # self.assertEqual(proto.critical_section_name, cs._handle.name) # cs_copy = critical_section_ops.CriticalSection.from_proto(proto) diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index 3f1ece4510578b5ac39849c577fffbb2a3be45a7..0754c3e0e30a340910a43a3ce86f6ca10afe848e 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -25,6 +25,7 @@ import re from tensorflow.contrib.framework.python.ops import add_arg_scope as contrib_add_arg_scope from tensorflow.contrib.framework.python.ops import gen_variable_ops from tensorflow.contrib.util import loader +from tensorflow.core.protobuf import saver_pb2 from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import dtypes @@ -32,9 +33,8 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import variable_scope -from tensorflow.python.ops import gen_state_ops -from tensorflow.python.platform import tf_logging as logging from tensorflow.python.platform import resource_loader +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver from tensorflow.python.training import training_util from tensorflow.python.util.deprecation import deprecated @@ -685,7 +685,8 @@ def assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=False, 'Variable %s missing in checkpoint %s', var, model_path) var_list = available_vars if var_list: - saver = tf_saver.Saver(var_list, reshape=reshape_variables) + saver = tf_saver.Saver(var_list, reshape=reshape_variables, + write_version=saver_pb2.SaverDef.V1) def callback(session): saver.restore(session, model_path) return callback diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index 5db34f0f8db93620b8b4a6b71f63b66ac718ee30..ff6f3b744190c9a7c74fb88878e5f13412251e79 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -55,6 +55,7 @@ py_test( name = "train_test", srcs = ["python/train_test.py"], srcs_version = "PY2AND3", + tags = ["notsan"], deps = [ ":features", ":namedtuples", @@ -353,6 +354,7 @@ py_test( name = "classifier_metrics_test", srcs = ["python/eval/python/classifier_metrics_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":classifier_metrics", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py index d9b07e62f89d61c72a34dfa844f11ad1238fb006..fdfabd07c13f689d075ecbb8786d725fa8a62d01 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py @@ -202,10 +202,13 @@ def get_graph_def_from_url_tarball(url, filename, tar_filename=None): A GraphDef loaded from a file in the downloaded tarball. """ if not (tar_filename and os.path.exists(tar_filename)): + def _progress(count, block_size, total_size): - sys.stdout.write('\r>> Downloading %s %.1f%%' % ( - url, float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.write('\r>> Downloading %s %.1f%%' % + (url, + float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() + tar_filename, _ = urllib.request.urlretrieve(url, tar_filename, _progress) with tarfile.open(tar_filename, 'r:gz') as tar: proto_str = tar.extractfile(filename).read() diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_test.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_test.py index 1e18c699ba93b5f524341c65d0a2db84556b65a2..61dc8646ddc10605561ae6b19e90f4739c346608 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_test.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_test.py @@ -181,7 +181,8 @@ class ClassifierMetricsTest(test.TestCase): batch_size = 3 img = array_ops.ones([batch_size, 299, 299, 3]) pool = _run_with_mock( - classifier_metrics.run_inception, img, + classifier_metrics.run_inception, + img, output_tensor=classifier_metrics.INCEPTION_FINAL_POOL) self.assertTrue(isinstance(pool, ops.Tensor)) @@ -195,9 +196,12 @@ class ClassifierMetricsTest(test.TestCase): batch_size = 3 img = array_ops.ones([batch_size, 299, 299, 3]) logits, pool = _run_with_mock( - classifier_metrics.run_inception, img, - output_tensor=[classifier_metrics.INCEPTION_OUTPUT, - classifier_metrics.INCEPTION_FINAL_POOL]) + classifier_metrics.run_inception, + img, + output_tensor=[ + classifier_metrics.INCEPTION_OUTPUT, + classifier_metrics.INCEPTION_FINAL_POOL + ]) self.assertTrue(isinstance(logits, ops.Tensor)) self.assertTrue(isinstance(pool, ops.Tensor)) @@ -209,8 +213,10 @@ class ClassifierMetricsTest(test.TestCase): def test_inception_score_graph(self): """Test `inception_score` graph construction.""" - score = _run_with_mock(classifier_metrics.inception_score, - array_ops.zeros([6, 299, 299, 3]), num_batches=3) + score = _run_with_mock( + classifier_metrics.inception_score, + array_ops.zeros([6, 299, 299, 3]), + num_batches=3) self.assertTrue(isinstance(score, ops.Tensor)) score.shape.assert_has_rank(0) @@ -248,12 +254,14 @@ class ClassifierMetricsTest(test.TestCase): array_ops.zeros([8, 10], dtype=dtypes.int32), p_logits, q) with self.assertRaisesRegexp(ValueError, 'must be floating type'): - classifier_metrics._kl_divergence( - p, array_ops.zeros([8, 10], dtype=dtypes.int32), q) + classifier_metrics._kl_divergence(p, + array_ops.zeros( + [8, 10], dtype=dtypes.int32), q) with self.assertRaisesRegexp(ValueError, 'must be floating type'): - classifier_metrics._kl_divergence( - p, p_logits, array_ops.zeros([10], dtype=dtypes.int32)) + classifier_metrics._kl_divergence(p, p_logits, + array_ops.zeros( + [10], dtype=dtypes.int32)) with self.assertRaisesRegexp(ValueError, 'must have rank 2'): classifier_metrics._kl_divergence(array_ops.zeros([8]), p_logits, q) @@ -266,8 +274,9 @@ class ClassifierMetricsTest(test.TestCase): def test_inception_score_value(self): """Test that `inception_score` gives the correct value.""" - logits = np.array([np.array([1, 2] * 500 + [4]), - np.array([4, 5] * 500 + [6])]) + logits = np.array( + [np.array([1, 2] * 500 + [4]), + np.array([4, 5] * 500 + [6])]) unused_image = array_ops.zeros([2, 299, 299, 3]) incscore = _run_with_mock(classifier_metrics.inception_score, unused_image) @@ -285,9 +294,11 @@ class ClassifierMetricsTest(test.TestCase): test_pool_real_a = np.float32(np.random.randn(512, 256)) test_pool_gen_a = np.float32(np.random.randn(512, 256)) - fid_op = _run_with_mock(classifier_metrics.frechet_classifier_distance, - test_pool_real_a, test_pool_gen_a, - classifier_fn=lambda x: x) + fid_op = _run_with_mock( + classifier_metrics.frechet_classifier_distance, + test_pool_real_a, + test_pool_gen_a, + classifier_fn=lambda x: x) with self.test_session() as sess: actual_fid = sess.run(fid_op) @@ -296,6 +307,33 @@ class ClassifierMetricsTest(test.TestCase): self.assertAllClose(expected_fid, actual_fid, 0.0001) + def test_frechet_classifier_distance_covariance(self): + """Test that `frechet_classifier_distance` takes covariance into account.""" + np.random.seed(0) + + # Make num_examples > num_features to ensure scipy's sqrtm function + # doesn't return a complex matrix. + test_pool_reals, test_pool_gens = [], [] + for i in range(1, 11, 2): + test_pool_reals.append(np.float32(np.random.randn(2048, 256) * i)) + test_pool_gens.append(np.float32(np.random.randn(2048, 256) * i)) + + fid_ops = [] + for i in range(len(test_pool_reals)): + fid_ops.append(_run_with_mock( + classifier_metrics.frechet_classifier_distance, + test_pool_reals[i], + test_pool_gens[i], + classifier_fn=lambda x: x)) + + fids = [] + with self.test_session() as sess: + for fid_op in fid_ops: + fids.append(sess.run(fid_op)) + + # Check that the FIDs increase monotonically. + self.assertTrue(all(fid_a < fid_b for fid_a, fid_b in zip(fids, fids[1:]))) + def test_trace_sqrt_product_value(self): """Test that `trace_sqrt_product` gives the correct value.""" np.random.seed(0) diff --git a/tensorflow/contrib/gan/python/eval/python/summaries_impl.py b/tensorflow/contrib/gan/python/eval/python/summaries_impl.py index 74811ff4096eb5215148f0565bf094b83408014c..0d1afad72da8a8e087239868e25ddebe23490d1e 100644 --- a/tensorflow/contrib/gan/python/eval/python/summaries_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/summaries_impl.py @@ -39,12 +39,13 @@ def _assert_is_image(data): data.shape[1:].assert_is_fully_defined() -def add_gan_model_image_summaries(gan_model, grid_size=4): +def add_gan_model_image_summaries(gan_model, grid_size=4, model_summaries=True): """Adds image summaries for real and fake images. Args: gan_model: A GANModel tuple. grid_size: The size of an image grid. + model_summaries: Also add summaries of the model. Raises: ValueError: If real and generated data aren't images. @@ -83,7 +84,9 @@ def add_gan_model_image_summaries(gan_model, grid_size=4): image_shape=generated_image_shape, num_channels=generated_channels), max_outputs=1) - add_gan_model_summaries(gan_model) + + if model_summaries: + add_gan_model_summaries(gan_model) def add_image_comparison_summaries(gan_model, num_comparisons=2, diff --git a/tensorflow/contrib/gan/python/eval/python/summaries_test.py b/tensorflow/contrib/gan/python/eval/python/summaries_test.py index a02d8772e130a2a927735e56c4272aba1f1a6996..45eb108586bed07434ac29595164745eac6054c1 100644 --- a/tensorflow/contrib/gan/python/eval/python/summaries_test.py +++ b/tensorflow/contrib/gan/python/eval/python/summaries_test.py @@ -72,8 +72,10 @@ def get_cyclegan_model(): class SummariesTest(test.TestCase): def _test_add_gan_model_image_summaries_impl(self, get_model_fn, - expected_num_summary_ops): - summaries.add_gan_model_image_summaries(get_model_fn(), grid_size=2) + expected_num_summary_ops, + model_summaries): + summaries.add_gan_model_image_summaries(get_model_fn(), grid_size=2, + model_summaries=model_summaries) self.assertEquals(expected_num_summary_ops, len(ops.get_collection(ops.GraphKeys.SUMMARIES))) @@ -82,10 +84,13 @@ class SummariesTest(test.TestCase): summary.merge_all().eval() def test_add_gan_model_image_summaries(self): - self._test_add_gan_model_image_summaries_impl(get_gan_model, 5) + self._test_add_gan_model_image_summaries_impl(get_gan_model, 5, True) + + def test_add_gan_model_image_summaries_no_model(self): + self._test_add_gan_model_image_summaries_impl(get_gan_model, 2, False) def test_add_gan_model_image_summaries_for_cyclegan(self): - self._test_add_gan_model_image_summaries_impl(get_cyclegan_model, 10) + self._test_add_gan_model_image_summaries_impl(get_cyclegan_model, 10, True) def _test_add_gan_model_summaries_impl(self, get_model_fn, expected_num_summary_ops): diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl.py b/tensorflow/contrib/gan/python/losses/python/losses_impl.py index 23a3b60cc0055917bfc5243b0ebdbaea7b61edb9..39588b7219ebac1cc4855532be3fcc38e6381134 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl.py @@ -305,6 +305,7 @@ def wasserstein_gradient_penalty( discriminator_fn, discriminator_scope, epsilon=1e-10, + target=1.0, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, @@ -324,6 +325,8 @@ def wasserstein_gradient_penalty( discriminator_scope: If not `None`, reuse discriminators from this scope. epsilon: A small positive number added for numerical stability when computing the gradient norm. + target: Optional Python number or `Tensor` indicating the target value of + gradient norm. Defaults to 1.0. weights: Optional `Tensor` whose rank is either 0, or the same rank as `real_data` and `generated_data`, and must be broadcastable to them (i.e., all dimensions must be either `1`, or the same as the @@ -374,7 +377,7 @@ def wasserstein_gradient_penalty( # For numerical stability, add epsilon to the sum before taking the square # root. Note tf.norm does not add epsilon. slopes = math_ops.sqrt(gradient_squares + epsilon) - penalties = math_ops.square(slopes - 1.0) + penalties = math_ops.square(slopes / target - 1.0) penalty = losses.compute_weighted_loss( penalties, weights, scope=scope, loss_collection=loss_collection, reduction=reduction) diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py index 56ac45554da3633149a61155a416fa7cb6cff553..dbaa624ae9d6a5a5949db692e52c0c1deb18b8df 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py @@ -481,6 +481,29 @@ class GradientPenaltyTest(test.TestCase, _PenaltyTest): }) self.assertAlmostEqual(self._expected_loss, loss, 5) + def test_loss_with_gradient_norm_target(self): + """Test loss value with non default gradient norm target.""" + generated_data = array_ops.placeholder(dtypes.float32, shape=(None, None)) + real_data = array_ops.placeholder(dtypes.float32, shape=(None, None)) + + loss = tfgan_losses.wasserstein_gradient_penalty( + generated_data, + real_data, + self._kwargs['generator_inputs'], + self._kwargs['discriminator_fn'], + self._kwargs['discriminator_scope'], + target=2.0) + + with self.test_session() as sess: + variables.global_variables_initializer().run() + loss = sess.run( + loss, + feed_dict={ + generated_data: self._generated_data_np, + real_data: self._real_data_np, + }) + self.assertAlmostEqual(1.0, loss, 5) + def test_reuses_scope(self): """Test that gradient penalty reuses discriminator scope.""" num_vars = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) diff --git a/tensorflow/contrib/gan/python/train.py b/tensorflow/contrib/gan/python/train.py index 5d0ac93aec7869bb1d9b8a174ba50d4bec2c2826..776eb11ecb1624544d24611d8fe6ca19768b8313 100644 --- a/tensorflow/contrib/gan/python/train.py +++ b/tensorflow/contrib/gan/python/train.py @@ -460,6 +460,7 @@ def gan_loss( # Auxiliary losses. gradient_penalty_weight=None, gradient_penalty_epsilon=1e-10, + gradient_penalty_target=1.0, mutual_information_penalty_weight=None, aux_cond_generator_weight=None, aux_cond_discriminator_weight=None, @@ -481,6 +482,9 @@ def gan_loss( small positive value used by the gradient penalty function for numerical stability. Note some applications will need to increase this value to avoid NaNs. + gradient_penalty_target: If `gradient_penalty_weight` is not None, a Python + number or `Tensor` indicating the target value of gradient norm. See the + CIFAR10 section of https://arxiv.org/abs/1710.10196. Defaults to 1.0. mutual_information_penalty_weight: If not `None`, must be a non-negative Python number or Tensor indicating how much to weight the mutual information penalty. See https://arxiv.org/abs/1606.03657 for more @@ -539,7 +543,10 @@ def gan_loss( # Add optional extra losses. if _use_aux_loss(gradient_penalty_weight): gp_loss = tfgan_losses.wasserstein_gradient_penalty( - model, epsilon=gradient_penalty_epsilon, add_summaries=add_summaries) + model, + epsilon=gradient_penalty_epsilon, + target=gradient_penalty_target, + add_summaries=add_summaries) dis_loss += gradient_penalty_weight * gp_loss if _use_aux_loss(mutual_information_penalty_weight): info_loss = tfgan_losses.mutual_information_penalty( diff --git a/tensorflow/contrib/graph_editor/reroute.py b/tensorflow/contrib/graph_editor/reroute.py index 7ffdbb7139281734917fdb715601b317eb58b82f..95c02a64d47c26e731ef2628fb551529e9bc3f4d 100644 --- a/tensorflow/contrib/graph_editor/reroute.py +++ b/tensorflow/contrib/graph_editor/reroute.py @@ -471,9 +471,10 @@ def remove_control_inputs(op, cops): if cop not in op.control_inputs: raise ValueError("{} is not a control_input of {}".format(op.name, cop.name)) + control_inputs = [cop for cop in op.control_inputs if cop not in cops] # pylint: disable=protected-access - op._control_inputs = [cop for cop in op._control_inputs if cop not in cops] - op._recompute_node_def() + op._remove_all_control_inputs() + op._add_control_inputs(control_inputs) # pylint: enable=protected-access @@ -496,9 +497,6 @@ def add_control_inputs(op, cops): if cop in op.control_inputs: raise ValueError("{} is already a control_input of {}".format(cop.name, op.name)) - # pylint: disable=protected-access - op._control_inputs += cops - op._recompute_node_def() - # pylint: enable=protected-access + op._add_control_inputs(cops) # pylint: disable=protected-access remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/hvx/README.md b/tensorflow/contrib/hvx/README.md index cb3a1087de5b83e7737e0dac2c04fbe152a9d676..163993a3f6bb1bedcdffb32944a98c7cc846878e 100644 --- a/tensorflow/contrib/hvx/README.md +++ b/tensorflow/contrib/hvx/README.md @@ -141,16 +141,16 @@ Configuring the installer for this system's environment... Launching installer... -An internal LaunchAnywhere application error has occured and this application cannot proceed. (LAX) +An internal LaunchAnywhere application error has occurred and this application cannot proceed. (LAX) Stack Trace: java.lang.IllegalArgumentException: Malformed \uxxxx encoding. - at java.util.Properties.loadConvert(Properties.java:574) - at java.util.Properties.load0(Properties.java:391) - at java.util.Properties.load(Properties.java:317) - at com.zerog.common.java.util.PropertiesUtil.loadProperties(Unknown Source) - at com.zerog.lax.LAX.(Unknown Source) - at com.zerog.lax.LAX.main(Unknown Source) + at java.util.Properties.loadConvert(Properties.java:574) + at java.util.Properties.load0(Properties.java:391) + at java.util.Properties.load(Properties.java:317) + at com.zerog.common.java.util.PropertiesUtil.loadProperties(Unknown Source) + at com.zerog.lax.LAX.(Unknown Source) + at com.zerog.lax.LAX.main(Unknown Source) ``` It can be solved by temporarily assigning the `PS1` environment variable to something simple, such as '$'. diff --git a/tensorflow/contrib/image/kernels/bipartite_match_op.cc b/tensorflow/contrib/image/kernels/bipartite_match_op.cc index 7d207c388b159c4ad0f25032811e97b153fd50d6..726adb07775e3243fdc96a7f1a00dbb0304d3dd9 100644 --- a/tensorflow/contrib/image/kernels/bipartite_match_op.cc +++ b/tensorflow/contrib/image/kernels/bipartite_match_op.cc @@ -85,7 +85,7 @@ class BipartiteMatchOp : public OpKernel { context->allocate_output(1, TensorShape({num_input_columns}), &column_to_row_match_indices)); - typename TTypes::ConstTensor distance_mat = + TTypes::ConstTensor distance_mat = input_distance_mat.shaped( {num_input_rows, num_input_columns}); diff --git a/tensorflow/contrib/image/python/kernel_tests/single_image_random_dot_stereograms_ops_test.py b/tensorflow/contrib/image/python/kernel_tests/single_image_random_dot_stereograms_ops_test.py index bf0c97245fc5c70469350ec66023f4d1474930e2..3f4029e558d92a2b6539456bf9cf49ec2d21c9f3 100644 --- a/tensorflow/contrib/image/python/kernel_tests/single_image_random_dot_stereograms_ops_test.py +++ b/tensorflow/contrib/image/python/kernel_tests/single_image_random_dot_stereograms_ops_test.py @@ -18,13 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - -from six.moves import xrange # pylint: disable=redefined-builtin - from tensorflow.contrib.image.python.ops.single_image_random_dot_stereograms \ import single_image_random_dot_stereograms -from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.platform import googletest diff --git a/tensorflow/contrib/kafka/BUILD b/tensorflow/contrib/kafka/BUILD index f7593aa462c4ca86d0ffc4f065e1aae849364561..1c3974871c62911c0cb47677eb92d28286837142 100644 --- a/tensorflow/contrib/kafka/BUILD +++ b/tensorflow/contrib/kafka/BUILD @@ -1,66 +1,93 @@ -package( - default_visibility = ["//visibility:private"], -) +package(default_visibility = ["//tensorflow:internal"]) licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs") -load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") -load("//tensorflow:tensorflow.bzl", "tf_kernel_library") -load("//tensorflow:tensorflow.bzl", "tf_py_test") +load( + "//tensorflow:tensorflow.bzl", + "tf_gen_op_wrapper_py", + "tf_kernel_library", + "tf_custom_op_library", + "tf_custom_op_py_library", + "tf_gen_op_libs", + "tf_py_test", +) -tf_kernel_library( - name = "kafka_kernels", +py_library( + name = "kafka", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_ops", + ], +) + +tf_custom_op_library( + name = "_dataset_ops.so", + srcs = ["ops/dataset_ops.cc"], + deps = [":dataset_kernels"], +) + +tf_gen_op_libs( + op_lib_names = ["dataset_ops"], +) + +cc_library( + name = "dataset_kernels", srcs = ["kernels/kafka_dataset_ops.cc"], - visibility = ["//visibility:public"], deps = [ - "//tensorflow/core:framework", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core/kernels:bounds_check_lib", - "//tensorflow/core/kernels:dataset", + "//tensorflow/core:framework_headers_lib", "//third_party/eigen3", - "@kafka//:kafka", + "@kafka", + "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) -tf_gen_op_libs( - op_lib_names = ["kafka_ops"], +py_library( + name = "dataset_ops", + srcs = [ + "python/ops/kafka_dataset_ops.py", + ], + srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:lib", + ":kafka_op_loader", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/util:nest", ], ) tf_gen_op_wrapper_py( - name = "gen_kafka_ops", - out = "python/ops/gen_kafka_ops.py", - require_shape_functions = True, - deps = [":kafka_ops_op_lib"], + name = "gen_dataset_ops", + out = "python/ops/gen_dataset_ops.py", + deps = ["//tensorflow/contrib/kafka:dataset_ops_op_lib"], ) -py_library( - name = "kafka", - srcs = [ - "__init__.py", - "python/ops/kafka_dataset_ops.py", +tf_kernel_library( + name = "dataset_ops_kernels", + deps = [ + ":dataset_kernels", + "//tensorflow/core:framework", + ], + alwayslink = 1, +) + +tf_custom_op_py_library( + name = "kafka_op_loader", + srcs = ["python/ops/kafka_op_loader.py"], + dso = ["//tensorflow/contrib/kafka:_dataset_ops.so"], + kernels = [ + ":dataset_ops_kernels", + "//tensorflow/contrib/kafka:dataset_ops_op_lib", ], srcs_version = "PY2AND3", - visibility = ["//visibility:public"], deps = [ - ":gen_kafka_ops", + ":gen_dataset_ops", "//tensorflow/contrib/util:util_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:platform", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - "//tensorflow/python/data/ops:readers", ], ) @@ -88,13 +115,17 @@ tf_py_test( ], tags = [ "manual", + "no_windows", + "notap", ], ) filegroup( name = "all_files", srcs = glob( - ["**/*"], + include = [ + "**/*", + ], exclude = [ "**/METADATA", "**/OWNERS", diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc index 88ef5f357113372b0a2d0cb13382ac980a61252d..a4cd4a2cc4b99b5906185bd2b942ed15c1ddf5e4 100644 --- a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc +++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc @@ -13,9 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/dataset.h" - -#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/dataset.h" #include "src-cpp/rdkafkacpp.h" diff --git a/tensorflow/contrib/kafka/ops/kafka_ops.cc b/tensorflow/contrib/kafka/ops/dataset_ops.cc similarity index 100% rename from tensorflow/contrib/kafka/ops/kafka_ops.cc rename to tensorflow/contrib/kafka/ops/dataset_ops.cc diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py index 94cf6b5ace6a10b7c0471d7d25b5bce789ac322b..621911876fc502ece76b08eb6c28697b3c12c863 100644 --- a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py @@ -18,21 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np -import os - from tensorflow.contrib.kafka.python.ops import kafka_dataset_ops from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.lib.io import python_io from tensorflow.python.ops import array_ops -from tensorflow.python.ops import io_ops from tensorflow.python.platform import test -from tensorflow.python.util import compat + class KafkaDatasetTest(test.TestCase): @@ -64,52 +56,58 @@ class KafkaDatasetTest(test.TestCase): with self.test_session() as sess: # Basic test: read from topic 0. - sess.run( - init_op, feed_dict={topics: ["test:0:0:4"], - num_epochs: 1}) + sess.run(init_op, feed_dict={topics: ["test:0:0:4"], num_epochs: 1}) for i in range(5): - self.assertEqual("D"+str(i), sess.run(get_next)) + self.assertEqual("D" + str(i), sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) # Basic test: read from topic 1. - sess.run( - init_op, feed_dict={topics: ["test:0:5:-1"], - num_epochs: 1}) + sess.run(init_op, feed_dict={topics: ["test:0:5:-1"], num_epochs: 1}) for i in range(5): - self.assertEqual("D"+str(i + 5), sess.run(get_next)) + self.assertEqual("D" + str(i + 5), sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) # Basic test: read from both topics. - sess.run(init_op, feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], - num_epochs: 1}) + sess.run( + init_op, + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 1 + }) for j in range(2): for i in range(5): - self.assertEqual("D"+str(i + j * 5), sess.run(get_next)) + self.assertEqual("D" + str(i + j * 5), sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) # Test repeated iteration through both files. - sess.run(init_op, feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], - num_epochs: 10}) + sess.run( + init_op, + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10 + }) for _ in range(10): for j in range(2): for i in range(5): - self.assertEqual("D"+str(i + j * 5), sess.run(get_next)) + self.assertEqual("D" + str(i + j * 5), sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) # Test batched and repeated iteration through both files. sess.run( init_batch_op, - feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], - num_epochs: 10, - batch_size: 5}) + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10, + batch_size: 5 + }) for _ in range(10): - self.assertAllEqual(["D"+str(i) for i in range(5)], + self.assertAllEqual(["D" + str(i) for i in range(5)], sess.run(get_next)) - self.assertAllEqual(["D"+str(i + 5) for i in range(5)], + self.assertAllEqual(["D" + str(i + 5) for i in range(5)], sess.run(get_next)) diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh index 7997c12731189e56fc491a0f4de8b19c39d262b2..adf027b8e714124cde2b4618546e20c6b7162e1f 100644 --- a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh @@ -1,4 +1,18 @@ #!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== set -e set -o pipefail diff --git a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py index e561f595a405280010a54d761bdb378ec0162ac0..a1624614d1ab1be31463c5cdc0b4cfb653165a0c 100644 --- a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py +++ b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py @@ -17,21 +17,24 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.kafka.python.ops import gen_kafka_ops -from tensorflow.contrib.util import loader -from tensorflow.python.data.ops.readers import Dataset -from tensorflow.python.framework import common_shapes +from tensorflow.contrib.kafka.python.ops import kafka_op_loader # pylint: disable=unused-import +from tensorflow.contrib.kafka.python.ops import gen_dataset_ops +from tensorflow.python.data.ops.dataset_ops import Dataset from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.platform import resource_loader + class KafkaDataset(Dataset): """A Kafka Dataset that consumes the message. """ - def __init__( - self, topics, servers="localhost", group="", eof=False, timeout=1000): + def __init__(self, + topics, + servers="localhost", + group="", + eof=False, + timeout=1000): """Create a KafkaReader. Args: @@ -51,14 +54,13 @@ class KafkaDataset(Dataset): servers, dtype=dtypes.string, name="servers") self._group = ops.convert_to_tensor( group, dtype=dtypes.string, name="group") - self._eof = ops.convert_to_tensor( - eof, dtype=dtypes.bool, name="eof") + self._eof = ops.convert_to_tensor(eof, dtype=dtypes.bool, name="eof") self._timeout = ops.convert_to_tensor( timeout, dtype=dtypes.int64, name="timeout") def _as_variant_tensor(self): - return gen_kafka_ops.kafka_dataset( - self._topics, self._servers, self._group, self._eof, self._timeout) + return gen_dataset_ops.kafka_dataset(self._topics, self._servers, + self._group, self._eof, self._timeout) @property def output_classes(self): diff --git a/tensorflow/contrib/kafka/python/ops/kafka_op_loader.py b/tensorflow/contrib/kafka/python/ops/kafka_op_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..ec2fdea962ef946d3f8f32b9e630b92649d612fe --- /dev/null +++ b/tensorflow/contrib/kafka/python/ops/kafka_op_loader.py @@ -0,0 +1,24 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python helper for loading kafka ops and kernels.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader + +_dataset_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("../../_dataset_ops.so")) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/BUILD b/tensorflow/contrib/kfac/python/kernel_tests/BUILD index f4ed978174a9ddd8b54a88e60bfb48a67a2e76d2..b0b1314d45cd907796000ebf45e1b3247c9f658f 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/BUILD +++ b/tensorflow/contrib/kfac/python/kernel_tests/BUILD @@ -113,6 +113,7 @@ py_test( name = "utils_test", srcs = ["utils_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ "//tensorflow/contrib/kfac/python/ops:utils", "//tensorflow/contrib/tpu", diff --git a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py index bfdb69ad02caaa57827e0ae6b3c9fc0d0ed03754..b12f7be76907dc206667eb8ee0c750f3b8db57fc 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py @@ -90,49 +90,51 @@ class EstimatorTest(test.TestCase): def testEstimatorInitManualRegistration(self): with self._graph.as_default(): # We should be able to build an estimator for only the registered vars. - estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection) + estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, + self.layer_collection) # Check that we throw an error if we try to build an estimator for vars # that were not manually registered. with self.assertRaises(ValueError): - estimator.FisherEstimator([self.weights, self.bias], 0.1, 0.2, + estimator.FisherEstimator(lambda: 0.2, [self.weights, self.bias], 0.1, self.layer_collection) # Check that we throw an error if we don't include registered variables, # i.e. self.weights with self.assertRaises(ValueError): - estimator.FisherEstimator([], 0.1, 0.2, self.layer_collection) + estimator.FisherEstimator(lambda: 0.2, [], 0.1, self.layer_collection) @test.mock.patch.object(utils.SubGraph, "variable_uses", return_value=42) def testVariableWrongNumberOfUses(self, mock_uses): with self.assertRaises(ValueError): - estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection) + estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, + self.layer_collection) def testInvalidEstimationMode(self): with self.assertRaises(ValueError): - estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection, - "not_a_real_mode") + estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, + self.layer_collection, "not_a_real_mode") def testModeListCorrect(self): with self._graph.as_default(): - est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + est = estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, self.layer_collection) self.assertItemsEqual(_ALL_ESTIMATION_MODES, est._gradient_fns.keys()) def testAllModesBuild(self): for mode in _ALL_ESTIMATION_MODES: with self._graph.as_default(): - estimator.FisherEstimator([self.weights], 0.1, 0.2, + estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, self.layer_collection, mode) def test_cov_update_thunks(self): """Ensures covariance update ops run once per global_step.""" with self._graph.as_default(), self.test_session() as sess: fisher_estimator = estimator.FisherEstimator( + damping_fn=lambda: 0.2, variables=[self.weights], layer_collection=self.layer_collection, - cov_ema_decay=0.0, - damping=0.0) + cov_ema_decay=0.0) # Construct an op that executes one covariance update per step. global_step = training_util.get_or_create_global_step() @@ -176,10 +178,10 @@ class EstimatorTest(test.TestCase): """Ensures inverse update ops run once per global_step.""" with self._graph.as_default(), self.test_session() as sess: fisher_estimator = estimator.FisherEstimator( + damping_fn=lambda: 0.2, variables=[self.weights], layer_collection=self.layer_collection, - cov_ema_decay=0.0, - damping=0.0) + cov_ema_decay=0.0) # Construct op that updates one inverse per global step. global_step = training_util.get_or_create_global_step() diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py index 82accd57f0c37d140238f1884fce956654d14227..fb4b3a241c1e9fd82e7bf630fd57295917048fbd 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py @@ -26,6 +26,7 @@ from tensorflow.contrib.kfac.python.ops import utils from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import state_ops @@ -236,10 +237,10 @@ class NaiveDiagonalFBTest(test.TestCase): self.assertAllClose(output_flat, explicit) -class FullyConnectedDiagonalFB(test.TestCase): +class FullyConnectedDiagonalFBTest(test.TestCase): def setUp(self): - super(FullyConnectedDiagonalFB, self).setUp() + super(FullyConnectedDiagonalFBTest, self).setUp() self.batch_size = 4 self.input_size = 6 @@ -375,6 +376,65 @@ class FullyConnectedDiagonalFB(test.TestCase): return multiply_result, multiply_inverse_result +class EmbeddingKFACFBTest(test.TestCase): + + def testInstantiateFactors(self): + with ops.Graph().as_default(): + random_seed.set_random_seed(200) + + # Create a Fisher Block. + vocab_size = 5 + block = fb.EmbeddingKFACFB(lc.LayerCollection(), vocab_size) + + # Add some examples. + inputs = array_ops.constant([[0, 1], [1, 2], [2, 3]]) + outputs = array_ops.constant([[0.], [1.], [2.]]) + block.register_additional_minibatch(inputs, outputs) + + # Instantiate factor's variables. Ensure it doesn't fail. + grads = outputs**2. + damping = array_ops.constant(0.) + block.instantiate_factors(([grads],), damping) + + def testMultiplyInverse(self): + with ops.Graph().as_default(), self.test_session() as sess: + random_seed.set_random_seed(200) + + # Create a Fisher Block. + vocab_size = 5 + block = fb.EmbeddingKFACFB(lc.LayerCollection(), vocab_size) + + # Add some examples. + inputs = array_ops.constant([[0, 1], [1, 2], [2, 3]]) + outputs = array_ops.constant([[0.], [1.], [2.]]) + block.register_additional_minibatch(inputs, outputs) + + # Instantiate factor's variables. Ensure it doesn't fail. + grads = outputs**2. + damping = array_ops.constant(0.) + block.instantiate_factors(([grads],), damping) + + # Create a sparse update. + indices = array_ops.constant([1, 3, 4]) + values = array_ops.constant([[1.], [1.], [1.]]) + sparse_vector = ops.IndexedSlices( + values, indices, dense_shape=[vocab_size, 1]) + dense_vector = array_ops.reshape([0., 1., 0., 1., 1.], [vocab_size, 1]) + + # Compare Fisher-vector product against explicit result. + result = block.multiply_inverse(sparse_vector) + expected_result = linalg_ops.matrix_solve(block.full_fisher_block(), + dense_vector) + + sess.run(tf_variables.global_variables_initializer()) + self.assertAlmostEqual( + sess.run(expected_result[1]), sess.run(result.values[0])) + self.assertAlmostEqual( + sess.run(expected_result[3]), sess.run(result.values[1])) + self.assertAlmostEqual( + sess.run(expected_result[4]), sess.run(result.values[2])) + + class FullyConnectedKFACBasicFBTest(test.TestCase): def testFullyConnectedKFACBasicFBInit(self): diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py index 753378d9f4a0d8762bafbee2ec27d6c71783dda1..66e18974abfadaad5d7a20b40d0b1352bfda67ee 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py @@ -89,6 +89,21 @@ class FisherFactorTestingDummy(ff.FisherFactor): def make_inverse_update_ops(self): return [] + def get_cov(self): + return NotImplementedError + + def left_multiply(self, x, damping): + return NotImplementedError + + def right_multiply(self, x, damping): + return NotImplementedError + + def left_multiply_inverse(self, x, damping): + return NotImplementedError + + def right_multiply_inverse(self, x, damping): + return NotImplementedError + class InverseProvidingFactorTestingDummy(ff.InverseProvidingFactor): """Dummy class to test the non-abstract methods on ff.InverseProvidingFactor. @@ -379,7 +394,7 @@ class NaiveDiagonalFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) - self.assertEqual([6, 1], factor.get_cov().get_shape().as_list()) + self.assertEqual([6, 1], factor.get_cov_var().get_shape().as_list()) def testNaiveDiagonalFactorInitFloat64(self): with tf_ops.Graph().as_default(): @@ -387,7 +402,7 @@ class NaiveDiagonalFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) - cov = factor.get_cov() + cov = factor.get_cov_var() self.assertEqual(cov.dtype, dtype) self.assertEqual([6, 1], cov.get_shape().as_list()) @@ -402,6 +417,29 @@ class NaiveDiagonalFactorTest(test.TestCase): self.assertAllClose([[0.75], [1.5]], new_cov) +class EmbeddingInputKroneckerFactorTest(test.TestCase): + + def testInitialization(self): + with tf_ops.Graph().as_default(): + input_ids = array_ops.constant([[0], [1], [4]]) + vocab_size = 5 + factor = ff.EmbeddingInputKroneckerFactor((input_ids,), vocab_size) + cov = factor.get_cov_var() + self.assertEqual(cov.shape.as_list(), [vocab_size]) + + def testCovarianceUpdateOp(self): + with tf_ops.Graph().as_default(): + input_ids = array_ops.constant([[0], [1], [4]]) + vocab_size = 5 + factor = ff.EmbeddingInputKroneckerFactor((input_ids,), vocab_size) + cov_update_op = factor.make_covariance_update_op(0.0) + + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + new_cov = sess.run(cov_update_op) + self.assertAllClose(np.array([1., 1., 0., 0., 1.]) / 3., new_cov) + + class FullyConnectedKroneckerFactorTest(test.TestCase): def _testFullyConnectedKroneckerFactorInit(self, diff --git a/tensorflow/contrib/kfac/python/ops/BUILD b/tensorflow/contrib/kfac/python/ops/BUILD index ee6549b109399766579b6ea18a987ae2c8275983..c26230c2a82ae9529ab13b523b9ec287d17debaf 100644 --- a/tensorflow/contrib/kfac/python/ops/BUILD +++ b/tensorflow/contrib/kfac/python/ops/BUILD @@ -144,10 +144,13 @@ py_library( ":fisher_estimator", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", + "//tensorflow/python:state_ops", "//tensorflow/python:training", + "//tensorflow/python:variable_scope", "//tensorflow/python:variables", ], ) diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py index a7b1f9d35c931fc44408be804479e758f28f7110..a7e268c48ae326a4d8fa5fe4a4ed15b8b83a0ed9 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator.py +++ b/tensorflow/contrib/kfac/python/ops/estimator.py @@ -83,9 +83,9 @@ class FisherEstimator(object): """ def __init__(self, + damping_fn, variables, cov_ema_decay, - damping, layer_collection, estimation_mode="gradients", colocate_gradients_with_ops=True, @@ -94,16 +94,12 @@ class FisherEstimator(object): """Create a FisherEstimator object. Args: + damping_fn: Function, accepts no arguments and returns damping value. variables: A list of the variables for which to estimate the Fisher. This must match the variables registered in layer_collection (if it is not None). cov_ema_decay: The decay factor used when calculating the covariance estimate moving averages. - damping: The damping factor used to stabilize training due to errors in - the local approximation with the Fisher information matrix, and to - regularize the update direction by making it closer to the gradient. - (Higher damping means the update looks more like a standard gradient - update - see Tikhonov regularization.) layer_collection: The layer collection object, which holds the fisher blocks, kronecker factors, and losses associated with the graph. @@ -135,10 +131,9 @@ class FisherEstimator(object): Raises: ValueError: If no losses have been registered with layer_collection. """ - + self._damping_fn = damping_fn self._cov_ema_decay = cov_ema_decay self._variables = variables - self._damping = damping self._estimation_mode = estimation_mode self._layers = layer_collection self._layers.create_subgraph() @@ -182,7 +177,7 @@ class FisherEstimator(object): @property def damping(self): - return self._damping + return self._damping_fn() def _apply_transformation(self, vecs_and_vars, transform): """Applies an block-wise transformation to the corresponding vectors. diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py index 0d2fa706f5853570bb8c04a9b9ac3378e2f2386e..cf38d28b43836dced8babe2ffa7853b1c4b1b369 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py @@ -92,10 +92,22 @@ def compute_pi_tracenorm(left_cov, right_cov): Returns: The computed scalar constant pi for these Kronecker Factors (as a Tensor). """ + + def _trace(cov): + if len(cov.shape) == 1: + # Diagonal matrix. + return math_ops.reduce_sum(cov) + elif len(cov.shape) == 2: + # Full matrix. + return math_ops.trace(cov) + else: + raise ValueError( + "What's the trace of a Tensor of rank %d?" % len(cov.shape)) + # Instead of dividing by the dim of the norm, we multiply by the dim of the # other norm. This works out the same in the ratio. - left_norm = math_ops.trace(left_cov) * right_cov.shape.as_list()[0] - right_norm = math_ops.trace(right_cov) * left_cov.shape.as_list()[0] + left_norm = _trace(left_cov) * right_cov.shape.as_list()[0] + right_norm = _trace(right_cov) * left_cov.shape.as_list()[0] return math_ops.sqrt(left_norm / right_norm) @@ -201,15 +213,15 @@ class FullFB(FisherBlock): self._factor.register_damped_inverse(damping) def multiply_inverse(self, vector): - inverse = self._factor.get_damped_inverse(self._damping) - out_flat = math_ops.matmul(inverse, utils.tensors_to_column(vector)) + vector_flat = utils.tensors_to_column(vector) + out_flat = self._factor.left_multiply_inverse( + vector_flat, self._damping) return utils.column_to_tensors(vector, out_flat) def multiply(self, vector): vector_flat = utils.tensors_to_column(vector) - out_flat = ( - math_ops.matmul(self._factor.get_cov(), vector_flat) + - self._damping * vector_flat) + out_flat = self._factor.left_multiply( + vector_flat, self._damping) return utils.column_to_tensors(vector, out_flat) def full_fisher_block(self): @@ -265,16 +277,20 @@ class NaiveDiagonalFB(FisherBlock): def multiply_inverse(self, vector): vector_flat = utils.tensors_to_column(vector) - out_flat = vector_flat / (self._factor.get_cov() + self._damping) + print("vector_flat: %s" % vector_flat) + out_flat = self._factor.left_multiply_inverse( + vector_flat, self._damping) + print("out_flat: %s" % out_flat) return utils.column_to_tensors(vector, out_flat) def multiply(self, vector): vector_flat = utils.tensors_to_column(vector) - out_flat = vector_flat * (self._factor.get_cov() + self._damping) + out_flat = self._factor.left_multiply( + vector_flat, self._damping) return utils.column_to_tensors(vector, out_flat) def full_fisher_block(self): - return array_ops.diag(array_ops.reshape(self._factor.get_cov(), (-1,))) + return self._factor.get_cov() def tensors_to_compute_grads(self): return self._params @@ -356,8 +372,9 @@ class FullyConnectedDiagonalFB(FisherBlock): Tensor of the same shape, corresponding to the inverse Fisher-vector product. """ - reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = reshaped_vect / (self._factor.get_cov() + self._damping) + reshaped_vec = utils.layer_params_to_mat2d(vector) + reshaped_out = self._factor.left_multiply_inverse( + reshaped_vec, self._damping) return utils.mat2d_to_layer_params(vector, reshaped_out) def multiply(self, vector): @@ -372,8 +389,9 @@ class FullyConnectedDiagonalFB(FisherBlock): Returns: Tensor of the same shape, corresponding to the Fisher-vector product. """ - reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = reshaped_vect * (self._factor.get_cov() + self._damping) + reshaped_vec = utils.layer_params_to_mat2d(vector) + reshaped_out = self._factor.left_multiply( + reshaped_vec, self._damping) return utils.mat2d_to_layer_params(vector, reshaped_out) def tensors_to_compute_grads(self): @@ -468,12 +486,14 @@ class ConvDiagonalFB(FisherBlock): def multiply_inverse(self, vector): reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = reshaped_vect / (self._factor.get_cov() + self._damping) + reshaped_out = self._factor.left_multiply_inverse( + reshaped_vect, self._damping) return utils.mat2d_to_layer_params(vector, reshaped_out) def multiply(self, vector): reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = reshaped_vect * (self._factor.get_cov() + self._damping) + reshaped_out = self._factor.left_multiply( + reshaped_vect, self._damping) return utils.mat2d_to_layer_params(vector, reshaped_out) def tensors_to_compute_grads(self): @@ -533,28 +553,24 @@ class KroneckerProductFB(FisherBlock): return 1.0 def multiply_inverse(self, vector): - left_factor_inv = self._input_factor.get_damped_inverse(self._input_damping) - right_factor_inv = self._output_factor.get_damped_inverse( - self._output_damping) reshaped_vector = utils.layer_params_to_mat2d(vector) - reshaped_out = math_ops.matmul(left_factor_inv, - math_ops.matmul(reshaped_vector, - right_factor_inv)) + reshaped_out = self._output_factor.right_multiply_inverse( + reshaped_vector, + self._output_damping) + reshaped_out = self._input_factor.left_multiply_inverse( + reshaped_out, self._input_damping) if self._renorm_coeff != 1.0: reshaped_out /= math_ops.cast( self._renorm_coeff, dtype=reshaped_out.dtype) return utils.mat2d_to_layer_params(vector, reshaped_out) def multiply(self, vector): - left_factor = self._input_factor.get_cov() - right_factor = self._output_factor.get_cov() reshaped_vector = utils.layer_params_to_mat2d(vector) - reshaped_out = ( - math_ops.matmul(reshaped_vector, right_factor) + - self._output_damping * reshaped_vector) - reshaped_out = ( - math_ops.matmul(left_factor, reshaped_out) + - self._input_damping * reshaped_out) + reshaped_out = self._output_factor.right_multiply( + reshaped_vector, + self._output_damping) + reshaped_out = self._input_factor.left_multiply( + reshaped_out, self._input_damping) if self._renorm_coeff != 1.0: reshaped_out *= math_ops.cast( self._renorm_coeff, dtype=reshaped_out.dtype) @@ -574,6 +590,74 @@ class KroneckerProductFB(FisherBlock): right_factor) +class EmbeddingKFACFB(KroneckerProductFB): + """K-FAC FisherBlock for embedding layers. + + This FisherBlock is similar to EmbeddingKFACFB, except that its + input factor is approximated by a diagonal matrix. In the case that each + example references exactly one embedding, this approximation is exact. + + Does not support bias parameters. + """ + + def __init__(self, layer_collection, vocab_size): + """Creates a EmbeddingKFACFB block. + + Args: + layer_collection: The collection of all layers in the K-FAC approximate + Fisher information matrix to which this FisherBlock belongs. + vocab_size: int. Size of vocabulary for this embedding layer. + """ + self._inputs = [] + self._outputs = [] + self._vocab_size = vocab_size + + super(EmbeddingKFACFB, self).__init__(layer_collection) + + def instantiate_factors(self, grads_list, damping): + """Instantiate Kronecker Factors for this FisherBlock. + + Args: + grads_list: List of list of Tensors. grads_list[i][j] is the + gradient of the loss with respect to 'outputs' from source 'i' and + tower 'j'. Each Tensor has shape [tower_minibatch_size, output_size]. + damping: 0-D Tensor or float. 'damping' * identity is approximately added + to this FisherBlock's Fisher approximation. + """ + # TODO(b/68033310): Validate which of, + # (1) summing on a single device (as below), or + # (2) on each device in isolation and aggregating + # is faster. + inputs = _concat_along_batch_dim(self._inputs) + grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + + self._input_factor = self._layer_collection.make_or_get_factor( # + fisher_factors.EmbeddingInputKroneckerFactor, # + ((inputs,), self._vocab_size)) + self._output_factor = self._layer_collection.make_or_get_factor( # + fisher_factors.FullyConnectedKroneckerFactor, # + (grads_list,)) + self._register_damped_input_and_output_inverses(damping) + + def tensors_to_compute_grads(self): + return self._outputs + + def register_additional_minibatch(self, inputs, outputs): + """Registers an additional minibatch to the FisherBlock. + + Args: + inputs: Tensor of shape [batch_size, input_size]. Inputs to the + matrix-multiply. + outputs: Tensor of shape [batch_size, output_size]. Layer preactivations. + """ + self._inputs.append(inputs) + self._outputs.append(outputs) + + @property + def num_registered_minibatches(self): + return len(self._inputs) + + class FullyConnectedKFACBasicFB(KroneckerProductFB): """K-FAC FisherBlock for fully-connected (dense) layers. diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks_lib.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks_lib.py index ac396309206fe09af65c2b70840a513fb25b579b..c04cf727fa958160d61c7a3638ec65f6c93c2f24 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks_lib.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks_lib.py @@ -29,6 +29,7 @@ _allowed_symbols = [ 'NaiveDiagonalFB', 'FullyConnectedDiagonalFB', 'KroneckerProductFB', + 'EmbeddingKFACFB', 'FullyConnectedKFACBasicFB', 'ConvKFCBasicFB', 'ConvDiagonalFB', @@ -36,7 +37,9 @@ _allowed_symbols = [ 'compute_pi_tracenorm', 'compute_pi_adjusted_damping', 'num_conv_locations', - 'normalize_damping' + 'normalize_damping', + 'LEFT_MULTIPLY', + 'RIGHT_MULTIPLY', ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index bcba18ae147c6ceca50bc9a2a17e01fc201d88c1..603d8b8b210279ee6d8f1de0ce10869fde23f4d9 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -25,13 +25,13 @@ import numpy as np import six from tensorflow.contrib.kfac.python.ops import utils +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops as tf_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn from tensorflow.python.ops import special_math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables @@ -112,54 +112,6 @@ def diagonal_covariance_initializer(shape, dtype, partition_info): # pylint: di return array_ops.ones(shape, dtype) -def extract_image_patches(image, ksizes, strides, padding, name=None): - """Extracts image patches for an N-dimensional convolution. - - This function is a compatibility wrapper over tf.extract_image_patches(), as - ExtractImagePatches isn't yet implemented in XLA. - - Args: - image: Tensor of shape [batch, in_x, in_y, ..., in_channels]. Input images. - All dimensions except 'batch' must be defined. - ksizes: [filter_x, filter_y, ...]. Spatial shape of filter in each - dimension. - strides: [stride_x, stride_y, ...]. Spatial stride for filter in each - dimension. - padding: str. "VALID" or "SAME". - name: str or None. name of Op. - - Returns: - result: [batch, out_x, out_y, ..., filter_x, filter_y, ..., in_channels]. - Contains image patches to which conv kernel would be applied for each - output location. [out_x, out_y, ...] depends on padding. - """ - if not utils.on_tpu(): - return array_ops.extract_image_patches( - image, - ksizes=([1] + list(ksizes) + [1]), - strides=([1] + list(strides) + [1]), - rates=[1, 1, 1, 1], - padding=padding, - name=name) - - with tf_ops.name_scope(name, "extract_image_patches", - [image, ksizes, strides, padding]): - batch = image.shape.as_list()[0] - in_channels = image.shape.as_list()[-1] - - # Map each input feature to a location in the output. - out_channels = np.prod(ksizes) * in_channels - filters = linalg_ops.eye(out_channels), - filters = array_ops.reshape(filters, ksizes + [in_channels, out_channels]) - - result = nn.convolution(image, filters, padding, strides=strides) - out_spatial = result.shape.as_list()[1:-1] - result = array_ops.reshape( - result, [batch or -1] + out_spatial + ksizes + [in_channels]) - - return result - - def compute_cov(tensor, tensor_right=None, normalizer=None): """Compute the empirical second moment of the rows of a 2D Tensor. @@ -259,12 +211,21 @@ def scalar_or_tensor_to_string(val): class FisherFactor(object): """Base class for objects modeling factors of approximate Fisher blocks. - Note that for blocks that aren't based on approximations, a 'factor' can - be the entire block itself, as is the case for the diagonal and full - representations. + A FisherFactor represents part of an approximate Fisher Information matrix. + For example, one approximation to the Fisher uses the Kronecker product of two + FisherFactors A and B, F = kron(A, B). FisherFactors are composed with + FisherBlocks to construct a block-diagonal approximation to the full Fisher. + + FisherFactors are backed by a single, non-trainable variable that is updated + by running FisherFactor.make_covariance_update_op(). The shape and type of + this variable is implementation specific. - Subclasses must implement the _compute_new_cov method, and the _var_scope - and _cov_shape properties. + Note that for blocks that aren't based on approximations, a 'factor' can + be the entire block itself, as is the case for the diagonal and full + representations. + + Subclasses must implement the _compute_new_cov() method, and the _var_scope + and _cov_shape properties. """ def __init__(self): @@ -272,16 +233,21 @@ class FisherFactor(object): @abc.abstractproperty def _var_scope(self): + """Variable scope for this FisherFactor instance. + + Returns: + string that unique identifies this FisherFactor instance. + """ pass @abc.abstractproperty def _cov_shape(self): - """The shape of the cov matrix.""" + """The shape of the variable backing this FisherFactor.""" pass @abc.abstractproperty def _num_sources(self): - """The number of things to sum over when computing cov. + """The number of things to sum over when updating covariance variable. The default make_covariance_update_op function will call _compute_new_cov with indices ranging from 0 to _num_sources-1. The typical situation is @@ -293,10 +259,12 @@ class FisherFactor(object): @abc.abstractproperty def _dtype(self): + """dtype for variable backing this factor.""" pass @property def _cov_initializer(self): + """Function for initializing covariance variable.""" return covariance_initializer def instantiate_covariance(self): @@ -311,6 +279,15 @@ class FisherFactor(object): @abc.abstractmethod def _compute_new_cov(self, idx=0): + """Computes minibatch-estimated covariance for a single source. + + Args: + idx: int in [0, self._num_sources). Which source to use when estimating + covariance. + + Returns: + Tensor of same shape as self.get_cov_var(). + """ pass def make_covariance_update_op(self, ema_decay): @@ -343,14 +320,101 @@ class FisherFactor(object): """Create and return update ops corresponding to registered computations.""" pass + @abc.abstractmethod def get_cov(self): + """Get full covariance matrix. + + Returns: + Tensor of shape [n, n]. Represents all parameter-parameter correlations + captured by this FisherFactor. + """ + pass + + def get_cov_var(self): + """Get variable backing this FisherFactor. + + May or may not be the same as self.get_cov() + + Returns: + Variable of shape self._cov_shape. + """ return self._cov + @abc.abstractmethod + def left_multiply(self, x, damping): + """Multiplies 'x' by the damped covariance of this factor. + + Let C be the covariance matrix this factor represents, and + D = C + damping * I be its damped variant. This method calculates + matmul(D, vec(x)). + + Args: + x: Tensor. Represents a single vector. Shape depends on implementation. + damping: 0-D Tensor. Damping to add to C's diagonal. + + Returns: + Tensor of same shape as 'x'. + """ + pass + + @abc.abstractmethod + def right_multiply(self, x, damping): + """Multiplies 'x' by the damped covariance of this factor. + + Let C be the covariance matrix this factor represents, and + D = C + damping * I be its damped variant. This method calculates + matmul(vec(x), D). + + Args: + x: Tensor. Represents a single vector. Shape depends on implementation. + damping: 0-D Tensor. Damping to add to C's diagonal. + + Returns: + Tensor of same shape as 'x'. + """ + pass + + @abc.abstractmethod + def left_multiply_inverse(self, x, damping): + """Multiplies 'x' by damped inverse of this factor. + + Let C be the covariance matrix this factor represents and + E = inv(C + damping * I) be its damped inverse. This method calculates + matmul(E, vec(x)). + + Args: + x: Tensor. Represents a single vector. Shape depends on implementation. + damping: 0-D Tensor. Damping to add to C's diagonal. + + Returns: + Tensor of same shape as 'x'. + """ + pass + + @abc.abstractmethod + def right_multiply_inverse(self, x, damping): + """Multiplies 'x' by damped inverse of this factor. + + Let C be the covariance matrix this factor represents and + E = inv(C + damping * I) be its damped inverse. This method calculates + matmul(vec(x), E). + + Args: + x: Tensor. Represents a single vector. Shape depends on implementation. + damping: 0-D Tensor. Damping to add to C's diagonal. + + Returns: + Tensor of same shape as 'x'. + """ + pass + class InverseProvidingFactor(FisherFactor): - """Base class for FisherFactors that maintain inverses, powers, etc of _cov. + """Base class for FisherFactors that maintain inverses explicitly. - Assumes that the _cov property is a square PSD matrix. + This class explicitly calculates and stores inverses of covariance matrices + provided by the underlying FisherFactor implementation. It is assumed that + vectors can be represented as 2-D matrices. Subclasses must implement the _compute_new_cov method, and the _var_scope and _cov_shape properties. @@ -485,6 +549,61 @@ class InverseProvidingFactor(FisherFactor): def reset_eigendecomp(self): self._eigendecomp = None + def get_cov(self): + # Variable contains full covariance matrix. + return self.get_cov_var() + + def left_multiply(self, x, damping): + n = self.get_cov().shape[0] + damped_cov = self.get_cov() + damping * array_ops.eye(n) + + if isinstance(x, tf_ops.IndexedSlices): + raise NotImplementedError( + "Left-multiply not yet supported for IndexedSlices.") + + if len(x.shape) != 2: + raise ValueError( + "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." + % (x,)) + + return math_ops.matmul(damped_cov, x) + + def right_multiply(self, x, damping): + n = self.get_cov().shape[0] + damped_cov = self.get_cov() + damping * array_ops.eye(n) + + if isinstance(x, tf_ops.IndexedSlices): + return utils.matmul_sparse_dense(x, damped_cov) + + if len(x.shape) != 2: + raise ValueError( + "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." + % (x,)) + + return math_ops.matmul(x, damped_cov) + + def left_multiply_inverse(self, x, damping): + if isinstance(x, tf_ops.IndexedSlices): + raise ValueError("Left-multiply not yet supported for IndexedSlices.") + + if x.shape.ndims != 2: + raise ValueError( + "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." + % (x,)) + + return math_ops.matmul(self.get_damped_inverse(damping), x) + + def right_multiply_inverse(self, x, damping): + if isinstance(x, tf_ops.IndexedSlices): + return utils.matmul_sparse_dense(x, self.get_damped_inverse(damping)) + + if x.shape.ndims != 2: + raise ValueError( + "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." + % (x,)) + + return math_ops.matmul(x, self.get_damped_inverse(damping)) + class FullFactor(InverseProvidingFactor): """FisherFactor for a full matrix representation of the Fisher of a parameter. @@ -530,7 +649,11 @@ class FullFactor(InverseProvidingFactor): class DiagonalFactor(FisherFactor): - """A base class for FisherFactors that use diagonal approximations.""" + """A base class for FisherFactors that use diagonal approximations. + + A DiagonalFactor's covariance variable can be of any shape, but must contain + exactly one entry per parameter. + """ def __init__(self): super(DiagonalFactor, self).__init__() @@ -542,6 +665,45 @@ class DiagonalFactor(FisherFactor): def make_inverse_update_ops(self): return [] + def get_cov(self): + # self.get_cov() could be any shape, but it must have one entry per + # parameter. Flatten it into a vector. + cov_diag_vec = array_ops.reshape(self.get_cov_var(), [-1]) + return array_ops.diag(cov_diag_vec) + + def left_multiply(self, x, damping): + damped_cov = self.get_cov_var() + damping + if isinstance(x, tf_ops.IndexedSlices): + return utils.matmul_diag_sparse(array_ops.reshape(damped_cov, [-1]), x) + + if x.shape != damped_cov.shape: + raise ValueError("x (%s) and cov (%s) must have same shape." % + (x, damped_cov)) + + return damped_cov * x + + def right_multiply(self, x, damping): + raise NotImplementedError("Only left-multiply is currently supported.") + + def left_multiply_inverse(self, x, damping): + inverse = 1. / (self.get_cov_var() + damping) + + if isinstance(x, tf_ops.IndexedSlices): + return utils.matmul_diag_sparse(array_ops.reshape(inverse, [-1]), x) + + if x.shape != inverse.shape: + raise ValueError("x (%s) and cov (%s) must have same shape." % + (x, inverse)) + + return inverse * x + + def right_multiply_inverse(self, x, damping): + raise NotImplementedError("Only left-multiply is currently supported.") + + def register_damped_inverse(self, damping): + # DiagonalFactors don't keep explicit inverses. + pass + class NaiveDiagonalFactor(DiagonalFactor): """FisherFactor for a diagonal approximation of any type of param's Fisher. @@ -553,6 +715,14 @@ class NaiveDiagonalFactor(DiagonalFactor): def __init__(self, params_grads, batch_size): + """Initializes NaiveDiagonalFactor instance. + + Args: + params_grads: Sequence of Tensors, each with same shape as parameters this + FisherFactor corresponds to. For example, the gradient of the loss with + respect to parameters. + batch_size: int or 0-D Tensor. Size + """ self._params_grads = tuple(utils.ensure_sequence(params_grad) for params_grad in params_grads) self._batch_size = batch_size @@ -567,7 +737,7 @@ class NaiveDiagonalFactor(DiagonalFactor): def _cov_shape(self): size = sum(param_grad.shape.num_elements() for param_grad in self._params_grads[0]) - return (size, 1) + return [size, 1] @property def _num_sources(self): @@ -584,6 +754,84 @@ class NaiveDiagonalFactor(DiagonalFactor): self._batch_size, params_grads_flat.dtype)) +class EmbeddingInputKroneckerFactor(DiagonalFactor): + r"""FisherFactor for input to an embedding layer. + + Given input_ids = [batch_size, input_size] representing indices into an + [vocab_size, embedding_size] embedding matrix, approximate input covariance by + a diagonal matrix, + + Cov(input_ids, input_ids) = + (1/batch_size) sum_{i} diag(n_hot(input[i]) ** 2). + + where n_hot() constructs an n-hot binary vector and diag() constructs a + diagonal matrix of size [vocab_size, vocab_size]. + """ + + def __init__(self, input_ids, vocab_size, dtype=None): + """Instantiate EmbeddingInputKroneckerFactor. + + Args: + input_ids: Tuple of Tensors of shape [batch_size, input_size] and dtype + int32. Indices into embedding matrix. + vocab_size: int or 0-D Tensor. Maximum value for entries in 'input_ids'. + dtype: dtype for covariance statistics. Must be a floating point type. + Defaults to float32. + """ + self._input_ids = input_ids + self._vocab_size = vocab_size + self._cov_dtype = dtype or dtypes.float32 + + super(EmbeddingInputKroneckerFactor, self).__init__() + + @property + def _var_scope(self): + return "ff_diag_embedding/" + scope_string_from_params(self._input_ids) + + @property + def _cov_shape(self): + return [self._vocab_size] + + @property + def _num_sources(self): + return len(self._input_ids) + + @property + def _dtype(self): + return self._cov_dtype + + def _compute_new_cov(self, idx=0): + with maybe_colocate_with(self._input_ids): + input_ids = self._input_ids[idx] + if len(input_ids.shape) > 2: + raise ValueError( + "Input to embeddings must have rank <= 2. Found rank %d." % len( + input_ids.shape)) + + batch_size = array_ops.shape(input_ids)[0] + + # Transform indices into one-hot vectors. + # + # TODO(b/72714822): There must be a faster way to construct the diagonal + # covariance matrix! This operation is O(batch_size * vocab_size), where + # it should be O(batch_size * input_size). + flat_input_ids = array_ops.reshape(input_ids, [-1]) + one_hots = array_ops.one_hot(flat_input_ids, + self._vocab_size) # [?, vocab_size] + + # Take average across examples. Note that, because all entries have + # magnitude zero or one, there's no need to square the entries. + # + # TODO(b/72714822): Support for SparseTensor, other kinds of aggregation + # within an example such as average. + # + # TODO(b/72714822): Support for partitioned embeddings. + new_cov = math_ops.reduce_sum(one_hots, axis=0) # [vocab_size] + new_cov /= math_ops.cast(batch_size, new_cov.dtype) + + return new_cov + + class FullyConnectedDiagonalFactor(DiagonalFactor): r"""FisherFactor for a diagonal approx of a fully-connected layer's Fisher. @@ -623,8 +871,9 @@ class FullyConnectedDiagonalFactor(DiagonalFactor): @property def _cov_shape(self): - return [self._inputs.shape[1] + self._has_bias, - self._outputs_grads[0].shape[1]] + input_size = self._inputs.shape[1] + self._has_bias + output_size = self._outputs_grads[0].shape[1] + return [input_size, output_size] @property def _num_sources(self): @@ -717,10 +966,11 @@ class ConvDiagonalFactor(DiagonalFactor): # TODO(b/64144716): there is potential here for a big savings in terms # of memory use. - patches = extract_image_patches( + patches = array_ops.extract_image_patches( self._inputs, - ksizes=[filter_height, filter_width], - strides=self._strides[1:-1], + ksizes=[1, filter_height, filter_width, 1], + strides=self._strides, + rates=[1, 1, 1, 1], padding=self._padding) if self._has_bias: @@ -864,10 +1114,11 @@ class ConvInputKroneckerFactor(InverseProvidingFactor): # TODO(b/64144716): there is potential here for a big savings in terms of # memory use. - patches = extract_image_patches( + patches = array_ops.extract_image_patches( self._inputs, - ksizes=[filter_height, filter_width], - strides=self._strides[1:-1], + ksizes=[1, filter_height, filter_width, 1], + strides=self._strides, + rates=[1, 1, 1, 1], padding=self._padding) flatten_size = (filter_height * filter_width * in_channels) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors_lib.py b/tensorflow/contrib/kfac/python/ops/fisher_factors_lib.py index ad93919149c287b1932dd2b6bd772c0dab26192d..2d8e378a932c16d48360bc4b15ff4f3239c0ed1f 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors_lib.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors_lib.py @@ -24,26 +24,15 @@ from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import _allowed_symbols = [ - "inverse_initializer", - "covariance_initializer", - "diagonal_covariance_initializer", - "scope_string_from_params", - "scope_string_from_name", - "scalar_or_tensor_to_string", - "FisherFactor", - "InverseProvidingFactor", - "FullFactor", - "DiagonalFactor", - "NaiveDiagonalFactor", - "FullyConnectedDiagonalFactor", - "FullyConnectedKroneckerFactor", - "ConvInputKroneckerFactor", - "ConvOutputKroneckerFactor", - "ConvDiagonalFactor", - "set_global_constants", - "maybe_colocate_with", - "compute_cov", - "append_homog" + "inverse_initializer", "covariance_initializer", + "diagonal_covariance_initializer", "scope_string_from_params", + "scope_string_from_name", "scalar_or_tensor_to_string", "FisherFactor", + "InverseProvidingFactor", "FullFactor", "DiagonalFactor", + "NaiveDiagonalFactor", "EmbeddingInputKroneckerFactor", + "FullyConnectedDiagonalFactor", "FullyConnectedKroneckerFactor", + "ConvInputKroneckerFactor", "ConvOutputKroneckerFactor", + "ConvDiagonalFactor", "set_global_constants", "maybe_colocate_with", + "compute_cov", "append_homog" ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/kfac/python/ops/layer_collection.py b/tensorflow/contrib/kfac/python/ops/layer_collection.py index 8d450f04f379701e46a18b2e34bbbd6fcfcce2bb..ce9005b9ce99a4efa5f2821c56e199dd2086482e 100644 --- a/tensorflow/contrib/kfac/python/ops/layer_collection.py +++ b/tensorflow/contrib/kfac/python/ops/layer_collection.py @@ -143,6 +143,7 @@ class LayerCollection(object): self._loss_dict = {} # {str: LossFunction} self._subgraph = None self._default_generic_approximation = APPROX_FULL_NAME + self._default_embedding_approximation = APPROX_KRONECKER_NAME self._default_fully_connected_approximation = APPROX_KRONECKER_NAME self._default_convolution_2d_approximation = APPROX_KRONECKER_NAME self._default_fully_connected_multi_approximation = ( @@ -178,6 +179,17 @@ class LayerCollection(object): """ return self._linked_parameters + @property + def default_embedding_approximation(self): + return self._default_embedding_approximation + + def set_default_embedding_approximation(self, value): + if value != APPROX_KRONECKER_NAME: + raise ValueError( + "{} is not a valid approximation for embedding variables.".format( + value)) + self._default_embedding_approximation = value + @property def default_generic_approximation(self): return self._default_generic_approximation @@ -417,6 +429,46 @@ class LayerCollection(object): else: return None + def register_embedding(self, + params, + inputs, + outputs, + approx=None, + reuse=VARIABLE_SCOPE): + """Registers a fully connnected layer. + + Args: + params: Embedding matrix of shape [vocab_size, embedding_size]. + inputs: Tensor of shape [batch_size, input_size] and dtype int32. Indices + into embedding matrix. + outputs: Tensor of shape [batch_size, output_size]. Outputs + produced by layer. + approx: str. Must be "kron". + reuse: bool or str. If True, reuse an existing FisherBlock. If False, + create a new FisherBlock. If "VARIABLE_SCOPE", use + tf.get_variable_scope().reuse. + + Raises: + ValueError: For improper value to 'approx'. + KeyError: If reuse == True but no FisherBlock found for 'params'. + ValueError: If reuse == True and FisherBlock found but of the wrong type. + """ + if approx is None: + approx = self._get_linked_approx(params) + if approx is None: + approx = self.default_embedding_approximation + + if approx != APPROX_KRONECKER_NAME: + raise ValueError("Bad value {} for approx.".format(approx)) + + if isinstance(params, (tuple, list)): + raise ValueError("Bias not supported.") + + vocab_size = int(params.shape[0]) + block = self.register_block( + params, fb.EmbeddingKFACFB(self, vocab_size), reuse=reuse) + block.register_additional_minibatch(inputs, outputs) + def register_fully_connected(self, params, inputs, diff --git a/tensorflow/contrib/kfac/python/ops/op_queue.py b/tensorflow/contrib/kfac/python/ops/op_queue.py index 831870fca451c585cb1a1dc6b24aad757e2bbaa8..b6d9d37a31a949b154b79e6f3677289a0d167373 100644 --- a/tensorflow/contrib/kfac/python/ops/op_queue.py +++ b/tensorflow/contrib/kfac/python/ops/op_queue.py @@ -18,7 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.ops import dataset_ops +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import ops as tf_ops diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py index 1974b07acfc879dc4bc844db9af88fd1043d6698..5d456bcb79ff00cedc1aaa7244cc8722d21f6e98 100644 --- a/tensorflow/contrib/kfac/python/ops/optimizer.py +++ b/tensorflow/contrib/kfac/python/ops/optimizer.py @@ -23,11 +23,14 @@ from tensorflow.contrib.kfac.python.ops import curvature_matrix_vector_products from tensorflow.contrib.kfac.python.ops import estimator as est # pylint enable=long-line +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training import gradient_descent @@ -61,6 +64,8 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): damping: The damping factor used to stabilize training due to errors in the local approximation with the Fisher information matrix, and to regularize the update direction by making it closer to the gradient. + If damping is adapted during training then this value is used for + initializing damping varaible. (Higher damping means the update looks more like a standard gradient update - see Tikhonov regularization.) layer_collection: The layer collection object, which holds the fisher @@ -105,10 +110,31 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): if variables is None: variables = tf_variables.trainable_variables() + # The below paramaters are required only if damping needs to be adapated. + # These parameters can be set by calling + # set_damping_adaptation_params() explicitly. + self._damping_adaptation_decay = 0.95 + self._damping_adaptation_interval = 5 + # Check section 6.5 KFAC paper. omega(1) = pow(damping decay, interval) + self._omega = ( + self._damping_adaptation_decay**self._damping_adaptation_interval) + self._adapt_damping = False + self._min_damping = 1e-5 + self._prev_train_batch = None + self._is_chief = False + self._loss_fn = None + self._damping_constant = damping + self._damping = None + self._rho = None + self._prev_loss = None + self._q_model_change = None + self._update_damping_op = None + + self._layers = layer_collection self._fisher_est = est.FisherEstimator( + lambda: self.damping, variables, cov_ema_decay, - damping, layer_collection, estimation_mode=estimation_mode, colocate_gradients_with_ops=colocate_gradients_with_ops, @@ -139,6 +165,60 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): super(KfacOptimizer, self).__init__(learning_rate, name=name) + def set_damping_adaptation_params(self, + is_chief, + prev_train_batch, + loss_fn, + min_damping=1e-5, + damping_adaptation_decay=0.99, + damping_adaptation_interval=5): + """Sets parameters required to adapt damping during training. + + When called, enables damping adaptation according to the Levenberg-Marquardt + style rule described in Section 6.5 of "Optimizing Neural Networks with + Kronecker-factored Approximate Curvature". + + Args: + is_chief: `Boolean`, `True` if the worker is chief. + prev_train_batch: Training data used to minimize loss in the previous + step. This will be used to evaluate loss by calling + `loss_fn(prev_train_batch)`. + loss_fn: `function` that takes as input training data tensor and returns + a scalar loss. + min_damping: `float`(Optional), Minimum value the damping parameter + can take. Default value 1e-5. + damping_adaptation_decay: `float`(Optional), The `damping` parameter is + multipled by the `damping_adaptation_decay` every + `damping_adaptation_interval` number of iterations. Default value 0.99. + damping_adaptation_interval: `int`(Optional), Number of steps in between + updating the `damping` parameter. Default value 5. + + Raises: + ValueError: If `set_damping_adaptation_params` is already called and the + the `adapt_damping` is `True`. + """ + if self._adapt_damping: + raise ValueError("Damping adaptation parameters already set.") + with variable_scope.variable_scope(self.get_name()): + self._adapt_damping = True + self._is_chief = is_chief + self._prev_train_batch = prev_train_batch + self._loss_fn = loss_fn + self._damping_adaptation_decay = damping_adaptation_decay + self._damping_adaptation_interval = damping_adaptation_interval + self._omega = ( + self._damping_adaptation_decay**self._damping_adaptation_interval) + self._min_damping = min_damping + + self._rho = variable_scope.get_variable( + "rho", shape=(), dtype=dtypes.float32, trainable=False) # LM ratio. + self._prev_loss = variable_scope.get_variable( + "prev_loss", shape=(), dtype=dtypes.float32, trainable=False) + self._q_model_change = variable_scope.get_variable( + "q_model_change", shape=(), dtype=dtypes.float32, trainable=False) + self._damping = variable_scope.get_variable( + "damping", initializer=self._damping_constant, trainable=False) + @property def cov_update_thunks(self): return self._fisher_est.cov_update_thunks @@ -169,14 +249,34 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): @property def damping(self): - return self._fisher_est.damping + if self._damping: + return self._damping + else: + return self._damping_constant + + @property + def damping_adaptation_interval(self): + return self._damping_adaptation_interval def minimize(self, *args, **kwargs): kwargs["var_list"] = kwargs.get("var_list") or self.variables if set(kwargs["var_list"]) != set(self.variables): raise ValueError("var_list doesn't match with set of Fisher-estimating " "variables.") - return super(KfacOptimizer, self).minimize(*args, **kwargs) + if self._adapt_damping and self._is_chief: + global_step = kwargs.get("global_step", None) + if not global_step: + raise KeyError("global_step needs to be passed to optimizer.minimize " + "if damping parameter is adapted.") + update_damping_op = self._update_damping(self._prev_train_batch, + global_step) + with ops.control_dependencies([update_damping_op]): + loss = args[0] + loss_assign_op = state_ops.assign(self._prev_loss, loss) + train_op = super(KfacOptimizer, self).minimize(*args, **kwargs) + return control_flow_ops.group(loss_assign_op, train_op) + else: + return super(KfacOptimizer, self).minimize(*args, **kwargs) def compute_gradients(self, *args, **kwargs): # args[1] could be our var_list @@ -296,6 +396,20 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): coeff = self._update_clip_coeff(grads_and_vars, precon_grads_and_vars) return [(pgrad * coeff, var) for pgrad, var in precon_grads_and_vars] + def _compute_prev_updates(self, variables): + """Computes previous updates as negative velocities scaled by learning rate. + + Args: + variables: List of variables in the graph that the update will be + applied to. + + Returns: + List of previous updates applied to the `variables`. + """ + return list( + -1 * self._learning_rate * self._zeros_slot(var, "velocity", self._name) + for var in variables) + def _compute_qmodel_hyperparams(self, precon_grads, prev_updates, grads, variables): """Compute optimal update hyperparameters from the quadratic model. @@ -374,9 +488,9 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): c = ops.convert_to_tensor([[_inner_product_list(grads, precon_grads)], [_inner_product_list(grads, prev_updates)]]) - sol = _two_by_two_solve(m, c) - alpha = -sol[0] - mu = -sol[1] + sol = -1. * _two_by_two_solve(m, c) + alpha = sol[0] + mu = sol[1] qmodel_change = 0.5 * math_ops.reduce_sum(sol * c) return alpha, mu, qmodel_change @@ -404,6 +518,52 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): return control_flow_ops.cond( math_ops.equal(m_22, 0.0), zero_prevupd_case, non_zero_prevupd_case) + def _assign_q_model_change(self, q_model_change): + """Assigns `q_model_change` to `self._q_model_change` if damping is adapted. + + Note only the chief worker does the assignment. + + Args: + q_model_change: Scalar tensor of type `float32`. + + Returns: + If `adapt_damping` is `True` then returns an assign op, Otherwise returns + a no_op(). + """ + if self._adapt_damping and self._is_chief: + q_model_assign_op = state_ops.assign(self._q_model_change, q_model_change) + else: + q_model_assign_op = control_flow_ops.no_op() + return q_model_assign_op + + def _compute_qmodel_hyperparams_wrapper(self, grads_and_vars, + precon_grads_and_vars): + """Wrapper function for `self._compute_qmodel_hyperparams`. + + Constructs a list of preconditioned gradients and variables. Also creates a + op to asssign the computed q model change to `self._q_model_change`. + + Args: + grads_and_vars: List of (gradient, variable) pairs. + precon_grads_and_vars: List of (preconditioned gradients, variable) + pairs. + + Returns: + (alpha, mu, q_model_assign_op), where alpha and mu are chosen to optimize + the quadratic model, `q_model_assign_op` assigns the computed q model + change to `self._q_model_change`. + """ + precon_grads = list( + precon_grad for (precon_grad, _) in precon_grads_and_vars) + grads = list(grad for (grad, _) in grads_and_vars) + variables = list(var for (_, var) in grads_and_vars) + prev_updates = self._compute_prev_updates(variables) + # Compute optimal velocity update parameters according to quadratic model + alpha, mu, q_model_change = self._compute_qmodel_hyperparams( + precon_grads, prev_updates, grads, variables) + + return alpha, mu, self._assign_q_model_change(q_model_change) + def _compute_update_steps(self, grads_and_vars): """Computes the update steps for the variables given the gradients. @@ -411,8 +571,10 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): grads_and_vars: List of (gradient, variable) pairs. Returns: - An 'Operation that computes the update steps for the given variables. + A list of tuple (assign_op ,var) where `assign_op` assigns the update + steps to `var`. """ + if self._momentum_type == "regular": # Compute "preconditioned" gradient. precon_grads_and_vars = self._fisher_est.multiply_inverse(grads_and_vars) @@ -423,8 +585,13 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): precon_grads_and_vars) # Update the velocity with this and return it as the step. - return self._update_velocities(precon_grads_and_vars, self._momentum) - + if self._adapt_damping and self._is_chief: + _, _, q_model_assign_op = self._compute_qmodel_hyperparams_wrapper( + grads_and_vars, precon_grads_and_vars) + with ops.control_dependencies([q_model_assign_op]): + return self._update_velocities(precon_grads_and_vars, self._momentum) + else: + return self._update_velocities(precon_grads_and_vars, self._momentum) elif self._momentum_type == "adam": # Update velocity. velocities_and_vars = self._update_velocities(grads_and_vars, @@ -436,23 +603,13 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): # Compute "preconditioned" gradient. precon_grads_and_vars = self._fisher_est.multiply_inverse(grads_and_vars) - # Extract out singleton lists from the tuple-lists - precon_grads = list( - precon_grad for (precon_grad, _) in precon_grads_and_vars) - grads = list(grad for (grad, _) in grads_and_vars) - variables = list(var for (_, var) in grads_and_vars) - # previous updates are the negative velocities (up to scaling by LR) - prev_updates = list( - -self._zeros_slot(var, "velocity", self._name) for var in variables) - # Compute optimal velocity update parameters according to quadratic model - alpha, mu, _ = self._compute_qmodel_hyperparams( - precon_grads, prev_updates, grads, variables) + alpha, mu, q_model_assign_op = self._compute_qmodel_hyperparams_wrapper( + grads_and_vars, precon_grads_and_vars) - # Update the velocity with precon_grads according to these params - # and return it as the step. - return self._update_velocities( - precon_grads_and_vars, mu, vec_coeff=-alpha) + with ops.control_dependencies([q_model_assign_op]): + return self._update_velocities( + precon_grads_and_vars, mu, vec_coeff=-alpha) def _update_velocities(self, vecs_and_vars, decay, vec_coeff=1.0): """Updates the velocities of the variables with the given vectors. @@ -482,6 +639,51 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): # Go through variable and update its associated part of the velocity vector. return [_update_velocity(vec, var) for vec, var in vecs_and_vars] + # TODO(b/73448937): Move all update damping code to a separate class/function. + def _update_damping(self, prev_batch, global_step): + """Adapts damping parameter. Check KFAC (Section 6.5) for the details. + + The damping parameter is updated according to the Levenberg-Marquardt rule + every `self._damping_adaptation_interval` iterations. + + Args: + prev_batch: Tensor or tuple of tensors which can be passed to + `self._loss_fn` to evaluate loss. + global_step: `Variable` which keeps track of number of times the training + variables have been updated. + Returns: + A `tf.cond` op which updates the damping parameter. + """ + def compute_damping(): + """"Adapts damping parameter based on "reduction ratio". + + Reduction ratio captures how closely the quadratic approximation to the + loss function approximates the actual loss within a trust region. The + damping update tries to make the damping as small as possible while + maintaining the property that the quadratic model remains a good local + approximation to the loss function. + + Returns: + An Op to assign newly computed damping value to `self._damping`. + """ + prev_batch_loss = self._loss_fn(prev_batch) + with ops.control_dependencies([prev_batch_loss]): + rho_assign = self._rho.assign( + (prev_batch_loss - self._prev_loss) / self._q_model_change) + with ops.control_dependencies([rho_assign]): + new_damping = control_flow_ops.case( + [(self._rho < 0.25, lambda: self.damping / self._omega), + (self._rho > 0.75, lambda: self.damping * self._omega)], + lambda: self.damping) + with ops.control_dependencies([new_damping]): + new_damping_min = math_ops.maximum(new_damping, self._min_damping) + return control_flow_ops.group(self._damping.assign(new_damping_min)) + + return control_flow_ops.cond( + math_ops.equal( + math_ops.mod(global_step + 1, self._damping_adaptation_interval), + 0), compute_damping, control_flow_ops.no_op) + def _inner_product_list(list1, list2): return math_ops.add_n( diff --git a/tensorflow/contrib/kfac/python/ops/utils.py b/tensorflow/contrib/kfac/python/ops/utils.py index e89508fa46b6e2ce278e5373e6c9d17203ad1ef2..88e6fb20e8f97528aea2a92752d79344c27bbf24 100644 --- a/tensorflow/contrib/kfac/python/ops/utils.py +++ b/tensorflow/contrib/kfac/python/ops/utils.py @@ -144,7 +144,9 @@ def layer_params_to_mat2d(vector): [-1, w_part.shape.as_list()[-1]]) return array_ops.concat( (w_part_reshaped, array_ops.reshape(b_part, [1, -1])), axis=0) - else: + elif isinstance(vector, ops.IndexedSlices): + return vector + else: # Tensor or Tensor-like. return array_ops.reshape(vector, [-1, vector.shape.as_list()[-1]]) @@ -163,6 +165,11 @@ def mat2d_to_layer_params(vector_template, mat2d): if isinstance(vector_template, (tuple, list)): w_part, b_part = mat2d[:-1], mat2d[-1] return array_ops.reshape(w_part, vector_template[0].shape), b_part + elif isinstance(vector_template, ops.IndexedSlices): + if not isinstance(mat2d, ops.IndexedSlices): + raise TypeError( + "If vector_template is an IndexedSlices, so should mat2d.") + return mat2d else: return array_ops.reshape(mat2d, vector_template.shape) @@ -234,19 +241,22 @@ class SubGraph(object): # Set of all ancestor Tensors, Ops to 'outputs'. self._members = set() - self._recurse_add(outputs) + self._iter_add(outputs) - def _recurse_add(self, nodes): - """Recursively adds all of nodes' ancestors.""" - for node in nodes: - if node in self._members: - continue - self._members.add(node) + def _iter_add(self, root): + """Iteratively adds all of nodes' ancestors using depth first search.""" + stack = [root] + while stack: + nodes = stack.pop() + for node in nodes: + if node in self._members: + continue + self._members.add(node) - if isinstance(node, ops.Tensor): - self._recurse_add((node.op,)) - elif isinstance(node, ops.Operation): - self._recurse_add(node.inputs) + if isinstance(node, ops.Tensor): + stack.append((node.op,)) + elif isinstance(node, ops.Operation): + stack.append(node.inputs) def is_member(self, node): """Check if 'node' is in this subgraph.""" @@ -420,5 +430,57 @@ def batch_execute(global_step, thunks, batch_size, name=None): return result +def matmul_sparse_dense(A, B, name=None): # pylint: disable=invalid-name + """Computes matmul(A, B) where A is sparse, B is dense. + + Args: + A: tf.IndexedSlices with dense shape [m, n]. + B: tf.Tensor with shape [n, k]. + name: str. Name of op. + + Returns: + tf.IndexedSlices resulting from matmul(A, B). + + Raises: + ValueError: If A doesn't represent a matrix. + ValueError: If B is not rank-2. + """ + with ops.name_scope(name, "matmul_sparse_dense", [A, B]): + if A.indices.shape.ndims != 1 or A.values.shape.ndims != 2: + raise ValueError("A must represent a matrix. Found: %s." % A) + if B.shape.ndims != 2: + raise ValueError("B must be a matrix.") + new_values = math_ops.matmul(A.values, B) + return ops.IndexedSlices( + new_values, + A.indices, + dense_shape=array_ops.stack([A.dense_shape[0], new_values.shape[1]])) + + +def matmul_diag_sparse(A_diag, B, name=None): # pylint: disable=invalid-name + """Computes matmul(A, B) where A is a diagonal matrix, B is sparse. + + Args: + A_diag: diagonal entries of matrix A of shape [m, m]. + B: tf.IndexedSlices. Represents matrix of shape [m, n]. + name: str. Name of op. + + Returns: + tf.IndexedSlices resulting from matmul(A, B). + + Raises: + ValueError: If A_diag is not rank-1. + ValueError: If B doesn't represent a matrix. + """ + with ops.name_scope(name, "matmul_diag_sparse", [A_diag, B]): + A_diag = ops.convert_to_tensor(A_diag) + if A_diag.shape.ndims != 1: + raise ValueError("A_diag must be a rank-1 Tensor.") + if B.indices.shape.ndims != 1 or B.values.shape.ndims != 2: + raise ValueError("B must represent a matrix. Found: %s." % B) + a = array_ops.gather(A_diag, B.indices) + a = array_ops.reshape(a, list(a.shape) + [1] * (B.values.shape.ndims - 1)) + return ops.IndexedSlices(a * B.values, B.indices, dense_shape=B.dense_shape) + # TODO(b/69623235): Add a function for finding tensors that share gradients # to eliminate redundant fisher factor computations. diff --git a/tensorflow/contrib/kfac/python/ops/utils_lib.py b/tensorflow/contrib/kfac/python/ops/utils_lib.py index fe8e39c212c2c3381f9aa6fdb9fdf423ff958481..8e424a794691484fdea7d8481677aa641c433d4c 100644 --- a/tensorflow/contrib/kfac/python/ops/utils_lib.py +++ b/tensorflow/contrib/kfac/python/ops/utils_lib.py @@ -40,6 +40,8 @@ _allowed_symbols = [ "fwd_gradients", "ensure_sequence", "batch_execute", + "matmul_sparse_dense", + "matmul_diag_sparse", ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/labeled_tensor/BUILD b/tensorflow/contrib/labeled_tensor/BUILD index 894e6f6946bb59810a9da2d304cc0dd43d25201d..544065dac6a10094a376c18e84521b1a26401cdd 100644 --- a/tensorflow/contrib/labeled_tensor/BUILD +++ b/tensorflow/contrib/labeled_tensor/BUILD @@ -70,6 +70,7 @@ py_test( "python/ops/core_test.py", ], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":_typecheck", ":core", diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core.py b/tensorflow/contrib/labeled_tensor/python/ops/core.py index abc18aa123bb4d40b54d22ec03257c5350118d13..0c6bba758b429a8c4112bc6abb2fae542b5dfc14 100644 --- a/tensorflow/contrib/labeled_tensor/python/ops/core.py +++ b/tensorflow/contrib/labeled_tensor/python/ops/core.py @@ -361,6 +361,10 @@ class LabeledTensor(object): def dtype(self): return self._tensor.dtype + @property + def shape(self): + return self._tensor.shape + @property def name(self): return self._tensor.name diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core_test.py b/tensorflow/contrib/labeled_tensor/python/ops/core_test.py index e70b4923749d89aba1bd0187857d762305daeb07..e378db56afb1d4f9463d2c9b0f1fa4c0feea8fb0 100644 --- a/tensorflow/contrib/labeled_tensor/python/ops/core_test.py +++ b/tensorflow/contrib/labeled_tensor/python/ops/core_test.py @@ -244,6 +244,9 @@ class LabeledTensorTest(test_util.Base): def test_dtype(self): self.assertEqual(self.lt.dtype, self.lt.tensor.dtype) + def test_shape(self): + self.assertEqual(self.lt.shape, self.lt.tensor.shape) + def test_get_shape(self): self.assertEqual(self.lt.get_shape(), self.lt.tensor.get_shape()) diff --git a/tensorflow/contrib/labeled_tensor/python/ops/ops.py b/tensorflow/contrib/labeled_tensor/python/ops/ops.py index c957b41a49b292225e547ce17b0c5a247810325a..3ba1026383ef146adb32197ae41b5c251155bf46 100644 --- a/tensorflow/contrib/labeled_tensor/python/ops/ops.py +++ b/tensorflow/contrib/labeled_tensor/python/ops/ops.py @@ -951,7 +951,7 @@ def define_reduce_op(op_name, reduce_fn): intermediate_axes.append(axis) reduce_op = reduce_fn( - labeled_tensor.tensor, reduction_dimensions, keep_dims=True) + labeled_tensor.tensor, reduction_dimensions, keepdims=True) reduce_lt = core.LabeledTensor(reduce_op, intermediate_axes) return squeeze(reduce_lt, axes_to_squeeze, name=scope) diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD index 852d06e1e3cc8f8deecd15b7436cd4e4a393ad66..cc7bbabf210ded9a31eb789fa8b94e8bde62ea43 100644 --- a/tensorflow/contrib/layers/BUILD +++ b/tensorflow/contrib/layers/BUILD @@ -188,6 +188,7 @@ py_test( size = "small", srcs = ["python/layers/normalization_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":layers_py", "//tensorflow/contrib/framework:framework_py", @@ -353,6 +354,7 @@ py_test( size = "small", srcs = ["python/ops/sparse_ops_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":layers_py", "//tensorflow/python:array_ops", diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py index ef419862b49f4d03d9b711c49155d4ae1252d5bc..337c9e06b870b2cca53fcdbf3d94225660e193c4 100644 --- a/tensorflow/contrib/layers/__init__.py +++ b/tensorflow/contrib/layers/__init__.py @@ -35,6 +35,7 @@ See the @{$python/contrib.layers} guide. @@fully_connected @@GDN @@gdn +@@images_to_sequence @@layer_norm @@linear @@max_pool2d @@ -50,6 +51,7 @@ See the @{$python/contrib.layers} guide. @@scale_gradient @@separable_conv2d @@separable_convolution2d +@@sequence_to_images @@softmax @@spatial_softmax @@stack diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py index b7d34d6435789e54403926a342481971e854b449..9ccb589d698ad83c9654f5523ccdcb35b031b3da 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column.py +++ b/tensorflow/contrib/layers/python/layers/feature_column.py @@ -154,6 +154,7 @@ from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import deprecation +from tensorflow.python.util import nest # Imports the core `InputLayer` symbol in contrib during development. @@ -554,28 +555,70 @@ def sparse_column_with_integerized_feature(column_name, class _SparseColumnHashed(_SparseColumn): """See `sparse_column_with_hash_bucket`.""" + def __new__(cls, + column_name, + is_integerized=False, + bucket_size=None, + lookup_config=None, + combiner="sum", + dtype=dtypes.string, + hash_keys=None): + if hash_keys is not None: + if not isinstance(hash_keys, list) or not hash_keys: + raise ValueError("hash_keys must be a non-empty list.") + if (any([not isinstance(key_pair, list) for key_pair in hash_keys]) or + any([len(key_pair) != 2 for key_pair in hash_keys]) or + any([not isinstance(key, int) for key in nest.flatten(hash_keys)])): + raise ValueError( + "Each element of hash_keys must be a pair of integers.") + obj = super(_SparseColumnHashed, cls).__new__( + cls, + column_name, + is_integerized=is_integerized, + bucket_size=bucket_size, + lookup_config=lookup_config, + combiner=combiner, + dtype=dtype) + obj.hash_keys = hash_keys + return obj + def _do_transform(self, input_tensor): if self.dtype.is_integer: sparse_values = string_ops.as_string(input_tensor.values) else: sparse_values = input_tensor.values - sparse_id_values = string_ops.string_to_hash_bucket_fast( - sparse_values, self.bucket_size, name="lookup") - return sparse_tensor_py.SparseTensor(input_tensor.indices, sparse_id_values, - input_tensor.dense_shape) + if self.hash_keys: + result = [] + for key in self.hash_keys: + sparse_id_values = string_ops.string_to_hash_bucket_strong( + sparse_values, self.bucket_size, key) + result.append( + sparse_tensor_py.SparseTensor(input_tensor.indices, + sparse_id_values, + input_tensor.dense_shape)) + return sparse_ops.sparse_concat(axis=1, sp_inputs=result, name="lookup") + else: + sparse_id_values = string_ops.string_to_hash_bucket_fast( + sparse_values, self.bucket_size, name="lookup") + return sparse_tensor_py.SparseTensor( + input_tensor.indices, sparse_id_values, input_tensor.dense_shape) def sparse_column_with_hash_bucket(column_name, hash_bucket_size, combiner="sum", - dtype=dtypes.string): + dtype=dtypes.string, + hash_keys=None): """Creates a _SparseColumn with hashed bucket configuration. Use this when your sparse features are in string or integer format, but you don't have a vocab file that maps each value to an integer ID. output_id = Hash(input_feature_string) % bucket_size + When hash_keys is set, multiple integer IDs would be created with each key + pair in the `hash_keys`. This is useful to reduce the collision of hashed ids. + Args: column_name: A string defining sparse column name. hash_bucket_size: An int that is > 1. The number of buckets. @@ -588,6 +631,9 @@ def sparse_column_with_hash_bucket(column_name, * "sqrtn": do l2 normalization on features in the column For more information: `tf.embedding_lookup_sparse`. dtype: The type of features. Only string and integer types are supported. + hash_keys: The hash keys to use. It is a list of lists of two uint64s. If + None, simple and fast hashing algorithm is used. Otherwise, multiple + strong hash ids would be produced with each two unit64s in this argument. Returns: A _SparseColumn with hashed bucket configuration @@ -600,7 +646,8 @@ def sparse_column_with_hash_bucket(column_name, column_name, bucket_size=hash_bucket_size, combiner=combiner, - dtype=dtype) + dtype=dtype, + hash_keys=hash_keys) class _SparseColumnKeys(_SparseColumn): diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py index fc8f153fe3abdc83aca5abfa9a4bb5f5d5531480..1de9ab705655db9863d9c7d2630f24283c83d44d 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_test.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py @@ -329,6 +329,55 @@ class FeatureColumnTest(test.TestCase): self.assertEqual(one_hot.sparse_id_column.name, "ids_weighted_by_weights") self.assertEqual(one_hot.length, 3) + def testOneHotColumnWithSparseColumnWithHashKeys(self): + input_values = ["marlo", "unknown", "omar"] + inputs = constant_op.constant(input_values) + hash_keys = [[10, 20], [20, 30]] + hash_column = fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=10, hash_keys=hash_keys) + columns_to_tensors = {} + columns_to_tensors["ids"] = inputs + hash_column.insert_transformed_feature(columns_to_tensors) + self.assertEqual(len(columns_to_tensors), 2) + self.assertTrue(hash_column in columns_to_tensors) + + one_hot_column = fc.one_hot_column(hash_column) + one_hot_output = one_hot_column._to_dnn_input_layer( + columns_to_tensors[hash_column]) + + expected = np.array([[0., 1., 0., 0., 0., 0., 0., 1., 0., + 0.], [0., 1., 0., 0., 0., 0., 0., 0., 0., 1.], + [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) + with self.test_session() as sess: + one_hot_value = sess.run(one_hot_output) + self.assertTrue(np.array_equal(one_hot_value, expected)) + + def testSparseColumnWithHashKeysWithUnexpectedHashKeys(self): + with self.assertRaisesRegexp(ValueError, + "hash_keys must be a non-empty list."): + fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=100, hash_keys=[]) + + with self.assertRaisesRegexp(ValueError, + "hash_keys must be a non-empty list."): + fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=100, hash_keys=1) + + with self.assertRaisesRegexp( + ValueError, "Each element of hash_keys must be a pair of integers."): + fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=100, hash_keys=[1, 2]) + + with self.assertRaisesRegexp( + ValueError, "Each element of hash_keys must be a pair of integers."): + fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=100, hash_keys=["key"]) + + with self.assertRaisesRegexp( + ValueError, "Each element of hash_keys must be a pair of integers."): + fc.sparse_column_with_hash_bucket( + column_name="ids", hash_bucket_size=100, hash_keys=[[1, 2.0]]) + def testMissingValueInOneHotColumnForWeightedSparseColumn(self): # Github issue 12583 ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"]) diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index fb7b2e315efb773770eda8c07e52c4850e48e4da..350bcb3bca11b4cad18ce863ab1496076477aa3c 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -51,7 +51,6 @@ from tensorflow.python.ops import standard_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training import moving_averages -from tensorflow.python.layers.maxout import maxout # TODO(b/28426988): Replace legacy_* fns migrated from slim. # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API. @@ -60,12 +59,12 @@ __all__ = [ 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'convolution3d', 'convolution3d_transpose', 'dense_to_sparse', - 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', - 'linear', 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', - 'relu6', 'repeat', 'scale_gradient', 'separable_conv2d', - 'separable_convolution2d', 'softmax', 'spatial_softmax', 'stack', - 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', - 'maxout' + 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', + 'images_to_sequence', 'layer_norm', 'linear', 'pool', 'max_pool2d', + 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', + 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', + 'sequence_to_images', 'softmax', 'spatial_softmax', 'stack', 'unit_norm', + 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout' ] DATA_FORMAT_NCHW = 'NCHW' @@ -518,8 +517,8 @@ def batch_norm(inputs, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.) - fused: if `True`, use a faster, fused implementation if possible. - If `None`, use the system recommended implementation. + fused: if `None` or `True`, use a faster, fused implementation if possible. + If `False`, use the system recommended implementation. data_format: A string. `NHWC` (default) and `NCHW` are supported. zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new pair of variables 'moving_mean/biased' and 'moving_mean/local_step'. @@ -779,7 +778,7 @@ def batch_norm(inputs, else: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.weighted_moments( - inputs, moments_axes, batch_weights, keep_dims=True) + inputs, moments_axes, batch_weights, keepdims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: @@ -1415,12 +1414,11 @@ def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None): outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. """ - with variable_scope.variable_scope( - scope, 'dense_to_sparse', [tensor]) as sc: + with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc: tensor = ops.convert_to_tensor(tensor) indices = array_ops.where( - math_ops.not_equal( - tensor, constant_op.constant(eos_token, tensor.dtype))) + math_ops.not_equal(tensor, constant_op.constant(eos_token, + tensor.dtype))) values = array_ops.gather_nd(tensor, indices) shape = array_ops.shape(tensor, out_type=dtypes.int64) outputs = sparse_tensor.SparseTensor(indices, values, shape) @@ -2187,6 +2185,36 @@ def layer_norm(inputs, return utils.collect_named_outputs(outputs_collections, sc.name, outputs) +@add_arg_scope +def images_to_sequence(inputs, + data_format=DATA_FORMAT_NHWC, + outputs_collections=None, + scope=None): + """Convert a batch of images into a batch of sequences. + Args: + inputs: a (num_images, height, width, depth) tensor + data_format: A string. `NHWC` (default) and `NCHW` are supported. + outputs_collections: The collections to which the outputs are added. + scope: Optional scope for name_scope. + Returns: + (width, num_images*height, depth) sequence tensor + """ + if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): + raise ValueError('data_format has to be either NCHW or NHWC.') + with ops.name_scope(scope, 'ImagesToSequence', [inputs]) as sc: + inputs = ops.convert_to_tensor(inputs) + df = ('channels_first' + if data_format and data_format.startswith('NC') else 'channels_last') + if df == 'channels_first': + inputs = array_ops.transpose(inputs, [0, 2, 3, 1]) + _, _, width, depth = inputs.get_shape().as_list() + s = array_ops.shape(inputs) + batch_size, height = s[0], s[1] + transposed = array_ops.transpose(inputs, [2, 0, 1, 3]) + outputs = array_ops.reshape(transposed, [width, batch_size * height, depth]) + return utils.collect_named_outputs(outputs_collections, sc, outputs) + + @add_arg_scope def max_pool2d(inputs, kernel_size, @@ -2666,6 +2694,39 @@ def separable_convolution2d( return utils.collect_named_outputs(outputs_collections, sc.name, outputs) +@add_arg_scope +def sequence_to_images(inputs, + height, + output_data_format='channels_last', + outputs_collections=None, + scope=None): + """Convert a batch of sequences into a batch of images. + Args: + inputs: (num_steps, num_batches, depth) sequence tensor + height: the height of the images + output_data_format: Format of output tensor. + Currently supports `'channels_first'` and `'channels_last'`. + outputs_collections: The collections to which the outputs are added. + scope: Optional scope for name_scope. + Returns: + A tensor representing the output of the operation. + """ + with ops.name_scope(scope, 'SequenceToImages', [inputs]) as sc: + inputs = ops.convert_to_tensor(inputs) + width, num_batches, depth = inputs.get_shape().as_list() + if num_batches is None: + num_batches = -1 + else: + num_batches = num_batches // height + reshaped = array_ops.reshape(inputs, + [width, num_batches, height, depth]) + if output_data_format == 'channels_first': + outputs = array_ops.transpose(reshaped, [1, 3, 2, 0]) + else: + outputs = array_ops.transpose(reshaped, [1, 2, 0, 3]) + return utils.collect_named_outputs(outputs_collections, sc, outputs) + + @add_arg_scope def softmax(logits, scope=None): """Performs softmax on Nth dimension of N-dimensional logit tensor. @@ -2686,7 +2747,7 @@ def softmax(logits, scope=None): logits_2d = array_ops.reshape(logits, [-1, num_logits]) predictions = nn.softmax(logits_2d) predictions = array_ops.reshape(predictions, array_ops.shape(logits)) - if context.in_graph_mode(): + if not context.executing_eagerly(): predictions.set_shape(logits.get_shape()) return predictions @@ -2776,9 +2837,9 @@ def spatial_softmax(features, softmax_attention = nn.softmax(features / temperature) expected_x = math_ops.reduce_sum( - pos_x * softmax_attention, [1], keep_dims=True) + pos_x * softmax_attention, [1], keepdims=True) expected_y = math_ops.reduce_sum( - pos_y * softmax_attention, [1], keep_dims=True) + pos_y * softmax_attention, [1], keepdims=True) expected_xy = array_ops.concat([expected_x, expected_y], 1) feature_keypoints = array_ops.reshape(expected_xy, [-1, num_channels.value * 2]) @@ -2879,6 +2940,53 @@ def unit_norm(inputs, dim, epsilon=1e-7, scope=None): return math_ops.div(inputs, array_ops.tile(lengths, multiples)) +@add_arg_scope +def maxout(inputs, num_units, axis=-1, scope=None): + """Adds a maxout op from https://arxiv.org/abs/1302.4389 + + "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron + Courville, + Yoshua Bengio + + Usually the operation is performed in the filter/channel dimension. This can + also be + used after fully-connected layers to reduce number of features. + + Arguments: + inputs: Tensor input + num_units: Specifies how many features will remain after maxout + in the `axis` dimension (usually channel). + This must be multiple of number of `axis`. + axis: The dimension where max pooling will be performed. Default is the + last dimension. + scope: Optional scope for variable_scope. + + Returns: + A `Tensor` representing the results of the pooling operation. + + Raises: + ValueError: if num_units is not multiple of number of features. + """ + with variable_scope.variable_scope(scope, 'MaxOut', [inputs]): + inputs = ops.convert_to_tensor(inputs) + shape = inputs.get_shape().as_list() + num_channels = shape[axis] + if num_channels % num_units: + raise ValueError('number of features({}) is not ' + 'a multiple of num_units({})'.format( + num_channels, num_units)) + shape[axis] = -1 + shape += [num_channels // num_units] + + # Dealing with batches with arbitrary sizes + for i in range(len(shape)): + if shape[i] is None: + shape[i] = array_ops.shape(inputs)[i] + outputs = math_ops.reduce_max( + array_ops.reshape(inputs, shape), -1, keepdims=False) + return outputs + + def poincare_normalize(x, axis=1, epsilon=1e-5, name=None): """Project into the Poincare ball with norm <= 1.0 - epsilon. @@ -2911,7 +3019,7 @@ def poincare_normalize(x, axis=1, epsilon=1e-5, name=None): """ with ops.name_scope(name, 'poincare_normalize', [x]) as name: x = ops.convert_to_tensor(x, name='x') - square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True) + square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True) x_inv_norm = math_ops.rsqrt(square_sum) x_inv_norm = math_ops.minimum((1. - epsilon) * x_inv_norm, 1.) return math_ops.multiply(x, x_inv_norm, name=name) diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 8945690db8ee233e61645c38e6e4d615c4f0da66..997f910a2a97567adbd7ffa3e81a31d2ae0bad7e 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -708,7 +708,7 @@ class Convolution2dTransposeTests(test.TestCase): _layers.convolution2d_transpose(images, 32, 3, data_format='CHWN') def testOutputSizeWithStrideOneSamePaddingNCHW(self): - # `NCHW` data fomat is only supported for `GPU` device. + # `NCHW` data format is only supported for `GPU` device. if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 32 @@ -1308,12 +1308,13 @@ class DenseToSparseTest(test.TestCase): expected_constant = np.reshape(np.arange(24, dtype=np.int64), (3, 4, 2)) tensor = constant_op.constant(expected_constant) sparse = _layers.dense_to_sparse(tensor) - dense = sparse_ops.sparse_to_dense( - sparse.indices, sparse.dense_shape, sparse.values) + dense = sparse_ops.sparse_to_dense(sparse.indices, sparse.dense_shape, + sparse.values) with self.test_session() as sess: constant = sess.run(dense) self.assertAllEqual(expected_constant, constant) + class DropoutTest(test.TestCase): def testCreateDropout(self): @@ -2195,7 +2196,7 @@ class BatchNormTest(test.TestCase): # After initialization moving_mean == 0 and moving_variance == 1. self.assertAllClose(mean, [0] * 3) self.assertAllClose(variance, [1] * 3) - # Simulate assigment from saver restore. + # Simulate assignment from saver restore. init_assigns = [ state_ops.assign(moving_mean, expected_mean), state_ops.assign(moving_variance, expected_var) @@ -2949,6 +2950,28 @@ class GDNTest(test.TestCase): self.assertAllClose(y, x * np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6) +class ImagesToSequenceTest(test.TestCase): + + def testInvalidDataFormat(self): + height, width = 7, 11 + images = np.random.uniform(size=(5, height, width, 2)) + with self.assertRaisesRegexp(ValueError, + 'data_format has to be either NCHW or NHWC.'): + _layers.images_to_sequence(images, data_format='CHWN') + + def testImagesToSequenceDims(self): + height, width = 7, 11 + images = np.random.uniform(size=(2, height, width, 5)).astype(np.float32) + output = _layers.images_to_sequence(images) + self.assertListEqual(output.get_shape().as_list(), [11, 14, 5]) + + def testImagesToSequenceNCHW(self): + height, width = 7, 11 + images = np.random.uniform(size=(2, 5, height, width)).astype(np.float32) + output = _layers.images_to_sequence(images, data_format='NCHW') + self.assertListEqual(output.get_shape().as_list(), [11, 14, 5]) + + class MaxPool2DTest(test.TestCase): def testInvalidDataFormat(self): @@ -3417,6 +3440,33 @@ class ScaleGradientTests(test.TestCase): np.testing.assert_array_equal([3 * 2], g_x.eval()) +class SequenceToImagesTest(test.TestCase): + + def testImagesToSequenceDims(self): + num_batches = 14 + num_time_steps = 11 + num_channels = 5 + desired_height = 7 + sequence = np.random.uniform(size=(num_time_steps, + num_batches, + num_channels)).astype(np.float32) + output = _layers.sequence_to_images(sequence, desired_height) + self.assertListEqual(output.get_shape().as_list(), [2, 7, 11, 5]) + + def testImagesToSequenceNCHW(self): + num_batches = 14 + num_time_steps = 11 + num_channels = 5 + desired_height = 7 + sequence = np.random.uniform(size=(num_time_steps, + num_batches, + num_channels)).astype(np.float32) + output = _layers.sequence_to_images(sequence, + desired_height, + output_data_format='channels_first') + self.assertListEqual(output.get_shape().as_list(), [2, 5, 7, 11]) + + class SoftmaxTests(test.TestCase): def setUp(self): @@ -4085,5 +4135,31 @@ class LegacyFullyConnectedTest(test.TestCase): _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax) +class MaxOutTest(test.TestCase): + + def test_simple(self): + inputs = random_ops.random_uniform((64, 10, 36), seed=1) + graph = _layers.maxout(inputs, num_units=3) + self.assertEqual(graph.get_shape().as_list(), [64, 10, 3]) + + def test_fully_connected(self): + inputs = random_ops.random_uniform((64, 50), seed=1) + graph = _layers.fully_connected(inputs, 50) + graph = _layers.maxout(graph, num_units=10) + self.assertEqual(graph.get_shape().as_list(), [64, 10]) + + def test_nchw(self): + inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1) + graph = _layers.conv2d(inputs, 10, 3, padding='SAME') + graph = _layers.maxout(graph, num_units=1) + self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1]) + + def test_invalid_shape(self): + inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1) + graph = _layers.conv2d(inputs, 3, 10) + with self.assertRaisesRegexp(ValueError, 'number of features'): + graph = _layers.maxout(graph, num_units=2) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py index cdceea6fee5bdb5aeb6537ea55d25ccf107def4c..69d927e1b3001d14dd1af2f890b07c1a57ab2cfc 100644 --- a/tensorflow/contrib/layers/python/layers/optimizers.py +++ b/tensorflow/contrib/layers/python/layers/optimizers.py @@ -41,7 +41,7 @@ OPTIMIZER_CLS_NAMES = { "Adagrad": train.AdagradOptimizer, "Adam": train.AdamOptimizer, "Ftrl": train.FtrlOptimizer, - "Momentum": lambda lr: train.MomentumOptimizer(lr, momentum=0.9), + "Momentum": lambda learning_rate: train.MomentumOptimizer(learning_rate, momentum=0.9), # pylint: disable=line-too-long "RMSProp": train.RMSPropOptimizer, "SGD": train.GradientDescentOptimizer, } diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py index 1ea25bd1a5685eb6f840e621b5739029a660aa0f..a4461a20e54c289886f1a1beb255de12fc054afe 100644 --- a/tensorflow/contrib/layers/python/layers/optimizers_test.py +++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py @@ -61,7 +61,8 @@ class OptimizersTest(test.TestCase): optimizers = [ "SGD", gradient_descent.GradientDescentOptimizer, gradient_descent.GradientDescentOptimizer(learning_rate=0.1), - lambda lr: gradient_descent.GradientDescentOptimizer(learning_rate=lr) + lambda lr: gradient_descent.GradientDescentOptimizer(learning_rate=lr), + "Momentum" ] for optimizer in optimizers: with ops.Graph().as_default() as g: diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD index 3c782b54a8559a6aac19d12ea11a9c76bffdb9c3..895f70eecfa3214b25a92891722a510d7294552c 100644 --- a/tensorflow/contrib/learn/BUILD +++ b/tensorflow/contrib/learn/BUILD @@ -115,6 +115,7 @@ py_test( size = "small", srcs = ["python/learn/learn_io/data_feeder_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/python:client_testlib", @@ -170,6 +171,7 @@ tf_py_test( "//tensorflow/python:variables", "//tensorflow/python/estimator", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) py_test( @@ -188,6 +190,7 @@ py_test( size = "small", srcs = ["python/learn/graph_actions_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/framework:framework_py", @@ -388,6 +391,7 @@ py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:lookup_ops", + "//tensorflow/python:math_ops", "//tensorflow/python:session", "//tensorflow/python:sparse_tensor", "//tensorflow/python:variables", @@ -425,6 +429,7 @@ py_test( size = "medium", srcs = ["python/learn/estimators/kmeans_test.py"], srcs_version = "PY2AND3", + tags = ["nomac"], # b/73741358 deps = [ ":learn", "//tensorflow/python:array_ops", @@ -583,6 +588,7 @@ py_test( size = "small", srcs = ["python/learn/learn_io/io_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/learn/python/learn/datasets", @@ -812,6 +818,7 @@ py_test( size = "small", srcs = ["python/learn/utils/saved_model_export_utils_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/layers:layers_py", diff --git a/tensorflow/contrib/learn/README.md b/tensorflow/contrib/learn/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d516bffc5e0327a3400068b35de5503e5a925a54 --- /dev/null +++ b/tensorflow/contrib/learn/README.md @@ -0,0 +1,143 @@ +EVERYTHING IN THIS DIRECTORY IS DEPRECATED. + +Using functions or classes will result in warnings. + +Instructions for converting to current alternatives are included in the +warnings. A high-level overview is below. + +## Canned Estimators + +Many canned estimators (subclasses of `Estimator`) have equivalents in core: +`DNNClassifier`, `DNNRegressor`, `DNNEstimator`, `LinearClassifier`, +`LinearRegressor`, `DNNLinearCombinedClassifier` and +`DNNLinearCombinedRegressor`. They are exposed under `tf.estimator`. +`DNNEstimator`, `LinearEstimator` and `DNNLinearCombinedEstimator` +are exposed under `tf.contrib.estimator`. + +To migrate to the new api, users need to take the following steps: + +* Replace `tf.contrib.learn` with `tf.estimator`. +* If you subclass any of the estimators, stop doing that. You should be able to + write a factory method that returns a canned estimator instead. If this is not + possible (if you override methods from the canned estimator), consider writing + a custom estimator instead. See `tf.estimator.Estimator`. +* Set `loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE` to preserve loss + reduction as the average over batch. +* Some optimizer-related arguments are no longer passed in the estimator + constructor. Instead, we provide methods that perform the same job by wrapping + an optimizer. Specifically: + * `gradient_clip_norm`: Use `tf.contrib.estimator.clip_gradients_by_norm` + * `embedding_lr_multipliers`: Not supported. + Other arguments: + * `input_layer_min_slice_size`: Replaced by `input_layer_partitioner` + * `enable_centered_bias`: Not supported. Dropping this argument is unlikely to + harm your model. + * `feature_engineering_fn`: Not supported. You can call your + `feature_engineering_fn` inside your input_fn: + ```python + def new_input_fn(): + features, labels = old_input_fn() + return feature_engineering_fn(features, labels) + ``` +* Use `tf.reshape` to reshape labels in your `input_fn`. `tf.estimator` + classifiers and regressors expect labels as a 2D Tensor of shape + `[batch_size, 1]`, or `[batch_size, n_labels]`. In contrast, + `tf.contrib.learn` classifiers and regressors supported labels with shape + `[batch_size]`. +* If you pass custom metrics from the `evaluate()` method call, use + `tf.contrib.estimator.add_metrics`. +* Replace your `serving_input_fn` with a `serving_input_receiver_fn`. + Note this should be entirely distinct from your training `input_fn`, so if you + previously had one `input_fn` with different "modes", you should now factor + that apart. Where the former returned either a simple `(features, labels)` + tuple or `InputFnOps`, you should now return a `ServingInputReceiver`. + If you were generating your `serving_input_fn` using the + `build_parsing_serving_input_fn` helper, you can simply drop in the + replacement `build_parsing_serving_input_receiver_fn`. + +Some remaining estimators/classes: + +* `DynamicRnnEstimator`: Consider a custom `model_fn`. +* `KMeansClustering`: Use `tf.contrib.factorization.KMeansClustering`. +* `LogisticRegressor`: Not supported. Instead, use `binary_classification_head` + with a custom `model_fn`, or with `DNNEstimator`. +* `StateSavingRnnEstimator`: Consider a custom `model_fn`. +* SVM: Consider a custom `model_fn`. +* `LinearComposableModel` and `DNNComposableModel`: Not supported. + Consider `tf.contrib.estimator.DNNEstimator`, or write a custom model_fn. +* `MetricSpec`: Deprecated. For adding custom metrics to canned Estimators, use + `tf.contrib.estimator.add_metrics`. + +## Estimator +`tf.contrib.learn.Estimator` is migrated to `tf.estimator.Estimator`. + +To migrate, users need to take the following steps: + +* Replace `tf.contrib.learn.Estimator` with `tf.estimator.Estimator`. +* If you pass a `config` argument to `Estimator`, this must be + `tf.estimator.RunConfig`. You may need to edit your code accordingly. +* Edit your `model_fn` to return `tf.estimator.EstimatorSpec`. Refer to + `EstimatorSpec` for documentation of specific fields. +* If your `model_fn` uses the `mode` argument, use `tf.estimator.ModeKeys`. + +Some related classes: +* `Evaluable`, `Trainable`: Not supported, merged into `tf.estimator.Estimator`. +* ExportStrategy: Replaced by `tf.estimator.Exporter`. + +## Head/MultiHead +These classes are now supported under `tf.contrib.estimator`, e.g. +`tf.contrib.estimator.multi_class_head` and `tf.contrib.estimator.multi_head`. + +Some differences: + +* `multi_class_head`: If you use `tf.contrib.learn.multi_class_head` with + `n_classes=2`, switch to `tf.contrib.estimator.binary_classification_head`. +* `loss_only_head`: Not supported. +* `poisson_regression_head`: Not supported (yet). +* `binary_svm_head`: Not supported (yet). +* `no_op_train_fn`: Replace it with `tf.no_op`. + +Some arguments are renamed, please refer to documentation. In addition: + +* `loss_fn`: Supported for `multi_label_head`. If you need it for other heads, + please open an issue. +* `metric_class_ids`: Not supported (yet). +* `enable_centered_bias`: Not supported. Dropping this argument is unlikely to + harm your model. +* `label_name`: Not needed in `tf.estimator`. If you don’t use `multi_head`, + drop this argument. If you use `multi_head`, refer to + `tf.contrib.estimator.multi_head` documentation. + +## Experiment Class - Distributed Training Tooling + +Switch to `tf.estimator.train_and_evaluate`. Some differences: + +* Most of the constructor arguments, like `train_input_fn`, `eval_input_fn`, + should be wrapped into `tf.estimator.TrainSpec` and `tf.estimator.EvalSpec`. +* Remove the `experiment_fn`. Instead, create the `Estimator`, + `train_spec` and `eval_spec`, then call `tf.estimator.train_and_evaluate` + directly. +* Inside `tf.estimator.EvalSpec`, the `exporter` field is the replacement + for `export_strategy`. To be precise, `tf.estimator.LatestExporter` is the + replacement for `tf.contrib.learn.make_export_strategy`. If you want to export + only at the end of training use `tf.estimator.FinalExporter`. +* If the `TF_CONFIG` environment variable is constructed manually, please read + the `train_and_evaluate` documentation for the new requirementds (in + particular, the chief node and evaluator node). + +## Others Classes and Functions + +* `tf.contrib.learn.datasets` is deprecated. We are adding ready to use datasets + to tensorflow/models. Many smaller datasets are available from other sources, + such as scikits.learn. Some Python processing may have to be written, but this + is straightforward to implement using the standard modules. +* `tf.contrib.learn.preprocessing`: Deprecated. The python-only preprocessing + functions are not a good fit for TensorFlow. Please use `tf.data`, and + consider tensorflow/transform for more complex use cases. +* `tf.contrib.learn.models`: Not supported, use canned estimators instead. +* `tf.contrib.learn.monitors`: Implement `SessionRunHook` instead. Hook + implementations are in `tf.train`. +* `tf.contrib.learn.learn_io`: Use the methods in `tf.estimator.inputs`, such as + `tf.estimator.inputs.numpy_input_fn`. Some utility functions have no + equivalent, we encourage the use of `tf.data`. + diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py index 3698af027e38f1063ad829c26eb179734968f813..79bd73faaf1301a2fc4999b64f88d30542577980 100644 --- a/tensorflow/contrib/learn/__init__.py +++ b/tensorflow/contrib/learn/__init__.py @@ -13,8 +13,11 @@ # limitations under the License. # ============================================================================== -# TODO(ptucker,ipolosukhin): Improve descriptions. -"""High level API for learning. +"""High level API for learning (DEPRECATED). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. See the @{$python/contrib.learn} guide. diff --git a/tensorflow/contrib/learn/python/__init__.py b/tensorflow/contrib/learn/python/__init__.py index bbebd5ab9792cb937219cf937f08c4d4e6e44a92..df23aeb2c433c2b4392f706730f715246ce01cea 100644 --- a/tensorflow/contrib/learn/python/__init__.py +++ b/tensorflow/contrib/learn/python/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level API for learning with TensorFlow.""" +"""High level API for learning with TensorFlow (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/__init__.py b/tensorflow/contrib/learn/python/learn/__init__.py index cdc67c77d5fd1df61016835dc75ba44feb458cf9..76e0e8ac8f19026086959f3b197cfd1a81e65a3e 100644 --- a/tensorflow/contrib/learn/python/learn/__init__.py +++ b/tensorflow/contrib/learn/python/learn/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level API for learning with TensorFlow.""" +"""High level API for learning with TensorFlow (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py index 2284ec46e971731af74f17678fc0d1d3888419e2..fed1c44d1970bf07c808ace817aa9972d7776d88 100644 --- a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py +++ b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py @@ -12,20 +12,47 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Some common SessionRunHook classes.""" +"""Some common SessionRunHook classes (deprected). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.training import basic_session_run_hooks +from tensorflow.python.util.deprecation import deprecated_alias # pylint: disable=invalid-name -LoggingTensorHook = basic_session_run_hooks.LoggingTensorHook -StopAtStepHook = basic_session_run_hooks.StopAtStepHook -CheckpointSaverHook = basic_session_run_hooks.CheckpointSaverHook -StepCounterHook = basic_session_run_hooks.StepCounterHook -NanLossDuringTrainingError = basic_session_run_hooks.NanLossDuringTrainingError -NanTensorHook = basic_session_run_hooks.NanTensorHook -SummarySaverHook = basic_session_run_hooks.SummarySaverHook +LoggingTensorHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.LoggingTensorHook', + 'tf.train.LoggingTensorHook', + basic_session_run_hooks.LoggingTensorHook) +StopAtStepHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.StopAtStepHook', + 'tf.train.StopAtStepHook', + basic_session_run_hooks.StopAtStepHook) +CheckpointSaverHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.CheckpointSaverHook', + 'tf.train.CheckpointSaverHook', + basic_session_run_hooks.CheckpointSaverHook) +StepCounterHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.StepCounterHook', + 'tf.train.StepCounterHook', + basic_session_run_hooks.StepCounterHook) +NanLossDuringTrainingError = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.NanLossDuringTrainingError', + 'tf.train.NanLossDuringTrainingError', + basic_session_run_hooks.NanLossDuringTrainingError) +NanTensorHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.NanTensorHook', + 'tf.train.NanTensorHook', + basic_session_run_hooks.NanTensorHook) +SummarySaverHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.SummarySaverHook', + 'tf.train.SummarySaverHook', + basic_session_run_hooks.SummarySaverHook) # pylint: enable=invalid-name diff --git a/tensorflow/contrib/learn/python/learn/datasets/__init__.py b/tensorflow/contrib/learn/python/learn/datasets/__init__.py index 7240b0de149051afa045a8113f9e9b212840c311..3c34712ac859d32f549468345950a93d2ed2aa56 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/__init__.py +++ b/tensorflow/contrib/learn/python/learn/datasets/__init__.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Dataset utilities and synthetic/reference datasets.""" +"""Dataset utilities and synthetic/reference datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.contrib.learn.python.learn.datasets import mnist from tensorflow.contrib.learn.python.learn.datasets import synthetic from tensorflow.contrib.learn.python.learn.datasets import text_datasets +from tensorflow.python.util.deprecation import deprecated # Export load_iris and load_boston. load_iris = base.load_iris @@ -51,6 +57,7 @@ SYNTHETIC = { } +@deprecated(None, 'Please use tf.data.') def load_dataset(name, size='small', test_with_fake_data=False): """Loads dataset by name. @@ -73,8 +80,9 @@ def load_dataset(name, size='small', test_with_fake_data=False): return DATASETS[name]() +@deprecated(None, 'Please use tf.data.') def make_dataset(name, n_samples=100, noise=None, seed=42, *args, **kwargs): - """Creates binary synthetic datasets + """Creates binary synthetic datasets. Args: name: str, name of the dataset to generate diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py index 18bf16e246bcb6c0a6a4ce75bc5c28d4e0d045e5..3b5c9b97c08a388e1f35249967b6cab26861f100 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/base.py +++ b/tensorflow/contrib/learn/python/learn/datasets/base.py @@ -12,7 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Base utilities for loading datasets.""" + +"""Base utilities for loading datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,19 +29,20 @@ import csv import os from os import path import random -import tempfile import time import numpy as np from six.moves import urllib -from tensorflow.contrib.framework import deprecated from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated + Dataset = collections.namedtuple('Dataset', ['data', 'target']) Datasets = collections.namedtuple('Datasets', ['train', 'validation', 'test']) +@deprecated(None, 'Use tf.data instead.') def load_csv_with_header(filename, target_dtype, features_dtype, @@ -55,6 +62,7 @@ def load_csv_with_header(filename, return Dataset(data=data, target=target) +@deprecated(None, 'Use tf.data instead.') def load_csv_without_header(filename, target_dtype, features_dtype, @@ -72,6 +80,7 @@ def load_csv_without_header(filename, return Dataset(data=data, target=target) +@deprecated(None, 'Use tf.data instead.') def shrink_csv(filename, ratio): """Create a smaller dataset of only 1/ratio of original data.""" filename_small = filename.replace('.', '_small.') @@ -86,6 +95,7 @@ def shrink_csv(filename, ratio): i += 1 +@deprecated(None, 'Use scikits.learn.datasets.') def load_iris(data_path=None): """Load Iris dataset. @@ -102,6 +112,7 @@ def load_iris(data_path=None): data_path, target_dtype=np.int, features_dtype=np.float) +@deprecated(None, 'Use scikits.learn.datasets.') def load_boston(data_path=None): """Load Boston housing dataset. @@ -118,7 +129,12 @@ def load_boston(data_path=None): data_path, target_dtype=np.float, features_dtype=np.float) -def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None): +@deprecated(None, 'Use the retry module or similar alternatives.') +def retry(initial_delay, + max_delay, + factor=2.0, + jitter=0.25, + is_retriable=None): """Simple decorator for wrapping retriable functions. Args: @@ -154,7 +170,7 @@ def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None): for delay in delays(): try: return fn(*args, **kwargs) - except Exception as e: # pylint: disable=broad-except) + except Exception as e: # pylint: disable=broad-except if is_retriable is None: continue @@ -178,11 +194,13 @@ def _is_retriable(e): return isinstance(e, IOError) and e.errno in _RETRIABLE_ERRNOS +@deprecated(None, 'Please use urllib or similar directly.') @retry(initial_delay=1.0, max_delay=16.0, is_retriable=_is_retriable) def urlretrieve_with_retry(url, filename=None): return urllib.request.urlretrieve(url, filename) +@deprecated(None, 'Please write your own downloading logic.') def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. diff --git a/tensorflow/contrib/learn/python/learn/datasets/mnist.py b/tensorflow/contrib/learn/python/learn/datasets/mnist.py index 37f9175015a239f763c7721cf36ab8063c0a3e32..abbb44c2f5b701829ce16f64eadd8ebc04c84e2c 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/mnist.py +++ b/tensorflow/contrib/learn/python/learn/datasets/mnist.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Functions for downloading and reading MNIST data.""" +"""Functions for downloading and reading MNIST data (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated # CVDF mirror of http://yann.lecun.com/exdb/mnist/ DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' @@ -37,6 +43,7 @@ def _read32(bytestream): return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] +@deprecated(None, 'Please use tf.data to implement this functionality.') def extract_images(f): """Extract the images into a 4D uint8 numpy array [index, y, x, depth]. @@ -65,6 +72,7 @@ def extract_images(f): return data +@deprecated(None, 'Please use tf.one_hot on tensors.') def dense_to_one_hot(labels_dense, num_classes): """Convert class labels from scalars to one-hot vectors.""" num_labels = labels_dense.shape[0] @@ -74,6 +82,7 @@ def dense_to_one_hot(labels_dense, num_classes): return labels_one_hot +@deprecated(None, 'Please use tf.data to implement this functionality.') def extract_labels(f, one_hot=False, num_classes=10): """Extract the labels into a 1D uint8 numpy array [index]. @@ -103,7 +112,15 @@ def extract_labels(f, one_hot=False, num_classes=10): class DataSet(object): + """Container class for a dataset (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def __init__(self, images, labels, @@ -210,6 +227,8 @@ class DataSet(object): return self._images[start:end], self._labels[start:end] +@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def read_data_sets(train_dir, fake_data=False, one_hot=False, @@ -275,5 +294,7 @@ def read_data_sets(train_dir, return base.Datasets(train=train, validation=validation, test=test) +@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def load_mnist(train_dir='MNIST-data'): return read_data_sets(train_dir) diff --git a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py index 6e0ba38941ce4650ede9f7210e284bde2ed8e6a9..a4848fa64a72f031ef35c0c3256e97a7326acd60 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py +++ b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Produce DBpedia datasets of a smaller size.""" +"""Produce DBpedia datasets of a smaller size (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py index 9a843168c27d9cae3f55efe4fe4c688d86c745f3..6a0e3350b3d1052249160a2a997a76de7a5040c3 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Synthetic dataset generators.""" +"""Synthetic dataset generators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,8 +26,10 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.learn.python.learn.datasets.base import Dataset +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def circles(n_samples=100, noise=None, seed=None, @@ -93,6 +100,7 @@ def circles(n_samples=100, return Dataset(data=X[indices], target=y[indices]) +@deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def spirals(n_samples=100, noise=None, seed=None, diff --git a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py index 2596a2ecaf1572506504831e8b08fab9b5dbc119..ce9466301728082f8e9d99c90989ba8fe623bcf0 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py +++ b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Text datasets.""" +"""Text datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -26,10 +31,12 @@ import numpy as np from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz' +@deprecated(None, 'See contrib/learn/README.md') def maybe_download_dbpedia(data_dir): """Download if DBpedia data is not present.""" train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv') @@ -41,6 +48,7 @@ def maybe_download_dbpedia(data_dir): tfile.extractall(data_dir) +@deprecated(None, 'See contrib/learn/README.md') def load_dbpedia(size='small', test_with_fake_data=False): """Get DBpedia datasets from CSV files.""" if not test_with_fake_data: diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py index 4981750c94c7ac31e23b7a3f71ca30e3c9573a20..3e64595f312bcc2a2e8dcba589fb993a249b684b 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py +++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""An estimator is a rule for calculating an estimate of a given quantity. +"""An estimator is a rule for calculating an estimate of a given quantity (deprecated). + +These classes are deprecated and replaced with `tf.estimator`. + +See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. # Estimators diff --git a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py index 15277415a1ce83dc1d4a334e60fe1933ba244df0..1f0e4663d060a3850e2002b27f809fde1db47e48 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== -"""sklearn cross-support.""" +"""sklearn cross-support (deprecated).""" from __future__ import absolute_import from __future__ import division @@ -132,6 +132,8 @@ class _TransformerMixin(): class NotFittedError(ValueError, AttributeError): """Exception class to raise if estimator is used before fitting. + USE OF THIS EXCEPTION IS DEPRECATED. + This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py index a02c726c74946d93b8e1726473db746220b00795..1fa58271e2b886cd143683a759145fd750791473 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py +++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""TensorFlow composable models used as building blocks for estimators.""" +"""TensorFlow composable models used as building blocks for estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -34,6 +39,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import variable_scope from tensorflow.python.summary import summary +from tensorflow.python.util.deprecation import deprecated class _ComposableModel(object): @@ -46,6 +52,7 @@ class _ComposableModel(object): _ComposableModel and its subclasses are not part of the public tf.learn API. """ + @deprecated(None, "Please use model_fns in tf.estimator.") def __init__(self, num_label_columns, optimizer, @@ -141,6 +148,10 @@ class _ComposableModel(object): class LinearComposableModel(_ComposableModel): """A _ComposableModel that implements linear regression. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Instances of this class can be used to build estimators through the use of composition. """ @@ -252,6 +263,10 @@ class LinearComposableModel(_ComposableModel): class DNNComposableModel(_ComposableModel): """A _ComposableModel that implements a DNN. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Instances of this class can be used to build estimators through the use of composition. """ diff --git a/tensorflow/contrib/learn/python/learn/estimators/constants.py b/tensorflow/contrib/learn/python/learn/estimators/constants.py index fc69e810244a182b864be856e6720f8584f7aa65..d2548946bc77dea7c452d61c7e2b6e12c3d6239a 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/constants.py +++ b/tensorflow/contrib/learn/python/learn/estimators/constants.py @@ -13,9 +13,11 @@ # limitations under the License. # ============================================================================== -"""Constants regarding Estimators. +"""Constants regarding Estimators (deprecated). -This file is obsoleted in the move of Estimator to core. +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. """ from __future__ import absolute_import from __future__ import division @@ -25,6 +27,8 @@ from __future__ import print_function class ProblemType(object): """Enum-like values for the type of problem that the model solves. + THIS CLASS IS DEPRECATED. + These values are used when exporting the model to produce the appropriate signature function for serving. diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py index 9d5f6c2bf969d7c85d251bf1b06a0307a41b2297..24b067b7e38b12df3d1d0c49f626344217218571 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/debug.py +++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Debug estimators. +"""Debug estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Debug estimators are bias-only estimators that can be used for debugging and as simple baselines. @@ -118,6 +122,10 @@ def debug_model_fn(features, labels, mode, params, config=None): class DebugClassifier(estimator.Estimator): """A classifier for TensorFlow Debug models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -237,6 +245,10 @@ class DebugClassifier(estimator.Estimator): class DebugRegressor(estimator.Estimator): """A regressor for TensorFlow Debug models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py index c17b41c0f767e19d9c3635a8f60347a49b297cfb..eabebb7e881558471c343c0573cc9a8f4a425312 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Deep Neural Network estimators.""" +"""Deep Neural Network estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -212,6 +217,10 @@ def _dnn_model_fn(features, labels, mode, params, config=None): class DNNClassifier(estimator.Estimator): """A classifier for TensorFlow DNN models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -521,6 +530,10 @@ class DNNClassifier(estimator.Estimator): class DNNRegressor(estimator.Estimator): """A regressor for TensorFlow DNN models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -796,6 +809,10 @@ class DNNRegressor(estimator.Estimator): class DNNEstimator(estimator.Estimator): """A Estimator for TensorFlow DNN models with user specified _Head. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py index 726612235050def6e7addb503cc6646a25de0e42..3d85533d92d17095bae9a69f229171e1bf61ba10 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow estimators for Linear and DNN joined training models.""" +"""TensorFlow estimators for Linear and DNN joined training models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -372,6 +377,10 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None): class DNNLinearCombinedEstimator(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. @@ -490,6 +499,10 @@ class DNNLinearCombinedEstimator(estimator.Estimator): class DNNLinearCombinedClassifier(estimator.Estimator): """A classifier for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. @@ -832,6 +845,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator): class DNNLinearCombinedRegressor(estimator.Estimator): """A regressor for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py index 69440e823ef1ed2d739f28bc14587891f2de80bb..a703dc66e922d48ceb64edc2a979061b8e45db49 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Estimator for Dynamic RNNs.""" +"""Estimator for Dynamic RNNs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -540,6 +545,12 @@ def _get_dynamic_rnn_model_fn( class DynamicRnnEstimator(estimator.Estimator): + """Dynamically unrolled RNN (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, problem_type, diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py index 63d0f1e1d454354948654e8ad4208a8852d356ca..5262e04e16ee85d1672dd495f05084ff07c8dd18 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Base Estimator class.""" +"""Base Estimator class (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -138,6 +143,7 @@ def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1): return df.input_builder, df.get_feed_dict_fn() +@deprecated(None, 'Please specify feature columns explicitly.') def infer_real_valued_columns_from_input_fn(input_fn): """Creates `FeatureColumn` objects for inputs defined by `input_fn`. @@ -158,6 +164,7 @@ def infer_real_valued_columns_from_input_fn(input_fn): return layers.infer_real_valued_columns(features) +@deprecated(None, 'Please specify feature columns explicitly.') def infer_real_valued_columns_from_input(x): """Creates `FeatureColumn` objects for inputs defined by input `x`. @@ -211,7 +218,7 @@ def _get_replica_device_setter(config): 'Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable', 'MutableHashTableV2', 'MutableHashTableOfTensors', 'MutableHashTableOfTensorsV2', 'MutableDenseHashTable', - 'MutableDenseHashTableV2' + 'MutableDenseHashTableV2', 'VarHandleOp' ] if config.task_type: @@ -389,6 +396,10 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, trainable.Trainable): """Abstract BaseEstimator class to train and evaluate TensorFlow models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Users should not instantiate or subclass this class. Instead, use an `Estimator`. """ @@ -399,6 +410,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, # TODO(wicke): Remove this once launcher takes over config functionality _Config = run_config.RunConfig # pylint: disable=invalid-name + @deprecated(None, 'Please replace uses of any Estimator from tf.contrib.learn' + ' with an Estimator from tf.estimator.*') def __init__(self, model_dir=None, config=None): """Initializes a BaseEstimator instance. @@ -1074,6 +1087,10 @@ def _identity_feature_engineering_fn(features, labels): class Estimator(BaseEstimator): """Estimator class is the basic TensorFlow model trainer/evaluator. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. """ def __init__(self, @@ -1458,8 +1475,14 @@ class Estimator(BaseEstimator): # For time of deprecation x,y from Estimator allow direct access. # pylint: disable=protected-access class SKCompat(sklearn.BaseEstimator): - """Scikit learn wrapper for TensorFlow Learn Estimator.""" + """Scikit learn wrapper for TensorFlow Learn Estimator. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please switch to the Estimator interface.') def __init__(self, estimator): self._estimator = estimator diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py index fd47710e3015de9ae6a453f98978b0ef8f88968c..e4c31396baf8271c49395a2b87b454dbc77177e2 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utils for Estimator.""" +"""Utils for Estimator (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index bc0e6fc0091c9b5419ab526855b404eb4a927e97..2b4b6eff39f4fc8a20a149edfc07d2f4f27a9bae 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -12,8 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Abstractions for the head(s) of a model. +"""Abstractions for the head(s) of a model (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. """ + from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -47,11 +52,16 @@ from tensorflow.python.summary import summary from tensorflow.python.training import training from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated class Head(object): """Interface for the head/top of a model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Given logits (or output of a hidden layer), a Head knows how to compute predictions, loss, default metric and export signature. It is meant to, @@ -177,11 +187,13 @@ class Head(object): raise NotImplementedError("Calling an abstract method.") +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def regression_head(label_name=None, weight_column_name=None, label_dimension=1, enable_centered_bias=False, - head_name=None): + head_name=None, + link_fn=None): """Creates a `Head` for linear regression. Args: @@ -199,6 +211,8 @@ def regression_head(label_name=None, head_name: name of the head. If provided, predictions, summary and metrics keys will be suffixed by `"/" + head_name` and the default variable scope will be `head_name`. + link_fn: link function to convert logits to predictions. If provided, + this link function will be used instead of identity. Returns: An instance of `Head` for linear regression. @@ -210,9 +224,10 @@ def regression_head(label_name=None, enable_centered_bias=enable_centered_bias, head_name=head_name, loss_fn=_mean_squared_loss, - link_fn=array_ops.identity) + link_fn=(link_fn if link_fn is not None else array_ops.identity)) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def poisson_regression_head(label_name=None, weight_column_name=None, label_dimension=1, @@ -251,6 +266,7 @@ def poisson_regression_head(label_name=None, # TODO(zakaria): Consider adding a _RegressionHead for logistic_regression +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_class_head(n_classes, label_name=None, weight_column_name=None, @@ -332,6 +348,7 @@ def multi_class_head(n_classes, label_keys=label_keys) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def binary_svm_head( label_name=None, weight_column_name=None, @@ -367,6 +384,7 @@ def binary_svm_head( thresholds=thresholds) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_label_head(n_classes, label_name=None, weight_column_name=None, @@ -427,6 +445,7 @@ def multi_label_head(n_classes, loss_fn=_wrap_custom_loss_fn(loss_fn) if loss_fn else None) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def loss_only_head(loss_fn, head_name=None): """Creates a Head that contains only loss terms. @@ -444,6 +463,7 @@ def loss_only_head(loss_fn, head_name=None): return _LossOnlyHead(loss_fn, head_name=head_name) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_head(heads, loss_weights=None): """Creates a MultiHead stemming from same logits/hidden layer. @@ -476,6 +496,7 @@ def multi_head(heads, loss_weights=None): return _MultiHead(heads, loss_merger=_weighted_loss_merger) +@deprecated(None, "Use 'lambda _: tf.no_op()'.") def no_op_train_fn(loss): del loss return control_flow_ops.no_op() diff --git a/tensorflow/contrib/learn/python/learn/estimators/head_test.py b/tensorflow/contrib/learn/python/learn/estimators/head_test.py index 3881bf533d642bef68fa9ab4ba908bbb8f7f8091..7c2d9bb0767cb979dae9c84b5342d129225677ed 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head_test.py @@ -33,6 +33,7 @@ from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import lookup_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables from tensorflow.python.ops.losses import losses as losses_lib from tensorflow.python.platform import test @@ -153,6 +154,25 @@ class RegressionHeadTest(test.TestCase): _assert_no_variables(self) _assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops) + def testRegressionWithLogitFn(self): + head = head_lib.regression_head(link_fn=math_ops.square) + def _assert_preditions(test_case, expected_predictions, model_fn_ops): + variables.initialize_local_variables().run() + test_case.assertAllClose(expected_predictions, + model_fn_ops.predictions["scores"].eval()) + with ops.Graph().as_default(), session.Session(): + model_fn_ops = head.create_model_fn_ops( + {}, + labels=((0.,), (1.,), (1.,)), + mode=model_fn.ModeKeys.TRAIN, + train_op_fn=head_lib.no_op_train_fn, + logits=((1.,), (1.,), (3.,))) + self._assert_output_alternatives(model_fn_ops) + _assert_summary_tags(self, ["loss"]) + _assert_no_variables(self) + _assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops) + _assert_preditions(self, ([1.0, 1.0, 9.0]), model_fn_ops) + def testRegressionWithInvalidLogits(self): head = head_lib.regression_head() with ops.Graph().as_default(), session.Session(): diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py index 8f9d6fc318a357853bdb8e3264f6691b410006b1..66ebcfd1d81904b9afe5be6bd1a648fe325e1e0b 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py +++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Implementation of k-means clustering on top of `Estimator` API. +"""Implementation of k-means clustering on top of `Estimator` API (deprecated). This module is deprecated. Please use @{tf.contrib.factorization.KMeansClustering} instead of @@ -153,7 +153,12 @@ def _kmeans_clustering_model_fn(features, labels, mode, params, config): # TODO(agarwal,ands): support sharded input. class KMeansClustering(estimator.Estimator): - """An Estimator for K-Means clustering.""" + """An Estimator for K-Means clustering. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE RANDOM_INIT = clustering_ops.RANDOM_INIT diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py index 37aa8b339622415d082933cdf66d2472a4119b48..64d7ecc68e7abb1d36a3eb098fedd8184d6e9d77 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/linear.py +++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Linear Estimators.""" +"""Linear Estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -305,6 +310,10 @@ class _SdcaUpdateWeightsHook(session_run_hook.SessionRunHook): class LinearClassifier(estimator.Estimator): """Linear classifier model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a linear model to classify instances into one of multiple possible classes. When number of possible classes is 2, this is binary classification. @@ -625,6 +634,10 @@ class LinearClassifier(estimator.Estimator): class LinearRegressor(estimator.Estimator): """Linear regressor model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a linear regression model to predict label value given observation of feature values. @@ -860,6 +873,10 @@ class LinearRegressor(estimator.Estimator): class LinearEstimator(estimator.Estimator): """Linear model with user specified head. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a generalized linear model to predict label value given observation of feature values. diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py index fb339160d58e09d4ffd50090f2dbbcec08bebe47..3cbcc6e98de1c915c302617e4591c9baa33adeaf 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py +++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Logistic regression (aka binary classifier) class. +"""Logistic regression (aka binary classifier) class (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This defines some useful basic metrics for using logistic regression to classify a binary event (0 vs 1). @@ -75,6 +79,10 @@ def LogisticRegressor( # pylint: disable=invalid-name feature_engineering_fn=None): """Builds a logistic regression Estimator for binary classification. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This method provides a basic Estimator with some additional metrics for custom binary classification models, including AUC, precision/recall and accuracy. diff --git a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py index 99388f116b345bd038f2985606c6203011597ea2..f264248e44d9aa48f26ee32e36746bd4c3145a8d 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py +++ b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py @@ -12,14 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Enum for metric keys.""" +"""Enum for metric keys (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function class MetricKey(object): - """Metric key strings.""" + """Metric key strings (deprecated).""" + LOSS = "loss" AUC = "auc" AUC_PR = "auc_precision_recall" diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py index 44e6c7c52dac524a22e9099e33e2aef82f8fe7ba..dcb161180c99ce71195c820217e8bdaf79d70901 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Classes and methods related to model_fn.""" +"""Classes and methods related to model_fn (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -37,10 +42,13 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import session_run_hook +from tensorflow.python.util.deprecation import deprecated class ModeKeys(object): - """Standard names for model modes. + """Standard names for model modes (deprecated). + + THIS CLASS IS DEPRECATED. The following standard keys are defined: @@ -65,8 +73,16 @@ class ModelFnOps( 'output_alternatives', 'training_chief_hooks', 'training_hooks', 'scaffold', 'mode' ])): - """Ops returned from a model_fn.""" + """Ops returned from a model_fn. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'When switching to tf.estimator.Estimator, use ' + 'tf.estimator.EstimatorSpec. You can use the `estimator_spec`' + ' method to create an equivalent one.') def __new__(cls, mode, predictions=None, diff --git a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py index f8d87b8914307a86eb2f46123a28ff11eb925eda..6fd2fc9d592cef4e44a640e2f27cb28b367d44d5 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py +++ b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Enum for model prediction keys. +"""Enum for model prediction keys (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This file is obsoleted in the move of Estimator to core. """ @@ -22,6 +26,8 @@ from __future__ import print_function class PredictionKey(object): + """THIS CLASS IS DEPRECATED.""" + CLASSES = "classes" PROBABILITIES = "probabilities" LOGITS = "logits" diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py index 2752bc2d90ee0f51b2c40ccc4d24a4eb21cff38f..215022e5d9e5d3cd5d6a96583b325b19a1719568 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py +++ b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Common operations for RNN Estimators.""" +"""Common operations for RNN Estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py index fd90fd1cc6277e7d80287aefdbab6134dac7c0d5..1d161093de01ef838d0c75ec9a39574c7529bd57 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py +++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Run Config.""" +"""Run Config (deprecated, use tf.estimator.RunConfig instead). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -29,11 +34,12 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as core_run_config from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib +from tensorflow.python.util.deprecation import deprecated # A list of the property names in RunConfig user allows to change. They will # not affect the execution framework, so when execution framework checks the -# `uid` of the RunConfig, it should be ingored. +# `uid` of the RunConfig, it should be ignored. _DEFAULT_UID_WHITE_LIST = [ 'tf_random_seed', 'save_summary_steps', @@ -47,6 +53,7 @@ _DEFAULT_UID_WHITE_LIST = [ class Environment(object): + """DEPRECATED CLASS.""" # For running general distributed training. CLOUD = 'cloud' # For running Google-internal distributed training. @@ -56,6 +63,7 @@ class Environment(object): class TaskType(object): + """DEPRECATED CLASS.""" MASTER = 'master' PS = 'ps' WORKER = 'worker' @@ -64,6 +72,8 @@ class TaskType(object): class ClusterConfig(object): """This class specifies the configurations for a distributed run. + THIS CLASS IS DEPRECATED. Use tf.estimator.RunConfig instead. + If you're using an `Estimator`, you should probably use the subclass RunConfig instead. """ @@ -211,10 +221,13 @@ class ClusterConfig(object): class RunConfig(ClusterConfig, core_run_config.RunConfig): """This class specifies the configurations for an `Estimator` run. - This class is the implementation of @{tf.estimator.RunConfig} interface. + This class is a deprecated implementation of @{tf.estimator.RunConfig} + interface. """ _USE_DEFAULT = 0 + @deprecated(None, 'When switching to tf.estimator.Estimator, use' + ' tf.estimator.RunConfig instead.') def __init__(self, master=None, num_cores=0, diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py index 0cea35e219a4457417a161a3ac4ac4292fd24f53..de78c72c3ae3ef14f5f7c46b1d47f82e8266c7c6 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Estimator for State Saving RNNs.""" +"""Estimator for State Saving RNNs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -528,6 +533,12 @@ def _get_rnn_model_fn(cell_type, class StateSavingRnnEstimator(estimator.Estimator): + """RNN with static unrolling and state saving (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, problem_type, diff --git a/tensorflow/contrib/learn/python/learn/estimators/svm.py b/tensorflow/contrib/learn/python/learn/estimators/svm.py index 72920d73c0c92886e54f533ad7fe170fe27d9870..3459997baba16fc0d4045e50819ecdd0e7121657 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/svm.py +++ b/tensorflow/contrib/learn/python/learn/estimators/svm.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Support Vector Machine (SVM) Estimator.""" +"""Support Vector Machine (SVM) Estimator (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -36,6 +41,10 @@ def _as_iterable(preds, output): class SVM(estimator.Estimator): """Support Vector Machine (SVM) model for binary classification. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Currently, only linear SVMs are supported. For the underlying optimization problem, the `SDCAOptimizer` is used. For performance and convergence tuning, the num_loss_partitions parameter passed to `SDCAOptimizer` (see `__init__()` diff --git a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py index a120bc6cc3975a3d4559d018c8aa74ff42a16d2d..71b5658dd174d2b47e33860844359f68e6768026 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py +++ b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorSignature class and utilities.""" +"""TensorSignature class and utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -33,6 +38,10 @@ class TensorSignature(collections.namedtuple( "TensorSignature", ["dtype", "shape", "is_sparse"])): """Signature of the `Tensor` object. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Useful to check compatibility of tensors. Example: diff --git a/tensorflow/contrib/learn/python/learn/estimators/test_data.py b/tensorflow/contrib/learn/python/learn/estimators/test_data.py index ed201bfc58f273e6587850032386c2686aea4148..e4b057b4f5a9e081c2d891bd9828ffc315e51e91 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/test_data.py +++ b/tensorflow/contrib/learn/python/learn/estimators/test_data.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Test data utilities.""" +"""Test data utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py index 8f6cd39864b437f163dd7c1140dc88755ce98529..10881ca885599bc81386e15f814a2687d907f63b 100644 --- a/tensorflow/contrib/learn/python/learn/evaluable.py +++ b/tensorflow/contrib/learn/python/learn/evaluable.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""`Evaluable` interface.""" +"""`Evaluable` interface (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,6 +28,10 @@ import abc class Evaluable(object): """Interface for objects that are evaluatable by, e.g., `Experiment`. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. """ __metaclass__ = abc.ABCMeta diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py index bec976afd2719138117976381669ca3292360480..9a7c4cd685b90cf3ac8922bdb031aa935c1aa64f 100644 --- a/tensorflow/contrib/learn/python/learn/experiment.py +++ b/tensorflow/contrib/learn/python/learn/experiment.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Experiment class collecting information needed for a single training run.""" +"""Experiment class collecting information for a single training run (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -25,7 +30,6 @@ import os import time from tensorflow.contrib.framework import deprecated -from tensorflow.contrib.framework import deprecated_args from tensorflow.contrib.framework.python.framework import experimental from tensorflow.contrib.learn.python.learn import evaluable from tensorflow.contrib.learn.python.learn import export_strategy @@ -118,6 +122,10 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener): class Experiment(object): """Experiment is a class containing all information needed to train a model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + After an experiment is created (by passing an Estimator and inputs for training and evaluation), an Experiment instance knows how to invoke training and eval loops in a sensible fashion for distributed training. @@ -125,16 +133,8 @@ class Experiment(object): # TODO(ispir): remove delay_workers_by_global_step and make global step based # waiting as only behavior. - @deprecated_args( - "2016-10-23", - "local_eval_frequency is deprecated as local_run will be renamed to " - "train_and_evaluate. Use min_eval_frequency and call train_and_evaluate " - "instead. Note, however, that the default for min_eval_frequency is 1, " - "meaning models will be evaluated every time a new checkpoint is " - "available. In contrast, the default for local_eval_frequency is None, " - "resulting in evaluation occurring only after training has completed. " - "min_eval_frequency is ignored when calling the deprecated local_run.", - "local_eval_frequency") + @deprecated(None, "Please switch to tf.estimator.train_and_evaluate. You will" + " also have to convert to a tf.estimator.Estimator.") def __init__(self, estimator, train_input_fn, @@ -152,7 +152,8 @@ class Experiment(object): export_strategies=None, train_steps_per_iteration=None, checkpoint_and_export=False, - saving_listeners=None): + saving_listeners=None, + check_interval_secs=5): """Constructor for `Experiment`. Creates an Experiment instance. None of the functions passed to this @@ -190,8 +191,9 @@ class Experiment(object): number of steps between evaluations. Of course, evaluation does not occur if no new snapshot is available, hence, this is the minimum. If 0, the evaluation will only happen after training. - If None, defaults to 1, unless model_dir is on GCS, in which case the - default is 1000. + If None, defaults to 1. To avoid checking for new checkpoints too + frequent, the interval is further limited to be at least + check_interval_secs between checks. delay_workers_by_global_step: if `True` delays training workers based on global step instead of time. export_strategies: Iterable of `ExportStrategy`s, or a single one, or @@ -215,7 +217,10 @@ class Experiment(object): saving_listeners: list of `CheckpointSaverListener` objects. Used by tf.estimator.Estimator for callbacks that run immediately before or after checkpoint savings. - + check_interval_secs: + Minimum time between subsequent checks for a new checkpoint. This + mostly applies if both min_eval_frequency and the time spent per + training step is low. Raises: ValueError: if `estimator` does not implement Estimator interface, or if export_strategies has the wrong type. @@ -261,13 +266,9 @@ class Experiment(object): self._continuous_eval_throttle_secs = continuous_eval_throttle_secs self._checkpoint_and_export = checkpoint_and_export self._saving_listeners = saving_listeners - # Using 1 on a non-cached file system requires a lot of overhead to - # read the checkpoint state file. This is particular bad on GCS, so - # we use a different default. This is a temporary band-aid, to be - # fixed holistically later (b/36498507). - default_min_eval_frequency = 1000 if _is_gcs(estimator.model_dir) else 1 self._min_eval_frequency = min_eval_frequency if ( - min_eval_frequency is not None) else default_min_eval_frequency + min_eval_frequency is not None) else 1 + self._check_interval_secs = check_interval_secs self._delay_workers_by_global_step = delay_workers_by_global_step self._train_monitors = train_monitors[:] if train_monitors else [] self._eval_hooks = eval_hooks[:] if eval_hooks else [] @@ -646,12 +647,19 @@ class Experiment(object): self._train_monitors += [saver_hook] else: if self._min_eval_frequency: + # Using low min_eval_frequency (default is 1) on a non-cached file + # system requires a lot of overhead to read the checkpoint state file. + # This is particular bad on GCS and CNS. See also b/36498507 for + # context. `check_interval_secs = 5` avoids polling a remote + # fileystem too often. + self._train_monitors += [ monitors.ValidationMonitor( input_fn=self._eval_input_fn, eval_steps=self._eval_steps, metrics=self._eval_metrics, every_n_steps=self._min_eval_frequency, + check_interval_secs=self._check_interval_secs, name=eval_dir_suffix, hooks=self._eval_hooks) ] @@ -928,7 +936,3 @@ def _new_attr_context(obj, attr): yield finally: setattr(obj, attr, saved) - - -def _is_gcs(model_dir): - return model_dir and model_dir.startswith("gs://") diff --git a/tensorflow/contrib/learn/python/learn/experiment_test.py b/tensorflow/contrib/learn/python/learn/experiment_test.py index 545d7d8924c0c10544e6113e2968b7ae3d2090fc..d10927a0cdd5c67c8d2a8e569153235ee175ec4d 100644 --- a/tensorflow/contrib/learn/python/learn/experiment_test.py +++ b/tensorflow/contrib/learn/python/learn/experiment_test.py @@ -674,37 +674,11 @@ class ExperimentTest(test.TestCase): def test_min_eval_frequency_defaults(self): def dummy_model_fn(features, labels): # pylint: disable=unused-argument pass - - # The default value when model_dir is on GCS is 1000 - estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket') - ex = experiment.Experiment( - estimator, train_input_fn=None, eval_input_fn=None) - self.assertEquals(ex._min_eval_frequency, 1000) - - # The default value when model_dir is not on GCS is 1 estimator = core_estimator.Estimator(dummy_model_fn, '/tmp/dummy') ex = experiment.Experiment( estimator, train_input_fn=None, eval_input_fn=None) self.assertEquals(ex._min_eval_frequency, 1) - # Make sure default not used when explicitly set - estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket') - ex = experiment.Experiment( - estimator, - min_eval_frequency=123, - train_input_fn=None, - eval_input_fn=None) - self.assertEquals(ex._min_eval_frequency, 123) - - # Make sure default not used when explicitly set as 0 - estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket') - ex = experiment.Experiment( - estimator, - min_eval_frequency=0, - train_input_fn=None, - eval_input_fn=None) - self.assertEquals(ex._min_eval_frequency, 0) - def test_continuous_train_and_eval(self): for est in self._estimators_for_tests(eval_dict={'global_step': 100}): if isinstance(est, core_estimator.Estimator): diff --git a/tensorflow/contrib/learn/python/learn/export_strategy.py b/tensorflow/contrib/learn/python/learn/export_strategy.py index 55a8b824312b89e0ac66513242191f4201ac212a..075cab536ecb5279e7e6f23abb0b70c75043a7ec 100644 --- a/tensorflow/contrib/learn/python/learn/export_strategy.py +++ b/tensorflow/contrib/learn/python/learn/export_strategy.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""ExportStrategy class represents different flavors of model export.""" +"""ExportStrategy class represents different flavors of model export (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,6 +26,7 @@ from __future__ import print_function import collections from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated __all__ = ['ExportStrategy'] @@ -30,6 +36,10 @@ class ExportStrategy( ['name', 'export_fn', 'strip_default_attrs'])): """A class representing a type of model export. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Typically constructed by a utility function specific to the exporter, such as `saved_model_export_utils.make_export_strategy()`. @@ -56,6 +66,8 @@ class ExportStrategy( forward compatibility of the resulting `SavedModel`. """ + @deprecated(None, 'Please switch to tf.estimator.train_and_evaluate, and use ' + 'tf.estimator.Exporter.') def __new__(cls, name, export_fn, strip_default_attrs=None): return super(ExportStrategy, cls).__new__( cls, name, export_fn, strip_default_attrs) diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py index 98365c05f663e5d2a06703457fc5663d7135f7d9..a997fab723a16dddf150aa9397863605e4e77933 100644 --- a/tensorflow/contrib/learn/python/learn/graph_actions.py +++ b/tensorflow/contrib/learn/python/learn/graph_actions.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level operations on graphs.""" +"""High level operations on graphs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -68,6 +73,7 @@ def clear_summary_writers(): return summary_io.SummaryWriterCache.clear() +@deprecated(None, 'Use `SummaryWriterCache.get` directly.') def get_summary_writer(logdir): """Returns single SummaryWriter per logdir in current run. diff --git a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py index 06c3782a471537cf3879450e6bd20899a35d96ac..8b133a4440d8cbc19abca64f972791fc16ade6f8 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tools to allow different io formats.""" +"""Tools to allow different io formats (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py index 7d666391cea3c0a52a2cb7e324c00d5f480710d5..e0a1948d95a727675dac8ff3ce9f55c35d5f8d8d 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Methods to allow dask.DataFrame.""" +"""Methods to allow dask.DataFrame (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,6 +26,8 @@ from __future__ import print_function import numpy as np +from tensorflow.python.util.deprecation import deprecated + try: # pylint: disable=g-import-not-at-top import dask.dataframe as dd @@ -60,6 +67,7 @@ def _construct_dask_df_with_divisions(df): return dd.Series(merge(dsk, df.dask), name, df.name, divisions) +@deprecated(None, 'Please feed input to tf.data to support dask.') def extract_dask_data(data): """Extract data from dask.Series or dask.DataFrame for predictors. @@ -81,6 +89,7 @@ def extract_dask_data(data): return data +@deprecated(None, 'Please feed input to tf.data to support dask.') def extract_dask_labels(labels): """Extract data from dask.Series or dask.DataFrame for labels. diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py index 96be8b1bc402479d5611965f27abb197363cb939..c45b1d186471125776d6536112aebb66bb5ad558 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Implementations of different data feeders to provide data for TF trainer.""" +"""Implementations of different data feeders to provide data for TF trainer (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" # TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues. @@ -31,6 +36,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.deprecation import deprecated # pylint: disable=g-multiple-import,g-bad-import-order from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels @@ -101,6 +107,7 @@ def _is_iterable(x): return hasattr(x, 'next') or hasattr(x, '__next__') +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_train_data_feeder(x, y, n_classes, @@ -188,6 +195,7 @@ def _batch_data(x, batch_size=None): yield np.matrix(chunk) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_predict_data_feeder(x, batch_size=None): """Returns an iterable for feeding into predict step. @@ -219,6 +227,7 @@ def setup_predict_data_feeder(x, batch_size=None): return [x] +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_processor_data_feeder(x): """Sets up processor iterable. @@ -233,6 +242,7 @@ def setup_processor_data_feeder(x): return x +@deprecated(None, 'Please convert numpy dtypes explicitly.') def check_array(array, dtype): """Checks array on dtype and converts it if different. @@ -275,8 +285,14 @@ def _check_dtype(dtype): class DataFeeder(object): - """Data feeder is an example class to sample data for TF trainer.""" + """Data feeder is an example class to sample data for TF trainer. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, x, y, @@ -563,6 +579,10 @@ class DataFeeder(object): class StreamingDataFeeder(DataFeeder): """Data feeder for TF trainer that reads data from iterator. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Streaming data feeder allows to read data as it comes it from disk or somewhere else. It's custom to have this iterators rotate infinetly over the dataset, to allow control of how much to learn on the trainer side. @@ -771,11 +791,16 @@ class StreamingDataFeeder(DataFeeder): class DaskDataFeeder(object): """Data feeder for that reads data from dask.Series and dask.DataFrame. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Numpy arrays can be serialized to disk and it's possible to do random seeks into them. DaskDataFeeder will remove requirement to have full dataset in the memory and still do random seeks for sampling of batches. """ + @deprecated(None, 'Please feed input to tf.data to support dask.') def __init__(self, x, y, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py index 884faf8335e2a3ca1d27d2d93b4c817131648774..f8aaa0c9e3e5b589a6ad47678dba3dc38de7c471 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to allow generator of dict with numpy arrays.""" +"""Methods to allow generator of dict with numpy arrays (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,8 +28,10 @@ from types import FunctionType from types import GeneratorType from tensorflow.python.estimator.inputs.queues.feeding_functions import _enqueue_data as enqueue_data +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Please use tf.data.') def generator_input_fn(x, target_key=None, batch_size=128, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py index 3a46c239688017f9204d2c6182a6f81cd325a417..9e816f54b6cf8dee84c6d62406ab3db700054d06 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to read data in the graph.""" +"""Methods to read data in the graph (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -34,11 +39,13 @@ from tensorflow.python.platform import gfile from tensorflow.python.summary import summary from tensorflow.python.training import input as input_ops from tensorflow.python.training import queue_runner +from tensorflow.python.util.deprecation import deprecated # Default name for key in the feature dict. KEY_FEATURE_NAME = '__key__' +@deprecated(None, 'Use tf.data.') def read_batch_examples(file_pattern, batch_size, reader, @@ -106,6 +113,7 @@ def read_batch_examples(file_pattern, return examples +@deprecated(None, 'Use tf.data.') def read_keyed_batch_examples(file_pattern, batch_size, reader, @@ -175,6 +183,7 @@ def read_keyed_batch_examples(file_pattern, seed=seed) +@deprecated(None, 'Use tf.data.') def read_keyed_batch_examples_shared_queue(file_pattern, batch_size, reader, @@ -452,6 +461,7 @@ def _read_keyed_batch_examples_helper(file_pattern, return queued_examples_with_keys +@deprecated(None, 'Use tf.data.') def read_keyed_batch_features(file_pattern, batch_size, features, @@ -540,6 +550,7 @@ def read_keyed_batch_features(file_pattern, name=scope) +@deprecated(None, 'Use tf.data.') def read_keyed_batch_features_shared_queue(file_pattern, batch_size, features, @@ -620,6 +631,7 @@ def read_keyed_batch_features_shared_queue(file_pattern, name=scope) +@deprecated(None, 'Use tf.data.') def queue_parsed_features(parsed_features, keys=None, feature_queue_capacity=100, @@ -742,6 +754,7 @@ def queue_parsed_features(parsed_features, return dequeued_keys, dequeued_parsed_features +@deprecated(None, 'Use tf.data.') def read_batch_features(file_pattern, batch_size, features, @@ -821,6 +834,7 @@ def read_batch_features(file_pattern, return features +@deprecated(None, 'Use tf.data.') def read_batch_record_features(file_pattern, batch_size, features, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py index 692438807fbd7febb156d4db73b5d3deba6c987d..29552d24f1eaa0d85a99c8b09f69d007e7e4fe9f 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py @@ -12,15 +12,22 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to allow dict of numpy arrays.""" +"""Methods to allow dict of numpy arrays (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.estimator.inputs.numpy_io import numpy_input_fn as core_numpy_input_fn +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Use tf.estimator.inputs.numpy_input_fn.') def numpy_input_fn(x, y=None, batch_size=128, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py index ede7558eafa9237dc63aa95a62e599c5e9755822..b4ef055f5ae484ec704ad42efcf2c00c4a7a4f56 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py @@ -13,13 +13,19 @@ # limitations under the License. # ============================================================================== -"""Methods to allow pandas.DataFrame.""" +"""Methods to allow pandas.DataFrame (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.estimator.inputs.pandas_io import pandas_input_fn as core_pandas_input_fn +from tensorflow.python.util.deprecation import deprecated try: # pylint: disable=g-import-not-at-top @@ -47,6 +53,7 @@ PANDAS_DTYPES = { } +@deprecated(None, 'Please use tf.estimator.inputs.pandas_input_fn') def pandas_input_fn(x, y=None, batch_size=128, @@ -66,6 +73,7 @@ def pandas_input_fn(x, target_column=target_column) +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_data(data): """Extract data from pandas.DataFrame for predictors. @@ -96,6 +104,7 @@ def extract_pandas_data(data): 'float, or bool. Found: ' + ', '.join(error_report)) +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_matrix(data): """Extracts numpy matrix from pandas DataFrame. @@ -111,6 +120,7 @@ def extract_pandas_matrix(data): return data.as_matrix() +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_labels(labels): """Extract data from pandas.DataFrame for labels. diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py index 2af723a0d64822e81fa0fbeb106ab812de6ab4e8..d719a3e488b9905ef7903e21d90dbaae0449735c 100644 --- a/tensorflow/contrib/learn/python/learn/learn_runner.py +++ b/tensorflow/contrib/learn/python/learn/learn_runner.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Runs an Experiment.""" +"""Runs an Experiment (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,7 @@ from tensorflow.contrib.learn.python.learn.estimators import run_config as run_c from tensorflow.contrib.learn.python.learn.experiment import Experiment from tensorflow.contrib.training.python.training import hparam as hparam_lib from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.deprecation import deprecated # TODO(xiejw): Refactor the learn_runner to make code reusable. @@ -99,6 +105,7 @@ def _wrapped_experiment_fn_with_uid_check(experiment_fn, require_hparams=False): return wrapped_experiment_fn +@deprecated(None, 'Use tf.estimator.train_and_evaluate.') def run(experiment_fn, output_dir=None, schedule=None, run_config=None, hparams=None): """Make and run an experiment. @@ -218,6 +225,7 @@ def run(experiment_fn, output_dir=None, schedule=None, run_config=None, return _execute_schedule(experiment, schedule) +@deprecated(None, 'Use tf.estimator.train_and_evaluate.') def tune(experiment_fn, tuner): """Tune an experiment with hyper-parameters. diff --git a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py index 7d9b1c7716f0ab1f2274ca53406175240b613027..ba2d067787c1dfd4e4820ecc916f1053e9f3cf60 100644 --- a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py +++ b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities to run and tune an Experiment. +"""Utilities to run and tune an Experiment (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. @@run @@tune diff --git a/tensorflow/contrib/learn/python/learn/metric_spec.py b/tensorflow/contrib/learn/python/learn/metric_spec.py index 6440bc204b8e339ff51311dcc87b36f556b94092..97220365d5dddb82b602369f06bea021a86d584f 100644 --- a/tensorflow/contrib/learn/python/learn/metric_spec.py +++ b/tensorflow/contrib/learn/python/learn/metric_spec.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""The metric spec class to flexibly connect models and metrics.""" +"""The metric spec class to flexibly connect models and metrics (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,7 @@ import six from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated def _assert_named_args(sentinel): @@ -223,6 +229,10 @@ def _adapt_metric_fn( class MetricSpec(object): """MetricSpec connects a model to metric functions. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + The MetricSpec class contains all information necessary to connect the output of a `model_fn` to the metrics (usually, streaming metrics) that are used in evaluation. @@ -284,6 +294,7 @@ class MetricSpec(object): """ + @deprecated(None, 'Use tf.estimator.EstimatorSpec.eval_metric_ops.') def __init__(self, metric_fn, prediction_key=None, diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py index 4283240d018c949bb35aeb12032d2ee8b75884a5..bd4bbf9f8c9ad7e8a0fc06d8c0dc24672536c158 100644 --- a/tensorflow/contrib/learn/python/learn/models.py +++ b/tensorflow/contrib/learn/python/learn/models.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Various high level TF models.""" +"""Various high level TF models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -28,8 +33,10 @@ from tensorflow.python.ops import array_ops as array_ops_ from tensorflow.python.ops import init_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.summary import summary +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Consider using a tf.estimator.LinearRegressor') def linear_regression_zero_init(x, y): """Linear regression subgraph with zero-value initial weights and bias. @@ -43,6 +50,7 @@ def linear_regression_zero_init(x, y): return linear_regression(x, y, init_mean=0.0, init_stddev=0.0) +@deprecated(None, 'Consider using a class from tf.estimator.LinearClassifier') def logistic_regression_zero_init(x, y): """Logistic regression subgraph with zero-value initial weights and bias. @@ -56,6 +64,7 @@ def logistic_regression_zero_init(x, y): return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0) +@deprecated(None, 'Consider using a class from tf.estimator.') def linear_regression(x, y, init_mean=None, init_stddev=1.0): """Creates linear regression TensorFlow subgraph. @@ -107,6 +116,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0): return losses_ops.mean_squared_error_regressor(x, y, weights, bias) +@deprecated(None, 'Consider using a class from tf.estimator.') def logistic_regression(x, y, class_weight=None, @@ -203,6 +213,7 @@ def _reverse_seq(input_seq, lengths): return result +@deprecated(None, 'Please consider `tf.nn.bidirectional_dynamic_rnn`.') def bidirectional_rnn(cell_fw, cell_bw, inputs, @@ -283,6 +294,7 @@ def bidirectional_rnn(cell_fw, # End of TensorFlow 0.7 +@deprecated(None, 'Please consider tensorflow/tensor2tensor.') def get_rnn_model(rnn_size, cell_type, num_layers, input_op_fn, bidirectional, target_predictor_fn, sequence_length, initial_state, attn_length, attn_size, attn_vec_size): diff --git a/tensorflow/contrib/learn/python/learn/monitored_session.py b/tensorflow/contrib/learn/python/learn/monitored_session.py index 22602e9f69d972505d83a66a6f9183b5e4d15c44..ac0433f1775feeed2ec3cf49291da01500bef01b 100644 --- a/tensorflow/contrib/learn/python/learn/monitored_session.py +++ b/tensorflow/contrib/learn/python/learn/monitored_session.py @@ -13,7 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""A wrapper of Session API which runs hooks.""" +"""A wrapper of Session API which runs hooks (deprecated). + +These are deprecated aliases for classes and functions in `tf.train`. Please use +those directly. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py index 51381a7427c919592b8e818c4b46dba974992610..77f7c73d5412d40b338eaff4cf04d99fd0892723 100644 --- a/tensorflow/contrib/learn/python/learn/monitors.py +++ b/tensorflow/contrib/learn/python/learn/monitors.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Monitors instrument the training process. +"""Monitors instrument the training process (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. @@get_default_monitors @@BaseMonitor @@ -59,6 +63,10 @@ from tensorflow.python.util import tf_inspect class BaseMonitor(object): """Base class for Monitors. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Defines basic interfaces of Monitors. Monitors can either be run on all workers or, more commonly, restricted to run exclusively on the elected chief worker. @@ -229,6 +237,10 @@ def _extract_output(outputs, request): class EveryN(BaseMonitor): """Base class for monitors that execute callbacks every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This class adds three new callbacks: - every_n_step_begin - every_n_step_end @@ -418,6 +430,10 @@ class StopAtStep(BaseMonitor): class PrintTensor(EveryN): """Prints given tensors every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This is an `EveryN` monitor and has consistent semantic for `every_n` and `first_n`. @@ -455,9 +471,12 @@ class PrintTensor(EveryN): class LoggingTrainable(EveryN): """Writes trainable variable values into log every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Write the tensors in trainable variables `every_n` steps, starting with the `first_n`th step. - """ def __init__(self, scope=None, every_n=100, first_n=1): @@ -493,7 +512,12 @@ class LoggingTrainable(EveryN): class SummarySaver(EveryN): - """Saves summaries every N steps.""" + """Saves summaries every N steps. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, summary_op, @@ -554,6 +578,10 @@ class SummarySaver(EveryN): class ValidationMonitor(EveryN): """Runs evaluation of a given estimator, at most every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note that the evaluation is done based on the saved checkpoint, which will usually be older than the current step. @@ -573,7 +601,8 @@ class ValidationMonitor(EveryN): early_stopping_rounds=None, early_stopping_metric="loss", early_stopping_metric_minimize=True, - name=None): + name=None, + check_interval_secs=5): """Initializes a ValidationMonitor. Args: @@ -600,6 +629,9 @@ class ValidationMonitor(EveryN): loss metrics like mean squared error, and False for performance metrics like accuracy. name: See `BaseEstimator.evaluate`. + check_interval_secs: Only check for new checkpoint if at least + `check_interval_secs` have passed. Ignore if None. Default is 5 secs. + Raises: ValueError: If both x and input_fn are provided. @@ -626,6 +658,8 @@ class ValidationMonitor(EveryN): self._early_stopped = False self._latest_path = None self._latest_path_step = None + self._last_checkpoint_check_time = None + self._check_interval_secs = check_interval_secs @property def early_stopped(self): @@ -690,6 +724,16 @@ class ValidationMonitor(EveryN): # that's what is being evaluated. if self._estimator is None: raise ValueError("Missing call to set_estimator.") + current_time = time.time() + if (self._check_interval_secs is not None and + self._last_checkpoint_check_time is not None and + current_time - self._last_checkpoint_check_time <= + self._check_interval_secs): + logging.debug( + "Skipping evaluation since less than %d seconds have passed since " + "last check for a new checkpoint.", self._check_interval_secs) + return False + self._last_checkpoint_check_time = current_time # Check that we are not running evaluation on the same checkpoint. latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir) if latest_path is None: @@ -740,6 +784,10 @@ class ValidationMonitor(EveryN): class CaptureVariable(EveryN): """Captures a variable's values into a collection. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This monitor is useful for unit testing. You should exercise caution when using this monitor in production, since it never discards values. @@ -778,6 +826,7 @@ class CaptureVariable(EveryN): self._var_values[step] = _extract_output(outputs, self._var_name) +@deprecation.deprecated(None, "Use tf.train.MonitoredTrainingSession.") def get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, @@ -812,6 +861,10 @@ def get_default_monitors(loss_op=None, class GraphDump(BaseMonitor): """Dumps almost all tensors in the graph at every step. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note, this is very expensive, prefer `PrintTensor` in production. """ @@ -901,7 +954,12 @@ class GraphDump(BaseMonitor): class ExportMonitor(EveryN): - """Monitor that exports Estimator every N steps.""" + """Monitor that exports Estimator every N steps. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ @deprecation.deprecated("2017-03-25", "ExportMonitor is deprecated. Please pass an " @@ -1024,7 +1082,12 @@ class ExportMonitor(EveryN): class CheckpointSaver(BaseMonitor): - """Saves checkpoints every N steps or N seconds.""" + """Saves checkpoints every N steps or N seconds. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, checkpoint_dir, @@ -1109,7 +1172,12 @@ class CheckpointSaver(BaseMonitor): class StepCounter(EveryN): - """Steps per second monitor.""" + """Steps per second monitor. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None): super(StepCounter, self).__init__(every_n_steps=every_n_steps) @@ -1149,6 +1217,10 @@ class NanLossDuringTrainingError(RuntimeError): class NanLoss(EveryN): """NaN Loss monitor. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training. """ diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py index b2b24776c60183113a5f936dd276ff312d6d0079..5c34d0ddb01f3bcdc407e6926e7c5b73be1863b4 100644 --- a/tensorflow/contrib/learn/python/learn/monitors_test.py +++ b/tensorflow/contrib/learn/python/learn/monitors_test.py @@ -385,7 +385,11 @@ class MonitorsTest(test.TestCase): estimator.evaluate.return_value = validation_outputs monitor = learn.monitors.ValidationMonitor( - x=constant_op.constant(2.0), every_n_steps=0, early_stopping_rounds=2) + x=constant_op.constant(2.0), + every_n_steps=0, + early_stopping_rounds=2, + check_interval_secs=None) + self._assert_validation_monitor(monitor) monitor.set_estimator(estimator) with ops.Graph().as_default() as g, self.test_session(g): diff --git a/tensorflow/contrib/learn/python/learn/ops/__init__.py b/tensorflow/contrib/learn/python/learn/ops/__init__.py index 33962e34cc685ce2c830a7bbfd1b5c626bcd8b31..efb1f47cf5bb2dcd0fb37b7b85cd8f170d56e4d1 100644 --- a/tensorflow/contrib/learn/python/learn/ops/__init__.py +++ b/tensorflow/contrib/learn/python/learn/ops/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Various TensorFlow Ops.""" +"""Various TensorFlow Ops (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py index fa3b7323e343371e986b763d30a8a44620894549..b3b067b8e1a4eb9f644e8e55587b3405d91a0189 100644 --- a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops to work with embeddings. +"""TensorFlow Ops to work with embeddings (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Note: categorical variables are handled via embeddings in many cases. For example, in case of words. diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py index b040ab3bb6c516158589a8e30d56fff1f7728951..92976d1539c7ddc226b81f903beee82b798ec8db 100644 --- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops for loss computation.""" +"""TensorFlow Ops for loss computation (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/ops/ops_test.py b/tensorflow/contrib/learn/python/learn/ops/ops_test.py index d0b9eb8abcbee187b6c53b7b419882f0a1e7da51..80d4923db37feb2a1304218f501ab51f9e0d9a14 100644 --- a/tensorflow/contrib/learn/python/learn/ops/ops_test.py +++ b/tensorflow/contrib/learn/python/learn/ops/ops_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.layers import conv2d from tensorflow.contrib.learn.python.learn import ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py index 45727faab4362abeab18f77861353eb53976023a..aa37cb4a76e2a6157bf077d327248353bd516472 100644 --- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops for Sequence to Sequence models.""" +"""TensorFlow Ops for Sequence to Sequence models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -26,8 +31,10 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import variable_scope as vs +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def sequence_classifier(decoding, labels, sampling_decoding=None, name=None): """Returns predictions and loss for sequence of predictions. @@ -57,6 +64,7 @@ def sequence_classifier(decoding, labels, sampling_decoding=None, name=None): return array_ops.stack(predictions, axis=1), loss +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None): """Processes inputs for Sequence to Sequence models. @@ -87,6 +95,7 @@ def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None): return in_x, in_y, out_y +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def rnn_decoder(decoder_inputs, initial_state, cell, scope=None): """RNN Decoder that creates training and sampling sub-graphs. @@ -123,6 +132,7 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None): return outputs, states, sampling_outputs, sampling_states +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def rnn_seq2seq(encoder_inputs, decoder_inputs, encoder_cell, diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py index 7bcc177d4ea0ab57f092d68888a72de2b2fd5edc..e8c6e1acf80f0791421bee59aff30e67bccb44b2 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Preprocessing tools useful for building models.""" +"""Preprocessing tools useful for building models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py index 154739d497ec1029026eaca1e93b37cd225f1050..faba3b2025e8abb51d1989c3fafbd5e711d6559b 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Implements preprocessing transformers for categorical variables.""" +"""Implements preprocessing transformers for categorical variables (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,8 @@ from __future__ import print_function import math import numpy as np +from tensorflow.python.util.deprecation import deprecated + # pylint: disable=g-bad-import-order from . import categorical_vocabulary from ..learn_io.data_feeder import setup_processor_data_feeder @@ -31,10 +38,16 @@ from ..learn_io.data_feeder import setup_processor_data_feeder class CategoricalProcessor(object): """Maps documents to sequences of word ids. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + As a common convention, Nan values are handled as unknown tokens. Both float('nan') and np.nan are accepted. """ + @deprecated(None, 'Please use tensorflow/transform or tf.data for sequence ' + 'processing.') def __init__(self, min_frequency=0, share=False, vocabularies=None): """Initializes a CategoricalProcessor instance. diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py index 5709955c49fba50ca4a299a443a2902bbd9c6b23..3ac370a6ab4423846e810900514445ad5269b680 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -"""Categorical vocabulary classes to map categories to indexes. +"""Categorical vocabulary classes to map categories to indexes (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Can be used for categorical variables, sparse variables and words. """ @@ -25,14 +29,21 @@ from __future__ import print_function import collections import six +from tensorflow.python.util.deprecation import deprecated + class CategoricalVocabulary(object): """Categorical variables vocabulary class. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Accumulates and provides mapping from classes to indexes. Can be easily used for words. """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, unknown_token="", support_reverse=True): self._unknown_token = unknown_token self._mapping = {unknown_token: 0} diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/text.py b/tensorflow/contrib/learn/python/learn/preprocessing/text.py index 3af2074c2a46f0258c04111fff0235ba8309625e..f2b6776be7789a9433bfe41eb9354b74347059ec 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/text.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/text.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Implements a number of text preprocessing utilities.""" +"""Implements a number of text preprocessing utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -24,6 +29,7 @@ import numpy as np import six from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated from .categorical_vocabulary import CategoricalVocabulary # pylint: disable=g-bad-import-order @@ -38,6 +44,7 @@ TOKENIZER_RE = re.compile(r"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\'\w\-]+", re.UNICODE) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def tokenizer(iterator): """Tokenizer generator. @@ -51,9 +58,16 @@ def tokenizer(iterator): yield TOKENIZER_RE.findall(value) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') class ByteProcessor(object): - """Maps documents into sequence of ids for bytes.""" + """Maps documents into sequence of ids for bytes. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, max_document_length): self.max_document_length = max_document_length @@ -108,8 +122,14 @@ class ByteProcessor(object): class VocabularyProcessor(object): - """Maps documents to sequences of word ids.""" + """Maps documents to sequences of word ids. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, max_document_length, min_frequency=0, diff --git a/tensorflow/contrib/learn/python/learn/session_run_hook.py b/tensorflow/contrib/learn/python/learn/session_run_hook.py index a8ba2be97206f2b974d256ad2c62c21a4e3e55d8..87edc9b720bdb3edcd5f2dcd1662d14da53c51cf 100644 --- a/tensorflow/contrib/learn/python/learn/session_run_hook.py +++ b/tensorflow/contrib/learn/python/learn/session_run_hook.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""This file is deprecated. Use tensorflow.python.training.session_run_hook.""" +"""This file is deprecated. Use `tensorflow.python.training.session_run_hook`. + +See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py index 919d415c302b8ec17202aad34ff0bee69bfee2c7..d663cf5fb79c428b0e70d66b0f1305f0559a05c9 100644 --- a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py +++ b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Wrapper for a Session-like object that handles threads and recovery. +"""Wrapper for a Session-like object that handles threads and recovery (deprecated). + +These are deprecated aliases for classes and functions in `tf.train`. Please use +those directly. Based on an original design of Illia Polosukhin. """ diff --git a/tensorflow/contrib/learn/python/learn/trainable.py b/tensorflow/contrib/learn/python/learn/trainable.py index 429b6040be21d8cbe1f2bba58090366552fdfbe7..a1a3f20dcd8cb5ff7baa559ac41d5e5c40780511 100644 --- a/tensorflow/contrib/learn/python/learn/trainable.py +++ b/tensorflow/contrib/learn/python/learn/trainable.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""`Trainable` interface.""" +"""`Trainable` interface (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,6 +28,8 @@ import abc class Trainable(object): """Interface for objects that are trainable by, e.g., `Experiment`. + + THIS CLASS IS DEPRECATED. """ __metaclass__ = abc.ABCMeta diff --git a/tensorflow/contrib/learn/python/learn/utils/__init__.py b/tensorflow/contrib/learn/python/learn/utils/__init__.py index 48978d0ac34cec2b18e6794dcf3b260bc3b683c4..66d8dc6fd43b383919a16515bc96be492a253bf6 100644 --- a/tensorflow/contrib/learn/python/learn/utils/__init__.py +++ b/tensorflow/contrib/learn/python/learn/utils/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Learn Utils.""" +"""TensorFlow Learn Utils (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py index cb34cb1d26b6812c7f3f39e9f965615de5a8ef07..3eacac7a3d3dcff4d39025fdee88e16e385b1b84 100644 --- a/tensorflow/contrib/learn/python/learn/utils/export.py +++ b/tensorflow/contrib/learn/python/learn/utils/export.py @@ -13,14 +13,18 @@ # limitations under the License. # ============================================================================== -"""Export utilities.""" +"""Export utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.framework import deprecated -from tensorflow.python.training import training_util from tensorflow.contrib.session_bundle import exporter from tensorflow.contrib.session_bundle import gc from tensorflow.python.client import session as tf_session @@ -32,6 +36,7 @@ from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver +from tensorflow.python.training import training_util @deprecated('2017-03-25', 'Please use Estimator.export_savedmodel() instead.') diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py index 226915987a4934626066b12810f579ae675107b2..916aecbea88b10bbef316ffb89d4c4d89667cb29 100644 --- a/tensorflow/contrib/learn/python/learn/utils/gc.py +++ b/tensorflow/contrib/learn/python/learn/utils/gc.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -r"""System for specifying garbage collection (GC) of path based data. +r"""System for specifying garbage collection (GC) of path based data (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This framework allows for GC of data specified by path names, for example files on disk. gc.Path objects each represent a single item stored at a path and may @@ -73,10 +77,12 @@ import os from tensorflow.python.platform import gfile from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated Path = collections.namedtuple('Path', 'path export_version') +@deprecated(None, 'Please implement your own file management or use Saver.') def largest_export_versions(n): """Creates a filter that keeps the largest n export versions. @@ -97,6 +103,7 @@ def largest_export_versions(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def one_of_every_n_export_versions(n): """Creates a filter that keeps one of every n export versions. @@ -128,6 +135,7 @@ def one_of_every_n_export_versions(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def mod_export_version(n): """Creates a filter that keeps every export that is a multiple of n. @@ -146,6 +154,7 @@ def mod_export_version(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def union(lf, rf): """Creates a filter that keeps the union of two filters. @@ -163,6 +172,7 @@ def union(lf, rf): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def negation(f): """Negate a filter. @@ -179,6 +189,7 @@ def negation(f): return keep +@deprecated(None, 'Please implement your own file name management.') def get_paths(base_dir, parser): """Gets a list of Paths in a given directory. diff --git a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py index b2521933e524e7ec24d73d4b5171f33e507dd88c..b92eb9fea8b7ccea56c781df74dcfa1cc5508e48 100644 --- a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py +++ b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities for creating input_fns. +"""Utilities for creating input_fns (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Contents of this file are moved to tensorflow/python/estimator/export.py. InputFnOps is renamed to ServingInputReceiver. @@ -32,13 +36,17 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops +from tensorflow.python.util.deprecation import deprecated class InputFnOps(collections.namedtuple('InputFnOps', ['features', 'labels', 'default_inputs'])): - """A return type for an input_fn. + """A return type for an input_fn (deprecated). + + THIS CLASS IS DEPRECATED. Please use tf.estimator.export.ServingInputReceiver + instead. This return type is currently only supported for serving input_fn. Training and eval input_fn should return a `(features, labels)` tuple. @@ -56,6 +64,8 @@ class InputFnOps(collections.namedtuple('InputFnOps', """ +@deprecated(None, 'Please use ' + 'tf.estimator.export.build_parsing_serving_input_receiver_fn.') def build_parsing_serving_input_fn(feature_spec, default_batch_size=None): """Build an input_fn appropriate for serving, expecting fed tf.Examples. @@ -84,6 +94,8 @@ def build_parsing_serving_input_fn(feature_spec, default_batch_size=None): return input_fn +@deprecated(None, 'Please use ' + 'tf.estimator.export.build_raw_serving_input_receiver_fn.') def build_default_serving_input_fn(features, default_batch_size=None): """Build an input_fn appropriate for serving, expecting feature Tensors. diff --git a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py index 6a63fb545a56e6040b0b0c3bbb6a17cd96925895..6dbaa15f8391b0044be8e30ca191753beb88db93 100644 --- a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py +++ b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""A simple script for inspect checkpoint files.""" +"""A simple script for inspect checkpoint files (deprecated).""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py index 1593380007b2799fb1d17e92408ab19a7b47fe1e..c7cdb4131215c388412407a008113de13bdd0934 100644 --- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py +++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities supporting export to SavedModel. +"""Utilities supporting export to SavedModel (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Some contents of this file are moved to tensorflow/python/estimator/export.py: @@ -52,8 +56,9 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.summary import summary_iterator from tensorflow.python.training import saver - from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated + # A key for use in the input_alternatives dict indicating the default input. # This is the input that will be expected when a serving request does not @@ -77,6 +82,7 @@ FEATURES_INPUT_ALTERNATIVE_KEY = 'features_input_alternative' _FALLBACK_DEFAULT_OUTPUT_ALTERNATIVE_KEY = 'default_output_alternative' +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def build_standardized_signature_def(input_tensors, output_tensors, problem_type): """Build a SignatureDef using problem type and input and output Tensors. @@ -156,6 +162,7 @@ def _is_regression_problem(problem_type, input_tensors, output_tensors): len(input_tensors) == 1 and len(output_tensors) == 1) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_input_alternatives(input_ops): """Obtain all input alternatives using the input_fn output and heuristics.""" input_alternatives = {} @@ -181,6 +188,7 @@ def get_input_alternatives(input_ops): return input_alternatives, features +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_output_alternatives(model_fn_ops, default_output_alternative_key=None): """Obtain all output alternatives using the model_fn output and heuristics. @@ -246,6 +254,7 @@ def get_output_alternatives(model_fn_ops, default_output_alternative_key=None): sorted(output_alternatives.keys()))) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def build_all_signature_defs(input_alternatives, output_alternatives, actual_default_output_alternative_key): """Build `SignatureDef`s from all pairs of input and output alternatives.""" @@ -279,6 +288,7 @@ def build_all_signature_defs(input_alternatives, output_alternatives, MAX_DIRECTORY_CREATION_ATTEMPTS = 10 +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_timestamped_export_dir(export_dir_base): """Builds a path to a new subdirectory within the base directory. @@ -317,6 +327,7 @@ def get_timestamped_export_dir(export_dir_base): '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS)) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_temp_export_dir(timestamped_export_dir): """Builds a directory name based on the argument but starting with 'temp-'. @@ -344,6 +355,7 @@ def _export_version_parser(path): return path._replace(export_version=int(filename)) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_most_recent_export(export_dir_base): """Locate the most recent SavedModel export in a directory of many exports. @@ -363,6 +375,7 @@ def get_most_recent_export(export_dir_base): return next(iter(results or []), None) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def garbage_collect_exports(export_dir_base, exports_to_keep): """Deletes older exports, retaining only a given number of the most recent. @@ -387,6 +400,7 @@ def garbage_collect_exports(export_dir_base, exports_to_keep): logging.warn('Can not delete %s recursively: %s', p.path, e) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def make_export_strategy(serving_input_fn, default_output_alternative_key=None, assets_extra=None, @@ -400,7 +414,7 @@ def make_export_strategy(serving_input_fn, `InputFnOps`. default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from ${tf.estimator.Estimator} + Must be `None` if the estimator inherits from @{tf.estimator.Estimator} or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -438,7 +452,7 @@ def make_export_strategy(serving_input_fn, The string path to the exported directory. Raises: - ValueError: If `estimator` is a ${tf.estimator.Estimator} instance + ValueError: If `estimator` is a @{tf.estimator.Estimator} instance and `default_output_alternative_key` was specified. """ if isinstance(estimator, core_estimator.Estimator): @@ -469,6 +483,8 @@ def make_export_strategy(serving_input_fn, return export_strategy.ExportStrategy('Servo', export_fn, strip_default_attrs) +@deprecated(None, + 'Use tf.estimator.export.build_parsing_serving_input_receiver_fn') def make_parsing_export_strategy(feature_columns, default_output_alternative_key=None, assets_extra=None, @@ -487,7 +503,7 @@ def make_parsing_export_strategy(feature_columns, that must be provided at serving time (excluding labels!). default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from ${tf.estimator.Estimator} + Must be `None` if the estimator inherits from @{tf.estimator.Estimator} or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -555,8 +571,14 @@ def _default_compare_fn(curr_best_eval_result, cand_eval_result): class BestModelSelector(object): - """A helper that keeps track of export selection candidates.""" + """A helper that keeps track of export selection candidates. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def __init__(self, event_file_pattern=None, compare_fn=None): """Constructor of this class. @@ -622,6 +644,7 @@ class BestModelSelector(object): return best_eval_result +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def make_best_model_export_strategy( serving_input_fn, exports_to_keep=1, @@ -707,6 +730,7 @@ def make_best_model_export_strategy( # TODO(b/67013778): Revisit this approach when corresponding changes to # TF Core are finalized. +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def extend_export_strategy(base_export_strategy, post_export_fn, post_export_name=None): @@ -741,7 +765,7 @@ def extend_export_strategy(base_export_strategy, The string path to the SavedModel indicated by post_export_fn. Raises: - ValueError: If `estimator` is a ${tf.estimator.Estimator} instance + ValueError: If `estimator` is a @{tf.estimator.Estimator} instance and `default_output_alternative_key` was specified or if post_export_fn does not return a valid directory. RuntimeError: If unable to create temporary or final export directory. diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index 7526f3ae0dbdb3d6827e9d7f690090b8438e4f6e..3f5fdc18bb8f47cceee8f81dd5ded02059344b8b 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -211,9 +211,8 @@ class SdcaModel(object): sums.append( math_ops.reduce_sum( math_ops.abs(math_ops.cast(weights, dtypes.float64)))) - sum = math_ops.add_n(sums) # SDCA L1 regularization cost is: l1 * sum(|weights|) - return self._options['symmetric_l1_regularization'] * sum + return self._options['symmetric_l1_regularization'] * math_ops.add_n(sums) def _l2_loss(self, l2): """Computes the (un-normalized) l2 loss of the model.""" @@ -225,9 +224,8 @@ class SdcaModel(object): sums.append( math_ops.reduce_sum( math_ops.square(math_ops.cast(weights, dtypes.float64)))) - sum = math_ops.add_n(sums) # SDCA L2 regularization cost is: l2 * sum(weights^2) / 2 - return l2 * sum / 2.0 + return l2 * math_ops.add_n(sums) / 2.0 def _convert_n_to_tensor(self, input_list, as_ref=False): """Converts input list to a set of tensors.""" diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index cc0e20f75ee74b3df2f46f8df179aa0c33741c5f..44c4a7e2ca8d019ca602c7f2b492cd1e70b17561 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -6,8 +6,11 @@ licenses(["notice"]) # Apache 2.0 load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts", "gen_selected_ops") +exports_files(["LICENSE"]) + exports_files(glob([ "testdata/*.bin", + "testdata/*.pb", "models/testdata/*", ])) @@ -25,11 +28,6 @@ config_setting( }, ) -load( - "//tensorflow:tensorflow.bzl", - "tf_cc_test", -) - cc_library( name = "schema_fbs_version", hdrs = ["version.h"], @@ -109,6 +107,7 @@ cc_library( srcs = [ "allocation.cc", "error_reporter.cc", + "graph_info.cc", "interpreter.cc", "model.cc", "nnapi_delegate.cc", @@ -118,6 +117,7 @@ cc_library( "allocation.h", "context.h", "error_reporter.h", + "graph_info.h", "interpreter.h", "model.h", "nnapi_delegate.h", @@ -166,6 +166,21 @@ cc_test( name = "interpreter_test", size = "small", srcs = ["interpreter_test.cc"], + deps = [ + ":framework", + ":string_util", + "//tensorflow/contrib/lite/kernels/internal:tensor_utils", + "//tensorflow/contrib/lite/schema:schema_fbs", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + +# Test graph utils +cc_test( + name = "graph_info_test", + size = "small", + srcs = ["graph_info_test.cc"], deps = [ ":framework", ":string_util", @@ -221,18 +236,18 @@ cc_test( # Model tests -cc_library( - name = "models_test_utils", - testonly = 1, - hdrs = ["models/test_utils.h"], - deps = select({ - "//tensorflow:android": [], - "//conditions:default": [ - "@com_google_absl//absl/strings", - "//tensorflow/core:test", - ], - }), -) +#cc_library( +# name = "models_test_utils", +# testonly = 1, +# hdrs = ["models/test_utils.h"], +# deps = select({ +# "//tensorflow:android": [], +# "//conditions:default": [ +# "@com_google_absl//absl/strings", +# "//tensorflow/core:test", +# ], +# }), +#) filegroup( name = "all_files", diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile index 7f316292724ea0baaf034d4e914773ad97a957d4..b4504f246a0f806d35d8c3d659717a86d2f2a4f5 100644 --- a/tensorflow/contrib/lite/Makefile +++ b/tensorflow/contrib/lite/Makefile @@ -27,10 +27,10 @@ LIBDIR := $(MAKEFILE_DIR)/gen/lib/ GENDIR := $(MAKEFILE_DIR)/gen/obj/ # Settings for the host compiler. -CXX := $(CC_PREFIX) gcc +CXX := $(CC_PREFIX)gcc CXXFLAGS := --std=c++11 -O3 -DNDEBUG -CC := $(CC_PREFIX) gcc -CFLAGS := +CC := $(CC_PREFIX)gcc +CFLAGS := -O3 -DNDEBUG LDOPTS := LDOPTS += -L/usr/local/lib ARFLAGS := -r @@ -57,10 +57,11 @@ LIBS := \ # If we're on Linux, also link in the dl library. ifeq ($(HOST_OS),LINUX) - LIBS += -ldl -lpthread + LIBS += -ldl endif include $(MAKEFILE_DIR)/ios_makefile.inc +include $(MAKEFILE_DIR)/rpi_makefile.inc # This library is the main target for this makefile. It will contain a minimal # runtime that can be linked in to other programs. diff --git a/tensorflow/contrib/lite/README.md b/tensorflow/contrib/lite/README.md index 55a524b207b258e794f97e68a96cf01dc60efb7f..df8c1c623c64eb410501d654cdccbb291a5995af 100644 --- a/tensorflow/contrib/lite/README.md +++ b/tensorflow/contrib/lite/README.md @@ -6,7 +6,7 @@ TensorFlow Lite uses many techniques for achieving low latency like optimizing t ![image](g3doc/TFLite-Architecture.jpg) # Getting Started with an Android Demo App -This section contains an example application using TensorFlow Lite for Android devices. The demo is a sample camera app that classifies images continuously using a quantized Mobilenet model. A device running Android 5.0 ( API 21) or higher is required to run the demo. +This section contains an example application using TensorFlow Lite for Android devices. The demo is a sample camera app that classifies images continuously using either a quantized Mobilenet model or a floating point Inception-v3 model. A device running Android 5.0 ( API 21) or higher is required to run the demo. There are 3 ways to get the demo app to your device - Download the prebuilt binary or @@ -29,9 +29,16 @@ The simplest way to compile the demo app, and try out changes to the project cod - Make sure the Android SDK version is greater than 26 and NDK version is greater than 14 (in the Android Studio Settings). - Import the `tensorflow/contrib/lite/java/demo` directory as a new Android Studio project. - Click through installing all the Gradle extensions it requests. - - Download the quantized Mobilenet TensorFlow Lite model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip) - - unzip and copy mobilenet_quant_v1_224.tflite to the assets directory: - `tensorflow/contrib/lite/java/demo/app/src/main/assets/` + - Either + - Download the quantized Mobilenet TensorFlow Lite model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip) + - unzip and copy mobilenet_quant_v1_224.tflite to the assets directory: + `tensorflow/contrib/lite/java/demo/app/src/main/assets/` + - Or download the floating point Inception-v3 model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip) + - unzip and copy inceptionv3_non_slim_2015.tflite to the assets directory + - change the chosen classifier in [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java) from + `classifier = new ImageClassifierQuantizedMobileNet(getActivity());` + to + `classifier = new ImageClassifierFloatInception(getActivity());` - Build and run the demo app ## Building TensorFlow Lite and the demo app from source @@ -84,7 +91,7 @@ Currently, we only support building the Android demo app within a Python 2 environment (due to a Bazel bug). ### More about the demo -The demo is resizing each camera image frame to (224 width * 224 height) to match the quantized Mobilenet model being used. The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch 224 * 224 is the width and height of the image 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. The Mobilenet model has 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The Mobilenet quantized model is bundled within the assets directory of the app. +The demo is resizing each camera image frame to (224 width * 224 height) to match the quantized Mobilenet model being used (299 * 299 for Inception-v3). The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch. 224 * 224 (299 * 299) is the width and height of the image. 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. Both models have 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The model file must be downloaded and bundled within the assets directory of the app. # iOS Demo App @@ -92,7 +99,7 @@ Similar to the Android demo app, there's an iOS camera app that uses exactly the This demo app requires a camera so it doesn't work with simulators. It need to be executed on a real iOS device. Follow the instructions to build and run the demo app: -1. Follow the Building section [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/ios.md#building) to build the universal iOS library for TensorFlow Lite. +1. Run `third_party/tensorflow/contrib/lite/examples/ios/download_models.sh` to download the model files used by the demo app. 1. Install [CocoaPods](https://cocoapods.org/) if it wasn't installed yet: `sudo gem install cocoapods`. 1. Run `pod install` in `tensorflow/contrib/lite/examples/ios/camera` to generate the workspace file. 1. Open the project by running `open tflite_camera_example.xcworkspace`, and build the app in XCode. @@ -142,7 +149,7 @@ Since we employ several formats, the following definitions may be useful: - SavedModel - A collection of GraphDef and CheckPoint together with a signature that labels input and output arguments to a model. A GraphDef and Checkpoint can be extracted from a saved model. - - TensorFlow lite model (.lite) - a serialized flatbuffer, containing TensorFlow lite operators and Tensors for the TensorFlow lite interpreter. This is most analogous to TensorFlow frozen GraphDefs. + - TensorFlow lite model (.tflite) - a serialized flatbuffer, containing TensorFlow lite operators and Tensors for the TensorFlow lite interpreter. This is most analogous to TensorFlow frozen GraphDefs. ### Freeze Graph To use this .pb GraphDef file within TensorFlow Lite, the application developer will need checkpoints containing trained weight parameters. The .pb contains only the structure of the graph. The process of merging the checkpoint values with the graph structure is known as "freezing" the graph. @@ -164,18 +171,18 @@ bazel-bin/tensorflow/python/tools/freeze_graph\ The user has to first build the freeze_graph script using bazel and then run the script. The input_binary flag has to be enabled to ensure that the protobuf is read and written in binary format. The user has to input the .pb and the .ckpt files to freeze the graph The output_node_names may not be obvious outside of the code that built the model. The easiest way to find them is to visualize the graph, either with graphviz, or [in tensorboard](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2/#3). -This frozen Graphdef is now ready to be converted to flatbuffer format (.lite) for use on Android or iOS. On Android users have the flexibility to use either the float or quantized versions of the frozen graphdef, if available, using the Tensorflow Optimizing Converter tool. +This frozen Graphdef is now ready to be converted to flatbuffer format (.tflite) for use on Android or iOS. On Android users have the flexibility to use either the float or quantized versions of the frozen graphdef, if available, using the Tensorflow Optimizing Converter tool. -Here is a sample command line to convert the frozen Graphdef to '.lite' format for The Tensorflow Optimizing Converter supports both float and quantized models, however, different configuration parameters are needed depending on whether a FLOAT or QUANTIZED mode is being used. +Here is a sample command line to convert the frozen Graphdef to '.tflite' format for The Tensorflow Optimizing Converter supports both float and quantized models, however, different configuration parameters are needed depending on whether a FLOAT or QUANTIZED mode is being used. (Here is a link to the pb [file](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz)). ``` bazel build tensorflow/contrib/lite/toco:toco -bazel-bin/tensorflow/contrib/lite/toco/toco -- \ +bazel-bin/tensorflow/contrib/lite/toco/toco \ --input_file=$(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \ --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \ - --output_file=/tmp/mobilenet_v1_1.0_224.lite --inference_type=FLOAT \ + --output_file=/tmp/mobilenet_v1_1.0_224.tflite --inference_type=FLOAT \ --input_type=FLOAT --input_arrays=input \ --output_arrays=MobilenetV1/Predictions/Reshape_1 --input_shapes=1,224,224,3 ``` @@ -211,7 +218,7 @@ and then visualize the resulting HTML file in a browser. ## Step 3. Use the TensorFlow Lite model for inference in a mobile app -After completion of Step 2 the developer should have a .lite model. +After completion of Step 2 the developer should have a .tflite model. ### For Android Because Android apps need to be written in Java, and core TensorFlow is in C++, a JNI library is provided to interface between the two. Its interface is aimed only at inference, so it provides the ability to load a graph, set up inputs, and run the model to calculate particular outputs. The full documentation for the set of methods can be seen [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/). The demo app is also open sourced on [github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app). diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc index 87b17c338e7afc33d32dd9688cc0825ac319dd19..8e47e2375e2e306c345a2b6caa2411abd9b3ceb0 100644 --- a/tensorflow/contrib/lite/arena_planner.cc +++ b/tensorflow/contrib/lite/arena_planner.cc @@ -128,6 +128,11 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { } TfLiteStatus ArenaPlanner::ExecuteAllocations(int first_node, int last_node) { + // Grow the size of `allocs_` if necessary. This allows allocating temporary + // tensors in op's `prepare` function. + TF_LITE_ENSURE(context_, graph_info_->num_tensors() >= allocs_.size()); + allocs_.resize(graph_info_->num_tensors()); + TF_LITE_ENSURE_STATUS(CalculateAllocations(first_node, last_node)); TF_LITE_ENSURE_STATUS(Commit()); diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 19829e4991651111e13fc1805f97daef8bc016a7..2813d1c347163e67c70983d3dd49773f4a4b4544 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -104,7 +104,7 @@ def tflite_jni_binary(name, """Builds a jni binary for TFLite.""" linkopts = linkopts + [ "-Wl,--version-script", # Export only jni functions & classes. - linkscript, + "$(location {})".format(linkscript), ] native.cc_binary( name=name, diff --git a/tensorflow/contrib/ndlstm/__init__.py b/tensorflow/contrib/lite/build_rpi_lib.sh old mode 100644 new mode 100755 similarity index 76% rename from tensorflow/contrib/ndlstm/__init__.py rename to tensorflow/contrib/lite/build_rpi_lib.sh index da89bb4ab605e1570a119a88d481f3ad3d1fee4c..3824b16412ed26a6cab79df3242da6017c3322b0 --- a/tensorflow/contrib/ndlstm/__init__.py +++ b/tensorflow/contrib/lite/build_rpi_lib.sh @@ -1,3 +1,4 @@ +#!/bin/bash -x # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,9 +14,9 @@ # limitations under the License. # ============================================================================== -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function +set -e -from tensorflow.contrib.ndlstm.python import lstm2d -from tensorflow.contrib.ndlstm.python import lstm1d +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR/../../.." + +CC_PREFIX=arm-linux-gnueabihf- make -j 3 -f tensorflow/contrib/lite/Makefile TARGET=RPI TARGET_ARCH=armv7 diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 5dbeadd16582ec586adab100b8a46e10182bd5ee..5fc8954743e5b3b458e5c2004f4378cbad6056c0 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -195,6 +195,10 @@ typedef struct { bool keep_dims; } TfLiteMeanParams; +typedef struct { + int num_splits; +} TfLiteSplitParams; + typedef struct { // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. // For now we will fix the maximum possible number of dimensions. diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..2218ea8eac5760ce3e0e09828fef4673489ab4cd --- /dev/null +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -0,0 +1,88 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ + +// DO NOT EDIT MANUALLY: This file is automatically generated by +// `schema_builtin_ops_header_generator.py`. + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// The enum for builtin operators. +// Note: CUSTOM and DELEGATE are 2 special ops which are not real biultin +// ops. +typedef enum { + kTfLiteBuiltinAdd = 0, + kTfLiteBuiltinAveragePool2d = 1, + kTfLiteBuiltinConcatenation = 2, + kTfLiteBuiltinConv2d = 3, + kTfLiteBuiltinDepthwiseConv2d = 4, + kTfLiteBuiltinDequantize = 6, + kTfLiteBuiltinEmbeddingLookup = 7, + kTfLiteBuiltinFullyConnected = 9, + kTfLiteBuiltinHashtableLookup = 10, + kTfLiteBuiltinL2Normalization = 11, + kTfLiteBuiltinL2Pool2d = 12, + kTfLiteBuiltinLocalResponseNormalization = 13, + kTfLiteBuiltinLogistic = 14, + kTfLiteBuiltinLshProjection = 15, + kTfLiteBuiltinLstm = 16, + kTfLiteBuiltinMaxPool2d = 17, + kTfLiteBuiltinMul = 18, + kTfLiteBuiltinRelu = 19, + kTfLiteBuiltinReluN1To1 = 20, + kTfLiteBuiltinRelu6 = 21, + kTfLiteBuiltinReshape = 22, + kTfLiteBuiltinResizeBilinear = 23, + kTfLiteBuiltinRnn = 24, + kTfLiteBuiltinSoftmax = 25, + kTfLiteBuiltinSpaceToDepth = 26, + kTfLiteBuiltinSvdf = 27, + kTfLiteBuiltinTanh = 28, + kTfLiteBuiltinConcatEmbeddings = 29, + kTfLiteBuiltinSkipGram = 30, + kTfLiteBuiltinCall = 31, + kTfLiteBuiltinCustom = 32, + kTfLiteBuiltinEmbeddingLookupSparse = 33, + kTfLiteBuiltinPad = 34, + kTfLiteBuiltinUnidirectionalSequenceRnn = 35, + kTfLiteBuiltinGather = 36, + kTfLiteBuiltinBatchToSpaceNd = 37, + kTfLiteBuiltinSpaceToBatchNd = 38, + kTfLiteBuiltinTranspose = 39, + kTfLiteBuiltinMean = 40, + kTfLiteBuiltinSub = 41, + kTfLiteBuiltinDiv = 42, + kTfLiteBuiltinSqueeze = 43, + kTfLiteBuiltinUnidirectionalSequenceLstm = 44, + kTfLiteBuiltinStridedSlice = 45, + kTfLiteBuiltinBidirectionalSequenceRnn = 46, + kTfLiteBuiltinExp = 47, + kTfLiteBuiltinTopkV2 = 48, + kTfLiteBuiltinSplit = 49, + kTfLiteBuiltinLogSoftmax = 50, + kTfLiteBuiltinDelegate = 51, + kTfLiteBuiltinBidirectionalSequenceLstm = 52, + kTfLiteBuiltinCast = 53, +} TfLiteBuiltinOperator; + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus +#endif // TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ +} diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index d6dfc20ae829b13e9cb45efcf9e14af5d4b69b48..ed7f4515fa4437d61a37be93616c28a046295c5a 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -38,6 +38,9 @@ extern "C" { typedef enum { kTfLiteOk = 0, kTfLiteError = 1 } TfLiteStatus; +// Forward declare so GetNode can use this is in Context. +typedef struct _TfLiteRegistration TfLiteRegistration; + #define kOptionalTensor (-1) // Fixed size list of integers. Used for dimensions and inputs/outputs tensor @@ -205,10 +208,57 @@ void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, // Resize the allocated data of a (dynamic) tensor. void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); +// A structure representing an instance of a node. +// This structure only exhibits the inputs, outputs and user defined data, not +// other features like the type. +typedef struct { + // Inputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* inputs; + + // Outputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* outputs; + + // Temporary tensors uses during the computations. This usually contains no + // tensors, but ops are allowed to change that if they need scratch space of + // any sort. + TfLiteIntArray* temporaries; + + // Opaque data provided by the node implementer through `Registration.init`. + void* user_data; + + // Opaque data provided to the node if the node is a builtin. This is usually + // a structure defined in builtin_op_data.h + void* builtin_data; + + // Custom initial data. This is the opaque data provided in the flatbuffer. + // WARNING: This is an experimental interface that is subject to change. + const void* custom_initial_data; + int custom_initial_data_size; +} TfLiteNode; + typedef struct TfLiteContext { // Number of tensors in the context. int tensors_size; - // An tensor of tensors in the interpreter context (of length `tensors_size`) + + // The execution plan contains a list of the node indices in execution + // order. execution_plan->size is the current number of nodes. And, + // execution_plan->data[0] is the first node that needs to be run. + // TfLiteDelegates can traverse the current execution plan by iterating + // through each member of this array and using GetNodeAndRegistration() to + // access details about a node. i.e. + // TfLiteIntArray* execution_plan; + // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan)); + // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) { + // int node_index = execution_plan->data[exec_index]; + // TfLiteNode* node; + // TfLiteRegistration* reg; + // context->GetNodeAndRegistration(context, node_index, &node, ®); + // } + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context, + TfLiteIntArray** execution_plan); + + // An array of tensors in the interpreter context (of length `tensors_size`) TfLiteTensor* tensors; // opaque full context ptr (an opaque c++ data structure) @@ -227,34 +277,24 @@ typedef struct TfLiteContext { TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add, int* first_new_tensor_index); + // Get a Tensor node by node_index. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*GetNodeAndRegistration)(struct TfLiteContext*, int node_index, + TfLiteNode** node, + TfLiteRegistration** registration); + + // Replace ops with one or more stub delegate operations. This function + // does not take ownership of `nodes_to_replace`. + TfLiteStatus (*ReplaceSubgraphsWithDelegateKernels)( + struct TfLiteContext*, TfLiteRegistration registration, + const TfLiteIntArray* nodes_to_replace); + // TODO(ahentz): we should create a more general mechanism for this sort of // library-global objects. void* gemm_context; } TfLiteContext; -// A structure representing an instance of a node. -// This structure only exhibits the inputs, outputs and user defined data, not -// other features like the type. -typedef struct { - // Inputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* inputs; - - // Outputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* outputs; - - // Temporary tensors uses during the computations. This usually contains no - // tensors, but ops are allowed to change that if they need scratch space of - // any sort. - TfLiteIntArray* temporaries; - - // Opaque data provided by the node implementer through `Registration.init`. - void* user_data; - - // Opaque data provided to the node if the node is a builtin. - void* builtin_data; -} TfLiteNode; - -typedef struct { +typedef struct _TfLiteRegistration { // Initializes the op from serialized data. // If a built-in op: // `buffer` is the op's params data (TfLiteLSTMParams*). @@ -291,8 +331,26 @@ typedef struct { // NN API. Note, it is the responsibility of the registration binder to // set this properly. int32_t builtin_code; + + // Custom op name. If the op is a builtin, this will be null. + // WARNING: This is an experimental interface that is subject to change. + const char* custom_name; } TfLiteRegistration; +// WARNING: This is an experimental interface that is subject to change. +typedef struct { + // Data that delegate needs to identify itself. This data is owned by the + // delegate. The delegate is owned in the user code, so the delegate is + // responsible for doing this when it is destroyed. + void* data_; + // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the + // delegate a view of the current graph through TfLiteContext*. It typically + // will look at the nodes and call ReplaceSubgraphsWithDelegateKernels() + // to ask the TensorFlow lite runtime to create macro-nodes to represent + // delegated subgraphs of the original graph. + TfLiteStatus (*Prepare)(TfLiteContext* context, void* data); +} TfLiteDelegate; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index e1b7b3613a041287ff3cc4eeff8afd7cfcede174..a93ed201d647ddf2359a57254a959871c13fb94f 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -36,8 +36,6 @@ ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_ NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" FARMHASH_URL="https://mirror.bazel.build/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz" FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/master.zip" -MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_ios_lite_float_2017_11_08.zip" -QUANTIZED_MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip" # TODO(petewarden): Some new code in Eigen triggers a clang bug with iOS arm64, # so work around it by patching the source. @@ -93,8 +91,6 @@ download_and_extract "${ABSL_URL}" "${DOWNLOADS_DIR}/absl" download_and_extract "${NEON_2_SSE_URL}" "${DOWNLOADS_DIR}/neon_2_sse" download_and_extract "${FARMHASH_URL}" "${DOWNLOADS_DIR}/farmhash" download_and_extract "${FLATBUFFERS_URL}" "${DOWNLOADS_DIR}/flatbuffers" -download_and_extract "${MODELS_URL}" "${DOWNLOADS_DIR}/models" -download_and_extract "${QUANTIZED_MODELS_URL}" "${DOWNLOADS_DIR}/quantized_models" replace_by_sed 's#static uint32x4_t p4ui_CONJ_XOR = vld1q_u32( conj_XOR_DATA );#static uint32x4_t p4ui_CONJ_XOR; // = vld1q_u32( conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" @@ -103,7 +99,4 @@ replace_by_sed 's#static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );#s replace_by_sed 's#static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );#static uint64x2_t p2ul_CONJ_XOR;// = vld1q_u64( p2ul_conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" -cp ${DOWNLOADS_DIR}/models/models/* tensorflow/contrib/lite/examples/ios/simple/data/ -cp ${DOWNLOADS_DIR}/quantized_models/* tensorflow/contrib/lite/examples/ios/camera/data/ - echo "download_dependencies.sh completed successfully." >&2 diff --git a/tensorflow/contrib/lite/examples/ios/camera/Podfile b/tensorflow/contrib/lite/examples/ios/camera/Podfile index 4ae6fb6b94e4489f63506b05a2f348b7daafd3b7..c7d3b1c966eaa0de71f5c37a6a77b3881e30ddd7 100644 --- a/tensorflow/contrib/lite/examples/ios/camera/Podfile +++ b/tensorflow/contrib/lite/examples/ios/camera/Podfile @@ -2,4 +2,4 @@ platform :ios, '8.0' inhibit_all_warnings! target 'tflite_camera_example' - pod 'TensorFlow-experimental' + pod 'TensorFlowLite' diff --git a/tensorflow/contrib/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj b/tensorflow/contrib/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj index c98183276bd60d2a0ad023ba26aad12572a02786..b0236e9c608ec35437bcfe79c51149a76f9f416e 100644 --- a/tensorflow/contrib/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj +++ b/tensorflow/contrib/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj @@ -16,7 +16,6 @@ 1CDB2D4E1ED3AA35007929E9 /* Info.plist in Resources */ = {isa = PBXBuildFile; fileRef = 1CDB2D4D1ED3AA35007929E9 /* Info.plist */; }; 54DC6C3C5F734F3A58069F0C /* libPods-tflite_camera_example.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 3BA8BF92C84895BFE59D8236 /* libPods-tflite_camera_example.a */; }; AC1F82661FBA3CBD0052BA77 /* labels.txt in Resources */ = {isa = PBXBuildFile; fileRef = AC1F82641FBA3CBD0052BA77 /* labels.txt */; }; - AC1F82691FBA3F930052BA77 /* libtensorflow-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = AC1F82681FBA3F930052BA77 /* libtensorflow-lite.a */; }; ACA1A4CA1FBB6C28009B8D86 /* mobilenet_quant_v1_224.tflite in Resources */ = {isa = PBXBuildFile; fileRef = ACA1A4C91FBB6C28009B8D86 /* mobilenet_quant_v1_224.tflite */; }; /* End PBXBuildFile section */ @@ -38,7 +37,6 @@ 3BC5BE4BBD09374D3E98F082 /* Pods-tflite_camera_example.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_camera_example.debug.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example.debug.xcconfig"; sourceTree = ""; }; 55ED318E8D29C8AFEF03DF1E /* Pods-tflite_camera_example.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_camera_example.release.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example.release.xcconfig"; sourceTree = ""; }; AC1F82641FBA3CBD0052BA77 /* labels.txt */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text; path = labels.txt; sourceTree = ""; }; - AC1F82681FBA3F930052BA77 /* libtensorflow-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libtensorflow-lite.a"; path = "../../../gen/lib/libtensorflow-lite.a"; sourceTree = ""; }; ACA1A4C91FBB6C28009B8D86 /* mobilenet_quant_v1_224.tflite */ = {isa = PBXFileReference; lastKnownFileType = file; path = mobilenet_quant_v1_224.tflite; sourceTree = ""; }; /* End PBXFileReference section */ @@ -47,7 +45,6 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( - AC1F82691FBA3F930052BA77 /* libtensorflow-lite.a in Frameworks */, 1CB47D491ED3AD1700DF7666 /* AVFoundation.framework in Frameworks */, 1CA5EB931ED3ABFB00247A34 /* CoreMedia.framework in Frameworks */, 54DC6C3C5F734F3A58069F0C /* libPods-tflite_camera_example.a in Frameworks */, @@ -60,7 +57,6 @@ 24D7686C331131624F4454A0 /* Frameworks */ = { isa = PBXGroup; children = ( - AC1F82681FBA3F930052BA77 /* libtensorflow-lite.a */, 1CB47D481ED3AD1700DF7666 /* AVFoundation.framework */, 1CA5EB921ED3ABFB00247A34 /* CoreMedia.framework */, 1C0D734A1ECCC460008C1DAB /* CoreGraphics.framework */, @@ -336,7 +332,6 @@ ../../../downloads/, ); IPHONEOS_DEPLOYMENT_TARGET = 8.0; - LIBRARY_SEARCH_PATHS = ../../../gen/lib/; MTL_ENABLE_DEBUG_INFO = YES; ONLY_ACTIVE_ARCH = YES; SDKROOT = iphoneos; @@ -384,7 +379,6 @@ ../../../downloads/, ); IPHONEOS_DEPLOYMENT_TARGET = 8.0; - LIBRARY_SEARCH_PATHS = ../../../gen/lib/; MTL_ENABLE_DEBUG_INFO = NO; SDKROOT = iphoneos; TARGETED_DEVICE_FAMILY = "1,2"; diff --git a/tensorflow/contrib/lite/examples/ios/download_models.sh b/tensorflow/contrib/lite/examples/ios/download_models.sh new file mode 100755 index 0000000000000000000000000000000000000000..ccd163758c5830dc9367e023dcb3a604e07ca5db --- /dev/null +++ b/tensorflow/contrib/lite/examples/ios/download_models.sh @@ -0,0 +1,57 @@ +#!/bin/bash +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -ex + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_ios_lite_float_2017_11_08.zip" +QUANTIZED_MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip" +DOWNLOADS_DIR=$(mktemp -d) + +cd $SCRIPT_DIR + +download_and_extract() { + local usage="Usage: download_and_extract URL DIR" + local url="${1:?${usage}}" + local dir="${2:?${usage}}" + echo "downloading ${url}" >&2 + mkdir -p "${dir}" + tempdir=$(mktemp -d) + tempdir2=$(mktemp -d) + + curl -L ${url} > ${tempdir}/zipped.zip + unzip ${tempdir}/zipped.zip -d ${tempdir2} + + # If the zip file contains nested directories, extract the files from the + # inner directory. + if ls ${tempdir2}/*/* 1> /dev/null 2>&1; then + # unzip has no strip components, so unzip to a temp dir, and move the + # files we want from the tempdir to destination. + cp -R ${tempdir2}/*/* ${dir}/ + else + cp -R ${tempdir2}/* ${dir}/ + fi + rm -rf ${tempdir2} ${tempdir} +} + +download_and_extract "${MODELS_URL}" "${DOWNLOADS_DIR}/models" +download_and_extract "${QUANTIZED_MODELS_URL}" "${DOWNLOADS_DIR}/quantized_models" + +file ${DOWNLOADS_DIR}/models + +cp ${DOWNLOADS_DIR}/models/models/* simple/data/ +cp ${DOWNLOADS_DIR}/quantized_models/* camera/data/ + diff --git a/tensorflow/contrib/lite/examples/ios/simple/Podfile b/tensorflow/contrib/lite/examples/ios/simple/Podfile index 1740ad64573a84fae6de0fcf284eb06afec67e25..e4aca2be82d437a0225d2c15d3e486b0344aa978 100644 --- a/tensorflow/contrib/lite/examples/ios/simple/Podfile +++ b/tensorflow/contrib/lite/examples/ios/simple/Podfile @@ -1,5 +1,5 @@ platform :ios, '8.0' inhibit_all_warnings! -target 'tf_simple_example' - pod 'TensorFlow-experimental' +target 'tflite_simple_example' + pod 'TensorFlowLite' diff --git a/tensorflow/contrib/lite/examples/ios/simple/RunModel-Info.plist b/tensorflow/contrib/lite/examples/ios/simple/RunModel-Info.plist index 1a3eaa8a2c18d1cd24dfd475d396b00ec4d86c9d..a19a43a7541e3d751116e868dbcbdd607d15ab4a 100644 --- a/tensorflow/contrib/lite/examples/ios/simple/RunModel-Info.plist +++ b/tensorflow/contrib/lite/examples/ios/simple/RunModel-Info.plist @@ -7,7 +7,7 @@ CFBundleDisplayName tflite-simple-example CFBundleExecutable - tf_simple_example + tflite_simple_example CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion diff --git a/tensorflow/contrib/lite/examples/ios/simple/simple.xcodeproj/project.pbxproj b/tensorflow/contrib/lite/examples/ios/simple/simple.xcodeproj/project.pbxproj index 9277c230b8cce1b5673a50d32d7640d52e2e8f9d..f5b8382d5ae4ac80a7edb52c34ebaf12ad65f4db 100644 --- a/tensorflow/contrib/lite/examples/ios/simple/simple.xcodeproj/project.pbxproj +++ b/tensorflow/contrib/lite/examples/ios/simple/simple.xcodeproj/project.pbxproj @@ -9,7 +9,7 @@ /* Begin PBXBuildFile section */ 1C0D734B1ECCC460008C1DAB /* CoreGraphics.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 1C0D734A1ECCC460008C1DAB /* CoreGraphics.framework */; }; 1CA45FFF1ECCC356002FA6A4 /* UIKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 1CA45FFE1ECCC356002FA6A4 /* UIKit.framework */; }; - 594C14AE1FB8F9B500EE8BFE /* libtensorflow-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 594C14AD1FB8F9B500EE8BFE /* libtensorflow-lite.a */; }; + 1E6F42DBB39A4A3871D4F848 /* libPods-tflite_simple_example.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 73DBC33C5DD9A526EE6D1EF2 /* libPods-tflite_simple_example.a */; }; 594C14B11FB9037100EE8BFE /* labels.txt in Resources */ = {isa = PBXBuildFile; fileRef = 594C14AF1FB9037100EE8BFE /* labels.txt */; }; 594C14B21FB9037100EE8BFE /* mobilenet_v1_1.0_224.tflite in Resources */ = {isa = PBXBuildFile; fileRef = 594C14B01FB9037100EE8BFE /* mobilenet_v1_1.0_224.tflite */; }; 59A3D0011CF4E68100C4259F /* AppDelegate.mm in Sources */ = {isa = PBXBuildFile; fileRef = 59A3CFF21CF4E68100C4259F /* AppDelegate.mm */; }; @@ -24,8 +24,7 @@ 1C0D73481ECCC41B008C1DAB /* CoreImage.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = CoreImage.framework; path = System/Library/Frameworks/CoreImage.framework; sourceTree = SDKROOT; }; 1C0D734A1ECCC460008C1DAB /* CoreGraphics.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = CoreGraphics.framework; path = System/Library/Frameworks/CoreGraphics.framework; sourceTree = SDKROOT; }; 1CA45FFE1ECCC356002FA6A4 /* UIKit.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = UIKit.framework; path = System/Library/Frameworks/UIKit.framework; sourceTree = SDKROOT; }; - 5911579B1CF4011C00C31E3A /* tf_simple_example.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = tf_simple_example.app; sourceTree = BUILT_PRODUCTS_DIR; }; - 594C14AD1FB8F9B500EE8BFE /* libtensorflow-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libtensorflow-lite.a"; path = "../../../gen/lib/libtensorflow-lite.a"; sourceTree = ""; }; + 5911579B1CF4011C00C31E3A /* tflite_simple_example.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = tflite_simple_example.app; sourceTree = BUILT_PRODUCTS_DIR; }; 594C14AF1FB9037100EE8BFE /* labels.txt */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text; path = labels.txt; sourceTree = ""; }; 594C14B01FB9037100EE8BFE /* mobilenet_v1_1.0_224.tflite */ = {isa = PBXFileReference; lastKnownFileType = file; path = mobilenet_v1_1.0_224.tflite; sourceTree = ""; }; 59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = ""; }; @@ -38,7 +37,9 @@ 59A3CFFE1CF4E68100C4259F /* RunModelViewController.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = RunModelViewController.h; sourceTree = ""; }; 59A3CFFF1CF4E68100C4259F /* RunModelViewController.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = RunModelViewController.mm; sourceTree = ""; }; 59A3D0001CF4E68100C4259F /* RunModelViewController.xib */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = file.xib; path = RunModelViewController.xib; sourceTree = ""; }; - 73DBC33C5DD9A526EE6D1EF2 /* libPods-tf_simple_example.a */ = {isa = PBXFileReference; explicitFileType = archive.ar; includeInIndex = 0; path = "libPods-tf_simple_example.a"; sourceTree = BUILT_PRODUCTS_DIR; }; + 5D6203B9FAEEB9824194DBE8 /* Pods-tflite_simple_example.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_simple_example.release.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_simple_example/Pods-tflite_simple_example.release.xcconfig"; sourceTree = ""; }; + 73DBC33C5DD9A526EE6D1EF2 /* libPods-tflite_simple_example.a */ = {isa = PBXFileReference; explicitFileType = archive.ar; includeInIndex = 0; path = "libPods-tflite_simple_example.a"; sourceTree = BUILT_PRODUCTS_DIR; }; + 987DD5BCAB2DD8B682674E20 /* Pods-tflite_simple_example.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_simple_example.debug.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_simple_example/Pods-tflite_simple_example.debug.xcconfig"; sourceTree = ""; }; /* End PBXFileReference section */ /* Begin PBXFrameworksBuildPhase section */ @@ -46,9 +47,9 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( - 594C14AE1FB8F9B500EE8BFE /* libtensorflow-lite.a in Frameworks */, 1C0D734B1ECCC460008C1DAB /* CoreGraphics.framework in Frameworks */, 1CA45FFF1ECCC356002FA6A4 /* UIKit.framework in Frameworks */, + 1E6F42DBB39A4A3871D4F848 /* libPods-tflite_simple_example.a in Frameworks */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -58,11 +59,10 @@ 24D7686C331131624F4454A0 /* Frameworks */ = { isa = PBXGroup; children = ( - 594C14AD1FB8F9B500EE8BFE /* libtensorflow-lite.a */, 1C0D734A1ECCC460008C1DAB /* CoreGraphics.framework */, 1C0D73481ECCC41B008C1DAB /* CoreImage.framework */, 1CA45FFE1ECCC356002FA6A4 /* UIKit.framework */, - 73DBC33C5DD9A526EE6D1EF2 /* libPods-tf_simple_example.a */, + 73DBC33C5DD9A526EE6D1EF2 /* libPods-tflite_simple_example.a */, ); name = Frameworks; sourceTree = ""; @@ -82,13 +82,14 @@ 59A3D0001CF4E68100C4259F /* RunModelViewController.xib */, 5911579C1CF4011C00C31E3A /* Products */, 24D7686C331131624F4454A0 /* Frameworks */, + 5CE7E4179B26BF77944D8637 /* Pods */, ); sourceTree = ""; }; 5911579C1CF4011C00C31E3A /* Products */ = { isa = PBXGroup; children = ( - 5911579B1CF4011C00C31E3A /* tf_simple_example.app */, + 5911579B1CF4011C00C31E3A /* tflite_simple_example.app */, ); name = Products; sourceTree = ""; @@ -103,24 +104,36 @@ path = data; sourceTree = ""; }; + 5CE7E4179B26BF77944D8637 /* Pods */ = { + isa = PBXGroup; + children = ( + 987DD5BCAB2DD8B682674E20 /* Pods-tflite_simple_example.debug.xcconfig */, + 5D6203B9FAEEB9824194DBE8 /* Pods-tflite_simple_example.release.xcconfig */, + ); + name = Pods; + sourceTree = ""; + }; /* End PBXGroup section */ /* Begin PBXNativeTarget section */ - 5911579A1CF4011C00C31E3A /* tf_simple_example */ = { + 5911579A1CF4011C00C31E3A /* tflite_simple_example */ = { isa = PBXNativeTarget; - buildConfigurationList = 591157B21CF4011D00C31E3A /* Build configuration list for PBXNativeTarget "tf_simple_example" */; + buildConfigurationList = 591157B21CF4011D00C31E3A /* Build configuration list for PBXNativeTarget "tflite_simple_example" */; buildPhases = ( + A507411BCC70190B9ABD2721 /* [CP] Check Pods Manifest.lock */, 591157971CF4011C00C31E3A /* Sources */, 591157981CF4011C00C31E3A /* Frameworks */, 591157991CF4011C00C31E3A /* Resources */, + 25E1671BDC7334C678FB5DFB /* [CP] Embed Pods Frameworks */, + 10976C49D86B7F8A59157601 /* [CP] Copy Pods Resources */, ); buildRules = ( ); dependencies = ( ); - name = tf_simple_example; + name = tflite_simple_example; productName = tf_ios_makefile_example; - productReference = 5911579B1CF4011C00C31E3A /* tf_simple_example.app */; + productReference = 5911579B1CF4011C00C31E3A /* tflite_simple_example.app */; productType = "com.apple.product-type.application"; }; /* End PBXNativeTarget section */ @@ -152,7 +165,7 @@ projectDirPath = ""; projectRoot = ""; targets = ( - 5911579A1CF4011C00C31E3A /* tf_simple_example */, + 5911579A1CF4011C00C31E3A /* tflite_simple_example */, ); }; /* End PBXProject section */ @@ -171,6 +184,57 @@ }; /* End PBXResourcesBuildPhase section */ +/* Begin PBXShellScriptBuildPhase section */ + 10976C49D86B7F8A59157601 /* [CP] Copy Pods Resources */ = { + isa = PBXShellScriptBuildPhase; + buildActionMask = 2147483647; + files = ( + ); + inputPaths = ( + ); + name = "[CP] Copy Pods Resources"; + outputPaths = ( + ); + runOnlyForDeploymentPostprocessing = 0; + shellPath = /bin/sh; + shellScript = "\"${SRCROOT}/Pods/Target Support Files/Pods-tflite_simple_example/Pods-tflite_simple_example-resources.sh\"\n"; + showEnvVarsInLog = 0; + }; + 25E1671BDC7334C678FB5DFB /* [CP] Embed Pods Frameworks */ = { + isa = PBXShellScriptBuildPhase; + buildActionMask = 2147483647; + files = ( + ); + inputPaths = ( + ); + name = "[CP] Embed Pods Frameworks"; + outputPaths = ( + ); + runOnlyForDeploymentPostprocessing = 0; + shellPath = /bin/sh; + shellScript = "\"${SRCROOT}/Pods/Target Support Files/Pods-tflite_simple_example/Pods-tflite_simple_example-frameworks.sh\"\n"; + showEnvVarsInLog = 0; + }; + A507411BCC70190B9ABD2721 /* [CP] Check Pods Manifest.lock */ = { + isa = PBXShellScriptBuildPhase; + buildActionMask = 2147483647; + files = ( + ); + inputPaths = ( + "${PODS_PODFILE_DIR_PATH}/Podfile.lock", + "${PODS_ROOT}/Manifest.lock", + ); + name = "[CP] Check Pods Manifest.lock"; + outputPaths = ( + "$(DERIVED_FILE_DIR)/Pods-tflite_simple_example-checkManifestLockResult.txt", + ); + runOnlyForDeploymentPostprocessing = 0; + shellPath = /bin/sh; + shellScript = "diff \"${PODS_PODFILE_DIR_PATH}/Podfile.lock\" \"${PODS_ROOT}/Manifest.lock\" > /dev/null\nif [ $? != 0 ] ; then\n # print error to STDERR\n echo \"error: The sandbox is not in sync with the Podfile.lock. Run 'pod install' or update your CocoaPods installation.\" >&2\n exit 1\nfi\n# This output is used by Xcode 'outputs' to avoid re-running this script phase.\necho \"SUCCESS\" > \"${SCRIPT_OUTPUT_FILE_0}\"\n"; + showEnvVarsInLog = 0; + }; +/* End PBXShellScriptBuildPhase section */ + /* Begin PBXSourcesBuildPhase section */ 591157971CF4011C00C31E3A /* Sources */ = { isa = PBXSourcesBuildPhase; @@ -274,6 +338,7 @@ }; 591157B31CF4011D00C31E3A /* Debug */ = { isa = XCBuildConfiguration; + baseConfigurationReference = 987DD5BCAB2DD8B682674E20 /* Pods-tflite_simple_example.debug.xcconfig */; buildSettings = { CLANG_DEBUG_INFORMATION_LEVEL = default; CODE_SIGN_IDENTITY = "iPhone Developer"; @@ -283,15 +348,10 @@ GCC_ENABLE_CPP_RTTI = YES; HEADER_SEARCH_PATHS = ( "$(inherited)", - ../../../../../../, - ../../../downloads/flatbuffers/include/, - ../../../downloads/eigen/, - ../../../downloads/, ); INFOPLIST_FILE = "$(SRCROOT)/RunModel-Info.plist"; IPHONEOS_DEPLOYMENT_TARGET = 9.2; LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks"; - LIBRARY_SEARCH_PATHS = ../../../gen/lib/; OTHER_CPLUSPLUSFLAGS = "$(OTHER_CFLAGS)"; OTHER_LDFLAGS = "$(inherited)"; PRODUCT_BUNDLE_IDENTIFIER = "com.google.tflite-simple-example"; @@ -304,6 +364,7 @@ }; 591157B41CF4011D00C31E3A /* Release */ = { isa = XCBuildConfiguration; + baseConfigurationReference = 5D6203B9FAEEB9824194DBE8 /* Pods-tflite_simple_example.release.xcconfig */; buildSettings = { CLANG_DEBUG_INFORMATION_LEVEL = default; CODE_SIGN_IDENTITY = "iPhone Developer"; @@ -313,15 +374,10 @@ GCC_ENABLE_CPP_RTTI = YES; HEADER_SEARCH_PATHS = ( "$(inherited)", - ../../../../../../, - ../../../downloads/flatbuffers/include/, - ../../../downloads/eigen/, - ../../../downloads/, ); INFOPLIST_FILE = "$(SRCROOT)/RunModel-Info.plist"; IPHONEOS_DEPLOYMENT_TARGET = 9.2; LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks"; - LIBRARY_SEARCH_PATHS = ../../../gen/lib/; ONLY_ACTIVE_ARCH = YES; OTHER_CPLUSPLUSFLAGS = "$(OTHER_CFLAGS)"; OTHER_LDFLAGS = "$(inherited)"; @@ -344,7 +400,7 @@ defaultConfigurationIsVisible = 0; defaultConfigurationName = Release; }; - 591157B21CF4011D00C31E3A /* Build configuration list for PBXNativeTarget "tf_simple_example" */ = { + 591157B21CF4011D00C31E3A /* Build configuration list for PBXNativeTarget "tflite_simple_example" */ = { isa = XCConfigurationList; buildConfigurations = ( 591157B31CF4011D00C31E3A /* Debug */, diff --git a/tensorflow/contrib/lite/examples/label_image/BUILD b/tensorflow/contrib/lite/examples/label_image/BUILD index d216cdf69ba8fd3de5a665e5d8b29aa5e01bff13..959347b5491514ddc13af57ea6f7385a0d39e418 100644 --- a/tensorflow/contrib/lite/examples/label_image/BUILD +++ b/tensorflow/contrib/lite/examples/label_image/BUILD @@ -43,8 +43,13 @@ cc_library( "label_image.h", ], deps = [ + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:schema_fbs_version", "//tensorflow/contrib/lite:string", + "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", + "//tensorflow/contrib/lite/schema:schema_fbs", ], ) diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h index 471fda2ba465aa5ccad2985a063a6855b7488a05..97343dde6b31694e5b2de20b35a7083fb8fe4a0e 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H -#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H +#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H_ +#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H_ #include "tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h" #include "tensorflow/contrib/lite/examples/label_image/label_image.h" @@ -31,8 +31,8 @@ void resize(T* out, uint8_t* in, int image_height, int image_width, int wanted_channels, Settings* s); // explicit instantiation -template void resize(uint8_t*, unsigned char*, int, int, int, int, - int, int, Settings*); +template void resize(uint8_t*, unsigned char*, int, int, int, int, int, + int, Settings*); template void resize(float*, unsigned char*, int, int, int, int, int, int, Settings*); diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h index 33ea695dda8a27ab2f0dd1c75538833debb26b95..2a64c1de725b601e9b6e9325d9faacb37df0e626 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h @@ -13,8 +13,14 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H -#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H +#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ +#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/string_util.h" +#include "tensorflow/contrib/lite/version.h" #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/interpreter.h" @@ -31,7 +37,6 @@ template void resize(T* out, uint8_t* in, int image_height, int image_width, int image_channels, int wanted_height, int wanted_width, int wanted_channels, Settings* s) { - int number_of_pixels = image_height * image_width * image_channels; std::unique_ptr interpreter(new Interpreter); @@ -45,7 +50,7 @@ void resize(T* out, uint8_t* in, int image_height, int image_width, interpreter->SetInputs({0, 1}); interpreter->SetOutputs({2}); - // set paramters of tensors + // set parameters of tensors TfLiteQuantizationParams quant; interpreter->SetTensorParametersReadWrite( 0, kTfLiteFloat32, "input", @@ -59,8 +64,11 @@ void resize(T* out, uint8_t* in, int image_height, int image_width, ops::builtin::BuiltinOpResolver resolver; TfLiteRegistration* resize_op = resolver.FindOp(BuiltinOperator_RESIZE_BILINEAR); - interpreter->AddNodeWithParameters({0, 1}, {2}, nullptr, 0, nullptr, - resize_op, nullptr); + auto* params = reinterpret_cast( + malloc(sizeof(TfLiteResizeBilinearParams))); + params->align_corners = false; + interpreter->AddNodeWithParameters({0, 1}, {2}, nullptr, 0, params, resize_op, + nullptr); interpreter->AllocateTensors(); @@ -92,4 +100,4 @@ void resize(T* out, uint8_t* in, int image_height, int image_width, } // namespace label_image } // namespace tflite -#endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H +#endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index a78900122efa540322a2f80fa3a98e6a8985ddd5..a91467d345fdce1268635a69a96939921dc170e8 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -151,14 +151,14 @@ void RunInference(Settings* s) { switch (interpreter->tensor(input)->type) { case kTfLiteFloat32: s->input_floating = true; - resize(interpreter->typed_tensor(input), in, - image_height, image_width, image_channels, - wanted_height, wanted_width, wanted_channels, s); + resize(interpreter->typed_tensor(input), in, image_height, + image_width, image_channels, wanted_height, wanted_width, + wanted_channels, s); break; case kTfLiteUInt8: resize(interpreter->typed_tensor(input), in, - image_height, image_width, image_channels, - wanted_height, wanted_width, wanted_channels, s); + image_height, image_width, image_channels, wanted_height, + wanted_width, wanted_channels, s); break; default: LOG(FATAL) << "cannot handle input type " @@ -188,9 +188,8 @@ void RunInference(Settings* s) { int output = interpreter->outputs()[0]; switch (interpreter->tensor(output)->type) { case kTfLiteFloat32: - get_top_n(interpreter->typed_output_tensor(0), - output_size, num_results, threshold, &top_results, - true); + get_top_n(interpreter->typed_output_tensor(0), output_size, + num_results, threshold, &top_results, true); break; case kTfLiteUInt8: get_top_n(interpreter->typed_output_tensor(0), diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index 204a489a93519309bb09238f1b2c8bbd4f1f19e4..d7cc854ebac08e79d346df0aca6e1fa56b490156 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -73,7 +73,7 @@ TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration* Register_SIN() { - static TfLiteRegistration r = {nullptr, nullptr, SinResize, SinEval}; + static TfLiteRegistration r = {nullptr, nullptr, SinPrepare, SinEval}; return &r; } ``` diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md new file mode 100644 index 0000000000000000000000000000000000000000..7a3a231626d0e1c71e474ff4ff16789ebe2901db --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/rpi.md @@ -0,0 +1,50 @@ +# TensorFlow Lite for Raspberry Pi + +## Cross compiling +### Installing toolchian +This has been tested on Ubuntu 16.04.3 64bit and Tensorflow devel docker image [tensorflow/tensorflow:nightly-devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/). + +To cross compiling TensorFlow Lite. First you should install the toolchain and libs. +```bash +sudo apt-get update +sudo apt-get install crossbuild-essential-armhf +``` +> If you are using docker, you may not use `sudo` + +### Building +Clone this Tensorflow repository, Run this script at the root of the repository to download all the dependencies: +> The Tensorflow repository is in `/tensorflow` if you are using `tensorflow/tensorflow:nightly-devel` docker image, just try it. +```bash +./tensorflow/contrib/lite/download_dependencies.sh +``` +Note than you only need to to this once. + +You should then be able to compile: +```bash +./tensorflow/contrib/lite/build_rpi_lib.sh +``` + +This should compile a static library in: +`tensorflow/contrib/lite/gen/lib/rpi_armv7/libtensorflow-lite.a`. + +## Native compiling +This has been tested on Raspberry Pi 3b, Raspbian GNU/Linux 9.1 (stretch), gcc version 6.3.0 20170516 (Raspbian 6.3.0-18+rpi1). + +Log in to you RPI, install the toolchain. +```bash +sudo apt-get instal build-essential +``` + +First, clone this TensorFlow repository. Run this at the root of the repository: +```bash +./tensorflow/contrib/lite/download_dependencies.sh +``` +Note than you only need to to this once. + +You should then be able to compile: +```bash +./tensorflow/contrib/lite/build_rpi_lib.sh +``` + +This should compile a static library in: +`tensorflow/contrib/lite/gen/lib/rpi_armv7/libtensorflow-lite.a`. diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 8e5e694a5cbe7f908572114db33c8257db6151f0..b1bbb7c67013acfb575cc1e9f9390ba191cbd08e 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -1,4 +1,4 @@ -# TensorFlow Compatibility Guide +# TensorFlow Lite & TensorFlow Compatibility Guide TensorFlow Lite supports a number of TensorFlow operations used in common inference models. As they are processed by the TensorFlow Lite Optimizing diff --git a/tensorflow/contrib/lite/graph_info.cc b/tensorflow/contrib/lite/graph_info.cc new file mode 100644 index 0000000000000000000000000000000000000000..e60ed2c2463cb621015ba725ca030e8d8c02f3c7 --- /dev/null +++ b/tensorflow/contrib/lite/graph_info.cc @@ -0,0 +1,224 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/graph_info.h" +#include + +namespace tflite { + +namespace { + +// Provide a range iterable wrapper for TfLiteIntArray* (C lists that TfLite +// C api uses. Can't use the google array_view, since we can't depend on even +// absl for embedded device reasons. +// TODO(aselle): Move this into central utilities. +class TfLiteIntArrayView { + public: + // Construct a view of a TfLiteIntArray*. Note, `int_array` should be non-null + // and this view does not take ownership of it. + explicit TfLiteIntArrayView(const TfLiteIntArray* int_array) + : int_array_(int_array) {} + + typedef const int* const_iterator; + const_iterator begin() const { return int_array_->data; } + const_iterator end() const { return &int_array_->data[int_array_->size]; } + + TfLiteIntArrayView(const TfLiteIntArrayView&) = default; + TfLiteIntArrayView& operator=(const TfLiteIntArrayView& rhs) = default; + + private: + const TfLiteIntArray* int_array_; +}; + +// Helper class that actually performs partitioning by subgraph. +// Outputs to a provided `subgraphs` structure. +// +// Example usage: +// PartitionGraphIntoIndependentSubgraphsImpl partitioner( +// info, nodes_to_part, subgraphs); +// partitioner.Partition(); +class PartitionGraphIntoIndependentSubgraphsImpl { + public: + PartitionGraphIntoIndependentSubgraphsImpl( + const GraphInfo* info, const TfLiteIntArray* nodes_to_partition, + std::vector* subgraphs) + : info_(info), + subgraphs_(subgraphs), + node_type_(info->num_nodes(), Subgraph::kTfNonPartition) { + // Populate the node_type_ map. + for (auto node_index : TfLiteIntArrayView(nodes_to_partition)) { + node_type_[node_index] = Subgraph::kTfPartition; + } + } + + // Actually partition the graph. + void Partition() { + // Initialize here to make Partition() re-entrant. + subgraphs_->clear(); + tensor_epochs_.clear(); + tensor_epochs_.resize(info_->num_tensors(), kEpochAlwaysReady); + node_epochs_.clear(); + node_epochs_.resize(info_->num_nodes(), kEpochNotReady); + // Set computed tensors to be kEpochNotReady (initializer set everything to + // AlwaysReady). + for (int node_index = 0; node_index < info_->num_nodes(); node_index++) { + const TfLiteNode& node = info_->node(node_index); + for (int output_tensor_index : TfLiteIntArrayView(node.outputs)) { + tensor_epochs_[output_tensor_index] = kEpochNotReady; + } + } + + // Do a graph traversal where each iteration in the loop is an epoch + // that corresponds to a subgraph that only contains nodes that are of + // the same node_type_. + while (true) { + BuildSubgraph(); + if (subgraphs_->back().nodes.empty()) { + subgraphs_->pop_back(); + break; + } + } + + // Mark model outputs as subgraph outputs. All the rest have already been + // identified. + for (int output_index : info_->outputs()) { + int output_epoch = tensor_epochs_[output_index]; + Subgraph& output_subgraph = (*subgraphs_)[output_epoch]; + output_subgraph.output_tensors.push_back(output_index); + } + // Make sure every subgraph's inputs and outputs are unique. Since the + // list of inputs and outputs is generated in a way that produces + // duplicates. + for (Subgraph& subgraph : *subgraphs_) { + // Sort and uniquefy using standard library algorithms. + auto uniquefy = [](std::vector* items) { + std::sort(items->begin(), items->end()); + auto last = std::unique(items->begin(), items->end()); + items->erase(last, items->end()); + }; + uniquefy(&subgraph.input_tensors); + uniquefy(&subgraph.output_tensors); + } + } + + private: + // Special integer values needed for tensor_epochs_ and node_epochs_. + enum { + // The node or tensor is not ready to be assigned an epoch. e.g. a node's + // inputs have not all been assigned epochs. + kEpochNotReady = -1, + // Used for tensor_epochs_. This means that the tensor is always ready. + // e.g. an input to the whole model or a constant that has no dependencies. + kEpochAlwaysReady = -2 + }; + + // Updates the node `node_index` and returns true if it is assigned to an + // epoch. False is returned if the node is already set to an epoch, its inputs + // are not all assigned to epochs, or if it cannot be assigned to the current + // epoch since the epoch's node_type doesn't match. + bool UpdateNode(int node_index) { + const TfLiteNode& node = info_->node(node_index); + Subgraph& current_subgraph = subgraphs_->back(); + int current_epoch = subgraphs_->size() - 1; + // Check if node is already done. + if (node_epochs_[node_index] != kEpochNotReady) { + return false; + } + // See if all dependencies of this node are already assigned to a + // subgraph. + for (int input_tensor_index : TfLiteIntArrayView(node.inputs)) { + if (tensor_epochs_[input_tensor_index] == kEpochNotReady) { + return false; + } + } + // When we are starting a new epoch, the first ready node defines + // the type of that epoch. + if (current_subgraph.type == Subgraph::kTfUnexplored) { + current_subgraph.type = node_type_[node_index]; + } + // The node gets assigned to this epoch if it is the same type as + // the epoch's assigned type. Note, if this is the current ready + // node encountered during this epoch, this condition will be + // automatically true. + if (current_subgraph.type == node_type_[node_index]) { + node_epochs_[node_index] = current_epoch; + current_subgraph.nodes.push_back(node_index); + // All outputs of this node now are assigned to this epoch as + // well. + for (int output_tensor_index : TfLiteIntArrayView(node.outputs)) { + tensor_epochs_[output_tensor_index] = current_epoch; + } + // Look at our inputs one more time to update that tensor's + // epochs' outputs + for (int input_tensor_index : TfLiteIntArrayView(node.inputs)) { + int input_epoch = tensor_epochs_[input_tensor_index]; + int node_epoch = current_epoch; + if (input_epoch != node_epoch) { + current_subgraph.input_tensors.push_back(input_tensor_index); + // Set inputs to be outputs of the subgraph where they reside. + // the if condition makes sure inputs to the whole computation + // are not included (i.e. those initialized to -2 above). + if (input_epoch >= 0) { + Subgraph& input_subgraph = (*subgraphs_)[input_epoch]; + input_subgraph.output_tensors.push_back(input_tensor_index); + } + } + } + return true; + } else { + return false; + } + } + + // Completely populates the current subgraph by doing graph traversal + void BuildSubgraph() { + subgraphs_->emplace_back(Subgraph()); + // loop until no more nodes can be updated. + while (true) { + bool did_something = false; + for (int node_index = 0; node_index < info_->num_nodes(); node_index++) { + if (UpdateNode(node_index)) { + did_something = true; + } + } + if (!did_something) return; + } + } + + // Temporary data needed for partitioning. + const GraphInfo* info_; + // List of subgraphs to populate + std::vector* subgraphs_; + std::vector node_type_; + // Maps from tensor index to the epoch in which it is assigned. Also special + // negative values of kEpochNotAssigned if not assigned, kEpochNotReady if it + // is an input or constant. + std::vector tensor_epochs_; + // Maps from tensor index to the epoch in which it is assigned. Also special + // negative values of kEpochNotAssigned if not assigned. + std::vector node_epochs_; +}; + +} // namespace + +TfLiteStatus PartitionGraphIntoIndependentSubgraphs( + const GraphInfo* info, const TfLiteIntArray* nodes_to_partition, + std::vector* subgraphs) { + PartitionGraphIntoIndependentSubgraphsImpl(info, nodes_to_partition, + subgraphs) + .Partition(); + return kTfLiteOk; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/graph_info.h b/tensorflow/contrib/lite/graph_info.h index 57690058c4630f75f8b23073f4ab44f27090c51b..313af5fb7574b42bcdd53b4baad06e4ccfb34053 100644 --- a/tensorflow/contrib/lite/graph_info.h +++ b/tensorflow/contrib/lite/graph_info.h @@ -48,6 +48,32 @@ class GraphInfo { virtual const std::vector& outputs() const = 0; }; +// Represents a subgraph of a TensorFlow Lite graph. +struct Subgraph { + enum Type { + kTfUnexplored = 0, // temporarily used during creation + kTfPartition, + kTfNonPartition + }; + Type type = kTfUnexplored; + // Nodes within the subgraph + std::vector nodes; + // Tensors that stride output from another subgraph that this depends on, + // or global inputs to the TensorFlow Lite full graph. + std::vector input_tensors; + // Outputs that are consumed by other subgraphs or are global output tensors. + // All output tensors of the nodes in the subgraph that do not appear in this + // list are intermediate results that can be potentially elided. + std::vector output_tensors; +}; + +// Partitions a list of node indices `nodes_to_partition` into subgraphs. +// Each subgraph is in dependency order (i.e. all members of the subgraph). +// `subgraphs` is assumed to be empty. +TfLiteStatus PartitionGraphIntoIndependentSubgraphs( + const GraphInfo* info, const TfLiteIntArray* nodes_to_partition, + std::vector* subgraphs); + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_GRAPH_INFO_H_ diff --git a/tensorflow/contrib/lite/graph_info_test.cc b/tensorflow/contrib/lite/graph_info_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..ea38b43993fef71c6820c7a978351d92d5420287 --- /dev/null +++ b/tensorflow/contrib/lite/graph_info_test.cc @@ -0,0 +1,270 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/contrib/lite/graph_info.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace { + +// Makes a TfLiteIntArray* from std::vector, must free with TfLiteIntFree(). +TfLiteIntArray* ConvertVector(const std::vector& x) { + TfLiteIntArray* lite = TfLiteIntArrayCreate(x.size()); + for (size_t i = 0; i < x.size(); i++) lite->data[i] = x[i]; + return lite; +} + +// A very simple test graph that supports setting in/out tensors on nodes. +class SimpleTestGraph : public GraphInfo { + public: + ~SimpleTestGraph() override { + for (auto& node : nodes_) { + TfLiteIntArrayFree(node.inputs); + TfLiteIntArrayFree(node.outputs); + } + } + + size_t num_tensors() const override { return tensors_.size(); } + size_t num_nodes() const override { return nodes_.size(); } + const TfLiteNode& node(size_t index) const override { return nodes_[index]; } + TfLiteTensor* tensor(size_t index) override { return &tensors_[index]; } + const std::vector& inputs() const override { return inputs_; } + const std::vector& outputs() const override { return outputs_; } + + void AddNode(const std::vector& inputs, + const std::vector& outputs) { + nodes_.push_back(TfLiteNode()); + TfLiteNode& node = nodes_.back(); + node.inputs = ConvertVector(inputs); + node.outputs = ConvertVector(outputs); + } + + void AddTensors(int count) { tensors_.resize(count + tensors_.size()); } + + void SetInputsAndOutputs(const std::vector& inputs, + const std::vector& outputs) { + inputs_ = inputs; + outputs_ = outputs; + } + + private: + std::vector nodes_; + std::vector tensors_; + std::vector inputs_; + std::vector outputs_; +}; + +// Partition a graph to generate a list of subgraphs. This wraps the API call +// we are testing and handles memory management and conversion to +// TfLiteIntArray. Populates `subgraphs` with resulting generated subgraphs. +void PartitionGraph(const SimpleTestGraph& graph, + const std::vector& nodes_to_partition, + std::vector* subgraphs) { + TfLiteIntArray* nodes_to_partition_int_array = + ConvertVector(nodes_to_partition); + PartitionGraphIntoIndependentSubgraphs(&graph, nodes_to_partition_int_array, + subgraphs); + TfLiteIntArrayFree(nodes_to_partition_int_array); +} + +// Check a generated list of subgraphs against the expected list of subgraphs. +void CheckPartitionSubgraphs(const std::vector& generated_subgraphs, + const std::vector& expected_subgraphs) { + ASSERT_EQ(generated_subgraphs.size(), expected_subgraphs.size()); + for (int subgraph_index = 0; subgraph_index < generated_subgraphs.size(); + subgraph_index++) { + EXPECT_EQ(generated_subgraphs[subgraph_index].nodes, + expected_subgraphs[subgraph_index].nodes); + EXPECT_EQ(generated_subgraphs[subgraph_index].input_tensors, + expected_subgraphs[subgraph_index].input_tensors); + EXPECT_EQ(generated_subgraphs[subgraph_index].output_tensors, + expected_subgraphs[subgraph_index].output_tensors); + } +} + +// Test an empty trivial graph with no partitions. +TEST(PartitionTest, Nodes0_PartitionNodes0) { + SimpleTestGraph graph; + std::vector nodes_to_partition = {}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + CheckPartitionSubgraphs(generated_subgraphs, {}); +} + +// Test a 1 node graph with no partitions. +// Input: tensor(0) -> node(0) -> tensor(1), nodes_to_partition=[] +// Output: [kTfNoPartition, tensor(0) -> node(0) -> tensor(1)] +TEST(PartitionTest, Nodes1PartitionNodes0) { + SimpleTestGraph graph; + graph.AddTensors(2); + graph.AddNode({0}, {1}); + graph.SetInputsAndOutputs({0}, {1}); + std::vector nodes_to_partition = {}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph; + expected_subgraph.type = Subgraph::kTfNonPartition; + expected_subgraph.nodes = {0}; + expected_subgraph.input_tensors = {0}; + expected_subgraph.output_tensors = {1}; + CheckPartitionSubgraphs(generated_subgraphs, {expected_subgraph}); +} + +// Test a 1 node graph with no inputs that is fully partitioned. +// Input: node(0) -> tensor(1), nodes_to_partition=[node0] +// Output: [kTfPartition, node(0) -> tensor(1)] +TEST(PartitionTest, Nodes1PartitionNodes0Inputs0) { + SimpleTestGraph graph; + graph.AddTensors(1); + graph.AddNode({}, {0}); + graph.SetInputsAndOutputs({}, {0}); + std::vector generated_subgraphs; + std::vector nodes_to_partition = {0}; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph; + expected_subgraph.type = Subgraph::kTfPartition; + expected_subgraph.nodes = {0}; + expected_subgraph.input_tensors = {}; + expected_subgraph.output_tensors = {0}; + CheckPartitionSubgraphs(generated_subgraphs, {expected_subgraph}); +} + +// Test a 1 node graph that is partitioned completely. +// Input: tensor(0) -> node(0) -> tensor(1), nodes_to_partition=[node0] +// Output: [kTfPartition, tensor(0) -> node(0) -> tensor(1)] +TEST(PartitionTest, Nodes1PartitionNodes1) { + SimpleTestGraph graph; + graph.AddTensors(2); + graph.AddNode({0}, {1}); + graph.SetInputsAndOutputs({0}, {1}); + std::vector nodes_to_partition = {0}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph; + expected_subgraph.type = Subgraph::kTfPartition; + expected_subgraph.nodes = {0}; + expected_subgraph.input_tensors = {0}; + expected_subgraph.output_tensors = {1}; + CheckPartitionSubgraphs(generated_subgraphs, {expected_subgraph}); +} + +// Test a 2 node graph where 1 node is partitioned and the other is not. +// Input: tensor(0) -> node(0) -> tensor(1) -> node(1) -> tensor(2), +// nodes_to_partition = [1] +// Output: [kTfNonPartition, tensor(0) -> node(0) -> tensor(1), +// kTfPartition, tensor(1) -> node(1), tensor(2)] +TEST(PartitionTest, Nodes2PartitionNodes1) { + SimpleTestGraph graph; + graph.AddTensors(3); + graph.AddNode({0}, {1}); + graph.AddNode({1}, {2}); + graph.SetInputsAndOutputs({0}, {2}); + std::vector nodes_to_partition = {1}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph0; + expected_subgraph0.type = Subgraph::kTfPartition; + expected_subgraph0.nodes = {0}; + expected_subgraph0.input_tensors = {0}; + expected_subgraph0.output_tensors = {1}; + Subgraph expected_subgraph1; + expected_subgraph1.type = Subgraph::kTfPartition; + expected_subgraph1.nodes = {1}; + expected_subgraph1.input_tensors = {1}; + expected_subgraph1.output_tensors = {2}; + CheckPartitionSubgraphs(generated_subgraphs, + {expected_subgraph0, expected_subgraph1}); +} + +// Test a 2 node graph where both nodes are fully partitioned. +// Input: tensor(0) -> node(0) -> tensor(1) -> node(1) -> tensor(2), +// nodes_to_partition = [0, 1] +// Output: [kTfPartition, tensor(0) -> node(0) -> node(1) -> tensor(1)] +TEST(PartitionTest, Nodes2PartitionNodes2) { + SimpleTestGraph graph; + graph.AddTensors(3); + graph.AddNode({0}, {1}); + graph.AddNode({1}, {2}); + graph.SetInputsAndOutputs({0}, {2}); + std::vector nodes_to_partition = {0, 1}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph0; + expected_subgraph0.type = Subgraph::kTfPartition; + expected_subgraph0.nodes = {0, 1}; + expected_subgraph0.input_tensors = {0}; + expected_subgraph0.output_tensors = {2}; + CheckPartitionSubgraphs(generated_subgraphs, {expected_subgraph0}); +} + +// Test a three node model where we want to partition nodes 0 and nodes +// 2, but nodes 0 and nodes 2 cannot be in the same subgraph since node 2 +// depends on node 1 which depends on node 0. Thus, we need to produce three +// subgraphs. +// +// Input: tensor(0) -> node(0) -> tensor(1) +// tensor(1) -> node(1) -> tensor(2) +// [tensor(2), tensor(1)] -> node(2) -> tensor(3) +// nodes_to_partition = [0, 2] +// Output: [[kTfPartition, tensor(0) -> node(0) -> tensor(1), +// [kTfNonPartition, tensor(1) -> node(1) -> tensor(2)], +// [kTfPartition, [tensor(2), tensor(1)] -> node(2) -> node(3)] +TEST(PartitionTest, Nodes3PartitionNodes2) { + SimpleTestGraph graph; + graph.AddTensors(4); + graph.AddNode({0}, {1}); + graph.AddNode({1}, {2}); + graph.AddNode({1, 2}, {3}); + graph.SetInputsAndOutputs({0}, {3}); + std::vector nodes_to_partition = {0, 2}; + std::vector generated_subgraphs; + PartitionGraph(graph, nodes_to_partition, &generated_subgraphs); + + Subgraph expected_subgraph0; + expected_subgraph0.type = Subgraph::kTfPartition; + expected_subgraph0.nodes = {0}; + expected_subgraph0.input_tensors = {0}; + expected_subgraph0.output_tensors = {1}; + Subgraph expected_subgraph1; + expected_subgraph1.type = Subgraph::kTfNonPartition; + expected_subgraph1.nodes = {1}; + expected_subgraph1.input_tensors = {1}; + expected_subgraph1.output_tensors = {2}; + Subgraph expected_subgraph2; + expected_subgraph2.type = Subgraph::kTfPartition; + expected_subgraph2.nodes = {2}; + expected_subgraph2.input_tensors = {1, 2}; + expected_subgraph2.output_tensors = {3}; + CheckPartitionSubgraphs( + generated_subgraphs, + {expected_subgraph0, expected_subgraph1, expected_subgraph2}); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 9dd60abc8639a0e051f717d5e41921271a78bf8a..0f5e17f0de0d828771e1fdbeac0e172f2ed9159c 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -25,13 +25,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/gemm_support.h" #include "tensorflow/contrib/lite/memory_planner.h" #include "tensorflow/contrib/lite/nnapi_delegate.h" - -namespace { - -// std::vector preallocation tuning. -constexpr const int kSlotsToReserve = 128; - -} // namespace +#include "tensorflow/contrib/lite/schema/schema_generated.h" namespace tflite { @@ -77,9 +71,15 @@ Interpreter::Interpreter(ErrorReporter* error_reporter) context_.tensors = nullptr; context_.tensors_size = 0; context_.gemm_context = nullptr; + + // Invalid to call these these except from TfLiteDelegate + context_.GetNodeAndRegistration = nullptr; + context_.ReplaceSubgraphsWithDelegateKernels = nullptr; + context_.GetExecutionPlan = nullptr; + // Reserve some space for the tensors to avoid excessive resizing. - tensors_.reserve(kSlotsToReserve); - nodes_and_registration_.reserve(kSlotsToReserve); + tensors_.reserve(kTensorsReservedCapacity); + nodes_and_registration_.reserve(kTensorsReservedCapacity); next_execution_plan_index_to_prepare_ = 0; UseNNAPI(false); } @@ -100,6 +100,81 @@ Interpreter::~Interpreter() { } } +TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( + TfLiteContext* context, TfLiteRegistration registration, + const TfLiteIntArray* nodes_to_replace) { + return static_cast(context->impl_) + ->ReplaceSubgraphsWithDelegateKernels(registration, nodes_to_replace); +} + +TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( + TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace) { + // Annotate the registration as DELEGATE op. + registration.builtin_code = BuiltinOperator_DELEGATE; + + // Analyze the graph to find all independent subgraphs that are either + // fully not-this-delegate or this-delegate computation. + InterpreterInfo info(this); + std::vector subgraphs; + PartitionGraphIntoIndependentSubgraphs(&info, nodes_to_replace, &subgraphs); + + execution_plan_.clear(); + for (auto& subgraph : subgraphs) { + // Turn subgraph.nodes into a TfLiteIntArray compatible data structure. + // TODO(aselle): Avoid this copy by constructing subgraph.nodes that way + // in the first place + subgraph.nodes.insert(subgraph.nodes.begin(), + static_cast(subgraph.nodes.size())); + // Subgraphs calimed by the delegate should have a "macro" op created, the + // other subgraphs (kTfNonPartition) just have their nodes added back to + // the execution plan. + switch (subgraph.type) { + case Subgraph::kTfNonPartition: + for (auto it = subgraph.nodes.begin() + 1; it != subgraph.nodes.end(); + ++it) { + execution_plan_.push_back(*it); + } + break; + case Subgraph::kTfPartition: { + void* builtin_data = nullptr; + int node_index; + // Create a node that represents computation of this subgraph. + AddNodeWithParameters( + subgraph.input_tensors, subgraph.output_tensors, + reinterpret_cast(subgraph.nodes.data()), + subgraph.nodes.size() * sizeof(subgraph.nodes[0]), builtin_data, + ®istration, &node_index); + } break; + case Subgraph::kTfUnexplored: + return kTfLiteError; + break; + } + } + return kTfLiteOk; +} + +// Gets an TfLiteIntArray* representing the execution plan. The interpreter owns +// this memory and it is only guaranteed to exist during the invocation of the +// delegate prepare. +TfLiteStatus Interpreter::GetExecutionPlan(TfLiteIntArray** execution_plan) { + // TODO(aselle): Do not make a copy here + plan_cache_.reset(TfLiteIntArrayCreate(execution_plan_.size())); + *execution_plan = plan_cache_.get(); + static_assert(sizeof(plan_cache_->data[0]) == sizeof(execution_plan_[0]), + "TfLiteIntArray and execution_plan do not contain same type."); + memcpy(plan_cache_->data, execution_plan_.data(), + sizeof(plan_cache_->data[0]) * execution_plan_.size()); + return kTfLiteOk; +} + +// WARNING: This is an experimental interface that is subject to change. +// Entry point for C node plugin API to get the execution plan +TfLiteStatus Interpreter::GetExecutionPlan(struct TfLiteContext* context, + TfLiteIntArray** execution_plan) { + return static_cast(context->impl_) + ->GetExecutionPlan(execution_plan); +} + TfLiteStatus Interpreter::SetInputs(std::vector inputs) { TF_LITE_ENSURE_OK(&context_, CheckTensorIndices("inputs", inputs.data(), inputs.size())); @@ -200,6 +275,7 @@ TfLiteStatus Interpreter::AddNodeWithParameters( int new_node_index = nodes_and_registration_.size(); if (node_index) *node_index = new_node_index; nodes_and_registration_.resize(nodes_and_registration_.size() + 1); + auto& node_and_reg = nodes_and_registration_.back(); TfLiteNode& node = node_and_reg.first; if (node.inputs) TfLiteIntArrayFree(node.inputs); @@ -219,7 +295,20 @@ TfLiteStatus Interpreter::AddNodeWithParameters( OpInit(*registration, reinterpret_cast(builtin_data_deleter.get()), 0); } + node.builtin_data = builtin_data_deleter.release(); + // TODO(ycling): Filling `custom_initial_data` and `custom_initial_data_size` + // properly for nodes generated by ReplaceSubgraphsWithDelegateKernels. + if (registration->builtin_code == BuiltinOperator_CUSTOM) { + // When it's a CUSTOM op, the `custom_options` field in the Flatbuffer + // `Operator` table is passed in. + node.custom_initial_data = init_data; + node.custom_initial_data_size = init_data_size; + } else { + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + } + node_and_reg.second = *registration; execution_plan_.push_back(new_node_index); return kTfLiteOk; @@ -257,6 +346,7 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt( TfLiteNode& node = nodes_and_registration_[node_index].first; const TfLiteRegistration& registration = nodes_and_registration_[node_index].second; + EnsureTensorsVectorCapacity(); if (OpPrepare(registration, &node) == kTfLiteError) { return kTfLiteError; } @@ -303,7 +393,6 @@ TfLiteStatus Interpreter::Invoke() { TfLiteStatus status = kTfLiteOk; if (nnapi_delegate_) { - TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); if (next_execution_plan_index_to_prepare_ == execution_plan_.size()) { TF_LITE_ENSURE_OK(&context_, nnapi_delegate_->Invoke(this)); return kTfLiteOk; @@ -335,6 +424,7 @@ TfLiteStatus Interpreter::Invoke() { TfLiteNode& node = nodes_and_registration_[node_index].first; const TfLiteRegistration& registration = nodes_and_registration_[node_index].second; + EnsureTensorsVectorCapacity(); if (OpInvoke(registration, &node) == kTfLiteError) { status = kTfLiteError; } @@ -389,6 +479,22 @@ TfLiteStatus Interpreter::AddTensors(TfLiteContext* context, int tensors_to_add, ->AddTensors(tensors_to_add, first_new_tensor_index); } +TfLiteStatus Interpreter::GetNodeAndRegistration( + int node_index, TfLiteNode** node, TfLiteRegistration** registration) { + TF_LITE_ENSURE(&context_, node_index < nodes_size() && node_index >= 0); + TF_LITE_ENSURE(&context_, node != nullptr && registration != nullptr); + *node = &nodes_and_registration_[node_index].first; + *registration = &nodes_and_registration_[node_index].second; + return kTfLiteOk; +} + +TfLiteStatus Interpreter::GetNodeAndRegistration( + struct TfLiteContext* context, int node_index, TfLiteNode** node, + TfLiteRegistration** registration) { + return static_cast(context->impl_) + ->GetNodeAndRegistration(node_index, node, registration); +} + TfLiteStatus Interpreter::SetTensorParametersReadOnly( int tensor_index, TfLiteType type, const char* name, const std::vector& dims, TfLiteQuantizationParams quantization, @@ -499,4 +605,20 @@ void Interpreter::SetNumThreads(int num_threads) { tflite::gemm_support::SetMaxNumThreads(&context_, num_threads); } +TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate) { + // TODO(aselle): Consider if it is worth storing pointers to delegates. + // Setup additional context interface + context_.GetNodeAndRegistration = GetNodeAndRegistration; + context_.ReplaceSubgraphsWithDelegateKernels = + ReplaceSubgraphsWithDelegateKernels; + context_.GetExecutionPlan = GetExecutionPlan; + + TfLiteStatus status = delegate->Prepare(&context_, delegate->data_); + // Remove additional context info. + context_.GetNodeAndRegistration = nullptr; + context_.ReplaceSubgraphsWithDelegateKernels = nullptr; + context_.GetExecutionPlan = nullptr; + return status; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 3b077c7a3540d40a0e8d53179a4ac1aba7e292a1..04c19644a026bff0f3693f7b05832393bafd0324 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/error_reporter.h" #include "tensorflow/contrib/lite/memory_planner.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" namespace tflite { @@ -80,6 +81,12 @@ class NNAPIDelegate; // foo.Invoke(); // +struct TfLiteIntArrayDeleter { + void operator()(TfLiteIntArray* a) { + if (a) TfLiteIntArrayFree(a); + } +}; + class Interpreter { public: // Instantiate an interpreter. All errors associated with reading and @@ -247,6 +254,25 @@ class Interpreter { // Set the number of threads available to the interpreter. void SetNumThreads(int num_threads); + // Allow a delegate to look at the graph and modify the graph to handle + // parts of the graph themselves. After this is called, the graph may + // contain new nodes that replace 1 more nodes. + TfLiteStatus ModifyGraphWithDelegate(TfLiteDelegate* delegate); + + // WARNING: This is a deprecated interface and will be removed as soon as + // possible. Please do not use it. + // TODO(impjdi): Remove this interface after resolving dependencies. + void set_model(const Model* model) { model_ = const_cast(model); } + Model* model() const { return model_; } + + // The default capacity of `tensors_` vector. + static constexpr int kTensorsReservedCapacity = 128; + // The capacity headroom of `tensors_` vector before calling ops' + // `prepare` and `invoke` function. In these functions, it's guaranteed + // allocating up to `kTensorsCapacityHeadroom` more tensors won't invalidate + // pointers to existing tensors. + static constexpr int kTensorsCapacityHeadroom = 16; + private: // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. @@ -325,6 +351,52 @@ class Interpreter { static TfLiteStatus AddTensors(TfLiteContext* context, int tensors_to_add, int* first_new_tensor_index); + // WARNING: This is an experimental API and subject to change. + // Entry point for C API ReplaceSubgraphsWithDelegateKernels + static TfLiteStatus ReplaceSubgraphsWithDelegateKernels( + TfLiteContext* context, TfLiteRegistration registration, + const TfLiteIntArray* nodes_to_replace); + + // Update the execution graph to replace some of the nodes with stub + // nodes. Specifically any node index that has `nodes[index]==1` will be + // slated for replacement with a delegate kernel specified by registration. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus ReplaceSubgraphsWithDelegateKernels( + TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace); + + // WARNING: This is an experimental interface that is subject to change. + // Gets the internal pointer to a TensorFlow lite node by node_index. + TfLiteStatus GetNodeAndRegistration(int node_index, TfLiteNode** node, + TfLiteRegistration** registration); + + // WARNING: This is an experimental interface that is subject to change. + // Entry point for C node plugin API to get a node by index. + static TfLiteStatus GetNodeAndRegistration(struct TfLiteContext*, + int node_index, TfLiteNode** node, + TfLiteRegistration** registration); + + // WARNING: This is an experimental interface that is subject to change. + // Gets an TfLiteIntArray* representing the execution plan. The caller owns + // this memory and must free it with TfLiteIntArrayFree(). + TfLiteStatus GetExecutionPlan(TfLiteIntArray** execution_plan); + + // WARNING: This is an experimental interface that is subject to change. + // Entry point for C node plugin API to get the execution plan + static TfLiteStatus GetExecutionPlan(struct TfLiteContext* context, + TfLiteIntArray** execution_plan); + + // Ensures that `tensors_` has at least `kTensorsCapacityHeadroom` extra + // capacity. Calling this function may invalidate existing pointers to + // tensors. After calling this function, adding `kTensorsCapacityHeadroom` + // more tensors won't invalidate the pointer to existing tensors. + void EnsureTensorsVectorCapacity() { + const int required_capacity = tensors_size() + kTensorsCapacityHeadroom; + if (required_capacity > tensors_.capacity()) { + tensors_.reserve(required_capacity); + context_.tensors = tensors_.data(); + } + } + // A pure C data structure used to communicate with the pure C plugin // interface. To avoid copying tensor metadata, this is also the definitive // structure to store tensors. @@ -372,10 +444,19 @@ class Interpreter { // subset of the node indices. std::vector execution_plan_; + // In the future, we'd like a TfLiteIntArray compatible representation. + // TODO(aselle): replace execution_plan_ with this. + std::unique_ptr plan_cache_; + // Whether to delegate to NN API std::unique_ptr nnapi_delegate_; std::unique_ptr memory_planner_; + + // WARNING: This is a deprecated interface and will be removed as soon as + // possible. Please do not use it. + // TODO(impjdi): Remove this interface after resolving dependencies. + Model* model_ = nullptr; }; } // namespace tflite diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index cfda19d72cd6d4de6e4f0bdc4d369439fef2293c..2e6727b32361ab771354a3954e5e4d8f9fa833a5 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -16,9 +16,10 @@ limitations under the License. #include "tensorflow/contrib/lite/interpreter.h" #include #include "tensorflow/contrib/lite/error_reporter.h" +#include "tensorflow/contrib/lite/kernels/internal/compatibility.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" #include "tensorflow/contrib/lite/string_util.h" #include "tensorflow/contrib/lite/testing/util.h" - namespace tflite { namespace { @@ -560,6 +561,46 @@ TEST(BasicInterpreter, TestCustomErrorReporter) { ASSERT_EQ(reporter.calls, 1); } +TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), + kTfLiteOk); + TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr}; + registration.prepare = [](TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* first_tensor = context->tensors; + + int new_tensor_index; + context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom, + &new_tensor_index); + EXPECT_EQ(first_tensor, context->tensors); + return kTfLiteOk; + }; + ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, + ®istration), + kTfLiteOk); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); +} + +TEST(InterpreterTensorsCapacityTest, TestExceedHeadroom) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), + kTfLiteOk); + TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr}; + registration.prepare = [](TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* first_tensor = context->tensors; + + int new_tensor_index; + context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom + 1, + &new_tensor_index); + EXPECT_NE(first_tensor, context->tensors); + return kTfLiteOk; + }; + ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, + ®istration), + kTfLiteOk); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); +} + // Test fixture that allows playing with execution plans. It creates a two // node graph that can be executed in either [0,1] order or [1,0] order. // The CopyOp records when it is invoked in the class member run_order_ @@ -687,6 +728,151 @@ TEST_F(TestExecutionPlan, NullExecutionPlan) { ASSERT_EQ(run_order_, std::vector()); } +// Build a kernel registration for an op that copies its one input +// to an output +TfLiteRegistration AddOpRegistration() { + TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + + reg.custom_name = "my_add"; + reg.builtin_code = tflite::BuiltinOperator_CUSTOM; + + reg.prepare = [](TfLiteContext* context, TfLiteNode* node) { + // Set output size to input size + TfLiteTensor* tensor0 = &context->tensors[node->inputs->data[0]]; + TfLiteTensor* tensor1 = &context->tensors[node->inputs->data[1]]; + TfLiteTensor* tensor2 = &context->tensors[node->outputs->data[0]]; + TfLiteIntArray* newSize = TfLiteIntArrayCopy(tensor0->dims); + TfLiteIntArray* newSizeOther = TfLiteIntArrayCopy(tensor1->dims); + TF_LITE_ENSURE_EQ(context, newSize->size, newSizeOther->size); + TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, tensor2, newSize)); + return kTfLiteOk; + }; + + reg.invoke = [](TfLiteContext* context, TfLiteNode* node) { + // Copy input data to output data. + TfLiteTensor* a0 = &context->tensors[node->inputs->data[0]]; + TfLiteTensor* a1 = &context->tensors[node->inputs->data[1]]; + TfLiteTensor* out = &context->tensors[node->outputs->data[0]]; + int num = a0->dims->data[0]; + for (int i = 0; i < num; i++) { + out->data.f[i] = a0->data.f[i] + a1->data.f[i]; + } + return kTfLiteOk; + }; + return reg; +} + +class TestDelegate : public ::testing::Test { + public: + TestDelegate() { + interpreter_.AddTensors(5); + interpreter_.SetInputs({0, 1}); + interpreter_.SetOutputs({3, 4}); + TfLiteQuantizationParams quant; + interpreter_.SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {3}, + quant); + interpreter_.SetTensorParametersReadWrite(1, kTfLiteFloat32, "", {3}, + quant); + interpreter_.SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {3}, + quant); + interpreter_.SetTensorParametersReadWrite(3, kTfLiteFloat32, "", {3}, + quant); + TfLiteRegistration reg = AddOpRegistration(); + interpreter_.AddNodeWithParameters({0, 0}, {2}, nullptr, 0, nullptr, ®); + interpreter_.AddNodeWithParameters({1, 1}, {3}, nullptr, 0, nullptr, ®); + interpreter_.AddNodeWithParameters({2, 1}, {4}, nullptr, 0, nullptr, ®); + } + + protected: + class SimpleDelegate { + public: + // Create a simple implementation of a TfLiteDelegate. We use the C++ class + // SimpleDelegate and it can produce a handle TfLiteDelegate that is + // value-copyable and compatible with TfLite. + explicit SimpleDelegate(const std::vector& nodes) : nodes_(nodes) { + delegate_.Prepare = [](TfLiteContext* context, + void* data) -> TfLiteStatus { + auto* simple = reinterpret_cast(data); + TfLiteIntArray* nodes_to_separate = + TfLiteIntArrayCreate(simple->nodes_.size()); + // Mark nodes that we want in TfLiteIntArray* structure. + int index = 0; + for (auto node_index : simple->nodes_) { + nodes_to_separate->data[index++] = node_index; + // make sure node is add + TfLiteNode* node; + TfLiteRegistration* reg; + context->GetNodeAndRegistration(context, node_index, &node, ®); + TFLITE_CHECK_EQ(reg->builtin_code, tflite::BuiltinOperator_CUSTOM); + TFLITE_CHECK_EQ(strcmp(reg->custom_name, "my_add"), 0); + } + // Check that all nodes are available + TfLiteIntArray* execution_plan; + TF_LITE_ENSURE_STATUS( + context->GetExecutionPlan(context, &execution_plan)); + for (int exec_index = 0; exec_index < execution_plan->size; + exec_index++) { + int node_index = execution_plan->data[exec_index]; + // Check that we are an identity map to start. + TFLITE_CHECK_EQ(exec_index, node_index); + TfLiteNode* node; + TfLiteRegistration* reg; + context->GetNodeAndRegistration(context, node_index, &node, ®); + TFLITE_CHECK_EQ(reg->builtin_code, tflite::BuiltinOperator_CUSTOM); + TFLITE_CHECK_EQ(strcmp(reg->custom_name, "my_add"), 0); + } + + context->ReplaceSubgraphsWithDelegateKernels( + context, FakeFusedRegistration(), nodes_to_separate); + TfLiteIntArrayFree(nodes_to_separate); + return kTfLiteOk; + }; + // Store type-punned data SimpleDelegate structure. + delegate_.data_ = reinterpret_cast(this); + } + + static TfLiteRegistration FakeFusedRegistration() { + TfLiteRegistration reg = {nullptr}; + reg.custom_name = "fake_fused_op"; + return reg; + } + + TfLiteDelegate* get_tf_lite_delegate() { return &delegate_; } + + private: + std::vector nodes_; + TfLiteDelegate delegate_; + }; + Interpreter interpreter_; +}; + +TEST_F(TestDelegate, BasicDelegate) { + interpreter_.Invoke(); + SimpleDelegate simple({0, 1, 2}); + interpreter_.ModifyGraphWithDelegate(simple.get_tf_lite_delegate()); + + ASSERT_EQ(interpreter_.execution_plan().size(), 1); + int node = interpreter_.execution_plan()[0]; + const auto* node_and_reg = interpreter_.node_and_registration(node); + ASSERT_EQ(node_and_reg->second.custom_name, + SimpleDelegate::FakeFusedRegistration().custom_name); +} + +TEST_F(TestDelegate, ComplexDeligate) { + interpreter_.Invoke(); + SimpleDelegate simple({1, 2}); + interpreter_.ModifyGraphWithDelegate(simple.get_tf_lite_delegate()); + + ASSERT_EQ(interpreter_.execution_plan().size(), 2); + // 0th should be a non-delegated original op + ASSERT_EQ(interpreter_.execution_plan()[0], 0); + // 1st should be a new macro op (3) which didn't exist) + ASSERT_EQ(interpreter_.execution_plan()[1], 3); + const auto* node_and_reg = interpreter_.node_and_registration(3); + ASSERT_EQ(node_and_reg->second.custom_name, + SimpleDelegate::FakeFusedRegistration().custom_name); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/ios_makefile.inc b/tensorflow/contrib/lite/ios_makefile.inc index 26cfe6c3e286ed603c2183986c697562e846889c..fc6594c3a04ba6aabba99bb631f85737baf389f1 100644 --- a/tensorflow/contrib/lite/ios_makefile.inc +++ b/tensorflow/contrib/lite/ios_makefile.inc @@ -22,6 +22,7 @@ ifeq ($(TARGET), IOS) IOS_ARCH := x86_64 CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \ -DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK \ + -DTFLITE_USE_APPLE_ACCELERATE_FOR_CONV \ -fembed-bitcode \ -Wno-c++11-narrowing \ -mno-thumb \ @@ -42,6 +43,7 @@ ifeq ($(TARGET), IOS) -O3 LDFLAGS := -fembed-bitcode \ -miphoneos-version-min=${MIN_SDK_VERSION} \ + -framework Accelerate \ -arch $(IOS_ARCH) OBJDIR := $(OBJDIR)ios_$(IOS_ARCH)/ LIBDIR := $(LIBDIR)ios_$(IOS_ARCH)/ diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt new file mode 100644 index 0000000000000000000000000000000000000000..572eccf90087c1c19874e40b950c1610f59cc9c2 --- /dev/null +++ b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt @@ -0,0 +1,1001 @@ +dummy +tench +goldfish +great white shark +tiger shark +hammerhead +electric ray +stingray +cock +hen +ostrich +brambling +goldfinch +house finch +junco +indigo bunting +robin +bulbul +jay +magpie +chickadee +water ouzel +kite +bald eagle +vulture +great grey owl +European fire salamander +common newt +eft +spotted salamander +axolotl +bullfrog +tree frog +tailed frog +loggerhead +leatherback turtle +mud turtle +terrapin +box turtle +banded gecko +common iguana +American chameleon +whiptail +agama +frilled lizard +alligator lizard +Gila monster +green lizard +African chameleon +Komodo dragon +African crocodile +American alligator +triceratops +thunder snake +ringneck snake +hognose snake +green snake +king snake +garter snake +water snake +vine snake +night snake +boa constrictor +rock python +Indian cobra +green mamba +sea snake +horned viper +diamondback +sidewinder +trilobite +harvestman +scorpion +black and gold garden spider +barn spider +garden spider +black widow +tarantula +wolf spider +tick +centipede +black grouse +ptarmigan +ruffed grouse +prairie chicken +peacock +quail +partridge +African grey +macaw +sulphur-crested cockatoo +lorikeet +coucal +bee eater +hornbill +hummingbird +jacamar +toucan +drake +red-breasted merganser +goose +black swan +tusker +echidna +platypus +wallaby +koala +wombat +jellyfish +sea anemone +brain coral +flatworm +nematode +conch +snail +slug +sea slug +chiton +chambered nautilus +Dungeness crab +rock crab +fiddler crab +king crab +American lobster +spiny lobster +crayfish +hermit crab +isopod +white stork +black stork +spoonbill +flamingo +little blue heron +American egret +bittern +crane +limpkin +European gallinule +American coot +bustard +ruddy turnstone +red-backed sandpiper +redshank +dowitcher +oystercatcher +pelican +king penguin +albatross +grey whale +killer whale +dugong +sea lion +Chihuahua +Japanese spaniel +Maltese dog +Pekinese +Shih-Tzu +Blenheim spaniel +papillon +toy terrier +Rhodesian ridgeback +Afghan hound +basset +beagle +bloodhound +bluetick +black-and-tan coonhound +Walker hound +English foxhound +redbone +borzoi +Irish wolfhound +Italian greyhound +whippet +Ibizan hound +Norwegian elkhound +otterhound +Saluki +Scottish deerhound +Weimaraner +Staffordshire bullterrier +American Staffordshire terrier +Bedlington terrier +Border terrier +Kerry blue terrier +Irish terrier +Norfolk terrier +Norwich terrier +Yorkshire terrier +wire-haired fox terrier +Lakeland terrier +Sealyham terrier +Airedale +cairn +Australian terrier +Dandie Dinmont +Boston bull +miniature schnauzer +giant schnauzer +standard schnauzer +Scotch terrier +Tibetan terrier +silky terrier +soft-coated wheaten terrier +West Highland white terrier +Lhasa +flat-coated retriever +curly-coated retriever +golden retriever +Labrador retriever +Chesapeake Bay retriever +German short-haired pointer +vizsla +English setter +Irish setter +Gordon setter +Brittany spaniel +clumber +English springer +Welsh springer spaniel +cocker spaniel +Sussex spaniel +Irish water spaniel +kuvasz +schipperke +groenendael +malinois +briard +kelpie +komondor +Old English sheepdog +Shetland sheepdog +collie +Border collie +Bouvier des Flandres +Rottweiler +German shepherd +Doberman +miniature pinscher +Greater Swiss Mountain dog +Bernese mountain dog +Appenzeller +EntleBucher +boxer +bull mastiff +Tibetan mastiff +French bulldog +Great Dane +Saint Bernard +Eskimo dog +malamute +Siberian husky +dalmatian +affenpinscher +basenji +pug +Leonberg +Newfoundland +Great Pyrenees +Samoyed +Pomeranian +chow +keeshond +Brabancon griffon +Pembroke +Cardigan +toy poodle +miniature poodle +standard poodle +Mexican hairless +timber wolf +white wolf +red wolf +coyote +dingo +dhole +African hunting dog +hyena +red fox +kit fox +Arctic fox +grey fox +tabby +tiger cat +Persian cat +Siamese cat +Egyptian cat +cougar +lynx +leopard +snow leopard +jaguar +lion +tiger +cheetah +brown bear +American black bear +ice bear +sloth bear +mongoose +meerkat +tiger beetle +ladybug +ground beetle +long-horned beetle +leaf beetle +dung beetle +rhinoceros beetle +weevil +fly +bee +ant +grasshopper +cricket +walking stick +cockroach +mantis +cicada +leafhopper +lacewing +dragonfly +damselfly +admiral +ringlet +monarch +cabbage butterfly +sulphur butterfly +lycaenid +starfish +sea urchin +sea cucumber +wood rabbit +hare +Angora +hamster +porcupine +fox squirrel +marmot +beaver +guinea pig +sorrel +zebra +hog +wild boar +warthog +hippopotamus +ox +water buffalo +bison +ram +bighorn +ibex +hartebeest +impala +gazelle +Arabian camel +llama +weasel +mink +polecat +black-footed ferret +otter +skunk +badger +armadillo +three-toed sloth +orangutan +gorilla +chimpanzee +gibbon +siamang +guenon +patas +baboon +macaque +langur +colobus +proboscis monkey +marmoset +capuchin +howler monkey +titi +spider monkey +squirrel monkey +Madagascar cat +indri +Indian elephant +African elephant +lesser panda +giant panda +barracouta +eel +coho +rock beauty +anemone fish +sturgeon +gar +lionfish +puffer +abacus +abaya +academic gown +accordion +acoustic guitar +aircraft carrier +airliner +airship +altar +ambulance +amphibian +analog clock +apiary +apron +ashcan +assault rifle +backpack +bakery +balance beam +balloon +ballpoint +Band Aid +banjo +bannister +barbell +barber chair +barbershop +barn +barometer +barrel +barrow +baseball +basketball +bassinet +bassoon +bathing cap +bath towel +bathtub +beach wagon +beacon +beaker +bearskin +beer bottle +beer glass +bell cote +bib +bicycle-built-for-two +bikini +binder +binoculars +birdhouse +boathouse +bobsled +bolo tie +bonnet +bookcase +bookshop +bottlecap +bow +bow tie +brass +brassiere +breakwater +breastplate +broom +bucket +buckle +bulletproof vest +bullet train +butcher shop +cab +caldron +candle +cannon +canoe +can opener +cardigan +car mirror +carousel +carpenter's kit +carton +car wheel +cash machine +cassette +cassette player +castle +catamaran +CD player +cello +cellular telephone +chain +chainlink fence +chain mail +chain saw +chest +chiffonier +chime +china cabinet +Christmas stocking +church +cinema +cleaver +cliff dwelling +cloak +clog +cocktail shaker +coffee mug +coffeepot +coil +combination lock +computer keyboard +confectionery +container ship +convertible +corkscrew +cornet +cowboy boot +cowboy hat +cradle +crane +crash helmet +crate +crib +Crock Pot +croquet ball +crutch +cuirass +dam +desk +desktop computer +dial telephone +diaper +digital clock +digital watch +dining table +dishrag +dishwasher +disk brake +dock +dogsled +dome +doormat +drilling platform +drum +drumstick +dumbbell +Dutch oven +electric fan +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa +file +fireboat +fire engine +fire screen +flagpole +flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn +frying pan +fur coat +garbage truck +gasmask +gas pump +goblet +go-kart +golf ball +golfcart +gondola +gong +gown +grand piano +greenhouse +grille +grocery store +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower +hand-held computer +handkerchief +hard disc +harmonica +harp +harvester +hatchet +holster +home theater +honeycomb +hook +hoopskirt +horizontal bar +horse cart +hourglass +iPod +iron +jack-o'-lantern +jean +jeep +jersey +jigsaw puzzle +jinrikisha +joystick +kimono +knee pad +knot +lab coat +ladle +lampshade +laptop +lawn mower +lens cap +letter opener +library +lifeboat +lighter +limousine +liner +lipstick +Loafer +lotion +loudspeaker +loupe +lumbermill +magnetic compass +mailbag +mailbox +maillot +maillot +manhole cover +maraca +marimba +mask +matchstick +maypole +maze +measuring cup +medicine chest +megalith +microphone +microwave +military uniform +milk can +minibus +miniskirt +minivan +missile +mitten +mixing bowl +mobile home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter +mountain bike +mountain tent +mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook +obelisk +oboe +ocarina +odometer +oil filter +organ +oscilloscope +overskirt +oxcart +oxygen mask +packet +paddle +paddlewheel +padlock +paintbrush +pajama +palace +panpipe +paper towel +parachute +parallel bars +park bench +parking meter +passenger car +patio +pay-phone +pedestal +pencil box +pencil sharpener +perfume +Petri dish +photocopier +pick +pickelhaube +picket fence +pickup +pier +piggy bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate +pitcher +plane +planetarium +plastic bag +plate rack +plow +plunger +Polaroid camera +pole +police van +poncho +pool table +pop bottle +pot +potter's wheel +power drill +prayer rug +printer +prison +projectile +projector +puck +punching bag +purse +quill +quilt +racer +racket +radiator +radio +radio telescope +rain barrel +recreational vehicle +reel +reflex camera +refrigerator +remote control +restaurant +revolver +rifle +rocking chair +rotisserie +rubber eraser +rugby ball +rule +running shoe +safe +safety pin +saltshaker +sandal +sarong +sax +scabbard +scale +school bus +schooner +scoreboard +screen +screw +screwdriver +seat belt +sewing machine +shield +shoe shop +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule +sliding door +slot +snorkel +snowmobile +snowplow +soap dispenser +soccer ball +sock +solar dish +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web +spindle +sports car +spotlight +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch +stove +strainer +streetcar +stretcher +studio couch +stupa +submarine +suit +sundial +sunglass +sunglasses +sunscreen +suspension bridge +swab +sweatshirt +swimming trunks +swing +switch +syringe +table lamp +tank +tape player +teapot +teddy +television +tennis ball +thatch +theater curtain +thimble +thresher +throne +tile roof +toaster +tobacco shop +toilet seat +torch +totem pole +tow truck +toyshop +tractor +trailer truck +tray +trench coat +tricycle +trimaran +tripod +triumphal arch +trolleybus +trombone +tub +turnstile +typewriter keyboard +umbrella +unicycle +upright +vacuum +vase +vault +velvet +vending machine +vestment +viaduct +violin +volleyball +waffle iron +wall clock +wallet +wardrobe +warplane +washbasin +washer +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool +worm fence +wreck +yawl +yurt +web site +comic book +crossword puzzle +street sign +traffic light +book jacket +menu +plate +guacamole +consomme +hot pot +trifle +ice cream +ice lolly +French loaf +bagel +pretzel +cheeseburger +hotdog +mashed potato +head cabbage +broccoli +cauliflower +zucchini +spaghetti squash +acorn squash +butternut squash +cucumber +artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple +banana +jackfruit +custard apple +pomegranate +hay +carbonara +chocolate sauce +dough +meat loaf +pizza +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff +coral reef +geyser +lakeside +promontory +sandbar +seashore +valley +volcano +ballplayer +groom +scuba diver +rapeseed +daisy +yellow lady's slipper +corn +acorn +hip +buckeye +coral fungus +agaric +gyromitra +stinkhorn +earthstar +hen-of-the-woods +bolete +ear +toilet tissue diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels.txt b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt similarity index 100% rename from tensorflow/contrib/lite/java/demo/app/src/main/assets/labels.txt rename to tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java index 74737a8b883d23684220dd32bbd7a9e8ab4b2123..9b9fdffab557060f0211a0ce361b002cc7d03956 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java +++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java @@ -296,7 +296,8 @@ public class Camera2BasicFragment extends Fragment public void onActivityCreated(Bundle savedInstanceState) { super.onActivityCreated(savedInstanceState); try { - classifier = new ImageClassifier(getActivity()); + // create either a new ImageClassifierQuantizedMobileNet or an ImageClassifierFloatInception + classifier = new ImageClassifierQuantizedMobileNet(getActivity()); } catch (IOException e) { Log.e(TAG, "Failed to initialize an image classifier."); } @@ -658,8 +659,7 @@ public class Camera2BasicFragment extends Fragment showToast("Uninitialized Classifier or invalid context."); return; } - Bitmap bitmap = - textureView.getBitmap(ImageClassifier.DIM_IMG_SIZE_X, ImageClassifier.DIM_IMG_SIZE_Y); + Bitmap bitmap = textureView.getBitmap(classifier.getImageSizeX(), classifier.getImageSizeY()); String textToShow = classifier.classifyFrame(bitmap); bitmap.recycle(); showToast(textToShow); diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java index e44c5ae6b48eda187079dd3a0a1bc563276d816e..c57bb348c5b386a59327c7b1bc769717ca755269 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java +++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java @@ -20,6 +20,9 @@ import android.content.res.AssetFileDescriptor; import android.graphics.Bitmap; import android.os.SystemClock; import android.util.Log; + +import org.tensorflow.lite.Interpreter; + import java.io.BufferedReader; import java.io.FileInputStream; import java.io.IOException; @@ -34,20 +37,15 @@ import java.util.Comparator; import java.util.List; import java.util.Map; import java.util.PriorityQueue; -import org.tensorflow.lite.Interpreter; -/** Classifies images with Tensorflow Lite. */ -public class ImageClassifier { +/** + * Classifies images with Tensorflow Lite. + */ +public abstract class ImageClassifier { /** Tag for the {@link Log}. */ private static final String TAG = "TfLiteCameraDemo"; - /** Name of the model file stored in Assets. */ - private static final String MODEL_PATH = "mobilenet_quant_v1_224.tflite"; - - /** Name of the label file stored in Assets. */ - private static final String LABEL_PATH = "labels.txt"; - /** Number of results to show in the UI. */ private static final int RESULTS_TO_SHOW = 3; @@ -56,23 +54,18 @@ public class ImageClassifier { private static final int DIM_PIXEL_SIZE = 3; - static final int DIM_IMG_SIZE_X = 224; - static final int DIM_IMG_SIZE_Y = 224; - /* Preallocated buffers for storing image data in. */ - private int[] intValues = new int[DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y]; + private int[] intValues = new int[getImageSizeX() * getImageSizeY()]; /** An instance of the driver class to run model inference with Tensorflow Lite. */ - private Interpreter tflite; + protected Interpreter tflite; /** Labels corresponding to the output of the vision model. */ private List labelList; /** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs. */ - private ByteBuffer imgData = null; + protected ByteBuffer imgData = null; - /** An array to hold inference results, to be feed into Tensorflow Lite as outputs. */ - private byte[][] labelProbArray = null; /** multi-stage low pass filter * */ private float[][] filterLabelProbArray = null; @@ -95,10 +88,13 @@ public class ImageClassifier { labelList = loadLabelList(activity); imgData = ByteBuffer.allocateDirect( - DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE); + DIM_BATCH_SIZE + * getImageSizeX() + * getImageSizeY() + * DIM_PIXEL_SIZE + * getNumBytesPerChannel()); imgData.order(ByteOrder.nativeOrder()); - labelProbArray = new byte[1][labelList.size()]; - filterLabelProbArray = new float[FILTER_STAGES][labelList.size()]; + filterLabelProbArray = new float[FILTER_STAGES][getNumLabels()]; Log.d(TAG, "Created a Tensorflow Lite Image Classifier."); } @@ -111,7 +107,7 @@ public class ImageClassifier { convertBitmapToByteBuffer(bitmap); // Here's where the magic happens!!! long startTime = SystemClock.uptimeMillis(); - tflite.run(imgData, labelProbArray); + runInference(); long endTime = SystemClock.uptimeMillis(); Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime)); @@ -125,12 +121,12 @@ public class ImageClassifier { } void applyFilter() { - int numLabels = labelList.size(); + int numLabels = getNumLabels(); // Low pass filter `labelProbArray` into the first stage of the filter. for (int j = 0; j < numLabels; ++j) { filterLabelProbArray[0][j] += - FILTER_FACTOR * (labelProbArray[0][j] - filterLabelProbArray[0][j]); + FILTER_FACTOR * (getProbability(j) - filterLabelProbArray[0][j]); } // Low pass filter each stage into the next. for (int i = 1; i < FILTER_STAGES; ++i) { @@ -142,7 +138,7 @@ public class ImageClassifier { // Copy the last stage filter output back to `labelProbArray`. for (int j = 0; j < numLabels; ++j) { - labelProbArray[0][j] = (byte)filterLabelProbArray[FILTER_STAGES - 1][j]; + setProbability(j, filterLabelProbArray[FILTER_STAGES - 1][j]); } } @@ -156,7 +152,7 @@ public class ImageClassifier { private List loadLabelList(Activity activity) throws IOException { List labelList = new ArrayList(); BufferedReader reader = - new BufferedReader(new InputStreamReader(activity.getAssets().open(LABEL_PATH))); + new BufferedReader(new InputStreamReader(activity.getAssets().open(getLabelPath()))); String line; while ((line = reader.readLine()) != null) { labelList.add(line); @@ -167,7 +163,7 @@ public class ImageClassifier { /** Memory-map the model file in Assets. */ private MappedByteBuffer loadModelFile(Activity activity) throws IOException { - AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_PATH); + AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(getModelPath()); FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor()); FileChannel fileChannel = inputStream.getChannel(); long startOffset = fileDescriptor.getStartOffset(); @@ -185,12 +181,10 @@ public class ImageClassifier { // Convert the image to floating point. int pixel = 0; long startTime = SystemClock.uptimeMillis(); - for (int i = 0; i < DIM_IMG_SIZE_X; ++i) { - for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) { + for (int i = 0; i < getImageSizeX(); ++i) { + for (int j = 0; j < getImageSizeY(); ++j) { final int val = intValues[pixel++]; - imgData.put((byte) ((val >> 16) & 0xFF)); - imgData.put((byte) ((val >> 8) & 0xFF)); - imgData.put((byte) (val & 0xFF)); + addPixelValue(val); } } long endTime = SystemClock.uptimeMillis(); @@ -199,9 +193,9 @@ public class ImageClassifier { /** Prints top-K labels, to be shown in UI as the results. */ private String printTopKLabels() { - for (int i = 0; i < labelList.size(); ++i) { + for (int i = 0; i < getNumLabels(); ++i) { sortedLabels.add( - new AbstractMap.SimpleEntry<>(labelList.get(i), (labelProbArray[0][i] & 0xff) / 255.0f)); + new AbstractMap.SimpleEntry<>(labelList.get(i), getNormalizedProbability(i))); if (sortedLabels.size() > RESULTS_TO_SHOW) { sortedLabels.poll(); } @@ -214,4 +208,89 @@ public class ImageClassifier { } return textToShow; } + + /** + * Get the name of the model file stored in Assets. + * + * @return + */ + protected abstract String getModelPath(); + + /** + * Get the name of the label file stored in Assets. + * + * @return + */ + protected abstract String getLabelPath(); + + /** + * Get the image size along the x axis. + * + * @return + */ + protected abstract int getImageSizeX(); + + /** + * Get the image size along the y axis. + * + * @return + */ + protected abstract int getImageSizeY(); + + /** + * Get the number of bytes that is used to store a single color channel value. + * + * @return + */ + protected abstract int getNumBytesPerChannel(); + + /** + * Add pixelValue to byteBuffer. + * + * @param pixelValue + */ + protected abstract void addPixelValue(int pixelValue); + + /** + * Read the probability value for the specified label This is either the original value as it was + * read from the net's output or the updated value after the filter was applied. + * + * @param labelIndex + * @return + */ + protected abstract float getProbability(int labelIndex); + + /** + * Set the probability value for the specified label. + * + * @param labelIndex + * @param value + */ + protected abstract void setProbability(int labelIndex, Number value); + + /** + * Get the normalized probability value for the specified label. This is the final value as it + * will be shown to the user. + * + * @return + */ + protected abstract float getNormalizedProbability(int labelIndex); + + /** + * Run inference using the prepared input in {@link #imgData}. Afterwards, the result will be + * provided by getProbability(). + * + *

This additional method is necessary, because we don't have a common base for different + * primitive data types. + */ + protected abstract void runInference(); + + /** + * Get the total number of labels. + * + * @return + */ + protected int getNumLabels() { + return labelList.size(); + } } diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java new file mode 100644 index 0000000000000000000000000000000000000000..be17b85e0cd93778fd123663595c43b730fb44f7 --- /dev/null +++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java @@ -0,0 +1,105 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package com.example.android.tflitecamerademo; + +import android.app.Activity; + +import java.io.IOException; + +/** + * This classifier works with the Inception-v3 slim model. + * It applies floating point inference rather than using a quantized model. + */ +public class ImageClassifierFloatInception extends ImageClassifier { + + /** + * The inception net requires additional normalization of the used input. + */ + private static final int IMAGE_MEAN = 128; + private static final float IMAGE_STD = 128.0f; + + /** + * An array to hold inference results, to be feed into Tensorflow Lite as outputs. + * This isn't part of the super class, because we need a primitive array here. + */ + private float[][] labelProbArray = null; + + /** + * Initializes an {@code ImageClassifier}. + * + * @param activity + */ + ImageClassifierFloatInception(Activity activity) throws IOException { + super(activity); + labelProbArray = new float[1][getNumLabels()]; + } + + @Override + protected String getModelPath() { + // you can download this file from + // https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip + return "inceptionv3_slim_2016.tflite"; + } + + @Override + protected String getLabelPath() { + return "labels_imagenet_slim.txt"; + } + + @Override + protected int getImageSizeX() { + return 299; + } + + @Override + protected int getImageSizeY() { + return 299; + } + + @Override + protected int getNumBytesPerChannel() { + // a 32bit float value requires 4 bytes + return 4; + } + + @Override + protected void addPixelValue(int pixelValue) { + imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + } + + @Override + protected float getProbability(int labelIndex) { + return labelProbArray[0][labelIndex]; + } + + @Override + protected void setProbability(int labelIndex, Number value) { + labelProbArray[0][labelIndex] = value.floatValue(); + } + + @Override + protected float getNormalizedProbability(int labelIndex) { + // TODO the following value isn't in [0,1] yet, but may be greater. Why? + return getProbability(labelIndex); + } + + @Override + protected void runInference() { + tflite.run(imgData, labelProbArray); + } +} diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java new file mode 100644 index 0000000000000000000000000000000000000000..156c895146940adfe71f111be6e354e02b75ea48 --- /dev/null +++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java @@ -0,0 +1,97 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package com.example.android.tflitecamerademo; + +import android.app.Activity; + +import java.io.IOException; + +/** + * This classifier works with the quantized MobileNet model. + */ +public class ImageClassifierQuantizedMobileNet extends ImageClassifier { + + /** + * An array to hold inference results, to be feed into Tensorflow Lite as outputs. + * This isn't part of the super class, because we need a primitive array here. + */ + private byte[][] labelProbArray = null; + + /** + * Initializes an {@code ImageClassifier}. + * + * @param activity + */ + ImageClassifierQuantizedMobileNet(Activity activity) throws IOException { + super(activity); + labelProbArray = new byte[1][getNumLabels()]; + } + + @Override + protected String getModelPath() { + // you can download this file from + // https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip + return "mobilenet_quant_v1_224.tflite"; + } + + @Override + protected String getLabelPath() { + return "labels_mobilenet_quant_v1_224.txt"; + } + + @Override + protected int getImageSizeX() { + return 224; + } + + @Override + protected int getImageSizeY() { + return 224; + } + + @Override + protected int getNumBytesPerChannel() { + // the quantized model uses a single byte only + return 1; + } + + @Override + protected void addPixelValue(int pixelValue) { + imgData.put((byte) ((pixelValue >> 16) & 0xFF)); + imgData.put((byte) ((pixelValue >> 8) & 0xFF)); + imgData.put((byte) (pixelValue & 0xFF)); + } + + @Override + protected float getProbability(int labelIndex) { + return labelProbArray[0][labelIndex]; + } + + @Override + protected void setProbability(int labelIndex, Number value) { + labelProbArray[0][labelIndex] = value.byteValue(); + } + + @Override + protected float getNormalizedProbability(int labelIndex) { + return (labelProbArray[0][labelIndex] & 0xff) / 255.0f; + } + + @Override + protected void runInference() { + tflite.run(imgData, labelProbArray); + } +} diff --git a/tensorflow/contrib/lite/java/proguard.flags b/tensorflow/contrib/lite/java/proguard.flags new file mode 100644 index 0000000000000000000000000000000000000000..8ee3d7e7ae728b27789336ac56208acdf13ee424 --- /dev/null +++ b/tensorflow/contrib/lite/java/proguard.flags @@ -0,0 +1,3 @@ +-keepclassmembers class org.tensorflow.lite.NativeInterpreterWrapper { + private long inferenceDurationNanoseconds; +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java index dd883d69d2065236ee29012b9bde99972aefbcf7..9e47e921a6a62527345984df7c6112cd38e7ea73 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java @@ -80,6 +80,9 @@ public final class Interpreter implements AutoCloseable { /** * Runs model inference if the model takes only one input, and provides only one output. * + *

Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please + * consider using {@link ByteBuffer} to feed input data for better performance. + * * @param input an array or multidimensional array, or a {@link ByteBuffer} of primitive types * including int, float, long, and byte. {@link ByteBuffer} is the preferred way to pass large * input data. When {@link ByteBuffer} is used, its content should remain unchanged until @@ -96,6 +99,9 @@ public final class Interpreter implements AutoCloseable { /** * Runs model inference if the model takes multiple inputs, or returns multiple outputs. * + *

Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please + * consider using {@link ByteBuffer} to feed input data for better performance. + * * @param inputs an array of input data. The inputs should be in the same order as inputs of the * model. Each input can be an array or multidimensional array, or a {@link ByteBuffer} of * primitive types including int, float, long, and byte. {@link ByteBuffer} is the preferred @@ -161,6 +167,27 @@ public final class Interpreter implements AutoCloseable { return wrapper.getOutputIndex(opName); } + /** + * Returns native inference timing. + *

IllegalArgumentException will be thrown if the model is not initialized by the + * {@link Interpreter}. + */ + public Long getLastNativeInferenceDurationNanoseconds() { + if (wrapper == null) { + throw new IllegalStateException("The interpreter has already been closed."); + } + return wrapper.getLastNativeInferenceDurationNanoseconds(); + } + + /** Turns on/off Android NNAPI for hardware acceleration when it is available. */ + public void setUseNNAPI(boolean useNNAPI) { + if (wrapper != null) { + wrapper.setUseNNAPI(useNNAPI); + } else { + throw new IllegalStateException("NativeInterpreterWrapper has already been closed."); + } + } + /** Release resources associated with the {@code Interpreter}. */ @Override public void close() { diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 5ee594dec492ad2fee22e603a6de311b3fed4cac..bca4a3cae603cd05635ff1b5a22f58c04180e8bf 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -35,6 +35,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { errorHandle = createErrorReporter(ERROR_BUFFER_SIZE); modelHandle = createModel(modelPath, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle); + isMemoryAllocated = true; } /** @@ -47,6 +48,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { errorHandle = createErrorReporter(ERROR_BUFFER_SIZE); modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle); + isMemoryAllocated = true; } /** Releases resources associated with this {@code NativeInterpreterWrapper}. */ @@ -59,6 +61,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { modelByteBuffer = null; inputsIndexes = null; outputsIndexes = null; + isMemoryAllocated = false; } /** Sets inputs, runs model inference and returns outputs. */ @@ -91,11 +94,21 @@ final class NativeInterpreterWrapper implements AutoCloseable { i, inputs.length)); } } + inferenceDurationNanoseconds = -1; long[] outputsHandles = - run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs); + run( + interpreterHandle, + errorHandle, + sizes, + dataTypes, + numsOfBytes, + inputs, + this, + isMemoryAllocated); if (outputsHandles == null || outputsHandles.length == 0) { throw new IllegalStateException("Interpreter has no outputs."); } + isMemoryAllocated = true; Tensor[] outputs = new Tensor[outputsHandles.length]; for (int i = 0; i < outputsHandles.length; ++i) { outputs[i] = Tensor.fromHandle(outputsHandles[i]); @@ -109,14 +122,18 @@ final class NativeInterpreterWrapper implements AutoCloseable { Object[] sizes, int[] dtypes, int[] numsOfBytes, - Object[] values); + Object[] values, + NativeInterpreterWrapper wrapper, + boolean memoryAllocated); /** Resizes dimensions of a specific input. */ void resizeInput(int idx, int[] dims) { - resizeInput(interpreterHandle, errorHandle, idx, dims); + if (resizeInput(interpreterHandle, errorHandle, idx, dims)) { + isMemoryAllocated = false; + } } - private static native void resizeInput( + private static native boolean resizeInput( long interpreterHandle, long errorHandle, int inputIdx, int[] dims); void setUseNNAPI(boolean useNNAPI) { @@ -236,6 +253,14 @@ final class NativeInterpreterWrapper implements AutoCloseable { } } + /** + * Gets the last inference duration in nanoseconds. It returns null if there is no previous + * inference run or the last inference run failed. + */ + Long getLastNativeInferenceDurationNanoseconds() { + return (inferenceDurationNanoseconds < 0) ? null : inferenceDurationNanoseconds; + } + private static final int ERROR_BUFFER_SIZE = 512; private long errorHandle; @@ -246,12 +271,16 @@ final class NativeInterpreterWrapper implements AutoCloseable { private int inputSize; + private long inferenceDurationNanoseconds = -1; + private MappedByteBuffer modelByteBuffer; private Map inputsIndexes; private Map outputsIndexes; + private boolean isMemoryAllocated = false; + private static native String[] getInputNames(long interpreterHandle); private static native String[] getOutputNames(long interpreterHandle); diff --git a/tensorflow/contrib/lite/java/src/main/native/BUILD b/tensorflow/contrib/lite/java/src/main/native/BUILD index 15806d57c8ed7a45d2db9b80e2aab8e22349ee3e..3571182ca92e959d54935cfdc76679ab69a8cfa9 100644 --- a/tensorflow/contrib/lite/java/src/main/native/BUILD +++ b/tensorflow/contrib/lite/java/src/main/native/BUILD @@ -11,6 +11,7 @@ licenses(["notice"]) # Apache 2.0 cc_library( name = "native_framework_only", srcs = [ + "duration_utils_jni.cc", "exception_jni.cc", "nativeinterpreterwrapper_jni.cc", "tensor_jni.cc", diff --git a/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc new file mode 100644 index 0000000000000000000000000000000000000000..0e08a04370592f6e3c92b5811fa7e163f808e03c --- /dev/null +++ b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc @@ -0,0 +1,38 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +namespace tflite { + +// Gets the elapsed wall-clock timespec. +timespec getCurrentTime() { + timespec time; + clock_gettime(CLOCK_MONOTONIC, &time); + return time; +} + +// Computes the time diff from two timespecs. Returns '-1' if 'stop' is earlier +// than 'start'. +jlong timespec_diff_nanoseconds(struct timespec* start, struct timespec* stop) { + jlong result = stop->tv_sec - start->tv_sec; + if (result < 0) return -1; + result = 1000000000 * result + (stop->tv_nsec - start->tv_nsec); + if (result < 0) return -1; + return result; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc index c346f9f92e360c0722ebac440d790da6441ceecf..475b467face2736f9a1b8f9e713fe2d86b9fa77e 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -149,6 +149,45 @@ TfLiteStatus checkInputs(JNIEnv* env, tflite::Interpreter* interpreter, return kTfLiteOk; } +// Checks whether there is any difference between dimensions of a tensor and a +// given dimensions. Returns true if there is difference, else false. +bool areDimsDifferent(JNIEnv* env, TfLiteTensor* tensor, jintArray dims) { + int num_dims = static_cast(env->GetArrayLength(dims)); + jint* ptr = env->GetIntArrayElements(dims, nullptr); + if (ptr == nullptr) { + throwException(env, kIllegalArgumentException, + "Empty dimensions of input array."); + return true; + } + if (tensor->dims->size != num_dims) { + return true; + } + for (int i = 0; i < num_dims; ++i) { + if (ptr[i] != tensor->dims->data[i]) { + return true; + } + } + env->ReleaseIntArrayElements(dims, ptr, JNI_ABORT); + return false; +} + +bool areInputDimensionsTheSame(JNIEnv* env, tflite::Interpreter* interpreter, + int input_size, jobjectArray sizes) { + if (interpreter->inputs().size() != input_size) { + return false; + } + for (int i = 0; i < input_size; ++i) { + int input_idx = interpreter->inputs()[i]; + jintArray dims = + static_cast(env->GetObjectArrayElement(sizes, i)); + TfLiteTensor* target = interpreter->tensor(input_idx); + if (areDimsDifferent(env, target, dims)) return false; + env->DeleteLocalRef(dims); + if (env->ExceptionCheck()) return false; + } + return true; +} + TfLiteStatus resizeInputs(JNIEnv* env, tflite::Interpreter* interpreter, int input_size, jobjectArray sizes) { for (int i = 0; i < input_size; ++i) { @@ -344,6 +383,15 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( throwException(env, kIllegalArgumentException, "Cannot create interpreter: %s", error_reporter->CachedErrorMessage()); + return 0; + } + // allocates memory + status = interpreter->AllocateTensors(); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, + "Can not allocate memory for the interpreter", + error_reporter->CachedErrorMessage()); + return 0; } return reinterpret_cast(interpreter.release()); } @@ -353,7 +401,7 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values) { + jobjectArray values, jobject wrapper, jboolean memory_allocated) { tflite::Interpreter* interpreter = convertLongToInterpreter(env, interpreter_handle); if (interpreter == nullptr) return nullptr; @@ -365,25 +413,29 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run( TfLiteStatus status = checkInputs(env, interpreter, input_size, data_types, nums_of_bytes, values, sizes); if (status != kTfLiteOk) return nullptr; - // resizes inputs - status = resizeInputs(env, interpreter, input_size, sizes); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, "Can not resize the input: %s", - error_reporter->CachedErrorMessage()); - return nullptr; - } - // allocates memory - status = interpreter->AllocateTensors(); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Can not allocate memory for the given inputs: %s", - error_reporter->CachedErrorMessage()); - return nullptr; + if (!memory_allocated || + !areInputDimensionsTheSame(env, interpreter, input_size, sizes)) { + // resizes inputs + status = resizeInputs(env, interpreter, input_size, sizes); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, "Can not resize the input: %s", + error_reporter->CachedErrorMessage()); + return nullptr; + } + // allocates memory + status = interpreter->AllocateTensors(); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, + "Can not allocate memory for the given inputs: %s", + error_reporter->CachedErrorMessage()); + return nullptr; + } } // sets inputs status = setInputs(env, interpreter, input_size, data_types, nums_of_bytes, values); if (status != kTfLiteOk) return nullptr; + timespec beforeInference = ::tflite::getCurrentTime(); // runs inference if (interpreter->Invoke() != kTfLiteOk) { throwException(env, kIllegalArgumentException, @@ -391,6 +443,17 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run( error_reporter->CachedErrorMessage()); return nullptr; } + timespec afterInference = ::tflite::getCurrentTime(); + jclass wrapper_clazz = env->GetObjectClass(wrapper); + jfieldID fid = + env->GetFieldID(wrapper_clazz, "inferenceDurationNanoseconds", "J"); + if (env->ExceptionCheck()) { + env->ExceptionClear(); + } else if (fid != nullptr) { + env->SetLongField( + wrapper, fid, + ::tflite::timespec_diff_nanoseconds(&beforeInference, &afterInference)); + } // returns outputs const std::vector& results = interpreter->outputs(); if (results.empty()) { @@ -438,29 +501,37 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( return outputs; } -JNIEXPORT void JNICALL +JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jint input_idx, jintArray dims) { BufferErrorReporter* error_reporter = convertLongToErrorReporter(env, error_handle); - if (error_reporter == nullptr) return; + if (error_reporter == nullptr) return JNI_FALSE; tflite::Interpreter* interpreter = convertLongToInterpreter(env, interpreter_handle); - if (interpreter == nullptr) return; + if (interpreter == nullptr) return JNI_FALSE; const int idx = static_cast(input_idx); if (idx < 0 || idx >= interpreter->inputs().size()) { throwException(env, kIllegalArgumentException, "Can not resize %d-th input for a model having %d inputs.", idx, interpreter->inputs().size()); + return JNI_FALSE; } - TfLiteStatus status = interpreter->ResizeInputTensor( - interpreter->inputs()[idx], convertJIntArrayToVector(env, dims)); - if (status != kTfLiteOk) { - throwException(env, kIllegalArgumentException, - "Failed to resize %d-th input: %s", idx, - error_reporter->CachedErrorMessage()); + // check whether it is resizing with the same dimensions. + TfLiteTensor* target = interpreter->tensor(input_idx); + bool is_changed = areDimsDifferent(env, target, dims); + if (is_changed) { + TfLiteStatus status = interpreter->ResizeInputTensor( + interpreter->inputs()[idx], convertJIntArrayToVector(env, dims)); + if (status != kTfLiteOk) { + throwException(env, kIllegalArgumentException, + "Failed to resize %d-th input: %s", idx, + error_reporter->CachedErrorMessage()); + return JNI_FALSE; + } } + return is_changed ? JNI_TRUE : JNI_FALSE; } JNIEXPORT void JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_delete( diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h index c52a7e4e439936344be26d5761fb5747db64794a..f7c2d9bf82a90e0e3156d18ba0bcaa30265942fa 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include #include #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/interpreter.h" @@ -28,6 +29,9 @@ limitations under the License. namespace tflite { // This is to be provided at link-time by a library. extern std::unique_ptr CreateOpResolver(); +extern timespec getCurrentTime(); +extern jlong timespec_diff_nanoseconds(struct timespec* start, + struct timespec* stop); } // namespace tflite #ifdef __cplusplus @@ -104,13 +108,14 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: - * Signature: (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;)[J + * Signature: + * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Ljava/lang/Object;Z)[J */ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values); + jobjectArray values, jobject wrapper, jboolean memory_allocated); /* * Class: org_tensorflow_lite_NativeInterpreterWrapper @@ -127,11 +132,12 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: - * Signature: (JJI[I) + * Signature: (JJI[I)Z * - * It resizes dimensions of a input. + * It returns true if resizing input tensor to different dimensions, else return + * false. */ -JNIEXPORT void JNICALL +JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jint input_idx, jintArray dims); diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index 424b3de6c97672e310c54230a7ac1204f46d9ac8..61d6c35ec86beebf78dd81e17e145863516802fa 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -218,4 +218,52 @@ public final class InterpreterTest { int index = interpreter.getOutputIndex("MobilenetV1/Predictions/Softmax"); assertThat(index).isEqualTo(0); } + + @Test + public void testTurnOffNNAPI() throws Exception { + Path path = MODEL_FILE.toPath(); + FileChannel fileChannel = + (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ)); + MappedByteBuffer mappedByteBuffer = + fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size()); + Interpreter interpreter = new Interpreter(mappedByteBuffer); + interpreter.setUseNNAPI(true); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + interpreter.run(fourD, parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.setUseNNAPI(false); + interpreter.run(fourD, parsedOutputs); + outputOneD = parsedOutputs[0][0][0]; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.close(); + fileChannel.close(); + } + + @Test + public void testTurnOnNNAPI() throws Exception { + Path path = MODEL_FILE.toPath(); + FileChannel fileChannel = + (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ)); + MappedByteBuffer mappedByteBuffer = + fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size()); + Interpreter interpreter = new Interpreter(mappedByteBuffer); + interpreter.setUseNNAPI(true); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + interpreter.run(fourD, parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.close(); + fileChannel.close(); + } } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java index 90323555d88419d837a76bca7de6d9998e388fca..6371fb59dc64c141ff24ca8dfefedbe763b5097d 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java @@ -94,6 +94,30 @@ public final class NativeInterpreterWrapperTest { wrapper.close(); } + @Test + public void testRunWithInputsOfSameDims() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, -6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + Tensor[] outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + float[][][][] parsedOutputs = new float[2][8][8][3]; + outputs[0].copyTo(parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, -19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + parsedOutputs = new float[2][8][8][3]; + outputs[0].copyTo(parsedOutputs); + outputOneD = parsedOutputs[0][0][0]; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + wrapper.close(); + } + @Test public void testRunWithInt() { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(INT_MODEL_PATH); @@ -417,4 +441,45 @@ public final class NativeInterpreterWrapperTest { assertThat(shape[1]).isEqualTo(3); assertThat(shape[2]).isEqualTo(1); } + + @Test + public void testGetInferenceLatency() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + Tensor[] outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isGreaterThan(0L); + wrapper.close(); + } + + @Test + public void testGetInferenceLatencyWithNewWrapper() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull(); + wrapper.close(); + } + + @Test + public void testGetLatencyAfterFailedInference() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + try { + wrapper.run(inputs); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e) + .hasMessageThat() + .contains("0-th input dimension should be [?,8,8,3], but found [?,8,7,3]"); + } + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull(); + wrapper.close(); + } } diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java index 8660cabf709e6531a5667a16e5cf43a93c7135bd..a5c13053d71374c0bdb8652bb22029d7025e20eb 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java @@ -32,4 +32,19 @@ public class TestHelper { throw new IllegalArgumentException("Interpreter has not initialized; Failed to setUseNNAPI."); } } + + /** + * Gets the last inference duration in nanoseconds. It returns null if there is no previous + * inference run or the last inference run failed. + * + * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code + * IllegalArgumentException} will be thrown. + */ + public static Long getLastNativeInferenceDurationNanoseconds(Interpreter interpreter) { + if (interpreter != null && interpreter.wrapper != null) { + return interpreter.wrapper.getLastNativeInferenceDurationNanoseconds(); + } else { + throw new IllegalArgumentException("Interpreter has not initialized; Failed to get latency."); + } + } } diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index a8ef0daede4f3b7eeffccf77263577002d512e2c..b1a29701e075739cf11f236d99d24c8f0ba47c93 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -5,15 +5,17 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") -load( - "//tensorflow:tensorflow.bzl", - "tf_cc_test", -) +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") +load("//tensorflow:tensorflow.bzl", "tf_cc_test") tf_cc_test( name = "optional_tensor_test", size = "small", srcs = ["optional_tensor_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -33,7 +35,7 @@ cc_library( "//tensorflow/contrib/lite:schema_fbs_version", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/testing:util", - "//tensorflow/core:lib", + "//tensorflow/core:tflite_portable_logging", "@com_google_googletest//:gtest", ], ) @@ -90,6 +92,10 @@ tf_cc_test( name = "kernel_util_test", size = "small", srcs = ["kernel_util_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":kernel_util", "//tensorflow/contrib/lite/testing:util", @@ -97,6 +103,17 @@ tf_cc_test( ], ) +tf_cc_test( + name = "test_util_test", + size = "small", + srcs = ["test_util_test.cc"], + deps = [ + ":test_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + cc_library( name = "builtin_ops", srcs = [ @@ -104,13 +121,17 @@ cc_library( "add.cc", "basic_rnn.cc", "batch_to_space_nd.cc", + "bidirectional_sequence_lstm.cc", "bidirectional_sequence_rnn.cc", + "cast.cc", "concatenation.cc", "conv.cc", "depthwise_conv.cc", + "dequantize.cc", "div.cc", "embedding_lookup.cc", "embedding_lookup_sparse.cc", + "exp.cc", "fully_connected.cc", "gather.cc", "hashtable_lookup.cc", @@ -128,10 +149,12 @@ cc_library( "skip_gram.cc", "space_to_batch_nd.cc", "space_to_depth.cc", + "split.cc", "squeeze.cc", "strided_slice.cc", "sub.cc", "svdf.cc", + "topk_v2.cc", "transpose.cc", "unidirectional_sequence_lstm.cc", "unidirectional_sequence_rnn.cc", @@ -172,6 +195,10 @@ tf_cc_test( name = "activations_test", size = "small", srcs = ["activations_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -184,6 +211,10 @@ tf_cc_test( name = "add_test", size = "small", srcs = ["add_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -196,6 +227,10 @@ tf_cc_test( name = "transpose_test", size = "small", srcs = ["transpose_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -210,6 +245,10 @@ tf_cc_test( name = "space_to_batch_nd_test", size = "small", srcs = ["space_to_batch_nd_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -222,6 +261,22 @@ tf_cc_test( name = "batch_to_space_nd_test", size = "small", srcs = ["batch_to_space_nd_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "cast_test", + size = "small", + srcs = ["cast_test.cc"], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -234,6 +289,10 @@ tf_cc_test( name = "concatenation_test", size = "small", srcs = ["concatenation_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -246,6 +305,10 @@ tf_cc_test( name = "conv_test", size = "small", srcs = ["conv_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -259,10 +322,27 @@ tf_cc_test( name = "depthwise_conv_test", size = "small", srcs = ["depthwise_conv_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "dequantize_test", + size = "small", + srcs = ["dequantize_test.cc"], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_absl//absl/memory", "@com_google_googletest//:gtest", ], ) @@ -271,6 +351,26 @@ tf_cc_test( name = "basic_rnn_test", size = "small", srcs = ["basic_rnn_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "bidirectional_sequence_lstm_test", + size = "small", + srcs = ["bidirectional_sequence_lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -283,6 +383,10 @@ tf_cc_test( name = "unidirectional_sequence_lstm_test", size = "small", srcs = ["unidirectional_sequence_lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -295,6 +399,9 @@ tf_cc_test( name = "bidirectional_sequence_rnn_test", size = "small", srcs = ["bidirectional_sequence_rnn_test.cc"], + tags = [ + "tflite_not_portable", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -307,6 +414,10 @@ tf_cc_test( name = "unidirectional_sequence_rnn_test", size = "small", srcs = ["unidirectional_sequence_rnn_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -319,6 +430,26 @@ tf_cc_test( name = "l2norm_test", size = "small", srcs = ["l2norm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "exp_test", + size = "small", + srcs = ["exp_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -331,6 +462,10 @@ tf_cc_test( name = "mean_test", size = "small", srcs = ["mean_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -343,6 +478,10 @@ tf_cc_test( name = "mul_test", size = "small", srcs = ["mul_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -355,6 +494,10 @@ tf_cc_test( name = "pad_test", size = "small", srcs = ["pad_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -367,6 +510,10 @@ tf_cc_test( name = "reshape_test", size = "small", srcs = ["reshape_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -379,6 +526,27 @@ tf_cc_test( name = "gather_test", size = "small", srcs = ["gather_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "topk_v2_test", + size = "small", + srcs = ["topk_v2_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -392,6 +560,10 @@ tf_cc_test( name = "resize_bilinear_test", size = "small", srcs = ["resize_bilinear_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -404,6 +576,10 @@ tf_cc_test( name = "svdf_test", size = "small", srcs = ["svdf_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -416,6 +592,10 @@ tf_cc_test( name = "embedding_lookup_test", size = "small", srcs = ["embedding_lookup_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -428,6 +608,10 @@ tf_cc_test( name = "embedding_lookup_sparse_test", size = "small", srcs = ["embedding_lookup_sparse_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -440,6 +624,10 @@ tf_cc_test( name = "fully_connected_test", size = "small", srcs = ["fully_connected_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -452,6 +640,10 @@ tf_cc_test( name = "local_response_norm_test", size = "small", srcs = ["local_response_norm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -464,6 +656,10 @@ tf_cc_test( name = "pooling_test", size = "small", srcs = ["pooling_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -476,6 +672,27 @@ tf_cc_test( name = "softmax_test", size = "small", srcs = ["softmax_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "//tensorflow/contrib/lite/kernels/internal:reference_base", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "log_softmax_test", + size = "small", + srcs = ["log_softmax_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -489,6 +706,10 @@ tf_cc_test( name = "lsh_projection_test", size = "small", srcs = ["lsh_projection_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -501,6 +722,10 @@ tf_cc_test( name = "hashtable_lookup_test", size = "small", srcs = ["hashtable_lookup_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -514,6 +739,10 @@ tf_cc_test( name = "lstm_test", size = "small", srcs = ["lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -526,6 +755,10 @@ tf_cc_test( name = "skip_gram_test", size = "small", srcs = ["skip_gram_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -539,6 +772,26 @@ tf_cc_test( name = "space_to_depth_test", size = "small", srcs = ["space_to_depth_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "split_test", + size = "small", + srcs = ["split_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -551,6 +804,10 @@ tf_cc_test( name = "squeeze_test", size = "small", srcs = ["squeeze_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -563,6 +820,10 @@ tf_cc_test( name = "strided_slice_test", size = "small", srcs = ["strided_slice_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -582,3 +843,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index 3c5c77815d0f2592ab549152b4d77f45b967a660..093761c43c1cb41ddb2245da13c963014b51271c 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -63,6 +63,33 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArrayCopy(input->dims)); } +TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TfLiteTensor* input = GetInput(context, node, 0); + TfLiteTensor* output = GetOutput(context, node, 0); + TF_LITE_ENSURE_EQ(context, input->type, output->type); + + if (input->type == kTfLiteUInt8) { + static constexpr int kInputIntegerBits = 4; + + const double input_real_multiplier = + input->params.scale * + static_cast(1 << (31 - kInputIntegerBits)); + + QuantizeMultiplierGreaterThanOne(input_real_multiplier, + &data->input_multiplier, + &data->input_left_shift); + data->input_range_radius = + CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } + + return context->ResizeTensor(context, output, + TfLiteIntArrayCopy(input->dims)); +} + TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) { OpData* data = reinterpret_cast(node->user_data); @@ -180,6 +207,7 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { } TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); TfLiteTensor* input = GetInput(context, node, 0); TfLiteTensor* output = GetOutput(context, node, 0); switch (input->type) { @@ -191,6 +219,14 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = std::tanh(*in); return kTfLiteOk; } break; + case kTfLiteUInt8: { + optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), + input->params.zero_point, data->input_range_radius, + data->input_multiplier, data->input_left_shift, + GetTensorData(output), + GetTensorDims(output)); + return kTfLiteOk; + } break; default: context->ReportError(context, "Only float32 supported currently."); return kTfLiteError; @@ -337,6 +373,21 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { } } +TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* input = GetInput(context, node, 0); + TfLiteTensor* output = GetOutput(context, node, 0); + switch (input->type) { + case kTfLiteFloat32: + optimized_ops::LogSoftmax( + GetTensorData(input), GetTensorDims(input), + GetTensorData(output), GetTensorDims(output)); + return kTfLiteOk; + default: + context->ReportError(context, "Only float32 supported currently."); + return kTfLiteError; + } +} + } // namespace activations TfLiteRegistration* Register_RELU() { @@ -361,8 +412,8 @@ TfLiteRegistration* Register_RELU6() { } TfLiteRegistration* Register_TANH() { - static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, - activations::GenericPrepare, + static TfLiteRegistration r = {activations::Init, activations::Free, + activations::TanhPrepare, activations::TanhEval}; return &r; } @@ -381,6 +432,13 @@ TfLiteRegistration* Register_SOFTMAX() { return &r; } +TfLiteRegistration* Register_LOG_SOFTMAX() { + static TfLiteRegistration r = {activations::Init, activations::Free, + activations::GenericPrepare, + activations::LogSoftmaxEval}; + return &r; +} + } // namespace builtin } // namespace ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 68d49944e51b043b6b82aa1589d22f6ebed37574..b9a96e3f79677c5a94ade86a6b334abb4c265fa1 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -52,6 +52,14 @@ class BaseActivationsOpModel : public SingleOpModel { BuildInterpreter({GetShape(input_)}); } + BaseActivationsOpModel(BuiltinOperator type, const TensorData &input, + const TensorData &output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(type, BuiltinOptions_NONE, 0); + BuildInterpreter({GetShape(input_)}); + } + protected: int input_; int output_; @@ -143,6 +151,27 @@ TEST(FloatActivationsOpTest, Tanh) { }))); } +TEST(QuantizedActivationsOpTest, Tanh) { + QuantizedActivationsOpModel m( + BuiltinOperator_TANH, + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -8, 8}, + /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, -1, 1}); + m.SetInput({ + 0, -6, 2, 4, // + -4, -2, 8, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.0, -0.999987, 0.964027, 0.999329, // + -0.996078, -0.96402, 0.99999, 0.76159, // + }, + 4 * (1. / 256)))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 226})); +} + TEST(FloatActivationsOpTest, Sigmoid) { FloatActivationsOpModel m(BuiltinOperator_LOGISTIC, /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); @@ -313,6 +342,47 @@ TEST(QuantizedActivationsOpTest, Softmax2D) { kQuantizedTolerance))); } +// This contains the same test values as the Softmax test, but reference answer +// generated via the following snippet of python: +// logits1 = tf.constant([[0, -6, 2, 4],[3, -2, 10, 1]], dtype=tf.float32) +// logits2 = tf.constant([[0,-6],[2,4],[3,-2],[10,1]], dtype=tf.float32) +// lsm1 = tf.nn.log_softmax(logits1) +// lsm2 = tf.nn.log_softmax(logits2) +// with tf.Session() as sess: +// print('lsm1', sess.run(lsm1)) +// print('lsm2', sess.run(lsm2)) + +TEST(FloatActivationsOpTest, LogSoftmax) { + FloatActivationsOpModel m(BuiltinOperator_LOG_SOFTMAX, + /*input=*/{TensorType_FLOAT32, {2, 4}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + -4.14297, -10.14297, -2.14297, -.142971, // + -7.00104, -12.00104, -.00104087, -9.00104, // + }))); + + // Same input, but a different shape. + FloatActivationsOpModel m2(BuiltinOperator_LOG_SOFTMAX, + /*input=*/{TensorType_FLOAT32, {4, 2}}); + m2.SetInput({ + 0, -6, // + 2, 4, // + 3, -2, // + 10, 1, // + }); + m2.Invoke(); + EXPECT_THAT(m2.GetOutput(), ElementsAreArray(ArrayFloatNear({ + -.00247565, -6.00247, // + -2.12692, -.126928, // + -.00671534, -5.00671, // + -.000123374, -9.00012, // + }))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc index 889239f93215a309d5434b209ebfc1f584c47849..bc438f99c6a72fdbc2794dee03524db6a7523834 100644 --- a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc @@ -116,7 +116,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Resize the output tensor if the output tensor is dynamic. if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } #define TF_LITE_BATCH_TO_SPACE_ND(type, scalar) \ diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc new file mode 100644 index 0000000000000000000000000000000000000000..a64ac42bc43336db928d2682e290f5263f3db0f4 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -0,0 +1,702 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace bidirectional_sequence_lstm { + +// Input Tensors of size {max_time, n_batch, n_input} +constexpr int kInputTensor = 0; + +// Forward LSTM cell tensors. +// Input weight tensors of size: {n_cell, n_input} +constexpr int kFwInputToInputWeightsTensor = 1; // Optional +constexpr int kFwInputToForgetWeightsTensor = 2; +constexpr int kFwInputToCellWeightsTensor = 3; +constexpr int kFwInputToOutputWeightsTensor = 4; + +// Recurrent weight tensors of size {n_cell, n_output} +constexpr int kFwRecurrentToInputWeightsTensor = 5; // Optional +constexpr int kFwRecurrentToForgetWeightsTensor = 6; +constexpr int kFwRecurrentToCellWeightsTensor = 7; +constexpr int kFwRecurrentToOutputWeightsTensor = 8; + +// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. +constexpr int kFwCellToInputWeightsTensor = 9; // Optional +constexpr int kFwCellToForgetWeightsTensor = 10; // Optional +constexpr int kFwCellToOutputWeightsTensor = 11; // Optional + +// Gates bias tensors of size {n_cell} +constexpr int kFwInputGateBiasTensor = 12; // Optional +constexpr int kFwForgetGateBiasTensor = 13; +constexpr int kFwCellGateBiasTensor = 14; +constexpr int kFwOutputGateBiasTensor = 15; + +// Projection weight tensor of size {n_output, n_cell} +constexpr int kFwProjectionWeightsTensor = 16; // Optional +// Projection bias tensor of size {n_output} +constexpr int kFwProjectionBiasTensor = 17; // Optional + +// Backward LSTM cell tensors. +// Input weight tensors of size: {n_cell, n_input} +constexpr int kBwInputToInputWeightsTensor = 18; // Optional +constexpr int kBwInputToForgetWeightsTensor = 19; +constexpr int kBwInputToCellWeightsTensor = 20; +constexpr int kBwInputToOutputWeightsTensor = 21; + +// Recurrent weight tensors of size {n_cell, n_output} +constexpr int kBwRecurrentToInputWeightsTensor = 22; // Optional +constexpr int kBwRecurrentToForgetWeightsTensor = 23; +constexpr int kBwRecurrentToCellWeightsTensor = 24; +constexpr int kBwRecurrentToOutputWeightsTensor = 25; + +// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. +constexpr int kBwCellToInputWeightsTensor = 26; // Optional +constexpr int kBwCellToForgetWeightsTensor = 27; // Optional +constexpr int kBwCellToOutputWeightsTensor = 28; // Optional + +// Gates bias tensors of size {n_cell} +constexpr int kBwInputGateBiasTensor = 29; // Optional +constexpr int kBwForgetGateBiasTensor = 30; +constexpr int kBwCellGateBiasTensor = 31; +constexpr int kBwOutputGateBiasTensor = 32; + +// Projection weight tensor of size {n_output, n_cell} +constexpr int kBwProjectionWeightsTensor = 33; // Optional +// Projection bias tensor of size {n_output} +constexpr int kBwProjectionBiasTensor = 34; // Optional + +// Output tensors. +constexpr int kFwScratchBufferTensor = 0; +constexpr int kFwOutputStateTensor = 1; +constexpr int kFwCellStateTensor = 2; +constexpr int kFwOutputTensor = 3; + +constexpr int kBwScratchBufferTensor = 4; +constexpr int kBwOutputStateTensor = 5; +constexpr int kBwCellStateTensor = 6; +constexpr int kBwOutputTensor = 7; + +// Check that input tensor dimensions matches with each other. +TfLiteStatus CheckLstmTensorDimensions( + TfLiteContext* context, TfLiteNode* node, int n_input, int n_output, + int n_cell, int input_to_input_weights_tensor, + int input_to_forget_weights_tensor, int input_to_cell_weights_tensor, + int input_to_output_weights_tensor, int recurrent_to_input_weights_tensor, + int recurrent_to_forget_weights_tensor, + int recurrent_to_cell_weights_tensor, + int recurrent_to_output_weights_tensor, int cell_to_input_weights_tensor, + int cell_to_forget_weights_tensor, int cell_to_output_weights_tensor, + int input_gate_bias_tensor, int forget_gate_bias_tensor, + int cell_gate_bias_tensor, int output_gate_bias_tensor, + int projection_weights_tensor, int projection_bias_tensor) { + auto* params = reinterpret_cast(node->builtin_data); + + // Making sure clipping parameters have valid values. + // == 0 means no clipping + // > 0 means clipping + TF_LITE_ENSURE(context, params->cell_clip >= 0); + TF_LITE_ENSURE(context, params->proj_clip >= 0); + + TfLiteTensor* input_to_input_weights = + GetOptionalInputTensor(context, node, input_to_input_weights_tensor); + if (input_to_input_weights) { + TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell); + TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input); + } + + TfLiteTensor* input_to_forget_weights = + GetInput(context, node, input_to_forget_weights_tensor); + TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell); + TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input); + + TfLiteTensor* input_to_cell_weights = + GetInput(context, node, input_to_cell_weights_tensor); + TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell); + TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input); + + TfLiteTensor* recurrent_to_input_weights = + GetOptionalInputTensor(context, node, recurrent_to_input_weights_tensor); + if (recurrent_to_input_weights) { + TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0], + n_cell); + TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1], + n_output); + } + + TfLiteTensor* recurrent_to_forget_weights = + GetInput(context, node, recurrent_to_forget_weights_tensor); + TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0], + n_cell); + TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1], + n_output); + + TfLiteTensor* recurrent_to_cell_weights = + GetInput(context, node, recurrent_to_cell_weights_tensor); + TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell); + TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1], + n_output); + + // We make sure the input-gate's parameters are either both present (regular + // LSTM) or not at all (CIFG-LSTM). + const bool cifg_weights_all_or_none = + ((input_to_input_weights != nullptr) && + (recurrent_to_input_weights != nullptr)) || + ((input_to_input_weights == nullptr) && + (recurrent_to_input_weights == nullptr)); + TF_LITE_ENSURE(context, cifg_weights_all_or_none == true); + + TfLiteTensor* cell_to_input_weights = + GetOptionalInputTensor(context, node, cell_to_input_weights_tensor); + if (cell_to_input_weights) { + TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1); + TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell); + } + + TfLiteTensor* cell_to_forget_weights = + GetOptionalInputTensor(context, node, cell_to_forget_weights_tensor); + if (cell_to_forget_weights) { + TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1); + TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell); + } + + TfLiteTensor* cell_to_output_weights = + GetOptionalInputTensor(context, node, cell_to_output_weights_tensor); + if (cell_to_output_weights) { + TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1); + TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell); + } + + // Making sure the peephole weights are there all or none. + const bool use_cifg = (input_to_input_weights == nullptr); + const bool peephole_weights_all_or_none = + ((cell_to_input_weights != nullptr || use_cifg) && + (cell_to_forget_weights != nullptr) && + (cell_to_output_weights != nullptr)) || + ((cell_to_input_weights == nullptr) && + (cell_to_forget_weights == nullptr) && + (cell_to_output_weights == nullptr)); + TF_LITE_ENSURE(context, peephole_weights_all_or_none == true); + + // Make sure the input gate bias is present only when not a CIFG-LSTM. + TfLiteTensor* input_gate_bias = + GetOptionalInputTensor(context, node, input_gate_bias_tensor); + if (use_cifg) { + TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr); + } else { + TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell); + } + + TfLiteTensor* forget_gate_bias = + GetInput(context, node, forget_gate_bias_tensor); + TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell); + + TfLiteTensor* cell_bias = GetInput(context, node, cell_gate_bias_tensor); + TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell); + + TfLiteTensor* output_gate_bias = + GetInput(context, node, output_gate_bias_tensor); + TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell); + + TfLiteTensor* projection_weights = + GetOptionalInputTensor(context, node, projection_weights_tensor); + if (projection_weights) { + TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output); + TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell); + } + + TfLiteTensor* projection_bias = + GetOptionalInputTensor(context, node, projection_bias_tensor); + if (projection_bias) { + TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output); + } + + // Making sure the projection tensors are consistent: + // 1) If projection weight is not present, then projection bias should not be + // present. + // 2) If projection weight is present, then projection bias is optional. + // TODO(ghodrat): make sure this is correct. + const bool projecton_tensors_consistent = + ((projection_weights != nullptr) || (projection_bias == nullptr)); + TF_LITE_ENSURE(context, projecton_tensors_consistent == true); + + return kTfLiteOk; +} + +TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, + TfLiteNode* node, int n_input, + int n_output, int n_cell) { + CheckLstmTensorDimensions( + context, node, n_input, n_output, n_cell, kFwInputToInputWeightsTensor, + kFwInputToForgetWeightsTensor, kFwInputToCellWeightsTensor, + kFwInputToOutputWeightsTensor, kFwRecurrentToInputWeightsTensor, + kFwRecurrentToForgetWeightsTensor, kFwRecurrentToCellWeightsTensor, + kFwRecurrentToOutputWeightsTensor, kFwCellToInputWeightsTensor, + kFwCellToForgetWeightsTensor, kFwCellToOutputWeightsTensor, + kFwInputGateBiasTensor, kFwForgetGateBiasTensor, kFwCellGateBiasTensor, + kFwOutputGateBiasTensor, kFwProjectionWeightsTensor, + kFwProjectionBiasTensor); + + CheckLstmTensorDimensions( + context, node, n_input, n_output, n_cell, kBwInputToInputWeightsTensor, + kBwInputToForgetWeightsTensor, kBwInputToCellWeightsTensor, + kBwInputToOutputWeightsTensor, kBwRecurrentToInputWeightsTensor, + kBwRecurrentToForgetWeightsTensor, kBwRecurrentToCellWeightsTensor, + kBwRecurrentToOutputWeightsTensor, kBwCellToInputWeightsTensor, + kBwCellToForgetWeightsTensor, kBwCellToOutputWeightsTensor, + kBwInputGateBiasTensor, kBwForgetGateBiasTensor, kBwCellGateBiasTensor, + kBwOutputGateBiasTensor, kBwProjectionWeightsTensor, + kBwProjectionBiasTensor); + + // Check if Forward and Backward tensors match along required dimensions. + return kTfLiteOk; +} + +// Resize the output, state and scratch tensors based on the sizes of the input +// tensors. Also check that the size of the input tensors match each other. +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + // Check we have all the inputs and outputs we need. + TF_LITE_ENSURE_EQ(context, node->inputs->size, 35); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 8); + + // Inferring batch size, number of outputs and sequence length and + // number of cells from the input tensors. + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TF_LITE_ENSURE(context, input->dims->size > 1); + const int max_time = input->dims->data[0]; + const int n_batch = input->dims->data[1]; + const int n_input = input->dims->data[2]; + + TfLiteTensor* fw_input_to_output_weights = + GetInput(context, node, kFwInputToOutputWeightsTensor); + const int n_fw_cell = fw_input_to_output_weights->dims->data[0]; + TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->data[1], + n_input); + + TfLiteTensor* fw_recurrent_to_output_weights = + GetInput(context, node, kFwRecurrentToOutputWeightsTensor); + TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->data[0], + n_fw_cell); + const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1]; + + // Check that input tensor dimensions matches with each other. + CheckInputTensorDimensions(context, node, n_input, n_fw_output, n_fw_cell); + + // Get the pointer to output, state and scratch buffer tensors. + TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor); + TfLiteTensor* fw_output_state = + GetOutput(context, node, kFwOutputStateTensor); + TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor); + // TODO(ghodrat): Modify this as soon as we have a finalized method for + // scratch buffers. + TfLiteTensor* fw_scratch_buffer = + GetOutput(context, node, kFwScratchBufferTensor); + + // Resize the output and output_state tensors. + TfLiteIntArray* fw_output_size = TfLiteIntArrayCreate(3); + fw_output_size->data[0] = max_time; + fw_output_size->data[1] = n_batch; + fw_output_size->data[2] = n_fw_output; + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, fw_output, fw_output_size)); + + TfLiteIntArray* fw_output_state_size = TfLiteIntArrayCreate(2); + fw_output_state_size->data[0] = n_batch; + fw_output_state_size->data[1] = n_fw_output; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_output_state, + fw_output_state_size)); + + // Resize the scratch buffer tensor. + TfLiteIntArray* fw_cell_size = TfLiteIntArrayCreate(2); + fw_cell_size->data[0] = n_batch; + fw_cell_size->data[1] = n_fw_cell; + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, fw_cell_state, fw_cell_size)); + + // Mark state tensors as persistent tensors. + fw_output_state->allocation_type = kTfLiteArenaRwPersistent; + fw_cell_state->allocation_type = kTfLiteArenaRwPersistent; + + TfLiteTensor* fw_input_to_input_weights = + GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor); + const bool fw_use_cifg = (fw_input_to_input_weights == nullptr); + TfLiteIntArray* fw_scratch_buffer_size = TfLiteIntArrayCreate(2); + fw_scratch_buffer_size->data[0] = n_batch; + if (fw_use_cifg) { + // Reserving space for Cell, Forget, Output gates + fw_scratch_buffer_size->data[1] = n_fw_cell * 3; + } else { + // Reserving space for Input, Cell, Forget, Output gates + fw_scratch_buffer_size->data[1] = n_fw_cell * 4; + } + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_scratch_buffer, + fw_scratch_buffer_size)); + // Same for the backward cell. + TfLiteTensor* bw_input_to_output_weights = + GetInput(context, node, kBwInputToOutputWeightsTensor); + const int n_bw_cell = bw_input_to_output_weights->dims->data[0]; + TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->data[1], + n_input); + + TfLiteTensor* bw_recurrent_to_output_weights = + GetInput(context, node, kBwRecurrentToOutputWeightsTensor); + TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->data[0], + n_bw_cell); + const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1]; + + // Check that input tensor dimensions matches with each other. + CheckInputTensorDimensions(context, node, n_input, n_bw_output, n_bw_cell); + + // Get the pointer to output, state and scratch buffer tensors. + TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor); + TfLiteTensor* bw_output_state = + GetOutput(context, node, kBwOutputStateTensor); + TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor); + // TODO(ghodrat): Modify this as soon as we have a finalized method for + // scratch buffers. + TfLiteTensor* bw_scratch_buffer = + GetOutput(context, node, kBwScratchBufferTensor); + + // Resize the output and output_state tensors. + TfLiteIntArray* bw_output_size = TfLiteIntArrayCreate(3); + bw_output_size->data[0] = max_time; + bw_output_size->data[1] = n_batch; + bw_output_size->data[2] = n_bw_output; + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, bw_output, bw_output_size)); + + TfLiteIntArray* bw_output_state_size = TfLiteIntArrayCreate(2); + bw_output_state_size->data[0] = n_batch; + bw_output_state_size->data[1] = n_bw_output; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_output_state, + bw_output_state_size)); + + // Resize the scratch buffer tensor. + TfLiteIntArray* bw_cell_size = TfLiteIntArrayCreate(2); + bw_cell_size->data[0] = n_batch; + bw_cell_size->data[1] = n_bw_cell; + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, bw_cell_state, bw_cell_size)); + + // Mark state tensors as persistent tensors. + bw_output_state->allocation_type = kTfLiteArenaRwPersistent; + bw_cell_state->allocation_type = kTfLiteArenaRwPersistent; + + TfLiteTensor* bw_input_to_input_weights = + GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor); + const bool bw_use_cifg = (bw_input_to_input_weights == nullptr); + TfLiteIntArray* bw_scratch_buffer_size = TfLiteIntArrayCreate(2); + bw_scratch_buffer_size->data[0] = n_batch; + if (bw_use_cifg) { + // Reserving space for Cell, Forget, Output gates + bw_scratch_buffer_size->data[1] = n_bw_cell * 3; + } else { + // Reserving space for Input, Cell, Forget, Output gates + bw_scratch_buffer_size->data[1] = n_bw_cell * 4; + } + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_scratch_buffer, + bw_scratch_buffer_size)); + return kTfLiteOk; +} + +// The LSTM Op engine. +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + + // Input tensor. + TfLiteTensor* input = GetInput(context, node, kInputTensor); + const int max_time = input->dims->data[0]; + const int n_batch = input->dims->data[1]; + const int n_input = input->dims->data[2]; + + // Tensors for the forward cell. + TfLiteTensor* fw_input_to_input_weights = + GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor); + TfLiteTensor* fw_input_to_forget_weights = + GetInput(context, node, kFwInputToForgetWeightsTensor); + TfLiteTensor* fw_input_to_cell_weights = + GetInput(context, node, kFwInputToCellWeightsTensor); + TfLiteTensor* fw_input_to_output_weights = + GetInput(context, node, kFwInputToOutputWeightsTensor); + + TfLiteTensor* fw_recurrent_to_input_weights = + GetOptionalInputTensor(context, node, kFwRecurrentToInputWeightsTensor); + TfLiteTensor* fw_recurrent_to_forget_weights = + GetInput(context, node, kFwRecurrentToForgetWeightsTensor); + TfLiteTensor* fw_recurrent_to_cell_weights = + GetInput(context, node, kFwRecurrentToCellWeightsTensor); + TfLiteTensor* fw_recurrent_to_output_weights = + GetInput(context, node, kFwRecurrentToOutputWeightsTensor); + + TfLiteTensor* fw_cell_to_input_weights = + GetOptionalInputTensor(context, node, kFwCellToInputWeightsTensor); + TfLiteTensor* fw_cell_to_forget_weights = + GetOptionalInputTensor(context, node, kFwCellToForgetWeightsTensor); + TfLiteTensor* fw_cell_to_output_weights = + GetOptionalInputTensor(context, node, kFwCellToOutputWeightsTensor); + + TfLiteTensor* fw_input_gate_bias = + GetOptionalInputTensor(context, node, kFwInputGateBiasTensor); + TfLiteTensor* fw_forget_gate_bias = + GetInput(context, node, kFwForgetGateBiasTensor); + TfLiteTensor* fw_cell_bias = GetInput(context, node, kFwCellGateBiasTensor); + TfLiteTensor* fw_output_gate_bias = + GetInput(context, node, kFwOutputGateBiasTensor); + + TfLiteTensor* fw_projection_weights = + GetOptionalInputTensor(context, node, kFwProjectionWeightsTensor); + TfLiteTensor* fw_projection_bias = + GetOptionalInputTensor(context, node, kFwProjectionBiasTensor); + + TfLiteTensor* fw_output_state = + GetOutput(context, node, kFwOutputStateTensor); + TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor); + TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor); + + // Tensors for the backward cell. + TfLiteTensor* bw_input_to_input_weights = + GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor); + TfLiteTensor* bw_input_to_forget_weights = + GetInput(context, node, kBwInputToForgetWeightsTensor); + TfLiteTensor* bw_input_to_cell_weights = + GetInput(context, node, kBwInputToCellWeightsTensor); + TfLiteTensor* bw_input_to_output_weights = + GetInput(context, node, kBwInputToOutputWeightsTensor); + + TfLiteTensor* bw_recurrent_to_input_weights = + GetOptionalInputTensor(context, node, kBwRecurrentToInputWeightsTensor); + TfLiteTensor* bw_recurrent_to_forget_weights = + GetInput(context, node, kBwRecurrentToForgetWeightsTensor); + TfLiteTensor* bw_recurrent_to_cell_weights = + GetInput(context, node, kBwRecurrentToCellWeightsTensor); + TfLiteTensor* bw_recurrent_to_output_weights = + GetInput(context, node, kBwRecurrentToOutputWeightsTensor); + + TfLiteTensor* bw_cell_to_input_weights = + GetOptionalInputTensor(context, node, kBwCellToInputWeightsTensor); + TfLiteTensor* bw_cell_to_forget_weights = + GetOptionalInputTensor(context, node, kBwCellToForgetWeightsTensor); + TfLiteTensor* bw_cell_to_output_weights = + GetOptionalInputTensor(context, node, kBwCellToOutputWeightsTensor); + + TfLiteTensor* bw_input_gate_bias = + GetOptionalInputTensor(context, node, kBwInputGateBiasTensor); + TfLiteTensor* bw_forget_gate_bias = + GetInput(context, node, kBwForgetGateBiasTensor); + TfLiteTensor* bw_cell_bias = GetInput(context, node, kBwCellGateBiasTensor); + TfLiteTensor* bw_output_gate_bias = + GetInput(context, node, kBwOutputGateBiasTensor); + + TfLiteTensor* bw_projection_weights = + GetOptionalInputTensor(context, node, kBwProjectionWeightsTensor); + TfLiteTensor* bw_projection_bias = + GetOptionalInputTensor(context, node, kBwProjectionBiasTensor); + + TfLiteTensor* bw_output_state = + GetOutput(context, node, kBwOutputStateTensor); + TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor); + TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor); + + // n_cell and n_output will be the same size when there is no projection. + const int n_fw_cell = fw_input_to_output_weights->dims->data[0]; + const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool fw_use_cifg = (fw_input_to_input_weights == nullptr); + const bool fw_use_peephole = (fw_cell_to_output_weights != nullptr); + + // Index the scratch buffers pointers to the global scratch buffer. + TfLiteTensor* fw_scratch_buffer = + GetOutput(context, node, kFwScratchBufferTensor); + float* fw_input_gate_scratch = nullptr; + float* fw_cell_scratch = nullptr; + float* fw_forget_gate_scratch = nullptr; + float* fw_output_gate_scratch = nullptr; + if (fw_use_cifg) { + fw_cell_scratch = fw_scratch_buffer->data.f; + fw_forget_gate_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch; + fw_output_gate_scratch = + fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch; + } else { + fw_input_gate_scratch = fw_scratch_buffer->data.f; + fw_cell_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch; + fw_forget_gate_scratch = + fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch; + fw_output_gate_scratch = + fw_scratch_buffer->data.f + 3 * n_fw_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + const float* fw_input_to_input_weights_ptr = + (fw_use_cifg) ? nullptr : fw_input_to_input_weights->data.f; + const float* fw_recurrent_to_input_weights_ptr = + (fw_use_cifg) ? nullptr : fw_recurrent_to_input_weights->data.f; + const float* fw_input_gate_bias_ptr = + (fw_use_cifg) ? nullptr : fw_input_gate_bias->data.f; + const float* fw_cell_to_input_weights_ptr = + (fw_use_peephole && !fw_use_cifg) ? fw_cell_to_input_weights->data.f + : nullptr; + const float* fw_cell_to_forget_weights_ptr = + (fw_use_peephole) ? fw_cell_to_forget_weights->data.f : nullptr; + const float* fw_cell_to_output_weights_ptr = + (fw_use_peephole) ? fw_cell_to_output_weights->data.f : nullptr; + const float* fw_projection_weights_ptr = (fw_projection_weights == nullptr) + ? nullptr + : fw_projection_weights->data.f; + const float* fw_projection_bias_ptr = + (fw_projection_bias == nullptr) ? nullptr : fw_projection_bias->data.f; + + // Loop through the sequence. + for (int t = 0; t < max_time; t++) { + const float* input_ptr_batch = input->data.f + t * n_batch * n_input; + float* output_ptr_time = fw_output->data.f + t * n_batch * n_fw_output; + + kernel_utils::LstmStep( + input_ptr_batch, fw_input_to_input_weights_ptr, + fw_input_to_forget_weights->data.f, fw_input_to_cell_weights->data.f, + fw_input_to_output_weights->data.f, fw_recurrent_to_input_weights_ptr, + fw_recurrent_to_forget_weights->data.f, + fw_recurrent_to_cell_weights->data.f, + fw_recurrent_to_output_weights->data.f, fw_cell_to_input_weights_ptr, + fw_cell_to_forget_weights_ptr, fw_cell_to_output_weights_ptr, + fw_input_gate_bias_ptr, fw_forget_gate_bias->data.f, + fw_cell_bias->data.f, fw_output_gate_bias->data.f, + fw_projection_weights_ptr, fw_projection_bias_ptr, params, n_batch, + n_fw_cell, n_input, n_fw_output, fw_output_state->data.f, + fw_cell_state->data.f, fw_input_gate_scratch, fw_forget_gate_scratch, + fw_cell_scratch, fw_output_gate_scratch, output_ptr_time); + } + + // n_cell and n_output will be the same size when there is no projection. + const int n_bw_cell = bw_input_to_output_weights->dims->data[0]; + const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool bw_use_cifg = (bw_input_to_input_weights == nullptr); + const bool bw_use_peephole = (bw_cell_to_output_weights != nullptr); + + // Index the scratch buffers pointers to the global scratch buffer. + TfLiteTensor* bw_scratch_buffer = + GetOutput(context, node, kBwScratchBufferTensor); + float* bw_input_gate_scratch = nullptr; + float* bw_cell_scratch = nullptr; + float* bw_forget_gate_scratch = nullptr; + float* bw_output_gate_scratch = nullptr; + if (bw_use_cifg) { + bw_cell_scratch = bw_scratch_buffer->data.f; + bw_forget_gate_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch; + bw_output_gate_scratch = + bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch; + } else { + bw_input_gate_scratch = bw_scratch_buffer->data.f; + bw_cell_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch; + bw_forget_gate_scratch = + bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch; + bw_output_gate_scratch = + bw_scratch_buffer->data.f + 3 * n_bw_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + const float* bw_input_to_input_weights_ptr = + (bw_use_cifg) ? nullptr : bw_input_to_input_weights->data.f; + const float* bw_recurrent_to_input_weights_ptr = + (bw_use_cifg) ? nullptr : bw_recurrent_to_input_weights->data.f; + const float* bw_input_gate_bias_ptr = + (bw_use_cifg) ? nullptr : bw_input_gate_bias->data.f; + const float* bw_cell_to_input_weights_ptr = + (bw_use_peephole && !bw_use_cifg) ? bw_cell_to_input_weights->data.f + : nullptr; + const float* bw_cell_to_forget_weights_ptr = + (bw_use_peephole) ? bw_cell_to_forget_weights->data.f : nullptr; + const float* bw_cell_to_output_weights_ptr = + (bw_use_peephole) ? bw_cell_to_output_weights->data.f : nullptr; + const float* bw_projection_weights_ptr = (bw_projection_weights == nullptr) + ? nullptr + : bw_projection_weights->data.f; + const float* bw_projection_bias_ptr = + (bw_projection_bias == nullptr) ? nullptr : bw_projection_bias->data.f; + + // Loop through the sequence backwards. + for (int t = max_time - 1; t >= 0; t--) { + const float* input_ptr_batch = input->data.f + t * n_batch * n_input; + float* output_ptr_time = bw_output->data.f + t * n_batch * n_bw_output; + + kernel_utils::LstmStep( + input_ptr_batch, bw_input_to_input_weights_ptr, + bw_input_to_forget_weights->data.f, bw_input_to_cell_weights->data.f, + bw_input_to_output_weights->data.f, bw_recurrent_to_input_weights_ptr, + bw_recurrent_to_forget_weights->data.f, + bw_recurrent_to_cell_weights->data.f, + bw_recurrent_to_output_weights->data.f, bw_cell_to_input_weights_ptr, + bw_cell_to_forget_weights_ptr, bw_cell_to_output_weights_ptr, + bw_input_gate_bias_ptr, bw_forget_gate_bias->data.f, + bw_cell_bias->data.f, bw_output_gate_bias->data.f, + bw_projection_weights_ptr, bw_projection_bias_ptr, params, n_batch, + n_bw_cell, n_input, n_bw_output, bw_output_state->data.f, + bw_cell_state->data.f, bw_input_gate_scratch, bw_forget_gate_scratch, + bw_cell_scratch, bw_output_gate_scratch, output_ptr_time); + } + + // Backward step. + return kTfLiteOk; +} + +} // namespace bidirectional_sequence_lstm + +TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM() { + static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, + bidirectional_sequence_lstm::Prepare, + bidirectional_sequence_lstm::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..cca857bac0633ded01d40273d2e9e8dde488d61e --- /dev/null +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc @@ -0,0 +1,1411 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// Unit test for TFLite Bidirectional LSTM op. + +#include +#include +#include + +#include +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class BidirectionalLSTMOpModel : public SingleOpModel { + public: + BidirectionalLSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, + int sequence_length, bool use_cifg, + bool use_peephole, bool use_projection_weights, + bool use_projection_bias, float cell_clip, + float proj_clip, + const std::vector>& input_shapes) + : n_batch_(n_batch), + n_input_(n_input), + n_fw_cell_(n_cell), + n_bw_cell_(n_cell), + n_fw_output_(n_output), + n_bw_output_(n_output), + sequence_length_(sequence_length) { + input_ = AddInput(TensorType_FLOAT32); + + if (use_cifg) { + fw_input_to_input_weights_ = AddNullInput(); + } else { + fw_input_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + + fw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32); + fw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32); + fw_input_to_output_weights_ = AddInput(TensorType_FLOAT32); + + if (use_cifg) { + fw_recurrent_to_input_weights_ = AddNullInput(); + } else { + fw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + + fw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32); + fw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32); + fw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32); + + if (use_peephole) { + if (use_cifg) { + fw_cell_to_input_weights_ = AddNullInput(); + } else { + fw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + fw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32); + fw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32); + } else { + fw_cell_to_input_weights_ = AddNullInput(); + fw_cell_to_forget_weights_ = AddNullInput(); + fw_cell_to_output_weights_ = AddNullInput(); + } + + if (use_cifg) { + fw_input_gate_bias_ = AddNullInput(); + } else { + fw_input_gate_bias_ = AddInput(TensorType_FLOAT32); + } + fw_forget_gate_bias_ = AddInput(TensorType_FLOAT32); + fw_cell_bias_ = AddInput(TensorType_FLOAT32); + fw_output_gate_bias_ = AddInput(TensorType_FLOAT32); + + if (use_projection_weights) { + fw_projection_weights_ = AddInput(TensorType_FLOAT32); + if (use_projection_bias) { + fw_projection_bias_ = AddInput(TensorType_FLOAT32); + } else { + fw_projection_bias_ = AddNullInput(); + } + } else { + fw_projection_weights_ = AddNullInput(); + fw_projection_bias_ = AddNullInput(); + } + + fw_scratch_buffer_ = AddOutput(TensorType_FLOAT32); + // TODO(ghodrat): Modify these states when we have a permanent solution for + // persistent buffer. + fw_output_state_ = AddOutput(TensorType_FLOAT32); + fw_cell_state_ = AddOutput(TensorType_FLOAT32); + fw_output_ = AddOutput(TensorType_FLOAT32); + + if (use_cifg) { + bw_input_to_input_weights_ = AddNullInput(); + } else { + bw_input_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + + bw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32); + bw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32); + bw_input_to_output_weights_ = AddInput(TensorType_FLOAT32); + + if (use_cifg) { + bw_recurrent_to_input_weights_ = AddNullInput(); + } else { + bw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + + bw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32); + bw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32); + bw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32); + + if (use_peephole) { + if (use_cifg) { + bw_cell_to_input_weights_ = AddNullInput(); + } else { + bw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32); + } + bw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32); + bw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32); + } else { + bw_cell_to_input_weights_ = AddNullInput(); + bw_cell_to_forget_weights_ = AddNullInput(); + bw_cell_to_output_weights_ = AddNullInput(); + } + + if (use_cifg) { + bw_input_gate_bias_ = AddNullInput(); + } else { + bw_input_gate_bias_ = AddInput(TensorType_FLOAT32); + } + bw_forget_gate_bias_ = AddInput(TensorType_FLOAT32); + bw_cell_bias_ = AddInput(TensorType_FLOAT32); + bw_output_gate_bias_ = AddInput(TensorType_FLOAT32); + + if (use_projection_weights) { + bw_projection_weights_ = AddInput(TensorType_FLOAT32); + if (use_projection_bias) { + bw_projection_bias_ = AddInput(TensorType_FLOAT32); + } else { + bw_projection_bias_ = AddNullInput(); + } + } else { + bw_projection_weights_ = AddNullInput(); + bw_projection_bias_ = AddNullInput(); + } + + bw_scratch_buffer_ = AddOutput(TensorType_FLOAT32); + // TODO(ghodrat): Modify these states when we have a permanent solution for + // persistent buffer. + bw_output_state_ = AddOutput(TensorType_FLOAT32); + bw_cell_state_ = AddOutput(TensorType_FLOAT32); + bw_output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOptions_LSTMOptions, + CreateLSTMOptions(builder_, ActivationFunctionType_TANH, + cell_clip, proj_clip) + .Union()); + BuildInterpreter(input_shapes); + } + + // Set weights in forward and backward cells to be the same. + void SetInputToInputWeights(std::initializer_list f) { + PopulateTensor(fw_input_to_input_weights_, f); + PopulateTensor(bw_input_to_input_weights_, f); + } + + void SetInputToForgetWeights(std::initializer_list f) { + PopulateTensor(fw_input_to_forget_weights_, f); + PopulateTensor(bw_input_to_forget_weights_, f); + } + + void SetInputToCellWeights(std::initializer_list f) { + PopulateTensor(fw_input_to_cell_weights_, f); + PopulateTensor(bw_input_to_cell_weights_, f); + } + + void SetInputToOutputWeights(std::initializer_list f) { + PopulateTensor(fw_input_to_output_weights_, f); + PopulateTensor(bw_input_to_output_weights_, f); + } + + void SetRecurrentToInputWeights(std::initializer_list f) { + PopulateTensor(fw_recurrent_to_input_weights_, f); + PopulateTensor(bw_recurrent_to_input_weights_, f); + } + + void SetRecurrentToForgetWeights(std::initializer_list f) { + PopulateTensor(fw_recurrent_to_forget_weights_, f); + PopulateTensor(bw_recurrent_to_forget_weights_, f); + } + + void SetRecurrentToCellWeights(std::initializer_list f) { + PopulateTensor(fw_recurrent_to_cell_weights_, f); + PopulateTensor(bw_recurrent_to_cell_weights_, f); + } + + void SetRecurrentToOutputWeights(std::initializer_list f) { + PopulateTensor(fw_recurrent_to_output_weights_, f); + PopulateTensor(bw_recurrent_to_output_weights_, f); + } + + void SetCellToInputWeights(std::initializer_list f) { + PopulateTensor(fw_cell_to_input_weights_, f); + PopulateTensor(bw_cell_to_input_weights_, f); + } + + void SetCellToForgetWeights(std::initializer_list f) { + PopulateTensor(fw_cell_to_forget_weights_, f); + PopulateTensor(bw_cell_to_forget_weights_, f); + } + + void SetCellToOutputWeights(std::initializer_list f) { + PopulateTensor(fw_cell_to_output_weights_, f); + PopulateTensor(bw_cell_to_output_weights_, f); + } + + void SetInputGateBias(std::initializer_list f) { + PopulateTensor(fw_input_gate_bias_, f); + PopulateTensor(bw_input_gate_bias_, f); + } + + void SetForgetGateBias(std::initializer_list f) { + PopulateTensor(fw_forget_gate_bias_, f); + PopulateTensor(bw_forget_gate_bias_, f); + } + + void SetCellBias(std::initializer_list f) { + PopulateTensor(fw_cell_bias_, f); + PopulateTensor(bw_cell_bias_, f); + } + + void SetOutputGateBias(std::initializer_list f) { + PopulateTensor(fw_output_gate_bias_, f); + PopulateTensor(bw_output_gate_bias_, f); + } + + void SetProjectionWeights(std::initializer_list f) { + PopulateTensor(fw_projection_weights_, f); + PopulateTensor(bw_projection_weights_, f); + } + + void SetProjectionBias(std::initializer_list f) { + PopulateTensor(fw_projection_bias_, f); + PopulateTensor(bw_projection_bias_, f); + } + + void ResetFwOutputAndCellStates() { + const int zero_buffer_size = n_fw_cell_ * n_batch_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(fw_output_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + PopulateTensor(fw_cell_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + void ResetBwOutputAndCellStates() { + const int zero_buffer_size = n_bw_cell_ * n_batch_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(bw_output_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + PopulateTensor(bw_cell_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetFwOutput() { return ExtractVector(fw_output_); } + std::vector GetBwOutput() { return ExtractVector(bw_output_); } + + int num_inputs() { return n_input_; } + int num_fw_outputs() { return n_fw_output_; } + int num_bw_outputs() { return n_bw_output_; } + int num_fw_cells() { return n_fw_cell_; } + int num_bw_cells() { return n_bw_cell_; } + int num_batches() { return n_batch_; } + int sequence_length() { return sequence_length_; } + + private: + int input_; + int fw_input_to_input_weights_; + int fw_input_to_forget_weights_; + int fw_input_to_cell_weights_; + int fw_input_to_output_weights_; + + int fw_recurrent_to_input_weights_; + int fw_recurrent_to_forget_weights_; + int fw_recurrent_to_cell_weights_; + int fw_recurrent_to_output_weights_; + + int fw_cell_to_input_weights_; + int fw_cell_to_forget_weights_; + int fw_cell_to_output_weights_; + + int fw_input_gate_bias_; + int fw_forget_gate_bias_; + int fw_cell_bias_; + int fw_output_gate_bias_; + + int fw_projection_weights_; + int fw_projection_bias_; + + int bw_input_to_input_weights_; + int bw_input_to_forget_weights_; + int bw_input_to_cell_weights_; + int bw_input_to_output_weights_; + + int bw_recurrent_to_input_weights_; + int bw_recurrent_to_forget_weights_; + int bw_recurrent_to_cell_weights_; + int bw_recurrent_to_output_weights_; + + int bw_cell_to_input_weights_; + int bw_cell_to_forget_weights_; + int bw_cell_to_output_weights_; + + int bw_input_gate_bias_; + int bw_forget_gate_bias_; + int bw_cell_bias_; + int bw_output_gate_bias_; + + int bw_projection_weights_; + int bw_projection_bias_; + + int fw_output_; + int fw_output_state_; + int fw_cell_state_; + int fw_scratch_buffer_; + + int bw_output_; + int bw_output_state_; + int bw_cell_state_; + int bw_scratch_buffer_; + + int n_batch_; + int n_input_; + int n_fw_cell_; + int n_bw_cell_; + int n_fw_output_; + int n_bw_output_; + int sequence_length_; +}; + +TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + const int sequence_length = 3; + + BidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false, + /*use_peephole=*/false, /*use_projection_weights=*/false, + /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor + + // Forward cell + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {0}, // cell_to_forget_weight tensor + {0}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + + // Backward cell + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {0}, // cell_to_forget_weight tensor + {0}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589, + -0.34550029, 0.04266912, -0.15680569, + -0.34856534, 0.43890524}); + + lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163, + -0.20583314, 0.44344562, 0.22077113, + -0.29909778}); + + lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935, + -0.31343272, -0.40032279, 0.44781327, + 0.01387155, -0.35593212}); + + lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829, + 0.40525138, 0.44272184, 0.03897077, -0.1556896, + 0.19487578}); + + lstm.SetInputGateBias({0., 0., 0., 0.}); + + lstm.SetCellBias({0., 0., 0., 0.}); + + lstm.SetForgetGateBias({1., 1., 1., 1.}); + + lstm.SetOutputGateBias({0., 0., 0., 0.}); + + lstm.SetRecurrentToInputWeights( + {-0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, + -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, + -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296}); + + lstm.SetRecurrentToCellWeights( + {-0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, + -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064}); + + lstm.SetRecurrentToForgetWeights( + {-0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, + -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004}); + + lstm.SetRecurrentToOutputWeights( + {0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, + 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136}); + + // Input should have n_input * sequence_length many values. + static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; + static float lstm_fw_golden_output[] = { + -0.02973187, 0.1229473, 0.20885126, -0.15358765, + -0.03716109, 0.12507336, 0.41193449, -0.20860538, + -0.15053082, 0.09120187, 0.24278517, -0.12222792}; + static float lstm_bw_golden_output[] = { + -0.0806187, 0.139077, 0.400476, -0.197842, + -0.0332076, 0.123838, 0.309777, -0.17621, + -0.0490733, 0.0739237, 0.067706, -0.0208124}; + + // Resetting cell_state and output_state + lstm.ResetFwOutputAndCellStates(); + lstm.ResetBwOutputAndCellStates(); + + float* batch0_start = lstm_input; + float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); + + lstm.SetInput(0, batch0_start, batch0_end); + + lstm.Invoke(); + + float* fw_golden_start = lstm_fw_golden_output; + float* fw_golden_end = + fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length(); + std::vector fw_expected; + fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end); + EXPECT_THAT(lstm.GetFwOutput(), + ElementsAreArray(ArrayFloatNear(fw_expected))); + + float* bw_golden_start = lstm_bw_golden_output; + float* bw_golden_end = + bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length(); + std::vector bw_expected; + bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end); + EXPECT_THAT(lstm.GetBwOutput(), + ElementsAreArray(ArrayFloatNear(bw_expected))); + + // Check reversed inputs. + static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.}; + + // Resetting cell_state and output_state + lstm.ResetFwOutputAndCellStates(); + lstm.ResetBwOutputAndCellStates(); + + batch0_start = lstm_input_reversed; + batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); + + lstm.SetInput(0, batch0_start, batch0_end); + + lstm.Invoke(); + + fw_expected.clear(); + for (int s = 0; s < lstm.sequence_length(); s++) { + fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs(); + fw_golden_end = fw_golden_start + lstm.num_fw_outputs(); + fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end); + } + EXPECT_THAT(lstm.GetBwOutput(), + ElementsAreArray(ArrayFloatNear(fw_expected))); + + bw_expected.clear(); + for (int s = 0; s < lstm.sequence_length(); s++) { + bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs(); + bw_golden_end = bw_golden_start + lstm.num_bw_outputs(); + bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end); + } + EXPECT_THAT(lstm.GetFwOutput(), + ElementsAreArray(ArrayFloatNear(bw_expected))); +} + +TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + const int sequence_length = 3; + + BidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/true, + /*use_peephole=*/true, /*use_projection_weights=*/false, + /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor + + {0, 0}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {0, 0}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {0}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + + {0, 0}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {0, 0}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {0}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, + 0.04717243, 0.48944736, -0.38535351, + -0.17212132}); + + lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988, + -0.3633365, -0.22755712, 0.28253698, 0.24407166, + 0.33826375}); + + lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593, + -0.09426838, -0.44257352, 0.54939759, + 0.01533556, 0.42751634}); + + lstm.SetCellBias({0., 0., 0., 0.}); + + lstm.SetForgetGateBias({1., 1., 1., 1.}); + + lstm.SetOutputGateBias({0., 0., 0., 0.}); + + lstm.SetRecurrentToCellWeights( + {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711, + 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004, + 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288, + 0.21193194}); + + lstm.SetRecurrentToForgetWeights( + {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827, + 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795, + -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349}); + + lstm.SetRecurrentToOutputWeights( + {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908, + -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835, + 0.50248802, 0.26114327, -0.43736315, 0.33149987}); + + lstm.SetCellToForgetWeights( + {0.47485286, -0.51955009, -0.24458408, 0.31544167}); + lstm.SetCellToOutputWeights( + {-0.17135078, 0.82760304, 0.85573703, -0.77109635}); + + static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; + static float lstm_fw_golden_output[] = { + -0.36444446, -0.00352185, 0.12886585, -0.05163646, + -0.42312205, -0.01218222, 0.24201041, -0.08124574, + -0.358325, -0.04621704, 0.21641694, -0.06471302}; + static float lstm_bw_golden_output[] = { + -0.401685, -0.0232794, 0.288642, -0.123074, -0.42915, -0.00871577, + 0.20912, -0.103567, -0.166398, -0.00486649, 0.0697471, -0.0537578}; + + // Resetting cell_state and output_state + lstm.ResetFwOutputAndCellStates(); + lstm.ResetBwOutputAndCellStates(); + + float* batch0_start = lstm_input; + float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); + + lstm.SetInput(0, batch0_start, batch0_end); + + lstm.Invoke(); + + float* fw_golden_start = lstm_fw_golden_output; + float* fw_golden_end = + fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length(); + std::vector fw_expected; + fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end); + EXPECT_THAT(lstm.GetFwOutput(), + ElementsAreArray(ArrayFloatNear(fw_expected))); + + float* bw_golden_start = lstm_bw_golden_output; + float* bw_golden_end = + bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length(); + std::vector bw_expected; + bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end); + EXPECT_THAT(lstm.GetBwOutput(), + ElementsAreArray(ArrayFloatNear(bw_expected))); + + // Check reversed inputs. + static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.}; + + // Resetting cell_state and output_state + lstm.ResetFwOutputAndCellStates(); + lstm.ResetBwOutputAndCellStates(); + + batch0_start = lstm_input_reversed; + batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); + + lstm.SetInput(0, batch0_start, batch0_end); + + lstm.Invoke(); + + fw_expected.clear(); + for (int s = 0; s < lstm.sequence_length(); s++) { + fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs(); + fw_golden_end = fw_golden_start + lstm.num_fw_outputs(); + fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end); + } + EXPECT_THAT(lstm.GetBwOutput(), + ElementsAreArray(ArrayFloatNear(fw_expected))); + + bw_expected.clear(); + for (int s = 0; s < lstm.sequence_length(); s++) { + bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs(); + bw_golden_end = bw_golden_start + lstm.num_bw_outputs(); + bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end); + } + EXPECT_THAT(lstm.GetFwOutput(), + ElementsAreArray(ArrayFloatNear(bw_expected))); +} + +TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 20; + const int n_output = 16; + const int sequence_length = 4; + + BidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false, + /*use_peephole=*/true, /*use_projection_weights=*/true, + /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {n_cell}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {n_output, n_cell}, // projection_weight tensor + {0}, // projection_bias tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {n_cell}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {n_output, n_cell}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights( + {0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, + 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, + -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, + -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, + -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, + -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, + -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, + 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, + 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, + 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, + -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, + 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, + -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, + -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, + -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, + 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, + -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, + -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, + -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, + -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}); + + lstm.SetInputToForgetWeights( + {-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236, + -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505, + -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495, + 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323, + 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421, + -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887, + -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791, + 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059, + 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068, + 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905, + 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605, + -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464, + 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506, + -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063, + -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375, + 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553, + 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353, + 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717, + -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371, + 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}); + + lstm.SetInputToCellWeights( + {-0.04580283, -0.09549462, -0.032418985, -0.06454633, + -0.043528453, 0.043018587, -0.049152344, -0.12418144, + -0.078985475, -0.07596889, 0.019484362, -0.11434962, + -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, + -0.025034338, -0.0028890965, 0.048929527, 0.06235075, + 0.10665918, -0.032036792, -0.08505916, -0.10843358, + -0.13002433, -0.036816437, -0.02130134, -0.016518239, + 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, + -0.10652836, -0.1037554, -0.13056071, -0.03266643, + -0.033702414, -0.006473424, -0.04611692, 0.014419339, + -0.025174323, 0.0396852, 0.081777506, 0.06157468, + 0.10210095, -0.009658194, 0.046511717, 0.03603906, + 0.0069369148, 0.015960095, -0.06507666, 0.09551598, + 0.053568836, 0.06408714, 0.12835667, -0.008714329, + -0.20211966, -0.12093674, 0.029450472, 0.2849013, + -0.029227901, 0.1164364, -0.08560263, 0.09941786, + -0.036999565, -0.028842626, -0.0033637602, -0.017012902, + -0.09720865, -0.11193351, -0.029155117, -0.017936034, + -0.009768936, -0.04223324, -0.036159635, 0.06505112, + -0.021742892, -0.023377212, -0.07221364, -0.06430552, + 0.05453865, 0.091149814, 0.06387331, 0.007518393, + 0.055960953, 0.069779344, 0.046411168, 0.10509911, + 0.07463894, 0.0075130584, 0.012850982, 0.04555431, + 0.056955688, 0.06555285, 0.050801456, -0.009862683, + 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}); + + lstm.SetInputToOutputWeights( + {-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, + -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, + 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, + -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, + -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, + 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, + -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, + -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, + -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, + -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, + 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, + 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, + 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, + -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, + 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, + 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, + -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, + 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, + -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, + -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}); + + lstm.SetInputGateBias( + {0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216, + -0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339, + -0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818, + 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196}); + + lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696, + 0.11098921, 0.15378423, 0.09263801, 0.09790885, + 0.09508917, 0.061199076, 0.07665568, -0.015443159, + -0.03499149, 0.046190713, 0.08895977, 0.10899629, + 0.40694186, 0.06030037, 0.012413437, -0.06108739}); + + lstm.SetCellBias({-0.024379363, 0.0055531194, 0.23377132, 0.033463873, + -0.1483596, -0.10639995, -0.091433935, 0.058573797, + -0.06809782, -0.07889636, -0.043246906, -0.09829136, + -0.4279842, 0.034901652, 0.18797937, 0.0075234566, + 0.016178843, 0.1749513, 0.13975595, 0.92058027}); + + lstm.SetOutputGateBias( + {0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795, + 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895, + 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149, + -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877}); + + lstm.SetRecurrentToInputWeights( + {-0.001374326, -0.078856036, 0.10672688, 0.029162422, + -0.11585556, 0.02557986, -0.13446963, -0.035785314, + -0.01244275, 0.025961924, -0.02337298, -0.044228926, + -0.055839065, -0.046598054, -0.010546039, -0.06900766, + 0.027239809, 0.022582639, -0.013296484, -0.05459212, + 0.08981, -0.045407712, 0.08682226, -0.06867011, + -0.14390695, -0.02916037, 0.000996957, 0.091420636, + 0.14283475, -0.07390571, -0.06402044, 0.062524505, + -0.093129106, 0.04860203, -0.08364217, -0.08119002, + 0.009352075, 0.22920375, 0.0016303885, 0.11583097, + -0.13732095, 0.012405723, -0.07551853, 0.06343048, + 0.12162708, -0.031923793, -0.014335606, 0.01790974, + -0.10650317, -0.0724401, 0.08554849, -0.05727212, + 0.06556731, -0.042729504, -0.043227166, 0.011683251, + -0.013082158, -0.029302018, -0.010899579, -0.062036745, + -0.022509435, -0.00964907, -0.01567329, 0.04260106, + -0.07787477, -0.11576462, 0.017356863, 0.048673786, + -0.017577527, -0.05527947, -0.082487635, -0.040137455, + -0.10820036, -0.04666372, 0.022746278, -0.07851417, + 0.01068115, 0.032956902, 0.022433773, 0.0026891115, + 0.08944216, -0.0685835, 0.010513544, 0.07228705, + 0.02032331, -0.059686817, -0.0005566496, -0.086984694, + 0.040414046, -0.1380399, 0.094208956, -0.05722982, + 0.012092817, -0.04989123, -0.086576, -0.003399834, + -0.04696032, -0.045747425, 0.10091314, 0.048676282, + -0.029037097, 0.031399418, -0.0040285117, 0.047237843, + 0.09504992, 0.041799378, -0.049185462, -0.031518843, + -0.10516937, 0.026374253, 0.10058866, -0.0033195973, + -0.041975245, 0.0073591834, 0.0033782164, -0.004325073, + -0.10167381, 0.042500053, -0.01447153, 0.06464186, + -0.017142897, 0.03312627, 0.009205989, 0.024138335, + -0.011337001, 0.035530265, -0.010912711, 0.0706555, + -0.005894094, 0.051841937, -0.1401738, -0.02351249, + 0.0365468, 0.07590991, 0.08838724, 0.021681072, + -0.10086113, 0.019608743, -0.06195883, 0.077335775, + 0.023646897, -0.095322326, 0.02233014, 0.09756986, + -0.048691444, -0.009579111, 0.07595467, 0.11480546, + -0.09801813, 0.019894179, 0.08502348, 0.004032281, + 0.037211012, 0.068537936, -0.048005626, -0.091520436, + -0.028379958, -0.01556313, 0.06554592, -0.045599163, + -0.01672207, -0.020169014, -0.011877351, -0.20212261, + 0.010889619, 0.0047078193, 0.038385306, 0.08540671, + -0.017140968, -0.0035865551, 0.016678626, 0.005633034, + 0.015963363, 0.00871737, 0.060130805, 0.028611384, + 0.10109069, -0.015060172, -0.07894427, 0.06401885, + 0.011584063, -0.024466386, 0.0047652307, -0.09041358, + 0.030737216, -0.0046374933, 0.14215417, -0.11823516, + 0.019899689, 0.006106124, -0.027092824, 0.0786356, + 0.05052217, -0.058925, -0.011402121, -0.024987547, + -0.0013661642, -0.06832946, -0.015667673, -0.1083353, + -0.00096863037, -0.06988685, -0.053350925, -0.027275559, + -0.033664223, -0.07978348, -0.025200296, -0.017207067, + -0.058403496, -0.055697463, 0.005798788, 0.12965427, + -0.062582195, 0.0013350133, -0.10482091, 0.0379771, + 0.072521195, -0.0029455067, -0.13797039, -0.03628521, + 0.013806405, -0.017858358, -0.01008298, -0.07700066, + -0.017081132, 0.019358726, 0.0027079724, 0.004635139, + 0.062634714, -0.02338735, -0.039547626, -0.02050681, + 0.03385117, -0.083611414, 0.002862572, -0.09421313, + 0.058618143, -0.08598433, 0.00972939, 0.023867095, + -0.053934585, -0.023203006, 0.07452513, -0.048767887, + -0.07314807, -0.056307215, -0.10433547, -0.06440842, + 0.04328182, 0.04389765, -0.020006588, -0.09076438, + -0.11652589, -0.021705797, 0.03345259, -0.010329105, + -0.025767034, 0.013057034, -0.07316461, -0.10145612, + 0.06358255, 0.18531723, 0.07759293, 0.12006465, + 0.1305557, 0.058638252, -0.03393652, 0.09622831, + -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845, + -0.005644518, 0.06857898, -0.12598175, -0.035084512, + 0.03156317, -0.12794146, -0.031963028, 0.04692781, + 0.030070418, 0.0071660685, -0.095516115, -0.004643372, + 0.040170413, -0.062104587, -0.0037324072, 0.0554317, + 0.08184801, -0.019164372, 0.06791302, 0.034257166, + -0.10307039, 0.021943003, 0.046745934, 0.0790918, + -0.0265588, -0.007824208, 0.042546265, -0.00977924, + -0.0002440307, -0.017384544, -0.017990116, 0.12252321, + -0.014512694, -0.08251313, 0.08861942, 0.13589665, + 0.026351685, 0.012641483, 0.07466548, 0.044301085, + -0.045414884, -0.051112458, 0.03444247, -0.08502782, + -0.04106223, -0.028126027, 0.028473156, 0.10467447}); + + lstm.SetRecurrentToForgetWeights( + {-0.057784554, -0.026057621, -0.068447545, -0.022581743, + 0.14811787, 0.10826372, 0.09471067, 0.03987225, + -0.0039523416, 0.00030638507, 0.053185795, 0.10572994, + 0.08414449, -0.022036452, -0.00066928595, -0.09203576, + 0.032950465, -0.10985798, -0.023809856, 0.0021431844, + -0.02196096, -0.00326074, 0.00058621005, -0.074678116, + -0.06193199, 0.055729095, 0.03736828, 0.020123724, + 0.061878487, -0.04729229, 0.034919553, -0.07585433, + -0.04421272, -0.044019096, 0.085488975, 0.04058006, + -0.06890133, -0.030951202, -0.024628663, -0.07672815, + 0.034293607, 0.08556707, -0.05293577, -0.033561368, + -0.04899627, 0.0241671, 0.015736353, -0.095442444, + -0.029564252, 0.016493602, -0.035026584, 0.022337519, + -0.026871363, 0.004780428, 0.0077918363, -0.03601621, + 0.016435321, -0.03263031, -0.09543275, -0.047392778, + 0.013454138, 0.028934088, 0.01685226, -0.086110644, + -0.046250615, -0.01847454, 0.047608484, 0.07339695, + 0.034546845, -0.04881143, 0.009128804, -0.08802852, + 0.03761666, 0.008096139, -0.014454086, 0.014361001, + -0.023502491, -0.0011840804, -0.07607001, 0.001856849, + -0.06509276, -0.006021153, -0.08570962, -0.1451793, + 0.060212336, 0.055259194, 0.06974018, 0.049454916, + -0.027794661, -0.08077226, -0.016179763, 0.1169753, + 0.17213494, -0.0056326236, -0.053934924, -0.0124349, + -0.11520337, 0.05409887, 0.088759385, 0.0019655675, + 0.0042065294, 0.03881498, 0.019844765, 0.041858196, + -0.05695512, 0.047233116, 0.038937137, -0.06542224, + 0.014429736, -0.09719407, 0.13908425, -0.05379757, + 0.012321099, 0.082840554, -0.029899208, 0.044217527, + 0.059855383, 0.07711018, -0.045319796, 0.0948846, + -0.011724666, -0.0033288454, -0.033542685, -0.04764985, + -0.13873616, 0.040668588, 0.034832682, -0.015319203, + -0.018715994, 0.046002675, 0.0599172, -0.043107376, + 0.0294216, -0.002314414, -0.022424703, 0.0030315618, + 0.0014641669, 0.0029166266, -0.11878115, 0.013738511, + 0.12375372, -0.0006038222, 0.029104086, 0.087442465, + 0.052958444, 0.07558703, 0.04817258, 0.044462286, + -0.015213451, -0.08783778, -0.0561384, -0.003008196, + 0.047060397, -0.002058388, 0.03429439, -0.018839769, + 0.024734668, 0.024614193, -0.042046934, 0.09597743, + -0.0043254104, 0.04320769, 0.0064070094, -0.0019131786, + -0.02558259, -0.022822596, -0.023273505, -0.02464396, + -0.10991725, -0.006240552, 0.0074488563, 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-0.08317623, 0.08594209, + 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, + 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, + 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, + 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, + 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, + -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, + -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, + 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, + -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, + -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, + 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, + 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, + 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, + -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, + -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, + -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, + 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, + -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, + 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, + 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, + -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, + -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, + -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, + 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, + -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, + -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, + -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, + 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, + 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, + 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656}); + + static float lstm_input[][20] = { + {// Batch0: 4 (input_sequence_size) * 5 (n_input) + 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386, + 0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199, + 0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, + + {// Batch1: 4 (input_sequence_size) * 5 (n_input) + 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260, + 0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485, + 0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}}; + + static float lstm_fw_golden_output[][64] = { + {// Batch0: 4 (input_sequence_size) * 16 (n_output) + -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, + -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, + -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, + 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, + -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, + -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, + 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, + 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, + 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, + 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, + -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, + -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, + 0.0286833, 0.00824207, 0.0264887, 0.0305169}, + {// Batch1: 4 (input_sequence_size) * 16 (n_output) + -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, + -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, + 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, + 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, + -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, + -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, + 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, + 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, + 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, + 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, + -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, + -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, + 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; + + static float lstm_combined_golden_output[][64] = { + { + -0.022014, 0.073544, -0.002235, 0.040068, -0.037136, -0.052788, + 0.075325, -0.029378, 0.024298, -0.07733 , -0.030674, -0.060229, + 0.040599, 0.011608, 0.042005, 0.045977, -0.039225, 0.076294, + 0.000735, 0.032852, -0.069869, -0.053312, 0.073527, -0.028136, + 0.021585, -0.102679, -0.004327, -0.043304, 0.072861, 0.027077, + 0.034558, 0.068292, -0.036292, 0.069832, -0.003032, 0.053829, + -0.043821, -0.072713, 0.085029, -0.040374, 0.020014, -0.104521, + -0.034504, -0.059759, 0.062569, 0.025652, 0.049306, 0.061189, + -0.025146, 0.079643, -0.005188, 0.033080, -0.048079, -0.048082, + 0.069369, -0.028900, 0.024572, -0.077547, -0.022517, -0.054477, + 0.038857, 0.013336, 0.043234, 0.044788}, + { + -0.039186, 0.070792, -0.005913, 0.02642, -0.068274, -0.05022, + 0.061444, -0.031241, 0.014996, -0.094544, -0.004146, -0.03464, + 0.058981, 0.026097, 0.039781, 0.058408, -0.031887, 0.069252, + 0.00576, 0.054062, -0.042801, -0.059974, 0.085272, -0.034453, + 0.026097, -0.0959, -0.031164, -0.058699, 0.06839, 0.020512, + 0.044727, 0.063609, -0.039863, 0.084819, -0.003909, 0.028666, + -0.075677, -0.045125, 0.070379, -0.033895, 0.022111, -0.097184, + -0.004921, -0.040851, 0.062316, 0.017435, 0.041437, 0.064568, + -0.039656, 0.060726, -0.003402, 0.036854, -0.056503, -0.058554, + 0.068588, -0.034879, 0.01352, -0.09962, -0.01434, -0.039505, + 0.065133, 0.024321, 0.038473, 0.062438 + }}; + + // Resetting cell_state and output_state + lstm.ResetFwOutputAndCellStates(); + lstm.ResetBwOutputAndCellStates(); + + for (int i = 0; i < lstm.sequence_length(); i++) { + float* batch0_start = lstm_input[0] + i * lstm.num_inputs(); + float* batch0_end = batch0_start + lstm.num_inputs(); + + lstm.SetInput(2 * i * lstm.num_inputs(), batch0_start, batch0_end); + + float* batch1_start = lstm_input[1] + i * lstm.num_inputs(); + float* batch1_end = batch1_start + lstm.num_inputs(); + lstm.SetInput((2 * i + 1) * lstm.num_inputs(), batch1_start, batch1_end); + } + + lstm.Invoke(); + + std::vector expected; + for (int i = 0; i < lstm.sequence_length(); i++) { + float* golden_start_batch0 = + lstm_fw_golden_output[0] + i * lstm.num_fw_outputs(); + float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs(); + float* golden_start_batch1 = + lstm_fw_golden_output[1] + i * lstm.num_fw_outputs(); + float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs(); + expected.insert(expected.end(), golden_start_batch0, golden_end_batch0); + expected.insert(expected.end(), golden_start_batch1, golden_end_batch1); + } + EXPECT_THAT(lstm.GetFwOutput(), ElementsAreArray(ArrayFloatNear(expected))); + + // Check if the sum of forward backward matches the golden. + expected.clear(); + for (int i = 0; i < lstm.sequence_length(); i++) { + float* golden_start_batch0 = + lstm_combined_golden_output[0] + i * lstm.num_fw_outputs(); + float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs(); + float* golden_start_batch1 = + lstm_combined_golden_output[1] + i * lstm.num_fw_outputs(); + float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs(); + expected.insert(expected.end(), golden_start_batch0, golden_end_batch0); + expected.insert(expected.end(), golden_start_batch1, golden_end_batch1); + } + + std::vector combined; + for (int i = 0; i < lstm.GetFwOutput().size(); ++i) { + combined.push_back(lstm.GetFwOutput()[i] + lstm.GetBwOutput()[i]); + } + EXPECT_THAT(combined, ElementsAreArray(ArrayFloatNear(expected))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/cast.cc b/tensorflow/contrib/lite/kernels/cast.cc new file mode 100644 index 0000000000000000000000000000000000000000..19942de7bc0c083f192a4b337b224b778d991140 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/cast.cc @@ -0,0 +1,99 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" +#include "tensorflow/contrib/lite/string_util.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace cast { +constexpr int kInputTensor = 0; +constexpr int kOutputTensor = 0; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + return context->ResizeTensor(context, output, + TfLiteIntArrayCopy(input->dims)); +} + +template +void copyCast(const FromT* in, ToT* out, int num_elements) { + std::transform(in, in + num_elements, out, + [](FromT a) { return static_cast(a); }); +} + +template +TfLiteStatus copyToTensor(const FromT* in, TfLiteTensor* out, + int num_elements) { + switch (out->type) { + case kTfLiteInt64: + copyCast(in, out->data.i64, num_elements); + break; + case kTfLiteInt32: + copyCast(in, out->data.i32, num_elements); + break; + case kTfLiteUInt8: + copyCast(in, out->data.uint8, num_elements); + break; + case kTfLiteFloat32: + copyCast(in, out->data.f, num_elements); + break; + default: + // Unsupported type. + return kTfLiteError; + } + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + const int num_elements = NumElements(input); + TF_LITE_ENSURE_EQ(context, num_elements, NumElements(output)); + switch (input->type) { + case kTfLiteInt64: + return copyToTensor(input->data.i64, output, num_elements); + case kTfLiteInt32: + return copyToTensor(input->data.i32, output, num_elements); + case kTfLiteUInt8: + return copyToTensor(input->data.uint8, output, num_elements); + case kTfLiteFloat32: + return copyToTensor(input->data.f, output, num_elements); + default: + // Unsupported type. + return kTfLiteError; + } + return kTfLiteOk; +} +} // namespace cast + +TfLiteRegistration* Register_CAST() { + static TfLiteRegistration r = {nullptr, nullptr, cast::Prepare, cast::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/cast_test.cc b/tensorflow/contrib/lite/kernels/cast_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4e56482a371550b6275a6380e2beebe3cef958ff --- /dev/null +++ b/tensorflow/contrib/lite/kernels/cast_test.cc @@ -0,0 +1,66 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class CastOpModel : public SingleOpModel { + public: + CastOpModel(const TensorData& input, const TensorData& output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_CAST, BuiltinOptions_CastOptions, + CreateCastOptions(builder_).Union()); + BuildInterpreter({GetShape(input_)}); + } + + int input() const { return input_; } + int output() const { return output_; } + + protected: + int input_; + int output_; +}; + +TEST(CastOpModel, CastIntToFloat) { + CastOpModel m({TensorType_INT64, {2, 3}}, {TensorType_FLOAT32, {2, 3}}); + m.PopulateTensor(m.input(), {100, 200, 300, 400, 500, 600}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({100.f, 200.f, 300.f, 400.f, 500.f, 600.f})); +} + +TEST(CastOpModel, CastFloatToInt) { + CastOpModel m({TensorType_FLOAT32, {3, 2}}, {TensorType_INT32, {3, 2}}); + m.PopulateTensor(m.input(), {100.f, 20.f, 3.f, 0.4f, 0.999f, 1.1f}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({100, 20, 3, 0, 0, 1})); +} + +} // namespace +} // namespace tflite +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/concatenation.cc b/tensorflow/contrib/lite/kernels/concatenation.cc index 7ff907531805887afea407684fdbaa65e98d619a..a619ada86af64c299f8e518a7493db20f1011a50 100644 --- a/tensorflow/contrib/lite/kernels/concatenation.cc +++ b/tensorflow/contrib/lite/kernels/concatenation.cc @@ -96,38 +96,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return context->ResizeTensor(context, output, output_size); } -template -class VectorOfInputs { - public: - VectorOfInputs(const TfLiteContext& context, const TfLiteIntArray& inputs) { - int num_inputs = inputs.size; - - all_data_.reserve(num_inputs); - all_dims_.reserve(num_inputs); - all_dims_ptr_.reserve(num_inputs); - - for (int i = 0; i < num_inputs; ++i) { - TfLiteTensor* input = &context.tensors[inputs.data[i]]; - all_data_.push_back(GetTensorData(input)); - all_dims_.push_back(GetTensorDims(input)); - } - - // Taking the pointer from inside a std::vector is only OK if the vector is - // never modified, so we populate all_dims in the previous loop and then we - // are free to grab iterators here. - for (int i = 0; i < num_inputs; ++i) { - all_dims_ptr_.push_back(&all_dims_[i]); - } - } - const T* const* data() const { return all_data_.data(); } - const Dims<4>* const* dims() const { return all_dims_ptr_.data(); } - - private: - std::vector all_data_; - std::vector> all_dims_; - std::vector*> all_dims_ptr_; -}; - template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { auto* params = @@ -141,7 +109,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // TODO(ycling): Activation function parameter is ignored. For now we dont have // a model with a Concatenation with fused activation function. #define TF_LITE_CONCATENATION(type, scalar) \ - VectorOfInputs all_inputs(*context, *node->inputs); \ + VectorOfTensors all_inputs(*context, *node->inputs); \ type::Concatenation( \ RemapDim(NumDimensions(output), axis), all_inputs.data(), \ all_inputs.dims(), node->inputs->size, GetTensorData(output), \ diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index 1fba3cbbce17fa35ac647912c21dae89384c8ca3..b93a416351cae34b2df8791e382a8a2cd38dcffb 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -51,11 +51,13 @@ enum KernelType { kCblasOptimized, }; +const int kTensorNotAllocated = -1; + struct OpData { // IDs are the arbitrary identifiers used by TF Lite to identify and access // memory buffers. - int im2col_id; - int hwcn_weights_id; + int im2col_id = kTensorNotAllocated; + int hwcn_weights_id = kTensorNotAllocated; TfLitePaddingValues padding; // The scaling factor from input to output (aka the 'real multiplier') can @@ -80,8 +82,6 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { // Instead, we allocate a new object to use as scratch space for im2col, and // to carry information from Prepare() to Eval(). auto* data = new OpData; - context->AddTensors(context, 1, &data->im2col_id); - context->AddTensors(context, 1, &data->hwcn_weights_id); gemm_support::IncrementUsageCounter(context); return data; } @@ -107,10 +107,66 @@ void TransposeFloatTensor(TfLiteTensor* input, TfLiteTensor* output) { } } +// Allocate temporary tensors (`im2col`, `hwcn_weights` if necessary). +// Note: `context->AddTensors` might invalidate pointers to existing tensors. +// Therefore the logic to add tensors are isolated into this function. +static TfLiteStatus AllocateTemporaryTensorsIfRequired(TfLiteContext* context, + TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + OpData* data = reinterpret_cast(node->user_data); + + TF_LITE_ENSURE(context, node->inputs->size >= 2); + TfLiteTensor* input = &context->tensors[node->inputs->data[0]]; + TfLiteTensor* filter = &context->tensors[node->inputs->data[1]]; + + int filter_width = filter->dims->data[2]; + int filter_height = filter->dims->data[1]; + + // We don't always need to allocate im2col. It is only used in some versions + // of the optimized Conv. This test just mimics something that happens inside + // optimized_ops.h, in order to avoid a DCHECK(!im2col_data). + data->need_im2col = + (params->stride_width != 1 || params->stride_height != 1 || + filter_width != 1 || filter_height != 1); + // If we're using the optimized multithreaded EigenTensor implementation of + // convolution, it expects the filter weights to be transposed compared to + // the normal TF Lite buffer format. Typical TF Lite weights are + // [filter_count, filter_height, filter_width, input_depth], but for the float + // implementation we need them as [filter_height, filter_width, input_depth, + // filter_count]. We get to that format by transposing, and create a temporary + // buffer to store the results. + // This path is only used for float processing, so only create the buffer if + // we're running with that data type. + data->need_hwcn_weights = (input->type == kTfLiteFloat32); + + int temporaries_count = 0; + if (data->need_im2col) { + data->im2col_index = temporaries_count; + if (data->im2col_id == kTensorNotAllocated) { + context->AddTensors(context, 1, &data->im2col_id); + } + ++temporaries_count; + } + if (data->need_hwcn_weights) { + data->hwcn_weights_index = temporaries_count; + if (data->hwcn_weights_id == kTensorNotAllocated) { + context->AddTensors(context, 1, &data->hwcn_weights_id); + } + ++temporaries_count; + } + + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(temporaries_count); + + return kTfLiteOk; +} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { auto* params = reinterpret_cast(node->builtin_data); OpData* data = reinterpret_cast(node->user_data); + TF_LITE_ENSURE_STATUS(AllocateTemporaryTensorsIfRequired(context, node)); + bool hasBias = node->inputs->size == 3; // Check number of inputs/outputs TF_LITE_ENSURE(context, hasBias || node->inputs->size == 2); @@ -118,6 +174,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; TfLiteTensor* input = &context->tensors[node->inputs->data[0]]; TfLiteTensor* filter = &context->tensors[node->inputs->data[1]]; + // Check dimensionality of input, filter TF_LITE_ENSURE_EQ(context, input->dims->size, 4); TF_LITE_ENSURE_EQ(context, filter->dims->size, 4); @@ -199,36 +256,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { if (output_status != kTfLiteOk) return output_status; - // We don't always need to allocate im2col. It is only used in some versions - // of the optimized Conv. This test just mimics something that happens inside - // optimized_ops.h, in order to avoid a DCHECK(!im2col_data). - data->need_im2col = - (params->stride_width != 1 || params->stride_height != 1 || - filter_width != 1 || filter_height != 1); - // If we're using the optimized multithreaded EigenTensor implementation of - // convolution, it expects the filter weights to be transposed compared to - // the normal TF Lite buffer format. Typical TF Lite weights are - // [filter_count, filter_height, filter_width, input_depth], but for the float - // implementation we need them as [filter_height, filter_width, input_depth, - // filter_count]. We get to that format by transposing, and create a temporary - // buffer to store the results. - // This path is only used for float processing, so only create the buffer if - // we're running with that data type. - data->need_hwcn_weights = (data_type == kTfLiteFloat32); - - int temporaries_count = 0; - if (data->need_im2col) { - data->im2col_index = temporaries_count; - ++temporaries_count; - } - if (data->need_hwcn_weights) { - data->hwcn_weights_index = temporaries_count; - ++temporaries_count; - } - - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(temporaries_count); - if (data->need_im2col) { node->temporaries->data[data->im2col_index] = data->im2col_id; @@ -344,7 +371,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, reference_ops::Conv(GetTensorData(input), GetTensorDims(input), GetTensorData(filter), GetTensorDims(filter), GetTensorData(bias), GetTensorDims(bias), - params->stride_width, params->stride_height, + params->stride_width, params->stride_height, 1, 1, data->padding.width, data->padding.height, output_activation_min, output_activation_max, GetTensorData(output), GetTensorDims(output), @@ -355,7 +382,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, optimized_ops::Conv(GetTensorData(input), GetTensorDims(input), GetTensorData(filter), GetTensorDims(filter), GetTensorData(bias), GetTensorDims(bias), - params->stride_width, params->stride_height, + params->stride_width, params->stride_height, 1, 1, data->padding.width, data->padding.height, output_activation_min, output_activation_max, GetTensorData(output), GetTensorDims(output), @@ -463,9 +490,7 @@ TfLiteRegistration* Register_CONVOLUTION_CBLAS_OPT() { } TfLiteRegistration* Register_CONV_2D() { -// TODO(ycling): Define a compilation flag and use CBLAS kernel when a -// fast CBLAS implementatino is available. -#ifdef TFLITE_USE_CBLAS_CONVOLUTION_KERNEL +#ifdef TFLITE_USE_APPLE_ACCELERATE_FOR_CONV return Register_CONVOLUTION_CBLAS_OPT(); #else return Register_CONVOLUTION_MULTITHREADED_OPT(); diff --git a/tensorflow/contrib/lite/kernels/dequantize.cc b/tensorflow/contrib/lite/kernels/dequantize.cc new file mode 100644 index 0000000000000000000000000000000000000000..e685f2465f627cf30e02564e6f16e1ec69e208e2 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/dequantize.cc @@ -0,0 +1,77 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace dequantize { + +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + input = GetInput(context, node, 0); + output = GetOutput(context, node, 0); + } + TfLiteTensor* input; + TfLiteTensor* output; +}; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + OpContext op_context(context, node); + + TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8); + + op_context.output->type = kTfLiteFloat32; + return context->ResizeTensor(context, op_context.output, + TfLiteIntArrayCopy(op_context.input->dims)); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + + auto zero_point = op_context.input->params.zero_point; + auto scale = op_context.input->params.scale; + + optimized_ops::Dequantize(GetTensorData(op_context.input), + GetTensorDims(op_context.input), zero_point, scale, + GetTensorData(op_context.output), + GetTensorDims(op_context.output)); + return kTfLiteOk; +} + +} // namespace dequantize + +TfLiteRegistration* Register_DEQUANTIZE_OPT() { + static TfLiteRegistration r = {nullptr, nullptr, dequantize::Prepare, + dequantize::Eval}; + return &r; +} + +TfLiteRegistration* Register_DEQUANTIZE() { return Register_DEQUANTIZE_OPT(); } + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/dequantize_test.cc b/tensorflow/contrib/lite/kernels/dequantize_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..fcd74206177a0a97db168338e3619d4b95c052a9 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/dequantize_test.cc @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class DequantizeOpModel : public SingleOpModel { + public: + DequantizeOpModel(std::initializer_list shape, float min, float max) { + input_ = AddInput({TensorType_UINT8, shape, min, max}); + output_ = AddOutput({TensorType_FLOAT32, shape}); + SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, + CreateDequantizeOptions(builder_).Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(SplitOpTest, FourDimensional) { + DequantizeOpModel m({2, 5}, -63.5, 64); + + m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/exp.cc b/tensorflow/contrib/lite/kernels/exp.cc new file mode 100644 index 0000000000000000000000000000000000000000..a9e79b742dc2c80ce4ed9a3aa786814265dcb660 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/exp.cc @@ -0,0 +1,92 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace exp { + +// This file has reference implementation of Exp. +enum KernelType { + kReference, +}; + +struct ExpContext { + ExpContext(TfLiteContext* context, TfLiteNode* node) { + input = GetInput(context, node, 0); + output = GetOutput(context, node, 0); + } + TfLiteTensor* input; + TfLiteTensor* output; +}; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + ExpContext op_context(context, node); + TfLiteIntArray* output_dims = TfLiteIntArrayCopy(op_context.input->dims); + op_context.output->type = op_context.input->type; + return context->ResizeTensor(context, op_context.output, output_dims); +} + +template +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + ExpContext op_context(context, node); + +#define TF_LITE_EXP(kernel_type, data_type) \ + kernel_type::Exp(GetTensorData(op_context.input), \ + NumElements(op_context.input), \ + GetTensorData(op_context.output)) + + // TODO(kanlig): supports half, bfloat16, float64, complex64, and complex128. + if (kernel_type == kReference) { + switch (op_context.input->type) { + case kTfLiteFloat32: + TF_LITE_EXP(reference_ops, float); + break; + default: + context->ReportError(context, + "Type %d is currently not supported by Exp.", + op_context.input->type); + return kTfLiteError; + } + } +#undef TF_LITE_EXP + return kTfLiteOk; +} + +} // namespace exp + +TfLiteRegistration* Register_EXP_REF() { + static TfLiteRegistration r = {nullptr, nullptr, exp::Prepare, + exp::Eval}; + return &r; +} + +// TODO(kanlig): add optimized implementation of Exp. +TfLiteRegistration* Register_EXP() { return Register_EXP_REF(); } + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/exp_test.cc b/tensorflow/contrib/lite/kernels/exp_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..eed67369a1f30e57cd29a3975a899db41938def0 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/exp_test.cc @@ -0,0 +1,70 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class ExpOpModel : public SingleOpModel { + public: + ExpOpModel(const TensorData& input, const TensorType& output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_EXP, BuiltinOptions_ExpOptions, + CreateExpOptions(builder_).Union()); + BuildInterpreter({GetShape(input_)}); + } + + template + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + template + std::vector GetOutput() { + return ExtractVector(output_); + } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + protected: + int input_; + int output_; +}; + +TEST(ExpOpTest, FloatTest) { + std::initializer_list data = {1.0, 0.0, -1.0, 1.0, 1.0, -1.0}; + ExpOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {2.71828, 1, 0.367879, 2.71828, 2.71828, 0.367879}))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index adedd58ff438ec9136f14e81e5cf93f6031890ef..aa3957bee133c8b51a82e9c62884ce365e086d2e 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -10,21 +10,25 @@ tflite_deps_intel = [ "@arm_neon_2_x86_sse", ] +HARD_FP_FLAGS_IF_APPLICABLE = select({ + "//tensorflow:android_arm": ["-mfloat-abi=softfp"], + "//tensorflow:android_arm64": ["-mfloat-abi=softfp"], + "//tensorflow:android_armeabi": ["-mfloat-abi=softfp"], + "//conditions:default": [], +}) + NEON_FLAGS_IF_APPLICABLE = select({ ":arm": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], ":armeabi-v7a": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], ":armv7a": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], "//conditions:default": [ "-O3", @@ -145,6 +149,7 @@ cc_library( "common.h", "optimized/depthwiseconv_float.h", "optimized/depthwiseconv_uint8.h", + "optimized/depthwiseconv_uint8_3x3_filter.h", "optimized/optimized_ops.h", ], copts = tflite_copts(), @@ -208,7 +213,10 @@ cc_library( "compatibility.h", "quantization_util.h", ], - deps = [":round"], + deps = [ + ":round", + ":types", + ], ) cc_test( @@ -283,7 +291,7 @@ cc_library( "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", ], - copts = NEON_FLAGS_IF_APPLICABLE, + copts = NEON_FLAGS_IF_APPLICABLE + HARD_FP_FLAGS_IF_APPLICABLE, deps = [ ":cpu_check", ":portable_tensor_utils", @@ -291,7 +299,7 @@ cc_library( "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite/kernels:activation_functor", "@arm_neon_2_x86_sse", - "@gemmlowp//:gemmlowp", + "@gemmlowp", ], ) @@ -305,6 +313,27 @@ cc_library( ], ) +# Audio support classes imported directly from TensorFlow. +cc_library( + name = "audio_utils", + srcs = [ + "mfcc.cc", + "mfcc_dct.cc", + "mfcc_mel_filterbank.cc", + "spectrogram.cc", + ], + hdrs = [ + "mfcc.h", + "mfcc_dct.h", + "mfcc_mel_filterbank.h", + "spectrogram.h", + ], + deps = [ + "//third_party/fft2d:fft2d_headers", + "@fft2d", + ], +) + cc_library( name = "tensor_utils", srcs = [ @@ -325,7 +354,7 @@ cc_library( "//tensorflow/contrib/lite/kernels:activation_functor", "//tensorflow/contrib/lite:builtin_op_data", "@arm_neon_2_x86_sse", - "@gemmlowp//:gemmlowp", + "@gemmlowp", ] + select({ ":arm": [ ":neon_tensor_utils", @@ -345,6 +374,9 @@ cc_library( ":ios_arm64": [ ":neon_tensor_utils", ], + ":ios_x86_64": [ + ":neon_tensor_utils", + ], ":x86_64": [ ":neon_tensor_utils", ], diff --git a/tensorflow/contrib/lite/kernels/internal/common.h b/tensorflow/contrib/lite/kernels/internal/common.h index fdeacedace59972dd108f8443ffad3b84f1e7e88..18601df22c1894dea6ce51f46ba815cd12dab095 100644 --- a/tensorflow/contrib/lite/kernels/internal/common.h +++ b/tensorflow/contrib/lite/kernels/internal/common.h @@ -102,6 +102,17 @@ inline int32 MultiplyByQuantizedMultiplierGreaterThanOne( quantized_multiplier); } +inline int32 MultiplyByQuantizedMultiplier(int32 x, int32 quantized_multiplier, + int shift) { + using gemmlowp::RoundingDivideByPOT; + using gemmlowp::SaturatingRoundingDoublingHighMul; + int left_shift = shift > 0 ? shift : 0; + int right_shift = shift > 0 ? 0 : -shift; + return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul( + x * (1 << left_shift), quantized_multiplier), + right_shift); +} + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 510395126ce3785b1d44fec1e0eb994c29ff0db7..f142374269606bdd3d4184af013749102666ab89 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -40,5 +40,152 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr, hidden_state_ptr_batch); } +void LstmStep( + const float* input_ptr_batch, const float* input_to_input_weights_ptr, + const float* input_to_forget_weights_ptr, + const float* input_to_cell_weights_ptr, + const float* input_to_output_weights_ptr, + const float* recurrent_to_input_weights_ptr, + const float* recurrent_to_forget_weights_ptr, + const float* recurrent_to_cell_weights_ptr, + const float* recurrent_to_output_weights_ptr, + const float* cell_to_input_weights_ptr, + const float* cell_to_forget_weights_ptr, + const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const float* projection_weights_ptr, + const float* projection_bias_ptr, const TfLiteLSTMParams* params, + int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, + float* cell_state_ptr, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* output_ptr_batch) { + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights_ptr == nullptr); + const bool use_peephole = (cell_to_output_weights_ptr != nullptr); + // Initialize scratch buffers with bias. + if (!use_cifg) { + tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, + input_gate_scratch); + } + tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, + forget_gate_scratch); + tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, + cell_scratch); + tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, + output_gate_scratch); + + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + forget_gate_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + output_gate_scratch, /*result_stride=*/1); + + // For each batch and cell: compute recurrent_weight * output_state. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, input_gate_scratch, + /*result_stride=*/1); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, forget_gate_scratch, + /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, output_gate_scratch, + /*result_stride=*/1); + + // For each batch and cell: update input gate. + if (!use_cifg) { + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch, + input_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, + input_gate_scratch); + } + + // For each batch and cell: update forget gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch, + forget_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, + forget_gate_scratch); + + // For each batch and cell: update the cell. + tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, + n_batch * n_cell, cell_state_ptr); + tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, + params->activation, cell_scratch); + if (use_cifg) { + tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, + forget_gate_scratch); + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); + } else { + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); + } + if (params->cell_clip > 0.0) { + tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, + params->cell_clip, cell_state_ptr); + } + + // For each batch and cell: update the output gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch, + output_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, + output_gate_scratch); + tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, + params->activation, cell_scratch); + tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, + n_batch * n_cell, output_gate_scratch); + + // For each batch: update the projection and output_state. + const bool use_projection_weight = (projection_weights_ptr != nullptr); + const bool use_projection_bias = (projection_bias_ptr != nullptr); + if (use_projection_weight) { + if (use_projection_bias) { + tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, + n_batch, output_ptr_batch); + } else { + tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch, + output_ptr_batch, /*result_stride=*/1); + if (params->proj_clip > 0.0) { + tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, + params->proj_clip, output_ptr_batch); + } + } else { + tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, + output_ptr_batch); + } + tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, + output_state_ptr); +} + } // namespace kernel_utils } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h index 9872d4500b862388ed4b96c97e3755f548e35d35..3ec60ee57a87833959a34ba95d32df15bea188a4 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h @@ -35,6 +35,42 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr, TfLiteFusedActivation activation, float* hidden_state_ptr_batch, float* output_ptr_batch); +// Performs an LSTM batch inference step for input specified by input_ptr_batch. +// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and +// biases (*_bias_ptr), and buffers (*_scratch), along with additional +// parameters: +// - params: various LSTM params including activation, clipping, etc., +// - n_batch: size of batch, +// - n_cell: number of cells (or units), +// - n_input: the input size, +// - n_output: the output size. +// +// The pointers to the cell and output state and the output are updated. Unless +// projection is specified output and output state contain the same data. +// +// The pointers with the suffix "_batch" point to data aligned in batch_major +// order, and each step processes batch_size many inputs from input_ptr_batch, +// and updates batch_size many cell and output states. +void LstmStep( + const float* input_ptr_batch, const float* input_to_input_weights_ptr, + const float* input_to_forget_weights_ptr, + const float* input_to_cell_weights_ptr, + const float* input_to_output_weights_ptr, + const float* recurrent_to_input_weights_ptr, + const float* recurrent_to_forget_weights_ptr, + const float* recurrent_to_cell_weights_ptr, + const float* recurrent_to_output_weights_ptr, + const float* cell_to_input_weights_ptr, + const float* cell_to_forget_weights_ptr, + const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const float* projection_weights_ptr, + const float* projection_bias_ptr, const TfLiteLSTMParams* params, + int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, + float* cell_state_ptr, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* output_ptr_batch); + } // namespace kernel_utils } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_KERNEL_UTILS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc.cc b/tensorflow/contrib/lite/kernels/internal/mfcc.cc new file mode 100644 index 0000000000000000000000000000000000000000..eafe0c7afee6fabd5a4a258aa5176e23f5e8d62a --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc.cc @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/contrib/lite/kernels/internal/mfcc.h" + +namespace tflite { +namespace internal { + +const double kDefaultUpperFrequencyLimit = 4000; +const double kDefaultLowerFrequencyLimit = 20; +const double kFilterbankFloor = 1e-12; +const int kDefaultFilterbankChannelCount = 40; +const int kDefaultDCTCoefficientCount = 13; + +Mfcc::Mfcc() + : initialized_(false), + lower_frequency_limit_(kDefaultLowerFrequencyLimit), + upper_frequency_limit_(kDefaultUpperFrequencyLimit), + filterbank_channel_count_(kDefaultFilterbankChannelCount), + dct_coefficient_count_(kDefaultDCTCoefficientCount) {} + +bool Mfcc::Initialize(int input_length, double input_sample_rate) { + bool initialized = mel_filterbank_.Initialize( + input_length, input_sample_rate, filterbank_channel_count_, + lower_frequency_limit_, upper_frequency_limit_); + initialized &= + dct_.Initialize(filterbank_channel_count_, dct_coefficient_count_); + initialized_ = initialized; + return initialized; +} + +void Mfcc::Compute(const std::vector& spectrogram_frame, + std::vector* output) const { + if (!initialized_) { + // LOG(ERROR) << "Mfcc not initialized."; + return; + } + std::vector working; + mel_filterbank_.Compute(spectrogram_frame, &working); + for (int i = 0; i < working.size(); ++i) { + double val = working[i]; + if (val < kFilterbankFloor) { + val = kFilterbankFloor; + } + working[i] = log(val); + } + dct_.Compute(working, output); +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc.h b/tensorflow/contrib/lite/kernels/internal/mfcc.h new file mode 100644 index 0000000000000000000000000000000000000000..d8500ecdcf38e5dcfe9eb89915501678455b3dd9 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc.h @@ -0,0 +1,78 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Basic class for computing MFCCs from spectrogram slices. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ + +#include + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_dct.h" +#include "tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h" + +namespace tflite { +namespace internal { + +class Mfcc { + public: + Mfcc(); + bool Initialize(int input_length, double input_sample_rate); + + // Input is a single squared-magnitude spectrogram frame. The input spectrum + // is converted to linear magnitude and weighted into bands using a + // triangular mel filterbank, and a discrete cosine transform (DCT) of the + // values is taken. Output is populated with the lowest dct_coefficient_count + // of these values. + void Compute(const std::vector& spectrogram_frame, + std::vector* output) const; + + void set_upper_frequency_limit(double upper_frequency_limit) { + // CHECK(!initialized_) << "Set frequency limits before calling + // Initialize."; + upper_frequency_limit_ = upper_frequency_limit; + } + + void set_lower_frequency_limit(double lower_frequency_limit) { + // CHECK(!initialized_) << "Set frequency limits before calling + // Initialize."; + lower_frequency_limit_ = lower_frequency_limit; + } + + void set_filterbank_channel_count(int filterbank_channel_count) { + /// CHECK(!initialized_) << "Set channel count before calling Initialize."; + filterbank_channel_count_ = filterbank_channel_count; + } + + void set_dct_coefficient_count(int dct_coefficient_count) { + // CHECK(!initialized_) << "Set coefficient count before calling + // Initialize."; + dct_coefficient_count_ = dct_coefficient_count; + } + + private: + MfccMelFilterbank mel_filterbank_; + MfccDct dct_; + bool initialized_; + double lower_frequency_limit_; + double upper_frequency_limit_; + int filterbank_channel_count_; + int dct_coefficient_count_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc new file mode 100644 index 0000000000000000000000000000000000000000..b0b7d181bdcf01688a387f33a3e64fc904324b50 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc @@ -0,0 +1,78 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_dct.h" + +#include + +namespace tflite { +namespace internal { + +MfccDct::MfccDct() : initialized_(false) {} + +bool MfccDct::Initialize(int input_length, int coefficient_count) { + coefficient_count_ = coefficient_count; + input_length_ = input_length; + + if (coefficient_count_ < 1) { + return false; + } + + if (input_length < 1) { + return false; + } + + if (coefficient_count_ > input_length_) { + return false; + } + + cosines_.resize(coefficient_count_); + double fnorm = sqrt(2.0 / input_length_); + // Some platforms don't have M_PI, so define a local constant here. + const double pi = atan(1) * 4; + double arg = pi / input_length_; + for (int i = 0; i < coefficient_count_; ++i) { + cosines_[i].resize(input_length_); + for (int j = 0; j < input_length_; ++j) { + cosines_[i][j] = fnorm * cos(i * arg * (j + 0.5)); + } + } + initialized_ = true; + return true; +} + +void MfccDct::Compute(const std::vector &input, + std::vector *output) const { + if (!initialized_) { + return; + } + + output->resize(coefficient_count_); + int length = input.size(); + if (length > input_length_) { + length = input_length_; + } + + for (int i = 0; i < coefficient_count_; ++i) { + double sum = 0.0; + for (int j = 0; j < length; ++j) { + sum += cosines_[i][j] * input[j]; + } + (*output)[i] = sum; + } +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h new file mode 100644 index 0000000000000000000000000000000000000000..a53f5cbd9bb70c7c9dd49672681140bb9cbd2f4f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h @@ -0,0 +1,43 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Basic minimal DCT class for MFCC speech processing. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ + +#include + +namespace tflite { +namespace internal { + +class MfccDct { + public: + MfccDct(); + bool Initialize(int input_length, int coefficient_count); + void Compute(const std::vector& input, + std::vector* output) const; + + private: + bool initialized_; + int coefficient_count_; + int input_length_; + std::vector > cosines_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc new file mode 100644 index 0000000000000000000000000000000000000000..c3deb33d91a47bfe54b7c84d2a615df2422f90cc --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc @@ -0,0 +1,204 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This code resamples the FFT bins, and smooths then with triangle-shaped +// weights to create a mel-frequency filter bank. For filter i centered at f_i, +// there is a triangular weighting of the FFT bins that extends from +// filter f_i-1 (with a value of zero at the left edge of the triangle) to f_i +// (where the filter value is 1) to f_i+1 (where the filter values returns to +// zero). + +// Note: this code fails if you ask for too many channels. The algorithm used +// here assumes that each FFT bin contributes to at most two channels: the +// right side of a triangle for channel i, and the left side of the triangle +// for channel i+1. If you ask for so many channels that some of the +// resulting mel triangle filters are smaller than a single FFT bin, these +// channels may end up with no contributing FFT bins. The resulting mel +// spectrum output will have some channels that are always zero. + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h" + +#include + +namespace tflite { +namespace internal { + +MfccMelFilterbank::MfccMelFilterbank() : initialized_(false) {} + +bool MfccMelFilterbank::Initialize(int input_length, double input_sample_rate, + int output_channel_count, + double lower_frequency_limit, + double upper_frequency_limit) { + num_channels_ = output_channel_count; + sample_rate_ = input_sample_rate; + input_length_ = input_length; + + if (num_channels_ < 1) { + // LOG(ERROR) << "Number of filterbank channels must be positive."; + return false; + } + + if (sample_rate_ <= 0) { + // LOG(ERROR) << "Sample rate must be positive."; + return false; + } + + if (input_length < 2) { + // LOG(ERROR) << "Input length must greater than 1."; + return false; + } + + if (lower_frequency_limit < 0) { + // LOG(ERROR) << "Lower frequency limit must be nonnegative."; + return false; + } + + if (upper_frequency_limit <= lower_frequency_limit) { + /// LOG(ERROR) << "Upper frequency limit must be greater than " + // << "lower frequency limit."; + return false; + } + + // An extra center frequency is computed at the top to get the upper + // limit on the high side of the final triangular filter. + center_frequencies_.resize(num_channels_ + 1); + const double mel_low = FreqToMel(lower_frequency_limit); + const double mel_hi = FreqToMel(upper_frequency_limit); + const double mel_span = mel_hi - mel_low; + const double mel_spacing = mel_span / static_cast(num_channels_ + 1); + for (int i = 0; i < num_channels_ + 1; ++i) { + center_frequencies_[i] = mel_low + (mel_spacing * (i + 1)); + } + + // Always exclude DC; emulate HTK. + const double hz_per_sbin = + 0.5 * sample_rate_ / static_cast(input_length_ - 1); + start_index_ = static_cast(1.5 + (lower_frequency_limit / hz_per_sbin)); + end_index_ = static_cast(upper_frequency_limit / hz_per_sbin); + + // Maps the input spectrum bin indices to filter bank channels/indices. For + // each FFT bin, band_mapper tells us which channel this bin contributes to + // on the right side of the triangle. Thus this bin also contributes to the + // left side of the next channel's triangle response. + band_mapper_.resize(input_length_); + int channel = 0; + for (int i = 0; i < input_length_; ++i) { + double melf = FreqToMel(i * hz_per_sbin); + if ((i < start_index_) || (i > end_index_)) { + band_mapper_[i] = -2; // Indicate an unused Fourier coefficient. + } else { + while ((center_frequencies_[channel] < melf) && + (channel < num_channels_)) { + ++channel; + } + band_mapper_[i] = channel - 1; // Can be == -1 + } + } + + // Create the weighting functions to taper the band edges. The contribution + // of any one FFT bin is based on its distance along the continuum between two + // mel-channel center frequencies. This bin contributes weights_[i] to the + // current channel and 1-weights_[i] to the next channel. + weights_.resize(input_length_); + for (int i = 0; i < input_length_; ++i) { + channel = band_mapper_[i]; + if ((i < start_index_) || (i > end_index_)) { + weights_[i] = 0.0; + } else { + if (channel >= 0) { + weights_[i] = + (center_frequencies_[channel + 1] - FreqToMel(i * hz_per_sbin)) / + (center_frequencies_[channel + 1] - center_frequencies_[channel]); + } else { + weights_[i] = (center_frequencies_[0] - FreqToMel(i * hz_per_sbin)) / + (center_frequencies_[0] - mel_low); + } + } + } + // Check the sum of FFT bin weights for every mel band to identify + // situations where the mel bands are so narrow that they don't get + // significant weight on enough (or any) FFT bins -- i.e., too many + // mel bands have been requested for the given FFT size. + std::vector bad_channels; + for (int c = 0; c < num_channels_; ++c) { + float band_weights_sum = 0.0; + for (int i = 0; i < input_length_; ++i) { + if (band_mapper_[i] == c - 1) { + band_weights_sum += (1.0 - weights_[i]); + } else if (band_mapper_[i] == c) { + band_weights_sum += weights_[i]; + } + } + // The lowest mel channels have the fewest FFT bins and the lowest + // weights sum. But given that the target gain at the center frequency + // is 1.0, if the total sum of weights is 0.5, we're in bad shape. + if (band_weights_sum < 0.5) { + bad_channels.push_back(c); + } + } + if (!bad_channels.empty()) { + /* + LOG(ERROR) << "Missing " << bad_channels.size() << " bands " + << " starting at " << bad_channels[0] + << " in mel-frequency design. " + << "Perhaps too many channels or " + << "not enough frequency resolution in spectrum. (" + << "input_length: " << input_length + << " input_sample_rate: " << input_sample_rate + << " output_channel_count: " << output_channel_count + << " lower_frequency_limit: " << lower_frequency_limit + << " upper_frequency_limit: " << upper_frequency_limit; + */ + } + initialized_ = true; + return true; +} + +// Compute the mel spectrum from the squared-magnitude FFT input by taking the +// square root, then summing FFT magnitudes under triangular integration windows +// whose widths increase with frequency. +void MfccMelFilterbank::Compute(const std::vector &input, + std::vector *output) const { + if (!initialized_) { + // LOG(ERROR) << "Mel Filterbank not initialized."; + return; + } + + if (input.size() <= end_index_) { + // LOG(ERROR) << "Input too short to compute filterbank"; + return; + } + + // Ensure output is right length and reset all values. + output->assign(num_channels_, 0.0); + + for (int i = start_index_; i <= end_index_; i++) { // For each FFT bin + double spec_val = sqrt(input[i]); + double weighted = spec_val * weights_[i]; + int channel = band_mapper_[i]; + if (channel >= 0) + (*output)[channel] += weighted; // Right side of triangle, downward slope + channel++; + if (channel < num_channels_) + (*output)[channel] += spec_val - weighted; // Left side of triangle + } +} + +double MfccMelFilterbank::FreqToMel(double freq) const { + return 1127.0 * log(1.0 + (freq / 700.0)); +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h new file mode 100644 index 0000000000000000000000000000000000000000..c1db28243eea39a694b7613ac7144dce9b294897 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h @@ -0,0 +1,63 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Basic class for applying a mel-scale mapping to a power spectrum. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ + +#include + +namespace tflite { +namespace internal { + +class MfccMelFilterbank { + public: + MfccMelFilterbank(); + bool Initialize(int input_length, // Number of unique FFT bins fftsize/2+1. + double input_sample_rate, int output_channel_count, + double lower_frequency_limit, double upper_frequency_limit); + + // Takes a squared-magnitude spectrogram slice as input, computes a + // triangular-mel-weighted linear-magnitude filterbank, and places the result + // in output. + void Compute(const std::vector& input, + std::vector* output) const; + + private: + double FreqToMel(double freq) const; + bool initialized_; + int num_channels_; + double sample_rate_; + int input_length_; + std::vector center_frequencies_; // In mel, for each mel channel. + + // Each FFT bin b contributes to two triangular mel channels, with + // proportion weights_[b] going into mel channel band_mapper_[b], and + // proportion (1 - weights_[b]) going into channel band_mapper_[b] + 1. + // Thus, weights_ contains the weighting applied to each FFT bin for the + // upper-half of the triangular band. + std::vector weights_; // Right-side weight for this fft bin. + + // FFT bin i contributes to the upper side of mel channel band_mapper_[i] + std::vector band_mapper_; + int start_index_; // Lowest FFT bin used to calculate mel spectrum. + int end_index_; // Highest FFT bin used to calculate mel spectrum. +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/cblas_conv.h b/tensorflow/contrib/lite/kernels/internal/optimized/cblas_conv.h index fcb9fac6713865f2d6f89755c785b179436cbc57..4a90e7e640ef29b675c236d8bbb479aa16560761 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/cblas_conv.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/cblas_conv.h @@ -19,9 +19,12 @@ limitations under the License. // The Conv implementation based on CBLAS interface. This is only used on iOS // for now, utilizing Apple's Accelerate framework. -// TODO(ycling): Update the BUILD file and integrate with Apple Accelerate -// Famework when it's available. +#if TFLITE_USE_APPLE_ACCELERATE_FOR_CONV +#include +#else #include "tensorflow/contrib/lite/kernels/internal/optimized/cblas_reference.h" +#endif + #include "tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h index e0eca2e736be00ff09737325f06b0035e77e3103..3a53d3ab07faf63250fc18fc846e0b8f5a39d9c4 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h @@ -34,7 +34,7 @@ inline bool TestCPUFeatureNeon() { #endif // __aarch64__ } -#elif defined USE_NEON || defined __ARM_NEON +#elif defined USE_NEON || defined __ARM_NEON inline bool TestCPUFeatureNeon() { return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h index e2c87df80bd927d823b150ed3799641796dfb4c7..7f6eea2d5d1cfd6f4e2a569760ecbe0d96f754c8 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h @@ -573,6 +573,46 @@ struct FloatDepthwiseConvKernel { } }; +template <> +struct FloatDepthwiseConvKernel { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Load the filters + float32x4_t filter_0 = vld1q_f32(filter_ptr + 4 * 0); + float32x4_t filter_1 = vld1q_f32(filter_ptr + 4 * 1); + float32x4_t filter_2 = vld1q_f32(filter_ptr + 4 * 2); + float32x4_t filter_3 = vld1q_f32(filter_ptr + 4 * 3); + float32x4_t filter_4 = vld1q_f32(filter_ptr + 4 * 4); + + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + // Load the inputs + const float input_val = *input_ptr; + input_ptr += input_ptr_increment; + // Load the accumulators from acc_buffer + float32x4_t acc_0 = vld1q_f32(acc_buffer_ptr + 4 * 0); + float32x4_t acc_1 = vld1q_f32(acc_buffer_ptr + 4 * 1); + float32x4_t acc_2 = vld1q_f32(acc_buffer_ptr + 4 * 2); + float32x4_t acc_3 = vld1q_f32(acc_buffer_ptr + 4 * 3); + float32x4_t acc_4 = vld1q_f32(acc_buffer_ptr + 4 * 4); + // Multiply-accumulate + acc_0 = vmlaq_n_f32(acc_0, filter_0, input_val); + acc_1 = vmlaq_n_f32(acc_1, filter_1, input_val); + acc_2 = vmlaq_n_f32(acc_2, filter_2, input_val); + acc_3 = vmlaq_n_f32(acc_3, filter_3, input_val); + acc_4 = vmlaq_n_f32(acc_4, filter_4, input_val); + // Store the accumulators back to acc_buffer + vst1q_f32(acc_buffer_ptr + 4 * 0, acc_0); + vst1q_f32(acc_buffer_ptr + 4 * 1, acc_1); + vst1q_f32(acc_buffer_ptr + 4 * 2, acc_2); + vst1q_f32(acc_buffer_ptr + 4 * 3, acc_3); + vst1q_f32(acc_buffer_ptr + 4 * 4, acc_4); + acc_buffer_ptr += 20; + } + } +}; + template <> struct FloatDepthwiseConvKernel { static void Run(int num_output_pixels, int input_depth, int depth_multiplier, @@ -926,6 +966,7 @@ inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, TFMINI_USE_DEPTHWISECONV_KERNEL(true, 8, 1) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 8) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 20) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 32) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 2, 1) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 3, 2) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h index fc5897896477711c46b06f10003acb10783d12af..08674a6c59192a71bae48eac467fecc7b7dff7b7 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h @@ -18,6 +18,7 @@ limitations under the License. #include "fixedpoint/fixedpoint.h" #include "public/gemmlowp.h" #include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { @@ -1205,6 +1206,55 @@ struct QuantizedDepthwiseConvKernel { } }; +template <> +struct QuantizedDepthwiseConvKernel { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const uint8* input_ptr, int16 input_offset, + int input_ptr_increment, const uint8* filter_ptr, + int16 filter_offset, int32* acc_buffer_ptr) { + // Load the filters, add filter_offset. + // NEON wants to load 8 bytes at a time, but 20 is not divisible by 8. + // We load the first 16 bytes into filter_u8_{0,1} as usual. + // Then we load the 8 last bytes into filter_u8_x (x for 'extra'). + // This is redundant: the first 4 bytes of filter_u8_x are the same + // as the last 4 bytes of filter_u8_x. + uint8x8_t filter_u8_0 = vld1_u8(filter_ptr + 8 * 0); + uint8x8_t filter_u8_1 = vld1_u8(filter_ptr + 8 * 1); + uint8x8_t filter_u8_x = vld1_u8(filter_ptr + 8 * 1 + 4); + int16x8_t filter_0 = vreinterpretq_s16_u16(vmovl_u8(filter_u8_0)); + int16x8_t filter_1 = vreinterpretq_s16_u16(vmovl_u8(filter_u8_1)); + int16x8_t filter_x = vreinterpretq_s16_u16(vmovl_u8(filter_u8_x)); + filter_0 = vaddq_s16(filter_0, vdupq_n_s16(filter_offset)); + filter_1 = vaddq_s16(filter_1, vdupq_n_s16(filter_offset)); + filter_x = vaddq_s16(filter_x, vdupq_n_s16(filter_offset)); + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + uint8 input_u8 = *input_ptr; + input_ptr += input_ptr_increment; + int16 input = static_cast(input_u8 + input_offset); + // Load the accumulators from acc_buffer + int32x4_t acc_0 = vld1q_s32(acc_buffer_ptr + 4 * 0); + int32x4_t acc_1 = vld1q_s32(acc_buffer_ptr + 4 * 1); + int32x4_t acc_2 = vld1q_s32(acc_buffer_ptr + 4 * 2); + int32x4_t acc_3 = vld1q_s32(acc_buffer_ptr + 4 * 3); + int32x4_t acc_4 = vld1q_s32(acc_buffer_ptr + 4 * 4); + // Multiply-accumulate + acc_0 = vmlal_n_s16(acc_0, vget_low_s16(filter_0), input); + acc_1 = vmlal_n_s16(acc_1, vget_high_s16(filter_0), input); + acc_2 = vmlal_n_s16(acc_2, vget_low_s16(filter_1), input); + acc_3 = vmlal_n_s16(acc_3, vget_high_s16(filter_1), input); + acc_4 = vmlal_n_s16(acc_4, vget_high_s16(filter_x), input); + // Store the accumulators back to acc_buffer + vst1q_s32(acc_buffer_ptr + 4 * 0, acc_0); + vst1q_s32(acc_buffer_ptr + 4 * 1, acc_1); + vst1q_s32(acc_buffer_ptr + 4 * 2, acc_2); + vst1q_s32(acc_buffer_ptr + 4 * 3, acc_3); + vst1q_s32(acc_buffer_ptr + 4 * 4, acc_4); + acc_buffer_ptr += 20; + } + } +}; + template <> struct QuantizedDepthwiseConvKernel { static void Run(int num_output_pixels, int input_depth, int depth_multiplier, @@ -1643,6 +1693,22 @@ inline void DepthwiseConv(const uint8* input_data, const Dims<4>& input_dims, const int output_width = ArraySize(output_dims, 1); TFLITE_DCHECK(output_depth == input_depth * depth_multiplier); +#ifdef __aarch64__ + // Call kernel optimized for depthwise convolutions using 3x3 filters, + // stride = 1, no padding, depth_multiplier = 1 and depth a multiple of 16. + if (filter_width == 3 && filter_height == 3 && depth_multiplier == 1 && + stride_width == 1 && stride_height == 1 && pad_width == 0 && + pad_height == 0 && (input_depth % 16) == 0) { + DepthwiseConv3by3FilterDepth16( + input_data, input_dims, input_offset, filter_data, filter_dims, + filter_offset, bias_data, bias_dims, stride_width, stride_height, + pad_width, pad_height, depth_multiplier, output_offset, + output_multiplier, output_shift, output_activation_min, + output_activation_max, output_data, output_dims); + return; + } +#endif + static const int kAccBufferMaxSize = 2048; int32 acc_buffer[kAccBufferMaxSize]; TFLITE_DCHECK_GE(kAccBufferMaxSize, output_depth); @@ -1691,6 +1757,7 @@ inline void DepthwiseConv(const uint8* input_data, const Dims<4>& input_dims, TFMINI_USE_DEPTHWISECONV_KERNEL(true, 8, 2) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 16, 1) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 16) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 20) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 32) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 8) TFMINI_USE_DEPTHWISECONV_KERNEL(true, 8, 1) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h new file mode 100644 index 0000000000000000000000000000000000000000..e0335b2c74cbf6d9d4f17d816f5802286628964f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h @@ -0,0 +1,653 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ + +#include "fixedpoint/fixedpoint.h" +#include "public/gemmlowp.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { +namespace optimized_ops { + +#ifdef __aarch64__ + +inline void preload_l1_keep(const uint8* ptr) { +#ifdef GEMMLOWP_ARM_64 + asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :); +#else + gemmlowp::Prefetch(ptr); +#endif +} + +// Implementation of quantized DepthwiseConv for 3x3 filters. + +// Below are helper structs to remove the use of arrays. +// There is an llvm bug that causes significant slowdown when using arrays for +// NEON intrinsics vector data types. +// See: https://bugs.llvm.org/show_bug.cgi?id=34945 + +struct Int32x16 { + int32x4_t v0, v1, v2, v3; +}; + +struct Int16x16 { + int16x8_t low, high; +}; + +struct Int16x16x3 { + Int16x16 v0, v1, v2; +}; + +struct Filter3x3x16 { + Int16x16x3 r0, r1, r2; +}; + +// Loads 3x3 filter of depth 16 and adds filter offsets. +inline Filter3x3x16 LoadFilterDepth16(const uint8* filter_ptr, + int32 filter_offset, int output_depth) { + Filter3x3x16 filter; + + uint8x8_t temp_u8_0, temp_u8_1, temp_u8_2, temp_u8_3, temp_u8_4, temp_u8_5, + temp_u8_6, temp_u8_7, temp_u8_8, temp_u8_9, temp_u8_10, temp_u8_11, + temp_u8_12, temp_u8_13, temp_u8_14, temp_u8_15, temp_u8_16, temp_u8_17; + int16x8_t filter_offset_vec = vdupq_n_s16(filter_offset); + + temp_u8_0 = vld1_u8(filter_ptr + 0 * output_depth); + temp_u8_1 = vld1_u8(filter_ptr + 0 * output_depth + 8); + temp_u8_2 = vld1_u8(filter_ptr + 1 * output_depth); + temp_u8_3 = vld1_u8(filter_ptr + 1 * output_depth + 8); + temp_u8_4 = vld1_u8(filter_ptr + 2 * output_depth); + temp_u8_5 = vld1_u8(filter_ptr + 2 * output_depth + 8); + + temp_u8_6 = vld1_u8(filter_ptr + 3 * output_depth); + temp_u8_7 = vld1_u8(filter_ptr + 3 * output_depth + 8); + temp_u8_8 = vld1_u8(filter_ptr + 4 * output_depth); + temp_u8_9 = vld1_u8(filter_ptr + 4 * output_depth + 8); + temp_u8_10 = vld1_u8(filter_ptr + 5 * output_depth); + temp_u8_11 = vld1_u8(filter_ptr + 5 * output_depth + 8); + + temp_u8_12 = vld1_u8(filter_ptr + 6 * output_depth); + temp_u8_13 = vld1_u8(filter_ptr + 6 * output_depth + 8); + temp_u8_14 = vld1_u8(filter_ptr + 7 * output_depth); + temp_u8_15 = vld1_u8(filter_ptr + 7 * output_depth + 8); + temp_u8_16 = vld1_u8(filter_ptr + 8 * output_depth); + temp_u8_17 = vld1_u8(filter_ptr + 8 * output_depth + 8); + + filter.r0.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_0)); + filter.r0.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_1)); + filter.r0.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_2)); + filter.r0.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_3)); + filter.r0.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_4)); + filter.r0.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_5)); + + filter.r1.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_6)); + filter.r1.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_7)); + filter.r1.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_8)); + filter.r1.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_9)); + filter.r1.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_10)); + filter.r1.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_11)); + + filter.r2.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_12)); + filter.r2.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_13)); + filter.r2.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_14)); + filter.r2.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_15)); + filter.r2.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_16)); + filter.r2.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_17)); + + filter.r0.v0.low = vaddq_s16(filter.r0.v0.low, filter_offset_vec); + filter.r0.v0.high = vaddq_s16(filter.r0.v0.high, filter_offset_vec); + filter.r0.v1.low = vaddq_s16(filter.r0.v1.low, filter_offset_vec); + filter.r0.v1.high = vaddq_s16(filter.r0.v1.high, filter_offset_vec); + filter.r0.v2.low = vaddq_s16(filter.r0.v2.low, filter_offset_vec); + filter.r0.v2.high = vaddq_s16(filter.r0.v2.high, filter_offset_vec); + + filter.r1.v0.low = vaddq_s16(filter.r1.v0.low, filter_offset_vec); + filter.r1.v0.high = vaddq_s16(filter.r1.v0.high, filter_offset_vec); + filter.r1.v1.low = vaddq_s16(filter.r1.v1.low, filter_offset_vec); + filter.r1.v1.high = vaddq_s16(filter.r1.v1.high, filter_offset_vec); + filter.r1.v2.low = vaddq_s16(filter.r1.v2.low, filter_offset_vec); + filter.r1.v2.high = vaddq_s16(filter.r1.v2.high, filter_offset_vec); + + filter.r2.v0.low = vaddq_s16(filter.r2.v0.low, filter_offset_vec); + filter.r2.v0.high = vaddq_s16(filter.r2.v0.high, filter_offset_vec); + filter.r2.v1.low = vaddq_s16(filter.r2.v1.low, filter_offset_vec); + filter.r2.v1.high = vaddq_s16(filter.r2.v1.high, filter_offset_vec); + filter.r2.v2.low = vaddq_s16(filter.r2.v2.low, filter_offset_vec); + filter.r2.v2.high = vaddq_s16(filter.r2.v2.high, filter_offset_vec); + + return filter; +} + +// Loads 3 input cells of depth 16 and adds input offsets. +inline Int16x16x3 LoadInputRowDepth16(const uint8* ptr, int input_depth, + int32 input_offset, + Int16x16x3 input_row) { + uint8x8_t temp_0, temp_1; + int16x8_t offset_vec = vdupq_n_s16(input_offset); + + temp_0 = vld1_u8(ptr + 0 * input_depth); + temp_1 = vld1_u8(ptr + 0 * input_depth + 8); + input_row.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v0.low = vaddq_s16(input_row.v0.low, offset_vec); + input_row.v0.high = vaddq_s16(input_row.v0.high, offset_vec); + + temp_0 = vld1_u8(ptr + 1 * input_depth); + temp_1 = vld1_u8(ptr + 1 * input_depth + 8); + input_row.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v1.low = vaddq_s16(input_row.v1.low, offset_vec); + input_row.v1.high = vaddq_s16(input_row.v1.high, offset_vec); + + temp_0 = vld1_u8(ptr + 2 * input_depth); + temp_1 = vld1_u8(ptr + 2 * input_depth + 8); + input_row.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v2.low = vaddq_s16(input_row.v2.low, offset_vec); + input_row.v2.high = vaddq_s16(input_row.v2.high, offset_vec); + + return input_row; +} + +// Performs multiply accumulate on 3 inputs of depth 16. +inline Int32x16 MultiplyAccumulateRowDepth16(Int32x16 output, + const Int16x16x3& filter_row, + const Int16x16x3& input_row) { + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v0.low), + vget_low_s16(input_row.v0.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v0.low), + vget_high_s16(input_row.v0.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v0.high), + vget_low_s16(input_row.v0.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v0.high), + vget_high_s16(input_row.v0.high)); + + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v1.low), + vget_low_s16(input_row.v1.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v1.low), + vget_high_s16(input_row.v1.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v1.high), + vget_low_s16(input_row.v1.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v1.high), + vget_high_s16(input_row.v1.high)); + + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v2.low), + vget_low_s16(input_row.v2.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v2.low), + vget_high_s16(input_row.v2.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v2.high), + vget_low_s16(input_row.v2.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v2.high), + vget_high_s16(input_row.v2.high)); + + return output; +} + +// Applies activation, offset and downquantize on a set of accumulator +// registers of depth 16. Stores results to output. +inline void DownquantizeAndStoreDepth16(Int32x16 acc, int32 output_multiplier, + int output_shift, + int32x4_t output_offset_vec, + int32x4_t output_activation_min_vec, + int32x4_t output_activation_max_vec, + uint8* output_ptr) { + // Fixed-point multiplication. + acc.v0 = vqrdmulhq_n_s32(acc.v0, output_multiplier); + acc.v1 = vqrdmulhq_n_s32(acc.v1, output_multiplier); + acc.v2 = vqrdmulhq_n_s32(acc.v2, output_multiplier); + acc.v3 = vqrdmulhq_n_s32(acc.v3, output_multiplier); + + using gemmlowp::RoundingDivideByPOT; + acc.v0 = RoundingDivideByPOT(acc.v0, output_shift); + acc.v1 = RoundingDivideByPOT(acc.v1, output_shift); + acc.v2 = RoundingDivideByPOT(acc.v2, output_shift); + acc.v3 = RoundingDivideByPOT(acc.v3, output_shift); + + // Add the output offset. + acc.v0 = vaddq_s32(acc.v0, output_offset_vec); + acc.v1 = vaddq_s32(acc.v1, output_offset_vec); + acc.v2 = vaddq_s32(acc.v2, output_offset_vec); + acc.v3 = vaddq_s32(acc.v3, output_offset_vec); + + // Apply the activation function. + acc.v0 = vmaxq_s32(acc.v0, output_activation_min_vec); + acc.v1 = vmaxq_s32(acc.v1, output_activation_min_vec); + acc.v2 = vmaxq_s32(acc.v2, output_activation_min_vec); + acc.v3 = vmaxq_s32(acc.v3, output_activation_min_vec); + + acc.v0 = vminq_s32(acc.v0, output_activation_max_vec); + acc.v1 = vminq_s32(acc.v1, output_activation_max_vec); + acc.v2 = vminq_s32(acc.v2, output_activation_max_vec); + acc.v3 = vminq_s32(acc.v3, output_activation_max_vec); + + // Saturating cast to uint8 and store to destination. + int16x4_t acc_tlla_s16 = vqmovn_s32(acc.v0); + int16x4_t acc_tllb_s16 = vqmovn_s32(acc.v1); + int16x4_t acc_tlha_s16 = vqmovn_s32(acc.v2); + int16x4_t acc_tlhb_s16 = vqmovn_s32(acc.v3); + + int16x8_t res_s16_0 = vcombine_s16(acc_tlla_s16, acc_tllb_s16); + int16x8_t res_s16_1 = vcombine_s16(acc_tlha_s16, acc_tlhb_s16); + uint8x8_t res_u8_0 = vqmovun_s16(res_s16_0); + uint8x8_t res_u8_1 = vqmovun_s16(res_s16_1); + vst1q_u8(output_ptr, vcombine_u8(res_u8_0, res_u8_1)); +} + +// A kernel that is optimized on the number of output cells in the x and y +// direction, and the stride. Assumes 3x3 filters of 16 depth. +template +struct ConvKernel3x3FilterDepth16 {}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 2, 1> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + // 16 depth accumulators for the 2 outputs. + Int32x16 acc0, acc1; + + // Accumulators for top filter. + acc0.v0 = vld1q_s32(bias_ptr); + acc0.v1 = vld1q_s32(bias_ptr + 4); + acc0.v2 = vld1q_s32(bias_ptr + 8); + acc0.v3 = vld1q_s32(bias_ptr + 12); + // Accumulators for bottom filter. + acc1.v0 = vld1q_s32(bias_ptr); + acc1.v1 = vld1q_s32(bias_ptr + 4); + acc1.v2 = vld1q_s32(bias_ptr + 8); + acc1.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row of top filter. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r0, input); + + // Do second row of top filter. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r1, input); + + // The inputs to second row of the top filter are also the inputs to the + // first row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r0, input); + + // Do third row of top filter. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r2, input); + + // The inputs to third row of the top filter are also the inputs to the + // second row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r1, input); + + // Do third row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 3 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc0, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + + DownquantizeAndStoreDepth16(acc1, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, + output_ptr + output_depth * output_width); + } +}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 2, 2> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + // 16 depth accumulators for the 2 outputs. + Int32x16 acc0, acc1; + + // Accumulators for top filter. + acc0.v0 = vld1q_s32(bias_ptr); + acc0.v1 = vld1q_s32(bias_ptr + 4); + acc0.v2 = vld1q_s32(bias_ptr + 8); + acc0.v3 = vld1q_s32(bias_ptr + 12); + // Accumulators for bottom filter. + acc1.v0 = vld1q_s32(bias_ptr); + acc1.v1 = vld1q_s32(bias_ptr + 4); + acc1.v2 = vld1q_s32(bias_ptr + 8); + acc1.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row of top filter. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r0, input); + + // Do second row of top filter. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r1, input); + + // Do third row of top filter. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r2, input); + + // The inputs to third row of the top filter are also the inputs + // to first row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r0, input); + + // Do second row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 3 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r1, input); + + // Do third row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 4 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc0, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + + DownquantizeAndStoreDepth16(acc1, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, + output_ptr + output_depth * output_width); + } +}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 1> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + Int32x16 acc; + acc.v0 = vld1q_s32(bias_ptr); + acc.v1 = vld1q_s32(bias_ptr + 4); + acc.v2 = vld1q_s32(bias_ptr + 8); + acc.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r0, input); + + // Do second row. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r1, input); + + // Do third row. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + } +}; + +inline void DepthwiseConv3by3FilterDepth16( + const uint8* input_data, const Dims<4>& input_dims, int32 input_offset, + const uint8* filter_data, const Dims<4>& filter_dims, int32 filter_offset, + const int32* bias_data, const Dims<4>& bias_dims, int stride_width, + int stride_height, int pad_width, int pad_height, int depth_multiplier, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int input_depth = ArraySize(input_dims, 0); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Algorithm assumes below constraints. It is optimized for depth multiplier + // of 1, 3x3 filter, no padding, strides 1 and 2. + TFLITE_DCHECK(output_depth == input_depth * depth_multiplier); + TFLITE_DCHECK(depth_multiplier == 1); + TFLITE_DCHECK(filter_height == 3); + TFLITE_DCHECK(filter_width == 3); + TFLITE_DCHECK(pad_height == 0); + TFLITE_DCHECK(pad_width == 0); + TFLITE_DCHECK(stride_width == 1); + TFLITE_DCHECK(stride_height == 1); + + // The number of outputs to process in the main loop. + const int num_x_outputs = 1; + const int num_y_outputs = 2; + + const int input_row_width = output_depth * (input_width + 2 * pad_width); + const int input_batch_size = + input_row_width * (input_height + 2 * pad_height); + const int output_batch_size = output_depth * output_width * output_height; + const int input_ptr_x_increment = input_depth * stride_width; + + // Calculate extents of non-boundary loop. + int out_x_start = 0; + for (; out_x_start < input_width; out_x_start++) { + int in_x = (out_x_start * stride_width) - pad_width; + if (in_x >= 0) { + break; + } + } + int out_x_end = output_width - 1; + for (; out_x_end >= 0; out_x_end--) { + int in_x = (out_x_end * stride_width) - pad_width; + int in_x_end = in_x + filter_width + (num_x_outputs - 1) * stride_width; + if (in_x_end <= input_width) { + out_x_end++; + break; + } + } + int out_y_start = 0; + for (; out_y_start < input_height; out_y_start++) { + int in_y = (out_y_start * stride_height) - pad_height; + if (in_y >= 0) { + break; + } + } + int out_y_end = output_height - 1; + for (; out_y_end >= 0; out_y_end--) { + int in_y = (out_y_end * stride_height) - pad_height; + int in_y_end = in_y + filter_height + (num_y_outputs - 1) * stride_height; + if (in_y_end <= input_height) { + out_y_end++; + break; + } + } + + // Offsets for preloading inputs. + const int i0 = 0; + const int i1 = input_depth; + const int i2 = 2 * input_depth; + const int i3 = input_row_width; + const int i4 = input_row_width + input_depth; + const int i5 = input_row_width + 2 * input_depth; + const int i6 = 2 * input_row_width; + const int i7 = 2 * input_row_width + input_depth; + const int i8 = 2 * input_row_width + 2 * input_depth; + const int i9 = 3 * input_row_width; + const int i10 = 3 * input_row_width + input_depth; + const int i11 = 3 * input_row_width + 2 * input_depth; + + for (int b = 0; b < batches; ++b) { + const int32* bias_ptr = bias_data; + const uint8* filter_ptr = filter_data; + + const int in_batch_offset = b * input_batch_size; + const int out_batch_offset = b * output_batch_size; + + int depth = 0; + for (; depth <= output_depth - 16; depth += 16) { + Filter3x3x16 filter = + LoadFilterDepth16(filter_ptr, filter_offset, output_depth); + + // Handle 1x2 outputs. + int out_y = out_y_start; + for (; out_y < out_y_end; out_y += num_y_outputs) { + int out_x = out_x_start; + + int in_y_offset = + stride_height * input_row_width * (out_y + pad_height); + int in_x_offset = stride_width * input_depth * (out_x + pad_width); + + const uint8* input_ptr = + input_data + depth + in_x_offset + in_y_offset + in_batch_offset; + + uint8* output_ptr = output_data + depth + (out_x * output_depth) + + (output_depth * output_width * out_y) + + out_batch_offset; + + // Preload inputs. If input depth is large, preload every value of the + // input for this depth range. Otherwise, preload only the first values + // of each row. + if (input_depth >= 32) { + preload_l1_keep(input_ptr + i0); + preload_l1_keep(input_ptr + i1); + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i3); + preload_l1_keep(input_ptr + i4); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i6); + preload_l1_keep(input_ptr + i7); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i9); + preload_l1_keep(input_ptr + i10); + preload_l1_keep(input_ptr + i11); + } else { + preload_l1_keep(input_ptr + i0); + preload_l1_keep(input_ptr + i3); + preload_l1_keep(input_ptr + i6); + preload_l1_keep(input_ptr + i9); + } + + for (; out_x < out_x_end; out_x += num_x_outputs) { + ConvKernel3x3FilterDepth16<1, 2, 1>::Run( + filter, input_ptr, input_depth, input_offset, input_row_width, + bias_ptr, output_offset, output_multiplier, output_shift, + output_activation_min, output_activation_max, output_ptr, + output_depth, output_width); + + input_ptr += input_ptr_x_increment * num_x_outputs; + output_ptr += output_depth * num_x_outputs; + + // Preload the next inputs depending on stride. + if (stride_width == 1) { + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i11); + } else if (stride_width == 2) { + preload_l1_keep(input_ptr + i1); + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i4); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i7); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i10); + preload_l1_keep(input_ptr + i11); + } + } + + // Handle the rest of the right side. + for (; out_x < output_width; out_x++) { + // This code path can only be reached if we're handling >1 x outputs + // at a time or support padding. + } + } + + // Handle the rest of the bottom side. + for (; out_y < output_height; out_y++) { + int out_x = out_x_start; + + int in_y_offset = + stride_height * input_row_width * (out_y + pad_height); + int in_x_offset = stride_width * input_depth * (out_x + pad_width); + + const uint8* input_ptr = + input_data + depth + in_x_offset + in_y_offset + in_batch_offset; + + uint8* output_ptr = output_data + depth + (out_x * output_depth) + + (output_depth * output_width * out_y) + + out_batch_offset; + + for (; out_x < output_width; out_x++) { + ConvKernel3x3FilterDepth16<1, 1>::Run( + filter, input_ptr, input_depth, input_offset, input_row_width, + bias_ptr, output_offset, output_multiplier, output_shift, + output_activation_min, output_activation_max, output_ptr, + output_depth, output_width); + + input_ptr += input_ptr_x_increment; + output_ptr += output_depth; + } + } + filter_ptr += 16; + bias_ptr += 16; + } + } +} + +#endif // __aarch64__ + +} // namespace optimized_ops +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index ea8502ae33bc2ee5965e3be30a7d9ee36604abe3..780401e052733cccae0cc34f495df090c1530624 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h" #ifdef USE_NEON diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index d5b0f45fd81d60aed2d0faafe01c9f7d236c4342..f1937228f62f9d9b5d6296cc54c0e3acb373a8ca 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -758,14 +758,90 @@ void Im2col(const T* input_data, const Dims<4>& input_dims, int stride, kwidth, byte_zero, output_data, output_dims); } +inline void DilatedConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + const float* bias_data, const Dims<4>& bias_dims, + int stride_width, int stride_height, + int dilation_width_factor, int dilation_height_factor, + int pad_width, int pad_height, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims, float* im2col_data, + const Dims<4>& im2col_dims) { + gemmlowp::ScopedProfilingLabel label("DilatedConv"); + // This is a copy of the reference Conv implementation. We do not currently + // have an optimized path for dilation. + (void)im2col_data; // only used in optimized code. + (void)im2col_dims; // only used in optimized code. + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 0); + const int output_depth = MatchingArraySize(filter_dims, 3, output_dims, 0); + if (bias_data) { + TFLITE_DCHECK_EQ(ArraySize(filter_dims, 3), ArraySize(bias_dims, 0)); + } + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + float total = 0.f; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // If the location is outside the bounds of the input image, + // use zero as a default value. + if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height)) { + float input_value = input_data[Offset(input_dims, in_channel, + in_x, in_y, batch)]; + float filter_value = + filter_data[Offset(filter_dims, in_channel, filter_x, + filter_y, out_channel)]; + total += (input_value * filter_value); + } + } + } + } + float bias_value = 0.0f; + if (bias_data) { + bias_value = bias_data[Offset(bias_dims, out_channel, 0, 0, 0)]; + } + output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] = + ActivationFunctionWithMinMax(total + bias_value, + output_activation_min, + output_activation_max); + } + } + } + } +} + inline void Conv(const float* input_data, const Dims<4>& input_dims, const float* filter_data, const Dims<4>& filter_dims, const float* bias_data, const Dims<4>& bias_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, float output_activation_min, - float output_activation_max, float* output_data, - const Dims<4>& output_dims, float* im2col_data, - const Dims<4>& im2col_dims) { + int stride_width, int stride_height, int dilation_width_factor, + int dilation_height_factor, int pad_width, int pad_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims, + float* im2col_data, const Dims<4>& im2col_dims) { + if ((dilation_width_factor != 1) || (dilation_height_factor != 1)) { + return DilatedConv(input_data, input_dims, filter_data, filter_dims, + bias_data, bias_dims, stride_width, stride_height, + dilation_width_factor, dilation_height_factor, pad_width, + pad_height, output_activation_min, output_activation_max, + output_data, output_dims, im2col_data, im2col_dims); + } + (void)im2col_data; (void)im2col_dims; gemmlowp::ScopedProfilingLabel label("Conv"); @@ -805,6 +881,23 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, output_activation_max); } +template +void Conv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + const float* bias_data, const Dims<4>& bias_dims, int stride_width, + int stride_height, int dilation_width_factor, + int dilation_height_factor, int pad_width, int pad_height, + float* output_data, const Dims<4>& output_dims, float* im2col_data, + const Dims<4>& im2col_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, + stride_width, stride_height, dilation_width_factor, + dilation_height_factor, pad_width, pad_height, output_activation_min, + output_activation_max, output_data, output_dims, im2col_data, + im2col_dims); +} + // legacy, for compatibility with old checked-in code template void Conv(const float* input_data, const Dims<4>& input_dims, @@ -816,7 +909,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims, float output_activation_min, output_activation_max; GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, - stride_width, stride_height, pad_width, pad_height, + stride_width, stride_height, 1, 1, pad_width, pad_height, output_activation_min, output_activation_max, output_data, output_dims, im2col_data, im2col_dims); } @@ -830,7 +923,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims, const Dims<4>& output_dims, float* im2col_data, const Dims<4>& im2col_dims) { Conv(input_data, input_dims, filter_data, filter_dims, bias_data, - bias_dims, stride, stride, pad_width, pad_height, output_data, + bias_dims, stride, stride, 1, 1, pad_width, pad_height, output_data, output_dims, im2col_data, im2col_dims); } @@ -2081,6 +2174,458 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, output_state_map.tanh(); } +#ifdef GEMMLOWP_NEON +// In the common case of batch size 1, a fully-connected node degenerates +// to a matrix*vector product. LSTM cells contain a fully-connected node; +// when quantized, this becomes a special type of GEMV operation where +// the output is 16bit-quantized, thus needs its own special path. +inline void GEMVForLstmCell(const uint8* input_data, const Dims<4>& input_dims, + const uint8* weights_data, + const Dims<4>& weights_dims, + uint8 weights_zero_point, const int32* bias_data, + const Dims<4>& bias_dims, int32 accum_multiplier, + int accum_shift, int16* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("GEMVForLstmCell"); + TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(weights_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(bias_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + TFLITE_DCHECK_EQ(ArraySize(output_dims, 1) * ArraySize(output_dims, 2) * + ArraySize(output_dims, 3), + 1); + const int input_size = input_dims.strides[3]; + const int output_size = MatchingArraySize(weights_dims, 1, output_dims, 0); + // This special fast path for quantized LSTM cells does not try to support + // odd sizes that we haven't encountered in any LSTM cell, that would + // require special code (that would go untested until any LSTM cell + // exercises it). We just guard our assumptions about size evenness with + // the following assertions. + TFLITE_DCHECK(!(output_size % 4)); + TFLITE_DCHECK(!(input_size % 8)); + const int32* bias_ptr = bias_data; + int16* output_ptr = output_data; + for (int out = 0; out < output_size; out += 4) { + int32x4_t acc_0 = vdupq_n_s32(0); + int32x4_t acc_1 = vdupq_n_s32(0); + int32x4_t acc_2 = vdupq_n_s32(0); + int32x4_t acc_3 = vdupq_n_s32(0); + const int16x8_t input_offset_vec = vdupq_n_s16(-128); + const int16x8_t weights_offset_vec = vdupq_n_s16(-weights_zero_point); + int in = 0; + // Handle 16 levels of depth at a time. + for (; in <= input_size - 16; in += 16) { + const uint8x16_t input_val_u8 = vld1q_u8(input_data + in); + const uint8* weights_ptr = weights_data + in + out * input_size; + uint8x16_t weights_val_u8_0 = vld1q_u8(weights_ptr + 0 * input_size); + uint8x16_t weights_val_u8_1 = vld1q_u8(weights_ptr + 1 * input_size); + uint8x16_t weights_val_u8_2 = vld1q_u8(weights_ptr + 2 * input_size); + uint8x16_t weights_val_u8_3 = vld1q_u8(weights_ptr + 3 * input_size); + int16x8_t input_val_0, input_val_1; + const uint8x8_t low = vget_low_u8(input_val_u8); + const uint8x8_t high = vget_high_u8(input_val_u8); + input_val_0 = vreinterpretq_s16_u16(vmovl_u8(low)); + input_val_1 = vreinterpretq_s16_u16(vmovl_u8(high)); + input_val_0 = vaddq_s16(input_val_0, input_offset_vec); + input_val_1 = vaddq_s16(input_val_1, input_offset_vec); + int16x8_t weights_val_0_0, weights_val_1_0, weights_val_2_0, + weights_val_3_0; + int16x8_t weights_val_0_1, weights_val_1_1, weights_val_2_1, + weights_val_3_1; + weights_val_0_0 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_0))), + weights_offset_vec); + weights_val_0_1 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_0))), + weights_offset_vec); + weights_val_1_0 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_1))), + weights_offset_vec); + weights_val_1_1 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_1))), + weights_offset_vec); + weights_val_2_0 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_2))), + weights_offset_vec); + weights_val_2_1 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_2))), + weights_offset_vec); + weights_val_3_0 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_3))), + weights_offset_vec); + weights_val_3_1 = vaddq_s16( + vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_3))), + weights_offset_vec); + acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_0), + vget_low_s16(input_val_0)); + acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_0), + vget_low_s16(input_val_0)); + acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_0), + vget_low_s16(input_val_0)); + acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_0), + vget_low_s16(input_val_0)); + acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_0), + vget_high_s16(input_val_0)); + acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_0), + vget_high_s16(input_val_0)); + acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_0), + vget_high_s16(input_val_0)); + acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_0), + vget_high_s16(input_val_0)); + acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_1), + vget_low_s16(input_val_1)); + acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_1), + vget_low_s16(input_val_1)); + acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_1), + vget_low_s16(input_val_1)); + acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_1), + vget_low_s16(input_val_1)); + acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_1), + vget_high_s16(input_val_1)); + acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_1), + vget_high_s16(input_val_1)); + acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_1), + vget_high_s16(input_val_1)); + acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_1), + vget_high_s16(input_val_1)); + } + // Handle 8 levels of depth at a time. + for (; in < input_size; in += 8) { + const uint8x8_t input_val_u8 = vld1_u8(input_data + in); + const uint8* weights_ptr = weights_data + in + out * input_size; + uint8x8_t weights_val_u8_0 = vld1_u8(weights_ptr + 0 * input_size); + uint8x8_t weights_val_u8_1 = vld1_u8(weights_ptr + 1 * input_size); + uint8x8_t weights_val_u8_2 = vld1_u8(weights_ptr + 2 * input_size); + uint8x8_t weights_val_u8_3 = vld1_u8(weights_ptr + 3 * input_size); + int16x8_t input_val; + input_val = vreinterpretq_s16_u16(vmovl_u8(input_val_u8)); + input_val = vaddq_s16(input_val, input_offset_vec); + int16x8_t weights_val_0, weights_val_1, weights_val_2, weights_val_3; + weights_val_0 = + vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_0)), + weights_offset_vec); + weights_val_1 = + vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_1)), + weights_offset_vec); + weights_val_2 = + vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_2)), + weights_offset_vec); + weights_val_3 = + vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_3)), + weights_offset_vec); + acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0), + vget_low_s16(input_val)); + acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1), + vget_low_s16(input_val)); + acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2), + vget_low_s16(input_val)); + acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3), + vget_low_s16(input_val)); + acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0), + vget_high_s16(input_val)); + acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1), + vget_high_s16(input_val)); + acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2), + vget_high_s16(input_val)); + acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3), + vget_high_s16(input_val)); + } + // Horizontally reduce accumulators + int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1, + pairwise_reduced_acc_2, pairwise_reduced_acc_3; + pairwise_reduced_acc_0 = + vpadd_s32(vget_low_s32(acc_0), vget_high_s32(acc_0)); + pairwise_reduced_acc_1 = + vpadd_s32(vget_low_s32(acc_1), vget_high_s32(acc_1)); + pairwise_reduced_acc_2 = + vpadd_s32(vget_low_s32(acc_2), vget_high_s32(acc_2)); + pairwise_reduced_acc_3 = + vpadd_s32(vget_low_s32(acc_3), vget_high_s32(acc_3)); + const int32x2_t reduced_lo = + vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1); + const int32x2_t reduced_hi = + vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3); + int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); + // Add bias values. + int32x4_t bias_vec = vld1q_s32(bias_ptr); + bias_ptr += 4; + reduced = vaddq_s32(reduced, bias_vec); + int left_shift = accum_shift > 0 ? accum_shift : 0; + int right_shift = accum_shift > 0 ? 0 : -accum_shift; + reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift)); + // Multiply by the fixed-point multiplier. + reduced = vqrdmulhq_n_s32(reduced, accum_multiplier); + // Rounding-shift-right. + using gemmlowp::RoundingDivideByPOT; + reduced = RoundingDivideByPOT(reduced, right_shift); + // Narrow values down to 16 bit signed. + const int16x4_t res16 = vqmovn_s32(reduced); + vst1_s16(output_ptr, res16); + output_ptr += 4; + } +} +#endif + +// Quantized LSTM cell. Currently just a copy of the reference impl in +// reference_ops.h. See the big function comment there, not replicating it +// here. +template +void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims, + const uint8* prev_activ_data_uint8, + const Dims<4>& prev_activ_dims, const uint8* weights_data_uint8, + const Dims<4>& weights_dims, const int32* bias_data_int32, + const Dims<4>& bias_dims, const int16* prev_state_data_int16, + const Dims<4>& prev_state_dims, int16* output_state_data_int16, + const Dims<4>& output_state_dims, uint8* output_activ_data_uint8, + const Dims<4>& output_activ_dims, uint8* concat_temp_data_uint8, + const Dims<4>& concat_temp_dims, int16* activ_temp_data_int16, + const Dims<4>& activ_temp_dims, int32 weights_zero_point, + int32 accum_multiplier, int accum_shift, + gemmlowp::GemmContext* gemm_context) { + gemmlowp::ScopedProfilingLabel label( + "LstmCell/quantized (8bit external, 16bit internal)"); + // Gather dimensions information, and perform consistency checks. + const int batches = + MatchingArraySize(input_dims, 3, prev_activ_dims, 3, prev_state_dims, 3, + output_state_dims, 3, output_activ_dims, 3); + const int height = + MatchingArraySize(input_dims, 2, prev_activ_dims, 2, prev_state_dims, 2, + output_state_dims, 2, output_activ_dims, 2); + const int width = + MatchingArraySize(input_dims, 1, prev_activ_dims, 1, prev_state_dims, 1, + output_state_dims, 1, output_activ_dims, 1); + TFLITE_CHECK_EQ(ArraySize(weights_dims, 2), 1); + TFLITE_CHECK_EQ(ArraySize(weights_dims, 3), 1); + const int input_depth = ArraySize(input_dims, 0); + const int prev_activ_depth = ArraySize(prev_activ_dims, 0); + const int total_input_depth = prev_activ_depth + input_depth; + TFLITE_CHECK_EQ(ArraySize(weights_dims, 0), total_input_depth); + TFLITE_CHECK_EQ(MatchingArraySize(bias_dims, 1, bias_dims, 2, bias_dims, 3), + 1); + const int intern_activ_depth = + MatchingArraySize(weights_dims, 1, bias_dims, 0); + TFLITE_CHECK_EQ(intern_activ_depth % 4, 0); + const int output_depth = + MatchingArraySize(prev_state_dims, 0, prev_activ_dims, 0, + output_state_dims, 0, output_activ_dims, 0); + TFLITE_CHECK_EQ(output_depth, intern_activ_depth / 4); + const int fc_batches = ArraySize(activ_temp_dims, 1) * + ArraySize(activ_temp_dims, 2) * + ArraySize(activ_temp_dims, 3); + const int fc_output_depth = + MatchingArraySize(weights_dims, 1, activ_temp_dims, 0); + const int fc_accum_depth = ArraySize(weights_dims, 0); + TFLITE_CHECK_EQ(fc_output_depth, 4 * output_depth); + + // Depth-concatenate prev_activ and input data together. + uint8 const* concat_input_arrays_data[2] = {input_data_uint8, + prev_activ_data_uint8}; + Dims<4> const* concat_input_arrays_dims[2] = {&input_dims, &prev_activ_dims}; + Concatenation( + 0, concat_input_arrays_data, concat_input_arrays_dims, 2, + concat_temp_data_uint8, concat_temp_dims); + + // Implementation of the fully connected node inside the LSTM cell. + // The operands are 8-bit integers, the accumulators are internally 32bit + // integers, and the output is 16-bit fixed-point with 3 integer bits so + // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that + // is explained in the function comment above. + bool gemm_already_performed = false; +#ifdef GEMMLOWP_NEON + if (fc_batches == 1 && !(fc_output_depth % 4) && !(fc_accum_depth % 8)) { + GEMVForLstmCell(concat_temp_data_uint8, concat_temp_dims, + weights_data_uint8, weights_dims, weights_zero_point, + bias_data_int32, bias_dims, accum_multiplier, accum_shift, + activ_temp_data_int16, activ_temp_dims); + gemm_already_performed = true; + } +#endif + if (!gemm_already_performed) { + gemmlowp::MatrixMap + weights_matrix(weights_data_uint8, fc_output_depth, fc_accum_depth); + gemmlowp::MatrixMap input_matrix( + concat_temp_data_uint8, fc_accum_depth, fc_batches); + gemmlowp::MatrixMap output_matrix( + activ_temp_data_int16, fc_output_depth, fc_batches); + typedef gemmlowp::VectorMap + ColVectorMap; + ColVectorMap bias_vector(bias_data_int32, fc_output_depth); + gemmlowp::OutputStageBiasAddition bias_addition_stage; + bias_addition_stage.bias_vector = bias_vector; + gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage; + scale_stage.result_offset_after_shift = 0; + scale_stage.result_fixedpoint_multiplier = accum_multiplier; + scale_stage.result_exponent = accum_shift; + gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage; + auto output_pipeline = std::make_tuple(bias_addition_stage, scale_stage, + saturating_cast_int16_stage); + gemmlowp::GemmWithOutputPipeline< + uint8, int16, gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( + gemm_context, weights_matrix, input_matrix, &output_matrix, + -weights_zero_point, -128, output_pipeline); + } + + // Rest of the LSTM cell: tanh and logistic math functions, and some adds + // and muls, all done in 16-bit fixed-point. + const int outer_size = batches * width * height; + const int16* input_gate_input_ptr = activ_temp_data_int16; + const int16* input_modulation_gate_input_ptr = + activ_temp_data_int16 + output_depth; + const int16* forget_gate_input_ptr = activ_temp_data_int16 + 2 * output_depth; + const int16* output_gate_input_ptr = activ_temp_data_int16 + 3 * output_depth; + const int16* prev_state_ptr = prev_state_data_int16; + int16* output_state_data_ptr = output_state_data_int16; + uint8* output_activ_data_ptr = output_activ_data_uint8; + + for (int b = 0; b < outer_size; ++b) { + int c = 0; +#ifdef GEMMLOWP_NEON + for (; c <= output_depth - 8; c += 8) { + // Define the fixed-point data types that we will use here. All use + // int16 as the underlying integer type i.e. all are 16-bit fixed-point. + // They only differ by the number of integral vs. fractional bits, + // determining the range of values that they can represent. + // + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8]. + // This is the range of the previous fully-connected node's output, + // which is our input here. + using F3 = gemmlowp::FixedPoint; + // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, + // 2^StateIntegerBits]. It's used to represent the internal state, whose + // number of integer bits is currently dictated by the model. See comment + // on the StateIntegerBits template parameter above. + using FS = gemmlowp::FixedPoint; + // Implementation of input gate, using fixed-point logistic function. + F3 input_gate_input = F3::FromRaw(vld1q_s16(input_gate_input_ptr)); + input_gate_input_ptr += 8; + F0 input_gate_output = gemmlowp::logistic(input_gate_input); + // Implementation of input modulation gate, using fixed-point tanh + // function. + F3 input_modulation_gate_input = + F3::FromRaw(vld1q_s16(input_modulation_gate_input_ptr)); + input_modulation_gate_input_ptr += 8; + F0 input_modulation_gate_output = + gemmlowp::tanh(input_modulation_gate_input); + // Implementation of forget gate, using fixed-point logistic function. + F3 forget_gate_input = F3::FromRaw(vld1q_s16(forget_gate_input_ptr)); + forget_gate_input_ptr += 8; + F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); + // Implementation of output gate, using fixed-point logistic function. + F3 output_gate_input = F3::FromRaw(vld1q_s16(output_gate_input_ptr)); + output_gate_input_ptr += 8; + F0 output_gate_output = gemmlowp::logistic(output_gate_input); + // Implementation of internal multiplication nodes, still in fixed-point. + F0 input_times_input_modulation = + input_gate_output * input_modulation_gate_output; + FS prev_state = FS::FromRaw(vld1q_s16(prev_state_ptr)); + prev_state_ptr += 8; + FS prev_state_times_forget_state = forget_gate_output * prev_state; + // Implementation of internal addition node, saturating. + FS new_state = gemmlowp::SaturatingAdd( + gemmlowp::Rescale(input_times_input_modulation), + prev_state_times_forget_state); + // Implementation of last internal Tanh node, still in fixed-point. + // Since a Tanh fixed-point implementation is specialized for a given + // number or integer bits, and each specialization can have a substantial + // code size, and we already used above a Tanh on an input with 3 integer + // bits, and per the table in the above function comment there is no + // significant accuracy to be lost by clamping to [-8, +8] for a + // 3-integer-bits representation, let us just do that. This helps people + // porting this to targets where code footprint must be minimized. + F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); + F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); + // Store the new internal state back to memory, as 16-bit integers. + // Note: here we store the original value with StateIntegerBits, not + // the rescaled 3-integer-bits value fed to tanh. + vst1q_s16(output_state_data_ptr, new_state.raw()); + output_state_data_ptr += 8; + // Down-scale the output activations to 8-bit integers, saturating, + // and store back to memory. + int16x8_t rescaled_output_activ = + gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); + int8x8_t int8_output_activ = vqmovn_s16(rescaled_output_activ); + uint8x8_t uint8_output_activ = + vadd_u8(vdup_n_u8(128), vreinterpret_u8_s8(int8_output_activ)); + vst1_u8(output_activ_data_ptr, uint8_output_activ); + output_activ_data_ptr += 8; + } +#endif + for (; c < output_depth; ++c) { + // Define the fixed-point data types that we will use here. All use + // int16 as the underlying integer type i.e. all are 16-bit fixed-point. + // They only differ by the number of integral vs. fractional bits, + // determining the range of values that they can represent. + // + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8]. + // This is the range of the previous fully-connected node's output, + // which is our input here. + using F3 = gemmlowp::FixedPoint; + // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, + // 2^StateIntegerBits]. It's used to represent the internal state, whose + // number of integer bits is currently dictated by the model. See comment + // on the StateIntegerBits template parameter above. + using FS = gemmlowp::FixedPoint; + // Implementation of input gate, using fixed-point logistic function. + F3 input_gate_input = F3::FromRaw(*input_gate_input_ptr++); + F0 input_gate_output = gemmlowp::logistic(input_gate_input); + // Implementation of input modulation gate, using fixed-point tanh + // function. + F3 input_modulation_gate_input = + F3::FromRaw(*input_modulation_gate_input_ptr++); + F0 input_modulation_gate_output = + gemmlowp::tanh(input_modulation_gate_input); + // Implementation of forget gate, using fixed-point logistic function. + F3 forget_gate_input = F3::FromRaw(*forget_gate_input_ptr++); + F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); + // Implementation of output gate, using fixed-point logistic function. + F3 output_gate_input = F3::FromRaw(*output_gate_input_ptr++); + F0 output_gate_output = gemmlowp::logistic(output_gate_input); + // Implementation of internal multiplication nodes, still in fixed-point. + F0 input_times_input_modulation = + input_gate_output * input_modulation_gate_output; + FS prev_state = FS::FromRaw(*prev_state_ptr++); + FS prev_state_times_forget_state = forget_gate_output * prev_state; + // Implementation of internal addition node, saturating. + FS new_state = gemmlowp::SaturatingAdd( + gemmlowp::Rescale(input_times_input_modulation), + prev_state_times_forget_state); + // Implementation of last internal Tanh node, still in fixed-point. + // Since a Tanh fixed-point implementation is specialized for a given + // number or integer bits, and each specialization can have a substantial + // code size, and we already used above a Tanh on an input with 3 integer + // bits, and per the table in the above function comment there is no + // significant accuracy to be lost by clamping to [-8, +8] for a + // 3-integer-bits representation, let us just do that. This helps people + // porting this to targets where code footprint must be minimized. + F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); + F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); + // Store the new internal state back to memory, as 16-bit integers. + // Note: here we store the original value with StateIntegerBits, not + // the rescaled 3-integer-bits value fed to tanh. + *output_state_data_ptr++ = new_state.raw(); + // Down-scale the output activations to 8-bit integers, saturating, + // and store back to memory. + int16 rescaled_output_activ = + gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); + int16 clamped_output_activ = + std::max(-128, std::min(127, rescaled_output_activ)); + *output_activ_data_ptr++ = 128 + clamped_output_activ; + } + input_gate_input_ptr += 3 * output_depth; + input_modulation_gate_input_ptr += 3 * output_depth; + forget_gate_input_ptr += 3 * output_depth; + output_gate_input_ptr += 3 * output_depth; + } +} + template void TensorFlowSplit(const Scalar* input_data, const Dims<4>& input_dims, int outputs_count, Scalar* const* output_data, @@ -2706,74 +3251,231 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - gemmlowp::ScopedProfilingLabel label("Softmax"); + gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int height = MatchingArraySize(input_dims, 2, output_dims, 2); + const int width = MatchingArraySize(input_dims, 1, output_dims, 1); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + + const int outer_size = batches * height * width; + + for (int b = 0; b < outer_size; ++b) { + const uint8* input_data_ptr = input_data + b * depth; + uint8* output_data_ptr = output_data + b * depth; + + // Determine the largest entry in the current row + uint8 max_in_row = 0; + { + int c = 0; +#ifdef USE_NEON + uint8x16_t max16_0 = vdupq_n_u8(0); + uint8x16_t max16_1 = vdupq_n_u8(0); + for (; c <= depth - 32; c += 32) { + max16_0 = vmaxq_u8(max16_0, vld1q_u8(input_data_ptr + c + 0)); + max16_1 = vmaxq_u8(max16_1, vld1q_u8(input_data_ptr + c + 16)); + } + uint8x16_t max16 = vmaxq_u8(max16_0, max16_1); + if (c <= depth - 16) { + max16 = vmaxq_u8(max16, vld1q_u8(input_data_ptr + c)); + c += 16; + } + uint8x8_t max8 = vmax_u8(vget_low_u8(max16), vget_high_u8(max16)); + if (c <= depth - 8) { + max8 = vmax_u8(max8, vld1_u8(input_data_ptr + c)); + c += 8; + } + uint8x8_t max4 = vmax_u8(max8, vext_u8(max8, max8, 4)); + uint8x8_t max2 = vmax_u8(max4, vext_u8(max4, max4, 2)); + uint8x8_t max1 = vpmax_u8(max2, max2); + max_in_row = vget_lane_u8(max1, 0); +#endif + for (; c < depth; ++c) { + max_in_row = std::max(max_in_row, input_data_ptr[c]); + } + } + +#ifdef USE_NEON + using FixedPointAccumInt32x4 = + gemmlowp::FixedPoint; + using FixedPointScaledDiffInt32x4 = + gemmlowp::FixedPoint; + using FixedPoint0Int32x4 = gemmlowp::FixedPoint; + FixedPoint0Int32x4 input_beta_multiplier_f0 = + FixedPoint0Int32x4::FromScalarRaw(input_beta_multiplier); + int16x8_t max_in_row_s16 = vdupq_n_s16(max_in_row); +#endif + + // Compute the sum of exponentials of the differences of entries in the + // current row from the largest entry in the current row. + FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); + { + int c = 0; +#ifdef USE_NEON + int32x4_t diff_min_s32 = vdupq_n_s32(diff_min); + FixedPointAccumInt32x4 sum_of_exps_0 = FixedPointAccumInt32x4::Zero(); + FixedPointAccumInt32x4 sum_of_exps_1 = FixedPointAccumInt32x4::Zero(); + FixedPointAccumInt32x4 zeros = FixedPointAccumInt32x4::Zero(); + for (; c <= depth - 8; c += 8) { + uint16x8_t input_u16 = vmovl_u8(vld1_u8(input_data_ptr + c)); + int16x8_t input_diff_s16 = + vsubq_s16(vreinterpretq_s16_u16(input_u16), max_in_row_s16); + int32x4_t input_diff_s32_0 = vmovl_s16(vget_low_s16(input_diff_s16)); + int32x4_t input_diff_s32_1 = vmovl_s16(vget_high_s16(input_diff_s16)); + int32x4_t mask_0 = + gemmlowp::MaskIfGreaterThanOrEqual(input_diff_s32_0, diff_min_s32); + int32x4_t mask_1 = + gemmlowp::MaskIfGreaterThanOrEqual(input_diff_s32_1, diff_min_s32); + FixedPointScaledDiffInt32x4 scaled_diff_0 = + input_beta_multiplier_f0 * + FixedPointScaledDiffInt32x4::FromRaw( + gemmlowp::ShiftLeft(input_diff_s32_0, input_beta_left_shift)); + FixedPointScaledDiffInt32x4 scaled_diff_1 = + input_beta_multiplier_f0 * + FixedPointScaledDiffInt32x4::FromRaw( + gemmlowp::ShiftLeft(input_diff_s32_1, input_beta_left_shift)); + FixedPointAccumInt32x4 exps_0 = + gemmlowp::Rescale( + exp_on_negative_values(scaled_diff_0)); + FixedPointAccumInt32x4 exps_1 = + gemmlowp::Rescale( + exp_on_negative_values(scaled_diff_1)); + FixedPointAccumInt32x4 masked_exps_0 = + SelectUsingMask(mask_0, exps_0, zeros); + FixedPointAccumInt32x4 masked_exps_1 = + SelectUsingMask(mask_1, exps_1, zeros); + sum_of_exps_0 = sum_of_exps_0 + masked_exps_0; + sum_of_exps_1 = sum_of_exps_1 + masked_exps_1; + } + int32x4_t sum_of_exps_reduced_4 = (sum_of_exps_0 + sum_of_exps_1).raw(); + int32x2_t sum_of_exps_reduced_2 = + vadd_s32(vget_low_s32(sum_of_exps_reduced_4), + vget_high_s32(sum_of_exps_reduced_4)); + int32x2_t sum_of_exps_reduced_1 = + vpadd_s32(sum_of_exps_reduced_2, sum_of_exps_reduced_2); + sum_of_exps = + FixedPointAccum::FromRaw(vget_lane_s32(sum_of_exps_reduced_1, 0)); +#endif + for (; c < depth; ++c) { + int32 input_diff = static_cast(input_data_ptr[c]) - max_in_row; + if (input_diff >= diff_min) { + const int32 input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_beta_multiplier, input_beta_left_shift); + const FixedPointScaledDiff scaled_diff_f8 = + FixedPointScaledDiff::FromRaw(input_diff_rescaled); + sum_of_exps = + sum_of_exps + gemmlowp::Rescale( + exp_on_negative_values(scaled_diff_f8)); + } + } + } + + // Compute the fixed-point multiplier and shift that we need to apply to + // perform a division by the above-computed sum-of-exponentials. + int32 fixed_sum_of_exps = sum_of_exps.raw(); + int headroom_plus_one = + __builtin_clz(static_cast(fixed_sum_of_exps)); + // This is the number of bits to the left of the binary point above 1.0. + // Consider fixed_sum_of_exps=1.25. In that case shifted_scale=0.8 and + // no later adjustment will be needed. + int num_bits_over_unit = kAccumulationIntegerBits - headroom_plus_one; + int32 shifted_sum_minus_one = static_cast( + (static_cast(fixed_sum_of_exps) << headroom_plus_one) - + (static_cast(1) << 31)); + FixedPoint0 shifted_scale = gemmlowp::one_over_one_plus_x_for_x_in_0_1( + FixedPoint0::FromRaw(shifted_sum_minus_one)); + + // Compute the quotients of exponentials of differences of entries in the + // current row from the largest entry, over the previously-computed sum of + // exponentials. + { + int c = 0; +#ifdef USE_NEON + int16x8_t diff_min_s16 = vdupq_n_s16(diff_min); + for (; c <= depth - 8; c += 8) { + uint16x8_t input_u16 = vmovl_u8(vld1_u8(input_data_ptr + c)); + int16x8_t input_diff_s16 = + vsubq_s16(vreinterpretq_s16_u16(input_u16), max_in_row_s16); + int32x4_t input_diff_s32_0 = vmovl_s16(vget_low_s16(input_diff_s16)); + int32x4_t input_diff_s32_1 = vmovl_s16(vget_high_s16(input_diff_s16)); + uint8x8_t mask = vmovn_u16(vcgeq_s16(input_diff_s16, diff_min_s16)); + FixedPointScaledDiffInt32x4 scaled_diff_0 = + input_beta_multiplier_f0 * + FixedPointScaledDiffInt32x4::FromRaw( + gemmlowp::ShiftLeft(input_diff_s32_0, input_beta_left_shift)); + FixedPointScaledDiffInt32x4 scaled_diff_1 = + input_beta_multiplier_f0 * + FixedPointScaledDiffInt32x4::FromRaw( + gemmlowp::ShiftLeft(input_diff_s32_1, input_beta_left_shift)); + FixedPoint0Int32x4 exp_0 = exp_on_negative_values(scaled_diff_0); + FixedPoint0Int32x4 exp_1 = exp_on_negative_values(scaled_diff_1); + int32x4_t output_s32_0 = gemmlowp::RoundingDivideByPOT( + vqrdmulhq_n_s32(exp_0.raw(), shifted_scale.raw()), + num_bits_over_unit + 31 - 8); + int32x4_t output_s32_1 = gemmlowp::RoundingDivideByPOT( + vqrdmulhq_n_s32(exp_1.raw(), shifted_scale.raw()), + num_bits_over_unit + 31 - 8); + int16x8_t output_s16 = + vcombine_s16(vqmovn_s32(output_s32_0), vqmovn_s32(output_s32_1)); + uint8x8_t output_u8 = vqmovun_s16(output_s16); + uint8x8_t masked_output = vbsl_u8(mask, output_u8, vdup_n_u8(0)); + vst1_u8(output_data_ptr + c, masked_output); + } +#endif + for (; c < depth; ++c) { + int32 input_diff = static_cast(input_data_ptr[c]) - max_in_row; + if (input_diff >= diff_min) { + const int32 input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_beta_multiplier, input_beta_left_shift); + const FixedPointScaledDiff scaled_diff_f8 = + FixedPointScaledDiff::FromRaw(input_diff_rescaled); + + FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); + int32 unsat_output = gemmlowp::RoundingDivideByPOT( + (shifted_scale * exp_in_0).raw(), num_bits_over_unit + 31 - 8); + + output_data_ptr[c] = std::max(std::min(unsat_output, 255), 0); + + } else { + output_data_ptr[c] = 0; + } + } + } + } +} + +// TODO(myenik): This is the same as the reference implementation, not actually +// optimized yet. +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); const int height = MatchingArraySize(input_dims, 2, output_dims, 2); const int width = MatchingArraySize(input_dims, 1, output_dims, 1); const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int b = 0; b < batches; ++b) { - for (int x = 0; x < width; ++x) { - for (int y = 0; y < height; ++y) { - uint8 max_in_row = 0; + for (int y = 0; y < height; ++y) { + for (int x = 0; x < width; ++x) { + // Find max element value which we'll use to ensure numerical stability + // taking advantage of the following equality: + // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C))) + float max = std::numeric_limits::lowest(); for (int c = 0; c < depth; ++c) { - max_in_row = - std::max(max_in_row, input_data[Offset(input_dims, c, x, y, b)]); + max = std::max(max, input_data[Offset(input_dims, c, x, y, b)]); } - FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); + // Compute sum. + float sum = 0.f; for (int c = 0; c < depth; ++c) { - int32 input_diff = - static_cast(input_data[Offset(input_dims, c, x, y, b)]) - - max_in_row; - if (input_diff >= diff_min) { - const int32 input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - sum_of_exps = - sum_of_exps + gemmlowp::Rescale( - exp_on_negative_values(scaled_diff_f8)); - } + sum += std::exp(input_data[Offset(input_dims, c, x, y, b)] - max); } - int32 fixed_sum_of_exps = sum_of_exps.raw(); - // TODO(starka): Use a NEON intrinsic like vclzq_u32 instead. - int headroom_plus_one = - __builtin_clz(static_cast(fixed_sum_of_exps)); - // This is the number of bits to the left of the binary point above 1.0. - // Consider fixed_sum_of_exps=1.25. In that case shifted_scale=0.8 and - // no later adjustment will be needed. - int num_bits_over_unit = kAccumulationIntegerBits - headroom_plus_one; - int32 shifted_sum_minus_one = static_cast( - (static_cast(fixed_sum_of_exps) << headroom_plus_one) - - (static_cast(1) << 31)); - - FixedPoint0 shifted_scale = gemmlowp::one_over_one_plus_x_for_x_in_0_1( - FixedPoint0::FromRaw(shifted_sum_minus_one)); - + // Compute result. + const float log_sum = std::log(sum); for (int c = 0; c < depth; ++c) { - int32 input_diff = - static_cast(input_data[Offset(input_dims, c, x, y, b)]) - - max_in_row; - if (input_diff >= diff_min) { - const int32 input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - - FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); - int32 unsat_output = gemmlowp::RoundingDivideByPOT( - (shifted_scale * exp_in_0).raw(), num_bits_over_unit + 31 - 8); - - output_data[Offset(output_dims, c, x, y, b)] = - std::max(std::min(unsat_output, 255), 0); - - } else { - output_data[Offset(output_dims, c, x, y, b)] = 0; - } + output_data[Offset(output_dims, c, x, y, b)] = + input_data[Offset(input_dims, c, x, y, b)] - max - log_sum; } } } @@ -2942,51 +3644,152 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, uint8* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int height = MatchingArraySize(input_dims, 2, output_dims, 2); - const int width = MatchingArraySize(input_dims, 1, output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < height; ++y) { - for (int x = 0; x < width; ++x) { - for (int c = 0; c < depth; ++c) { - const uint8 input_val_u8 = input_data[Offset(input_dims, c, x, y, b)]; - const int32 input_val_centered = - static_cast(input_val_u8) - input_zero_point; - uint8 output_val; - if (input_val_centered <= -input_range_radius) { - output_val = 0; - } else if (input_val_centered >= input_range_radius) { - output_val = 255; - } else { - const int32 input_val_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_val_centered, input_multiplier, input_left_shift); - using FixedPoint4 = gemmlowp::FixedPoint; - using FixedPoint0 = gemmlowp::FixedPoint; - const FixedPoint4 input_val_f4 = - FixedPoint4::FromRaw(input_val_rescaled); - const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); - - using gemmlowp::RoundingDivideByPOT; - int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); - // TODO(mjmatthews): properly wire through this zero offset - output_val_s32 += 127; - if (output_val_s32 == -1) { - // May underflow since we cannot properly represent -1.0f - output_val_s32 = 0; - } - TFLITE_DCHECK_GE(output_val_s32, 0); - TFLITE_DCHECK_LE(output_val_s32, 255); - output_val = static_cast(output_val_s32); - } - output_data[Offset(output_dims, c, x, y, b)] = output_val; - } + // Note that this is almost the exact same code as in Logistic(). + gemmlowp::ScopedProfilingLabel label("Tanh"); + /* batches */ MatchingArraySize(input_dims, 3, output_dims, 3); + /* height */ MatchingArraySize(input_dims, 2, output_dims, 2); + /* width */ MatchingArraySize(input_dims, 1, output_dims, 1); + /* depth */ MatchingArraySize(input_dims, 0, output_dims, 0); + const int size = RequiredBufferSizeForDims(input_dims); + + int c = 0; + int32_t output_zero_point = 128; +#ifdef USE_NEON + // Handle 16 values at a time + for (; c <= size - 16; c += 16) { + // Read input uint8 values, cast to int16 and subtract input_zero_point + uint8x16_t input_val_u8 = vld1q_u8(input_data + c); + int16x8_t input_val_centered_0 = + vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(input_val_u8))), + vdupq_n_s16(input_zero_point)); + int16x8_t input_val_centered_1 = + vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(input_val_u8))), + vdupq_n_s16(input_zero_point)); + + // Prepare the bit masks that we will use at the end to implement the logic + // that was expressed in the scalar code with branching: + // if (input_val_centered < -input_range_radius) { + // output_val = 0; + // } else if (input_val_centered > input_range_radius) { + // output_val = 255; + // } else { + // ... + uint16x8_t mask_rightclamp_0 = + vcgtq_s16(input_val_centered_0, vdupq_n_s16(input_range_radius)); + uint16x8_t mask_rightclamp_1 = + vcgtq_s16(input_val_centered_1, vdupq_n_s16(input_range_radius)); + uint16x8_t mask_leftclamp_0 = + vcgeq_s16(input_val_centered_0, vdupq_n_s16(-input_range_radius)); + uint16x8_t mask_leftclamp_1 = + vcgeq_s16(input_val_centered_1, vdupq_n_s16(-input_range_radius)); + uint8x16_t mask_rightclamp = vcombine_u8(vshrn_n_u16(mask_rightclamp_0, 8), + vshrn_n_u16(mask_rightclamp_1, 8)); + uint8x16_t mask_leftclamp = vcombine_u8(vshrn_n_u16(mask_leftclamp_0, 8), + vshrn_n_u16(mask_leftclamp_1, 8)); + + // This performs what is expressed in the scalar code as + // const int32 input_val_rescaled = + // MultiplyByQuantizedMultiplierGreaterThanOne( + // input_val_centered, input_multiplier, input_left_shift); + int32x4_t input_val_rescaled_0 = + vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_0)), + vdupq_n_s32(input_left_shift)); + int32x4_t input_val_rescaled_1 = + vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_0)), + vdupq_n_s32(input_left_shift)); + int32x4_t input_val_rescaled_2 = + vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_1)), + vdupq_n_s32(input_left_shift)); + int32x4_t input_val_rescaled_3 = + vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_1)), + vdupq_n_s32(input_left_shift)); + input_val_rescaled_0 = + vqrdmulhq_n_s32(input_val_rescaled_0, input_multiplier); + input_val_rescaled_1 = + vqrdmulhq_n_s32(input_val_rescaled_1, input_multiplier); + input_val_rescaled_2 = + vqrdmulhq_n_s32(input_val_rescaled_2, input_multiplier); + input_val_rescaled_3 = + vqrdmulhq_n_s32(input_val_rescaled_3, input_multiplier); + + // Invoke gemmlowp::tanh on FixedPoint wrapping int32x4_t + using FixedPoint4 = gemmlowp::FixedPoint; + using FixedPoint0 = gemmlowp::FixedPoint; + const FixedPoint4 input_val_f4_0 = + FixedPoint4::FromRaw(input_val_rescaled_0); + const FixedPoint4 input_val_f4_1 = + FixedPoint4::FromRaw(input_val_rescaled_1); + const FixedPoint4 input_val_f4_2 = + FixedPoint4::FromRaw(input_val_rescaled_2); + const FixedPoint4 input_val_f4_3 = + FixedPoint4::FromRaw(input_val_rescaled_3); + const FixedPoint0 output_val_f0_0 = gemmlowp::tanh(input_val_f4_0); + const FixedPoint0 output_val_f0_1 = gemmlowp::tanh(input_val_f4_1); + const FixedPoint0 output_val_f0_2 = gemmlowp::tanh(input_val_f4_2); + const FixedPoint0 output_val_f0_3 = gemmlowp::tanh(input_val_f4_3); + + // Divide by 2^24 as in the scalar code + using gemmlowp::RoundingDivideByPOT; + int32x4_t output_val_s32_0 = RoundingDivideByPOT(output_val_f0_0.raw(), 24); + int32x4_t output_val_s32_1 = RoundingDivideByPOT(output_val_f0_1.raw(), 24); + int32x4_t output_val_s32_2 = RoundingDivideByPOT(output_val_f0_2.raw(), 24); + int32x4_t output_val_s32_3 = RoundingDivideByPOT(output_val_f0_3.raw(), 24); + + // Add the output zero point + int32x4_t output_zero_point_s32 = vdupq_n_s32(output_zero_point); + output_val_s32_0 = vaddq_s32(output_val_s32_0, output_zero_point_s32); + output_val_s32_1 = vaddq_s32(output_val_s32_1, output_zero_point_s32); + output_val_s32_2 = vaddq_s32(output_val_s32_2, output_zero_point_s32); + output_val_s32_3 = vaddq_s32(output_val_s32_3, output_zero_point_s32); + + // Cast output values to uint8, saturating + int16x8_t output_val_s16_0 = vcombine_s16(vqmovn_s32(output_val_s32_0), + vqmovn_s32(output_val_s32_1)); + int16x8_t output_val_s16_1 = vcombine_s16(vqmovn_s32(output_val_s32_2), + vqmovn_s32(output_val_s32_3)); + uint8x16_t output_val_u8 = vcombine_u8(vqmovun_s16(output_val_s16_0), + vqmovun_s16(output_val_s16_1)); + + // Perform the bit-masking with the bit masks computed at the beginning, + // see the comment there. + output_val_u8 = vorrq_u8(output_val_u8, mask_rightclamp); + output_val_u8 = vandq_u8(output_val_u8, mask_leftclamp); + + // Store back to memory + vst1q_u8(output_data + c, output_val_u8); + } +#endif + // Leftover loop: handle one value at a time with scalar code. + for (; c < size; ++c) { + const uint8 input_val_u8 = input_data[c]; + const int32 input_val_centered = + static_cast(input_val_u8) - input_zero_point; + uint8 output_val; + if (input_val_centered < -input_range_radius) { + output_val = 0; + } else if (input_val_centered > input_range_radius) { + output_val = 255; + } else { + const int32 input_val_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_val_centered, input_multiplier, input_left_shift); + using FixedPoint4 = gemmlowp::FixedPoint; + using FixedPoint0 = gemmlowp::FixedPoint; + const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); + const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); + using gemmlowp::RoundingDivideByPOT; + int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); + output_val_s32 += output_zero_point; + if (output_val_s32 == 256) { + output_val_s32 = 255; } + TFLITE_DCHECK_GE(output_val_s32, 0); + TFLITE_DCHECK_LE(output_val_s32, 255); + output_val = static_cast(output_val_s32); } + output_data[c] = output_val; } } - inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, int32 zero_point, double scale, float* output_data, const Dims<4>& output_dims) { @@ -3894,6 +4697,107 @@ void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, } } +template +void Transpose(const T* input, const Dims<4>& input_dims, T* output, + const Dims<4>& output_dims, const int* permuted_axes) { + int out_sizes[4]; + // Compute the inverse permutation array so we can do an output centered + // transpose. Also, check to make sure output_dims is matching input_dims. + for (int k = 0; k < 4; k++) { + out_sizes[k] = + MatchingArraySize(input_dims, permuted_axes[k], output_dims, k); + } + + // Naive transpose loop (iterate on output index and compute input index). + int o[4]; // loop index (on output). + int i[4]; + for (o[3] = 0; o[3] < out_sizes[3]; o[3]++) { + i[permuted_axes[3]] = o[3]; + for (o[2] = 0; o[2] < out_sizes[2]; o[2]++) { + i[permuted_axes[2]] = o[2]; + for (o[1] = 0; o[1] < out_sizes[1]; o[1]++) { + i[permuted_axes[1]] = o[1]; + for (o[0] = 0; o[0] < out_sizes[0]; o[0]++) { + i[permuted_axes[0]] = o[0]; + output[Offset(output_dims, o)] = input[Offset(input_dims, i)]; + } + } + } + } +} + +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("TransposeConv"); + // THIS FUNCTION IS A COPY FROM reference_ops.h. + // To optimize, start by using the conv code with transposed weights for the + // case of stride_height = stride_width = 1. + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a "scatter" + // access pattern, where we loop through all the input elements, computing + // their influence on the output, rather than looping through the output + // elements in the typical "gather" access pattern of a conv. We therefore + // must initialize the output array to zero. + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] = + 0.0f; + } + } + } + } + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < input_depth; + ++out_channel) { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + float input_value = input_data[Offset(input_dims, in_channel, + in_x, in_y, batch)]; + float filter_value = + filter_data[Offset(filter_dims, out_channel, filter_x, + filter_y, in_channel)]; + output_data[Offset(output_dims, out_channel, out_x, out_y, + batch)] += input_value * filter_value; + } + } + } + } + } + } + } + } +} + } // namespace optimized_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc index 98f2e365c5249a6c28673fc185ebec34cc2105b2..18be6777a5caeb45a4ffabd8b7f1793de7b053f8 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc @@ -22,27 +22,20 @@ limitations under the License. namespace tflite { -void QuantizeMultiplierSmallerThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* right_shift) { - TFLITE_CHECK(double_multiplier >= 0.); - TFLITE_CHECK(double_multiplier < 1.); +void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, + int* shift) { if (double_multiplier == 0.) { *quantized_multiplier = 0; - *right_shift = 0; + *shift = 0; return; } - TFLITE_CHECK(double_multiplier > 0.); - const double q = std::frexp(double_multiplier, right_shift); - *right_shift *= -1; - + const double q = std::frexp(double_multiplier, shift); auto q_fixed = static_cast(TfLiteRound(q * (1ll << 31))); TFLITE_CHECK(q_fixed <= (1ll << 31)); if (q_fixed == (1ll << 31)) { q_fixed /= 2; - --*right_shift; + ++*shift; } - TFLITE_CHECK_GE(*right_shift, 0); TFLITE_CHECK_LE(q_fixed, std::numeric_limits::max()); *quantized_multiplier = static_cast(q_fixed); } @@ -50,17 +43,20 @@ void QuantizeMultiplierSmallerThanOne(double double_multiplier, void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t* quantized_multiplier, int* left_shift) { - TFLITE_CHECK(double_multiplier > 1.); - const double q = std::frexp(double_multiplier, left_shift); - auto q_fixed = static_cast(TfLiteRound(q * (1ll << 31))); - TFLITE_CHECK(q_fixed <= (1ll << 31)); - if (q_fixed == (1ll << 31)) { - q_fixed /= 2; - ++*left_shift; - } + TFLITE_CHECK_GT(double_multiplier, 1.); + QuantizeMultiplier(double_multiplier, quantized_multiplier, left_shift); TFLITE_CHECK_GE(*left_shift, 0); - TFLITE_CHECK_LE(q_fixed, std::numeric_limits::max()); - *quantized_multiplier = static_cast(q_fixed); +} + +void QuantizeMultiplierSmallerThanOne(double double_multiplier, + int32_t* quantized_multiplier, + int* right_shift) { + TFLITE_CHECK_LT(double_multiplier, 1.); + TFLITE_CHECK_GT(double_multiplier, 0.); + int shift; + QuantizeMultiplier(double_multiplier, quantized_multiplier, &shift); + TFLITE_CHECK_LE(shift, 0); + *right_shift = -shift; } void PreprocessSoftmaxScaling(double beta, double input_scale, diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.h b/tensorflow/contrib/lite/kernels/internal/quantization_util.h index efb7191c8deb2a23ea5473ab131d2b6537202765..f7706c793883f012d2b66cf3d3167a59afe31f91 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.h +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.h @@ -12,15 +12,94 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ -#define PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ +#include #include +#include + +#include "tensorflow/contrib/lite/kernels/internal/compatibility.h" +#include "tensorflow/contrib/lite/kernels/internal/round.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { +// Given the min and max values of a float array, return +// reasonable quantization parameters to use for this array. +template +QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { + const T qmin = std::numeric_limits::min(); + const T qmax = std::numeric_limits::max(); + const double qmin_double = qmin; + const double qmax_double = qmax; + // 0 should always be a representable value. Let's assume that the initial + // min,max range contains 0. + TFLITE_CHECK_LE(rmin, 0.); + TFLITE_CHECK_GE(rmax, 0.); + if (rmin == rmax) { + // Special case where the min,max range is a point. Should be {0}. + TFLITE_CHECK_EQ(rmin, 0.); + TFLITE_CHECK_EQ(rmax, 0.); + QuantizationParams quantization_params; + quantization_params.zero_point = 0; + quantization_params.scale = 0.; + return quantization_params; + } + + // General case. + // + // First determine the scale. + const double scale = (rmax - rmin) / (qmax_double - qmin_double); + + // Zero-point computation. + // First the initial floating-point computation. The zero-point can be + // determined from solving an affine equation for any known pair + // (real value, corresponding quantized value). + // We know two such pairs: (rmin, qmin) and (rmax, qmax). + // The arithmetic error on the zero point computed from either pair + // will be roughly machine_epsilon * (sum of absolute values of terms) + // so we want to use the variant that adds the smaller terms. + const double zero_point_from_min = qmin_double - rmin / scale; + const double zero_point_from_max = qmax_double - rmax / scale; + const double zero_point_from_min_error = + std::abs(qmin_double) + std::abs(rmin / scale); + const double zero_point_from_max_error = + std::abs(qmax_double) + std::abs(rmax / scale); + + const double zero_point_double = + zero_point_from_min_error < zero_point_from_max_error + ? zero_point_from_min + : zero_point_from_max; + + // Now we need to nudge the zero point to be an integer + // (our zero points are integer, and this is motivated by the requirement + // to be able to represent the real value "0" exactly as a quantized value, + // which is required in multiple places, for example in Im2col with SAME + // padding). + T nudged_zero_point = 0; + if (zero_point_double < qmin_double) { + nudged_zero_point = qmin; + } else if (zero_point_double > qmax_double) { + nudged_zero_point = qmax; + } else { + nudged_zero_point = static_cast(round(zero_point_double)); + } + // The zero point should always be in the range of quantized value, + // [qmin, qmax]. + TFLITE_CHECK_GE(nudged_zero_point, qmin); + TFLITE_CHECK_LE(nudged_zero_point, qmax); + + // Finally, store the result nudged quantization params. + QuantizationParams quantization_params; + quantization_params.zero_point = nudged_zero_point; + quantization_params.scale = scale; + return quantization_params; +} + // Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of its exponent. +// significand, and shift representation of NEGATIVE its exponent --- +// this is intended as a RIGHT-shift. // // Restricted to the case where the multiplier < 1 (and non-negative). void QuantizeMultiplierSmallerThanOne(double double_multiplier, @@ -35,6 +114,16 @@ void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t* quantized_multiplier, int* left_shift); +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of its exponent. +// +// Handles an arbitrary positive multiplier. The 'shift' output-value is +// basically the 'floating-point exponent' of the multiplier: +// Negative for a right-shift (when the multiplier is <1), positive for a +// left-shift (when the multiplier is >1) +void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, + int* shift); + // This first creates a multiplier in a double equivalent of // Q(input_integer_bits).(31-input_integer_bits) representation, with extra // precision in the double's fractional bits. It then splits the result into @@ -52,4 +141,4 @@ int CalculateInputRadius(int input_integer_bits, int input_left_shift); } // namespace tflite -#endif // PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc index d6f306e2cbae3c780b3d773638ba46cd2abf02f5..4ae2085c30ed08790eaff27c7921909d47687707 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc @@ -22,6 +22,51 @@ namespace { using ::testing::Pair; +// Example taken from http://www.tensorflow.org/performance/quantization +// +// Quantized | Float +// --------- | ----- +// 0 | -10.0 +// 255 | 30.0 +// 128 | 10.0 +TEST(QuantizationUtilTest, ChooseQuantizationParams) { + QuantizationParams qp = ChooseQuantizationParams(-10.0, 30.0); + EXPECT_NEAR(qp.scale, 0.156863, 1e-5); + EXPECT_EQ(qp.zero_point, 64); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMinBoundary) { + QuantizationParams qp = ChooseQuantizationParams(0.0, 30.0); + EXPECT_NEAR(qp.scale, 0.117647, 1e-5); + EXPECT_EQ(qp.zero_point, 0); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroNotInRange) { + // Assumption is that zero is within the range. + EXPECT_DEATH(ChooseQuantizationParams(10.0, 30.0), ""); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangePositive) { + // Assumption is that zero is within the range. + EXPECT_DEATH(ChooseQuantizationParams(30.0, 30.0), ""); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangeZero) { + QuantizationParams qp = ChooseQuantizationParams(0.0, 0.0); + EXPECT_NEAR(qp.scale, 0.0, 1e-5); + EXPECT_EQ(qp.zero_point, 0); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMaxBoundary) { + QuantizationParams qp = ChooseQuantizationParams(-10.0, 0.0); + EXPECT_NEAR(qp.scale, 0.039216, 1e-5); + EXPECT_EQ(qp.zero_point, 255); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsInvalidRange) { + EXPECT_DEATH(ChooseQuantizationParams(10.0, -30.0), ""); +} + TEST(QuantizationUtilTest, QuantizeMultiplierSmallerThanOne) { auto quantize = [](double d) { int32_t q; @@ -31,7 +76,7 @@ TEST(QuantizationUtilTest, QuantizeMultiplierSmallerThanOne) { }; EXPECT_DEATH(quantize(-0.1), ""); - EXPECT_THAT(quantize(0.0), Pair(0, 0)); + EXPECT_DEATH(quantize(0.0), ""); EXPECT_THAT(quantize(0.25), Pair(1073741824, 1)); // Around 0.5 we can see the change in exponent and how we try hard to diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 40e5c48a4ca6047a5c1cad0c8805ac8f281b357c..84f6cf6e4fff0edcc0e6a1b37f76847f24b7cc3b 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -157,11 +157,11 @@ inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, inline void Conv(const float* input_data, const Dims<4>& input_dims, const float* filter_data, const Dims<4>& filter_dims, const float* bias_data, const Dims<4>& bias_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, float output_activation_min, - float output_activation_max, float* output_data, - const Dims<4>& output_dims, float* im2col_data, - const Dims<4>& im2col_dims) { + int stride_width, int stride_height, int dilation_width_factor, + int dilation_height_factor, int pad_width, int pad_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims, + float* im2col_data, const Dims<4>& im2col_dims) { (void)im2col_data; // only used in optimized code. (void)im2col_dims; // only used in optimized code. const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); @@ -186,8 +186,9 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, for (int filter_y = 0; filter_y < filter_height; ++filter_y) { for (int filter_x = 0; filter_x < filter_width; ++filter_x) { for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; // If the location is outside the bounds of the input image, // use zero as a default value. if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && @@ -216,6 +217,23 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, } } +template +void Conv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + const float* bias_data, const Dims<4>& bias_dims, int stride_width, + int stride_height, int dilation_width_factor, + int dilation_height_factor, int pad_width, int pad_height, + float* output_data, const Dims<4>& output_dims, float* im2col_data, + const Dims<4>& im2col_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, + stride_width, stride_height, dilation_width_factor, + dilation_height_factor, pad_width, pad_height, output_activation_min, + output_activation_max, output_data, output_dims, im2col_data, + im2col_dims); +} + // legacy, for compatibility with old checked-in code template void Conv(const float* input_data, const Dims<4>& input_dims, @@ -227,7 +245,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims, float output_activation_min, output_activation_max; GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, - stride_width, stride_height, pad_width, pad_height, + stride_width, stride_height, 1, 1, pad_width, pad_height, output_activation_min, output_activation_max, output_data, output_dims, im2col_data, im2col_dims); } @@ -241,7 +259,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims, const Dims<4>& output_dims, float* im2col_data, const Dims<4>& im2col_dims) { Conv(input_data, input_dims, filter_data, filter_dims, bias_data, - bias_dims, stride, stride, pad_width, pad_height, output_data, + bias_dims, stride, stride, 1, 1, pad_width, pad_height, output_data, output_dims, im2col_data, im2col_dims); } @@ -1358,6 +1376,278 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, } } +// Quantized LSTM cell implementation. +// The quantization of the input, output arrays is as follows: +// - The input activations are quantized as uint8 on the interval +// [-1, 127/128]. +// The rationale for that is that that is the natural interval for output +// activations (see next point) and these need to be concatenated together. +// We could accommodate different ranges by re-scaling, but we empirically +// found that setting the input activations range to be [-1, 127/128] in the +// first place, removing the need for re-scaling, greatly improves accuracy. +// - The output activations are quantized as uint8 on the interval +// [-1, 127/128]. +// The rationale for that is that the definition of a LSTM cell makes them +// intrinsically constrained in [-1, 1]; tweaking that to [-1, 127/128] +// makes for simpler, more accurate fixed-point arithmetic. +// - The output-at-previous-timestep state array is obviously quantized as +// the output activations. +// - The internal LSTM memory (not the output-at-previous-timestep, the other +// internal state array) is int16-quantized and may use any power-of-two, +// symmetric range i.e. [-2^N, 2^N * 32767/32768] for any N, which we call +// StateIntegerBits below, see the below discussion of that template +// parameter ("The StateIntegerBits template parameter"). +// - The output of the internal fully-connected node is int16-quantized +// on the interval [-8, 8 * 32767/32768], the rationale for which is +// explained just below ("Why [-8, 8] for fully-connected output?"). +// +// +// === The StateIntegerBits template parameter === +// +// The StateIntegerBits template parameter controls the fixed-point format used +// to represent the internal memory of the LSTM cell (not the +// output-at-previous-timestep, the other internal state array). It's currently +// a template parameter so that the model can control that. The most typical +// value for StateIntegerBits is 4. Other plausible values are anywhere between +// 3 and 5. We might eventually standardize on a single supported value, e.g. 4, +// and drop that template parameter. The reason why it can't be a runtime +// parameter is that this controls the fixed-point format used, i.e. we need to +// generate actually different code based on it. In particular, we generate code +// for a fixed-point tanh() implementation for that format, which internally +// uses a fixed-point exp() implementation, which internally uses a +// barrel-shifter with a number of steps that depends on StateIntegerBits. +// Another consequence of that is that a higher value of StateIntegerBits +// results in a more expensive implementation (more barrel shifter steps +// needed). +// +// +// === Why [-8, 8] for fully-connected output? === +// +// This array is only fed to Logistic and Tanh functions, for which +// the quantized implementation will want to use fixed-point arithmetic, +// requiring a power-of-two representation interval. Thus, we should right +// away quantize this array to a power-of-two interval; otherwise, +// implementation will need to rescale that, losing any benefit that a tighter +// representation interval might otherwise yield, while introducting some +// numerical error and computational overhead. +// +// Now, Logistic and Tanh +// are nearly constant (nearly equal to their horizontal asymptotes) +// outside of a small bounded interval around 0: +// +// Logistic(4) = 1 - 1.8e-2 Tanh(4) = 1 - 6.7e-4 +// Logistic(8) = 1 - 3.4e-4 Tanh(8) = 1 - 2.3e-7 +// Logistic(16) = 1 - 1.1e-7 Tanh(16) = 1 - 2.5e-14 +// +// From this, we see that clamping to [-4, 4] would be too inaccurate +// (the error of 1.8e-2 on Logistic would be felt even in 8bit precision) +// while clamping to [-16, 16] would make no difference even in float32. +// However, for a fixed-point implementation in 16-bit integers, using 5 +// integer bits to represent the [-16, 16] range would leave only 11 +// fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive +// representable values. Notice that that is higher than the +// worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. +// Using [-8, 8] thus seems like the better compromise overall, enjoying +// an increment of 2.4e-4 between representable values and a worst-case +// clamping error of 3.4e-4, both better than the increment of 4.9e-4 with +// [-16, 16]. +// +// Moreover, all other things being equal, it is nice to choose the narrower +// representation range, as that makes the implementation of fixed-point +// math functions a little cheaper (each integer bit requires an additional +// barrel-shifter atep in the implementation of exp(-x)). That is further +// reason to prefer [-8, 8] over [-16, 16]. The choice of [-16, 16] would make +// sense for 32-bit float or 32-bit fixed-point quantization, but we are +// aiming for 16-bit fixed-point quantization of these internal nodes here. +// +template +void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims, + const uint8* prev_activ_data_uint8, + const Dims<4>& prev_activ_dims, const uint8* weights_data_uint8, + const Dims<4>& weights_dims, const int32* bias_data_int32, + const Dims<4>& bias_dims, const int16* prev_state_data_int16, + const Dims<4>& prev_state_dims, int16* output_state_data_int16, + const Dims<4>& output_state_dims, uint8* output_activ_data_uint8, + const Dims<4>& output_activ_dims, uint8* concat_temp_data_uint8, + const Dims<4>& concat_temp_dims, int16* activ_temp_data_int16, + const Dims<4>& activ_temp_dims, int32 weights_zero_point, + int32 accum_multiplier, int accum_shift, + gemmlowp::GemmContext* gemm_context) { + (void)gemm_context; // only used in optimized code. + + // Gather dimensions information, and perform consistency checks. + const int batches = + MatchingArraySize(input_dims, 3, prev_activ_dims, 3, prev_state_dims, 3, + output_state_dims, 3, output_activ_dims, 3); + const int height = + MatchingArraySize(input_dims, 2, prev_activ_dims, 2, prev_state_dims, 2, + output_state_dims, 2, output_activ_dims, 2); + const int width = + MatchingArraySize(input_dims, 1, prev_activ_dims, 1, prev_state_dims, 1, + output_state_dims, 1, output_activ_dims, 1); + TFLITE_CHECK_EQ(ArraySize(weights_dims, 2), 1); + TFLITE_CHECK_EQ(ArraySize(weights_dims, 3), 1); + const int input_depth = ArraySize(input_dims, 0); + const int prev_activ_depth = ArraySize(prev_activ_dims, 0); + const int total_input_depth = prev_activ_depth + input_depth; + TFLITE_CHECK_EQ(ArraySize(weights_dims, 0), total_input_depth); + TFLITE_CHECK_EQ(MatchingArraySize(bias_dims, 1, bias_dims, 2, bias_dims, 3), + 1); + const int intern_activ_depth = + MatchingArraySize(weights_dims, 1, bias_dims, 0); + TFLITE_CHECK_EQ(intern_activ_depth % 4, 0); + const int output_depth = + MatchingArraySize(prev_state_dims, 0, prev_activ_dims, 0, + output_state_dims, 0, output_activ_dims, 0); + TFLITE_CHECK_EQ(output_depth, intern_activ_depth / 4); + const int fc_batches = ArraySize(activ_temp_dims, 1) * + ArraySize(activ_temp_dims, 2) * + ArraySize(activ_temp_dims, 3); + const int fc_output_depth = + MatchingArraySize(weights_dims, 1, activ_temp_dims, 0); + const int fc_accum_depth = ArraySize(weights_dims, 0); + TFLITE_CHECK_EQ(fc_output_depth, 4 * output_depth); + + // Depth-concatenate prev_activ and input data together. + uint8 const* concat_input_arrays_data[2] = {input_data_uint8, + prev_activ_data_uint8}; + Dims<4> const* concat_input_arrays_dims[2] = {&input_dims, &prev_activ_dims}; + Concatenation( + 0, concat_input_arrays_data, concat_input_arrays_dims, 2, + concat_temp_data_uint8, concat_temp_dims); + + // Implementation of the fully connected node inside the LSTM cell. + // The operands are 8-bit integers, the accumulators are internally 32bit + // integers, and the output is 16-bit fixed-point with 3 integer bits so + // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that + // is explained in the function comment above. + for (int b = 0; b < fc_batches; ++b) { + for (int out_c = 0; out_c < fc_output_depth; ++out_c) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32 accum = bias_data_int32[out_c]; + // Accumulation loop. + for (int d = 0; d < fc_accum_depth; ++d) { + int16 input_val = concat_temp_data_uint8[b * fc_accum_depth + d] - 128; + int16 weights_val = + weights_data_uint8[out_c * fc_accum_depth + d] - weights_zero_point; + accum += input_val * weights_val; + } + // Down-scale the final int32 accumulator to the scale used by our + // (16-bit, using 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + accum = + MultiplyByQuantizedMultiplier(accum, accum_multiplier, accum_shift); + // Saturate, cast to int16, and store to the temporary activations array. + accum = std::max(-32768, std::min(32767, accum)); + activ_temp_data_int16[out_c + fc_output_depth * b] = accum; + } + } + + // Rest of the LSTM cell: tanh and logistic math functions, and some adds + // and muls, all done in 16-bit fixed-point. + const int outer_size = batches * width * height; + for (int b = 0; b < outer_size; ++b) { + for (int c = 0; c < output_depth; ++c) { + // Define the fixed-point data types that we will use here. All use + // int16 as the underlying integer type i.e. all are 16-bit fixed-point. + // They only differ by the number of integral vs. fractional bits, + // determining the range of values that they can represent. + // + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8]. + // This is the range of the previous fully-connected node's output, + // which is our input here. + using F3 = gemmlowp::FixedPoint; + // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, + // 2^StateIntegerBits]. It's used to represent the internal state, whose + // number of integer bits is currently dictated by the model. See comment + // on the StateIntegerBits template parameter above. + using FS = gemmlowp::FixedPoint; + // Implementation of input gate, using fixed-point logistic function. + F3 input_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 0 * output_depth + c]); + F0 input_gate_output = gemmlowp::logistic(input_gate_input); + // Implementation of input modulation gate, using fixed-point tanh + // function. + F3 input_modulation_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 1 * output_depth + c]); + F0 input_modulation_gate_output = + gemmlowp::tanh(input_modulation_gate_input); + // Implementation of forget gate, using fixed-point logistic function. + F3 forget_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 2 * output_depth + c]); + F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); + // Implementation of output gate, using fixed-point logistic function. + F3 output_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 3 * output_depth + c]); + F0 output_gate_output = gemmlowp::logistic(output_gate_input); + // Implementation of internal multiplication nodes, still in fixed-point. + F0 input_times_input_modulation = + input_gate_output * input_modulation_gate_output; + FS prev_state = FS::FromRaw(prev_state_data_int16[b * output_depth + c]); + FS prev_state_times_forget_state = forget_gate_output * prev_state; + // Implementation of internal addition node, saturating. + FS new_state = gemmlowp::SaturatingAdd( + gemmlowp::Rescale(input_times_input_modulation), + prev_state_times_forget_state); + // Implementation of last internal Tanh node, still in fixed-point. + // Since a Tanh fixed-point implementation is specialized for a given + // number or integer bits, and each specialization can have a substantial + // code size, and we already used above a Tanh on an input with 3 integer + // bits, and per the table in the above function comment there is no + // significant accuracy to be lost by clamping to [-8, +8] for a + // 3-integer-bits representation, let us just do that. This helps people + // porting this to targets where code footprint must be minimized. + F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); + F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); + // Store the new internal state back to memory, as 16-bit integers. + // Note: here we store the original value with StateIntegerBits, not + // the rescaled 3-integer-bits value fed to tanh. + output_state_data_int16[b * output_depth + c] = new_state.raw(); + // Down-scale the output activations to 8-bit integers, saturating, + // and store back to memory. + int16 rescaled_output_activ = + gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); + int16 clamped_output_activ = + std::max(-128, std::min(127, rescaled_output_activ)); + output_activ_data_uint8[b * output_depth + c] = + 128 + clamped_output_activ; + } + } +} + +template +void TensorFlowSplit(const Scalar* input_data, const Dims<4>& input_dims, + int axis, int outputs_count, Scalar* const* output_data, + const Dims<4>* const* output_dims) { + const int batches = ArraySize(*output_dims[0], 3); + const int height = ArraySize(*output_dims[0], 2); + const int width = ArraySize(*output_dims[0], 1); + const int depth = ArraySize(*output_dims[0], 0); + + const int slice_size = ArraySize(*output_dims[0], axis); + + for (int i = 0; i < outputs_count; ++i) { + int offset = i * slice_size * input_dims.strides[axis]; + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < height; ++y) { + for (int x = 0; x < width; ++x) { + for (int c = 0; c < depth; ++c) { + auto out = Offset(*output_dims[i], c, x, y, b); + auto in = Offset(input_dims, c, x, y, b); + output_data[i][out] = input_data[offset + in]; + } + } + } + } + } +} + template void TensorFlowSplit(const Scalar* input_data, const Dims<4>& input_dims, int outputs_count, Scalar* const* output_data, @@ -1368,28 +1658,12 @@ void TensorFlowSplit(const Scalar* input_data, const Dims<4>& input_dims, /* height = */ MatchingArraySize(*output_dims[i], 2, input_dims, 2); /* width = */ MatchingArraySize(*output_dims[i], 1, input_dims, 1); } - const int batches = MatchingArraySize(*output_dims[0], 3, input_dims, 3); - const int height = MatchingArraySize(*output_dims[0], 2, input_dims, 2); - const int width = MatchingArraySize(*output_dims[0], 1, input_dims, 1); // for now we dont have a model with a TensorFlowSplit // with fused activation function. TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < height; ++y) { - for (int x = 0; x < width; ++x) { - int in_c = 0; - for (int i = 0; i < outputs_count; ++i) { - const int depth = ArraySize(*output_dims[i], 0); - for (int c = 0; c < depth; ++c) { - output_data[i][Offset(*output_dims[i], c, x, y, b)] = - input_data[Offset(input_dims, in_c, x, y, b)]; - in_c++; - } - } - TFLITE_DCHECK(in_c == ArraySize(input_dims, 0)); - } - } - } + + TensorFlowSplit(input_data, input_dims, /*axis=*/0, outputs_count, + output_data, output_dims); } // TODO(benoitjacob) make this a proper reference impl without Eigen! @@ -1960,6 +2234,41 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, } } +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int height = MatchingArraySize(input_dims, 2, output_dims, 2); + const int width = MatchingArraySize(input_dims, 1, output_dims, 1); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < height; ++y) { + for (int x = 0; x < width; ++x) { + // Find max element value which we'll use to ensure numerical stability + // taking advantage of the following equality: + // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C))) + float max = std::numeric_limits::lowest(); + for (int c = 0; c < depth; ++c) { + max = std::max(max, input_data[Offset(input_dims, c, x, y, b)]); + } + + // Compute sum. + float sum = 0.f; + for (int c = 0; c < depth; ++c) { + sum += std::exp(input_data[Offset(input_dims, c, x, y, b)] - max); + } + + // Compute result. + const float log_sum = std::log(sum); + for (int c = 0; c < depth; ++c) { + output_data[Offset(output_dims, c, x, y, b)] = + input_data[Offset(input_dims, c, x, y, b)] - max - log_sum; + } + } + } + } +} + inline void Logistic(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); @@ -2047,6 +2356,7 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, uint8* output_data, const Dims<4>& output_dims) { + const int32 output_zero_point = 128; const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); const int height = MatchingArraySize(input_dims, 2, output_dims, 2); const int width = MatchingArraySize(input_dims, 1, output_dims, 1); @@ -2075,11 +2385,9 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, using gemmlowp::RoundingDivideByPOT; int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); - // TODO(mjmatthews): properly wire through this zero offset - output_val_s32 += 127; - if (output_val_s32 == -1) { - // May underflow since we cannot properly represent -1.0f - output_val_s32 = 0; + output_val_s32 += output_zero_point; + if (output_val_s32 == 256) { + output_val_s32 = 255; } TFLITE_DCHECK_GE(output_val_s32, 0); TFLITE_DCHECK_LE(output_val_s32, 255); @@ -2531,6 +2839,14 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims, } } +template +inline void Exp(const T* input_data, const size_t num_elements, + T* output_data) { + for (size_t idx = 0; idx < num_elements; ++idx) { + output_data[idx] = exp(input_data[idx]); + } +} + template inline void Mean(T* input_data, const int* input_dims, const int input_num_dims, T* output_data, const int* output_dims, @@ -2583,9 +2899,11 @@ inline void Mean(T* input_data, const int* input_dims, const int input_num_dims, for (int idx = 0; idx < num_resolved_axis; ++idx) { num_elements_in_axis *= static_cast(input_dims[resolved_axis[idx]]); } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = static_cast(static_cast(output_data[idx]) / - num_elements_in_axis); + if (num_elements_in_axis > 0) { + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = static_cast(static_cast(output_data[idx]) / + num_elements_in_axis); + } } } @@ -2766,6 +3084,67 @@ void Transpose(const T* input, const Dims<4>& input_dims, T* output, } } +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a "scatter" + // access pattern, where we loop through all the input elements, computing + // their influence on the output, rather than looping through the output + // elements in the typical "gather" access pattern of a conv. We therefore + // must initialize the output array to zero. + for (int i = 0; i < RequiredBufferSizeForDims(output_dims); i++) { + output_data[i] = 0.0f; + } + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + float input_value = input_data[Offset(input_dims, in_channel, + in_x, in_y, batch)]; + float filter_value = + filter_data[Offset(filter_dims, out_channel, filter_x, + filter_y, in_channel)]; + output_data[Offset(output_dims, out_channel, out_x, out_y, + batch)] += input_value * filter_value; + } + } + } + } + } + } + } + } +} + } // namespace reference_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc new file mode 100644 index 0000000000000000000000000000000000000000..66ca694dc47bb15e0db526d742e62589ba3a3e04 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc @@ -0,0 +1,244 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/spectrogram.h" + +#include + +#include "third_party/fft2d/fft.h" + +namespace tflite { +namespace internal { + +using std::complex; + +namespace { +// Returns the default Hann window function for the spectrogram. +void GetPeriodicHann(int window_length, std::vector* window) { + // Some platforms don't have M_PI, so define a local constant here. + const double pi = std::atan(1) * 4; + window->resize(window_length); + for (int i = 0; i < window_length; ++i) { + (*window)[i] = 0.5 - 0.5 * cos((2 * pi * i) / window_length); + } +} +} // namespace + +bool Spectrogram::Initialize(int window_length, int step_length) { + std::vector window; + GetPeriodicHann(window_length, &window); + return Initialize(window, step_length); +} + +inline int Log2Floor(uint n) { + if (n == 0) return -1; + int log = 0; + uint value = n; + for (int i = 4; i >= 0; --i) { + int shift = (1 << i); + uint x = value >> shift; + if (x != 0) { + value = x; + log += shift; + } + } + assert(value == 1); + return log; +} + +inline int Log2Ceiling(uint n) { + int floor = Log2Floor(n); + if (n == (n & ~(n - 1))) // zero or a power of two + return floor; + else + return floor + 1; +} + +inline uint NextPowerOfTwo(uint value) { + int exponent = Log2Ceiling(value); + // DCHECK_LT(exponent, std::numeric_limits::digits); + return 1 << exponent; +} + +bool Spectrogram::Initialize(const std::vector& window, + int step_length) { + window_length_ = window.size(); + window_ = window; // Copy window. + if (window_length_ < 2) { + // LOG(ERROR) << "Window length too short."; + initialized_ = false; + return false; + } + + step_length_ = step_length; + if (step_length_ < 1) { + // LOG(ERROR) << "Step length must be positive."; + initialized_ = false; + return false; + } + + fft_length_ = NextPowerOfTwo(window_length_); + // CHECK(fft_length_ >= window_length_); + output_frequency_channels_ = 1 + fft_length_ / 2; + + // Allocate 2 more than what rdft needs, so we can rationalize the layout. + fft_input_output_.assign(fft_length_ + 2, 0.0); + + int half_fft_length = fft_length_ / 2; + fft_double_working_area_.assign(half_fft_length, 0.0); + fft_integer_working_area_.assign(2 + static_cast(sqrt(half_fft_length)), + 0); + // Set flag element to ensure that the working areas are initialized + // on the first call to cdft. It's redundant given the assign above, + // but keep it as a reminder. + fft_integer_working_area_[0] = 0; + input_queue_.clear(); + samples_to_next_step_ = window_length_; + initialized_ = true; + return true; +} + +template +bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>* output) { + if (!initialized_) { + // LOG(ERROR) << "ComputeComplexSpectrogram() called before successful call + // " + // << "to Initialize()."; + return false; + } + // CHECK(output); + output->clear(); + int input_start = 0; + while (GetNextWindowOfSamples(input, &input_start)) { + // DCHECK_EQ(input_queue_.size(), window_length_); + ProcessCoreFFT(); // Processes input_queue_ to fft_input_output_. + // Add a new slice vector onto the output, to save new result to. + output->resize(output->size() + 1); + // Get a reference to the newly added slice to fill in. + auto& spectrogram_slice = output->back(); + spectrogram_slice.resize(output_frequency_channels_); + for (int i = 0; i < output_frequency_channels_; ++i) { + // This will convert double to float if it needs to. + spectrogram_slice[i] = complex( + fft_input_output_[2 * i], fft_input_output_[2 * i + 1]); + } + } + return true; +} +// Instantiate it four ways: +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); + +template +bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, + std::vector>* output) { + if (!initialized_) { + // LOG(ERROR) << "ComputeSquaredMagnitudeSpectrogram() called before " + // << "successful call to Initialize()."; + return false; + } + // CHECK(output); + output->clear(); + int input_start = 0; + while (GetNextWindowOfSamples(input, &input_start)) { + // DCHECK_EQ(input_queue_.size(), window_length_); + ProcessCoreFFT(); // Processes input_queue_ to fft_input_output_. + // Add a new slice vector onto the output, to save new result to. + output->resize(output->size() + 1); + // Get a reference to the newly added slice to fill in. + auto& spectrogram_slice = output->back(); + spectrogram_slice.resize(output_frequency_channels_); + for (int i = 0; i < output_frequency_channels_; ++i) { + // Similar to the Complex case, except storing the norm. + // But the norm function is known to be a performance killer, + // so do it this way with explicit real and imagninary temps. + const double re = fft_input_output_[2 * i]; + const double im = fft_input_output_[2 * i + 1]; + // Which finally converts double to float if it needs to. + spectrogram_slice[i] = re * re + im * im; + } + } + return true; +} +// Instantiate it four ways: +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); + +// Return true if a full window of samples is prepared; manage the queue. +template +bool Spectrogram::GetNextWindowOfSamples(const std::vector& input, + int* input_start) { + auto input_it = input.begin() + *input_start; + int input_remaining = input.end() - input_it; + if (samples_to_next_step_ > input_remaining) { + // Copy in as many samples are left and return false, no full window. + input_queue_.insert(input_queue_.end(), input_it, input.end()); + *input_start += input_remaining; // Increases it to input.size(). + samples_to_next_step_ -= input_remaining; + return false; // Not enough for a full window. + } else { + // Copy just enough into queue to make a new window, then trim the + // front off the queue to make it window-sized. + input_queue_.insert(input_queue_.end(), input_it, + input_it + samples_to_next_step_); + *input_start += samples_to_next_step_; + input_queue_.erase( + input_queue_.begin(), + input_queue_.begin() + input_queue_.size() - window_length_); + // DCHECK_EQ(window_length_, input_queue_.size()); + samples_to_next_step_ = step_length_; // Be ready for next time. + return true; // Yes, input_queue_ now contains exactly a window-full. + } +} + +void Spectrogram::ProcessCoreFFT() { + for (int j = 0; j < window_length_; ++j) { + fft_input_output_[j] = input_queue_[j] * window_[j]; + } + // Zero-pad the rest of the input buffer. + for (int j = window_length_; j < fft_length_; ++j) { + fft_input_output_[j] = 0.0; + } + const int kForwardFFT = 1; // 1 means forward; -1 reverse. + // This real FFT is a fair amount faster than using cdft here. + rdft(fft_length_, kForwardFFT, &fft_input_output_[0], + &fft_integer_working_area_[0], &fft_double_working_area_[0]); + // Make rdft result look like cdft result; + // unpack the last real value from the first position's imag slot. + fft_input_output_[fft_length_] = fft_input_output_[1]; + fft_input_output_[fft_length_ + 1] = 0; + fft_input_output_[1] = 0; +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.h b/tensorflow/contrib/lite/kernels/internal/spectrogram.h new file mode 100644 index 0000000000000000000000000000000000000000..b77a68f7dfe6edb07ec4e5db540c673b2d6f6d6e --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.h @@ -0,0 +1,110 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Class for generating spectrogram slices from a waveform. +// Initialize() should be called before calls to other functions. Once +// Initialize() has been called and returned true, The Compute*() functions can +// be called repeatedly with sequential input data (ie. the first element of the +// next input vector directly follows the last element of the previous input +// vector). Whenever enough audio samples are buffered to produce a +// new frame, it will be placed in output. Output is cleared on each +// call to Compute*(). This class is thread-unsafe, and should only be +// called from one thread at a time. +// With the default parameters, the output of this class should be very +// close to the results of the following MATLAB code: +// overlap_samples = window_length_samples - step_samples; +// window = hann(window_length_samples, 'periodic'); +// S = abs(spectrogram(audio, window, overlap_samples)).^2; + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ + +#include +#include +#include + +#include "third_party/fft2d/fft.h" + +namespace tflite { +namespace internal { + +class Spectrogram { + public: + Spectrogram() : initialized_(false) {} + ~Spectrogram() {} + + // Initializes the class with a given window length and step length + // (both in samples). Internally a Hann window is used as the window + // function. Returns true on success, after which calls to Process() + // are possible. window_length must be greater than 1 and step + // length must be greater than 0. + bool Initialize(int window_length, int step_length); + + // Initialize with an explicit window instead of a length. + bool Initialize(const std::vector& window, int step_length); + + // Processes an arbitrary amount of audio data (contained in input) + // to yield complex spectrogram frames. After a successful call to + // Initialize(), Process() may be called repeatedly with new input data + // each time. The audio input is buffered internally, and the output + // vector is populated with as many temporally-ordered spectral slices + // as it is possible to generate from the input. The output is cleared + // on each call before the new frames (if any) are added. + // + // The template parameters can be float or double. + template + bool ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>* output); + + // This function works as the one above, but returns the power + // (the L2 norm, or the squared magnitude) of each complex value. + template + bool ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, + std::vector>* output); + + // Return reference to the window function used internally. + const std::vector& GetWindow() const { return window_; } + + // Return the number of frequency channels in the spectrogram. + int output_frequency_channels() const { return output_frequency_channels_; } + + private: + template + bool GetNextWindowOfSamples(const std::vector& input, + int* input_start); + void ProcessCoreFFT(); + + int fft_length_; + int output_frequency_channels_; + int window_length_; + int step_length_; + bool initialized_; + int samples_to_next_step_; + + std::vector window_; + std::vector fft_input_output_; + std::deque input_queue_; + + // Working data areas for the FFT routines. + std::vector fft_integer_working_area_; + std::vector fft_double_working_area_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/tensor.h b/tensorflow/contrib/lite/kernels/internal/tensor.h index dfe76c2afd40c692063710a4d98464b55e40feb9..62e38e0d4c3e023d0ed2242fc9438b096b86dc59 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor.h @@ -81,6 +81,51 @@ inline Dims<4> GetTensorDims(const TfLiteTensor* tensor) { return GetTensorDims(dims->data, dims->size); } +// A list of tensors in a format that can be used by kernels like split and +// concatenation. +template +class VectorOfTensors { + public: + // Build with the tensors in 'tensor_list'. + VectorOfTensors(const TfLiteContext& context, + const TfLiteIntArray& tensor_list) { + int num_tensors = tensor_list.size; + + all_data_.reserve(num_tensors); + all_dims_.reserve(num_tensors); + all_dims_ptr_.reserve(num_tensors); + + for (int i = 0; i < num_tensors; ++i) { + TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; + all_data_.push_back(GetTensorData(t)); + all_dims_.push_back(GetTensorDims(t)); + } + + // Taking the pointer from inside a std::vector is only OK if the vector is + // never modified, so we populate all_dims in the previous loop and then we + // are free to grab iterators here. + for (int i = 0; i < num_tensors; ++i) { + all_dims_ptr_.push_back(&all_dims_[i]); + } + } + // Return a pointer to the data pointers of all tensors in the list. For + // example: + // float* const* f = v.data(); + // f[0][1] is the second element of the first tensor. + T* const* data() const { return all_data_.data(); } + + // Return a pointer the dim pointers of all tensors in the list. For + // example: + // const Dims<4>* const* d = v.dims(); + // dims[1] are the dimensions of the second tensor in the list. + const Dims<4>* const* dims() const { return all_dims_ptr_.data(); } + + private: + std::vector all_data_; + std::vector> all_dims_; + std::vector*> all_dims_ptr_; +}; + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_TENSOR_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index afe131b06ec41201395e80aa5415fd7db990f8d4..293538fcbb6406d6065d8efd25adb3b163638c92 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -21,6 +21,22 @@ namespace tflite { enum class FusedActivationFunctionType : uint8 { kNone, kRelu6, kRelu1, kRelu }; +// Quantization parameters, determining the mapping of quantized values +// to real values (i.e. determining how quantized values are mathematically +// interpreted). +// +// The correspondence is as follows: +// +// real_value = scale * (quantized_value - zero_point); +// +// In other words, zero_point designates which quantized value corresponds to +// the real 0 value, and scale designates the difference between the real values +// corresponding to consecutive quantized values differing by 1. +struct QuantizationParams { + int32 zero_point = 0; + double scale = 0.0; +}; + template struct Dims { int sizes[N]; diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..62820a2f5113cb6ae252386aaf3842135383b79f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc @@ -0,0 +1,112 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// Unit test for TFLite LOG_SOFTMAX op. + +#include +#include +#include + +#include +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +class LogSoftmaxOpModel : public SingleOpModel { + public: + LogSoftmaxOpModel(int batches, int size) + : batches_(batches), input_size_(size) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_LOG_SOFTMAX, BuiltinOptions_LogSoftmaxOptions, + CreateLogSoftmaxOptions(builder_).Union()); + BuildInterpreter({{batches_, input_size_}}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; + + int batches_; + int input_size_; +}; + +TEST(LogSoftmaxOpTest, SimpleTest) { + LogSoftmaxOpModel m(/*batches=*/2, /*size=*/5); + m.SetInput({ + 1.0, 2.0, 3.0, 4.0, 5.0, // b = 0 + -1.0, -2.0, -3.0, -4.0, -5.0, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-4.45191431, -3.45191431, -2.45191431, -1.45191443, -0.4519144, + -0.4519144, -1.45191443, -2.45191431, -3.45191431, -4.45191431}, + 1e-6))); +} + +TEST(LogSoftmaxOpTest, CompareWithTFmini) { + const int batch_size = 2; + const int input_size = 5; + static float input_buffer[] = { + 1.0, 2.0, 3.0, 4.0, 5.0, // b = 0 + -1.0, -2.0, -3.0, -4.0, -5.0, // b = 1 + }; + + LogSoftmaxOpModel m(batch_size, input_size); + + m.SetInput(0, input_buffer, input_buffer + input_size * batch_size); + + m.Invoke(); + + std::unique_ptr output_buffer(new float[input_size * batch_size]); + static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, + {1, 0, 0, input_size}}; + tflite::reference_ops::LogSoftmax(input_buffer, input_dims, + output_buffer.get(), input_dims); + + std::vector expected; + expected.insert(expected.end(), output_buffer.get(), + output_buffer.get() + input_size * batch_size); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(expected, 1e-6))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 6c06264d845c24e71647b6fd2374734be32383ef..b9255b23a5573788e6290633723313b6db7b4f76 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -377,127 +378,54 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; } - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell, - n_batch, input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell, - n_batch, forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell, - n_batch, output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights->data.f, n_cell, n_input, input->data.f, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights->data.f, n_cell, n_input, input->data.f, n_batch, - forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights->data.f, n_cell, n_input, input->data.f, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights->data.f, n_cell, n_input, input->data.f, n_batch, - output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights->data.f, n_cell, n_output, output_state->data.f, - n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, - cell_state->data.f, n_batch * n_cell, - cell_state->data.f); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state->data.f); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell, - params->cell_clip, cell_state->data.f); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output, - n_batch, output->data.f); - } else { - tensor_utils::ZeroVector(output->data.f, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights->data.f, n_output, n_cell, output_gate_scratch, - n_batch, output->data.f, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output->data.f, n_batch * n_output, - params->proj_clip, output->data.f); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output->data.f); - } - tensor_utils::CopyVector(output->data.f, n_batch * n_output, - output_state->data.f); + // Check optional tensors, the respective pointers can be null. + const float* input_to_input_weights_ptr = + (use_cifg) ? nullptr : input_to_input_weights->data.f; + const float* recurrent_to_input_weights_ptr = + (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; + const float* input_gate_bias_ptr = + (use_cifg) ? nullptr : input_gate_bias->data.f; + const float* cell_to_input_weights_ptr = + (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; + const float* cell_to_forget_weights_ptr = + (use_peephole) ? cell_to_forget_weights->data.f : nullptr; + const float* cell_to_output_weights_ptr = + (use_peephole) ? cell_to_output_weights->data.f : nullptr; + const float* projection_weights_ptr = + (projection_weights == nullptr) ? nullptr : projection_weights->data.f; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const float* input_ptr_batch = input->data.f; + const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; + const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; + const float* input_to_output_weights_ptr = input_to_output_weights->data.f; + const float* recurrent_to_forget_weights_ptr = + recurrent_to_forget_weights->data.f; + const float* recurrent_to_cell_weights_ptr = + recurrent_to_cell_weights->data.f; + const float* recurrent_to_output_weights_ptr = + recurrent_to_output_weights->data.f; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + float* output_ptr_batch = output->data.f; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, input_to_forget_weights_ptr, + input_to_cell_weights_ptr, input_to_output_weights_ptr, + recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr, + recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr, + cell_to_input_weights_ptr, cell_to_forget_weights_ptr, + cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, + cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, + projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, + output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch, + cell_scratch, output_gate_scratch, output_ptr_batch); return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/mean.cc index ec1c40202761e3789462a4740e5547eba654b0f9..aff19581ea56f94c08638b7b388ae181f566cf4f 100644 --- a/tensorflow/contrib/lite/kernels/mean.cc +++ b/tensorflow/contrib/lite/kernels/mean.cc @@ -183,8 +183,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(resolved_axis->bytes, resolved_axis); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } #define TF_LITE_MEAN(kernel_type, data_type) \ diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/mean_test.cc index c4c53c2ded351849e7c458fc754c36395a25ebd0..2d6d4bc2da4b75289ee27c3f2a12787216716d44 100644 --- a/tensorflow/contrib/lite/kernels/mean_test.cc +++ b/tensorflow/contrib/lite/kernels/mean_test.cc @@ -74,7 +74,7 @@ class MeanOpDynamicModel : public BaseMeanOpModel { } }; -TEST(ConstMeanOpTest, NotKeepDims) { +TEST(ConstFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -86,7 +86,7 @@ TEST(ConstMeanOpTest, NotKeepDims) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); } -TEST(ConstMeanOpTest, KeepDims) { +TEST(ConstFloatMeanOpTest, KeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -99,7 +99,7 @@ TEST(ConstMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } -TEST(DynamicMeanOpTest, NotKeepDims) { +TEST(DynamicFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -114,7 +114,7 @@ TEST(DynamicMeanOpTest, NotKeepDims) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); } -TEST(DynamicMeanOpTest, KeepDims) { +TEST(DynamicFloatMeanOpTest, KeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -130,6 +130,70 @@ TEST(DynamicMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } +TEST(DynamicFloatMeanOpTest, Scale) { + std::initializer_list data = {9.527}; + MeanOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +TEST(ConstUint8MeanOpTest, NotKeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({12, 13})); +} + +TEST(ConstUint8MeanOpTest, KeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({10, 12, 14})); +} + +TEST(DynamicUint8MeanOpTest, NotKeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}}, + {TensorType_INT32, {4}}, false); + std::initializer_list axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({12, 13})); +} + +TEST(DynamicUint8MeanOpTest, KeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}}, + {TensorType_INT32, {2}}, true); + std::initializer_list axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({10, 12, 14})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/pad.cc b/tensorflow/contrib/lite/kernels/pad.cc index 48114e5a4069abf864a996141c7b0906301d9809..c29da3862e84d6756bf5ef34b2ca06307b0a065d 100644 --- a/tensorflow/contrib/lite/kernels/pad.cc +++ b/tensorflow/contrib/lite/kernels/pad.cc @@ -101,7 +101,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Resize the output tensor if the output tensor is dynamic. if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } // TODO(nupurgarg): Change kernel implementation to take in int* instead of diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 1fb779fd5174a255b7d34322c57dc084f68d8c3f..9537b79a9ae4cb1eebaecb3fb2cb446726eb2baa 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -49,6 +49,7 @@ TfLiteRegistration* Register_MUL(); TfLiteRegistration* Register_L2_NORMALIZATION(); TfLiteRegistration* Register_LOCAL_RESPONSE_NORMALIZATION(); TfLiteRegistration* Register_LSTM(); +TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM(); TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); TfLiteRegistration* Register_PAD(); TfLiteRegistration* Register_RESHAPE(); @@ -58,8 +59,14 @@ TfLiteRegistration* Register_SPACE_TO_DEPTH(); TfLiteRegistration* Register_GATHER(); TfLiteRegistration* Register_TRANSPOSE(); TfLiteRegistration* Register_MEAN(); +TfLiteRegistration* Register_SPLIT(); TfLiteRegistration* Register_SQUEEZE(); TfLiteRegistration* Register_STRIDED_SLICE(); +TfLiteRegistration* Register_EXP(); +TfLiteRegistration* Register_TOPK_V2(); +TfLiteRegistration* Register_LOG_SOFTMAX(); +TfLiteRegistration* Register_CAST(); +TfLiteRegistration* Register_DEQUANTIZE(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -94,6 +101,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, Register_LOCAL_RESPONSE_NORMALIZATION()); AddBuiltin(BuiltinOperator_LSTM, Register_LSTM()); + AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + Register_BIDIRECTIONAL_SEQUENCE_LSTM()); AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, Register_UNIDIRECTIONAL_SEQUENCE_LSTM()); AddBuiltin(BuiltinOperator_PAD, Register_PAD()); @@ -106,8 +115,14 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_MEAN, Register_MEAN()); AddBuiltin(BuiltinOperator_DIV, Register_DIV()); AddBuiltin(BuiltinOperator_SUB, Register_SUB()); + AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT()); AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE()); AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); + AddBuiltin(BuiltinOperator_EXP, Register_EXP()); + AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); + AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); + AddBuiltin(BuiltinOperator_CAST, Register_CAST()); + AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); } TfLiteRegistration* BuiltinOpResolver::FindOp( diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear.cc b/tensorflow/contrib/lite/kernels/resize_bilinear.cc index c5d60cae3ab0b203299e04a25c392519a2a23b75..9e3e19c09a4012ebdadbc2a7c2ba06c4bfefd206 100644 --- a/tensorflow/contrib/lite/kernels/resize_bilinear.cc +++ b/tensorflow/contrib/lite/kernels/resize_bilinear.cc @@ -85,7 +85,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (IsDynamicTensor(output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, input, size, output)); - TfLiteTensorRealloc(output->bytes, output); } if (output->type == kTfLiteFloat32) { diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc index e2e1873f770fad889137b43d87585602162819f7..d8c9e352f00627eee45ae836b720f2af77140538 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc @@ -111,7 +111,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Resize the output tensor if the output tensor is dynamic. if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } #define TF_LITE_SPACE_TO_BATCH_ND(type, scalar) \ diff --git a/tensorflow/contrib/lite/kernels/split.cc b/tensorflow/contrib/lite/kernels/split.cc new file mode 100644 index 0000000000000000000000000000000000000000..b524c79f8779b0119781679c0af9fe354e38ad4f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/split.cc @@ -0,0 +1,159 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace split { + +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + params = reinterpret_cast(node->builtin_data); + axis = GetInput(context, node, 0); + input = GetInput(context, node, 1); + } + TfLiteSplitParams* params; + TfLiteTensor* axis; + TfLiteTensor* input; +}; + +TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) { + for (int i = 0; i < NumOutputs(node); ++i) { + SetTensorToDynamic(GetOutput(context, node, i)); + } + return kTfLiteOk; +} + +TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node, + TfLiteTensor* axis, TfLiteTensor* input, + int num_splits) { + int axis_value = GetTensorData(axis)[0]; + if (axis_value < 0) { + axis_value += NumDimensions(input); + } + + const int input_size = SizeOfDimension(input, axis_value); + TF_LITE_ENSURE_MSG(context, input_size % num_splits == 0, + "Not an even split"); + const int slice_size = input_size / num_splits; + + for (int i = 0; i < NumOutputs(node); ++i) { + TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims); + output_dims->data[axis_value] = slice_size; + TfLiteTensor* output = GetOutput(context, node, i); + TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims)); + } + + return kTfLiteOk; +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + + OpContext op_context(context, node); + + TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits); + + auto input_type = op_context.input->type; + TF_LITE_ENSURE(context, + input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8); + for (int i = 0; i < NumOutputs(node); ++i) { + GetOutput(context, node, i)->type = input_type; + } + + // If we know the contents of the 'axis' tensor, resize all outputs. + // Otherwise, wait until Eval(). + if (IsConstantTensor(op_context.axis)) { + return ResizeOutputTensors(context, node, op_context.axis, op_context.input, + op_context.params->num_splits); + } else { + return UseDynamicOutputTensors(context, node); + } +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + + // When the 'axis' tensor is non-const we can't resize output tensors in + // Prepare(), and we have to do it now. + if (!IsConstantTensor(op_context.axis)) { + TF_LITE_ENSURE_OK( + context, + ResizeOutputTensors(context, node, op_context.axis, op_context.input, + op_context.params->num_splits)); + } + + int axis_value = GetTensorData(op_context.axis)[0]; + if (axis_value < 0) { + axis_value += NumDimensions(op_context.input); + } + axis_value = RemapDim(NumDimensions(op_context.input), axis_value); + + // TODO(ahentz): Our usage of VectorOfTensors could be optimized by + // calculating it in Prepare, unless we defer shape calculation. + // TODO(ahentz): We can improve the optimized_ops version to handle other + // cases too. +#define TF_LITE_SPLIT(scalar) \ + VectorOfTensors all_outputs(*context, *node->outputs); \ + if (axis_value == NumDimensions(op_context.input)) { \ + optimized_ops::TensorFlowSplit( \ + GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), NumOutputs(node), all_outputs.data(), \ + all_outputs.dims()); \ + } else { \ + reference_ops::TensorFlowSplit( \ + GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), axis_value, NumOutputs(node), \ + all_outputs.data(), all_outputs.dims()); \ + } + switch (op_context.input->type) { + case kTfLiteFloat32: { + TF_LITE_SPLIT(float); + break; + } + case kTfLiteUInt8: { + TF_LITE_SPLIT(uint8_t); + break; + } + default: + context->ReportError(context, + "Only float32 and uint8 are currently supported."); + return kTfLiteError; + } +#undef TF_LITE_SPLIT + + return kTfLiteOk; +} + +} // namespace split + +TfLiteRegistration* Register_SPLIT() { + static TfLiteRegistration r = {nullptr, nullptr, split::Prepare, split::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/split_test.cc b/tensorflow/contrib/lite/kernels/split_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..61a0759c6475795c06a9b55d3586d2b818f298b2 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/split_test.cc @@ -0,0 +1,147 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +constexpr int kAxisIsATensor = -1000; + +class SplitOpModel : public SingleOpModel { + public: + SplitOpModel(const TensorData& input, int num_splits, + int axis = kAxisIsATensor) { + if (axis == kAxisIsATensor) { + axis_ = AddInput({TensorType_INT32, {1}}); + } else { + axis_ = AddConstInput(TensorType_INT32, {axis}, {1}); + } + input_ = AddInput(input); + for (int i = 0; i < num_splits; ++i) { + outputs_.push_back(AddOutput(input.type)); + } + SetBuiltinOp(BuiltinOperator_SPLIT, BuiltinOptions_SplitOptions, + CreateSplitOptions(builder_, num_splits).Union()); + if (axis == kAxisIsATensor) { + BuildInterpreter({GetShape(axis_), GetShape(input_)}); + } else { + BuildInterpreter({{}, GetShape(input_)}); + } + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetAxis(int axis) { PopulateTensor(axis_, {axis}); } + + std::vector GetOutput(int i) { + return ExtractVector(outputs_[i]); + } + std::vector GetOutputShape(int i) { return GetTensorShape(outputs_[i]); } + + private: + int input_; + int axis_; + std::vector outputs_; +}; + +using TensorValues = std::initializer_list; + +void Check(int axis, int num_splits, std::initializer_list input_shape, + std::initializer_list output_shape, + const TensorValues& input_data, + const std::vector& output_data) { + auto debug = [&](int i) { + std::stringstream ss; + ss << "for output tensor " << i << " axis=" << axis + << " and num_splits=" << num_splits; + return ss.str(); + }; + SplitOpModel m({TensorType_FLOAT32, input_shape}, num_splits); + m.SetInput(input_data); + m.SetAxis(axis); + m.Invoke(); + for (int i = 0; i < num_splits; ++i) { + EXPECT_THAT(m.GetOutput(i), ElementsAreArray(output_data[i])) << debug(i); + EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shape)) + << debug(i); + } + + SplitOpModel const_m({TensorType_FLOAT32, input_shape}, num_splits, axis); + const_m.SetInput(input_data); + const_m.Invoke(); + for (int i = 0; i < num_splits; ++i) { + EXPECT_THAT(const_m.GetOutput(i), ElementsAreArray(output_data[i])) + << debug(i); + EXPECT_THAT(const_m.GetOutputShape(i), ElementsAreArray(output_shape)) + << debug(i); + } +} + +TEST(SplitOpTest, FourDimensional) { + Check(/*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 5, 6, 7, 8}, + {9, 10, 11, 12, 13, 14, 15, 16}, + }); + Check(/*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 9, 10, 11, 12}, + {5, 6, 7, 8, 13, 14, 15, 16}, + }); + Check(/*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 5, 6, 9, 10, 13, 14}, + {3, 4, 7, 8, 11, 12, 15, 16}, + }); + Check(/*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 3, 5, 7, 9, 11, 13, 15}, + {2, 4, 6, 8, 10, 12, 14, 16}, + }); +} + +TEST(SplitOpTest, OneDimensional) { + Check(/*axis=*/0, /*num_splits=*/8, {8}, {1}, {1, 2, 3, 4, 5, 6, 7, 8}, + {{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}}); +} + +TEST(SplitOpTest, NegativeAxis) { + Check(/*axis=*/-4, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 5, 6, 7, 8}, + {9, 10, 11, 12, 13, 14, 15, 16}, + }); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc index c4ffdf79d3aa7d47b9747bdf4208f8317d9fd22e..3907a6620d5d6a5480d0055e5f1f04bf93f0601a 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice.cc @@ -48,7 +48,7 @@ struct StridedSliceContext { output = GetOutput(context, node, kOutputTensor); dims = NumDimensions(input); } - TfLiteStridedSliceParams* params; + const TfLiteStridedSliceParams* params; TfLiteTensor* input; TfLiteTensor* begin; TfLiteTensor* end; @@ -181,7 +181,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } std::vector starts; @@ -200,18 +199,18 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { strides.emplace_back(1); } - op_context.params->begin_mask = + int begin_mask = ReverseMaskBits(op_context.params->begin_mask, op_context.dims); - op_context.params->end_mask = + int end_mask = ReverseMaskBits(op_context.params->end_mask, op_context.dims); - op_context.params->shrink_axis_mask = + int shrink_axis_mask = ReverseMaskBits(op_context.params->shrink_axis_mask, op_context.dims); #define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ kernel_type::StridedSlice( \ GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), op_context.params->begin_mask, \ - op_context.params->end_mask, op_context.params->shrink_axis_mask, \ + GetTensorDims(op_context.input), \ + begin_mask, end_mask, shrink_axis_mask, \ starts, stops, strides, GetTensorData(op_context.output), \ GetTensorDims(op_context.output)) diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc index 6f56aa6bf38781e860e33e8ac3b6a0bb8b50bb01..373310bd87370a670a847cf5328633956028a850 100644 --- a/tensorflow/contrib/lite/kernels/test_util.cc +++ b/tensorflow/contrib/lite/kernels/test_util.cc @@ -187,6 +187,7 @@ void SingleOpModel::BuildInterpreter( for (const auto& shape : input_shapes) { int input_idx = interpreter_->inputs()[i++]; if (input_idx == kOptionalTensor) continue; + if (shape.empty()) continue; CHECK(interpreter_->ResizeInputTensor(input_idx, shape) == kTfLiteOk); } CHECK(interpreter_->AllocateTensors() == kTfLiteOk) diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index 7d476ba1eaffbb24fb77390c0e71c32d60b6411e..a9064d54e7704d52eefa34f6bf446ec1cfe68fe1 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -39,10 +39,10 @@ inline std::vector Quantize(const std::vector& data, float scale, int32_t zero_point) { std::vector q; for (float f : data) { - q.push_back(std::max( + q.push_back(static_cast(std::max( std::numeric_limits::min(), - std::min(std::numeric_limits::max(), - static_cast(std::round(zero_point + (f / scale)))))); + std::min(std::numeric_limits::max(), + std::round(zero_point + (f / scale)))))); } return q; } diff --git a/tensorflow/contrib/lite/kernels/test_util_test.cc b/tensorflow/contrib/lite/kernels/test_util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..1e10e89061213b6fcabd404310893dd97a51d83f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/test_util_test.cc @@ -0,0 +1,51 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +TEST(TestUtilTest, QuantizeVector) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/1.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 1, 1, 255}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +TEST(TestUtilTest, QuantizeVectorScalingDown) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/10.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 0, 0, 100}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +TEST(TestUtilTest, QuantizeVectorScalingUp) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/0.1, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 5, 10, 255}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/topk_v2.cc b/tensorflow/contrib/lite/kernels/topk_v2.cc new file mode 100644 index 0000000000000000000000000000000000000000..807e84609f8b23d25324d99d26086331d78a0684 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/topk_v2.cc @@ -0,0 +1,232 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" +namespace tflite { +namespace ops { +namespace builtin { +namespace topk_v2 { +constexpr int kInputTensor = 0; +constexpr int kInputTopK = 1; +constexpr int kOutputIndexes = 0; +constexpr int kOutputValues = 1; + +namespace { +TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* top_k = GetInput(context, node, kInputTopK); + // INT32 number of top results is supported. + TF_LITE_ENSURE_EQ(context, top_k->type, kTfLiteInt32); + // Check that the tensor contains only one value. + TF_LITE_ENSURE_EQ(context, NumDimensions(top_k), 1); + TF_LITE_ENSURE_EQ(context, NumElements(top_k), 1); + const int32 k = top_k->data.i32[0]; + + TfLiteTensor* input = GetInput(context, node, kInputTensor); + const int num_dimensions = NumDimensions(input); + // Check that input has one or more dimensions. + TF_LITE_ENSURE_MSG(context, input->dims->size >= 1, + "TopK k input must have 1 or more dimensions."); + // Check that k is less or equal the internal dimension. + TF_LITE_ENSURE_MSG(context, k <= input->dims->data[num_dimensions - 1], + "TopK k is higher than the internal dimension."); + + TfLiteIntArray* output_indexes_shape = TfLiteIntArrayCreate(num_dimensions); + TfLiteIntArray* output_values_shape = TfLiteIntArrayCreate(num_dimensions); + for (int i = 0; i < num_dimensions - 1; ++i) { + output_indexes_shape->data[i] = input->dims->data[i]; + output_values_shape->data[i] = input->dims->data[i]; + } + output_indexes_shape->data[num_dimensions - 1] = k; + output_values_shape->data[num_dimensions - 1] = k; + TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes); + TfLiteTensor* output_values = GetOutput(context, node, kOutputValues); + auto resize_tensor = [context](TfLiteTensor* tensor, TfLiteIntArray* new_size, + TfLiteIntArray* delete_on_error) { + TfLiteStatus status = context->ResizeTensor(context, tensor, new_size); + if (status != kTfLiteOk) { + TfLiteIntArrayFree(new_size); + if (delete_on_error != nullptr) { + TfLiteIntArrayFree(delete_on_error); + } + } + return status; + }; + TF_LITE_ENSURE_OK(context, resize_tensor(output_indexes, output_indexes_shape, + output_values_shape)); + TF_LITE_ENSURE_OK(context, + resize_tensor(output_values, output_values_shape, nullptr)); + return kTfLiteOk; +} + +// The class that collects top indexes of k values. Based on template +// tensorflow::gtl::TopN<> but, for optimization, +// it re-uses the same container. +template +class TopContainer { + public: + TopContainer() = delete; + TopContainer(int32 k, int32 row_size) : k_(k) { + container_.reserve(std::min(k, row_size) + 1); + } + + void start_collecting(const T* values) { + values_ = values; + container_.clear(); + } + void push(int32 a) { + auto comparator = [this](int32 a, int32 b) { return compare_fun(a, b); }; + if (container_.size() <= k_) { + container_.push_back(a); + if (container_.size() == k_ + 1) { + std::make_heap(container_.begin(), container_.end(), comparator); + std::pop_heap(container_.begin(), container_.end(), comparator); + } + } else if (comparator(a, container_.front())) { + container_.back() = a; + std::push_heap(container_.begin(), container_.end(), comparator); + std::pop_heap(container_.begin(), container_.end(), comparator); + } + } + + const std::vector& sorted_result() { + auto comparator = [this](int32 a, int32 b) { return compare_fun(a, b); }; + if (container_.size() <= k_) { + std::sort(container_.begin(), container_.end(), comparator); + } else { + std::sort_heap(container_.begin(), container_.end() - 1, comparator); + container_.resize(k_); + } + return container_; + } + + private: + int32 k_; + std::vector container_; + const T* values_ = nullptr; + + bool compare_fun(int32 a, int32 b) const { + if (values_[b] < values_[a]) { + return true; + } else if (values_[b] > values_[a]) { + return false; + } else { + return a < b; + } + } +}; + +// Mostly modeled on tensorflow/core/kernels/topk_op.cc for CPU. +template +void TopK(int32 row_size, int32 num_rows, const T* data, int32 k, + int32* output_indexes, T* output_values) { + TopContainer topc(k, row_size); + for (int row = 0; row < num_rows; ++row) { + const T* values_row = data + row * row_size; + topc.start_collecting(values_row); + for (int32 c = 0; c < row_size; ++c) { + topc.push(c); + } + + // Prepare output buffers. + int32* indexes_row = output_indexes + row * k; + T* output_row = output_values + row * k; + // We always assume that the output is sorted. + const auto& top_k = topc.sorted_result(); + std::copy(top_k.begin(), top_k.end(), indexes_row); + std::transform(top_k.begin(), top_k.end(), output_row, + [values_row](const int32 loc) { return values_row[loc]; }); + } +} + +} // namespace + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + // Check that the inputs and outputs have the right sizes and types. + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2); + + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output_values = GetOutput(context, node, kOutputValues); + TF_LITE_ENSURE_EQ(context, input->type, output_values->type); + + TfLiteTensor* top_k = GetInput(context, node, kInputTopK); + TF_LITE_ENSURE_EQ(context, top_k->type, kTfLiteInt32); + + // Set output dynamic if the input is not const. + if (IsConstantTensor(top_k)) { + TF_LITE_ENSURE_OK(context, ResizeOutput(context, node)); + } else { + TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes); + TfLiteTensor* output_values = GetOutput(context, node, kOutputValues); + SetTensorToDynamic(output_indexes); + SetTensorToDynamic(output_values); + } + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* output_values = GetOutput(context, node, kOutputValues); + TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes); + if (IsDynamicTensor(output_values)) { + TF_LITE_ENSURE_OK(context, ResizeOutput(context, node)); + } + TfLiteTensor* top_k = GetInput(context, node, kInputTopK); + const int32 k = top_k->data.i32[0]; + // The tensor can have more than 2 dimensions or even be a vector, the code + // anyway calls the internal dimension as row; + TfLiteTensor* input = GetInput(context, node, kInputTensor); + const int32 row_size = input->dims->data[input->dims->size - 1]; + int32 num_rows = 1; + for (int i = 0; i < input->dims->size - 1; ++i) { + num_rows *= input->dims->data[i]; + } + switch (output_values->type) { + case kTfLiteFloat32: + TopK(row_size, num_rows, input->data.f, k, output_indexes->data.i32, + output_values->data.f); + break; + case kTfLiteUInt8: + TopK(row_size, num_rows, input->data.uint8, k, output_indexes->data.i32, + output_values->data.uint8); + break; + case kTfLiteInt32: + TopK(row_size, num_rows, input->data.i32, k, output_indexes->data.i32, + output_values->data.i32); + break; + case kTfLiteInt64: + TopK(row_size, num_rows, input->data.i64, k, output_indexes->data.i32, + output_values->data.i64); + break; + default: + context->ReportError(context, "Type is currently not supported by TopK."); + return kTfLiteError; + } + + return kTfLiteOk; +} +} // namespace topk_v2 +TfLiteRegistration* Register_TOPK_V2() { + static TfLiteRegistration r = {nullptr, nullptr, topk_v2::Prepare, + topk_v2::Eval}; + return &r; +} +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/topk_v2_test.cc b/tensorflow/contrib/lite/kernels/topk_v2_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..29f2a057cd45e1cded3ff1aa0f0fdcad666ce2fa --- /dev/null +++ b/tensorflow/contrib/lite/kernels/topk_v2_test.cc @@ -0,0 +1,155 @@ + +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class TopKV2OpModel : public SingleOpModel { + public: + TopKV2OpModel(std::initializer_list input_shape, TensorType input_type, + int top_k) { + input_ = AddInput(input_type); + top_k_ = AddInput(TensorType_INT32); + output_indexes_ = AddOutput(TensorType_INT32); + output_values_ = AddOutput(input_type); + SetBuiltinOp(BuiltinOperator_TOPK_V2, BuiltinOptions_TopKV2Options, 0); + BuildInterpreter({input_shape, {1}}); + PopulateTensor(top_k_, {top_k}); + } + + void SetInputFloat(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInputUInt8(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInputInt32(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInputInt64(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetIndexes() { + return ExtractVector(output_indexes_); + } + + std::vector GetValuesFloat() { + return ExtractVector(output_values_); + } + + std::vector GetValuesUInt8() { + return ExtractVector(output_values_); + } + + std::vector GetValuesInt32() { + return ExtractVector(output_values_); + } + + std::vector GetValuesInt64() { + return ExtractVector(output_values_); + } + + protected: + int input_; + int top_k_; + int output_indexes_; + int output_values_; +}; + +// The test where the tensor dimension is equal to top. +TEST(TopKV2OpTest, EqualFloat) { + TopKV2OpModel m({2, 2}, TensorType_FLOAT32, 2); + m.SetInputFloat({-2.0, 0.2, 0.8, 0.1}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({1, 0, 0, 1})); + EXPECT_THAT(m.GetValuesFloat(), + ElementsAreArray(ArrayFloatNear({0.2, -2.0, 0.8, 0.1}))); +} + +// Test when internal dimension is k+1. +TEST(TopKV2OpTest, BorderFloat) { + TopKV2OpModel m({2, 3}, TensorType_FLOAT32, 2); + m.SetInputFloat({-2.0, -3.0, 0.2, 0.8, 0.1, -0.1}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({2, 0, 0, 1})); + EXPECT_THAT(m.GetValuesFloat(), + ElementsAreArray(ArrayFloatNear({0.2, -2.0, 0.8, 0.1}))); +} +// Test when internal dimension is higher than k. +TEST(TopKV2OpTest, LargeFloat) { + TopKV2OpModel m({2, 4}, TensorType_FLOAT32, 2); + m.SetInputFloat({-2.0, -3.0, -4.0, 0.2, 0.8, 0.1, -0.1, -0.8}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({3, 0, 0, 1})); + EXPECT_THAT(m.GetValuesFloat(), + ElementsAreArray(ArrayFloatNear({0.2, -2.0, 0.8, 0.1}))); +} + +// Test 1D case. +TEST(TopKV2OpTest, VectorFloat) { + TopKV2OpModel m({8}, TensorType_FLOAT32, 2); + m.SetInputFloat({-2.0, -3.0, -4.0, 0.2, 0.8, 0.1, -0.1, -0.8}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({4, 3})); + EXPECT_THAT(m.GetValuesFloat(), ElementsAreArray(ArrayFloatNear({0.8, 0.2}))); +} + +// Check that uint8 works. +TEST(TopKV2OpTest, TypeUint8) { + TopKV2OpModel m({2, 3}, TensorType_UINT8, 2); + m.SetInputUInt8({1, 2, 3, 251, 250, 249}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({2, 1, 0, 1})); + EXPECT_THAT(m.GetValuesUInt8(), ElementsAreArray({3, 2, 251, 250})); +} + +// Check that int32 works. +TEST(TopKV2OpTest, TypeInt32) { + TopKV2OpModel m({2, 3}, TensorType_INT32, 2); + m.SetInputInt32({1, 2, 3, 10251, 10250, 10249}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({2, 1, 0, 1})); + EXPECT_THAT(m.GetValuesInt32(), ElementsAreArray({3, 2, 10251, 10250})); +} + +// Check that int64 works. +TEST(TopKV2OpTest, TypeInt64) { + TopKV2OpModel m({2, 3}, TensorType_INT64, 2); + m.SetInputInt64({1, 2, 3, -1, -2, -3}); + m.Invoke(); + EXPECT_THAT(m.GetIndexes(), ElementsAreArray({2, 1, 0, 1})); + EXPECT_THAT(m.GetValuesInt64(), ElementsAreArray({3, 2, -1, -2})); +} +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/transpose.cc b/tensorflow/contrib/lite/kernels/transpose.cc index 093814bc4494041809d6e82965519e38a8997f49..d3c10a9bb7b07404ccd8cfe2636473a622b91787 100644 --- a/tensorflow/contrib/lite/kernels/transpose.cc +++ b/tensorflow/contrib/lite/kernels/transpose.cc @@ -90,7 +90,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Resize the output tensor if the output tensor is dynamic. if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } // Reverse the permuted axes and convert to 4D due to the way Dims are diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 9cdb58714edb5fee771fc45f3c53a570f8fb28d1..508a570e2e5fd52cfad28baf45824ef25061e13f 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -380,135 +381,57 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; } + // Check optional tensors, the respective pointers can be null. + const float* input_to_input_weights_ptr = + (use_cifg) ? nullptr : input_to_input_weights->data.f; + const float* recurrent_to_input_weights_ptr = + (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; + const float* input_gate_bias_ptr = + (use_cifg) ? nullptr : input_gate_bias->data.f; + const float* cell_to_input_weights_ptr = + (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; + const float* cell_to_forget_weights_ptr = + (use_peephole) ? cell_to_forget_weights->data.f : nullptr; + const float* cell_to_output_weights_ptr = + (use_peephole) ? cell_to_output_weights->data.f : nullptr; + const float* projection_weights_ptr = + (projection_weights == nullptr) ? nullptr : projection_weights->data.f; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; + const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; + const float* input_to_output_weights_ptr = input_to_output_weights->data.f; + const float* recurrent_to_forget_weights_ptr = + recurrent_to_forget_weights->data.f; + const float* recurrent_to_cell_weights_ptr = + recurrent_to_cell_weights->data.f; + const float* recurrent_to_output_weights_ptr = + recurrent_to_output_weights->data.f; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + for (int t = 0; t < max_time; t++) { - const float* input_ptr_time = input->data.f + t * n_batch * n_input; - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell, - n_batch, input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell, - n_batch, forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell, - n_batch, output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights->data.f, n_cell, n_input, input_ptr_time, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, input_gate_scratch, - /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, forget_gate_scratch, - /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, output_gate_scratch, - /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, - cell_state->data.f, n_batch * n_cell, - cell_state->data.f); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell, - params->cell_clip, cell_state->data.f); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, - output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - float* output_ptr_time = output->data.f + t * n_batch * n_output; - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output, - n_batch, output_ptr_time); - } else { - tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights->data.f, n_output, n_cell, output_gate_scratch, - n_batch, output_ptr_time, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output_ptr_time, n_batch * n_output, - params->proj_clip, output_ptr_time); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output_ptr_time); - } - tensor_utils::CopyVector(output_ptr_time, n_batch * n_output, - output_state->data.f); + const float* input_ptr_batch = input->data.f + t * n_batch * n_input; + float* output_ptr_batch = output->data.f + t * n_batch * n_output; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, + input_to_forget_weights_ptr, input_to_cell_weights_ptr, + input_to_output_weights_ptr, recurrent_to_input_weights_ptr, + recurrent_to_forget_weights_ptr, recurrent_to_cell_weights_ptr, + recurrent_to_output_weights_ptr, cell_to_input_weights_ptr, + cell_to_forget_weights_ptr, cell_to_output_weights_ptr, + input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, + output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr, + params, n_batch, n_cell, n_input, n_output, output_state_ptr, + cell_state_ptr, input_gate_scratch, forget_gate_scratch, cell_scratch, + output_gate_scratch, output_ptr_batch); } return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 14b6709964b54a6532273a69cca51c560b1cc103..8c456e70da2842c9972be723ad764f1658bd310d 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -124,19 +124,25 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() { auto opcodes = model_->operator_codes(); for (const OperatorCode* opcode : *opcodes) { TfLiteRegistration* registration = nullptr; - - if (opcode->builtin_code() != BuiltinOperator_CUSTOM) { - auto x = opcode->builtin_code(); - flatbuffer_op_index_to_registration_types_.push_back(x); - registration = op_resolver_.FindOp(x); + auto builtin_code = opcode->builtin_code(); + if (builtin_code > BuiltinOperator_MAX || + builtin_code < BuiltinOperator_MIN) { + error_reporter_->Report( + "Op builtin_code out or range: %d. Are you using old TFLite binary " + "with newer model?", + builtin_code); + status = kTfLiteError; + } else if (builtin_code != BuiltinOperator_CUSTOM) { + flatbuffer_op_index_to_registration_types_.push_back(builtin_code); + registration = op_resolver_.FindOp(builtin_code); if (registration == nullptr) { error_reporter_->Report("Didn't find op for builtin opcode '%s'\n", - EnumNameBuiltinOperator(x)); + EnumNameBuiltinOperator(builtin_code)); status = kTfLiteError; } } else if (!opcode->custom_code()) { error_reporter_->Report( - "Operator with builtin_code==0 has no custom_code.\n"); + "Operator with CUSTOM builtin_code has no custom_code.\n"); status = kTfLiteError; } else { const char* name = opcode->custom_code()->c_str(); @@ -278,6 +284,11 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_RELU_N1_TO_1: case BuiltinOperator_RELU6: case BuiltinOperator_CONCAT_EMBEDDINGS: + case BuiltinOperator_EXP: + case BuiltinOperator_TOPK_V2: + case BuiltinOperator_LOG_SOFTMAX: + case BuiltinOperator_CAST: + case BuiltinOperator_DEQUANTIZE: break; case BuiltinOperator_LSH_PROJECTION: { TfLiteLSHProjectionParams* params = @@ -453,6 +464,7 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: case BuiltinOperator_LSTM: { TfLiteLSTMParams* params = MallocPOD(); @@ -533,6 +545,14 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_SPLIT: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_SplitOptions()) { + params->num_splits = schema_params->num_splits(); + } + builtin_data = reinterpret_cast(params); + break; + } case BuiltinOperator_SQUEEZE: { auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_SqueezeOptions()) { @@ -556,6 +576,11 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_DELEGATE: { + // TODO(ycling): Revisit when supporting saving delegated models. + error_reporter->Report("DELEGATE op shouldn't exist in model."); + break; + } } return builtin_data; } @@ -769,6 +794,8 @@ TfLiteStatus InterpreterBuilder::operator()( return cleanup_and_error(); } + (**interpreter).set_model(model_); + // Parse inputs/outputs (**interpreter).SetInputs(FlatBufferIntArrayToVector(subgraph->inputs())); (**interpreter).SetOutputs(FlatBufferIntArrayToVector(subgraph->outputs())); diff --git a/tensorflow/contrib/lite/models/BUILD b/tensorflow/contrib/lite/models/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..6a1255b586ef04b80159156a78f0c4569a4661c5 --- /dev/null +++ b/tensorflow/contrib/lite/models/BUILD @@ -0,0 +1,26 @@ +# Model tests +package( + default_visibility = ["//visibility:public"], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") + +exports_files(glob([ + "testdata/*", +])) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/lite/models/speech_test.cc b/tensorflow/contrib/lite/models/speech_test.cc index daa8c3100b64e9290256aa14a6ab641f19174a0a..a354179a9480c136d65f83836d81f69c2089fdbe 100644 --- a/tensorflow/contrib/lite/models/speech_test.cc +++ b/tensorflow/contrib/lite/models/speech_test.cc @@ -97,7 +97,12 @@ bool ConvertCsvData(const string& model_name, const string& in_name, return true; } -TEST(SpeechTest, HotwordOkGoogleRank1Test) { +class SpeechTest : public ::testing::TestWithParam { + protected: + int GetMaxInvocations() { return GetParam(); } +}; + +TEST_P(SpeechTest, HotwordOkGoogleRank1Test) { std::stringstream os; ASSERT_TRUE(ConvertCsvData( "speech_hotword_model_rank1.tflite", "speech_hotword_model_in.csv", @@ -105,11 +110,11 @@ TEST(SpeechTest, HotwordOkGoogleRank1Test) { /*output_tensor=*/"18", /*persistent_tensors=*/"4", /*sequence_size=*/40, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } -TEST(SpeechTest, HotwordOkGoogleRank2Test) { +TEST_P(SpeechTest, HotwordOkGoogleRank2Test) { std::stringstream os; ASSERT_TRUE(ConvertCsvData( "speech_hotword_model_rank2.tflite", "speech_hotword_model_in.csv", @@ -117,11 +122,11 @@ TEST(SpeechTest, HotwordOkGoogleRank2Test) { /*output_tensor=*/"18", /*persistent_tensors=*/"1", /*sequence_size=*/40, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } -TEST(SpeechTest, SpeakerIdOkGoogleTest) { +TEST_P(SpeechTest, SpeakerIdOkGoogleTest) { std::stringstream os; ASSERT_TRUE(ConvertCsvData( "speech_speakerid_model.tflite", "speech_speakerid_model_in.csv", @@ -130,11 +135,11 @@ TEST(SpeechTest, SpeakerIdOkGoogleTest) { /*persistent_tensors=*/"19,20,40,41,61,62", /*sequence_size=*/80, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } -TEST(SpeechTest, AsrAmTest) { +TEST_P(SpeechTest, AsrAmTest) { std::stringstream os; ASSERT_TRUE( ConvertCsvData("speech_asr_am_model.tflite", "speech_asr_am_model_in.csv", @@ -143,7 +148,7 @@ TEST(SpeechTest, AsrAmTest) { /*persistent_tensors=*/"19,20,40,41,61,62,82,83,103,104", /*sequence_size=*/320, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } @@ -151,15 +156,16 @@ TEST(SpeechTest, AsrAmTest) { // through the interpreter and stored the sum of all the output, which was them // compared for correctness. In this test we are comparing all the intermediate // results. -TEST(SpeechTest, AsrLmTest) { +TEST_P(SpeechTest, AsrLmTest) { std::ifstream in_file; testing::TfLiteDriver test_driver(/*use_nnapi=*/false); ASSERT_TRUE(Init("speech_asr_lm_model.test_spec", &test_driver, &in_file)); - ASSERT_TRUE(testing::ParseAndRunTests(&in_file, &test_driver)) + ASSERT_TRUE( + testing::ParseAndRunTests(&in_file, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } -TEST(SpeechTest, EndpointerTest) { +TEST_P(SpeechTest, EndpointerTest) { std::stringstream os; ASSERT_TRUE(ConvertCsvData( "speech_endpointer_model.tflite", "speech_endpointer_model_in.csv", @@ -168,11 +174,11 @@ TEST(SpeechTest, EndpointerTest) { /*persistent_tensors=*/"28,29,49,50", /*sequence_size=*/320, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } -TEST(SpeechTest, TtsTest) { +TEST_P(SpeechTest, TtsTest) { std::stringstream os; ASSERT_TRUE(ConvertCsvData("speech_tts_model.tflite", "speech_tts_model_in.csv", @@ -181,9 +187,19 @@ TEST(SpeechTest, TtsTest) { /*persistent_tensors=*/"25,26,46,47,67,68,73", /*sequence_size=*/334, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); - ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver)) + ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) << test_driver.GetErrorMessage(); } +// Define two instantiations. The "ShortTests" instantiations is used when +// running the tests on Android, in order to prevent timeouts (It takes about +// 200s just to bring up the Android emulator.) +static const int kAllInvocations = -1; +static const int kFirstFewInvocations = 10; +INSTANTIATE_TEST_CASE_P(LongTests, SpeechTest, + ::testing::Values(kAllInvocations)); +INSTANTIATE_TEST_CASE_P(ShortTests, SpeechTest, + ::testing::Values(kFirstFewInvocations)); + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index da9ceec2f1401745ba477824bf494ee5b0ee1187..9d00d965d397d3af0dd454609901685ba965ded6 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -323,6 +323,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: case tflite::BuiltinOperator_EMBEDDING_LOOKUP: case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: + case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: case tflite::BuiltinOperator_L2_NORMALIZATION: case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: @@ -335,12 +336,19 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_GATHER: case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: + case tflite::BuiltinOperator_TOPK_V2: case tflite::BuiltinOperator_TRANSPOSE: case tflite::BuiltinOperator_MEAN: case tflite::BuiltinOperator_DIV: case tflite::BuiltinOperator_SUB: + case tflite::BuiltinOperator_SPLIT: case tflite::BuiltinOperator_SQUEEZE: case tflite::BuiltinOperator_STRIDED_SLICE: + case tflite::BuiltinOperator_EXP: + case tflite::BuiltinOperator_LOG_SOFTMAX: + case tflite::BuiltinOperator_DEQUANTIZE: + case tflite::BuiltinOperator_DELEGATE: + case tflite::BuiltinOperator_CAST: FATAL("Op code %d is currently not delegated to NNAPI", builtin); nn_op_type = -1; // set to invalid break; diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 2d8c49b7d7a5ae5c180f100a399a1870679c455f..82feae0f0041997949212613c654a5695f468d56 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -28,6 +28,7 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/framework:framework_py", + "//tensorflow/core:protos_all_py", "//tensorflow/python:platform", ], ) diff --git a/tensorflow/contrib/lite/python/op_hint.py b/tensorflow/contrib/lite/python/op_hint.py index 7c587e38b16dc3011fc7c8bef4eec4d0ea99ec21..9a3971228a683211e84b4c55d3a3e8d574b5ed94 100644 --- a/tensorflow/contrib/lite/python/op_hint.py +++ b/tensorflow/contrib/lite/python/op_hint.py @@ -73,6 +73,7 @@ import itertools as _itertools import uuid as _uuid from tensorflow.contrib import framework as _framework +from tensorflow.core.framework import attr_value_pb2 as _attr_value_pb2 from tensorflow.python.framework import ops as _ops from tensorflow.python.ops import array_ops as _array_ops from tensorflow.python.util.all_util import remove_undocumented @@ -133,10 +134,17 @@ class OpHint(object): def augmented_identity(arg): identity_op = _array_ops.identity(arg) - attr = identity_op.op.node_def.attr - attr[OpHint.FUNCTION_NAME_ATTR].s = self._function_name - attr[OpHint.FUNCTION_UUID_ATTR].s = self._unique_function_id - attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i = self._curr_input_index + # pylint: disable=protected-access + identity_op.op._set_attr( + OpHint.FUNCTION_NAME_ATTR, + _attr_value_pb2.AttrValue(s=self._function_name)) + identity_op.op._set_attr( + OpHint.FUNCTION_UUID_ATTR, + _attr_value_pb2.AttrValue(s=self._unique_function_id)) + identity_op.op._set_attr( + OpHint.FUNCTION_INPUT_INDEX_ATTR, + _attr_value_pb2.AttrValue(i=self._curr_input_index)) + # pylint: enable=protected-access self._curr_input_index += 1 return identity_op @@ -154,10 +162,17 @@ class OpHint(object): def augmented_identity(arg): identity_op = _array_ops.identity(arg) - attr = identity_op.op.node_def.attr - attr[OpHint.FUNCTION_NAME_ATTR].s = self._function_name - attr[OpHint.FUNCTION_UUID_ATTR].s = self._unique_function_id - attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i = self._curr_output_index + # pylint: disable=protected-access + identity_op.op._set_attr( + OpHint.FUNCTION_NAME_ATTR, + _attr_value_pb2.AttrValue(s=self._function_name)) + identity_op.op._set_attr( + OpHint.FUNCTION_UUID_ATTR, + _attr_value_pb2.AttrValue(s=self._unique_function_id)) + identity_op.op._set_attr( + OpHint.FUNCTION_OUTPUT_INDEX_ATTR, + _attr_value_pb2.AttrValue(i=self._curr_output_index)) + # pylint: enable=protected-access self._curr_output_index += 1 return identity_op diff --git a/tensorflow/contrib/lite/rpi_makefile.inc b/tensorflow/contrib/lite/rpi_makefile.inc new file mode 100644 index 0000000000000000000000000000000000000000..832ef5824bea86a368184bd7e3d17915739e9d46 --- /dev/null +++ b/tensorflow/contrib/lite/rpi_makefile.inc @@ -0,0 +1,33 @@ +# Settings for Raspberry Pi. +ifeq ($(TARGET), RPI) + ifeq ($(TARGET_ARCH), armv7) + CXXFLAGS += \ + -march=armv7-a \ + -mfpu=neon-vfpv4 \ + -funsafe-math-optimizations \ + -ftree-vectorize + + CCFLAGS += \ + -march=armv7-a \ + -mfpu=neon-vfpv4 \ + -funsafe-math-optimizations \ + -ftree-vectorize + + LDFLAGS := \ + -Wl,--no-export-dynamic \ + -Wl,--exclude-libs,ALL \ + -Wl,--gc-sections \ + -Wl,--as-needed + endif + + LIBS := \ + -lstdc++ \ + -lpthread \ + -lm \ + -ldl + + OBJDIR := $(OBJDIR)rpi_$(TARGET_ARCH)/ + LIBDIR := $(LIBDIR)rpi_$(TARGET_ARCH)/ + BINDIR := $(BINDIR)rpi_$(TARGET_ARCH)/ + DEPDIR := $(DEPDIR)rpi_$(TARGET_ARCH)/ +endif diff --git a/tensorflow/contrib/lite/schema/BUILD b/tensorflow/contrib/lite/schema/BUILD index 54167ddd9a5a003d0ff21e6627a1dbe94afa3e87..da65ec659c7ab39348d2b7911aceaa9dbdd2654b 100644 --- a/tensorflow/contrib/lite/schema/BUILD +++ b/tensorflow/contrib/lite/schema/BUILD @@ -5,6 +5,7 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 load("//tensorflow:tensorflow.bzl", "py_test") +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") py_binary( name = "upgrade_schema", @@ -80,3 +81,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD b/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..0148149a6adc141d67e82808f7e8c72ddb7e309a --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD @@ -0,0 +1,43 @@ +package(default_visibility = [ + "//visibility:public", +]) + +licenses(["notice"]) # Apache 2.0 + +cc_library( + name = "generator", + srcs = ["generator.cc"], + hdrs = ["generator.h"], + deps = [ + "//tensorflow/contrib/lite/schema:schema_fbs", + ], +) + +cc_binary( + name = "generate", + srcs = ["generate.cc"], + deps = [ + ":generator", + ], +) + +cc_test( + name = "generator_test", + srcs = ["generator_test.cc"], + deps = [ + ":generator", + "@com_google_googletest//:gtest", + ], +) + +cc_test( + name = "consistency_test", + srcs = ["consistency_test.cc"], + data = [ + "//tensorflow/contrib/lite:builtin_ops.h", + ], + deps = [ + ":generator", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/README.md b/tensorflow/contrib/lite/schema/builtin_ops_header/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f20d4f664e62fdd52e55339e45b9603307a2b671 --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/README.md @@ -0,0 +1,12 @@ +# Builtin Ops Header Generator. + +This directory contains a code generator to generate a pure C header for +builtin op definition. + +Whenever you add a new builtin op, please execute: + +```sh +bazel run \ + //tensorflow/contrib/lite/schema/builtin_ops_header:generate > \ + tensorflow/contrib/lite/builtin_ops.h +``` diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/consistency_test.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/consistency_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..d55c125c117db3c1b8d67ab0b674abe2e7c39d94 --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/consistency_test.cc @@ -0,0 +1,47 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include "tensorflow/contrib/lite/schema/builtin_ops_header/generator.h" + +namespace { + +const char* kHeaderFileName = + "tensorflow/contrib/lite/builtin_ops.h"; + +// The test ensures that `builtin_ops.h` is consistent with the FlatBuffer +// schema definition. When the schema is modified, it's required to run the +// generator to re-generate the header. +// Please see README.md for more details. +TEST(BuiltinOpsHeaderTest, TestConsistency) { + std::ifstream input_stream(kHeaderFileName, std::ios::binary); + ASSERT_TRUE(input_stream); + std::string file_content((std::istreambuf_iterator(input_stream)), + std::istreambuf_iterator()); + + std::ostringstream output_stream; + tflite::builtin_ops_header::GenerateHeader(output_stream); + std::string generated_content = output_stream.str(); + + EXPECT_EQ(file_content, generated_content); +} + +} // anonymous namespace + +int main(int argc, char** argv) { + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generate.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/generate.cc new file mode 100644 index 0000000000000000000000000000000000000000..72a28987b8d4863b0f03f7861177940177edd884 --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generate.cc @@ -0,0 +1,25 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/schema/builtin_ops_header/generator.h" + +// This executable is used to generate builtin_ops.h in TensorFlow Lite. +// Please see README.md for more details. +int main() { + if (!tflite::builtin_ops_header::GenerateHeader(std::cout)) { + std::cerr << "Failed to generate the header file.\n"; + } + return 0; +} diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc new file mode 100644 index 0000000000000000000000000000000000000000..08bcfe451685f488be2c3bc180f2dfc43dfe4f05 --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc @@ -0,0 +1,135 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/schema/builtin_ops_header/generator.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" + +namespace tflite { +namespace builtin_ops_header { + +namespace { +const char* kFileHeader = + R"(/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ + +// DO NOT EDIT MANUALLY: This file is automatically generated by +// `schema_builtin_ops_header_generator.py`. + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// The enum for builtin operators. +// Note: CUSTOM and DELEGATE are 2 special ops which are not real biultin +// ops. +typedef enum { +)"; + +const char* kFileFooter = + R"(} TfLiteBuiltinOperator; + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus +#endif // TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ +} +)"; +} // anonymous namespace + +bool IsValidInputEnumName(const std::string& name) { + const char* begin = name.c_str(); + const char* ch = begin; + while (*ch != '\0') { + // If it's not the first character, expect an underscore. + if (ch != begin) { + if (*ch != '_') { + return false; + } + ++ch; + } + + // Expecting a word with upper case letters or digits, like "CONV", + // "CONV2D", "2D"...etc. + bool empty = true; + while (isupper(*ch) || isdigit(*ch)) { + // It's not empty if at least one character is consumed. + empty = false; + ++ch; + } + if (empty) { + return false; + } + } + return true; +} + +std::string ConstantizeVariableName(const std::string& name) { + std::string result = "kTfLiteBuiltin"; + bool uppercase = true; + for (char input_char : name) { + if (input_char == '_') { + uppercase = true; + } else if (uppercase) { + result += toupper(input_char); + uppercase = false; + } else { + result += tolower(input_char); + } + } + + return result; +} + +bool GenerateHeader(std::ostream& os) { + auto enum_names = tflite::EnumNamesBuiltinOperator(); + + // Check if all the input enum names are valid. + for (auto enum_value : EnumValuesBuiltinOperator()) { + auto enum_name = enum_names[enum_value]; + if (!IsValidInputEnumName(enum_name)) { + std::cerr << "Invalid input enum name: " << enum_name << std::endl; + return false; + } + } + + os << kFileHeader; + for (auto enum_value : EnumValuesBuiltinOperator()) { + auto enum_name = enum_names[enum_value]; + os << " "; + os << ConstantizeVariableName(enum_name); + os << " = "; + os << enum_value; + os << ",\n"; + } + os << kFileFooter; + return true; +} + +} // namespace builtin_ops_header +} // namespace tflite diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.h b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.h new file mode 100644 index 0000000000000000000000000000000000000000..3241ff83d599ed8a476fc1d5a88c26143ebfbaf2 --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.h @@ -0,0 +1,38 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// An utility library to generate pure C header for builtin ops definition. +#ifndef TENSORFLOW_CONTRIB_LITE_SCHEMA_BUILTIN_OPS_HEADER_GENERATOR_H_ +#define TENSORFLOW_CONTRIB_LITE_SCHEMA_BUILTIN_OPS_HEADER_GENERATOR_H_ + +#include + +namespace tflite { +namespace builtin_ops_header { + +// Check if the input enum name (from the Flatbuffer definition) is valid. +bool IsValidInputEnumName(const std::string& name); + +// Convert the enum name from Flatbuffer convention to C enum name convention. +// E.g. `L2_POOL_2D` becomes `kTfLiteBuiltinL2Pool2d`. +std::string ConstantizeVariableName(const std::string& name); + +// The function generates a pure C header for builtin ops definition, and write +// it to the output stream. +bool GenerateHeader(std::ostream& os); + +} // namespace builtin_ops_header +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_SCHEMA_BUILTIN_OPS_HEADER_GENERATOR_H_ diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generator_test.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/generator_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..a7dc8e1b0486eda6e09f38a209dca95c0317a1fb --- /dev/null +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generator_test.cc @@ -0,0 +1,63 @@ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/schema/builtin_ops_header/generator.h" +#include +#include + +namespace { + +using tflite::builtin_ops_header::ConstantizeVariableName; +using tflite::builtin_ops_header::IsValidInputEnumName; + +TEST(TestIsValidInputEnumName, TestWithValidInputNames) { + EXPECT_TRUE(IsValidInputEnumName("ADD")); + EXPECT_TRUE(IsValidInputEnumName("CONV_2D")); + EXPECT_TRUE(IsValidInputEnumName("L2_POOL_2D")); +} + +TEST(TestIsValidInputEnumName, TestWithLeadingUnderscore) { + EXPECT_FALSE(IsValidInputEnumName("_ADD")); + EXPECT_FALSE(IsValidInputEnumName("_CONV_2D")); +} + +TEST(TestIsValidInputEnumName, TestWithLowerCase) { + EXPECT_FALSE(IsValidInputEnumName("_AdD")); + EXPECT_FALSE(IsValidInputEnumName("_COnV_2D")); +} + +TEST(TestIsValidInputEnumName, TestWithOtherCharacters) { + EXPECT_FALSE(IsValidInputEnumName("_AdD!2D")); + EXPECT_FALSE(IsValidInputEnumName("_COnV?2D")); +} + +TEST(TestIsValidInputEnumName, TestWithDoubleUnderscores) { + EXPECT_FALSE(IsValidInputEnumName("ADD__2D")); + EXPECT_FALSE(IsValidInputEnumName("CONV__2D")); +} + +TEST(TestConstantizeVariableName, TestWithValidInputNames) { + EXPECT_EQ(ConstantizeVariableName("ADD"), "kTfLiteBuiltinAdd"); + EXPECT_EQ(ConstantizeVariableName("CONV_2D"), "kTfLiteBuiltinConv2d"); + EXPECT_EQ(ConstantizeVariableName("L2_POOL_2D"), "kTfLiteBuiltinL2Pool2d"); +} + +} // anonymous namespace + +int main(int argc, char** argv) { + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 36cc2724eb1d927d39cff25a46a57aca4f572547..04387fed33fb8d99cd862a5c5fe0c8465f84337e 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -75,7 +75,7 @@ enum BuiltinOperator : byte { CONV_2D = 3, DEPTHWISE_CONV_2D = 4, // DEPTH_TO_SPACE = 5, - // DEQUANTIZE = 6, + DEQUANTIZE = 6, EMBEDDING_LOOKUP = 7, // FLOOR = 8, FULLY_CONNECTED = 9, @@ -120,6 +120,16 @@ enum BuiltinOperator : byte { UNIDIRECTIONAL_SEQUENCE_LSTM = 44, STRIDED_SLICE = 45, BIDIRECTIONAL_SEQUENCE_RNN = 46, + EXP = 47, + TOPK_V2 = 48, + SPLIT = 49, + LOG_SOFTMAX = 50, + // DELEGATE is a special op type for the operations which are delegated to + // other backends. + // WARNING: Experimental interface, subject to change + DELEGATE = 51, + BIDIRECTIONAL_SEQUENCE_LSTM = 52, + CAST = 53, } // Options for the builtin operators. @@ -156,6 +166,12 @@ union BuiltinOptions { SqueezeOptions, SequenceRNNOptions, StridedSliceOptions, + ExpOptions, + TopKV2Options, + SplitOptions, + LogSoftmaxOptions, + CastOptions, + DequantizeOptions, } enum Padding : byte { SAME, VALID } @@ -315,6 +331,9 @@ table DivOptions { fused_activation_function:ActivationFunctionType; } +table TopKV2Options { +} + enum CombinerType : byte { SUM = 0, MEAN = 1, @@ -332,6 +351,9 @@ table GatherOptions { table TransposeOptions { } +table ExpOptions { +} + table MeanOptions { keep_dims: bool; } @@ -340,6 +362,10 @@ table SqueezeOptions { squeeze_dims:[int]; } +table SplitOptions { + num_splits: int; +} + table StridedSliceOptions { begin_mask: int; end_mask: int; @@ -348,6 +374,15 @@ table StridedSliceOptions { shrink_axis_mask: int; } +table LogSoftmaxOptions { +} + +table CastOptions { +} + +table DequantizeOptions { +} + // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a // builtin, or a string if the operator is custom. table OperatorCode { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index e2ac0b9d1e05cdc7e89da32107044320d6e4ea5a..b922de20816bafc6e01c4bb66ddeef839be2bbd8 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ // automatically generated by the FlatBuffers compiler, do not modify + #ifndef FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ #define FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ @@ -108,6 +109,9 @@ struct SubOptionsT; struct DivOptions; struct DivOptionsT; +struct TopKV2Options; +struct TopKV2OptionsT; + struct EmbeddingLookupSparseOptions; struct EmbeddingLookupSparseOptionsT; @@ -117,15 +121,30 @@ struct GatherOptionsT; struct TransposeOptions; struct TransposeOptionsT; +struct ExpOptions; +struct ExpOptionsT; + struct MeanOptions; struct MeanOptionsT; struct SqueezeOptions; struct SqueezeOptionsT; +struct SplitOptions; +struct SplitOptionsT; + struct StridedSliceOptions; struct StridedSliceOptionsT; +struct LogSoftmaxOptions; +struct LogSoftmaxOptionsT; + +struct CastOptions; +struct CastOptionsT; + +struct DequantizeOptions; +struct DequantizeOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -153,15 +172,27 @@ enum TensorType { }; inline TensorType (&EnumValuesTensorType())[6] { - static TensorType values[] = {TensorType_FLOAT32, TensorType_FLOAT16, - TensorType_INT32, TensorType_UINT8, - TensorType_INT64, TensorType_STRING}; + static TensorType values[] = { + TensorType_FLOAT32, + TensorType_FLOAT16, + TensorType_INT32, + TensorType_UINT8, + TensorType_INT64, + TensorType_STRING + }; return values; } inline const char **EnumNamesTensorType() { - static const char *names[] = {"FLOAT32", "FLOAT16", "INT32", "UINT8", - "INT64", "STRING", nullptr}; + static const char *names[] = { + "FLOAT32", + "FLOAT16", + "INT32", + "UINT8", + "INT64", + "STRING", + nullptr + }; return names; } @@ -176,6 +207,7 @@ enum BuiltinOperator { BuiltinOperator_CONCATENATION = 2, BuiltinOperator_CONV_2D = 3, BuiltinOperator_DEPTHWISE_CONV_2D = 4, + BuiltinOperator_DEQUANTIZE = 6, BuiltinOperator_EMBEDDING_LOOKUP = 7, BuiltinOperator_FULLY_CONNECTED = 9, BuiltinOperator_HASHTABLE_LOOKUP = 10, @@ -215,108 +247,133 @@ enum BuiltinOperator { BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, BuiltinOperator_STRIDED_SLICE = 45, BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, + BuiltinOperator_EXP = 47, + BuiltinOperator_TOPK_V2 = 48, + BuiltinOperator_SPLIT = 49, + BuiltinOperator_LOG_SOFTMAX = 50, + BuiltinOperator_DELEGATE = 51, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, + BuiltinOperator_CAST = 53, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN + BuiltinOperator_MAX = BuiltinOperator_CAST }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[44] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[52] { static BuiltinOperator values[] = { - BuiltinOperator_ADD, - BuiltinOperator_AVERAGE_POOL_2D, - BuiltinOperator_CONCATENATION, - BuiltinOperator_CONV_2D, - BuiltinOperator_DEPTHWISE_CONV_2D, - BuiltinOperator_EMBEDDING_LOOKUP, - BuiltinOperator_FULLY_CONNECTED, - BuiltinOperator_HASHTABLE_LOOKUP, - BuiltinOperator_L2_NORMALIZATION, - BuiltinOperator_L2_POOL_2D, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - BuiltinOperator_LOGISTIC, - BuiltinOperator_LSH_PROJECTION, - BuiltinOperator_LSTM, - BuiltinOperator_MAX_POOL_2D, - BuiltinOperator_MUL, - BuiltinOperator_RELU, - BuiltinOperator_RELU_N1_TO_1, - BuiltinOperator_RELU6, - BuiltinOperator_RESHAPE, - BuiltinOperator_RESIZE_BILINEAR, - BuiltinOperator_RNN, - BuiltinOperator_SOFTMAX, - BuiltinOperator_SPACE_TO_DEPTH, - BuiltinOperator_SVDF, - BuiltinOperator_TANH, - BuiltinOperator_CONCAT_EMBEDDINGS, - BuiltinOperator_SKIP_GRAM, - BuiltinOperator_CALL, - BuiltinOperator_CUSTOM, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - BuiltinOperator_PAD, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_GATHER, - BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOperator_TRANSPOSE, - BuiltinOperator_MEAN, - BuiltinOperator_SUB, - BuiltinOperator_DIV, - BuiltinOperator_SQUEEZE, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN}; + BuiltinOperator_ADD, + BuiltinOperator_AVERAGE_POOL_2D, + BuiltinOperator_CONCATENATION, + BuiltinOperator_CONV_2D, + BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOperator_DEQUANTIZE, + BuiltinOperator_EMBEDDING_LOOKUP, + BuiltinOperator_FULLY_CONNECTED, + BuiltinOperator_HASHTABLE_LOOKUP, + BuiltinOperator_L2_NORMALIZATION, + BuiltinOperator_L2_POOL_2D, + BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + BuiltinOperator_LOGISTIC, + BuiltinOperator_LSH_PROJECTION, + BuiltinOperator_LSTM, + BuiltinOperator_MAX_POOL_2D, + BuiltinOperator_MUL, + BuiltinOperator_RELU, + BuiltinOperator_RELU_N1_TO_1, + BuiltinOperator_RELU6, + BuiltinOperator_RESHAPE, + BuiltinOperator_RESIZE_BILINEAR, + BuiltinOperator_RNN, + BuiltinOperator_SOFTMAX, + BuiltinOperator_SPACE_TO_DEPTH, + BuiltinOperator_SVDF, + BuiltinOperator_TANH, + BuiltinOperator_CONCAT_EMBEDDINGS, + BuiltinOperator_SKIP_GRAM, + BuiltinOperator_CALL, + BuiltinOperator_CUSTOM, + BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, + BuiltinOperator_PAD, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_GATHER, + BuiltinOperator_BATCH_TO_SPACE_ND, + BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOperator_TRANSPOSE, + BuiltinOperator_MEAN, + BuiltinOperator_SUB, + BuiltinOperator_DIV, + BuiltinOperator_SQUEEZE, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_STRIDED_SLICE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_EXP, + BuiltinOperator_TOPK_V2, + BuiltinOperator_SPLIT, + BuiltinOperator_LOG_SOFTMAX, + BuiltinOperator_DELEGATE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_CAST + }; return values; } inline const char **EnumNamesBuiltinOperator() { - static const char *names[] = {"ADD", - "AVERAGE_POOL_2D", - "CONCATENATION", - "CONV_2D", - "DEPTHWISE_CONV_2D", - "", - "", - "EMBEDDING_LOOKUP", - "", - "FULLY_CONNECTED", - "HASHTABLE_LOOKUP", - "L2_NORMALIZATION", - "L2_POOL_2D", - "LOCAL_RESPONSE_NORMALIZATION", - "LOGISTIC", - "LSH_PROJECTION", - "LSTM", - "MAX_POOL_2D", - "MUL", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "RESHAPE", - "RESIZE_BILINEAR", - "RNN", - "SOFTMAX", - "SPACE_TO_DEPTH", - "SVDF", - "TANH", - "CONCAT_EMBEDDINGS", - "SKIP_GRAM", - "CALL", - "CUSTOM", - "EMBEDDING_LOOKUP_SPARSE", - "PAD", - "UNIDIRECTIONAL_SEQUENCE_RNN", - "GATHER", - "BATCH_TO_SPACE_ND", - "SPACE_TO_BATCH_ND", - "TRANSPOSE", - "MEAN", - "SUB", - "DIV", - "SQUEEZE", - "UNIDIRECTIONAL_SEQUENCE_LSTM", - "STRIDED_SLICE", - "BIDIRECTIONAL_SEQUENCE_RNN", - nullptr}; + static const char *names[] = { + "ADD", + "AVERAGE_POOL_2D", + "CONCATENATION", + "CONV_2D", + "DEPTHWISE_CONV_2D", + "", + "DEQUANTIZE", + "EMBEDDING_LOOKUP", + "", + "FULLY_CONNECTED", + "HASHTABLE_LOOKUP", + "L2_NORMALIZATION", + "L2_POOL_2D", + "LOCAL_RESPONSE_NORMALIZATION", + "LOGISTIC", + "LSH_PROJECTION", + "LSTM", + "MAX_POOL_2D", + "MUL", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "RESHAPE", + "RESIZE_BILINEAR", + "RNN", + "SOFTMAX", + "SPACE_TO_DEPTH", + "SVDF", + "TANH", + "CONCAT_EMBEDDINGS", + "SKIP_GRAM", + "CALL", + "CUSTOM", + "EMBEDDING_LOOKUP_SPARSE", + "PAD", + "UNIDIRECTIONAL_SEQUENCE_RNN", + "GATHER", + "BATCH_TO_SPACE_ND", + "SPACE_TO_BATCH_ND", + "TRANSPOSE", + "MEAN", + "SUB", + "DIV", + "SQUEEZE", + "UNIDIRECTIONAL_SEQUENCE_LSTM", + "STRIDED_SLICE", + "BIDIRECTIONAL_SEQUENCE_RNN", + "EXP", + "TOPK_V2", + "SPLIT", + "LOG_SOFTMAX", + "DELEGATE", + "BIDIRECTIONAL_SEQUENCE_LSTM", + "CAST", + nullptr + }; return names; } @@ -359,83 +416,104 @@ enum BuiltinOptions { BuiltinOptions_SqueezeOptions = 30, BuiltinOptions_SequenceRNNOptions = 31, BuiltinOptions_StridedSliceOptions = 32, + BuiltinOptions_ExpOptions = 33, + BuiltinOptions_TopKV2Options = 34, + BuiltinOptions_SplitOptions = 35, + BuiltinOptions_LogSoftmaxOptions = 36, + BuiltinOptions_CastOptions = 37, + BuiltinOptions_DequantizeOptions = 38, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_StridedSliceOptions + BuiltinOptions_MAX = BuiltinOptions_DequantizeOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[33] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[39] { static BuiltinOptions values[] = { - BuiltinOptions_NONE, - BuiltinOptions_Conv2DOptions, - BuiltinOptions_DepthwiseConv2DOptions, - BuiltinOptions_ConcatEmbeddingsOptions, - BuiltinOptions_LSHProjectionOptions, - BuiltinOptions_Pool2DOptions, - BuiltinOptions_SVDFOptions, - BuiltinOptions_RNNOptions, - BuiltinOptions_FullyConnectedOptions, - BuiltinOptions_SoftmaxOptions, - BuiltinOptions_ConcatenationOptions, - BuiltinOptions_AddOptions, - BuiltinOptions_L2NormOptions, - BuiltinOptions_LocalResponseNormalizationOptions, - BuiltinOptions_LSTMOptions, - BuiltinOptions_ResizeBilinearOptions, - BuiltinOptions_CallOptions, - BuiltinOptions_ReshapeOptions, - BuiltinOptions_SkipGramOptions, - BuiltinOptions_SpaceToDepthOptions, - BuiltinOptions_EmbeddingLookupSparseOptions, - BuiltinOptions_MulOptions, - BuiltinOptions_PadOptions, - BuiltinOptions_GatherOptions, - BuiltinOptions_BatchToSpaceNDOptions, - BuiltinOptions_SpaceToBatchNDOptions, - BuiltinOptions_TransposeOptions, - BuiltinOptions_MeanOptions, - BuiltinOptions_SubOptions, - BuiltinOptions_DivOptions, - BuiltinOptions_SqueezeOptions, - BuiltinOptions_SequenceRNNOptions, - BuiltinOptions_StridedSliceOptions}; + BuiltinOptions_NONE, + BuiltinOptions_Conv2DOptions, + BuiltinOptions_DepthwiseConv2DOptions, + BuiltinOptions_ConcatEmbeddingsOptions, + BuiltinOptions_LSHProjectionOptions, + BuiltinOptions_Pool2DOptions, + BuiltinOptions_SVDFOptions, + BuiltinOptions_RNNOptions, + BuiltinOptions_FullyConnectedOptions, + BuiltinOptions_SoftmaxOptions, + BuiltinOptions_ConcatenationOptions, + BuiltinOptions_AddOptions, + BuiltinOptions_L2NormOptions, + BuiltinOptions_LocalResponseNormalizationOptions, + BuiltinOptions_LSTMOptions, + BuiltinOptions_ResizeBilinearOptions, + BuiltinOptions_CallOptions, + BuiltinOptions_ReshapeOptions, + BuiltinOptions_SkipGramOptions, + BuiltinOptions_SpaceToDepthOptions, + BuiltinOptions_EmbeddingLookupSparseOptions, + BuiltinOptions_MulOptions, + BuiltinOptions_PadOptions, + BuiltinOptions_GatherOptions, + BuiltinOptions_BatchToSpaceNDOptions, + BuiltinOptions_SpaceToBatchNDOptions, + BuiltinOptions_TransposeOptions, + BuiltinOptions_MeanOptions, + BuiltinOptions_SubOptions, + BuiltinOptions_DivOptions, + BuiltinOptions_SqueezeOptions, + BuiltinOptions_SequenceRNNOptions, + BuiltinOptions_StridedSliceOptions, + BuiltinOptions_ExpOptions, + BuiltinOptions_TopKV2Options, + BuiltinOptions_SplitOptions, + BuiltinOptions_LogSoftmaxOptions, + BuiltinOptions_CastOptions, + BuiltinOptions_DequantizeOptions + }; return values; } inline const char **EnumNamesBuiltinOptions() { - static const char *names[] = {"NONE", - "Conv2DOptions", - "DepthwiseConv2DOptions", - "ConcatEmbeddingsOptions", - "LSHProjectionOptions", - "Pool2DOptions", - "SVDFOptions", - "RNNOptions", - "FullyConnectedOptions", - "SoftmaxOptions", - "ConcatenationOptions", - "AddOptions", - "L2NormOptions", - "LocalResponseNormalizationOptions", - "LSTMOptions", - "ResizeBilinearOptions", - "CallOptions", - "ReshapeOptions", - "SkipGramOptions", - "SpaceToDepthOptions", - "EmbeddingLookupSparseOptions", - "MulOptions", - "PadOptions", - "GatherOptions", - "BatchToSpaceNDOptions", - "SpaceToBatchNDOptions", - "TransposeOptions", - "MeanOptions", - "SubOptions", - "DivOptions", - "SqueezeOptions", - "SequenceRNNOptions", - "StridedSliceOptions", - nullptr}; + static const char *names[] = { + "NONE", + "Conv2DOptions", + "DepthwiseConv2DOptions", + "ConcatEmbeddingsOptions", + "LSHProjectionOptions", + "Pool2DOptions", + "SVDFOptions", + "RNNOptions", + "FullyConnectedOptions", + "SoftmaxOptions", + "ConcatenationOptions", + "AddOptions", + "L2NormOptions", + "LocalResponseNormalizationOptions", + "LSTMOptions", + "ResizeBilinearOptions", + "CallOptions", + "ReshapeOptions", + "SkipGramOptions", + "SpaceToDepthOptions", + "EmbeddingLookupSparseOptions", + "MulOptions", + "PadOptions", + "GatherOptions", + "BatchToSpaceNDOptions", + "SpaceToBatchNDOptions", + "TransposeOptions", + "MeanOptions", + "SubOptions", + "DivOptions", + "SqueezeOptions", + "SequenceRNNOptions", + "StridedSliceOptions", + "ExpOptions", + "TopKV2Options", + "SplitOptions", + "LogSoftmaxOptions", + "CastOptions", + "DequantizeOptions", + nullptr + }; return names; } @@ -444,206 +522,182 @@ inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { return EnumNamesBuiltinOptions()[index]; } -template -struct BuiltinOptionsTraits { +template struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_NONE; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; }; -template <> -struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = - BuiltinOptions_DepthwiseConv2DOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; }; -template <> -struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = - BuiltinOptions_ConcatEmbeddingsOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; }; -template <> -struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = - BuiltinOptions_LocalResponseNormalizationOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; }; -template <> -struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = - BuiltinOptions_EmbeddingLookupSparseOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_MeanOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; }; -template <> -struct BuiltinOptionsTraits { +template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {} - BuiltinOptionsUnion(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT - : type(BuiltinOptions_NONE), - value(nullptr) { - std::swap(type, u.type); - std::swap(value, u.value); - } + BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT : + type(BuiltinOptions_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } BuiltinOptionsUnion(const BuiltinOptionsUnion &) FLATBUFFERS_NOEXCEPT; - BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) - FLATBUFFERS_NOEXCEPT { - BuiltinOptionsUnion t(u); - std::swap(type, t.type); - std::swap(value, t.value); - return *this; - } - BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT { - std::swap(type, u.type); - std::swap(value, u.value); - return *this; - } + BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) FLATBUFFERS_NOEXCEPT + { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } ~BuiltinOptionsUnion() { Reset(); } void Reset(); #ifndef FLATBUFFERS_CPP98_STL template - void Set(T &&val) { + void Set(T&& val) { Reset(); type = BuiltinOptionsTraits::enum_value; if (type != BuiltinOptions_NONE) { @@ -652,342 +706,317 @@ struct BuiltinOptionsUnion { } #endif // FLATBUFFERS_CPP98_STL - static void *UnPack(const void *obj, BuiltinOptions type, - const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; Conv2DOptionsT *AsConv2DOptions() { - return type == BuiltinOptions_Conv2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; } const Conv2DOptionsT *AsConv2DOptions() const { - return type == BuiltinOptions_Conv2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; } DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() { - return type == BuiltinOptions_DepthwiseConv2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; } const DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const { - return type == BuiltinOptions_DepthwiseConv2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; } ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() { - return type == BuiltinOptions_ConcatEmbeddingsOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; } const ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const { - return type == BuiltinOptions_ConcatEmbeddingsOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; } LSHProjectionOptionsT *AsLSHProjectionOptions() { - return type == BuiltinOptions_LSHProjectionOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; } const LSHProjectionOptionsT *AsLSHProjectionOptions() const { - return type == BuiltinOptions_LSHProjectionOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; } Pool2DOptionsT *AsPool2DOptions() { - return type == BuiltinOptions_Pool2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; } const Pool2DOptionsT *AsPool2DOptions() const { - return type == BuiltinOptions_Pool2DOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; } SVDFOptionsT *AsSVDFOptions() { - return type == BuiltinOptions_SVDFOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; } const SVDFOptionsT *AsSVDFOptions() const { - return type == BuiltinOptions_SVDFOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; } RNNOptionsT *AsRNNOptions() { - return type == BuiltinOptions_RNNOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; } const RNNOptionsT *AsRNNOptions() const { - return type == BuiltinOptions_RNNOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; } FullyConnectedOptionsT *AsFullyConnectedOptions() { - return type == BuiltinOptions_FullyConnectedOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; } const FullyConnectedOptionsT *AsFullyConnectedOptions() const { - return type == BuiltinOptions_FullyConnectedOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; } SoftmaxOptionsT *AsSoftmaxOptions() { - return type == BuiltinOptions_SoftmaxOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; } const SoftmaxOptionsT *AsSoftmaxOptions() const { - return type == BuiltinOptions_SoftmaxOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; } ConcatenationOptionsT *AsConcatenationOptions() { - return type == BuiltinOptions_ConcatenationOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; } const ConcatenationOptionsT *AsConcatenationOptions() const { - return type == BuiltinOptions_ConcatenationOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; } AddOptionsT *AsAddOptions() { - return type == BuiltinOptions_AddOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; } const AddOptionsT *AsAddOptions() const { - return type == BuiltinOptions_AddOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; } L2NormOptionsT *AsL2NormOptions() { - return type == BuiltinOptions_L2NormOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; } const L2NormOptionsT *AsL2NormOptions() const { - return type == BuiltinOptions_L2NormOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; } LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() { - return type == BuiltinOptions_LocalResponseNormalizationOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; } - const LocalResponseNormalizationOptionsT * - AsLocalResponseNormalizationOptions() const { - return type == BuiltinOptions_LocalResponseNormalizationOptions - ? reinterpret_cast( - value) - : nullptr; + const LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const { + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; } LSTMOptionsT *AsLSTMOptions() { - return type == BuiltinOptions_LSTMOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; } const LSTMOptionsT *AsLSTMOptions() const { - return type == BuiltinOptions_LSTMOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; } ResizeBilinearOptionsT *AsResizeBilinearOptions() { - return type == BuiltinOptions_ResizeBilinearOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; } const ResizeBilinearOptionsT *AsResizeBilinearOptions() const { - return type == BuiltinOptions_ResizeBilinearOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; } CallOptionsT *AsCallOptions() { - return type == BuiltinOptions_CallOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; } const CallOptionsT *AsCallOptions() const { - return type == BuiltinOptions_CallOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; } ReshapeOptionsT *AsReshapeOptions() { - return type == BuiltinOptions_ReshapeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; } const ReshapeOptionsT *AsReshapeOptions() const { - return type == BuiltinOptions_ReshapeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; } SkipGramOptionsT *AsSkipGramOptions() { - return type == BuiltinOptions_SkipGramOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; } const SkipGramOptionsT *AsSkipGramOptions() const { - return type == BuiltinOptions_SkipGramOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; } SpaceToDepthOptionsT *AsSpaceToDepthOptions() { - return type == BuiltinOptions_SpaceToDepthOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; } const SpaceToDepthOptionsT *AsSpaceToDepthOptions() const { - return type == BuiltinOptions_SpaceToDepthOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; } EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() { - return type == BuiltinOptions_EmbeddingLookupSparseOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; } const EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const { - return type == BuiltinOptions_EmbeddingLookupSparseOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; } MulOptionsT *AsMulOptions() { - return type == BuiltinOptions_MulOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; } const MulOptionsT *AsMulOptions() const { - return type == BuiltinOptions_MulOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; } PadOptionsT *AsPadOptions() { - return type == BuiltinOptions_PadOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; } const PadOptionsT *AsPadOptions() const { - return type == BuiltinOptions_PadOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; } GatherOptionsT *AsGatherOptions() { - return type == BuiltinOptions_GatherOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; } const GatherOptionsT *AsGatherOptions() const { - return type == BuiltinOptions_GatherOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; } BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() { - return type == BuiltinOptions_BatchToSpaceNDOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; } const BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const { - return type == BuiltinOptions_BatchToSpaceNDOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; } SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() { - return type == BuiltinOptions_SpaceToBatchNDOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; } const SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const { - return type == BuiltinOptions_SpaceToBatchNDOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; } TransposeOptionsT *AsTransposeOptions() { - return type == BuiltinOptions_TransposeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; } const TransposeOptionsT *AsTransposeOptions() const { - return type == BuiltinOptions_TransposeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; } MeanOptionsT *AsMeanOptions() { - return type == BuiltinOptions_MeanOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_MeanOptions ? + reinterpret_cast(value) : nullptr; } const MeanOptionsT *AsMeanOptions() const { - return type == BuiltinOptions_MeanOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_MeanOptions ? + reinterpret_cast(value) : nullptr; } SubOptionsT *AsSubOptions() { - return type == BuiltinOptions_SubOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; } const SubOptionsT *AsSubOptions() const { - return type == BuiltinOptions_SubOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; } DivOptionsT *AsDivOptions() { - return type == BuiltinOptions_DivOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; } const DivOptionsT *AsDivOptions() const { - return type == BuiltinOptions_DivOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; } SqueezeOptionsT *AsSqueezeOptions() { - return type == BuiltinOptions_SqueezeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; } const SqueezeOptionsT *AsSqueezeOptions() const { - return type == BuiltinOptions_SqueezeOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; } SequenceRNNOptionsT *AsSequenceRNNOptions() { - return type == BuiltinOptions_SequenceRNNOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; } const SequenceRNNOptionsT *AsSequenceRNNOptions() const { - return type == BuiltinOptions_SequenceRNNOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; } StridedSliceOptionsT *AsStridedSliceOptions() { - return type == BuiltinOptions_StridedSliceOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; } const StridedSliceOptionsT *AsStridedSliceOptions() const { - return type == BuiltinOptions_StridedSliceOptions - ? reinterpret_cast(value) - : nullptr; + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; + } + ExpOptionsT *AsExpOptions() { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + const ExpOptionsT *AsExpOptions() const { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + TopKV2OptionsT *AsTopKV2Options() { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + const TopKV2OptionsT *AsTopKV2Options() const { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + SplitOptionsT *AsSplitOptions() { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + const SplitOptionsT *AsSplitOptions() const { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + LogSoftmaxOptionsT *AsLogSoftmaxOptions() { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + const LogSoftmaxOptionsT *AsLogSoftmaxOptions() const { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + CastOptionsT *AsCastOptions() { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + const CastOptionsT *AsCastOptions() const { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + DequantizeOptionsT *AsDequantizeOptions() { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const DequantizeOptionsT *AsDequantizeOptions() const { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; } }; -bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, - BuiltinOptions type); -bool VerifyBuiltinOptionsVector( - flatbuffers::Verifier &verifier, - const flatbuffers::Vector> *values, - const flatbuffers::Vector *types); +bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); +bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); enum Padding { Padding_SAME = 0, @@ -997,12 +1026,19 @@ enum Padding { }; inline Padding (&EnumValuesPadding())[2] { - static Padding values[] = {Padding_SAME, Padding_VALID}; + static Padding values[] = { + Padding_SAME, + Padding_VALID + }; return values; } inline const char **EnumNamesPadding() { - static const char *names[] = {"SAME", "VALID", nullptr}; + static const char *names[] = { + "SAME", + "VALID", + nullptr + }; return names; } @@ -1024,15 +1060,26 @@ enum ActivationFunctionType { inline ActivationFunctionType (&EnumValuesActivationFunctionType())[6] { static ActivationFunctionType values[] = { - ActivationFunctionType_NONE, ActivationFunctionType_RELU, - ActivationFunctionType_RELU_N1_TO_1, ActivationFunctionType_RELU6, - ActivationFunctionType_TANH, ActivationFunctionType_SIGN_BIT}; + ActivationFunctionType_NONE, + ActivationFunctionType_RELU, + ActivationFunctionType_RELU_N1_TO_1, + ActivationFunctionType_RELU6, + ActivationFunctionType_TANH, + ActivationFunctionType_SIGN_BIT + }; return values; } inline const char **EnumNamesActivationFunctionType() { - static const char *names[] = {"NONE", "RELU", "RELU_N1_TO_1", "RELU6", - "TANH", "SIGN_BIT", nullptr}; + static const char *names[] = { + "NONE", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "TANH", + "SIGN_BIT", + nullptr + }; return names; } @@ -1050,14 +1097,21 @@ enum LSHProjectionType { }; inline LSHProjectionType (&EnumValuesLSHProjectionType())[3] { - static LSHProjectionType values[] = {LSHProjectionType_UNKNOWN, - LSHProjectionType_SPARSE, - LSHProjectionType_DENSE}; + static LSHProjectionType values[] = { + LSHProjectionType_UNKNOWN, + LSHProjectionType_SPARSE, + LSHProjectionType_DENSE + }; return values; } inline const char **EnumNamesLSHProjectionType() { - static const char *names[] = {"UNKNOWN", "SPARSE", "DENSE", nullptr}; + static const char *names[] = { + "UNKNOWN", + "SPARSE", + "DENSE", + nullptr + }; return names; } @@ -1075,13 +1129,21 @@ enum CombinerType { }; inline CombinerType (&EnumValuesCombinerType())[3] { - static CombinerType values[] = {CombinerType_SUM, CombinerType_MEAN, - CombinerType_SQRTN}; + static CombinerType values[] = { + CombinerType_SUM, + CombinerType_MEAN, + CombinerType_SQRTN + }; return values; } inline const char **EnumNamesCombinerType() { - static const char *names[] = {"SUM", "MEAN", "SQRTN", nullptr}; + static const char *names[] = { + "SUM", + "MEAN", + "SQRTN", + nullptr + }; return names; } @@ -1097,12 +1159,17 @@ enum CustomOptionsFormat { }; inline CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] { - static CustomOptionsFormat values[] = {CustomOptionsFormat_FLEXBUFFERS}; + static CustomOptionsFormat values[] = { + CustomOptionsFormat_FLEXBUFFERS + }; return values; } inline const char **EnumNamesCustomOptionsFormat() { - static const char *names[] = {"FLEXBUFFERS", nullptr}; + static const char *names[] = { + "FLEXBUFFERS", + nullptr + }; return names; } @@ -1117,13 +1184,18 @@ struct QuantizationParametersT : public flatbuffers::NativeTable { std::vector max; std::vector scale; std::vector zero_point; - QuantizationParametersT() {} + QuantizationParametersT() { + } }; -struct QuantizationParameters FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef QuantizationParametersT NativeTableType; - enum { VT_MIN = 4, VT_MAX = 6, VT_SCALE = 8, VT_ZERO_POINT = 10 }; + enum { + VT_MIN = 4, + VT_MAX = 6, + VT_SCALE = 8, + VT_ZERO_POINT = 10 + }; const flatbuffers::Vector *min() const { return GetPointer *>(VT_MIN); } @@ -1137,20 +1209,20 @@ struct QuantizationParameters FLATBUFFERS_FINAL_CLASS return GetPointer *>(VT_ZERO_POINT); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_MIN) && - verifier.Verify(min()) && VerifyOffset(verifier, VT_MAX) && - verifier.Verify(max()) && VerifyOffset(verifier, VT_SCALE) && - verifier.Verify(scale()) && VerifyOffset(verifier, VT_ZERO_POINT) && - verifier.Verify(zero_point()) && verifier.EndTable(); - } - QuantizationParametersT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - QuantizationParametersT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_MIN) && + verifier.Verify(min()) && + VerifyOffset(verifier, VT_MAX) && + verifier.Verify(max()) && + VerifyOffset(verifier, VT_SCALE) && + verifier.Verify(scale()) && + VerifyOffset(verifier, VT_ZERO_POINT) && + verifier.Verify(zero_point()) && + verifier.EndTable(); + } + QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct QuantizationParametersBuilder { @@ -1165,16 +1237,14 @@ struct QuantizationParametersBuilder { void add_scale(flatbuffers::Offset> scale) { fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); } - void add_zero_point( - flatbuffers::Offset> zero_point) { + void add_zero_point(flatbuffers::Offset> zero_point) { fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); } explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - QuantizationParametersBuilder &operator=( - const QuantizationParametersBuilder &); + QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -1196,23 +1266,21 @@ inline flatbuffers::Offset CreateQuantizationParameters( return builder_.Finish(); } -inline flatbuffers::Offset -CreateQuantizationParametersDirect( +inline flatbuffers::Offset CreateQuantizationParametersDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *min = nullptr, const std::vector *max = nullptr, const std::vector *scale = nullptr, const std::vector *zero_point = nullptr) { return tflite::CreateQuantizationParameters( - _fbb, min ? _fbb.CreateVector(*min) : 0, + _fbb, + min ? _fbb.CreateVector(*min) : 0, max ? _fbb.CreateVector(*max) : 0, scale ? _fbb.CreateVector(*scale) : 0, zero_point ? _fbb.CreateVector(*zero_point) : 0); } -flatbuffers::Offset CreateQuantizationParameters( - flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct TensorT : public flatbuffers::NativeTable { typedef Tensor TableType; @@ -1221,7 +1289,10 @@ struct TensorT : public flatbuffers::NativeTable { uint32_t buffer; std::string name; std::unique_ptr quantization; - TensorT() : type(TensorType_FLOAT32), buffer(0) {} + TensorT() + : type(TensorType_FLOAT32), + buffer(0) { + } }; struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -1239,7 +1310,9 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { TensorType type() const { return static_cast(GetField(VT_TYPE, 0)); } - uint32_t buffer() const { return GetField(VT_BUFFER, 0); } + uint32_t buffer() const { + return GetField(VT_BUFFER, 0); + } const flatbuffers::String *name() const { return GetPointer(VT_NAME); } @@ -1247,20 +1320,20 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { return GetPointer(VT_QUANTIZATION); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) && - verifier.Verify(shape()) && VerifyField(verifier, VT_TYPE) && + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.Verify(shape()) && + VerifyField(verifier, VT_TYPE) && VerifyField(verifier, VT_BUFFER) && - VerifyOffset(verifier, VT_NAME) && verifier.Verify(name()) && + VerifyOffset(verifier, VT_NAME) && + verifier.Verify(name()) && VerifyOffset(verifier, VT_QUANTIZATION) && - verifier.VerifyTable(quantization()) && verifier.EndTable(); + verifier.VerifyTable(quantization()) && + verifier.EndTable(); } - TensorT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = - nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct TensorBuilder { @@ -1278,11 +1351,11 @@ struct TensorBuilder { void add_name(flatbuffers::Offset name) { fbb_.AddOffset(Tensor::VT_NAME, name); } - void add_quantization( - flatbuffers::Offset quantization) { + void add_quantization(flatbuffers::Offset quantization) { fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); } - explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { + explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } TensorBuilder &operator=(const TensorBuilder &); @@ -1296,7 +1369,8 @@ struct TensorBuilder { inline flatbuffers::Offset CreateTensor( flatbuffers::FlatBufferBuilder &_fbb, flatbuffers::Offset> shape = 0, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, + TensorType type = TensorType_FLOAT32, + uint32_t buffer = 0, flatbuffers::Offset name = 0, flatbuffers::Offset quantization = 0) { TensorBuilder builder_(_fbb); @@ -1311,17 +1385,20 @@ inline flatbuffers::Offset CreateTensor( inline flatbuffers::Offset CreateTensorDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *shape = nullptr, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, + TensorType type = TensorType_FLOAT32, + uint32_t buffer = 0, const char *name = nullptr, flatbuffers::Offset quantization = 0) { return tflite::CreateTensor( - _fbb, shape ? _fbb.CreateVector(*shape) : 0, type, buffer, - name ? _fbb.CreateString(name) : 0, quantization); + _fbb, + shape ? _fbb.CreateVector(*shape) : 0, + type, + buffer, + name ? _fbb.CreateString(name) : 0, + quantization); } -flatbuffers::Offset CreateTensor( - flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct Conv2DOptionsT : public flatbuffers::NativeTable { typedef Conv2DOptions TableType; @@ -1333,7 +1410,8 @@ struct Conv2DOptionsT : public flatbuffers::NativeTable { : padding(Padding_SAME), stride_w(0), stride_h(0), - fused_activation_function(ActivationFunctionType_NONE) {} + fused_activation_function(ActivationFunctionType_NONE) { + } }; struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -1347,11 +1425,14 @@ struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { Padding padding() const { return static_cast(GetField(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField(VT_STRIDE_H, 0); } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1361,22 +1442,16 @@ struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - Conv2DOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - Conv2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct Conv2DOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_padding(Padding padding) { - fbb_.AddElement(Conv2DOptions::VT_PADDING, - static_cast(padding), 0); + fbb_.AddElement(Conv2DOptions::VT_PADDING, static_cast(padding), 0); } void add_stride_w(int32_t stride_w) { fbb_.AddElement(Conv2DOptions::VT_STRIDE_W, stride_w, 0); @@ -1384,13 +1459,11 @@ struct Conv2DOptionsBuilder { void add_stride_h(int32_t stride_h) { fbb_.AddElement(Conv2DOptions::VT_STRIDE_H, stride_h, 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &); @@ -1402,10 +1475,11 @@ struct Conv2DOptionsBuilder { }; inline flatbuffers::Offset CreateConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + flatbuffers::FlatBufferBuilder &_fbb, + Padding padding = Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { Conv2DOptionsBuilder builder_(_fbb); builder_.add_stride_h(stride_h); builder_.add_stride_w(stride_w); @@ -1414,9 +1488,7 @@ inline flatbuffers::Offset CreateConv2DOptions( return builder_.Finish(); } -flatbuffers::Offset CreateConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct Pool2DOptionsT : public flatbuffers::NativeTable { typedef Pool2DOptions TableType; @@ -1432,7 +1504,8 @@ struct Pool2DOptionsT : public flatbuffers::NativeTable { stride_h(0), filter_width(0), filter_height(0), - fused_activation_function(ActivationFunctionType_NONE) {} + fused_activation_function(ActivationFunctionType_NONE) { + } }; struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -1448,15 +1521,20 @@ struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { Padding padding() const { return static_cast(GetField(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField(VT_STRIDE_H, 0); } - int32_t filter_width() const { return GetField(VT_FILTER_WIDTH, 0); } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + int32_t filter_width() const { + return GetField(VT_FILTER_WIDTH, 0); + } int32_t filter_height() const { return GetField(VT_FILTER_HEIGHT, 0); } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1468,22 +1546,16 @@ struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - Pool2DOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - Pool2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct Pool2DOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_padding(Padding padding) { - fbb_.AddElement(Pool2DOptions::VT_PADDING, - static_cast(padding), 0); + fbb_.AddElement(Pool2DOptions::VT_PADDING, static_cast(padding), 0); } void add_stride_w(int32_t stride_w) { fbb_.AddElement(Pool2DOptions::VT_STRIDE_W, stride_w, 0); @@ -1497,13 +1569,11 @@ struct Pool2DOptionsBuilder { void add_filter_height(int32_t filter_height) { fbb_.AddElement(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &); @@ -1515,11 +1585,13 @@ struct Pool2DOptionsBuilder { }; inline flatbuffers::Offset CreatePool2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, int32_t filter_width = 0, + flatbuffers::FlatBufferBuilder &_fbb, + Padding padding = Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t filter_width = 0, int32_t filter_height = 0, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { Pool2DOptionsBuilder builder_(_fbb); builder_.add_filter_height(filter_height); builder_.add_filter_width(filter_width); @@ -1530,9 +1602,7 @@ inline flatbuffers::Offset CreatePool2DOptions( return builder_.Finish(); } -flatbuffers::Offset CreatePool2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { typedef DepthwiseConv2DOptions TableType; @@ -1546,11 +1616,11 @@ struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { stride_w(0), stride_h(0), depth_multiplier(0), - fused_activation_function(ActivationFunctionType_NONE) {} + fused_activation_function(ActivationFunctionType_NONE) { + } }; -struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef DepthwiseConv2DOptionsT NativeTableType; enum { VT_PADDING = 4, @@ -1562,14 +1632,17 @@ struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS Padding padding() const { return static_cast(GetField(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField(VT_STRIDE_H, 0); } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } int32_t depth_multiplier() const { return GetField(VT_DEPTH_MULTIPLIER, 0); } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1580,22 +1653,16 @@ struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - DepthwiseConv2DOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - DepthwiseConv2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct DepthwiseConv2DOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_padding(Padding padding) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, - static_cast(padding), 0); + fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, static_cast(padding), 0); } void add_stride_w(int32_t stride_w) { fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); @@ -1604,21 +1671,16 @@ struct DepthwiseConv2DOptionsBuilder { fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); } void add_depth_multiplier(int32_t depth_multiplier) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, - depth_multiplier, 0); + fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement( - DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - DepthwiseConv2DOptionsBuilder &operator=( - const DepthwiseConv2DOptionsBuilder &); + DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -1627,10 +1689,12 @@ struct DepthwiseConv2DOptionsBuilder { }; inline flatbuffers::Offset CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, int32_t depth_multiplier = 0, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + flatbuffers::FlatBufferBuilder &_fbb, + Padding padding = Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t depth_multiplier = 0, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { DepthwiseConv2DOptionsBuilder builder_(_fbb); builder_.add_depth_multiplier(depth_multiplier); builder_.add_stride_h(stride_h); @@ -1640,34 +1704,33 @@ inline flatbuffers::Offset CreateDepthwiseConv2DOptions( return builder_.Finish(); } -flatbuffers::Offset CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable { typedef ConcatEmbeddingsOptions TableType; int32_t num_channels; std::vector num_columns_per_channel; std::vector embedding_dim_per_channel; - ConcatEmbeddingsOptionsT() : num_channels(0) {} + ConcatEmbeddingsOptionsT() + : num_channels(0) { + } }; -struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef ConcatEmbeddingsOptionsT NativeTableType; enum { VT_NUM_CHANNELS = 4, VT_NUM_COLUMNS_PER_CHANNEL = 6, VT_EMBEDDING_DIM_PER_CHANNEL = 8 }; - int32_t num_channels() const { return GetField(VT_NUM_CHANNELS, 0); } + int32_t num_channels() const { + return GetField(VT_NUM_CHANNELS, 0); + } const flatbuffers::Vector *num_columns_per_channel() const { - return GetPointer *>( - VT_NUM_COLUMNS_PER_CHANNEL); + return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); } const flatbuffers::Vector *embedding_dim_per_channel() const { - return GetPointer *>( - VT_EMBEDDING_DIM_PER_CHANNEL); + return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1675,43 +1738,31 @@ struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && verifier.Verify(num_columns_per_channel()) && VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && - verifier.Verify(embedding_dim_per_channel()) && verifier.EndTable(); + verifier.Verify(embedding_dim_per_channel()) && + verifier.EndTable(); } - ConcatEmbeddingsOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - ConcatEmbeddingsOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct ConcatEmbeddingsOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_num_channels(int32_t num_channels) { - fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, - num_channels, 0); + fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); } - void add_num_columns_per_channel( - flatbuffers::Offset> - num_columns_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, - num_columns_per_channel); + void add_num_columns_per_channel(flatbuffers::Offset> num_columns_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); } - void add_embedding_dim_per_channel( - flatbuffers::Offset> - embedding_dim_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, - embedding_dim_per_channel); + void add_embedding_dim_per_channel(flatbuffers::Offset> embedding_dim_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel); } explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - ConcatEmbeddingsOptionsBuilder &operator=( - const ConcatEmbeddingsOptionsBuilder &); + ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -1719,13 +1770,11 @@ struct ConcatEmbeddingsOptionsBuilder { } }; -inline flatbuffers::Offset -CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_channels = 0, - flatbuffers::Offset> - num_columns_per_channel = 0, - flatbuffers::Offset> - embedding_dim_per_channel = 0) { +inline flatbuffers::Offset CreateConcatEmbeddingsOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, + flatbuffers::Offset> num_columns_per_channel = 0, + flatbuffers::Offset> embedding_dim_per_channel = 0) { ConcatEmbeddingsOptionsBuilder builder_(_fbb); builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); builder_.add_num_columns_per_channel(num_columns_per_channel); @@ -1733,61 +1782,54 @@ CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, return builder_.Finish(); } -inline flatbuffers::Offset -CreateConcatEmbeddingsOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0, +inline flatbuffers::Offset CreateConcatEmbeddingsOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, const std::vector *num_columns_per_channel = nullptr, const std::vector *embedding_dim_per_channel = nullptr) { return tflite::CreateConcatEmbeddingsOptions( - _fbb, num_channels, - num_columns_per_channel - ? _fbb.CreateVector(*num_columns_per_channel) - : 0, - embedding_dim_per_channel - ? _fbb.CreateVector(*embedding_dim_per_channel) - : 0); + _fbb, + num_channels, + num_columns_per_channel ? _fbb.CreateVector(*num_columns_per_channel) : 0, + embedding_dim_per_channel ? _fbb.CreateVector(*embedding_dim_per_channel) : 0); } -flatbuffers::Offset CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct LSHProjectionOptionsT : public flatbuffers::NativeTable { typedef LSHProjectionOptions TableType; LSHProjectionType type; - LSHProjectionOptionsT() : type(LSHProjectionType_UNKNOWN) {} + LSHProjectionOptionsT() + : type(LSHProjectionType_UNKNOWN) { + } }; -struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef LSHProjectionOptionsT NativeTableType; - enum { VT_TYPE = 4 }; + enum { + VT_TYPE = 4 + }; LSHProjectionType type() const { return static_cast(GetField(VT_TYPE, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TYPE) && verifier.EndTable(); + VerifyField(verifier, VT_TYPE) && + verifier.EndTable(); } - LSHProjectionOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - LSHProjectionOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct LSHProjectionOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_type(LSHProjectionType type) { - fbb_.AddElement(LSHProjectionOptions::VT_TYPE, - static_cast(type), 0); + fbb_.AddElement(LSHProjectionOptions::VT_TYPE, static_cast(type), 0); } explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &); @@ -1806,25 +1848,29 @@ inline flatbuffers::Offset CreateLSHProjectionOptions( return builder_.Finish(); } -flatbuffers::Offset CreateLSHProjectionOptions( - flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SVDFOptionsT : public flatbuffers::NativeTable { typedef SVDFOptions TableType; int32_t rank; ActivationFunctionType fused_activation_function; SVDFOptionsT() - : rank(0), fused_activation_function(ActivationFunctionType_NONE) {} + : rank(0), + fused_activation_function(ActivationFunctionType_NONE) { + } }; struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SVDFOptionsT NativeTableType; - enum { VT_RANK = 4, VT_FUSED_ACTIVATION_FUNCTION = 6 }; - int32_t rank() const { return GetField(VT_RANK, 0); } + enum { + VT_RANK = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + int32_t rank() const { + return GetField(VT_RANK, 0); + } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1832,14 +1878,9 @@ struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - SVDFOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SVDFOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SVDFOptionsBuilder { @@ -1848,13 +1889,11 @@ struct SVDFOptionsBuilder { void add_rank(int32_t rank) { fbb_.AddElement(SVDFOptions::VT_RANK, rank, 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &); @@ -1866,57 +1905,51 @@ struct SVDFOptionsBuilder { }; inline flatbuffers::Offset CreateSVDFOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t rank = 0, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + flatbuffers::FlatBufferBuilder &_fbb, + int32_t rank = 0, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { SVDFOptionsBuilder builder_(_fbb); builder_.add_rank(rank); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateSVDFOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct RNNOptionsT : public flatbuffers::NativeTable { typedef RNNOptions TableType; ActivationFunctionType fused_activation_function; - RNNOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + RNNOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef RNNOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - RNNOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - RNNOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct RNNOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } RNNOptionsBuilder &operator=(const RNNOptionsBuilder &); @@ -1929,16 +1962,13 @@ struct RNNOptionsBuilder { inline flatbuffers::Offset CreateRNNOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { RNNOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SequenceRNNOptionsT : public flatbuffers::NativeTable { typedef SequenceRNNOptions TableType; @@ -1946,16 +1976,21 @@ struct SequenceRNNOptionsT : public flatbuffers::NativeTable { ActivationFunctionType fused_activation_function; SequenceRNNOptionsT() : time_major(false), - fused_activation_function(ActivationFunctionType_NONE) {} + fused_activation_function(ActivationFunctionType_NONE) { + } }; struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SequenceRNNOptionsT NativeTableType; - enum { VT_TIME_MAJOR = 4, VT_FUSED_ACTIVATION_FUNCTION = 6 }; - bool time_major() const { return GetField(VT_TIME_MAJOR, 0) != 0; } + enum { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1963,30 +1998,22 @@ struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - SequenceRNNOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SequenceRNNOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SequenceRNNOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_time_major(bool time_major) { - fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, - static_cast(time_major), 0); + fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &); @@ -1998,18 +2025,16 @@ struct SequenceRNNOptionsBuilder { }; inline flatbuffers::Offset CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { SequenceRNNOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); builder_.add_time_major(time_major); return builder_.Finish(); } -flatbuffers::Offset CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { typedef BidirectionalSequenceRNNOptions TableType; @@ -2017,17 +2042,21 @@ struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { ActivationFunctionType fused_activation_function; BidirectionalSequenceRNNOptionsT() : time_major(false), - fused_activation_function(ActivationFunctionType_NONE) {} + fused_activation_function(ActivationFunctionType_NONE) { + } }; -struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef BidirectionalSequenceRNNOptionsT NativeTableType; - enum { VT_TIME_MAJOR = 4, VT_FUSED_ACTIVATION_FUNCTION = 6 }; - bool time_major() const { return GetField(VT_TIME_MAJOR, 0) != 0; } + enum { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -2035,37 +2064,25 @@ struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - BidirectionalSequenceRNNOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct BidirectionalSequenceRNNOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_time_major(bool time_major) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, - static_cast(time_major), 0); - } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement( - BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); - } - explicit BidirectionalSequenceRNNOptionsBuilder( - flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - BidirectionalSequenceRNNOptionsBuilder &operator=( - const BidirectionalSequenceRNNOptionsBuilder &); + BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -2073,63 +2090,52 @@ struct BidirectionalSequenceRNNOptionsBuilder { } }; -inline flatbuffers::Offset -CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); builder_.add_time_major(time_major); return builder_.Finish(); } -flatbuffers::Offset -CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct FullyConnectedOptionsT : public flatbuffers::NativeTable { typedef FullyConnectedOptions TableType; ActivationFunctionType fused_activation_function; FullyConnectedOptionsT() - : fused_activation_function(ActivationFunctionType_NONE) {} + : fused_activation_function(ActivationFunctionType_NONE) { + } }; -struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef FullyConnectedOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - FullyConnectedOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - FullyConnectedOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct FullyConnectedOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &); @@ -2142,39 +2148,38 @@ struct FullyConnectedOptionsBuilder { inline flatbuffers::Offset CreateFullyConnectedOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { FullyConnectedOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SoftmaxOptionsT : public flatbuffers::NativeTable { typedef SoftmaxOptions TableType; float beta; - SoftmaxOptionsT() : beta(0.0f) {} + SoftmaxOptionsT() + : beta(0.0f) { + } }; struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SoftmaxOptionsT NativeTableType; - enum { VT_BETA = 4 }; - float beta() const { return GetField(VT_BETA, 0.0f); } + enum { + VT_BETA = 4 + }; + float beta() const { + return GetField(VT_BETA, 0.0f); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BETA) && verifier.EndTable(); + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); } - SoftmaxOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SoftmaxOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SoftmaxOptionsBuilder { @@ -2184,7 +2189,7 @@ struct SoftmaxOptionsBuilder { fbb_.AddElement(SoftmaxOptions::VT_BETA, beta, 0.0f); } explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &); @@ -2196,32 +2201,36 @@ struct SoftmaxOptionsBuilder { }; inline flatbuffers::Offset CreateSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, float beta = 0.0f) { + flatbuffers::FlatBufferBuilder &_fbb, + float beta = 0.0f) { SoftmaxOptionsBuilder builder_(_fbb); builder_.add_beta(beta); return builder_.Finish(); } -flatbuffers::Offset CreateSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct ConcatenationOptionsT : public flatbuffers::NativeTable { typedef ConcatenationOptions TableType; int32_t axis; ActivationFunctionType fused_activation_function; ConcatenationOptionsT() - : axis(0), fused_activation_function(ActivationFunctionType_NONE) {} + : axis(0), + fused_activation_function(ActivationFunctionType_NONE) { + } }; -struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef ConcatenationOptionsT NativeTableType; - enum { VT_AXIS = 4, VT_FUSED_ACTIVATION_FUNCTION = 6 }; - int32_t axis() const { return GetField(VT_AXIS, 0); } + enum { + VT_AXIS = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -2229,14 +2238,9 @@ struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - ConcatenationOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - ConcatenationOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct ConcatenationOptionsBuilder { @@ -2245,13 +2249,11 @@ struct ConcatenationOptionsBuilder { void add_axis(int32_t axis) { fbb_.AddElement(ConcatenationOptions::VT_AXIS, axis, 0); } - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &); @@ -2263,57 +2265,51 @@ struct ConcatenationOptionsBuilder { }; inline flatbuffers::Offset CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0, + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { ConcatenationOptionsBuilder builder_(_fbb); builder_.add_axis(axis); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct AddOptionsT : public flatbuffers::NativeTable { typedef AddOptions TableType; ActivationFunctionType fused_activation_function; - AddOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + AddOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef AddOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - AddOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - AddOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct AddOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } AddOptionsBuilder &operator=(const AddOptionsBuilder &); @@ -2326,55 +2322,48 @@ struct AddOptionsBuilder { inline flatbuffers::Offset CreateAddOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { AddOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateAddOptions( - flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct MulOptionsT : public flatbuffers::NativeTable { typedef MulOptions TableType; ActivationFunctionType fused_activation_function; - MulOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + MulOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef MulOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - MulOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - MulOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct MulOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } MulOptionsBuilder &operator=(const MulOptionsBuilder &); @@ -2387,55 +2376,48 @@ struct MulOptionsBuilder { inline flatbuffers::Offset CreateMulOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { MulOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct L2NormOptionsT : public flatbuffers::NativeTable { typedef L2NormOptions TableType; ActivationFunctionType fused_activation_function; - L2NormOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + L2NormOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef L2NormOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - L2NormOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - L2NormOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct L2NormOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &); @@ -2448,16 +2430,13 @@ struct L2NormOptionsBuilder { inline flatbuffers::Offset CreateL2NormOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { L2NormOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateL2NormOptions( - flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { typedef LocalResponseNormalizationOptions TableType; @@ -2466,61 +2445,66 @@ struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { float alpha; float beta; LocalResponseNormalizationOptionsT() - : radius(0), bias(0.0f), alpha(0.0f), beta(0.0f) {} + : radius(0), + bias(0.0f), + alpha(0.0f), + beta(0.0f) { + } }; -struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef LocalResponseNormalizationOptionsT NativeTableType; - enum { VT_RADIUS = 4, VT_BIAS = 6, VT_ALPHA = 8, VT_BETA = 10 }; - int32_t radius() const { return GetField(VT_RADIUS, 0); } - float bias() const { return GetField(VT_BIAS, 0.0f); } - float alpha() const { return GetField(VT_ALPHA, 0.0f); } - float beta() const { return GetField(VT_BETA, 0.0f); } + enum { + VT_RADIUS = 4, + VT_BIAS = 6, + VT_ALPHA = 8, + VT_BETA = 10 + }; + int32_t radius() const { + return GetField(VT_RADIUS, 0); + } + float bias() const { + return GetField(VT_BIAS, 0.0f); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + float beta() const { + return GetField(VT_BETA, 0.0f); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_RADIUS) && VerifyField(verifier, VT_BIAS) && VerifyField(verifier, VT_ALPHA) && - VerifyField(verifier, VT_BETA) && verifier.EndTable(); + VerifyField(verifier, VT_BETA) && + verifier.EndTable(); } - LocalResponseNormalizationOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - LocalResponseNormalizationOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const LocalResponseNormalizationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct LocalResponseNormalizationOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_radius(int32_t radius) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, - radius, 0); + fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); } void add_bias(float bias) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, - 0.0f); + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); } void add_alpha(float alpha) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, - 0.0f); + fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); } void add_beta(float beta) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, - 0.0f); + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); } - explicit LocalResponseNormalizationOptionsBuilder( - flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - LocalResponseNormalizationOptionsBuilder &operator=( - const LocalResponseNormalizationOptionsBuilder &); + LocalResponseNormalizationOptionsBuilder &operator=(const LocalResponseNormalizationOptionsBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -2528,10 +2512,12 @@ struct LocalResponseNormalizationOptionsBuilder { } }; -inline flatbuffers::Offset -CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t radius = 0, float bias = 0.0f, - float alpha = 0.0f, float beta = 0.0f) { +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t radius = 0, + float bias = 0.0f, + float alpha = 0.0f, + float beta = 0.0f) { LocalResponseNormalizationOptionsBuilder builder_(_fbb); builder_.add_beta(beta); builder_.add_alpha(alpha); @@ -2540,11 +2526,7 @@ CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, return builder_.Finish(); } -flatbuffers::Offset -CreateLocalResponseNormalizationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const LocalResponseNormalizationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct LSTMOptionsT : public flatbuffers::NativeTable { typedef LSTMOptions TableType; @@ -2554,41 +2536,43 @@ struct LSTMOptionsT : public flatbuffers::NativeTable { LSTMOptionsT() : fused_activation_function(ActivationFunctionType_NONE), cell_clip(0.0f), - proj_clip(0.0f) {} + proj_clip(0.0f) { + } }; struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef LSTMOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4, VT_CELL_CLIP = 6, VT_PROJ_CLIP = 8 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); } - float cell_clip() const { return GetField(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField(VT_PROJ_CLIP, 0.0f); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && VerifyField(verifier, VT_CELL_CLIP) && - VerifyField(verifier, VT_PROJ_CLIP) && verifier.EndTable(); + VerifyField(verifier, VT_PROJ_CLIP) && + verifier.EndTable(); } - LSTMOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - LSTMOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct LSTMOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } void add_cell_clip(float cell_clip) { fbb_.AddElement(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); @@ -2597,7 +2581,7 @@ struct LSTMOptionsBuilder { fbb_.AddElement(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); } explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &); @@ -2610,9 +2594,9 @@ struct LSTMOptionsBuilder { inline flatbuffers::Offset CreateLSTMOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f) { LSTMOptionsBuilder builder_(_fbb); builder_.add_proj_clip(proj_clip); builder_.add_cell_clip(cell_clip); @@ -2620,9 +2604,7 @@ inline flatbuffers::Offset CreateLSTMOptions( return builder_.Finish(); } -flatbuffers::Offset CreateLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { typedef ResizeBilinearOptions TableType; @@ -2657,7 +2639,7 @@ struct ResizeBilinearOptionsBuilder { fbb_.AddElement(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); } explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &); @@ -2681,25 +2663,27 @@ flatbuffers::Offset CreateResizeBilinearOptions(flatbuffe struct CallOptionsT : public flatbuffers::NativeTable { typedef CallOptions TableType; uint32_t subgraph; - CallOptionsT() : subgraph(0) {} + CallOptionsT() + : subgraph(0) { + } }; struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef CallOptionsT NativeTableType; - enum { VT_SUBGRAPH = 4 }; - uint32_t subgraph() const { return GetField(VT_SUBGRAPH, 0); } + enum { + VT_SUBGRAPH = 4 + }; + uint32_t subgraph() const { + return GetField(VT_SUBGRAPH, 0); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SUBGRAPH) && verifier.EndTable(); + VerifyField(verifier, VT_SUBGRAPH) && + verifier.EndTable(); } - CallOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - CallOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct CallOptionsBuilder { @@ -2709,7 +2693,7 @@ struct CallOptionsBuilder { fbb_.AddElement(CallOptions::VT_SUBGRAPH, subgraph, 0); } explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } CallOptionsBuilder &operator=(const CallOptionsBuilder &); @@ -2721,41 +2705,37 @@ struct CallOptionsBuilder { }; inline flatbuffers::Offset CreateCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t subgraph = 0) { + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t subgraph = 0) { CallOptionsBuilder builder_(_fbb); builder_.add_subgraph(subgraph); return builder_.Finish(); } -flatbuffers::Offset CreateCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct PadOptionsT : public flatbuffers::NativeTable { typedef PadOptions TableType; - PadOptionsT() {} + PadOptionsT() { + } }; struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef PadOptionsT NativeTableType; bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && verifier.EndTable(); + return VerifyTableStart(verifier) && + verifier.EndTable(); } - PadOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - PadOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct PadOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } PadOptionsBuilder &operator=(const PadOptionsBuilder &); @@ -2772,45 +2752,42 @@ inline flatbuffers::Offset CreatePadOptions( return builder_.Finish(); } -flatbuffers::Offset CreatePadOptions( - flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct ReshapeOptionsT : public flatbuffers::NativeTable { typedef ReshapeOptions TableType; std::vector new_shape; - ReshapeOptionsT() {} + ReshapeOptionsT() { + } }; struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef ReshapeOptionsT NativeTableType; - enum { VT_NEW_SHAPE = 4 }; + enum { + VT_NEW_SHAPE = 4 + }; const flatbuffers::Vector *new_shape() const { return GetPointer *>(VT_NEW_SHAPE); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NEW_SHAPE) && - verifier.Verify(new_shape()) && verifier.EndTable(); + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NEW_SHAPE) && + verifier.Verify(new_shape()) && + verifier.EndTable(); } - ReshapeOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - ReshapeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct ReshapeOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_new_shape( - flatbuffers::Offset> new_shape) { + void add_new_shape(flatbuffers::Offset> new_shape) { fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); } explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &); @@ -2833,39 +2810,34 @@ inline flatbuffers::Offset CreateReshapeOptionsDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *new_shape = nullptr) { return tflite::CreateReshapeOptions( - _fbb, new_shape ? _fbb.CreateVector(*new_shape) : 0); + _fbb, + new_shape ? _fbb.CreateVector(*new_shape) : 0); } -flatbuffers::Offset CreateReshapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { typedef SpaceToBatchNDOptions TableType; - SpaceToBatchNDOptionsT() {} + SpaceToBatchNDOptionsT() { + } }; -struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SpaceToBatchNDOptionsT NativeTableType; bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && verifier.EndTable(); + return VerifyTableStart(verifier) && + verifier.EndTable(); } - SpaceToBatchNDOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SpaceToBatchNDOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SpaceToBatchNDOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &); @@ -2882,36 +2854,30 @@ inline flatbuffers::Offset CreateSpaceToBatchNDOptions( return builder_.Finish(); } -flatbuffers::Offset CreateSpaceToBatchNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { typedef BatchToSpaceNDOptions TableType; - BatchToSpaceNDOptionsT() {} + BatchToSpaceNDOptionsT() { + } }; -struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef BatchToSpaceNDOptionsT NativeTableType; bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && verifier.EndTable(); + return VerifyTableStart(verifier) && + verifier.EndTable(); } - BatchToSpaceNDOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - BatchToSpaceNDOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct BatchToSpaceNDOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &); @@ -2928,9 +2894,7 @@ inline flatbuffers::Offset CreateBatchToSpaceNDOptions( return builder_.Finish(); } -flatbuffers::Offset CreateBatchToSpaceNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SkipGramOptionsT : public flatbuffers::NativeTable { typedef SkipGramOptions TableType; @@ -2938,13 +2902,22 @@ struct SkipGramOptionsT : public flatbuffers::NativeTable { int32_t max_skip_size; bool include_all_ngrams; SkipGramOptionsT() - : ngram_size(0), max_skip_size(0), include_all_ngrams(false) {} + : ngram_size(0), + max_skip_size(0), + include_all_ngrams(false) { + } }; struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SkipGramOptionsT NativeTableType; - enum { VT_NGRAM_SIZE = 4, VT_MAX_SKIP_SIZE = 6, VT_INCLUDE_ALL_NGRAMS = 8 }; - int32_t ngram_size() const { return GetField(VT_NGRAM_SIZE, 0); } + enum { + VT_NGRAM_SIZE = 4, + VT_MAX_SKIP_SIZE = 6, + VT_INCLUDE_ALL_NGRAMS = 8 + }; + int32_t ngram_size() const { + return GetField(VT_NGRAM_SIZE, 0); + } int32_t max_skip_size() const { return GetField(VT_MAX_SKIP_SIZE, 0); } @@ -2958,14 +2931,9 @@ struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_INCLUDE_ALL_NGRAMS) && verifier.EndTable(); } - SkipGramOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SkipGramOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SkipGramOptionsBuilder { @@ -2975,15 +2943,13 @@ struct SkipGramOptionsBuilder { fbb_.AddElement(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); } void add_max_skip_size(int32_t max_skip_size) { - fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, - 0); + fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); } void add_include_all_ngrams(bool include_all_ngrams) { - fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, - static_cast(include_all_ngrams), 0); + fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast(include_all_ngrams), 0); } explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &); @@ -2995,8 +2961,10 @@ struct SkipGramOptionsBuilder { }; inline flatbuffers::Offset CreateSkipGramOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t ngram_size = 0, - int32_t max_skip_size = 0, bool include_all_ngrams = false) { + flatbuffers::FlatBufferBuilder &_fbb, + int32_t ngram_size = 0, + int32_t max_skip_size = 0, + bool include_all_ngrams = false) { SkipGramOptionsBuilder builder_(_fbb); builder_.add_max_skip_size(max_skip_size); builder_.add_ngram_size(ngram_size); @@ -3004,33 +2972,32 @@ inline flatbuffers::Offset CreateSkipGramOptions( return builder_.Finish(); } -flatbuffers::Offset CreateSkipGramOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SpaceToDepthOptionsT : public flatbuffers::NativeTable { typedef SpaceToDepthOptions TableType; int32_t block_size; - SpaceToDepthOptionsT() : block_size(0) {} + SpaceToDepthOptionsT() + : block_size(0) { + } }; -struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SpaceToDepthOptionsT NativeTableType; - enum { VT_BLOCK_SIZE = 4 }; - int32_t block_size() const { return GetField(VT_BLOCK_SIZE, 0); } + enum { + VT_BLOCK_SIZE = 4 + }; + int32_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BLOCK_SIZE) && verifier.EndTable(); + VerifyField(verifier, VT_BLOCK_SIZE) && + verifier.EndTable(); } - SpaceToDepthOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SpaceToDepthOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SpaceToDepthOptionsBuilder { @@ -3040,7 +3007,7 @@ struct SpaceToDepthOptionsBuilder { fbb_.AddElement(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); } explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &); @@ -3052,54 +3019,49 @@ struct SpaceToDepthOptionsBuilder { }; inline flatbuffers::Offset CreateSpaceToDepthOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0) { + flatbuffers::FlatBufferBuilder &_fbb, + int32_t block_size = 0) { SpaceToDepthOptionsBuilder builder_(_fbb); builder_.add_block_size(block_size); return builder_.Finish(); } -flatbuffers::Offset CreateSpaceToDepthOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SubOptionsT : public flatbuffers::NativeTable { typedef SubOptions TableType; ActivationFunctionType fused_activation_function; - SubOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + SubOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SubOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - SubOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SubOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SubOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SubOptionsBuilder &operator=(const SubOptionsBuilder &); @@ -3112,55 +3074,48 @@ struct SubOptionsBuilder { inline flatbuffers::Offset CreateSubOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { SubOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateSubOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct DivOptionsT : public flatbuffers::NativeTable { typedef DivOptions TableType; ActivationFunctionType fused_activation_function; - DivOptionsT() : fused_activation_function(ActivationFunctionType_NONE) {} + DivOptionsT() + : fused_activation_function(ActivationFunctionType_NONE) { + } }; struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef DivOptionsT NativeTableType; - enum { VT_FUSED_ACTIVATION_FUNCTION = 4 }; + enum { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; ActivationFunctionType fused_activation_function() const { - return static_cast( - GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); } - DivOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - DivOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct DivOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_fused_activation_function( - ActivationFunctionType fused_activation_function) { - fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast(fused_activation_function), 0); + void add_fused_activation_function(ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } DivOptionsBuilder &operator=(const DivOptionsBuilder &); @@ -3173,59 +3128,91 @@ struct DivOptionsBuilder { inline flatbuffers::Offset CreateDivOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = - ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { DivOptionsBuilder builder_(_fbb); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } -flatbuffers::Offset CreateDivOptions( - flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TopKV2OptionsT : public flatbuffers::NativeTable { + typedef TopKV2Options TableType; + TopKV2OptionsT() { + } +}; + +struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TopKV2OptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TopKV2OptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTopKV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + TopKV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable { typedef EmbeddingLookupSparseOptions TableType; CombinerType combiner; - EmbeddingLookupSparseOptionsT() : combiner(CombinerType_SUM) {} + EmbeddingLookupSparseOptionsT() + : combiner(CombinerType_SUM) { + } }; -struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef EmbeddingLookupSparseOptionsT NativeTableType; - enum { VT_COMBINER = 4 }; + enum { + VT_COMBINER = 4 + }; CombinerType combiner() const { return static_cast(GetField(VT_COMBINER, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COMBINER) && verifier.EndTable(); + VerifyField(verifier, VT_COMBINER) && + verifier.EndTable(); } - EmbeddingLookupSparseOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct EmbeddingLookupSparseOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_combiner(CombinerType combiner) { - fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, - static_cast(combiner), 0); + fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast(combiner), 0); } - explicit EmbeddingLookupSparseOptionsBuilder( - flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } - EmbeddingLookupSparseOptionsBuilder &operator=( - const EmbeddingLookupSparseOptionsBuilder &); + EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &); flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); auto o = flatbuffers::Offset(end); @@ -3233,42 +3220,40 @@ struct EmbeddingLookupSparseOptionsBuilder { } }; -inline flatbuffers::Offset -CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, - CombinerType combiner = CombinerType_SUM) { +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions( + flatbuffers::FlatBufferBuilder &_fbb, + CombinerType combiner = CombinerType_SUM) { EmbeddingLookupSparseOptionsBuilder builder_(_fbb); builder_.add_combiner(combiner); return builder_.Finish(); } -flatbuffers::Offset -CreateEmbeddingLookupSparseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct GatherOptionsT : public flatbuffers::NativeTable { typedef GatherOptions TableType; int32_t axis; - GatherOptionsT() : axis(0) {} + GatherOptionsT() + : axis(0) { + } }; struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef GatherOptionsT NativeTableType; - enum { VT_AXIS = 4 }; - int32_t axis() const { return GetField(VT_AXIS, 0); } + enum { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS) && verifier.EndTable(); + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); } - GatherOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - GatherOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct GatherOptionsBuilder { @@ -3278,7 +3263,7 @@ struct GatherOptionsBuilder { fbb_.AddElement(GatherOptions::VT_AXIS, axis, 0); } explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } GatherOptionsBuilder &operator=(const GatherOptionsBuilder &); @@ -3290,41 +3275,37 @@ struct GatherOptionsBuilder { }; inline flatbuffers::Offset CreateGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0) { + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { GatherOptionsBuilder builder_(_fbb); builder_.add_axis(axis); return builder_.Finish(); } -flatbuffers::Offset CreateGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct TransposeOptionsT : public flatbuffers::NativeTable { typedef TransposeOptions TableType; - TransposeOptionsT() {} + TransposeOptionsT() { + } }; struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef TransposeOptionsT NativeTableType; bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && verifier.EndTable(); + return VerifyTableStart(verifier) && + verifier.EndTable(); } - TransposeOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - TransposeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct TransposeOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &); @@ -3341,43 +3322,82 @@ inline flatbuffers::Offset CreateTransposeOptions( return builder_.Finish(); } -flatbuffers::Offset CreateTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpOptionsT : public flatbuffers::NativeTable { + typedef ExpOptions TableType; + ExpOptionsT() { + } +}; + +struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + ExpOptionsBuilder &operator=(const ExpOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct MeanOptionsT : public flatbuffers::NativeTable { typedef MeanOptions TableType; bool keep_dims; - MeanOptionsT() : keep_dims(false) {} + MeanOptionsT() + : keep_dims(false) { + } }; struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef MeanOptionsT NativeTableType; - enum { VT_KEEP_DIMS = 4 }; - bool keep_dims() const { return GetField(VT_KEEP_DIMS, 0) != 0; } + enum { + VT_KEEP_DIMS = 4 + }; + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && - VerifyField(verifier, VT_KEEP_DIMS) && verifier.EndTable(); + VerifyField(verifier, VT_KEEP_DIMS) && + verifier.EndTable(); } - MeanOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - MeanOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + MeanOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct MeanOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_keep_dims(bool keep_dims) { - fbb_.AddElement(MeanOptions::VT_KEEP_DIMS, - static_cast(keep_dims), 0); + fbb_.AddElement(MeanOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); } explicit MeanOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } MeanOptionsBuilder &operator=(const MeanOptionsBuilder &); @@ -3389,52 +3409,49 @@ struct MeanOptionsBuilder { }; inline flatbuffers::Offset CreateMeanOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) { + flatbuffers::FlatBufferBuilder &_fbb, + bool keep_dims = false) { MeanOptionsBuilder builder_(_fbb); builder_.add_keep_dims(keep_dims); return builder_.Finish(); } -flatbuffers::Offset CreateMeanOptions( - flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SqueezeOptionsT : public flatbuffers::NativeTable { typedef SqueezeOptions TableType; std::vector squeeze_dims; - SqueezeOptionsT() {} + SqueezeOptionsT() { + } }; struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SqueezeOptionsT NativeTableType; - enum { VT_SQUEEZE_DIMS = 4 }; + enum { + VT_SQUEEZE_DIMS = 4 + }; const flatbuffers::Vector *squeeze_dims() const { return GetPointer *>(VT_SQUEEZE_DIMS); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SQUEEZE_DIMS) && - verifier.Verify(squeeze_dims()) && verifier.EndTable(); + verifier.Verify(squeeze_dims()) && + verifier.EndTable(); } - SqueezeOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SqueezeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SqueezeOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_squeeze_dims( - flatbuffers::Offset> squeeze_dims) { + void add_squeeze_dims(flatbuffers::Offset> squeeze_dims) { fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); } explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &); @@ -3457,14 +3474,67 @@ inline flatbuffers::Offset CreateSqueezeOptionsDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *squeeze_dims = nullptr) { return tflite::CreateSqueezeOptions( - _fbb, squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0); + _fbb, + squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0); } -flatbuffers::Offset CreateSqueezeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -struct StridedSliceOptionsT : public flatbuffers::NativeTable { +struct SplitOptionsT : public flatbuffers::NativeTable { + typedef SplitOptions TableType; + int32_t num_splits; + SplitOptionsT() + : num_splits(0) { + } +}; + +struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitOptionsT NativeTableType; + enum { + VT_NUM_SPLITS = 4 + }; + int32_t num_splits() const { + return GetField(VT_NUM_SPLITS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_SPLITS) && + verifier.EndTable(); + } + SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_splits(int32_t num_splits) { + fbb_.AddElement(SplitOptions::VT_NUM_SPLITS, num_splits, 0); + } + explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + SplitOptionsBuilder &operator=(const SplitOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_splits = 0) { + SplitOptionsBuilder builder_(_fbb); + builder_.add_num_splits(num_splits); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StridedSliceOptionsT : public flatbuffers::NativeTable { typedef StridedSliceOptions TableType; int32_t begin_mask; int32_t end_mask; @@ -3476,11 +3546,11 @@ struct StridedSliceOptionsT : public flatbuffers::NativeTable { end_mask(0), ellipsis_mask(0), new_axis_mask(0), - shrink_axis_mask(0) {} + shrink_axis_mask(0) { + } }; -struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS - : private flatbuffers::Table { +struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef StridedSliceOptionsT NativeTableType; enum { VT_BEGIN_MASK = 4, @@ -3489,8 +3559,12 @@ struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS VT_NEW_AXIS_MASK = 10, VT_SHRINK_AXIS_MASK = 12 }; - int32_t begin_mask() const { return GetField(VT_BEGIN_MASK, 0); } - int32_t end_mask() const { return GetField(VT_END_MASK, 0); } + int32_t begin_mask() const { + return GetField(VT_BEGIN_MASK, 0); + } + int32_t end_mask() const { + return GetField(VT_END_MASK, 0); + } int32_t ellipsis_mask() const { return GetField(VT_ELLIPSIS_MASK, 0); } @@ -3509,14 +3583,9 @@ struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS VerifyField(verifier, VT_SHRINK_AXIS_MASK) && verifier.EndTable(); } - StridedSliceOptionsT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - StridedSliceOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct StridedSliceOptionsBuilder { @@ -3529,19 +3598,16 @@ struct StridedSliceOptionsBuilder { fbb_.AddElement(StridedSliceOptions::VT_END_MASK, end_mask, 0); } void add_ellipsis_mask(int32_t ellipsis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, - ellipsis_mask, 0); + fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); } void add_new_axis_mask(int32_t new_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, - new_axis_mask, 0); + fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); } void add_shrink_axis_mask(int32_t shrink_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, - shrink_axis_mask, 0); + fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); } explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &); @@ -3553,8 +3619,11 @@ struct StridedSliceOptionsBuilder { }; inline flatbuffers::Offset CreateStridedSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t begin_mask = 0, - int32_t end_mask = 0, int32_t ellipsis_mask = 0, int32_t new_axis_mask = 0, + flatbuffers::FlatBufferBuilder &_fbb, + int32_t begin_mask = 0, + int32_t end_mask = 0, + int32_t ellipsis_mask = 0, + int32_t new_axis_mask = 0, int32_t shrink_axis_mask = 0) { StridedSliceOptionsBuilder builder_(_fbb); builder_.add_shrink_axis_mask(shrink_axis_mask); @@ -3565,20 +3634,143 @@ inline flatbuffers::Offset CreateStridedSliceOptions( return builder_.Finish(); } -flatbuffers::Offset CreateStridedSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogSoftmaxOptionsT : public flatbuffers::NativeTable { + typedef LogSoftmaxOptions TableType; + LogSoftmaxOptionsT() { + } +}; + +struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogSoftmaxOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogSoftmaxOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogSoftmaxOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogSoftmaxOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CastOptionsT : public flatbuffers::NativeTable { + typedef CastOptions TableType; + CastOptionsT() { + } +}; + +struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CastOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CastOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + CastOptionsBuilder &operator=(const CastOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCastOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + CastOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DequantizeOptionsT : public flatbuffers::NativeTable { + typedef DequantizeOptions TableType; + DequantizeOptionsT() { + } +}; + +struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DequantizeOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DequantizeOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDequantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DequantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; std::string custom_code; - OperatorCodeT() : builtin_code(BuiltinOperator_ADD) {} + OperatorCodeT() + : builtin_code(BuiltinOperator_ADD) { + } }; struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef OperatorCodeT NativeTableType; - enum { VT_BUILTIN_CODE = 4, VT_CUSTOM_CODE = 6 }; + enum { + VT_BUILTIN_CODE = 4, + VT_CUSTOM_CODE = 6 + }; BuiltinOperator builtin_code() const { return static_cast(GetField(VT_BUILTIN_CODE, 0)); } @@ -3589,30 +3781,25 @@ struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { return VerifyTableStart(verifier) && VerifyField(verifier, VT_BUILTIN_CODE) && VerifyOffset(verifier, VT_CUSTOM_CODE) && - verifier.Verify(custom_code()) && verifier.EndTable(); + verifier.Verify(custom_code()) && + verifier.EndTable(); } - OperatorCodeT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - OperatorCodeT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct OperatorCodeBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_builtin_code(BuiltinOperator builtin_code) { - fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, - static_cast(builtin_code), 0); + fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, static_cast(builtin_code), 0); } void add_custom_code(flatbuffers::Offset custom_code) { fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); } explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { + : fbb_(_fbb) { start_ = fbb_.StartTable(); } OperatorCodeBuilder &operator=(const OperatorCodeBuilder &); @@ -3638,12 +3825,12 @@ inline flatbuffers::Offset CreateOperatorCodeDirect( BuiltinOperator builtin_code = BuiltinOperator_ADD, const char *custom_code = nullptr) { return tflite::CreateOperatorCode( - _fbb, builtin_code, custom_code ? _fbb.CreateString(custom_code) : 0); + _fbb, + builtin_code, + custom_code ? _fbb.CreateString(custom_code) : 0); } -flatbuffers::Offset CreateOperatorCode( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct OperatorT : public flatbuffers::NativeTable { typedef Operator TableType; @@ -3655,7 +3842,8 @@ struct OperatorT : public flatbuffers::NativeTable { CustomOptionsFormat custom_options_format; OperatorT() : opcode_index(0), - custom_options_format(CustomOptionsFormat_FLEXBUFFERS) {} + custom_options_format(CustomOptionsFormat_FLEXBUFFERS) { + } }; struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -3679,398 +3867,304 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { return GetPointer *>(VT_OUTPUTS); } BuiltinOptions builtin_options_type() const { - return static_cast( - GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); + return static_cast(GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); } const void *builtin_options() const { return GetPointer(VT_BUILTIN_OPTIONS); } - template - const T *builtin_options_as() const; + template const T *builtin_options_as() const; const Conv2DOptions *builtin_options_as_Conv2DOptions() const { - return builtin_options_type() == BuiltinOptions_Conv2DOptions - ? static_cast(builtin_options()) - : nullptr; - } - const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() - const { - return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions - ? static_cast(builtin_options()) - : nullptr; - } - const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() - const { - return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_Conv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const { + return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const { + return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions ? static_cast(builtin_options()) : nullptr; } const LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const { - return builtin_options_type() == BuiltinOptions_LSHProjectionOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_LSHProjectionOptions ? static_cast(builtin_options()) : nullptr; } const Pool2DOptions *builtin_options_as_Pool2DOptions() const { - return builtin_options_type() == BuiltinOptions_Pool2DOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_Pool2DOptions ? static_cast(builtin_options()) : nullptr; } const SVDFOptions *builtin_options_as_SVDFOptions() const { - return builtin_options_type() == BuiltinOptions_SVDFOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SVDFOptions ? static_cast(builtin_options()) : nullptr; } const RNNOptions *builtin_options_as_RNNOptions() const { - return builtin_options_type() == BuiltinOptions_RNNOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_RNNOptions ? static_cast(builtin_options()) : nullptr; } - const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() - const { - return builtin_options_type() == BuiltinOptions_FullyConnectedOptions - ? static_cast(builtin_options()) - : nullptr; + const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const { + return builtin_options_type() == BuiltinOptions_FullyConnectedOptions ? static_cast(builtin_options()) : nullptr; } const SoftmaxOptions *builtin_options_as_SoftmaxOptions() const { - return builtin_options_type() == BuiltinOptions_SoftmaxOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SoftmaxOptions ? static_cast(builtin_options()) : nullptr; } const ConcatenationOptions *builtin_options_as_ConcatenationOptions() const { - return builtin_options_type() == BuiltinOptions_ConcatenationOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_ConcatenationOptions ? static_cast(builtin_options()) : nullptr; } const AddOptions *builtin_options_as_AddOptions() const { - return builtin_options_type() == BuiltinOptions_AddOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_AddOptions ? static_cast(builtin_options()) : nullptr; } const L2NormOptions *builtin_options_as_L2NormOptions() const { - return builtin_options_type() == BuiltinOptions_L2NormOptions - ? static_cast(builtin_options()) - : nullptr; - } - const LocalResponseNormalizationOptions * - builtin_options_as_LocalResponseNormalizationOptions() const { - return builtin_options_type() == - BuiltinOptions_LocalResponseNormalizationOptions - ? static_cast( - builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_L2NormOptions ? static_cast(builtin_options()) : nullptr; + } + const LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const { + return builtin_options_type() == BuiltinOptions_LocalResponseNormalizationOptions ? static_cast(builtin_options()) : nullptr; } const LSTMOptions *builtin_options_as_LSTMOptions() const { - return builtin_options_type() == BuiltinOptions_LSTMOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_LSTMOptions ? static_cast(builtin_options()) : nullptr; } - const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() - const { - return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions - ? static_cast(builtin_options()) - : nullptr; + const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const { + return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions ? static_cast(builtin_options()) : nullptr; } const CallOptions *builtin_options_as_CallOptions() const { - return builtin_options_type() == BuiltinOptions_CallOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_CallOptions ? static_cast(builtin_options()) : nullptr; } const ReshapeOptions *builtin_options_as_ReshapeOptions() const { - return builtin_options_type() == BuiltinOptions_ReshapeOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_ReshapeOptions ? static_cast(builtin_options()) : nullptr; } const SkipGramOptions *builtin_options_as_SkipGramOptions() const { - return builtin_options_type() == BuiltinOptions_SkipGramOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SkipGramOptions ? static_cast(builtin_options()) : nullptr; } const SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const { - return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions ? static_cast(builtin_options()) : nullptr; } - const EmbeddingLookupSparseOptions * - builtin_options_as_EmbeddingLookupSparseOptions() const { - return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions - ? static_cast( - builtin_options()) - : nullptr; + const EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const { + return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast(builtin_options()) : nullptr; } const MulOptions *builtin_options_as_MulOptions() const { - return builtin_options_type() == BuiltinOptions_MulOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_MulOptions ? static_cast(builtin_options()) : nullptr; } const PadOptions *builtin_options_as_PadOptions() const { - return builtin_options_type() == BuiltinOptions_PadOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr; } const GatherOptions *builtin_options_as_GatherOptions() const { - return builtin_options_type() == BuiltinOptions_GatherOptions - ? static_cast(builtin_options()) - : nullptr; - } - const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() - const { - return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions - ? static_cast(builtin_options()) - : nullptr; - } - const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() - const { - return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr; + } + const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const { + return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions ? static_cast(builtin_options()) : nullptr; + } + const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const { + return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions ? static_cast(builtin_options()) : nullptr; } const TransposeOptions *builtin_options_as_TransposeOptions() const { - return builtin_options_type() == BuiltinOptions_TransposeOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; } const MeanOptions *builtin_options_as_MeanOptions() const { - return builtin_options_type() == BuiltinOptions_MeanOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_MeanOptions ? static_cast(builtin_options()) : nullptr; } const SubOptions *builtin_options_as_SubOptions() const { - return builtin_options_type() == BuiltinOptions_SubOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; } const DivOptions *builtin_options_as_DivOptions() const { - return builtin_options_type() == BuiltinOptions_DivOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_DivOptions ? static_cast(builtin_options()) : nullptr; } const SqueezeOptions *builtin_options_as_SqueezeOptions() const { - return builtin_options_type() == BuiltinOptions_SqueezeOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SqueezeOptions ? static_cast(builtin_options()) : nullptr; } const SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const { - return builtin_options_type() == BuiltinOptions_SequenceRNNOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_SequenceRNNOptions ? static_cast(builtin_options()) : nullptr; } const StridedSliceOptions *builtin_options_as_StridedSliceOptions() const { - return builtin_options_type() == BuiltinOptions_StridedSliceOptions - ? static_cast(builtin_options()) - : nullptr; + return builtin_options_type() == BuiltinOptions_StridedSliceOptions ? static_cast(builtin_options()) : nullptr; + } + const ExpOptions *builtin_options_as_ExpOptions() const { + return builtin_options_type() == BuiltinOptions_ExpOptions ? static_cast(builtin_options()) : nullptr; + } + const TopKV2Options *builtin_options_as_TopKV2Options() const { + return builtin_options_type() == BuiltinOptions_TopKV2Options ? static_cast(builtin_options()) : nullptr; + } + const SplitOptions *builtin_options_as_SplitOptions() const { + return builtin_options_type() == BuiltinOptions_SplitOptions ? static_cast(builtin_options()) : nullptr; + } + const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { + return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; + } + const CastOptions *builtin_options_as_CastOptions() const { + return builtin_options_type() == BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; + } + const DequantizeOptions *builtin_options_as_DequantizeOptions() const { + return builtin_options_type() == BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } CustomOptionsFormat custom_options_format() const { - return static_cast( - GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); + return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_OPCODE_INDEX) && - VerifyOffset(verifier, VT_INPUTS) && verifier.Verify(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.Verify(outputs()) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.Verify(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.Verify(outputs()) && VerifyField(verifier, VT_BUILTIN_OPTIONS_TYPE) && VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && - VerifyBuiltinOptions(verifier, builtin_options(), - builtin_options_type()) && + VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.Verify(custom_options()) && VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT) && verifier.EndTable(); } - OperatorT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - OperatorT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; -template <> -inline const Conv2DOptions *Operator::builtin_options_as() - const { +template<> inline const Conv2DOptions *Operator::builtin_options_as() const { return builtin_options_as_Conv2DOptions(); } -template <> -inline const DepthwiseConv2DOptions * -Operator::builtin_options_as() const { +template<> inline const DepthwiseConv2DOptions *Operator::builtin_options_as() const { return builtin_options_as_DepthwiseConv2DOptions(); } -template <> -inline const ConcatEmbeddingsOptions * -Operator::builtin_options_as() const { +template<> inline const ConcatEmbeddingsOptions *Operator::builtin_options_as() const { return builtin_options_as_ConcatEmbeddingsOptions(); } -template <> -inline const LSHProjectionOptions * -Operator::builtin_options_as() const { +template<> inline const LSHProjectionOptions *Operator::builtin_options_as() const { return builtin_options_as_LSHProjectionOptions(); } -template <> -inline const Pool2DOptions *Operator::builtin_options_as() - const { +template<> inline const Pool2DOptions *Operator::builtin_options_as() const { return builtin_options_as_Pool2DOptions(); } -template <> -inline const SVDFOptions *Operator::builtin_options_as() const { +template<> inline const SVDFOptions *Operator::builtin_options_as() const { return builtin_options_as_SVDFOptions(); } -template <> -inline const RNNOptions *Operator::builtin_options_as() const { +template<> inline const RNNOptions *Operator::builtin_options_as() const { return builtin_options_as_RNNOptions(); } -template <> -inline const FullyConnectedOptions * -Operator::builtin_options_as() const { +template<> inline const FullyConnectedOptions *Operator::builtin_options_as() const { return builtin_options_as_FullyConnectedOptions(); } -template <> -inline const SoftmaxOptions *Operator::builtin_options_as() - const { +template<> inline const SoftmaxOptions *Operator::builtin_options_as() const { return builtin_options_as_SoftmaxOptions(); } -template <> -inline const ConcatenationOptions * -Operator::builtin_options_as() const { +template<> inline const ConcatenationOptions *Operator::builtin_options_as() const { return builtin_options_as_ConcatenationOptions(); } -template <> -inline const AddOptions *Operator::builtin_options_as() const { +template<> inline const AddOptions *Operator::builtin_options_as() const { return builtin_options_as_AddOptions(); } -template <> -inline const L2NormOptions *Operator::builtin_options_as() - const { +template<> inline const L2NormOptions *Operator::builtin_options_as() const { return builtin_options_as_L2NormOptions(); } -template <> -inline const LocalResponseNormalizationOptions * -Operator::builtin_options_as() const { +template<> inline const LocalResponseNormalizationOptions *Operator::builtin_options_as() const { return builtin_options_as_LocalResponseNormalizationOptions(); } -template <> -inline const LSTMOptions *Operator::builtin_options_as() const { +template<> inline const LSTMOptions *Operator::builtin_options_as() const { return builtin_options_as_LSTMOptions(); } -template <> -inline const ResizeBilinearOptions * -Operator::builtin_options_as() const { +template<> inline const ResizeBilinearOptions *Operator::builtin_options_as() const { return builtin_options_as_ResizeBilinearOptions(); } -template <> -inline const CallOptions *Operator::builtin_options_as() const { +template<> inline const CallOptions *Operator::builtin_options_as() const { return builtin_options_as_CallOptions(); } -template <> -inline const ReshapeOptions *Operator::builtin_options_as() - const { +template<> inline const ReshapeOptions *Operator::builtin_options_as() const { return builtin_options_as_ReshapeOptions(); } -template <> -inline const SkipGramOptions *Operator::builtin_options_as() - const { +template<> inline const SkipGramOptions *Operator::builtin_options_as() const { return builtin_options_as_SkipGramOptions(); } -template <> -inline const SpaceToDepthOptions * -Operator::builtin_options_as() const { +template<> inline const SpaceToDepthOptions *Operator::builtin_options_as() const { return builtin_options_as_SpaceToDepthOptions(); } -template <> -inline const EmbeddingLookupSparseOptions * -Operator::builtin_options_as() const { +template<> inline const EmbeddingLookupSparseOptions *Operator::builtin_options_as() const { return builtin_options_as_EmbeddingLookupSparseOptions(); } -template <> -inline const MulOptions *Operator::builtin_options_as() const { +template<> inline const MulOptions *Operator::builtin_options_as() const { return builtin_options_as_MulOptions(); } -template <> -inline const PadOptions *Operator::builtin_options_as() const { +template<> inline const PadOptions *Operator::builtin_options_as() const { return builtin_options_as_PadOptions(); } -template <> -inline const GatherOptions *Operator::builtin_options_as() - const { +template<> inline const GatherOptions *Operator::builtin_options_as() const { return builtin_options_as_GatherOptions(); } -template <> -inline const BatchToSpaceNDOptions * -Operator::builtin_options_as() const { +template<> inline const BatchToSpaceNDOptions *Operator::builtin_options_as() const { return builtin_options_as_BatchToSpaceNDOptions(); } -template <> -inline const SpaceToBatchNDOptions * -Operator::builtin_options_as() const { +template<> inline const SpaceToBatchNDOptions *Operator::builtin_options_as() const { return builtin_options_as_SpaceToBatchNDOptions(); } -template <> -inline const TransposeOptions *Operator::builtin_options_as() - const { +template<> inline const TransposeOptions *Operator::builtin_options_as() const { return builtin_options_as_TransposeOptions(); } -template <> -inline const MeanOptions *Operator::builtin_options_as() const { +template<> inline const MeanOptions *Operator::builtin_options_as() const { return builtin_options_as_MeanOptions(); } -template <> -inline const SubOptions *Operator::builtin_options_as() const { +template<> inline const SubOptions *Operator::builtin_options_as() const { return builtin_options_as_SubOptions(); } -template <> -inline const DivOptions *Operator::builtin_options_as() const { +template<> inline const DivOptions *Operator::builtin_options_as() const { return builtin_options_as_DivOptions(); } -template <> -inline const SqueezeOptions *Operator::builtin_options_as() - const { +template<> inline const SqueezeOptions *Operator::builtin_options_as() const { return builtin_options_as_SqueezeOptions(); } -template <> -inline const SequenceRNNOptions * -Operator::builtin_options_as() const { +template<> inline const SequenceRNNOptions *Operator::builtin_options_as() const { return builtin_options_as_SequenceRNNOptions(); } -template <> -inline const StridedSliceOptions * -Operator::builtin_options_as() const { +template<> inline const StridedSliceOptions *Operator::builtin_options_as() const { return builtin_options_as_StridedSliceOptions(); } +template<> inline const ExpOptions *Operator::builtin_options_as() const { + return builtin_options_as_ExpOptions(); +} + +template<> inline const TopKV2Options *Operator::builtin_options_as() const { + return builtin_options_as_TopKV2Options(); +} + +template<> inline const SplitOptions *Operator::builtin_options_as() const { + return builtin_options_as_SplitOptions(); +} + +template<> inline const LogSoftmaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogSoftmaxOptions(); +} + +template<> inline const CastOptions *Operator::builtin_options_as() const { + return builtin_options_as_CastOptions(); +} + +template<> inline const DequantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_DequantizeOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -4084,21 +4178,19 @@ struct OperatorBuilder { fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); } void add_builtin_options_type(BuiltinOptions builtin_options_type) { - fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, - static_cast(builtin_options_type), 0); + fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast(builtin_options_type), 0); } void add_builtin_options(flatbuffers::Offset builtin_options) { fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); } - void add_custom_options( - flatbuffers::Offset> custom_options) { + void add_custom_options(flatbuffers::Offset> custom_options) { fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); } void add_custom_options_format(CustomOptionsFormat custom_options_format) { - fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, - static_cast(custom_options_format), 0); + fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); } - explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { + explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } OperatorBuilder &operator=(const OperatorBuilder &); @@ -4110,14 +4202,14 @@ struct OperatorBuilder { }; inline flatbuffers::Offset CreateOperator( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, flatbuffers::Offset> inputs = 0, flatbuffers::Offset> outputs = 0, BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, flatbuffers::Offset> custom_options = 0, - CustomOptionsFormat custom_options_format = - CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { OperatorBuilder builder_(_fbb); builder_.add_custom_options(custom_options); builder_.add_builtin_options(builtin_options); @@ -4130,25 +4222,26 @@ inline flatbuffers::Offset CreateOperator( } inline flatbuffers::Offset CreateOperatorDirect( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, const std::vector *inputs = nullptr, const std::vector *outputs = nullptr, BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, const std::vector *custom_options = nullptr, - CustomOptionsFormat custom_options_format = - CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { return tflite::CreateOperator( - _fbb, opcode_index, inputs ? _fbb.CreateVector(*inputs) : 0, - outputs ? _fbb.CreateVector(*outputs) : 0, builtin_options_type, + _fbb, + opcode_index, + inputs ? _fbb.CreateVector(*inputs) : 0, + outputs ? _fbb.CreateVector(*outputs) : 0, + builtin_options_type, builtin_options, custom_options ? _fbb.CreateVector(*custom_options) : 0, custom_options_format); } -flatbuffers::Offset CreateOperator( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SubGraphT : public flatbuffers::NativeTable { typedef SubGraph TableType; @@ -4157,7 +4250,8 @@ struct SubGraphT : public flatbuffers::NativeTable { std::vector outputs; std::vector> operators; std::string name; - SubGraphT() {} + SubGraphT() { + } }; struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -4170,8 +4264,7 @@ struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_NAME = 12 }; const flatbuffers::Vector> *tensors() const { - return GetPointer> *>( - VT_TENSORS); + return GetPointer> *>(VT_TENSORS); } const flatbuffers::Vector *inputs() const { return GetPointer *>(VT_INPUTS); @@ -4180,41 +4273,36 @@ struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { return GetPointer *>(VT_OUTPUTS); } const flatbuffers::Vector> *operators() const { - return GetPointer< - const flatbuffers::Vector> *>( - VT_OPERATORS); + return GetPointer> *>(VT_OPERATORS); } const flatbuffers::String *name() const { return GetPointer(VT_NAME); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TENSORS) && + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TENSORS) && verifier.Verify(tensors()) && verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && verifier.Verify(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.Verify(outputs()) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.Verify(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.Verify(outputs()) && VerifyOffset(verifier, VT_OPERATORS) && verifier.Verify(operators()) && verifier.VerifyVectorOfTables(operators()) && - VerifyOffset(verifier, VT_NAME) && verifier.Verify(name()) && + VerifyOffset(verifier, VT_NAME) && + verifier.Verify(name()) && verifier.EndTable(); } - SubGraphT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo( - SubGraphT *_o, - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct SubGraphBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_tensors( - flatbuffers::Offset>> - tensors) { + void add_tensors(flatbuffers::Offset>> tensors) { fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); } void add_inputs(flatbuffers::Offset> inputs) { @@ -4223,15 +4311,14 @@ struct SubGraphBuilder { void add_outputs(flatbuffers::Offset> outputs) { fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); } - void add_operators( - flatbuffers::Offset>> - operators) { + void add_operators(flatbuffers::Offset>> operators) { fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); } void add_name(flatbuffers::Offset name) { fbb_.AddOffset(SubGraph::VT_NAME, name); } - explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { + explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } SubGraphBuilder &operator=(const SubGraphBuilder &); @@ -4244,12 +4331,10 @@ struct SubGraphBuilder { inline flatbuffers::Offset CreateSubGraph( flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset>> - tensors = 0, + flatbuffers::Offset>> tensors = 0, flatbuffers::Offset> inputs = 0, flatbuffers::Offset> outputs = 0, - flatbuffers::Offset>> - operators = 0, + flatbuffers::Offset>> operators = 0, flatbuffers::Offset name = 0) { SubGraphBuilder builder_(_fbb); builder_.add_name(name); @@ -4272,38 +4357,36 @@ inline flatbuffers::Offset CreateSubGraphDirect( tensors ? _fbb.CreateVector>(*tensors) : 0, inputs ? _fbb.CreateVector(*inputs) : 0, outputs ? _fbb.CreateVector(*outputs) : 0, - operators ? _fbb.CreateVector>(*operators) - : 0, + operators ? _fbb.CreateVector>(*operators) : 0, name ? _fbb.CreateString(name) : 0); } -flatbuffers::Offset CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct BufferT : public flatbuffers::NativeTable { typedef Buffer TableType; std::vector data; - BufferT() {} + BufferT() { + } }; struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef BufferT NativeTableType; - enum { VT_DATA = 4 }; + enum { + VT_DATA = 4 + }; const flatbuffers::Vector *data() const { return GetPointer *>(VT_DATA); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_DATA) && - verifier.Verify(data()) && verifier.EndTable(); + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DATA) && + verifier.Verify(data()) && + verifier.EndTable(); } - BufferT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = - nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct BufferBuilder { @@ -4312,7 +4395,8 @@ struct BufferBuilder { void add_data(flatbuffers::Offset> data) { fbb_.AddOffset(Buffer::VT_DATA, data); } - explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { + explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } BufferBuilder &operator=(const BufferBuilder &); @@ -4334,13 +4418,12 @@ inline flatbuffers::Offset CreateBuffer( inline flatbuffers::Offset CreateBufferDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *data = nullptr) { - return tflite::CreateBuffer(_fbb, - data ? _fbb.CreateVector(*data) : 0); + return tflite::CreateBuffer( + _fbb, + data ? _fbb.CreateVector(*data) : 0); } -flatbuffers::Offset CreateBuffer( - flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct ModelT : public flatbuffers::NativeTable { typedef Model TableType; @@ -4349,7 +4432,9 @@ struct ModelT : public flatbuffers::NativeTable { std::vector> subgraphs; std::string description; std::vector> buffers; - ModelT() : version(0) {} + ModelT() + : version(0) { + } }; struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { @@ -4361,24 +4446,20 @@ struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_DESCRIPTION = 10, VT_BUFFERS = 12 }; - uint32_t version() const { return GetField(VT_VERSION, 0); } - const flatbuffers::Vector> *operator_codes() - const { - return GetPointer< - const flatbuffers::Vector> *>( - VT_OPERATOR_CODES); + uint32_t version() const { + return GetField(VT_VERSION, 0); + } + const flatbuffers::Vector> *operator_codes() const { + return GetPointer> *>(VT_OPERATOR_CODES); } const flatbuffers::Vector> *subgraphs() const { - return GetPointer< - const flatbuffers::Vector> *>( - VT_SUBGRAPHS); + return GetPointer> *>(VT_SUBGRAPHS); } const flatbuffers::String *description() const { return GetPointer(VT_DESCRIPTION); } const flatbuffers::Vector> *buffers() const { - return GetPointer> *>( - VT_BUFFERS); + return GetPointer> *>(VT_BUFFERS); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -4391,16 +4472,14 @@ struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { verifier.VerifyVectorOfTables(subgraphs()) && VerifyOffset(verifier, VT_DESCRIPTION) && verifier.Verify(description()) && - VerifyOffset(verifier, VT_BUFFERS) && verifier.Verify(buffers()) && - verifier.VerifyVectorOfTables(buffers()) && verifier.EndTable(); + VerifyOffset(verifier, VT_BUFFERS) && + verifier.Verify(buffers()) && + verifier.VerifyVectorOfTables(buffers()) && + verifier.EndTable(); } - ModelT *UnPack( - const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = - nullptr) const; - static flatbuffers::Offset Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; struct ModelBuilder { @@ -4409,26 +4488,20 @@ struct ModelBuilder { void add_version(uint32_t version) { fbb_.AddElement(Model::VT_VERSION, version, 0); } - void add_operator_codes( - flatbuffers::Offset< - flatbuffers::Vector>> - operator_codes) { + void add_operator_codes(flatbuffers::Offset>> operator_codes) { fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); } - void add_subgraphs( - flatbuffers::Offset>> - subgraphs) { + void add_subgraphs(flatbuffers::Offset>> subgraphs) { fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); } void add_description(flatbuffers::Offset description) { fbb_.AddOffset(Model::VT_DESCRIPTION, description); } - void add_buffers( - flatbuffers::Offset>> - buffers) { + void add_buffers(flatbuffers::Offset>> buffers) { fbb_.AddOffset(Model::VT_BUFFERS, buffers); } - explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { + explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { start_ = fbb_.StartTable(); } ModelBuilder &operator=(const ModelBuilder &); @@ -4440,14 +4513,12 @@ struct ModelBuilder { }; inline flatbuffers::Offset CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - flatbuffers::Offset>> - operator_codes = 0, - flatbuffers::Offset>> - subgraphs = 0, + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + flatbuffers::Offset>> operator_codes = 0, + flatbuffers::Offset>> subgraphs = 0, flatbuffers::Offset description = 0, - flatbuffers::Offset>> - buffers = 0) { + flatbuffers::Offset>> buffers = 0) { ModelBuilder builder_(_fbb); builder_.add_buffers(buffers); builder_.add_description(description); @@ -4458,2010 +4529,1346 @@ inline flatbuffers::Offset CreateModel( } inline flatbuffers::Offset CreateModelDirect( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - const std::vector> *operator_codes = - nullptr, + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + const std::vector> *operator_codes = nullptr, const std::vector> *subgraphs = nullptr, const char *description = nullptr, const std::vector> *buffers = nullptr) { return tflite::CreateModel( - _fbb, version, - operator_codes ? _fbb.CreateVector>( - *operator_codes) - : 0, - subgraphs ? _fbb.CreateVector>(*subgraphs) - : 0, + _fbb, + version, + operator_codes ? _fbb.CreateVector>(*operator_codes) : 0, + subgraphs ? _fbb.CreateVector>(*subgraphs) : 0, description ? _fbb.CreateString(description) : 0, buffers ? _fbb.CreateVector>(*buffers) : 0); } -flatbuffers::Offset CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, - const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -inline QuantizationParametersT *QuantizationParameters::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new QuantizationParametersT(); UnPackTo(_o, _resolver); return _o; } -inline void QuantizationParameters::UnPackTo( - QuantizationParametersT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = min(); - if (_e) { - _o->min.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->min[_i] = _e->Get(_i); - } - } - }; - { - auto _e = max(); - if (_e) { - _o->max.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->max[_i] = _e->Get(_i); - } - } - }; - { - auto _e = scale(); - if (_e) { - _o->scale.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->scale[_i] = _e->Get(_i); - } - } - }; - { - auto _e = zero_point(); - if (_e) { - _o->zero_point.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->zero_point[_i] = _e->Get(_i); - } - } - }; + { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } }; + { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } }; + { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } }; + { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } }; } -inline flatbuffers::Offset QuantizationParameters::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateQuantizationParameters(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateQuantizationParameters( - flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const QuantizationParametersT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0; auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0; auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0; - auto _zero_point = - _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; - return tflite::CreateQuantizationParameters(_fbb, _min, _max, _scale, - _zero_point); + auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; + return tflite::CreateQuantizationParameters( + _fbb, + _min, + _max, + _scale, + _zero_point); } -inline TensorT *Tensor::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new TensorT(); UnPackTo(_o, _resolver); return _o; } -inline void Tensor::UnPackTo( - TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = shape(); - if (_e) { - _o->shape.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->shape[_i] = _e->Get(_i); - } - } - }; - { - auto _e = type(); - _o->type = _e; - }; - { - auto _e = buffer(); - _o->buffer = _e; - }; - { - auto _e = name(); - if (_e) _o->name = _e->str(); - }; - { - auto _e = quantization(); - if (_e) - _o->quantization = - std::unique_ptr(_e->UnPack(_resolver)); - }; + { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } }; + { auto _e = type(); _o->type = _e; }; + { auto _e = buffer(); _o->buffer = _e; }; + { auto _e = name(); if (_e) _o->name = _e->str(); }; + { auto _e = quantization(); if (_e) _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); }; } -inline flatbuffers::Offset Tensor::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateTensor(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateTensor( - flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const TensorT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; auto _type = _o->type; auto _buffer = _o->buffer; auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _quantization = _o->quantization - ? CreateQuantizationParameters( - _fbb, _o->quantization.get(), _rehasher) - : 0; - return tflite::CreateTensor(_fbb, _shape, _type, _buffer, _name, - _quantization); + auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; + return tflite::CreateTensor( + _fbb, + _shape, + _type, + _buffer, + _name, + _quantization); } -inline Conv2DOptionsT *Conv2DOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new Conv2DOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void Conv2DOptions::UnPackTo( - Conv2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = padding(); - _o->padding = _e; - }; - { - auto _e = stride_w(); - _o->stride_w = _e; - }; - { - auto _e = stride_h(); - _o->stride_h = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = padding(); _o->padding = _e; }; + { auto _e = stride_w(); _o->stride_w = _e; }; + { auto _e = stride_h(); _o->stride_h = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset Conv2DOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateConv2DOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const Conv2DOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _padding = _o->padding; auto _stride_w = _o->stride_w; auto _stride_h = _o->stride_h; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateConv2DOptions(_fbb, _padding, _stride_w, _stride_h, - _fused_activation_function); + return tflite::CreateConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _fused_activation_function); } -inline Pool2DOptionsT *Pool2DOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new Pool2DOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void Pool2DOptions::UnPackTo( - Pool2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = padding(); - _o->padding = _e; - }; - { - auto _e = stride_w(); - _o->stride_w = _e; - }; - { - auto _e = stride_h(); - _o->stride_h = _e; - }; - { - auto _e = filter_width(); - _o->filter_width = _e; - }; - { - auto _e = filter_height(); - _o->filter_height = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = padding(); _o->padding = _e; }; + { auto _e = stride_w(); _o->stride_w = _e; }; + { auto _e = stride_h(); _o->stride_h = _e; }; + { auto _e = filter_width(); _o->filter_width = _e; }; + { auto _e = filter_height(); _o->filter_height = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset Pool2DOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreatePool2DOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreatePool2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const Pool2DOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _padding = _o->padding; auto _stride_w = _o->stride_w; auto _stride_h = _o->stride_h; auto _filter_width = _o->filter_width; auto _filter_height = _o->filter_height; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreatePool2DOptions(_fbb, _padding, _stride_w, _stride_h, - _filter_width, _filter_height, - _fused_activation_function); + return tflite::CreatePool2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _filter_width, + _filter_height, + _fused_activation_function); } -inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new DepthwiseConv2DOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void DepthwiseConv2DOptions::UnPackTo( - DepthwiseConv2DOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = padding(); - _o->padding = _e; - }; - { - auto _e = stride_w(); - _o->stride_w = _e; - }; - { - auto _e = stride_h(); - _o->stride_h = _e; - }; - { - auto _e = depth_multiplier(); - _o->depth_multiplier = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = padding(); _o->padding = _e; }; + { auto _e = stride_w(); _o->stride_w = _e; }; + { auto _e = stride_h(); _o->stride_h = _e; }; + { auto _e = depth_multiplier(); _o->depth_multiplier = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset DepthwiseConv2DOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const DepthwiseConv2DOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _padding = _o->padding; auto _stride_w = _o->stride_w; auto _stride_h = _o->stride_h; auto _depth_multiplier = _o->depth_multiplier; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateDepthwiseConv2DOptions(_fbb, _padding, _stride_w, - _stride_h, _depth_multiplier, - _fused_activation_function); + return tflite::CreateDepthwiseConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _depth_multiplier, + _fused_activation_function); } -inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new ConcatEmbeddingsOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void ConcatEmbeddingsOptions::UnPackTo( - ConcatEmbeddingsOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = num_channels(); - _o->num_channels = _e; - }; - { - auto _e = num_columns_per_channel(); - if (_e) { - _o->num_columns_per_channel.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->num_columns_per_channel[_i] = _e->Get(_i); - } - } - }; - { - auto _e = embedding_dim_per_channel(); - if (_e) { - _o->embedding_dim_per_channel.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->embedding_dim_per_channel[_i] = _e->Get(_i); - } - } - }; + { auto _e = num_channels(); _o->num_channels = _e; }; + { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } }; + { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } }; } -inline flatbuffers::Offset -ConcatEmbeddingsOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset -CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const ConcatEmbeddingsOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _num_channels = _o->num_channels; - auto _num_columns_per_channel = - _o->num_columns_per_channel.size() - ? _fbb.CreateVector(_o->num_columns_per_channel) - : 0; - auto _embedding_dim_per_channel = - _o->embedding_dim_per_channel.size() - ? _fbb.CreateVector(_o->embedding_dim_per_channel) - : 0; - return tflite::CreateConcatEmbeddingsOptions(_fbb, _num_channels, - _num_columns_per_channel, - _embedding_dim_per_channel); -} - -inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { + auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0; + auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0; + return tflite::CreateConcatEmbeddingsOptions( + _fbb, + _num_channels, + _num_columns_per_channel, + _embedding_dim_per_channel); +} + +inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new LSHProjectionOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void LSHProjectionOptions::UnPackTo( - LSHProjectionOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = type(); - _o->type = _e; - }; + { auto _e = type(); _o->type = _e; }; } -inline flatbuffers::Offset LSHProjectionOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateLSHProjectionOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateLSHProjectionOptions( - flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const LSHProjectionOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _type = _o->type; - return tflite::CreateLSHProjectionOptions(_fbb, _type); + return tflite::CreateLSHProjectionOptions( + _fbb, + _type); } -inline SVDFOptionsT *SVDFOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SVDFOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SVDFOptions::UnPackTo( - SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = rank(); - _o->rank = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = rank(); _o->rank = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset SVDFOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSVDFOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSVDFOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SVDFOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _rank = _o->rank; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateSVDFOptions(_fbb, _rank, _fused_activation_function); + return tflite::CreateSVDFOptions( + _fbb, + _rank, + _fused_activation_function); } -inline RNNOptionsT *RNNOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new RNNOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void RNNOptions::UnPackTo( - RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset RNNOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateRNNOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const RNNOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateRNNOptions(_fbb, _fused_activation_function); + return tflite::CreateRNNOptions( + _fbb, + _fused_activation_function); } -inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SequenceRNNOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SequenceRNNOptions::UnPackTo( - SequenceRNNOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = time_major(); - _o->time_major = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = time_major(); _o->time_major = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset SequenceRNNOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSequenceRNNOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SequenceRNNOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _time_major = _o->time_major; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateSequenceRNNOptions(_fbb, _time_major, - _fused_activation_function); + return tflite::CreateSequenceRNNOptions( + _fbb, + _time_major, + _fused_activation_function); } -inline BidirectionalSequenceRNNOptionsT * -BidirectionalSequenceRNNOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new BidirectionalSequenceRNNOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void BidirectionalSequenceRNNOptions::UnPackTo( - BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = time_major(); - _o->time_major = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = time_major(); _o->time_major = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset -BidirectionalSequenceRNNOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset -CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const BidirectionalSequenceRNNOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const BidirectionalSequenceRNNOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _time_major = _o->time_major; auto _fused_activation_function = _o->fused_activation_function; return tflite::CreateBidirectionalSequenceRNNOptions( - _fbb, _time_major, _fused_activation_function); + _fbb, + _time_major, + _fused_activation_function); } -inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new FullyConnectedOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void FullyConnectedOptions::UnPackTo( - FullyConnectedOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset FullyConnectedOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateFullyConnectedOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const FullyConnectedOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateFullyConnectedOptions(_fbb, _fused_activation_function); + return tflite::CreateFullyConnectedOptions( + _fbb, + _fused_activation_function); } -inline SoftmaxOptionsT *SoftmaxOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SoftmaxOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SoftmaxOptions::UnPackTo( - SoftmaxOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = beta(); - _o->beta = _e; - }; + { auto _e = beta(); _o->beta = _e; }; } -inline flatbuffers::Offset SoftmaxOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSoftmaxOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SoftmaxOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _beta = _o->beta; - return tflite::CreateSoftmaxOptions(_fbb, _beta); + return tflite::CreateSoftmaxOptions( + _fbb, + _beta); } -inline ConcatenationOptionsT *ConcatenationOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new ConcatenationOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void ConcatenationOptions::UnPackTo( - ConcatenationOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = axis(); - _o->axis = _e; - }; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = axis(); _o->axis = _e; }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset ConcatenationOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateConcatenationOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const ConcatenationOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _axis = _o->axis; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateConcatenationOptions(_fbb, _axis, - _fused_activation_function); + return tflite::CreateConcatenationOptions( + _fbb, + _axis, + _fused_activation_function); } -inline AddOptionsT *AddOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new AddOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void AddOptions::UnPackTo( - AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset AddOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateAddOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateAddOptions( - flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const AddOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateAddOptions(_fbb, _fused_activation_function); + return tflite::CreateAddOptions( + _fbb, + _fused_activation_function); } -inline MulOptionsT *MulOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new MulOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void MulOptions::UnPackTo( - MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset MulOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateMulOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const MulOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateMulOptions(_fbb, _fused_activation_function); + return tflite::CreateMulOptions( + _fbb, + _fused_activation_function); } -inline L2NormOptionsT *L2NormOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new L2NormOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void L2NormOptions::UnPackTo( - L2NormOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset L2NormOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateL2NormOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateL2NormOptions( - flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const L2NormOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateL2NormOptions(_fbb, _fused_activation_function); + return tflite::CreateL2NormOptions( + _fbb, + _fused_activation_function); } -inline LocalResponseNormalizationOptionsT * -LocalResponseNormalizationOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new LocalResponseNormalizationOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void LocalResponseNormalizationOptions::UnPackTo( - LocalResponseNormalizationOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = radius(); - _o->radius = _e; - }; - { - auto _e = bias(); - _o->bias = _e; - }; - { - auto _e = alpha(); - _o->alpha = _e; - }; - { - auto _e = beta(); - _o->beta = _e; - }; + { auto _e = radius(); _o->radius = _e; }; + { auto _e = bias(); _o->bias = _e; }; + { auto _e = alpha(); _o->alpha = _e; }; + { auto _e = beta(); _o->beta = _e; }; } -inline flatbuffers::Offset -LocalResponseNormalizationOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const LocalResponseNormalizationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset -CreateLocalResponseNormalizationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const LocalResponseNormalizationOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const LocalResponseNormalizationOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _radius = _o->radius; auto _bias = _o->bias; auto _alpha = _o->alpha; auto _beta = _o->beta; - return tflite::CreateLocalResponseNormalizationOptions(_fbb, _radius, _bias, - _alpha, _beta); + return tflite::CreateLocalResponseNormalizationOptions( + _fbb, + _radius, + _bias, + _alpha, + _beta); } -inline LSTMOptionsT *LSTMOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new LSTMOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void LSTMOptions::UnPackTo( - LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; - { - auto _e = cell_clip(); - _o->cell_clip = _e; - }; - { - auto _e = proj_clip(); - _o->proj_clip = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; + { auto _e = cell_clip(); _o->cell_clip = _e; }; + { auto _e = proj_clip(); _o->proj_clip = _e; }; } -inline flatbuffers::Offset LSTMOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateLSTMOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const LSTMOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; auto _cell_clip = _o->cell_clip; auto _proj_clip = _o->proj_clip; - return tflite::CreateLSTMOptions(_fbb, _fused_activation_function, _cell_clip, - _proj_clip); + return tflite::CreateLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip); } -inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new ResizeBilinearOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void ResizeBilinearOptions::UnPackTo( - ResizeBilinearOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; + { auto _e = align_corners(); _o->align_corners = _e; }; } -inline flatbuffers::Offset ResizeBilinearOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateResizeBilinearOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateResizeBilinearOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const ResizeBilinearOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - return tflite::CreateResizeBilinearOptions(_fbb); + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _align_corners = _o->align_corners; + return tflite::CreateResizeBilinearOptions( + _fbb, + _align_corners); } -inline CallOptionsT *CallOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new CallOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void CallOptions::UnPackTo( - CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = subgraph(); - _o->subgraph = _e; - }; + { auto _e = subgraph(); _o->subgraph = _e; }; } -inline flatbuffers::Offset CallOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateCallOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const CallOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _subgraph = _o->subgraph; - return tflite::CreateCallOptions(_fbb, _subgraph); + return tflite::CreateCallOptions( + _fbb, + _subgraph); } -inline PadOptionsT *PadOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new PadOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void PadOptions::UnPackTo( - PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; } -inline flatbuffers::Offset PadOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreatePadOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreatePadOptions( - flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const PadOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - return tflite::CreatePadOptions(_fbb); + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreatePadOptions( + _fbb); } -inline ReshapeOptionsT *ReshapeOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new ReshapeOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void ReshapeOptions::UnPackTo( - ReshapeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = new_shape(); - if (_e) { - _o->new_shape.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->new_shape[_i] = _e->Get(_i); - } - } - }; + { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } }; } -inline flatbuffers::Offset ReshapeOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateReshapeOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateReshapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const ReshapeOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0; - return tflite::CreateReshapeOptions(_fbb, _new_shape); + return tflite::CreateReshapeOptions( + _fbb, + _new_shape); } -inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SpaceToBatchNDOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SpaceToBatchNDOptions::UnPackTo( - SpaceToBatchNDOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; } -inline flatbuffers::Offset SpaceToBatchNDOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSpaceToBatchNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SpaceToBatchNDOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - return tflite::CreateSpaceToBatchNDOptions(_fbb); + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateSpaceToBatchNDOptions( + _fbb); } -inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new BatchToSpaceNDOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void BatchToSpaceNDOptions::UnPackTo( - BatchToSpaceNDOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; } -inline flatbuffers::Offset BatchToSpaceNDOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateBatchToSpaceNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const BatchToSpaceNDOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - return tflite::CreateBatchToSpaceNDOptions(_fbb); + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateBatchToSpaceNDOptions( + _fbb); } -inline SkipGramOptionsT *SkipGramOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SkipGramOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SkipGramOptions::UnPackTo( - SkipGramOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = ngram_size(); - _o->ngram_size = _e; - }; - { - auto _e = max_skip_size(); - _o->max_skip_size = _e; - }; - { - auto _e = include_all_ngrams(); - _o->include_all_ngrams = _e; - }; + { auto _e = ngram_size(); _o->ngram_size = _e; }; + { auto _e = max_skip_size(); _o->max_skip_size = _e; }; + { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; }; } -inline flatbuffers::Offset SkipGramOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSkipGramOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSkipGramOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SkipGramOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _ngram_size = _o->ngram_size; auto _max_skip_size = _o->max_skip_size; auto _include_all_ngrams = _o->include_all_ngrams; - return tflite::CreateSkipGramOptions(_fbb, _ngram_size, _max_skip_size, - _include_all_ngrams); + return tflite::CreateSkipGramOptions( + _fbb, + _ngram_size, + _max_skip_size, + _include_all_ngrams); } -inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SpaceToDepthOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SpaceToDepthOptions::UnPackTo( - SpaceToDepthOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = block_size(); - _o->block_size = _e; - }; + { auto _e = block_size(); _o->block_size = _e; }; } -inline flatbuffers::Offset SpaceToDepthOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSpaceToDepthOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSpaceToDepthOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SpaceToDepthOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _block_size = _o->block_size; - return tflite::CreateSpaceToDepthOptions(_fbb, _block_size); + return tflite::CreateSpaceToDepthOptions( + _fbb, + _block_size); } -inline SubOptionsT *SubOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SubOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SubOptions::UnPackTo( - SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset SubOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSubOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSubOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SubOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateSubOptions(_fbb, _fused_activation_function); + return tflite::CreateSubOptions( + _fbb, + _fused_activation_function); } -inline DivOptionsT *DivOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new DivOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void DivOptions::UnPackTo( - DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = fused_activation_function(); - _o->fused_activation_function = _e; - }; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; } -inline flatbuffers::Offset DivOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateDivOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateDivOptions( - flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const DivOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; - return tflite::CreateDivOptions(_fbb, _fused_activation_function); + return tflite::CreateDivOptions( + _fbb, + _fused_activation_function); +} + +inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new TopKV2OptionsT(); + UnPackTo(_o, _resolver); + return _o; } -inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTopKV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateTopKV2Options( + _fbb); +} + +inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new EmbeddingLookupSparseOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void EmbeddingLookupSparseOptions::UnPackTo( - EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = combiner(); - _o->combiner = _e; - }; + { auto _e = combiner(); _o->combiner = _e; }; } -inline flatbuffers::Offset -EmbeddingLookupSparseOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset -CreateEmbeddingLookupSparseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - const EmbeddingLookupSparseOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const EmbeddingLookupSparseOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _combiner = _o->combiner; - return tflite::CreateEmbeddingLookupSparseOptions(_fbb, _combiner); + return tflite::CreateEmbeddingLookupSparseOptions( + _fbb, + _combiner); } -inline GatherOptionsT *GatherOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new GatherOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void GatherOptions::UnPackTo( - GatherOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = axis(); - _o->axis = _e; - }; + { auto _e = axis(); _o->axis = _e; }; } -inline flatbuffers::Offset GatherOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateGatherOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const GatherOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _axis = _o->axis; - return tflite::CreateGatherOptions(_fbb, _axis); + return tflite::CreateGatherOptions( + _fbb, + _axis); } -inline TransposeOptionsT *TransposeOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new TransposeOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void TransposeOptions::UnPackTo( - TransposeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; } -inline flatbuffers::Offset TransposeOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateTransposeOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const TransposeOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - return tflite::CreateTransposeOptions(_fbb); + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateTransposeOptions( + _fbb); } -inline MeanOptionsT *MeanOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ExpOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExpOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateExpOptions( + _fbb); +} + +inline MeanOptionsT *MeanOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new MeanOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void MeanOptions::UnPackTo( - MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void MeanOptions::UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = keep_dims(); - _o->keep_dims = _e; - }; + { auto _e = keep_dims(); _o->keep_dims = _e; }; } -inline flatbuffers::Offset MeanOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset MeanOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateMeanOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateMeanOptions( - flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const MeanOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MeanOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _keep_dims = _o->keep_dims; - return tflite::CreateMeanOptions(_fbb, _keep_dims); + return tflite::CreateMeanOptions( + _fbb, + _keep_dims); } -inline SqueezeOptionsT *SqueezeOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SqueezeOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void SqueezeOptions::UnPackTo( - SqueezeOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = squeeze_dims(); - if (_e) { - _o->squeeze_dims.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->squeeze_dims[_i] = _e->Get(_i); - } - } - }; + { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } }; } -inline flatbuffers::Offset SqueezeOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSqueezeOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSqueezeOptions( - flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SqueezeOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - auto _squeeze_dims = - _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; - return tflite::CreateSqueezeOptions(_fbb, _squeeze_dims); -} - -inline StridedSliceOptionsT *StridedSliceOptions::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; + return tflite::CreateSqueezeOptions( + _fbb, + _squeeze_dims); +} + +inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new SplitOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_splits(); _o->num_splits = _e; }; +} + +inline flatbuffers::Offset SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSplitOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_splits = _o->num_splits; + return tflite::CreateSplitOptions( + _fbb, + _num_splits); +} + +inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new StridedSliceOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void StridedSliceOptions::UnPackTo( - StridedSliceOptionsT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = begin_mask(); - _o->begin_mask = _e; - }; - { - auto _e = end_mask(); - _o->end_mask = _e; - }; - { - auto _e = ellipsis_mask(); - _o->ellipsis_mask = _e; - }; - { - auto _e = new_axis_mask(); - _o->new_axis_mask = _e; - }; - { - auto _e = shrink_axis_mask(); - _o->shrink_axis_mask = _e; - }; + { auto _e = begin_mask(); _o->begin_mask = _e; }; + { auto _e = end_mask(); _o->end_mask = _e; }; + { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; }; + { auto _e = new_axis_mask(); _o->new_axis_mask = _e; }; + { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; }; } -inline flatbuffers::Offset StridedSliceOptions::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateStridedSliceOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateStridedSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const StridedSliceOptionsT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _begin_mask = _o->begin_mask; auto _end_mask = _o->end_mask; auto _ellipsis_mask = _o->ellipsis_mask; auto _new_axis_mask = _o->new_axis_mask; auto _shrink_axis_mask = _o->shrink_axis_mask; - return tflite::CreateStridedSliceOptions(_fbb, _begin_mask, _end_mask, - _ellipsis_mask, _new_axis_mask, - _shrink_axis_mask); + return tflite::CreateStridedSliceOptions( + _fbb, + _begin_mask, + _end_mask, + _ellipsis_mask, + _new_axis_mask, + _shrink_axis_mask); +} + +inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new LogSoftmaxOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogSoftmaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogSoftmaxOptions( + _fbb); +} + +inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new CastOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCastOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateCastOptions( + _fbb); +} + +inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new DequantizeOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDequantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateDequantizeOptions( + _fbb); } -inline OperatorCodeT *OperatorCode::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); return _o; } -inline void OperatorCode::UnPackTo( - OperatorCodeT *_o, - const flatbuffers::resolver_function_t *_resolver) const { +inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = builtin_code(); - _o->builtin_code = _e; - }; - { - auto _e = custom_code(); - if (_e) _o->custom_code = _e->str(); - }; + { auto _e = builtin_code(); _o->builtin_code = _e; }; + { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); }; } -inline flatbuffers::Offset OperatorCode::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateOperatorCode(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateOperatorCode( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const OperatorCodeT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _builtin_code = _o->builtin_code; - auto _custom_code = - _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); - return tflite::CreateOperatorCode(_fbb, _builtin_code, _custom_code); + auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); + return tflite::CreateOperatorCode( + _fbb, + _builtin_code, + _custom_code); } -inline OperatorT *Operator::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorT(); UnPackTo(_o, _resolver); return _o; } -inline void Operator::UnPackTo( - OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = opcode_index(); - _o->opcode_index = _e; - }; - { - auto _e = inputs(); - if (_e) { - _o->inputs.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->inputs[_i] = _e->Get(_i); - } - } - }; - { - auto _e = outputs(); - if (_e) { - _o->outputs.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->outputs[_i] = _e->Get(_i); - } - } - }; - { - auto _e = builtin_options_type(); - _o->builtin_options.type = _e; - }; - { - auto _e = builtin_options(); - if (_e) - _o->builtin_options.value = - BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); - }; - { - auto _e = custom_options(); - if (_e) { - _o->custom_options.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->custom_options[_i] = _e->Get(_i); - } - } - }; - { - auto _e = custom_options_format(); - _o->custom_options_format = _e; - }; + { auto _e = opcode_index(); _o->opcode_index = _e; }; + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }; + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }; + { auto _e = builtin_options_type(); _o->builtin_options.type = _e; }; + { auto _e = builtin_options(); if (_e) _o->builtin_options.value = BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); }; + { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->custom_options[_i] = _e->Get(_i); } } }; + { auto _e = custom_options_format(); _o->custom_options_format = _e; }; } -inline flatbuffers::Offset Operator::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateOperator(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateOperator( - flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const OperatorT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _opcode_index = _o->opcode_index; auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; auto _builtin_options_type = _o->builtin_options.type; auto _builtin_options = _o->builtin_options.Pack(_fbb); - auto _custom_options = - _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; + auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; auto _custom_options_format = _o->custom_options_format; - return tflite::CreateOperator(_fbb, _opcode_index, _inputs, _outputs, - _builtin_options_type, _builtin_options, - _custom_options, _custom_options_format); + return tflite::CreateOperator( + _fbb, + _opcode_index, + _inputs, + _outputs, + _builtin_options_type, + _builtin_options, + _custom_options, + _custom_options_format); } -inline SubGraphT *SubGraph::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new SubGraphT(); UnPackTo(_o, _resolver); return _o; } -inline void SubGraph::UnPackTo( - SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = tensors(); - if (_e) { - _o->tensors.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->tensors[_i] = - std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); - } - } - }; - { - auto _e = inputs(); - if (_e) { - _o->inputs.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->inputs[_i] = _e->Get(_i); - } - } - }; - { - auto _e = outputs(); - if (_e) { - _o->outputs.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->outputs[_i] = _e->Get(_i); - } - } - }; - { - auto _e = operators(); - if (_e) { - _o->operators.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->operators[_i] = - std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); - } - } - }; - { - auto _e = name(); - if (_e) _o->name = _e->str(); - }; + { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }; + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }; + { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operators[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; + { auto _e = name(); if (_e) _o->name = _e->str(); }; } -inline flatbuffers::Offset SubGraph::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateSubGraph(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const SubGraphT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; - auto _tensors = - _o->tensors.size() - ? _fbb.CreateVector>( - _o->tensors.size(), - [](size_t i, _VectorArgs *__va) { - return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), - __va->__rehasher); - }, - &_va) - : 0; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _tensors = _o->tensors.size() ? _fbb.CreateVector> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0; auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; - auto _operators = _o->operators.size() - ? _fbb.CreateVector>( - _o->operators.size(), - [](size_t i, _VectorArgs *__va) { - return CreateOperator( - *__va->__fbb, __va->__o->operators[i].get(), - __va->__rehasher); - }, - &_va) - : 0; + auto _operators = _o->operators.size() ? _fbb.CreateVector> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0; auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - return tflite::CreateSubGraph(_fbb, _tensors, _inputs, _outputs, _operators, - _name); + return tflite::CreateSubGraph( + _fbb, + _tensors, + _inputs, + _outputs, + _operators, + _name); } -inline BufferT *Buffer::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new BufferT(); UnPackTo(_o, _resolver); return _o; } -inline void Buffer::UnPackTo( - BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = data(); - if (_e) { - _o->data.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->data[_i] = _e->Get(_i); - } - } - }; + { auto _e = data(); if (_e) { _o->data.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->data[_i] = _e->Get(_i); } } }; } -inline flatbuffers::Offset Buffer::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateBuffer(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateBuffer( - flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const BufferT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; - return tflite::CreateBuffer(_fbb, _data); + return tflite::CreateBuffer( + _fbb, + _data); } -inline ModelT *Model::UnPack( - const flatbuffers::resolver_function_t *_resolver) const { +inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new ModelT(); UnPackTo(_o, _resolver); return _o; } -inline void Model::UnPackTo( - ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = version(); - _o->version = _e; - }; - { - auto _e = operator_codes(); - if (_e) { - _o->operator_codes.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->operator_codes[_i] = - std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); - } - } - }; - { - auto _e = subgraphs(); - if (_e) { - _o->subgraphs.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->subgraphs[_i] = - std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); - } - } - }; - { - auto _e = description(); - if (_e) _o->description = _e->str(); - }; - { - auto _e = buffers(); - if (_e) { - _o->buffers.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->buffers[_i] = - std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); - } - } - }; + { auto _e = version(); _o->version = _e; }; + { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->operator_codes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; + { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; + { auto _e = description(); if (_e) _o->description = _e->str(); }; + { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; } -inline flatbuffers::Offset Model::Pack( - flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { return CreateModel(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, - const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { - flatbuffers::FlatBufferBuilder *__fbb; - const ModelT *__o; - const flatbuffers::rehasher_function_t *__rehasher; - } _va = {&_fbb, _o, _rehasher}; - (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _version = _o->version; - auto _operator_codes = - _o->operator_codes.size() - ? _fbb.CreateVector>( - _o->operator_codes.size(), - [](size_t i, _VectorArgs *__va) { - return CreateOperatorCode(*__va->__fbb, - __va->__o->operator_codes[i].get(), - __va->__rehasher); - }, - &_va) - : 0; - auto _subgraphs = _o->subgraphs.size() - ? _fbb.CreateVector>( - _o->subgraphs.size(), - [](size_t i, _VectorArgs *__va) { - return CreateSubGraph( - *__va->__fbb, __va->__o->subgraphs[i].get(), - __va->__rehasher); - }, - &_va) - : 0; - auto _description = - _o->description.empty() ? 0 : _fbb.CreateString(_o->description); - auto _buffers = - _o->buffers.size() - ? _fbb.CreateVector>( - _o->buffers.size(), - [](size_t i, _VectorArgs *__va) { - return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), - __va->__rehasher); - }, - &_va) - : 0; - return tflite::CreateModel(_fbb, _version, _operator_codes, _subgraphs, - _description, _buffers); -} - -inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, - const void *obj, BuiltinOptions type) { + auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); + auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite::CreateModel( + _fbb, + _version, + _operator_codes, + _subgraphs, + _description, + _buffers); +} + +inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { switch (type) { case BuiltinOptions_NONE: { return true; @@ -6515,8 +5922,7 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, return verifier.VerifyTable(ptr); } case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = - reinterpret_cast(obj); + auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } case BuiltinOptions_LSTMOptions: { @@ -6595,28 +6001,47 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } - default: - return false; + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return false; } } -inline bool VerifyBuiltinOptionsVector( - flatbuffers::Verifier &verifier, - const flatbuffers::Vector> *values, - const flatbuffers::Vector *types) { +inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; if (values->size() != types->size()) return false; for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyBuiltinOptions(verifier, values->Get(i), - types->GetEnum(i))) { + if (!VerifyBuiltinOptions( + verifier, values->Get(i), types->GetEnum(i))) { return false; } } return true; } -inline void *BuiltinOptionsUnion::UnPack( - const void *obj, BuiltinOptions type, - const flatbuffers::resolver_function_t *resolver) { +inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) { switch (type) { case BuiltinOptions_Conv2DOptions: { auto ptr = reinterpret_cast(obj); @@ -6667,8 +6092,7 @@ inline void *BuiltinOptionsUnion::UnPack( return ptr->UnPack(resolver); } case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = - reinterpret_cast(obj); + auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } case BuiltinOptions_LSTMOptions: { @@ -6747,14 +6171,35 @@ inline void *BuiltinOptionsUnion::UnPack( auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } - default: - return nullptr; + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; } } -inline flatbuffers::Offset BuiltinOptionsUnion::Pack( - flatbuffers::FlatBufferBuilder &_fbb, - const flatbuffers::rehasher_function_t *_rehasher) const { +inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { switch (type) { case BuiltinOptions_Conv2DOptions: { auto ptr = reinterpret_cast(value); @@ -6805,10 +6250,8 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack( return CreateL2NormOptions(_fbb, ptr, _rehasher).Union(); } case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = - reinterpret_cast(value); - return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher) - .Union(); + auto ptr = reinterpret_cast(value); + return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union(); } case BuiltinOptions_LSTMOptions: { auto ptr = reinterpret_cast(value); @@ -6886,32 +6329,50 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack( auto ptr = reinterpret_cast(value); return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union(); } - default: - return 0; + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + return CreateExpOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + return CreateTopKV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + return CreateSplitOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + return CreateCastOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); + } + default: return 0; } } -inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) - FLATBUFFERS_NOEXCEPT : type(u.type), - value(nullptr) { +inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FLATBUFFERS_NOEXCEPT : type(u.type), value(nullptr) { switch (type) { case BuiltinOptions_Conv2DOptions: { value = new Conv2DOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_DepthwiseConv2DOptions: { - value = new DepthwiseConv2DOptionsT( - *reinterpret_cast(u.value)); + value = new DepthwiseConv2DOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_ConcatEmbeddingsOptions: { - value = new ConcatEmbeddingsOptionsT( - *reinterpret_cast(u.value)); + value = new ConcatEmbeddingsOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_LSHProjectionOptions: { - value = new LSHProjectionOptionsT( - *reinterpret_cast(u.value)); + value = new LSHProjectionOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_Pool2DOptions: { @@ -6927,18 +6388,15 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_FullyConnectedOptions: { - value = new FullyConnectedOptionsT( - *reinterpret_cast(u.value)); + value = new FullyConnectedOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SoftmaxOptions: { - value = - new SoftmaxOptionsT(*reinterpret_cast(u.value)); + value = new SoftmaxOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_ConcatenationOptions: { - value = new ConcatenationOptionsT( - *reinterpret_cast(u.value)); + value = new ConcatenationOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_AddOptions: { @@ -6950,8 +6408,7 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_LocalResponseNormalizationOptions: { - value = new LocalResponseNormalizationOptionsT( - *reinterpret_cast(u.value)); + value = new LocalResponseNormalizationOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_LSTMOptions: { @@ -6959,8 +6416,7 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_ResizeBilinearOptions: { - value = new ResizeBilinearOptionsT( - *reinterpret_cast(u.value)); + value = new ResizeBilinearOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_CallOptions: { @@ -6968,23 +6424,19 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_ReshapeOptions: { - value = - new ReshapeOptionsT(*reinterpret_cast(u.value)); + value = new ReshapeOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SkipGramOptions: { - value = - new SkipGramOptionsT(*reinterpret_cast(u.value)); + value = new SkipGramOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SpaceToDepthOptions: { - value = new SpaceToDepthOptionsT( - *reinterpret_cast(u.value)); + value = new SpaceToDepthOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_EmbeddingLookupSparseOptions: { - value = new EmbeddingLookupSparseOptionsT( - *reinterpret_cast(u.value)); + value = new EmbeddingLookupSparseOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_MulOptions: { @@ -7000,18 +6452,15 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_BatchToSpaceNDOptions: { - value = new BatchToSpaceNDOptionsT( - *reinterpret_cast(u.value)); + value = new BatchToSpaceNDOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SpaceToBatchNDOptions: { - value = new SpaceToBatchNDOptionsT( - *reinterpret_cast(u.value)); + value = new SpaceToBatchNDOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_TransposeOptions: { - value = new TransposeOptionsT( - *reinterpret_cast(u.value)); + value = new TransposeOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_MeanOptions: { @@ -7027,18 +6476,39 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) break; } case BuiltinOptions_SqueezeOptions: { - value = - new SqueezeOptionsT(*reinterpret_cast(u.value)); + value = new SqueezeOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SequenceRNNOptions: { - value = new SequenceRNNOptionsT( - *reinterpret_cast(u.value)); + value = new SequenceRNNOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_StridedSliceOptions: { - value = new StridedSliceOptionsT( - *reinterpret_cast(u.value)); + value = new StridedSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ExpOptions: { + value = new ExpOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TopKV2Options: { + value = new TopKV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SplitOptions: { + value = new SplitOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + value = new LogSoftmaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CastOptions: { + value = new CastOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DequantizeOptions: { + value = new DequantizeOptionsT(*reinterpret_cast(u.value)); break; } default: @@ -7208,8 +6678,37 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } - default: + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; break; + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; } value = nullptr; type = BuiltinOptions_NONE; @@ -7219,25 +6718,33 @@ inline const tflite::Model *GetModel(const void *buf) { return flatbuffers::GetRoot(buf); } -inline const char *ModelIdentifier() { return "TFL3"; } +inline const char *ModelIdentifier() { + return "TFL3"; +} inline bool ModelBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier(buf, ModelIdentifier()); + return flatbuffers::BufferHasIdentifier( + buf, ModelIdentifier()); } -inline bool VerifyModelBuffer(flatbuffers::Verifier &verifier) { +inline bool VerifyModelBuffer( + flatbuffers::Verifier &verifier) { return verifier.VerifyBuffer(ModelIdentifier()); } -inline const char *ModelExtension() { return "tflite"; } +inline const char *ModelExtension() { + return "tflite"; +} -inline void FinishModelBuffer(flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { +inline void FinishModelBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { fbb.Finish(root, ModelIdentifier()); } inline std::unique_ptr UnPackModel( - const void *buf, const flatbuffers::resolver_function_t *res = nullptr) { + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { return std::unique_ptr(GetModel(buf)->UnPack(res)); } diff --git a/tensorflow/contrib/lite/simple_memory_arena.h b/tensorflow/contrib/lite/simple_memory_arena.h index 0c5e00a1f2e6a3303556ec54d8e50e8398644bf5..0535522374c63459d029c252ebe94628cf3122d5 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.h +++ b/tensorflow/contrib/lite/simple_memory_arena.h @@ -36,9 +36,9 @@ struct ArenaAlloc { } }; -// This small class is responsible for allocating, dealocating and reusing +// This small class is responsible for allocating, deallocating and reusing // dynamic memory from a common underlying buffer. The arena can be used in -// scenarios when the pattern of memory allocations and dealocations is +// scenarios when the pattern of memory allocations and deallocations is // repetitive, e.g. running NN inference in multiple iterations. class SimpleMemoryArena { public: diff --git a/tensorflow/contrib/lite/special_rules.bzl b/tensorflow/contrib/lite/special_rules.bzl new file mode 100644 index 0000000000000000000000000000000000000000..54083c49182c707620cbd231b957405cfe24be92 --- /dev/null +++ b/tensorflow/contrib/lite/special_rules.bzl @@ -0,0 +1,6 @@ +"""External versions of build rules that differ outside of Google.""" + +def tflite_portable_test_suite(**kwargs): + """This is a no-op outside of Google.""" + _ignore = [kwargs] + pass diff --git a/tensorflow/contrib/lite/testdata/multi_add.pb b/tensorflow/contrib/lite/testdata/multi_add.pb new file mode 100644 index 0000000000000000000000000000000000000000..e95a20841fb2b320bd77994d9dda157d79311dd6 --- /dev/null +++ b/tensorflow/contrib/lite/testdata/multi_add.pb @@ -0,0 +1,26 @@ + +I +a Placeholder" /device:CPU:0* +shape:* +dtype0 +I +b Placeholder" /device:CPU:0* +dtype0* +shape: +I +c Placeholder" /device:CPU:0* +dtype0* +shape: +I +d Placeholder" /device:CPU:0* +dtype0* +shape: +& +iAddbc" /device:CPU:0* +T0 +& +xAddai" /device:CPU:0* +T0 +& +yAdddi" /device:CPU:0* +T0" \ No newline at end of file diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index b949045128fc15b6abe8f6c59d63dfd2b47c3c30..631601656dbad59b7cf0b9b81276d42ea9322cb6 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -8,6 +8,7 @@ load( "//tensorflow/contrib/lite:build_def.bzl", "gen_zipped_test_files", ) +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") load( "//tensorflow:tensorflow.bzl", "tf_cc_test", @@ -25,6 +26,7 @@ gen_zipped_test_files( "conv.zip", "depthwiseconv.zip", "div.zip", + "exp.zip", "fully_connected.zip", "fused_batch_norm.zip", "gather.zip", @@ -32,6 +34,8 @@ gen_zipped_test_files( "l2_pool.zip", "l2norm.zip", "local_response_norm.zip", + "log_softmax.zip", + "lstm.zip", "max_pool.zip", "mean.zip", "mul.zip", @@ -45,9 +49,11 @@ gen_zipped_test_files( "softmax.zip", "space_to_batch_nd.zip", "space_to_depth.zip", + "split.zip", "squeeze.zip", "strided_slice.zip", "sub.zip", + "topk.zip", "transpose.zip", ], ) @@ -121,6 +127,21 @@ cc_test( ], ) +cc_library( + name = "join", + hdrs = ["join.h"], +) + +cc_test( + name = "join_test", + size = "small", + srcs = ["join_test.cc"], + deps = [ + ":join", + "@com_google_googletest//:gtest_main", + ], +) + cc_library( name = "tflite_driver", srcs = ["tflite_driver.cc"], @@ -195,6 +216,125 @@ cc_binary( ], ) +cc_library( + name = "tf_driver", + srcs = ["tf_driver.cc"], + hdrs = ["tf_driver.h"], + deps = [ + ":join", + ":split", + ":test_runner", + "//tensorflow/core:core_cpu", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:tensorflow", + ], +) + +cc_test( + name = "tf_driver_test", + size = "small", + srcs = ["tf_driver_test.cc"], + data = ["//tensorflow/contrib/lite:testdata/multi_add.pb"], + tags = [ + "tflite_not_portable", + ], + deps = [ + ":tf_driver", + "@com_google_googletest//:gtest_main", + ], +) + +cc_library( + name = "generate_testspec", + testonly = 1, + srcs = ["generate_testspec.cc"], + hdrs = ["generate_testspec.h"], + deps = [ + ":join", + ":split", + ":tf_driver", + "//tensorflow/core:framework", + ], +) + +cc_test( + name = "generate_testspec_test", + size = "small", + srcs = ["generate_testspec_test.cc"], + tags = [ + "tflite_not_portable", + ], + deps = [ + ":generate_testspec", + "@com_google_googletest//:gtest_main", + ], +) + +cc_library( + name = "tflite_diff_util", + testonly = 1, + srcs = ["tflite_diff_util.cc"], + hdrs = ["tflite_diff_util.h"], + deps = [ + ":generate_testspec", + ":parse_testdata_lib", + ":split", + ":tflite_driver", + ":util", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:string", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], +) + +cc_library( + name = "tflite_diff_flags", + testonly = 1, + hdrs = ["tflite_diff_flags.h"], + deps = [ + ":split", + ":tflite_diff_util", + ] + select({ + "//conditions:default": [ + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + ], + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib", + ], + }), +) + +tf_cc_test( + name = "tflite_diff_example_test", + size = "medium", + srcs = ["tflite_diff_example_test.cc"], + args = [ + "--tensorflow_model=third_party/tensorflow/contrib/lite/testdata/multi_add.pb", + "--tflite_model=third_party/tensorflow/contrib/lite/testdata/multi_add.bin", + "--input_layer=a,b,c,d", + "--input_layer_type=float,float,float,float", + "--input_layer_shape=1,3,4,3:1,3,4,3:1,3,4,3:1,3,4,3", + "--output_layer=x,y", + ], + data = [ + "//tensorflow/contrib/lite:testdata/multi_add.bin", + "//tensorflow/contrib/lite:testdata/multi_add.pb", + ], + tags = [ + "no_cuda_on_cpu_tap", + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":tflite_diff_flags", + ":tflite_diff_util", + ], +) + tf_cc_test( name = "generated_examples_zip_test", size = "large", @@ -207,7 +347,10 @@ tf_cc_test( ], data = [":optest"], shard_count = 20, - tags = ["no_oss"], + tags = [ + "no_oss", + "tflite_not_portable", + ], deps = [ ":parse_testdata_lib", ":tflite_driver", @@ -241,3 +384,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 147400ec37c606308244cc862bbee5b88ba553ec..5488b71fcf644070710acc4b2b2886e9a96facb6 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -36,6 +36,7 @@ import traceback import zipfile import numpy as np from six import StringIO +from six.moves import xrange # TODO(aselle): Disable GPU for now os.environ["CUDA_VISIBLE_DEVICES"] = "-1" @@ -46,6 +47,7 @@ from google.protobuf import text_format # TODO(aselle): switch to TensorFlow's resource_loader from tensorflow.contrib.lite.testing import generate_examples_report as report_lib from tensorflow.python.framework import graph_util as tf_graph_util +from tensorflow.python.ops import rnn parser = argparse.ArgumentParser(description="Script to generate TFLite tests.") parser.add_argument("output_path", @@ -99,16 +101,32 @@ KNOWN_BUGS = { r"batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\]": "70594634", # BatchToSpaceND only supports 4D tensors. r"batch_to_space_nd.*input_shape=\[8,2,2,2,1,1\]": "70594733", - # Div will use floordiv - r"div.*int32": "72051395" + # Div will use floordiv. + r"div.*int32": "72051395", + # TOCO require matching dimensions in strided_slice. + r"strided_slice.*begin=\[0\].*end=\[1\].*": "73170889", + # No support for SplitV + r"split.*num_or_size_splits=\[2,2\]": "73377559", } +class ExtraTocoOptions(object): + """Additonal toco options besides input, output, shape.""" + + def __init__(self): + # Whether to ignore control dependency nodes. + self.drop_control_dependency = False + # Allow custom ops in the toco conversion. + self.allow_custom_ops = False + # Rnn states that are used to support rnn / lstm cells. + self.rnn_states = None + + def toco_options(data_types, input_arrays, output_arrays, shapes, - drop_control_dependency): + extra_toco_options=ExtraTocoOptions()): """Create TOCO options to process a model. Args: @@ -116,8 +134,7 @@ def toco_options(data_types, input_arrays: names of the input tensors output_arrays: name of the output tensors shapes: shapes of the input tensors - drop_control_dependency: whether to ignore control dependency nodes. - + extra_toco_options: additional toco options Returns: the options in a string. """ @@ -133,37 +150,15 @@ def toco_options(data_types, " --input_arrays=%s" % ",".join(input_arrays) + " --input_shapes=%s" % shape_str + " --output_arrays=%s" % ",".join(output_arrays)) - if drop_control_dependency: + if extra_toco_options.drop_control_dependency: s += " --drop_control_dependency" + if extra_toco_options.allow_custom_ops: + s += " --allow_custom_ops" + if extra_toco_options.rnn_states: + s += (" --rnn_states='" + extra_toco_options.rnn_states + "'") return s -def write_toco_options(filename, - data_types, - input_arrays, - output_arrays, - shapes, - drop_control_dependency=False): - """Create TOCO options to process a model. - - Args: - filename: Filename to write the options to. - data_types: input and inference types used by TOCO. - input_arrays: names of the input tensors - output_arrays: names of the output tensors - shapes: shapes of the input tensors - drop_control_dependency: whether to ignore control dependency nodes. - """ - with open(filename, "w") as fp: - fp.write( - toco_options( - data_types=data_types, - input_arrays=input_arrays, - output_arrays=output_arrays, - shapes=shapes, - drop_control_dependency=drop_control_dependency)) - - def write_examples(fp, examples): """Given a list `examples`, write a text format representation. @@ -241,7 +236,7 @@ def create_tensor_data(dtype, shape, min_value=-100, max_value=100): if dtype in (tf.float32, tf.float16): value = (max_value-min_value)*np.random.random_sample(shape)+min_value elif dtype in (tf.int32, tf.uint8, tf.int64): - value = np.random.random_integers(min_value, max_value, shape) + value = np.random.randint(min_value, max_value+1, shape) return value.astype(dtype) @@ -281,12 +276,14 @@ def make_control_dep_tests(zip_path): return [input_values], sess.run( outputs, feed_dict=dict(zip(inputs, [input_values]))) + extra_toco_options = ExtraTocoOptions() + extra_toco_options.drop_control_dependency = True make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs, - drop_control_dependency=True) + extra_toco_options) def toco_convert(graph_def_str, input_tensors, output_tensors, - drop_control_dependency=False): + extra_toco_options): """Convert a model's graph def into a tflite model. NOTE: this currently shells out to the toco binary, but we would like @@ -294,9 +291,9 @@ def toco_convert(graph_def_str, input_tensors, output_tensors, Args: graph_def_str: Graph def proto in serialized string format. - input_tensors: List of input tensor tuples `(name, shape, type)` - output_tensors: List of output tensors (names) - drop_control_dependency: whether to ignore control dependency nodes. + input_tensors: List of input tensor tuples `(name, shape, type)`. + output_tensors: List of output tensors (names). + extra_toco_options: Additional toco options. Returns: output tflite model, log_txt from conversion @@ -308,7 +305,7 @@ def toco_convert(graph_def_str, input_tensors, output_tensors, input_arrays=[x[0] for x in input_tensors], shapes=[x[1] for x in input_tensors], output_arrays=output_tensors, - drop_control_dependency=drop_control_dependency) + extra_toco_options=extra_toco_options) with tempfile.NamedTemporaryFile() as graphdef_file, \ tempfile.NamedTemporaryFile() as output_file, \ @@ -327,11 +324,18 @@ def toco_convert(graph_def_str, input_tensors, output_tensors, return (None if exit_code != 0 else output_file.read()), log +def normalize_output_name(output_name): + """Remove :0 suffix from tensor names.""" + return output_name.split(":")[0] if output_name.endswith( + ":0") else output_name + + def make_zip_of_tests(zip_path, test_parameters, make_graph, make_test_inputs, - drop_control_dependency=False): + extra_toco_options=ExtraTocoOptions(), + use_frozen_graph=False): """Helper to make a zip file of a bunch of TensorFlow models. This does a cartestian product of the dictionary of test_parameters and @@ -349,7 +353,9 @@ def make_zip_of_tests(zip_path, `[input1, input2, ...], [output1, output2, ...]` make_test_inputs: function taking `curr_params`, `session`, `input_tensors`, `output_tensors` and returns tuple `(input_values, output_values)`. - drop_control_dependency: whether to ignore control dependency nodes. + extra_toco_options: Additional toco options. + use_frozen_graph: Whether or not freeze graph before toco converter. + Raises: RuntimeError: if there are toco errors that can't be ignored. """ @@ -409,21 +415,25 @@ def make_zip_of_tests(zip_path, return None, report report["toco"] = report_lib.FAILED report["tf"] = report_lib.SUCCESS - # Convert graph to toco + input_tensors = [(input_tensor.name.split(":")[0], + input_tensor.get_shape(), input_tensor.dtype) + for input_tensor in inputs] + output_tensors = [normalize_output_name(out.name) for out in outputs] + graph_def = freeze_graph( + sess, + tf.global_variables() + inputs + + outputs) if use_frozen_graph else sess.graph_def tflite_model_binary, toco_log = toco_convert( - sess.graph_def.SerializeToString(), - [(input_tensor.name.split(":")[0], input_tensor.get_shape(), - input_tensor.dtype) for input_tensor in inputs], - [out.name.split(":")[0] - for out in outputs], drop_control_dependency) + graph_def.SerializeToString(), input_tensors, output_tensors, + extra_toco_options) report["toco"] = (report_lib.SUCCESS if tflite_model_binary is not None else report_lib.FAILED) report["toco_log"] = toco_log if FLAGS.save_graphdefs: archive.writestr(label + ".pb", - text_format.MessageToString(sess.graph_def), + text_format.MessageToString(graph_def), zipfile.ZIP_DEFLATED) if tflite_model_binary: @@ -745,6 +755,65 @@ def make_mean_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_exp_tests(zip_path): + """Make a set of tests to do exp.""" + + test_parameters = [{ + "input_dtype": [tf.float32], + "input_shape": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + }] + + def build_graph(parameters): + """Build the exp op testing graph.""" + input_tensor = tf.placeholder( + dtype=parameters["input_dtype"], + name="input", + shape=parameters["input_shape"]) + + out = tf.exp(input_tensor) + return [input_tensor], [out] + + def build_inputs(parameters, sess, inputs, outputs): + values = [ + create_tensor_data(parameters["input_dtype"], parameters["input_shape"], + min_value=-100, max_value=9) + ] + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + +def make_log_softmax_tests(zip_path): + """Make a set of tests to do log_softmax.""" + + test_parameters = [{ + "input_dtype": [tf.float32], + "input_shape": [[1, 100], [4, 2], [5, 224]], + }] + + def build_graph(parameters): + """Build the log_softmax op testing graph.""" + input_tensor = tf.placeholder( + dtype=parameters["input_dtype"], + name="input", + shape=parameters["input_shape"]) + + out = tf.nn.log_softmax(input_tensor) + return [input_tensor], [out] + + def build_inputs(parameters, sess, inputs, outputs): + values = [ + create_tensor_data( + parameters["input_dtype"], + parameters["input_shape"], + min_value=-100, + max_value=9) + ] + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_binary_op_tests_func(binary_operator): """Return a function that does a test on a binary operator.""" return lambda zip_path: make_binary_op_tests(zip_path, binary_operator) @@ -995,8 +1064,31 @@ def make_depthwiseconv_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_split_tests(zip_path): + """Make a set of tests to do tf.split.""" + + test_parameters = [{ + "input_shape": [[1, 3, 4, 6], [2, 4, 1], [6, 4], [8]], + "num_or_size_splits": [1, 2, 3, 4, 5, [2, 2]], + "axis": [0, 1, 2, 3, -4, -3, -2, -1], + }] + + def build_graph(parameters): + input_tensor = tf.placeholder( + dtype=tf.float32, name="input", shape=parameters["input_shape"]) + out = tf.split( + input_tensor, parameters["num_or_size_splits"], parameters["axis"]) + return [input_tensor], out + + def build_inputs(parameters, sess, inputs, outputs): + values = [create_tensor_data(np.float32, parameters["input_shape"])] + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_concatenation_tests(zip_path): - """Make a set of tests to do concatenatinon.""" + """Make a set of tests to do concatenation.""" test_parameters = [{ "base_shape": [[1, 3, 4, 3], [3, 4]], @@ -1568,6 +1660,19 @@ def make_strided_slice_tests(zip_path): "shrink_axis_mask": [None, 1, 8, 11, 15, -1], "constant_indices": [False, True], }, + # + { + "dtype": [tf.float32], + "index_type": [tf.int32], + "input_shape": [[12, 2, 2, 5]], + "begin": [[0]], + "end": [[1]], + "strides": [[1]], + "begin_mask": [0], + "end_mask": [0], + "shrink_axis_mask": [1], + "constant_indices": [True], + }, # 2-D { "dtype": [tf.float32, tf.int32, tf.int64], @@ -1583,7 +1688,7 @@ def make_strided_slice_tests(zip_path): }, # Negative strides { - "dtype": [tf.float32, tf.int32, tf.int64], + "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[2, 3]], "begin": [[0, -1]], @@ -1656,6 +1761,84 @@ def make_strided_slice_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_lstm_tests(zip_path): + """Make a set of tests to do basic Lstm cell.""" + + test_parameters = [ + { + "dtype": [tf.float32], + "num_batchs": [1], + "time_step_size": [1], + "input_vec_size": [3], + "num_cells": [4], + }, + ] + + def build_graph(parameters): + """Build a simple graph with BasicLSTMCell.""" + + num_batchs = parameters["num_batchs"] + time_step_size = parameters["time_step_size"] + input_vec_size = parameters["input_vec_size"] + num_cells = parameters["num_cells"] + inputs_after_split = [] + for i in xrange(time_step_size): + one_timestamp_input = tf.placeholder( + dtype=parameters["dtype"], + name="split_{}".format(i), + shape=[num_batchs, input_vec_size]) + inputs_after_split.append(one_timestamp_input) + # Currently lstm identifier has a few limitations: only supports + # forget_bias == 0, inner state activiation == tanh. + # TODO(zhixianyan): Add another test with forget_bias == 1. + # TODO(zhixianyan): Add another test with relu as activation. + lstm_cell = tf.contrib.rnn.BasicLSTMCell( + num_cells, forget_bias=0.0, state_is_tuple=True) + cell_outputs, _ = rnn.static_rnn( + lstm_cell, inputs_after_split, dtype=tf.float32) + out = cell_outputs[-1] + return inputs_after_split, [out] + + def build_inputs(parameters, sess, inputs, outputs): + """Feed inputs, assign vairables, and freeze graph.""" + + with tf.variable_scope("", reuse=True): + kernel = tf.get_variable("rnn/basic_lstm_cell/kernel") + bias = tf.get_variable("rnn/basic_lstm_cell/bias") + kernel_values = create_tensor_data( + parameters["dtype"], [kernel.shape[0], kernel.shape[1]], -1, 1) + bias_values = create_tensor_data(parameters["dtype"], [bias.shape[0]], 0, + 1) + sess.run(tf.group(kernel.assign(kernel_values), bias.assign(bias_values))) + + num_batchs = parameters["num_batchs"] + time_step_size = parameters["time_step_size"] + input_vec_size = parameters["input_vec_size"] + input_values = [] + for _ in xrange(time_step_size): + tensor_data = create_tensor_data(parameters["dtype"], + [num_batchs, input_vec_size], 0, 1) + input_values.append(tensor_data) + out = sess.run(outputs, feed_dict=dict(zip(inputs, input_values))) + return input_values, out + + # TODO(zhixianyan): Automatically generate rnn_states for lstm cell. + extra_toco_options = ExtraTocoOptions() + extra_toco_options.rnn_states = ( + "{state_array:rnn/BasicLSTMCellZeroState/zeros," + "back_edge_source_array:rnn/basic_lstm_cell/Add_1,size:4}," + "{state_array:rnn/BasicLSTMCellZeroState/zeros_1," + "back_edge_source_array:rnn/basic_lstm_cell/Mul_2,size:4}") + + make_zip_of_tests( + zip_path, + test_parameters, + build_graph, + build_inputs, + extra_toco_options, + use_frozen_graph=True) + + def make_l2_pool(input_tensor, ksize, strides, padding, data_format): """Given an input perform a sequence of TensorFlow ops to produce l2pool.""" return tf.sqrt(tf.nn.avg_pool( @@ -1663,6 +1846,32 @@ def make_l2_pool(input_tensor, ksize, strides, padding, data_format): padding=padding, data_format=data_format)) +def make_topk_tests(zip_path): + """Make a set of tests to do gather.""" + + test_parameters = [{ + "input_dtype": [tf.float32, tf.int32], + "input_shape": [[10], [5, 20]], + }] + + def build_graph(parameters): + """Build the gather op testing graph.""" + input_value = tf.placeholder( + dtype=parameters["input_dtype"], + name="input", + shape=parameters["input_shape"]) + k = tf.constant(3, name="k") + out = tf.nn.top_k(input_value, k) + return [input_value], [out[1]] + + def build_inputs(parameters, sess, inputs, outputs): + input_value = create_tensor_data(parameters["input_dtype"], + parameters["input_shape"]) + return [input_value], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value]))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + # Toco binary path provided by the generate rule. bin_path = None @@ -1711,10 +1920,15 @@ def main(unused_args): "sigmoid.zip": make_sigmoid_tests, "softmax.zip": make_softmax_tests, "space_to_depth.zip": make_space_to_depth_tests, + "topk.zip": make_topk_tests, + "split.zip": make_split_tests, "transpose.zip": make_transpose_tests, "mean.zip": make_mean_tests, "squeeze.zip": make_squeeze_tests, "strided_slice.zip": make_strided_slice_tests, + "exp.zip": make_exp_tests, + "log_softmax.zip": make_log_softmax_tests, + "lstm.zip": make_lstm_tests, } out = FLAGS.zip_to_output bin_path = FLAGS.toco diff --git a/tensorflow/contrib/lite/testing/generate_testspec.cc b/tensorflow/contrib/lite/testing/generate_testspec.cc new file mode 100644 index 0000000000000000000000000000000000000000..eb3deafb6986e877f0a553a8b6f712102af4caca --- /dev/null +++ b/tensorflow/contrib/lite/testing/generate_testspec.cc @@ -0,0 +1,88 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/testing/generate_testspec.h" +#include "tensorflow/contrib/lite/testing/join.h" +#include "tensorflow/contrib/lite/testing/split.h" +#include "tensorflow/contrib/lite/testing/tf_driver.h" +#include "tensorflow/core/framework/types.h" + +namespace tflite { +namespace testing { + +void GenerateTestSpecFromTensorflowModel( + std::iostream& stream, const string& tensorflow_model_path, + const string& tflite_model_path, const std::vector& input_layer, + const std::vector& input_layer_type, + const std::vector& input_layer_shape, + const std::vector& output_layer) { + CHECK_EQ(input_layer.size(), input_layer_type.size()); + CHECK_EQ(input_layer.size(), input_layer_shape.size()); + + // Initialize random functions. + static unsigned int seed = 0; + std::function float_rand = [](int idx) { + return static_cast(rand_r(&seed)) / RAND_MAX - 0.5f; + }; + + // Generate inputs. + std::vector input_values; + input_values.resize(input_layer.size()); + for (int i = 0; i < input_layer.size(); i++) { + tensorflow::DataType type; + CHECK(DataTypeFromString(input_layer_type[i], &type)); + auto shape = Split(input_layer_shape[i], ","); + + switch (type) { + case tensorflow::DT_FLOAT: { + const auto& data = GenerateRandomTensor(shape, float_rand); + input_values[i] = Join(data.data(), data.size(), ","); + break; + } + default: + + fprintf(stderr, "Unsupported type %d when generating testspec\n", type); + return; + } + } + + // Invoke tensorflow model. + TfDriver runner(input_layer, input_layer_type, input_layer_shape, + output_layer); + runner.LoadModel(tensorflow_model_path); + for (int i = 0; i < input_values.size(); i++) { + runner.SetInput(i, input_values[i]); + } + runner.Invoke(); + + // Write test spec. + stream << "load_model: " << tflite_model_path << "\n"; + stream << "reshape {\n"; + for (const auto& shape : input_layer_shape) { + stream << " input: \"" << shape << "\"\n"; + } + stream << "}\n"; + stream << "invoke {\n"; + for (const auto& value : input_values) { + stream << " input: \"" << value << "\"\n"; + } + for (int i = 0; i < output_layer.size(); i++) { + stream << " output: \"" << runner.ReadOutput(i) << "\"\n"; + } + stream << "}\n"; +} + +} // namespace testing +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/generate_testspec.h b/tensorflow/contrib/lite/testing/generate_testspec.h new file mode 100644 index 0000000000000000000000000000000000000000..3529ee709b66625fff6e2a35b78e47f3778f0fe7 --- /dev/null +++ b/tensorflow/contrib/lite/testing/generate_testspec.h @@ -0,0 +1,64 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_ +#define TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_ + +#include +#include +#include + +namespace tflite { +namespace testing { + +// Generate test spec by executing TensorFlow model on random inputs. +// The test spec can be consumed by ParseAndRunTests. +// See test spec format in parse_testdata.h +// +// Inputs: +// stream: mutable iostream that contains the contents of test spec. +// tensorflow_model_path: path to TensorFlow model. +// tflite_model_path: path to tflite_model_path that the test spec runs +// against. input_layer: names of input tensors. Example: input1 +// input_layer_type: datatypes of input tensors. Example: float +// input_layer_shape: shapes of input tensors, separated by comma. example: +// 1,3,4 output_layer: names of output tensors. Example: output +void GenerateTestSpecFromTensorflowModel( + std::iostream& stream, const string& tensorflow_model_path, + const string& tflite_model_path, const std::vector& input_layer, + const std::vector& input_layer_type, + const std::vector& input_layer_shape, + const std::vector& output_layer); + +// Generates random values that are filled into the tensor. +// random_func returns the generated random element at given index. +template +std::vector GenerateRandomTensor(const std::vector& shape, + const std::function& random_func) { + int64_t num_elements = 1; + for (const int dim : shape) { + num_elements *= dim; + } + + std::vector result(num_elements); + for (int i = 0; i < num_elements; i++) { + result[i] = random_func(i); + } + return result; +} + +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_ diff --git a/tensorflow/contrib/lite/testing/generate_testspec_test.cc b/tensorflow/contrib/lite/testing/generate_testspec_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..2a97b757a413246c9ad9b5f453741b13e381c903 --- /dev/null +++ b/tensorflow/contrib/lite/testing/generate_testspec_test.cc @@ -0,0 +1,54 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/testing/generate_testspec.h" + +#include +#include + +namespace tflite { +namespace testing { +namespace { + +TEST(GenerateRandomTensor, FloatValue) { + static unsigned int seed = 0; + std::function float_rand = [](int idx) { + return static_cast(rand_r(&seed)) / RAND_MAX - 0.5f; + }; + + std::set values; + float sum_x_square = 0.0f; + float sum_x = 0.0f; + for (int i = 0; i < 100; i++) { + const auto& data = GenerateRandomTensor({1, 3, 4}, float_rand); + for (float value : data) { + values.insert(value); + sum_x_square += value * value; + sum_x += value; + } + } + + // Eech round, generated tensor has different values. + EXPECT_GT(values.size(), 200); + int num = 1 * 3 * 4 * 100; + float stddev = sum_x_square / num - (sum_x / num) * (sum_x / num); + + // Stddev is greater than 1/2 stddev of uniform distribution: (B-A)^2 / 12 + float minstddev = 1.0f / 12 / 2; + EXPECT_GT(stddev, minstddev); +} + +} // namespace +} // namespace testing +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 5ea3e21f6a1636d1e7029bed8e75b2f68f656103..86606d12393b94567fbe1fceb6d708b266efe4a8 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -92,6 +92,9 @@ std::map kBrokenTests = { // Transpose only supports 1D-4D input tensors. {R"(^\/transpose.*input_shape=\[.,.,.,.,.\])", "71545879"}, + + // Lstm kernel gets different results on tsan, asan, msan. + {R"(^\/lstmdtype=tf.float32.*)", "73830845"}, }; // Allows test data to be unzipped into a temporary directory and makes @@ -242,6 +245,7 @@ INSTANTIATE_TESTS(constant) INSTANTIATE_TESTS(control_dep) INSTANTIATE_TESTS(conv) INSTANTIATE_TESTS(depthwiseconv) +INSTANTIATE_TESTS(exp) INSTANTIATE_TESTS(fully_connected) INSTANTIATE_TESTS(fused_batch_norm) INSTANTIATE_TESTS(gather) @@ -249,6 +253,7 @@ INSTANTIATE_TESTS(global_batch_norm) INSTANTIATE_TESTS(l2norm) INSTANTIATE_TESTS(l2_pool) INSTANTIATE_TESTS(local_response_norm) +INSTANTIATE_TESTS(log_softmax) INSTANTIATE_TESTS(max_pool) INSTANTIATE_TESTS(mul) INSTANTIATE_TESTS(pad) @@ -261,8 +266,10 @@ INSTANTIATE_TESTS(sigmoid) INSTANTIATE_TESTS(softmax) INSTANTIATE_TESTS(space_to_depth) INSTANTIATE_TESTS(sub) +INSTANTIATE_TESTS(split) INSTANTIATE_TESTS(div) INSTANTIATE_TESTS(transpose) +INSTANTIATE_TESTS(lstm) INSTANTIATE_TESTS(mean) INSTANTIATE_TESTS(squeeze) INSTANTIATE_TESTS(strided_slice) diff --git a/tensorflow/compiler/xla/array2d.cc b/tensorflow/contrib/lite/testing/join.h similarity index 51% rename from tensorflow/compiler/xla/array2d.cc rename to tensorflow/contrib/lite/testing/join.h index 418587c1f75c7249f92e925455d40685d870c57a..ce8c072a21c6e61e8ab8ae12ba52418e6144009a 100644 --- a/tensorflow/compiler/xla/array2d.cc +++ b/tensorflow/contrib/lite/testing/join.h @@ -12,25 +12,31 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_JOIN_H_ +#define TENSORFLOW_CONTRIB_LITE_TESTING_JOIN_H_ -#include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/ptr_util.h" - -namespace xla { - -std::unique_ptr> MakeLinspaceArray2D(float from, float to, - int64 n1, int64 n2) { - auto array = MakeUnique>(n1, n2); - int64 count = n1 * n2; - float step = (count > 1) ? (to - from) / (count - 1) : 0.0f; - auto set = [&array, n1, n2](int64 index, float value) { - (*array)(index / n2, index % n2) = value; - }; - for (int64 i = 0; i < count - 1; ++i) { - set(i, from + i * step); +#include +#include +#include + +namespace tflite { +namespace testing { + +// Join a list of data separated by delimieter. +template +string Join(T* data, size_t len, const string& delimiter) { + if (len == 0 || data == nullptr) { + return ""; } - set(count - 1, to); - return array; + std::stringstream result; + result << data[0]; + for (int i = 1; i < len; i++) { + result << delimiter << data[i]; + } + return result.str(); } -} // namespace xla +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TESTING_JOIN_H_ diff --git a/tensorflow/contrib/lite/testing/join_test.cc b/tensorflow/contrib/lite/testing/join_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..bd04528381f6d31164728a5cabbf8753e9b8d2b8 --- /dev/null +++ b/tensorflow/contrib/lite/testing/join_test.cc @@ -0,0 +1,43 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/testing/join.h" + +#include +#include + +namespace tflite { +namespace testing { +namespace { + +TEST(JoinTest, JoinInt) { + std::vector data = {1, 2, 3}; + EXPECT_EQ(Join(data.data(), data.size(), ","), "1,2,3"); +} + +TEST(JoinTest, JoinFloat) { + float data[] = {1.0, -3, 2.3, 1e-5}; + EXPECT_EQ(Join(data, 4, " "), "1 -3 2.3 1e-05"); +} + +TEST(JoinTest, JoinNullData) { EXPECT_THAT(Join(nullptr, 3, ","), ""); } + +TEST(JoinTest, JoinZeroData) { + std::vector data; + EXPECT_THAT(Join(data.data(), 0, ","), ""); +} + +} // namespace +} // namespace testing +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/parse_testdata.cc b/tensorflow/contrib/lite/testing/parse_testdata.cc index 0caef0fe2201a668b2235a98304eb353072a3c2f..389688d552051ea735ce71533943af33df5059ef 100644 --- a/tensorflow/contrib/lite/testing/parse_testdata.cc +++ b/tensorflow/contrib/lite/testing/parse_testdata.cc @@ -192,27 +192,25 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, int model_outputs = interpreter->outputs().size(); TF_LITE_ENSURE_EQ(context, model_outputs, example.outputs.size()); for (size_t i = 0; i < interpreter->outputs().size(); i++) { + bool tensors_differ = false; int output_index = interpreter->outputs()[i]; if (const float* data = interpreter->typed_tensor(output_index)) { for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) { float computed = data[idx]; float reference = example.outputs[0].flat_data[idx]; float diff = std::abs(computed - reference); - bool error_is_large = false; // For very small numbers, try absolute error, otherwise go with // relative. - if (std::abs(reference) < kRelativeThreshold) { - error_is_large = (diff > kAbsoluteThreshold); - } else { - error_is_large = (diff > kRelativeThreshold * std::abs(reference)); - } - if (error_is_large) { + bool local_tensors_differ = + std::abs(reference) < kRelativeThreshold + ? diff > kAbsoluteThreshold + : diff > kRelativeThreshold * std::abs(reference); + if (local_tensors_differ) { fprintf(stdout, "output[%zu][%zu] did not match %f vs reference %f\n", i, idx, data[idx], reference); - return kTfLiteError; + tensors_differ = local_tensors_differ; } } - fprintf(stderr, "\n"); } else if (const int32_t* data = interpreter->typed_tensor(output_index)) { for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) { @@ -221,10 +219,9 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, if (std::abs(computed - reference) > 0) { fprintf(stderr, "output[%zu][%zu] did not match %d vs reference %d\n", i, idx, computed, reference); - return kTfLiteError; + tensors_differ = true; } } - fprintf(stderr, "\n"); } else if (const int64_t* data = interpreter->typed_tensor(output_index)) { for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) { @@ -235,14 +232,15 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, "output[%zu][%zu] did not match %" PRId64 " vs reference %" PRId64 "\n", i, idx, computed, reference); - return kTfLiteError; + tensors_differ = true; } } - fprintf(stderr, "\n"); } else { fprintf(stderr, "output[%zu] was not float or int data\n", i); return kTfLiteError; } + fprintf(stderr, "\n"); + if (tensors_differ) return kTfLiteError; } return kTfLiteOk; } @@ -319,8 +317,9 @@ class Reshape : public Message { // This is the top-level message in a test file. class TestData : public Message { public: - explicit TestData(TestRunner* test_runner) : test_runner_(test_runner) {} - + explicit TestData(TestRunner* test_runner) + : test_runner_(test_runner), num_invocations_(0), max_invocations_(-1) {} + void SetMaxInvocations(int max) { max_invocations_ = max; } void SetField(const std::string& name, const std::string& value) override { if (name == "load_model") { test_runner_->LoadModel(value); @@ -334,7 +333,12 @@ class TestData : public Message { Message* AddChild(const std::string& s) override { if (s == "invoke") { test_runner_->AllocateTensors(); - return Store(new Invoke(test_runner_)); + if (max_invocations_ == -1 || num_invocations_ < max_invocations_) { + ++num_invocations_; + return Store(new Invoke(test_runner_)); + } else { + return nullptr; + } } else if (s == "reshape") { return Store(new Reshape(test_runner_)); } @@ -343,10 +347,14 @@ class TestData : public Message { private: TestRunner* test_runner_; + int num_invocations_; + int max_invocations_; }; -bool ParseAndRunTests(std::istream* input, TestRunner* test_runner) { +bool ParseAndRunTests(std::istream* input, TestRunner* test_runner, + int max_invocations) { TestData test_data(test_runner); + test_data.SetMaxInvocations(max_invocations); Message::Read(input, &test_data); return test_runner->IsValid() && test_runner->GetOverallSuccess(); } diff --git a/tensorflow/contrib/lite/testing/parse_testdata.h b/tensorflow/contrib/lite/testing/parse_testdata.h index 7ebf362eb99c5f4cf6ea3654cf71e13ff1de99b3..d94361d735e2be8dc130dc8d6bf0bb5c822ebb7c 100644 --- a/tensorflow/contrib/lite/testing/parse_testdata.h +++ b/tensorflow/contrib/lite/testing/parse_testdata.h @@ -66,7 +66,8 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, const Example&); // output: "12,3,4,545,3" // output: "0.01,0.02" // } -bool ParseAndRunTests(std::istream* input, TestRunner* test_runner); +bool ParseAndRunTests(std::istream* input, TestRunner* test_runner, + int max_invocations = -1); } // namespace testing } // namespace tflite diff --git a/tensorflow/contrib/lite/testing/test_runner.h b/tensorflow/contrib/lite/testing/test_runner.h index 60eaafa474a01887bee12b031b1f59cc5c91f173..05770beee23275ebe210606dbfd2b33eea17612d 100644 --- a/tensorflow/contrib/lite/testing/test_runner.h +++ b/tensorflow/contrib/lite/testing/test_runner.h @@ -68,6 +68,10 @@ class TestRunner { // satisfied. virtual bool CheckResults() = 0; + // Read contents of tensor into csv format. + // The given 'id' is guaranteed to be one of the ids returned by GetOutputs(). + virtual string ReadOutput(int id) = 0; + // Set the base path for loading models. void SetModelBaseDir(const string& path) { model_base_dir_ = path; diff --git a/tensorflow/contrib/lite/testing/test_runner_test.cc b/tensorflow/contrib/lite/testing/test_runner_test.cc index f712a5347a042990ae5adb9d44325dd683193168..3f04aa20bd7de813f0acd3f5897d5ab2df6c0fd7 100644 --- a/tensorflow/contrib/lite/testing/test_runner_test.cc +++ b/tensorflow/contrib/lite/testing/test_runner_test.cc @@ -31,6 +31,7 @@ class ConcreteTestRunner : public TestRunner { void ResetTensor(int id) override {} void SetInput(int id, const string& csv_values) override {} void SetExpectation(int id, const string& csv_values) override {} + string ReadOutput(int id) override { return ""; } void Invoke() override {} bool CheckResults() override { return true; } bool CheckFloatSizes(size_t bytes, size_t values) { diff --git a/tensorflow/contrib/lite/testing/tf_driver.cc b/tensorflow/contrib/lite/testing/tf_driver.cc new file mode 100644 index 0000000000000000000000000000000000000000..2c253bb1983e5ddc5bc12858c929585d1bcee710 --- /dev/null +++ b/tensorflow/contrib/lite/testing/tf_driver.cc @@ -0,0 +1,182 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/testing/tf_driver.h" + +#include +#include + +#include "tensorflow/contrib/lite/testing/join.h" +#include "tensorflow/contrib/lite/testing/split.h" +#include "tensorflow/core/lib/gtl/array_slice.h" + +namespace tflite { +namespace testing { + +namespace { + +tensorflow::Tensor CreateTensor(const tensorflow::DataType type, + const std::vector& dim) { + tensorflow::TensorShape shape{gtl::ArraySlice{ + reinterpret_cast(dim.data()), dim.size()}}; + return {type, shape}; +} + +template +void FillTensorWithData(tensorflow::Tensor* tensor, const string& csv_values) { + auto data = tensor->flat(); + + const auto& values = testing::Split(csv_values, ","); + for (int i = 0; i < values.size(); i++) { + data(i) = values[i]; + } +} + +template +void FillTensorWithZeros(tensorflow::Tensor* tensor) { + auto data = tensor->flat(); + for (int i = 0; i < tensor->NumElements(); i++) { + data(i) = 0; + } +} + +template +string TensorDataToCsvString(const tensorflow::Tensor& tensor) { + const auto& data = tensor.flat(); + return Join(data.data(), data.size(), ","); +} + +} // namespace + +TfDriver::TfDriver(const std::vector& input_layer, + const std::vector& input_layer_type, + const std::vector& input_layer_shape, + const std::vector& output_layer) + : input_names_(input_layer), output_names_(output_layer) { + CHECK_EQ(input_layer.size(), input_layer_type.size()); + CHECK_EQ(input_layer.size(), input_layer_shape.size()); + + input_ids_.resize(input_layer.size()); + input_tensors_.reserve(input_layer.size()); + input_types_.resize(input_layer.size()); + input_shapes_.resize(input_layer.size()); + for (int i = 0; i < input_layer.size(); i++) { + input_ids_[i] = i; + input_tensors_[input_layer[i]] = {}; + CHECK(DataTypeFromString(input_layer_type[i], &input_types_[i])); + input_shapes_[i] = Split(input_layer_shape[i], ","); + } + + output_ids_.resize(output_layer.size()); + output_tensors_.reserve(output_layer.size()); + for (int i = 0; i < output_layer.size(); i++) { + output_ids_[i] = i; + } +} + +void TfDriver::LoadModel(const string& bin_file_path) { + if (!IsValid()) return; + std::cout << std::endl << "Loading model: " << bin_file_path << std::endl; + std::ifstream model(bin_file_path); + if (model.fail()) { + Invalidate("Failed to find the model"); + return; + } + + tensorflow::GraphDef graphdef; + if (!graphdef.ParseFromIstream(&model)) { + Invalidate("Failed to parse tensorflow graphdef"); + return; + } + + tensorflow::SessionOptions options; + session_.reset(tensorflow::NewSession(options)); + auto status = session_->Create(graphdef); + if (!status.ok()) { + Invalidate("Failed to create session"); + } +} + +void TfDriver::SetInput(int id, const string& csv_values) { + if (!IsValid()) return; + + auto tensor = CreateTensor(input_types_[id], input_shapes_[id]); + switch (input_types_[id]) { + case tensorflow::DT_FLOAT: { + FillTensorWithData(&tensor, csv_values); + break; + } + case tensorflow::DT_INT32: { + FillTensorWithData(&tensor, csv_values); + break; + } + default: + fprintf(stderr, "Unsupported type %d in SetInput\n", input_types_[id]); + Invalidate("Unsupported tensor data type"); + return; + } + input_tensors_[input_names_[id]] = tensor; +} + +void TfDriver::ResetTensor(int id) { + if (!IsValid()) return; + auto tensor = input_tensors_[input_names_[id]]; + switch (input_types_[id]) { + case tensorflow::DT_FLOAT: { + FillTensorWithZeros(&tensor); + break; + } + case tensorflow::DT_INT32: { + FillTensorWithZeros(&tensor); + break; + } + default: + fprintf(stderr, "Unsupported type %d in ResetTensor\n", input_types_[id]); + Invalidate("Unsupported tensor data type"); + return; + } +} + +void TfDriver::ReshapeTensor(int id, const string& csv_values) { + input_shapes_[id] = Split(csv_values, ","); + input_tensors_[input_names_[id]] = + CreateTensor(input_types_[id], input_shapes_[id]); + ResetTensor(id); +} + +string TfDriver::ReadOutput(int id) { + if (!IsValid()) return ""; + switch (output_tensors_[id].dtype()) { + case tensorflow::DT_FLOAT: + return TensorDataToCsvString(output_tensors_[id]); + case tensorflow::DT_INT32: + return TensorDataToCsvString(output_tensors_[id]); + default: + fprintf(stderr, "Unsupported type %d in ResetTensor\n", input_types_[id]); + Invalidate("Unsupported tensor data type"); + return ""; + } +} + +void TfDriver::Invoke() { + if (!IsValid()) return; + auto status = session_->Run({input_tensors_.begin(), input_tensors_.end()}, + output_names_, {}, &output_tensors_); + if (!status.ok()) { + Invalidate("Failed to invoke interpreter"); + } +} + +} // namespace testing +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/tf_driver.h b/tensorflow/contrib/lite/testing/tf_driver.h new file mode 100644 index 0000000000000000000000000000000000000000..b766f85c4ddee9fb7b1513c264d4159e694770ca --- /dev/null +++ b/tensorflow/contrib/lite/testing/tf_driver.h @@ -0,0 +1,75 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TF_DRIVER_H_ +#define TENSORFLOW_CONTRIB_LITE_TESTING_TF_DRIVER_H_ + +#include +#include + +#include "tensorflow/contrib/lite/testing/split.h" +#include "tensorflow/contrib/lite/testing/test_runner.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/public/session.h" + +namespace tflite { +namespace testing { + +// A test runner that feeds inputs into Tensorflow and generates outputs. +class TfDriver : public TestRunner { + public: + explicit TfDriver(const std::vector& input_layer, + const std::vector& input_layer_type, + const std::vector& input_layer_shape, + const std::vector& output_layer); + ~TfDriver() override {} + + void LoadModel(const string& bin_file_path) override; + void SetInput(int id, const string& csv_values) override; + void Invoke() override; + string ReadOutput(int id) override; + + const std::vector& GetInputs() override { return input_ids_; } + const std::vector& GetOutputs() override { return output_ids_; } + void ReshapeTensor(int id, const string& csv_values) override; + // Note: ResetTensor only works for input tensor. + void ResetTensor(int id) override; + + // no-op. SetInput will overwrite existing data . + void AllocateTensors() override {} + // no-op. Tf driver is not supposed to check the results. + void SetExpectation(int id, const string& csv_values) override {} + // tf driver is not supposed to check the results. + bool CheckResults() override { return false; } + + private: + std::unique_ptr session_; + std::vector input_ids_; + std::vector input_names_; + std::vector> input_shapes_; + std::vector input_types_; + std::unordered_map input_tensors_; + + std::vector output_ids_; + std::vector output_names_; + std::vector<::tensorflow::Tensor> output_tensors_; +}; + +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TESTING_TF_DRIVER_H_ diff --git a/tensorflow/contrib/lite/testing/tf_driver_test.cc b/tensorflow/contrib/lite/testing/tf_driver_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..c0faa4676adc3e846ad398bb203b77b99a2ba360 --- /dev/null +++ b/tensorflow/contrib/lite/testing/tf_driver_test.cc @@ -0,0 +1,56 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/testing/tf_driver.h" + +#include +#include + +namespace tflite { +namespace testing { +namespace { + +using ::testing::ElementsAre; + +TEST(TfDriverTest, SimpleTest) { + std::unique_ptr runner( + new TfDriver({"a", "b", "c", "d"}, {"float", "float", "float", "float"}, + {"1,8,8,3", "1,8,8,3", "1,8,8,3", "1,8,8,3"}, {"x", "y"})); + + runner->LoadModel( + "third_party/tensorflow/contrib/lite/testdata/multi_add.pb"); + EXPECT_TRUE(runner->IsValid()) << runner->GetErrorMessage(); + + ASSERT_THAT(runner->GetInputs(), ElementsAre(0, 1, 2, 3)); + ASSERT_THAT(runner->GetOutputs(), ElementsAre(0, 1)); + + for (int i : {0, 1, 2, 3}) { + runner->ReshapeTensor(i, "1,2,2,1"); + } + ASSERT_TRUE(runner->IsValid()); + + runner->SetInput(0, "0.1,0.2,0.3,0.4"); + runner->SetInput(1, "0.001,0.002,0.003,0.004"); + runner->SetInput(2, "0.001,0.002,0.003,0.004"); + runner->SetInput(3, "0.01,0.02,0.03,0.04"); + runner->ResetTensor(2); + runner->Invoke(); + + ASSERT_EQ(runner->ReadOutput(0), "0.101,0.202,0.303,0.404"); + ASSERT_EQ(runner->ReadOutput(1), "0.011,0.022,0.033,0.044"); +} + +} // namespace +} // namespace testing +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..3817e68111dbaaf2a38ceff9fbc38f30f303cb5f --- /dev/null +++ b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc @@ -0,0 +1,28 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/testing/tflite_diff_flags.h" +#include "tensorflow/contrib/lite/testing/tflite_diff_util.h" + +int main(int argc, char** argv) { + ::tflite::testing::DiffOptions options = + ::tflite::testing::ParseTfliteDiffFlags(&argc, argv); + for (int i = 0; i < 100; i++) { + if (!tflite::testing::RunDiffTest(options)) { + return 1; + } + } + return 0; +} diff --git a/tensorflow/contrib/lite/testing/tflite_diff_flags.h b/tensorflow/contrib/lite/testing/tflite_diff_flags.h new file mode 100644 index 0000000000000000000000000000000000000000..5f1129d501b7235f1202b704cf36904e07b8720e --- /dev/null +++ b/tensorflow/contrib/lite/testing/tflite_diff_flags.h @@ -0,0 +1,70 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ +#define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ + +#include "tensorflow/contrib/lite/testing/split.h" +#include "tensorflow/contrib/lite/testing/tflite_diff_util.h" +#include "tensorflow/core/util/command_line_flags.h" + +namespace tflite { +namespace testing { + +DiffOptions ParseTfliteDiffFlags(int* argc, char** argv) { + struct { + string tensorflow_model; + string tflite_model; + string input_layer; + string input_layer_type; + string input_layer_shape; + string output_layer; + } values; + + std::vector flags = { + tensorflow::Flag("tensorflow_model", &values.tensorflow_model, + "Path of tensorflow model."), + tensorflow::Flag("tflite_model", &values.tflite_model, + "Path of tensorflow lite model."), + tensorflow::Flag("input_layer", &values.input_layer, + "Names of input tensors, separated by comma. Example: " + "input_1,input_2"), + tensorflow::Flag("input_layer_type", &values.input_layer_type, + "Data types of input tensors, separated by comma. " + "Example: float,int"), + tensorflow::Flag( + "input_layer_shape", &values.input_layer_shape, + "Shapes of input tensors, separated by colon. Example: 1,3,4,1:2"), + tensorflow::Flag("output_layer", &values.output_layer, + "Names of output tensors, separated by comma. Example " + "output_1,output_2"), + }; + + bool success = tensorflow::Flags::Parse(argc, argv, flags); + if (!success || (*argc == 2 && !strcmp(argv[1], "--helpfull"))) { + fprintf(stderr, "%s", tensorflow::Flags::Usage(argv[0], flags).c_str()); + } + + return {values.tensorflow_model, + values.tflite_model, + Split(values.input_layer, ","), + Split(values.input_layer_type, ","), + Split(values.input_layer_shape, ":"), + Split(values.output_layer, ",")}; +} + +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.cc b/tensorflow/contrib/lite/testing/tflite_diff_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..9ef4e1f66c7d31c746c18d63495e760585d4af9e --- /dev/null +++ b/tensorflow/contrib/lite/testing/tflite_diff_util.cc @@ -0,0 +1,41 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/testing/generate_testspec.h" +#include "tensorflow/contrib/lite/testing/parse_testdata.h" +#include "tensorflow/contrib/lite/testing/tflite_diff_util.h" +#include "tensorflow/contrib/lite/testing/tflite_driver.h" + +namespace tflite { +namespace testing { + +bool RunDiffTest(const DiffOptions& options) { + std::stringstream tflite_stream; + GenerateTestSpecFromTensorflowModel( + tflite_stream, options.tensorflow_model, options.tflite_model, + options.input_layer, options.input_layer_type, options.input_layer_shape, + options.output_layer); + TfLiteDriver tflite_driver(/*use_nnapi=*/true); + tflite_driver.LoadModel(options.tflite_model); + std::cout << tflite_stream.str(); + return tflite::testing::ParseAndRunTests(&tflite_stream, &tflite_driver); +} +} // namespace testing + +} // namespace tflite diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.h b/tensorflow/contrib/lite/testing/tflite_diff_util.h new file mode 100644 index 0000000000000000000000000000000000000000..326fa6c3e28000dee9b6eb9cc5b3a6c5c87e28d0 --- /dev/null +++ b/tensorflow/contrib/lite/testing/tflite_diff_util.h @@ -0,0 +1,51 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_ +#define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_ + +#include + +#include "tensorflow/contrib/lite/string.h" + +namespace tflite { +namespace testing { + +// Configurations to run Tflite diff test. +struct DiffOptions { + // Path of tensorflow model. + string tensorflow_model; + // Path of tensorflow lite model. + string tflite_model; + // Names of input tensors. + // Example: input_1,input_2 + std::vector input_layer; + // Data types of input tensors. + // Example: float,int + std::vector input_layer_type; + // Shapes of input tensors, separated by comma. + // Example: 1,3,4,1 + std::vector input_layer_shape; + // Names of output tensors. + // Example output_1,output_2 + std::vector output_layer; +}; + +// Run a single TensorFLow Lite diff test with a given options. +bool RunDiffTest(const DiffOptions& options); + +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_ diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc index bae639ea95318a16c963269de5e55afcb681d4c5..613223f3d4ff212cb8672494243b2d7a1d06b3db 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.cc +++ b/tensorflow/contrib/lite/testing/tflite_driver.cc @@ -106,8 +106,8 @@ class TfLiteDriver::Expectation { if (error_is_large) { good_output = false; if (verbose) { - std::cerr << " index " << i << ": " << reference - << " != " << computed << std::endl; + std::cerr << " index " << i << ": got " << computed + << ", but expected " << reference << std::endl; } } } @@ -203,6 +203,10 @@ void TfLiteDriver::SetInput(int id, const string& csv_values) { void TfLiteDriver::SetExpectation(int id, const string& csv_values) { if (!IsValid()) return; auto* tensor = interpreter_->tensor(id); + if (expected_output_.count(id) != 0) { + fprintf(stderr, "Overriden expectation for tensor %d\n", id); + Invalidate("Overriden expectation"); + } expected_output_[id].reset(new Expectation); switch (tensor->type) { case kTfLiteFloat32: diff --git a/tensorflow/contrib/lite/testing/tflite_driver.h b/tensorflow/contrib/lite/testing/tflite_driver.h index 25689a9fb42c06fa3f8f2f92064cf59e8c331637..02b7de1534e648734d7bc53154afa42f2ef256b4 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.h +++ b/tensorflow/contrib/lite/testing/tflite_driver.h @@ -45,6 +45,7 @@ class TfLiteDriver : public TestRunner { void SetExpectation(int id, const string& csv_values) override; void Invoke() override; bool CheckResults() override; + string ReadOutput(int id) override { return "no-op"; } private: class Expectation; diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 20c156a93262568cf0c6c349b44fbf3d3afa5bc4..031db2bd7cd793c2043bc9ce412921ade44c704b 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -174,6 +174,7 @@ cc_library( "graph_transformations/convert_pure_conv_to_depthwise.cc", "graph_transformations/convert_reorder_axes.cc", "graph_transformations/convert_trivial_addn_to_add.cc", + "graph_transformations/convert_trivial_stack_to_reshape.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", "graph_transformations/create_im2col_arrays.cc", "graph_transformations/dequantize.cc", @@ -185,10 +186,14 @@ cc_library( "graph_transformations/fuse_binary_into_preceding_affine.cc", "graph_transformations/graph_transformations.cc", "graph_transformations/hardcode_min_max.cc", + "graph_transformations/identify_dilated_conv.cc", "graph_transformations/identify_l2_normalization.cc", "graph_transformations/identify_l2_pool.cc", "graph_transformations/identify_lstm.cc", + "graph_transformations/identify_lstm_merge_inputs.cc", + "graph_transformations/identify_lstm_split_inputs.cc", "graph_transformations/identify_relu1.cc", + "graph_transformations/lstm_utils.cc", "graph_transformations/make_initial_dequantize_operator.cc", "graph_transformations/propagate_array_data_types.cc", "graph_transformations/propagate_fixed_sizes.cc", @@ -204,6 +209,7 @@ cc_library( "graph_transformations/remove_trivial_passthrough.h", "graph_transformations/remove_trivial_quantized_activation_func.cc", "graph_transformations/remove_trivial_reshape.cc", + "graph_transformations/remove_trivial_slice.cc", "graph_transformations/remove_unused_op.cc", "graph_transformations/reorder_activation_functions.cc", "graph_transformations/resolve_batch_normalization.cc", @@ -216,8 +222,10 @@ cc_library( "graph_transformations/resolve_constant_shape_or_rank.cc", "graph_transformations/resolve_constant_stack.cc", "graph_transformations/resolve_constant_strided_slice.cc", + "graph_transformations/resolve_constant_transpose.cc", "graph_transformations/resolve_constant_unary.cc", "graph_transformations/resolve_mean_attributes.cc", + "graph_transformations/resolve_multiply_by_zero.cc", "graph_transformations/resolve_pad_attributes.cc", "graph_transformations/resolve_reorder_axes.cc", "graph_transformations/resolve_reshape_attributes.cc", @@ -232,9 +240,12 @@ cc_library( "graph_transformations/resolve_tensorflow_tile.cc", "graph_transformations/resolve_transpose_attributes.cc", "graph_transformations/unfuse_activation_functions.cc", + "graph_transformations/unpartition_embedding_lookup.cc", + "graph_transformations/unroll_batch_matmul.cc", ], hdrs = [ "graph_transformations/graph_transformations.h", + "graph_transformations/lstm_utils.h", ], visibility = ["//visibility:public"], deps = [ @@ -245,6 +256,7 @@ cc_library( ":tooling_util", ":types_proto_cc", "//tensorflow/core:lib", + "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", ], ) @@ -317,6 +329,7 @@ cc_library( ":toco_graphviz_dump_options", ":toco_port", ":types_proto_cc", + "//tensorflow/contrib/lite/kernels/internal:quantization_util", "//tensorflow/core:lib", "@com_google_absl//absl/strings", "@protobuf_archive//:protobuf_headers", diff --git a/tensorflow/contrib/lite/toco/args.h b/tensorflow/contrib/lite/toco/args.h index b97a4720a7c4e69f8b69574475d19e0522cfe86d..59a6115920614d38900c0370708324c122384420 100644 --- a/tensorflow/contrib/lite/toco/args.h +++ b/tensorflow/contrib/lite/toco/args.h @@ -229,6 +229,7 @@ struct ParsedTocoFlags { // Deprecated flags Arg input_type; Arg input_types; + Arg debug_disable_recurrent_cell_fusion = Arg(false); Arg drop_control_dependency = Arg(false); }; diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index c726eb6d8678e2703f5acba8b3d8d740186939f5..c8352741b44cd627ff9edb9c4677b994c4cb9a09 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -142,14 +142,8 @@ NodeProperties GetPropertiesForArray(const Model& model, // Append array shape to the label. auto& array = model.GetArray(array_name); - - if (array.data_type == ArrayDataType::kFloat) { - AppendF(&node_properties.label, "\\nType: float"); - } else if (array.data_type == ArrayDataType::kInt32) { - AppendF(&node_properties.label, "\\nType: int32"); - } else if (array.data_type == ArrayDataType::kUint8) { - AppendF(&node_properties.label, "\\nType: uint8"); - } + AppendF(&node_properties.label, "\\nType: %s", + ArrayDataTypeName(array.data_type)); if (array.has_shape()) { auto& array_shape = array.shape(); @@ -199,12 +193,12 @@ NodeProperties GetPropertiesForArray(const Model& model, } if (array.minmax) { - AppendF(&node_properties.label, "\\nMinMax: [%.3g, %.3g]", + AppendF(&node_properties.label, "\\nMinMax: [%.7g, %.7g]", array.minmax->min, array.minmax->max); } if (array.quantization_params) { - AppendF(&node_properties.label, "\\nQuantization: %.3g * (x - %d)", + AppendF(&node_properties.label, "\\nQuantization: %7g * (x - %d)", array.quantization_params->scale, array.quantization_params->zero_point); } diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index be6d506bf3d17ce42bf97da222670fb06680fa13..695def7ba347ba6e93d26674bf693d25ee80ec43 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -46,6 +46,32 @@ using tensorflow::TensorProto; namespace toco { namespace { +tensorflow::DataType GetTensorFlowDataType(ArrayDataType data_type) { + switch (data_type) { + case ArrayDataType::kBool: + return tensorflow::DT_BOOL; + case ArrayDataType::kFloat: + return tensorflow::DT_FLOAT; + case ArrayDataType::kUint8: + return tensorflow::DT_UINT8; + case ArrayDataType::kInt32: + return tensorflow::DT_INT32; + case ArrayDataType::kInt64: + return tensorflow::DT_INT64; + case ArrayDataType::kString: + return tensorflow::DT_STRING; + default: + case ArrayDataType::kNone: + LOG(FATAL) << "Unsupported data type: " << static_cast(data_type); + return tensorflow::DT_INVALID; + } +} + +tensorflow::DataType GetTensorFlowDataType(const Model& model, + const string& array_name) { + return GetTensorFlowDataType(model.GetArray(array_name).data_type); +} + // TensorFlow sometimes forbids what it calls "legacy scalars", // which are 1-D shapes where the unique shape size is 1. // See OpKernel::IsLegacyScalar and OpKernel::allow_legacy_scalars. @@ -212,6 +238,30 @@ void ConvertIntTensorConst(const Model& model, const string& name, } } +void CreateIntTensorConst(const string& name, const std::vector& data, + const std::vector& shape, + GraphDef* tensorflow_graph) { + if (HasAlreadyExportedConst(name, *tensorflow_graph)) { + return; + } + auto* const_op = tensorflow_graph->add_node(); + const_op->set_op("Const"); + const_op->set_name(name); + (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); + auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor(); + tensor->set_dtype(DT_INT32); + for (auto index : data) { + tensor->add_int_val(index); + } + auto* tensor_shape = tensor->mutable_tensor_shape(); + int num_elements = 1; + for (int size : shape) { + tensor_shape->add_dim()->set_size(size); + num_elements *= size; + } + CHECK_EQ(num_elements, data.size()); +} + void CreateMatrixShapeTensorConst(const string& name, int rows, int cols, GraphDef* tensorflow_graph) { if (HasAlreadyExportedConst(name, *tensorflow_graph)) { @@ -341,6 +391,84 @@ void ConvertConvOperator(const Model& model, const ConvOperator& src_op, } } +void ConvertDilatedConvOperator(const Model& model, const ConvOperator& src_op, + GraphDef* tensorflow_graph) { + CHECK((src_op.dilation_width_factor > 1) || + (src_op.dilation_height_factor > 1)) + << "Conv operator must have height or width dilation factor > 1. " + "Otherwise, use regular conv op."; + CHECK_EQ(src_op.stride_width, 1) + << "Dilated AND strided convolution is unsupported"; + CHECK_EQ(src_op.stride_height, 1) + << "Dilated AND strided convolution is unsupported"; + + // Emulate dilated convolution with a chain of SpaceToBatchND -> Conv -> + // BatchToSpaceND ops. + + // Compute padding + const auto& input_array = model.GetArray(src_op.inputs[0]); + const auto& input_shape = input_array.shape(); + CHECK_EQ(input_shape.dimensions_count(), 4); + int height_mod_dilation = input_shape.dims(1) % src_op.dilation_height_factor; + int pad_height; + if (height_mod_dilation) { + pad_height = src_op.dilation_height_factor - height_mod_dilation; + } else { + pad_height = 0; + } + int pad_width; + int width_mod_dilation = input_shape.dims(2) % src_op.dilation_width_factor; + if (width_mod_dilation) { + pad_width = src_op.dilation_width_factor - width_mod_dilation; + } else { + pad_width = 0; + } + + // SpaceToBatchND op "collapses" the spatially separated elements together + string stb_output = src_op.outputs[0] + "/dilated_conv_SpaceToBatch"; + auto* stb_op = tensorflow_graph->add_node(); + stb_op->set_op("SpaceToBatchND"); + stb_op->set_name(stb_output); + *stb_op->add_input() = src_op.inputs[0]; + (*stb_op->mutable_attr())["T"].set_type(DT_FLOAT); + string block_shape = src_op.outputs[0] + "/dilated_conv_block_shape"; + CreateIntTensorConst( + block_shape, + {src_op.dilation_height_factor, src_op.dilation_width_factor}, {2}, + tensorflow_graph); + *stb_op->add_input() = block_shape; + (*stb_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32); + string stb_paddings = src_op.outputs[0] + "/dilated_conv_paddings"; + CreateIntTensorConst(stb_paddings, {0, pad_height, pad_width, 0}, {2, 2}, + tensorflow_graph); + *stb_op->add_input() = stb_paddings; + (*stb_op->mutable_attr())["Tpaddings"].set_type(DT_INT32); + + // Perform a regular conv on the "collapsed" elements + ConvOperator conv_op; + string conv_output = src_op.outputs[0] + "/dilated_conv_Conv2D"; + conv_op.inputs = src_op.inputs; + conv_op.inputs[0] = stb_output; + conv_op.outputs = {conv_output}; + conv_op.padding.type = src_op.padding.type; + conv_op.stride_width = src_op.stride_width; + conv_op.stride_height = src_op.stride_height; + conv_op.dilation_width_factor = 1; + conv_op.dilation_height_factor = 1; + ConvertConvOperator(model, conv_op, tensorflow_graph); + + // BatchToSpaceND op restores elements to their original layout + auto* bts_op = tensorflow_graph->add_node(); + bts_op->set_op("BatchToSpaceND"); + bts_op->set_name(src_op.outputs[0]); + *bts_op->add_input() = conv_output; + (*bts_op->mutable_attr())["T"].set_type(DT_FLOAT); + *bts_op->add_input() = block_shape; + (*bts_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32); + *bts_op->add_input() = stb_paddings; + (*bts_op->mutable_attr())["Tcrops"].set_type(DT_INT32); +} + void ConvertDepthwiseConvOperator(const Model& model, const DepthwiseConvOperator& src_op, GraphDef* tensorflow_graph) { @@ -420,6 +548,38 @@ void ConvertDepthwiseConvOperator(const Model& model, } } +void ConvertTransposeConvOperator(const Model& model, + const TransposeConvOperator& src_op, + GraphDef* tensorflow_graph) { + auto* conv2d_op = tensorflow_graph->add_node(); + conv2d_op->set_op("Conv2DBackpropInput"); + conv2d_op->set_name(src_op.outputs[0]); + *conv2d_op->add_input() = src_op.inputs[0]; + *conv2d_op->add_input() = src_op.inputs[1]; + *conv2d_op->add_input() = src_op.inputs[2]; + (*conv2d_op->mutable_attr())["T"].set_type(DT_FLOAT); + const string& weights_array_name = WalkUpToConstantArray( + model, src_op.inputs[TransposeConvOperator::WEIGHTS]); + const auto& weights_array = model.GetArray(weights_array_name); + CHECK(weights_array.buffer->type == ArrayDataType::kFloat); + ConvertFloatTensorConst(model, weights_array_name, AxesOrder::kOHWI, + AxesOrder::kHWIO, tensorflow_graph); + auto& strides = (*conv2d_op->mutable_attr())["strides"]; + strides.mutable_list()->add_i(1); + strides.mutable_list()->add_i(src_op.stride_height); + strides.mutable_list()->add_i(src_op.stride_width); + strides.mutable_list()->add_i(1); + string padding; + if (src_op.padding.type == PaddingType::kSame) { + padding = "SAME"; + } else if (src_op.padding.type == PaddingType::kValid) { + padding = "VALID"; + } else { + LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; + } + (*conv2d_op->mutable_attr())["padding"].set_s(padding); +} + void ConvertDepthToSpaceOperator(const Model& model, const DepthToSpaceOperator& src_op, GraphDef* tensorflow_graph) { @@ -445,14 +605,23 @@ void ConvertSpaceToDepthOperator(const Model& model, void ConvertFullyConnectedOperator(const Model& model, const FullyConnectedOperator& src_op, GraphDef* tensorflow_graph) { - const string reshape_output = src_op.outputs[0] + "/reshape"; - const string reshape_shape = src_op.outputs[0] + "/reshape/shape"; + // Reshape input activations to have the shape expected by the MatMul. + const string reshape_output = + AvailableArrayName(model, src_op.outputs[0] + "/reshape"); + const string reshape_shape = + AvailableArrayName(model, reshape_output + "/shape"); + const auto& fc_weights_array = model.GetArray(src_op.inputs[1]); + const auto& fc_weights_shape = fc_weights_array.shape(); + CHECK_EQ(fc_weights_shape.dimensions_count(), 2); + CreateMatrixShapeTensorConst(reshape_shape, fc_weights_shape.dims(1), -1, + tensorflow_graph); auto* reshape_op = tensorflow_graph->add_node(); reshape_op->set_op("Reshape"); reshape_op->set_name(reshape_output); reshape_op->add_input(src_op.inputs[0]); reshape_op->add_input(reshape_shape); - (*reshape_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*reshape_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); const bool has_bias = src_op.inputs.size() >= 3; string matmul_output = src_op.outputs[0]; @@ -460,38 +629,43 @@ void ConvertFullyConnectedOperator(const Model& model, matmul_output += "/matmul"; } + // Transpose the RHS input from column-major to row-major to match TensorFlow + // expectations. This is the inverse of the transpose we do during + // ResolveTensorFlowMatMul. + const string transpose_output = + AvailableArrayName(model, matmul_output + "/transpose_weights"); + const string transpose_perm = + AvailableArrayName(model, transpose_output + "/perm"); + CreateIntTensorConst(transpose_perm, {1, 0}, {2}, tensorflow_graph); + auto transpose_op = tensorflow_graph->add_node(); + transpose_op->set_op("Transpose"); + transpose_op->set_name(transpose_output); + *transpose_op->add_input() = src_op.inputs[1]; + *transpose_op->add_input() = transpose_perm; + (*transpose_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[1])); + (*transpose_op->mutable_attr())["Tperm"].set_type(DT_INT32); + auto* matmul_op = tensorflow_graph->add_node(); matmul_op->set_op("MatMul"); - matmul_op->set_name(matmul_output); *matmul_op->add_input() = reshape_output; - *matmul_op->add_input() = src_op.inputs[1]; - (*matmul_op->mutable_attr())["T"].set_type(DT_FLOAT); + *matmul_op->add_input() = transpose_op->name(); + (*matmul_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); (*matmul_op->mutable_attr())["transpose_a"].set_b(false); (*matmul_op->mutable_attr())["transpose_b"].set_b(false); CHECK(model.HasArray(src_op.inputs[1])); - const string& fc_weights_name = - WalkUpToConstantArray(model, src_op.inputs[1]); - const auto& fc_weights_array = model.GetArray(fc_weights_name); - const auto& fc_weights_shape = fc_weights_array.shape(); - CHECK_EQ(fc_weights_shape.dimensions_count(), 2); - CreateMatrixShapeTensorConst(reshape_shape, fc_weights_shape.dims(1), -1, - tensorflow_graph); - - CHECK(fc_weights_array.buffer); - CHECK(fc_weights_array.buffer->type == ArrayDataType::kFloat); - const float* fc_weights_data = - fc_weights_array.GetBuffer().data.data(); - ConvertFloatTensorConst(fc_weights_name, fc_weights_shape, fc_weights_data, - AxesOrder::kCR, AxesOrder::kRC, tensorflow_graph); + // Add the bias, if it exists. if (has_bias) { auto* biasadd_op = tensorflow_graph->add_node(); biasadd_op->set_op("BiasAdd"); biasadd_op->set_name(src_op.outputs[0]); biasadd_op->add_input(matmul_output); biasadd_op->add_input(src_op.inputs[2]); - (*biasadd_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*biasadd_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); CHECK(model.HasArray(src_op.inputs[2])); const auto& bias_array = model.GetArray(src_op.inputs[2]); // TODO(b/62904716) Bias arrays should be 1-D, and used directly. @@ -657,6 +831,46 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, (*softmax_op->mutable_attr())["T"].set_type(DT_FLOAT); } +void ConvertLogSoftmaxOperator(const Model& model, + const LogSoftmaxOperator& src_op, + GraphDef* tensorflow_graph) { + string softmax_input; + Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); + if (providing_op != nullptr && + providing_op->type == OperatorType::kTensorFlowReshape) { + softmax_input = src_op.inputs[0]; + } else { + // Insert a reshape operator that reduces the dimensions down to the 2 that + // are required for TensorFlow Logits. + const string reshape_output = + src_op.outputs[0] + "/log_softmax_insert_reshape"; + const string softmax_size = src_op.outputs[0] + "/log_softmax_insert_size"; + softmax_input = reshape_output; + + auto* reshape_op = tensorflow_graph->add_node(); + reshape_op->set_op("Reshape"); + reshape_op->set_name(reshape_output); + *reshape_op->add_input() = src_op.inputs[0]; + *reshape_op->add_input() = softmax_size; + (*reshape_op->mutable_attr())["T"].set_type(DT_FLOAT); + + const auto& input_shape = model.GetArray(src_op.inputs[0]).shape(); + int32 flattened_size = 1; + for (int i = 0; i < input_shape.dimensions_count() - 1; ++i) { + flattened_size *= input_shape.dims(i); + } + const std::vector shape_data = { + flattened_size, input_shape.dims(input_shape.dimensions_count() - 1)}; + CreateReshapeShapeTensorConst(softmax_size, shape_data, tensorflow_graph); + } + + auto* log_softmax_op = tensorflow_graph->add_node(); + log_softmax_op->set_op("LogSoftmax"); + log_softmax_op->set_name(src_op.outputs[0]); + *log_softmax_op->add_input() = softmax_input; + (*log_softmax_op->mutable_attr())["T"].set_type(DT_FLOAT); +} + void ConvertL2NormalizationOperator(const L2NormalizationOperator& src_op, GraphDef* tensorflow_graph) { const string square_output = src_op.outputs[0] + "/square"; @@ -799,7 +1013,8 @@ void ConvertConcatenationOperator(const Model& model, *dc_op->add_input() = input; } *dc_op->add_input() = dummy_axis; - (*dc_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*dc_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); (*dc_op->mutable_attr())["Tidx"].set_type(DT_INT32); (*dc_op->mutable_attr())["N"].set_i(src_op.inputs.size()); } @@ -813,7 +1028,8 @@ void ConvertTensorFlowReshapeOperator(const Model& model, CHECK_EQ(src_op.inputs.size(), 2); *reshape_op->add_input() = src_op.inputs[0]; *reshape_op->add_input() = src_op.inputs[1]; - (*reshape_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*reshape_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); const auto& shape_array = model.GetArray(src_op.inputs[1]); QCHECK(shape_array.data_type == ArrayDataType::kInt32) << "Only int32 shape is supported."; @@ -910,24 +1126,6 @@ void ConvertSplitOperator(const Model& model, tensorflow_graph); } -tensorflow::DataType GetTensorFlowDataType(const Model& model, - const string& array_name) { - auto& dtype = model.GetArray(array_name).data_type; - CHECK(dtype == ArrayDataType::kFloat || dtype == ArrayDataType::kInt32 || - dtype == ArrayDataType::kUint8 || dtype == ArrayDataType::kInt64); - if (dtype == ArrayDataType::kFloat) { - return tensorflow::DT_FLOAT; - } else if (dtype == ArrayDataType::kInt32) { - return tensorflow::DT_INT32; - } else if (dtype == ArrayDataType::kUint8) { - return tensorflow::DT_UINT8; - } else if (dtype == ArrayDataType::kInt64) { - return tensorflow::DT_INT64; - } else { - LOG(FATAL) << "Wrong data type"; - } -} - void ConvertCastOperator(const Model& model, const CastOperator& src_op, GraphDef* tensorflow_graph) { auto* cast_op = tensorflow_graph->add_node(); @@ -982,6 +1180,113 @@ void ConvertArgMaxOperator(const Model& model, const ArgMaxOperator& src_op, GetTensorFlowDataType(model, src_op.outputs[0])); } +void ConvertTransposeOperator(const Model& model, + const TransposeOperator& src_op, + GraphDef* tensorflow_graph) { + auto* transpose_op = tensorflow_graph->add_node(); + transpose_op->set_op("Transpose"); + transpose_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *transpose_op->add_input() = src_op.inputs[0]; + *transpose_op->add_input() = src_op.inputs[1]; + (*transpose_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); + (*transpose_op->mutable_attr())["Tperm"].set_type( + GetTensorFlowDataType(model, src_op.inputs[1])); +} + +void ConvertTensorFlowShapeOperator(const Model& model, + const TensorFlowShapeOperator& src_op, + GraphDef* tensorflow_graph) { + auto* shape_op = tensorflow_graph->add_node(); + shape_op->set_op("Shape"); + shape_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 1); + *shape_op->add_input() = src_op.inputs[0]; + (*shape_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); + (*shape_op->mutable_attr())["out_type"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); +} + +void ConvertRankOperator(const Model& model, const RankOperator& src_op, + GraphDef* tensorflow_graph) { + auto* rank_op = tensorflow_graph->add_node(); + rank_op->set_op("Rank"); + rank_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 1); + *rank_op->add_input() = src_op.inputs[0]; + (*rank_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); +} + +void ConvertRangeOperator(const Model& model, const RangeOperator& src_op, + GraphDef* tensorflow_graph) { + auto* range_op = tensorflow_graph->add_node(); + range_op->set_op("Range"); + range_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 3); + *range_op->add_input() = src_op.inputs[0]; + *range_op->add_input() = src_op.inputs[1]; + *range_op->add_input() = src_op.inputs[2]; + (*range_op->mutable_attr())["Tidx"].set_type( + GetTensorFlowDataType(src_op.dtype)); +} + +void ConvertStackOperator(const Model& model, const StackOperator& src_op, + GraphDef* tensorflow_graph) { + auto* stack_op = tensorflow_graph->add_node(); + stack_op->set_op("Stack"); + stack_op->set_name(src_op.outputs[0]); + for (const auto& input : src_op.inputs) { + *stack_op->add_input() = input; + } + (*stack_op->mutable_attr())["elem_type"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); + (*stack_op->mutable_attr())["axis"].set_i(src_op.axis); +} + +void ConvertFillOperator(const Model& model, const FillOperator& src_op, + GraphDef* tensorflow_graph) { + auto* fill_op = tensorflow_graph->add_node(); + fill_op->set_op("Fill"); + fill_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *fill_op->add_input() = src_op.inputs[0]; + *fill_op->add_input() = src_op.inputs[1]; + (*fill_op->mutable_attr())["index_type"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); + (*fill_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[1])); +} + +void ConvertFloorDivOperator(const Model& model, const FloorDivOperator& src_op, + GraphDef* tensorflow_graph) { + auto* floor_div_op = tensorflow_graph->add_node(); + floor_div_op->set_op("FloorDiv"); + floor_div_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *floor_div_op->add_input() = src_op.inputs[0]; + *floor_div_op->add_input() = src_op.inputs[1]; + (*floor_div_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); +} + +void ConvertExpandDimsOperator(const Model& model, + const ExpandDimsOperator& src_op, + GraphDef* tensorflow_graph) { + auto* expand_dims_op = tensorflow_graph->add_node(); + expand_dims_op->set_op("ExpandDims"); + expand_dims_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *expand_dims_op->add_input() = src_op.inputs[0]; + *expand_dims_op->add_input() = src_op.inputs[1]; + (*expand_dims_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); + (*expand_dims_op->mutable_attr())["Tdim"].set_type( + GetTensorFlowDataType(model, src_op.inputs[1])); +} + void ConvertResizeBilinearOperator(const Model& model, const ResizeBilinearOperator& src_op, GraphDef* tensorflow_graph) { @@ -1048,8 +1353,9 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // Write weights const string weights_output = base + "weights"; CHECK(model.HasArray(src_op.inputs[LstmCellOperator::WEIGHTS_INPUT])); - const auto& weights_array = - model.GetArray(src_op.inputs[LstmCellOperator::WEIGHTS_INPUT]); + const string weights_name = WalkUpToConstantArray( + model, src_op.inputs[LstmCellOperator::WEIGHTS_INPUT]); + const auto& weights_array = model.GetArray(weights_name); // Convert 4D FullyConnected weights into 2D matrix const auto& weights_shape = weights_array.shape(); CHECK_EQ(weights_shape.dimensions_count(), 2); @@ -1074,8 +1380,9 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // Write biases const string biases_output = base + "biases"; CHECK(model.HasArray(src_op.inputs[LstmCellOperator::BIASES_INPUT])); - const auto& bias_array = - model.GetArray(src_op.inputs[LstmCellOperator::BIASES_INPUT]); + const string bias_name = WalkUpToConstantArray( + model, src_op.inputs[LstmCellOperator::BIASES_INPUT]); + const auto& bias_array = model.GetArray(bias_name); // TODO(b/62904716) Bias arrays should be 1-D, and used directly. Shape bias_shape_1d = bias_array.shape(); UnextendShape(&bias_shape_1d, 1); @@ -1391,6 +1698,17 @@ void ConvertTensorFlowMaximumOperator(const Model& model, (*sub_op->mutable_attr())["T"].set_type(data_type); } +void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op, + GraphDef* tensorflow_graph) { + auto* topk_op = tensorflow_graph->add_node(); + topk_op->set_op("TOPKV2"); + topk_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *topk_op->add_input() = src_op.inputs[0]; + *topk_op->add_input() = src_op.inputs[1]; + (*topk_op->mutable_attr())["sorted"].set_b(true); +} + void ConvertOperator(const Model& model, const Operator& src_op, GraphDef* tensorflow_graph) { if (src_op.fused_activation_function != FusedActivationFunctionType::kNone) { @@ -1399,8 +1717,13 @@ void ConvertOperator(const Model& model, const Operator& src_op, } if (src_op.type == OperatorType::kConv) { - ConvertConvOperator(model, static_cast(src_op), - tensorflow_graph); + const ConvOperator& conv_op = static_cast(src_op); + if ((conv_op.dilation_width_factor != 1) || + (conv_op.dilation_height_factor != 1)) { + return ConvertDilatedConvOperator(model, conv_op, tensorflow_graph); + } else { + ConvertConvOperator(model, conv_op, tensorflow_graph); + } } else if (src_op.type == OperatorType::kDepthwiseConv) { ConvertDepthwiseConvOperator( model, static_cast(src_op), @@ -1447,6 +1770,10 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kSoftmax) { ConvertSoftmaxOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kLogSoftmax) { + ConvertLogSoftmaxOperator(model, + static_cast(src_op), + tensorflow_graph); } else if (src_op.type == OperatorType::kLocalResponseNormalization) { ConvertLocalResponseNormalizationOperator( static_cast(src_op), @@ -1535,6 +1862,39 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kArgMax) { ConvertArgMaxOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTopK_V2) { + ConvertTopKV2Operator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kTranspose) { + ConvertTransposeOperator( + model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTensorFlowShape) { + ConvertTensorFlowShapeOperator( + model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kRank) { + ConvertRankOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kRange) { + ConvertRangeOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kStack) { + ConvertStackOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kFill) { + ConvertFillOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kFloorDiv) { + ConvertFloorDivOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kExpandDims) { + ConvertExpandDimsOperator(model, + static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kTransposeConv) { + ConvertTransposeConvOperator( + model, static_cast(src_op), + tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } @@ -1624,6 +1984,30 @@ void ExportTensorFlowGraphDefImplementation(const Model& model, } } // namespace +void EncodeConstantArraysMinMaxByWrappingThemInFakeQuantNodes(Model* model) { + for (const auto& array_kv : model->GetArrayMap()) { + const string& array_name = array_kv.first; + Array& array = *array_kv.second; + if (!array.buffer || !array.minmax) { + continue; + } + const string& wrapped_array_name = + AvailableArrayName(*model, array_name + "/data"); + Array& wrapped_array = model->GetOrCreateArray(wrapped_array_name); + wrapped_array.data_type = array.data_type; + wrapped_array.copy_shape(array.shape()); + wrapped_array.buffer = std::move(array.buffer); + FakeQuantOperator* fakequant_op = new FakeQuantOperator; + fakequant_op->inputs = {wrapped_array_name}; + fakequant_op->outputs = {array_name}; + fakequant_op->minmax.reset(new MinMax); + *fakequant_op->minmax = *array.minmax; + const auto& it = FindOpWithInput(*model, array_name); + model->operators.emplace(it, fakequant_op); + } + CheckInvariants(*model); +} + void ExportTensorFlowGraphDef(const Model& model, string* output_file_contents) { CHECK(output_file_contents->empty()); diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.h b/tensorflow/contrib/lite/toco/export_tensorflow.h index 79682153a8fd143c4934095567764b886bd776af..d7310bb75f258cde25236da2a9269f18234784e4 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.h +++ b/tensorflow/contrib/lite/toco/export_tensorflow.h @@ -22,6 +22,8 @@ namespace toco { void ExportTensorFlowGraphDef(const Model& model, string* output_file_contents); +void EncodeConstantArraysMinMaxByWrappingThemInFakeQuantNodes(Model* model); + } // namespace toco #endif // TENSORFLOW_CONTRIB_LITE_TOCO_EXPORT_TENSORFLOW_H_ diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index 7e152f5ba887088c98055596f8245b82fbc86eaa..372c52558973f4aacc180ac44b9e95a5e9b199ef 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -23,7 +23,7 @@ curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_ bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=FLOAT \ @@ -101,7 +101,7 @@ direction, let us just give an example of that: ``` bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/foo.lite \ + --input_file=/tmp/foo.tflite \ --output_file=/tmp/foo.pb \ --input_format=TFLITE \ --output_format=TENSORFLOW_GRAPHDEF \ @@ -130,7 +130,7 @@ flatbuffer is done like this: bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/some_quantized_graph.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=QUANTIZED_UINT8 \ @@ -207,7 +207,7 @@ curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_ bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=FLOAT \ @@ -235,7 +235,7 @@ curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_ bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=FLOAT \ @@ -308,7 +308,7 @@ curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_ bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=FLOAT \ @@ -415,7 +415,7 @@ curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_ bazel run --config=opt \ //tensorflow/contrib/lite/toco:toco -- \ --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ - --output_file=/tmp/foo.lite \ + --output_file=/tmp/foo.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --inference_type=FLOAT \ diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc new file mode 100644 index 0000000000000000000000000000000000000000..0615b5e6c6db910ee847188427b416fd812aa141 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc @@ -0,0 +1,81 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "absl/strings/str_cat.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ConvertTrivialStackToReshape::Run(Model* model, std::size_t op_index) { + auto stack_it = model->operators.begin() + op_index; + if (stack_it->get()->type != OperatorType::kStack) { + return false; + } + auto* stack_op = static_cast(stack_it->get()); + if (stack_op->inputs.size() > 1) { + // Not trivial. + return false; + } + CHECK_EQ(stack_op->outputs.size(), 1); + + const auto& input_array = model->GetArray(stack_op->inputs[0]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return false; + } + if (input_array.shape().dimensions_count() == 0) { + // Input array cannot be 0-D. + // (Unsure if this is TF behavior, but was required to get a test to pass.) + return false; + } + + AddMessageF("Converting trivial %s to a reshape", LogName(*stack_op)); + + // Note that we could convert to ExpandDims but toco prefers reshapes. + auto* reshape_op = new TensorFlowReshapeOperator; + reshape_op->inputs = {stack_op->inputs[0]}; + reshape_op->outputs = stack_op->outputs; + + // Create shape param. + string shape_array_name = + AvailableArrayName(*model, stack_op->outputs[0] + "_shape"); + Array& shape_array = model->GetOrCreateArray(shape_array_name); + *(shape_array.mutable_shape()->mutable_dims()) = { + 1 + input_array.shape().dimensions_count()}; + reshape_op->inputs.push_back(shape_array_name); + shape_array.data_type = ArrayDataType::kInt32; + auto& shape_buffer = shape_array.GetMutableBuffer(); + shape_buffer.data.push_back(1); + for (int dim : input_array.shape().dims()) { + shape_buffer.data.push_back(dim); + } + + // Replace the operator in the graph. + const auto reshape_it = model->operators.emplace(stack_it, reshape_op); + stack_it = reshape_it + 1; + CHECK_EQ(stack_it->get(), stack_op); + model->operators.erase(stack_it); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index cf90ebe99697fe8a40b4c707e70fdc5318123854..f0739990adc988a1b178a374aa542d728c6415b5 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -115,6 +115,7 @@ void RunGraphTransformations(Model* model, const string& message, DECLARE_GRAPH_TRANSFORMATION(ConvertExpandDimsToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) +DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialStackToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertReorderAxes) DECLARE_GRAPH_TRANSFORMATION(EnsureBiasVectors) @@ -124,7 +125,10 @@ DECLARE_GRAPH_TRANSFORMATION(FuseBinaryIntoPrecedingAffine) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Normalization) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Pool) DECLARE_GRAPH_TRANSFORMATION(IdentifyLstmCell) +DECLARE_GRAPH_TRANSFORMATION(SplitLstmCellInputs) +DECLARE_GRAPH_TRANSFORMATION(MergeLstmCellInputs) DECLARE_GRAPH_TRANSFORMATION(IdentifyRelu1) +DECLARE_GRAPH_TRANSFORMATION(IdentifyDilatedConv) DECLARE_GRAPH_TRANSFORMATION(MakeInitialDequantizeOperator) DECLARE_GRAPH_TRANSFORMATION(PropagateArrayDataTypes) DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes) @@ -136,6 +140,7 @@ DECLARE_GRAPH_TRANSFORMATION(RemoveTensorFlowIdentity) DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialBinaryOperator) DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialConcatenation) DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialConcatenationInput) +DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialSlice) DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialQuantizedActivationFunc) DECLARE_GRAPH_TRANSFORMATION(RemoveUnusedOp) DECLARE_GRAPH_TRANSFORMATION(ResolveBatchNormalization) @@ -154,8 +159,10 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowSwitch) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowTile) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFakeQuant) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantConcatenation) +DECLARE_GRAPH_TRANSFORMATION(ResolveConstantTranspose) DECLARE_GRAPH_TRANSFORMATION(DropFakeQuant) DECLARE_GRAPH_TRANSFORMATION(UnfuseActivationFunctions) +DECLARE_GRAPH_TRANSFORMATION(UnrollBatchMatMul) DECLARE_GRAPH_TRANSFORMATION(ResolveSpaceToBatchNDAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolveBatchToSpaceNDAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolvePadAttributes) @@ -168,7 +175,9 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantShapeOrRank) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStack) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStridedSlice) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFill) +DECLARE_GRAPH_TRANSFORMATION(ResolveMultiplyByZero) DECLARE_GRAPH_TRANSFORMATION(Dequantize) +DECLARE_GRAPH_TRANSFORMATION(UnpartitionEmbeddingLookup) class ResolveReshapeAttributes : public GraphTransformation { public: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc index f1892136cfe5ea8b82b579226f3c16b279781f49..938d76386d6f315abfe6fe55b133cb4d19014f01 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc @@ -125,6 +125,27 @@ bool HardcodeMinMaxForConcatenation(Model* model, Operator* op) { return changed; } +bool HardcodeMinMaxForSplit(Model* model, Operator* op) { + for (const auto& output : op->outputs) { + if (model->GetArray(output).minmax) { + LOG(WARNING) << "Skipping min-max setting for " << LogName(*op) + << " because output " << output << " already has min-max."; + return false; + } + } + // Data is in second input. + auto& input_array = model->GetArray(op->inputs[1]); + if (!input_array.minmax) { + return false; + } else { + for (const auto& output : op->outputs) { + auto& array = model->GetArray(output); + array.GetOrCreateMinMax() = *input_array.minmax; + } + return true; + } +} + // The output of average or max pooling is within the same range as its input. bool HardcodeMinMaxForAverageOrMaxPool(Model* model, Operator* op) { auto& output_array = model->GetArray(op->outputs[0]); @@ -177,6 +198,106 @@ bool HardcodeMinMaxForOutput(Model* model, Operator* op, double min, output_minmax.max = max; return true; } + +// Propagates MinMax from any of the listed arrays, to all others. +// If multiple of these arrays have MinMax, then these are required +// to agree with each other. +bool PropagateMinMaxAmongArrays(Model* model, + const std::vector array_names) { + string reference_array_name; + MinMax* reference_minmax = nullptr; + for (const string& array_name : array_names) { + if (model->GetArray(array_name).minmax) { + reference_array_name = array_name; + reference_minmax = model->GetArray(array_name).minmax.get(); + break; + } + } + // No MinMax info is available to propagate. + if (!reference_minmax) { + return false; + } + bool changed = false; + for (const string& array_name : array_names) { + auto& array = model->GetArray(array_name); + if (array.minmax) { + CHECK(*array.minmax == *reference_minmax) + << "Both the following arrays have minmax, and they disagree: " + << reference_array_name << " and " << array_name + << ". Expected that either only one of them would have minmax, or at " + "least that they would agree."; + } else { + array.GetOrCreateMinMax() = *reference_minmax; + changed = true; + } + } + return changed; +} + +bool HardcodeMinMaxForLstmCell(Model* model, Operator* op) { + CHECK_EQ(op->inputs.size(), LstmCellOperator::NUM_INPUTS); + CHECK_EQ(op->outputs.size(), LstmCellOperator::NUM_OUTPUTS); + + bool changed = false; + changed |= PropagateMinMaxAmongArrays( + model, {op->inputs[LstmCellOperator::PREV_STATE_INPUT], + op->outputs[LstmCellOperator::STATE_OUTPUT]}); + + auto& input_activations = + model->GetArray(op->inputs[LstmCellOperator::DATA_INPUT]); + if (!input_activations.minmax) { + auto& minmax = input_activations.GetOrCreateMinMax(); + minmax.min = -1; + minmax.max = 127. / 128.; + changed = true; + } + + auto& prev_output_activations = + model->GetArray(op->inputs[LstmCellOperator::PREV_ACTIV_INPUT]); + if (!prev_output_activations.minmax) { + auto& minmax = prev_output_activations.GetOrCreateMinMax(); + minmax.min = -1; + minmax.max = 127. / 128.; + changed = true; + } + + auto& output_concat_temp = + model->GetArray(op->outputs[LstmCellOperator::CONCAT_TEMP]); + if (!output_concat_temp.minmax) { + auto& minmax = output_concat_temp.GetOrCreateMinMax(); + minmax.min = -1; + minmax.max = 127. / 128.; + changed = true; + } + + auto& output_activations = + model->GetArray(op->outputs[LstmCellOperator::ACTIV_OUTPUT]); + if (!output_activations.minmax) { + auto& minmax = output_activations.GetOrCreateMinMax(); + minmax.min = -1; + minmax.max = 127. / 128.; + changed = true; + } + + // (This comment should morph into proper documentation for + // quantization of LSTM models. It isn't just a local implementation detail, + // the training code for LSTM models needs to be adjusted to that.) + // + // Finally, output_activations_temp holds the output of the fully-connected + // node inside the LSTM cell. For it, we hardcode a minmax of [-8, 8]. + // The rationale for that is given in a lengthy comment on the LstmCell + // quantized runtime implementation in reference_ops.h. + auto& output_activations_temp = + model->GetArray(op->outputs[LstmCellOperator::ACTIV_TEMP]); + if (!output_activations_temp.minmax) { + auto& minmax = output_activations_temp.GetOrCreateMinMax(); + minmax.min = -8; + minmax.max = 8 * 32767. / 32768.; + changed = true; + } + + return changed; +} } // namespace bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { @@ -196,6 +317,10 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForConcatenation(model, op); break; + case OperatorType::kTensorFlowSplit: + changed = HardcodeMinMaxForSplit(model, op); + break; + case OperatorType::kAveragePool: case OperatorType::kMaxPool: changed = HardcodeMinMaxForAverageOrMaxPool(model, op); @@ -225,6 +350,10 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForOutput(model, op, -127. / 128., 1.0); break; + case OperatorType::kLstmCell: + changed = HardcodeMinMaxForLstmCell(model, op); + break; + default: break; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc new file mode 100644 index 0000000000000000000000000000000000000000..ae3301f467de5714230e731b4bab87ddc1637201 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc @@ -0,0 +1,213 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +// A dilated convolution can be emulated with a regular convolution by chaining +// SpaceToBatch and BatchToSpace ops before and after it: +// +// SpaceToBatchND -> Conv2D -> BatchToSpaceND +// +// This method was common before Conv2D fully supported dilated convolution in +// TensorFlow. This transformation detects this "emulation", and replaces it +// with a true dilated convolution, eliminating the SpaceToBatch and +// BatchtoSpace ops. +// +// Detecting this alone would be relatively easy. However, in practice some +// extra ops are used, so we detect the following patterns: +// +// +// SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BatchToSpaceND -> BiasAdd +// +// SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> Pad -> BatchToSpaceND -> +// BiasAdd +// +// SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BiasAdd -> BatchToSpaceND +// +// SpaceToBatchND -> Conv2D -> Pad -> BatchToSpaceND -> BiasAdd +// +// SpaceToBatchND -> Conv2D -> BatchToSpaceND -> BiasAdd +// +// +// The Expand/Squeeze combination is used to adapt a 3D array (such as in +// WaveNet) to the 4D arrays that Conv2D requires. Padding and BiasAdd are +// thrown in just for the extra headache. Padding adapts non-conforming input +// sizes, and can be discarded. The bias is necessary, so is kept. + +bool IdentifyDilatedConv::Run(Model* model, std::size_t op_index) { + const auto it = model->operators.begin() + op_index; + auto* stb_op = it->get(); + + // 1. IDENTIFY OPERATORS + // *************************************************************************** + // SpaceToBatch Op. + if (stb_op->type != OperatorType::kSpaceToBatchND) { + return false; + } + if (stb_op->inputs.size() != 3) { + return false; + } + CHECK_EQ(stb_op->outputs.size(), 1); + // Extract the dilation factor from Input[1] of SpaceToBatch + // TODO(mjmatthews): Support 2D dilation factors. + const auto& block_shape_array = model->GetArray(stb_op->inputs[1]); + if (!block_shape_array.buffer) { + return false; + } + CHECK_EQ(block_shape_array.shape().dimensions_count(), 1); + int dilation_factor = + block_shape_array.Array::GetBuffer().data[0]; + + // Expand Op + auto* post_stb_op = GetOpWithInput(*model, stb_op->outputs[0]); + if (!post_stb_op) { + return false; + } + bool has_expand_op = false; + if (post_stb_op->type == OperatorType::kExpandDims) { + has_expand_op = true; + CHECK_EQ(post_stb_op->inputs.size(), 2); + CHECK_EQ(post_stb_op->outputs.size(), 1); + } + + // Conv Op + ConvOperator* conv_op = dynamic_cast( + has_expand_op ? GetOpWithInput(*model, post_stb_op->outputs[0]) + : GetOpWithInput(*model, stb_op->outputs[0])); + if (!conv_op || conv_op->type != OperatorType::kConv) { + return false; + } + if (conv_op->inputs.size() != 2) { + // The conv op must only have weights, no bias. + return false; + } + CHECK_EQ(conv_op->outputs.size(), 1); + + // Squeeze Op + auto* post_conv_op = GetOpWithInput(*model, conv_op->outputs[0]); + if (!post_conv_op) { + return false; + } + if (has_expand_op) { + if (post_conv_op->type != OperatorType::kSqueeze) { + // If an expand op was used, the post-conv op must be a squeeze op + return false; + } + CHECK_EQ(post_conv_op->inputs.size(), 1); + CHECK_EQ(post_conv_op->outputs.size(), 1); + } + + // Pad Op + const auto* pad_op = has_expand_op + ? GetOpWithInput(*model, post_conv_op->outputs[0]) + : GetOpWithInput(*model, conv_op->outputs[0]); + bool has_pad_op = false; + if (pad_op->type == OperatorType::kPad) { + has_pad_op = true; + CHECK_EQ(pad_op->inputs.size(), 2); + CHECK_EQ(pad_op->outputs.size(), 1); + } + // TODO(mjmatthews): Perform validity checking on padding dimensions. + + // Pre-BatchToSpace Bias Op + auto* next_op = has_pad_op + ? GetOpWithInput(*model, pad_op->outputs[0]) + : has_expand_op + ? GetOpWithInput(*model, post_conv_op->outputs[0]) + : GetOpWithInput(*model, conv_op->outputs[0]); + bool has_bias_before_bts = false; + if (next_op->type == OperatorType::kAdd) { + has_bias_before_bts = true; + } + auto final_op = GetOpWithInput(*model, next_op->outputs[0]); + + // BatchToSpace Op + const auto* bts_op = has_bias_before_bts ? final_op : next_op; + if (bts_op->type != OperatorType::kBatchToSpaceND) { + return false; + } + CHECK_EQ(bts_op->inputs.size(), 3); + CHECK_EQ(bts_op->outputs.size(), 1); + + // Post-BatchToSpace Bias Op + Operator* bias_add_op = !has_bias_before_bts ? final_op : next_op; + if (bias_add_op->type != OperatorType::kAdd) { + // Bias op is required before or after BatchToSpace + return false; + } + CHECK_EQ(bias_add_op->inputs.size(), 2); + CHECK_EQ(bias_add_op->outputs.size(), 1); + + LOG(INFO) << "Identified sub-network emulating dilated convolution."; + + // 2. RE-WIRE OPERATORS + // *************************************************************************** + // Re-use the existing Conv2D op. + conv_op->dilation_width_factor = dilation_factor; + conv_op->dilation_height_factor = dilation_factor; + conv_op->padding.type = PaddingType::kSame; + + // Rewire the ops to bypass SpaceToBatch, BatchToSpace, and Pad. + bias_add_op->outputs[0] = final_op->outputs[0]; + if (has_expand_op) { + bias_add_op->inputs[0] = post_conv_op->outputs[0]; + post_conv_op->inputs[0] = conv_op->outputs[0]; + conv_op->inputs[0] = post_stb_op->outputs[0]; + post_stb_op->inputs[0] = stb_op->inputs[0]; + } else { + bias_add_op->inputs[0] = conv_op->outputs[0]; + conv_op->inputs[0] = stb_op->inputs[0]; + } + // TODO(mjmatthews): Connect bias directly into the Conv2D? + + // 3. DELETE LEFTOVER OPERATORS + // *************************************************************************** + // Order is important. Delete the output array first, then the op, then it's + // redundant inputs. + // BatchToSpace Op + DeleteArrayIfUnused(bts_op->outputs[0], model); + std::vector bts_op_inputs = bts_op->inputs; + model->operators.erase(FindOp(*model, bts_op)); + DeleteArrayIfUnused(bts_op_inputs[1], model); + DeleteArrayIfUnused(bts_op_inputs[2], model); + + // Pad Op if present + if (has_pad_op) { + DeleteArrayIfUnused(pad_op->outputs[0], model); + std::vector pad_op_inputs = pad_op->inputs; + model->operators.erase(FindOp(*model, pad_op)); + DeleteArrayIfUnused(pad_op_inputs[1], model); + } + + // SpaceToBatch Op + DeleteArrayIfUnused(stb_op->outputs[0], model); + std::vector stb_op_inputs = stb_op->inputs; + model->operators.erase(FindOp(*model, stb_op)); + DeleteArrayIfUnused(stb_op_inputs[1], model); + DeleteArrayIfUnused(stb_op_inputs[2], model); + + LOG(INFO) << "Replaced with Dilated Conv2D op outputting \"" + << conv_op->outputs[0] << "\"."; + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc new file mode 100644 index 0000000000000000000000000000000000000000..45335fd78c99a577d535770d78acf4fcd6c04531 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc @@ -0,0 +1,185 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include "absl/memory/memory.h" +#include "absl/strings/string_view.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +bool MergeLstmCellInputs::Run(Model* model, std::size_t op_index) { + // Find lstm cell. + auto op_it = model->operators.begin() + op_index; + auto src_op = op_it->get(); + if (src_op->type != OperatorType::kLstmCell) { + return false; + } + + // Already a compact LstmCell with LstmCellOperator::NUM_INPUTS of inputs, + // do not need to merge cell inputs. + if (src_op->inputs.size() == LstmCellOperator::NUM_INPUTS) { + return false; + } + + // Identify prev_activ_input, prev_state_input as required Op inputs, + // using the rnn_states in the model flag. + string prev_activ_input; + if (!GetMatchingRnnArray(model, src_op->outputs[kOutputTensor], + &prev_activ_input)) { + return false; + } + string prev_state_input; + if (!GetMatchingRnnArray(model, src_op->outputs[kCellStateTensor], + &prev_state_input)) { + return false; + } + + // Get LstmCell's cell, input, output size. + int num_cell = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) + .shape() + .dims(0); + int num_input = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) + .shape() + .dims(1); + int num_output = + model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor]) + .shape() + .dims(1); + + // Make sure n_cell and n_output are equal as there is no projection. + CHECK_EQ(num_cell, num_output); + + // Create tensorflow_graphdef style's one big weight tensor. + const string base_name(FindLongestCommonPrefix( + src_op->outputs[kOutputTensor], src_op->outputs[kCellStateTensor])); + string merged_weights = AvailableArrayName(*model, base_name + "weights"); + auto& array = model->GetOrCreateArray(merged_weights); + array.data_type = ArrayDataType::kFloat; + int weights_dim1 = 4 * num_cell; + int weights_dim2 = num_input + num_output; + Shape shape = Shape({weights_dim1, weights_dim2}); + array.copy_shape(shape); + auto& buffer = array.GetMutableBuffer(); + buffer.data.resize(weights_dim1 * weights_dim2); + + // Merge 8 small weight tensors to 1 weight tensor. + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kInputToInputWeightsTensor]), 0, 0); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kInputToCellWeightsTensor]), num_cell, 0); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kInputToForgetWeightsTensor]), + num_cell * 2, 0); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kInputToOutputWeightsTensor]), + num_cell * 3, 0); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor]), 0, + num_input); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kRecurrentToCellWeightsTensor]), num_cell, + num_input); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kRecurrentToForgetWeightsTensor]), + num_cell * 2, num_input); + CopyArrayToSubArray( + buffer, weights_dim2, + model->GetArray(src_op->inputs[kRecurrentToOutputWeightsTensor]), + num_cell * 3, num_input); + + // Create tensorflow_graphdef style's one big bias tensor. + string merged_biases = AvailableArrayName(*model, base_name + "biases"); + auto& bias_array = model->GetOrCreateArray(merged_biases); + bias_array.data_type = ArrayDataType::kFloat; + bias_array.copy_shape(Shape({weights_dim1})); + auto& bias_buffer = bias_array.GetMutableBuffer(); + bias_buffer.data.resize(weights_dim1); + + // Merge 4 small bias tensors into a big one. + CopyArrayToSubArray(bias_buffer, weights_dim2, + model->GetArray(src_op->inputs[kInputGateBiasTensor]), 0, + 0); + CopyArrayToSubArray(bias_buffer, weights_dim2, + model->GetArray(src_op->inputs[kCellGateBiasTensor]), + num_cell, 0); + CopyArrayToSubArray(bias_buffer, weights_dim2, + model->GetArray(src_op->inputs[kForgetGateBiasTensor]), + num_cell * 2, 0); + CopyArrayToSubArray(bias_buffer, weights_dim2, + model->GetArray(src_op->inputs[kOutputGateBiasTensor]), + num_cell * 3, 0); + + // Emplace a new LSTM cell operator (use basic 5 inputs kernel). + auto lstm_cell_op = absl::make_unique(); + + // Compact LstmCell's 5 inputs. + lstm_cell_op->inputs.resize(LstmCellOperator::NUM_INPUTS); + lstm_cell_op->inputs[LstmCellOperator::DATA_INPUT] = + src_op->inputs[kInputTensor]; + lstm_cell_op->inputs[LstmCellOperator::WEIGHTS_INPUT] = merged_weights; + lstm_cell_op->inputs[LstmCellOperator::BIASES_INPUT] = merged_biases; + lstm_cell_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT] = prev_activ_input; + lstm_cell_op->inputs[LstmCellOperator::PREV_STATE_INPUT] = prev_state_input; + + // Reorder LstmCell's 4 outputs. + lstm_cell_op->outputs.resize(LstmCellOperator::NUM_OUTPUTS); + lstm_cell_op->outputs[LstmCellOperator::ACTIV_OUTPUT] = + src_op->outputs[kOutputTensor]; + lstm_cell_op->outputs[LstmCellOperator::STATE_OUTPUT] = + src_op->outputs[kCellStateTensor]; + lstm_cell_op->outputs[LstmCellOperator::CONCAT_TEMP] = + src_op->outputs[kScratchBufferTensor]; + lstm_cell_op->outputs[LstmCellOperator::ACTIV_TEMP] = + src_op->outputs[kOutputStateTensor]; + + // Add the op into model. + model->operators.emplace(op_it, std::move(lstm_cell_op)); + AddMessageF("Creating compact LstmCell replacing previous lstm cell"); + + // Delete arrays and operators replaced by the LSTM cell operator. Order is + // important - DeleteArrayIfUnused() only succeeds if dependent operators + // have been removed first. Start at the output and work towards the input. + // Erase curr lstm op being replaced. + DeleteArrayIfUnused(src_op->inputs[kInputToInputWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kInputToForgetWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kInputToCellWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kInputToOutputWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kRecurrentToInputWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kRecurrentToForgetWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kRecurrentToCellWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kRecurrentToOutputWeightsTensor], model); + DeleteArrayIfUnused(src_op->inputs[kInputGateBiasTensor], model); + DeleteArrayIfUnused(src_op->inputs[kForgetGateBiasTensor], model); + DeleteArrayIfUnused(src_op->inputs[kCellGateBiasTensor], model); + DeleteArrayIfUnused(src_op->inputs[kOutputGateBiasTensor], model); + model->operators.erase(FindOp(*model, src_op)); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc new file mode 100644 index 0000000000000000000000000000000000000000..eca717680af281018b919c27068ba5d9f5699d69 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc @@ -0,0 +1,171 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include "absl/memory/memory.h" +#include "absl/strings/string_view.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { + // Find lstm cell. + auto op_it = model->operators.begin() + op_index; + auto curr_op = op_it->get(); + if (curr_op->type != OperatorType::kLstmCell) { + return false; + } + + // Already an extended LstmCell with kExtendedLstmInputCount of inputs, + // do not need to split cell inputs. + if (curr_op->inputs.size() == kExtendedLstmInputCount) { + return false; + } + + // Make sure the WEIGHTS_INPUT and BIASES_INPUT are constant arrays, + // that are able to be split into smaller weight and bias tensors. + if (!IsConstantParameterArray( + *model, curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]) || + !IsConstantParameterArray( + *model, curr_op->inputs[LstmCellOperator::BIASES_INPUT])) { + return false; + } + + // Make sure propagate_fixed_sizes has defined the size of the output. + if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) + .has_shape()) { + return false; + } + + // Emplace a new LstmCell operator with extended inputs (kernel/lstm.cc). + auto lstm_cell_op = absl::make_unique(); + lstm_cell_op->inputs.resize(kExtendedLstmInputCount); + int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT]) + .shape() + .dims(1); + + // n_cell and n_output have the same size when there is no projection. + int num_cell = + model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) + .shape() + .dims(1); + int num_output = num_cell; + + // Data input. + lstm_cell_op->inputs[kInputTensor] = + curr_op->inputs[LstmCellOperator::ACTIV_OUTPUT]; + + // Get original weight tensor and decompose 1 tensor to 8 sub tensors. + Array& kernel = + model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]); + const string base_name(FindLongestCommonPrefix( + curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT], + curr_op->outputs[LstmCellOperator::STATE_OUTPUT])); + + // Input weight tensors of size {n_cell, n_input}. + CopySubArrayToArray( + model, &(lstm_cell_op->inputs[kInputToInputWeightsTensor]), + base_name + "weight_i_i", num_cell, num_input, kernel, 0, 0); + CopySubArrayToArray(model, &(lstm_cell_op->inputs[kInputToCellWeightsTensor]), + base_name + "weight_c_i", num_cell, num_input, kernel, + num_cell, 0); + CopySubArrayToArray( + model, &(lstm_cell_op->inputs[kInputToForgetWeightsTensor]), + base_name + "weight_f_i", num_cell, num_input, kernel, num_cell * 2, 0); + CopySubArrayToArray( + model, &(lstm_cell_op->inputs[kInputToOutputWeightsTensor]), + base_name + "weight_o_i", num_cell, num_input, kernel, num_cell * 3, 0); + + // Recurrent weight tensors of size {n_cell, n_output}. + CopySubArrayToArray( + model, &(lstm_cell_op->inputs[kRecurrentToInputWeightsTensor]), + base_name + "weight_i_r", num_cell, num_output, kernel, 0, num_input); + CopySubArrayToArray(model, + &(lstm_cell_op->inputs[kRecurrentToCellWeightsTensor]), + base_name + "weight_c_r", num_cell, num_output, kernel, + num_cell, num_input); + CopySubArrayToArray(model, + &(lstm_cell_op->inputs[kRecurrentToForgetWeightsTensor]), + base_name + "weight_f_r", num_cell, num_output, kernel, + num_cell * 2, num_input); + CopySubArrayToArray(model, + &(lstm_cell_op->inputs[kRecurrentToOutputWeightsTensor]), + base_name + "weight_o_r", num_cell, num_output, kernel, + num_cell * 3, num_input); + + // Peephole (optional). + CreateOptionalArray(model, &(lstm_cell_op->inputs[kCellToInputWeightsTensor]), + base_name + "peephole_c_i"); + CreateOptionalArray(model, + &(lstm_cell_op->inputs[kCellToForgetWeightsTensor]), + base_name + "peephole_c_f"); + CreateOptionalArray(model, + &(lstm_cell_op->inputs[kCellToOutputWeightsTensor]), + base_name + "peephole_c_o"); + + // Get original bias tensor and decompose 1 tensor to 4 sub tensors + Array& bias = + model->GetArray(curr_op->inputs[LstmCellOperator::BIASES_INPUT]); + CopySubArrayToArray(model, &(lstm_cell_op->inputs[kInputGateBiasTensor]), + base_name + "bias_i", num_cell, 1, bias, 0, 0); + CopySubArrayToArray(model, &(lstm_cell_op->inputs[kCellGateBiasTensor]), + base_name + "bias_c", num_cell, 1, bias, num_cell, 0); + CopySubArrayToArray(model, &(lstm_cell_op->inputs[kForgetGateBiasTensor]), + base_name + "bias_f", num_cell, 1, bias, num_cell * 2, 0); + CopySubArrayToArray(model, &(lstm_cell_op->inputs[kOutputGateBiasTensor]), + base_name + "bias_o", num_cell, 1, bias, num_cell * 3, 0); + + // Projection (optional). + CreateOptionalArray(model, &(lstm_cell_op->inputs[kProjectionWeightsTensor]), + base_name + "proj_weight"); + CreateOptionalArray(model, &(lstm_cell_op->inputs[kProjectionBiasTensor]), + base_name + "proj_bias"); + + // Reorder LstmCell's outputs. + lstm_cell_op->outputs.resize(LstmCellOperator::NUM_OUTPUTS); + lstm_cell_op->outputs[kScratchBufferTensor] = + curr_op->outputs[LstmCellOperator::CONCAT_TEMP]; + lstm_cell_op->outputs[kOutputStateTensor] = + curr_op->outputs[LstmCellOperator::ACTIV_TEMP]; + lstm_cell_op->outputs[kCellStateTensor] = + curr_op->outputs[LstmCellOperator::STATE_OUTPUT]; + lstm_cell_op->outputs[kOutputTensor] = + curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]; + + // Add the op into model. + model->operators.emplace(op_it, std::move(lstm_cell_op)); + AddMessageF("Creating extended LstmCell replacing previous lstm cell"); + + // Delete arrays and operators replaced by the LSTM cell operator. Order is + // important - DeleteArrayIfUnused() only succeeds if dependent operators + // have been removed first. Start at the output and work towards the input. + // Erase curr lstm op being replaced. + DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT], model); + DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::BIASES_INPUT], model); + DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT], + model); + DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT], + model); + model->operators.erase(FindOp(*model, curr_op)); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc index d36e95060937d6af0789766bcb29ae70cef4569d..de6d8889fb4ccdb56e9639ab0dd7d093bfa4b908 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc @@ -57,45 +57,60 @@ int GetSingleScalarInputIndexOfBinaryOp(Model* model, const Operator* op, } // namespace bool IdentifyRelu1::Run(Model* model, std::size_t op_index) { - const auto maximum_it = model->operators.begin() + op_index; - const auto* maximum_op = maximum_it->get(); - if (maximum_op->type != OperatorType::kTensorFlowMaximum) { + // Follow sequences of min+max and max+min. First get the leading op. + const auto op_it = model->operators.begin() + op_index; + const auto* op_0 = op_it->get(); + if (op_0->type != OperatorType::kTensorFlowMinimum && + op_0->type != OperatorType::kTensorFlowMaximum) { return false; } - CHECK_EQ(maximum_op->inputs.size(), 2); - if (maximum_op->outputs.size() != 1) { - return false; - } - int scalar_input_index = - GetSingleScalarInputIndexOfBinaryOp(model, maximum_op, -1.0f); - if (scalar_input_index == -1) { + + // Get the paired op and ensure it's the counter to the first. + const auto* op_1 = GetOpWithInput(*model, op_0->outputs[0]); + if (!op_1 || + (op_1->type != OperatorType::kTensorFlowMinimum && + op_1->type != OperatorType::kTensorFlowMaximum) || + op_0->type == op_1->type) { return false; } - const auto* minimum_op = GetOpWithInput(*model, maximum_op->outputs[0]); - if (!minimum_op || minimum_op->type != OperatorType::kTensorFlowMinimum) { + + const auto* min_op = + op_0->type == OperatorType::kTensorFlowMinimum ? op_0 : op_1; + const auto* max_op = + op_0->type == OperatorType::kTensorFlowMaximum ? op_0 : op_1; + + CHECK_EQ(min_op->inputs.size(), 2); + CHECK_EQ(max_op->inputs.size(), 2); + if (min_op->outputs.size() != 1 || max_op->outputs.size() != 1) { return false; } - if (GetSingleScalarInputIndexOfBinaryOp(model, minimum_op, 1.0f) == -1) { + + // Get the original input to the min+max pair. + int min_scalar_input_index = + GetSingleScalarInputIndexOfBinaryOp(model, min_op, 1.0f); + int max_scalar_input_index = + GetSingleScalarInputIndexOfBinaryOp(model, max_op, -1.0f); + if (min_scalar_input_index == -1 || max_scalar_input_index == -1) { return false; } - CHECK_EQ(minimum_op->inputs.size(), 2); + int op_0_scalar_input_index = + op_0 == min_op ? min_scalar_input_index : max_scalar_input_index; - // Create and emplace Relu1 node + // Create and emplace Relu1 node. auto* relu1_op = new Relu1Operator; - relu1_op->inputs = {maximum_op->inputs[!scalar_input_index]}; - relu1_op->outputs = minimum_op->outputs; - model->operators.emplace(maximum_it, relu1_op); + relu1_op->inputs = {op_0->inputs[!op_0_scalar_input_index]}; + relu1_op->outputs = op_1->outputs; + model->operators.emplace(op_it, relu1_op); AddMessageF("Creating %s replacing equivalent subgraph", LogName(*relu1_op)); - // Erase Maximum scalar input & operator - model->EraseArray(maximum_op->inputs[scalar_input_index]); - model->operators.erase(FindOperator(model, maximum_op)); - - // Erase Minimum inputs & operator - model->EraseArray(minimum_op->inputs[0]); - model->EraseArray(minimum_op->inputs[1]); - model->operators.erase(FindOperator(model, minimum_op)); + // Erase op scalar inputs & operators. Note that we preserve the non-scalar + // input to the first op as that's been redirected to the relu1_op. + DeleteArrayIfUsedOnce(op_0->inputs[op_0_scalar_input_index], model); + DeleteArrayIfUsedOnce(op_1->inputs[0], model); + DeleteArrayIfUsedOnce(op_1->inputs[1], model); + model->operators.erase(FindOperator(model, op_0)); + model->operators.erase(FindOperator(model, op_1)); return true; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.cc b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..910a96058979887972b41f27b2e570e8cb5b4f4c --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.cc @@ -0,0 +1,97 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" + +namespace toco { + +void CreateOptionalArray(Model* model, string* input_array_buffer, + const string& array_name) { + *input_array_buffer = array_name; + model->CreateOptionalArray(array_name); +} + +void CopyArrayData(const Buffer& src_buffer, + int src_stride, int src_start_idx1, int src_start_idx2, + Buffer* dst_buffer, int dst_stride, + int dst_start_idx1, int dst_start_idx2, int dim1_copy_size, + int dim2_copy_size) { + int src_offset = src_start_idx1 * src_stride + src_start_idx2; + int dst_offset = dst_start_idx1 * dst_stride + dst_start_idx2; + for (int i = 0; i < dim1_copy_size; i++) { + for (int j = 0; j < dim2_copy_size; j++) { + int idx_src = src_offset + i * src_stride + j; + int idx_dst = dst_offset + i * dst_stride + j; + dst_buffer->data[idx_dst] = src_buffer.data[idx_src]; + } + } +} + +Buffer* CreateFloatArrayBuffer(Model* model, + string* array_name, + const Shape& shape) { + *array_name = AvailableArrayName(*model, *array_name); + auto& array = model->GetOrCreateArray(*array_name); + array.data_type = ArrayDataType::kFloat; + array.copy_shape(shape); + Buffer* buffer = + &(array.GetMutableBuffer()); + buffer->data.resize(RequiredBufferSizeForShape(shape)); + return buffer; +} + +void CopySubArrayToArray(Model* model, string* array_name, + const string& tensor_name, int dim1_size, + int dim2_size, const Array& original_array, + int start_idx1, int start_idx2) { + // Determine whether it's bias or not, create shape, buffer. + bool is_bias = dim2_size == 1; + Shape shape = is_bias ? Shape({dim1_size}) : Shape({dim1_size, dim2_size}); + Buffer* buffer = + CreateFloatArrayBuffer(model, array_name, shape); + auto& orig_buffer = original_array.GetBuffer(); + + // Copy data from big tensor. + CopyArrayData(orig_buffer, is_bias ? 1 : original_array.shape().dims(1), + start_idx1, start_idx2, buffer, dim2_size, 0, 0, dim1_size, + dim2_size); +} + +void CopyArrayToSubArray(Buffer& tensor_buffer, + int tensor_stride, const Array& sub_array, + int start_idx1, int start_idx2) { + // Get tensor data. + bool is_bias = sub_array.shape().dims().size() == 1; + int dim1_copy_size = sub_array.shape().dims()[0]; + int dim2_copy_size = is_bias ? 1 : sub_array.shape().dims(1); + auto& sub_buffer = sub_array.GetBuffer(); + + // Copy data from sub tensor. + CopyArrayData(sub_buffer, dim2_copy_size, 0, 0, &tensor_buffer, + is_bias ? 1 : tensor_stride, start_idx1, start_idx2, + dim1_copy_size, dim2_copy_size); +} + +bool GetMatchingRnnArray(Model* model, const string& back_edge_source_array, + string* rnn_array) { + for (const auto& rnn_state : model->flags.rnn_states()) { + if (rnn_state.back_edge_source_array() == back_edge_source_array) { + *rnn_array = rnn_state.state_array(); + return true; + } + } + return false; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..881c2d4dc892625d4640cac867a2f49c24b638f5 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h @@ -0,0 +1,102 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +// For consistency with the parameters defined in extended LstmCell's kernel +// (tensorflow/contrib/lite/kernels/lstm.cc), +// use lowercase for these constants. + +enum ExtendedLstmCellInputs { + kInputTensor = 0, + kInputToInputWeightsTensor = 1, // Optional + kInputToForgetWeightsTensor = 2, + kInputToCellWeightsTensor = 3, + kInputToOutputWeightsTensor = 4, + kRecurrentToInputWeightsTensor = 5, // Optional + kRecurrentToForgetWeightsTensor = 6, + kRecurrentToCellWeightsTensor = 7, + kRecurrentToOutputWeightsTensor = 8, + kCellToInputWeightsTensor = 9, // Optional + kCellToForgetWeightsTensor = 10, // Optional + kCellToOutputWeightsTensor = 11, // Optional + kInputGateBiasTensor = 12, // Optional + kForgetGateBiasTensor = 13, + kCellGateBiasTensor = 14, + kOutputGateBiasTensor = 15, + kProjectionWeightsTensor = 16, // Optional + kProjectionBiasTensor = 17, // Optional + kExtendedLstmInputCount = 18 +}; + +enum ExtendedLstmCellOutputs { + kScratchBufferTensor = 0, + kOutputStateTensor = 1, + kCellStateTensor = 2, + kOutputTensor = 3 +}; + +// Create optional array used for optional tensor in ExtendedLstmCell inputs. +void CreateOptionalArray(Model* model, string* input_array_buffer, + const string& array_name); + +// Create float array and get its buffer. +Buffer* CreateFloatArrayBuffer(Model* model, + string* array_name, + const Shape& shape); + +// Copy data from one array to the other one (supports 1D and 2D array), +// for 1D array, the 2nd dim's size is 1. +// Arguments: +// src_buffer: the source buffer +// src_stride: the stride of source buffer, i.e., 2nd dim's size +// src_start_idx1: the 1st dim index of start point in src matrix +// src_start_idx2: the 2nd dim index of start point in src matrix +// dst_buffer: the destination buffer +// dst_stride: the stride of destination buffer, i.e., 2nd dim's size +// dst_start_idx1: the 1st dim index of start point in dst matrix +// dst_start_idx2: the 2nd dim index of start point in dst matrix +// dim1_copy_size: 1st dim size of copy data +// dim2_copy_size: 2nd dim size of copy data +void CopyArrayData(const Buffer& src_buffer, + int src_stride, int src_start_idx1, int src_start_idx2, + Buffer* dst_buffer, int dst_stride, + int dst_start_idx1, int dst_start_idx2, int dim1_copy_size, + int dim2_copy_size); + +// Copy a subset of array data and create a smaller array, +// mostly used for spliting weights and bias for Lstm cell. +void CopySubArrayToArray(Model* model, string* array_name, + const string& tensor_name, int dim1_size, + int dim2_size, const Array& original_array, + int start_idx1, int start_idx2); + +// Copy array data to a large array's submatrix, +// mostly used for merging weights and bias for Lstm cell. +void CopyArrayToSubArray(Buffer& tensor_buffer, + int tensor_stride, const Array& sub_array, + int start_idx1, int start_idx2); + +// Get mating rnn array inputs using rnn_states flag. +bool GetMatchingRnnArray(Model* model, const string& back_edge_source_array, + string* rnn_array); + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc index d83603e9a2c59ae74a5e5fda5b11178740336bfb..935da9f966ca63095faa17476be3a559d1a0193a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc @@ -85,8 +85,8 @@ bool AddDequantizeOperatorToInput(const string& input_name, const Operator* op, auto& dequantized_input_minmax = dequantized_input_array.GetOrCreateMinMax(); dequantized_input_minmax = input_minmax; auto& input_qparams = input_array.GetOrCreateQuantizationParams(); - GetQuantizationParamsFromMinMax( - model->flags, input_minmax, &input_qparams); + GetQuantizationParamsFromMinMax(input_minmax, + &input_qparams); transformation->AddMessageF( "Created %s" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index f0d107232b4517115aa3f64b39b825dbaffb83ce..778da39bf13563cbbdbe54f1140595b057253ae3 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -71,6 +71,11 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { CHECK_GE(op->inputs.size(), 2); const ArrayDataType data_type = model->GetArray(op->inputs[1]).data_type; SetDataTypeForAllOutputs(model, op, data_type); + } else if (op->type == OperatorType::kTransposeConv) { + // These operators produce an output with the same type as their 3rd input + CHECK_GE(op->inputs.size(), 3); + const ArrayDataType data_type = model->GetArray(op->inputs[2]).data_type; + SetDataTypeForAllOutputs(model, op, data_type); } else if (op->type == OperatorType::kCast) { // Data type of the Cast op is specified. CHECK_EQ(op->outputs.size(), 1); @@ -97,10 +102,13 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); } else if (op->type == OperatorType::kTensorFlowUnsupported) { auto* unsupported_op = static_cast(op); - if (unsupported_op->output_data_types.size() != op->outputs.size()) { + // Some output tensors from the op could be eliminated by optimization. + // This can make unsupported_op->output_data_types have more elements than + // op->outputs. + if (unsupported_op->output_data_types.size() < op->outputs.size()) { return false; } - for (int i = 0; i < unsupported_op->output_data_types.size(); ++i) { + for (int i = 0; i < op->outputs.size(); ++i) { auto output = op->outputs[i]; auto data_type = unsupported_op->output_data_types[i]; model->GetArray(output).data_type = data_type; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index fa7e70d90b421eba7545e458a59c915b5ac183ef..375848a7d408b58eab8692a15376b5b2e04b34af 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -31,17 +31,22 @@ namespace { void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth, int kheight, int stride_width, int stride_height, + int dilation_width_factor, int dilation_height_factor, PaddingType padding_type, Shape* output_shape, FixedPadding* fixed_padding) { const int input_width = input_shape.dims(2); const int input_height = input_shape.dims(1); const int batch = input_shape.dims(0); + int dilated_kwidth = dilation_width_factor * (kwidth - 1) + 1; + int dilated_kheight = dilation_height_factor * (kheight - 1) + 1; + int output_height = 0; int output_width = 0; if (padding_type == PaddingType::kValid) { - output_height = (input_height + stride_height - kheight) / stride_height; - output_width = (input_width + stride_width - kwidth) / stride_width; + output_height = + (input_height + stride_height - dilated_kheight) / stride_height; + output_width = (input_width + stride_width - dilated_kwidth) / stride_width; } else if (padding_type == PaddingType::kSame) { output_height = (input_height + stride_height - 1) / stride_height; output_width = (input_width + stride_width - 1) / stride_width; @@ -49,10 +54,12 @@ void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth, LOG(FATAL) << "Only supporting SAME or VALID padding"; } - fixed_padding->height = std::max( - 0, ((output_height - 1) * stride_height + kheight - input_height) / 2); + fixed_padding->height = std::max(0, ((output_height - 1) * stride_height + + dilated_kheight - input_height) / + 2); fixed_padding->width = std::max( - 0, ((output_width - 1) * stride_width + kwidth - input_width) / 2); + 0, + ((output_width - 1) * stride_width + dilated_kwidth - input_width) / 2); // Actually had to debug a situation where those were negative due to bad // propagation of placeholder -1 sizes in TensorFlowReshape. @@ -61,23 +68,42 @@ void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth, output_shape->ReplaceDims({batch, output_height, output_width, output_depth}); } -void ComputeBinaryOperatorOutputSize(const Shape& input_shape1, - const Shape& input_shape2, +void ComputeBinaryOperatorOutputSize(const Shape& input_shape_x, + const Shape& input_shape_y, Array* output_array) { - const int size1 = RequiredBufferSizeForShape(input_shape1); - const int size2 = RequiredBufferSizeForShape(input_shape2); - if (size1 > size2) { - output_array->copy_shape(input_shape1); - } else if (size2 > size1) { - output_array->copy_shape(input_shape2); - } else { - CHECK_EQ(size1, size2); - const int dims1 = input_shape1.dimensions_count(); - const int dims2 = input_shape2.dimensions_count(); - if (dims1 >= dims2) { - output_array->copy_shape(input_shape1); + // This matches the code in BroadcastBinaryOpShapeFn from tensorflow. + // It zips together the two input shapes and pads with 1 to make them the + // same length. For each dimension we broadcast if either dimension is 1 and + // otherwise expect them to match. + int rank_x = input_shape_x.dimensions_count(); + int rank_y = input_shape_y.dimensions_count(); + int rank_out = std::max(rank_x, rank_y); + std::vector* dims_out = output_array->mutable_shape()->mutable_dims(); + dims_out->clear(); + dims_out->reserve(rank_out); + for (int i = 0; i < rank_out; ++i) { + int dim_x = i < (rank_out - rank_x) + ? 1 + : input_shape_x.dims(i - (rank_out - rank_x)); + bool dim_y_is_one = i < (rank_out - rank_y); + int dim_y = dim_y_is_one ? 1 : input_shape_y.dims(i - (rank_out - rank_y)); + if (dim_x == -1 || dim_y == -1) { + // One or both dimensions is unknown. + QCHECK(false) << "Shapes must be specified"; + } else if (dim_x == 1 || dim_y == 1) { + // Broadcast one dimension to the other that is 1. + if (dim_x == 1 && !dim_y_is_one) { + // Broadcast dim_y to dim_x (1). + dims_out->push_back(dim_y); + } else { + // Broadcast dim_x to dim_y (1). + DCHECK_EQ(dim_y, 1); + dims_out->push_back(dim_x); + } } else { - output_array->copy_shape(input_shape2); + // Expect the dimensions to match. + CHECK_EQ(dim_x, dim_y) << "Dimensions must match"; + dims_out->push_back(dim_x); } } CHECK(output_array->has_shape()); @@ -147,7 +173,8 @@ void ProcessConvOperator(Model* model, ConvOperator* op) { const int kheight = weights_shape.dims(1); const int kwidth = weights_shape.dims(2); ComputeConvSizes(input_shape, output_depth, kwidth, kheight, op->stride_width, - op->stride_height, op->padding.type, + op->stride_height, op->dilation_width_factor, + op->dilation_height_factor, op->padding.type, output_array.mutable_shape(), &op->padding.GetOrCreateFixedPadding()); CHECK_EQ(output_array.shape().dimensions_count(), 4); @@ -163,6 +190,116 @@ void ProcessConvOperator(Model* model, ConvOperator* op) { } } +void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { + // TransposeConv is unique in that it is specifically given the output shape + // as a 1D array on it's 1st input. Theoretically then, resolving the output + // shape is as easy as waiting for this input to be resolved. However, we also + // have to calculate the padding which requires the weights shape. So, we + // might as well calculate the output shape and ensure it matches the + // specified one + + // Check if we have already run. + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + return; + } + + // SPECIFIED OUTPUT SHAPE + // The below is the specified, or prescribed output shape, _given_ to the + // operator as an input. + auto& specified_output_shape_array = + model->GetArray(op->inputs[TransposeConvOperator::OUTPUT_SHAPE]); + if (!specified_output_shape_array.has_shape() || + !specified_output_shape_array.buffer) { + // Yield until the specified output shape is resolved as a constant + return; + } + + CHECK(specified_output_shape_array.data_type == ArrayDataType::kInt32) + << "TransposeConv input_dims must be int32"; + + CHECK(specified_output_shape_array.shape().dimensions_count() == 1 && + specified_output_shape_array.shape().dims(0) == 4) + << "TransposeConv requires a 1D, 4 element array on it's 0th input " + "specifying the output shape. \"" + << op->inputs[TransposeConvOperator::OUTPUT_SHAPE] << "\" had shape " + << toco::ShapeToString(specified_output_shape_array.shape()); + + // COMPUTE PADDING + // We require the weights shape to calculate padding. + const auto& weights_array = + model->GetArray(op->inputs[TransposeConvOperator::WEIGHTS]); + if (!weights_array.has_shape()) { + // Yield until weights dims have been resolved. + return; + } + const auto& weights_shape = weights_array.shape(); + CHECK_EQ(weights_shape.dimensions_count(), 4) + << "TransposeConv weights must have 4 input dimensions. Input weights \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + CHECK(weights_shape.dims(0) == 1 && weights_shape.dims(3) == 1) + << "TransposeConv weights dimensions must begin and end with 1. Input " + "weights \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + // Compute padding + const int kheight = weights_shape.dims(1); + const int kwidth = weights_shape.dims(2); + op->padding.GetOrCreateFixedPadding(); + if (op->padding.type == PaddingType::kValid) { + op->padding.fixed->height = 0; + op->padding.fixed->width = 0; + } else if (op->padding.type == PaddingType::kSame) { + op->padding.fixed->height = (kheight - 1) / 2; + op->padding.fixed->width = (kwidth - 1) / 2; + } else { + LOG(FATAL) << "TransposeConv only supports SAME or VALID padding"; + } + + // VALIDATE OUTPUT SHAPE + // Compute the output shape from the input and weights shapes to verify it + // agrees with the specified output shape. + const auto& input_array = + model->GetArray(op->inputs[TransposeConvOperator::DATA_INPUT]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return; + } + const auto& input_shape = input_array.shape(); + CHECK_EQ(input_shape.dimensions_count(), 4) + << "TransposeConv input shape must have 4 dimensions. Input \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + // Compute output shape + const int input_width = input_shape.dims(2); + const int input_height = input_shape.dims(1); + int output_height = op->stride_height * (input_height - 1); + int output_width = op->stride_width * (input_width - 1); + if (op->padding.type == PaddingType::kValid) { + output_height += kheight; + output_width += kwidth; + } else if (op->padding.type == PaddingType::kSame) { + output_height += 1; + output_width += 1; + } + + CHECK(specified_output_shape_array.GetBuffer().data == + std::vector({input_shape.dims(0), output_height, output_width, + weights_shape.dims(3)})) + << "Specified output shape: " << ShapeToString(output_array.shape()) + << ", does not agree with shape computed from input data and weights: [" + << input_shape.dims(0) << ", " << output_height << ", " << output_width + << ", " << weights_shape.dims(3) << "]."; + + // SUCCESS: Set the op's output shape according to the specified output shape. + *(output_array.mutable_shape()->mutable_dims()) = + specified_output_shape_array.GetBuffer().data; +} + void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { if (!EnsureBiasVectorShape(model, op)) { return; @@ -203,7 +340,7 @@ void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { const int kheight = weights_shape.dims(1); const int kwidth = weights_shape.dims(2); ComputeConvSizes(input_shape, output_depth, kwidth, kheight, op->stride_width, - op->stride_height, op->padding.type, + op->stride_height, 1, 1, op->padding.type, model->GetArray(output_name).mutable_shape(), &op->padding.GetOrCreateFixedPadding()); } @@ -631,15 +768,34 @@ void ProcessTensorFlowSplitOperator(Model* model, TensorFlowSplitOperator* op) { } const Shape& input_shape = input_array.shape(); - // This code is slightly suspect. The TensorFlow docs say that the axis - // selection defaults to 0, but we are splitting across the final axis. - const int input_dims_count = input_shape.dimensions_count(); - const int input_depth = input_shape.dims(input_dims_count - 1); - CHECK_EQ(input_depth % op->num_split, 0); - const int split_depth = input_depth / op->num_split; + // Yield until axis is constant. + if (!IsConstantParameterArray(*model, op->inputs[0])) { + return; + } + + const auto& axis_array = model->GetArray(op->inputs[0]); + + // Yield until axis dims have been resolved. + if (!axis_array.has_shape()) { + return; + } + + CHECK(axis_array.data_type == ArrayDataType::kInt32) + << "Axis array must be int32."; + CHECK_EQ(RequiredBufferSizeForShape(axis_array.shape()), 1) + << "Axis array must be scalar."; + + int axis = axis_array.GetBuffer().data[0]; + if (axis < 0) { + axis += input_shape.dimensions_count(); + } + + const int split_dim = input_shape.dims(axis); + CHECK_EQ(split_dim % op->num_split, 0); + const int split_depth = split_dim / op->num_split; Shape output_shape = input_shape; - (*output_shape.mutable_dims())[input_dims_count - 1] = split_depth; + (*output_shape.mutable_dims())[axis] = split_depth; CHECK_EQ(op->outputs.size(), op->num_split); for (const auto& output : op->outputs) { @@ -659,7 +815,7 @@ void ProcessAveragePoolOperator(Model* model, AveragePoolOperator* op) { const string& output_name = op->outputs[0]; const int output_depth = input_shape.dims(3); ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight, - op->stride_width, op->stride_height, op->padding.type, + op->stride_width, op->stride_height, 1, 1, op->padding.type, model->GetArray(output_name).mutable_shape(), &op->padding.GetOrCreateFixedPadding()); } @@ -676,7 +832,7 @@ void ProcessMaxPoolOperator(Model* model, MaxPoolOperator* op) { const string& output_name = op->outputs[0]; const int output_depth = input_shape.dims(3); ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight, - op->stride_width, op->stride_height, op->padding.type, + op->stride_width, op->stride_height, 1, 1, op->padding.type, model->GetArray(output_name).mutable_shape(), &op->padding.GetOrCreateFixedPadding()); } @@ -695,7 +851,7 @@ void ProcessL2PoolOperator(Model* model, L2PoolOperator* op) { const string& output_name = op->outputs[0]; const int output_depth = input_shape.dims(3); ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight, - op->stride_width, op->stride_height, op->padding.type, + op->stride_width, op->stride_height, 1, 1, op->padding.type, model->GetArray(output_name).mutable_shape(), &op->padding.GetOrCreateFixedPadding()); } @@ -728,9 +884,8 @@ void ProcessResizeBilinearOperator(Model* model, ResizeBilinearOperator* op) { } void ProcessLstmCellOperator(Model* model, LstmCellOperator* op) { - // I/O arrays should be allocated on creation of op. - QCHECK_EQ(op->inputs.size(), LstmCellOperator::NUM_INPUTS); - QCHECK_EQ(op->outputs.size(), LstmCellOperator::NUM_OUTPUTS); + // Only required for compact LstmCell with default NUM_INPUTS of inputs. + if (op->inputs.size() != LstmCellOperator::NUM_INPUTS) return; const auto& input_array = model->GetArray(op->inputs[LstmCellOperator::DATA_INPUT]); @@ -945,6 +1100,43 @@ void ProcessGatherOperator(Model* model, GatherOperator* op) { } } +void ProcessTopkV2Operator(Model* model, TopKV2Operator* op) { + const auto& input_values = model->GetArray(op->inputs[0]); + const auto& input_k = model->GetArray(op->inputs[1]); + auto& output_indexes = model->GetArray(op->outputs[0]); + auto& output_values = model->GetArray(op->outputs[1]); + + // Bail if we already know the output shape. + if (output_indexes.has_shape()) { + QCHECK(output_values.has_shape()); + return; + } + + // Yield until input dims have been resolved. + if (!input_values.has_shape()) { + return; + } + + const auto& input_values_shape = input_values.shape(); + auto output_indexes_dims = output_indexes.mutable_shape()->mutable_dims(); + auto output_values_dims = output_values.mutable_shape()->mutable_dims(); + for (int dim = 0; dim < input_values_shape.dimensions_count() - 1; dim++) { + output_indexes_dims->push_back(input_values_shape.dims(dim)); + output_values_dims->push_back(input_values_shape.dims(dim)); + } + // If the value is initialized, we can specify the last dimension, otherwise + // unknown. + if (input_k.buffer) { + const int32_t k_value = input_k.GetBuffer().data[0]; + output_indexes_dims->push_back(k_value); + output_values_dims->push_back(k_value); + + } else { + output_indexes_dims->push_back(0); + output_values_dims->push_back(0); + } +} + void ProcessPadOperator(Model* model, PadOperator* op) { CHECK_EQ(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); @@ -1218,7 +1410,8 @@ void ProcessTransposeOperator(Model* model, TransposeOperator* op) { std::vector const& perm = perm_array.GetBuffer().data; CHECK_EQ(perm.size(), input_shape.dimensions_count()) - << "Transpose permutation input must be same length as input dimensions"; + << "Transpose permutation input " << op->inputs[1] + << " must be same length as input dimensions"; std::vector* output_dims = output_array.mutable_shape()->mutable_dims(); for (int i = 0; i < perm.size(); i++) { int axis = perm[i]; @@ -1275,6 +1468,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kRelu1: case OperatorType::kRelu6: case OperatorType::kSoftmax: + case OperatorType::kLogSoftmax: case OperatorType::kLogistic: case OperatorType::kTanh: case OperatorType::kLocalResponseNormalization: @@ -1288,12 +1482,15 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kTensorFlowAssert: case OperatorType::kCast: case OperatorType::kFloor: + case OperatorType::kExp: ProcessSimpleOperator(model, op); break; case OperatorType::kGather: ProcessGatherOperator(model, static_cast(op)); break; - + case OperatorType::kTopK_V2: + ProcessTopkV2Operator(model, static_cast(op)); + break; case OperatorType::kAdd: case OperatorType::kSub: case OperatorType::kMul: @@ -1315,8 +1512,8 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessConvOperator(model, static_cast(op)); break; case OperatorType::kTransposeConv: - // Unimplemented, hopefully another graph transformation will drop it or - // rewrite it. + ProcessTransposeConvOperator(model, + static_cast(op)); break; case OperatorType::kDepthwiseConv: ProcessDepthwiseConvOperator(model, @@ -1424,6 +1621,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kLstmCell: ProcessLstmCellOperator(model, static_cast(op)); break; + case OperatorType::kBatchMatMul: case OperatorType::kTensorFlowMatMul: // MatMul operators are converted to FullyConnected, after which their // shapes are propagated. @@ -1454,6 +1652,12 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kTranspose: ProcessTransposeOperator(model, static_cast(op)); break; + case OperatorType::kDynamicPartition: + case OperatorType::kDynamicStitch: + // DynamicPartition/DynamicStitch are currently only supported for + // transforms that remove them, so we avoid propagating shapes through + // them and let things settle once they've been removed. + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index 139c19022ed45e0243ca7c1a84717a5d79bb81cf..4fd26e4325a7ff53762d9acd6f6d3463bba5b830 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -49,7 +49,7 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kTensorFlowReshape || type == OperatorType::kTanh || type == OperatorType::kMul || type == OperatorType::kSpaceToDepth || - type == OperatorType::kDepthToSpace; + type == OperatorType::kDepthToSpace || type == OperatorType::kLstmCell; } template @@ -100,10 +100,19 @@ void QuantizeArray(GraphTransformation* transformation, Model* model, void QuantizeArray(GraphTransformation* transformation, Model* model, const string& name, ArrayDataType quantized_data_type, const QuantizationParams& quantization_params) { - switch (quantized_data_type) { + ArrayDataType adjusted_data_type = quantized_data_type; + auto& array = model->GetArray(name); + if (array.final_data_type == ArrayDataType::kInt16) { + adjusted_data_type = array.final_data_type; + } + + switch (adjusted_data_type) { case ArrayDataType::kUint8: return QuantizeArray(transformation, model, name, quantization_params); + case ArrayDataType::kInt16: + return QuantizeArray(transformation, model, name, + quantization_params); case ArrayDataType::kInt32: return QuantizeArray(transformation, model, name, quantization_params); @@ -163,6 +172,102 @@ const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) { "proceed with quantization."; } +struct QuantizationPoints { + int64 min_value; + int64 max_value; + int64 central_value; +}; + +template +QuantizationPoints GetQuantizationPoints() { + QuantizationPoints qp; + using Integer = DataType; + qp.min_value = std::numeric_limits::min(); + qp.max_value = std::numeric_limits::max(); + // eg [-128,127]... + qp.central_value = (qp.min_value / 2 + // -128 -> -64. + (qp.max_value - 1) / 2 + // 127 -> 63. + 1); + return qp; +} + +QuantizationPoints GetQuantizationPoints(ArrayDataType data_type) { + switch (data_type) { + case ArrayDataType::kUint8: + return GetQuantizationPoints(); + case ArrayDataType::kInt16: + return GetQuantizationPoints(); + case ArrayDataType::kInt32: + return GetQuantizationPoints(); + default: + LOG(FATAL) << "Unhandled case."; + } +} + +ArrayDataType GetQuantizedDataType(const Array& array, + ArrayDataType default_type) { + switch (array.final_data_type) { + case ArrayDataType::kInt8: + case ArrayDataType::kUint8: + case ArrayDataType::kInt16: + case ArrayDataType::kUint16: + case ArrayDataType::kInt32: + case ArrayDataType::kUint32: + case ArrayDataType::kInt64: + case ArrayDataType::kUint64: + return array.final_data_type; + case ArrayDataType::kFloat: + case ArrayDataType::kNone: + return default_type; + default: + LOG(FATAL) << "Unhandled final quantization type " + << static_cast(array.final_data_type); + } +} + +void GetQuantizationParams(ArrayDataType data_type, const MinMax& minmax, + QuantizationParams* quantization_params) { + switch (data_type) { + case ArrayDataType::kInt8: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint8: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt16: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint16: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt32: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint32: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt64: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint64: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kFloat: + case ArrayDataType::kNone: + default: + LOG(FATAL) << "Unhandled final quantization type " + << static_cast(data_type); + } +} + bool ChooseQuantizationForOperatorInput( GraphTransformation* transformation, Model* model, const Operator& op, std::size_t input_index, ArrayDataType* quantized_data_type, @@ -172,46 +277,70 @@ bool ChooseQuantizationForOperatorInput( if (array.data_type != ArrayDataType::kFloat) { return false; } + + // Quantization of bias vectors + bool is_bias_vector = false; + int activations_input_index; + int weights_input_index; if (op.type == OperatorType::kConv || op.type == OperatorType::kDepthwiseConv || op.type == OperatorType::kFullyConnected) { if (input_index == 2) { - // Quantization of bias vector. - // We need both of the mandatory inputs (input activations and weights) to - // have - // been already quantized. - const auto& input_activations = model->GetArray(op.inputs[0]); - const auto& input_weights = model->GetArray(op.inputs[1]); - if (!input_activations.quantization_params || - !input_weights.quantization_params) { - return false; - } - const auto input_activations_scale = - input_activations.quantization_params->scale; - const auto input_weights_scale = input_weights.quantization_params->scale; - quantization_params->scale = - input_activations_scale * input_weights_scale; - quantization_params->zero_point = 0; - *quantized_data_type = ArrayDataType::kInt32; - transformation->AddMessageF( - "Input array %s is a bias vector. Choosing quantization params " - "accordingly.", - input); - return true; + is_bias_vector = true; + activations_input_index = 0; + weights_input_index = 1; + } + } + if (op.type == OperatorType::kLstmCell) { + if (input_index == LstmCellOperator::BIASES_INPUT) { + is_bias_vector = true; + activations_input_index = LstmCellOperator::DATA_INPUT; + weights_input_index = LstmCellOperator::WEIGHTS_INPUT; } } + if (is_bias_vector) { + // Quantization of bias vector. + // We need both of the mandatory inputs (input activations and weights) to + // have been already quantized. + const auto& input_activations = + model->GetArray(op.inputs[activations_input_index]); + const auto& input_weights = model->GetArray(op.inputs[weights_input_index]); + if (!input_activations.quantization_params || + !input_weights.quantization_params) { + return false; + } + const auto input_activations_scale = + input_activations.quantization_params->scale; + const auto input_weights_scale = input_weights.quantization_params->scale; + quantization_params->scale = input_activations_scale * input_weights_scale; + quantization_params->zero_point = 0; + *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kInt32); + transformation->AddMessageF( + "Input array %s is a bias vector. Choosing quantization params " + "accordingly.", + input); + return true; + } const MinMax& minmax = GetOrComputeMinMax(model, input); - GetQuantizationParamsFromMinMax(model->flags, minmax, - quantization_params); + + if (op.type == OperatorType::kLstmCell) { + if (input_index == LstmCellOperator::PREV_STATE_INPUT) { + *quantized_data_type = ArrayDataType::kInt16; + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + return true; + } + } + + *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); transformation->AddMessageF( "For input array %s with min=%g" ", max=%g" - ", chose to quantize as uint8 with zero_point=%d" + ", chose to quantize as %s with zero_point=%d" ", scale=%g", - input, minmax.min, minmax.max, quantization_params->zero_point, - quantization_params->scale); - *quantized_data_type = ArrayDataType::kUint8; + input, minmax.min, minmax.max, ArrayDataTypeName(*quantized_data_type), + quantization_params->zero_point, quantization_params->scale); return true; } @@ -233,16 +362,18 @@ bool IsExactlyRepresentable(double real_value, ArrayDataType data_type, return true; } +// Quantized data type is preset to the type of the input before this function. bool ChooseHardcodedQuantizationForOperatorOutput( - const Operator& op, ArrayDataType* quantized_data_type, + const Operator& op, const Array& array, ArrayDataType* quantized_data_type, QuantizationParams* quantization_params) { if (op.type == OperatorType::kL2Normalization) { // L2Normalization has range: [-1, 1]. // 0 should be exactly representable, as values will typically be centered // around 0, with many values near 0. - *quantized_data_type = ArrayDataType::kUint8; - quantization_params->zero_point = 128; - quantization_params->scale = 1. / 128.; + *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type); + const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type); + quantization_params->zero_point = qp.central_value; + quantization_params->scale = 1. / (qp.central_value - qp.min_value); CHECK( IsExactlyRepresentable(0., *quantized_data_type, *quantization_params)); return true; @@ -255,18 +386,20 @@ bool ChooseHardcodedQuantizationForOperatorOutput( // will typically exploit the symmetry logistic(-x) = 1 - logistic(x), and // the glueing of the two halves of the graph will only be seamless if we // are accurately representing logistic(0) == 0.5. - *quantized_data_type = ArrayDataType::kUint8; + *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type); + const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type); quantization_params->zero_point = 0; - quantization_params->scale = 1. / 256.; + quantization_params->scale = 1. / (qp.max_value + 1); CHECK(IsExactlyRepresentable(0.5, *quantized_data_type, *quantization_params)); return true; } if (op.type == OperatorType::kTanh) { // Tanh has the range: [-1, 1]. - *quantized_data_type = ArrayDataType::kUint8; - quantization_params->zero_point = 127; - quantization_params->scale = 1. / 128.; + *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type); + const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type); + quantization_params->zero_point = qp.central_value; + quantization_params->scale = 1. / (qp.central_value - qp.min_value); // 0 should be exactly representable, as values will typically be centered // around 0, with many values near 0. CHECK( @@ -285,8 +418,9 @@ bool ChooseQuantizationForOperatorOutput( if (array.data_type != ArrayDataType::kFloat) { return false; } - if (ChooseHardcodedQuantizationForOperatorOutput(op, quantized_data_type, - quantization_params)) { + *quantized_data_type = model->GetArray(op.inputs[0]).data_type; + if (ChooseHardcodedQuantizationForOperatorOutput( + op, array, quantized_data_type, quantization_params)) { transformation->AddMessageF( "Output array %s is produced by a %s operator. Choosing fixed " "quantization params accordingly.", @@ -294,12 +428,21 @@ bool ChooseQuantizationForOperatorOutput( return true; } if ((op.type == OperatorType::kDepthToSpace) || - (op.type == OperatorType::kSpaceToDepth)) { - // DepthToSpace and SpaceToDepth should preserve the quantization parameters - // of the input array, as these are simple reshape operations. - const auto& input_quantization_params = - model->GetArray(op.inputs[0]).GetQuantizationParams(); - *quantized_data_type = ArrayDataType::kUint8; + (op.type == OperatorType::kSpaceToDepth) || + (op.type == OperatorType::kTensorFlowReshape) || + (op.type == OperatorType::kTensorFlowSplit) || + (op.type == OperatorType::kConcatenation)) { + int data_input_index = 0; + if (op.type == OperatorType::kTensorFlowSplit) { + data_input_index = 1; + } + // Copying and rearrangement ops should preserve the quantization parameters + // of the input array. + const auto& input_array = model->GetArray(op.inputs[data_input_index]); + const auto& input_quantization_params = input_array.GetQuantizationParams(); + *quantized_data_type = + GetQuantizedDataType(input_array, ArrayDataType::kUint8); + *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type); quantization_params->zero_point = input_quantization_params.zero_point; quantization_params->scale = input_quantization_params.scale; @@ -310,15 +453,22 @@ bool ChooseQuantizationForOperatorOutput( return true; } const MinMax& minmax = GetOrComputeMinMax(model, output); - GetQuantizationParamsFromMinMax(model->flags, minmax, - quantization_params); - *quantized_data_type = ArrayDataType::kUint8; + if (op.type == OperatorType::kLstmCell) { + if (output_index == LstmCellOperator::STATE_OUTPUT || + output_index == LstmCellOperator::ACTIV_TEMP) { + *quantized_data_type = ArrayDataType::kInt16; + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + return true; + } + } + *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); transformation->AddMessageF( "For output array %s with min=%g, max=%g" - ", chose to quantize as uint8 with zero_point=%d" + ", chose to quantize as %s with zero_point=%d" ", scale=%g", - output, minmax.min, minmax.max, quantization_params->zero_point, - quantization_params->scale); + output, minmax.min, minmax.max, ArrayDataTypeName(*quantized_data_type), + quantization_params->zero_point, quantization_params->scale); return true; } @@ -405,41 +555,52 @@ bool Quantize::Run(Model* model, std::size_t op_index) { if (ChooseQuantizationForOperatorInput(this, model, op, input_index, &quantized_data_type, &quantization_params)) { - changed = true; const auto& input = op.inputs[input_index]; if (IsConstantParameterArray(*model, input)) { QuantizeArray(this, model, input, quantized_data_type, quantization_params); - } else if (toco::IsRnnStateArray(*model, input)) { - // Simply Quantize the Array - auto& array = model->GetArray(op.inputs[input_index]); - array.GetOrCreateQuantizationParams() = quantization_params; - array.data_type = quantized_data_type; + changed = true; } else { auto dequantize_it = FindOpWithOutput(*model, input); - CHECK(dequantize_it != model->operators.end()) - << "Cannot quantize input \"" << input - << "\" on operator with output \"" << op.outputs[0] - << "\". Nothing feeding input."; - auto* dequantize_op = dequantize_it->get(); - CHECK(dequantize_op->type == OperatorType::kDequantize) - << "Cannot quantize input \"" << input - << "\" on operator with output \"" << op.outputs[0] - << "\". Input is not fed by a Dequantize operator."; - op.inputs[input_index] = dequantize_op->inputs[0]; - // Check if the output of that Dequantize op was not used by any - // other operator. We will then erase that Dequantize op. - if (!CountOpsWithInput(*model, dequantize_op->outputs[0])) { - // If any of the model's output_arrays was pointing to the - // Dequantize op's output, let it point to the Dequantize op's - // input instead. - for (int i = 0; i < model->flags.output_arrays_size(); i++) { - if (model->flags.output_arrays(i) == dequantize_op->outputs[0]) { - model->flags.set_output_arrays(i, dequantize_op->inputs[0]); + if (dequantize_it != model->operators.end()) { + auto* dequantize_op = dequantize_it->get(); + CHECK(dequantize_op->type == OperatorType::kDequantize); + op.inputs[input_index] = dequantize_op->inputs[0]; + // Check if the output of that Dequantize op was not used by any + // other operator. We will then erase that Dequantize op. + if (!CountOpsWithInput(*model, dequantize_op->outputs[0])) { + // If any of the model's output_arrays was pointing to the + // Dequantize op's output, let it point to the Dequantize op's + // input instead. + for (int i = 0; i < model->flags.output_arrays_size(); i++) { + if (model->flags.output_arrays(i) == dequantize_op->outputs[0]) { + model->flags.set_output_arrays(i, dequantize_op->inputs[0]); + } + } + model->EraseArray(dequantize_op->outputs[0]); + model->operators.erase(dequantize_it); + } + changed = true; + } else { + // This input array is not produced by a Dequantize op. + // We have encountered this situation in RNN graphs, whose cyclic + // nature defeats the basic assumption underlying the quantization + // algorithm implemented here. For now, when we have seen this + // happening, the array in question was a RNN state array itself, + // so let us just implement this case here, and guard that assumption + // with a CHECK. A more general fix would involve revisiting the + // design of this whole Quantization transformation. + bool is_rnn_state_array = false; + for (const auto& rnn_state : model->flags.rnn_states()) { + if (rnn_state.state_array() == input) { + is_rnn_state_array = true; + break; } } - model->EraseArray(dequantize_op->outputs[0]); - model->operators.erase(dequantize_it); + CHECK(is_rnn_state_array); + QuantizeArray(this, model, input, quantized_data_type, + quantization_params); + changed = true; } } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc index 587f171bbf823408a45083c36d52f1d38c300123..aa93ace03af300f9cbd3f9c6620a6a58b9329aa4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc @@ -60,7 +60,9 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation, for (int i = 0; i < passthru_op->inputs.size(); i++) { if (!model->GetArray(passthru_op->inputs[i]).buffer) { count_nonconstant_input_arrays++; - main_input_array_index = i; + if (count_nonconstant_input_arrays == 1) { + main_input_array_index = i; + } } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_slice.cc new file mode 100644 index 0000000000000000000000000000000000000000..0cbbcd7c814d38e32ee55e9d9271adf532d20924 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_slice.cc @@ -0,0 +1,69 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +bool IsSliceTrivial(const Model& model, const Operator& op, + RemoveTrivialSlice* transformation) { + CHECK(op.type == OperatorType::kSlice); + + // Slices are trivial if they are slicing the entire input contents. + const auto& input_array = model.GetArray(op.inputs[0]); + const auto& output_array = model.GetArray(op.outputs[0]); + if (input_array.has_shape() && output_array.has_shape()) { + if (input_array.shape() == output_array.shape()) { + transformation->AddMessageF( + "%s is trivial because its input and output shapes are equal", + LogName(op)); + return true; + } + } + + return false; +} + +} // namespace + +bool RemoveTrivialSlice::Run(Model* model, std::size_t op_index) { + const auto reshape_it = model->operators.begin() + op_index; + auto* slice_op = reshape_it->get(); + if (slice_op->type != OperatorType::kSlice) { + return false; + } + + if (!IsSliceTrivial(*model, *slice_op, this)) { + return false; + } + + AddMessageF("Removing trivial %s", LogName(*slice_op)); + + CHECK_EQ(slice_op->inputs.size(), 3); + return RemoveTrivialPassthroughOp(this, model, op_index); +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc index cabbc4d313be3069053f056eb0de45c37ba2e7a4..30a005c789bb12e880e8e4534088d99ebacba84a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc @@ -62,6 +62,20 @@ bool ReorderActivationFunctions::Run(Model* model, std::size_t op_index) { return false; } + // If the ac_op was originally producing an output_array we can't reorder as + // otherwise the output array would change. It'd be nice to still be able to + // reorder but if code is relying on the fetch names instead of array indices + // this won't work. + for (int i = 0; i < model->flags.output_arrays_size(); ++i) { + if (model->flags.output_arrays(i) == ac_op->outputs[0]) { + AddMessageF( + "Not exchanging activation function with %s to preserve output array " + "name %s", + LogName(*exchange_op), ac_op->outputs[0]); + return false; + } + } + // Rewire by changing inputs, including all consumers. Operator* consumer = GetFirstOpWithInput(*model, ac_op_output); while (consumer) { @@ -75,6 +89,10 @@ bool ReorderActivationFunctions::Run(Model* model, std::size_t op_index) { ac_op->inputs[0] = exchange_op_input; exchange_op->inputs[0] = ac_op_output; + // Clear shapes; this will allow shape propagation to fix the sizes for us. + model->GetOrCreateArray(ac_op->outputs[0]).clear_shape(); + model->GetOrCreateArray(exchange_op->outputs[0]).clear_shape(); + // Finally, reorder operators. Note that this only works when there are no // other direct descendents of the exchange_op. ac_op.swap(exchange_op); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc index db68968bad1e6fbf020c6ac82d6871b5a071b29e..064810b53e7c3bee4601204c9dbd976c374a6a60 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc @@ -190,7 +190,7 @@ bool ResolveConstantConcatenation::Run(Model* model, std::size_t op_index) { // Remove all the resolved arrays. for (const string& input_name : concat_op->inputs) { // Check to prevent removal of shared tensors - if(CountOpsWithInput(*model, input_name) == 1) { + if (CountOpsWithInput(*model, input_name) == 1) { model->EraseArray(input_name); } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc index 81fe37d7e017c6e2440de34cc2daedf7fb2a422e..625d90205a801ad7c3fc1026c9cedc9b509f920d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc @@ -50,12 +50,13 @@ bool ResolveConstantFakeQuant::Run(Model* model, std::size_t op_index) { output_array.data_type = ArrayDataType::kFloat; CHECK(!output_array.buffer); const auto& input_buffer = input_array.GetBuffer(); + output_array.GetOrCreateMinMax() = *fakequant_op->minmax; auto& output_buffer = output_array.GetMutableBuffer(); const int size = input_buffer.data.size(); output_buffer.data.resize(size); QuantizationParams qparams; - GetQuantizationParamsFromMinMax( - model->flags, *fakequant_op->minmax, &qparams); + GetQuantizationParamsFromMinMax(*fakequant_op->minmax, + &qparams); for (int i = 0; i < size; i++) { const double src_val = input_buffer.data[i]; const double unclamped_quantized_val = diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc new file mode 100644 index 0000000000000000000000000000000000000000..4f984bfde55b3457694bb411bbfdf30723c7066e --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc @@ -0,0 +1,180 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +// Transposes an array up to rank 4. +// This is ShuffleArrayTemplate with non-enum permutation. +template +void Transpose(Model* model, const Array& input_array, + const std::vector& perm, Array* output_array) { + const Shape& input_shape = input_array.shape(); + const std::vector>& input_data = + input_array.GetBuffer().data; + + const Shape& output_shape = output_array->shape(); + std::vector>& output_data = + output_array->GetMutableBuffer().data; + output_data.resize(RequiredBufferSizeForShape(output_shape)); + + CHECK(input_shape.dimensions_count() == output_shape.dimensions_count()); + const int dim = input_shape.dimensions_count(); + CHECK_LE(dim, 4); + CHECK(perm.size() >= dim); + for (int i = 0; i < dim; i++) { + CHECK(perm[i] >= 0 && perm[i] < dim); + CHECK(input_shape.dims(perm[i]) == output_shape.dims(i)); + } + Shape extended_input_shape = input_shape; + ExtendShape(&extended_input_shape, 4); + Shape extended_output_shape = output_shape; + ExtendShape(&extended_output_shape, 4); + std::vector extended_perm; + ExtendShuffle(perm, 4, &extended_perm); + + const std::vector& extended_input_dims = extended_input_shape.dims(); + const std::vector& extended_output_dims = extended_output_shape.dims(); + + // TODO(starka): Rework to handle different numbers of dimensions. + int input_strides[4]; + input_strides[3] = 1; + input_strides[2] = extended_input_dims[3]; + input_strides[1] = input_strides[2] * extended_input_dims[2]; + input_strides[0] = input_strides[1] * extended_input_dims[1]; + const int input_stride_0 = input_strides[extended_perm[3]]; + const int input_stride_1 = input_strides[extended_perm[2]]; + const int input_stride_2 = input_strides[extended_perm[1]]; + const int input_stride_3 = input_strides[extended_perm[0]]; + + const int output_size_0 = extended_output_dims[3]; + const int output_size_1 = extended_output_dims[2]; + const int output_size_2 = extended_output_dims[1]; + const int output_size_3 = extended_output_dims[0]; + const int output_stride_0 = 1; + const int output_stride_1 = output_size_0; + const int output_stride_2 = output_stride_1 * output_size_1; + const int output_stride_3 = output_stride_2 * output_size_2; + + for (int i3 = 0; i3 < output_size_3; i3++) { + const DataType* const input_ptr_3 = + input_data.data() + i3 * input_stride_3; + DataType* const output_ptr_3 = + output_data.data() + i3 * output_stride_3; + for (int i2 = 0; i2 < output_size_2; i2++) { + const DataType* const input_ptr_2 = + input_ptr_3 + i2 * input_stride_2; + DataType* const output_ptr_2 = output_ptr_3 + i2 * output_stride_2; + for (int i1 = 0; i1 < output_size_1; i1++) { + const DataType* input_ptr = input_ptr_2 + i1 * input_stride_1; + DataType* output_ptr = output_ptr_2 + i1 * output_stride_1; + DataType* const output_ptr_end = + output_ptr + output_size_0 * output_stride_0; + while (output_ptr != output_ptr_end) { + *output_ptr = *input_ptr; + input_ptr += input_stride_0; + output_ptr += output_stride_0; + } + } + } + } +} + +} // namespace + +bool ResolveConstantTranspose::Run(Model* model, std::size_t op_index) { + auto it = model->operators.begin() + op_index; + const auto* base_op = it->get(); + if (base_op->type != OperatorType::kTranspose) { + return false; + } + const auto* op = static_cast(base_op); + + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 1); + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.data_type == ArrayDataType::kNone) { + // Yield until the output type has been set by PropagateArrayDataTypes. + return false; + } + if (!output_array.has_shape()) { + // Yield until the output shape has been set by PropagateFixedShapes. + return false; + } + + // We require constant inputs. + if (!IsConstantParameterArray(*model, op->inputs[0]) || + !IsConstantParameterArray(*model, op->inputs[1])) { + return false; + } + const Array& input_array = model->GetArray(op->inputs[0]); + + if (input_array.minmax) { + output_array.GetOrCreateMinMax() = input_array.GetMinMax(); + } + + if (op->perm.empty()) { + // Yield until perm has been populated by ResolveTransposeAttributes. + return false; + } + + // We currently only support 1-4 dimensions. + CHECK_LE(op->perm.size(), 4); + + CHECK(!output_array.buffer); + switch (output_array.data_type) { + case ArrayDataType::kFloat: + Transpose(model, input_array, op->perm, + &output_array); + break; + case ArrayDataType::kUint8: + Transpose(model, input_array, op->perm, + &output_array); + break; + case ArrayDataType::kInt32: + Transpose(model, input_array, op->perm, + &output_array); + break; + case ArrayDataType::kInt64: + Transpose(model, input_array, op->perm, + &output_array); + break; + default: + LOG(FATAL) << "Unsupported data type given to Transpose op with output \"" + << op->outputs[0] << "\""; + break; + } + + // Erase input arrays if no longer used. + for (const auto& input : op->inputs) { + if (IsDiscardableArray(*model, input) && + CountOpsWithInput(*model, input) == 1) { + model->EraseArray(input); + } + } + + // Erase the operator. + model->operators.erase(it); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index 1cd2aff28c68eaba4e9b18d8e2c2803834328696..d96b3d522d3d3475496cc8a2ad3c1752daa0c842 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -138,15 +138,34 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { memcpy(output_float_data.data(), (*input_float_data).data(), output_buffer_size * sizeof(output_float_data[0])); } else if (unary_op->type == OperatorType::kTensorFlowSum) { - // At the moment only full reduction across all dimensions is supported. - for (int i = 0; i < output_dims_count; i++) { - CHECK_EQ(output_shape.dims(i), 1); + CHECK_EQ(unary_op->inputs.size(), 2) << "Sum needs 2 inputs"; + if (!IsConstantParameterArray(*model, unary_op->inputs[1])) { + AddMessageF("Axis input is non-constant"); + return false; } - float sum = 0.f; - for (int i = 0; i < input_buffer_size; i++) { - sum += (*input_float_data)[i]; + auto& axis_array = model->GetArray(unary_op->inputs[1]); + CHECK(axis_array.data_type == ArrayDataType::kInt32); + int axis = axis_array.GetBuffer().data[0]; + CHECK_LT(axis, input_shape.dimensions_count()) << "Axis out of bounds"; + + // We currently only handle reduction on axis 0. + CHECK_EQ(axis, 0) << "Only reduction along axis 0 is supported"; + // We currently only handle 1-D and 2-D input tensors. + CHECK_LE(input_shape.dimensions_count(), 2) << "Rank >2 not yet supported"; + // We only support keep_dims=true; shape prop will need to change otherwise. + auto sum_op = static_cast(unary_op); + CHECK(sum_op->keep_dims) << "Only keep_dims=true is supported"; + + std::vector indices(input_shape.dimensions_count()); + for (int i = 0; i < input_shape.dims(1); ++i) { + indices[1] = i; + float sum = 0.f; + for (int j = 0; j < input_shape.dims(0); ++j) { + indices[0] = j; + sum += (*input_float_data)[Offset(input_shape, indices)]; + } + output_float_data[i] = sum; } - output_float_data[0] = sum; } else if (unary_op->type == OperatorType::kTensorFlowMin) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_multiply_by_zero.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_multiply_by_zero.cc new file mode 100644 index 0000000000000000000000000000000000000000..37beb41dfc5904fc6ace79ebea2420d2ab92fbfb --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_multiply_by_zero.cc @@ -0,0 +1,152 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +namespace { + +template +bool AreAllBufferElementsZero(const std::vector& buffer_data) { + for (auto x : buffer_data) { + if (x != 0) { + return false; + } + } + return true; +} + +template +void FillArrayWithZeros(Array* array) { + CHECK(array->data_type == Type); + std::vector>& data = array->GetMutableBuffer().data; + data.resize(RequiredBufferSizeForShape(array->shape())); + for (size_t i = 0; i < data.size(); i++) { + data[i] = 0; + } +} + +} // namespace + +// Removes a multiplication by array of constant zeros by making the output +// array an array of constant zeros and removing the input arrays if they are no +// longer needed. +bool ResolveMultiplyByZero::Run(Model* model, std::size_t op_index) { + const auto mul_it = model->operators.begin() + op_index; + auto* mul_op = mul_it->get(); + if (mul_op->type != OperatorType::kMul) { + return false; + } + const auto& output_array_name = mul_op->outputs[0]; + auto& output_array = model->GetArray(output_array_name); + + // Yield if the output shape is not known yet. + if (!output_array.has_shape()) { + return false; + } + + // This transformation only handles the case where one operand is all 0's and + // the other is non-constant. Other cases are handled by constant propagation + // or the trivial binary removal pass. + const bool is_input_constant[2] = { + IsConstantParameterArray(*model, mul_op->inputs[0]), + IsConstantParameterArray(*model, mul_op->inputs[1]), + }; + if (!is_input_constant[0] && !is_input_constant[1]) { + // Neither input is constant, so nothing we can resolve here. + return false; + } + if (is_input_constant[0] && is_input_constant[1]) { + // Both inputs are constants. That's a job for constants propagation, not + // for us to handle here. + return false; + } + const int index_of_constant_input = is_input_constant[0] ? 0 : 1; + const int index_of_variable_input = is_input_constant[0] ? 1 : 0; + CHECK(is_input_constant[index_of_constant_input]); + CHECK(!is_input_constant[index_of_variable_input]); + + const auto& constant_input_array = + model->GetArray(mul_op->inputs[index_of_constant_input]); + + CHECK(constant_input_array.data_type == output_array.data_type); + switch (output_array.data_type) { + case ArrayDataType::kFloat: { + const auto& constant_input_data = + constant_input_array.GetBuffer().data; + if (!AreAllBufferElementsZero>( + constant_input_data)) { + return false; + } + FillArrayWithZeros(&output_array); + } break; + case ArrayDataType::kUint8: { + const auto& constant_input_data = + constant_input_array.GetBuffer().data; + if (!AreAllBufferElementsZero>( + constant_input_data)) { + return false; + } + FillArrayWithZeros(&output_array); + } break; + case ArrayDataType::kInt32: { + const auto& constant_input_data = + constant_input_array.GetBuffer().data; + if (!AreAllBufferElementsZero>( + constant_input_data)) { + return false; + } + FillArrayWithZeros(&output_array); + } break; + case ArrayDataType::kInt64: { + const auto& constant_input_data = + constant_input_array.GetBuffer().data; + if (!AreAllBufferElementsZero>( + constant_input_data)) { + return false; + } + FillArrayWithZeros(&output_array); + } break; + default: + AddMessageF( + "Cannot resolve multiply by 0 because of unsupported data type\n"); + return false; + } + + // Erase input arrays to the multiply if no longer used + if (IsDiscardableArray(*model, mul_op->inputs[0]) && + CountOpsWithInput(*model, mul_op->inputs[0]) == 1) { + model->EraseArray(mul_op->inputs[0]); + } + if (IsDiscardableArray(*model, mul_op->inputs[1]) && + CountOpsWithInput(*model, mul_op->inputs[1]) == 1) { + model->EraseArray(mul_op->inputs[1]); + } + + // Erase the multiply operator. + model->operators.erase(mul_it); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc index 5c68f87f6ccd912a94213c95a59a78076b0e768b..bc70db0bd8c26319fa140616de96452260a01058 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc @@ -60,16 +60,7 @@ bool ResolveReorderAxes::Run(Model* model, std::size_t op_index) { const auto& output_array_name = reorder_op->outputs[0]; auto& input_array = model->GetArray(input_array_name); auto& output_array = model->GetArray(output_array_name); - string constant_input_array_name = input_array_name; if (!input_array.buffer) { - const auto* op_producing_input = GetOpWithOutput(*model, input_array_name); - if (op_producing_input && - op_producing_input->type == OperatorType::kFakeQuant) { - constant_input_array_name = op_producing_input->inputs[0]; - } - } - auto& constant_input_array = model->GetArray(constant_input_array_name); - if (!constant_input_array.buffer) { return false; } // Yield until output dims have been resolved. @@ -77,14 +68,14 @@ bool ResolveReorderAxes::Run(Model* model, std::size_t op_index) { return false; } // Reorder the input array dims and buffer data - if (constant_input_array.buffer->type == ArrayDataType::kFloat) { - ReorderAxes( - reorder_op->input_axes_order, reorder_op->output_axes_order, - &constant_input_array, &output_array); - } else if (constant_input_array.buffer->type == ArrayDataType::kInt32) { - ReorderAxes( - reorder_op->input_axes_order, reorder_op->output_axes_order, - &constant_input_array, &output_array); + if (input_array.buffer->type == ArrayDataType::kFloat) { + ReorderAxes(reorder_op->input_axes_order, + reorder_op->output_axes_order, + &input_array, &output_array); + } else if (input_array.buffer->type == ArrayDataType::kInt32) { + ReorderAxes(reorder_op->input_axes_order, + reorder_op->output_axes_order, + &input_array, &output_array); } else { LOG(FATAL) << "Cannot ReorderAxes unless input buffer is float or uint8."; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc index ad1e56888e53133c5a84cc0e3d5e76b7ef3b29b4..f38203c80fcb7ab8bc1639129fd98e4e342e5cb7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc @@ -29,7 +29,36 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { if (matmul_it->get()->type != OperatorType::kTensorFlowMatMul) { return false; } - const auto* matmul_op = matmul_it->get(); + const auto* matmul_op = + static_cast(matmul_it->get()); + + // Reorder the axes on the second input. TensorFlow uses row-major ordering + // on both inputs, however this is inefficient for the FullyConnected + // operator. We'll transpose the second input to be in column-major order now + // and let constant propagation optimize things (if possible). + auto* transpose_op = new TransposeOperator; + transpose_op->inputs = { + matmul_op->inputs[1], + CreateInt32Array( + model, + AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose/perm"), + {1, 0})}; + transpose_op->outputs = { + AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose")}; + model->GetOrCreateArray(transpose_op->outputs[0]); + model->operators.emplace(matmul_it, transpose_op); + + // Refresh iterator. + matmul_it = model->operators.begin(); + for (; matmul_it != model->operators.end(); ++matmul_it) { + if (matmul_it->get() == matmul_op) { + break; + } + } + DCHECK_EQ(matmul_it->get(), matmul_op); + + string input_lhs = matmul_op->inputs[0]; + string input_rhs = transpose_op->outputs[0]; // Find the op producing the array passed to this MatMul auto previous_op_it = model->operators.begin(); @@ -47,22 +76,26 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { } Operator* previous_op = (found) ? previous_op_it->get() : nullptr; - // construct the new FullyConnectedOperator + // Construct the new FullyConnectedOperator. auto* fc_op = new FullyConnectedOperator; fc_op->outputs = matmul_op->outputs; - // insert the newly constructed FullyConnectedOperator - auto fc_it = model->operators.emplace(matmul_it, fc_op); + // Insert the newly constructed FullyConnectedOperator. + model->operators.emplace(matmul_it, fc_op) + 1; - // refresh invalidated iterator - matmul_it = fc_it + 1; + // Refresh iterator. + matmul_it = model->operators.begin(); + for (; matmul_it != model->operators.end(); ++matmul_it) { + if (matmul_it->get() == matmul_op) { + break; + } + } DCHECK_EQ(matmul_it->get(), matmul_op); // The way that TensorFlow encodes FullyConnected ops is as a pair // (Reshape, MatMul), so we want to remove the Reshape op and rewrite the - // MatMul - // op as a FullyConnected. However, TensorFlow skips the Reshape ops if the - // input doesn't need reshaping, so we can't just match (Reshape, MatMul) + // MatMul op as a FullyConnected. However, TensorFlow skips the Reshape ops if + // the input doesn't need reshaping, so we can't just match (Reshape, MatMul) // pairs. if (previous_op && previous_op->type == OperatorType::kTensorFlowReshape) { AddMessageF("Combining %s and %s into %s", LogName(*previous_op), @@ -72,7 +105,7 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { model->EraseArray(previous_op_output); } CHECK_EQ(previous_op->inputs.size(), 2); - fc_op->inputs = {previous_op->inputs[0], matmul_op->inputs[1]}; + input_lhs = previous_op->inputs[0]; // Only remove Reshape node if no other node uses its output. if (CountOpsWithInput(*model, previous_op_output) == 1) { const auto& previous_op_shape = previous_op->inputs[1]; @@ -95,9 +128,10 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { } else { AddMessageF("Replacing %s by a FullyConnected operator", LogName(*matmul_op)); - fc_op->inputs = {matmul_op->inputs[0], matmul_op->inputs[1]}; } + fc_op->inputs = {input_lhs, input_rhs}; + // erase the MatMul operator model->operators.erase(matmul_it); return true; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD b/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD index 893149878293c9ef2740effe331d3b6c51b49983..2f94f9cd8a9ab24809fb3d137b5d05ab12f43003 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD +++ b/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD @@ -18,6 +18,17 @@ tf_cc_test( ], ) +tf_cc_test( + name = "lstm_utils_test", + srcs = ["lstm_utils_test.cc"], + deps = [ + "//tensorflow/contrib/lite/toco:graph_transformations", + "//tensorflow/contrib/lite/toco:model", + "//tensorflow/contrib/lite/toco:tooling_util", + "@com_google_googletest//:gtest_main", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/lite/toco/graph_transformations/tests/lstm_utils_test.cc b/tensorflow/contrib/lite/toco/graph_transformations/tests/lstm_utils_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..6aae0775d3445daf7d990bcce09d335c5f686601 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/tests/lstm_utils_test.cc @@ -0,0 +1,442 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include + +#include +#include +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +namespace { + +// A gmock matcher that check that elements of a float vector match to a given +// tolerance. +std::vector> ArrayFloatNear( + const std::vector& values, float max_abs_error = 1e-5) { + std::vector> matchers; + matchers.reserve(values.size()); + for (const float& v : values) { + matchers.emplace_back(testing::FloatNear(v, max_abs_error)); + } + return matchers; +} +} // namespace + +class CopyArrayDataTest : public ::testing::Test { + public: + CopyArrayDataTest() {} + + void PrepareBuffers(Model* model, std::initializer_list src_data, + int src_dim_1, int src_dim_2, + std::initializer_list dst_data, int dst_dim_1, + int dst_dim_2) { + string src_array = "src_array"; + src_buffer_ = CreateFloatArrayBuffer( + model, &src_array, + src_dim_2 == 1 ? Shape({src_dim_1}) : Shape({src_dim_1, src_dim_2})); + PopulateBuffer(src_buffer_, src_data); + string dst_array = "dst_array"; + dst_buffer_ = CreateFloatArrayBuffer( + model, &dst_array, + dst_dim_2 == 1 ? Shape({dst_dim_1}) : Shape({dst_dim_1, dst_dim_2})); + PopulateBuffer(dst_buffer_, dst_data); + } + + Buffer* GetSrcBuffer() { return src_buffer_; } + Buffer* GetDstBuffer() { return dst_buffer_; } + + void PopulateBuffer(Buffer* buffer, + const std::vector& init_data) { + for (int i = 0; i < init_data.size(); i++) { + buffer->data[i] = init_data[i]; + } + } + void UpdateBuffer(Buffer* buffer, + std::initializer_list data) { + buffer->data.resize(data.size()); + PopulateBuffer(buffer, data); + } + + private: + Buffer* src_buffer_; + Buffer* dst_buffer_; +}; + +// Copy from 1 big 2D array to 8 smaller ones. +TEST_F(CopyArrayDataTest, CopyFromBigArrayToSmallerArrayes2D) { + // Init src_buffer, dst_buffer. + Model model; + std::initializer_list large_tf_weight_data = { + -0.320407, -0.108683, 0.406358, -0.410811, -0.285786, -0.15769, + -0.194201, 0.170866, 0.084135, 0.201878, 0.21519, -0.284458, + 0.495906, -0.073818, 0.045578, 0.149816, -0.447073, -0.453578, + 0.116766, 0.21808, 0.047326, -0.001985, 0.402193, 0.315517, + 0.38258, 0.43599, 0.11986, 0.465195, 0.33548, -0.118789, + -0.414159, 0.049269, 0.156108, 0.093459, -0.129103, -0.086274, + 0.186188, -0.324923, 0.4117, -0.344439, 0.240465, -0.343331, + -0.463082, -0.231706, -0.487465, -0.186592, -0.020756, -0.239007, + 0.364817, 0.459106, -0.171447, -0.006542, 0.204032, -0.375317, + -0.041911, 0.051664, 0.320483, 0.155899, 0.156555, -0.249823, + -0.353107, 0.031563, -0.340771, -0.052532, 0.134631, -0.257957, + -0.50141, 0.486939, -0.43853, 0.268426, -0.08754, -0.109447, + -0.502462, -0.028055, -0.121838, -0.046016, 0.105309, -0.070774, + 0.495683, -0.475088, 0.048654, -0.38582, 0.411018, -0.315606, + 0.349628, 0.21698, 0.258989, -0.097902, 0.331218, 0.034602, + 0.418069, -0.089025, -0.417513, 0.07609, 0.393821, 0.404733, + -0.055418, -0.43903, -0.447049, 0.013125, 0.278503, 0.459869, + 0.143755, -0.177335, -0.162247, -0.432371, 0.153714, -0.047403, + -0.446775, -0.418363, 0.019743, 0.042025}; + std::initializer_list tflite_lstm_input_weight = {0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0}; + PrepareBuffers(&model, large_tf_weight_data, /*src_dim_1=*/16, + /*src_dim_2=*/7, tflite_lstm_input_weight, + /*dst_dim_1=*/4, /*dst_dim_2=*/3); + + // Copy src starts at (0,0), size (4,3). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/3, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + std::vector expected = {-0.320407, -0.108683, 0.406358, 0.170866, + 0.084135, 0.201878, 0.045578, 0.149816, + -0.447073, -0.001985, 0.402193, 0.315517}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (4,0), size (4,3). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/4, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/3, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + expected = {0.33548, -0.118789, -0.414159, -0.086274, 0.186188, -0.324923, + -0.463082, -0.231706, -0.487465, 0.459106, -0.171447, -0.006542}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (8,0), size (4,3). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/8, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/3, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + expected = {0.320483, 0.155899, 0.156555, -0.052532, 0.134631, -0.257957, + -0.08754, -0.109447, -0.502462, -0.070774, 0.495683, -0.475088}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (12,0), size (4,3). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/12, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/3, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + expected = {0.349628, 0.21698, 0.258989, -0.089025, -0.417513, 0.07609, + -0.447049, 0.013125, 0.278503, -0.432371, 0.153714, -0.047403}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // New dst_buffer with size 16. + std::initializer_list tflite_lstm_recurrent_weight = { + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + PrepareBuffers(&model, large_tf_weight_data, /*src_dim_1=*/16, + /*src_dim_2=*/7, tflite_lstm_recurrent_weight, + /*dst_dim_1=*/4, /*dst_dim_2=*/4); + + // Copy src starts at (0,3), size (4,4). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/0, + /*src_start_idx2=*/3, GetDstBuffer(), /*dst_stride=*/4, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + expected = {-0.410811, -0.285786, -0.15769, -0.194201, 0.21519, -0.284458, + 0.495906, -0.073818, -0.453578, 0.116766, 0.21808, 0.047326, + 0.38258, 0.43599, 0.11986, 0.465195}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (4,3), size (4,4). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/4, + /*src_start_idx2=*/3, GetDstBuffer(), /*dst_stride=*/4, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + expected = {0.049269, 0.156108, 0.093459, -0.129103, 0.4117, -0.344439, + 0.240465, -0.343331, -0.186592, -0.020756, -0.239007, 0.364817, + 0.204032, -0.375317, -0.041911, 0.051664}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (8,3), size (4,4). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/8, + /*src_start_idx2=*/3, GetDstBuffer(), /*dst_stride=*/4, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + expected = {-0.249823, -0.353107, 0.031563, -0.340771, -0.50141, 0.486939, + -0.43853, 0.268426, -0.028055, -0.121838, -0.046016, 0.105309, + 0.048654, -0.38582, 0.411018, -0.315606}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy src starts at (12,3), size (4,4). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/7, /*src_start_idx1=*/12, + /*src_start_idx2=*/3, GetDstBuffer(), /*dst_stride=*/4, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + expected = {-0.097902, 0.331218, 0.034602, 0.418069, 0.393821, 0.404733, + -0.055418, -0.43903, 0.459869, 0.143755, -0.177335, -0.162247, + -0.446775, -0.418363, 0.019743, 0.042025}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); +} + +// Copy from 1 big 1D array to 4 small ones. +TEST_F(CopyArrayDataTest, CopyFromBigArrayToSmallerArrayes1D) { + // Init src_buffer, dst_buffer. + Model model; + std::initializer_list large_tf_bias_data = { + 0.980304, 0.419808, 0.080278, 0.728548, 0.581674, 0.672433, + 0.434190, 0.844357, 0.229587, 0.785629, 0.022065, 0.753082, + 0.422080, 0.539481, 0.878386, 0.168965}; + std::initializer_list tflite_lstm_i_bias = {0, 0, 0, 0}; + PrepareBuffers(&model, large_tf_bias_data, /*src_dim_1=*/16, + /*src_dim_2=*/1, tflite_lstm_i_bias, + /*dst_dim_1=*/4, /*dst_dim_2=*/1); + + // Copy starts at (0,), size (4,). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + std::vector expected = {0.980304, 0.419808, 0.080278, 0.728548}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy starts at (4,), size (4,). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/4, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + expected = {0.581674, 0.672433, 0.434190, 0.844357}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy starts at (8,), size (4,). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/8, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + expected = {0.229587, 0.785629, 0.022065, 0.753082}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); + + // Copy starts at (12,), size (4,). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/12, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + expected = {0.422080, 0.539481, 0.878386, 0.168965}; + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); +} + +// Copy from 8 small 2D arrayes to 1 big one. +TEST_F(CopyArrayDataTest, CopyFromSmallArrayesToBigArray2D) { + // Init src_buffer, dst_buffer. + Model model; + std::initializer_list large_tf_weights_data = { + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + + // Copy dst starts (0, 0), size (4, 3). + std::initializer_list tflite_lstm_i2i_weight = { + -0.320407, -0.108683, 0.406358, 0.170866, 0.084135, 0.201878, + 0.045578, 0.149816, -0.447073, -0.001985, 0.402193, 0.315517}; + PrepareBuffers(&model, tflite_lstm_i2i_weight, /*src_dim_1=*/4, + /*src_dim_2=*/3, large_tf_weights_data, + /*dst_dim_1=*/16, /*dst_dim_2=*/7); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/3, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + + // Copy dst starts (4, 0), size (4, 3). + std::initializer_list tflite_lstm_i2c_weight = { + 0.33548, -0.118789, -0.414159, -0.086274, 0.186188, -0.324923, + -0.463082, -0.231706, -0.487465, 0.459106, -0.171447, -0.006542}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_i2c_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/3, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/4, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + + // Copy dst starts (8, 0), size (4, 3). + std::initializer_list tflite_lstm_i2f_weight = { + 0.320483, 0.155899, 0.156555, -0.052532, 0.134631, -0.257957, + -0.08754, -0.109447, -0.502462, -0.070774, 0.495683, -0.475088}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_i2f_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/3, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/8, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + + // Copy dst starts (12, 0), size (4, 3). + std::initializer_list tflite_lstm_i2o_weight = { + 0.349628, 0.21698, 0.258989, -0.089025, -0.417513, 0.07609, + -0.447049, 0.013125, 0.278503, -0.432371, 0.153714, -0.047403}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_i2o_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/3, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/12, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/3); + + // Copy dst starts (0, 3), size (4, 4). + std::initializer_list tflite_lstm_i2r_weight = { + -0.410811, -0.285786, -0.15769, -0.194201, 0.21519, -0.284458, + 0.495906, -0.073818, -0.453578, 0.116766, 0.21808, 0.047326, + 0.38258, 0.43599, 0.11986, 0.465195}; + UpdateBuffer(GetSrcBuffer(), tflite_lstm_i2r_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/4, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/3, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + + // Copy dst starts (4, 3), size (4, 4). + std::initializer_list tflite_lstm_c2r_weight = { + 0.049269, 0.156108, 0.093459, -0.129103, 0.4117, -0.344439, + 0.240465, -0.343331, -0.186592, -0.020756, -0.239007, 0.364817, + 0.204032, -0.375317, -0.041911, 0.051664}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_c2r_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/4, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/4, /*dst_start_idx2=*/3, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + + // Copy dst starts (8, 3), size (4, 4). + std::initializer_list tflite_lstm_f2r_weight = { + -0.249823, -0.353107, 0.031563, -0.340771, -0.50141, 0.486939, + -0.43853, 0.268426, -0.028055, -0.121838, -0.046016, 0.105309, + 0.048654, -0.38582, 0.411018, -0.315606}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_f2r_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/4, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/8, /*dst_start_idx2=*/3, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + + // Copy dst starts (12, 3), size (4, 4). + std::initializer_list tflite_lstm_o2r_weight = { + -0.097902, 0.331218, 0.034602, 0.418069, 0.393821, 0.404733, + -0.055418, -0.43903, 0.459869, 0.143755, -0.177335, -0.162247, + -0.446775, -0.418363, 0.019743, 0.042025}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_o2r_weight); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/4, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/7, + /*dst_start_idx1=*/12, /*dst_start_idx2=*/3, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/4); + + std::vector expected = { + -0.320407, -0.108683, 0.406358, -0.410811, -0.285786, -0.15769, + -0.194201, 0.170866, 0.084135, 0.201878, 0.21519, -0.284458, + 0.495906, -0.073818, 0.045578, 0.149816, -0.447073, -0.453578, + 0.116766, 0.21808, 0.047326, -0.001985, 0.402193, 0.315517, + 0.38258, 0.43599, 0.11986, 0.465195, 0.33548, -0.118789, + -0.414159, 0.049269, 0.156108, 0.093459, -0.129103, -0.086274, + 0.186188, -0.324923, 0.4117, -0.344439, 0.240465, -0.343331, + -0.463082, -0.231706, -0.487465, -0.186592, -0.020756, -0.239007, + 0.364817, 0.459106, -0.171447, -0.006542, 0.204032, -0.375317, + -0.041911, 0.051664, 0.320483, 0.155899, 0.156555, -0.249823, + -0.353107, 0.031563, -0.340771, -0.052532, 0.134631, -0.257957, + -0.50141, 0.486939, -0.43853, 0.268426, -0.08754, -0.109447, + -0.502462, -0.028055, -0.121838, -0.046016, 0.105309, -0.070774, + 0.495683, -0.475088, 0.048654, -0.38582, 0.411018, -0.315606, + 0.349628, 0.21698, 0.258989, -0.097902, 0.331218, 0.034602, + 0.418069, -0.089025, -0.417513, 0.07609, 0.393821, 0.404733, + -0.055418, -0.43903, -0.447049, 0.013125, 0.278503, 0.459869, + 0.143755, -0.177335, -0.162247, -0.432371, 0.153714, -0.047403, + -0.446775, -0.418363, 0.019743, 0.042025}; + + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); +} + +// Copy from 4 small 1D arrayes to 1 big one. +TEST_F(CopyArrayDataTest, CopyFromSmallArrayesToBigArray1D) { + // Init src_buffer, dst_buffer. + Model model; + std::initializer_list large_tf_bias_data = {0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0}; + + std::initializer_list tflite_lstm_i_bias = {0.980304, 0.419808, + 0.080278, 0.728548}; + + PrepareBuffers(&model, tflite_lstm_i_bias, /*src_dim_1=*/4, + /*src_dim_2=*/1, large_tf_bias_data, + /*dst_dim_1=*/16, /*dst_dim_2=*/1); + + // Copy starts at (0,), size (4,). + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/0, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + + // Copy starts at (4,), size (4,). + std::initializer_list tflite_lstm_cell_bias = {0.581674, 0.672433, + 0.434190, 0.844357}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_cell_bias); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/4, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + + // Copy starts at (8,0), size (4,). + std::initializer_list tflite_lstm_forget_bias = {0.229587, 0.785629, + 0.022065, 0.753082}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_forget_bias); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/8, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + + // Copy starts at (12,), size (4,). + std::initializer_list tflite_lstm_output_bias = {0.422080, 0.539481, + 0.878386, 0.168965}; + PopulateBuffer(GetSrcBuffer(), tflite_lstm_output_bias); + CopyArrayData(*(GetSrcBuffer()), + /*src_stride=*/1, /*src_start_idx1=*/0, + /*src_start_idx2=*/0, GetDstBuffer(), /*dst_stride=*/1, + /*dst_start_idx1=*/12, /*dst_start_idx2=*/0, + /*dim1_copy_size=*/4, /*dim2_copy_size=*/1); + + std::vector expected = {0.980304, 0.419808, 0.080278, 0.728548, + 0.581674, 0.672433, 0.434190, 0.844357, + 0.229587, 0.785629, 0.022065, 0.753082, + 0.422080, 0.539481, 0.878386, 0.168965}; + + EXPECT_THAT(GetDstBuffer()->data, ElementsAreArray(ArrayFloatNear(expected))); +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc new file mode 100644 index 0000000000000000000000000000000000000000..419fb9a79928985860ec0378fd3f33045fab0ff7 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc @@ -0,0 +1,237 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +bool UnpartitionEmbeddingLookup::Run(Model* model, std::size_t op_index) { + // Collapses a partitioned tf.nn.embedding_lookup back into a single Gather. + // https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup + // This transform attempts to identify the len(params) > 1 case and collapse + // it to the len(params) = 1 case by concatenating the original params and + // reversing the partitioning. + // + // If len(params) to the tf.nn.embedding_lookup == 1, the whole op becomes + // simply a gather: + // https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/python/ops/embedding_ops.py#L150 + // + // Notes on this implementation: + // - only supports partition_strategy='mod' + // + // A rough graph of a partitioned embedding_lookup looks like: + // (ids)--+-->FloorDiv--+-->DynamicPartition-->[[Gather]]--\ + // \-->FloorMod--/ | + // V | + // Range-->DynamicPartition-------->DynamicStitch<---------/ + // (const) V + // (embeddings) + + // First look for the final DynamicStitch. + auto op_it = model->operators.begin() + op_index; + if (op_it->get()->type != OperatorType::kDynamicStitch) { + return false; + } + auto* stitch_op = static_cast(op_it->get()); + + // Split up the DynamicStitch inputs into the indices and data. + std::vector stitch_indices_inputs; + std::vector stitch_data_inputs; + for (size_t i = 0; i < stitch_op->num_partitions; ++i) { + stitch_indices_inputs.push_back(stitch_op->inputs[i]); + } + for (size_t i = stitch_op->num_partitions; i < stitch_op->num_partitions * 2; + ++i) { + stitch_data_inputs.push_back(stitch_op->inputs[i]); + } + + // Validate all indices come from the same DynamicPartition. + DynamicPartitionOperator* indices_partition_op = nullptr; + for (const string& indices_partition_output_name : stitch_indices_inputs) { + auto* op = GetOpWithOutput(*model, indices_partition_output_name); + CHECK(op) << "Source of " << indices_partition_output_name << " not found"; + if (op->type != OperatorType::kDynamicPartition) { + AddMessageF( + "Skipping because indices input %s into " + "%s is unexpected", + LogName(*op), LogName(*stitch_op)); + return false; + } + if (!indices_partition_op) { + indices_partition_op = static_cast(op); + } else { + // Ensure this is the same op as previous ones. + if (op != indices_partition_op) { + AddMessageF( + "Skipping because indices input %s into " + "%s is from a different source op than others", + LogName(*op), LogName(*stitch_op)); + return false; + } + } + } + CHECK(indices_partition_op) << "No indices inputs"; + + // The data for the indices must be a constant range of the array shape. + if (!IsConstantParameterArray(*model, indices_partition_op->inputs[0])) { + AddMessageF("Skipping because indices partition data is non-constant"); + return false; + } + auto& indices_data_array = model->GetArray(indices_partition_op->inputs[0]); + if (indices_data_array.data_type == ArrayDataType::kNone) { + // Yield until data types are propagated. + return false; + } + CHECK(indices_data_array.data_type == ArrayDataType::kInt32) + << "Indices partition inputs must be int32"; + const auto& indices_data_buffer = + indices_data_array.GetBuffer().data; + for (size_t i = 0; i < indices_data_buffer.size(); ++i) { + CHECK_EQ(indices_data_buffer[i], i) << "Indices range must be identity"; + } + + // Find all of the gathers used for the data inputs. + std::vector gather_ops; + for (const string& gather_output_name : stitch_data_inputs) { + auto* op = GetOpWithOutput(*model, gather_output_name); + CHECK(op) << "Source of " << gather_output_name << " not found"; + if (op->type != OperatorType::kGather) { + AddMessageF( + "Skipping because data input %s into %s " + "is unexpected", + LogName(*op), LogName(*stitch_op)); + return false; + } + gather_ops.push_back(static_cast(op)); + } + + // Validate all gathers come from the same DynamicPartition. + DynamicPartitionOperator* data_partition_op = nullptr; + for (auto* gather_op : gather_ops) { + auto* op = GetOpWithOutput(*model, gather_op->inputs[1]); + CHECK(op) << "Source of " << gather_op->inputs[1] << " not found"; + if (op->type != OperatorType::kDynamicPartition) { + AddMessageF( + "Skipping because data input %s into " + "%s is unexpected", + LogName(*op), LogName(*gather_op)); + return false; + } + if (!data_partition_op) { + data_partition_op = static_cast(op); + } else { + // Ensure this is the same op as previous ones. + if (op != data_partition_op) { + AddMessageF( + "Skipping because data input %s into " + "%s is from a different source op than others", + LogName(*op), LogName(*gather_op)); + return false; + } + } + } + CHECK(data_partition_op) << "No data inputs"; + + // Validate the partition ops have the same sizes. + CHECK_EQ(indices_partition_op->num_partitions, + data_partition_op->num_partitions) + << "Indices and data partition ops have differing dimensions"; + int num_partitions = indices_partition_op->num_partitions; + + // Partition strategy of 'mod' gives us a FloorMod and FloorDiv. + // The gather partition uses the FloorDiv as the data and FloorMod as the + // partitions and the indices use the FloorMod as their partitions. + Operator* div_op = GetOpWithOutput(*model, data_partition_op->inputs[0]); + Operator* mod_op = GetOpWithOutput(*model, data_partition_op->inputs[1]); + CHECK(div_op && div_op->type == OperatorType::kFloorDiv) + << "Unsupported partition strategy"; + CHECK(mod_op && mod_op->type == OperatorType::kFloorMod) + << "Unsupported partition strategy"; + CHECK_EQ(mod_op, GetOpWithOutput(*model, indices_partition_op->inputs[1])) + << "Indices and data parition ops require the same partition strategy " + "and inputs"; + + // Glob together all of the gather data. This is not yet in the correct order. + auto* gather_params_concat_op = new ConcatenationOperator; + for (const auto& gather_op : gather_ops) { + gather_params_concat_op->inputs.push_back(gather_op->inputs[0]); + } + gather_params_concat_op->outputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_unpartitioned")); + op_it = model->operators.emplace(op_it, gather_params_concat_op) + 1; + model->GetOrCreateArray(gather_params_concat_op->outputs[0]); + + // Permute the gather params to undo the partitioning that was originally + // done. + auto* gather_params_permute_op = new GatherOperator; + gather_params_permute_op->inputs.push_back( + gather_params_concat_op->outputs[0]); + gather_params_permute_op->inputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted/perm")); + gather_params_permute_op->outputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted")); + op_it = model->operators.emplace(op_it, gather_params_permute_op) + 1; + model->GetOrCreateArray(gather_params_permute_op->outputs[0]); + const auto& partition_array = model->GetArray(gather_ops[0]->inputs[0]); + const auto& partition_array_dims = partition_array.shape().dims(); + auto& perm_array = + model->GetOrCreateArray(gather_params_permute_op->inputs[1]); + perm_array.data_type = ArrayDataType::kInt32; + perm_array.mutable_shape()->ReplaceDims( + {num_partitions * partition_array_dims[0]}); + auto& perm_data = perm_array.GetMutableBuffer().data; + perm_data.resize(RequiredBufferSizeForShape(perm_array.shape())); + // NOTE: this is what relies on the partition_strategy. + for (int i = 0; i < num_partitions * partition_array_dims[0]; ++i) { + int p = i % num_partitions; + perm_data[i] = p * partition_array_dims[0] + i / num_partitions; + } + + // Insert the new unpartitioned gather op. + auto* merged_gather_op = new GatherOperator; + merged_gather_op->inputs = {gather_params_permute_op->outputs[0], + mod_op->inputs[0]}; + merged_gather_op->outputs = {stitch_op->outputs[0]}; + model->operators.emplace(op_it, merged_gather_op); + + AddMessageF( + "Replacing suspected partitioned tf.nn.embedding_lookup (starting at %s " + "+ %s and ending at %s) with a single unpartitioned gather %s", + LogName(*div_op), LogName(*mod_op), LogName(*stitch_op), + LogName(*merged_gather_op)); + + // Ensure the stitch output array is dead, as we don't want whatever was in it + // previously now that we've redefined it. It'll be recreated when needed. + model->EraseArray(stitch_op->outputs[0]); + model->GetOrCreateArray(merged_gather_op->outputs[0]); + + // Erase all the original ops. + DeleteOpAndArraysIfUnused(model, div_op); + DeleteOpAndArraysIfUnused(model, mod_op); + for (auto* gather_op : gather_ops) { + DeleteOpAndArraysIfUnused(model, gather_op); + } + DeleteOpAndArraysIfUnused(model, indices_partition_op); + DeleteOpAndArraysIfUnused(model, data_partition_op); + DeleteOpAndArraysIfUnused(model, stitch_op); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc new file mode 100644 index 0000000000000000000000000000000000000000..da81ea2ff3b4ab0bee0550874a9c4ea1044a3579 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc @@ -0,0 +1,172 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +// Unrolls a BatchMatMul on the batch dimension. +// We need to slice each batch out of the inputs, matmul them individually, then +// stack them all back together at the end. +// +// This transform effectively looks like: +// result_slices = [] +// for bat in B: +// slice_a = tf.reshape(tf.slice(a, [bat, 0, 0], [1, M, N]), [M, N]) +// slice_b = tf.reshape(tf.slice(b, [bat, 0, 0], [1, M, N]), [M, N]) +// slice_c = tf.matmul(slice_a, slice_b) +// result_slices[bat] = slice_c +// result = tf.stack(result_slices) +bool UnrollBatchMatMul::Run(Model* model, std::size_t op_index) { + auto batch_op_it = model->operators.begin() + op_index; + if (batch_op_it->get()->type != OperatorType::kBatchMatMul) { + return false; + } + const auto* batch_op = + static_cast(batch_op_it->get()); + + // We must have the shape of at least one input to know our batch size. + const auto& input_array_a = model->GetArray(batch_op->inputs[0]); + const auto& input_array_b = model->GetArray(batch_op->inputs[1]); + if (!input_array_a.has_shape() || !input_array_b.has_shape()) return false; + + // We only support the rank 3 case. If you are batching on rank > 3 you'll + // have to figure that out. + CHECK_EQ(input_array_a.shape().dimensions_count(), + input_array_b.shape().dimensions_count()) + << "Input dimensions must have the same rank"; + if (input_array_a.shape().dimensions_count() == 2) { + // This is really just a MatMul. This likely means that someone hand-crafted + // a graphdef with a BatchMatMul when they really wanted a MatMul. + AddMessageF("Replacing non-batch BatchMatMul %s by a MatMul operator", + LogName(*batch_op)); + auto* matmul_op = new TensorFlowMatMulOperator; + matmul_op->inputs = batch_op->inputs; + matmul_op->outputs = batch_op->outputs; + const auto matmul_op_it = model->operators.emplace(batch_op_it, matmul_op); + batch_op_it = matmul_op_it + 1; + CHECK_EQ(batch_op_it->get(), batch_op); + model->operators.erase(batch_op_it); + return true; + } + CHECK_EQ(input_array_a.shape().dimensions_count(), 3) + << "Input arrays must have rank 3"; + + // Perform the matmul for each slice of the batch. + int batch_count = input_array_a.shape().dims(0); + AddMessageF("Unrolling BatchMatMul %s %d times", LogName(*batch_op), + batch_count); + auto tail_it = batch_op_it; + std::vector stack_inputs; + for (int batch = 0; batch < batch_count; ++batch) { + std::string batch_name = + std::string(batch_op->outputs[0]) + "_b" + std::to_string(batch); + + // tf.slice(a, ...). + auto* slice_a_op = new SliceOperator; + slice_a_op->inputs = { + batch_op->inputs[0], + CreateInt32Array(model, batch_name + "/slice_a/slice/begin", + {batch, 0, 0}), + CreateInt32Array( + model, batch_name + "/slice_a/slice/size", + {1, input_array_a.shape().dims(1), input_array_a.shape().dims(2)}), + }; + slice_a_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_a")}; + auto& slice_a_op_output = model->GetOrCreateArray(slice_a_op->outputs[0]); + slice_a_op_output.data_type = input_array_a.data_type; + tail_it = model->operators.emplace(tail_it, slice_a_op) + 1; + + // Reshape to remove the first dimension ([1,M,N] -> [M,N]). + auto* slice_a_reshape_op = new TensorFlowReshapeOperator; + slice_a_reshape_op->inputs = { + slice_a_op->outputs[0], + CreateInt32Array(model, batch_name + "/slice_a/reshape/shape", + {-1, input_array_a.shape().dims(2)})}; + slice_a_reshape_op->outputs = { + AvailableArrayName(*model, batch_name + "/slice_a/reshape")}; + auto& slice_a_reshape_op_output = + model->GetOrCreateArray(slice_a_reshape_op->outputs[0]); + slice_a_reshape_op_output.data_type = input_array_a.data_type; + tail_it = model->operators.emplace(tail_it, slice_a_reshape_op) + 1; + + // tf.slice(b, ...). + auto* slice_b_op = new SliceOperator; + slice_b_op->inputs = { + batch_op->inputs[1], + CreateInt32Array(model, batch_name + "/slice_b/slice/begin", {0, 0, 0}), + CreateInt32Array( + model, batch_name + "/slice_b/slice/size", + {1, input_array_b.shape().dims(1), input_array_b.shape().dims(2)}), + }; + slice_b_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_b")}; + auto& slice_b_op_output = model->GetOrCreateArray(slice_b_op->outputs[0]); + slice_b_op_output.data_type = input_array_b.data_type; + tail_it = model->operators.emplace(tail_it, slice_b_op) + 1; + + // Reshape to remove the first dimension ([1,M,N] -> [M,N]). + auto* slice_b_reshape_op = new TensorFlowReshapeOperator; + slice_b_reshape_op->inputs = { + slice_b_op->outputs[0], + CreateInt32Array(model, batch_name + "/slice_b/reshape/shape", + {-1, input_array_b.shape().dims(2)})}; + slice_b_reshape_op->outputs = { + AvailableArrayName(*model, batch_name + "/slice_b/reshape")}; + auto& slice_b_reshape_op_output = + model->GetOrCreateArray(slice_b_reshape_op->outputs[0]); + slice_b_reshape_op_output.data_type = input_array_b.data_type; + tail_it = model->operators.emplace(tail_it, slice_b_reshape_op) + 1; + + // tf.matmul(slice_a, slice_b). + auto* matmul_op = new TensorFlowMatMulOperator; + matmul_op->inputs = {slice_a_reshape_op->outputs[0], + slice_b_reshape_op->outputs[0]}; + matmul_op->outputs = {AvailableArrayName(*model, batch_name)}; + auto& matmul_op_output = model->GetOrCreateArray(matmul_op->outputs[0]); + matmul_op_output.data_type = input_array_a.data_type; + tail_it = model->operators.emplace(tail_it, matmul_op) + 1; + + // Add to stack. + stack_inputs.push_back(matmul_op->outputs[0]); + } + + // The stack that will join all the individual matmul results together. + auto* stack_op = new StackOperator; + stack_op->inputs = stack_inputs; + stack_op->outputs = {batch_op->outputs[0]}; + stack_op->axis = 0; + model->operators.emplace(tail_it, stack_op); + + // Remove the old batch matmul now that we've unrolled. + batch_op_it = model->operators.begin(); + for (; batch_op_it != model->operators.end(); ++batch_op_it) { + if (batch_op_it->get() == batch_op) { + break; + } + } + CHECK(batch_op_it != model->operators.end()); + CHECK(batch_op_it->get() == batch_op); + model->operators.erase(batch_op_it); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index c12706e52dd6257d7267bc4fb14ffb0053f3d69c..50aeafdf8d0108655c07e0f42d2f3a7b0741a767 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -21,6 +21,7 @@ limitations under the License. #include "google/protobuf/map.h" #include "google/protobuf/text_format.h" +#include "absl/memory/memory.h" #include "absl/strings/match.h" #include "absl/strings/numbers.h" #include "absl/strings/str_cat.h" @@ -271,6 +272,39 @@ void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { } } +void ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { + CHECK_EQ(input_tensor.dtype(), DT_BOOL); + const auto& input_shape = input_tensor.tensor_shape(); + CHECK_LE(input_shape.dim_size(), 4); + ImportShape(input_shape.dim(), output_array->mutable_shape()); + int input_flat_size = 1; + for (int k = 0; k < input_shape.dim_size(); k++) { + input_flat_size *= input_shape.dim(k).size(); + } + auto& output_bool_data = + output_array->GetMutableBuffer().data; + output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()), + false); + if (input_tensor.bool_val_size()) { + for (int i = 0; i < input_tensor.bool_val_size(); i++) { + output_bool_data[i] = input_tensor.bool_val(i); + } + } else if (input_tensor.tensor_content().size() == input_flat_size) { + std::vector buf(input_tensor.tensor_content().size()); + toco::port::CopyToBuffer(input_tensor.tensor_content(), buf.data()); + for (int i = 0; i < input_tensor.tensor_content().size(); i++) { + output_bool_data[i] = static_cast(buf[i]); + } + } else { + // Some graphs have bool const nodes without actual value... + // assuming that 'false' is implied. + // So far only encountered that in an array with 1 entry, let's + // require that until we encounter a graph where that's not the case. + CHECK_EQ(output_bool_data.size(), 1); + output_bool_data[0] = false; + } +} + void ImportStringArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_STRING); const auto& input_shape = input_tensor.tensor_shape(); @@ -317,6 +351,18 @@ void CheckInputsCount(const NodeDef& node, << " input(s) other than control dependencies: " << node.DebugString(); } +template +string CreateConstArray(Model* model, string const& name, + std::vector > const& data) { + // Utility function to create a const 1D array, useful for input parameters. + string array_name = toco::AvailableArrayName(*model, name); + auto& array = model->GetOrCreateArray(array_name); + array.data_type = T; + array.mutable_shape()->mutable_dims()->emplace_back(data.size()); + array.GetMutableBuffer().data = data; + return array_name; +} + void ConvertConstOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -346,6 +392,10 @@ void ConvertConstOperator(const NodeDef& node, array.data_type = ArrayDataType::kString; ImportStringArray(tensor, &array); break; + case DT_BOOL: + array.data_type = ArrayDataType::kBool; + ImportBoolArray(tensor, &array); + break; default: array.data_type = ArrayDataType::kNone; // do nothing, silently ignore the Const data. @@ -364,7 +414,7 @@ void ConvertConvOperator(const NodeDef& node, // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. - if (node.attr().count("data_format")) { + if (HasAttr(node, "data_format")) { CHECK_EQ(GetStringAttr(node, "data_format"), "NHWC"); } CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT); @@ -398,6 +448,17 @@ void ConvertConvOperator(const NodeDef& node, CHECK_EQ(strides.i(3), 1); conv->stride_height = strides.i(1); conv->stride_width = strides.i(2); + if (HasAttr(node, "dilations")) { + const auto& dilations = GetListAttr(node, "dilations"); + CHECK_EQ(dilations.i_size(), 4); + CHECK_EQ(dilations.i(0), 1); + CHECK_EQ(dilations.i(3), 1); + conv->dilation_height_factor = dilations.i(1); + conv->dilation_width_factor = dilations.i(2); + } else { + conv->dilation_height_factor = 1; + conv->dilation_width_factor = 1; + } const auto& padding = GetStringAttr(node, "padding"); if (padding == "SAME") { conv->padding.type = PaddingType::kSame; @@ -417,7 +478,7 @@ void ConvertDepthwiseConvOperator(const NodeDef& node, // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. - if (node.attr().count("data_format")) { + if (HasAttr(node, "data_format")) { CHECK_EQ(GetStringAttr(node, "data_format"), "NHWC"); } CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT); @@ -839,6 +900,7 @@ void ConvertSwitchOperator(const NodeDef& node, op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op); } + void ConvertSoftmaxOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -854,6 +916,18 @@ void ConvertSoftmaxOperator(const NodeDef& node, model->operators.emplace_back(softmax); } +void ConvertLogSoftmaxOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "LogSoftmax"); + CheckInputsCount(node, tf_import_flags, 1); + const auto& input_name = node.input(0); + auto* log_softmax = new LogSoftmaxOperator; + log_softmax->inputs.push_back(input_name); + log_softmax->outputs.push_back(node.name()); + model->operators.emplace_back(log_softmax); +} + void ConvertLRNOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -962,49 +1036,37 @@ void ConvertReshapeOperator(const NodeDef& node, model->operators.emplace_back(op); } +void ConvertBatchMatMulOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CheckInputsCount(node, tf_import_flags, 2); + + // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions + CHECK(!HasAttr(node, "adj_a") || (GetBoolAttr(node, "adj_a") == false)); + CHECK(!HasAttr(node, "adj_b") || (GetBoolAttr(node, "adj_b") == false)); + + auto* batch_matmul = new BatchMatMulOperator; + batch_matmul->inputs = {node.input(0), node.input(1)}; + batch_matmul->outputs = {node.name()}; + model->operators.emplace_back(batch_matmul); +} + void ConvertMatMulOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CheckInputsCount(node, tf_import_flags, 2); - if (node.op() == "MatMul") { - // Transpose flags should be easy to support, but we don't have a - // GraphDef with them to test on at the moment. - CHECK_EQ(GetBoolAttr(node, "transpose_a"), false); - CHECK_EQ(GetBoolAttr(node, "transpose_b"), false); - CHECK(!HasAttr(node, "adjoint_a") || - (GetBoolAttr(node, "adjoint_a") == false)); - CHECK(!HasAttr(node, "adjoint_b") || - (GetBoolAttr(node, "adjoint_b") == false)); - } else if (node.op() == "BatchMatMul") { - // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions - CHECK(!HasAttr(node, "adj_a") || (GetBoolAttr(node, "adj_a") == false)); - CHECK(!HasAttr(node, "adj_b") || (GetBoolAttr(node, "adj_b") == false)); - } else { - LOG(FATAL) << "op must be 'MatMul' or 'BatchMatMul'"; - } - const auto& input_name = node.input(0); - const auto& weights_name = node.input(1); - const auto& reordered_weights_name = weights_name + "_reordered"; - // Check if a ReorderAxesOperator was already created for these weights - // (that happens when multiple layers share the same weights). - const Operator* existing_reorder = - GetOpWithOutput(*model, reordered_weights_name); - if (existing_reorder) { - // Check that it is safe to rely on the _reordered naming of the output - // array! - CHECK(existing_reorder->type == OperatorType::kReorderAxes); - } else { - // Create a new ReorderAxesOperator - auto* reorder = new ReorderAxesOperator; - reorder->inputs = {weights_name}; - reorder->outputs = {reordered_weights_name}; - reorder->input_axes_order = AxesOrder::kRC; - reorder->output_axes_order = AxesOrder::kCR; - model->operators.emplace_back(reorder); - } + // Transpose flags should be easy to support, but we don't have a + // GraphDef with them to test on at the moment. + CHECK_EQ(GetBoolAttr(node, "transpose_a"), false); + CHECK_EQ(GetBoolAttr(node, "transpose_b"), false); + CHECK(!HasAttr(node, "adjoint_a") || + (GetBoolAttr(node, "adjoint_a") == false)); + CHECK(!HasAttr(node, "adjoint_b") || + (GetBoolAttr(node, "adjoint_b") == false)); + auto* matmul = new TensorFlowMatMulOperator; - matmul->inputs = {input_name, reordered_weights_name}; + matmul->inputs = {node.input(0), node.input(1)}; matmul->outputs = {node.name()}; model->operators.emplace_back(matmul); } @@ -1386,12 +1448,8 @@ void ConvertFusedBatchNormOperator(const NodeDef& node, const string& moving_variance_input = node.input(4); // Create an array holding the epsilon value (typically, 0.001). - const string epsilon_array_name = node.name() + "_epsilon_array"; - auto& epsilon_array = model->GetOrCreateArray(epsilon_array_name); - epsilon_array.data_type = ArrayDataType::kFloat; - *epsilon_array.mutable_shape()->mutable_dims() = {1}; - epsilon_array.GetMutableBuffer().data.push_back( - GetFloatAttr(node, "epsilon")); + const string epsilon_array_name = CreateConstArray( + model, node.name() + "_epsilon_array", {GetFloatAttr(node, "epsilon")}); // Add epsilon to the moving variance. const string epsilon_add_op_name = node.name() + "_epsilon"; @@ -1459,6 +1517,17 @@ void ConvertBatchToSpaceNDOperator(const NodeDef& node, model->operators.emplace_back(op); } +void ConvertExpOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "Exp"); + CheckInputsCount(node, tf_import_flags, 1); + auto* op = new ExpOperator; + op->inputs.push_back(node.input(0)); + op->outputs.push_back(node.name()); + model->operators.emplace_back(op); +} + void ConvertMeanOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -1508,16 +1577,56 @@ void ConvertTransposeConvOperator(const NodeDef& node, CHECK_EQ(node.op(), "Conv2DBackpropInput"); CheckInputsCount(node, tf_import_flags, 3); auto* op = new TransposeConvOperator; - op->inputs.push_back(node.input(2)); - op->inputs.push_back(node.input(1)); op->inputs.push_back(node.input(0)); + op->inputs.push_back(node.input(1)); + op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); const auto& strides = GetListAttr(node, "strides"); - CHECK_EQ(strides.i_size(), 4); - CHECK_EQ(strides.i(0), 1); op->stride_height = strides.i(1); op->stride_width = strides.i(2); - CHECK_EQ(strides.i(3), 1); + CHECK_EQ(strides.i_size(), 4) + << "Can only import TransposeConv ops with 4D strides. TensorFlow op \"" + << node.name() << "\" has " << strides.i_size() << "D strides."; + CHECK((strides.i(0) == 1) && (strides.i(3) == 1)) + << "Can only import TransposeConv ops with striding along the height " + "(1st) or width (2nd) axis. TensorFlow op \"" + << node.name() << "\" had strides:[ " << strides.i(0) << ", " + << strides.i(1) << ", " << strides.i(2) << ", " << strides.i(3) << "]."; + op->stride_height = strides.i(1); + op->stride_width = strides.i(2); + if (HasAttr(node, "dilations")) { + const auto& dilations = GetListAttr(node, "dilations"); + CHECK_EQ(dilations.i_size(), 4) + << "Dilation unsupported in TransposeConv. TensorFlow op \"" + << node.name() << "\" had dilations"; + CHECK((dilations.i(0) == 1) && (dilations.i(1) == 1) && + (dilations.i(1) == 1) && (dilations.i(3) == 1)) + << "Dilation unsupported in TransposeConv. TensorFlow op \"" + << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " + << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) + << "]."; + } + + const string& weights_name = node.input(TransposeConvOperator::WEIGHTS); + const string& transposed_weights_name = weights_name + "_transposed"; + // Check if a TransposeOperator was already created for these weights + // (can happen when multiple layers share the same weights). + const Operator* existing_transpose = + GetOpWithOutput(*model, transposed_weights_name); + if (existing_transpose) { + CHECK(existing_transpose->type == OperatorType::kTranspose); + } else { + // Transpose weights from HWIO order to OHWI order, which is more efficient + // for computation + TransposeOperator* transpose = new TransposeOperator; + string perm_array = CreateConstArray( + model, node.name() + "_transpose_perm", {3, 0, 1, 2}); + transpose->inputs = {weights_name, perm_array}; + transpose->outputs = {transposed_weights_name}; + model->operators.emplace_back(transpose); + } + op->inputs[1] = transposed_weights_name; + auto const& padding = GetStringAttr(node, "padding"); if (padding == "SAME") { op->padding.type = PaddingType::kSame; @@ -1569,7 +1678,7 @@ void ConvertFloorDivOperator(const NodeDef& node, void ConvertFloorModOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CHECK(node.op() == "FloorMod"); + CHECK_EQ(node.op(), "FloorMod"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new FloorModOperator; op->inputs.push_back(node.input(0)); @@ -1804,6 +1913,63 @@ bool InlineAllFunctions(GraphDef* graphdef) { } return graph_modified; } + +void ConvertTopKV2Operator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK((node.op() == "TopK") || (node.op() == "TopKV2")); + auto op = absl::make_unique(); + op->inputs.push_back(node.input(0)); + // K can be encoded as attr (TopK) convert it to a const. + if (HasAttr(node, "k")) { + string k_array = CreateConstArray( + model, node.name() + "k", {GetIntAttr(node, "k")}); + op->inputs.push_back(k_array); + } else { + CheckInputsCount(node, tf_import_flags, 2); + op->inputs.push_back(node.input(1)); + } + // The op has two outputs. + op->outputs.push_back(node.name() + ":0"); + op->outputs.push_back(node.name() + ":1"); + model->operators.emplace_back(op.release()); +} + +void ConvertDynamicPartitionOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + auto op = absl::make_unique(); + CHECK(HasAttr(node, "num_partitions")); + op->num_partitions = GetIntAttr(node, "num_partitions"); + CheckInputsCount(node, tf_import_flags, 2); + op->inputs.push_back(node.input(0)); + op->inputs.push_back(node.input(1)); + CHECK_GT(op->num_partitions, 1); + op->outputs.push_back(node.name()); // Implicit :0. + for (int i = 1; i < op->num_partitions; ++i) { + op->outputs.push_back(node.name() + ":" + std::to_string(i)); + } + model->operators.emplace_back(op.release()); +} + +void ConvertDynamicStitchOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + // The parallel and non-parallel variants are the same besides whether they + // have a parallel loop; there are no behavioral differences. + CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch"); + auto op = absl::make_unique(); + CHECK(HasAttr(node, "N")); + op->num_partitions = GetIntAttr(node, "N"); + // Expect all ID partitions + all value partitions. + CheckInputsCount(node, tf_import_flags, op->num_partitions * 2); + for (int i = 0; i < op->num_partitions * 2; ++i) { + op->inputs.push_back(node.input(i)); + } + op->outputs.push_back(node.name()); + model->operators.emplace_back(op.release()); +} + } // namespace std::unique_ptr ImportTensorFlowGraphDef( @@ -1859,7 +2025,9 @@ std::unique_ptr ImportTensorFlowGraphDef( ConvertAvgPoolOperator(node, tf_import_flags, model); } else if (node.op() == "Reshape") { ConvertReshapeOperator(node, tf_import_flags, model); - } else if (node.op() == "MatMul" || node.op() == "BatchMatMul") { + } else if (node.op() == "BatchMatMul") { + ConvertBatchMatMulOperator(node, tf_import_flags, model); + } else if (node.op() == "MatMul") { ConvertMatMulOperator(node, tf_import_flags, model); } else if (node.op() == "Div" || node.op() == "RealDiv") { ConvertDivOperator(node, tf_import_flags, model); @@ -1898,6 +2066,8 @@ std::unique_ptr ImportTensorFlowGraphDef( ConvertLRNOperator(node, tf_import_flags, model); } else if (node.op() == "Softmax") { ConvertSoftmaxOperator(node, tf_import_flags, model); + } else if (node.op() == "LogSoftmax") { + ConvertLogSoftmaxOperator(node, tf_import_flags, model); } else if (node.op() == "All") { ConvertAllOperator(node, tf_import_flags, model); } else if (node.op() == "Assert") { @@ -1981,6 +2151,15 @@ std::unique_ptr ImportTensorFlowGraphDef( ConvertTransposeOperator(node, tf_import_flags, model); } else if (node.op() == "ArgMax") { ConvertArgMaxOperator(node, tf_import_flags, model); + } else if (node.op() == "Exp") { + ConvertExpOperator(node, tf_import_flags, model); + } else if (node.op() == "TopK" || node.op() == "TopKV2") { + ConvertTopKV2Operator(node, tf_import_flags, model); + } else if (node.op() == "DynamicPartition") { + ConvertDynamicPartitionOperator(node, tf_import_flags, model); + } else if (node.op() == "DynamicStitch" || + node.op() == "ParallelDynamicStitch") { + ConvertDynamicStitchOperator(node, tf_import_flags, model); } else { ConvertUnsupportedOperator(node, tf_import_flags, model); } diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 447618ec8519f20ccdf346fc766415b5b829c2b5..3fa0089cba15fbfed869b5c33883741c35493cda 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -29,12 +29,15 @@ limitations under the License. namespace toco { +using tflite::QuantizationParams; + enum class OperatorType { kNone, // General-purpose neural network operators. kAdd, kAddN, kAveragePool, + kBatchMatMul, kBatchNormalization, kConv, kConcatenation, @@ -43,6 +46,7 @@ enum class OperatorType { kSpaceToDepth, kDequantize, kDiv, + kExp, kExpandDims, kFill, kFloorDiv, @@ -62,6 +66,7 @@ enum class OperatorType { kRelu1, kRelu6, kSoftmax, + kLogSoftmax, kSub, kTanh, kTransposeConv, @@ -111,6 +116,9 @@ enum class OperatorType { kTensorFlowSwitch, kTensorFlowTile, kTranspose, + kTopK_V2, + kDynamicPartition, + kDynamicStitch, // An unsupported TF operation. It's only needed to be able to represent TF // graph internally and is expected to be dropped by graph transformations. kTensorFlowUnsupported, @@ -156,12 +164,17 @@ enum class AxesOrder { // may be involved only in debug-only subgraphs that we may not be interested // in actually supporting). enum class ArrayDataType { - kNone, + kNone, // 0 kBool, kFloat, + kInt8, kUint8, + kInt16, // 5 + kUint16, kInt32, + kUint32, kInt64, + kUint64, // 10 kString }; @@ -181,18 +194,38 @@ struct DataTypeImpl { typedef float Type; }; template <> +struct DataTypeImpl { + typedef int8 Type; +}; +template <> struct DataTypeImpl { typedef uint8 Type; }; template <> +struct DataTypeImpl { + typedef int16 Type; +}; +template <> +struct DataTypeImpl { + typedef uint16 Type; +}; +template <> struct DataTypeImpl { typedef int32 Type; }; template <> +struct DataTypeImpl { + typedef uint32 Type; +}; +template <> struct DataTypeImpl { typedef int64 Type; }; template <> +struct DataTypeImpl { + typedef uint64 Type; +}; +template <> struct DataTypeImpl { typedef string Type; }; @@ -215,6 +248,8 @@ struct GenericBuffer { // in containers and have the containers call the right subclass destructor. virtual ~GenericBuffer() {} + virtual int Length() const = 0; + const ArrayDataType type; protected: @@ -227,6 +262,8 @@ template struct Buffer : GenericBuffer { Buffer() : GenericBuffer(A) {} + int Length() const override { return data.size(); } + std::vector> data; }; @@ -330,7 +367,8 @@ struct ConvOperator : Operator { // A dilation_rate of 0 is invalid and this field is an optional attribute. // Thus initializing it to 1 to allow default conv behavior when the // attribute is not present. - int dilation_rate = 1; + int dilation_width_factor = 1; + int dilation_height_factor = 1; }; // Depthwise-separable convolution operator. @@ -712,6 +750,19 @@ struct TensorFlowIdentityOperator : Operator { TensorFlowIdentityOperator() : Operator(OperatorType::kTensorFlowIdentity) {} }; +// Batch matrix multiplication operator. This comes from the (deprecated) +// tf.batch_matmul or a tf.matmul that has rank 3. dims(0) is the batch count +// and it can be trivially unrolled into a series of matmuls on each element. +// +// Inputs: +// inputs[0]: required: the left-hand side matrix +// inputs[1]: required: the right-hand side matrix +// +// TensorFlow equivalent: MatMul +struct BatchMatMulOperator : Operator { + BatchMatMulOperator() : Operator(OperatorType::kBatchMatMul) {} +}; + // General matrix multiplication operator. We don't want to support general // matrix multiplication at inference time, so we resolve it during tooling // to more specific operator types, namely, FullyConnected. @@ -797,19 +848,40 @@ struct SqueezeOperator : Operator { }; // Inputs: -// inputs[0]: required: the input activations array -// inputs[1]: required: the Conv weights -// channel. +// inputs[0]: required: the output shape +// inputs[1]: required: the weights +// inputs[2]: required: the input activations array +// NOTE: The input activations is NOT the first input. +// // // Outputs: // outputs[0]: required: the output activations array // // TensorFlow equivalent: Conv2DBackpropInput struct TransposeConvOperator : Operator { + enum Inputs { + OUTPUT_SHAPE = 0, + WEIGHTS = 1, + DATA_INPUT = 2, + }; + TransposeConvOperator() : Operator(OperatorType::kTransposeConv) {} Padding padding; int stride_width = 0; int stride_height = 0; + // Dilation is possible with transpose convolution, but Tensorflow does not + // currently support it, so we omit it. +}; + +// Given a tensor input, this operation calculates element-wise exponential +// (y = e^x). +// +// Inputs: +// inputs[0]: required: input tensor +// +// TensorFlow equivalent: Exp +struct ExpOperator : Operator { + ExpOperator() : Operator(OperatorType::kExp) {} }; // Given a tensor input, this operation inserts a dimension of 1 at the @@ -1216,6 +1288,16 @@ struct SoftmaxOperator : Operator { float beta = 0.f; }; +// LogSoftmax activation function. +// +// Inputs: +// inputs[0]: required: the logits input array +// +// TensorFlow equivalent: LogSoftmax +struct LogSoftmaxOperator : Operator { + LogSoftmaxOperator() : Operator(OperatorType::kLogSoftmax) {} +}; + // Cast operator. // // Inputs: @@ -1338,6 +1420,38 @@ struct SvdfOperator : Operator { int rank; }; +// TopKV2 operator. +// +// Inputs: +// input tensor and top_k scalar. +struct TopKV2Operator : Operator { + TopKV2Operator() : Operator(OperatorType::kTopK_V2) {} +}; + +// DynamicPartition operator: +// +// Inputs: +// inputs[0]: required: data. +// inputs[1]: required: partitions. +// +// TensorFlow equivalent: DynamicPartition +struct DynamicPartitionOperator : Operator { + DynamicPartitionOperator() : Operator(OperatorType::kDynamicPartition) {} + int num_partitions; +}; + +// DynamicStitch operator: +// +// Inputs: +// inputs[0,N): required: indices. +// inputs[N,2N): required: data. +// +// TensorFlow equivalent: DynamicStitch/ParallelDynamicStitch +struct DynamicStitchOperator : Operator { + DynamicStitchOperator() : Operator(OperatorType::kDynamicStitch) {} + int num_partitions; +}; + // Alloc's are used for transient arrays only. An Alloc specifies which interval // of the "transient_data" workspace buffer passed to inference functions, is to // be used for the transient array at hand. The 'start' and 'end' values are @@ -1351,22 +1465,6 @@ inline bool operator<(const Alloc& a, const Alloc& b) { return a.start < b.start; } -// Quantization parameters, determining the mapping of quantized values -// to real values (i.e. determining how quantized values are mathematically -// interpreted). -// -// The correspondence is as follows: -// -// real_value = scale * (quantized_value - zero_point); -// -// In other words, zero_point designates which quantized value corresponds to -// the real 0 value, and scale designates the difference between the real values -// corresponding to consecutive quantized values differing by 1. -struct QuantizationParams { - int32 zero_point = 0; - double scale = 0.; -}; - class Shape { public: // For Shape, we stick to half-way encapsulation for now: @@ -1544,7 +1642,7 @@ class Model { bool HasArray(const string& name) const { return arrays.count(name) > 0; } Array& GetArray(const string& name) const { - DCHECK(HasArray(name)); + DCHECK(HasArray(name)) << "Array not found: " << name; return *arrays.at(name); } Array& GetOrCreateArray(const string& name) { diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto index e4b39b34e85e4d703c1b41cb68f8139abd1f6279..867b86f31d16b502a7aeb92cb3d8c96117630cd2 100644 --- a/tensorflow/contrib/lite/toco/model_flags.proto +++ b/tensorflow/contrib/lite/toco/model_flags.proto @@ -96,9 +96,11 @@ message RnnState { // model that does not already contain such MinMax information. message ArraysExtraInfo { message Entry { + // Next ID to use: 5. optional string name = 1; optional float min = 2; optional float max = 3; + optional IODataType data_type = 4; } repeated Entry entries = 1; } diff --git a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc index 664e828c19dca1117b81113f723416541f48d621..646d048496c27955aa641fd01a35d8acfbd8dd90 100644 --- a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc +++ b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc @@ -103,11 +103,11 @@ class ResolveSvdfTest : public ::testing::Test { // Add the float vector as an attribute to the node. (*node->mutable_attr())["dtype"].set_type(tensorflow::DT_FLOAT); tensorflow::TensorProto* allocated_tensor = new tensorflow::TensorProto; - tensorflow::TensorShapeProto* allocated_tesnor_shape = + tensorflow::TensorShapeProto* allocated_tensor_shape = new tensorflow::TensorShapeProto; - auto tensor_shape_dim0 = allocated_tesnor_shape->add_dim(); + auto tensor_shape_dim0 = allocated_tensor_shape->add_dim(); tensor_shape_dim0->set_size(values.size()); - allocated_tensor->set_allocated_tensor_shape(allocated_tesnor_shape); + allocated_tensor->set_allocated_tensor_shape(allocated_tensor_shape); allocated_tensor->set_tensor_content( string(reinterpret_cast(values.data()), values.size() * sizeof(float))); @@ -122,11 +122,11 @@ class ResolveSvdfTest : public ::testing::Test { // Add the float vector as an attribute to the node. (*node->mutable_attr())["dtype"].set_type(tensorflow::DT_INT32); tensorflow::TensorProto* allocated_tensor = new tensorflow::TensorProto; - tensorflow::TensorShapeProto* allocated_tesnor_shape = + tensorflow::TensorShapeProto* allocated_tensor_shape = new tensorflow::TensorShapeProto; - auto tensor_shape_dim0 = allocated_tesnor_shape->add_dim(); + auto tensor_shape_dim0 = allocated_tensor_shape->add_dim(); tensor_shape_dim0->set_size(values.size()); - allocated_tensor->set_allocated_tensor_shape(allocated_tesnor_shape); + allocated_tensor->set_allocated_tensor_shape(allocated_tensor_shape); allocated_tensor->set_tensor_content( string(reinterpret_cast(values.data()), values.size() * sizeof(int))); diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index 5b1ab514b23248cd98e66847185d0e8b9fe2d6aa..d2aeb7811497aa52da133eead9effa9d52a7f5c3 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -64,6 +64,9 @@ void ImportTensors(const ::tflite::Model& input_model, Model* model) { auto shape = input_tensor->shape(); if (shape) { + // If the shape is 0-dimensional, make sure to record it as such, + // as oppose to leaving the array without a shape. + array.mutable_shape()->mutable_dims()->clear(); for (int i = 0; i < shape->Length(); ++i) { auto d = shape->Get(i); array.mutable_shape()->mutable_dims()->push_back(d); diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 04aaedd59d76ce63a80e80debba48f2b085d8db9..f2cc4ef71f71902e363ac4cddd3695446af30c7d 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -524,6 +524,28 @@ class Transpose TocoOperator* op) const override {} }; +class Lstm : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + // Current toco converter only supports tanh, no clip. + return ::tflite::CreateLSTMOptions(*builder, /*fused_activation_function=*/ + ::tflite::ActivationFunctionType_TANH, + /*cell_clip=*/0.0, + /*proj_clip=*/0.0); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + // Only support tanh activation, so check that tflite type is tanh. + CHECK(options.fused_activation_function() == + ::tflite::ActivationFunctionType_TANH); + } +}; + class Mean : public BuiltinOperator { public: @@ -579,15 +601,21 @@ class Squeeze } }; -class Split : public CustomOperator { +class Split + : public BuiltinOperator { public: - using CustomOperator::CustomOperator; - void WriteOptions(const TocoOperator& op, - flexbuffers::Builder* fbb) const override { - fbb->Int("num_split", op.num_split); + using BuiltinOperator::BuiltinOperator; + + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateSplitOptions(*builder, op.num_split); } - void ReadOptions(const flexbuffers::Map& m, TocoOperator* op) const override { - op->num_split = m["num_split"].AsInt64(); + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->num_split = options.num_splits(); } }; @@ -615,6 +643,20 @@ class StridedSlice } }; +class TopK_V2 : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateTopKV2Options(*builder); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override {} +}; + class TensorFlowUnsupported : public BaseOperator { public: using BaseOperator::BaseOperator; @@ -777,15 +819,20 @@ std::vector> BuildOperatorList() { OperatorType::kResizeBilinear)); ops.emplace_back( new Squeeze(::tflite::BuiltinOperator_SQUEEZE, OperatorType::kSqueeze)); + ops.emplace_back(new Split(::tflite::BuiltinOperator_SPLIT, + OperatorType::kTensorFlowSplit)); ops.emplace_back(new StridedSlice(::tflite::BuiltinOperator_STRIDED_SLICE, OperatorType::kStridedSlice)); + ops.emplace_back( + new TopK_V2(::tflite::BuiltinOperator_TOPK_V2, OperatorType::kTopK_V2)); + ops.emplace_back( + new Lstm(::tflite::BuiltinOperator_LSTM, OperatorType::kLstmCell)); // Custom Operators. ops.emplace_back(new Cast("CAST", OperatorType::kCast)); ops.emplace_back( new DepthToSpace("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); ops.emplace_back(new FakeQuant("FAKE_QUANT", OperatorType::kFakeQuant)); - ops.emplace_back(new Split("SPLIT", OperatorType::kTensorFlowSplit)); ops.emplace_back(new TensorFlowUnsupported( "TENSORFLOW_UNSUPPORTED", OperatorType::kTensorFlowUnsupported)); @@ -811,6 +858,9 @@ std::vector> BuildOperatorList() { "LOGISTIC", OperatorType::kLogistic)); ops.emplace_back( new SimpleOperator("TANH", OperatorType::kTanh)); + ops.emplace_back(new SimpleOperator("EXP", OperatorType::kExp)); + ops.emplace_back(new SimpleOperator( + "LOG_SOFTMAX", OperatorType::kLogSoftmax)); return ops; } diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 796534be53cba0ea772e974cd8173c0b4c12e6c3..9c19f8d4649acf40fdd85b78874f7b18798533f2 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -106,6 +106,9 @@ TEST_F(OperatorTest, SimpleOperators) { CheckSimpleOperator("RELU6", OperatorType::kRelu6); CheckSimpleOperator("LOGISTIC", OperatorType::kLogistic); CheckSimpleOperator("TANH", OperatorType::kTanh); + CheckSimpleOperator("EXP", OperatorType::kExp); + CheckSimpleOperator("LOG_SOFTMAX", + OperatorType::kLogSoftmax); } TEST_F(OperatorTest, BuiltinAdd) { @@ -379,6 +382,13 @@ TEST_F(OperatorTest, StridedSlice) { EXPECT_EQ(op.shrink_axis_mask, output_toco_op->shrink_axis_mask); } +TEST_F(OperatorTest, BuiltinTopKV2) { + TopKV2Operator op; + auto output_toco_op = SerializeAndDeserialize( + GetOperator("TOPK_V2", OperatorType::kTopK_V2), op); + ASSERT_NE(nullptr, output_toco_op.get()); +} + TEST_F(OperatorTest, TensorFlowUnsupported) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; diff --git a/tensorflow/contrib/lite/toco/tflite/types.cc b/tensorflow/contrib/lite/toco/tflite/types.cc index b4c2851502a40a1ca36965d4ddd2c8a15b8fe60f..0afd2f3df57caf3214dd198bfa2ee75fa7a8fd7b 100644 --- a/tensorflow/contrib/lite/toco/tflite/types.cc +++ b/tensorflow/contrib/lite/toco/tflite/types.cc @@ -90,6 +90,8 @@ flatbuffers::Offset> DataBuffer::Serialize( return CopyBuffer(array, builder); case ArrayDataType::kInt32: return CopyBuffer(array, builder); + case ArrayDataType::kInt64: + return CopyBuffer(array, builder); case ArrayDataType::kString: return CopyBuffer(array, builder); case ArrayDataType::kUint8: diff --git a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc index c5a62fdb620ee7d6b7195f6e8e2bc3cb208feb10..0f67c2de728532b5b8101b3514811a78a3b3bc38 100644 --- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc @@ -112,6 +112,11 @@ bool ParseTocoFlagsFromCommandLineFlags( "If true, ignore control dependency requirements in input TensorFlow " "GraphDef. Otherwise an error will be raised upon control dependency " "inputs."), + Flag("debug_disable_recurrent_cell_fusion", + parsed_flags.debug_disable_recurrent_cell_fusion.bind(), + parsed_flags.debug_disable_recurrent_cell_fusion.default_value(), + "If true, disable fusion of known identifiable cell subgraphs into " + "cells. This includes, for example, specific forms of LSTM cell."), }; bool asked_for_help = *argc == 2 && (!strcmp(argv[1], "--help") || !strcmp(argv[1], "-help")); diff --git a/tensorflow/contrib/lite/toco/toco_flags.proto b/tensorflow/contrib/lite/toco/toco_flags.proto index 3b9d7e22570b66aef2c9fc819e5ab4ec38e179f5..3237147a736f97f65953ca965420fcea934820a4 100644 --- a/tensorflow/contrib/lite/toco/toco_flags.proto +++ b/tensorflow/contrib/lite/toco/toco_flags.proto @@ -36,7 +36,8 @@ enum FileFormat { // are not normally encoded in model files and in general may not be thought // of as properties of models, instead describing how models are to be // processed in the context of the present tooling job. -// Next Id: 13 +// +// Next ID to use: 14. message TocoFlags { // Input file format optional FileFormat input_format = 1; @@ -136,4 +137,8 @@ message TocoFlags { // - Default to false if the output format is TENSORFLOW_GRAPHDEF. // - Default to true in all other cases. optional bool drop_control_dependency = 12; + + // Disables transformations that fuse subgraphs such as known LSTMs (not all + // LSTMs are identified). + optional bool debug_disable_recurrent_cell_fusion = 13; } diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index b715881774bf00f6cf2a50452a5b5c59c647ade6..42e0a89017e1510a805469a493a769116607c03f 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -53,18 +53,22 @@ void MakeGeneralGraphTransformationsSet( CHECK(transformations->empty()); transformations->Add(new ConvertExpandDimsToReshape); transformations->Add(new ConvertTrivialAddNToAdd); + transformations->Add(new ConvertTrivialStackToReshape); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); transformations->Add(new ResolveReshapeAttributes); + transformations->Add(new ResolveTransposeAttributes); transformations->Add(new PropagateArrayDataTypes); transformations->Add(new PropagateFixedSizes); transformations->Add(new RemoveTensorFlowAssert); transformations->Add(new RemoveTensorFlowIdentity); transformations->Add(new RemoveTrivialConcatenation); transformations->Add(new RemoveTrivialConcatenationInput); + transformations->Add(new RemoveTrivialSlice); transformations->Add(new RemoveUnusedOp); transformations->Add(new EnsureBiasVectors); transformations->Add(new ResolveReorderAxes); + transformations->Add(new UnrollBatchMatMul); transformations->Add(new ResolveTensorFlowMatMul); transformations->Add(new FuseBinaryIntoPrecedingAffine); transformations->Add(new FuseBinaryIntoFollowingAffine); @@ -75,12 +79,15 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveConstantRange); transformations->Add(new ResolveConstantStack); transformations->Add(new ResolveConstantStridedSlice); + transformations->Add(new ResolveConstantTranspose); transformations->Add(new ResolveConstantUnaryOperator); transformations->Add(new ResolveTensorFlowMerge); transformations->Add(new ResolveSqueezeAttributes); transformations->Add(new ResolveTensorFlowSwitch); transformations->Add(new ResolveTensorFlowTile); transformations->Add(new ResolveTensorFlowConcat); + transformations->Add(new ResolveMultiplyByZero); + transformations->Add(new IdentifyDilatedConv); transformations->Add(new IdentifyL2Normalization); transformations->Add(new IdentifyL2Pool); transformations->Add(new IdentifyRelu1); @@ -92,9 +99,10 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveStridedSliceAttributes); transformations->Add(new ResolveSliceAttributes); transformations->Add(new ResolveMeanAttributes); - transformations->Add(new ResolveTransposeAttributes); transformations->Add(new ResolveConstantShapeOrRank); transformations->Add(new MakeInitialDequantizeOperator); + transformations->Add(new ResolveConstantFakeQuant); + transformations->Add(new UnpartitionEmbeddingLookup); } bool SupportsQuantization(FileFormat format) { @@ -106,7 +114,8 @@ bool SupportsFusedActivationFunction(FileFormat format) { } bool SupportsLstmCell(FileFormat format) { - return (format == TENSORFLOW_GRAPHDEF || format == GRAPHVIZ_DOT); + return (format == TENSORFLOW_GRAPHDEF || format == GRAPHVIZ_DOT || + format == TFLITE); } bool SupportsPreallocatedWorkspace(FileFormat format) { @@ -182,20 +191,23 @@ std::unique_ptr Import(const TocoFlags& toco_flags, } void Transform(const TocoFlags& toco_flags, Model* model) { + // Clean up after import. + SetFinalDataTypeOnInputs(toco_flags, model); + UseArraysExtraInfo(model); + FinishBuildingRNNStates(model); + const FileFormat output_format = toco_flags.output_format(); const IODataType inference_type = toco_flags.inference_type(); const bool quantize_output = - SupportsQuantization(output_format) && inference_type == QUANTIZED_UINT8; + SupportsQuantization(output_format) && + (inference_type == QUANTIZED_UINT8 || inference_type == QUANTIZED_INT16); if (quantize_output) { QCHECK_NE(toco_flags.inference_input_type(), FLOAT) << "Quantized inference is not allowed with float inputs."; } - SetFinalDataTypeOnInputs(toco_flags, model); - UseArraysExtraInfo(model); - // Remove unused ops before performing any other optimizations. This is to // stop optimizations from crossing the input/output boundaries. For example // this will stop BatchNorm fusing if the output node is in between a conv @@ -212,9 +224,6 @@ void Transform(const TocoFlags& toco_flags, Model* model) { } else { transformations.Add(new UnfuseActivationFunctions); } - if (output_format != TENSORFLOW_GRAPHDEF) { - transformations.Add(new ResolveConstantFakeQuant); - } if (toco_flags.drop_fake_quant()) { transformations.Add(new DropFakeQuant); } else { @@ -227,9 +236,15 @@ void Transform(const TocoFlags& toco_flags, Model* model) { } } transformations.Add(new ConvertPureConvToDepthwise); - // TFLite export does not yet support fused LSTM cell. if (SupportsLstmCell(output_format)) { - transformations.Add(new IdentifyLstmCell); + if (!toco_flags.debug_disable_recurrent_cell_fusion()) { + transformations.Add(new IdentifyLstmCell); + } + if (output_format == TFLITE) { + transformations.Add(new toco::SplitLstmCellInputs); + } else { + transformations.Add(new toco::MergeLstmCellInputs); + } } transformations.Add(new ResolveConstantConcatenation); RunGraphTransformations(model, "general graph transformations", @@ -267,6 +282,10 @@ void Transform(const TocoFlags& toco_flags, Model* model) { dequantization_transformations); } + if (output_format == TENSORFLOW_GRAPHDEF) { + EncodeConstantArraysMinMaxByWrappingThemInFakeQuantNodes(model); + } + LogDump(kLogLevelModelChanged, "AFTER TRANSFORMATIONS", *model); if (output_format != GRAPHVIZ_DOT && output_format != TFLITE) { diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index ff8bc471b7f41bb0c9d51b8b51637412c09dadfc..48aad89b8c0f1984a692488e0565dda0e9c58852 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -25,6 +25,7 @@ limitations under the License. #include "absl/strings/str_cat.h" #include "absl/strings/str_join.h" #include "absl/strings/str_replace.h" +#include "absl/strings/str_split.h" #include "tensorflow/contrib/lite/toco/dump_graphviz.h" #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h" @@ -61,6 +62,37 @@ string LogName(const Operator& op) { } } +string ArrayDataTypeName(ArrayDataType data_type) { + switch (data_type) { + case ArrayDataType::kFloat: + return "Float"; + case ArrayDataType::kInt8: + return "Int8"; + case ArrayDataType::kUint8: + return "Uint8"; + case ArrayDataType::kInt16: + return "Int16"; + case ArrayDataType::kUint16: + return "Uint16"; + case ArrayDataType::kInt32: + return "Int32"; + case ArrayDataType::kUint32: + return "Uint32"; + case ArrayDataType::kInt64: + return "Int64"; + case ArrayDataType::kUint64: + return "Uint64"; + case ArrayDataType::kString: + return "String"; + case ArrayDataType::kBool: + return "Bool"; + case ArrayDataType::kNone: + return "None"; + default: + LOG(FATAL) << "Unhandled array data type " << static_cast(data_type); + } +} + bool IsInputArray(const Model& model, const string& name) { for (const auto& input_array : model.flags.input_arrays()) { if (input_array.name() == name) { @@ -110,13 +142,32 @@ int CountOpsWithInput(const Model& model, const string& array_name) { } bool DeleteArrayIfUnused(const string& array_name, Model* model) { - if (CountOpsWithInput(*model, array_name) == 0) { + if (IsDiscardableArray(*model, array_name) && + CountOpsWithInput(*model, array_name) == 0) { model->EraseArray(array_name); return true; } return false; } +bool DeleteArrayIfUsedOnce(const string& array_name, Model* model) { + if (IsDiscardableArray(*model, array_name) && + CountOpsWithInput(*model, array_name) == 1) { + model->EraseArray(array_name); + return true; + } + return false; +} + +void DeleteOpAndArraysIfUnused(Model* model, Operator* op) { + for (const string& array_name : op->inputs) { + DeleteArrayIfUsedOnce(array_name, model); + } + auto op_it = FindOp(*model, op); + CHECK(op_it != model->operators.end()); + model->operators.erase(op_it); +} + std::vector>::const_iterator FindOpWithOutput( const Model& model, const string& array_name) { for (auto it = model.operators.begin(); it != model.operators.end(); ++it) { @@ -159,6 +210,18 @@ std::vector>::const_iterator FindOpWithInput( return model.operators.end(); } +std::vector>::iterator FindOpWithInput( + Model& model, const string& array_name) { + for (auto it = model.operators.begin(); it != model.operators.end(); ++it) { + for (auto& input : it->get()->inputs) { + if (input == array_name) { + return it; + } + } + } + return model.operators.end(); +} + std::vector>::const_iterator FindOp( const Model& model, const Operator* op) { for (auto it = model.operators.begin(); it != model.operators.end(); ++it) { @@ -217,6 +280,7 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Add) HANDLE_OPERATORTYPENAME_CASE(AddN) HANDLE_OPERATORTYPENAME_CASE(AveragePool) + HANDLE_OPERATORTYPENAME_CASE(BatchMatMul) HANDLE_OPERATORTYPENAME_CASE(BatchNormalization) HANDLE_OPERATORTYPENAME_CASE(Conv) HANDLE_OPERATORTYPENAME_CASE(Concatenation) @@ -238,6 +302,7 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Relu6) HANDLE_OPERATORTYPENAME_CASE(ReorderAxes) HANDLE_OPERATORTYPENAME_CASE(Softmax) + HANDLE_OPERATORTYPENAME_CASE(LogSoftmax) HANDLE_OPERATORTYPENAME_CASE(Div) HANDLE_OPERATORTYPENAME_CASE(Tanh) HANDLE_OPERATORTYPENAME_CASE(TensorFlowAll) @@ -288,7 +353,11 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Mean) HANDLE_OPERATORTYPENAME_CASE(Svdf) HANDLE_OPERATORTYPENAME_CASE(ArgMax) + HANDLE_OPERATORTYPENAME_CASE(TopK_V2) HANDLE_OPERATORTYPENAME_CASE(TensorFlowUnsupported) + HANDLE_OPERATORTYPENAME_CASE(Exp) + HANDLE_OPERATORTYPENAME_CASE(DynamicPartition) + HANDLE_OPERATORTYPENAME_CASE(DynamicStitch) default: LOG(FATAL) << "Unhandled op type"; #undef HANDLE_OPERATORTYPENAME_CASE @@ -307,6 +376,7 @@ string HelpfulOperatorTypeName(const Operator& op) { bool OperatorSupportsFusedActivation(OperatorType type) { switch (type) { case OperatorType::kConcatenation: + case OperatorType::kGather: case OperatorType::kSlice: case OperatorType::kSqueeze: case OperatorType::kTensorFlowReshape: @@ -335,48 +405,9 @@ void LogSummary(int log_level, const Model& model) { void LogArray(int log_level, const Model& model, const string& name) { const auto& array = model.GetArray(name); VLOG(log_level) << "Array: " << name; - switch (array.data_type) { - case ArrayDataType::kNone: - VLOG(log_level) << " Data type:"; - break; - case ArrayDataType::kFloat: - VLOG(log_level) << " Data type: kFloat"; - break; - case ArrayDataType::kInt32: - VLOG(log_level) << " Data type: kInt32"; - break; - case ArrayDataType::kUint8: - VLOG(log_level) << " Data type: kUint8"; - break; - case ArrayDataType::kString: - VLOG(log_level) << " Data type: kString"; - break; - default: - VLOG(log_level) << " Data type: other (numerical value: " - << static_cast(array.data_type) << ")"; - break; - } - switch (array.final_data_type) { - case ArrayDataType::kNone: - VLOG(log_level) << " Final type:"; - break; - case ArrayDataType::kFloat: - VLOG(log_level) << " Final type: kFloat"; - break; - case ArrayDataType::kInt32: - VLOG(log_level) << " Final type: kInt32"; - break; - case ArrayDataType::kUint8: - VLOG(log_level) << " Final type: kUint8"; - break; - case ArrayDataType::kString: - VLOG(log_level) << " Final type: kString"; - break; - default: - VLOG(log_level) << " Final type: other (numerical value: " - << static_cast(array.data_type) << ")"; - break; - } + VLOG(log_level) << " Data type: " << ArrayDataTypeName(array.data_type); + VLOG(log_level) << " Final type: " + << ArrayDataTypeName(array.final_data_type); if (array.buffer) { VLOG(log_level) << " Constant Buffer"; } @@ -406,7 +437,7 @@ void LogArray(int log_level, const Model& model, const string& name) { } if (array.quantization_params) { VLOG(log_level) << " QuantizationParams: zero_point=" - << array.quantization_params->zero_point + << static_cast(array.quantization_params->zero_point) << ", scale=" << array.quantization_params->scale; } } @@ -606,6 +637,14 @@ bool IsConstantParameterArray(const Model& model, const string& name) { } namespace { +// Take an array name, which may be something like "name:3_5" and make it +// acceptable as a TF node name, say "name_3_5"; +string SanitizeNameForTFNode(const string& array_name) { + auto node_name = array_name; + std::replace(node_name.begin(), node_name.end(), ':', '_'); + return node_name; +} + void CheckInputArraysAreNotOutputArrays(const ModelFlags& model_flags) { for (const auto& input_array : model_flags.input_arrays()) { for (const string& output_array : model_flags.output_arrays()) { @@ -685,12 +724,10 @@ void CheckNoMissingArray(const Model& model) { for (const auto& op : model.operators) { for (const auto& input : op->inputs) { CHECK(model.HasArray(input) || model.optional_arrays.count(input)) - << "Input: " << input << " missing for op: " - << op->outputs[0] << "."; + << "Input: " << input << " missing for op: " << op->outputs[0] << "."; } for (const auto& output : op->outputs) { - CHECK(model.HasArray(output)) << "Output: " << output - << " missing."; + CHECK(model.HasArray(output)) << "Output: " << output << " missing."; } } CheckNonExistentIOArrays(model); @@ -771,7 +808,10 @@ void FixNoOrphanedArray(Model* model) { } } -void CheckArrayFieldsConsistent(const Model& model) { +// Apply checks to arrays individually (for-each fashion). +// +// Check consistency of array fields, check name. +void CheckEachArray(const Model& model) { for (const auto& array_entry : model.GetArrayMap()) { const auto& array = array_entry.second; if (array->has_shape()) { @@ -782,10 +822,28 @@ void CheckArrayFieldsConsistent(const Model& model) { // It's OK to have a buffer or an alloc, but not both. // (Since allocs are for transient arrays without a buffer). CHECK(!array->buffer || !array->alloc); - // If there is a buffer, its type should be consistent with data_type. if (array->buffer) { + // If there is a buffer, its type should be consistent with data_type. CHECK(array->buffer->type == array->data_type); + // The presence of a fixed buffer should imply the presence of a fixed + // shape. + CHECK(array->has_shape()); + // The shape flat-size should agree with the buffer length. + CHECK_EQ(array->buffer->Length(), + RequiredBufferSizeForShape(array->shape())); } + + // Check name. Either "name_with_suffix_8", "name_with_port:3", but not + // "name_with_both:3_8". + const string& name = array_entry.first; + auto colon_pos = name.find_first_of(":"); + if (colon_pos != string::npos) { + CHECK_EQ(name.substr(colon_pos + 1).find_first_not_of("0123456789"), + string::npos) + << "Array name must only have digits after colon"; + } + CHECK_GT(colon_pos, 0) + << "First character of array name must not be a colon."; } } @@ -934,7 +992,7 @@ void CheckInvariants(const Model& model) { CheckNonAsciiIOArrays(model.flags); CheckNoMissingArray(model); CheckNoOrphanedArray(model); - CheckArrayFieldsConsistent(model); + CheckEachArray(model); CheckOperatorOrdering(model); } @@ -1026,9 +1084,6 @@ void CreateOrCheckRnnStateArray(const string& name, int size, Model* model) { if (array.has_shape()) { num_dims = array.shape().dimensions_count(); } - CHECK(array.data_type == ArrayDataType::kFloat || - array.data_type == ArrayDataType::kNone); - array.data_type = ArrayDataType::kFloat; if (!array.has_shape() && num_dims >= 0) { Shape* shape = array.mutable_shape(); std::vector dims; @@ -1052,7 +1107,7 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { } } if (!dst_input_array) { - // specified_input_array from model_flags is not found in model->flags. + // Specified_input_array from model_flags is not found in model->flags. // Match a name-less specified input array when there can be no ambiguity // as there is only 1 input array. if (model->flags.input_arrays_size() == 1 && @@ -1155,7 +1210,7 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { << "This model does not define output arrays, so a " "--output_arrays flag must be given on the command-line."; - for (const auto& input_array_proto : model->flags.input_arrays()) { + for (auto& input_array_proto : *model->flags.mutable_input_arrays()) { auto& input_array = model->GetOrCreateArray(input_array_proto.name()); if (input_array_proto.has_data_type()) { const ArrayDataType specified_type = @@ -1199,6 +1254,11 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { for (int i = 0; i < input_array_dims.size(); i++) { CHECK_EQ(input_array_dims[i], input_array_proto.shape().dims(i)); } + } else { + for (int i = 0; i < input_array.shape().dimensions_count(); i++) { + input_array_proto.mutable_shape()->add_dims( + input_array.shape().dims(i)); + } } } @@ -1296,12 +1356,23 @@ int ElementSize(ArrayDataType data_type) { switch (data_type) { case ArrayDataType::kFloat: return 4; - case ArrayDataType::kInt32: - return 4; + case ArrayDataType::kInt8: + return 1; case ArrayDataType::kUint8: return 1; + case ArrayDataType::kInt16: + return 2; + case ArrayDataType::kUint16: + return 2; + case ArrayDataType::kInt32: + return 4; + case ArrayDataType::kUint32: + return 4; case ArrayDataType::kInt64: return 8; + case ArrayDataType::kUint64: + return 8; + // Usually not critical limitation because strings are only input and/or // output. case ArrayDataType::kString: @@ -1348,19 +1419,23 @@ bool IsAllocatableTransientArray(const Model& model, const string& array_name) { } string AvailableArrayName(const Model& model, const string& name) { - if (!model.HasArray(name) && !model.IsOptionalArray(name)) { - return name; + string sanitized_name = SanitizeNameForTFNode(name); + if (!model.HasArray(sanitized_name) && + !model.IsOptionalArray(sanitized_name)) { + return sanitized_name; } const int kNumSuffixesToTry = 1000; for (int i = 0; i < kNumSuffixesToTry; i++) { - const string& name_with_suffix = toco::port::StringF("%s_%d", name, i); + const string& name_with_suffix = + toco::port::StringF("%s_%d", sanitized_name, i); if (!model.HasArray(name_with_suffix) && !model.IsOptionalArray(name_with_suffix)) { return name_with_suffix; } } - LOG(FATAL) << "Could not find an available array name starting with " << name - << ". Tried " << kNumSuffixesToTry << " suffixes, all were taken!"; + LOG(FATAL) << "Could not find an available array name starting with " + << sanitized_name << ". Tried " << kNumSuffixesToTry + << " suffixes, all were taken!"; return ""; } @@ -1399,6 +1474,21 @@ bool IsArrayFullyConnectedWeights(const Model& model, const string& name) { return is_fc_weights; } +string CreateInt32Array(Model* model, const string& param_name, + const std::vector& value) { + auto param_array_name = AvailableArrayName(*model, param_name); + auto& param_array = model->GetOrCreateArray(param_array_name); + param_array.mutable_shape()->ReplaceDims({static_cast(value.size())}); + param_array.data_type = ArrayDataType::kInt32; + auto& param_array_data = + param_array.GetMutableBuffer().data; + param_array_data.resize(RequiredBufferSizeForShape(param_array.shape())); + for (int i = 0; i < value.size(); ++i) { + param_array_data[i] = value[i]; + } + return param_array_name; +} + bool EstimateArithmeticOpsCount(const Model& model, int64* result) { int64 total = 0; for (const auto& op : model.operators) { @@ -1446,6 +1536,7 @@ bool EstimateArithmeticOpsCount(const Model& model, int64* result) { } case OperatorType::kLogistic: case OperatorType::kSoftmax: + case OperatorType::kLogSoftmax: case OperatorType::kTanh: { const auto& output_array = model.GetArray(op->outputs[0]); if (!output_array.has_shape()) { @@ -1545,10 +1636,6 @@ void GetShuffleShape(AxesOrder input_axes_order, AxesOrder output_axes_order, } } -namespace { - -// Extend shuffle is designed to match ExtendShape, which pads the shape with -// unit dimensions at the beginning. void ExtendShuffle(const std::vector& input_shuffle, int newdim, std::vector* extended_shuffle) { *extended_shuffle = input_shuffle; @@ -1563,8 +1650,6 @@ void ExtendShuffle(const std::vector& input_shuffle, int newdim, } } -} // end anonymous namespace - void ShuffleDims(const Shape& input_shape, AxesOrder input_axes_order, AxesOrder output_axes_order, Shape* output_shape) { if (input_axes_order == AxesOrder::kHWIM && @@ -1724,7 +1809,10 @@ bool IsDiscardableArray(const Model& model, const string& array_name) { void CheckFinalDataTypesSatisfied(const Model& model) { for (const auto& array_entry : model.GetArrayMap()) { const auto& array = *array_entry.second; - if (array.final_data_type != ArrayDataType::kNone) { + // If the final data type is int16, the data type may be float, for example + // after dequantization. + if (array.final_data_type != ArrayDataType::kNone && + array.final_data_type != ArrayDataType::kInt16) { CHECK(array.final_data_type == array.data_type) << "Array \"" << array_entry.first << "\" has mis-matching actual and final data types (" @@ -1740,6 +1828,8 @@ ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type) { return ArrayDataType::kFloat; case QUANTIZED_UINT8: return ArrayDataType::kUint8; + case QUANTIZED_INT16: + return ArrayDataType::kInt16; case INT32: return ArrayDataType::kInt32; case INT64: @@ -1749,33 +1839,40 @@ ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type) { } } +void FinishBuildingRNNStates(Model* model) { + for (const auto& rnn_state : model->flags.rnn_states()) { + if (!model->HasArray(rnn_state.back_edge_source_array()) || + !model->HasArray(rnn_state.state_array())) { + CHECK(model->HasArray(rnn_state.back_edge_source_array())); + CHECK(model->HasArray(rnn_state.state_array())); + continue; + } + const auto& src_array = model->GetArray(rnn_state.back_edge_source_array()); + auto& dst_array = model->GetArray(rnn_state.state_array()); + if (src_array.data_type == ArrayDataType::kNone && + dst_array.data_type == ArrayDataType::kNone) { + dst_array.data_type = ArrayDataType::kFloat; + } + } +} + void UseArraysExtraInfo(Model* model) { for (const auto& entry : model->flags.arrays_extra_info().entries()) { QCHECK(model->HasArray(entry.name())) << "ArraysExtraInfo refers to non-existent array name: " << entry.name(); - auto& minmax = model->GetArray(entry.name()).GetOrCreateMinMax(); - minmax.min = entry.min(); - minmax.max = entry.max(); - } -} - -bool IsRnnSourceArray(const toco::Model& model, const string& array_name) { - for (const auto& rnn_state : model.flags.rnn_states()) { - if (array_name == rnn_state.back_edge_source_array()) { - return true; + auto& array = model->GetArray(entry.name()); + auto& minmax = array.GetOrCreateMinMax(); + if (entry.has_min() || entry.has_max()) { + CHECK_EQ(entry.has_min(), entry.has_max()); + minmax.min = entry.min(); + minmax.max = entry.max(); } - } - return false; -} - -bool IsRnnStateArray(const toco::Model& model, const string& array_name) { - for (const auto& rnn_state : model.flags.rnn_states()) { - if (array_name == rnn_state.state_array()) { - return true; + if (entry.has_data_type()) { + array.final_data_type = + ConvertIODataTypeToArrayDataType(entry.data_type()); } } - return false; } } // namespace toco diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index a023bab1a0a0a09f3afe7bcd5afa787c1962874d..05360e3b0a8510f57724d98dab0e2097b1062ced 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -28,6 +28,7 @@ limitations under the License. #if TOCO_SUPPORT_PORTABLE_PROTOS #include "third_party/protobuf/src/google/protobuf/text_format.h" #endif // TOCO_SUPPORT_PORTABLE_PROTOS +#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/runtime/types.h" @@ -54,12 +55,19 @@ absl::string_view FindLongestCommonPrefix(absl::string_view a, absl::string_view b); string LogName(const Operator& op); +string ArrayDataTypeName(ArrayDataType data_type); + bool IsInputArray(const Model& model, const string& name); bool IsArrayConsumed(const Model& model, const string& name); int CountTrueOutputs(const Model& model, const Operator& op); int CountOpsWithInput(const Model& model, const string& array_name); bool DeleteArrayIfUnused(const string& array_name, Model* model); +bool DeleteArrayIfUsedOnce(const string& array_name, Model* model); + +// Deletes the op and any of its input and output arrays if they are unused +// after the op has been deleted. +void DeleteOpAndArraysIfUnused(Model* model, Operator* op); std::vector>::const_iterator FindOpWithOutput( const Model& model, const string& array_name); @@ -67,10 +75,13 @@ Operator* GetOpWithOutput(const Model& model, const string& array_name); std::vector>::iterator FindOpWithOutput( Model& model, const string& array_name); -Operator* GetOpWithOutput(const Model& model, const string& array_name); std::vector>::const_iterator FindOpWithInput( const Model& model, const string& array_name); + +std::vector>::iterator FindOpWithInput( + Model& model, const string& array_name); + Operator* GetOpWithInput(const Model& model, const string& array_name); Operator* GetFirstOpWithInput(const Model& model, const string& array_name); @@ -136,75 +147,14 @@ void FixNoOrphanedArray(Model* model); void ResolveModelFlags(const ModelFlags& model_flags, Model* model); template -void GetQuantizationParamsFromMinMax(const ModelFlags& model_flags, - const MinMax& minmax, +void GetQuantizationParamsFromMinMax(const MinMax& minmax, QuantizationParams* quantization_params) { using Integer = DataType; - const Integer qmin = std::numeric_limits::min(); - const Integer qmax = std::numeric_limits::max(); - const double qmin_double = qmin; - const double qmax_double = qmax; const double rmin = minmax.min; const double rmax = minmax.max; - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - CHECK_LE(rmin, 0.); - CHECK_GE(rmax, 0.); - if (rmin == rmax) { - // Special case where the min,max range is a point. Should be {0}. - CHECK_EQ(rmin, 0.); - CHECK_EQ(rmax, 0.); - quantization_params->zero_point = 0; - quantization_params->scale = 0.; - return; - } - // General case. - // - // First determine the scale. - const double scale = (rmax - rmin) / (qmax_double - qmin_double); - - // Zero-point computation. - // First the initial floating-point computation. The zero-point can be - // determined from solving an affine equation for any known pair - // (real value, corresponding quantized value). - // We know two such pairs: (rmin, qmin) and (rmax, qmax). - // The arithmetic error on the zero point computed from either pair - // will be roughly machine_epsilon * (sum of absolute values of terms) - // so we want to use the variant that adds the smaller terms. - const double zero_point_from_min = qmin_double - rmin / scale; - const double zero_point_from_max = qmax_double - rmax / scale; - const double zero_point_from_min_error = - std::abs(qmin_double) + std::abs(rmin / scale); - const double zero_point_from_max_error = - std::abs(qmax_double) + std::abs(rmax / scale); - - const double zero_point_double = - zero_point_from_min_error < zero_point_from_max_error - ? zero_point_from_min - : zero_point_from_max; - - // Now we need to nudge the zero point to be an integer - // (our zero points are integer, and this is motivated by the requirement - // to be able to represent the real value "0" exactly as a quantized value, - // which is required in multiple places, for example in Im2col with SAME - // padding). - Integer nudged_zero_point = 0; - if (zero_point_double < qmin_double) { - nudged_zero_point = qmin; - } else if (zero_point_double > qmax_double) { - nudged_zero_point = qmax; - } else { - nudged_zero_point = static_cast(std::round(zero_point_double)); - } - // The zero point should always be in the range of quantized value, - // [qmin, qmax]. - CHECK_GE(nudged_zero_point, qmin); - CHECK_LE(nudged_zero_point, qmax); - - // Finally, store the result nudged quantization params. - quantization_params->zero_point = nudged_zero_point; - quantization_params->scale = scale; + *quantization_params = + ::tflite::ChooseQuantizationParams(rmin, rmax); } void CheckIsReadyForQuantization(const Model& model); @@ -255,6 +205,11 @@ void PrintArrayShape(Model* model, const string& name); void MakeArrayDims(int num_dims, int batch, int height, int width, int depth, std::vector* out_dims); +// Defines a constant int32 array with the provided values formatted for use +// as op parameters. +string CreateInt32Array(Model* model, const string& param_name, + const std::vector& value); + bool EstimateArithmeticOpsCount(const Model& model, int64* result); int AxesCount(AxesOrder axes_order); @@ -264,6 +219,11 @@ int AxesCount(AxesOrder axes_order); void GetShuffleShape(AxesOrder input_axes_order, AxesOrder output_axes_order, std::vector* shuffle); +// Extend shuffle is designed to match ExtendShape, which pads the shape with +// unit dimensions at the beginning. +void ExtendShuffle(const std::vector& input_shuffle, int newdim, + std::vector* extended_shuffle); + void ShuffleDims(const Shape& input_shape, AxesOrder input_axes_order, AxesOrder output_axes_order, Shape* output_shape); void ShuffleArray(const Shape& input_shape, AxesOrder input_axes_order, @@ -283,10 +243,24 @@ void CheckFinalDataTypesSatisfied(const Model& model); ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type); -void UseArraysExtraInfo(Model* model); +// The process of building models varies according to the import format. +// +// (a) In some cases, such as model-proto format, the model should be fully +// specified. In these cases, no extra action should be taken by this function. +// (b) In other cases, such as TF graphdef format, the desired types of RNN +// arrays are not specified directly in the model, neither can they be inferred. +// However, we can set the types of RNN destination arrays to float. This breaks +// any cycles such as when resolution of the type of an RNN source array depends +// on the type of its destination array. +// +// This function is applied after the main import, after resolution of flags and +// after application of ArraysExtraInfo. It only defaults destination RNN arrays +// to float. If the model is subsequently quantized, it is assumed that the +// model contains sufficient information for that to be completed. If it is +// already quantized, then case (a) should hold. +void FinishBuildingRNNStates(Model* model); -bool IsRnnSourceArray(const toco::Model& model, const string& array_name); -bool IsRnnStateArray(const toco::Model& model, const string& array_name); +void UseArraysExtraInfo(Model* model); } // namespace toco diff --git a/tensorflow/contrib/lite/toco/types.proto b/tensorflow/contrib/lite/toco/types.proto index 318fd4b7b2c2df093562e73c3fe707675ee98876..03bd6150bc86bb27221814cd191b17f1a09585fa 100644 --- a/tensorflow/contrib/lite/toco/types.proto +++ b/tensorflow/contrib/lite/toco/types.proto @@ -34,4 +34,7 @@ enum IODataType { // String, not quantized STRING = 5; + + // Int16, quantized + QUANTIZED_INT16 = 6; } diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 6786b1618456637aecfd870b9984af65b59784f6..54df724f793c2e53ac088165812a8cff8db97313 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -4,6 +4,7 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") load("//tensorflow:tensorflow.bzl", "tf_cc_binary") py_binary( @@ -111,7 +112,11 @@ cc_test( name = "verifier_test", size = "small", srcs = ["verifier_test.cc"], + tags = [ + "tflite_not_portable", + ], deps = [ + ":mutable_op_resolver", ":verifier", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", @@ -123,3 +128,5 @@ cc_test( "@flatbuffers", ], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc index 726e2aaa3162591593cd2abd6384eb55baf0aef4..59c74205f0a311ec12ff87f46622041605fb493b 100644 --- a/tensorflow/contrib/lite/tools/verifier.cc +++ b/tensorflow/contrib/lite/tools/verifier.cc @@ -155,11 +155,11 @@ bool VerifyTensors(const Model& model, ErrorReporter* error_reporter) { } for (const auto& subgraph : *model.subgraphs()) { if (!subgraph->tensors()) { - return true; + continue; } for (const auto& tensor : *subgraph->tensors()) { if (!tensor->buffer()) { - return true; + continue; } if (tensor->buffer() >= model.buffers()->size()) { ReportError(error_reporter, "Invalid tensor buffer index: %d", @@ -187,9 +187,33 @@ bool VerifyTensors(const Model& model, ErrorReporter* error_reporter) { return true; } +bool VerifyOps(const Model& model, const OpResolver& resolver, + ErrorReporter* error_reporter) { + if (!model.operator_codes()) { + return true; + } + for (const auto& opcode : *model.operator_codes()) { + if (opcode->builtin_code() == BuiltinOperator_CUSTOM) { + if (!resolver.FindOp(opcode->custom_code()->c_str())) { + ReportError(error_reporter, "Unsupported custom op: %s", + opcode->custom_code()->c_str()); + return false; + } + } else { + if (!resolver.FindOp(opcode->builtin_code())) { + ReportError(error_reporter, "Unsupported builtin op: %s", + EnumNameBuiltinOperator(opcode->builtin_code())); + return false; + } + } + } + return true; +} + } // namespace -bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter) { +bool Verify(const void* buf, size_t len, const OpResolver& resolver, + ErrorReporter* error_reporter) { const Model* model = VerifyFlatbufferAndGetModel(buf, len); if (model == nullptr) { ReportError(error_reporter, "Invalid flatbuffer format"); @@ -202,6 +226,9 @@ bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter) { if (!VerifyTensors(*model, error_reporter)) { return false; } + if (!VerifyOps(*model, resolver, error_reporter)) { + return false; + } return true; } } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h index d2bf3c91d54225098c1f254c26971e8bb962f791..c2ee11215c861ed7b27696a8d786bb6e2a48e930 100644 --- a/tensorflow/contrib/lite/tools/verifier.h +++ b/tensorflow/contrib/lite/tools/verifier.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/contrib/lite/error_reporter.h" +#include "tensorflow/contrib/lite/model.h" namespace tflite { @@ -26,7 +27,9 @@ namespace tflite { // Currently, it verifies: // * The file is following a legit flatbuffer schema. // * The model is in supported version. -bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter); +// * All ops used in the model are supported by OpResolver. +bool Verify(const void* buf, size_t len, const OpResolver& resolver, + ErrorReporter* error_reporter); } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc index 87f6854e9e67c0389949c8d72a476036051d1c0f..b3e611f999b2837efbf8876bd989db44c408b8c7 100644 --- a/tensorflow/contrib/lite/tools/verifier_test.cc +++ b/tensorflow/contrib/lite/tools/verifier_test.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/contrib/lite/error_reporter.h" #include "tensorflow/contrib/lite/schema/schema_generated.h" #include "tensorflow/contrib/lite/testing/util.h" +#include "tensorflow/contrib/lite/tools/mutable_op_resolver.h" #include "tensorflow/contrib/lite/tools/verifier.h" #include "tensorflow/contrib/lite/version.h" #include "tensorflow/core/framework/numeric_types.h" @@ -40,6 +41,19 @@ class TfLiteFlatbufferModelBuilder { CreateBuffer(builder_, builder_.CreateVector(std::vector{}))); } + TfLiteFlatbufferModelBuilder(const std::vector& builtin_ops, + const std::vector& custom_ops) { + buffers_.push_back( + CreateBuffer(builder_, builder_.CreateVector(std::vector{}))); + + for (const auto& iter : builtin_ops) { + resolver_.AddBuiltin(iter, &fake_op_); + } + for (const auto& iter : custom_ops) { + resolver_.AddCustom(iter.data(), &fake_op_); + } + } + void AddTensor(const std::vector& shape, tflite::TensorType type, const std::vector& buffer, const char* name) { int buffer_index = 0; @@ -79,11 +93,13 @@ class TfLiteFlatbufferModelBuilder { bool Verify() { return tflite::Verify(builder_.GetBufferPointer(), builder_.GetSize(), - DefaultErrorReporter()); + resolver_, DefaultErrorReporter()); } private: FlatBufferBuilder builder_; + MutableOpResolver resolver_; + TfLiteRegistration fake_op_; std::vector> operators_; std::vector> operator_codes_; std::vector> tensors_; @@ -98,11 +114,11 @@ TEST(VerifyModel, TestEmptyModel) { ::tflite::FinishModelBuffer(builder, model); ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); + MutableOpResolver{}, DefaultErrorReporter())); } TEST(VerifyModel, TestSimpleModel) { - TfLiteFlatbufferModelBuilder builder; + TfLiteFlatbufferModelBuilder builder({}, {"test"}); builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "test"); builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4, 5, 6}, "input"); builder.AddTensor( @@ -116,7 +132,8 @@ TEST(VerifyModel, TestSimpleModel) { TEST(VerifyModel, TestCorruptedData) { std::string model = "123"; - ASSERT_FALSE(Verify(model.data(), model.size(), /*error_reporter=*/nullptr)); + ASSERT_FALSE(Verify(model.data(), model.size(), MutableOpResolver{}, + /*error_reporter=*/nullptr)); } TEST(VerifyModel, TestUnsupportedVersion) { @@ -125,7 +142,7 @@ TEST(VerifyModel, TestUnsupportedVersion) { /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); + MutableOpResolver{}, DefaultErrorReporter())); } TEST(VerifyModel, TestRandomModificationIsNotAllowed) { @@ -140,7 +157,7 @@ TEST(VerifyModel, TestRandomModificationIsNotAllowed) { for (int i = 0; i < model_content.size(); i++) { model_content[i] = (model_content[i] + 137) % 255; EXPECT_FALSE(Verify(model_content.data(), model_content.size(), - DefaultErrorReporter())) + MutableOpResolver{}, DefaultErrorReporter())) << "Fail at position: " << i; } } @@ -188,7 +205,7 @@ TEST(VerifyModel, TensorBufferIsNotValid) { ::tflite::FinishModelBuffer(builder, model); ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); + MutableOpResolver{}, DefaultErrorReporter())); } TEST(VerifyModel, StringTensorHasInvalidNumString) { @@ -229,6 +246,37 @@ TEST(VerifyModel, StringTensorIsLargerThanRequired) { ASSERT_FALSE(builder.Verify()); } +TEST(VerifyModel, AllOpsAreSupported) { + TfLiteFlatbufferModelBuilder builder({BuiltinOperator_ADD}, {"CustomOp"}); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input1"); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input2"); + builder.AddTensor({2, 3}, TensorType_UINT8, {}, "output"); + builder.AddOperator({0, 1}, {2}, BuiltinOperator_ADD, nullptr); + builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "CustomOp"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, UseUnsupportedBuiltinOps) { + TfLiteFlatbufferModelBuilder builder({BuiltinOperator_SUB}, {"CustomOp"}); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input1"); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input2"); + builder.AddTensor({2, 3}, TensorType_UINT8, {}, "output"); + builder.AddOperator({0, 1}, {2}, BuiltinOperator_ADD, nullptr); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, UseUnsupportedCustomOps) { + TfLiteFlatbufferModelBuilder builder({BuiltinOperator_ADD}, {"NewOp"}); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input1"); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input2"); + builder.AddTensor({2, 3}, TensorType_UINT8, {}, "output"); + builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "Not supported"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + // TODO(yichengfan): make up malicious files to test with. } // namespace tflite diff --git a/tensorflow/contrib/lookup/BUILD b/tensorflow/contrib/lookup/BUILD index 8ca03f4193f260ce32f942ccaf76a8260b282156..0a6edc33c57435e9466b27cb1d33570d7a98cb12 100644 --- a/tensorflow/contrib/lookup/BUILD +++ b/tensorflow/contrib/lookup/BUILD @@ -46,6 +46,7 @@ tf_py_test( "//tensorflow/python:variables", ], grpc_enabled = True, + tags = ["no_windows"], # TODO: needs investigation on Windows ) filegroup( diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py index a430dac4ec43ce31f0b5aaae5e7b0b51d25c9632..62f1c810fc72ba7e27c4553006c947f8fa0ef629 100644 --- a/tensorflow/contrib/lookup/lookup_ops.py +++ b/tensorflow/contrib/lookup/lookup_ops.py @@ -341,23 +341,21 @@ class MutableHashTable(LookupInterface): # training to work correctly. Use the node name if no shared_name has been # explicitly specified. use_node_name_sharing = checkpoint and shared_name is None - # pylint: disable=protected-access if self._default_value.get_shape().ndims == 0: - self._table_ref = gen_lookup_ops._mutable_hash_table_v2( + self._table_ref = gen_lookup_ops.mutable_hash_table_v2( shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype, value_dtype=value_dtype, name=name) else: - self._table_ref = gen_lookup_ops._mutable_hash_table_of_tensors_v2( + self._table_ref = gen_lookup_ops.mutable_hash_table_of_tensors_v2( shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype, value_dtype=value_dtype, value_shape=self._default_value.get_shape(), name=name) - # pylint: enable=protected-access super(MutableHashTable, self).__init__(key_dtype, value_dtype, self._table_ref.op.name.split( "/")[-1]) @@ -378,9 +376,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name) + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -406,8 +402,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_find" % self._name, (self._table_ref, keys, self._default_value)) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, keys, self._default_value, name=name) values.set_shape(keys.get_shape().concatenate(self._value_shape)) @@ -437,7 +432,7 @@ class MutableHashTable(LookupInterface): [self._table_ref, keys, values]) as name: with ops.colocate_with(self._table_ref): # pylint: disable=protected-access - op = gen_lookup_ops._lookup_table_insert_v2( + op = gen_lookup_ops.lookup_table_insert_v2( self._table_ref, keys, values, name=name) return op @@ -454,8 +449,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_export_values" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2( + exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2( self._table_ref, self._key_dtype, self._value_dtype, name=name) exported_values.set_shape(exported_keys.get_shape().concatenate( @@ -477,7 +471,7 @@ class MutableHashTable(LookupInterface): def restore(self, restored_tensors, unused_restored_shapes): # pylint: disable=protected-access with ops.colocate_with(self.op._table_ref): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op._table_ref, restored_tensors[0], restored_tensors[1]) @@ -551,8 +545,7 @@ class MutableDenseHashTable(LookupInterface): # explicitly specified. use_node_name_sharing = checkpoint and shared_name is None empty_key = ops.convert_to_tensor(empty_key, dtype=key_dtype) - # pylint: disable=protected-access - self._table_ref = gen_lookup_ops._mutable_dense_hash_table_v2( + self._table_ref = gen_lookup_ops.mutable_dense_hash_table_v2( empty_key=empty_key, shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, @@ -560,7 +553,6 @@ class MutableDenseHashTable(LookupInterface): value_shape=self._value_shape, initial_num_buckets=initial_num_buckets, name=name) - # pylint: enable=protected-access super(MutableDenseHashTable, self).__init__( key_dtype, value_dtype, self._table_ref.op.name.split("/")[-1]) @@ -580,8 +572,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name) + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -607,8 +598,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_find" % self._name, [self._table_ref, keys]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, keys, self._default_value, name=name) if keys.get_shape().ndims is not None and keys.get_shape().ndims > 0: @@ -640,8 +630,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_insert" % self._name, [self._table_ref, keys, values]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - op = gen_lookup_ops._lookup_table_insert_v2( + op = gen_lookup_ops.lookup_table_insert_v2( self._table_ref, keys, values, name=name) return op @@ -658,8 +647,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_export_values" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2( + exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2( self._table_ref, self._key_dtype, self._value_dtype, name=name) exported_values.set_shape(exported_keys.get_shape().concatenate( @@ -681,5 +669,5 @@ class MutableDenseHashTable(LookupInterface): def restore(self, restored_tensors, unused_restored_shapes): # pylint: disable=protected-access with ops.colocate_with(self.op._table_ref): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op._table_ref, restored_tensors[0], restored_tensors[1]) diff --git a/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py b/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py index c3a57ba51bcf0a292490dfaa9e556f6e5811ed66..2b9eee4ef7b418e2b90d388d2f165537b8660a9a 100644 --- a/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py +++ b/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py @@ -50,16 +50,12 @@ def pairwise_distance(feature, squared=False): pairwise_distances: 2-D Tensor of size [number of data, number of data]. """ pairwise_distances_squared = math_ops.add( + math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True), math_ops.reduce_sum( - math_ops.square(feature), - axis=[1], - keep_dims=True), - math_ops.reduce_sum( - math_ops.square( - array_ops.transpose(feature)), + math_ops.square(array_ops.transpose(feature)), axis=[0], - keep_dims=True)) - 2.0 * math_ops.matmul( - feature, array_ops.transpose(feature)) + keepdims=True)) - 2.0 * math_ops.matmul(feature, + array_ops.transpose(feature)) # Deal with numerical inaccuracies. Set small negatives to zero. pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0) @@ -132,10 +128,10 @@ def masked_maximum(data, mask, dim=1): masked_maximums: N-D `Tensor`. The maximized dimension is of size 1 after the operation. """ - axis_minimums = math_ops.reduce_min(data, dim, keep_dims=True) + axis_minimums = math_ops.reduce_min(data, dim, keepdims=True) masked_maximums = math_ops.reduce_max( - math_ops.multiply( - data - axis_minimums, mask), dim, keep_dims=True) + axis_minimums + math_ops.multiply(data - axis_minimums, mask), dim, + keepdims=True) + axis_minimums return masked_maximums @@ -151,10 +147,10 @@ def masked_minimum(data, mask, dim=1): masked_minimums: N-D `Tensor`. The minimized dimension is of size 1 after the operation. """ - axis_maximums = math_ops.reduce_max(data, dim, keep_dims=True) + axis_maximums = math_ops.reduce_max(data, dim, keepdims=True) masked_minimums = math_ops.reduce_min( - math_ops.multiply( - data - axis_maximums, mask), dim, keep_dims=True) + axis_maximums + math_ops.multiply(data - axis_maximums, mask), dim, + keepdims=True) + axis_maximums return masked_minimums @@ -202,8 +198,7 @@ def triplet_semihard_loss(labels, embeddings, margin=1.0): mask_final = array_ops.reshape( math_ops.greater( math_ops.reduce_sum( - math_ops.cast( - mask, dtype=dtypes.float32), 1, keep_dims=True), + math_ops.cast(mask, dtype=dtypes.float32), 1, keepdims=True), 0.0), [batch_size, batch_size]) mask_final = array_ops.transpose(mask_final) @@ -290,7 +285,7 @@ def npairs_loss(labels, embeddings_anchor, embeddings_positive, labels_remapped = math_ops.to_float( math_ops.equal(labels, array_ops.transpose(labels))) - labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keep_dims=True) + labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True) # Add the softmax loss. xent_loss = nn.softmax_cross_entropy_with_logits( @@ -395,7 +390,7 @@ def npairs_loss_multilabel(sparse_labels, embeddings_anchor, multilabel_adjacency_matrix = _build_multilabel_adjacency(sparse_labels) labels_remapped = math_ops.to_float(multilabel_adjacency_matrix) - labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keep_dims=True) + labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True) # Add the softmax loss. xent_loss = nn.softmax_cross_entropy_with_logits( @@ -448,10 +443,10 @@ def lifted_struct_loss(labels, embeddings, margin=1.0): # Safe maximum: Temporarily shift negative distances # above zero before taking max. # this is to take the max only among negatives. - row_minimums = math_ops.reduce_min(diff, 1, keep_dims=True) + row_minimums = math_ops.reduce_min(diff, 1, keepdims=True) row_negative_maximums = math_ops.reduce_max( - math_ops.multiply( - diff - row_minimums, mask), 1, keep_dims=True) + row_minimums + math_ops.multiply(diff - row_minimums, mask), 1, + keepdims=True) + row_minimums # Compute the loss. # Keep track of matrix of maximums where M_ij = max(m_i, m_j) @@ -467,10 +462,11 @@ def lifted_struct_loss(labels, embeddings, margin=1.0): array_ops.transpose(max_elements), [-1, 1]) loss_exp_left = array_ops.reshape( - math_ops.reduce_sum(math_ops.multiply( - math_ops.exp( - diff_tiled - max_elements_vect), - mask_tiled), 1, keep_dims=True), [batch_size, batch_size]) + math_ops.reduce_sum( + math_ops.multiply( + math_ops.exp(diff_tiled - max_elements_vect), mask_tiled), + 1, + keepdims=True), [batch_size, batch_size]) loss_mat = max_elements + math_ops.log( loss_exp_left + array_ops.transpose(loss_exp_left)) @@ -686,7 +682,7 @@ def _find_loss_augmented_facility_idx(pairwise_distances, labels, chosen_ids, array_ops.reshape(pairwise_distances_candidate, [1, -1]) ], 0), axis=0, - keep_dims=True), [num_candidates, -1]), + keepdims=True), [num_candidates, -1]), axis=1) nmi_scores = array_ops.zeros([num_candidates]) diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index 81327407d44b4317b7aecb964a689a35aa35c163..05e8d9064bea748c935859f5f9b4c7e646f504cf 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -677,6 +677,7 @@ endif # TEGRA TF_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS)) # Add in any extra files that don't fit the patterns easily TF_CC_SRCS += tensorflow/contrib/makefile/downloads/fft2d/fftsg.c +TF_CC_SRCS += tensorflow/core/common_runtime/gpu/gpu_id_manager.cc # Also include the op and kernel definitions. TF_CC_SRCS += $(shell cat $(MAKEFILE_DIR)/tf_op_files.txt) PBT_CC_SRCS := $(shell cat $(MAKEFILE_DIR)/tf_pb_text_files.txt) diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md index 0613de2cabe2065f1e4a816f2295d41b69159c10..995230dfa848532dc2a50b85f58d19ba264f293e 100644 --- a/tensorflow/contrib/makefile/README.md +++ b/tensorflow/contrib/makefile/README.md @@ -130,6 +130,105 @@ adb shell '/data/local/tmp/benchmark \ For more details, see the [benchmark documentation](../../tools/benchmark). +## CUDA support for Tegra devices running Android (Nvidia Shield TV, etc) + +With the release of TF 1.6 and JetPack for Android 3.2 (currently pending), you can now build a version of TensorFlow for compatible devices according to the following instructions which will receive the full benefits of GPU acceleration. + +#### Environment setup: + +First, download and install JetPack for Android version 3.2 or greater from [Nvidia](https://developers.nvidia.com). Note that as of the TF 1.6 release the JetPack for Android 3.2 release is still pending, and regular JetPack for L4T will not work. + +```bash +git clone https://github.com/tensorflow/tensorflow.git +cd tensorflow +JETPACK=$HOME/JetPack_Android_3.2 +TEGRA_LIBS="$JETPACK/cuDNN/aarch64/cuda/lib64/libcudnn.so $JETPACK/cuda-9.0/extras/CUPTI/lib64/libcupti.so $JETPACK/cuda/targets/aarch64-linux-androideabi/lib64/libcufft.so" +``` + +#### Building all CUDA-enabled native binaries: +This will build CUDA-enabled versions of libtensorflow_inference.so and the benchmark binary. (libtensorflow_demo.so will also be built incidentally, but it does not support CUDA) + +```bash +NDK_ROOT=$JETPACK/android-ndk-r13b +CC_PREFIX=ccache tensorflow/contrib/makefile/build_all_android.sh -s tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in -t "libtensorflow_inference.so libtensorflow_demo.so all" -a tegra +``` +(add -T on subsequent builds to skip protobuf downloading/building) + + +#### Testing the CUDA-enabled benchmark via adb: +Build binaries first as above, then run: + +```bash +adb shell mkdir -p /data/local/tmp/lib64 +adb push $TEGRA_LIBS /data/local/tmp/lib64 +adb push tensorflow/contrib/makefile/gen/bin/android_arm64-v8a/benchmark /data/local/tmp +wget https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk +unzip tensorflow_demo.apk -d /tmp/tensorflow_demo +adb push /tmp/tensorflow_demo/assets/*.pb /data/local/tmp +adb shell "LD_LIBRARY_PATH=/data/local/tmp/lib64 /data/local/tmp/benchmark --graph=/data/local/tmp/tensorflow_inception_graph.pb" +``` + +#### Building the CUDA-enabled TensorFlow AAR with Bazel: +Build the native binaries first as above. Then, build the aar and package the native libs by executing the following: +```bash +mkdir -p /tmp/tf/jni/arm64-v8a +cp tensorflow/contrib/makefile/gen/lib/android_tegra/libtensorflow_*.so /tmp/tf/jni/arm64-v8a/ +cp $TEGRA_LIBS /tmp/tf/jni/arm64-v8a +bazel build //tensorflow/contrib/android:android_tensorflow_inference_java.aar +cp bazel-bin/tensorflow/contrib/android/android_tensorflow_inference_java.aar /tmp/tf/tensorflow.aar +cd /tmp/tf +chmod +w tensorflow.aar +zip -ur tensorflow.aar $(find jni -name *.so) +``` + +#### Building the CUDA-enabled TensorFlow Android demo with Bazel: +Build binaries first as above, then edit tensorflow/examples/android/BUILD and replace: +``` + srcs = [ + ":libtensorflow_demo.so", + "//tensorflow/contrib/android:libtensorflow_inference.so", + ], +``` +with: +``` +srcs = glob(["libs/arm64-v8a/*.so"]), +``` + +Then run: +```bash +# Create dir for native libs +mkdir -p tensorflow/examples/android/libs/arm64-v8a + +# Copy JetPack libs +cp $TEGRA_LIBS tensorflow/examples/android/libs/arm64-v8a + +# Copy native TensorFlow libraries +cp tensorflow/contrib/makefile/gen/lib/android_arm64-v8a/libtensorflow_*.so tensorflow/examples/android/libs/arm64-v8a/ + +# Build APK +bazel build -c opt --fat_apk_cpu=arm64-v8a tensorflow/android:tensorflow_demo + +# Install +adb install -r -f bazel-bin/tensorflow/examples/android/tensorflow_demo.apk +``` + +#### Building the CUDA-enabled Android demo with gradle/Android Studio: + +Add tensorflow/examples/android as an Android project in Android Studio as normal. + +Edit build.gradle and: +* set nativeBuildSystem = 'makefile' +* set cpuType = 'arm64-v8a' +* in "buildNativeMake", replace cpuType with 'tegra' (optional speedups like -T and ccache also work) +* set the environment "NDK_ROOT" var to $JETPACK/android-ndk-r13b + +Click "build apk" to build. + +Install: +```bash +adb install -r -f tensorflow/examples/android/gradleBuild/outputs/apk/debug/android-debug.apk +``` + ## iOS _Note: To use this library in an iOS application, see related instructions in @@ -268,7 +367,7 @@ selectively register only for the operators used in your graph. ```bash tensorflow/contrib/makefile/build_all_ios.sh -a arm64 -g $HOME/graphs/inception/tensorflow_inception_graph.pb ``` -Please note this is an aggresive optimization of the operators and the resulting library may not work with other graphs but will reduce the size of the final library. +Please note this is an aggressive optimization of the operators and the resulting library may not work with other graphs but will reduce the size of the final library. The `compile_ios_tensorflow.sh` script can take optional command-line arguments. The first argument will be passed as a C++ optimization flag and defaults to diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index f67c5161861d20ba15ba165491365cdfd0239047..fc88f59e0948e1d3ed7cce9b809bf30ba280af12 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -52,7 +52,7 @@ shift $((OPTIND - 1)) if [ "$ARCH" == "tegra" ]; then if [[ -z "${JETPACK}" ]]; then - export JETPACK="$HOME/JetPack_Android_3.0" + export JETPACK="$HOME/JetPack_Android_3.2" fi if [ ! -d ${JETPACK} ]; then echo "Can't find Jetpack at ${JETPACK}" diff --git a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh index 203ff4f890a3b0ed32caa1406508b100dd47bcad..421ddd210fd5b1ac6487918d5797eab5953316df 100755 --- a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh +++ b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh @@ -36,7 +36,7 @@ while getopts "bc:Eps" opt_name; do b) BUILD_ONLY="true";; c) TEST_COUNT="${OPTARG}";; E) ENABLE_EXPERIMENTAL_HEXNN_OPS="true";; - p) USE_PREBUILT_HEXAOGON_BINARIES="true";; + p) USE_PREBUILT_HEXAGON_BINARIES="true";; s) SKIP_DOWNLOAD_IF_EXIST="true";; *) usage;; esac @@ -49,7 +49,7 @@ if [[ -z "${NDK_ROOT}" ]]; then exit 1 fi -if [[ "${USE_PREBUILT_HEXAOGON_BINARIES}" != "true" && +if [[ "${USE_PREBUILT_HEXAGON_BINARIES}" != "true" && -z "${QUALCOMM_SDK}" ]]; then echo "QUALCOMM_SDK is empty" 1>&2 usage @@ -84,7 +84,7 @@ rm -rf "${GEN_DIR}" mkdir -p "${GEN_LIBS_DIR}" mkdir -p "${GEN_DOWNLOAD_DIR}" -if [[ "${USE_PREBUILT_HEXAOGON_BINARIES}" == "true" ]]; then +if [[ "${USE_PREBUILT_HEXAGON_BINARIES}" == "true" ]]; then echo "Download prebuilt hexagon binaries" if [[ "${BUILD_ONLY}" != "true" ]]; then CONTROLLER_PUSH_DEST="/data/local/tmp" diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD index 9de664c822bf7a9abf7b8082f444c61dfa45f499..e90c525113348532a3ebdadde7e712bf2d98cee9 100644 --- a/tensorflow/contrib/metrics/BUILD +++ b/tensorflow/contrib/metrics/BUILD @@ -43,6 +43,7 @@ py_library( "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python:weights_broadcast_ops", + "//tensorflow/python/ops/distributions", ], ) diff --git a/tensorflow/contrib/metrics/__init__.py b/tensorflow/contrib/metrics/__init__.py index d3dce46bfb6e9c77cc7ae107b323a9bc7074c47e..de02dc8f457364450929776035829d86035d706b 100644 --- a/tensorflow/contrib/metrics/__init__.py +++ b/tensorflow/contrib/metrics/__init__.py @@ -16,6 +16,7 @@ See the @{$python/contrib.metrics} guide. +@@auc_with_confidence_intervals @@streaming_accuracy @@streaming_mean @@streaming_recall @@ -83,6 +84,7 @@ from tensorflow.contrib.metrics.python.ops.confusion_matrix_ops import confusion from tensorflow.contrib.metrics.python.ops.histogram_ops import auc_using_histogram from tensorflow.contrib.metrics.python.ops.metric_ops import aggregate_metric_map from tensorflow.contrib.metrics.python.ops.metric_ops import aggregate_metrics +from tensorflow.contrib.metrics.python.ops.metric_ops import auc_with_confidence_intervals from tensorflow.contrib.metrics.python.ops.metric_ops import cohen_kappa from tensorflow.contrib.metrics.python.ops.metric_ops import count from tensorflow.contrib.metrics.python.ops.metric_ops import precision_recall_at_equal_thresholds diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index c2340d037761aa5ebd0dbea128cadb1b65f1a788..0fee584f8e243e12c7e28e18fe70383551d1af96 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -38,6 +38,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import weights_broadcast_ops +from tensorflow.python.ops.distributions.normal import Normal from tensorflow.python.util.deprecation import deprecated # Epsilon constant used to represent extremely small quantity. @@ -1196,6 +1197,295 @@ def streaming_dynamic_auc(labels, return auc, update_op +def _compute_placement_auc(labels, predictions, weights, alpha, + logit_transformation, is_valid): + """Computes the AUC and asymptotic normally distributed confidence interval. + + The calculations are achieved using the fact that AUC = P(Y_1>Y_0) and the + concept of placement values for each labeled group, as presented by Delong and + Delong (1988). The actual algorithm used is a more computationally efficient + approach presented by Sun and Xu (2014). This could be slow for large batches, + but has the advantage of not having its results degrade depending on the + distribution of predictions. + + Args: + labels: A `Tensor` of ground truth labels with the same shape as + `predictions` with values of 0 or 1 and type `int64`. + predictions: A 1-D `Tensor` of predictions whose values are `float64`. + weights: `Tensor` whose rank is either 0, or the same rank as `labels`. + alpha: Confidence interval level desired. + logit_transformation: A boolean value indicating whether the estimate should + be logit transformed prior to calculating the confidence interval. Doing + so enforces the restriction that the AUC should never be outside the + interval [0,1]. + is_valid: A bool tensor describing whether the input is valid. + + Returns: + A 1-D `Tensor` containing the area-under-curve, lower, and upper confidence + interval values. + """ + # Disable the invalid-name checker so that we can capitalize the name. + # pylint: disable=invalid-name + AucData = collections_lib.namedtuple('AucData', ['auc', 'lower', 'upper']) + # pylint: enable=invalid-name + + # If all the labels are the same or if number of observations are too few, + # AUC isn't well-defined + size = array_ops.size(predictions, out_type=dtypes.int32) + + # Count the total number of positive and negative labels in the input. + total_0 = math_ops.reduce_sum( + math_ops.cast(1 - labels, weights.dtype) * weights) + total_1 = math_ops.reduce_sum( + math_ops.cast(labels, weights.dtype) * weights) + + # Sort the predictions ascending, as well as + # (i) the corresponding labels and + # (ii) the corresponding weights. + ordered_predictions, indices = nn.top_k(predictions, k=size, sorted=True) + ordered_predictions = array_ops.reverse( + ordered_predictions, axis=array_ops.zeros(1, dtypes.int32)) + indices = array_ops.reverse(indices, axis=array_ops.zeros(1, dtypes.int32)) + ordered_labels = array_ops.gather(labels, indices) + ordered_weights = array_ops.gather(weights, indices) + + # We now compute values required for computing placement values. + + # We generate a list of indices (segmented_indices) of increasing order. An + # index is assigned for each unique prediction float value. Prediction + # values that are the same share the same index. + _, segmented_indices = array_ops.unique(ordered_predictions) + + # We create 2 tensors of weights. weights_for_true is non-zero for true + # labels. weights_for_false is non-zero for false labels. + float_labels_for_true = math_ops.cast(ordered_labels, dtypes.float32) + float_labels_for_false = 1.0 - float_labels_for_true + weights_for_true = ordered_weights * float_labels_for_true + weights_for_false = ordered_weights * float_labels_for_false + + # For each set of weights with the same segmented indices, we add up the + # weight values. Note that for each label, we deliberately rely on weights + # for the opposite label. + weight_totals_for_true = math_ops.segment_sum(weights_for_false, + segmented_indices) + weight_totals_for_false = math_ops.segment_sum(weights_for_true, + segmented_indices) + + # These cumulative sums of weights importantly exclude the current weight + # sums. + cum_weight_totals_for_true = math_ops.cumsum(weight_totals_for_true, + exclusive=True) + cum_weight_totals_for_false = math_ops.cumsum(weight_totals_for_false, + exclusive=True) + + # Compute placement values using the formula. Values with the same segmented + # indices and labels share the same placement values. + placements_for_true = ( + (cum_weight_totals_for_true + weight_totals_for_true / 2.0) / + (math_ops.reduce_sum(weight_totals_for_true) + _EPSILON)) + placements_for_false = ( + (cum_weight_totals_for_false + weight_totals_for_false / 2.0) / + (math_ops.reduce_sum(weight_totals_for_false) + _EPSILON)) + + # We expand the tensors of placement values (for each label) so that their + # shapes match that of predictions. + placements_for_true = array_ops.gather(placements_for_true, segmented_indices) + placements_for_false = array_ops.gather(placements_for_false, + segmented_indices) + + # Select placement values based on the label for each index. + placement_values = ( + placements_for_true * float_labels_for_true + + placements_for_false * float_labels_for_false) + + # Split placement values by labeled groups. + placement_values_0 = placement_values * math_ops.cast( + 1 - ordered_labels, weights.dtype) + weights_0 = ordered_weights * math_ops.cast( + 1 - ordered_labels, weights.dtype) + placement_values_1 = placement_values * math_ops.cast( + ordered_labels, weights.dtype) + weights_1 = ordered_weights * math_ops.cast( + ordered_labels, weights.dtype) + + # Calculate AUC using placement values + auc_0 = (math_ops.reduce_sum(weights_0 * (1. - placement_values_0)) / + (total_0 + _EPSILON)) + auc_1 = (math_ops.reduce_sum(weights_1 * (placement_values_1)) / + (total_1 + _EPSILON)) + auc = array_ops.where(math_ops.less(total_0, total_1), auc_1, auc_0) + + # Calculate variance and standard error using the placement values. + var_0 = ( + math_ops.reduce_sum( + weights_0 * math_ops.square(1. - placement_values_0 - auc_0)) / + (total_0 - 1. + _EPSILON)) + var_1 = ( + math_ops.reduce_sum( + weights_1 * math_ops.square(placement_values_1 - auc_1)) / + (total_1 - 1. + _EPSILON)) + auc_std_err = math_ops.sqrt( + (var_0 / (total_0 + _EPSILON)) + (var_1 / (total_1 + _EPSILON))) + + # Calculate asymptotic normal confidence intervals + std_norm_dist = Normal(loc=0., scale=1.) + z_value = std_norm_dist.quantile((1.0 - alpha) / 2.0) + if logit_transformation: + estimate = math_ops.log(auc / (1. - auc + _EPSILON)) + std_err = auc_std_err / (auc * (1. - auc + _EPSILON)) + transformed_auc_lower = estimate + (z_value * std_err) + transformed_auc_upper = estimate - (z_value * std_err) + def inverse_logit_transformation(x): + exp_negative = math_ops.exp(math_ops.negative(x)) + return 1. / (1. + exp_negative + _EPSILON) + + auc_lower = inverse_logit_transformation(transformed_auc_lower) + auc_upper = inverse_logit_transformation(transformed_auc_upper) + else: + estimate = auc + std_err = auc_std_err + auc_lower = estimate + (z_value * std_err) + auc_upper = estimate - (z_value * std_err) + + ## If estimate is 1 or 0, no variance is present so CI = 1 + ## n.b. This can be misleading, since number obs can just be too low. + lower = array_ops.where( + math_ops.logical_or( + math_ops.equal(auc, array_ops.ones_like(auc)), + math_ops.equal(auc, array_ops.zeros_like(auc))), + auc, auc_lower) + upper = array_ops.where( + math_ops.logical_or( + math_ops.equal(auc, array_ops.ones_like(auc)), + math_ops.equal(auc, array_ops.zeros_like(auc))), + auc, auc_upper) + + # If all the labels are the same, AUC isn't well-defined (but raising an + # exception seems excessive) so we return 0, otherwise we finish computing. + trivial_value = array_ops.constant(0.0) + + return AucData(*control_flow_ops.cond( + is_valid, lambda: [auc, lower, upper], lambda: [trivial_value]*3)) + + +def auc_with_confidence_intervals(labels, + predictions, + weights=None, + alpha=0.95, + logit_transformation=True, + metrics_collections=(), + updates_collections=(), + name=None): + """Computes the AUC and asymptotic normally distributed confidence interval. + + USAGE NOTE: this approach requires storing all of the predictions and labels + for a single evaluation in memory, so it may not be usable when the evaluation + batch size and/or the number of evaluation steps is very large. + + Computes the area under the ROC curve and its confidence interval using + placement values. This has the advantage of being resilient to the + distribution of predictions by aggregating across batches, accumulating labels + and predictions and performing the final calculation using all of the + concatenated values. + + Args: + labels: A `Tensor` of ground truth labels with the same shape as `labels` + and with values of 0 or 1 whose values are castable to `int64`. + predictions: A `Tensor` of predictions whose values are castable to + `float64`. Will be flattened into a 1-D `Tensor`. + weights: Optional `Tensor` whose rank is either 0, or the same rank as + `labels`. + alpha: Confidence interval level desired. + logit_transformation: A boolean value indicating whether the estimate should + be logit transformed prior to calculating the confidence interval. Doing + so enforces the restriction that the AUC should never be outside the + interval [0,1]. + metrics_collections: An optional iterable of collections that `auc` should + be added to. + updates_collections: An optional iterable of collections that `update_op` + should be added to. + name: An optional name for the variable_scope that contains the metric + variables. + + Returns: + auc: A 1-D `Tensor` containing the current area-under-curve, lower, and + upper confidence interval values. + update_op: An operation that concatenates the input labels and predictions + to the accumulated values. + + Raises: + ValueError: If `labels`, `predictions`, and `weights` have mismatched shapes + or if `alpha` isn't in the range (0,1). + """ + if not (alpha > 0 and alpha < 1): + raise ValueError('alpha must be between 0 and 1; currently %.02f' % alpha) + + if weights is None: + weights = array_ops.ones_like(predictions) + + with variable_scope.variable_scope( + name, + default_name='auc_with_confidence_intervals', + values=[labels, predictions, weights]): + + predictions, labels, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access + predictions=predictions, + labels=labels, + weights=weights) + + total_weight = math_ops.reduce_sum(weights) + + weights = array_ops.reshape(weights, [-1]) + predictions = array_ops.reshape( + math_ops.cast(predictions, dtypes.float64), [-1]) + labels = array_ops.reshape(math_ops.cast(labels, dtypes.int64), [-1]) + + with ops.control_dependencies([ + check_ops.assert_greater_equal( + labels, + array_ops.zeros_like(labels, dtypes.int64), + message='labels must be 0 or 1, at least one is <0'), + check_ops.assert_less_equal( + labels, + array_ops.ones_like(labels, dtypes.int64), + message='labels must be 0 or 1, at least one is >1'), + ]): + preds_accum, update_preds = streaming_concat( + predictions, name='concat_preds') + labels_accum, update_labels = streaming_concat(labels, + name='concat_labels') + weights_accum, update_weights = streaming_concat( + weights, name='concat_weights') + update_op_for_valid_case = control_flow_ops.group( + update_labels, update_preds, update_weights) + + # Only perform updates if this case is valid. + all_labels_positive_or_0 = math_ops.logical_and( + math_ops.equal(math_ops.reduce_min(labels), 0), + math_ops.equal(math_ops.reduce_max(labels), 1)) + sums_of_weights_at_least_1 = math_ops.greater_equal(total_weight, 1.0) + is_valid = math_ops.logical_and(all_labels_positive_or_0, + sums_of_weights_at_least_1) + + update_op = control_flow_ops.cond( + sums_of_weights_at_least_1, + lambda: update_op_for_valid_case, control_flow_ops.no_op) + + auc = _compute_placement_auc( + labels_accum, + preds_accum, + weights_accum, + alpha=alpha, + logit_transformation=logit_transformation, + is_valid=is_valid) + + if updates_collections: + ops.add_to_collections(updates_collections, update_op) + if metrics_collections: + ops.add_to_collections(metrics_collections, auc) + return auc, update_op + + def precision_recall_at_equal_thresholds(labels, predictions, weights=None, @@ -1226,7 +1516,7 @@ def precision_recall_at_equal_thresholds(labels, predictions: A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]`. weights: Optional; If provided, a `Tensor` that has the same dtype as, - and broadcastable to, `predictions`. This tensor is multplied by counts. + and broadcastable to, `predictions`. This tensor is multiplied by counts. num_thresholds: Optional; Number of thresholds, evenly distributed in `[0, 1]`. Should be `>= 2`. Defaults to 201. Note that the number of bins is 1 less than `num_thresholds`. Using an even `num_thresholds` value @@ -3356,7 +3646,7 @@ def cohen_kappa(labels, `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.contrib.metrics.cohen_kappa is not supported' 'when eager execution is enabled.') if num_classes < 2: @@ -3430,6 +3720,7 @@ def cohen_kappa(labels, __all__ = [ + 'auc_with_confidence_intervals', 'aggregate_metric_map', 'aggregate_metrics', 'cohen_kappa', diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index e067f08babd9a900e876545d427c91e5ff808f04..33eb655fb660f0ecdfe1c5ab870d7f17690ae3ff 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -2128,6 +2128,205 @@ class StreamingDynamicAUCTest(test.TestCase): self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-5) +class AucWithConfidenceIntervalsTest(test.TestCase): + + def setUp(self): + np.random.seed(1) + ops.reset_default_graph() + + def _testResultsEqual(self, expected_dict, gotten_result): + """Tests that 2 results (dicts) represent the same data. + + Args: + expected_dict: A dictionary with keys that are the names of properties + of PrecisionRecallData and whose values are lists of floats. + gotten_result: A AucWithConfidenceIntervalData object. + """ + gotten_dict = {k: t.eval() for k, t in gotten_result._asdict().items()} + self.assertItemsEqual( + list(expected_dict.keys()), list(gotten_dict.keys())) + + for key, expected_values in expected_dict.items(): + self.assertAllClose(expected_values, gotten_dict[key]) + + def _testCase(self, predictions, labels, expected_result, weights=None): + """Performs a test given a certain scenario of labels, predictions, weights. + + Args: + predictions: The predictions tensor. Of type float32. + labels: The labels tensor. Of type bool. + expected_result: The expected result (dict) that maps to tensors. + weights: Optional weights tensor. + """ + with self.test_session() as sess: + predictions_tensor = constant_op.constant( + predictions, dtype=dtypes_lib.float32) + labels_tensor = constant_op.constant(labels, dtype=dtypes_lib.int64) + weights_tensor = None + if weights: + weights_tensor = constant_op.constant(weights, dtype=dtypes_lib.float32) + gotten_result, update_op = ( + metric_ops.auc_with_confidence_intervals( + labels=labels_tensor, + predictions=predictions_tensor, + weights=weights_tensor)) + + sess.run(variables.local_variables_initializer()) + sess.run(update_op) + + self._testResultsEqual(expected_result, gotten_result) + + def testAucAllCorrect(self): + self._testCase( + predictions=[0., 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0], + labels=[0, 0, 1, 0, 0, 1, 0, 1, 1, 0], + expected_result={ + 'auc': 0.66666667, + 'lower': 0.27826795, + 'upper': 0.91208512, + }) + + def testAucUnorderedInput(self): + self._testCase( + predictions=[1.0, 0.6, 0., 0.3, 0.4, 0.2, 0.5, 0.3, 0.6, 0.8], + labels=[0, 1, 0, 1, 0, 0, 1, 0, 0, 1], + expected_result={ + 'auc': 0.66666667, + 'lower': 0.27826795, + 'upper': 0.91208512, + }) + + def testAucWithWeights(self): + self._testCase( + predictions=[0., 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0], + labels=[0, 0, 1, 0, 0, 1, 0, 1, 1, 0], + weights=[0.5, 0.6, 1.2, 1.5, 2.0, 2.0, 1.5, 1.2, 0.6, 0.5], + expected_result={ + 'auc': 0.65151515, + 'lower': 0.28918604, + 'upper': 0.89573906, + }) + + def testAucEqualOne(self): + self._testCase( + predictions=[0, 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0], + labels=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], + expected_result={ + 'auc': 1.0, + 'lower': 1.0, + 'upper': 1.0, + }) + + def testAucEqualZero(self): + self._testCase( + predictions=[0, 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.6, 0.8, 1.0], + labels=[1, 1, 1, 1, 1, 0, 0, 0, 0, 0], + expected_result={ + 'auc': 0.0, + 'lower': 0.0, + 'upper': 0.0, + }) + + def testNonZeroOnePredictions(self): + self._testCase( + predictions=[2.5, -2.5, .5, -.5, 1], + labels=[1, 0, 1, 0, 0], + expected_result={ + 'auc': 0.83333333, + 'lower': 0.15229267, + 'upper': 0.99286517, + }) + + def testAllLabelsOnes(self): + self._testCase( + predictions=[1., 1., 1., 1., 1.], + labels=[1, 1, 1, 1, 1], + expected_result={ + 'auc': 0., + 'lower': 0., + 'upper': 0., + }) + + def testAllLabelsZeros(self): + self._testCase( + predictions=[0., 0., 0., 0., 0.], + labels=[0, 0, 0, 0, 0], + expected_result={ + 'auc': 0., + 'lower': 0., + 'upper': 0., + }) + + def testWeightSumLessThanOneAll(self): + self._testCase( + predictions=[1., 1., 0., 1., 0., 0.], + labels=[1, 1, 1, 0, 0, 0], + weights=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], + expected_result={ + 'auc': 0., + 'lower': 0., + 'upper': 0., + }) + + def testWithMultipleUpdates(self): + batch_size = 50 + num_batches = 100 + labels = np.array([]) + predictions = np.array([]) + tf_labels = variables.Variable(array_ops.ones(batch_size, dtypes_lib.int32), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.int32) + tf_predictions = variables.Variable( + array_ops.ones(batch_size), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.float32) + auc, update_op = metrics.auc_with_confidence_intervals(tf_labels, + tf_predictions) + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in xrange(num_batches): + new_labels = np.random.randint(0, 2, size=batch_size) + noise = np.random.normal(0.0, scale=0.2, size=batch_size) + new_predictions = 0.4 + 0.2 * new_labels + noise + labels = np.concatenate([labels, new_labels]) + predictions = np.concatenate([predictions, new_predictions]) + sess.run(tf_labels.assign(new_labels)) + sess.run(tf_predictions.assign(new_predictions)) + sess.run(update_op) + expected_auc = _np_auc(predictions, labels) + self.assertAllClose(expected_auc, auc.auc.eval()) + + def testExceptionOnFloatLabels(self): + with self.test_session() as sess: + predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32) + labels = constant_op.constant([0.7, 0, 1, 0, 1]) + _, update_op = metrics.auc_with_confidence_intervals(labels, predictions) + sess.run(variables.local_variables_initializer()) + self.assertRaises(TypeError, sess.run(update_op)) + + def testExceptionOnGreaterThanOneLabel(self): + with self.test_session() as sess: + predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32) + labels = constant_op.constant([2, 1, 0, 1, 0]) + _, update_op = metrics.auc_with_confidence_intervals(labels, predictions) + sess.run(variables.local_variables_initializer()) + with self.assertRaisesRegexp( + errors_impl.InvalidArgumentError, + '.*labels must be 0 or 1, at least one is >1.*'): + sess.run(update_op) + + def testExceptionOnNegativeLabel(self): + with self.test_session() as sess: + predictions = constant_op.constant([1, 0.5, 0, 1, 0], dtypes_lib.float32) + labels = constant_op.constant([1, 0, -1, 1, 0]) + _, update_op = metrics.auc_with_confidence_intervals(labels, predictions) + sess.run(variables.local_variables_initializer()) + with self.assertRaisesRegexp( + errors_impl.InvalidArgumentError, + '.*labels must be 0 or 1, at least one is <0.*'): + sess.run(update_op) + + class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): def setUp(self): diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index d286750c257e9a78a82c95c1fc872b3ca6972203..52b659c69fdfc507e6259e928d79c65471f2f025 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -134,7 +134,7 @@ $ bazel-bin/$examples_dir/cifar10/cifar10_eval --run_once ### Block Sparsity -For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is supported for weight tensors with rank 2 only. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). +For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is only supported for weight tensors which can be squeezed to rank 2. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). The convolution layer tensors are always pruned used block dimensions of [1,1]. ## References diff --git a/tensorflow/contrib/model_pruning/python/layers/layers.py b/tensorflow/contrib/model_pruning/python/layers/layers.py index dfebb9a6794056dd43b0699ccbcc5797f2f172f7..988748ad75bdf72f1da3f4e1c6e85aabb04a5954 100644 --- a/tensorflow/contrib/model_pruning/python/layers/layers.py +++ b/tensorflow/contrib/model_pruning/python/layers/layers.py @@ -21,7 +21,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np import six from tensorflow.contrib.framework.python.ops import add_arg_scope diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index d16af9da19816211ee22f6ea48a347f0b9a4e612..86963be4b8aee396704752bab87e0a6b49ab1a49 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -523,7 +523,8 @@ class Pruning(object): """Performs block-granular masking of the weights. Block pruning occurs only if the block_height or block_width is > 1 and - if the weight tensor has ndims = 2. Otherwise, elementwise pruning occurs. + if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise + pruning occurs. Args: weights: The weight tensor that needs to be masked. threshold: The current threshold value. The function will compute a new @@ -540,7 +541,8 @@ class Pruning(object): Raises: ValueError: if block pooling function is not AVG or MAX """ - if weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: + squeezed_weights = array_ops.squeeze(weights) + if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: return self._update_mask(weights, threshold) if self._block_pooling_function not in ['AVG', 'MAX']: @@ -549,9 +551,11 @@ class Pruning(object): with ops.name_scope(weights.op.name + '_pruning_ops'): abs_weights = math_ops.abs( - array_ops.reshape( - weights, [1, weights.get_shape()[0], - weights.get_shape()[1], 1])) + array_ops.reshape(weights, [ + 1, + squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1], 1 + ])) pool_window = [self._block_dim[0], self._block_dim[1]] pooled_weights = nn_ops.pool( abs_weights, @@ -572,9 +576,10 @@ class Pruning(object): array_ops.ones(self._block_dim)) sliced_mask = array_ops.slice( updated_mask, [0, 0], - [weights.get_shape()[0], - weights.get_shape()[1]]) - return smoothed_threshold, sliced_mask + [squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1]]) + return smoothed_threshold, array_ops.reshape(sliced_mask, + array_ops.shape(weights)) def _get_mask_assign_ops(self): # Make sure the assignment ops have not already been added to the list diff --git a/tensorflow/contrib/model_pruning/python/pruning_test.py b/tensorflow/contrib/model_pruning/python/pruning_test.py index 1767b4bb94a9bb56bc6a4933423ad27d8cf3ed35..89e65713197afc6ed37346cb67a6e9be3fa9290f 100644 --- a/tensorflow/contrib/model_pruning/python/pruning_test.py +++ b/tensorflow/contrib/model_pruning/python/pruning_test.py @@ -140,6 +140,23 @@ class PruningTest(test.TestCase): [0.0, -0.3, 0.0, -0.4]]) expected_mask = [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]] + self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, + expected_mask) + self._blockMasking(param_list + ["block_pooling_function=AVG"], weights_avg, + expected_mask) + + def testBlockMaskingWithHigherDimensions(self): + param_list = ["block_height=2", "block_width=2", "threshold_decay=0"] + + # Weights as in testBlockMasking, but with one extra dimension. + weights_avg = constant_op.constant( + [[[0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], [0.3, 0.3, 0.4, 0.4], + [0.3, 0.3, 0.4, 0.4]]]) + weights_max = constant_op.constant( + [[[0.1, 0.0, 0.2, 0.0], [0.0, -0.1, 0.0, -0.2], [0.3, 0.0, 0.4, 0.0], + [0.0, -0.3, 0.0, -0.4]]]) + expected_mask = [[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]]] + self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, expected_mask) self._blockMasking(param_list + ["block_pooling_function=AVG"], diff --git a/tensorflow/contrib/mpi/mpi_utils.h b/tensorflow/contrib/mpi/mpi_utils.h index fa297c28cb47d43ba927ab941854bd472d90b465..df055ff56731140b3bd09704c70e65f81362f763 100644 --- a/tensorflow/contrib/mpi/mpi_utils.h +++ b/tensorflow/contrib/mpi/mpi_utils.h @@ -24,6 +24,8 @@ limitations under the License. #include "tensorflow/core/lib/strings/str_util.h" +// Skip MPI C++ bindings support, this matches the usage in other places +#define OMPI_SKIP_MPICXX #include "third_party/mpi/mpi.h" #define MPI_CHECK(cmd) \ do { \ diff --git a/tensorflow/contrib/nccl/python/ops/nccl_ops.py b/tensorflow/contrib/nccl/python/ops/nccl_ops.py index 8dc038b9ac992de7db8b762e3697c6693099e192..794372a1f4b0dcc41bcf0da611f5bc2ec9301973 100644 --- a/tensorflow/contrib/nccl/python/ops/nccl_ops.py +++ b/tensorflow/contrib/nccl/python/ops/nccl_ops.py @@ -267,5 +267,5 @@ def _check_device(tensor, expected=None): def _check_graph_mode(): - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError('Nccl ops are not supported in eager mode') diff --git a/tensorflow/contrib/ndlstm/BUILD b/tensorflow/contrib/ndlstm/BUILD deleted file mode 100644 index 8403f841884d4640ce8156ff4db46868dbe1788c..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/BUILD +++ /dev/null @@ -1,92 +0,0 @@ -# Description: -# Contains classes implementing 1D and 2D LSTMs for image and signal -# processing problems. - -licenses(["notice"]) # Apache 2.0 - -exports_files(["LICENSE"]) - -package(default_visibility = ["//tensorflow:__subpackages__"]) - -load("//tensorflow:tensorflow.bzl", "tf_py_test") - -py_library( - name = "ndlstm", - srcs = [ - "__init__.py", - "python/__init__.py", - "python/lstm1d.py", - "python/lstm2d.py", - "python/misc.py", - ], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/contrib/framework:framework_py", - "//tensorflow/contrib/layers:layers_py", - "//tensorflow/contrib/rnn:rnn_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - "//tensorflow/python:ops", - "//tensorflow/python:platform", - "//tensorflow/python:random_ops", - "//tensorflow/python:rnn", - "//tensorflow/python:rnn_cell", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - ], -) - -tf_py_test( - name = "lstm1d_test", - srcs = ["python/lstm1d_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:variables", - ], -) - -tf_py_test( - name = "lstm2d_test", - srcs = ["python/lstm2d_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:variables", - ], -) - -tf_py_test( - name = "misc_test", - srcs = ["python/misc_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:variables", - ], -) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/contrib/ndlstm/README.md b/tensorflow/contrib/ndlstm/README.md deleted file mode 100644 index 7ccb57f1b34a24af7d776f7dbb12a2a00bb5ca30..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/README.md +++ /dev/null @@ -1,31 +0,0 @@ -Library of multidimensional LSTM models and related code. - -# 2D LSTM code - -The 2D LSTM layers take tensors of the form (batch_size, height, width, -depth), compatible with convolutional layers, as inputs. The library -transposes and reshapes these tensors in a way that allows batches of -images to be processed by LSTMs. - -The library currently provides: - - - a separable 2D LSTM layer - - a simple 2D convolutional layer that can be swapped out against 2D LSTM - - layers to reduce images to sequences and images to final state vectors - - layers for sequence classification, pixel-wise classification - -# Other Dimensions - -There is 1D LSTM code in `lstm1d.py`. This code implements 1D LSTM versions -suitable as a basis for higher dimensional LSTMs. It is intended for constant -batch size and uses a different layout. Although the code is perfectly fine for -1D use, you may find other 1D LSTM implementations to be more convenient if you -are interested in sequence problems. - -# Upcoming Changes - - - PyramidLSTM - - support for 3D and 4D - - optional use of native fused LSTM op - - easy-to-use command line drivers and examples - - operators for patch-wise processing diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py deleted file mode 100644 index 2e2e9086c00b3e7766678b5eb6dca47dc9a5ddcc..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/lstm1d.py +++ /dev/null @@ -1,184 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""LSTM layers for sequences.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.contrib.framework.python.ops import variables -from tensorflow.python.framework import constant_op -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import rnn -from tensorflow.python.ops import rnn_cell -from tensorflow.python.ops import variable_scope - - -def _shape(tensor): - return tensor.get_shape().as_list() - - -def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False): - """Run an LSTM, either forward or backward. - - This is a 1D LSTM implementation using unrolling and the TensorFlow - LSTM op. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - - Returns: - Output sequence (length, batch_size, noutput) - - """ - with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]): - length, batch_size, _ = _shape(inputs) - lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm_cell.state_size]) - output_u = [] - inputs_u = array_ops.unstack(inputs) - if reverse: - inputs_u = list(reversed(inputs_u)) - for i in xrange(length): - if i > 0: - variable_scope.get_variable_scope().reuse_variables() - output, state = lstm_cell(inputs_u[i], state) - output_u += [output] - if reverse: - output_u = list(reversed(output_u)) - outputs = array_ops.stack(output_u) - return outputs - - -def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): - """Run an LSTM, either forward or backward. - - This is a 1D LSTM implementation using dynamic_rnn and - the TensorFlow LSTM op. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - - Returns: - Output sequence (length, batch_size, noutput) - """ - with variable_scope.variable_scope(scope, "SeqLstm", [inputs]): - lstm_cell = rnn_cell.BasicLSTMCell(noutput) - if reverse: - inputs = array_ops.reverse_v2(inputs, [0]) - outputs, _ = rnn.dynamic_rnn( - lstm_cell, inputs, time_major=True, dtype=inputs.dtype) - if reverse: - outputs = array_ops.reverse_v2(outputs, [0]) - return outputs - - -def ndlstm_base(inputs, noutput, scope=None, reverse=False, dynamic=True): - """Implements a 1D LSTM, either forward or backward. - - This is a base case for multidimensional LSTM implementations, which - tend to be used differently from sequence-to-sequence - implementations. For general 1D sequence to sequence - transformations, you may want to consider another implementation - from TF slim. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - dynamic: use dynamic_rnn - - Returns: - Output sequence (length, batch_size, noutput) - - """ - # TODO(tmb) maybe add option for other LSTM implementations, like - # slim.rnn.basic_lstm_cell - if dynamic: - return ndlstm_base_dynamic(inputs, noutput, scope=scope, reverse=reverse) - else: - return ndlstm_base_unrolled(inputs, noutput, scope=scope, reverse=reverse) - - -def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False): - """Run an LSTM across all steps and returns only the final state. - - Args: - inputs: (length, batch_size, depth) tensor - noutput: size of output vector - scope: optional scope name - name: optional name for output tensor - reverse: run in reverse - - Returns: - Batch of size (batch_size, noutput). - """ - with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]): - length, batch_size, _ = _shape(inputs) - lstm = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm.state_size]) - inputs_u = array_ops.unstack(inputs) - if reverse: - inputs_u = list(reversed(inputs_u)) - for i in xrange(length): - if i > 0: - variable_scope.get_variable_scope().reuse_variables() - output, state = lstm(inputs_u[i], state) - outputs = array_ops.reshape(output, [batch_size, noutput], name=name) - return outputs - - -def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None): - """Run a softmax layer over all the time steps of an input sequence. - - Args: - inputs: (length, batch_size, depth) tensor - noutput: output depth - scope: optional scope name - name: optional name for output tensor - linear_name: name for linear (pre-softmax) output - - Returns: - A tensor of size (length, batch_size, noutput). - - """ - length, _, ninputs = _shape(inputs) - inputs_u = array_ops.unstack(inputs) - output_u = [] - with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]): - initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1) - initial_b = constant_op.constant(0.1, shape=[noutput]) - w = variables.model_variable("weights", initializer=initial_w) - b = variables.model_variable("biases", initializer=initial_b) - for i in xrange(length): - with variable_scope.variable_scope(scope, "SequenceSoftmaxStep", - [inputs_u[i]]): - # TODO(tmb) consider using slim.fully_connected(..., - # activation_fn=tf.nn.softmax) - linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name) - output = nn_ops.softmax(linear) - output_u += [output] - outputs = array_ops.stack(output_u, name=name) - return outputs diff --git a/tensorflow/contrib/ndlstm/python/lstm1d_test.py b/tensorflow/contrib/ndlstm/python/lstm1d_test.py deleted file mode 100644 index 49b15cc814cc54aaea7c67c4e509e5aa144e063e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/lstm1d_test.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for 1D LSTM.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import lstm1d as lstm1d_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.ops import gradient_checker -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -lstm1d = lstm1d_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class Lstm1DTest(test.TestCase): - - def testSequenceToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 1, 5)) - outputs = lstm1d.ndlstm_base(inputs, 8) - variables.global_variables_initializer().run() - names = [v.name for v in variables.trainable_variables()] - self.assertEqual(len(names), 2) - result = outputs.eval() - self.assertEqual(tuple(result.shape), (17, 1, 8)) - - def testSequenceToSequenceGradient(self): - with self.test_session(): - size = (17, 1, 15) - output_size = (17, 1, 8) - inputs = constant_op.constant(_rand(*size)) - outputs = lstm1d.ndlstm_base(inputs, 8, dynamic=False) - variables.global_variables_initializer().run() - gradients = gradients_impl.gradients(outputs, inputs) - if 1: # pylint: disable=using-constant-test - gradients = gradients_impl.gradients(outputs, inputs)[0].eval() - self.assertEqual(gradients.shape, size) - else: - # TODO(tmb) tf.test.compute_gradient error is currently broken - # with dynamic_rnn. Enable this test case eventually. - err = gradient_checker.compute_gradient_error( - inputs, size, outputs, output_size, delta=1e-4) - self.assert_(not np.isnan(err)) - self.assert_(err < 0.1) - - def testSequenceToSequenceGradientReverse(self): - with self.test_session(): - size = (17, 1, 15) - output_size = (17, 1, 8) - inputs = constant_op.constant(_rand(*size)) - outputs = lstm1d.ndlstm_base(inputs, 8, reverse=1, dynamic=False) - variables.global_variables_initializer().run() - if 1: # pylint: disable=using-constant-test - gradients = gradients_impl.gradients(outputs, inputs)[0].eval() - self.assertEqual(gradients.shape, size) - else: - # TODO(tmb) tf.test.compute_gradient error is currently broken - # with dynamic_rnn. Enable this test case eventually. - err = gradient_checker.compute_gradient_error( - inputs, size, outputs, output_size, delta=1e-4) - self.assert_(not np.isnan(err)) - self.assert_(err < 0.1) - - def testSequenceToFinalDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 6, 5)) - outputs = lstm1d.sequence_to_final(inputs, 8) - variables.global_variables_initializer().run() - names = [v.name for v in variables.trainable_variables()] - self.assertEqual(len(names), 2) - result = outputs.eval() - self.assertEqual(tuple(result.shape), (6, 8)) - - def testSequenceSoftmaxDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 1, 5)) - outputs = lstm1d.sequence_softmax(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (17, 1, 8)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/ndlstm/python/lstm2d.py b/tensorflow/contrib/ndlstm/python/lstm2d.py deleted file mode 100644 index ebbb4ccf11b219e86578d05e99a7a02ebe08271e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/lstm2d.py +++ /dev/null @@ -1,213 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""A small library of functions dealing with LSTMs applied to images. - -Tensors in this library generally have the shape (num_images, height, width, -depth). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.ndlstm.python import lstm1d -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import variable_scope - - -def _shape(tensor): - """Get the shape of a tensor as an int list.""" - return tensor.get_shape().as_list() - - -def images_to_sequence(tensor): - """Convert a batch of images into a batch of sequences. - - Args: - tensor: a (num_images, height, width, depth) tensor - - Returns: - (width, num_images*height, depth) sequence tensor - """ - - num_image_batches, height, width, depth = _shape(tensor) - transposed = array_ops.transpose(tensor, [2, 0, 1, 3]) - return array_ops.reshape(transposed, - [width, num_image_batches * height, depth]) - - -def sequence_to_images(tensor, num_image_batches): - """Convert a batch of sequences into a batch of images. - - Args: - tensor: (num_steps, num_batches, depth) sequence tensor - num_image_batches: the number of image batches - - Returns: - (num_images, height, width, depth) tensor - """ - - width, num_batches, depth = _shape(tensor) - height = num_batches // num_image_batches - reshaped = array_ops.reshape(tensor, - [width, num_image_batches, height, depth]) - return array_ops.transpose(reshaped, [1, 2, 0, 3]) - - -def horizontal_lstm(images, num_filters_out, scope=None): - """Run an LSTM bidirectionally over all the rows of each image. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output depth - scope: optional scope name - - Returns: - (num_images, height, width, num_filters_out) tensor, where - num_steps is width and new num_batches is num_image_batches * height - """ - with variable_scope.variable_scope(scope, "HorizontalLstm", [images]): - batch_size, _, _, _ = _shape(images) - sequence = images_to_sequence(images) - with variable_scope.variable_scope("lr"): - hidden_sequence_lr = lstm1d.ndlstm_base(sequence, num_filters_out // 2) - with variable_scope.variable_scope("rl"): - hidden_sequence_rl = (lstm1d.ndlstm_base( - sequence, num_filters_out - num_filters_out // 2, reverse=1)) - output_sequence = array_ops.concat([hidden_sequence_lr, hidden_sequence_rl], - 2) - output = sequence_to_images(output_sequence, batch_size) - return output - - -def get_blocks(images, kernel_size): - """Split images in blocks - - Args: - images: (num_images, height, width, depth) tensor - kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of - of the pooling. Can be an int if both values are the same. - - Returns: - (num_images, height/kernel_height, width/kernel_width, - depth*kernel_height*kernel_width) tensor - """ - with variable_scope.variable_scope("image_blocks"): - batch_size, height, width, chanels = _shape(images) - - if height % kernel_size[0] != 0: - offset = array_ops.zeros([batch_size, - kernel_size[0] - (height % kernel_size[0]), - width, - chanels]) - images = array_ops.concat([images, offset], 1) - batch_size, height, width, chanels = _shape(images) - if width % kernel_size[1] != 0: - offset = array_ops.zeros([batch_size, - height, - kernel_size[1] - (width % kernel_size[1]), - chanels]) - images = array_ops.concat([images, offset], 2) - batch_size, height, width, chanels = _shape(images) - - h, w = int(height / kernel_size[0]), int(width / kernel_size[1]) - features = kernel_size[1] * kernel_size[0] * chanels - - lines = array_ops.split(images, h, axis=1) - line_blocks = [] - for line in lines: - line = array_ops.transpose(line, [0, 2, 3, 1]) - line = array_ops.reshape(line, [batch_size, w, features]) - line_blocks.append(line) - - return array_ops.stack(line_blocks, axis=1) - - -def separable_lstm(images, num_filters_out, - kernel_size=None, nhidden=None, scope=None): - """Run bidirectional LSTMs first horizontally then vertically. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of - of the pooling. Can be an int if both values are the same. Set to None for - not using blocks - nhidden: hidden layer depth - scope: optional scope name - - Returns: - (num_images, height/kernel_height, width/kernel_width, - num_filters_out) tensor - """ - with variable_scope.variable_scope(scope, "SeparableLstm", [images]): - if nhidden is None: - nhidden = num_filters_out - if kernel_size is not None: - images = get_blocks(images, kernel_size) - hidden = horizontal_lstm(images, nhidden) - with variable_scope.variable_scope("vertical"): - transposed = array_ops.transpose(hidden, [0, 2, 1, 3]) - output_transposed = horizontal_lstm(transposed, num_filters_out) - output = array_ops.transpose(output_transposed, [0, 2, 1, 3]) - return output - - -def reduce_to_sequence(images, num_filters_out, scope=None): - """Reduce an image to a sequence by scanning an LSTM vertically. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - scope: optional scope name - - Returns: - A (width, num_images, num_filters_out) sequence. - """ - with variable_scope.variable_scope(scope, "ReduceToSequence", [images]): - batch_size, height, width, depth = _shape(images) - transposed = array_ops.transpose(images, [1, 0, 2, 3]) - reshaped = array_ops.reshape(transposed, - [height, batch_size * width, depth]) - reduced = lstm1d.sequence_to_final(reshaped, num_filters_out) - output = array_ops.reshape(reduced, [batch_size, width, num_filters_out]) - return output - - -def reduce_to_final(images, num_filters_out, nhidden=None, scope=None): - """Reduce an image to a final state by running two LSTMs. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - nhidden: hidden layer depth (defaults to num_filters_out) - scope: optional scope name - - Returns: - A (num_images, num_filters_out) batch. - """ - with variable_scope.variable_scope(scope, "ReduceToFinal", [images]): - nhidden = nhidden or num_filters_out - batch_size, height, width, depth = _shape(images) - transposed = array_ops.transpose(images, [1, 0, 2, 3]) - reshaped = array_ops.reshape(transposed, - [height, batch_size * width, depth]) - with variable_scope.variable_scope("reduce1"): - reduced = lstm1d.sequence_to_final(reshaped, nhidden) - transposed_hidden = array_ops.reshape(reduced, - [batch_size, width, nhidden]) - hidden = array_ops.transpose(transposed_hidden, [1, 0, 2]) - with variable_scope.variable_scope("reduce2"): - output = lstm1d.sequence_to_final(hidden, num_filters_out) - return output diff --git a/tensorflow/contrib/ndlstm/python/lstm2d_test.py b/tensorflow/contrib/ndlstm/python/lstm2d_test.py deleted file mode 100644 index f1b37d701b868438dcbac4e713ccc2136dacd983..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/lstm2d_test.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for 2D LSTMs.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import lstm2d as lstm2d_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import test_util -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -lstm2d = lstm2d_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class Lstm2DTest(test_util.TensorFlowTestCase): - - def testImagesToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.images_to_sequence(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (11, 14, 5)) - - def testSequenceToImagesDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(11, 14, 5)) - outputs = lstm2d.sequence_to_images(inputs, 2) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7, 11, 5)) - - def testImagesAndSequenceDims(self): - with self.test_session(): - size = (2, 7, 11, 5) - inputs = constant_op.constant(_rand(*size)) - sequence = lstm2d.images_to_sequence(inputs) - outputs = lstm2d.sequence_to_images(sequence, size[0]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), size) - - def testSeparableLstmDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.separable_lstm(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7, 11, 8)) - - def testSeparableLstmDimsBlocks(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.separable_lstm(inputs, 8, kernel_size=[2, 2]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 4, 6, 8)) - - def testReduceToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.reduce_to_sequence(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 11, 8)) - - def testReduceToFinalDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.reduce_to_final(inputs, 8, 12) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 8)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/ndlstm/python/misc.py b/tensorflow/contrib/ndlstm/python/misc.py deleted file mode 100644 index 38eeff84ca4e5afbe45d6c9e0c52af9ae86de24f..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/misc.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Miscellaneous functions useful for nD-LSTM models. - -Some of these functions duplicate functionality in tfslim with -slightly different interfaces. - -Tensors in this library generally have the shape (num_images, height, width, -depth). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.layers.python.layers import layers -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import sparse_ops - - -def _shape(tensor): - """Get the shape of a tensor as an int list.""" - return tensor.get_shape().as_list() - - -def pixels_as_vector(images, scope=None): - """Reduce images to vectors by combining all pixels.""" - with ops.name_scope(scope, "PixelsAsVector", [images]): - batch_size, height, width, depth = _shape(images) - return array_ops.reshape(images, [batch_size, height * width * depth]) - - -def pool_as_vector(images, scope=None): - """Reduce images to vectors by averaging all pixels.""" - with ops.name_scope(scope, "PoolAsVector", [images]): - return math_ops.reduce_mean(images, [1, 2]) - - -def one_hot_planes(labels, num_classes, scope=None): - """Compute 1-hot encodings for planes. - - Given a label, this computes a label image that contains - 1 at all pixels in the plane corresponding to the target - class and 0 in all other planes. - - Args: - labels: (batch_size,) tensor - num_classes: number of classes - scope: optional scope name - - Returns: - Tensor of shape (batch_size, 1, 1, num_classes) with a 1-hot encoding. - """ - with ops.name_scope(scope, "OneHotPlanes", [labels]): - batch_size, = _shape(labels) - batched = layers.one_hot_encoding(labels, num_classes) - return array_ops.reshape(batched, [batch_size, 1, 1, num_classes]) - - -def one_hot_mask(labels, num_classes, scope=None): - """Compute 1-hot encodings for masks. - - Given a label image, this computes the one hot encoding at - each pixel. - - Args: - labels: (batch_size, width, height, 1) tensor containing labels. - num_classes: number of classes - scope: optional scope name - - Returns: - Tensor of shape (batch_size, width, height, num_classes) with - a 1-hot encoding. - """ - with ops.name_scope(scope, "OneHotMask", [labels]): - height, width, depth = _shape(labels) - assert depth == 1 - sparse_labels = math_ops.to_int32(array_ops.reshape(labels, [-1, 1])) - sparse_size, _ = _shape(sparse_labels) - indices = array_ops.reshape(math_ops.range(0, sparse_size, 1), [-1, 1]) - concated = array_ops.concat([indices, sparse_labels], 1) - dense_result = sparse_ops.sparse_to_dense(concated, - [sparse_size, num_classes], 1.0, - 0.0) - result = array_ops.reshape(dense_result, [height, width, num_classes]) - return result diff --git a/tensorflow/contrib/ndlstm/python/misc_test.py b/tensorflow/contrib/ndlstm/python/misc_test.py deleted file mode 100644 index fac9023da3b23b89a5494358c6e7ad82c12f9bdf..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/ndlstm/python/misc_test.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Miscellaneous tests.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import misc as misc_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import test_util -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -misc = misc_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class LstmMiscTest(test_util.TensorFlowTestCase): - - def testPixelsAsVectorDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = misc.pixels_as_vector(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7 * 11 * 5)) - - def testPoolAsVectorDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = misc.pool_as_vector(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 5)) - - def testOneHotPlanes(self): - with self.test_session(): - inputs = constant_op.constant([0, 1, 3]) - outputs = misc.one_hot_planes(inputs, 4) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (3, 1, 1, 4)) - target = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) - self.assertAllClose(result.reshape(-1), target.reshape(-1)) - - def testOneHotMask(self): - with self.test_session(): - data = np.array([[0, 1, 2], [2, 0, 1]]).reshape(2, 3, 1) - inputs = constant_op.constant(data) - outputs = misc.one_hot_mask(inputs, 3) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 3, 3)) - target = np.array([[[1, 0, 0], [0, 1, 0]], [[0, 1, 0], [0, 0, 1]], - [[0, 0, 1], [1, 0, 0]]]).transpose(1, 2, 0) - self.assertAllClose(result.reshape(-1), target.reshape(-1)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/nn/python/ops/alpha_dropout.py b/tensorflow/contrib/nn/python/ops/alpha_dropout.py index d7b61a584478f701726248a41c4992382189223d..2f92d05ba81f30a91f68f3c3ec51b6695d3d0371 100644 --- a/tensorflow/contrib/nn/python/ops/alpha_dropout.py +++ b/tensorflow/contrib/nn/python/ops/alpha_dropout.py @@ -18,7 +18,6 @@ from __future__ import print_function import numbers -from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util @@ -26,7 +25,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_impl def alpha_dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name diff --git a/tensorflow/contrib/nn/python/ops/alpha_dropout_test.py b/tensorflow/contrib/nn/python/ops/alpha_dropout_test.py index 2ff978ab89727c0ba2a8654013466838732377e4..54a98e6f142b7ba58c9418a8ac88269d38944aab 100644 --- a/tensorflow/contrib/nn/python/ops/alpha_dropout_test.py +++ b/tensorflow/contrib/nn/python/ops/alpha_dropout_test.py @@ -21,7 +21,6 @@ from __future__ import print_function from tensorflow.contrib.nn.python.ops.alpha_dropout import alpha_dropout from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import nn_impl diff --git a/tensorflow/contrib/opt/python/training/addsign_test.py b/tensorflow/contrib/opt/python/training/addsign_test.py index bd19ee3e7ac514448c6d79272abb86a154f55e9a..08d45ed73f3ae4b580d7078272e79fef22ef67c5 100644 --- a/tensorflow/contrib/opt/python/training/addsign_test.py +++ b/tensorflow/contrib/opt/python/training/addsign_test.py @@ -97,7 +97,7 @@ class AddSignTest(test.TestCase): global_step=global_step) neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), global_step=global_step) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -108,13 +108,13 @@ class AddSignTest(test.TestCase): # last 3 steps with negative gradient (sign(gm) should be -1) for t in range(1, 8): if t < 5: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1]), global_step=global_step) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(neg_update) elif t > 1: opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py index 716ee9cdf704a14a6e433c7f92ccb91739f70655..5763593b81497f5d6945ff1e5d000042d295c093 100644 --- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py @@ -150,7 +150,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer): self._global_map = ea_custom_getter._global_map if moving_rate is None: - self._moving_rate = BETA / communication_period / num_worker + self._moving_rate = self.BETA / communication_period / num_worker else: self._moving_rate = moving_rate if rho is None: diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py index d68ad23d65500cc2348459cdc53030c2ea08373a..9ce50bfe1054072b315adecb87f1ba729dfe0d83 100644 --- a/tensorflow/contrib/opt/python/training/moving_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py @@ -83,7 +83,7 @@ class MovingAverageOptimizer(optimizer.Optimizer): self._optimizer = opt self._ema = moving_averages.ExponentialMovingAverage( average_decay, num_updates=num_updates) - self._variable_map = None + self._swapped_variable_name_map = None self._sequential_update = sequential_update def compute_gradients(self, *args, **kwargs): @@ -93,7 +93,7 @@ class MovingAverageOptimizer(optimizer.Optimizer): train_op = self._optimizer.apply_gradients( grads_and_vars, global_step=global_step, name=name) var_list = [x[1] for x in grads_and_vars if x[0] is not None] - self._variable_map = {} + self._swapped_variable_name_map = {} if self._sequential_update: with ops.control_dependencies([train_op]): ma_op = self._ema.apply(var_list) @@ -102,9 +102,9 @@ class MovingAverageOptimizer(optimizer.Optimizer): for v in var_list: v_avg = self._ema.average(v) - self._variable_map[v.op.name] = v_avg - self._variable_map[v_avg.op.name] = v - return control_flow_ops.group(train_op, ma_op, name="train_with_avg") + self._swapped_variable_name_map[v.op.name] = v_avg.op.name + self._swapped_variable_name_map[v_avg.op.name] = v.op.name + return control_flow_ops.group(train_op, ma_op, name='train_with_avg') def swapping_saver(self, var_list=None, name='swapping_saver', **kwargs): """Create a saver swapping moving averages and variables. @@ -129,22 +129,45 @@ class MovingAverageOptimizer(optimizer.Optimizer): Raises: RuntimeError: If apply_gradients or minimize has not been called before. + ValueError: If var_list is provided and contains some variables but not + their moving average counterpart. """ - if self._variable_map is None: + if self._swapped_variable_name_map is None: raise RuntimeError('Must call apply_gradients or minimize before ' 'creating the swapping_saver') if var_list is None: var_list = variables.global_variables() if not isinstance(var_list, dict): var_list = saver.BaseSaverBuilder.OpListToDict(var_list) + + # OpListToDict converts variables to tensors. We make sure we can get + # the unique variable name for normal and resource vaiables. + def get_v_name(tensor): + if tensor.op.type == 'ReadVariableOp': + return tensor.op.inputs[0].op.name + else: + return tensor.op.name + + v_name_to_tensor = {} + for tensor in six.itervalues(var_list): + v_name = get_v_name(tensor) + v_name_to_tensor[v_name] = tensor + # Now swap variables and moving averages swapped_var_list = {} - for k, v in six.iteritems(var_list): - v_swap = self._variable_map.get(v.op.name, None) - if v_swap: - swapped_var_list[k] = v_swap - else: - swapped_var_list[k] = v + for k, tensor in six.iteritems(var_list): + v_name = get_v_name(tensor) + swapped_v_name = self._swapped_variable_name_map.get(v_name, None) + tensor_to_save = tensor + if swapped_v_name is not None: + if swapped_v_name in v_name_to_tensor: + tensor_to_save = v_name_to_tensor[swapped_v_name] + else: + raise ValueError( + ('Variable to swap %s is not part of variables to save. ' + 'This breaks MovingAverageOptimizer.') % swapped_v_name) + swapped_var_list[k] = tensor_to_save + # Build the swapping saver. return saver.Saver(swapped_var_list, name=name, **kwargs) diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py index 60929add198f2e69b5acc2eb5516dafc82b1f3ba..85e3e8d3791f2331ed249c0b7f67a3dbde4fca08 100644 --- a/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py +++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py @@ -24,6 +24,10 @@ import six from tensorflow.contrib.opt.python.training import moving_average_optimizer from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import gradient_descent @@ -33,13 +37,26 @@ from tensorflow.python.training import saver class MovingAverageOptimizerTest(test.TestCase): def testRun(self): + self._helpTestRun(use_resource=False) + + def testRunUseResource(self): + # Test that MovingAverageOptimizer works with resource variables. + self._helpTestRun(use_resource=True) + + def _helpTestRun(self, use_resource=False): for sequential_update in [True, False]: for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session() as sess: + with self.test_session(graph=ops.Graph()) as sess: orig_val0 = [1.0, 2.0] orig_val1 = [3.0, 4.0] - var0 = variables.Variable(orig_val0, name='var0', dtype=dtype) - var1 = variables.Variable(orig_val1, name='var1', dtype=dtype) + var0 = variable_scope.get_variable( + 'var0', + initializer=constant_op.constant(orig_val0, dtype=dtype), + use_resource=use_resource) + var1 = variable_scope.get_variable( + 'var1', + initializer=constant_op.constant(orig_val1, dtype=dtype), + use_resource=use_resource) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) @@ -52,22 +69,63 @@ class MovingAverageOptimizerTest(test.TestCase): save_path = os.path.join(save_dir, 'model') update = opt.apply_gradients( list(six.moves.zip([grads0, grads1], [var0, var1]))) + global_vars = variables.global_variables() + ema_var0 = [ + v for v in global_vars + if v.op.name == 'var0/ExponentialMovingAverage' + ][0] + ema_var1 = [ + v for v in global_vars + if v.op.name == 'var1/ExponentialMovingAverage' + ][0] + perturb = control_flow_ops.group([ + state_ops.assign_add(var0, [1.0, 1.0]), + state_ops.assign_add(var1, [2.0, 2.0]), + state_ops.assign_add(ema_var0, [3.0, 3.0]), + state_ops.assign_add(ema_var1, [4.0, 4.0]) + ]) + + # Test taht saver with missing ema variables will fail. + with self.assertRaisesRegexp(ValueError, r'Variable to swap'): + opt.swapping_saver(var_list=[var0]) + train_saver = opt.swapping_saver() + train_saver_subset = opt.swapping_saver(var_list=[var0, ema_var0]) inference_saver = saver.Saver() variables.global_variables_initializer().run() # Step 1. update.run() - val0 = var0.eval() - val1 = var1.eval() self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval()) self.assertAllCloseAccordingToType([2.98, 3.98], var1.eval()) + if sequential_update: + self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval()) + self.assertAllCloseAccordingToType([2.99, 3.99], ema_var1.eval()) # Test that the swapping saver save/restore operation is identity. train_saver.save(sess, save_path) train_saver.restore(sess, save_path) - val0 = var0.eval() - val1 = var1.eval() self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval()) self.assertAllCloseAccordingToType([2.98, 3.98], var1.eval()) + if sequential_update: + self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval()) + self.assertAllCloseAccordingToType([2.99, 3.99], ema_var1.eval()) + # Test that the subset saver saves the EMA variable as well. + if sequential_update: + subset_save_path = save_path + '_subset' + train_saver_subset.save(sess, subset_save_path) + perturb.run() + self.assertAllCloseAccordingToType([1.8, 2.8], var0.eval()) + self.assertAllCloseAccordingToType([3.9, 4.9], ema_var0.eval()) + self.assertAllCloseAccordingToType([4.98, 5.98], var1.eval()) + self.assertAllCloseAccordingToType([6.99, 7.99], ema_var1.eval()) + # Restoring should only restore var0 and ema_var0. + train_saver_subset.restore(sess, subset_save_path) + self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval()) + self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval()) + self.assertAllCloseAccordingToType([4.98, 5.98], var1.eval()) + self.assertAllCloseAccordingToType([6.99, 7.99], ema_var1.eval()) + # Restore back to previou state. + train_saver.restore(sess, save_path) + # If updates are parallel, this is not always true after the 1st step. if sequential_update: # Test that the normal saver will have the averaged variables. diff --git a/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py b/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py index b0a257d264f83ae0a54cdc0e9265d6e7098b7b56..825c08a09a05894df1656a9bb6981f1862195244 100644 --- a/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py +++ b/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py @@ -21,12 +21,9 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.opt.python.training import nadam_optimizer -from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test diff --git a/tensorflow/contrib/opt/python/training/powersign_test.py b/tensorflow/contrib/opt/python/training/powersign_test.py index ff7b1a72d47d8ef54980905323bcaf358c988a82..5214082dd66f00eadadad71d50f7e00b178b8c10 100644 --- a/tensorflow/contrib/opt/python/training/powersign_test.py +++ b/tensorflow/contrib/opt/python/training/powersign_test.py @@ -99,7 +99,7 @@ class PowerSignTest(test.TestCase): neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), global_step=global_step) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -110,13 +110,13 @@ class PowerSignTest(test.TestCase): # last 3 steps with negative gradient (sign(gm) should be -1) for t in range(1, 8): if t < 5: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1]), global_step=global_step) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(neg_update) elif t > 1: opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), diff --git a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py index 74036082f0ca2bae23b30deb1b1986befd6601d8..3c0b8394be51e8744b5461a00a99ead5e45d90b2 100644 --- a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py +++ b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py @@ -109,7 +109,7 @@ class VariableClippingOptimizer(optimizer.Optimizer): def _clip_dense(self, var): with self._maybe_colocate_with(var): - updated_var_value = var._ref() # pylint: disable=protected-access + updated_var_value = var.read_value() normalized_var = clip_ops.clip_by_norm( updated_var_value, self._max_norm, self._vars_to_clip_dims[var]) delta = updated_var_value - normalized_var diff --git a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py index 6a09f70f442131da7da2a4e98a238f21c3ccb6ec..348623d8f8d0c2ed60f559eca281343722038100 100644 --- a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py +++ b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py @@ -18,13 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function - +# pylint: disable=unused-import from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample from tensorflow.contrib.util import loader from tensorflow.python.platform import resource_loader +# pylint: enable=unused-import _periodic_resample_op = loader.load_op_library( resource_loader.get_path_to_datafile('_periodic_resample_op.so')) diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py index 379fa7fd5c2a22b5b16a21cca8c2ea8afdcaeefa..6531183cb59af774299eb767cce111d2ec6f32b4 100644 --- a/tensorflow/contrib/py2tf/__init__.py +++ b/tensorflow/contrib/py2tf/__init__.py @@ -23,6 +23,7 @@ from __future__ import print_function from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl.api import convert +from tensorflow.contrib.py2tf.impl.api import converted_call from tensorflow.contrib.py2tf.impl.api import graph_ready from tensorflow.contrib.py2tf.impl.api import to_code from tensorflow.contrib.py2tf.impl.api import to_graph @@ -30,7 +31,8 @@ from tensorflow.contrib.py2tf.pyct.transformer import PyFlowParseError from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'to_graph', 'to_code', 'convert', 'graph_ready', 'utils', 'PyFlowParseError' + 'to_graph', 'to_code', 'convert', 'graph_ready', 'converted_call', 'utils', + 'PyFlowParseError' ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index cb9dec74ffc30c9d8c87a223083abb2650dfd2fa..1da2ccb75bba5b11820f8c8559097b321ecebacf 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -18,15 +18,18 @@ py_library( name = "converters", srcs = [ "asserts.py", - "break_canonicalization.py", + "break_statements.py", "builtin_functions.py", "call_trees.py", - "continue_canonicalization.py", + "continue_statements.py", "control_flow.py", "decorators.py", - "for_canonicalization.py", + "for_loops.py", + "list_comprehension.py", "logical_expressions.py", + "name_scopes.py", "side_effect_guards.py", + "single_return.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], @@ -44,9 +47,11 @@ py_library( visibility = ["//tensorflow:__subpackages__"], deps = [ ":converters", + "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", + "@six_archive//:six", ], ) @@ -56,51 +61,49 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) py_test( - name = "break_canonicalization_test", - srcs = ["break_canonicalization_test.py"], + name = "break_statements_test", + srcs = ["break_statements_test.py"], srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) py_test( - name = "call_trees_test", - srcs = ["call_trees_test.py"], + name = "builtin_functions_test", + srcs = ["builtin_functions_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) py_test( - name = "decorators_test", - srcs = ["decorators_test.py"], + name = "call_trees_test", + srcs = ["call_trees_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/impl", "//tensorflow/python:client_testlib", ], ) py_test( - name = "continue_canonicalization_test", - srcs = ["continue_canonicalization_test.py"], + name = "continue_statements_test", + srcs = ["continue_statements_test.py"], srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -111,26 +114,33 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) py_test( - name = "builtin_functions_test", - srcs = ["builtin_functions_test.py"], + name = "decorators_test", + srcs = ["decorators_test.py"], srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) py_test( - name = "for_canonicalization_test", - srcs = ["for_canonicalization_test.py"], + name = "for_loops_test", + srcs = ["for_loops_test.py"], srcs_version = "PY2AND3", + deps = [ + ":test_lib", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "name_scopes_test", + srcs = ["name_scopes_test.py"], deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -138,13 +148,22 @@ py_test( ], ) +py_test( + name = "list_comprehension_test", + srcs = ["list_comprehension_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":test_lib", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "logical_expressions_test", srcs = ["logical_expressions_test.py"], srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -153,6 +172,21 @@ py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], srcs_version = "PY2AND3", + tags = [ + # TODO(mdan): Fix. + "flaky", + "notap", + ], + deps = [ + ":test_lib", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "single_return_test", + srcs = ["single_return_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", diff --git a/tensorflow/contrib/py2tf/converters/asserts.py b/tensorflow/contrib/py2tf/converters/asserts.py index 2d6ee1d09829b538815dbb9794868c13f51578fc..5b9b8e772bed82df2429fd6cb94dbf7b565e22b3 100644 --- a/tensorflow/contrib/py2tf/converters/asserts.py +++ b/tensorflow/contrib/py2tf/converters/asserts.py @@ -35,7 +35,7 @@ class AssertsTransformer(transformer.Base): # Note: The lone tf.Assert call will be wrapped with control_dependencies # by side_effect_guards. template = """ - tf.Assert(test, [tf.constant(msg)]) + tf.Assert(test, [msg]) """ if node.msg is None: diff --git a/tensorflow/contrib/py2tf/converters/break_canonicalization.py b/tensorflow/contrib/py2tf/converters/break_statements.py similarity index 100% rename from tensorflow/contrib/py2tf/converters/break_canonicalization.py rename to tensorflow/contrib/py2tf/converters/break_statements.py diff --git a/tensorflow/contrib/py2tf/converters/break_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/break_statements_test.py similarity index 53% rename from tensorflow/contrib/py2tf/converters/break_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/break_statements_test.py index 54c4d99361f00ba2b5b79323f5feddcbbdfc99e8..095fcdff07d44ecc6b9bb7f8d3e2c7c43df72a02 100644 --- a/tensorflow/contrib/py2tf/converters/break_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/break_statements_test.py @@ -12,25 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for break_canonicalization module.""" +"""Tests for break_statements module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.converters import break_canonicalization -from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import break_statements from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.platform import test -class TestNamer(control_flow.SymbolNamer): - - def new_symbol(self, name_root, _): - return name_root - - class BreakCanonicalizationTest(converter_test_base.TestCase): def test_basic_break(self): @@ -44,15 +36,15 @@ class BreakCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = break_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = break_statements.transform(node, self.ctx) - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with self.compiled(node) as result: + self.assertEqual(test_fn(0), result.test_fn(0)) + self.assertEqual(test_fn(1), result.test_fn(1)) + self.assertEqual(test_fn(2), result.test_fn(2)) + self.assertEqual(test_fn(3), result.test_fn(3)) + self.assertEqual(test_fn(4), result.test_fn(4)) def test_basic_break_for_loop(self): @@ -76,16 +68,17 @@ class BreakCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = break_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = break_statements.transform(node, self.ctx) - # The break is incompletely canonicalized. Everything is in place, but - # the loop does not break. - self.assertEqual(test_equiv_fn([]), result.test_fn([])) - self.assertEqual(test_equiv_fn([1]), result.test_fn([1])) - self.assertEqual(test_equiv_fn([2]), result.test_fn([2])) - self.assertEqual(test_equiv_fn([1, 2, 3, 4]), result.test_fn([1, 2, 3, 4])) + with self.compiled(node) as result: + # The break is incompletely canonicalized. Everything is in place, but + # the loop does not break. + self.assertEqual(test_equiv_fn([]), result.test_fn([])) + self.assertEqual(test_equiv_fn([1]), result.test_fn([1])) + self.assertEqual(test_equiv_fn([2]), result.test_fn([2])) + self.assertEqual( + test_equiv_fn([1, 2, 3, 4]), result.test_fn([1, 2, 3, 4])) def test_continue_deeply_nested(self): @@ -104,15 +97,15 @@ class BreakCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v, u, w - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = break_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = break_statements.transform(node, self.ctx) - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with self.compiled(node) as result: + self.assertEqual(test_fn(0), result.test_fn(0)) + self.assertEqual(test_fn(1), result.test_fn(1)) + self.assertEqual(test_fn(2), result.test_fn(2)) + self.assertEqual(test_fn(3), result.test_fn(3)) + self.assertEqual(test_fn(4), result.test_fn(4)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py index 3e56634106c2c9c1e4c334d4e61cedee395853a9..b5aa9756da6a139e542e9a0ead86cf4cc8207449 100644 --- a/tensorflow/contrib/py2tf/converters/builtin_functions.py +++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py @@ -25,33 +25,50 @@ from tensorflow.contrib.py2tf.pyct import transformer class BuiltinFunctionTransformer(transformer.Base): - """Transforms Print nodes to Call so they can be handled as functions.""" + """Handles builtin functions. + + This transformer only covers functions that are translated into a + TF equivalent, like `len`. + """ def __init__(self, context): super(BuiltinFunctionTransformer, self).__init__(context) - def _convert_len(self, node): + # pylint:disable=invalid-name + + def _convert_builtin(self, node): template = """ - tf.shape(args)[0] + py2tf_utils.dynamic_builtin(func, args) """ - new_call = templates.replace(template, args=node.args)[0].value - return new_call + return templates.replace(template, func=node.func, args=node.args)[0].value - # pylint:disable=invalid-name + def _convert_print(self, node): + template = """ + py2tf_utils.dynamic_print(args) + """ + return templates.replace(template, args=node.args)[0].value def visit_Call(self, node): self.generic_visit(node) # TODO(mdan): This won't work if the function was hidden. - if isinstance(node.func, gast.Name) and node.func.id == 'len': - return self._convert_len(node) + if isinstance(node.func, gast.Name) and node.func.id in ('len',): + return self._convert_builtin(node) + # Print needs to be handled separately because it can be read as statement. + if isinstance(node.func, gast.Name) and node.func.id == 'print': + return self._convert_print(node) return node def visit_Print(self, node): self.generic_visit(node) + args = node.values + # Following is the case when calling print(a, b) + if len(args) == 1 and isinstance(args[0], gast.Tuple): + args = args[0].elts template = """ fname(args) """ - return templates.replace(template, fname='print', args=node.values) + function_call = templates.replace(template, fname='print', args=args)[0] + return self.visit(function_call) # pylint:enable=invalid-name diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions_test.py b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py index be76066242856c85784221166a70299187b11b14..eb60a1d8ae2b56907df8f3ffafe7604883cfc2a9 100644 --- a/tensorflow/contrib/py2tf/converters/builtin_functions_test.py +++ b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py @@ -18,13 +18,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import gast +import sys + +import six from tensorflow.contrib.py2tf.converters import builtin_functions from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import script_ops from tensorflow.python.platform import test @@ -37,25 +40,77 @@ class BuiltinFunctionsTest(converter_test_base.TestCase): node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'tf', array_ops) - with self.test_session() as sess: - self.assertEqual(3, - sess.run( - result.test_fn(constant_op.constant([0, 0, 0])))) + with self.compiled(node, array_ops.shape) as result: + with self.test_session() as sess: + self.assertEqual(3, + sess.run( + result.test_fn(constant_op.constant([0, 0, 0])))) + + self.assertEqual(3, result.test_fn([0, 0, 0])) - def test_print(self): + def test_print_with_op(self): def test_fn(a): print(a) node = self.parse_and_analyze(test_fn, {'print': print}) node = builtin_functions.transform(node, self.ctx) - result = compiler.ast_to_object(node) - result.test_fn('a') - self.assertTrue(isinstance(node.body[0].body[0].value, gast.Call)) + # Note: it's relevant not to include script_ops.py_func here, to verify + # that tf.Print is used. + with self.compiled(node, logging_ops.Print) as result: + with self.test_session() as sess: + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + result.test_fn('a') + sess.run(sess.graph.get_operations()) + self.assertEqual(out_capturer.getvalue(), 'a\n') + finally: + sys.stdout = sys.__stdout__ + + def test_print_with_op_multiple_values(self): + + def test_fn(a, b): + print(a, b) + + node = self.parse_and_analyze(test_fn, {'print': print}) + node = builtin_functions.transform(node, self.ctx) + + # Note: it's relevant not to include script_ops.py_func here, to verify + # that tf.Print is used. + with self.compiled(node, logging_ops.Print) as result: + with self.test_session() as sess: + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + result.test_fn('a', 1) + sess.run(sess.graph.get_operations()) + self.assertEqual(out_capturer.getvalue(), 'a 1\n') + finally: + sys.stdout = sys.__stdout__ + + def test_print_with_py_func(self): + + def test_fn(a, b, c): + print(a, b, c) + + node = self.parse_and_analyze(test_fn, {'print': print}) + node = builtin_functions.transform(node, self.ctx) + + # Note: it's relevant not to include logging_ops.Print here, to verify + # that py_func is used. + with self.compiled(node, script_ops.py_func) as result: + with self.test_session() as sess: + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + result.test_fn('a', 1, [2, 3]) + sess.run(sess.graph.get_operations()) + self.assertEqual(out_capturer.getvalue(), 'a 1 [2, 3]\n') + finally: + sys.stdout = sys.__stdout__ if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py index 834baf258d3b5a0eee02e79ccdf0ebfc61b2c9df..ca8726f9160d106ebd82e01e399e65fb77b02aab 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees.py +++ b/tensorflow/contrib/py2tf/converters/call_trees.py @@ -27,10 +27,10 @@ import types import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import templates from tensorflow.contrib.py2tf.pyct import transformer -from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno from tensorflow.python.util import tf_inspect @@ -73,9 +73,8 @@ class CallTreeTransformer(transformer.Base): self.uncompiled_modules = uncompiled_modules self.nocompile_decorators = nocompile_decorators - # pylint:disable=invalid-name - def _resolve_name(self, node): + """Used to resolve decorator info.""" if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): @@ -100,7 +99,13 @@ class CallTreeTransformer(transformer.Base): (owner_type, node.attr)) return None + def _function_is_compilable(self, target_entity): + """Determines whether an entity can be compiled at all.""" + # TODO(mdan): This is just a placeholder. Implement. + return not isinstance(target_entity, types.BuiltinFunctionType) + def _should_compile(self, node, fqn): + """Determines whether an entity should be compiled in the context.""" for i in range(1, len(fqn)): if fqn[:i] in self.uncompiled_modules: return False @@ -142,33 +147,6 @@ class CallTreeTransformer(transformer.Base): return True - def _determine_function_owner(self, m): - # TODO(mdan): The parent type should be known at analysis. Use that instead. - if hasattr(m, 'im_class'): # Python 2 - return m.im_class - if hasattr(m, '__qualname__'): # Python 3 - # Object attributes: should be bound to "self". - if hasattr(m, '__self__'): - return type(m.__self__) - - # Class attributes: should have the owner name in their namespace. - qn = m.__qualname__.split('.') - if len(qn) < 2: - return None - owner_name, func_name = qn[-2:] - if func_name != m.__name__: - raise ValueError('Inconsistent names detected ' - '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % - (func_name, m.__name__, m)) - if owner_name == '': - return None - if owner_name not in self.context.namespace: - raise ValueError( - 'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' % - (owner_name, m, self.context.namespace)) - return self.context.namespace[owner_name] - return None - def _rename_compilable_function(self, node): assert anno.hasanno(node.func, 'live_val') assert anno.hasanno(node.func, 'fqn') @@ -183,7 +161,11 @@ class CallTreeTransformer(transformer.Base): target_fqn, live_entity=target_entity) do_rename = True else: - owner_type = self._determine_function_owner(target_entity) + if anno.hasanno(node.func, 'parent_type'): + owner_type = anno.getanno(node.func, 'parent_type') + else: + # Fallback - not reliable. + owner_type = inspect_utils.getmethodclass(target_entity) new_name, do_rename = self.context.namer.compiled_function_name( target_fqn, live_entity=target_entity, owner_type=owner_type) @@ -197,28 +179,41 @@ class CallTreeTransformer(transformer.Base): return node def _wrap_to_py_func_no_return(self, node): - args_scope = anno.getanno(node, NodeAnno.ARGS_SCOPE) # TODO(mdan): Properly handle varargs, kwargs, etc. template = """ - def wrapper(args): - call(args) - return 1 - tf.py_func(wrapper, [args], [tf.int64]) + py2tf_utils.wrap_py_func(func, None, (original_args,), True) """ - wrapper_def, call_expr = templates.replace( - template, - call=node.func, - wrapper=self.context.namer.compiled_function_name(node.func.id)[0], - args=tuple(args_scope.used)) - anno.setanno(call_expr.value, NodeAnno.ARGS_SCOPE, args_scope) - # TODO(mdan): Rename this annotation to 'graph_ready' - anno.setanno(wrapper_def, anno.Basic.SKIP_PROCESSING, True) - - return (wrapper_def, call_expr) + return templates.replace(template, func=node.func, original_args=node.args) + + def _insert_dynamic_conversion(self, node): + """Inlines a dynamic conversion for a dynamic function.""" + # TODO(mdan): Pass information on the statically compiled functions. + # Having access to the statically compiled functions can help avoid + # unnecessary compilation. + # For example, this would lead to function `a` being compiled twice: + # + # def a(): + # v = b + # b() + # def b(): + # a() + # + # This is really a problem with recursive calls, which currently can + # only be gated by a static condition, and should be rare. + # TODO(mdan): It probably makes sense to use dynamic conversion every time. + # Before we could convert all the time though, we'd need a reasonable + # caching mechanism. + template = """ + py2tf_api.converted_call(func, True, False, {}, original_args) + """ + call_expr = templates.replace( + template, func=node.func, original_args=node.args) + new_call = call_expr[0].value + # TODO(mdan): Improve the template mechanism to better support this. + new_call.keywords = node.keywords + return new_call - def _function_is_compilable(self, target_entity): - # TODO(mdan): This is just a placeholder. Implement. - return not isinstance(target_entity, types.BuiltinFunctionType) + # pylint:disable=invalid-name def visit_Expr(self, node): if isinstance(node.value, gast.Call): @@ -259,9 +254,9 @@ class CallTreeTransformer(transformer.Base): raise NotImplementedError('py_func with return values') else: if self.context.recursive: - raise NotImplementedError('Could not resolve target function.') + node = self._insert_dynamic_conversion(node) else: - # TODO(mdan): Double check. Is this reachable code? + # Unresolved functions are allowed in non-recursive mode. pass return node diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py index e63c10de0fed72333a6d571f9b9a4f1cb50b5f1d..d482a9ef7897388839bbf8f9e4bfc5839d42b2d7 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees_test.py +++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py @@ -20,23 +20,11 @@ from __future__ import print_function from tensorflow.contrib.py2tf.converters import call_trees from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.framework import constant_op from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class TestNamer(call_trees.FunctionNamer): - - def compiled_function_name(self, - original_fqn, - live_entity=None, - owner_type=None): - if owner_type is not None: - return None, False - return ('renamed_%s' % '_'.join(original_fqn)), True - - class CallTreesTest(converter_test_base.TestCase): def test_basic(self): @@ -50,14 +38,29 @@ class CallTreesTest(converter_test_base.TestCase): def test_fn_2(a): return test_fn_1(a) + 1 - node = self.parse_and_analyze( - test_fn_2, {'test_fn_1': test_fn_1}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) + node = call_trees.transform(node, self.ctx, (), ()) + + with self.compiled(node) as result: + # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 + # manually. + result.renamed_test_fn_1 = renamed_test_fn_1 + self.assertEquals(3, result.test_fn_2(1)) + + def test_dynamic_function(self): + + def test_fn_1(): + raise ValueError('This should be masked by the mock.') + + def test_fn_2(f): + return f() + 3 + + node = self.parse_and_analyze(test_fn_2, {}) node = call_trees.transform(node, self.ctx, (), ()) - result = compiler.ast_to_object(node) - # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 manually. - setattr(result, 'renamed_test_fn_1', renamed_test_fn_1) - self.assertEquals(3, result.test_fn_2(1)) + with self.compiled(node) as result: + # 10 = 7 (from the mock) + 3 (from test_fn_2) + self.assertEquals(10, result.test_fn_2(test_fn_1)) def test_simple_methods(self): @@ -71,13 +74,36 @@ class CallTreesTest(converter_test_base.TestCase): node = self.parse_and_analyze( TestClass.test_fn_2, {'TestClass': TestClass}, - namer=TestNamer(), + namer=converter_test_base.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) node = call_trees.transform(node, self.ctx, (), ()) - result = compiler.ast_to_object(node) - tc = TestClass() - self.assertEquals(3, result.test_fn_2(tc, 1)) + with self.compiled(node) as result: + tc = TestClass() + self.assertEquals(3, result.test_fn_2(tc, 1)) + + def test_py_func_wrap_no_retval(self): + + def test_fn(a): + setattr(a, 'foo', 'bar') + + node = self.parse_and_analyze(test_fn, {'setattr': setattr}) + node = call_trees.transform(node, self.ctx, (), ()) + + with self.compiled(node) as result: + with self.test_session() as sess: + # The function has no return value, so we do some tricks to grab the + # generated py_func node and ensure its effect only happens at graph + # execution. + + class Dummy(object): + pass + + a = Dummy() + result.test_fn(a) + self.assertFalse(hasattr(a, 'foo')) + sess.run(sess.graph.get_operations()[0]) + self.assertEquals('bar', a.foo) def test_uncompiled_modules(self): @@ -86,26 +112,22 @@ class CallTreesTest(converter_test_base.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self.parse_and_analyze( - test_fn, { - 'math_ops': math_ops, - 'constant_op': constant_op - }, - namer=TestNamer()) + node = self.parse_and_analyze(test_fn, { + 'math_ops': math_ops, + 'constant_op': constant_op + }) node = call_trees.transform(node, self.ctx, set(((math_ops.__name__,), (constant_op.__name__,))), ()) - result = compiler.ast_to_object(node) - setattr(result, 'math_ops', math_ops) - setattr(result, 'constant_op', constant_op) - - with self.test_session() as sess: - # Not renamed, because the converter doesn't rename the definition itself. - # (the caller is responsible for that). - result_tensor = result.test_fn(constant_op.constant(1)) - result_val = sess.run(result_tensor) - self.assertEquals(3, result_val) + with self.compiled(node) as result: + result.math_ops = math_ops + result.constant_op = constant_op + with self.test_session() as sess: + # Not renamed, because the converter doesn't rename the definition + # itself (the caller is responsible for that). + result_tensor = result.test_fn(constant_op.constant(1)) + self.assertEquals(3, sess.run(result_tensor)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/continue_canonicalization.py b/tensorflow/contrib/py2tf/converters/continue_statements.py similarity index 100% rename from tensorflow/contrib/py2tf/converters/continue_canonicalization.py rename to tensorflow/contrib/py2tf/converters/continue_statements.py diff --git a/tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/continue_statements_test.py similarity index 51% rename from tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/continue_statements_test.py index 4b188195595e4ea4be4359eb87830d587c1de1de..a598dcd1aed29478b7e3fe27e3c1b20010247dd9 100644 --- a/tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/continue_statements_test.py @@ -12,25 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for continue_canonicalization module.""" +"""Tests for continue_statements module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.converters import continue_canonicalization -from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import continue_statements from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.platform import test -class TestNamer(control_flow.SymbolNamer): - - def new_symbol(self, name_root, _): - return name_root - - class ContinueCanonicalizationTest(converter_test_base.TestCase): def test_basic_continue(self): @@ -44,15 +36,15 @@ class ContinueCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = continue_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = continue_statements.transform(node, self.ctx) - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with self.compiled(node) as result: + self.assertEqual(test_fn(0), result.test_fn(0)) + self.assertEqual(test_fn(1), result.test_fn(1)) + self.assertEqual(test_fn(2), result.test_fn(2)) + self.assertEqual(test_fn(3), result.test_fn(3)) + self.assertEqual(test_fn(4), result.test_fn(4)) def test_basic_continue_for_loop(self): @@ -65,14 +57,14 @@ class ContinueCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = continue_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = continue_statements.transform(node, self.ctx) - self.assertEqual(test_fn([]), result.test_fn([])) - self.assertEqual(test_fn([1]), result.test_fn([1])) - self.assertEqual(test_fn([2]), result.test_fn([2])) - self.assertEqual(test_fn([1, 2, 3]), result.test_fn([1, 2, 3])) + with self.compiled(node) as result: + self.assertEqual(test_fn([]), result.test_fn([])) + self.assertEqual(test_fn([1]), result.test_fn([1])) + self.assertEqual(test_fn([2]), result.test_fn([2])) + self.assertEqual(test_fn([1, 2, 3]), result.test_fn([1, 2, 3])) def test_continue_deeply_nested(self): @@ -91,15 +83,15 @@ class ContinueCanonicalizationTest(converter_test_base.TestCase): v.append(x) return v, u, w - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = continue_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = continue_statements.transform(node, self.ctx) - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with self.compiled(node) as result: + self.assertEqual(test_fn(0), result.test_fn(0)) + self.assertEqual(test_fn(1), result.test_fn(1)) + self.assertEqual(test_fn(2), result.test_fn(2)) + self.assertEqual(test_fn(3), result.test_fn(3)) + self.assertEqual(test_fn(4), result.test_fn(4)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/control_flow.py b/tensorflow/contrib/py2tf/converters/control_flow.py index a256c074a807b6d8ff3a2573f6d57e82624c0229..762c26f0c77e13c077761ceec41cb29db9149a35 100644 --- a/tensorflow/contrib/py2tf/converters/control_flow.py +++ b/tensorflow/contrib/py2tf/converters/control_flow.py @@ -21,6 +21,7 @@ from __future__ import print_function import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import ast_util from tensorflow.contrib.py2tf.pyct import templates from tensorflow.contrib.py2tf.pyct import transformer from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno @@ -42,25 +43,6 @@ class SymbolNamer(object): raise NotImplementedError() -class SymbolRenamer(gast.NodeTransformer): - """Transformer that can rename symbols to a simple names.""" - - def __init__(self, name_map): - self.name_map = name_map - - def _process(self, node): - qn = anno.getanno(node, anno.Basic.QN) - if qn in self.name_map: - return gast.Name(self.name_map[qn], node.ctx, None) - return node - - def visit_Name(self, node): - return self._process(node) - - def visit_Attribute(self, node): - return self._process(node) - - class ControlFlowTransformer(transformer.Base): """Transforms control flow structures like loops an conditionals.""" @@ -72,6 +54,49 @@ class ControlFlowTransformer(transformer.Base): def visit_For(self, node): assert False, 'for statement should have been canonicalized at this point' + def _create_cond_branch(self, body_name, aliased_orig_names, + aliased_new_names, body, returns): + if aliased_orig_names: + template = """ + def body_name(): + aliased_new_names, = aliased_orig_names, + body + return (returns,) + """ + return templates.replace( + template, + body_name=body_name, + body=body, + aliased_orig_names=aliased_orig_names, + aliased_new_names=aliased_new_names, + returns=returns) + else: + template = """ + def body_name(): + body + return (returns,) + """ + return templates.replace( + template, body_name=body_name, body=body, returns=returns) + + def _create_cond_expr(self, results, test, body_name, orelse_name): + if results is not None: + template = """ + results = py2tf_utils.run_cond(test, body_name, orelse_name) + """ + return templates.replace( + template, + test=test, + results=results, + body_name=body_name, + orelse_name=orelse_name) + else: + template = """ + py2tf_utils.run_cond(test, body_name, orelse_name) + """ + return templates.replace( + template, test=test, body_name=body_name, orelse_name=orelse_name) + def visit_If(self, node): self.generic_visit(node) @@ -85,7 +110,7 @@ class ControlFlowTransformer(transformer.Base): raise ValueError( 'The else branch creates new symbols that the if branch does not.') - all_modified = tuple(body_scope.modified | orelse_scope.modified) + modified = tuple(body_scope.modified | orelse_scope.modified) all_referenced = body_scope.referenced | orelse_scope.referenced # Alias the closure variables inside the conditional functions @@ -99,61 +124,44 @@ class ControlFlowTransformer(transformer.Base): self.context.namer.new_symbol(s.ssf(), all_referenced) for s in aliased_orig_names) alias_map = dict(zip(aliased_orig_names, aliased_new_names)) - node_body = node.body - node_body = [SymbolRenamer(alias_map).visit(n) for n in node_body] - node_orelse = node.orelse - node_orelse = [SymbolRenamer(alias_map).visit(n) for n in node_orelse] - - if len(all_modified) == 1: - results = all_modified[0] + node_body = ast_util.rename_symbols(node.body, alias_map) + node_orelse = ast_util.rename_symbols(node.orelse, alias_map) + + if not modified: + # When the cond would return no value, we leave the cond called without + # results. That in turn should trigger the side effect guards. The + # branch functions will return a dummy value that ensures cond + # actually has some return value as well. + results = None + elif len(modified) == 1: + results = modified[0] else: - results = gast.Tuple([s.ast() for s in all_modified], None) + results = gast.Tuple([s.ast() for s in modified], None) - if aliased_orig_names: - template = """ - def body_name(): - aliased_new_names, = aliased_orig_names, - body - return (all_results,) - def orelse_name(): - aliased_new_names, = aliased_orig_names, - orelse - return (all_results,) - results = tf.cond(test, body_name, orelse_name) - """ - body_name = self.context.namer.new_symbol('if_true', all_referenced) - return templates.replace( - template, - test=node.test, - body_name=body_name, - body=node_body, - orelse_name=self.context.namer.new_symbol('if_false', all_referenced), - orelse=node_orelse, - aliased_orig_names=tuple(aliased_orig_names), - aliased_new_names=tuple(aliased_new_names), - all_results=tuple(alias_map[s] if s in aliased_orig_names else s - for s in all_modified), - results=results) + body_name = self.context.namer.new_symbol('if_true', all_referenced) + orelse_name = self.context.namer.new_symbol('if_false', all_referenced) + if modified: + body_returns = tuple( + alias_map[s] if s in aliased_orig_names else s for s in modified) else: - template = """ - def body_name(): - body - return (all_results,) - def orelse_name(): - orelse - return (all_results,) - results = tf.cond(test, body_name, orelse_name) - """ - body_name = self.context.namer.new_symbol('if_true', all_referenced) - return templates.replace( - template, - test=node.test, - body_name=body_name, - body=node_body, - orelse_name=self.context.namer.new_symbol('if_false', all_referenced), - orelse=node_orelse, - all_results=tuple(s for s in all_modified), - results=results) + body_returns = templates.replace('tf.ones(())')[0].value + + body_def = self._create_cond_branch( + body_name, + aliased_orig_names=tuple(aliased_orig_names), + aliased_new_names=tuple(aliased_new_names), + body=node_body, + returns=body_returns) + orelse_def = self._create_cond_branch( + orelse_name, + aliased_orig_names=tuple(aliased_orig_names), + aliased_new_names=tuple(aliased_new_names), + body=node_orelse, + returns=body_returns) + cond_expr = self._create_cond_expr(results, node.test, body_name, + orelse_name) + + return body_def + orelse_def + cond_expr def visit_While(self, node): self.generic_visit(node) @@ -163,6 +171,14 @@ class ControlFlowTransformer(transformer.Base): all_referenced = body_scope.referenced state = list(body_closure) + if not state: + # TODO(mdan): Implement this properly. + # To complete this statement, we need to check whether any variable + # created inside the body scope is used before being modified outside the + # scope. This should be done during activity analysis, and in general + # should cover the case where variables may not be initialized. + raise ValueError('cannot convert while loop: no outputs') + state_ssf = [ self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state ] @@ -179,11 +195,8 @@ class ControlFlowTransformer(transformer.Base): else: state_ast_tuple = gast.Tuple([n.ast() for n in state], None) - node_body = node.body - node_body = [SymbolRenamer(ssf_map).visit(n) for n in node_body] - - test = node.test - test = SymbolRenamer(ssf_map).visit(test) + node_body = ast_util.rename_symbols(node.body, ssf_map) + test = ast_util.rename_symbols(node.test, ssf_map) template = """ def test_name(state_ssf): @@ -191,7 +204,7 @@ class ControlFlowTransformer(transformer.Base): def body_name(state_ssf): body return state_ssf, - state_ast_tuple = tf.while_loop(test_name, body_name, [state]) + state_ast_tuple = py2tf_utils.run_while(test_name, body_name, [state]) """ node = templates.replace( template, diff --git a/tensorflow/contrib/py2tf/converters/control_flow_test.py b/tensorflow/contrib/py2tf/converters/control_flow_test.py index f192bf1b465f6d88578107268d7974cd97a01623..b785b284a7fb7a0257551326c88b44a341b295ba 100644 --- a/tensorflow/contrib/py2tf/converters/control_flow_test.py +++ b/tensorflow/contrib/py2tf/converters/control_flow_test.py @@ -20,23 +20,11 @@ from __future__ import print_function from tensorflow.contrib.py2tf.converters import control_flow from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.framework import constant_op from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test -class TestNamer(control_flow.SymbolNamer): - - def new_symbol(self, name_root, used): - i = 0 - while True: - name = '%s%d' % (name_root, i) - if name not in used: - return name - i += 1 - - class ControlFlowTest(converter_test_base.TestCase): def test_simple_while(self): @@ -49,14 +37,13 @@ class ControlFlowTest(converter_test_base.TestCase): i += 1 return s, i, n - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'tf', control_flow_ops) - with self.test_session() as sess: - self.assertEqual((10, 5, 5), - sess.run(result.test_fn(constant_op.constant(5)))) + with self.compiled(node, control_flow_ops.while_loop) as result: + with self.test_session() as sess: + self.assertEqual((10, 5, 5), + sess.run(result.test_fn(constant_op.constant(5)))) def test_while_single_var(self): @@ -65,13 +52,12 @@ class ControlFlowTest(converter_test_base.TestCase): n -= 1 return n - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'tf', control_flow_ops) - with self.test_session() as sess: - self.assertEqual(0, sess.run(result.test_fn(constant_op.constant(5)))) + with self.compiled(node, control_flow_ops.while_loop) as result: + with self.test_session() as sess: + self.assertEqual(0, sess.run(result.test_fn(constant_op.constant(5)))) def test_simple_if(self): @@ -84,16 +70,15 @@ class ControlFlowTest(converter_test_base.TestCase): b = 2 * n return a, b - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'tf', control_flow_ops) - with self.test_session() as sess: - self.assertEqual((-1, 0), sess.run( - result.test_fn(constant_op.constant(1)))) - self.assertEqual((0, -2), - sess.run(result.test_fn(constant_op.constant(-1)))) + with self.compiled(node, control_flow_ops.cond) as result: + with self.test_session() as sess: + self.assertEqual((-1, 0), + sess.run(result.test_fn(constant_op.constant(1)))) + self.assertEqual((0, -2), + sess.run(result.test_fn(constant_op.constant(-1)))) def test_if_single_var(self): @@ -102,13 +87,12 @@ class ControlFlowTest(converter_test_base.TestCase): n = -n return n - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'tf', control_flow_ops) - with self.test_session() as sess: - self.assertEqual(-1, sess.run(result.test_fn(constant_op.constant(1)))) + with self.compiled(node, control_flow_ops.cond) as result: + with self.test_session() as sess: + self.assertEqual(-1, sess.run(result.test_fn(constant_op.constant(1)))) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py index bcb96c81ae762f90159797c929a261a5b7d4fa83..1f98d8469c1b3032fe6babb5a63dde1747027f21 100644 --- a/tensorflow/contrib/py2tf/converters/converter_test_base.py +++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py @@ -18,8 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import contextlib +import imp + +from tensorflow.contrib.py2tf import utils +from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import pretty_printer from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct.static_analysis import activity from tensorflow.contrib.py2tf.pyct.static_analysis import live_values @@ -27,7 +33,67 @@ from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.platform import test +class FakeNamer(object): + + def new_symbol(self, name_root, used): + i = 0 + while True: + name = '%s%d' % (name_root, i) + if name not in used: + return name + i += 1 + + def compiled_function_name(self, + original_fqn, + live_entity=None, + owner_type=None): + del live_entity + if owner_type is not None: + return None, False + return ('renamed_%s' % '_'.join(original_fqn)), True + + +class FakeNoRenameNamer(FakeNamer): + + def compiled_function_name(self, original_fqn, **_): + return str(original_fqn), False + + class TestCase(test.TestCase): + """Base class for unit tests in this module. Contains relevant utilities.""" + + @contextlib.contextmanager + def compiled(self, node, *symbols): + source = None + + self.dynamic_calls = [] + def converted_call(*args): + """Mock version of api.converted_call.""" + self.dynamic_calls.append(args) + return 7 + + try: + result, source = compiler.ast_to_object(node) + result.tf = self.make_fake_mod('fake_tf', *symbols) + result.py2tf_utils = utils + result.py2tf_api = self.make_fake_mod('fake_api', converted_call) + yield result + except Exception: # pylint:disable=broad-except + if source is None: + print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False)) + else: + print('Offending compiled code:\n%s' % source) + raise + + def make_fake_mod(self, name, *symbols): + fake_mod = imp.new_module(name) + for s in symbols: + setattr(fake_mod, s.__name__, s) + return fake_mod + + def attach_namespace(self, module, **ns): + for k, v in ns.items(): + setattr(module, k, v) def parse_and_analyze(self, test_fn, @@ -35,15 +101,17 @@ class TestCase(test.TestCase): namer=None, arg_types=None, include_type_analysis=True, + owner_type=None, recursive=True): node, source = parser.parse_entity(test_fn) ctx = context.EntityContext( - namer=namer, + namer=namer or FakeNamer(), source_code=source, source_file=None, namespace=namespace, arg_values=None, arg_types=arg_types, + owner_type=owner_type, recursive=recursive) node = qual_names.resolve(node) node = activity.resolve(node, ctx) diff --git a/tensorflow/contrib/py2tf/converters/decorators.py b/tensorflow/contrib/py2tf/converters/decorators.py index 3f620c1cd2d9b75f82410754a7e812e13eabe3ae..68bf241ef33292f0581ccb3c44f313f853c92ba7 100644 --- a/tensorflow/contrib/py2tf/converters/decorators.py +++ b/tensorflow/contrib/py2tf/converters/decorators.py @@ -33,6 +33,7 @@ class DecoratorsTransformer(gast.NodeTransformer): def __init__(self, remove_decorators): self.remove_decorators = remove_decorators + self.additional_dependencies = set() # pylint:disable=invalid-name @@ -44,13 +45,38 @@ class DecoratorsTransformer(gast.NodeTransformer): dec_func = dec.func else: dec_func = dec + + # Special cases. + # TODO(mdan): Is there any way we can treat these more generically? + # We may want to forego using decorators altogether if we can't + # properly support them. + if isinstance(dec_func, gast.Name) and dec_func.id in ('classmethod',): + # Assumption: decorators are only visible in the AST when converting + # a function inline (via another decorator). + # In that case, the converted function is no longer part of the + # original object that it was declared into. + # This is currently verified by tests. + continue + if not anno.hasanno(dec_func, 'live_val'): raise ValueError( 'Could not resolve decorator: %s' % pretty_printer.fmt(dec_func)) + dec_value = anno.getanno(dec_func, 'live_val') if dec_value not in self.remove_decorators: - kept_decorators.append(dec) - node.decorator_list = kept_decorators + kept_decorators.append((dec, dec_value)) + + for _, dec_value in kept_decorators: + if dec_value.__module__ == '__main__': + raise ValueError( + 'decorator "%s" was not allowed because it is declared ' + 'in the module "%s". To fix this, declare it in a separate ' + 'module that we can import it from.' % (dec_value, + dec_value.__module__)) + else: + self.additional_dependencies.add(dec_value) + + node.decorator_list = [dec for dec, _ in kept_decorators] return node # pylint:enable=invalid-name @@ -59,4 +85,4 @@ class DecoratorsTransformer(gast.NodeTransformer): def transform(node, remove_decorators): transformer = DecoratorsTransformer(remove_decorators) node = transformer.visit(node) - return node + return node, transformer.additional_dependencies diff --git a/tensorflow/contrib/py2tf/converters/decorators_test.py b/tensorflow/contrib/py2tf/converters/decorators_test.py index f50d593043aeb76d63beb3cb6c301122c9ed8948..c75e5461746f27d14a54b7ac06e7f77d868372c8 100644 --- a/tensorflow/contrib/py2tf/converters/decorators_test.py +++ b/tensorflow/contrib/py2tf/converters/decorators_test.py @@ -18,78 +18,121 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import textwrap +from functools import wraps from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.converters import decorators from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.platform import test -from tensorflow.python.util import tf_inspect -class DecoratorsTest(converter_test_base.TestCase): +# The Python parser only briefly captures decorators into the AST. +# The interpreter desugars them on load, and the decorated function loses any +# trace of the decorator (which is notmally what you would expect, since +# they are meant to be transparent). +# However, decorators are still visible when you analyze the function +# from inside a decorator, before it was applied - as is the case +# with our conversion decorators. - def test_function_decorator(self): - def function_decorator(): +def simple_decorator(f): + return lambda a: f(a) + 1 - def decorator(f): - return lambda a: f(a) + 1 - return decorator +def self_removing_decorator(removing_wrapper): + def decorator(f): + @wraps(f) + def wrapper(*args): + # This removing wrapper is defined in the test below. This setup is so + # intricate just to simulate how we use the transformer in practice. + transformed_f = removing_wrapper(f, (self_removing_decorator,)) + return transformed_f(*args) + 1 + return wrapper + return decorator - # The Python parser does capture decorators into the AST. - # However, the interpreter desugars them on load, and refering to the - # decorated function at runtime usually loses any trace of the decorator. - # Below is an example when that doesn't happen. - def static_wrapper(): - @function_decorator() - def test_fn(a): # pylint:disable=unused-variable - return a +class DecoratorsTest(converter_test_base.TestCase): + + def _remover_wrapper(self, f, remove_decorators): + namespace = { + 'self_removing_decorator': self_removing_decorator, + 'simple_decorator': simple_decorator + } + node = self.parse_and_analyze(f, namespace) + node, _ = decorators.transform(node, remove_decorators=remove_decorators) + result, _ = compiler.ast_to_object(node) + return getattr(result, f.__name__) - node = self.parse_and_analyze(static_wrapper, - {'function_decorator': function_decorator}) - node = node.body[0].body[0] + def test_noop(self): - node = decorators.transform(node, remove_decorators=()) - result = compiler.ast_to_object( - node, - source_prefix=textwrap.dedent(tf_inspect.getsource(function_decorator))) - self.assertEqual(2, result.test_fn(1)) + def test_fn(a): + return a - node = decorators.transform(node, remove_decorators=(function_decorator,)) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node, deps = decorators.transform(node, remove_decorators=()) + result, _ = compiler.ast_to_object(node) + + self.assertFalse(deps) self.assertEqual(1, result.test_fn(1)) - def test_simple_decorator(self): + def test_function(self): - def simple_decorator(f): - return lambda a: f(a) + 1 + @self_removing_decorator(self._remover_wrapper) + def test_fn(a): + return a - # The Python parser does capture decorators into the AST. - # However, the interpreter desugars them upon load, and refering to the - # decorated function at runtime usually loses any trace of the decorator. - # Below is an example when that doesn't happen. - def static_wrapper(): + # 2 = 1 (a) + 1 (decorator applied exactly once) + self.assertEqual(2, test_fn(1)) - @simple_decorator - def test_fn(a): # pylint:disable=unused-variable + def test_method(self): + + class TestClass(object): + + @self_removing_decorator(self._remover_wrapper) + def test_fn(self, a): return a - node = self.parse_and_analyze(static_wrapper, - {'simple_decorator': simple_decorator}) - node = node.body[0].body[0] + # 2 = 1 (a) + 1 (decorator applied exactly once) + self.assertEqual(2, TestClass().test_fn(1)) - node = decorators.transform(node, remove_decorators=()) - result = compiler.ast_to_object( - node, - source_prefix=textwrap.dedent(tf_inspect.getsource(simple_decorator))) - self.assertEqual(2, result.test_fn(1)) + def test_multiple_decorators(self): - node = decorators.transform(node, remove_decorators=(simple_decorator,)) - result = compiler.ast_to_object(node) - self.assertEqual(1, result.test_fn(1)) + class TestClass(object): + + # Note that reversing the order of this two doesn't work. + @classmethod + @self_removing_decorator(self._remover_wrapper) + def test_fn(cls, a): + return a + + # 2 = 1 (a) + 1 (decorator applied exactly once) + self.assertEqual(2, TestClass.test_fn(1)) + + def test_nested_decorators(self): + + @self_removing_decorator(self._remover_wrapper) + def test_fn(a): + @simple_decorator + def inner_fn(b): + return b + 11 + return inner_fn(a) + + with self.assertRaises(ValueError): + test_fn(1) + + # TODO(mdan): Uncomment this test once converter_test_base is updated. + # (can't do it now because it has unrelated pending changes) + # def test_nested_decorators(self): + # + # @self_removing_decorator(self._remover_wrapper) + # def test_fn(a): + # @imported_decorator + # def inner_fn(b): + # return b + 11 + # return inner_fn(a) + # + # # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn) + # self.assertEqual(14, test_fn(1)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/for_canonicalization.py b/tensorflow/contrib/py2tf/converters/for_loops.py similarity index 100% rename from tensorflow/contrib/py2tf/converters/for_canonicalization.py rename to tensorflow/contrib/py2tf/converters/for_loops.py diff --git a/tensorflow/contrib/py2tf/converters/for_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/for_loops_test.py similarity index 64% rename from tensorflow/contrib/py2tf/converters/for_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/for_loops_test.py index 142bd4aea12fa26468372472cb8a08e1f6b0e8ac..70a367d3b517e528b67f260d607431d324d2ab7d 100644 --- a/tensorflow/contrib/py2tf/converters/for_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/for_loops_test.py @@ -12,25 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for for_canonicalization module.""" +"""Tests for for_loops module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.converters import control_flow from tensorflow.contrib.py2tf.converters import converter_test_base -from tensorflow.contrib.py2tf.converters import for_canonicalization -from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.converters import for_loops from tensorflow.python.platform import test -class TestNamer(control_flow.SymbolNamer): - - def new_symbol(self, name_root, _): - return name_root - - class ControlFlowTest(converter_test_base.TestCase): def test_basic_for(self): @@ -41,14 +33,14 @@ class ControlFlowTest(converter_test_base.TestCase): s += e return s - node = self.parse_and_analyze(test_fn, {}, namer=TestNamer()) - node = for_canonicalization.transform(node, self.ctx) - result = compiler.ast_to_object(node) + node = self.parse_and_analyze(test_fn, {}) + node = for_loops.transform(node, self.ctx) - l = [1, 2, 3] - self.assertEqual(test_fn(l), result.test_fn(l)) - l = [] - self.assertEqual(test_fn(l), result.test_fn(l)) + with self.compiled(node) as result: + l = [1, 2, 3] + self.assertEqual(test_fn(l), result.test_fn(l)) + l = [] + self.assertEqual(test_fn(l), result.test_fn(l)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/list_comprehension.py b/tensorflow/contrib/py2tf/converters/list_comprehension.py new file mode 100644 index 0000000000000000000000000000000000000000..e8744831100e4852919b5cd1253b74acea4d790d --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/list_comprehension.py @@ -0,0 +1,80 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Canonicalizing list comprehensions into for and if statements. + +e.g. +result = [x * x for x in xs] + +becomes + +result = [] +for x in xs: + elt = x * x + result.append(elt) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer + + +class ListCompCanonicalizationTransformer(transformer.Base): + """NodeTransformer to canonicalize list comprehensions.""" + + def __init__(self, context): + super(ListCompCanonicalizationTransformer, self).__init__(context) + + def make_update_list_node(self, list_, elt): + return templates.replace('list_.append(elt)', list_=list_, elt=elt)[0] + + def instantiate_list_node(self): + return parser.parse_str('[]').body[0].value + + def visit_Assign(self, node): + if not isinstance(node.value, gast.ListComp): + return node + if len(node.targets) > 1: + raise ValueError('Only support single assignment.') + return self.canonicalize_listcomp(node.targets[0], node.value) + + def canonicalize_listcomp(self, result_node, list_comp_node): + + make_list = templates.replace( + 'list_ = create_list', + list_=result_node, + create_list=self.instantiate_list_node()) + loop_body = self.make_update_list_node(result_node, list_comp_node.elt) + + for gen in reversed(list_comp_node.generators): + for gen_if in reversed(gen.ifs): + loop_body = templates.replace( + 'if test: loop_body', test=gen_if, loop_body=loop_body) + loop_body = templates.replace( + 'for target in iter_: loop_body', + iter_=gen.iter, + target=gen.target, + loop_body=loop_body) + + return make_list + loop_body + + +def transform(node, context): + return ListCompCanonicalizationTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/converters/list_comprehension_test.py b/tensorflow/contrib/py2tf/converters/list_comprehension_test.py new file mode 100644 index 0000000000000000000000000000000000000000..025fac11e41e6771fbb9b80ff3da70dc3ceec73e --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/list_comprehension_test.py @@ -0,0 +1,75 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for list_comprehension module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import list_comprehension +from tensorflow.python.platform import test + + +class ListCompTest(converter_test_base.TestCase): + + def test_basic(self): + + def test_fn(l): + s = [e * e for e in l] + return s + + node = self.parse_and_analyze(test_fn, {}) + node = list_comprehension.transform(node, self.ctx) + + with self.compiled(node) as result: + l = [1, 2, 3] + self.assertEqual(test_fn(l), result.test_fn(l)) + l = [] + self.assertEqual(test_fn(l), result.test_fn(l)) + + def test_multiple_generators(self): + + def test_fn(l): + s = [e * e for sublist in l for e in sublist] + return s + + node = self.parse_and_analyze(test_fn, {}) + node = list_comprehension.transform(node, self.ctx) + + with self.compiled(node) as result: + l = [[1], [2], [3]] + self.assertEqual(test_fn(l), result.test_fn(l)) + l = [] + self.assertEqual(test_fn(l), result.test_fn(l)) + + def test_conds(self): + + def test_fn(l): + s = [e * e for e in l if e > 1] + return s + + node = self.parse_and_analyze(test_fn, {}) + node = list_comprehension.transform(node, self.ctx) + + with self.compiled(node) as result: + l = [1, 2, 3] + self.assertEqual(test_fn(l), result.test_fn(l)) + l = [] + self.assertEqual(test_fn(l), result.test_fn(l)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/converters/logical_expressions.py b/tensorflow/contrib/py2tf/converters/logical_expressions.py index df980d41c9c57e325bee9a1fa870d9c95f46ea41..766aa11efd54ff620ee9e8b845d3dc0da3e20c46 100644 --- a/tensorflow/contrib/py2tf/converters/logical_expressions.py +++ b/tensorflow/contrib/py2tf/converters/logical_expressions.py @@ -23,52 +23,107 @@ from __future__ import print_function import gast -from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer -class LogicalExpressionTransformer(gast.NodeTransformer): +# TODO(mdan): Properly extrack boolean ops according to lazy eval rules. +# Note that this isn't completely safe either, because tensors may have control +# dependencies. +# Note that for loops that should be done after the loop was converted to +# tf.while_loop so that the expanded conditionals are properly scoped. + +# Used to signal that an operand is safe for non-lazy evaluation. +SAFE_BOOLEAN_OPERAND = 'SAFE_BOOLEAN_OPERAND' + + +class LogicalExpressionTransformer(transformer.Base): """Converts logical expressions to corresponding TF calls.""" - def __init__(self): + def __init__(self, context): + super(LogicalExpressionTransformer, self).__init__(context) # TODO(mdan): Look into replacing with bitwise operators instead. self.op_mapping = { - gast.And: 'tf.logical_and', - gast.Or: 'tf.logical_or', - gast.Not: 'tf.logical_not', - gast.Eq: 'tf.equal', + gast.And: 'logical_and', + gast.Eq: 'equal', + gast.Gt: 'greater', + gast.GtE: 'greater_equal', + gast.Lt: 'less', + gast.LtE: 'less_equal', + gast.Not: 'logical_not', + gast.NotEq: 'not_equal', + gast.Or: 'logical_or', + gast.USub: 'negative', } + def _expect_simple_symbol(self, operand): + if isinstance(operand, gast.Name): + return + if anno.hasanno(operand, SAFE_BOOLEAN_OPERAND): + return + raise NotImplementedError( + 'only simple local variables are supported in logical and compound ' + 'comparison expressions; for example, we support "a or b" but not ' + '"a.x or b"; for a workaround, assign the expression to a local ' + 'variable and use that instead, for example "tmp = a.x", "tmp or b"') + + def _matching_tf_op(self, operator): + op_type = type(operator) + mapped_op = self.op_mapping.get(op_type) + if not mapped_op: + raise NotImplementedError('operator %s is not yet supported' % op_type) + return mapped_op + + def _inline_tf_op(self, op_name, args): + template = """ + tf.op_name(args) + """ + replacement = templates.replace(template, op_name=op_name, args=args) + # It's a body with a single expression, we want its value. + n = replacement[0].value + anno.setanno(n, SAFE_BOOLEAN_OPERAND, True) + return n + def visit_Compare(self, node): node = self.generic_visit(node) - if len(node.ops) > 1: - raise NotImplementedError() - cmp_type = type(node.ops[0]) - if cmp_type in self.op_mapping: - tf_function = parser.parse_str(self.op_mapping[cmp_type]).body[0].value - return gast.Call( - func=tf_function, args=[node.left, node.comparators[0]], keywords=[]) - return node + ops_and_comps = list(zip(node.ops, node.comparators)) + left = node.left + op_tree = None + + # Repeated comparisons are converted to conjunctions: + # a < b < c -> a < b and b < c + while ops_and_comps: + op, right = ops_and_comps.pop(0) + binary_comparison = self._inline_tf_op(self._matching_tf_op(op), + (left, right)) + if isinstance(left, gast.Name) and isinstance(right, gast.Name): + anno.setanno(binary_comparison, SAFE_BOOLEAN_OPERAND, True) + if op_tree: + self._expect_simple_symbol(right) + op_tree = self._inline_tf_op('logical_and', + (binary_comparison, op_tree)) + else: + op_tree = binary_comparison + left = right + assert op_tree is not None + return op_tree def visit_UnaryOp(self, node): node = self.generic_visit(node) - if isinstance(node.op, gast.Not): - tf_function = parser.parse_str(self.op_mapping[type( - node.op)]).body[0].value - node = gast.Call(func=tf_function, args=[node.operand], keywords=[]) - return node + return self._inline_tf_op(self._matching_tf_op(node.op), node.operand) def visit_BoolOp(self, node): - # TODO(mdan): A normalizer may be useful here. Use ANF? node = self.generic_visit(node) - tf_function = parser.parse_str(self.op_mapping[type(node.op)]).body[0].value - left = node.values[0] - for i in range(1, len(node.values)): - left = gast.Call( - func=tf_function, args=[left, node.values[i]], keywords=[]) - return left - - -def transform(node): - transformer = LogicalExpressionTransformer() - node = transformer.visit(node) - return node + node_values = node.values + right = node.values.pop() + self._expect_simple_symbol(right) + while node_values: + left = node_values.pop() + self._expect_simple_symbol(left) + right = self._inline_tf_op(self._matching_tf_op(node.op), (left, right)) + return right + + +def transform(node, context): + return LogicalExpressionTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/converters/logical_expressions_test.py b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py index d711065099b24ad814104e6460e6ca551b31b3e6..eb28c309a429f2267cc1ae1f6f65a8cde0ad91b8 100644 --- a/tensorflow/contrib/py2tf/converters/logical_expressions_test.py +++ b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.converters import logical_expressions -from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -33,13 +32,12 @@ class GradientsFunctionTest(converter_test_base.TestCase): return a == b node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node) - result = compiler.ast_to_object(node) - setattr(result, 'tf', math_ops) + node = logical_expressions.transform(node, self.ctx) - with self.test_session() as sess: - self.assertTrue(sess.run(result.test_fn(1, 1))) - self.assertFalse(sess.run(result.test_fn(1, 2))) + with self.compiled(node, math_ops.equal) as result: + with self.test_session() as sess: + self.assertTrue(sess.run(result.test_fn(1, 1))) + self.assertFalse(sess.run(result.test_fn(1, 2))) def test_bool_ops(self): @@ -47,12 +45,12 @@ class GradientsFunctionTest(converter_test_base.TestCase): return (a or b) and (a or b or c) node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node) - result = compiler.ast_to_object(node) - setattr(result, 'tf', math_ops) + node = logical_expressions.transform(node, self.ctx) - with self.test_session() as sess: - self.assertTrue(sess.run(result.test_fn(True, False, True))) + with self.compiled(node, math_ops.logical_or, + math_ops.logical_and) as result: + with self.test_session() as sess: + self.assertTrue(sess.run(result.test_fn(True, False, True))) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/name_scopes.py b/tensorflow/contrib/py2tf/converters/name_scopes.py new file mode 100644 index 0000000000000000000000000000000000000000..c702823fcf047fcad3254318bd323d2b8fddd700 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/name_scopes.py @@ -0,0 +1,52 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Wraps a function body with a `name_scope` of the function name. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer + + +class FunctionNameScopeTransformer(transformer.Base): + """Wrap a function body with a `name_scope` of the function name.""" + + def __init__(self, context): + super(FunctionNameScopeTransformer, self).__init__(context) + self._function_level = 0 + + def visit_FunctionDef(self, node): + self._function_level += 1 + try: + self.generic_visit(node) + finally: + self._function_level -= 1 + scope_name = node.name + if self._function_level == 0 and self.context.owner_type is not None: + scope_name = '{}/{}'.format(self.context.owner_type.__name__, scope_name) + node.body = templates.replace( + 'with tf.name_scope(scope_name): body', + scope_name=gast.Str(scope_name), + body=node.body) + return node + + +def transform(node, context): + return FunctionNameScopeTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/converters/name_scopes_test.py b/tensorflow/contrib/py2tf/converters/name_scopes_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a8ca341602ee5f06dbb812643a58794339d98afe --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/name_scopes_test.py @@ -0,0 +1,92 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for for_canonicalization module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import name_scopes +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.platform import test + + +class FunctionNameScopeTransformer(converter_test_base.TestCase): + + def test_basic_name(self): + + def test_fn(l): + a = 5 + l += a + return l + + node = self.parse_and_analyze(test_fn, {}) + node = name_scopes.transform(node, self.ctx) + + with self.compiled(node, ops.name_scope) as result: + result_op = result.test_fn(constant_op.constant([1, 2, 3])) + self.assertIn('test_fn/', result_op.op.name) + + def test_nested_name(self): + + def test_fn(l): + + def body(i): + return i**2 + + l += [4] + return body(l) + + node = self.parse_and_analyze(test_fn, {}) + node = name_scopes.transform(node, self.ctx) + + with self.compiled(node, ops.name_scope) as result: + result_op = result.test_fn(constant_op.constant([1, 2, 3])) + first_result_input_name = result_op.op.inputs[0].name + second_result_input_name = result_op.op.inputs[1].name + self.assertIn('test_fn/', first_result_input_name) + self.assertNotIn('body/', first_result_input_name) + self.assertIn('test_fn/body/', second_result_input_name) + + def test_class_name(self): + + class TestClass(object): + + def test_fn(self, l): + + def body(i): + return i**2 + + l += [4] + return body(l) + + # Note that 'TestClass' was needed in the namespace here. + node = self.parse_and_analyze( + TestClass, {'TestClass': TestClass}, owner_type=TestClass) + node = name_scopes.transform(node, self.ctx) + + with self.compiled(node, ops.name_scope) as result: + result_op = result.TestClass().test_fn(constant_op.constant([1, 2, 3])) + first_result_input_name = result_op.op.inputs[0].name + second_result_input_name = result_op.op.inputs[1].name + self.assertIn('TestClass/test_fn/', first_result_input_name) + self.assertNotIn('body/', first_result_input_name) + self.assertIn('TestClass/test_fn/body/', second_result_input_name) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py index 7ece8135d674f1af60ced16d6c9976681bb62376..30976b3ec6db5a6607023ac804d9d54cfb296190 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards.py @@ -34,11 +34,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from contextlib import contextmanager - import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import ast_util +from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct import templates from tensorflow.contrib.py2tf.pyct import transformer from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno @@ -64,45 +64,56 @@ class SideEffectGuardTransformer(transformer.Base): def __init__(self, context): super(SideEffectGuardTransformer, self).__init__(context) - self.indent_next = False - self.next_indent_owner = None # pylint:disable=invalid-name def _visit_and_reindent(self, nodes): new_nodes = [] current_dest = new_nodes + alias_map = {} + reindent_requested = False for n in nodes: n = self.visit(n) + # NOTE: the order in which these statements execute is important; in + # particular, watch out for ending up with cycles in the AST. + if alias_map: + n = ast_util.rename_symbols(n, alias_map) if isinstance(n, (list, tuple)): current_dest.extend(n) else: current_dest.append(n) - if self.indent_next: - assert self.next_indent_owner is not None - current_dest.append(self.next_indent_owner) - current_dest = self.next_indent_owner.body - self.next_indent_owner = None - self.indent_next = False - if not current_dest: + if anno.hasanno(n, anno.Basic.INDENT_BLOCK_REMAINDER): + reindent_requested = True + new_dest, new_alias_map = anno.getanno( + n, anno.Basic.INDENT_BLOCK_REMAINDER) + anno.delanno(n, anno.Basic.INDENT_BLOCK_REMAINDER) + new_alias_map.update(alias_map) + alias_map = new_alias_map + current_dest = new_dest + if reindent_requested and not current_dest: # TODO(mdan): There may still be something that could be done. raise ValueError('Unable to insert statement into the computation flow: ' - 'it is not followed by any computation that can we can ' - 'condition on the statement.') + 'it is not followed by any computation which ' + 'the statement could gate.') return new_nodes def visit_FunctionDef(self, node): node.body = self._visit_and_reindent(node.body) return node - def _gate_symbols(self, guard_statement, guarded_args): - # TODO(mdan): This won't work for variables. - template = """ - (args,) = (tf.identity(a) for a in (args,)) - """ - guards = templates.replace(template, args=tuple(guarded_args)) - guard_statement.body.extend(guards) - return guard_statement + def visit_With(self, node): + node.body = self._visit_and_reindent(node.body) + return node + + def visit_If(self, node): + node.body = self._visit_and_reindent(node.body) + node.orelse = self._visit_and_reindent(node.orelse) + return node + + def visit_While(self, node): + node.body = self._visit_and_reindent(node.body) + node.orelse = self._visit_and_reindent(node.orelse) + return node def visit_Expr(self, node): self.generic_visit(node) @@ -111,30 +122,65 @@ class SideEffectGuardTransformer(transformer.Base): # opt.minimize(loss) # or: # tf.py_func(...) - template = """ - with py2tf_utils.control_dependency_on_returns(tf, call): - # TODO(mdan): Also insert ops to re-fetch if variables are involved? - pass # Will be removed below. - """ - # TODO(mdan): This is brittle. Reorganize the mechanism. - statements = templates.replace(template, call=node.value) - control_deps_guard = statements[-1] - control_deps_guard.body = [] # First, attempt to gate future evaluation of args. If that's not # possible, gate all remaining statements (and that may fail too, see # _visit_and_reindent. args_scope = anno.getanno(node.value, NodeAnno.ARGS_SCOPE) - guarded_args = tuple(args_scope.used & (args_scope.parent.modified - | args_scope.parent.returned)) + # NOTE: We can't guard object attributes because they may not be writable. + # In addition, avoid renaming well-known names. + # TODO(mdan): Move these names into config. + unguarded_names = (qual_names.QN('self'), qual_names.QN('tf')) + guarded_args = tuple(s for s in args_scope.used + if not s.is_composite() and s not in unguarded_names) + + # TODO(mdan): Include all arguments which depended on guarded_args too. + # For example, the following will still cause a race: + # tf.assign(a, a + 1) + # b = a + 1 + # tf.assign(a, a + 1) # Control deps here should include `b` + # c = b + 1 + # Or maybe we should just raise an "unsafe assign" error? + if guarded_args: - node = tuple(statements[:-1]) + ( - self._gate_symbols(control_deps_guard, guarded_args),) + # The aliases may need new names to avoid incorrectly making them local. + # TODO(mdan): This is brutal. It will even rename modules - any fix? + need_alias = tuple( + s for s in guarded_args if s not in args_scope.parent.modified) + aliased_new_names = tuple( + qual_names.QN( + self.context.namer.new_symbol( + s.ssf(), args_scope.parent.referenced)) for s in need_alias) + alias_map = dict(zip(need_alias, aliased_new_names)) + if len(guarded_args) == 1: + s, = guarded_args + aliased_guarded_args = alias_map.get(s, s) + else: + aliased_guarded_args = gast.Tuple( + [alias_map.get(s, s).ast() for s in guarded_args], None) + + template = """ + with py2tf_utils.control_dependency_on_returns(call): + aliased_guarded_args = py2tf_utils.alias_tensors(guarded_args) + """ + control_deps_guard = templates.replace( + template, + call=node.value, + aliased_guarded_args=aliased_guarded_args, + guarded_args=guarded_args)[-1] else: - node = tuple(statements[:-1]) - # The mechanism will insert the guard statement later. - self.indent_next = True - self.next_indent_owner = control_deps_guard + alias_map = {} + + template = """ + with py2tf_utils.control_dependency_on_returns(call): + pass + """ + control_deps_guard = templates.replace(template, call=node.value)[-1] + control_deps_guard.body = [] + + node = control_deps_guard + anno.setanno(node, anno.Basic.INDENT_BLOCK_REMAINDER, + (node.body, alias_map)) return node # pylint:enable=invalid-name diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py index dea09ecc3ff8c566e7bd6c440bc1a146aaf15121..463db2e770213ba9636d2537b095a77dece5d8f6 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py @@ -18,46 +18,147 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.converters import side_effect_guards -from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test -class TestNamer(side_effect_guards.SymbolNamer): +class SideEffectGuardsTest(converter_test_base.TestCase): - def new_symbol(self, name_root, _): - return name_root + def test_side_effect_on_return_only_variable(self): + tf = None -class SideEffectGuardsTest(converter_test_base.TestCase): + def test_fn(a): + tf.assign(a, a + 1) + return a + + node = self.parse_and_analyze(test_fn, {}) + node = side_effect_guards.transform(node, self.ctx) + + with self.compiled(node, state_ops.assign) as result: + self.assertEqual(len(node.body[0].body), 1) + with self.test_session() as sess: + v = variables.Variable(2) + sess.run(v.initializer) + # NOTE: We don't expect the assignment to execute in this case, because + # variables cannot be reliably guarded. + self.assertEqual(2, sess.run(result.test_fn(v))) + + def test_side_effect_on_used_variable(self): - def test_transform(self): + tf = None def test_fn(a): - state_ops.assign(a, a + 1) + tf.assign(a, a + 1) + return a + 1 + + node = self.parse_and_analyze(test_fn, {}) + node = side_effect_guards.transform(node, self.ctx) + + with self.compiled(node, state_ops.assign) as result: + self.assertEqual(len(node.body[0].body), 1) + with self.test_session() as sess: + v = variables.Variable(2) + sess.run(v.initializer) + # NOTE: Unlike test_side_effect_on_return_only_variable, the variable + # was used in the local scope and so we could catch the assign's side + # effect. + self.assertEqual(4, sess.run(result.test_fn(v))) + + def test_side_effect_on_tensor(self): + + tf = None + + def test_fn(a): + tf.Assert(a > 0, ['expected in throw']) return a - node = self.parse_and_analyze( - test_fn, {'state_ops': state_ops}, namer=TestNamer()) + node = self.parse_and_analyze(test_fn, {}) node = side_effect_guards.transform(node, self.ctx) - result = compiler.ast_to_object(node) - setattr(result, 'state_ops', state_ops) - setattr(result, 'py2tf_utils', utils) - - # TODO(mdan): Configure the namespaces instead of doing these hacks. - ops.identity = array_ops.identity - setattr(result, 'tf', ops) - - with self.test_session() as sess: - v = variables.Variable(2) - sess.run(v.initializer) - self.assertEqual(3, sess.run(result.test_fn(v))) + + with self.compiled(node, control_flow_ops.Assert) as result: + self.assertEqual(len(node.body[0].body), 1) + with self.test_session() as sess: + # NOTE: In this case we can also capture the side effect because the + # argument is a tensor ans we can wrap it inside an identity. + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + 'expected in throw'): + sess.run(result.test_fn(constant_op.constant(-1))) + + def test_multiline_block(self): + + tf = None + + def test_fn(a): + tf.assign(a, a + 1) + b = a + 1 + tf.assign(a, b + 1) + c = b + 1 + d = c + 1 + return d + + node = self.parse_and_analyze(test_fn, {}) + node = side_effect_guards.transform(node, self.ctx) + + with self.compiled(node, state_ops.assign) as result: + self.assertEqual(len(node.body[0].body), 1) + with self.test_session() as sess: + v = variables.Variable(2) + sess.run(v.initializer) + self.assertEqual(6, sess.run(result.test_fn(v))) + + def test_multiline_nested_block(self): + + tf = None + + def test_fn(a): + with tf.name_scope('foo'): + tf.assign(a, a + 1) + b = a + 1 + c = b + 1 + d = c + 1 + return d + + node = self.parse_and_analyze(test_fn, {}) + node = side_effect_guards.transform(node, self.ctx) + + with self.compiled(node, state_ops.assign, ops.name_scope) as result: + self.assertEqual(len(node.body[0].body[0].body), 1) + with self.test_session() as sess: + v = variables.Variable(2) + sess.run(v.initializer) + self.assertEqual(6, sess.run(result.test_fn(v))) + + def test_multiline_block_unsafe(self): + + tf = None + + def test_fn(a): + tf.assign(a, a + 1) + b = a + 1 + tf.assign(a, a + 1) + c = b + 1 + d = c + 1 + return d + + node = self.parse_and_analyze(test_fn, {}) + node = side_effect_guards.transform(node, self.ctx) + + with self.compiled(node, state_ops.assign) as result: + self.assertEqual(len(node.body[0].body), 1) + with self.test_session() as sess: + v = variables.Variable(2) + sess.run(v.initializer) + # NOTE: This intentionally highlights the flakiness. The test should be + # tightened down once that is solved. + self.assertTrue(sess.run(result.test_fn(v)) in (6, 7)) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/single_return.py b/tensorflow/contrib/py2tf/converters/single_return.py new file mode 100644 index 0000000000000000000000000000000000000000..1194b98f5ebeffa79a41fc3b32aa79ffd8cc407b --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/single_return.py @@ -0,0 +1,317 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Canonicalizes functions with multiple returns to use just one.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import ast_util +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer +from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno + + +# TODO(mdan): Move this logic into transformer_base. +class BodyVisitor(transformer.Base): + """Walks breadth- or depth-first the list-of-nodes bodies of AST nodes.""" + + def __init__(self, context, depth_first=False): + self.depth_first = depth_first + self.changes_made = False + super(BodyVisitor, self).__init__(context) + + def visit_nodelist(self, nodelist): + for node in nodelist: + if isinstance(node, list): + node = self.visit_nodelist(node) + else: + node = self.generic_visit(node) + return nodelist + + def visit_If(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_For(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_While(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_Try(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + node.finalbody = self.visit_nodelist(node.finalbody) + for i in range(len(node.handlers)): + node.handlers[i].body = self.visit_nodelist(node.handlers[i].body) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_With(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_FunctionDef(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + self.generic_visit(node) + if not self.depth_first: + node = self.generic_visit(node) + return node + + +class FoldElse(BodyVisitor): + + def visit_nodelist(self, nodelist): + for i in range(len(nodelist)): + node = nodelist[i] + if isinstance(node, gast.If): + true_branch_returns = isinstance(node.body[-1], gast.Return) + false_branch_returns = len(node.orelse) and isinstance( + node.orelse[-1], gast.Return) + # If the last node in the if body is a return, + # then every line after this if statement effectively + # belongs in the else. + if true_branch_returns and not false_branch_returns: + for j in range(i + 1, len(nodelist)): + nodelist[i].orelse.append(ast_util.copy_clean(nodelist[j])) + if nodelist[i + 1:]: + self.changes_made = True + return nodelist[:i + 1] + elif not true_branch_returns and false_branch_returns: + for j in range(i + 1, len(nodelist)): + nodelist[i].body.append(ast_util.copy_clean(nodelist[j])) + if nodelist[i + 1:]: + self.changes_made = True + return nodelist[:i + 1] + elif true_branch_returns and false_branch_returns: + if nodelist[i + 1:]: + raise ValueError( + 'Unreachable code after conditional where both branches return.' + ) + return nodelist + elif isinstance(node, gast.Return) and nodelist[i + 1:]: + raise ValueError( + 'Cannot have statements after a return in the same basic block') + return nodelist + + +def contains_return(node): + for n in gast.walk(node): + if isinstance(n, gast.Return): + return True + return False + + +class LiftReturn(transformer.Base): + """Move return statements out of If and With blocks.""" + + def __init__(self, context): + self.changes_made = False + self.common_return_name = None + super(LiftReturn, self).__init__(context) + + def visit_If(self, node): + # Depth-first traversal of if statements + node = self.generic_visit(node) + + # We check if both branches return, and if so, lift the return out of the + # conditional. We don't enforce that the true and false branches either + # both return or both do not, because FoldElse might move a return + # into a branch after this transform completes. FoldElse and LiftReturn + # are alternately run until the code reaches a fixed point. + true_branch_returns = isinstance(node.body[-1], gast.Return) + false_branch_returns = len(node.orelse) and isinstance( + node.orelse[-1], gast.Return) + if true_branch_returns and false_branch_returns: + node.body[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.body[-1].value)[0] + node.orelse[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.orelse[-1].value)[0] + return_node = templates.replace('return a', a=self.common_return_name)[0] + self.changes_made = True + return [node, return_node] + else: + return node + + def visit_With(self, node): + # Depth-first traversal of syntax + node = self.generic_visit(node) + + # If the with statement returns, lift the return + if isinstance(node.body[-1], gast.Return): + node.body[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.body[-1].value)[0] + return_node = templates.replace('return a', a=self.common_return_name)[0] + node = self.generic_visit(node) + self.changes_made = True + return [node, return_node] + else: + return node + + def visit_FunctionDef(self, node): + # Ensure we're doing depth-first traversal + last_return_name = self.common_return_name + body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) + referenced_names = body_scope.referenced + self.common_return_name = self.context.namer.new_symbol( + 'return_', referenced_names) + node = self.generic_visit(node) + self.common_return_name = last_return_name + return node + + +class DetectReturnInUnsupportedControlFlow(gast.NodeVisitor): + """Throws an error if code returns inside loops or try/except.""" + + # First, throw an error if we detect a return statement in a loop. + # TODO(alexbw): we need to learn to handle returns inside a loop, + # but don't currently have the TF constructs to do so (need something + # that looks vaguely like a goto). + + def __init__(self): + self.cant_return = False + super(DetectReturnInUnsupportedControlFlow, self).__init__() + + def visit_While(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_For(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Try(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Return(self, node): + if self.cant_return: + raise ValueError( + 'Pyflow currently does not support `return` statements in loops. ' + 'Try assigning to a variable in the while loop, and returning ' + 'outside of the loop') + + +class DetectReturnInConditional(gast.NodeVisitor): + """Assert that no return statements are present in conditionals.""" + + def __init__(self): + self.cant_return = False + super(DetectReturnInConditional, self).__init__() + + def visit_If(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Return(self, node): + if self.cant_return: + raise ValueError( + 'After transforms, a conditional contained a `return `statement, ' + 'which is not allowed. This is a bug, and should not happen.') + + +class DetectReturnInFunctionDef(gast.NodeVisitor): + + def visit_FunctionDef(self, node): + self.generic_visit(node) + if not contains_return(node): + raise ValueError( + 'Each function definition should contain at least one return.') + + +def transform(node, context): + """Ensure a function has only a single return. + + This transforms an AST node with multiple returns successively into containing + only a single return node. + There are a few restrictions on what we can handle: + - An AST being transformed must contain at least one return. + - No returns allowed in loops. We have to know the type of the return value, + and we currently don't have either a type inference system to discover it, + nor do we have a mechanism for late type binding in TensorFlow. + - After all transformations are finished, a Return node is not allowed inside + control flow. If we were unable to move a return outside of control flow, + this is an error. + + Args: + node: an AST node to transform + context: a context object + + Returns: + new_node: an AST with a single return value + + Raises: + ValueError: if the AST is structured so that we can't perform the + transform. + """ + # Make sure that the function has at least one return statement + # TODO(alexbw): turning off this assertion for now -- + # we need to not require this in e.g. class constructors. + # DetectReturnInFunctionDef().visit(node) + + # Make sure there's no returns in unsupported locations (loops, try/except) + DetectReturnInUnsupportedControlFlow().visit(node) + + while True: + + # Try to lift all returns out of if statements and with blocks + lr = LiftReturn(context) + node = lr.visit(node) + changes_made = lr.changes_made + fe = FoldElse(context) + node = fe.visit(node) + changes_made = changes_made or fe.changes_made + + if not changes_made: + break + + # Make sure we've scrubbed all returns from conditionals + DetectReturnInConditional().visit(node) + + return node diff --git a/tensorflow/contrib/py2tf/converters/single_return_test.py b/tensorflow/contrib/py2tf/converters/single_return_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea7a9d6d3e25c8dafd8f211994c8fe99bd0e781 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/single_return_test.py @@ -0,0 +1,189 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for single_return module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import single_return +from tensorflow.python.framework.ops import name_scope +from tensorflow.python.platform import test + + +class SingleReturnTest(converter_test_base.TestCase): + + def compiled_fn(self, test_fn, *args): + node = self.parse_and_analyze(test_fn, {}) + node = single_return.transform(node, self.ctx) + module = self.compiled(node, *args) + return module + + def test_noop(self): + # Noop + def test_fn(x): + return x + + with self.compiled_fn(test_fn) as result: + self.assertEqual(test_fn(2.0), result.test_fn(2.0)) + + def test_return_expression(self): + # ANF + def test_fn(x): + return x * x + + with self.compiled_fn(test_fn) as result: + x = 2 + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_merge(self): + # Simple merge + def test_fn(x): + if x > 0: + return x + else: + return x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_orphan_branch(self): + + def test_fn(x): + if x > 0: + return x + + with self.assertRaises(ValueError): + self.compiled_fn(test_fn) + + def test_lift_body_into_false_branch(self): + + def test_fn(x): + if x > 0: + return x + return x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_lift_body_into_true_branch(self): + + def test_fn(x): + if x < 0: + x *= x + else: + # TODO(alexbw): linter bug here that requires us suppress this warning. + return x # pylint: disable=undefined-loop-variable + return x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_nested_if(self): + + def test_fn(x): + if x > 0: + if x < 5: + return x + else: + return x * x + else: + return x * x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2, 5]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_context_manager(self): + + def test_fn(x): + + with name_scope(''): + return x * x + + with self.compiled_fn(test_fn) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_context_manager_in_conditional(self): + + def test_fn(x): + if x > 0: + with name_scope(''): + return x * x + else: + return x + + with self.compiled_fn(test_fn, name_scope) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def text_conditional_in_context_manager(self): + + def test_fn(x): + with name_scope(''): + if x > 0: + return x * x + else: + return x + + with self.compiled_fn(test_fn) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_no_return(self): + + def test_fn(x): + x *= x + + with self.compiled_fn(test_fn) as result: + self.assertEqual(test_fn(2), result.test_fn(2)) + + def test_nested_functiondefs(self): + + def test_fn(x): + + def inner_fn(y): + if y > 0: + return y * y + else: + return y + + return inner_fn(x) + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_loop(self): + + def test_fn(x): + for _ in range(10): + return x + return x + + with self.assertRaises(ValueError): + self.compiled_fn(test_fn) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/impl/BUILD b/tensorflow/contrib/py2tf/impl/BUILD index 22f0c25cabcd44261c0b42091b50969500db2193..90ffabbc9bf4524ec2ebf54b6dd847bd8768a486 100644 --- a/tensorflow/contrib/py2tf/impl/BUILD +++ b/tensorflow/contrib/py2tf/impl/BUILD @@ -28,6 +28,7 @@ py_library( "//tensorflow/contrib/py2tf/converters", "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", + "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", "@six_archive//:six", ], @@ -39,6 +40,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":impl", + "//tensorflow/contrib/py2tf/utils", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py index 85d40f31580d156bf719e059bb3580a068595cb5..883b304089024363f41cabde2cb74c49f01ae836 100644 --- a/tensorflow/contrib/py2tf/impl/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -26,7 +26,10 @@ import six from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.impl import conversion from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.utils import builtins +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect # TODO(mdan): Properly document the type hints. @@ -83,7 +86,7 @@ def convert_inline(f, *args, **kwargs): return convert(arg_value_hints)(f)(*args, **kwargs) -def convert(recursive=False, arg_types=None): +def convert(recursive=False, verbose=False, arg_types=None): """Decorator that compiles a function to graph mode. The decorator is dynamic - invoking compilation whenever the decorated @@ -92,6 +95,7 @@ def convert(recursive=False, arg_types=None): Args: recursive: Whether to recusrively convert any functions that the decorator function may call. + verbose: Whether to output the compiled code in the logs. arg_types: See to_graph. Returns: @@ -108,27 +112,7 @@ def convert(recursive=False, arg_types=None): @wraps(f) def wrapper(*args, **kwargs): - """Wrapper that calls the compiled version of the wrapped function.""" - partial_types = () - arg_values = {} - arg_names = tf_inspect.getargspec(f)[0] - for name, arg in zip(arg_names, args): - arg_values[name] = arg - arg_class = arg.__class__ - # If arg_value_hints specifies any name, use that instead. - if name not in arg_types: - arg_types[name] = (arg_class.__name__, arg_class) - if name == 'self' and tf_inspect.isclass(arg_class): - # Annotated methods need to specify that their owner type is partial, - # otherwise other members they call will not be converted. - partial_types = (arg_class,) - wrapped = to_graph( - f, - recursive=recursive, - arg_values=arg_values, - arg_types=arg_types, - partial_types=partial_types) - return wrapped(*args, **kwargs) + return converted_call(f, recursive, verbose, arg_types, *args, **kwargs) # Sometimes the decorator is just desugared, making it impossible to detect. # This attribute makes detection easier. @@ -138,8 +122,81 @@ def convert(recursive=False, arg_types=None): return decorator +def converted_call(f, recursive, verbose, arg_types, *args, **kwargs): + """Compiles a function call inline.""" + # TODO(mdan): This needs cleanup. + # In particular, we may want to avoid renaming functions altogether. + + if conversion.is_whitelisted_for_graph(f): + return f(*args, **kwargs) + + unknown_arg_value = object() # Sentinel for arguments of unknown value + + if tf_inspect.isbuiltin(f): + return builtins.dynamic_builtin(f, *args, **kwargs) + + if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): + # Regular functions + target_entity = f + arg_map_target = f + effective_args = args + f_class = inspect_utils.getmethodclass(f) + + if f_class is not None: + partial_types = (f_class,) + else: + partial_types = () + + elif tf_inspect.isclass(f): + # Constructors + target_entity = f + arg_map_target = f.__init__ + effective_args = (unknown_arg_value,) + args + partial_types = () + + elif hasattr(f, '__call__') and hasattr(f, '__class__'): + # Callable objects + target_entity = f.__call__ + arg_map_target = f.__call__ + effective_args = (f,) + args + partial_types = (f.__class__,) + + else: + NotImplementedError('unknown callable type "%s"' % type(f)) + + arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs) + for name, arg in arg_values.items(): + if arg is unknown_arg_value: + continue + arg_class = arg.__class__ + # If arg_value_hints specifies any name, use that instead. + if name not in arg_types: + arg_types[name] = (arg_class.__name__, arg_class) + + # When called from within a decorator, this is the only indication that + # the function is a method - it appears that the decorator is applied + # before the method is bound. + if not partial_types: + if 'self' in arg_values: + if tf_inspect.isclass(arg_values['self'].__class__): + partial_types = (arg_values['self'].__class__,) + elif 'cls' in arg_values: + if tf_inspect.isclass(arg_values['cls']): + partial_types = (arg_values['cls'],) + + converted_f = to_graph( + target_entity, + recursive=recursive, + verbose=verbose, + arg_values=arg_values, + arg_types=arg_types, + partial_types=partial_types) + return converted_f(*effective_args, **kwargs) + + def to_graph(e, recursive=True, + verbose=False, arg_values=None, arg_types=None, partial_types=None): @@ -155,6 +212,7 @@ def to_graph(e, e: A Python entity. recursive: Whether to recusrively convert any functions that the decorator function may call. + verbose: Whether to output the compiled code in the logs. arg_values: A dict containing value hints for symbols like function parameters. arg_types: A dict containing type hints for symbols like function @@ -170,22 +228,26 @@ def to_graph(e, conversion_map = conversion.ConversionMap( recursive=recursive, nocompile_decorators=(convert, graph_ready, convert_inline), - partial_types=partial_types) + partial_types=partial_types, + api_module=tf_inspect.getmodule(to_graph)) _, name = conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) module = gast.Module([]) for import_line in config.COMPILED_IMPORT_STATEMENTS: - module.body.append(parser.parse_str(import_line)) + module.body.extend(parser.parse_str(import_line).body) for dep in conversion_map.dependency_cache.values(): module.body.append(dep) - compiled_node = compiler.ast_to_object(module) + compiled_node, compiled_src = compiler.ast_to_object(module) # The compiled code should see everything the entry function saw. # TODO(mdan): This might not work well if the call tree spans modules? if tf_inspect.isfunction(e): - compiled_node.__dict__.update(six.get_function_globals(e)) - + compiled_node.__dict__.update(inspect_utils.getnamespace(e)) compiled_fn = getattr(compiled_node, name) + + if verbose: + logging.info('Compiled output of %s:\n\n%s\n', e, compiled_src) + return compiled_fn @@ -213,7 +275,8 @@ def to_code(e, conversion_map = conversion.ConversionMap( recursive=recursive, nocompile_decorators=(convert, graph_ready, convert_inline), - partial_types=partial_types) + partial_types=partial_types, + api_module=tf_inspect.getmodule(to_graph)) conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) imports = '\n'.join(config.COMPILED_IMPORT_STATEMENTS) diff --git a/tensorflow/contrib/py2tf/impl/api_test.py b/tensorflow/contrib/py2tf/impl/api_test.py index dbd079a3ca6d09824f24c6f0bd7647758d3a5552..13f8e66018920a5b13f8bd3f00c67d3bbdd519aa 100644 --- a/tensorflow/contrib/py2tf/impl/api_test.py +++ b/tensorflow/contrib/py2tf/impl/api_test.py @@ -18,21 +18,26 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl import api from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.framework import constant_op -from tensorflow.python.ops import math_ops from tensorflow.python.platform import test +tf = utils.fake_tf() + + class ApiTest(test.TestCase): def setUp(self): - config.DEFAULT_UNCOMPILED_MODULES.add((math_ops.__name__,)) config.COMPILED_IMPORT_STATEMENTS = ( - 'from tensorflow.python.ops ' - 'import control_flow_ops as tf',) + 'from __future__ import print_function', + 'from tensorflow.contrib.py2tf import utils as ' + 'py2tf_utils', + 'tf = py2tf_utils.fake_tf()' + ) def test_decorator_recurses(self): @@ -45,7 +50,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -61,11 +66,11 @@ class ApiTest(test.TestCase): class TestClass(object): def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=False) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -82,11 +87,11 @@ class ApiTest(test.TestCase): @api.graph_ready def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -109,7 +114,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -131,7 +136,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= api.convert_inline(self.called_member, a) return x @@ -147,11 +152,11 @@ class ApiTest(test.TestCase): class TestClass(object): def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= api.graph_ready(self.called_member(a)) return x @@ -164,7 +169,7 @@ class ApiTest(test.TestCase): def test_to_graph_basic(self): def test_fn(x, s): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= 2 return x @@ -176,14 +181,14 @@ class ApiTest(test.TestCase): def test_to_code_basic(self): def test_fn(x, s): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x /= 2 return x compiled_code = api.to_code(test_fn) # Just check for some key words and that it is parseable Python code. - self.assertRegexpMatches(compiled_code, 'tf\\.while_loop') + self.assertRegexpMatches(compiled_code, 'py2tf_utils\\.run_while') self.assertIsNotNone(parser.parse_str(compiled_code)) diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py index 6525806a0933dd9f0a237e278bb70b88346bea27..bdbc6663dd65ed66c55ad2d2e52428084bbea219 100644 --- a/tensorflow/contrib/py2tf/impl/config.py +++ b/tensorflow/contrib/py2tf/impl/config.py @@ -18,6 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils + + PYTHON_LITERALS = { 'None': None, 'False': False, @@ -27,14 +30,21 @@ PYTHON_LITERALS = { DEFAULT_UNCOMPILED_MODULES = set(( ('tensorflow',), + (utils.__name__,), + + # All of tensorflow's subpackages. Unlike the root tf module, they don't + # have well-known names. Not refering to the module directly to avoid + # circular imports. + (utils.__name__[:-len('.contrib.py2tf.utils')],), )) NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) # TODO(mdan): Also allow controlling the generated names (for testability). -# TODO(mdan): Verify that these names are not hidden by generated code. -# TODO(mdan): Make sure copybara renames the reference below. COMPILED_IMPORT_STATEMENTS = ( + 'from __future__ import print_function', 'import tensorflow as tf', + 'from tensorflow.contrib.py2tf.impl import api as ' + 'py2tf_api', 'from tensorflow.contrib.py2tf import utils as ' 'py2tf_utils') diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py index ff4f159975578dada45542df39f7ebbb61dd2e36..96e7b1a53e0442adf8c92a88c20c15e2670a8e46 100644 --- a/tensorflow/contrib/py2tf/impl/conversion.py +++ b/tensorflow/contrib/py2tf/impl/conversion.py @@ -19,26 +19,30 @@ from __future__ import division from __future__ import print_function import gast -import six +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.converters import asserts -from tensorflow.contrib.py2tf.converters import break_canonicalization +from tensorflow.contrib.py2tf.converters import break_statements from tensorflow.contrib.py2tf.converters import builtin_functions from tensorflow.contrib.py2tf.converters import call_trees -from tensorflow.contrib.py2tf.converters import continue_canonicalization +from tensorflow.contrib.py2tf.converters import continue_statements from tensorflow.contrib.py2tf.converters import control_flow from tensorflow.contrib.py2tf.converters import decorators -from tensorflow.contrib.py2tf.converters import for_canonicalization +from tensorflow.contrib.py2tf.converters import for_loops from tensorflow.contrib.py2tf.converters import logical_expressions +from tensorflow.contrib.py2tf.converters import name_scopes from tensorflow.contrib.py2tf.converters import side_effect_guards +from tensorflow.contrib.py2tf.converters import single_return from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.impl import naming from tensorflow.contrib.py2tf.pyct import context +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct.static_analysis import activity from tensorflow.contrib.py2tf.pyct.static_analysis import live_values from tensorflow.contrib.py2tf.pyct.static_analysis import type_info +from tensorflow.contrib.py2tf.utils import type_hints from tensorflow.python.util import tf_inspect @@ -46,7 +50,9 @@ from tensorflow.python.util import tf_inspect class ConversionMap(object): - """ConversionMaps keep track of converting function hierarchies. + """ConversionMap keeps track of converting function hierarchies. + + This object is mutable, and is updated as functions are converted. Attributes: recursive: Whether to recusrively convert any functions that the decorator @@ -55,18 +61,26 @@ class ConversionMap(object): off. dependency_cache: dict[object]: ast; maps original entities to their converted AST + additional_imports: set(object); additional entities which for any reason + cannot be attached after loading and need to be explicitly imported + in the generated code name_map: dict[string]: string; maps original entities to the name of their converted counterparts + api_module: A reference to the api module. The reference needs to be passed + to avoid circular dependencies. """ # TODO(mdan): Rename to ConversionContext, and pull in additional flags. - def __init__(self, recursive, nocompile_decorators, partial_types): + def __init__(self, recursive, nocompile_decorators, partial_types, + api_module): self.recursive = recursive self.nocompile_decorators = nocompile_decorators self.partial_types = partial_types if partial_types else () self.dependency_cache = {} + self.additional_imports = set() self.name_map = {} + self.api_module = api_module def new_namer(self, namespace): return naming.Namer(namespace, self.recursive, self.name_map, @@ -87,6 +101,24 @@ class ConversionMap(object): self.dependency_cache[original_entity] = converted_ast +def is_whitelisted_for_graph(o): + """Check whether an entity is whitelisted for use in graph mode. + + Examples of whitelisted entities include all members of the tensorflow + package. + + Args: + o: A Python entity. + Returns: + Boolean + """ + m = tf_inspect.getmodule(o) + for prefix, in config.DEFAULT_UNCOMPILED_MODULES: + if m.__name__.startswith(prefix): + return True + return False + + def entity_to_graph(o, conversion_map, arg_values, arg_types): """Compile a Python entity into equivalent TensorFlow. @@ -126,14 +158,20 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): conversion_map.add_to_cache(o, node) if conversion_map.recursive: - for obj in conversion_map.name_map.keys(): - if obj not in conversion_map.dependency_cache: - if (hasattr(obj, 'im_class') and - getattr(obj, 'im_class') not in conversion_map.partial_types): - # Class members are converted with their objects, unless they're - # only converted partially. - continue - entity_to_graph(obj, conversion_map, {}, {}) + while True: + candidate = None + for obj in conversion_map.name_map.keys(): + if obj not in conversion_map.dependency_cache: + candidate = obj + break + if candidate is None: + break + if (hasattr(candidate, 'im_class') and + getattr(candidate, 'im_class') not in conversion_map.partial_types): + # Class members are converted with their objects, unless they're + # only converted partially. + continue + entity_to_graph(candidate, conversion_map, {}, {}) return node, new_name @@ -141,11 +179,12 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): def class_to_graph(c, conversion_map): """Specialization of `entity_to_graph` for classes.""" converted_members = {} - members = tf_inspect.getmembers(c, predicate=tf_inspect.ismethod) + method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m) + members = tf_inspect.getmembers(c, predicate=method_filter) if not members: - raise ValueError('Cannot convert %s: it has no member methods.') + raise ValueError('Cannot convert %s: it has no member methods.' % c) - class_globals = None + class_namespace = None for _, m in members: node, _ = function_to_graph( m, @@ -154,37 +193,49 @@ def class_to_graph(c, conversion_map): arg_types={'self': (c.__name__, c)}, owner_type=c) # TODO(mdan): Do not assume all members have the same view of globals. - if class_globals is None: - class_globals = six.get_function_globals(m) + if class_namespace is None: + class_namespace = inspect_utils.getnamespace(m) converted_members[m] = node - namer = conversion_map.new_namer(class_globals) + namer = conversion_map.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) node = gast.ClassDef( class_name, bases=[], keywords=[], - body=converted_members.values(), + body=list(converted_members.values()), decorator_list=[]) return node, class_name +def _add_self_references(namespace, api_module): + """Self refs are only required for analysis and are not used directly.""" + # Manually add the utils namespace which may be used from generated code. + if 'py2tf_util' not in namespace: + namespace['py2tf_utils'] = utils + elif namespace['py2tf_utils'] != utils: + raise ValueError( + 'The module name "py2tf_utils" is reserved and may not be used.') + + # We also make reference to the api module for dynamic conversion, but + # to avoid circular references we don't import it here. + if 'py2tf_api' not in namespace: + namespace['py2tf_api'] = api_module + elif namespace['py2tf_api'] != api_module: + raise ValueError( + 'The module name "py2tf_api" is reserved and may not be used.') + + def function_to_graph(f, conversion_map, arg_values, arg_types, owner_type=None): """Specialization of `entity_to_graph` for callable functions.""" node, source = parser.parse_entity(f) node = node.body[0] - namespace = six.get_function_globals(f) - - # This is needed for non-global functions. - closure = six.get_function_closure(f) - if closure: - for e in closure: - if callable(e.cell_contents): - fn = e.cell_contents - namespace[fn.__name__] = fn + namespace = inspect_utils.getnamespace(f) + _add_self_references(namespace, conversion_map.api_module) namer = conversion_map.new_namer(namespace) + ctx = context.EntityContext( namer=namer, source_code=source, @@ -192,8 +243,10 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, namespace=namespace, arg_values=arg_values, arg_types=arg_types, - recursive=conversion_map.recursive) - node = node_to_graph(node, ctx, conversion_map.nocompile_decorators) + owner_type=owner_type, + recursive=conversion_map.recursive, + type_annotation_func=type_hints.set_element_type) + node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators) # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type) @@ -204,6 +257,9 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, node.name = new_name conversion_map.update_name_map(namer) + # TODO(mdan): Use this at compilation. + conversion_map.additional_imports.update(deps) + return node, new_name @@ -242,26 +298,28 @@ def node_to_graph(node, ctx, nocompile_decorators): # to re-run the analysis. node = _static_analysis_pass(node, ctx) + # Past this point, line numbers are no longer accurate so we ignore the # source. # TODO(mdan): Is it feasible to reconstruct intermediate source code? ctx.source_code = None - node = decorators.transform(node, nocompile_decorators) - node = break_canonicalization.transform(node, ctx) + node, deps = decorators.transform(node, nocompile_decorators) + node = break_statements.transform(node, ctx) node = asserts.transform(node, ctx) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. - node = continue_canonicalization.transform(node, ctx) + node = continue_statements.transform(node, ctx) ctx.namespace['len'] = len node = _static_analysis_pass(node, ctx) - node = for_canonicalization.transform(node, ctx) - # for_canonicalization may insert new global references. + node = single_return.transform(node, ctx) + + node = _static_analysis_pass(node, ctx) + node = for_loops.transform(node, ctx) + # for_loops may insert new global references. node = builtin_functions.transform(node, ctx) - # builtin_functions may insert new global references. - ctx.namespace['print'] = print node = _static_analysis_pass(node, ctx) node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES, @@ -270,7 +328,8 @@ def node_to_graph(node, ctx, nocompile_decorators): # control_flow may create new symbols and change scopes. node = _static_analysis_pass(node, ctx) - node = logical_expressions.transform(node) + node = logical_expressions.transform(node, ctx) node = side_effect_guards.transform(node, ctx) + node = name_scopes.transform(node, ctx) - return node + return node, deps diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py index 3888958f19b9fa13b759924c5188722e500e30a1..9ff256aace7a0e7ac5e7ac07e580b8bed7d8df6f 100644 --- a/tensorflow/contrib/py2tf/impl/conversion_test.py +++ b/tensorflow/contrib/py2tf/impl/conversion_test.py @@ -20,15 +20,26 @@ from __future__ import print_function import gast +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl import conversion +from tensorflow.python.framework import constant_op from tensorflow.python.platform import test class ConversionTest(test.TestCase): + def test_is_whitelisted_for_graph(self): + + def test_fn(): + return constant_op.constant(1) + + self.assertFalse(conversion.is_whitelisted_for_graph(test_fn)) + self.assertTrue(conversion.is_whitelisted_for_graph(utils)) + self.assertTrue(conversion.is_whitelisted_for_graph(constant_op.constant)) + def test_entity_to_graph_unsupported_types(self): with self.assertRaises(ValueError): - conversion_map = conversion.ConversionMap(True, (), ()) + conversion_map = conversion.ConversionMap(True, (), (), None) conversion.entity_to_graph('dummy', conversion_map, None, None) def test_entity_to_graph_callable(self): @@ -36,7 +47,7 @@ class ConversionTest(test.TestCase): def f(a): return a - conversion_map = conversion.ConversionMap(True, (), ()) + conversion_map = conversion.ConversionMap(True, (), (), None) ast, new_name = conversion.entity_to_graph(f, conversion_map, None, None) self.assertTrue(isinstance(ast, gast.FunctionDef), ast) self.assertEqual('tf__f', new_name) @@ -49,14 +60,17 @@ class ConversionTest(test.TestCase): def f(a): return g(a) - conversion_map = conversion.ConversionMap(True, (), ()) + conversion_map = conversion.ConversionMap(True, (), (), None) conversion.entity_to_graph(f, conversion_map, None, None) self.assertTrue(f in conversion_map.dependency_cache) self.assertTrue(g in conversion_map.dependency_cache) self.assertEqual('tf__f', conversion_map.dependency_cache[f].name) + # need the extra .body[0] in order to step past the with tf.name_scope('f') + # that is added automatically self.assertEqual( - 'tf__g', conversion_map.dependency_cache[f].body[0].value.func.id) + 'tf__g', + conversion_map.dependency_cache[f].body[0].body[0].value.func.id) self.assertEqual('tf__g', conversion_map.dependency_cache[g].name) diff --git a/tensorflow/contrib/py2tf/impl/naming.py b/tensorflow/contrib/py2tf/impl/naming.py index d31462cba060bf6c04eefbad3ce7f166db994ab3..51326091de13715c32d0a79279f1d3274e48ad10 100644 --- a/tensorflow/contrib/py2tf/impl/naming.py +++ b/tensorflow/contrib/py2tf/impl/naming.py @@ -115,8 +115,14 @@ class Namer(object): else: raise ValueError('Unexpected symbol type "%s"' % type(s)) + pieces = name_root.split('_') + if pieces[-1].isdigit(): + name_root = '_'.join(pieces[:-1]) + n = int(pieces[-1]) + else: + n = 0 new_name = name_root - n = 0 + while (new_name in self.global_namespace or new_name in all_reserved_locals or new_name in self.generated_names): n += 1 diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD index 054eb17fb6a0eba38c58c46a657c3ad16b4773dc..edec5f7712d08247437c9e95d743e59dafffcd7b 100644 --- a/tensorflow/contrib/py2tf/pyct/BUILD +++ b/tensorflow/contrib/py2tf/pyct/BUILD @@ -1,5 +1,7 @@ licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE"]) + load("//tensorflow:tensorflow.bzl", "py_test") filegroup( @@ -19,9 +21,10 @@ py_library( srcs = [ "__init__.py", "anno.py", + "ast_util.py", "compiler.py", "context.py", - "copier.py", + "inspect_utils.py", "parser.py", "pretty_printer.py", "qual_names.py", @@ -48,6 +51,17 @@ py_test( ], ) +py_test( + name = "ast_util_test", + srcs = ["ast_util_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":pyct", + "//tensorflow/python:client_testlib", + "@gast_archive//:gast", + ], +) + py_test( name = "compiler_test", srcs = ["compiler_test.py"], @@ -60,8 +74,8 @@ py_test( ) py_test( - name = "copier_test", - srcs = ["copier_test.py"], + name = "inspect_utils_test", + srcs = ["inspect_utils_test.py"], srcs_version = "PY2AND3", deps = [ ":pyct", @@ -90,6 +104,16 @@ py_test( ], ) +py_test( + name = "qual_names_test", + srcs = ["qual_names_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":pyct", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "templates_test", srcs = ["templates_test.py"], diff --git a/tensorflow/contrib/py2tf/pyct/anno.py b/tensorflow/contrib/py2tf/pyct/anno.py index c6d41f9e128a31b4c3d513615da8c6d0fe51c29d..cc4a7edf02ed7556c9a552d8730e4c7875038c83 100644 --- a/tensorflow/contrib/py2tf/pyct/anno.py +++ b/tensorflow/contrib/py2tf/pyct/anno.py @@ -39,6 +39,11 @@ class Basic(NoValue): QN = 'Qualified name, as it appeared in the code.' SKIP_PROCESSING = ( 'This node should be preserved as is and not processed any further.') + INDENT_BLOCK_REMAINDER = ( + 'When a node is annotated with this, the remainder of the block should ' + 'be indented below it. The annotation contains a tuple ' + '(new_body, name_map), where `new_body` is the new indented block and ' + '`name_map` allows renaming symbols.') def getanno(node, key, field_name='___pyct_anno'): @@ -57,3 +62,16 @@ def setanno(node, key, value, field_name='___pyct_anno'): # So that the annotations survive gast_to_ast() and ast_to_gast() if field_name not in node._fields: node._fields += (field_name,) + + +def delanno(node, key, field_name='___pyct_anno'): + annotations = getattr(node, field_name) + del annotations[key] + if not annotations: + delattr(node, field_name) + node._fields = tuple(f for f in node._fields if f != field_name) + + +def copyanno(from_node, to_node, key, field_name='___pyct_anno'): + if hasanno(from_node, key, field_name): + setanno(to_node, key, getanno(from_node, key, field_name), field_name) diff --git a/tensorflow/contrib/py2tf/pyct/anno_test.py b/tensorflow/contrib/py2tf/pyct/anno_test.py index 19e3b4576210c3715620fc7002c91c5130b46ed0..6c29918fdfaaa0224f20a2c3cb2ea8088f3eb52b 100644 --- a/tensorflow/contrib/py2tf/pyct/anno_test.py +++ b/tensorflow/contrib/py2tf/pyct/anno_test.py @@ -24,6 +24,9 @@ from tensorflow.contrib.py2tf.pyct import anno from tensorflow.python.platform import test +# TODO(mdan): Consider strong types instead of primitives. + + class AnnoTest(test.TestCase): def test_basic(self): @@ -37,6 +40,22 @@ class AnnoTest(test.TestCase): self.assertTrue(anno.hasanno(node, 'foo')) self.assertEqual(3, anno.getanno(node, 'foo')) + anno.delanno(node, 'foo') + self.assertFalse(anno.hasanno(node, 'foo')) + with self.assertRaises(AttributeError): + anno.getanno(node, 'foo') + + def test_copyanno(self): + node_1 = ast.Name() + anno.setanno(node_1, 'foo', 3) + + node_2 = ast.Name() + anno.copyanno(node_1, node_2, 'foo') + anno.copyanno(node_1, node_2, 'bar') + + self.assertTrue(anno.hasanno(node_2, 'foo')) + self.assertFalse(anno.hasanno(node_2, 'bar')) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/copier.py b/tensorflow/contrib/py2tf/pyct/ast_util.py similarity index 73% rename from tensorflow/contrib/py2tf/pyct/copier.py rename to tensorflow/contrib/py2tf/pyct/ast_util.py index 41598fdc995a187fe5ff8ed7f86dad0a96d62fe5..f916775b9cf3cec960ec2896c334f1d737862205 100644 --- a/tensorflow/contrib/py2tf/pyct/copier.py +++ b/tensorflow/contrib/py2tf/pyct/ast_util.py @@ -66,3 +66,31 @@ def copy_clean(node): return tuple(copier.visit(n) for n in node) else: return copier.visit(node) + + +class SymbolRenamer(gast.NodeTransformer): + """Transformer that can rename symbols to a simple names.""" + + def __init__(self, name_map): + self.name_map = name_map + + def _process(self, node): + qn = anno.getanno(node, anno.Basic.QN) + if qn in self.name_map: + return gast.Name(str(self.name_map[qn]), node.ctx, None) + return self.generic_visit(node) + + def visit_Name(self, node): + return self._process(node) + + def visit_Attribute(self, node): + return self._process(node) + + +def rename_symbols(node, name_map): + renamer = SymbolRenamer(name_map) + if isinstance(node, list): + return [renamer.visit(n) for n in node] + elif isinstance(node, tuple): + return tuple(renamer.visit(n) for n in node) + return renamer.visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/copier_test.py b/tensorflow/contrib/py2tf/pyct/ast_util_test.py similarity index 56% rename from tensorflow/contrib/py2tf/pyct/copier_test.py rename to tensorflow/contrib/py2tf/pyct/ast_util_test.py index a6b35eda1492b7981557a5a515abdd36b05c8c87..a871ccad6fc7ea1487e41fd6da3ce6120bdcbcbd 100644 --- a/tensorflow/contrib/py2tf/pyct/copier_test.py +++ b/tensorflow/contrib/py2tf/pyct/ast_util_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for copier module.""" +"""Tests for ast_util module.""" from __future__ import absolute_import from __future__ import division @@ -20,11 +20,37 @@ from __future__ import print_function import ast -from tensorflow.contrib.py2tf.pyct import copier +from tensorflow.contrib.py2tf.pyct import ast_util +from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.python.platform import test -class CopierTest(test.TestCase): +class AstUtilTest(test.TestCase): + + def test_rename_symbols(self): + node = ast.Tuple([ + ast.Name('a', ast.Load()), + ast.Name('b', ast.Load()), + ast.Attribute(ast.Name('b', None), 'c', ast.Store()), + ast.Attribute( + ast.Attribute(ast.Name('b', None), 'c', ast.Load()), 'd', None) + ], None) + node = qual_names.resolve(node) + node = ast_util.rename_symbols( + node, { + qual_names.QN('a'): + qual_names.QN('renamed_a'), + qual_names.QN(qual_names.QN('b'), attr='c'): + qual_names.QN('renamed_b_c'), + }) + + self.assertEqual(node.elts[0].id, 'renamed_a') + self.assertTrue(isinstance(node.elts[0].ctx, ast.Load)) + self.assertEqual(node.elts[1].id, 'b') + self.assertEqual(node.elts[2].id, 'renamed_b_c') + self.assertTrue(isinstance(node.elts[2].ctx, ast.Store)) + self.assertEqual(node.elts[3].value.id, 'renamed_b_c') + self.assertTrue(isinstance(node.elts[3].value.ctx, ast.Load)) def test_copy_clean(self): ret = ast.Return( @@ -43,7 +69,7 @@ class CopierTest(test.TestCase): body=[ret], decorator_list=[], returns=None) - new_node = copier.copy_clean(node) + new_node = ast_util.copy_clean(node) self.assertFalse(node is new_node) self.assertFalse(ret is new_node.body[0]) self.assertFalse(hasattr(new_node.body[0], '__foo')) diff --git a/tensorflow/contrib/py2tf/pyct/compiler.py b/tensorflow/contrib/py2tf/pyct/compiler.py index fc71469d1eaeb92352e3b50cb743621d7e5eb1d5..507dbc7ed3de9c0b8874164e97a3d1d149e42423 100644 --- a/tensorflow/contrib/py2tf/pyct/compiler.py +++ b/tensorflow/contrib/py2tf/pyct/compiler.py @@ -22,6 +22,7 @@ from __future__ import division from __future__ import print_function # TODO(mdan): Use six for compatibility here. +import atexit import imp import os import tempfile @@ -38,10 +39,14 @@ def ast_to_source(node, indentation): astor.string_repr.pretty_string) generator.visit(node) generator.result.append('\n') - return astor.source_repr.pretty_source(generator.result).lstrip() + # In some versions of Python, literals may appear as actual values. This + # ensures everything is string. + code = map(str, generator.result) + return astor.source_repr.pretty_source(code).lstrip() -def ast_to_object(node, indentation=' ', source_prefix=None): +def ast_to_object( + node, indentation=' ', source_prefix=None, delete_on_exit=True): """Return the Python objects represented by given AST. Compiling the AST code this way ensures that the source code is readable by @@ -51,6 +56,8 @@ def ast_to_object(node, indentation=' ', source_prefix=None): node: The code to compile, as an AST object. indentation: The string to use for indentation. source_prefix: Optional string to print as-is into the source file. + delete_on_exit: Whether to delete the temporary file used for compilation + on exit. Returns: A module object containing the compiled source code. @@ -63,4 +70,6 @@ def ast_to_object(node, indentation=' ', source_prefix=None): f.write(source_prefix) f.write('\n') f.write(source) - return imp.load_source(module_name, f.name) + if delete_on_exit: + atexit.register(lambda: os.remove(f.name)) + return imp.load_source(module_name, f.name), source diff --git a/tensorflow/contrib/py2tf/pyct/compiler_test.py b/tensorflow/contrib/py2tf/pyct/compiler_test.py index e0cde43566310b99bac5035285154fde906fa127..243f4c81538f5853a01ff444f2ff16ccf7cd5d62 100644 --- a/tensorflow/contrib/py2tf/pyct/compiler_test.py +++ b/tensorflow/contrib/py2tf/pyct/compiler_test.py @@ -23,11 +23,28 @@ import textwrap import gast from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect class CompilerTest(test.TestCase): + def test_parser_compile_idempotent(self): + + def test_fn(x): + a = True + b = '' + if a: + b = x + 1 + return b + + self.assertEqual( + textwrap.dedent(tf_inspect.getsource(test_fn)), + tf_inspect.getsource( + compiler.ast_to_object( + parser.parse_entity(test_fn)[0].body[0])[0].test_fn)) + def test_ast_to_source(self): node = gast.If( test=gast.Num(1), @@ -41,6 +58,7 @@ class CompilerTest(test.TestCase): targets=[gast.Name('a', gast.Store(), None)], value=gast.Str('c')) ]) + self.assertEqual( textwrap.dedent(""" if 1: @@ -70,15 +88,19 @@ class CompilerTest(test.TestCase): decorator_list=[], returns=None) - mod = compiler.ast_to_object(node) + module, source = compiler.ast_to_object(node) - self.assertEqual(2, mod.f(1)) - with open(mod.__file__, 'r') as temp_output: + expected_source = """ + def f(a): + return a + 1 + """ + self.assertEqual( + textwrap.dedent(expected_source).strip(), + source.strip()) + self.assertEqual(2, module.f(1)) + with open(module.__file__, 'r') as temp_output: self.assertEqual( - textwrap.dedent(""" - def f(a): - return a + 1 - """).strip(), + textwrap.dedent(expected_source).strip(), temp_output.read().strip()) diff --git a/tensorflow/contrib/py2tf/pyct/context.py b/tensorflow/contrib/py2tf/pyct/context.py index fef74ebefa290369c7310af6d7e4faeef44d9aee..b34015cfd2888f0dbeb6492b9e7335d561bf4763 100644 --- a/tensorflow/contrib/py2tf/pyct/context.py +++ b/tensorflow/contrib/py2tf/pyct/context.py @@ -22,6 +22,8 @@ from __future__ import print_function class EntityContext(object): """Contains information about an entity, like source code. + In general, objects of this class should be considered immutable. + Attributes: namer: Namer that matches the contract of all converters. source_code: The entity's source code. @@ -30,14 +32,18 @@ class EntityContext(object): (excluding parameters). arg_values: Dict[str->*], containing parameter values, if known. arg_types: Dict[str->*], containing parameter types, if known. + owner_type: The surrounding class type of the function, if present. """ + # TODO(mdan): Remove the default and update tests. def __init__(self, namer, source_code, source_file, namespace, arg_values, - arg_types, recursive): + arg_types, owner_type, recursive, type_annotation_func=None): self.namer = namer self.source_code = source_code self.source_file = source_file self.namespace = namespace self.arg_values = {} if arg_values is None else arg_values self.arg_types = {} if arg_types is None else arg_types + self.owner_type = owner_type self.recursive = recursive + self.type_annotation_func = type_annotation_func diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d19c6ed75e0f0651781d6e1ed80f7be11fb8a5a4 --- /dev/null +++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py @@ -0,0 +1,119 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Live entity inspection utilities. + +This module contains whatever inspect doesn't offer out of the box. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools + +import six + +from tensorflow.python.util import tf_inspect + + +def getnamespace(f): + """Returns the complete namespace of a function. + + Namespace is defined here as the mapping of all non-local variables to values. + This includes the globals and the closure variables. Note that this captures + the entire globals collection of the function, and may contain extra symbols + that it does not actually use. + + Args: + f: User defined function. + Returns: + A dict mapping symbol names to values. + """ + namespace = dict(six.get_function_globals(f)) + closure = six.get_function_closure(f) + freevars = six.get_function_code(f).co_freevars + if freevars and closure: + for name, cell in zip(freevars, closure): + namespace[name] = cell.cell_contents + return namespace + + +def getmethodclass(m): + """Resolves a function's owner, e.g. a method's class. + + Note that this returns the object that the function was retrieved from, not + necessarily the class where it was defined. + + This function relies on Python stack frame support in the interpreter, and + has the same limitations that inspect.currentframe. + + Limitations. This function will only work correctly if the owned class is + visible in the caller's global or local variables. + + Args: + m: A user defined function + + Returns: + The class that this function was retrieved from, or None if the function + is not an object or class method, or the class that owns the object or + method is not visible to m. + + Raises: + ValueError: if the class could not be resolved for any unexpected reason. + """ + + # Instance method and class methods: should be bound to a non-null "self". + # If self is a class, then it's a class method. + if hasattr(m, '__self__'): + if m.__self__: + if tf_inspect.isclass(m.__self__): + return m.__self__ + return type(m.__self__) + + # Class, static and unbound methods: search all defined classes in any + # namespace. This is inefficient but more robust method. + owners = [] + caller_frame = tf_inspect.currentframe().f_back + try: + # TODO(mdan): This doesn't consider cell variables. + # TODO(mdan): This won't work if the owner is hidden inside a container. + # Cell variables may be pulled using co_freevars and the closure. + for v in itertools.chain(caller_frame.f_locals.values(), + caller_frame.f_globals.values()): + if hasattr(v, m.__name__): + candidate = getattr(v, m.__name__) + # Py2 methods may be bound or unbound, extract im_func to get the + # underlying function. + if hasattr(candidate, 'im_func'): + candidate = candidate.im_func + if hasattr(m, 'im_func'): + m = m.im_func + if candidate is m: + owners.append(v) + finally: + del caller_frame + + if owners: + if len(owners) == 1: + return owners[0] + + # If multiple owners are found, and are not subclasses, raise an error. + owner_types = tuple(o if tf_inspect.isclass(o) else type(o) for o in owners) + for o in owner_types: + if tf_inspect.isclass(o) and issubclass(o, tuple(owner_types)): + return o + raise ValueError('Found too many owners of %s: %s' % (m, owners)) + + return None diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5528ac851f74bd7b7dacdbe7b930945afa8c9783 --- /dev/null +++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py @@ -0,0 +1,230 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for unspect_utils module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from functools import wraps + +import six + +from tensorflow.contrib.py2tf.pyct import inspect_utils +from tensorflow.python.platform import test + + +def decorator(f): + return f + + +def function_decorator(): + def dec(f): + return f + return dec + + +def wrapping_decorator(): + def dec(f): + def replacement(*_): + return None + + @wraps(f) + def wrapper(*args, **kwargs): + return replacement(*args, **kwargs) + return wrapper + return dec + + +class TestClass(object): + + def member_function(self): + pass + + @decorator + def decorated_member(self): + pass + + @function_decorator() + def fn_decorated_member(self): + pass + + @wrapping_decorator() + def wrap_decorated_member(self): + pass + + @staticmethod + def static_method(): + pass + + @classmethod + def class_method(cls): + pass + + +def free_function(): + pass + + +def factory(): + return free_function + + +def free_factory(): + def local_function(): + pass + return local_function + + +class InspectUtilsTest(test.TestCase): + + def test_getnamespace_globals(self): + ns = inspect_utils.getnamespace(factory) + self.assertEqual(ns['free_function'], free_function) + + def test_getnamespace_hermetic(self): + + # Intentionally hiding the global function to make sure we don't overwrite + # it in the global namespace. + free_function = object() # pylint:disable=redefined-outer-name + + def test_fn(): + return free_function + + ns = inspect_utils.getnamespace(test_fn) + globs = six.get_function_globals(test_fn) + self.assertTrue(ns['free_function'] is free_function) + self.assertFalse(globs['free_function'] is free_function) + + def test_getnamespace_locals(self): + + def called_fn(): + return 0 + + closed_over_list = [] + closed_over_primitive = 1 + + def local_fn(): + closed_over_list.append(1) + local_var = 1 + return called_fn() + local_var + closed_over_primitive + + ns = inspect_utils.getnamespace(local_fn) + self.assertEqual(ns['called_fn'], called_fn) + self.assertEqual(ns['closed_over_list'], closed_over_list) + self.assertEqual(ns['closed_over_primitive'], closed_over_primitive) + self.assertTrue('local_var' not in ns) + + def test_getmethodclass(self): + + self.assertEqual( + inspect_utils.getmethodclass(free_function), None) + self.assertEqual( + inspect_utils.getmethodclass(free_factory()), None) + + self.assertEqual( + inspect_utils.getmethodclass(TestClass.member_function), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(TestClass.decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(TestClass.fn_decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(TestClass.wrap_decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(TestClass.static_method), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(TestClass.class_method), + TestClass) + + test_obj = TestClass() + self.assertEqual( + inspect_utils.getmethodclass(test_obj.member_function), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.fn_decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.wrap_decorated_member), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.static_method), + TestClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.class_method), + TestClass) + + def test_getmethodclass_locals(self): + + def local_function(): + pass + + class LocalClass(object): + + def member_function(self): + pass + + @decorator + def decorated_member(self): + pass + + @function_decorator() + def fn_decorated_member(self): + pass + + @wrapping_decorator() + def wrap_decorated_member(self): + pass + + self.assertEqual( + inspect_utils.getmethodclass(local_function), None) + + self.assertEqual( + inspect_utils.getmethodclass(LocalClass.member_function), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(LocalClass.decorated_member), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(LocalClass.fn_decorated_member), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(LocalClass.wrap_decorated_member), + LocalClass) + + test_obj = LocalClass() + self.assertEqual( + inspect_utils.getmethodclass(test_obj.member_function), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.decorated_member), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.fn_decorated_member), + LocalClass) + self.assertEqual( + inspect_utils.getmethodclass(test_obj.wrap_decorated_member), + LocalClass) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/pyct/qual_names.py b/tensorflow/contrib/py2tf/pyct/qual_names.py index 11e3838467783807530576413bec20c9904f873f..2ffda03868026c12816cfe8dfd0f00a59bd9ba55 100644 --- a/tensorflow/contrib/py2tf/pyct/qual_names.py +++ b/tensorflow/contrib/py2tf/pyct/qual_names.py @@ -31,23 +31,43 @@ from tensorflow.contrib.py2tf.pyct import anno class QN(object): - """Represents a qualified name. - - """ - - def __init__(self, base, attr=None): - if attr: + """Represents a qualified name.""" + + def __init__(self, base, attr=None, subscript=None): + if attr is not None and subscript is not None: + raise ValueError('A QN can only be either an attr or a subscript, not ' + 'both: attr={}, subscript={}.'.format(attr, subscript)) + self._has_attr = False + self._has_subscript = False + if attr is not None: if not isinstance(base, QN): raise ValueError('For attribute QNs, base must be a QN.') self._parent = base - self.qn = base.qn + (attr,) + # TODO(mdan): Get rid of the tuple - it can only have 1 or 2 elements now. + self.qn = (base, attr) + self._has_attr = True + elif subscript is not None: + if not isinstance(base, QN): + raise ValueError('For subscript QNs, base must be a QN.') + self._parent = base + self.qn = (base, subscript) + self._has_subscript = True else: + if not isinstance(base, str): + raise ValueError('For simple QNs, base must be a string.') + assert '.' not in base and '[' not in base and ']' not in base self._parent = None - self.qn = tuple(base.split('.')) + self.qn = (base,) def is_composite(self): return len(self.qn) > 1 + def has_subscript(self): + return self._has_subscript + + def has_attr(self): + return self._has_attr + @property def parent(self): if self._parent is None: @@ -55,24 +75,41 @@ class QN(object): return self._parent def __hash__(self): - return hash(self.qn) + return hash(self.qn + (self._has_attr, self._has_subscript)) def __eq__(self, other): - return self.qn == other.qn + return (isinstance(other, QN) and self.qn == other.qn and + self.has_subscript() == other.has_subscript() and + self.has_attr() == other.has_attr()) def __str__(self): - return '.'.join(self.qn) + if self.has_subscript(): + return str(self.qn[0]) + '[' + str(self.qn[1]) + ']' + if self.has_attr(): + return '.'.join(map(str, self.qn)) + else: + return str(self.qn[0]) def __repr__(self): return str(self) def ssf(self): """Simple symbol form.""" - return '_'.join(self.qn) + ssfs = [n.ssf() if isinstance(n, QN) else n for n in self.qn] + ssf_string = '' + for i in range(0, len(self.qn) - 1): + if self.has_subscript(): + delimiter = '_sub_' + else: + delimiter = '_' + ssf_string += ssfs[i] + delimiter + return ssf_string + ssfs[-1] def ast(self): # The caller must adjust the context appropriately. - if self.is_composite(): + if self.has_subscript(): + return gast.Subscript(self.parent.ast(), str(self.qn[-1]), None) + if self.has_attr(): return gast.Attribute(self.parent.ast(), self.qn[-1], None) return gast.Name(self.qn[0], None, None) @@ -91,7 +128,22 @@ class QnResolver(gast.NodeTransformer): def visit_Attribute(self, node): self.generic_visit(node) anno.setanno(node, anno.Basic.QN, - QN(anno.getanno(node.value, anno.Basic.QN), node.attr)) + QN(anno.getanno(node.value, anno.Basic.QN), attr=node.attr)) + return node + + def visit_Subscript(self, node): + if not isinstance(node.slice, gast.Index): + raise NotImplementedError('range and multi-dimensional indexing are not' + ' yet supported') + self.generic_visit(node) + if isinstance(node.slice.value, gast.Num) or isinstance( + node.slice.value, gast.Str): + raise NotImplementedError('constant subscripts are not yet supported') + else: + subscript = anno.getanno(node.slice.value, anno.Basic.QN) + anno.setanno(node, anno.Basic.QN, + QN(anno.getanno(node.value, anno.Basic.QN), + subscript=subscript)) return node diff --git a/tensorflow/contrib/py2tf/pyct/qual_names_test.py b/tensorflow/contrib/py2tf/pyct/qual_names_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9eaaaf9d4cc2cf4362ae5924a6183530276d5093 --- /dev/null +++ b/tensorflow/contrib/py2tf/pyct/qual_names_test.py @@ -0,0 +1,200 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for qual_names module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import textwrap + +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct.qual_names import QN +from tensorflow.contrib.py2tf.pyct.qual_names import resolve +from tensorflow.python.platform import test + + +class QNTest(test.TestCase): + + def test_basic(self): + a = QN('a') + self.assertEqual(a.qn, ('a',)) + self.assertEqual(str(a), 'a') + self.assertEqual(a.ssf(), 'a') + self.assertEqual(a.ast().id, 'a') + self.assertFalse(a.is_composite()) + with self.assertRaises(ValueError): + _ = a.parent + + a_b = QN(a, attr='b') + self.assertEqual(a_b.qn, (a, 'b')) + self.assertEqual(str(a_b), 'a.b') + self.assertEqual(a_b.ssf(), 'a_b') + self.assertEqual(a_b.ast().value.id, 'a') + self.assertEqual(a_b.ast().attr, 'b') + self.assertTrue(a_b.is_composite()) + self.assertEqual(a_b.parent.qn, ('a',)) + + def test_subscripts(self): + a = QN('a') + b = QN('b') + a_sub_b = QN(a, subscript=b) + self.assertEqual(a_sub_b.qn, (a, b)) + self.assertEqual(str(a_sub_b), 'a[b]') + self.assertEqual(a_sub_b.ssf(), 'a_sub_b') + self.assertEqual(a_sub_b.ast().value.id, 'a') + self.assertEqual(a_sub_b.ast().slice, 'b') + self.assertTrue(a_sub_b.is_composite()) + self.assertTrue(a_sub_b.has_subscript()) + self.assertEqual(a_sub_b.parent.qn, ('a',)) + + c = QN('c') + b_sub_c = QN(b, subscript=c) + a_sub_b_sub_c = QN(a, subscript=b_sub_c) + self.assertEqual(a_sub_b_sub_c.qn, (a, b_sub_c)) + self.assertTrue(a_sub_b.is_composite()) + self.assertTrue(a_sub_b_sub_c.is_composite()) + self.assertTrue(a_sub_b.has_subscript()) + self.assertTrue(a_sub_b_sub_c.has_subscript()) + self.assertEqual(b_sub_c.qn, (b, c)) + self.assertEqual(str(a_sub_b_sub_c), 'a[b[c]]') + self.assertEqual(a_sub_b_sub_c.ssf(), 'a_sub_b_sub_c') + self.assertEqual(a_sub_b_sub_c.ast().value.id, 'a') + self.assertEqual(a_sub_b_sub_c.ast().slice, 'b[c]') + self.assertEqual(b_sub_c.ast().slice, 'c') + self.assertEqual(a_sub_b_sub_c.parent.qn, ('a',)) + with self.assertRaises(ValueError): + QN('a', 'b') + + def test_equality(self): + a = QN('a') + a2 = QN('a') + a_b = QN(a, attr='b') + self.assertEqual(a2.qn, ('a',)) + with self.assertRaises(ValueError): + _ = a.parent + + a_b2 = QN(a, attr='b') + self.assertEqual(a_b2.qn, (a, 'b')) + self.assertEqual(a_b2.parent.qn, ('a',)) + + self.assertTrue(a2 == a) + self.assertFalse(a2 is a) + + self.assertTrue(a_b.parent == a) + self.assertTrue(a_b2.parent == a) + + self.assertTrue(a_b2 == a_b) + self.assertFalse(a_b2 is a_b) + self.assertFalse(a_b2 == a) + a_sub_b = QN(a, subscript='b') + a_sub_b2 = QN(a, subscript='b') + self.assertTrue(a_sub_b == a_sub_b2) + self.assertFalse(a_sub_b == a_b) + + def test_nested_attrs_subscripts(self): + a = QN('a') + b = QN('b') + c = QN('c') + b_sub_c = QN(b, subscript=c) + a_sub_b_sub_c = QN(a, subscript=b_sub_c) + + b_dot_c = QN(b, attr=c) + a_sub__b_dot_c = QN(a, subscript=b_dot_c) + + a_sub_b = QN(a, subscript=b) + a_sub_b__dot_c = QN(a_sub_b, attr=c) + + a_dot_b = QN(a, attr=b) + a_dot_b_sub_c = QN(a_dot_b, subscript=c) + + self.assertEqual(str(a_sub_b_sub_c), 'a[b[c]]') + self.assertEqual(str(a_sub__b_dot_c), 'a[b.c]') + self.assertEqual(str(a_sub_b__dot_c), 'a[b].c') + self.assertEqual(str(a_dot_b_sub_c), 'a.b[c]') + + self.assertFalse(a_sub_b_sub_c == a_sub__b_dot_c) + self.assertFalse(a_sub_b_sub_c == a_sub_b__dot_c) + self.assertFalse(a_sub_b_sub_c == a_dot_b_sub_c) + + self.assertFalse(a_sub__b_dot_c == a_sub_b__dot_c) + self.assertFalse(a_sub__b_dot_c == a_dot_b_sub_c) + + self.assertFalse(a_sub_b__dot_c == a_dot_b_sub_c) + + def test_hashable(self): + d = {QN('a'): 'a', QN('b'): 'b'} + self.assertEqual(d[QN('a')], 'a') + self.assertEqual(d[QN('b')], 'b') + self.assertTrue(QN('c') not in d) + + +class QNResolverTest(test.TestCase): + + def assertQNStringIs(self, node, qn_str): + self.assertEqual(str(anno.getanno(node, anno.Basic.QN)), qn_str) + + def test_resolve(self): + samples = """ + a + a.b + (c, d.e) + [f, (g.h.i)] + j(k, l) + """ + nodes = resolve(parser.parse_str(textwrap.dedent(samples))) + nodes = tuple(n.value for n in nodes.body) + + self.assertQNStringIs(nodes[0], 'a') + self.assertQNStringIs(nodes[1], 'a.b') + self.assertQNStringIs(nodes[2].elts[0], 'c') + self.assertQNStringIs(nodes[2].elts[1], 'd.e') + self.assertQNStringIs(nodes[3].elts[0], 'f') + self.assertQNStringIs(nodes[3].elts[1], 'g.h.i') + self.assertQNStringIs(nodes[4].func, 'j') + self.assertQNStringIs(nodes[4].args[0], 'k') + self.assertQNStringIs(nodes[4].args[1], 'l') + + def test_subscript_resolve(self): + samples = """ + x[i] + x[i.b] + a.b[c] + a.b[x.y] + a[z[c]] + a[b[c[d]]] + a[b].c + a.b.c[d].e.f + a.b[c[d]].e.f + a.b[c[d.e.f].g].h + """ + nodes = resolve(parser.parse_str(textwrap.dedent(samples))) + nodes = tuple(n.value for n in nodes.body) + + self.assertQNStringIs(nodes[0], 'x[i]') + self.assertQNStringIs(nodes[1], 'x[i.b]') + self.assertQNStringIs(nodes[2], 'a.b[c]') + self.assertQNStringIs(nodes[3], 'a.b[x.y]') + self.assertQNStringIs(nodes[4], 'a[z[c]]') + self.assertQNStringIs(nodes[5], 'a[b[c[d]]]') + self.assertQNStringIs(nodes[6], 'a[b].c') + self.assertQNStringIs(nodes[7], 'a.b.c[d].e.f') + self.assertQNStringIs(nodes[8], 'a.b[c[d]].e.f') + self.assertQNStringIs(nodes[9], 'a.b[c[d.e.f].g].h') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD index fbfce18c60cca4b105e7de3c3ea7b9c3438f6b2a..2799b56a0042e99b8f8b38100d07c5afaef9f424 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD @@ -60,6 +60,7 @@ py_test( deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/utils", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py index 1c93e1603113d48176af7a97a0f37321e6f67586..87fc8c979c4e3310fb3aa82b0f23d909b0170cda 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py @@ -24,6 +24,7 @@ import gast from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import transformer +from tensorflow.contrib.py2tf.pyct.qual_names import QN from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno # TODO(mdan): Add support for PY3 (e.g. Param vs arg). @@ -70,13 +71,33 @@ class Scope(object): tuple(self.modified)) def copy_from(self, other): + """Recursively copies the contents of this scope from another scope.""" + if (self.parent is None) != (other.parent is None): + raise ValueError('cannot copy scopes of different structures') + if other.parent is not None: + self.parent.copy_from(other.parent) + self.isolated = other.isolated self.modified = copy.copy(other.modified) self.created = copy.copy(other.created) self.used = copy.copy(other.used) self.params = copy.copy(other.params) self.returned = copy.copy(other.returned) + @classmethod + def copy_of(cls, other): + if other.parent is not None: + parent = cls.copy_of(other.parent) + else: + parent = None + new_copy = cls(parent) + new_copy.copy_from(other) + return new_copy + def merge_from(self, other): + if (self.parent is None) != (other.parent is None): + raise ValueError('cannot merge scopes of different structures') + if other.parent is not None: + self.parent.merge_from(other.parent) self.modified |= other.modified self.created |= other.created self.used |= other.used @@ -224,19 +245,38 @@ class ActivityAnalizer(transformer.Base): # modifies the parent state causing the other child blocks to be # processed incorrectly. So we need to checkpoint the parent scope so that # each child sees the same context. - before_parent = Scope(None) - before_parent.copy_from(self.scope) + before_parent = Scope.copy_of(self.scope) after_children = [] for child, scope_name in children: self.scope.copy_from(before_parent) parent = self._process_block_node(parent, child, scope_name) - after_child = Scope(None) - after_child.copy_from(self.scope) + after_child = Scope.copy_of(self.scope) after_children.append(after_child) for after_child in after_children: self.scope.merge_from(after_child) return parent + def visit_FunctionDef(self, node): + if self.scope: + qn = QN(node.name) + self.scope.mark_write(qn) + current_scope = self.scope + fndef_scope = Scope(current_scope, isolated=True) + self.scope = fndef_scope + self.generic_visit(node) + anno.setanno(node, NodeAnno.BODY_SCOPE, fndef_scope) + self.scope = current_scope + return node + + def visit_With(self, node): + current_scope = self.scope + with_scope = Scope(current_scope, isolated=False) + self.scope = with_scope + self.generic_visit(node) + anno.setanno(node, NodeAnno.BODY_SCOPE, with_scope) + self.scope = current_scope + return node + def visit_If(self, node): self.visit(node.test) node = self._process_parallel_blocks(node, diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py index e1eb954a5efef4d6a00ac492e7c85394d54e28c9..b16d15b39d8eb4c444cbc50ae62baa3a8fcc7841 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py @@ -45,7 +45,7 @@ class ScopeTest(test.TestCase): scope.mark_read(QN('bar')) self.assertFalse(scope.has(QN('bar'))) - def test_copy(self): + def test_copy_from(self): scope = activity.Scope(None) scope.mark_write(QN('foo')) @@ -65,6 +65,17 @@ class ScopeTest(test.TestCase): self.assertTrue(QN('bar') in scope.created) self.assertFalse(QN('bar') in other.created) + def test_copy_of(self): + scope = activity.Scope(None) + scope.mark_read(QN('foo')) + + self.assertTrue(QN('foo') in activity.Scope.copy_of(scope).used) + + child_scope = activity.Scope(scope) + child_scope.mark_read(QN('bar')) + + self.assertTrue(QN('bar') in activity.Scope.copy_of(child_scope).used) + def test_nesting(self): scope = activity.Scope(None) scope.mark_write(QN('foo')) @@ -108,6 +119,7 @@ class ActivityAnalizerTest(test.TestCase): namespace={}, arg_values=None, arg_types=None, + owner_type=None, recursive=True) node = qual_names.resolve(node) node = activity.resolve(node, ctx) @@ -132,7 +144,7 @@ class ActivityAnalizerTest(test.TestCase): anno.getanno(node.body[0].body[2].value, NodeAnno.IS_LOCAL)) # b in return b - def assertScopeIs(self, scope, used, modified, created): + def assertScopeIsRmc(self, scope, used, modified, created): self.assertItemsEqual(used, tuple(str(s) for s in scope.used)) self.assertItemsEqual(modified, tuple(str(s) for s in scope.modified)) self.assertItemsEqual(created, tuple(str(s) for s in scope.created)) @@ -158,7 +170,7 @@ class ActivityAnalizerTest(test.TestCase): print_args_scope = anno.getanno(print_node, NodeAnno.ARGS_SCOPE) # We basically need to detect which variables are captured by the call # arguments. - self.assertScopeIs(print_args_scope, ('a', 'b'), (), ()) + self.assertScopeIsRmc(print_args_scope, ('a', 'b'), (), ()) def test_call(self): @@ -172,7 +184,7 @@ class ActivityAnalizerTest(test.TestCase): call_node = node.body[0].body[2].value # We basically need to detect which variables are captured by the call # arguments. - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(call_node, NodeAnno.ARGS_SCOPE), ('a', 'b'), (), ()) def test_while(self): @@ -186,10 +198,10 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) while_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(while_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'), ('c',)) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(while_node, NodeAnno.BODY_SCOPE).parent, ('a', 'b', 'c'), ('b', 'c'), ('a', 'b', 'c')) @@ -204,9 +216,9 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) for_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(for_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'), ('c',)) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(for_node, NodeAnno.BODY_SCOPE).parent, ('a', 'b', 'c'), ('b', 'c', '_'), ('a', 'b', 'c', '_')) @@ -225,20 +237,66 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) if_node = node.body[0].body[0] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('x', 'y', 'z'), ('y', 'z')) # TODO(mdan): Double check: is it ok to not mark a local symbol as not read? - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE).parent, ('x', 'z', 'u'), ('x', 'y', 'z', 'u'), ('x', 'y', 'z', 'u')) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE), ('x', 'y'), ('x', 'y', 'u'), ('y', 'u')) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE).parent, ('x', 'z', 'u'), ('x', 'y', 'z', 'u'), ('x', 'y', 'z', 'u')) + def test_nested_if_else_creation(self): + + def test_fn(b): + if b > 0: + if b < 5: + a = b + else: + a = b * b + return a + + node = self._parse_and_analyze(test_fn) + inner_if_node = node.body[0].body[0].body[0] + self.assertScopeIsRmc( + anno.getanno(inner_if_node, NodeAnno.BODY_SCOPE), ('b',), ('a',), + ('a',)) + self.assertScopeIsRmc( + anno.getanno(inner_if_node, NodeAnno.ORELSE_SCOPE), ('b',), ('a',), + ('a',)) + + def test_function_def(self): + + def test_fn(a): + + def f(x): + y = x * x + return y + + b = a + for i in a: + c = b + b -= f(i) + return b, c + + node = self._parse_and_analyze(test_fn) + fndef_node = node.body[0].body[0] + + self.assertScopeIsRmc( + anno.getanno(fndef_node, + NodeAnno.BODY_SCOPE).parent, ('b', 'i', 'f', 'c', 'a'), + ('f', 'b', 'c', 'i'), ('f', 'a', 'b', 'c', 'i')) + self.assertScopeIsRmc( + anno.getanno(fndef_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('y',), ( + 'x', + 'y', + )) + def test_call_with_composite_names(self): def foo(*_): @@ -256,13 +314,13 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) call_node = node.body[0].body[0].value - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(call_node, NodeAnno.ARGS_SCOPE), ('a', 'a.b', 'a.c'), (), ()) if_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('a',), ('a.b',), ()) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE), ('a', 'a.c', 'd', 'd.e', 'f'), ('a.c', 'd', 'd.e', 'f'), ('d', 'f')) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py index 9c0a9a9e74eccb3d22840032e8f0c2b81e051e7e..0388be5d252389f2f3516c8b27828905d6475589 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py @@ -86,6 +86,7 @@ class LiveValueResolver(transformer.Base): if not hasattr(parent_object, node.attr): raise AttributeError('%s has no attribute %s' % (parent_object, node.attr)) + anno.setanno(node, 'parent_type', type(parent_object)) anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'fqn') + (node.attr,)) # TODO(mdan): Investigate the role built-in annotations can play here. @@ -96,6 +97,7 @@ class LiveValueResolver(transformer.Base): # This would not hold for dynamic members like function attributes. # For the dynamic case, we simply leave the node without an annotation, # and let downstream consumers figure out what to do. + anno.setanno(node, 'parent_type', parent_type) anno.setanno(node, 'live_val', getattr(parent_type, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'type_fqn') + (node.attr,)) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py index 9f64689401e3594a77fbdd7b6f02880bd6e90492..c133a455b3dd328689102634c6076f366212ac25 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py @@ -46,6 +46,7 @@ class LiveValuesResolverTest(test.TestCase): namespace=namespace, arg_values=None, arg_types=arg_types, + owner_type=None, recursive=True) node = qual_names.resolve(node) node = activity.resolve(node, ctx) @@ -102,6 +103,7 @@ class LiveValuesResolverTest(test.TestCase): arg_types={'self': (TestClass.__name__, TestClass)}) func_node = node.body[0].body[0].value.func self.assertEquals(TestClass.member, anno.getanno(func_node, 'live_val')) + self.assertEquals(TestClass, anno.getanno(func_node, 'parent_type')) self.assertEquals(('TestClass', 'member'), anno.getanno(func_node, 'fqn')) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py index 8203bda0f9a792a5b24b9abb25d8f39b61625748..5556a58c025da695bcef10352c597c7c8dd612d9 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py @@ -14,9 +14,29 @@ # ============================================================================== """Type resolution. +This analyzer uses known live values to further infer object types. This +may include for instance constructed objects and object member functions. + +In addition, the analyzer will also process annotations for TF (staged) type +annotations. + Requires annotations generated by LiveValuesResolver. """ +# TODO(mdan): This would be more robust with a CFG. +# Situations with multiple reaching modifications (e.g. modified inside and +# outside a control flow statement) should be more robustly detected and +# analyzed. + +# TODO(mdan): Look into using Python AST's type annotation fields instead. +# It would be desirable to use that mechanism if we can. +# Some caveats to consider: We may need to annotate other nodes like +# Attribute. It may also not be feasible for us to faithfully to replicate +# PY3's type annotations where it isn't available. It would also require us +# to design rigorous type definitions that can accommodate Python types +# as well as TensorFLow dtypes and shapes. + + from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -29,7 +49,7 @@ from tensorflow.python.util import tf_inspect class Scope(object): - """Encloses symbol value references. + """Tracks symbol value references. Attributes: values: A dict mapping string to gast.Node, containing the value that was @@ -138,11 +158,14 @@ class TypeInfoResolver(transformer.Base): elif isinstance(node.ctx, gast.Load) and self.scope.hasval(qn): # E.g. if we had # a = b - # then for future references to `a` we should have traced_source = `b` - traced_source = self.scope.getval(qn) - if anno.hasanno(traced_source, 'type'): - anno.setanno(node, 'type', anno.getanno(traced_source, 'type')) - anno.setanno(node, 'type_fqn', anno.getanno(traced_source, 'type_fqn')) + # then for future references to `a` we should have definition = `b` + definition = self.scope.getval(qn) + if anno.hasanno(definition, 'type'): + anno.setanno(node, 'type', anno.getanno(definition, 'type')) + anno.setanno(node, 'type_fqn', anno.getanno(definition, 'type_fqn')) + if anno.hasanno(definition, 'element_type'): + anno.setanno(node, 'element_type', + anno.getanno(definition, 'element_type')) return node def _process_variable_assignment(self, source, targets): @@ -181,6 +204,34 @@ class TypeInfoResolver(transformer.Base): self._process_variable_assignment(node.value, node.targets) return node + def visit_Call(self, node): + if anno.hasanno(node.func, 'live_val'): + # Symbols targeted by the "set_type" marker function are assigned the data + # type that it specified. + if (anno.getanno(node.func, 'live_val') is + self.context.type_annotation_func): + # Expecting the actual type to be the second argument. + if len(node.args) != 2: + raise ValueError('"%s" must have exactly two parameters' + % self.context.type_annotation_func) + if not anno.hasanno(node.args[0], anno.Basic.QN): + raise ValueError('the first argument of "%s" must by a symbol' + % self.context.type_annotation_func) + if not anno.hasanno(node.args[1], 'live_val'): + raise ValueError( + 'the second argument of "%s" must be statically resolvable' % + self.context.type_annotation_func) + target_symbol = anno.getanno(node.args[0], anno.Basic.QN) + element_type = anno.getanno(node.args[1], 'live_val') + # Find the definition of this symbol and annotate it with the given + # data type. That in turn will cause future uses of the symbol + # to receive the same type annotation. + definition = self.scope.getval(target_symbol) + anno.setanno(node, 'element_type', element_type) + anno.setanno(definition, 'element_type', element_type) + # TODO(mdan): Should we update references between definition and here? + return self.generic_visit(node) + def resolve(node, context): return TypeInfoResolver(context).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py index 3659f949db9910534870d8dd9e42fd4ee8297253..0d9d5a85f055b170ea6e493e8ac185f1298ebf3c 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser @@ -56,7 +57,10 @@ class ScopeTest(test.TestCase): class TypeInfoResolverTest(test.TestCase): - def _parse_and_analyze(self, test_fn, namespace, arg_types=None): + def _parse_and_analyze(self, + test_fn, + namespace, + arg_types=None): node, source = parser.parse_entity(test_fn) ctx = context.EntityContext( namer=None, @@ -65,7 +69,9 @@ class TypeInfoResolverTest(test.TestCase): namespace=namespace, arg_values=None, arg_types=arg_types, - recursive=True) + owner_type=None, + recursive=True, + type_annotation_func=utils.set_element_type) node = qual_names.resolve(node) node = activity.resolve(node, ctx) node = live_values.resolve(node, ctx, {}) @@ -174,6 +180,22 @@ class TypeInfoResolverTest(test.TestCase): method_call = node.body[0].body[1].value.func self.assertFalse(anno.hasanno(method_call, 'live_val')) + def test_type_annotation(self): + + class Foo(object): + pass + + def test_fn(): + f = [] + f = utils.set_element_type(f, Foo) + return f + + node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) + f_def = node.body[0].body[0].value + self.assertEqual(anno.getanno(f_def, 'element_type'), Foo) + f_ref = node.body[0].body[1].value + self.assertEqual(anno.getanno(f_ref, 'element_type'), Foo) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/templates.py b/tensorflow/contrib/py2tf/pyct/templates.py index 1039fc871393b1b4a388ce56d5beb563a37ee565..cdd71dc56de33cde46d6115085350a321093d792 100644 --- a/tensorflow/contrib/py2tf/pyct/templates.py +++ b/tensorflow/contrib/py2tf/pyct/templates.py @@ -26,7 +26,7 @@ import textwrap import gast -from tensorflow.contrib.py2tf.pyct import copier +from tensorflow.contrib.py2tf.pyct import ast_util from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import qual_names @@ -59,7 +59,7 @@ class ReplaceTransformer(gast.NodeTransformer): repl = self.replacements[node.name] if not isinstance(repl, (gast.Name, ast.Name)): raise ValueError( - 'A function name can only be replaced by a Name node. Found: %s', + 'A function name can only be replaced by a Name node. Found: %s' % repl) node.name = repl.id return node @@ -68,16 +68,33 @@ class ReplaceTransformer(gast.NodeTransformer): if isinstance(node, gast.Attribute): self._set_inner_child_context(node.value, ctx) node.ctx = gast.Load() + elif isinstance(node, gast.Tuple): + for e in node.elts: + self._set_inner_child_context(e, ctx) + node.ctx = ctx elif isinstance(node, gast.Name): node.ctx = ctx + elif isinstance(node, (gast.Str, gast.Num)): + pass else: raise ValueError('unexpected node type "%s"' % node) + def visit_Attribute(self, node): + node = self.generic_visit(node) + if node.attr not in self.replacements: + return node + repl = self.replacements[node.attr] + if not isinstance(repl, gast.Name): + raise ValueError( + 'An attribute can only be replaced by a Name node. Found: %s' % repl) + node.attr = repl.id + return node + def visit_Name(self, node): if node.id not in self.replacements: return node - new_nodes = copier.copy_clean(self.replacements[node.id]) + new_nodes = ast_util.copy_clean(self.replacements[node.id]) if isinstance(new_nodes, gast.AST): new_nodes = [new_nodes] @@ -148,3 +165,17 @@ def replace(template, **replacements): if isinstance(results, list): return [qual_names.resolve(r) for r in results] return qual_names.resolve(results) + + +def replace_as_expression(template, **replacements): + """Variant of replace that generates expressions, instead of code blocks.""" + replacement = replace(template, **replacements) + if len(replacement) != 1: + raise ValueError( + 'single expression expected; for more general templates use replace') + node = replacement[0] + if not isinstance(node, gast.Expr): + raise ValueError( + 'the template is expected to generate an expression node; instead ' + 'found %s' % node) + return node.value diff --git a/tensorflow/contrib/py2tf/pyct/templates_test.py b/tensorflow/contrib/py2tf/pyct/templates_test.py index 0e3d07e378972de67d67d9ef7fd9bef351c5b5be..d7835b80a7f53c3ba012d01cac34b68c57bfe348 100644 --- a/tensorflow/contrib/py2tf/pyct/templates_test.py +++ b/tensorflow/contrib/py2tf/pyct/templates_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import imp + import gast from tensorflow.contrib.py2tf.pyct import compiler @@ -34,7 +36,8 @@ class TemplatesTest(test.TestCase): """ node = templates.replace(template, b=('a', 'c'))[0] - result = compiler.ast_to_object(node) + result, _ = compiler.ast_to_object(node) + self.assertEquals((2, 3), result.test_fn(2, 3)) def test_replace_variable(self): @@ -46,7 +49,7 @@ class TemplatesTest(test.TestCase): """ node = templates.replace(template, a='b')[0] - result = compiler.ast_to_object(node) + result, _ = compiler.ast_to_object(node) self.assertEquals(7, result.test_fn(2)) def test_replace_function_name(self): @@ -58,10 +61,10 @@ class TemplatesTest(test.TestCase): """ node = templates.replace(template, fname='test_fn')[0] - result = compiler.ast_to_object(node) + result, _ = compiler.ast_to_object(node) self.assertEquals(7, result.test_fn(2)) - def test_code_block(self): + def test_replace_code_block(self): template = """ def test_fn(a): block @@ -75,9 +78,46 @@ class TemplatesTest(test.TestCase): gast.Name('a', None, None) ], gast.BinOp(gast.Name('a', None, None), gast.Add(), gast.Num(1))), ] * 2)[0] - result = compiler.ast_to_object(node) + result, _ = compiler.ast_to_object(node) self.assertEquals(3, result.test_fn(1)) + def test_replace_attribute(self): + template = """ + def test_fn(a): + return a.foo + """ + + node = templates.replace(template, foo='b')[0] + result, _ = compiler.ast_to_object(node) + mod = imp.new_module('test') + mod.b = 3 + self.assertEquals(3, result.test_fn(mod)) + + with self.assertRaises(ValueError): + templates.replace(template, foo=1) + + def replace_as_expression(self): + template = """ + foo(a) + """ + + node = templates.replace(template, foo='bar', a='baz') + self.assertTrue(node is gast.Call) + self.assertEqual(node.func.id, 'bar') + self.assertEqual(node.func.args[0].id, 'baz') + + def replace_as_expression_restrictions(self): + template = """ + foo(a) + bar(b) + """ + with self.assertRaises(ValueError): + templates.replace_as_expression(template) + with self.assertRaises(ValueError): + templates.replace('') + with self.assertRaises(ValueError): + templates.replace('a = b') + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/transformer.py b/tensorflow/contrib/py2tf/pyct/transformer.py index 877d52af016af720424c8a56257fec9ab64611cb..57016bb4ce84776dfc8dfbe380322a03eb4b37b8 100644 --- a/tensorflow/contrib/py2tf/pyct/transformer.py +++ b/tensorflow/contrib/py2tf/pyct/transformer.py @@ -44,6 +44,12 @@ class Base(gast.NodeTransformer): self._col_offset = 0 self.context = context + def debug_print(self, node): + """Helper method useful for debugging.""" + if __debug__: + print(pretty_printer.fmt(node)) + return node + def visit(self, node): source_code = self.context.source_code source_file = self.context.source_file diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD index 01804aa8834f23851dbc7af3ae9082645639ffbc..ccc76a79a8fa3b86bd79344fa338662055f4dc54 100644 --- a/tensorflow/contrib/py2tf/utils/BUILD +++ b/tensorflow/contrib/py2tf/utils/BUILD @@ -1,5 +1,7 @@ licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE"]) + load("//tensorflow:tensorflow.bzl", "py_test") filegroup( @@ -18,11 +20,21 @@ py_library( name = "utils", srcs = [ "__init__.py", + "builtins.py", "context_managers.py", + "misc.py", + "multiple_dispatch.py", + "py_func.py", + "tensor_list.py", + "testing.py", + "type_check.py", + "type_hints.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ + "//tensorflow/python:script_ops", + "@six_archive//:six", ], ) @@ -35,3 +47,55 @@ py_test( "//tensorflow/python:client_testlib", ], ) + +py_test( + name = "misc_test", + srcs = ["misc_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "multiple_dispatch_test", + srcs = ["multiple_dispatch_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "py_func_test", + srcs = ["py_func_test.py"], + srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "type_check_test", + srcs = ["type_check_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "tensor_list_test", + srcs = ["tensor_list_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + "//tensorflow/python:list_ops", + ], +) diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py index bca33e89e99cd5939683ad10a2eb17db243af2ef..997c81588728555d20fbf5e48a68c4ea1475857f 100644 --- a/tensorflow/contrib/py2tf/utils/__init__.py +++ b/tensorflow/contrib/py2tf/utils/__init__.py @@ -18,4 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin +from tensorflow.contrib.py2tf.utils.builtins import dynamic_print from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns +from tensorflow.contrib.py2tf.utils.misc import alias_tensors +from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond +from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while +from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func +from tensorflow.contrib.py2tf.utils.testing import fake_tf +from tensorflow.contrib.py2tf.utils.type_check import is_tensor +from tensorflow.contrib.py2tf.utils.type_hints import set_element_type diff --git a/tensorflow/contrib/py2tf/utils/builtins.py b/tensorflow/contrib/py2tf/utils/builtins.py new file mode 100644 index 0000000000000000000000000000000000000000..0a50b80b60101afaa9aa0f445079727e9708ac35 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/builtins.py @@ -0,0 +1,73 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Builtin conversion utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils import py_func +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.util import tf_inspect + + +def dynamic_builtin(f, *args, **kwargs): + """Converts a builtin function call inline.""" + if not tf_inspect.isbuiltin(f): + return f(*args, **kwargs) + + if f is len: + return dynamic_len(*args, **kwargs) + + raise NotImplementedError('The "%s" builtin is not yet supported.' % f) + + +def dynamic_len(list_or_tensor): + """Implementation of len using dynamic dispatch.""" + if tensor_util.is_tensor(list_or_tensor): + shape = list_or_tensor.shape + if not shape: + raise ValueError( + 'len requires non-zero rank for tensor "%s"' % list_or_tensor) + return array_ops.shape(list_or_tensor)[0] + + return len(list_or_tensor) + + +def is_tf_print_compatible(value): + # TODO(mdan): Enable once we can reliably test this. + # This is currently disabled because we can't capture the output of + # op kernels from Python. + del value + return False + + +def dynamic_print(*values): + """Implementartion of print using dynamic dispatch. + + The function attempts to use tf.Print if all the values are compatible. + Otherwise, it will fall back to py_func. + + Args: + *values: values to print + Returns: + A dummy value indicating the print completed. If tf. + """ + + if all(map(is_tf_print_compatible, values)): + return logging_ops.Print(1, values) + return py_func.wrap_py_func(print, None, values, use_dummy_return=True) diff --git a/tensorflow/contrib/py2tf/utils/builtins_test.py b/tensorflow/contrib/py2tf/utils/builtins_test.py new file mode 100644 index 0000000000000000000000000000000000000000..19a72c63ecc873c52abde18e481221fc782ad490 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/builtins_test.py @@ -0,0 +1,78 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for builtins module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import sys + +import six + +from tensorflow.contrib.py2tf.utils import builtins +from tensorflow.python.framework import constant_op +from tensorflow.python.platform import test + + +class BuiltinsTest(test.TestCase): + + def test_dynamic_len_tf_scalar(self): + a = constant_op.constant(1) + + with self.assertRaises(ValueError): + with self.test_session() as sess: + sess.run(builtins.dynamic_builtin(len, a)) + + def test_dynamic_len_tf_array(self): + a = constant_op.constant([1, 2, 3]) + + with self.test_session() as sess: + self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a))) + + def test_dynamic_len_tf_matrix(self): + a = constant_op.constant([[1, 2], [3, 4]]) + + with self.test_session() as sess: + self.assertEqual(2, sess.run(builtins.dynamic_builtin(len, a))) + + def test_dynamic_len_py_list(self): + a = [3] * 5 + + self.assertEqual(5, builtins.dynamic_builtin(len, a)) + + def test_dynamic_print_tf(self): + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + with self.test_session() as sess: + sess.run(builtins.dynamic_print('test message', 1)) + self.assertEqual(out_capturer.getvalue(), 'test message 1\n') + finally: + sys.stdout = sys.__stdout__ + + def test_dynamic_print_complex(self): + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + with self.test_session() as sess: + sess.run(builtins.dynamic_print('test message', [1, 2])) + self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n') + finally: + sys.stdout = sys.__stdout__ + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/utils/context_managers.py b/tensorflow/contrib/py2tf/utils/context_managers.py index 47d98399971da039a8987ea17039c8b44bfa3b61..38d9e11fe9069722b9023fee848bf53e1f72de6a 100644 --- a/tensorflow/contrib/py2tf/utils/context_managers.py +++ b/tensorflow/contrib/py2tf/utils/context_managers.py @@ -20,14 +20,15 @@ from __future__ import print_function import contextlib +from tensorflow.python.framework import ops -def control_dependency_on_returns(tf, return_value): + +def control_dependency_on_returns(return_value): """Create a TF control dependency on the return values of a function. If the function had no return value, a no-op context is returned. Args: - tf: The TensorFlow module. return_value: The return value to set as control dependency. Returns: @@ -38,4 +39,4 @@ def control_dependency_on_returns(tf, return_value): # TODO(mdan): Filter to tensor objects. if not isinstance(return_value, (list, tuple)): return_value = (return_value,) - return tf.control_dependencies(return_value) + return ops.control_dependencies(return_value) diff --git a/tensorflow/contrib/py2tf/utils/context_managers_test.py b/tensorflow/contrib/py2tf/utils/context_managers_test.py index c903f082528118aba2d0163b25a38178b99a17e7..633ba93540e696889a6b2b71b40b999da39d48ff 100644 --- a/tensorflow/contrib/py2tf/utils/context_managers_test.py +++ b/tensorflow/contrib/py2tf/utils/context_managers_test.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.contrib.py2tf.utils import context_managers from tensorflow.python.framework import constant_op -from tensorflow.python.framework import ops from tensorflow.python.platform import test @@ -28,14 +27,14 @@ class ContextManagersTest(test.TestCase): def test_control_dependency_on_returns(self): # Just dry run them. - with context_managers.control_dependency_on_returns(ops, None): + with context_managers.control_dependency_on_returns(None): pass with context_managers.control_dependency_on_returns( - ops, constant_op.constant(1)): + constant_op.constant(1)): pass with context_managers.control_dependency_on_returns( - ops, [constant_op.constant(1), - constant_op.constant(2)]): + [constant_op.constant(1), + constant_op.constant(2)]): pass diff --git a/tensorflow/contrib/py2tf/utils/misc.py b/tensorflow/contrib/py2tf/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..1b06caf0bdeb6f4a079e33f2e887d2dca017adc2 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/misc.py @@ -0,0 +1,50 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Miscellaneous utilities that don't fit anywhere else.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops + + +def alias_tensors(*args): + """Wrap any Tensor arguments with an identity op. + + Any other argument, including Variables, is returned unchanged. + + Args: + *args: Any arguments. Must contain at least one element. + + Returns: + Same as *args, with Tensor instances replaced as described. + + Raises: + ValueError: If args doesn't meet the requirements. + """ + + def alias_if_tensor(a): + return array_ops.identity(a) if isinstance(a, ops.Tensor) else a + + # TODO(mdan): Recurse into containers? + # TODO(mdan): Anything we can do about variables? Fake a scope reuse? + if len(args) > 1: + return (alias_if_tensor(a) for a in args) + elif len(args) == 1: + return alias_if_tensor(args[0]) + + raise ValueError('at least one argument required') diff --git a/tensorflow/contrib/py2tf/utils/misc_test.py b/tensorflow/contrib/py2tf/utils/misc_test.py new file mode 100644 index 0000000000000000000000000000000000000000..8aedd4cd64798660cc07364c45487399986c9be6 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/misc_test.py @@ -0,0 +1,54 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for misc module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils.misc import alias_tensors +from tensorflow.python.framework.constant_op import constant +from tensorflow.python.ops.variables import Variable +from tensorflow.python.platform import test + + +class MiscTest(test.TestCase): + + def test_alias_single_tensor(self): + a = constant(1) + + new_a = alias_tensors(a) + self.assertFalse(new_a is a) + with self.test_session() as sess: + self.assertEqual(1, sess.run(new_a)) + + def test_alias_tensors(self): + a = constant(1) + v = Variable(2) + s = 'a' + l = [1, 2, 3] + + new_a, new_v, new_s, new_l = alias_tensors(a, v, s, l) + + self.assertFalse(new_a is a) + self.assertTrue(new_v is v) + self.assertTrue(new_s is s) + self.assertTrue(new_l is l) + with self.test_session() as sess: + self.assertEqual(1, sess.run(new_a)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/utils/multiple_dispatch.py b/tensorflow/contrib/py2tf/utils/multiple_dispatch.py new file mode 100644 index 0000000000000000000000000000000000000000..a855fdc075941915035d1e3380846ff912803494 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/multiple_dispatch.py @@ -0,0 +1,90 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for type-dependent behavior used in py2tf-generated code.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.contrib.py2tf.utils.type_check import is_tensor +from tensorflow.python.ops import control_flow_ops + + +def run_cond(condition, true_fn, false_fn): + """Type-dependent functional conditional. + + Args: + condition: A Tensor or Python bool. + true_fn: A Python callable implementing the true branch of the conditional. + false_fn: A Python callable implementing the false branch of the + conditional. + + Returns: + result: The result of calling the appropriate branch. If condition is a + Tensor, tf.cond will be used. Otherwise, a standard Python if statement will + be ran. + """ + if is_tensor(condition): + return control_flow_ops.cond(condition, true_fn, false_fn) + else: + return py_cond(condition, true_fn, false_fn) + + +def py_cond(condition, true_fn, false_fn): + if condition: + return true_fn() + else: + return false_fn() + + +def run_while(cond_fn, body_fn, init_args): + """Type-dependent functional while loop. + + Args: + cond_fn: A Python callable implementing the stop conditions of the loop. + body_fn: A Python callable implementing the body of the loop. + init_args: The initial values of the arguments that will be passed to both + cond_fn and body_fn. + + Returns: + result: A list of values with the same shape and type as init_args. If any + of the init_args, or any variables closed-over in cond_fn are Tensors, + tf.while_loop will be used, otherwise a Python while loop will be ran. + + Raises: + ValueError: if init_args is not a tuple or list with one or more elements. + """ + if not isinstance(init_args, (tuple, list)) or not init_args: + raise ValueError( + 'init_args must be a non-empty list or tuple, found %s' % init_args) + + # TODO(alexbw): statically determine all active variables in cond_fn, + # and pass them directly + closure_vars = tuple( + [c.cell_contents for c in six.get_function_closure(cond_fn) or []]) + possibly_tensors = tuple(init_args) + closure_vars + if is_tensor(*possibly_tensors): + return control_flow_ops.while_loop(cond_fn, body_fn, init_args) + else: + return py_while_loop(cond_fn, body_fn, init_args) + + +def py_while_loop(cond_fn, body_fn, init_args): + state = init_args + while cond_fn(*state): + state = body_fn(*state) + return state diff --git a/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py b/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5bb4d4086b002211eebb86783bb7212c707a1418 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py @@ -0,0 +1,69 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for multiple_dispatch.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from tensorflow.contrib.py2tf.utils import multiple_dispatch +from tensorflow.python.client.session import Session +from tensorflow.python.framework.constant_op import constant +from tensorflow.python.platform import test + + +class MultipleDispatchTest(test.TestCase): + + def test_run_cond_python(self): + true_fn = lambda: 2.0 + false_fn = lambda: 3.0 + self.assertEqual(multiple_dispatch.run_cond(True, true_fn, false_fn), 2.0) + self.assertEqual(multiple_dispatch.run_cond(False, true_fn, false_fn), 3.0) + + def test_run_cond_tf(self): + + true_fn = lambda: constant([2.0]) + false_fn = lambda: constant([3.0]) + with Session() as sess: + out = multiple_dispatch.run_cond(constant(True), true_fn, false_fn) + self.assertEqual(sess.run(out), 2.0) + out = multiple_dispatch.run_cond(constant(False), true_fn, false_fn) + self.assertEqual(sess.run(out), 3.0) + + def test_run_while_python(self): + cond_fn = lambda x, t, s: x > t + body_fn = lambda x, t, s: (x * s, t, s) + + x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [3.0, 1.0, 0.5]) + self.assertEqual(x, 0.75) + + x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [3.0, 4.0, 0.5]) + self.assertEqual(x, 3.0) + + def test_run_while_tf(self): + cond_fn = lambda x, t, s: x > t + body_fn = lambda x, t, s: (x * s, t, s) + + with Session() as sess: + x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, + [constant(3.0), 1.0, 0.5]) + self.assertEqual(sess.run(x), 0.75) + + x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, + [constant(3.0), 4.0, 0.5]) + self.assertEqual(sess.run(x), 3.0) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/utils/py_func.py b/tensorflow/contrib/py2tf/utils/py_func.py new file mode 100644 index 0000000000000000000000000000000000000000..838872d092a3ab07e965180eff4fec7ff6c4ccf9 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/py_func.py @@ -0,0 +1,69 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Pyfunc creation utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import script_ops + + +def wrap_py_func(f, return_dtypes, arguments, use_dummy_return=False): + """Helper that wraps a callable to py_func. + + The helper passes tensor arguments through the py_func interface. Non-tensor + arguments are allowed, and will be passed to f directly. Note that non-tensor + arguments are captured by f will not update every time the wrapper is + called (this is consistent with its argument list, which only includes + the tensor arguments). In general, it's safest not to reuse this wrapper. + + Args: + f: Callable + return_dtypes: DType, tuple, list or None, the data type for each of f's + return value. None if f has no return values or use_dummy_return is + True. + arguments: Arguments for f + use_dummy_return: If True, the function will return a dummy value of 1 + and discard its actual return value. + Returns: + The return values of f converted to tensor. + Raises: + ValueError: if the arguments are incorrect. + """ + + if return_dtypes and use_dummy_return: + raise ValueError('if use_dummy_return is True, return_dtypes must be empty') + + n = len(arguments) + arg_is_tensor = tuple(map(tensor_util.is_tensor, arguments)) + index_in_tensor_list = [0] * n + i = 0 + for j in range(n): + index_in_tensor_list[j] = i + if arg_is_tensor[j]: + i += 1 + + def f_wrapper(*tensor_args): + f_args = tuple(tensor_args[index_in_tensor_list[i]] + if arg_is_tensor[i] else arguments[i] for i in range(n)) + retval = f(*f_args) + return 1 if use_dummy_return else retval + + return script_ops.py_func( + f_wrapper, tuple(arguments[i] for i in range(n) if arg_is_tensor[i]), + dtypes.int64 if use_dummy_return else return_dtypes) diff --git a/tensorflow/contrib/py2tf/utils/py_func_test.py b/tensorflow/contrib/py2tf/utils/py_func_test.py new file mode 100644 index 0000000000000000000000000000000000000000..776b5309c6f027bb2008aa83d48e4155e817ed97 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/py_func_test.py @@ -0,0 +1,91 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for wrap_py_func module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils import py_func +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.platform import test + + +class PyFuncTest(test.TestCase): + + def test_wrap_py_func_simple(self): + + def test_fn(a, b, c): + return a + b + c + + with self.test_session() as sess: + tensor_1 = constant_op.constant(1) + self.assertEqual(3, + sess.run( + py_func.wrap_py_func(test_fn, dtypes.int64, + (1, tensor_1, 1)))) + self.assertEqual(3, + sess.run( + py_func.wrap_py_func(test_fn, dtypes.int64, + (1, 1, 1)))) + self.assertEqual(3, + sess.run( + py_func.wrap_py_func(test_fn, dtypes.int64, + (tensor_1, 1, tensor_1)))) + + def test_wrap_py_func_complex_args(self): + + class TestClass(object): + + def __init__(self): + self.foo = 5 + + def test_fn(a, b): + return a * b.foo + + with self.test_session() as sess: + self.assertEqual(35, + sess.run( + py_func.wrap_py_func(test_fn, dtypes.int64, + (7, TestClass())))) + self.assertEqual( + 35, + sess.run( + py_func.wrap_py_func(test_fn, dtypes.int64, + (constant_op.constant(7), TestClass())))) + + def test_wrap_py_func_dummy_return(self): + + side_counter = [0] + + def test_fn(_): + side_counter[0] += 1 + + with self.test_session() as sess: + self.assertEqual(1, + sess.run( + py_func.wrap_py_func(test_fn, None, (5,), True))) + self.assertEqual([1], side_counter) + self.assertEqual(1, + sess.run( + py_func.wrap_py_func(test_fn, None, + (constant_op.constant(5),), + True))) + self.assertEqual([2], side_counter) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/utils/tensor_list.py b/tensorflow/contrib/py2tf/utils/tensor_list.py new file mode 100644 index 0000000000000000000000000000000000000000..2556f412891b4f0b954af5a6f0193341a6a5020a --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/tensor_list.py @@ -0,0 +1,68 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""A typed list in Python.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.ops import list_ops +from tensorflow.python.ops import tensor_array_ops + + +def dynamic_list_append(target, element): + """Converts a list append call inline.""" + if isinstance(target, tensor_array_ops.TensorArray): + return target.write(target.size(), element) + # TODO(mdan): What's the right way to check this? + # TODO(mdan): We may not need this branch. + # It may be possible to use TensorList alone if the loop body will not + # require wrapping it, although we'd have to think about an autoboxing + # mechanism for lists received as parameter. + if isinstance(target, ops.Tensor): + return list_ops.tensor_list_push_back(target, element) + + # Python targets (including TensorList): fallback to their original append. + target.append(element) + return target + + +class TensorList(object): + """Tensor list wrapper API-compatible with Python built-in list.""" + + def __init__(self, shape, dtype): + self.dtype = dtype + self.shape = shape + self.clear() + + def append(self, value): + self.list_ = list_ops.tensor_list_push_back(self.list_, value) + + def pop(self): + self.list_, value = list_ops.tensor_list_pop_back(self.list_, self.dtype) + return value + + def clear(self): + self.list_ = list_ops.empty_tensor_list(self.shape, self.dtype) + + def count(self): + return list_ops.tensor_list_length(self.list_) + + def __getitem__(self, key): + return list_ops.tensor_list_get_item(self.list_, key, self.dtype) + + def __setitem__(self, key, value): + self.list_ = list_ops.tensor_list_set_item(self.list_, key, value) diff --git a/tensorflow/contrib/py2tf/utils/tensor_list_test.py b/tensorflow/contrib/py2tf/utils/tensor_list_test.py new file mode 100644 index 0000000000000000000000000000000000000000..110e4d105e934d9d752afc2ccc0c53c99b70d41d --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/tensor_list_test.py @@ -0,0 +1,117 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for PyFlow list.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils import tensor_list as tl +from tensorflow.python.client.session import Session +from tensorflow.python.eager import context +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework.constant_op import constant +from tensorflow.python.ops import list_ops +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.platform import test + + +class TensorListTest(test.TestCase): + + def _shape(self, shape_tuple): + return constant(shape_tuple, dtypes.int32) + + def test_dynamic_list_append(self): + l = [] + l = tl.dynamic_list_append(l, 1) + self.assertListEqual(l, [1]) + + l = list_ops.empty_tensor_list(self._shape(()), dtypes.int32) + l = tl.dynamic_list_append(l, 1) + s = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) + with self.test_session() as sess: + self.assertAllEqual(sess.run(s), [1]) + + l = tensor_array_ops.TensorArray(dtypes.int32, size=0, dynamic_size=True) + l = tl.dynamic_list_append(l, 1) + s = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(s), [1]) + + l = tl.TensorList(self._shape(()), dtypes.int32) + l = tl.dynamic_list_append(l, 1) + with self.test_session() as sess: + self.assertAllEqual(sess.run(l[0]), 1) + + def test_list_append_python(self): + with context.eager_mode(): + a = constant(3.0) + l = tl.TensorList(a.shape, a.dtype) + l.append(a) + self.assertEqual(l.count().numpy(), 1) + l.append(a) + self.assertEqual(l.count().numpy(), 2) + _ = l.pop() + self.assertEqual(l.count().numpy(), 1) + a2 = l.pop() + self.assertEqual(l.count().numpy(), 0) + self.assertEqual(a.numpy(), a2.numpy()) + + def test_list_index_python(self): + with context.eager_mode(): + a = constant(3.0) + b = constant(2.0) + l = tl.TensorList(a.shape, a.dtype) + l.append(a) + self.assertEqual(l[0].numpy(), a.numpy()) + l[0] = ops.convert_to_tensor(b) + self.assertEqual(l[0].numpy(), b.numpy()) + + def test_list_append_tf(self): + a = constant(3.0) + l = tl.TensorList(a.shape, a.dtype) + l.append(a) + c1 = l.count() + l.append(a) + c2 = l.count() + _ = l.pop() + c3 = l.count() + a2 = l.pop() + c4 = l.count() + with Session() as sess: + c1, c2, c3, c4, a, a2 = sess.run([c1, c2, c3, c4, a, a2]) + self.assertEqual(c1, 1) + self.assertEqual(c2, 2) + self.assertEqual(c3, 1) + self.assertEqual(c4, 0) + self.assertEqual(a, a2) + + def test_list_index_tf(self): + a = constant(3.0) + b = constant(2.0) + l = tl.TensorList(a.shape, a.dtype) + l.append(a) + l0 = l[0] + l[0] = b + l1 = l[0] + with self.test_session() as sess: + l0, l1, a, b = sess.run([l0, l1, a, b]) + self.assertEqual(l0, a) + self.assertEqual(l1, b) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/quantize/python/copy_graph.py b/tensorflow/contrib/py2tf/utils/testing.py similarity index 68% rename from tensorflow/contrib/quantize/python/copy_graph.py rename to tensorflow/contrib/py2tf/utils/testing.py index 0376fcba82b99feabdba3b683f9db9a32db51efb..cb4785d0dc0f4674b3560418daeb6733364b21e7 100644 --- a/tensorflow/contrib/quantize/python/copy_graph.py +++ b/tensorflow/contrib/py2tf/utils/testing.py @@ -12,21 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utility to copy a tf.Graph.""" +"""Testing utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function +import imp + from tensorflow.python.framework import ops -from tensorflow.python.training import saver as saver_lib +from tensorflow.python.ops import math_ops -def CopyGraph(graph): - """Return a copy of graph.""" - meta_graph = saver_lib.export_meta_graph( - graph=graph, collection_list=graph.get_all_collection_keys()) - graph_copy = ops.Graph() - with graph_copy.as_default(): - _ = saver_lib.import_meta_graph(meta_graph) - return graph_copy +def fake_tf(): + """Creates a fake module that looks like TensorFlow, for testing.""" + mod = imp.new_module('tensorflow') + mod_contents = dict() + mod_contents.update(math_ops.__dict__) + mod_contents.update(ops.__dict__) + mod_contents.update(mod.__dict__) + mod.__dict__.update(mod_contents) + return mod diff --git a/tensorflow/contrib/py2tf/utils/type_check.py b/tensorflow/contrib/py2tf/utils/type_check.py new file mode 100644 index 0000000000000000000000000000000000000000..9ca2dec872c8a9ca7bedaa8603f70e3214a3e24a --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/type_check.py @@ -0,0 +1,33 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities used in py2tf-generated code.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import tensor_util + + +def is_tensor(*args): + """Check if all arguments are tensors. + + Args: + *args: Python objects that may or may not be tensors. + + Returns: + True if all *args are TensorFlow types, False if one or more are not. + """ + return any([tensor_util.is_tensor(a) for a in args]) diff --git a/tensorflow/contrib/py2tf/utils/type_check_test.py b/tensorflow/contrib/py2tf/utils/type_check_test.py new file mode 100644 index 0000000000000000000000000000000000000000..7d0428e9cccecdc67511e236bc00655a055aea29 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/type_check_test.py @@ -0,0 +1,43 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for type_check.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy + +from tensorflow.contrib.py2tf.utils import type_check +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test + + +class TypeCheckTest(test.TestCase): + + def test_checks(self): + self.assertTrue(type_check.is_tensor(constant_op.constant([1, 2, 3]))) + self.assertTrue( + type_check.is_tensor(test_util.variables.Variable([1, 2, 3]))) + self.assertTrue( + type_check.is_tensor( + test_util.array_ops.placeholder(test_util.dtypes.float32))) + self.assertFalse(type_check.is_tensor(3)) + self.assertFalse(type_check.is_tensor(numpy.eye(3))) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py b/tensorflow/contrib/py2tf/utils/type_hints.py similarity index 50% rename from tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py rename to tensorflow/contrib/py2tf/utils/type_hints.py index 9f7a95f138f7fd3e726f095dc16f41abb6182e17..aeb9e545610460afbe364dfcfc7a54b9aede29fe 100644 --- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py +++ b/tensorflow/contrib/py2tf/utils/type_hints.py @@ -12,40 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Csiszar f-Divergence and helpers. +"""No-op utilities that provide static type hints. -See ${python/contrib.bayesflow.csiszar_divergence}. +These are used when the data type is not known at creation, for instance in the +case of empty lists. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.csiszar_divergence_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = [ - 'amari_alpha', - 'arithmetic_geometric', - 'chi_square', - 'csiszar_vimco', - 'dual_csiszar_function', - 'jeffreys', - 'jensen_shannon', - 'kl_forward', - 'kl_reverse', - 'log1p_abs', - 'modified_gan', - 'monte_carlo_csiszar_f_divergence', - 'pearson', - 'squared_hellinger', - 'symmetrized_csiszar_function', - 'total_variation', - 't_power', - 'triangular', -] +def set_element_type(entity, dtype, shape=None): + """Indicates that the entity is expected hold items of specified type. -remove_undocumented(__name__, _allowed_symbols) + This function is a no-op. Its presence merely marks the data type of its + argument. The staged TensorFlow ops will reflect and assert this data type. + + Args: + entity: A Tensor or TensorArray. + dtype: TensorFlow dtype value to assert for entity. + shape: Optional shape to assert for entity. + Returns: + The value of entity, unchanged. + """ + del dtype + del shape + return entity diff --git a/tensorflow/contrib/quantization/README.md b/tensorflow/contrib/quantization/README.md new file mode 100644 index 0000000000000000000000000000000000000000..359950aaf3d89c1f3e8fda21cbd27fb89217d918 --- /dev/null +++ b/tensorflow/contrib/quantization/README.md @@ -0,0 +1,7 @@ +The contrib/quantization package exposes a few TensorFlow quantization operations. + +If you are looking for quantized training rewrites that allow for training +quantized models that work with +[TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/), you should look at +the [contrib/quantize](https://www.tensorflow.org/api_docs/python/tf/contrib/quantize) +package. diff --git a/tensorflow/contrib/quantize/BUILD b/tensorflow/contrib/quantize/BUILD index b7d525a1fa203fd150642c18304759e1a9c48c4b..aec9f47ccb20349c08bbe2fd813ee24a807f9fe3 100644 --- a/tensorflow/contrib/quantize/BUILD +++ b/tensorflow/contrib/quantize/BUILD @@ -13,6 +13,20 @@ py_library( deps = [], ) +py_test( + name = "common_test", + size = "small", + srcs = ["python/common_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":common", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + "//tensorflow/python:session", + ], +) + py_library( name = "graph_matcher", srcs = [ @@ -75,7 +89,9 @@ py_library( ":graph_matcher", ":input_to_ops", "//tensorflow/contrib/graph_editor:graph_editor_py", + "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", + "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", "//tensorflow/python:layers", "//tensorflow/python:math_ops", @@ -83,6 +99,7 @@ py_library( "//tensorflow/python:nn_ops", "//tensorflow/python:ops", "//tensorflow/python:training", + "//tensorflow/python:util", "//tensorflow/python:variables", ], ) @@ -92,7 +109,6 @@ py_test( srcs = ["python/fold_batch_norms_test.py"], srcs_version = "PY2AND3", deps = [ - ":copy_graph", ":fold_batch_norms", "//tensorflow/contrib/layers:layers_py", "//tensorflow/python:array_ops", @@ -107,31 +123,7 @@ py_test( "//tensorflow/python:random_ops", "//tensorflow/python:random_seed", "//tensorflow/python:session", - "//tensorflow/python:variables", - ], -) - -py_library( - name = "copy_graph", - srcs = ["python/copy_graph.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/python:framework_ops", "//tensorflow/python:training", - ], -) - -py_test( - name = "copy_graph_test", - size = "small", - srcs = ["python/copy_graph_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":copy_graph", - "//tensorflow/python:constant_op", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform_test", "//tensorflow/python:variables", ], ) @@ -162,7 +154,6 @@ py_test( "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "//tensorflow/python:session", "//tensorflow/python:variables", @@ -174,7 +165,7 @@ py_library( srcs = ["python/quantize.py"], srcs_version = "PY2AND3", deps = [ - ":common", + ":graph_matcher", ":input_to_ops", ":quant_ops", "//tensorflow/contrib/graph_editor:graph_editor_py", @@ -221,7 +212,6 @@ py_test( "//tensorflow/python:math_ops", "//tensorflow/python:nn_ops", "//tensorflow/python:platform_test", - "//tensorflow/python:training", ], ) @@ -233,12 +223,9 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - ":copy_graph", ":fold_batch_norms", ":quantize", - "//tensorflow/python:framework_ops", "//tensorflow/python:util", - "//tensorflow/python:variables", ], ) @@ -251,13 +238,11 @@ py_test( ":quantize_graph", "//tensorflow/contrib/layers:layers_py", "//tensorflow/python:array_ops", - "//tensorflow/python:constant_op", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", "//tensorflow/python:init_ops", "//tensorflow/python:nn_ops", "//tensorflow/python:platform_test", - "//tensorflow/python:variables", ], ) diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index 40541729da5fd9d0ae75579e11f20999337de124..348c824a4072c3329ac4a3441c19c71598bc9c03 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -1,9 +1,9 @@ +# Quantized Training Rewrites + tf.contrib.quantize provides tools for transforming graphs to include ops to model quantization of weights, biases and activations during both training and inference. This is done using the -[fake quantization op] -(https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization), -which is described below: +[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). Recent literature has shown that fixed point networks provide comparable performance to floating point networks [1]. This is achieved by modeling the @@ -14,56 +14,52 @@ updated at high precision as this is needed to ensure sufficient precision in accumulating tiny adjustments to the parameters. However, for the forward pass, the parameters and activations are quantized to the desired lower precision. -![drawing](g3doc/drawings/Fake_Quantization.jpg) - -###Forward pass - - - - -\begin{equation*} -f_Q(x) = \Delta\text{ }round\left(\frac{sat\left(x\right)-x_{min}}{\Delta}\right) -\end{equation*} - - -where - -$$ -\begin{equation*} -sat(x) = -\left\{ - \begin{array}{ll} - x_{min} & \mbox{if } x \le x_{min} \\ - x & \mbox{if } x_{min} \leq x \leq x_{max} \\ - x_{max} & \mbox{if } x_{max} \le x - \end{array} -\right. -\end{equation*} -$$ - - -where $$\Delta$$ is the Quantizer Step size, given by -$$\Delta =\frac{x_{max} - x_{min} }{255} $$ and $$x_{min} $$ and $$x_{max}$$ are -the minimum and maximum values of the variable under consideration. Note that -the rounding performed is deterministic and corresponds to asymmetric rounding, -which is supported in almost all hardware platforms. - -###Backward pass -For the backward pass, we model the quantizer as a piecewise linear block, with -derivatives that are non-zero only in the linear region. - - - -\begin{equation*} -\frac{df_Q(x)}{dx}=1, x_{min} \leq x \leq x_{max},\text{ 0 elsewhere } -\end{equation*} - -Therefore, the backward pass through the quantizer reduces to passing through -the gradients as long as the inputs to the quantizer are in the linear region. -Otherwise, the gradients are set to zero. - -Note that the quantizer is fully specified by the min and max values of the -variables being quantized. +## How to use the Rewrites + +tf.contrib.quantize provides two rewrites, one to train for quantization and +one to create a [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) +compatible eval graph. + +``` +# Build forward pass of model. +… +loss = tf.losses.get_total_loss() + +# Call the training rewrite which rewrites the graph in-place with FakeQuantization nodes +# and folds batchnorm for training. +# It is often needed to finetune a floating point model for quantization with this training tool. +# When training from scratch, quant_delay can be used to activate quantization after +# training to convergence with the float graph, effectively finetuning the model. +tf.contrib.quantize.create_training_graph(quant_delay=2000000) + +# Call backward pass optimizer as usual. +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +optimizer.minimize(loss) +``` + +Additionally, the rewritten eval graph is non-trivially different from the +training graph due the effects of quantization on batch normalization. Thus, +we offer a separate rewrite for the eval_graph. + +``` +# Build eval model +… +logits = tf.nn.softmax_cross_entropy_with_logits(...) + +# Call the eval rewrite which rewrites the graph in-place with FakeQuantization nodes +# and fold batchnorm for eval. +tf.contrib.quantize.create_eval_graph() + +# Save the checkpoint and eval graph proto to disk for freezing and providing to TFLite. +with open(eval_graph_file, ‘w’) as f: + f.write(str(g.as_graph_def())) +saver = tf.train.Saver() +saver.save(sess, checkpoint_name) +``` + +These rewrites are an active area of research and experimentation, so the +rewrites and quantized training will likely not work across all models, though +we hope to work towards generalizing these techniques. [1] P.Gysel, "HARDWARE-ORIENTED APPROXIMATION OF CONVOLUTIONAL diff --git a/tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg b/tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg deleted file mode 100644 index fdc7ae40cec757cc0a93d50eca6c8698a4697d07..0000000000000000000000000000000000000000 Binary files a/tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg and /dev/null differ diff --git a/tensorflow/contrib/quantize/python/common.py b/tensorflow/contrib/quantize/python/common.py index d0b0674c31239ee903f5ab7ef9ae0262bb20d189..3a1fa61e43986af1a1315d5a9e6f010e802ea157 100644 --- a/tensorflow/contrib/quantize/python/common.py +++ b/tensorflow/contrib/quantize/python/common.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Constants used across this package.""" +"""Common utilities used across this package.""" from __future__ import absolute_import from __future__ import division @@ -21,6 +21,12 @@ from __future__ import print_function import collections import re +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope + # Skip all operations that are backprop related or export summaries. SKIPPED_PREFIXES = ( 'gradients/', 'RMSProp/', 'Adagrad/', 'Const_', 'HistogramSummary', @@ -86,3 +92,31 @@ def _GetOperationByNameDontThrow(graph, name): return graph.get_operation_by_name(name) except KeyError: return None + + +def CreateOrGetQuantizationStep(): + """Returns a Tensor of the number of steps the quantized graph has run. + + Returns: + Quantization step Tensor. + """ + quantization_step_name = 'fake_quantization_step' + quantization_step_tensor_name = quantization_step_name + '/AssignAdd:0' + g = ops.get_default_graph() + try: + return g.get_tensor_by_name(quantization_step_tensor_name) + except KeyError: + # Create in proper graph and base name_scope. + with g.name_scope(None): + quantization_step_tensor = variable_scope.get_variable( + quantization_step_name, + shape=[], + dtype=dtypes.int64, + initializer=init_ops.zeros_initializer(), + trainable=False, + collections=[ops.GraphKeys.GLOBAL_VARIABLES]) + with g.name_scope(quantization_step_tensor.op.name + '/'): + # We return the incremented variable tensor. Since this is used in conds + # for quant_delay and freeze_bn_delay, it will run once per graph + # execution. + return state_ops.assign_add(quantization_step_tensor, 1) diff --git a/tensorflow/contrib/quantize/python/common_test.py b/tensorflow/contrib/quantize/python/common_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d6237fe5e38d905bf262d7be3746b9ee6046da47 --- /dev/null +++ b/tensorflow/contrib/quantize/python/common_test.py @@ -0,0 +1,59 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for common utilities in this package.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.quantize.python import common +from tensorflow.python.client import session +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import variables +from tensorflow.python.platform import googletest + + +class CommonTest(test_util.TensorFlowTestCase): + + def testCreateOrGetQuantizationStep(self): + g = ops.Graph() + with session.Session(graph=g) as sess: + quantization_step_tensor = common.CreateOrGetQuantizationStep() + + # Check that operations are added to the graph. + num_nodes = len(g.get_operations()) + self.assertGreater(num_nodes, 0) + + # Check that getting the quantization step doesn't change the graph. + get_quantization_step_tensor = common.CreateOrGetQuantizationStep() + self.assertEqual(quantization_step_tensor, get_quantization_step_tensor) + self.assertEqual(num_nodes, len(g.get_operations())) + + # Ensure that running the graph increments the quantization step. + sess.run(variables.global_variables_initializer()) + step_val = sess.run(quantization_step_tensor) + self.assertEqual(step_val, 1) + + # Ensure that even running a graph that depends on the quantization step + # multiple times only executes it once. + a = quantization_step_tensor + 1 + b = a + quantization_step_tensor + _, step_val = sess.run([b, quantization_step_tensor]) + self.assertEqual(step_val, 2) + + +if __name__ == '__main__': + googletest.main() diff --git a/tensorflow/contrib/quantize/python/copy_graph_test.py b/tensorflow/contrib/quantize/python/copy_graph_test.py deleted file mode 100644 index 7ff9ad9f8412d7076bf12d6cf10772244444013f..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/quantize/python/copy_graph_test.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for copy_graph.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.quantize.python import copy_graph -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.ops import variables -from tensorflow.python.platform import googletest - - -class CopyGraphTest(test_util.TensorFlowTestCase): - - def _CompareNodeInGraph(self, node, graph): - graph_node = graph.get_operation_by_name(node.name) - self.assertEqual(str(node.node_def), str(graph_node.node_def)) - - def testCopyGraph(self): - graph = ops.Graph() - with graph.as_default(): - a = constant_op.constant(1.0) - b = variables.Variable(2.0) - c = a + b - graph_copy = copy_graph.CopyGraph(graph) - # Ensure that the three original nodes are in the new graph. - # import_meta_graph also adds a saver node to the graph which we don't care - # about in this specific use case. - for tensor in [a, b, c]: - self._CompareNodeInGraph(tensor.op, graph_copy) - # Test that the graph collections are the same. - for key in graph.get_all_collection_keys(): - self.assertEqual( - len(graph.get_collection(key)), - len(graph_copy.get_collection(key)), 'Collection %s differs.') - - -if __name__ == '__main__': - googletest.main() diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py index 7fa0d484ec94f90a817630f2eb985c0cdc0e4c04..1f0648bbb6cf4579739cb2d4fbcfb478aaa5836d 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py @@ -31,11 +31,10 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops -from tensorflow.python.training import training_util from tensorflow.python.util import compat -def FoldBatchNorms(graph, freeze_batch_norm_delay=None, is_training=True): +def FoldBatchNorms(graph, is_training, freeze_batch_norm_delay=None): """Finds batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -43,25 +42,22 @@ def FoldBatchNorms(graph, freeze_batch_norm_delay=None, is_training=True): Args: graph: Graph to walk and modify. - freeze_batch_norm_delay: How many steps to wait before freezing - moving mean and variance and using them for batch normalization. This value - is used only when is_training is True. - is_training: Bool, true if training - + is_training: Bool, true if training. + freeze_batch_norm_delay: How many steps to wait before freezing moving mean + and variance and using them for batch normalization. This value is used + only when is_training is True. Raises: ValueError: When batch norm folding fails. """ _FoldFusedBatchNorms( - graph, - freeze_batch_norm_delay=freeze_batch_norm_delay, - is_training=is_training) + graph, is_training, freeze_batch_norm_delay=freeze_batch_norm_delay) _FoldUnfusedBatchNorms( graph, - freeze_batch_norm_delay=freeze_batch_norm_delay, - is_training=is_training) + is_training=is_training, + freeze_batch_norm_delay=freeze_batch_norm_delay) -def _FoldFusedBatchNorms(graph, freeze_batch_norm_delay, is_training): +def _FoldFusedBatchNorms(graph, is_training, freeze_batch_norm_delay): """Finds fused batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -69,9 +65,9 @@ def _FoldFusedBatchNorms(graph, freeze_batch_norm_delay, is_training): Args: graph: Graph to walk and modify. - freeze_batch_norm_delay: How many steps to wait before freezing - moving mean and variance and using them for batch normalization - is_training: Bool, true if training + is_training: Bool, true if training. + freeze_batch_norm_delay: How many steps to wait before freezing moving mean + and variance and using them for batch normalization. Raises: ValueError: When batch norm folding fails. @@ -198,7 +194,7 @@ def _FindFusedBatchNorms(graph): layer_op = match_result.get_op(layer_pattern) layer_tensor = match_result.get_tensor(layer_pattern) bn_op = match_result.get_op(batch_norm_pattern) - batch_epsilon_tensor = bn_op.get_attr('epsilon') + batch_epsilon = bn_op.get_attr('epsilon') # In the MatMul case, the output of batch norm is reshaped back into a # 2D tensor, so the output_tensor is the output of the Reshape op. @@ -211,6 +207,11 @@ def _FindFusedBatchNorms(graph): continue output_tensor = output_reshape_op.outputs[0] + # Ensure that the output tensor has consumers, otherwise this is a dangling + # node and not a match. + if not output_tensor.consumers(): + continue + input_tensor = match_result.get_tensor(input_pattern) weight_tensor = match_result.get_tensor(weight_pattern) gamma_tensor = match_result.get_tensor(gamma_pattern) @@ -274,7 +275,7 @@ def _FindFusedBatchNorms(graph): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon_tensor=batch_epsilon_tensor) + batch_epsilon=batch_epsilon) def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, @@ -305,10 +306,10 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, Args: context: The scope under which we look for batch norm params match: Object containg required batch norm tensors for correction - computation + computation. freeze_batch_norm_delay: Delay in steps at which computation switches from regular batch norm to frozen mean and variance. - fused_batch_norm: Bool, true if fused batch norm is used + fused_batch_norm: Bool, true if fused batch norm is used. Returns: A tuple of correction_scale, correction_recip, correction_offset @@ -317,9 +318,8 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, g = ops.get_default_graph() with g.name_scope(context + '/batch_norm_correction'): recip_sigma_mv = math_ops.rsqrt( - match.moving_variance_tensor + match.batch_epsilon_tensor) - recip_sigma = math_ops.rsqrt( - match.variance_tensor + match.batch_epsilon_tensor) + match.moving_variance_tensor + match.batch_epsilon) + recip_sigma = math_ops.rsqrt(match.variance_tensor + match.batch_epsilon) correction_scale = math_ops.divide( recip_sigma_mv, recip_sigma, name='scale_compute') correction_scale = array_ops.identity( @@ -334,7 +334,7 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, if freeze_batch_norm_delay is not None: use_mv_avg = math_ops.greater_equal( - training_util.get_or_create_global_step(), + common.CreateOrGetQuantizationStep(), freeze_batch_norm_delay, name='use_moving_average') else: @@ -418,7 +418,7 @@ def _FoldFusedBatchNormGrad(op, unused_grad_y, grad_mean, grad_var, unused_1, return (dmean_dx + dvar_dx), None, None, None, None -def _FoldUnfusedBatchNorms(graph, freeze_batch_norm_delay, is_training): +def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay): """Finds unfused batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -426,9 +426,9 @@ def _FoldUnfusedBatchNorms(graph, freeze_batch_norm_delay, is_training): Args: graph: Graph to walk and modify. - freeze_batch_norm_delay: How many steps to wait before freezing - moving mean and variance and using them for batch normalization - is_training: Bool, True if training + is_training: Bool, True if training. + freeze_batch_norm_delay: How many steps to wait before freezing moving mean + and variance and using them for batch normalization. Raises: ValueError: When batch norm folding fails. @@ -438,6 +438,9 @@ def _FoldUnfusedBatchNorms(graph, freeze_batch_norm_delay, is_training): for bn in common.BatchNormGroups(graph): has_scaling = _HasScaling(graph, input_to_ops_map, bn) + if not _IsValidUnfusedBatchNorm(graph, bn): + continue + # The mangling code intimately depends on BatchNorm node's internals. original_op, folded_op = _CreateFoldedOp( graph, @@ -466,24 +469,32 @@ def _FoldUnfusedBatchNorms(graph, freeze_batch_norm_delay, is_training): raise ValueError('Unexpected inputs to op: %s' % add_bypass.name) +def _IsValidUnfusedBatchNorm(graph, context): + """Checks that the output of the unfused batch norm has consumers.""" + add_shift = graph.get_operation_by_name( + context + '/BatchNorm/batchnorm/add_1') + # Ensure that the output tensor of batch norm has consumers, otherwise this + # is a dangling node and not a match. + return bool(add_shift.outputs[0].consumers()) + + def _GetBatchNormParams(graph, context, has_scaling): """Extracts relevant tensors for folding batch norms. Args: graph: Graph to inspect. context: The scope under which we look for batch norm params - has_scaling: Bool that specifies if scaling is done as part of batch - norm + has_scaling: Bool that specifies if scaling is done as part of batch norm. Returns: - _BatchNormMatch containing all required batch norm parameters + _BatchNormMatch containing all required batch norm parameters. """ gamma_tensor = None batch_mean_tensor = None batch_variance_tensor = None moving_mean_tensor = None moving_variance_tensor = None - batch_epsilon_tensor = None + batch_epsilon = None bn_decay_mean_tensor = None bn_decay_var_tensor = None @@ -514,7 +525,7 @@ def _GetBatchNormParams(graph, context, has_scaling): if op.name.endswith(op_suffix_moving_variance): moving_variance_tensor = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_epsilon): - batch_epsilon_tensor = graph.get_tensor_by_name(op.name + ':0') + batch_epsilon = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_bn_decay_mean): bn_decay_mean_tensor = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_bn_decay_var): @@ -540,7 +551,7 @@ def _GetBatchNormParams(graph, context, has_scaling): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon_tensor=batch_epsilon_tensor) + batch_epsilon=batch_epsilon) def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, @@ -554,20 +565,20 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, Args: graph: Graph to modify. context: String, batch norm context, i.e. node into which BatchNorm is - nested. + nested. has_scaling: Whether the batch norm has scaling enabled. - freeze_batch_norm_delay: How many steps to wait before freezing - moving mean and variance and using them for batch normalization - is_training: Bool, true if training + freeze_batch_norm_delay: How many steps to wait before freezing moving mean + and variance and using them for batch normalization. + is_training: Bool, true if training. Raises: ValueError: When operation type is not supported, or input and output tensor - shapes mismatch for created operations: mul_fold, add_fold. + shapes mismatch for created operations: mul_fold, add_fold. Returns: A pair of Operations, the first is the original consumer node of the batch - norm (../BatchNorm/batchnorm/add_1), the second is the consumer node of - the folded graph (add_fold). + norm (../BatchNorm/batchnorm/add_1), the second is the consumer node of + the folded graph (add_fold). """ mul_scale_name = 'mul_1' if has_scaling else 'mul' mul_scale = graph.get_operation_by_name(context + @@ -642,7 +653,7 @@ def _CloneOp(op, new_name, new_inputs): op: Operation to modify. new_name: String, a new name to set on cloned op. new_inputs: A list of tuples (idx, tensor), each input with corresponding - index will be replaced by the given Tensor in the cloned op. + index will be replaced by the given Tensor in the cloned op. Returns: Operation, the cloned op. @@ -821,7 +832,7 @@ class _BatchNormMatch(object): def __init__(self, layer_op, bn_op, output_tensor, input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, variance_tensor, moving_mean_tensor, moving_variance_tensor, - bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon_tensor): + bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon): self._layer_op = layer_op self._bn_op = bn_op self._output_tensor = output_tensor @@ -835,7 +846,7 @@ class _BatchNormMatch(object): self._moving_variance_tensor = moving_variance_tensor self._bn_decay_mean_tensor = bn_decay_mean_tensor self._bn_decay_var_tensor = bn_decay_var_tensor - self._batch_epsilon_tensor = batch_epsilon_tensor + self._batch_epsilon = batch_epsilon @property def layer_op(self): @@ -882,8 +893,8 @@ class _BatchNormMatch(object): return self._moving_variance_tensor @property - def batch_epsilon_tensor(self): - return self._batch_epsilon_tensor + def batch_epsilon(self): + return self._batch_epsilon @property def bn_decay_mean_tensor(self): diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py index 330bd8a6474c18b236b635d930e7a1df9594d84f..c90a18ab0357f1bcbc5d8ccd48edf894d7baf5f9 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.layers.python.layers import layers -from tensorflow.contrib.quantize.python import copy_graph from tensorflow.contrib.quantize.python import fold_batch_norms from tensorflow.python.client import session from tensorflow.python.framework import dtypes @@ -34,6 +33,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest +from tensorflow.python.training import saver as saver_lib batch_norm = layers.batch_norm conv2d = layers.conv2d @@ -379,7 +379,7 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): if with_bypass: node = math_ops.add(inputs, node, name='test/Add') relu_node = relu(node, name='test/' + relu_op_name) - folded_g = copy_graph.CopyGraph(unfolded_g) + folded_g = self._CopyGraph(unfolded_g) with folded_g.as_default(): fold_batch_norms.FoldBatchNorms( folded_g, @@ -462,5 +462,15 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): out_op = graph.get_operation_by_name(out_op_name) self.assertIn(op.outputs[0].name, [str(t.name) for t in out_op.inputs]) + def _CopyGraph(self, graph): + """Return a copy of graph.""" + meta_graph = saver_lib.export_meta_graph( + graph=graph, collection_list=graph.get_all_collection_keys()) + graph_copy = ops.Graph() + with graph_copy.as_default(): + _ = saver_lib.import_meta_graph(meta_graph) + return graph_copy + + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/contrib/quantize/python/quant_ops.py b/tensorflow/contrib/quantize/python/quant_ops.py index f80d427ff0a6573ecd6562c443182797b5d22527..0a8e35080cb08f71dc28e33c6138a12656e5a5ea 100644 --- a/tensorflow/contrib/quantize/python/quant_ops.py +++ b/tensorflow/contrib/quantize/python/quant_ops.py @@ -53,7 +53,7 @@ def LastValueQuantize(inputs, init_max=6.0, updates_collection=ops.GraphKeys.UPDATE_OPS, vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES, - scope=None, + name_prefix='LastValueQuant', reuse=None, is_training=True, num_bits=8, @@ -73,7 +73,7 @@ def LastValueQuantize(inputs, computation. vars_collection: (Optional) collection where to store variables for quantization interval ends. - scope: Optional scope for variable_scope. + name_prefix: name_prefix for created nodes. reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. is_training: Whether the op is applied to a training or eval graph. @@ -84,13 +84,13 @@ def LastValueQuantize(inputs, a tensor containing quantized values. """ with variable_scope.variable_scope( - scope, 'LastValueQuantize', values=[inputs], reuse=reuse): + None, default_name=name_prefix, values=[inputs], reuse=reuse): input_shape = inputs.get_shape() input_dim = len(input_shape) if per_channel: # Only support quantizing 1-, 2- and 4-dimensional tensors. assert input_dim in [1, 2, 4], ('Expected 1D, 2D or 4D input, was: %s in ' - ' scope: %s' % (input_shape, scope)) + ' scope: %s' % (input_shape, name_prefix)) min_max_shape = [input_shape[-1]] else: min_max_shape = [] @@ -165,7 +165,7 @@ def MovingAvgQuantize(inputs, ema_decay=0.999, updates_collection=ops.GraphKeys.UPDATE_OPS, vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES, - scope=None, + name_prefix='MovingAvgQuantize', reuse=None, is_training=True, num_bits=8, @@ -186,7 +186,7 @@ def MovingAvgQuantize(inputs, computation. vars_collection: (Optional) collection where to store variables for quantization interval ends. - scope: Optional scope for variable_scope. + name_prefix: name_prefix for created nodes. reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. is_training: Whether the op is applied to a training or eval graph. @@ -197,13 +197,13 @@ def MovingAvgQuantize(inputs, a tensor containing quantized values. """ with variable_scope.variable_scope( - scope, 'MovingAvgQuantize', values=[inputs], reuse=reuse): + None, default_name=name_prefix, values=[inputs], reuse=reuse): input_shape = inputs.get_shape() input_dim = len(input_shape) if per_channel: # Only support quantizing 1-, 2- and 4-dimensional tensors. assert input_dim in [1, 2, 4], ('Expected 1D, 2D or 4D input, was: %s in ' - ' scope: %s' % (input_shape, scope)) + ' scope: %s' % (input_shape, name_prefix)) min_max_shape = [input_shape[-1]] else: min_max_shape = [] diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py index 50a2b4c91c9e7a2681f6041646a023a4225fb0c5..5fd806d195dce671d079386ea4b6c89042e26cf6 100644 --- a/tensorflow/contrib/quantize/python/quantize.py +++ b/tensorflow/contrib/quantize/python/quantize.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Logic to update a Tensorflow model graph with quantization operations.""" +"""Logic to update a TensorFlow model graph with quantization operations.""" from __future__ import absolute_import from __future__ import division @@ -21,37 +21,37 @@ from __future__ import print_function import re from tensorflow.contrib import graph_editor from tensorflow.contrib.quantize.python import common +from tensorflow.contrib.quantize.python import graph_matcher from tensorflow.contrib.quantize.python import input_to_ops from tensorflow.contrib.quantize.python import quant_ops from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.training import training_util -# Operation types used to select operations of interest. +# Quantizable operation types that are supported by the quantization rewrite. _QUANTIZABLE_TYPES = {'Conv2D', 'MatMul', 'DepthwiseConv2dNative'} -# Custom key for storing and retrieving update ops used by quantizing nodes. -_UPDATE_QUANT_OPS = 'update_quant_ops' +# Activations that are supported by the quantization rewrite. +_ACTIVATION_TYPES = {'Relu', 'Relu6', 'Identity'} + +# Weight types that are supported by the quantization rewrite. +# TODO(suharshs): Add support for ResourceVariable. +_WEIGHT_TYPES = {'Variable', 'VariableV2'} def Quantize(graph, + is_training, weight_bits=8, - weight_narrow_range=False, activation_bits=8, ema_decay=0.999, quant_delay=None, - vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES, - is_training=True, - quantize_folded_weights_use_ema=False): + vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES): """Updates graph with quantization operations. Args: graph: Graph to modify. + is_training: Whether quantizing training graph or eval graph. weight_bits: Number of bits to use for quantizing weights. - weight_narrow_range: Whether to use a more efficient narrow range for - weights quantization. With weight_narrow_range true, the range is - [1; 2^weight_bits - 1], with it false [0; 2^weight_bits - 1]. activation_bits: Number of bits to use for quantizing activations. ema_decay: (Optional) Float, EMA decay parameter. EMA is used to update quantization intervals for quantizing activations (see here about EMA: @@ -61,346 +61,292 @@ def Quantize(graph, training. vars_collection: (Optional) Collection where to store the variables for quantization interval ends. - is_training: (Optional) Whether quantizing training graph or eval graph. - quantize_folded_weights_use_ema: (Optional, default False) Whether to - quantize weights after batchnorm-folding with exponential average - quantization. Raises: ValueError: When quantization fails. """ - context = _QuantizeContext(graph, weight_bits, weight_narrow_range, - activation_bits, ema_decay, quant_delay, - vars_collection, is_training, - quantize_folded_weights_use_ema) - - graph_ops = graph.get_operations() - - # Filter out backprop and summary related operations, leave only interesting - # op types. - def _IsInterestingOpWithWeights(op): - return (op.type in _QUANTIZABLE_TYPES and - not op.name.startswith(common.SKIPPED_PREFIXES)) - - for op in (op for op in graph_ops if _IsInterestingOpWithWeights(op)): - if op.name.endswith('/depthwise'): - # Separable convolution may consist of 2 convolution nodes. If so, skip - # .../depthwise and only quantize the top one. - separable_conv = context.GetOperationByNameDontThrow( - op.name[:-len('/depthwise')]) - if separable_conv and separable_conv.type == 'Conv2D': - continue - # Quantize add ops that come after Conv2D or DepthwiseConv2dNative. - if op.type in ['Conv2D', 'DepthwiseConv2dNative']: - add_context_re = re.search(r'^(.*)/[^/]+/', op.name) - if add_context_re is not None: - context.add_contexts.add(add_context_re.group(1)) - if not op.name.endswith('_Fold'): - folded_op = context.GetOperationByNameDontThrow(op.name + '_Fold') - # Do nothing if found, it will be quantized when it is iterated over. - if not folded_op: - context.QuantizeOpWithWeights(op, folded=False) - else: - context.QuantizeOpWithWeights(op, folded=True) - - context.QuantizeAddContexts() - - # Once all quantization ops have been inserted in the graph, collect update - # ops for their variables and modify the TF Slim update barrier (see - # https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py) - # to depend on them. - try: - update_barrier = graph.get_operation_by_name('update_barrier') - except KeyError: - # In evaluation graph, this barrier may not exist. - return None - update_quant_ops = graph.get_collection_ref(_UPDATE_QUANT_OPS) - graph_editor.add_control_inputs(update_barrier, update_quant_ops) - - -class _QuantizeContext(object): - """Context holds references needed for quantization.""" - - def __init__(self, - graph, - weight_bits, - weight_narrow_range, - activation_bits, - ema_decay=0.999, - quant_delay=None, - vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES, - is_training=True, - quantize_folded_weights_use_ema=False): - """Initializes context to hold references needed for quantization. - - Args: - graph: Graph to modify. - weight_bits: Number of bits to use for quantizing weights. - weight_narrow_range: Whether to use a more efficient narrow range for - weights quantization. With weight_narrow_range true, the range is - [1; 2^weight_bits - 1], with it false [0; 2^weight_bits - 1]. - activation_bits: Number of bits to use for quantizing activations. - ema_decay: (Optional) Float, EMA decay parameter. - quant_delay: (Optional, default None) Int, count of global steps for which - to delay quantization. This helps weights stabilize at the start of - training. - vars_collection: (Optional) Collection where to store the variables for - quantization interval ends. - is_training: (Optional) Whether quantizing training or eval graph. - quantize_folded_weights_use_ema: (Optional, default False) Whether to - quantize weights after batchnorm-folding with exponential average - quantization. - """ - self.graph = graph - self.weight_bits = weight_bits - self.weight_narrow_range = weight_narrow_range - self.activation_bits = activation_bits - self.ema_decay = ema_decay - self.quant_delay = quant_delay - self.vars_collection = vars_collection - self.is_training = is_training - self.quantize_folded_weights_use_ema = quantize_folded_weights_use_ema - self.input_to_ops_map = input_to_ops.InputToOps(graph) - self.add_contexts = set() - - def QuantizeAddContexts(self): - """Quantizes all add ops in self.add_contexts.""" - # Loop through sorted self.add_contexts so that op creation is - # deterministic. This is needed when using multiple worker replicas so that - # the ops can be initialized consistently. - for add_context in sorted(self.add_contexts): - add_op = self.GetOperationByNamesDontThrow([ - add_context + '/Add', add_context + '/add']) - if add_op is not None: - self._InsertQuantOp( - add_context, - add_op, - self.input_to_ops_map.ConsumerOperations(add_op), - name='add_quant', - moving_avg=True, - bits=self.activation_bits, - narrow_range=False) - - def QuantizeOpWithWeights(self, op, folded): - """Quantizes around the specific operation with or without batch norm. - - Args: - op: Operation to quantize. - folded: Operation has been folded and needs special handling if True. - Raises: - ValueError: When quantization fails. - """ - # Op name component before the last slash will be used as context. - context = re.search(r'^(.*)/([^/]+)', op.name).group(1) - - # Quantize weights. - if folded: - producer_op = self.graph.get_operation_by_name(context + '/mul_fold') - else: - try: - input_idx = next(i for i, v in enumerate(op.inputs) - if '/weights/' in v.name or - '/depthwise_weights' in v.name) - except StopIteration: - raise ValueError('No inputs to quantize for op: %s' % op) - producer_op = op.inputs[input_idx].op - - # If batch norm is used, the folded weights depend on the batch std, hence - # it is sensible to use EMA during training to smooth out the noise. This is - # controlled by the flag quantize_folded_weights_use_ema. Its default is - # False for backward compatibility. - # If there is no batch norm, weights do not depend on the batch and using - # the latest value of min and max is more efficient. - weight_use_ema = folded and self.quantize_folded_weights_use_ema - self._InsertQuantOp( - context, - producer_op, [op], - name='weights_quant', - moving_avg=weight_use_ema, - delay_requested=weight_use_ema, - bits=self.weight_bits, - narrow_range=self.weight_narrow_range) - - # Important: do not quantize biases here. During inference they are - # quantized to 32 bits, which is much finer than 8 bit quantization and - # depends on weight and input activation ranges. - - # Find activation and (optionally) Add operations to quantize. - activation_op, add_op, add_context = self._GetReluAndAddOperations(context, - op) - if add_op: - original_context = context - context = add_context - - # Quantize activation outputs. - consumer_ops = self.input_to_ops_map.ConsumerOperations(activation_op) - self._InsertQuantOp( + input_to_ops_map = input_to_ops.InputToOps(graph) + for layer_match in _FindLayersToQuantize(graph): + # Quantize the weights. + context = _GetContextFromOp(layer_match.layer_op) + _InsertQuantOp( context, - activation_op, + 'weights_quant', + layer_match.weight_tensor.op, [layer_match.layer_op], + is_training, + moving_avg=False, + ema_decay=ema_decay, + quant_delay=quant_delay, + narrow_range=True, + vars_collection=vars_collection, + bits=weight_bits) + + # Quantize the activations. + consumer_ops = input_to_ops_map.ConsumerOperations( + layer_match.activation_op) + add_context = context + if layer_match.bypass_op: + add_context = re.search(r'^(.*)/([^/]+)', context).group(1) + _InsertQuantOp( + add_context, + 'act_quant', + layer_match.activation_op, consumer_ops, - name='act_quant', + is_training, moving_avg=True, - init_min=0.0, - bits=self.activation_bits, - narrow_range=False) - - # When a bypass connection was found, also quantize Add op input. - if add_op: - def _QuantizeAddInput(add_input): - if folded: - return add_input.op.name.endswith('/add_fold') - else: - return add_input.op.name.startswith(original_context + '/') - - for add_input in add_op.inputs: - if _QuantizeAddInput(add_input): - self._InsertQuantOp( - original_context, - add_input.op, [add_op], - name='conv_quant', - moving_avg=True, - bits=self.activation_bits, - narrow_range=False) - - def _GetReluAndAddOperations(self, context, op): - """Looks up a Relu* and Add operations in given context. - - Args: - context: Context where to look for operations. - op: Operation to quantize. - - Returns: - A triplet (Operation, Operation, string), the first element is an end - point operation, the second is Add operation (optional), the third element - is string context where the Add operation was found (optional). - - Raises: - ValueError: When operations cannot be found. - """ - activation_op = common.GetEndpointActivationOp(self.graph, context) - if activation_op: - return activation_op, None, None - - if '/' in context: - # If no activation op is there, look for them one level up. - add_context = re.search(r'^(.*)/([^/]+)', context).group(1) - activation_op = common.GetEndpointActivationOp(self.graph, add_context) - if not activation_op: - # Still no Relu, can happen on the top layer, just find the next node up, - # make sure it is BiasAdd. - consumers = [c for outp in op.outputs for c in outp.consumers()] - if len(consumers) != 1 or consumers[0].type != 'BiasAdd': - raise ValueError('Failed to quantize op: %s, %s' % (op.name, op.type)) - return consumers[0], None, None - if add_context: - add_op = self.GetOperationByNamesDontThrow([ - add_context + '/Add', add_context + '/add']) - return activation_op, add_op, add_context - else: - raise ValueError('Failed to quantize op: %s, %s' % (op.name, op.type)) - - def GetOperationByNameDontThrow(self, name): - """Returns an Operation with the given name. - - Args: - name: Name of Operation to return. - - Returns: - The Operation with the given name. None if the name does not correspond to - any operation in the graph. - """ - try: - return self.graph.get_operation_by_name(name) - except KeyError: - return None - - def GetOperationByNamesDontThrow(self, names): - """Returns an Operation with one of the given names. - - Args: - names: Names of Operation to return. - - Returns: - The Operation with one of the given names. None if none of the names - corresponds to any operation in the graph. - """ - for name in names: - op = self.GetOperationByNameDontThrow(name) - if op is not None: - return op - return None - - def _InsertQuantOp( - self, - context, - producer, - consumers, - name, - moving_avg=True, - init_min=-6.0, - init_max=6.0, - delay_requested=True, - bits=8, - narrow_range=False,): - """Inserts a quant op between a producer op and (multiple) consumer ops. - - Args: - context: Context where producer and consumer operations are nested. - producer: Producer operation of the pairs where quantization will be - inserted. - consumers: Consumer operations of the pairs. - name: Name for the new quantization op within the context. - moving_avg: Specifies whether to use exponential moving average or just - the last value seen. - init_min: Starting minimum value for the new quantization op. - init_max: Starting maximum value for the new quantization op. - delay_requested: If true, implement quantization delay where needed. - False value explicitly disables delay quantization everywhere. - bits: Number of bits to use for quantization, must be between 2 and 8. - narrow_range: Whether to use the narrow quantization range - [1; 2^bits - 1] or wide range [0; 2^bits - 1]. - Raises: - ValueError: When producer operation is not directly connected to the - consumer operation. - """ - scope = context + '/' + name - inputs = producer.outputs[0] - if moving_avg: - quant = (quant_ops.MovingAvgQuantize( - inputs, - init_min=init_min, - init_max=init_max, - ema_decay=self.ema_decay, - is_training=self.is_training, - num_bits=bits, - narrow_range=narrow_range, - updates_collection=_UPDATE_QUANT_OPS, - vars_collection=self.vars_collection, - scope=scope)) - else: - quant = (quant_ops.LastValueQuantize( - inputs, - init_min=init_min, - init_max=init_max, - is_training=self.is_training, - num_bits=bits, - narrow_range=narrow_range, - updates_collection=_UPDATE_QUANT_OPS, - vars_collection=self.vars_collection, - scope=scope)) - - if delay_requested and self.quant_delay and self.quant_delay > 0: - activate_quant = math_ops.greater_equal( - training_util.get_or_create_global_step(), - self.quant_delay, - name=scope + '/activate_quant') - quant = control_flow_ops.cond( - activate_quant, - lambda: quant, - lambda: inputs, - name=scope + '/delayed_quant') - - nodes_modified_count = graph_editor.reroute_ts( - [quant], [inputs], can_modify=consumers) - if nodes_modified_count != len(consumers): - raise ValueError('Some inputs not quantized for ops: [%s]' % - ', '.join([consumer.name for consumer in consumers])) + ema_decay=ema_decay, + quant_delay=quant_delay, + vars_collection=vars_collection, + bits=activation_bits, + init_min=0.0) + + # Quantize the inputs and output to the bypass (if it exists). The input to + # the bypass is the bias add, and the output is the activation. + if layer_match.bypass_op is not None: + _InsertQuantOp( + context, + 'conv_quant', + layer_match.bias_add_op, [layer_match.bypass_op], + is_training, + moving_avg=True, + ema_decay=ema_decay, + quant_delay=quant_delay, + vars_collection=vars_collection, + bits=activation_bits) + _InsertQuantOp( + add_context, + 'add_quant', + layer_match.bypass_op, + input_to_ops_map.ConsumerOperations(layer_match.bypass_op), + is_training, + moving_avg=True, + ema_decay=ema_decay, + quant_delay=quant_delay, + vars_collection=vars_collection, + bits=activation_bits) + + +def _FindLayersToQuantize(graph): + """Matches layers in graph to quantize. + + Args: + graph: Graph to perform match on. + + Yields: + _LayerMatches. + """ + input_pattern = graph_matcher.OpTypePattern('*') + weight_var_pattern = graph_matcher.OpTypePattern('|'.join(_WEIGHT_TYPES)) + weight_pattern = graph_matcher.OpTypePattern( + 'Identity', inputs=[weight_var_pattern]) + + folded_weight_pattern = graph_matcher.OpTypePattern('Mul') + + # The weights inputs to the layer operation can either be from the Variable or + # the folded weight (Mul). + layer_pattern = graph_matcher.OpTypePattern( + '|'.join(_QUANTIZABLE_TYPES), + inputs=[ + input_pattern, + graph_matcher.OneofPattern([weight_pattern, folded_weight_pattern]) + ]) + + folded_bias_mul_pattern = graph_matcher.OpTypePattern( + 'Mul', inputs=[graph_matcher.OpTypePattern('*'), layer_pattern]) + post_layer_op_correction_pattern = graph_matcher.OpTypePattern( + 'Add', inputs=[folded_bias_mul_pattern, + graph_matcher.OpTypePattern('*')]) + folded_bias_add_pattern = graph_matcher.OpTypePattern( + 'Add', + inputs=[ + post_layer_op_correction_pattern, + graph_matcher.OpTypePattern('*') + ]) + + bias_add_pattern = graph_matcher.OpTypePattern( + 'Add|BiasAdd', inputs=[layer_pattern, '*']) + + # The bias can come from the bias add or the folded bias add. + bypass_pattern_a = graph_matcher.OpTypePattern( + 'Add', + inputs=[ + graph_matcher.OneofPattern( + [bias_add_pattern, folded_bias_add_pattern]), '*' + ]) + bypass_pattern_b = graph_matcher.OpTypePattern( + 'Add', + inputs=[ + '*', + graph_matcher.OneofPattern( + [bias_add_pattern, folded_bias_add_pattern]) + ]) + + # The input to the activation can come from bias add, fold bias add or the + # bypasses. + activation_pattern = graph_matcher.OpTypePattern( + '|'.join(_ACTIVATION_TYPES), + inputs=[ + graph_matcher.OneofPattern([ + bias_add_pattern, folded_bias_add_pattern, bypass_pattern_a, + bypass_pattern_b + ]) + ]) + + layer_matcher = graph_matcher.GraphMatcher(activation_pattern) + for match_result in layer_matcher.match_graph(graph): + layer_op = match_result.get_op(layer_pattern) + weight_tensor = match_result.get_tensor(weight_pattern) + if weight_tensor is None: + weight_tensor = match_result.get_tensor(folded_weight_pattern) + activation_op = match_result.get_op(activation_pattern) + bias_add_op = match_result.get_op(bias_add_pattern) + if bias_add_op is None: + bias_add_op = match_result.get_op(folded_bias_add_pattern) + bypass_op = match_result.get_op(bypass_pattern_a) + if bypass_op is None: + bypass_op = match_result.get_op(bypass_pattern_b) + yield _LayerMatch(layer_op, weight_tensor, activation_op, bypass_op, + bias_add_op) + + # Match the final layer, where there will not be an activation and instead + # the output of the final BiasAdd must be quantized, so we treat it as the + # 'activation_op' in the _LayerMatch. + # TODO(suharshs): Figure out how to quantize this final layer across many + # models. + final_layer_matcher = graph_matcher.GraphMatcher(bias_add_pattern) + for match_result in final_layer_matcher.match_graph(graph): + layer_op = match_result.get_op(layer_pattern) + weight_tensor = match_result.get_tensor(weight_pattern) + activation_op = match_result.get_op(bias_add_pattern) + yield _LayerMatch(layer_op, weight_tensor, activation_op, None, None) + + +class _LayerMatch(object): + """Contains all information related to a matched Layer.""" + + def __init__(self, layer_op, weight_tensor, activation_op, bypass_op, + bias_add_op): + self._layer_op = layer_op + self._weight_tensor = weight_tensor + self._activation_op = activation_op + self._bypass_op = bypass_op + self._bias_add_op = bias_add_op + + @property + def layer_op(self): + return self._layer_op + + @property + def weight_tensor(self): + return self._weight_tensor + + @property + def activation_op(self): + return self._activation_op + + @property + def bypass_op(self): + return self._bypass_op + + @property + def bias_add_op(self): + return self._bias_add_op + + +def _InsertQuantOp(context, + name, + producer, + consumers, + is_training, + moving_avg=True, + init_min=-6.0, + init_max=6.0, + bits=8, + ema_decay=0.999, + quant_delay=None, + vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES, + narrow_range=False): + """Inserts a quant op between a producer op and (multiple) consumer ops. + + Args: + context: Context w,here producer and consumer operations are nested. + name: Name for the new quantization op within the context. + producer: Producer operation of the pairs where quantization will be + inserted. + consumers: Consumer operations of the pairs. + is_training: Whether quantizing training graph or eval graph. + moving_avg: Specifies whether to use exponential moving average or just + the last value seen. + init_min: Starting minimum value for the new quantization op. + init_max: Starting maximum value for the new quantization op. + bits: Number of bits to use for quantization, must be between 2 and 8. + ema_decay: (Optional) Float, EMA decay parameter. EMA is used to update + quantization intervals for quantizing activations (see here about EMA: + https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average). + quant_delay: (Optional, default None) Int, count of global steps for which + to delay quantization. This helps weights stabilize at the start of + training. + vars_collection: (Optional) Collection where to store the variables for + quantization interval ends. + narrow_range: Whether to use the narrow quantization range + [1; 2^bits - 1] or wide range [0; 2^bits - 1]. + Raises: + ValueError: When producer operation is not directly connected to the + consumer operation. + """ + name_prefix = _AddContextToName(context, name) + inputs = producer.outputs[0] + if moving_avg: + quant = ( + quant_ops.MovingAvgQuantize( + inputs, + init_min=init_min, + init_max=init_max, + ema_decay=ema_decay, + is_training=is_training, + num_bits=bits, + narrow_range=narrow_range, + vars_collection=vars_collection, + name_prefix=name_prefix)) + else: + quant = ( + quant_ops.LastValueQuantize( + inputs, + init_min=init_min, + init_max=init_max, + is_training=is_training, + num_bits=bits, + narrow_range=narrow_range, + vars_collection=vars_collection, + name_prefix=name_prefix)) + + if quant_delay and quant_delay > 0: + activate_quant = math_ops.greater_equal( + common.CreateOrGetQuantizationStep(), + quant_delay, + name=name_prefix + '/activate_quant') + quant = control_flow_ops.cond( + activate_quant, + lambda: quant, + lambda: inputs, + name=name_prefix + '/delayed_quant') + + nodes_modified_count = graph_editor.reroute_ts( + [quant], [inputs], can_modify=consumers) + if nodes_modified_count != len(consumers): + raise ValueError('Some inputs not quantized for ops: [%s]' % ', '.join( + [consumer.name for consumer in consumers])) + + +def _GetContextFromOp(op): + """Gets the root context name from the op name.""" + context_re = re.search(r'^(.*)/([^/]+)', op.name) + if context_re: + return context_re.group(1) + return '' + + +def _AddContextToName(context, name): + """Adds the context to the name if it exists.""" + if not context: + return name + return context + '/' + name diff --git a/tensorflow/contrib/quantize/python/quantize_graph.py b/tensorflow/contrib/quantize/python/quantize_graph.py index 89b744c559170e7d9e502d3d8610afaca2c549b7..5a3a74cec4864ad3808d485849334c81f569d300 100644 --- a/tensorflow/contrib/quantize/python/quantize_graph.py +++ b/tensorflow/contrib/quantize/python/quantize_graph.py @@ -18,187 +18,198 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.quantize.python import copy_graph from tensorflow.contrib.quantize.python import fold_batch_norms from tensorflow.contrib.quantize.python import quantize from tensorflow.python.framework import ops -from tensorflow.python.ops import variables -def _create_graph(input_graph, - is_training, - elements=None, - device_name_or_function=None): - """Returns a transformed training input_graph for simulated quantization. +def _create_graph(input_graph=None, + is_training=True, + weight_bits=8, + activation_bits=8, + quant_delay=None, + freeze_bn_delay=None): + """Rewrites an input_graph in place for simulated quantization. - The forward pass has fake quantization ops inserted to simulate the error - introduced by quantization. + The graph has fake quantization ops inserted to simulate the error + introduced by quantization. Since the graph is transformed in place, + the expected behavior of previously held references to nodes and tensors may + change. Args: - input_graph: The tf.Graph to be transformed. + input_graph: The tf.Graph to be transformed, if None then defaults to the + default graph. is_training: Whether quantizing training or eval graph. - elements: (Optional) List of Tensors and Operations in input_graph whose - corresponding elements in the new graph will be returned. - device_name_or_function: (Optional) The device name or function to use. - - Returns: - g is new tf.Graph that is rewritten for simulated quantization. - l is a list of Tensors/Operations in g corresponding to the provided input - elements, if elements is not None. + weight_bits: Number of bits to use for quantizing weights. + activation_bits: Number of bits to use for quantizing activations. + quant_delay: Number of steps after which weights and activations are + quantized during training. + freeze_bn_delay: Number of steps after which moving mean and variance are + frozen and used instead of batch statistics during training. + freeze_bn_delay should be greater than quant_delay and should correspond + to the number of steps when training has almost converged Raises: ValueError: If elements contains an element that isn't a tf.Tensor or - tf.Operation. + tf.Operation. """ - # TODO(suharshs): Describe the process in more detail in the doc string. - g = copy_graph.CopyGraph(input_graph) - if is_training: - # TODO(raghuramank): Need to make freeze_batch_norm_delay - # a function of the batch size. For now setting this to 250 epochs - # This corresponds to 5 million steps at a batch size of 64. - freeze_batch_norm_delay = 5000000 - else: - freeze_batch_norm_delay = None - with g.as_default(): - with ops.device(device_name_or_function): - fold_batch_norms.FoldBatchNorms( - g, - freeze_batch_norm_delay=freeze_batch_norm_delay, - is_training=is_training) - quantize.Quantize(g, is_training=is_training) - if elements is None: - return g - - return_elements = [] - for element in elements: - if isinstance(element, (ops.Tensor, variables.Variable)): - return_elements.append(g.get_tensor_by_name(element.name)) - elif isinstance(element, ops.Operation): - return_elements.append(g.get_operation_by_name(element.name)) - else: - raise ValueError( - 'elements must consist of Tensor or Operation objects, got: ', - str(element)) - return g, return_elements - - -def create_training_graph(input_graph, - elements=None, - device_name_or_function=None): - """Returns a transformed training input_graph for simulated quantization. - - The forward pass has fake quantization ops inserted to simulate the error - introduced by quantization. + + if input_graph is None: + input_graph = ops.get_default_graph() + with input_graph.as_default(): + fold_batch_norms.FoldBatchNorms( + input_graph, + freeze_batch_norm_delay=freeze_bn_delay, + is_training=is_training) + quantize.Quantize( + input_graph, + is_training, + quant_delay=quant_delay, + weight_bits=weight_bits, + activation_bits=activation_bits) + + +def create_training_graph(input_graph=None, quant_delay=0): + """Rewrites a training input_graph in place for simulated quantization. + + The graph has fake quantization ops inserted to simulate the error + introduced by quantization. Since the graph is transformed in place, + the expected behavior of previously held references to nodes and tensors may + change. + + The default value of quant_delay is suitable for finetuning an already trained + floating point model (recommended). + If one wants to train a quantized model from scratch, quant_delay should be + set to the number of steps it take the floating point model to converge. + Quantization will be activated at this point and effectively finetune the + model. If quant_delay is not provided when training from scratch, training can + often fail. Args: input_graph: The tf.Graph to be transformed. - elements: (Optional) List of Tensors and Operations in input_graph whose - corresponding elements in the new graph will be returned. - device_name_or_function: (Optional) The device name or function to use. - - Returns: - g is new tf.Graph that is rewritten for simulated quantization. - l is a list of Tensors/Operations in g corresponding to the provided input - elements, if elements is not None. + quant_delay: Number of steps after which weights and activations are + quantized during training. Raises: ValueError: If elements contains an element that isn't a tf.Tensor or - tf.Operation. + tf.Operation. """ - return _create_graph( + # TODO(raghuramank) Need to have freeze_bn_delay be a function of batch size + # Currently the values below are hardcoded for mobilenetV1 on imagenet + # Please use the experimental API if you need to tune these values. + if quant_delay == 0: + # Corresponds to case of restoring from a floating point checkpoint + # In this case, we can freeze the moving mean and variance early on and + # switch to using them during training. Therefore, freeze_bn_delay is set to + # 2e5. + freeze_bn_delay = int(2e5) + else: + # If training from scratch, set freeze_bn_delay to 100 epochs after quant + # delay. With a batch size of 64, this corresponds to 20000*100=2M steps. + freeze_bn_delay = quant_delay + int(2e6) + + _create_graph( input_graph=input_graph, is_training=True, - elements=elements, - device_name_or_function=device_name_or_function) + quant_delay=quant_delay, + freeze_bn_delay=freeze_bn_delay) -def create_eval_graph(input_graph, elements=None, device_name_or_function=None): - """Returns a transformed eval input_graph for simulated quantization. +def create_eval_graph(input_graph=None): + """Rewrites an eval input_graph in place for simulated quantization. - The forward pass has fake quantization ops inserted to simulate the error - introduced by quantization. + The graph has fake quantization ops inserted to simulate the error + introduced by quantization. Since the graph is transformed in place, + the expected behavior of previously held references to nodes and tensors may + change. Args: - input_graph: The tf.Graph to be transformed. - elements: (Optional) List of Tensors and Operations in input_graph whose - corresponding elements in the new graph will be returned. - device_name_or_function: (Optional) The device name or function to use. - - Returns: - g is new tf.Graph that is rewritten for simulated quantization. - l is a list of Tensors/Operations in g corresponding to the provided input - elements, if elements is not None. + input_graph: The tf.Graph to be transformed, if None then defaults to the + default graph. Raises: ValueError: If elements contains an element that isn't a tf.Tensor or - tf.Operation. + tf.Operation. """ - return _create_graph( - input_graph=input_graph, - is_training=False, - elements=elements, - device_name_or_function=device_name_or_function) + _create_graph(input_graph=input_graph, is_training=False) -def experimental_create_training_graph(input_graph, - elements=None, - device_name_or_function=None): - """Returns a transformed training input_graph for simulated quantization. +def experimental_create_training_graph(input_graph=None, + weight_bits=8, + activation_bits=8, + quant_delay=0, + freeze_bn_delay=int(2e5)): + """Rewrites a training input_graph in place for simulated quantization. This function has additional experimental options not (yet) available to create_training_graph. The resulting behavior may be undefined. - The forward pass has fake quantization ops inserted to simulate the error - introduced by quantization. - Args: - input_graph: The tf.Graph to be transformed. - elements: (Optional) List of Tensors and Operations in input_graph whose - corresponding elements in the new graph will be returned. - device_name_or_function: (Optional) The device name or function to use. + The graph has fake quantization ops inserted to simulate the error + introduced by quantization. Since the graph is transformed in place, + the expected behavior of previously held references to nodes and tensors may + change. + + The default value of quant_delay is suitable for finetuning an already trained + floating point model (recommended). + If one wants to train a quantized model from scratch, quant_delay should be + set to the number of steps it take the floating point model to converge. + Quantization will be activated at this point and effectively finetune the + model. If quant_delay is not provided when training from scratch, training can + often fail. - Returns: - g is new tf.Graph that is rewritten for simulated quantization. - l is a list of Tensors/Operations in g corresponding to the provided input - elements, if elements is not None. + Args: + input_graph: The tf.Graph to be transformed,if None then defaults to the + default graph. + weight_bits: Number of bits to use for quantizing weights. + activation_bits: Number of bits to use for quantizing activations. + quant_delay: Number of steps after which weights and activations are + quantized during training. + freeze_bn_delay: Number of steps after which moving mean and variance are + frozen and used instead of batch statistics during training. + freeze_bn_delay should be greater than quant_delay and should correspond + to when training has almost converged Raises: ValueError: If elements contains an element that isn't a tf.Tensor or tf.Operation. """ - return _create_graph( + + _create_graph( input_graph=input_graph, is_training=True, - elements=elements, - device_name_or_function=device_name_or_function) + weight_bits=weight_bits, + activation_bits=activation_bits, + quant_delay=quant_delay, + freeze_bn_delay=freeze_bn_delay) -def experimental_create_eval_graph(input_graph, - elements=None, - device_name_or_function=None): - """Returns a transformed eval input_graph for simulated quantization. +def experimental_create_eval_graph(input_graph=None, + weight_bits=8, + activation_bits=8): + """Rewrites an eval input_graph in place for simulated quantization. This function has additional experimental options not (yet) available to create_eval_graph. The resulting behavior may be undefined. - The forward pass has fake quantization ops inserted to simulate the error - introduced by quantization. + + The graph has fake quantization ops inserted to simulate the error + introduced by quantization. Since the graph is transformed in place, + the expected behavior of previously held references to nodes and tensors may + change. Args: - input_graph: The tf.Graph to be transformed. - elements: (Optional) List of Tensors and Operations in input_graph whose - corresponding elements in the new graph will be returned. - device_name_or_function: (Optional) The device name or function to use. + input_graph: The tf.Graph to be transformed, if None then defaults to the + default graph. + weight_bits: Number of bits to use for quantizing weights. + activation_bits: Number of bits to use for quantizing activations. + - Returns: - g is new tf.Graph that is rewritten for simulated quantization. - l is a list of Tensors/Operations in g corresponding to the provided input - elements, if elements is not None. Raises: ValueError: If elements contains an element that isn't a tf.Tensor or - tf.Operation. + tf.Operation. """ - return _create_graph( + _create_graph( input_graph=input_graph, is_training=False, - elements=elements, - device_name_or_function=device_name_or_function) + weight_bits=weight_bits, + activation_bits=activation_bits) diff --git a/tensorflow/contrib/quantize/python/quantize_graph_test.py b/tensorflow/contrib/quantize/python/quantize_graph_test.py index 514862a0ab5b796718a04aa65a46e7a7e3b86330..b9d03c1bc059fe7bcce75978f503cbbf76090dbd 100644 --- a/tensorflow/contrib/quantize/python/quantize_graph_test.py +++ b/tensorflow/contrib/quantize/python/quantize_graph_test.py @@ -20,13 +20,11 @@ from __future__ import print_function from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.quantize.python import quantize_graph -from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -34,7 +32,7 @@ class QuantizeGraphTest(test_util.TensorFlowTestCase): # We have a lot of other tests that test the details of the rewrite, here we # just the specific features of the quantize_graph API. - def _RunTestOverParameters(self, test_fn): + def _RunTestOverAllRewrites(self, test_fn): rewrite_fns = [ quantize_graph.create_training_graph, quantize_graph.create_eval_graph, @@ -44,85 +42,202 @@ class QuantizeGraphTest(test_util.TensorFlowTestCase): for fn in rewrite_fns: test_fn(fn) - def testReturnedElements(self): - self._RunTestOverParameters(self._TestReturnElements) + def _RunTestOverTrainingRewrites(self, test_fn): + rewrite_fns = [ + quantize_graph.create_training_graph, + quantize_graph.experimental_create_training_graph, + ] + for fn in rewrite_fns: + test_fn(fn) - def _TestReturnElements(self, fn): - graph = ops.Graph() - with graph.as_default(): - a = constant_op.constant(1.0) - b = variables.Variable(2.0) - c = a + b - elements = [a, b, c.op] - q_graph, returned_elements = fn(graph, elements=elements) - # Make sure q_graph is different from graph. - self.assertTrue(graph != q_graph) - # Check that the returned elements are part of the new graph. - for returned_element in returned_elements: - self.assertEqual(q_graph, returned_element.graph) - # Check that the elements match with the one from the input graph. - for element, returned_element in zip(elements, returned_elements): - self.assertEqual(element.name, returned_element.name) - - def testNoReturnElements(self): - self._RunTestOverParameters(self._TestNoReturnElements) - - def _TestNoReturnElements(self, fn): - graph = ops.Graph() - with graph.as_default(): - a = constant_op.constant(1.0) - b = variables.Variable(2.0) - _ = a + b - q_graph = fn(graph) - # Check that quantize_graph didn't return a tuple when elements isn't - # provided. - self.assertTrue(isinstance(q_graph, ops.Graph)) - # Make sure q_graph is different from graph. - self.assertTrue(graph != q_graph) - - def testDeviceName(self): - self._RunTestOverParameters(self._TestDeviceName) - - def _TestDeviceName(self, fn): + def _RunTestOverEvalRewrites(self, test_fn): + rewrite_fns = [ + quantize_graph.create_eval_graph, + quantize_graph.experimental_create_eval_graph, + ] + for fn in rewrite_fns: + test_fn(fn) + + def _RunTestOverExperimentalRewrites(self, test_fn): + rewrite_fns = [ + quantize_graph.experimental_create_training_graph, + quantize_graph.experimental_create_eval_graph, + ] + for fn in rewrite_fns: + test_fn(fn) + + def testRewrite(self): + self._RunTestOverAllRewrites(self._TestRewrite) + + def _TestRewrite(self, rewrite_fn): graph = ops.Graph() with graph.as_default(): - batch_size, height, width, depth = 5, 128, 128, 3 - inputs = array_ops.zeros((batch_size, height, width, depth)) - conv = layers.conv2d( - inputs, - 32, [5, 5], - stride=2, - padding='SAME', - weights_initializer=self._WeightInit(0.09), - activation_fn=None, - scope='test') - _ = nn_ops.relu6(conv) - - device_name = '/job:oink/task:0/device:CPU:0' - q_graph = fn(graph, device_name_or_function=device_name) + self._ConvLayer() orig_variable_names = set( [v.name for v in graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)]) - q_variables = q_graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) - # Ensure that variables were added. - self.assertTrue(len(orig_variable_names) < len(q_variables)) - # All added variables should have the specified device name. - for var in q_variables: - if var.name not in orig_variable_names: - self.assertEqual(var.device, device_name) - def _WeightInit(self, stddev): - """Returns truncated normal variable initializer. + rewrite_fn(graph) - Function is defined purely to shorten the name so that it stops wrapping. - - Args: - stddev: Standard deviation of normal variable. + q_variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + # Ensure that variables were added. + self.assertTrue(len(orig_variable_names) < len(q_variables)) - Returns: - An initialized that initialzes with a truncated normal variable. - """ - return init_ops.truncated_normal_initializer(stddev=stddev) + def testDefaultGraph(self): + self._RunTestOverAllRewrites(self._TestRewrite) + + def _TestDefaultGraph(self, rewrite_fn): + # Tests that the default graph is correctly used when no args are provided + # to rewrite_fn. + with ops.Graph().as_default() as g: + self._ConvLayer() + orig_variable_names = set( + [v.name for v in g.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)]) + rewrite_fn() + + q_variables = g.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + # Ensure that variables were added. + self.assertTrue(len(orig_variable_names) < len(q_variables)) + + def testQuantDelay(self): + self._RunTestOverTrainingRewrites(self._TestQuantDelay) + + def _TestQuantDelay(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + quant_delay = 100 + rewrite_fn(quant_delay=quant_delay) + + quant_delay_found = False + for op in g.get_operations(): + # Check to see if the quant_delay is correctly set. + if 'activate_quant' in op.name and op.type == 'Const': + quant_delay_found = True + const_value = str(op.get_attr('value')) + self.assertTrue(('int64_val: %i' % quant_delay) in const_value) + self.assertTrue(quant_delay_found) + + def testWeightBits(self): + self._RunTestOverExperimentalRewrites(self._TestWeightBits) + + def _TestWeightBits(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + weight_bits = 4 + rewrite_fn(weight_bits=weight_bits) + + weights_quant_found = False + for op in g.get_operations(): + # Check to see if FakeQuant operations for weights have the right bits + # set. + if 'weights_quant' in op.name and op.type == 'FakeQuantWithMinMaxVars': + weights_quant_found = True + self.assertEqual(op.get_attr('num_bits'), weight_bits) + self.assertTrue(weights_quant_found) + + def testActivationBits(self): + self._RunTestOverExperimentalRewrites(self._TestActivationBits) + + def _TestActivationBits(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + activation_bits = 4 + rewrite_fn(activation_bits=activation_bits) + + act_quant_found = False + for op in g.get_operations(): + # Check to see if FakeQuant operations for activations have the right bits + # set. + act_quant_names = ['act_quant', 'conv_quant', 'add_quant'] + if any(s in op.name + for s in act_quant_names) and op.type == 'FakeQuantWithMinMaxVars': + act_quant_found = True + self.assertEqual(op.get_attr('num_bits'), activation_bits) + self.assertTrue(act_quant_found) + + def testTrainingQuantization(self): + self._RunTestOverTrainingRewrites(self._TestTrainingQuantization) + + def _TestTrainingQuantization(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + rewrite_fn() + + # Ensure that FakeQuant and variable update nodes were found. + quant_found = False + assign_min_last_found = False + assign_min_ema_found = False + assign_max_last_found = False + assign_max_ema_found = False + for op in g.get_operations(): + # Check that FakeQuant operations were added. + if op.type == 'FakeQuantWithMinMaxVars': + quant_found = True + # Check that update operations for the added min max variables exist in + # the graph. + if 'AssignMinLast' in op.name: + assign_min_last_found = True + elif 'AssignMinEma' in op.name: + assign_min_ema_found = True + elif 'AssignMaxLast' in op.name: + assign_max_last_found = True + elif 'AssignMaxEma' in op.name: + assign_max_ema_found = True + self.assertTrue(assign_min_last_found) + self.assertTrue(assign_min_ema_found) + self.assertTrue(assign_max_last_found) + self.assertTrue(assign_max_ema_found) + self.assertTrue(quant_found) + + def testEvalQuantization(self): + self._RunTestOverEvalRewrites(self._TestEvalQuantization) + + def _TestEvalQuantization(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + rewrite_fn() + + # Ensure that FakeQuant and variable update nodes were found. + quant_found = False + for op in g.get_operations(): + # Check that FakeQuant operations were added. + if op.type == 'FakeQuantWithMinMaxVars': + quant_found = True + # Check that update operations for the added min max variables don't + # exist in the graph. + update_names = [ + 'AssignMinLast', 'AssignMinEma', 'AssignMaxLast', 'AssignMaxEma' + ] + self.assertFalse(any(s in op.name for s in update_names)) + self.assertTrue(quant_found) + + def testIdempotent(self): + self._RunTestOverAllRewrites(self._TestIdempotent) + + def _TestIdempotent(self, rewrite_fn): + with ops.Graph().as_default() as g: + self._ConvLayer() + rewrite_fn() + graph_def_before = str(g.as_graph_def()) + # Ensuring that calling the rewrite again doesn't add more nodes. + rewrite_fn() + graph_def_after = str(g.as_graph_def()) + self.assertEqual(graph_def_before, graph_def_after) + + def _ConvLayer(self): + """Add a basic convolution layer to the default graph.""" + batch_size, height, width, depth = 5, 128, 128, 3 + inputs = array_ops.zeros((batch_size, height, width, depth)) + weight_init = init_ops.truncated_normal_initializer + conv = layers.conv2d( + inputs, + 32, [5, 5], + stride=2, + padding='SAME', + weights_initializer=weight_init(0.09), + activation_fn=None, + scope='test') + _ = nn_ops.relu6(conv) if __name__ == '__main__': diff --git a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py index 57dab03f162629f84adf1d15521b05f4014c4a80..dd73f6c86048b9d75f2ad9808155007eed7079ec 100644 --- a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py +++ b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py @@ -29,7 +29,6 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.platform import googletest -from tensorflow.python.training import training batch_norm = layers.batch_norm conv2d = layers.conv2d @@ -73,8 +72,6 @@ class QuantizeTest(test_util.TensorFlowTestCase): """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -90,8 +87,8 @@ class QuantizeTest(test_util.TensorFlowTestCase): update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') + quantize.Quantize(graph, True, quant_delay=delay) - quantize.Quantize(graph, quant_delay=delay) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) @@ -101,7 +98,11 @@ class QuantizeTest(test_util.TensorFlowTestCase): scope + '/weights_quant/AssignMaxLast', scope + '/weights/read' ] self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + '/Conv2D' + if delay and delay > 0: + output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' + else: + output_op_name = scope + '/Conv2D' + self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -129,6 +130,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._TestIdempotent(graph) def testQuantize_Conv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( @@ -148,8 +150,6 @@ class QuantizeTest(test_util.TensorFlowTestCase): """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 @@ -164,8 +164,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - - quantize.Quantize(graph, quant_delay=delay) + quantize.Quantize(graph, True, quant_delay=delay) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + @@ -176,7 +175,10 @@ class QuantizeTest(test_util.TensorFlowTestCase): scope + '/weights_quant/AssignMaxLast', scope + '/weights/read' ] self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + '/MatMul' + if delay and delay > 0: + output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' + else: + output_op_name = scope + '/MatMul' self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -203,6 +205,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._TestIdempotent(graph) def testQuantize_FCWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( @@ -222,8 +225,6 @@ class QuantizeTest(test_util.TensorFlowTestCase): """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -239,8 +240,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - - quantize.Quantize(graph, quant_delay=delay) + quantize.Quantize(graph, True, quant_delay=delay) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + @@ -252,7 +252,10 @@ class QuantizeTest(test_util.TensorFlowTestCase): scope + '/depthwise_weights/read' ] self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + '/depthwise' + if delay and delay > 0: + output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' + else: + output_op_name = scope + '/depthwise' self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -279,6 +282,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._TestIdempotent(graph) def testQuantize_DepthwiseConv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( @@ -316,40 +320,11 @@ class QuantizeTest(test_util.TensorFlowTestCase): for params in parameters_list: test_fn(params[0], params[1], params[2], params[3], params[4]) - def _TestQuantize_Conv2dWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm): - """Tests quantization: inputs -> Conv2d with batch norm -> Activation. - - Args: - activation: Callable that returns an Operation, a factory method for the - Activation. - activation_op_name: String, name of the Activation operation. - with_bypass: Bool, when true there is an extra connection added from - inputs to just before Activation. - delay: Int (optional), delay in number of steps until quantization starts. - fused_batch_norm: Bool, when true use FusedBatchNorm. - """ - self._testQuantize_Conv2dWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=True) - self._testQuantize_Conv2dWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=False) - def testQuantize_Conv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_Conv2dWithBatchNorm) - def _testQuantize_Conv2dWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm, - use_ema): + def _TestQuantize_Conv2dWithBatchNorm(self, activation, activation_op_name, + with_bypass, delay, fused_batch_norm): """Tests quantization: inputs -> Conv2d with batch norm -> Activation. Args: @@ -360,12 +335,9 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. - use_ema: Bool, when true uses EMA quantization for BN folded weights. """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -392,25 +364,20 @@ class QuantizeTest(test_util.TensorFlowTestCase): with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - fold_batch_norms.FoldBatchNorms(graph) - - quantize.Quantize( - graph, quant_delay=delay, quantize_folded_weights_use_ema=use_ema) + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, True, quant_delay=delay) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) expected_inputs = [ - scope + '/weights_quant/' + ('AssignMinEma' - if use_ema else 'AssignMinLast'), - scope + '/weights_quant/' + ('AssignMaxEma' - if use_ema else 'AssignMaxLast'), - scope + '/mul_fold' + scope + '/weights_quant/' + 'AssignMinLast', + scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' ] self._AssertInputOpsAre(weights_quant, expected_inputs) output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if (delay and use_ema) else '/Conv2D_Fold') + if delay else '/Conv2D_Fold') self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -437,41 +404,13 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) - - def _TestQuantize_FCWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm): - """Tests quantization: inputs -> FC with batch norm -> Activation. - - Args: - activation: Callable that returns an Operation, a factory method for the - Activation. - activation_op_name: String, name of the Activation operation. - with_bypass: Bool, when true there is an extra connection added from - inputs to just before Activation. - delay: Int (optional), delay in number of steps until quantization starts. - fused_batch_norm: Bool, when true use FusedBatchNorm. - """ - self._testQuantize_FCWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=True) - self._testQuantize_FCWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=False) + self._TestIdempotent(graph) def testQuantize_FCWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_FCWithBatchNorm) - def _testQuantize_FCWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm, - use_ema): + def _TestQuantize_FCWithBatchNorm(self, activation, activation_op_name, + with_bypass, delay, fused_batch_norm): """Tests quantization: inputs -> FC with batch norm -> Activation. Args: @@ -482,12 +421,9 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. - use_ema: Bool, when true uses EMA quantization for BN folded weights. """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 @@ -511,25 +447,21 @@ class QuantizeTest(test_util.TensorFlowTestCase): with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - fold_batch_norms.FoldBatchNorms(graph) + fold_batch_norms.FoldBatchNorms(graph, is_training=True) - quantize.Quantize( - graph, quant_delay=delay, quantize_folded_weights_use_ema=use_ema) + quantize.Quantize(graph, True, quant_delay=delay) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) expected_inputs = [ - scope + '/weights_quant/' + ('AssignMinEma' - if use_ema else 'AssignMinLast'), - scope + '/weights_quant/' + ('AssignMaxEma' - if use_ema else 'AssignMaxLast'), - scope + '/mul_fold' + scope + '/weights_quant/' + 'AssignMinLast', + scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' ] self._AssertInputOpsAre(weights_quant, expected_inputs) output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if delay and use_ema else '/MatMul_Fold') + if delay else '/MatMul_Fold') self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -556,43 +488,15 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) - - def _TestQuantize_DepthwiseConv2dWithBatchNorm( - self, activation, activation_op_name, with_bypass, delay, - fused_batch_norm): - """Tests quantization: inputs -> DWConv2d with batch norm -> Activation. - - Args: - activation: Callable that returns an Operation, a factory method for the - Activation. - activation_op_name: String, name of the Activation operation. - with_bypass: Bool, when true there is an extra connection added from - inputs to just before Activation. - delay: Int (optional), delay in number of steps until quantization starts. - fused_batch_norm: Bool, when true use FusedBatchNorm. - """ - self._testQuantize_DepthwiseConv2dWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=True) - self._testQuantize_DepthwiseConv2dWithBatchNorm( - activation, - activation_op_name, - with_bypass, - delay, - fused_batch_norm, - use_ema=False) + self._TestIdempotent(graph) def testQuantize_DepthwiseConv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters( self._TestQuantize_DepthwiseConv2dWithBatchNorm) - def _testQuantize_DepthwiseConv2dWithBatchNorm( + def _TestQuantize_DepthwiseConv2dWithBatchNorm( self, activation, activation_op_name, with_bypass, delay, - fused_batch_norm, use_ema): + fused_batch_norm): """Tests quantization: inputs -> DWConv2d with batch norm -> Activation. Args: @@ -603,12 +507,9 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. - use_ema: Bool, when true uses EMA quantization for BN folded weights. """ graph = ops.Graph() with graph.as_default(): - training.create_global_step(graph) - batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -635,24 +536,20 @@ class QuantizeTest(test_util.TensorFlowTestCase): with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - fold_batch_norms.FoldBatchNorms(graph) + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, True, quant_delay=delay) - quantize.Quantize( - graph, quant_delay=delay, quantize_folded_weights_use_ema=use_ema) quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) expected_inputs = [ - scope + '/weights_quant/' + ('AssignMinEma' - if use_ema else 'AssignMinLast'), - scope + '/weights_quant/' + ('AssignMaxEma' - if use_ema else 'AssignMaxLast'), - scope + '/mul_fold' + scope + '/weights_quant/' + 'AssignMinLast', + scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' ] self._AssertInputOpsAre(weights_quant, expected_inputs) output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if delay and use_ema else '/depthwise_Fold') + if delay else '/depthwise_Fold') self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: @@ -679,6 +576,17 @@ class QuantizeTest(test_util.TensorFlowTestCase): output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._TestIdempotent(graph) + + def _TestIdempotent(self, graph): + # Ensure that calling the rewrite again doesn't change the graph. + graph_def_before = str(graph.as_graph_def()) + with graph.as_default(): + # Ensuring that calling the rewrite again doesn't add more nodes. + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, True) + graph_def_after = str(graph.as_graph_def()) + self.assertEqual(graph_def_before, graph_def_after) def _BatchNormParams(self, fused=False): return {'center': True, 'scale': True, 'decay': 1.0 - 0.003, 'fused': fused} diff --git a/tensorflow/contrib/quantize/python/quantize_test.py b/tensorflow/contrib/quantize/python/quantize_test.py index 1e4dd7cf67dbfbd16386fd740c7dcc83e05ad82a..ef59475167137e203db2f6ca7f43c7b8f1938060 100644 --- a/tensorflow/contrib/quantize/python/quantize_test.py +++ b/tensorflow/contrib/quantize/python/quantize_test.py @@ -35,7 +35,15 @@ separable_conv2d = layers.separable_conv2d class QuantizeTest(test_util.TensorFlowTestCase): + def _RunTestOverParameters(self, test_fn): + params = [True, False] + for is_training in params: + test_fn(is_training) + def testInsertQuantOpFailsWhenOpsNotConnected(self): + pass + + def _TestInsertQuantOpFailsWhenOpsNotConnected(self, is_training): graph = ops.Graph() with graph.as_default(): batch_size, height, width, depth = 5, 128, 128, 3 @@ -45,17 +53,18 @@ class QuantizeTest(test_util.TensorFlowTestCase): activation_fn=None, scope='test') relu = nn_ops.relu6(inputs) - context = quantize._QuantizeContext(graph=graph, weight_bits=8, - weight_narrow_range=True, - activation_bits=8) # Inserting a quantization op between two unconnected ops should fail with # ValueError. with self.assertRaises(ValueError) as err: - context._InsertQuantOp('test', conv.op, [relu.op], 'FailingQuantOp') + quantize._InsertQuantOp('test', is_training, conv.op, [relu.op], + 'FailingQuantOp') self.assertEqual( str(err.exception), 'Some inputs not quantized for ops: [Relu6]') def testInsertQuantOpForAddAfterConv2d(self): + self._RunTestOverParameters(self._TestInsertQuantOpForAddAfterConv2d) + + def _TestInsertQuantOpForAddAfterConv2d(self, is_training): graph = ops.Graph() with graph.as_default(): batch_size, height, width, depth = 5, 128, 128, 3 @@ -70,8 +79,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - quantize.Quantize(graph=graph, weight_bits=8, weight_narrow_range=True, - activation_bits=8) + quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8) quantization_node_name = 'FakeQuantWithMinMaxVars' add_quant = graph.get_operation_by_name('test/add_quant/' + @@ -79,6 +87,10 @@ class QuantizeTest(test_util.TensorFlowTestCase): self.assertEqual(add_quant.type, quantization_node_name) def testInsertQuantOpForAddAfterSeparableConv2d(self): + self._RunTestOverParameters( + self._TestInsertQuantOpForAddAfterSeparableConv2d) + + def _TestInsertQuantOpForAddAfterSeparableConv2d(self, is_training): graph = ops.Graph() with graph.as_default(): batch_size, height, width, depth = 5, 128, 128, 3 @@ -94,14 +106,35 @@ class QuantizeTest(test_util.TensorFlowTestCase): with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - quantize.Quantize(graph=graph, weight_bits=8, weight_narrow_range=True, - activation_bits=8) + quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8) quantization_node_name = 'FakeQuantWithMinMaxVars' add_quant = graph.get_operation_by_name('test/add_quant/' + quantization_node_name) self.assertEqual(add_quant.type, quantization_node_name) + def testFinalLayerQuantized(self): + self._RunTestOverParameters(self._TestFinalLayerQuantized) + + def _TestFinalLayerQuantized(self, is_training): + graph = ops.Graph() + with graph.as_default(): + batch_size, height, width, depth = 5, 128, 128, 3 + input1 = array_ops.zeros((batch_size, height, width, depth)) + _ = conv2d( + input1, + 32, [5, 5], + stride=2, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=None, + scope='test') + # Ensure that the a FakeQuant operation is in the outputs of the BiasAdd. + bias_add_op = graph.get_operation_by_name('test/BiasAdd') + quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8) + self.assertTrue('FakeQuantWithMinMaxVars' in + [op.type for op in bias_add_op.outputs[0].consumers()]) + def _WeightInit(self, stddev): """Returns truncated normal variable initializer. diff --git a/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py b/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py index 60a193db4c7f084d3262a69e2b8c5df66273e138..468886da20021646089bd1d222da1ebd4b5c7822 100644 --- a/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py +++ b/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import numpy as np -import unittest from tensorflow.contrib.reduce_slice_ops.python.ops import reduce_slice_ops from tensorflow.python.framework.test_util import TensorFlowTestCase diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD index 27f0a7f58f78135f1d73ae04bd1e76ef496fa549..54c66271cd43c85bc7be139dbdf70b9178ad1622 100644 --- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD +++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD @@ -38,7 +38,6 @@ py_test( size = "small", srcs = ["python/ops/remote_fused_graph_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":remote_fused_graph_ops_py", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/contrib/rnn/__init__.py b/tensorflow/contrib/rnn/__init__.py index c568c6760fd67b1902b0c1e6dc1aa439cb63de9b..67f31785b57fddef67733c18c3b744322532c28c 100644 --- a/tensorflow/contrib/rnn/__init__.py +++ b/tensorflow/contrib/rnn/__init__.py @@ -18,6 +18,7 @@ See @{$python/contrib.rnn} guide. @@RNNCell +@@LayerRNNCell @@BasicRNNCell @@BasicLSTMCell @@GRUCell @@ -68,6 +69,10 @@ See @{$python/contrib.rnn} guide. @@static_bidirectional_rnn @@stack_bidirectional_dynamic_rnn @@stack_bidirectional_rnn + + +@@transpose_batch_time +@@best_effort_input_batch_size """ from __future__ import absolute_import @@ -85,6 +90,8 @@ from tensorflow.contrib.rnn.python.ops.lstm_ops import * from tensorflow.contrib.rnn.python.ops.rnn import * from tensorflow.contrib.rnn.python.ops.rnn_cell import * +from tensorflow.python.ops.rnn import _best_effort_input_batch_size as best_effort_input_batch_size +from tensorflow.python.ops.rnn import _transpose_batch_time as transpose_batch_time from tensorflow.python.ops.rnn import static_bidirectional_rnn from tensorflow.python.ops.rnn import static_rnn from tensorflow.python.ops.rnn import static_state_saving_rnn diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 9b84635e85e8d300be4a77a4cc261b70d14ae2ac..d41fc0b3ac1cee4eacc88cb0f41df1f9ee59e7c3 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -39,8 +39,6 @@ from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as variables_lib from tensorflow.python.platform import test -from tensorflow.python.framework import test_util -from tensorflow.contrib.rnn.python.ops import rnn_cell as contrib_rnn_cell # pylint: enable=protected-access Linear = core_rnn_cell._Linear # pylint: disable=invalid-name @@ -167,9 +165,10 @@ class RNNCellTest(test.TestCase): m = array_ops.zeros([1, 2]) g, _ = contrib_rnn_cell.SRUCell(2)(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) # Smoke test self.assertAllClose(res[0], [[0.55255556, 0.55255556]]) @@ -188,6 +187,8 @@ class RNNCellTest(test.TestCase): ], state_is_tuple=False) self.assertEqual(cell.dtype, None) + self.assertEqual("cell-0", cell._checkpoint_dependencies[0].name) + self.assertEqual("cell-1", cell._checkpoint_dependencies[1].name) g, out_m = cell(x, m) # Layer infers the input type. self.assertEqual(cell.dtype, dtype.name) diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index 57521c6a9ba0b2d66639017b09c541e270276323..de5df912921932056526e1e6dc5dbb905735f775 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -869,7 +869,7 @@ class LSTMTest(test.TestCase): num_proj = 4 max_length = 8 sequence_length = [4, 6] - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) @@ -934,8 +934,7 @@ class LSTMTest(test.TestCase): if in_graph_mode: self.assertAllEqual(outputs_static, outputs_dynamic) else: - self.assertAllEqual( - array_ops.stack(outputs_static).numpy(), outputs_dynamic.numpy()) + self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) @test_util.run_in_graph_and_eager_modes() @@ -946,7 +945,7 @@ class LSTMTest(test.TestCase): num_proj = 4 max_length = 8 sequence_length = [4, 6] - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) @@ -1022,10 +1021,9 @@ class LSTMTest(test.TestCase): if in_graph_mode: self.assertAllEqual(outputs_static, outputs_dynamic) else: - self.assertAllEqual( - array_ops.stack(outputs_static).numpy(), outputs_dynamic.numpy()) - state_static = [s.numpy() for s in nest.flatten(state_static)] - state_dynamic = [s.numpy() for s in nest.flatten(state_dynamic)] + self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) + state_static = nest.flatten(state_static) + state_dynamic = nest.flatten(state_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) def _testDynamicEquivalentToStaticRNN(self, use_sequence_length): @@ -1043,7 +1041,7 @@ class LSTMTest(test.TestCase): else: sequence_length = None - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() # TODO(b/68017812): Eager ignores operation seeds, so we need to create a # single cell and reuse it across the static and dynamic RNNs. Remove this diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py index 7b883ebc5d7756f1bdf445f900500a4b89e6cffd..564caac1db6a76eb9cf9d053ca32e383acc1581a 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py @@ -1031,57 +1031,92 @@ class RNNCellTest(test.TestCase): num_units = 4 number_of_groups = 1 - with self.test_session() as sess: - with variable_scope.variable_scope( - "root1", initializer=init_ops.constant_initializer(0.5)): - x = array_ops.ones([batch_size, num_units]) - # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM - gcell = contrib_rnn_cell.GLSTMCell( - num_units=num_units, number_of_groups=number_of_groups) - cell = rnn_cell.LSTMCell(num_units=num_units) - self.assertTrue(isinstance(gcell.state_size, tuple)) - zero_state = gcell.zero_state( - batch_size=batch_size, dtype=dtypes.float32) - gh, gs = gcell(x, zero_state) - h, g = cell(x, zero_state) + # Try with input dimension equal to num_units or not. + for num_inputs in [num_units, num_units + number_of_groups]: + with self.test_session() as sess: + with variable_scope.variable_scope( + "root1_%d" % num_inputs, + initializer=init_ops.constant_initializer(0.5)): + x = array_ops.ones([batch_size, num_inputs]) + # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM + gcell = contrib_rnn_cell.GLSTMCell( + num_units=num_units, number_of_groups=number_of_groups) + cell = rnn_cell.LSTMCell(num_units=num_units) + self.assertTrue(isinstance(gcell.state_size, tuple)) + zero_state = gcell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + gh, gs = gcell(x, zero_state) + h, g = cell(x, zero_state) - sess.run([variables.global_variables_initializer()]) - glstm_result = sess.run([gh, gs]) - lstm_result = sess.run([h, g]) + sess.run([variables.global_variables_initializer()]) + glstm_result = sess.run([gh, gs]) + lstm_result = sess.run([h, g]) - self.assertAllClose(glstm_result[0], lstm_result[0], 1e-5) - self.assertAllClose(glstm_result[1], lstm_result[1], 1e-5) + self.assertAllClose(glstm_result[0], lstm_result[0], 1e-5) + self.assertAllClose(glstm_result[1], lstm_result[1], 1e-5) # Test that G-LSTM subgroup act like corresponding sub-LSTMs batch_size = 2 num_units = 4 number_of_groups = 2 - with self.test_session() as sess: + # Try with num_inputs equal to or not equal to num_units. + for num_inputs in [num_units, num_units + number_of_groups]: + with self.test_session() as sess: + with variable_scope.variable_scope( + "root2_%d" % num_inputs, + initializer=init_ops.constant_initializer(0.5)): + # input for G-LSTM with 2 groups + glstm_input = array_ops.ones([batch_size, num_inputs]) + gcell = contrib_rnn_cell.GLSTMCell( + num_units=num_units, number_of_groups=number_of_groups) + gcell_zero_state = gcell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + gh, gs = gcell(glstm_input, gcell_zero_state) + + # input for LSTM cell simulating single G-LSTM group + lstm_input = array_ops.ones( + [batch_size, num_inputs / number_of_groups]) + # note division by number_of_groups. This cell one simulates G-LSTM + # group + cell = rnn_cell.LSTMCell(num_units=int(num_units / number_of_groups)) + cell_zero_state = cell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + h, g = cell(lstm_input, cell_zero_state) + + sess.run([variables.global_variables_initializer()]) + [gh_res, h_res] = sess.run([gh, h]) + self.assertAllClose(gh_res[:, 0:int(num_units / number_of_groups)], + h_res, 1e-5) + self.assertAllClose(gh_res[:, int(num_units / number_of_groups):], + h_res, 1e-5) + + def testGLSTMCellFailure(self): + batch_size = 2 + num_units = 4 + number_of_groups = 2 + with self.test_session(): with variable_scope.variable_scope( - "root2", initializer=init_ops.constant_initializer(0.5)): - # input for G-LSTM with 2 groups - glstm_input = array_ops.ones([batch_size, num_units]) + "glstm_failure", initializer=init_ops.constant_initializer(0.5)): gcell = contrib_rnn_cell.GLSTMCell( num_units=num_units, number_of_groups=number_of_groups) gcell_zero_state = gcell.zero_state( batch_size=batch_size, dtype=dtypes.float32) - gh, gs = gcell(glstm_input, gcell_zero_state) - # input for LSTM cell simulating single G-LSTM group - lstm_input = array_ops.ones([batch_size, num_units / number_of_groups]) - # note division by number_of_groups. This cell one simulates G-LSTM group - cell = rnn_cell.LSTMCell(num_units=int(num_units / number_of_groups)) - cell_zero_state = cell.zero_state( - batch_size=batch_size, dtype=dtypes.float32) - h, g = cell(lstm_input, cell_zero_state) + # Try an input with statically-unknown innermost dimension. + glstm_input = array_ops.placeholder( + dtypes.float32, shape=[batch_size, None]) + with self.assertRaisesRegexp(ValueError, + "input size must be statically known"): + gcell(glstm_input, gcell_zero_state) - sess.run([variables.global_variables_initializer()]) - [gh_res, h_res] = sess.run([gh, h]) - self.assertAllClose(gh_res[:, 0:int(num_units / number_of_groups)], - h_res, 1e-5) - self.assertAllClose(gh_res[:, int(num_units / number_of_groups):], - h_res, 1e-5) + # Try an input whose innermost dimension isn't divisible into groups. + glstm_input = array_ops.placeholder( + dtypes.float32, shape=[batch_size, 3]) + with self.assertRaisesRegexp( + ValueError, + r"input size \(3\) must be divisible by number_of_groups \(2\)"): + gcell(glstm_input, gcell_zero_state) class LayerNormBasicLSTMCellTest(test.TestCase): diff --git a/tensorflow/contrib/rnn/python/ops/gru_ops.py b/tensorflow/contrib/rnn/python/ops/gru_ops.py index 4c964ec201f153d6c8293d3bf93bc231ff8f751d..81ca12317be484ba420b7bbfac822e91d6d38bff 100644 --- a/tensorflow/contrib/rnn/python/ops/gru_ops.py +++ b/tensorflow/contrib/rnn/python/ops/gru_ops.py @@ -32,7 +32,7 @@ from tensorflow.python.util.deprecation import deprecated_args _gru_ops_so = loader.load_op_library( resource_loader.get_path_to_datafile("_gru_ops.so")) -LayerRNNCell = rnn_cell_impl._LayerRNNCell # pylint: disable=invalid-name,protected-access +LayerRNNCell = rnn_cell_impl.LayerRNNCell # pylint: disable=invalid-name @ops.RegisterGradient("GRUBlockCell") diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py index 04f342cd18271425068b2b02c2937236c900c5e2..4eb4fbcd92f0d7cb3bee712862c8950a1971b632 100644 --- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py +++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import resource_loader _lstm_ops_so = loader.load_op_library( resource_loader.get_path_to_datafile("_lstm_ops.so")) -LayerRNNCell = rnn_cell_impl._LayerRNNCell # pylint: disable=invalid-name,protected-access +LayerRNNCell = rnn_cell_impl.LayerRNNCell # pylint: disable=invalid-name # pylint: disable=invalid-name @@ -572,9 +572,8 @@ class LSTMBlockWrapper(base_layer.Layer): def _gather_states(self, data, indices, batch_size): """Produce `out`, s.t. out(i, j) = data(indices(i), i, j).""" - mod_indices = indices * batch_size + math_ops.range(batch_size) - return array_ops.gather( - array_ops.reshape(data, [-1, self.num_units]), mod_indices) + return array_ops.gather_nd( + data, array_ops.stack([indices, math_ops.range(batch_size)], axis=1)) class LSTMBlockFusedCell(LSTMBlockWrapper): diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index 6af9db3f15d6040133bfffdc1891a833e625958c..50a16d3d4e8de4948b96b7b7d9fdcd85a25f105f 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -32,12 +32,12 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_impl # pylint: disable=unused-import from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import partitioned_variables # pylint: disable=unused-import from tensorflow.python.ops import random_ops from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope as vs -from tensorflow.python.ops import partitioned_variables -from tensorflow.python.ops import nn_impl from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import nest @@ -424,8 +424,9 @@ class TimeFreqLSTMCell(rnn_cell_impl.RNNCell): "W_O_diag", shape=[self._num_units], dtype=dtype) # initialize the first freq state to be zero - m_prev_freq = array_ops.zeros([int(inputs.get_shape()[0]), self._num_units], - dtype) + m_prev_freq = array_ops.zeros( + [inputs.shape[0].value or inputs.get_shape()[0], self._num_units], + dtype) for fq in range(len(freq_inputs)): c_prev = array_ops.slice(state, [0, 2 * fq * self._num_units], [-1, self._num_units]) @@ -2130,7 +2131,7 @@ class Conv1DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_1d_lstm_cell", **kwargs): """Construct Conv1DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv1DLSTMCell, self).__init__(conv_ndims=1, **kwargs) + super(Conv1DLSTMCell, self).__init__(conv_ndims=1, name=name, **kwargs) class Conv2DLSTMCell(ConvLSTMCell): @@ -2141,7 +2142,7 @@ class Conv2DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_2d_lstm_cell", **kwargs): """Construct Conv2DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv2DLSTMCell, self).__init__(conv_ndims=2, **kwargs) + super(Conv2DLSTMCell, self).__init__(conv_ndims=2, name=name, **kwargs) class Conv3DLSTMCell(ConvLSTMCell): @@ -2152,7 +2153,7 @@ class Conv3DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_3d_lstm_cell", **kwargs): """Construct Conv3DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv3DLSTMCell, self).__init__(conv_ndims=3, **kwargs) + super(Conv3DLSTMCell, self).__init__(conv_ndims=3, name=name, **kwargs) def _conv(args, filter_size, num_features, bias, bias_start=0.0): @@ -2224,6 +2225,13 @@ class GLSTMCell(rnn_cell_impl.RNNCell): O. Kuchaiev and B. Ginsburg "Factorization Tricks for LSTM Networks", ICLR 2017 workshop. + + In brief, a G-LSTM cell consists of one LSTM sub-cell per group, where each + sub-cell operates on an evenly-sized sub-vector of the input and produces an + evenly-sized sub-vector of the output. For example, a G-LSTM cell with 128 + units and 4 groups consists of 4 LSTMs sub-cells with 32 units each. If that + G-LSTM cell is fed a 200-dim input, then each sub-cell receives a 50-dim part + of the input and produces a 32-dim part of the output. """ def __init__(self, @@ -2319,9 +2327,12 @@ class GLSTMCell(rnn_cell_impl.RNNCell): """Run one step of G-LSTM. Args: - inputs: input Tensor, 2D, [batch x num_units]. - state: this must be a tuple of state Tensors, both `2-D`, - with column sizes `c_state` and `m_state`. + inputs: input Tensor, 2D, [batch x num_inputs]. num_inputs must be + statically-known and evenly divisible into groups. The innermost + vectors of the inputs are split into evenly-sized sub-vectors and fed + into the per-group LSTM sub-cells. + state: this must be a tuple of state Tensors, both `2-D`, with column + sizes `c_state` and `m_state`. Returns: A tuple containing: @@ -2336,11 +2347,24 @@ class GLSTMCell(rnn_cell_impl.RNNCell): Raises: ValueError: If input size cannot be inferred from inputs via - static shape inference. + static shape inference, or if the input shape is incompatible + with the number of groups. """ (c_prev, m_prev) = state self._batch_size = inputs.shape[0].value or array_ops.shape(inputs)[0] + + # If the input size is statically-known, calculate and validate its group + # size. Otherwise, use the output group size. + input_size = inputs.shape[1].value + if input_size is None: + raise ValueError("input size must be statically known") + if input_size % self._number_of_groups != 0: + raise ValueError( + "input size (%d) must be divisible by number_of_groups (%d)" % + (input_size, self._number_of_groups)) + input_group_size = int(input_size / self._number_of_groups) + dtype = inputs.dtype scope = vs.get_variable_scope() with vs.variable_scope(scope, initializer=self._initializer): @@ -2353,8 +2377,7 @@ class GLSTMCell(rnn_cell_impl.RNNCell): with vs.variable_scope("group%d" % group_id): x_g_id = array_ops.concat( [ - self._get_input_for_group(inputs, group_id, - self._group_shape[0]), + self._get_input_for_group(inputs, group_id, input_group_size), self._get_input_for_group(m_prev, group_id, self._group_shape[0]) ], @@ -2682,7 +2705,7 @@ class LayerNormLSTMCell(rnn_cell_impl.RNNCell): return m, new_state -class SRUCell(rnn_cell_impl._LayerRNNCell): +class SRUCell(rnn_cell_impl.LayerRNNCell): """SRU, Simple Recurrent Unit Implementation based on diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD index 245fe07f2bcdaddb2bc47c0e1234dc1f19bd85e3..b10757df47a4c38b98692c3d1995b3f040f0b478 100644 --- a/tensorflow/contrib/saved_model/BUILD +++ b/tensorflow/contrib/saved_model/BUILD @@ -53,6 +53,7 @@ py_test( size = "small", srcs = ["python/saved_model/reader_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows visibility = ["//visibility:private"], deps = [ ":saved_model_py", diff --git a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc index 64973ccccdc962757a727d7183bd70e94edcfd1b..dfa12e873a6aca806031c48d6f92e0432d0ea6e0 100644 --- a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc +++ b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc @@ -80,12 +80,12 @@ class GatherTreeOp : public OpKernel { max_sequence_lengths.shape().DebugString())); Tensor* beams; OP_REQUIRES_OK(ctx, ctx->allocate_output(0, step_ids_shape, &beams)); - typename TTypes::ConstTensor step_ids_t = step_ids.tensor(); - typename TTypes::ConstTensor parent_ids_t = parent_ids.tensor(); + typename TTypes::ConstTensor step_ids_t(step_ids.tensor()); + typename TTypes::ConstTensor parent_ids_t(parent_ids.tensor()); typename TTypes::ConstVec max_seq_lens_t = max_sequence_lengths.vec(); - typename TTypes::ConstScalar end_token_t = end_token.scalar(); - typename TTypes::Tensor beams_t = beams->tensor(); + typename TTypes::ConstScalar end_token_t(end_token.scalar()); + typename TTypes::Tensor beams_t(beams->tensor()); const T end_token_value = end_token_t(); functor::GatherTree()(ctx, device, step_ids_t, parent_ids_t, max_seq_lens_t, end_token_value, beams_t); diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index d6184d61095f727f9dcab56fe59e2601868c1624..554eb24e5260724a905b099091bf8aea461554cf 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -724,7 +724,7 @@ def _mask_probs(probs, eos_token, finished): eos_token, vocab_size, dtype=probs.dtype, - on_value=0., + on_value=ops.convert_to_tensor(0., dtype=probs.dtype), off_value=probs.dtype.min) finished_probs = array_ops.tile( array_ops.reshape(finished_row, [1, 1, -1]), diff --git a/tensorflow/contrib/session_bundle/BUILD b/tensorflow/contrib/session_bundle/BUILD index 67011c8fef6c4f54db2626ffe7ae1299bddbb352..3ad88a8a22966a072e0c43d6c41369333aba5cbe 100644 --- a/tensorflow/contrib/session_bundle/BUILD +++ b/tensorflow/contrib/session_bundle/BUILD @@ -165,6 +165,7 @@ py_test( name = "gc_test", srcs = ["gc_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows visibility = ["//visibility:private"], deps = [ ":gc", diff --git a/tensorflow/contrib/session_bundle/bundle_shim.py b/tensorflow/contrib/session_bundle/bundle_shim.py index 69db594f8ae52e608b34cff74650889aaf41a21e..1db97020a2a81f4d034543e722a6cb7ba823f44a 100644 --- a/tensorflow/contrib/session_bundle/bundle_shim.py +++ b/tensorflow/contrib/session_bundle/bundle_shim.py @@ -134,9 +134,8 @@ def _convert_named_signatures_to_signature_def(signatures): signature_constants.PREDICT_OUTPUTS] # TODO(pdudnik): what if there are other signatures? Mimic cr/140900781 once # it is submitted. - if (input_signature.WhichOneof("type") != - legacy_constants.GENERIC_SIGNATURE or - output_signature.WhichOneof("type") != + if (input_signature.WhichOneof("type") != legacy_constants.GENERIC_SIGNATURE + or output_signature.WhichOneof("type") != legacy_constants.GENERIC_SIGNATURE): raise RuntimeError("Named input and output signatures can only be " "up-converted if they are generic signature. " diff --git a/tensorflow/contrib/session_bundle/gc.py b/tensorflow/contrib/session_bundle/gc.py index 249c23c88f3043403e322b73b6c9df97e932a92a..514cc0f652c8d174bdb9bff2b2cf1ea38fdd7b1f 100644 --- a/tensorflow/contrib/session_bundle/gc.py +++ b/tensorflow/contrib/session_bundle/gc.py @@ -70,7 +70,6 @@ import heapq import math import os -from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.platform import gfile from tensorflow.python.util.deprecation import deprecated diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py index bca2e01d7bbefb18fd69a0eba27e3afb8f636724..a8b5deff6ca3a4a756d31b904e577f08f6155fd7 100644 --- a/tensorflow/contrib/signal/python/ops/spectral_ops.py +++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py @@ -144,7 +144,7 @@ def inverse_stft_window_fn(frame_step, overlaps = -(-frame_length // frame_step) # Ceiling division. denom = array_ops.pad(denom, [(0, overlaps * frame_step - frame_length)]) denom = array_ops.reshape(denom, [overlaps, frame_step]) - denom = math_ops.reduce_sum(denom, 0, keep_dims=True) + denom = math_ops.reduce_sum(denom, 0, keepdims=True) denom = array_ops.tile(denom, [overlaps, 1]) denom = array_ops.reshape(denom, [overlaps * frame_step]) diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md index c7a54cb9a2e9535efbdc179f1463cef379ebb1f9..2d9df8f27ee98431f51fd39c168325b8f625dce9 100644 --- a/tensorflow/contrib/slim/README.md +++ b/tensorflow/contrib/slim/README.md @@ -145,7 +145,7 @@ regular_variables_and_model_variables = slim.get_variables() How does this work? When you create a model variable via TF-Slim's layers or directly via the `slim.model_variable` function, TF-Slim adds the variable to -a the `tf.GraphKeys.MODEL_VARIABLES` collection. What if you have your own +the `tf.GraphKeys.MODEL_VARIABLES` collection. What if you have your own custom layers or variable creation routine but still want TF-Slim to manage or be aware of your model variables? TF-Slim provides a convenience function for adding the model variable to its collection: diff --git a/tensorflow/contrib/slim/python/slim/data/BUILD b/tensorflow/contrib/slim/python/slim/data/BUILD index 5daabbd62e7e63608a7a86a8b7fb0bc0d570b28b..7aa1684839184900547e3304358315246ac2b140 100644 --- a/tensorflow/contrib/slim/python/slim/data/BUILD +++ b/tensorflow/contrib/slim/python/slim/data/BUILD @@ -61,6 +61,7 @@ py_test( name = "dataset_data_provider_test", srcs = ["dataset_data_provider_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":dataset", ":dataset_data_provider", diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py index ad5e985487190e72b9eb2809da964f3d7b34ef94..b3343aef47d9f352c3bcbef4afbe8f9bf2560e6d 100644 --- a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py +++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py @@ -221,7 +221,7 @@ def parallel_read(data_sources, the data will be cycled through indefinitely. num_readers: a integer, number of Readers to create. reader_kwargs: an optional dict, of kwargs for the reader. - shuffle: boolean, wether should shuffle the files and the records by using + shuffle: boolean, whether should shuffle the files and the records by using RandomShuffleQueue as common_queue. dtypes: A list of types. The length of dtypes must equal the number of elements in each record. If it is None it will default to diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py index 0544404e9e252cca6d3650b805b91be25d705eea..b3b61e1dfe5671a7fbbee20b0c577ee5fad0fb9b 100644 --- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py +++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py @@ -349,7 +349,8 @@ class Image(ItemHandler): shape=None, channels=3, dtype=dtypes.uint8, - repeated=False): + repeated=False, + dct_method=''): """Initializes the image. Args: @@ -368,6 +369,11 @@ class Image(ItemHandler): tf.decode_raw, repeated: if False, decodes a single image. If True, decodes a variable number of image strings from a 1D tensor of strings. + dct_method: An optional string. Defaults to empty string. It only takes + effect when image format is jpeg, used to specify a hint about the + algorithm used for jpeg decompression. Currently valid values + are ['INTEGER_FAST', 'INTEGER_ACCURATE']. The hint may be ignored, for + example, the jpeg library does not have that specific option. """ if not image_key: image_key = 'image/encoded' @@ -381,6 +387,7 @@ class Image(ItemHandler): self._channels = channels self._dtype = dtype self._repeated = repeated + self._dct_method = dct_method def tensors_to_item(self, keys_to_tensors): """See base class.""" @@ -406,9 +413,25 @@ class Image(ItemHandler): A tensor that represents decoded image of self._shape, or (?, ?, self._channels) if self._shape is not specified. """ + def decode_image(): - """Decodes a png or jpg based on the headers.""" - return image_ops.decode_image(image_buffer, self._channels) + """Decodes a image based on the headers.""" + return image_ops.decode_image(image_buffer, channels=self._channels) + + def decode_jpeg(): + """Decodes a jpeg image with specified '_dct_method'.""" + return image_ops.decode_jpeg( + image_buffer, channels=self._channels, dct_method=self._dct_method) + + def check_jpeg(): + """Checks if an image is jpeg.""" + # For jpeg, we directly use image_ops.decode_jpeg rather than decode_image + # in order to feed the jpeg specify parameter 'dct_method'. + return control_flow_ops.cond( + image_ops.is_jpeg(image_buffer), + decode_jpeg, + decode_image, + name='cond_jpeg') def decode_raw(): """Decodes a raw image.""" @@ -420,7 +443,7 @@ class Image(ItemHandler): math_ops.equal(image_format, 'RAW')): decode_raw, } image = control_flow_ops.case( - pred_fn_pairs, default=decode_image, exclusive=True) + pred_fn_pairs, default=check_jpeg, exclusive=True) image.set_shape([None, None, self._channels]) if self._shape is not None: diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index f5a9299d263450ba89617f38bf7a4c5cbc359cb1..c24bd048512daaae116e732ac437f7c9b6f6d7fc 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -42,7 +42,7 @@ from tensorflow.python.platform import flags from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary import summary_iterator -from tensorflow.python.training import input +from tensorflow.python.training import input # pylint: disable=redefined-builtin from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import session_run_hook @@ -236,7 +236,7 @@ class SingleEvaluationTest(test.TestCase): def _prepareCheckpoint(self, checkpoint_path): init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) - saver = saver_lib.Saver() + saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) with self.test_session() as sess: sess.run(init_op) saver.save(sess, checkpoint_path) diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py index 83f33806e055cca764c9f52f318c5b71f70e0551..6a200de1ea172b4ccb38c0f5d889566ccaeef893 100644 --- a/tensorflow/contrib/slim/python/slim/learning.py +++ b/tensorflow/contrib/slim/python/slim/learning.py @@ -738,7 +738,7 @@ def train(train_op, if summary_writer is not None: train_step_kwargs['summary_writer'] = sv.summary_writer - total_loss = 0 + total_loss = None should_retry = True while should_retry: try: @@ -771,10 +771,10 @@ def train(train_op, logging.info('Stopping Training.') sv.request_stop() break - except errors.OutOfRangeError: + except errors.OutOfRangeError as e: # OutOfRangeError is thrown when epoch limit per # tf.train.limit_epochs is reached. - logging.info('Caught OutOfRangeError. Stopping Training.') + logging.info('Caught OutOfRangeError. Stopping Training. %s', e) if logdir and sv.is_chief: logging.info('Finished training! Saving model to disk.') sv.saver.save(sess, sv.save_path, global_step=sv.global_step) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py index 7b609ae96b20a5c3d078777cc8fbb475e5eebb1b..a1282847bef981717d7fdb1474adbbaaae4621c0 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py @@ -47,8 +47,8 @@ def _get_linear_equations_tests(dtype_, use_static_shape_, shape_): a_np = np.dot(a_np.T, a_np) # jacobi preconditioner jacobi_np = np.zeros_like(a_np) - jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = (1.0 / - a_np.diagonal()) + jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = ( + 1.0 / a_np.diagonal()) rhs_np = np.random.uniform( low=-1.0, high=1.0, size=shape_[0]).astype(dtype_) x_np = np.zeros_like(rhs_np) @@ -66,18 +66,30 @@ def _get_linear_equations_tests(dtype_, use_static_shape_, shape_): x = array_ops.placeholder(dtype_) jacobi = array_ops.placeholder(dtype_) operator = util.create_operator(a) - preconditioners = [None, util.identity_operator(a), - util.create_operator(jacobi)] + preconditioners = [ + None, util.identity_operator(a), + util.create_operator(jacobi) + ] cg_results = [] for preconditioner in preconditioners: cg_graph = linear_equations.conjugate_gradient( - operator, rhs, preconditioner=preconditioner, - x=x, tol=tol, max_iter=max_iter) + operator, + rhs, + preconditioner=preconditioner, + x=x, + tol=tol, + max_iter=max_iter) if use_static_shape_: cg_val = sess.run(cg_graph) else: - cg_val = sess.run(cg_graph, feed_dict={a: a_np, rhs: rhs_np, x: x_np, - jacobi: jacobi_np}) + cg_val = sess.run( + cg_graph, + feed_dict={ + a: a_np, + rhs: rhs_np, + x: x_np, + jacobi: jacobi_np + }) norm_r0 = np.linalg.norm(rhs_np) norm_r = np.linalg.norm(cg_val.r) self.assertLessEqual(norm_r, tol * norm_r0) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py index 12e94369cbae462c21867657119cd2dd9ee29651..5d7534657bff27f7169e6a97bf4b03d4f6a35bc9 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py @@ -85,9 +85,11 @@ class UtilTest(test.TestCase): op_shape_val, ax_val, aty_val = sess.run([op_shape, ax, aty]) else: op_shape_val, ax_val, aty_val = sess.run( - [op_shape, ax, aty], feed_dict={a: a_np, - x: x_np, - y: y_np}) + [op_shape, ax, aty], feed_dict={ + a: a_np, + x: x_np, + y: y_np + }) self.assertAllEqual(op_shape_val, [3, 2]) self.assertAllClose(ax_val, x_np) self.assertAllClose(aty_val, y_np) diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py index 4dfaa97ac9834ca3c13a9f8e8d721ddaba33bf7d..d791d467639b572e7831c1d1a582aa15585649b6 100644 --- a/tensorflow/contrib/solvers/python/ops/linear_equations.py +++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import linalg_ops @@ -84,10 +85,9 @@ def conjugate_gradient(operator, cg_state = collections.namedtuple("CGState", ["i", "x", "r", "p", "gamma"]) def stopping_criterion(i, state): - return math_ops.logical_and(i < max_iter, - linalg_ops.norm(state.r) > tol) + return math_ops.logical_and(i < max_iter, linalg_ops.norm(state.r) > tol) - def cg_step(i, state): + def cg_step(i, state): # pylint: disable=missing-docstring z = operator.apply(state.p) alpha = state.gamma / util.dot(state.p, z) x = state.x + alpha * state.p @@ -108,8 +108,7 @@ def conjugate_gradient(operator, rhs = array_ops.expand_dims(rhs, -1) if x is None: x = array_ops.expand_dims( - array_ops.zeros( - n, dtype=rhs.dtype.base_dtype), -1) + array_ops.zeros(n, dtype=rhs.dtype.base_dtype), -1) r0 = rhs else: x = array_ops.expand_dims(x, -1) @@ -119,7 +118,7 @@ def conjugate_gradient(operator, else: p0 = preconditioner.apply(r0) gamma0 = util.dot(r0, p0) - tol = tol * linalg_ops.norm(r0) + tol *= linalg_ops.norm(r0) i = constant_op.constant(0, dtype=dtypes.int32) state = cg_state(i=i, x=x, r=r0, p=p0, gamma=gamma0) _, state = control_flow_ops.while_loop(stopping_criterion, cg_step, diff --git a/tensorflow/contrib/sparsemax/python/ops/sparsemax.py b/tensorflow/contrib/sparsemax/python/ops/sparsemax.py index 73a5cf1e9287ea4e4350d88165744cf12db954bb..890ca20f4cabd65146e803e54e554a5c97e72427 100644 --- a/tensorflow/contrib/sparsemax/python/ops/sparsemax.py +++ b/tensorflow/contrib/sparsemax/python/ops/sparsemax.py @@ -23,7 +23,6 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn -from tensorflow.python.platform import resource_loader __all__ = ["sparsemax"] diff --git a/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py b/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py index ba18f89e16c76a6ef3cb05df0c13f62eace6bbb1..582d1e6136df4d3ad3c8108ae9607d5fef519145 100644 --- a/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py +++ b/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.util import loader -from tensorflow.python.platform import resource_loader from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops diff --git a/tensorflow/contrib/specs/BUILD b/tensorflow/contrib/specs/BUILD index 4b688690aef513dd683817b0b5c2ba4cb50f73d9..084953a0a226cde46ebd9d2031d20cb839180ca8 100644 --- a/tensorflow/contrib/specs/BUILD +++ b/tensorflow/contrib/specs/BUILD @@ -23,7 +23,6 @@ py_library( srcs_version = "PY2AND3", deps = [ "//tensorflow/contrib/layers:layers_py", - "//tensorflow/contrib/ndlstm", "//tensorflow/python:array_ops", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:logging_ops", diff --git a/tensorflow/contrib/specs/README.md b/tensorflow/contrib/specs/README.md index b764e6e714ea907cd4474a07843bda300a8e4c8b..bcf34e601f1ffe3ab7a8c0d2ad573da4c8c977e9 100644 --- a/tensorflow/contrib/specs/README.md +++ b/tensorflow/contrib/specs/README.md @@ -59,17 +59,6 @@ Reshaping: - `Squeeze` = tf.squeeze - `Expand` = tf.expand_dims -Multidimensional LSTM: - -These are intended as alternatives to 2D convolutions. For sequence models, -there will be other modeling primitives. - - - `Lstm2` = Fun(lstm2d.separable_lstm) # 2D-to-2D - - `Lstm2to1` = Fun(lstm2d.reduce_to_sequence) # 2D-to-1D - - `Lstm2to0` = Fun(lstm2d.reduce_to_final) # 2D-to-vector - - `Clstm2(n, m)` is a `Cl(n, [3,3])` followed by `Lstm2(m)` - - `Dws(n)` is a depthwise convolution `Cs(n, [1, 1])` - Other: - `Id` = identity diff --git a/tensorflow/contrib/specs/python/__init__.py b/tensorflow/contrib/specs/python/__init__.py index 52db61e421a52f4106ab1e2a4d7ee5c100b6b4bc..b6cc754023859f8d3668545dd5c2fd1d1581ecf5 100644 --- a/tensorflow/contrib/specs/python/__init__.py +++ b/tensorflow/contrib/specs/python/__init__.py @@ -18,10 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# pylint: disable=wildcard-import,g-importing-member +# pylint: disable=wildcard-import,g-importing-member,redefined-builtin from tensorflow.contrib.specs.python.params_ops import * from tensorflow.contrib.specs.python.specs import * from tensorflow.contrib.specs.python.specs_lib import * from tensorflow.contrib.specs.python.specs_ops import * from tensorflow.contrib.specs.python.summaries import * -# pylint: enable=wildcard-import +# pylint: enable=wildcard-import,redefined-builtin diff --git a/tensorflow/contrib/specs/python/specs_ops.py b/tensorflow/contrib/specs/python/specs_ops.py index a6bd4d16c284a8b1a370005a7c55d3b74b4fbf95..49b989b8d0fc83a3793263a2b59a98a8fe292c6a 100644 --- a/tensorflow/contrib/specs/python/specs_ops.py +++ b/tensorflow/contrib/specs/python/specs_ops.py @@ -23,8 +23,6 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.layers.python.layers import layers -from tensorflow.contrib.ndlstm.python import lstm1d -from tensorflow.contrib.ndlstm.python import lstm2d from tensorflow.contrib.specs.python import specs_lib from tensorflow.python.ops import array_ops from tensorflow.python.ops import logging_ops @@ -122,17 +120,6 @@ Sig = Fun(math_ops.sigmoid) Tanh = Fun(math_ops.tanh) Smax = Fun(nn_ops.softmax) -# 2D LSTM - -Lstm2 = Fun(lstm2d.separable_lstm) -Lstm2to1 = Fun(lstm2d.reduce_to_sequence) # 2D to 1D -Lstm2to0 = Fun(lstm2d.reduce_to_final) # 2D to depth-only - - -def Clstm2(n, *args, **kw): - """2D LSTM with 3x3 pre-convolution.""" - return Cl(n, [3, 3]) | Lstm2(*args, **kw) - def Dws(n): """Depth-wise convolution + sigmoid (used after LSTM).""" @@ -143,13 +130,6 @@ def Dwm(n): """Depth-wise convolution + softmax (used after LSTM).""" return Cm(n, [1, 1]) - -# 1D LSTM - -Lstm1 = Fun(lstm1d.ndlstm_base) -Lstm1to0 = Fun(lstm1d.sequence_to_final) # 1D to depth-only -Ssm = Fun(lstm1d.sequence_softmax) - # Sharing of Variables diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py index 41782a9fc9ada3d8a1ff30847971aea18f0ca1c7..9a4ad36793542a83105ad0dc1ef7c0624a6c1f99 100644 --- a/tensorflow/contrib/specs/python/specs_test.py +++ b/tensorflow/contrib/specs/python/specs_test.py @@ -149,36 +149,6 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (10, 20)) self.assertEqual(summaries.tf_spec_structure(spec, inputs), "_ sig sig") - def testLstm2(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 64, 64, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 64, 64, 15)) - - def testLstm2to1(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2to1(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 64, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 64, 15)) - - def testLstm2to0(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2to0(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 15)) - def testKeywordRestriction(self): with self.test_session(): inputs = constant_op.constant(_rand(10, 20)) diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index 7d3b8b7437a9ff5aaa0834db79bca8883cd679c8..2d6d7ea6a3eff2562ba8def4117e3aa6f818b6fd 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -18,6 +18,42 @@ The operations in this package are safe to use with eager execution turned on or off. It has a more flexible API that allows summaries to be written directly from ops to places other than event log files, rather than propagating protos from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. + +To use with eager execution enabled, write your code as follows: + +global_step = tf.train.get_or_create_global_step() +summary_writer = tf.contrib.summary.create_file_writer( + train_dir, flush_millis=10000) +with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): + # model code goes here + # and in it call + tf.contrib.summary.scalar("loss", my_loss) + # In this case every call to tf.contrib.summary.scalar will generate a record + # ... + +To use it with graph execution, write your code as follows: + +global_step = tf.train.get_or_create_global_step() +summary_writer = tf.contrib.summary.create_file_writer( + train_dir, flush_millis=10000) +with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): + # model definition code goes here + # and in it call + tf.contrib.summary.scalar("loss", my_loss) + # In this case every call to tf.contrib.summary.scalar will generate an op, + # note the need to run tf.contrib.summary.all_summary_ops() to make sure these + # ops get executed. + # ... + train_op = .... + +with tf.Session(...) as sess: + tf.global_variables_initializer().run() + tf.contrib.summary.initialize(graph=tf.get_default_graph()) + # ... + while not_done_training: + sess.run([train_op, tf.contrib.summary.all_summary_ops()]) + # ... + """ from __future__ import absolute_import diff --git a/tensorflow/contrib/summary/summary_ops.py b/tensorflow/contrib/summary/summary_ops.py index a6968d8b2a67809e3e63d099ad9448efd619b4d9..a61ce04ca2d448cb15ef77f553f39463802af5cb 100644 --- a/tensorflow/contrib/summary/summary_ops.py +++ b/tensorflow/contrib/summary/summary_ops.py @@ -110,7 +110,7 @@ class SummaryWriter(object): def __init__(self, resource): self._resource = resource - if context.in_eager_mode(): + if context.executing_eagerly() and self._resource is not None: self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device="cpu:0") @@ -154,10 +154,12 @@ def initialize( to @{tf.get_default_session}. Raises: - RuntimeError: If in eager mode, or if the current thread has no - default @{tf.contrib.summary.SummaryWriter}. + RuntimeError: If the current thread has no default + @{tf.contrib.summary.SummaryWriter}. ValueError: If session wasn't passed and no default session. """ + if context.executing_eagerly(): + return if context.context().summary_writer_resource is None: raise RuntimeError("No default tf.contrib.summary.SummaryWriter found") if session is None: @@ -267,7 +269,7 @@ def _make_summary_writer(name, factory, **kwargs): resource = gen_summary_ops.summary_writer(shared_name=name) # TODO(apassos): Consider doing this instead. # node = factory(resource, **kwargs) - # if not context.in_eager_mode(): + # if not context.executing_eagerly(): # ops.get_default_session().run(node) ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, factory(resource, **kwargs)) @@ -292,13 +294,9 @@ def all_summary_ops(): Returns: The summary ops. - - Raises: - RuntimeError: If in Eager mode. """ - if context.in_eager_mode(): - raise RuntimeError( - "tf.contrib.summary.all_summary_ops is only supported in graph mode.") + if context.executing_eagerly(): + return None return ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access @@ -311,7 +309,7 @@ def summary_writer_initializer_op(): Raises: RuntimeError: If in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "tf.contrib.summary.summary_writer_initializer_op is only " "supported in graph mode.") @@ -479,7 +477,7 @@ def graph(param, step=None, name=None): Raises: TypeError: If `param` isn't already a @{tf.Tensor} in graph mode. """ - if not context.in_eager_mode() and not isinstance(param, ops.Tensor): + if not context.executing_eagerly() and not isinstance(param, ops.Tensor): raise TypeError("graph() needs a tf.Tensor (e.g. tf.placeholder) in graph " "mode, but was: %s" % type(param)) writer = context.context().summary_writer_resource diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index dfaa4182bb867cc03480320eaf1804da36206655..bb7215f879411e91a1c47b87f5caede63fffea74 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -29,7 +29,6 @@ from tensorflow.core.framework import types_pb2 from tensorflow.python.eager import function from tensorflow.python.eager import test from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import state_ops @@ -59,12 +58,6 @@ _NUMPY_NUMERIC_TYPES = { class TargetTest(test_util.TensorFlowTestCase): - def testInvalidDirectory(self): - logdir = '/tmp/apath/that/doesnt/exist' - self.assertFalse(gfile.Exists(logdir)) - with self.assertRaises(errors.NotFoundError): - summary_ops.create_file_writer(logdir, max_queue=0, name='t0') - def testShouldRecordSummary(self): self.assertFalse(summary_ops.should_record_summaries()) with summary_ops.always_record_summaries(): diff --git a/tensorflow/contrib/summary/summary_test_internal.py b/tensorflow/contrib/summary/summary_test_internal.py new file mode 100644 index 0000000000000000000000000000000000000000..d0d3384735fb1eb1a048c7aa6da0037ee9fc6936 --- /dev/null +++ b/tensorflow/contrib/summary/summary_test_internal.py @@ -0,0 +1,60 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Internal helpers for tests in this directory.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import sqlite3 + +from tensorflow.contrib.summary import summary_ops +from tensorflow.python.framework import test_util + + +class SummaryDbTest(test_util.TensorFlowTestCase): + """Helper for summary database testing.""" + + def setUp(self): + super(SummaryDbTest, self).setUp() + self.db_path = os.path.join(self.get_temp_dir(), 'DbTest.sqlite') + if os.path.exists(self.db_path): + os.unlink(self.db_path) + self.db = sqlite3.connect(self.db_path) + self.create_db_writer = functools.partial( + summary_ops.create_db_writer, + db_uri=self.db_path, + experiment_name='experiment', + run_name='run', + user_name='user') + + def tearDown(self): + self.db.close() + super(SummaryDbTest, self).tearDown() + + +def get_one(db, q, *p): + return db.execute(q, p).fetchone()[0] + + +def get_all(db, q, *p): + return unroll(db.execute(q, p).fetchall()) + + +def unroll(list_of_tuples): + return sum(list_of_tuples, ()) diff --git a/tensorflow/contrib/summary/summary_test_util.py b/tensorflow/contrib/summary/summary_test_util.py index bda57e6a0ca8e1ddb979a80de276911c7738f0aa..8506c4be9c4ca8305b62da17c7246e6e18313bd3 100644 --- a/tensorflow/contrib/summary/summary_test_util.py +++ b/tensorflow/contrib/summary/summary_test_util.py @@ -21,6 +21,7 @@ from __future__ import print_function import functools import os + import sqlite3 from tensorflow.contrib.summary import summary_ops diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD index 58a7fa095d8356229fdb5879bea99d316113c828..07b6b1f142ba4d113c0d45db5a8a2ce3f66f4154 100644 --- a/tensorflow/contrib/tensor_forest/BUILD +++ b/tensorflow/contrib/tensor_forest/BUILD @@ -497,6 +497,7 @@ py_library( ":tensor_forest_v4_ops_py", "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py", "//tensorflow/contrib/framework:framework_py", + "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_py", "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", @@ -552,7 +553,6 @@ py_test( srcs = ["client/random_forest_test.py"], srcs_version = "PY2AND3", tags = [ - "no_windows", "nomac", # b/63258195 "notsan", ], diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h index 04e6b0a735320dd024e326a94ef910593a326245..dc3e9fe79d32a19930d500b62b520eddb4b41aa8 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h @@ -468,7 +468,7 @@ class FixedSizeSparseClassificationGrowStats : public ClassificationStats { void PackToProto(FertileSlot* slot) const override; void InitLeafClassStats(int best_split_index, LeafStat* left_stats, - LeafStat* right_stats) const; + LeafStat* right_stats) const override; protected: void ClassificationAddSplitStats() override { diff --git a/tensorflow/contrib/tensorboard/BUILD b/tensorflow/contrib/tensorboard/BUILD index 2e0a46ffe432341a423ac159deb7745d9ef15374..1e7dd79ae77e5d1c413bdafe877c876fdd44051d 100644 --- a/tensorflow/contrib/tensorboard/BUILD +++ b/tensorflow/contrib/tensorboard/BUILD @@ -9,6 +9,7 @@ exports_files(["LICENSE"]) # For platform specific build config load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library") +load("//tensorflow:tensorflow.bzl", "py_test") tf_proto_library( name = "protos_all", diff --git a/tensorflow/contrib/tensorboard/db/summary_file_writer.cc b/tensorflow/contrib/tensorboard/db/summary_file_writer.cc index d891e86e53f4d760bfaea0e67601cfda037a4564..85b3e7231bcb433e9510522597c03c5f764f06cf 100644 --- a/tensorflow/contrib/tensorboard/db/summary_file_writer.cc +++ b/tensorflow/contrib/tensorboard/db/summary_file_writer.cc @@ -42,14 +42,14 @@ class SummaryFileWriter : public SummaryWriterInterface { if (is_dir.code() != tensorflow::error::NOT_FOUND) { return is_dir; } - TF_RETURN_IF_ERROR(env_->CreateDir(logdir)); + TF_RETURN_IF_ERROR(env_->RecursivelyCreateDir(logdir)); } mutex_lock ml(mu_); events_writer_ = tensorflow::MakeUnique(io::JoinPath(logdir, "events")); - if (!events_writer_->InitWithSuffix(filename_suffix)) { - return errors::Unknown("Could not initialize events writer."); - } + TF_RETURN_WITH_CONTEXT_IF_ERROR( + events_writer_->InitWithSuffix(filename_suffix), + "Could not initialize events writer."); last_flush_ = env_->NowMicros(); is_initialized_ = true; return Status::OK(); @@ -151,9 +151,8 @@ class SummaryFileWriter : public SummaryWriterInterface { events_writer_->WriteEvent(*e); } queue_.clear(); - if (!events_writer_->Flush()) { - return errors::InvalidArgument("Could not flush events file."); - } + TF_RETURN_WITH_CONTEXT_IF_ERROR(events_writer_->Flush(), + "Could not flush events file."); last_flush_ = env_->NowMicros(); return Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 28f571e1f015435b33406d0b802bb8e059c6e5ee..906cc3f0344e7cb641589bd522e33d658150d3b5 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -1,5 +1,6 @@ # Description: -# Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow. +# Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow +# and provide TensorRT operators and converter package. # APIs are meant to change over time. package(default_visibility = ["//tensorflow:__subpackages__"]) @@ -8,7 +9,19 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) +load( + "//tensorflow:tensorflow.bzl", + "tf_cc_test", + "tf_copts", + "tf_cuda_library", + "tf_custom_op_library", + "tf_custom_op_library_additional_deps", + "tf_gen_op_libs", + "tf_gen_op_wrapper_py", +) load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test") +load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") +load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") load( "@local_config_tensorrt//:build_defs.bzl", "if_tensorrt", @@ -32,6 +45,234 @@ tf_cuda_cc_test( ]), ) +tf_custom_op_library( + name = "python/ops/_trt_engine_op.so", + srcs = [ + "ops/trt_calib_op.cc", + "ops/trt_engine_op.cc", + ], + deps = [ + ":trt_engine_op_kernel", + ":trt_shape_function", + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +tf_cuda_library( + name = "trt_shape_function", + srcs = ["shape_fn/trt_shfn.cc"], + hdrs = ["shape_fn/trt_shfn.h"], + visibility = ["//visibility:public"], + deps = [ + ":trt_logging", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]) + tf_custom_op_library_additional_deps(), +) + +cc_library( + name = "trt_engine_op_kernel", + srcs = [ + "kernels/trt_calib_op.cc", + "kernels/trt_engine_op.cc", + ], + hdrs = [ + "kernels/trt_calib_op.h", + "kernels/trt_engine_op.h", + ], + copts = tf_copts(), + visibility = ["//visibility:public"], + deps = [ + ":trt_logging", + ":trt_resources", + "//tensorflow/core:gpu_headers_lib", + "//tensorflow/core:lib_proto_parsing", + "//tensorflow/core:stream_executor_headers_lib", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]) + tf_custom_op_library_additional_deps(), + # TODO(laigd) + alwayslink = 1, # buildozer: disable=alwayslink-with-hdrs +) + +tf_gen_op_libs( + op_lib_names = [ + "trt_engine_op", + "trt_calib_op", + ], + deps = if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +tf_cuda_library( + name = "trt_logging", + srcs = ["log/trt_logger.cc"], + hdrs = ["log/trt_logger.h"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +tf_gen_op_wrapper_py( + name = "trt_engine_op", + gen_locally = True, + deps = [ + ":trt_calib_op_op_lib", + ":trt_engine_op_op_lib", + ":trt_logging", + ":trt_shape_function", + ], +) + +tf_custom_op_py_library( + name = "trt_engine_op_loader", + srcs = ["python/ops/trt_engine_op.py"], + dso = [ + ":python/ops/_trt_engine_op.so", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:resources", + ], +) + +py_library( + name = "init_py", + srcs = [ + "__init__.py", + "python/__init__.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":trt_convert_py", + ":trt_ops_py", + "//tensorflow/python:errors", + ], +) + +py_library( + name = "trt_ops_py", + srcs_version = "PY2AND3", + deps = [ + ":trt_engine_op", + ":trt_engine_op_loader", + ], +) + +py_library( + name = "trt_convert_py", + srcs = ["python/trt_convert.py"], + srcs_version = "PY2AND3", + deps = [ + ":wrap_conversion", + ], +) + +tf_py_wrap_cc( + name = "wrap_conversion", + srcs = ["trt_conversion.i"], + copts = tf_copts(), + deps = [ + ":trt_conversion", + "//tensorflow/core:framework_lite", + "//util/python:python_headers", + ], +) + +tf_cuda_library( + name = "trt_resources", + srcs = [ + "resources/trt_int8_calibrator.cc", + "resources/trt_resource_manager.cc", + ], + hdrs = [ + "resources/trt_int8_calibrator.h", + "resources/trt_resource_manager.h", + "resources/trt_resources.h", + ], + deps = [ + ":trt_logging", + "//tensorflow/core:framework_headers_lib", + "//tensorflow/core:framework_lite", + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +# Library for the node-level conversion portion of TensorRT operation creation +tf_cuda_library( + name = "trt_conversion", + srcs = [ + "convert/convert_graph.cc", + "convert/convert_nodes.cc", + ], + hdrs = [ + "convert/convert_graph.h", + "convert/convert_nodes.h", + ], + deps = [ + ":segment", + ":trt_logging", + ":trt_resources", + "//tensorflow/core/grappler:grappler_item", + "//tensorflow/core/grappler:utils", + "//tensorflow/core:framework", + "//tensorflow/core:framework_lite", + "//tensorflow/core:graph", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/grappler:devices", + "//tensorflow/core/grappler/clusters:virtual_cluster", + "//tensorflow/core/grappler/costs:graph_properties", + "//tensorflow/core/grappler/optimizers:constant_folding", + "//tensorflow/core/grappler/optimizers:layout_optimizer", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]) + tf_custom_op_library_additional_deps(), +) + +# Library for the segmenting portion of TensorRT operation creation +cc_library( + name = "segment", + srcs = ["segment/segment.cc"], + hdrs = [ + "segment/segment.h", + "segment/union_find.h", + ], + linkstatic = 1, + deps = [ + "//tensorflow/core:graph", + "//tensorflow/core:lib_proto_parsing", + "//tensorflow/core:protos_all_cc", + "@protobuf_archive//:protobuf_headers", + ], +) + +tf_cc_test( + name = "segment_test", + size = "small", + srcs = ["segment/segment_test.cc"], + deps = [ + ":segment", + "//tensorflow/c:c_api", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md new file mode 100644 index 0000000000000000000000000000000000000000..461e627e99c38fe492dc8a8a1c9fb06e6cfda3a2 --- /dev/null +++ b/tensorflow/contrib/tensorrt/README.md @@ -0,0 +1,25 @@ +Using TensorRT in TensorFlow +============================ + +This module provides necessary bindings and introduces TRT_engine_op +operator that wraps a subgraph in TensorRT. This is still a work in progress +but should be useable with most common graphs. + +Compilation +----------- + +In order to compile the module, you need to have a local TensorRT +installation (libnvinfer.so and respective include files). During the +configuration step, TensorRT should be enabled and installation path +should be set. If installed through package managers (deb,rpm), +configure script should find the necessary components from the system +automatically. If installed from tar packages, user has to set path to +location where the library is installed during configuration. + +```shell +bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package +bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/ +``` + +After the installation of tensorflow package, TensorRT transformation +will be available. An example use can be found in test/test_tftrt.py directory diff --git a/tensorflow/contrib/tensorrt/__init__.py b/tensorflow/contrib/tensorrt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..140ad4828208ae4844a49bf664955b50cd9e51cd --- /dev/null +++ b/tensorflow/contrib/tensorrt/__init__.py @@ -0,0 +1,35 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Exposes the python wrapper for TensorRT graph transforms.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import errors + +# pylint: disable=unused-import,wildcard-import,g-import-not-at-top +try: + from tensorflow.contrib.tensorrt.python import * +except errors.NotFoundError as e: + no_trt_message = ( + '**** Failed to initialize TensorRT. This is either because the TensorRT' + ' installation path is not in LD_LIBRARY_PATH, or because you do not have' + ' it installed. If not installed, please go to' + ' https://developer.nvidia.com/tensorrt to download and install' + ' TensorRT ****') + print(no_trt_message) + raise e +# pylint: enable=unused-import,wildcard-import,g-import-not-at-top diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc new file mode 100644 index 0000000000000000000000000000000000000000..eea8c8efa28c1b7f9a1979311e920ca5c89f830a --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -0,0 +1,427 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/convert/convert_graph.h" + +#include +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/segment/segment.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/grappler/clusters/virtual_cluster.h" +#include "tensorflow/core/grappler/costs/graph_properties.h" +#include "tensorflow/core/grappler/devices.h" +#include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/optimizers/constant_folding.h" +#include "tensorflow/core/grappler/optimizers/layout_optimizer.h" +#include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" +#include "tensorflow/core/protobuf/device_properties.pb.h" // NOLINT + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +namespace convert { +namespace { + +bool IsTensorRTCandidate(const tensorflow::NodeDef& node_def) { + // LINT.IfChange + // TODO(jie): Segmentation shouldn't associated with op name. + // Split it into a registration for each kernel. + static const std::set candidate_ops = { + "Identity", + "Const", + "Conv2D", + "MaxPool", + "BiasAdd", + "Relu", + "Add", + "Mul", + "Sub", + "Rsqrt", + "Pad", + "Mean", + "AvgPool", + "ConcatV2", + "DepthwiseConv2dNative", + "FusedBatchNorm", + "FusedBatchNormV2", + // TODO(ben,jie): ... + }; + // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) + return candidate_ops.count(node_def.op()); +} + +void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, + const std::set& subgraph_node_ids, + tensorflow::EdgeSet* incoming_edges) { + for (int node_id : subgraph_node_ids) { + const tensorflow::Node* node = graph.FindNodeId(node_id); + for (const tensorflow::Edge* edge : node->in_edges()) { + if (!subgraph_node_ids.count(edge->src()->id()) && + !edge->src()->IsSource()) { + incoming_edges->insert(edge); + } else { + VLOG(2) << edge->src()->name() << " N, "; + } + } + } +} + +void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, + const std::set& subgraph_node_ids, + tensorflow::EdgeSet* outgoing_edges) { + for (int node_id : subgraph_node_ids) { + const tensorflow::Node* node = graph.FindNodeId(node_id); + for (const tensorflow::Edge* edge : node->out_edges()) { + if (!subgraph_node_ids.count(edge->dst()->id()) && + !edge->dst()->IsSink()) { + VLOG(2) << edge->dst()->name() << " Y, "; + outgoing_edges->insert(edge); + } else { + VLOG(2) << edge->dst()->name() << " N, "; + } + } + } +} + +std::pair ParseTensorName(string name, int default_idx = 0) { + int idx = default_idx; + size_t sep = name.find_last_of(':'); + if (sep != string::npos) { + name = name.substr(0, sep); + idx = std::stoi(name.substr(sep + 1)); + } + return std::make_pair(name, idx); +} + +std::unordered_map> BuildTensorNameMap( + const std::vector& tensor_names) { + std::unordered_map> result; + for (string const& tensor_name : tensor_names) { + string node_name; + int index; + std::tie(node_name, index) = ParseTensorName(tensor_name); + result[node_name].push_back(index); + } + return result; +} +// TODO(sami): convert references to pointers +struct ConvertGraphParams { + ConvertGraphParams( + tensorflow::Graph& inp_graph, + const std::vector& output_node_names, + const std::set& subgraph_node_id_numbers, + size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, + const tensorflow::grappler::GraphProperties& current_graph_properties, + std::unordered_map>* output_edges, + int engine_precision_mode) + : graph(inp_graph), + output_names(output_node_names), + subgraph_node_ids(subgraph_node_id_numbers), + max_batch_size(max_supported_batch_size), + max_workspace_size_bytes(max_consumed_workspace_size_bytes), + graph_properties(current_graph_properties), + output_edge_map(output_edges), + precision_mode(engine_precision_mode) {} + tensorflow::Graph& graph; + const std::vector& output_names; + const std::set& subgraph_node_ids; + size_t max_batch_size; + size_t max_workspace_size_bytes; + const tensorflow::grappler::GraphProperties& graph_properties; + std::unordered_map>* output_edge_map; + int precision_mode; + std::vector> subgraph_inputs; + std::vector> subgraph_outputs; + tensorflow::EdgeSet subgraph_incoming_edges; + tensorflow::EdgeSet subgraph_outgoing_edges; +}; + +static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { + GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, + &p->subgraph_incoming_edges); + for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { + p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); + } + auto output_name_to_index_map = BuildTensorNameMap(p->output_names); + std::set> subgraph_outputs_set; + // Collect outputs referenced from output_names + for (int node_id : p->subgraph_node_ids) { + tensorflow::Node* node = p->graph.FindNodeId(node_id); + if (output_name_to_index_map.count(node->name())) { + for (int index : output_name_to_index_map.at(node->name())) { + subgraph_outputs_set.insert({node_id, index}); + } + } + } + GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, + &p->subgraph_outgoing_edges); + for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { + subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); + } + p->subgraph_outputs.reserve(subgraph_outputs_set.size()); + p->subgraph_outputs.insert(p->subgraph_outputs.begin(), + subgraph_outputs_set.begin(), + subgraph_outputs_set.end()); + return tensorflow::Status::OK(); +}; + +tensorflow::Status GetCalibNode(ConvertGraphParams* params) { + TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); + tensorflow::NodeDef trt_node_def; + SubGraphParams s(params->graph, params->subgraph_node_ids, + params->subgraph_inputs, params->subgraph_outputs, + params->max_batch_size, params->max_workspace_size_bytes, + params->graph_properties, params->output_edge_map, + &trt_node_def, params->precision_mode); + TF_RETURN_IF_ERROR(InjectCalibrationNode(s)); + tensorflow::Status status; + tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); + + TF_RETURN_IF_ERROR(status); + + for (auto in_edge : + params->subgraph_incoming_edges) { // loop over incoming edges and + // attach them to calib node + // tensorflow::Node* src_node = in_edge->src(); + auto src_output = in_edge->src_output(); + auto dst_node = in_edge->dst(); + auto dst_input = in_edge->dst_input(); + VLOG(1) << " update edge " << trt_node->name() << ":" << src_output + << " -> " << dst_node->name() << ":" << dst_input; + TF_RETURN_IF_ERROR( + params->graph.UpdateEdge(trt_node, src_output, dst_node, dst_input)); + } + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { + TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); + tensorflow::NodeDef trt_node_def; + + SubGraphParams s(params->graph, params->subgraph_node_ids, + params->subgraph_inputs, params->subgraph_outputs, + params->max_batch_size, params->max_workspace_size_bytes, + params->graph_properties, params->output_edge_map, + &trt_node_def, params->precision_mode); + TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(s)); + tensorflow::Status status; + tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); + + // AddNode does not wire edges. + // Re-map incoming edges to use the new TRT node instead of the orig subgraph + std::map, int> subgraph_edge_to_input_map; + for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { + subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); + } + for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { + std::pair old_src = {edge->src()->id(), edge->src_output()}; + int new_src_output = subgraph_edge_to_input_map.at(old_src); + params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, + new_src_output); + params->graph.RemoveEdge(edge); + } + + VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + } + + TF_RETURN_IF_ERROR(status); + + // Re-map outgoing edges to use the new TRT node instead of the orig subgraph + std::map, int> subgraph_edge_to_output_map; + for (size_t i = 0; i < params->subgraph_outputs.size(); ++i) { + subgraph_edge_to_output_map.insert({params->subgraph_outputs.at(i), i}); + } + TF_RETURN_IF_ERROR(status); + for (const tensorflow::Edge* edge : params->subgraph_outgoing_edges) { + std::pair old_src = {edge->src()->id(), edge->src_output()}; + int new_src_output = subgraph_edge_to_output_map.at(old_src); + TF_RETURN_IF_ERROR(params->graph.UpdateEdge( + trt_node, new_src_output, edge->dst(), edge->dst_input())); + } + // Remove the original subgraph + for (int node_id : params->subgraph_node_ids) { + tensorflow::Node* node = params->graph.FindNodeId(node_id); + // Don't remove the input placeholders + if (node->type_string() == "Placeholder") { + continue; + } + params->graph.RemoveNode(node); + } + return tensorflow::Status::OK(); +} + +tensorflow::Status BuildNodeMap( + const tensorflow::Graph& graph, + std::unordered_map* node_map) { + for (auto* node : graph.op_nodes()) { + if (!node_map->insert({node->name(), node}).second) { + return tensorflow::errors::AlreadyExists( + "Node name is not unique in graph: " + node->name()); + } + } + return tensorflow::Status::OK(); +} + +} // namespace +tensorflow::Status ConvertCalibGraphToInferGraph( + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph) { + VLOG(0) << "Starting Calib Conversion"; + tensorflow::Graph graph(tensorflow::OpRegistry::Global()); + TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( + tensorflow::GraphConstructorOptions(), graph_def, &graph)); + // get calib nodes + std::vector calib_nodes; + for (auto node : graph.op_nodes()) { + if (node->type_string() == "TRTCalibOp") { + VLOG(1) << "Found Calib Node"; + calib_nodes.push_back(node); + } + } + VLOG(0) << "Num Calib nodes in graph= " << calib_nodes.size(); + if (calib_nodes.size() == 0) + return tensorflow::errors::FailedPrecondition( + "Graph doesn't contain any calibration nodes!." + " Please generate calibration graph and run calibration first"); + for (auto n : calib_nodes) { + TF_RETURN_IF_ERROR( + tensorrt::convert::ConvertCalibrationNodeToEngineNode(graph, n)); + } + graph.ToGraphDef(infer_graph); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertGraphDefToTensorRT( + const tensorflow::GraphDef& graph_def, + const std::vector& output_names, size_t max_batch_size, + size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, + int precision_mode = FP32MODE, int minimum_segment_size = 3) { + // optimization pass + tensorflow::grappler::GrapplerItem item; + item.fetch = output_names; + tensorflow::GraphDef gdef; + + // Layout optimization + item.graph = graph_def; + tensorflow::grappler::LayoutOptimizer optimizer; + tensorflow::grappler::Cluster* cluster; + + // virtual cluster + tensorflow::DeviceProperties device_properties; + + device_properties.set_type("GPU"); + device_properties.mutable_environment()->insert({"architecture", "6"}); + cluster = + new tensorflow::grappler::VirtualCluster({{"/GPU:0", device_properties}}); + + // single machine + int num_cpu_cores = tensorflow::grappler::GetNumAvailableLogicalCPUCores(); + int num_gpus = tensorflow::grappler::GetNumAvailableGPUs(); + VLOG(2) << "cpu_cores: " << num_cpu_cores; + VLOG(2) << "gpus: " << num_gpus; + + TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef)); + + // constant folding + item.graph = gdef; + tensorflow::grappler::ConstantFolding fold(nullptr); + TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef)); + + // AJ refactoring shape inference through grappler/GraphProperties. + tensorflow::grappler::GraphProperties static_graph_properties(item); + TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(false)); + // Build full graph + tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), + gdef.library()); + tensorflow::Graph graph(flib); + TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( + tensorflow::GraphConstructorOptions(), gdef, &graph)); + + // Segment the graph into subgraphs that can be converted to TensorRT + tensorflow::tensorrt::segment::SegmentOptions segment_options; + + // TODO(ben,jie,sami): exclude output nodes (DISCUSS IT) + for (auto node : output_names) { + segment_options.exclude_node_list.insert(node); + } + + // TODO(sami): this should be passed as a knob!!!! + segment_options.minimum_segment_size = minimum_segment_size; + tensorflow::tensorrt::segment::SegmentNodesVector segments; + TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( + gdef, IsTensorRTCandidate, segment_options, &segments)); + if (segments.size() > 1) { + VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size(); + } + std::unordered_map node_map; + TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map)); + std::unordered_map> output_edge_map; + int count = 0; + float total_num_nodes_in_segments = 0.; + for (auto s : segments) { + total_num_nodes_in_segments += s.size(); + } + for (const std::set& subgraph_node_names : segments) { + std::set subgraph_node_ids; + size_t max_mem_per_engine = + max_workspace_size_bytes * + ((float)subgraph_node_names.size() / total_num_nodes_in_segments); + std::stringstream oss; + for (const string& node_name : subgraph_node_names) { + oss << " " << node_name; + subgraph_node_ids.insert(node_map.at(node_name)->id()); + } + VLOG(2) << "Subgraph nodes" << oss.str(); + ConvertGraphParams p(graph, output_names, subgraph_node_ids, max_batch_size, + max_mem_per_engine, static_graph_properties, + &output_edge_map, precision_mode); + if (precision_mode == INT8MODE) { + TF_RETURN_IF_ERROR(GetCalibNode(&p)); + } else { + tensorflow::Status status = ConvertSubGraphToTensorRT(&p); + if (status != tensorflow::Status::OK()) { + LOG(WARNING) << "subgraph conversion error for subgraph_index:" << count + << " due to: \n" + << status.ToString() << " SKIPPING......"; + } + count++; + } + } + graph.ToGraphDef(new_graph_def); + return tensorflow::Status::OK(); +} + +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h new file mode 100644 index 0000000000000000000000000000000000000000..e1596e89e229813245b35e568cd349b738c2d879 --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_ + +#include + +#include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { +namespace convert { + +// This method converts an already generated calibration graph which was used in +// calibration runs to an inference graph +tensorflow::Status ConvertCalibGraphToInferGraph( + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def); + +// max_batch_size: maximum batch size which can be used for inference for +// optimization targets inference run with max batch size. +// max_workspace_size_bytes: The upper bound of memory allowence for +// engine building. +tensorflow::Status ConvertGraphDefToTensorRT( + const tensorflow::GraphDef& graph_def, + const std::vector& output_names, size_t max_batch_size, + size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, + int precision_mode, int minimum_segment_size); + +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_ diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc new file mode 100644 index 0000000000000000000000000000000000000000..4c00630cfeed032f063dacd1bed05a92ba11738a --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -0,0 +1,2662 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" +#include "tensorflow/core/framework/node_def.pb.h" // NOLINT +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/tensor_shape.pb.h" // NOLINT +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/tensor_coding.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +// Check if the types are equal. Cast to int first so that failure log message +// would work! +#define CHECK_EQ_TYPE(val1, val2) CHECK_EQ((int)val1, (int)val2) + +namespace tensorflow { +namespace tensorrt { +namespace convert { +using ::tensorflow::strings::StrCat; + +namespace { + +inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, + nvinfer1::DataType* trt_dtype) { + switch (tf_dtype) { + case tensorflow::DataType::DT_FLOAT: + *trt_dtype = nvinfer1::DataType::kFLOAT; + break; + case tensorflow::DataType::DT_INT8: + *trt_dtype = nvinfer1::DataType::kINT8; + break; + case tensorflow::DataType::DT_HALF: + *trt_dtype = nvinfer1::DataType::kHALF; + break; + default: + return tensorflow::errors::InvalidArgument( + "Unsupported data type " + tensorflow::DataTypeString(tf_dtype)); + } + return tensorflow::Status::OK(); +} + +inline nvinfer1::Dims GetTensorShape(const tensorflow::Tensor& tensor) { + nvinfer1::Dims dims; + dims.nbDims = tensor.dims(); + for (int i = 0; i < dims.nbDims; i++) { + dims.d[i] = tensor.dim_size(i); + } + return dims; +} + +inline int64_t GetShapeSize(nvinfer1::Dims shape) { + // Returns total number of elements in shape + int64_t count = 1; + for (int d = 0; d < shape.nbDims; ++d) { + count *= shape.d[d]; + } + return count; +} + +static std::vector> CreateSamePadding( + const nvinfer1::DimsHW& stride, const nvinfer1::DimsHW& kernel, + const std::vector& input_dims) { + std::vector> padding(input_dims.size()); + CHECK_EQ((size_t)stride.nbDims, input_dims.size()); // TODO(jie): N+C? NC+? + + for (size_t i = 0; i < input_dims.size(); ++i) { + // Formula to calculate the padding + int p = ((input_dims[i] - 1) / stride.d[i]) * stride.d[i] + kernel.d[i] - + input_dims[i]; + p = (p > 0) ? p : 0; + + // Right precedence padding, like in TensorFlow + int left = p / 2; + int right = p - left; + + VLOG(2) << "PADDING_" << i << " pre: " << left << ", post: " << right + << "paras: " << input_dims[i] << ", " << stride.d[i] << ", " + << "kernel: " << kernel.d[i]; + padding[i] = {left, right}; + } + return padding; +} + +string GetCommonNameScope(const string& op_name_a, const string& op_name_b) { + size_t last_scope_separator = 0; + for (size_t i = 0; i < std::min(op_name_a.size(), op_name_b.size()); ++i) { + if (op_name_a[i] != op_name_b[i]) { + break; + } else if (op_name_a[i] == '/') { + last_scope_separator = i + 1; + } + } + return op_name_a.substr(0, last_scope_separator); +} + +class TRT_ShapedWeights { + public: + TRT_ShapedWeights(tensorflow::DataType type, const void* values, + nvinfer1::Dims shape) + : shape_(shape), type_(type), values_(values), empty_weight_flag_(false) { + // Note: this->shape.type[] is not used + } + + explicit TRT_ShapedWeights(tensorflow::DataType type) + : shape_(), type_(type), values_(nullptr), empty_weight_flag_(true) {} + + TRT_ShapedWeights(const TRT_ShapedWeights& rhs) + : shape_(rhs.shape_), + type_(rhs.type_), + values_(rhs.values_), + empty_weight_flag_(rhs.empty_weight_flag_) {} + + int64_t count() const { + int64_t c = 1; + for (int i = 0; i < shape_.nbDims; i++) c *= shape_.d[i]; + return c; + } + + nvinfer1::Weights GetWeightsForTRT() const { + nvinfer1::DataType trt_type(nvinfer1::DataType::kFLOAT); + TF_CHECK_OK(ConvertDType(type_, &trt_type)); + if (empty_weight_flag_) return nvinfer1::Weights{trt_type, nullptr, 0}; + + // Note: this->shape.type[] is not used + return nvinfer1::Weights{trt_type, GetValues(), GetShapeSize(shape_)}; + } + + const void* GetValues() const { return values_; } + + void SetValues(const void* values) { values_ = values; } + + size_t size_bytes() const { + int type_size = tensorflow::DataTypeSize(this->type_); + return this->count() * type_size; + } + + // Default converter + operator nvinfer1::Weights() const { return GetWeightsForTRT(); } + + nvinfer1::Dims shape_; + tensorflow::DataType type_; + + private: + const void* values_; + bool empty_weight_flag_; +}; + +class TRT_TensorOrWeights { + public: + explicit TRT_TensorOrWeights(nvinfer1::ITensor* tensor) + : tensor_(tensor), weights_(DT_FLOAT), variant_(TRT_NODE_TENSOR) {} + explicit TRT_TensorOrWeights(const TRT_ShapedWeights& weights) + : tensor_(nullptr), weights_(weights), variant_(TRT_NODE_WEIGHTS) {} + TRT_TensorOrWeights(const TRT_TensorOrWeights& rhs) + : tensor_(rhs.tensor_), weights_(rhs.weights_), variant_(rhs.variant_) {} + ~TRT_TensorOrWeights() {} + + bool is_tensor() const { return variant_ == TRT_NODE_TENSOR; } + bool is_weights() const { return variant_ == TRT_NODE_WEIGHTS; } + + nvinfer1::ITensor* tensor() { + CHECK_EQ(is_tensor(), true); + return tensor_; + } + const nvinfer1::ITensor* tensor() const { + CHECK_EQ(is_tensor(), true); + return tensor_; + } + TRT_ShapedWeights& weights() { + CHECK_EQ(is_weights(), true); + return weights_; + } + const TRT_ShapedWeights& weights() const { + CHECK_EQ(is_weights(), true); + return weights_; + } + nvinfer1::Dims shape() const { + if (is_tensor()) { + return tensor()->getDimensions(); + } else { + return weights().shape_; + } + } + + private: + nvinfer1::ITensor* tensor_; + TRT_ShapedWeights weights_; + enum { TRT_NODE_TENSOR, TRT_NODE_WEIGHTS } variant_; +}; + +class TFAttrs { + public: + explicit TFAttrs(const tensorflow::NodeDef& tf_node) { + for (const auto& attr : tf_node.attr()) { + attrs_.insert({attr.first, &attr.second}); + } + } + bool count(string key) const { return attrs_.count(key); } + tensorflow::AttrValue const* at(string key) const { + if (!attrs_.count(key)) { + LOG(FATAL) << "Attribute not found: " << key; + } + return attrs_.at(key); + } + template + T get(string key) const; + template + T get(string key, const T& default_value) const { + return attrs_.count(key) ? this->get(key) : default_value; + } + + private: + typedef std::map AttrMap; + AttrMap attrs_; +}; + +template <> +string TFAttrs::get(string key) const { + return this->at(key)->s(); +} + +template <> +std::vector TFAttrs::get>(string key) const { + auto attr = this->at(key)->list().i(); + return std::vector(attr.begin(), attr.end()); +} + +template <> +std::vector TFAttrs::get>(string key) const { + auto attr = this->at(key)->list().s(); + return std::vector(attr.begin(), attr.end()); +} +template <> +nvinfer1::Dims TFAttrs::get(string key) const { + auto values = this->get>(key); + nvinfer1::Dims dims; + dims.nbDims = values.size(); + std::copy(values.begin(), values.end(), dims.d); + // Note: No dimension type information is included + return dims; +} + +template <> +nvinfer1::DataType TFAttrs::get(string key) const { + nvinfer1::DataType trt_dtype(nvinfer1::DataType::kFLOAT); + TF_CHECK_OK(ConvertDType(this->at(key)->type(), &trt_dtype)); + return trt_dtype; +} + +template <> +tensorflow::DataType TFAttrs::get(string key) const { + return this->at(key)->type(); +} + +template <> +float TFAttrs::get(string key) const { + return this->at(key)->f(); +} + +template <> +bool TFAttrs::get(string key) const { + return this->at(key)->b(); +} + +// TODO(jie): reorder4 & reorder2 should be merged? +template +void Reorder4(nvinfer1::DimsNCHW shape, const T* idata, + nvinfer1::DimsNCHW istrides, T* odata, + nvinfer1::DimsNCHW ostrides) { + for (int n = 0; n < shape.n(); ++n) { + for (int c = 0; c < shape.c(); ++c) { + for (int h = 0; h < shape.h(); ++h) { + for (int w = 0; w < shape.w(); ++w) { + odata[n * ostrides.n() + c * ostrides.c() + h * ostrides.h() + + w * ostrides.w()] = idata[n * istrides.n() + c * istrides.c() + + h * istrides.h() + w * istrides.w()]; + } + } + } + } +} + +template +void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides, + T* odata, nvinfer1::DimsHW ostrides) { + for (int h = 0; h < shape.h(); ++h) { + for (int w = 0; w < shape.w(); ++w) { + odata[h * ostrides.h() + w * ostrides.w()] = + idata[h * ostrides.h() + w * ostrides.w()]; + } + } +} + +// TODO(jie): fallback to tensorflow!! +void ReorderCKtoKC(const TRT_ShapedWeights& iweights, + TRT_ShapedWeights* oweights) { + int c = iweights.shape_.d[0]; + int k = iweights.shape_.d[1]; + oweights->shape_.d[0] = k; + oweights->shape_.d[1] = c; + nvinfer1::DimsHW istrides = {1, k}; + nvinfer1::DimsHW ostrides = {c, 1}; + switch (iweights.type_) { + case tensorflow::DataType::DT_FLOAT: { + Reorder2({k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + case tensorflow::DataType::DT_HALF: { + Reorder2( + {k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + default: + LOG(FATAL) << "Unsupported type in reorder expected fp32 or fp16 but got " + << DataTypeString(iweights.type_); + } +} + +void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights, + TRT_ShapedWeights* oweights, int num_groups) { + CHECK_EQ(iweights.type_, oweights->type_); + CHECK_EQ(iweights.size_bytes(), oweights->size_bytes()); + int r = iweights.shape_.d[0]; + int s = iweights.shape_.d[1]; + // TRT requires GKcRS, while TF depthwise has RSCK + // where c=1, C=G + VLOG(2) << "num_groups: " << num_groups; + int c = iweights.shape_.d[2] / num_groups; + VLOG(2) << "c" << iweights.shape_.d[2] << " then " << c; + int k = iweights.shape_.d[3] * num_groups; + VLOG(2) << "k" << iweights.shape_.d[3] << " then " << k; + oweights->shape_.d[0] = k / num_groups; + oweights->shape_.d[1] = c * num_groups; + oweights->shape_.d[2] = r; + oweights->shape_.d[3] = s; + nvinfer1::DimsNCHW istrides = {1, k, s * k * c, c * k}; + nvinfer1::DimsNCHW ostrides = {c * r * s, r * s, s, 1}; + switch (iweights.type_) { + case tensorflow::DataType::DT_FLOAT: { + Reorder4({k, c, r, s}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + case tensorflow::DataType::DT_HALF: { + Reorder4( + {k, c, r, s}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + + default: + LOG(FATAL) << "Unsupported type, expected fp32 or fp16 but got " + << DataTypeString(iweights.type_); + } +} + +struct InferDeleter { + template + void operator()(T* obj) const { + if (obj) { + obj->destroy(); + } + } +}; + +template +inline std::shared_ptr infer_object(T* obj) { + return std::shared_ptr(obj, InferDeleter()); +} + +class Converter; + +using OpConverter = + std::function&, + std::vector*)>; + +class Converter { + std::unordered_map trt_tensors_; + std::unordered_map op_registry_; + nvinfer1::INetworkDefinition* trt_network_; + std::list> temp_bufs_; + tensorflow::tensorrt::TRTWeightStore* weight_store_; + bool fp16_; + void register_op_converters(); + std::vector get_inputs( + const tensorflow::NodeDef& node_def) { + std::vector inputs; + for (auto const& input_name : node_def.input()) { + /************************************************************************* + * TODO(jie) handle case 1) here + * Normalizes the inputs and extracts associated metadata: + * 1) Inputs can contain a colon followed by a suffix of characters. + * That suffix may be a single number (e.g. inputName:1) or several + * word characters separated from a number by a colon + * (e.g. inputName:foo:1). The + * latter case is used to denote inputs and outputs of functions. + * 2) Control dependency inputs contain caret at the beginning and we + * remove this and annotate the edge as a control dependency. + ************************************************************************/ + string name = input_name[0] == '^' ? input_name.substr(1) : input_name; + auto first = name.find_first_of(':'); + if (first != string::npos && first + 2 == name.size() && + name[first + 1] == '0') + name.erase(first); + + VLOG(2) << "retrieve input: " << name; + if (trt_tensors_.count(name)) { + inputs.push_back(trt_tensors_.at(name)); + } else { + LOG(FATAL) << "input: " << name << " not availabled for node at, " + << node_def.name(); + } + } + return inputs; + } + + public: + explicit Converter(nvinfer1::INetworkDefinition* trt_network, + tensorflow::tensorrt::TRTWeightStore* ws, bool fp16) + : trt_network_(trt_network), weight_store_(ws), fp16_(fp16) { + this->register_op_converters(); + } + tensorflow::tensorrt::TRTWeightStore* weight_store() { return weight_store_; } + TRT_ShapedWeights get_temp_weights(tensorflow::DataType type, + nvinfer1::Dims shape) { + TRT_ShapedWeights weights(type, nullptr, shape); + // TODO(jie): check weights size_bytes. 0 means type error + weight_store_->store_.push_back(std::vector(weights.size_bytes())); + weights.SetValues(weight_store_->store_.back().data()); + return weights; + } + bool isFP16() { return fp16_; }; + TRT_ShapedWeights get_temp_weights_like(const TRT_ShapedWeights& weights) { + return this->get_temp_weights(weights.type_, weights.shape_); + } + + tensorflow::Status convert_node(const tensorflow::NodeDef& node_def) { + std::vector inputs = this->get_inputs(node_def); + string op = node_def.op(); + if (!op_registry_.count(op)) { + return tensorflow::errors::Unimplemented( + "No converter registered for op: " + op); + } + OpConverter op_converter = op_registry_.at(op); + std::vector outputs; + TF_RETURN_IF_ERROR(op_converter(*this, node_def, inputs, &outputs)); + for (size_t i = 0; i < outputs.size(); ++i) { + TRT_TensorOrWeights output = outputs.at(i); + // TODO(jie): tf protobuf seems to be omitting the :0 suffix + string output_name = node_def.name(); + if (i != 0) output_name = StrCat(output_name, ":", i); + if (output.is_tensor()) { + output.tensor()->setName(output_name.c_str()); + } + VLOG(2) << "Write out tensor: " << output_name; + if (!trt_tensors_.insert({output_name, output}).second) { + return tensorflow::errors::AlreadyExists( + "Output tensor already exists for op: " + op); + } + } + return tensorflow::Status::OK(); + } + + nvinfer1::INetworkDefinition* network() { return trt_network_; } + + TRT_TensorOrWeights get_tensor(string name) { + if (!trt_tensors_.count(name)) { + return TRT_TensorOrWeights(nullptr); + } + return trt_tensors_.at(name); + } + + bool insert_input_tensor(string name, nvinfer1::ITensor* tensor) { + return trt_tensors_.insert({name, TRT_TensorOrWeights(tensor)}).second; + } + + nvinfer1::ITensor* TransposeTensor(nvinfer1::ITensor* input_tensor, + std::vector order) { + auto dims = input_tensor->getDimensions(); + + // TODO(jie): change the return to status and properly exit + if (order.size() - 1 != size_t(dims.nbDims)) + LOG(ERROR) << "Dimension does not match, fail gracefully"; + + nvinfer1::IShuffleLayer* layer = this->network()->addShuffle(*input_tensor); + nvinfer1::Permutation permutation; + for (int32_t i = 0; i < dims.nbDims; ++i) { + permutation.order[i] = order[i + 1] - 1; + } + layer->setFirstTranspose(permutation); + + nvinfer1::Dims reshape_dims; + reshape_dims.nbDims = dims.nbDims; + for (int32_t i = 0; i < reshape_dims.nbDims; ++i) { + reshape_dims.d[i] = 0; + reshape_dims.type[i] = dims.type[i]; + } + layer->setReshapeDimensions(reshape_dims); + return layer->getOutput(0); + } +}; + +// **************************************************************************** +// Constant folding functions +// TODO(jie): once optimizer kicks in, we should have done constant folding +// there. +//*****************************************************************************/ +struct LambdaFactory { + enum class OP_CATEGORY : int { RSQRT = 0, NEG, ADD, MUL, SUB }; + OP_CATEGORY op; + + template + std::function unary() { + switch (op) { + case OP_CATEGORY::RSQRT: { + VLOG(2) << "RSQRT GETS DONE"; + return [](T t) -> T { return 1.0 / sqrt(t); }; + } + case OP_CATEGORY::NEG: + return [](T t) -> T { return -t; }; + default: + VLOG(2) << "Not supported op for unary: " << static_cast(op); + return nullptr; + } + } + + template + std::function binary() { + switch (op) { + case OP_CATEGORY::ADD: + return [](T l, T r) -> T { return l + r; }; + case OP_CATEGORY::SUB: + return [](T l, T r) -> T { return l - r; }; + case OP_CATEGORY::MUL: + return [](T l, T r) -> T { return l * r; }; + default: + LOG(WARNING) << "Not supported op for binary: " << static_cast(op); + } + return [](T l, T r) -> T { + LOG(FATAL) << "Unsupported op type "; + return l; + }; + } + + template + std::function broadcast_r(T val) { + VLOG(2) << "LAMBDA VAL : " << val; + switch (op) { + case OP_CATEGORY::ADD: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return l + val; + }; + // Return [val](T l)-> T {return l+val;}; + case OP_CATEGORY::SUB: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return l - val; + }; + case OP_CATEGORY::MUL: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return l * val; + }; + default: + LOG(WARNING) << "Not supported op for binary: " << static_cast(op); + } + return [val](T l) -> T { + LOG(FATAL) << "Unsupported op type "; + return l; + }; + } + + template + std::function broadcast_l(T val) { + VLOG(2) << "LAMBDA VAL : " << val; + switch (op) { + case OP_CATEGORY::ADD: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return val + l; + }; + case OP_CATEGORY::SUB: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return val - l; + }; + case OP_CATEGORY::MUL: + return [val](T l) -> T { + VLOG(2) << "LAMBDA VAL : " << val; + return val * l; + }; + default: + LOG(ERROR) << "Not supported op for binary: " << static_cast(op); + } + return [val](T l) -> T { + LOG(FATAL) << "Unsupported op type "; + return l; + }; + } +}; + +template <> +std::function LambdaFactory::unary() { + switch (op) { + case OP_CATEGORY::RSQRT: { + VLOG(2) << "RSQRT GETS DONE"; + return [](Eigen::half t) -> Eigen::half { + return Eigen::half(1.0 / sqrt(float(t))); + }; + } + case OP_CATEGORY::NEG: + return [](Eigen::half t) -> Eigen::half { return -t; }; + default: + VLOG(2) << "Not supported op for unary: " << static_cast(op); + return nullptr; + } +} +tensorflow::Status UnaryCompute(const TRT_ShapedWeights& iweights, + TRT_ShapedWeights* oweights, + LambdaFactory unary_op) { + CHECK_EQ(iweights.type_, oweights->type_); + switch (iweights.type_) { + case tensorflow::DataType::DT_FLOAT: { + auto inp = static_cast(iweights.GetValues()); + auto oup = static_cast(const_cast(oweights->GetValues())); + std::transform(inp, inp + iweights.count(), oup, unary_op.unary()); + break; + } + case tensorflow::DataType::DT_HALF: { + auto inp = static_cast(iweights.GetValues()); + auto oup = + static_cast(const_cast(oweights->GetValues())); + std::transform(inp, inp + iweights.count(), oup, + unary_op.unary()); + break; + } + default: + return tensorflow::errors::Unimplemented( + "Data type not supported: " + + tensorflow::DataTypeString(iweights.type_)); + } + return tensorflow::Status::OK(); +} + +tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l, + const TRT_ShapedWeights& iweights_r, + TRT_ShapedWeights* oweights, + LambdaFactory binary_op) { + // Assume iweights_l.type == iweight_r.type + CHECK_EQ(iweights_l.type_, oweights->type_); + CHECK_EQ(iweights_r.type_, oweights->type_); + VLOG(2) << "SANITY CHECK!"; + + switch (iweights_l.type_) { + case tensorflow::DataType::DT_FLOAT: { + auto inp_l = static_cast(iweights_l.GetValues()); + auto inp_r = static_cast(iweights_r.GetValues()); + auto oup = static_cast(const_cast(oweights->GetValues())); + + if (iweights_l.count() != iweights_r.count()) { + // We only supports broadcast of RankZero + if (iweights_l.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_l); + std::transform(inp_r, inp_r + iweights_r.count(), oup, + binary_op.broadcast_l(*inp_l)); + } else if (iweights_r.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_r); + std::transform(inp_l, inp_l + iweights_l.count(), oup, + binary_op.broadcast_r(*inp_r)); + } else { + return tensorflow::errors::Unimplemented( + "Binary op with non-rankZero broadcast not supported"); + } + } else { + std::transform(inp_l, inp_l + iweights_l.count(), inp_r, oup, + binary_op.binary()); + } + break; + } + case tensorflow::DataType::DT_HALF: { + auto inp_l = static_cast(iweights_l.GetValues()); + auto inp_r = static_cast(iweights_r.GetValues()); + auto oup = + static_cast(const_cast(oweights->GetValues())); + + if (iweights_l.count() != iweights_r.count()) { + // We only supports broadcast of RankZero + if (iweights_l.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_l); + std::transform(inp_r, inp_r + iweights_r.count(), oup, + binary_op.broadcast_l(*inp_l)); + } else if (iweights_r.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_r); + std::transform(inp_l, inp_l + iweights_l.count(), oup, + binary_op.broadcast_r(*inp_r)); + } else { + return tensorflow::errors::Unimplemented( + "Binary op with non-rankZero broadcast not supported"); + } + } else { + std::transform(inp_l, inp_l + iweights_l.count(), inp_r, oup, + binary_op.binary()); + } + break; + } + default: + return tensorflow::errors::Unimplemented( + "Data type not supported: " + + tensorflow::DataTypeString(iweights_l.type_)); + } + + return tensorflow::Status::OK(); +} + +tensorflow::Status ConstantFoldUnary( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + TRT_ShapedWeights weights_input = inputs.at(0).weights(); + + // Allocate output weights + TRT_ShapedWeights weights_output = ctx.get_temp_weights_like(weights_input); + + // FIXME assume type matches input weights + // Get trt type & shape + // Maybe this part has to be moved into the block of rsqrt later + // Check type consistency + CHECK_EQ(weights_input.type_, + TFAttrs(node_def).get("T")); + + LambdaFactory unary_op; + if (node_def.op() == "Rsqrt") { + // Compute rsqrt + unary_op.op = LambdaFactory::OP_CATEGORY::RSQRT; + auto ret = UnaryCompute(weights_input, &weights_output, unary_op); + // Pass the output + if (ret == tensorflow::Status::OK()) { + outputs->push_back(TRT_TensorOrWeights(weights_output)); + } + return ret; + } else { + return tensorflow::errors::Unimplemented("Binary op not supported: " + + node_def.op()); + } +} + +// TODO(jie,ben) broadcast is needed yet not implemented +// Let's get the simple stuff working first. Maybe we should fall back to TF +// approach for constant folding +tensorflow::Status ConstantFoldBinary( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + TRT_ShapedWeights weights_input_l = inputs.at(0).weights(); + TRT_ShapedWeights weights_input_r = inputs.at(1).weights(); + + // Check type consistency + CHECK_EQ(weights_input_l.type_, weights_input_r.type_); + + if (weights_input_l.shape_.nbDims != weights_input_r.shape_.nbDims) + return tensorflow::errors::Unimplemented( + "Binary op implicit broadcast not supported: " + node_def.op()); + + // TODO(jie): constant fold should really fall back to TF. + int num_dims = weights_input_l.shape_.nbDims; + nvinfer1::Dims output_shape; + output_shape.nbDims = num_dims; + VLOG(2) << "nb_dims: " << num_dims + << ", the other: " << weights_input_r.shape_.nbDims; + for (int i = 0; i < num_dims; i++) { + if (weights_input_l.shape_.d[i] == weights_input_r.shape_.d[i]) { + output_shape.d[i] = weights_input_l.shape_.d[i]; + } else if (weights_input_l.shape_.d[i] == 1 || + weights_input_r.shape_.d[i] == 1) { + output_shape.d[i] = + std::max(weights_input_l.shape_.d[i], weights_input_r.shape_.d[i]); + } else { + return tensorflow::errors::Unimplemented( + "Binary op with incompatible shape at, " + node_def.op()); + } + VLOG(2) << "left: " << weights_input_l.shape_.d[i] + << "right: " << weights_input_r.shape_.d[i] + << "output: " << output_shape.d[i]; + } + + // FIXME assume type matches input weights + // Get trt type & shape + TFAttrs attrs(node_def); + // Maybe this part has to be moved into the block of rsqrt later + tensorflow::DataType dtype = attrs.get("T"); + + // Allocate output weights + TRT_ShapedWeights weights_output = ctx.get_temp_weights(dtype, output_shape); + + LambdaFactory binary_op; + if (node_def.op() == "Sub") { + binary_op.op = LambdaFactory::OP_CATEGORY::SUB; + } else if (node_def.op() == "Mul") { + binary_op.op = LambdaFactory::OP_CATEGORY::MUL; + } else if (node_def.op() == "Add") { + binary_op.op = LambdaFactory::OP_CATEGORY::ADD; + } else { + return tensorflow::errors::Unimplemented("Binary op not supported: " + + node_def.op()); + } + auto ret = BinaryCompute(weights_input_l, weights_input_r, &weights_output, + binary_op); + + // Pass the output + if (ret == tensorflow::Status::OK()) { + outputs->push_back(TRT_TensorOrWeights(weights_output)); + } + + return ret; +} + +// TODO(jie): broadcast is needed yet not implemented. +// Only implemented channel wise for the time being +tensorflow::Status BinaryTensorOpWeight( + Converter& ctx, const tensorflow::NodeDef& node_def, + const nvinfer1::ITensor* tensor, TRT_ShapedWeights weights, + std::vector* outputs) { + // FIXME assume type matches input weights + // Get trt type & shape + // Maybe this part has to be moved into the block of rsqrt later + + // Check type consistency + nvinfer1::DataType ttype; + TF_CHECK_OK(ConvertDType(weights.type_, &ttype)); + + // Check scale mode + auto dims_w = weights.shape_; + auto dims_t = tensor->getDimensions(); + + // default to element-wise + auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; + + // TODO(jie): maybe use a permuatation instead to support more cases; + bool permutation_flag = false; + + if (weights.count() == 1) { + VLOG(2) << "UNIFORM"; + scale_mode = nvinfer1::ScaleMode::kUNIFORM; + } else { + // no broadcasting on Batch dimension; + VLOG(2) << "WEIGHTS DIM: " << dims_w.nbDims + << " tensor DIM: " << dims_t.nbDims; + if (dims_w.nbDims == dims_t.nbDims + 1) { + if (dims_w.d[0] == 1) { + for (int i = 1; i < dims_w.nbDims; i++) { + dims_w.d[i - 1] = dims_w.d[i]; + } + dims_w.nbDims--; + } else { + return tensorflow::errors::InvalidArgument( + "Binary op cannot operate on batch, " + node_def.name()); + } + } + + if (dims_w.nbDims == dims_t.nbDims && dims_w.d[0] == dims_t.d[0]) { + scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; + // default is element; + for (int i = 1; i < dims_w.nbDims; i++) { + if (dims_w.d[i] != dims_t.d[i]) { + // if dimension does not match, switch back to channel; + VLOG(2) << "channel"; + scale_mode = nvinfer1::ScaleMode::kCHANNEL; + break; + } + } + // if channel as candidate, validate it + if (scale_mode == nvinfer1::ScaleMode::kCHANNEL) { + for (int i = 1; i < dims_w.nbDims; i++) { + if (dims_w.d[i] != 1) + return tensorflow::errors::InvalidArgument( + "Weight shape not compatible at, " + node_def.name()); + } + } else { + VLOG(2) << "elementwise"; + } + } else if (dims_w.nbDims == 1 && + dims_w.d[0] == dims_t.d[dims_t.nbDims - 1]) { + // channel wise and broadcast required; + permutation_flag = true; + scale_mode = nvinfer1::ScaleMode::kCHANNEL; + } else { + return tensorflow::errors::InvalidArgument( + "Weight shape not compatible at, " + node_def.name()); + } + } + + // transpose last dimension + std::vector permutation(dims_t.nbDims + 1); + if (permutation_flag) { + if (scale_mode == nvinfer1::ScaleMode::kCHANNEL && dims_t.nbDims > 1) { + // we swap the last dimension into channel for trt. + // because of tensorflow default broadcasting rules. + for (int i = 0; i < static_cast(permutation.size()); i++) { + permutation[i] = i; + } + permutation[1] = dims_t.nbDims; + permutation[dims_t.nbDims] = 1; + tensor = ctx.TransposeTensor(const_cast(tensor), + permutation); + } else { + return tensorflow::errors::InvalidArgument( + "Transpose cannot be applied, " + node_def.name()); + } + } + + // prepare weights + TRT_ShapedWeights shift_weights(weights.type_); + TRT_ShapedWeights scale_weights(weights.type_); + TRT_ShapedWeights power_weights(weights.type_); + + // Maybe I should do a switch + if (node_def.op() == "Sub") { + TRT_ShapedWeights neg_weights = ctx.get_temp_weights_like(weights); + LambdaFactory unary_op; + unary_op.op = LambdaFactory::OP_CATEGORY::NEG; + TF_RETURN_IF_ERROR(UnaryCompute(weights, &neg_weights, unary_op)); + shift_weights = neg_weights; + } else if (node_def.op() == "Mul") { + scale_weights = weights; + } else if (node_def.op() == "Add") { + shift_weights = weights; + } else { + return tensorflow::errors::Unimplemented("Binary op not supported: " + + node_def.op()); + } + + nvinfer1::IScaleLayer* layer = ctx.network()->addScale( + *const_cast(tensor), scale_mode, shift_weights, + scale_weights, power_weights); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + // transpose back dimension + if (permutation_flag) { + output_tensor = ctx.TransposeTensor(output_tensor, permutation); + } + + // Pass the output + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +enum class ConvolutionType { DEFAULT, DEPTHWISE_CONV }; + +tensorflow::Status ConvertConv2DHelper( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs, + int group // group ==0 specifies depthwise conv +) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + + TFAttrs attrs(node_def); + + int h_index = 2; + int w_index = 3; + auto data_format = attrs.get("data_format"); + if (data_format == "NHWC") { + tensor = ctx.TransposeTensor(const_cast(tensor), + {0, 3, 1, 2}); + h_index = 1; + w_index = 2; + // TODO(jie): transpose it + } + + // tensor after transpose (NCHW) + auto tensor_dim = tensor->getDimensions(); + + int num_groups = group; + if (num_groups == 0) // depthwise convolution + num_groups = tensor_dim.d[0]; + VLOG(2) << "groups count: " << num_groups; + + TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); + TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_rsck); + ReorderRSCKToKCRS(weights_rsck, &weights, num_groups); + TRT_ShapedWeights biases(weights.type_); + int noutput = weights.shape_.d[0] * num_groups; + nvinfer1::DimsHW kernel_size; + kernel_size.h() = weights.shape_.d[2]; + kernel_size.w() = weights.shape_.d[3]; + VLOG(2) << "kernel size: " << kernel_size.h() << ", " << kernel_size.w(); + + // TODO(jie): stride. (NHWC/NCHW) + auto tf_stride = attrs.get>("strides"); + VLOG(2) << "h_INDEX" << h_index << ", w_index " << w_index; + VLOG(2) << "stride!!!: " << tf_stride[0] << tf_stride[1] << tf_stride[2] + << tf_stride[3]; + nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); + + std::vector> padding; + // TODO(jie): padding. + if (attrs.get("padding") == "SAME") { + // This is NCHW tensor with no batch dimension. + // 1 -> h + // 2 -> w + padding = CreateSamePadding( + stride, kernel_size, + {static_cast(tensor_dim.d[1]), static_cast(tensor_dim.d[2])}); + } else { + padding = {{0, 0}, {0, 0}}; + } + + if (padding[0].first != padding[0].second || + padding[1].first != padding[1].second) { + // TODO(jie): handle asymmetric padding + VLOG(2) << "Padding!!!: " << padding[0].first << padding[0].second + << padding[1].first << padding[1].second; + + auto dim_before = tensor->getDimensions(); + VLOG(2) << "TENSOR before: " << dim_before.d[0] << ", " << dim_before.d[1] + << dim_before.d[2] << ", " << dim_before.d[3]; + auto pad_layer = ctx.network()->addPadding( + *const_cast(tensor), + nvinfer1::DimsHW(padding[0].first, padding[1].first), + nvinfer1::DimsHW(padding[0].second, padding[1].second)); + padding = {{0, 0}, {0, 0}}; + tensor = pad_layer->getOutput(0); + auto dim_after = tensor->getDimensions(); + VLOG(2) << "TENSOR after: " << dim_after.d[0] << ", " << dim_after.d[1] + << dim_after.d[2] << ", " << dim_after.d[3]; + } + + nvinfer1::IConvolutionLayer* layer = + ctx.network()->addConvolution(*const_cast(tensor), + noutput, kernel_size, weights, biases); + + layer->setStride(stride); + layer->setPadding({padding[0].first, padding[1].first}); + layer->setName(node_def.name().c_str()); + layer->setNbGroups(num_groups); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + auto dim_after = output_tensor->getDimensions(); + VLOG(2) << "TENSOR out: " << dim_after.d[0] << ", " << dim_after.d[1] << ", " + << dim_after.d[2] << ", " << dim_after.d[3]; + + if (data_format == "NHWC") { + // TODO(jie): transpose it back! + output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); + } else { + VLOG(2) << "NCHW !!!!"; + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertConv2DHelper( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs, ConvolutionType type) { + switch (type) { + case ConvolutionType::DEFAULT: + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, 1); + case ConvolutionType::DEPTHWISE_CONV: + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, 0); + } + return tensorflow::errors::Unimplemented("unsupported convolution type at, " + + node_def.name()); +} + +tensorflow::Status BinaryTensorOpTensor( + Converter& ctx, const tensorflow::NodeDef& node_def, + const nvinfer1::ITensor* tensor_l, const nvinfer1::ITensor* tensor_r, + std::vector* outputs) { + static const std::unordered_map ops{ + {"Add", nvinfer1::ElementWiseOperation::kSUM}, + {"Mul", nvinfer1::ElementWiseOperation::kPROD}, + {"Sub", nvinfer1::ElementWiseOperation::kSUB}, + {"Div", nvinfer1::ElementWiseOperation::kDIV}, + }; + + // FIXME assume type matches input weights + // get trt type & shape + TFAttrs attrs(node_def); + // maybe this part has to be moved into the block of rsqrt later + nvinfer1::DataType dtype = attrs.get("T"); + + // check type consistency + CHECK_EQ_TYPE(tensor_l->getType(), dtype); + CHECK_EQ_TYPE(tensor_r->getType(), dtype); + auto op_pair = ops.find(node_def.op()); + if (op_pair == ops.end()) + return tensorflow::errors::Unimplemented( + "binary op: " + node_def.op() + + " not supported at: " + node_def.name()); + + nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( + *const_cast(tensor_l), + *const_cast(tensor_r), op_pair->second); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + // pass the output + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertPlaceholder( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + VLOG(2) << "Placeholder should have been replace already"; + return tensorflow::errors::Unimplemented("cannot convert Placeholder op"); + // OK this make sense since we are supposed to replace it with input + TFAttrs attrs(node_def); + nvinfer1::DataType dtype = attrs.get("dtype"); + nvinfer1::Dims dims = attrs.get("shape"); + + dims.nbDims--; + for (int i = 0; i < dims.nbDims; i++) dims.d[i] = dims.d[i + 1]; + + nvinfer1::ITensor* output = + ctx.network()->addInput(node_def.name().c_str(), dtype, dims); + if (!output) { + return tensorflow::errors::InvalidArgument("Failed to create Input layer"); + } + outputs->push_back(TRT_TensorOrWeights(output)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertConv2D(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, + ConvolutionType::DEFAULT); +} + +tensorflow::Status ConvertConv2DDepthwise( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, + ConvolutionType::DEPTHWISE_CONV); +} + +tensorflow::Status ConvertPool(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + TFAttrs attrs(node_def); + + int h_index = 2; + int w_index = 3; + auto data_format = attrs.get("data_format"); + if (data_format == "NHWC") { + h_index = 1; + w_index = 2; + tensor = ctx.TransposeTensor(const_cast(tensor), + {0, 3, 1, 2}); + } else { + VLOG(2) << "NCHW !!!!"; + } + nvinfer1::PoolingType type; + // TODO(jie): support other pooling type + if (node_def.op() == "MaxPool") + type = nvinfer1::PoolingType::kMAX; + else if (node_def.op() == "AvgPool") + type = nvinfer1::PoolingType::kAVERAGE; + else + return tensorflow::errors::Unimplemented("Only supports Max pool"); + + // TODO(jie): NCHW + auto tf_stride = attrs.get>("strides"); + nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); + + auto tf_kernel = attrs.get>("ksize"); + nvinfer1::DimsHW ksize(tf_kernel[h_index], tf_kernel[w_index]); + + auto tensor_dim = tensor->getDimensions(); + std::vector> padding; + // TODO(jie): padding. + if (attrs.get("padding") == "SAME") { + // This is NCHW tensor with no batch dimension. + // 1 -> h + // 2 -> w + padding = CreateSamePadding( + stride, ksize, + {static_cast(tensor_dim.d[1]), static_cast(tensor_dim.d[2])}); + } else if (attrs.get("padding") == "VALID") { + // No padding for valid padding here + VLOG(2) << "No padding added for VALID padding in pool" << node_def.name(); + padding = {{0, 0}, {0, 0}}; + } else { + return tensorflow::errors::Unimplemented( + "Current MaxPool cannot support padding other than SAME"); + } + + if (padding[0].first != padding[0].second || + padding[1].first != padding[1].second) { + // TODO(jie): handle asymmetric padding + VLOG(2) << "Padding!!!: " << padding[0].first << padding[0].second + << padding[1].first << padding[1].second; + auto pad_layer = ctx.network()->addPadding( + *const_cast(tensor), + nvinfer1::DimsHW(padding[0].first, padding[1].first), + nvinfer1::DimsHW(padding[0].second, padding[1].second)); + padding = {{0, 0}, {0, 0}}; + tensor = pad_layer->getOutput(0); + } + + nvinfer1::IPoolingLayer* layer = ctx.network()->addPooling( + *const_cast(tensor), type, ksize); + + layer->setStride(stride); + layer->setPadding({padding[0].first, padding[1].first}); + layer->setName(node_def.name().c_str()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + if (data_format == "NHWC") { + // TODO(jie): transpose it back! + output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); + } else { + VLOG(2) << "NCHW !!!!"; + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertActivation( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + nvinfer1::IActivationLayer* layer = ctx.network()->addActivation( + *const_cast(tensor), nvinfer1::ActivationType::kRELU); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertScale(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) + return tensorflow::errors::Unimplemented( + "Only supports tensor op weight for now, at " + node_def.name()); + // Implement tensor binaryOp weight [channel wise] for now; + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + + // TODO(jie): handle NHWC/NCHW transpose; + TRT_ShapedWeights weights = inputs.at(1).weights(); + TRT_ShapedWeights empty_weights(weights.type_); + + TFAttrs attrs(node_def); + + // Transpose NHWC + auto data_format = attrs.get("data_format"); + if (data_format == "NHWC") { + tensor = ctx.TransposeTensor(const_cast(tensor), + {0, 3, 1, 2}); + // TODO(jie): transpose it + } else { + VLOG(2) << "NCHW !!!!"; + } + + auto dims = tensor->getDimensions(); + VLOG(2) << "tensor dimensions: " << dims.nbDims; + for (int i = 0; i < dims.nbDims; i++) { + VLOG(2) << "i: " << dims.d[i]; + } + dims = weights.shape_; + VLOG(2) << "tensor dimensions: " << dims.nbDims; + for (int i = 0; i < dims.nbDims; i++) { + VLOG(2) << "i: " << dims.d[i]; + } + + nvinfer1::ScaleMode mode = nvinfer1::ScaleMode::kCHANNEL; + if (weights.shape_.d[0] == 1) { + mode = nvinfer1::ScaleMode::kUNIFORM; + } + + nvinfer1::IScaleLayer* layer = + ctx.network()->addScale(*const_cast(tensor), mode, + weights, empty_weights, empty_weights); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + if (data_format == "NHWC") { + // TODO(jie): transpose it back! + output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); + } else { + VLOG(2) << "NCHW !!!!"; + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertConst(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const auto& weights_tensor = node_def.attr().at("value").tensor(); + + // Get trt type & shape + TFAttrs attrs(node_def); + const tensorflow::DataType dtype = attrs.get("dtype"); + + // Create shaped weights as output + tensorflow::Tensor tensor; + if (!tensor.FromProto(weights_tensor)) + return tensorflow::errors::Internal("Cannot parse weight tensor proto: " + + node_def.name()); + + TRT_ShapedWeights weights(dtype); + if (!weights_tensor.float_val().empty()) { + VLOG(2) << "SCALAR!!!" << node_def.name(); + nvinfer1::Dims scalar_shape; + if (tensor.dims() > 0) { + VLOG(2) << "dimensions: " << tensor.dims(); + VLOG(2) << "size: " << weights_tensor.float_val_size(); + scalar_shape = GetTensorShape(tensor); + for (int i = 0; i < scalar_shape.nbDims; i++) + VLOG(2) << scalar_shape.d[i]; + if (GetShapeSize(scalar_shape) != weights_tensor.float_val_size()) { + if (weights_tensor.float_val_size() == 1 || + scalar_shape.d[0] == weights_tensor.float_val_size()) { + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.float_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + } else { + LOG(FATAL) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + } + } + } else { + VLOG(2) << "Dimensions: " << tensor.dims(); + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.float_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) { + scalar_shape.d[i] = 0; + scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; + } + } + if (ctx.isFP16()) { + auto dtype_new = tensorflow::DataType::DT_HALF; + size_t len_data = tensorflow::DataTypeSize(dtype_new); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + tensorflow::Tensor temp_tensor(tensorflow::DT_HALF, tensor.shape()); + auto half_tensor = temp_tensor.flat(); + Eigen::DefaultDevice defd; + half_tensor.device(defd) = + tensor.flat().template cast(); + memcpy(dst, half_tensor.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype_new, dst, scalar_shape); + } else { + size_t len_data = tensorflow::DataTypeSize(dtype); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + std::vector tensor_data( + weights_tensor.float_val().begin(), + weights_tensor.float_val() + .end()); // make a local copy first to flatten + memcpy(dst, tensor_data.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype, dst, scalar_shape); + } + } else if (!weights_tensor.int_val().empty()) { + VLOG(2) << "int!!!" << node_def.name(); + nvinfer1::Dims scalar_shape; + if (tensor.dims() > 0) { + VLOG(2) << "dimensions: " << tensor.dims(); + scalar_shape = GetTensorShape(tensor); + if (GetShapeSize(scalar_shape) != weights_tensor.int_val_size()) { + if (weights_tensor.int_val_size() == 1 || + scalar_shape.d[0] == weights_tensor.int_val_size()) { + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.int_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + } else { + LOG(FATAL) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + } + } + } else { + VLOG(2) << "dimensions: " << tensor.dims(); + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.int_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) { + scalar_shape.d[i] = 0; + scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; + } + } + if (ctx.isFP16()) { + auto dtype_new = tensorflow::DataType::DT_HALF; + size_t len_data = tensorflow::DataTypeSize(dtype_new); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + tensorflow::Tensor temp_tensor(tensorflow::DT_HALF, tensor.shape()); + TTypes::Flat half_tensor = temp_tensor.flat(); + Eigen::DefaultDevice defd; + switch (dtype) { + case (tensorflow::DT_INT32): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_INT16): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_INT8): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_UINT8): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + default: + return tensorflow::errors::InvalidArgument( + "Datatype " + tensorflow::DataTypeString(dtype) + + " for FP16 conversion"); + break; + }; + memcpy(dst, half_tensor.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype_new, dst, scalar_shape); + } else { + size_t len_data = tensorflow::DataTypeSize(dtype); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + size_t len_tensor = weights_tensor.int_val_size() * sizeof(int32); + len_data = std::max(len_data, len_tensor); + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + std::vector tensor_data( + weights_tensor.int_val().begin(), + weights_tensor.int_val() + .end()); // make a local copy first to flatten + // doesn't have to be contigous + memcpy(dst, tensor_data.data(), len_tensor); // store into weight store + weights = TRT_ShapedWeights(dtype, dst, scalar_shape); + } + } else if (!weights_tensor.tensor_content().empty()) { + VLOG(2) << "TENSOR!!!" << node_def.name(); + const auto& content = weights_tensor.tensor_content(); + + weights = ctx.get_temp_weights(dtype, GetTensorShape(tensor)); + if (content.size() > 0) { + const int dtype_size = tensorflow::DataTypeSize(dtype); + CHECK_EQ(0, content.size() % dtype_size) + << "Tensor content size (" << content.size() + << ") is not a multiple of " << dtype_size; + port::CopyToArray( + content, static_cast(const_cast(weights.GetValues()))); + } + } else { + return tensorflow::errors::Unimplemented( + "Not supported constant type, at " + node_def.name()); + } + // Pass the output + outputs->push_back(TRT_TensorOrWeights(weights)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertIdentity( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + outputs->push_back(inputs.at(0)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertBinary(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2) + return tensorflow::errors::FailedPrecondition( + "Binary ops require two tensor input, at " + node_def.name()); + + if (inputs.at(0).is_weights() && inputs.at(1).is_weights()) + return ConstantFoldBinary(ctx, node_def, inputs, outputs); + + if (inputs.at(0).is_tensor() && inputs.at(1).is_weights()) + return BinaryTensorOpWeight(ctx, node_def, inputs.at(0).tensor(), + inputs.at(1).weights(), outputs); + + if (inputs.at(0).is_weights() && inputs.at(1).is_tensor()) + return BinaryTensorOpWeight(ctx, node_def, inputs.at(1).tensor(), + inputs.at(0).weights(), outputs); + + if (inputs.at(0).is_tensor() && inputs.at(1).is_tensor()) + return BinaryTensorOpTensor(ctx, node_def, inputs.at(0).tensor(), + inputs.at(1).tensor(), outputs); + + return tensorflow::errors::Unknown("Binary op input error, at " + + node_def.name()); +} + +tensorflow::Status ConvertUnary(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 1) + return tensorflow::errors::FailedPrecondition( + "Unary ops require single tensor input, at " + node_def.name()); + + if (inputs.at(0).is_weights()) + return ConstantFoldUnary(ctx, node_def, inputs, outputs); + else if (inputs.at(0).is_tensor()) + return tensorflow::errors::Unimplemented( + "Unary op for tensor not supported, at " + node_def.name()); + + return tensorflow::errors::Unknown("Binary op input error, at " + + node_def.name()); +} + +tensorflow::Status ConvertReduce(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) + return tensorflow::errors::InvalidArgument( + "Input expects tensor and weights, at" + node_def.name()); + + // Implement tensor binaryOp weight [channel wise] for now; + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + auto dims = tensor->getDimensions(); + // Restore implicit batch dimension + int nb_dims = dims.nbDims + 1; + + TRT_ShapedWeights index_list = inputs.at(1).weights(); + + TFAttrs attrs(node_def); + // TODO(jie): handle data type. + // Index type here is done through TF type, so I can leverage their + // EnumToDataType for my cast + auto index_type = attrs.get("Tidx"); + + // Only expect to handle INT32 as attributes for now + if (index_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented("Tidx supports only DT_INT32"); + auto index_list_data = + static_cast(const_cast(index_list.GetValues())); + + // Hack warning: have to fall back to pool layer since reduce is not in public + // TRT yet. + if (nb_dims != 4) + return tensorflow::errors::InvalidArgument( + "TRT only support reduce on 4 dimensional tensors, at" + + node_def.name()); + if (index_list.count() > 2) + return tensorflow::errors::InvalidArgument( + "TRT cannot support reduce on more than 2 dimensions, at" + + node_def.name()); + + std::set idx_set; + // We cannot operate on Channel. permutation flag used to transpose tensor + int permuted_index = -1; + for (int i = 0; i < index_list.count(); i++) { + if (index_list_data[i] == 0) + return tensorflow::errors::InvalidArgument("TRT cannot reduce at 0, at" + + node_def.name()); + if (index_list_data[i] == 1) permuted_index = 1; + + idx_set.emplace(index_list_data[i]); + } + + std::vector permutation_order(nb_dims); + nvinfer1::DimsHW pool_kernel; + if (permuted_index == 1) { + for (int i = 2; i < nb_dims; i++) { + if (idx_set.count(i) == 0) { + permuted_index = i; + break; + } + } + for (int i = 0; i < nb_dims; i++) permutation_order[i] = i; + + permutation_order[permuted_index] = 1; + permutation_order[1] = permuted_index; + + // Apply permutation before extracting dimension for pool_kernel + tensor = ctx.TransposeTensor(const_cast(tensor), + permutation_order); + } + + // Apply permutation before extracting dimension for pool_kernel + pool_kernel.d[0] = (idx_set.count(2) || permuted_index == 2) ? dims.d[1] : 1; + pool_kernel.d[1] = (idx_set.count(3) || permuted_index == 3) ? dims.d[2] : 1; + + nvinfer1::ITensor* output_tensor; + + if (node_def.op() == "Mean") { + nvinfer1::IPoolingLayer* layer = + ctx.network()->addPooling(*const_cast(tensor), + nvinfer1::PoolingType::kAVERAGE, pool_kernel); + output_tensor = layer->getOutput(0); + } else { + return tensorflow::errors::Unimplemented( + "Op not supported " + node_def.op() + " , at " + node_def.name()); + } + if (permuted_index != -1) { + // Apply permutation before extracting dimension for pool_kernel + output_tensor = ctx.TransposeTensor( + const_cast(output_tensor), permutation_order); + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertPad(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) + return tensorflow::errors::InvalidArgument( + "Input expects tensor and weights, at" + node_def.name()); + + // Implement tensor binaryOp weight [channel wise] for now; + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + auto dims = tensor->getDimensions(); + // Restore implicit batch dimension + int nb_dims = dims.nbDims + 1; + + TRT_ShapedWeights pads = inputs.at(1).weights(); + + TFAttrs attrs(node_def); + // Padding type here is done through TF type + // so I can leverage their EnumToDataType for my cast + auto padding_type = attrs.get("Tpaddings"); + // TODO(jie): handle data type conversion for TRT? + + if (pads.shape_.d[0] != nb_dims || pads.shape_.d[1] != 2) + return tensorflow::errors::InvalidArgument( + "Pad only supports explicit padding on 4 dimensional tensor, at " + + node_def.name()); + + // Only expect to handle INT32 as attributes for now + if (padding_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented( + "Tpaddings supports only DT_INT32"); + auto pad_data = static_cast(const_cast(pads.GetValues())); + + std::vector pad_index; + for (int i = 0; i < nb_dims; i++) { + if (pad_data[2 * i] != 0 || pad_data[2 * i + 1] != 0) + pad_index.push_back(i); + } + + // No padding at all, we should exit + if (pad_index.size() == 0) { + outputs->push_back(inputs.at(0)); + return tensorflow::Status::OK(); + } + + // Only supports padding on less than 2 axis GIE-2579 + if (pad_index.size() > 2) + return tensorflow::errors::InvalidArgument( + "Padding layer does not support padding on > 2"); + + // Padding on batch dimension is not supported + if (pad_index[0] == 0) + return tensorflow::errors::InvalidArgument( + "Padding layer does not support padding on batch dimension"); + + // Not doing the legit thing here. ignoring padding on dim 1 and 3; + // TODO(jie): implement pad as uff parser + if (pad_index.size() == 2 && pad_index[0] == 0 && pad_index[1] == 3) + return tensorflow::errors::Unimplemented( + "Padding layer does not support padding on dimension 1 and 3 yet"); + + bool legit_pad = true; + nvinfer1::DimsHW pre_padding(0, 0); + nvinfer1::DimsHW post_padding(0, 0); + + std::vector permuted_pad_index(pad_index); + if (pad_index[0] == 1) { + legit_pad = false; + tensor = ctx.TransposeTensor(const_cast(tensor), + {0, 3, 2, 1}); + permuted_pad_index[0] = 3; + } + + for (size_t i = 0; i < pad_index.size(); i++) { + int index = pad_index[i]; + if (permuted_pad_index[i] == 2) { + pre_padding.h() = pad_data[index * 2]; + post_padding.h() = pad_data[index * 2 + 1]; + } else if (permuted_pad_index[i] == 3) { + pre_padding.w() = pad_data[index * 2]; + post_padding.w() = pad_data[index * 2 + 1]; + } + } + + nvinfer1::IPaddingLayer* layer = ctx.network()->addPadding( + *const_cast(tensor), pre_padding, post_padding); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + if (!legit_pad) + output_tensor = ctx.TransposeTensor( + const_cast(output_tensor), {0, 3, 2, 1}); + + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertConcat(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + // not including the last input (axis) here + int input_size = static_cast(inputs.size()) - 1; + + if (!inputs.at(0).is_tensor()) + return tensorflow::errors::InvalidArgument( + "Concat in TRT support only Tensor input, at " + node_def.name()); + + // We are retrieving the axis + TRT_ShapedWeights axis = inputs.at(input_size).weights(); + + TFAttrs attrs(node_def); + // auto attr_size = attrs.at("N")->i(); + // auto data_type = attrs.get("T"); + auto index_type = attrs.get("Tidx"); + + // TODO(jie): handle data type + // Only expect to handle INT32 as index attributes for now + if (index_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented( + "Tidx supports only DT_INT32, at " + node_def.name()); + + int index = *(static_cast(const_cast(axis.GetValues()))); + + // TODO(jie): early termination with no-op (attr_size==1) + + auto dim = inputs.at(0).tensor()->getDimensions(); + // dimension check + if (index > dim.nbDims + 1) + return tensorflow::errors::InvalidArgument( + "Concatenate on axis out of dimension range, at " + node_def.name()); + + if (index == 0) + return tensorflow::errors::InvalidArgument( + "Concatenate on batch dimension not supported, at " + node_def.name()); + + // incase we need permutation; + std::vector permutation_order(dim.nbDims + 1); + + for (int i = 0; i < dim.nbDims + 1; i++) permutation_order[i] = i; + + if (index != 1) { + permutation_order[1] = index - 1; + permutation_order[index - 1] = 1; + } + + std::vector inputs_vec; + // Shap chack (all input tensor should have same shape) + // starting from 0 since we are probably also doing transpose here; + for (int i = 0; i < input_size; i++) { + auto tensor_i = inputs.at(i).tensor(); + auto dim_i = tensor_i->getDimensions(); + if (dim_i.nbDims != dim.nbDims) + return tensorflow::errors::InvalidArgument( + "Concatenate receives inputs with inconsistent dimensions, at " + + node_def.name()); + + for (int j = 0; j < dim.nbDims; j++) { + // check dimension consistency on non-concatenate axis + if (j != index - 1 && dim_i.d[j] != dim.d[j]) + return tensorflow::errors::InvalidArgument( + "Concatenate receives inputs with inconsistent shape, at" + + node_def.name()); + } + + // TRT does concatenation only on channel! + if (index != 1) + tensor_i = ctx.TransposeTensor(const_cast(tensor_i), + permutation_order); + + inputs_vec.push_back(tensor_i); + } + + // nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + nvinfer1::IConcatenationLayer* layer = ctx.network()->addConcatenation( + const_cast(inputs_vec.data()), + inputs_vec.size()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + if (index != 1) { + output_tensor = ctx.TransposeTensor(output_tensor, permutation_order); + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertFusedBatchNorm( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + TFAttrs attrs(node_def); + float epsilon = attrs.get("epsilon"); + auto data_format = attrs.get("data_format"); + if (data_format != "NCHW") { + return tensorflow::errors::Unimplemented( + "only data_format=NCHW is supported, at " + node_def.name()); + } + bool is_training = attrs.get("is_training"); + if (is_training) { + return tensorflow::errors::Unimplemented( + "only is_training=false is supported, at " + node_def.name()); + } + nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + TRT_ShapedWeights scale_weights = inputs.at(1).weights(); + TRT_ShapedWeights offset_weights = inputs.at(2).weights(); + TRT_ShapedWeights mean_weights = inputs.at(3).weights(); + TRT_ShapedWeights variance_weights = inputs.at(4).weights(); + TRT_ShapedWeights dummy_power_weights(scale_weights.type_); + TRT_ShapedWeights combined_scale_weights = + ctx.get_temp_weights_like(scale_weights); + TRT_ShapedWeights combined_offset_weights = + ctx.get_temp_weights_like(offset_weights); + size_t nweight = scale_weights.count(); + if ((scale_weights.type_ == offset_weights.type_) && + (mean_weights.type_ == variance_weights.type_) && + (scale_weights.type_ == variance_weights.type_)) { + if ((scale_weights.type_ != tensorflow::DataType::DT_FLOAT) && + (scale_weights.type_ != tensorflow::DataType::DT_HALF)) { + return tensorflow::errors::Unimplemented( + "only float32 or float16 weight data type is supported, for node " + + node_def.name() + " got " + + tensorflow::DataTypeString(scale_weights.type_)); + } + if (scale_weights.type_ == tensorflow::DT_FLOAT) { + for (size_t i = 0; i < nweight; ++i) { + float scale = (static_cast(scale_weights.GetValues()))[i]; + float offset = + (static_cast(offset_weights.GetValues()))[i]; + float mean = (static_cast(mean_weights.GetValues()))[i]; + float variance = + (static_cast(variance_weights.GetValues()))[i]; + float& combined_scale_ref = const_cast( + static_cast(combined_scale_weights.GetValues()))[i]; + float& combined_offset_ref = const_cast( + static_cast(combined_offset_weights.GetValues()))[i]; + combined_scale_ref = scale / sqrtf(variance + epsilon); + combined_offset_ref = offset - mean * combined_scale_ref; + } + } else { + const Eigen::half* scale_vals = + (static_cast(scale_weights.GetValues())); + const Eigen::half* off_vals = + (static_cast(offset_weights.GetValues())); + const Eigen::half* mean_vals = + (static_cast(mean_weights.GetValues())); + const Eigen::half* variance_vals = + (static_cast(variance_weights.GetValues())); + Eigen::half* comb_scale_vals = const_cast( + static_cast(combined_scale_weights.GetValues())); + Eigen::half* comb_off_vals = const_cast( + static_cast(combined_offset_weights.GetValues())); + for (size_t i = 0; i < nweight; ++i) { + float scale(scale_vals[i]); + float offset(off_vals[i]); + float mean(mean_vals[i]); + float variance(variance_vals[i]); + float combined_scale_ref = scale / sqrtf(variance + epsilon); + comb_scale_vals[i] = Eigen::half(combined_scale_ref); + float combined_offset_ref = offset - mean * combined_scale_ref; + comb_off_vals[i] = Eigen::half(combined_offset_ref); + } + } + } + nvinfer1::IScaleLayer* layer = ctx.network()->addScale( + *const_cast(tensor), nvinfer1::ScaleMode::kCHANNEL, + combined_offset_weights.GetWeightsForTRT(), + combined_scale_weights.GetWeightsForTRT(), + dummy_power_weights.GetWeightsForTRT()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertMatMul(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + + // TODO(jie): transpose! + TFAttrs attrs(node_def); + + TRT_ShapedWeights weights_ck = inputs.at(1).weights(); + TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_ck); + ReorderCKtoKC(weights_ck, &weights); + TRT_ShapedWeights biases(weights.type_); + + int noutput = weights.shape_.d[0]; + + nvinfer1::IFullyConnectedLayer* layer = ctx.network()->addFullyConnected( + *const_cast(tensor), noutput, weights, biases); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertReshape( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) + return tensorflow::errors::InvalidArgument( + "Input expects tensor and weights, at" + node_def.name()); + + // implement tensor binaryOp weight [channel wise] for now; + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + auto dims = tensor->getDimensions(); + // restore implicit batch dimension + + TRT_ShapedWeights shape = inputs.at(1).weights(); + + TFAttrs attrs(node_def); + + auto padding_type = attrs.get("Tshape"); + + if (shape.shape_.nbDims != 1) + return tensorflow::errors::InvalidArgument( + "reshape new shape is not 1 dimensional, at " + node_def.name()); + + // Only expect to handle INT32 as attributes for now + if (padding_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented( + "reshape new shape supports only DT_INT32, at " + node_def.name()); + + auto shape_data = static_cast(const_cast(shape.GetValues())); + + if (shape_data[0] != -1) + return tensorflow::errors::InvalidArgument( + "reshape new shape first dimension is not -1, at " + node_def.name()); + + auto shape_num_dims = shape.shape_.d[0]; + VLOG(2) << "shape dimensions: " << shape_num_dims; + int volume_w = 1; + for (int i = 1; i < shape.shape_.d[0]; i++) volume_w *= shape_data[i]; + + int volume_t = 1; + for (int i = 0; i < dims.nbDims; i++) volume_t *= dims.d[i]; + + VLOG(2) << "volume: " << volume_t << " volume weights: " << volume_w; + if (volume_w != volume_t) + return tensorflow::errors::InvalidArgument( + "volume does not agree between tensor and new shape, at " + + node_def.name()); + + nvinfer1::IShuffleLayer* layer = + ctx.network()->addShuffle(*const_cast(tensor)); + + nvinfer1::Dims reshape_dims; + VLOG(2) << "new dimension: " << shape_num_dims - 1; + reshape_dims.nbDims = shape_num_dims - 1; + for (int32_t i = 0; i < reshape_dims.nbDims; ++i) { + reshape_dims.d[i] = shape_data[i + 1]; + } + layer->setReshapeDimensions(reshape_dims); + VLOG(2) << "new dimension: " << shape_num_dims - 1; + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + auto dims_output = output_tensor->getDimensions(); + VLOG(2) << "output tensor dimension:" << dims_output.nbDims; + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +void Converter::register_op_converters() { + // vgg_16 slim implementation + op_registry_["Placeholder"] = ConvertPlaceholder; + op_registry_["Conv2D"] = ConvertConv2D; + op_registry_["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; + op_registry_["Relu"] = ConvertActivation; + op_registry_["MaxPool"] = ConvertPool; + op_registry_["AvgPool"] = ConvertPool; + // This could be really handled as ConvertBinary + op_registry_["BiasAdd"] = ConvertScale; + op_registry_["Const"] = ConvertConst; + // TODO(ben,jie): this is a temp hack. + op_registry_["Identity"] = ConvertIdentity; // Identity should be removed + + // resnet_50_v1 slim implementation + op_registry_["Add"] = ConvertBinary; + op_registry_["Mul"] = ConvertBinary; + op_registry_["Sub"] = ConvertBinary; + op_registry_["Rsqrt"] = ConvertUnary; + op_registry_["Mean"] = ConvertReduce; + op_registry_["Pad"] = ConvertPad; + // TODO(ben,jie): Add more ops + + op_registry_["ConcatV2"] = ConvertConcat; + op_registry_["MatMul"] = ConvertMatMul; + op_registry_["Reshape"] = ConvertReshape; + op_registry_["FusedBatchNorm"] = ConvertFusedBatchNorm; + op_registry_["FusedBatchNormV2"] = ConvertFusedBatchNorm; +} + +} // namespace +tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { + return tensorflow::errors::Unimplemented("Not implemented yet"); +} +tensorflow::Status ConvertCalibrationNodeToEngineNode( + tensorflow::Graph& graph, tensorflow::Node* c_node) { + const auto ndef = c_node->def(); + + TFAttrs attrs(ndef); + std::vector segment_nodes( + attrs.get>("segment_nodes")); + std::vector output_nodes( + attrs.get>("segment_output_names")); + std::vector input_names( + attrs.get>("input_names")); + string res_name = attrs.get("resource_name"); + VLOG(1) << "Node name " << c_node->name() << " res_name " << res_name; + string engine_name = "my_trt_op"; + { + const auto node_id = tensorflow::str_util::Split(res_name, "_"); + engine_name += node_id.back(); + } + std::map node_maps; + + for (auto n : graph.op_nodes()) { + node_maps.insert({n->name(), n}); + } + VLOG(1) << "Output Nodes:"; + std::vector out_types; + std::vector out_edges; + for (auto& i : output_nodes) { + auto node_port = tensorflow::str_util::Split(i, ":"); + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + auto out_node_name = node_port.at(0); + if (node_port.size() > 1) { + VLOG(1) << "Multi port output" << node_port.at(0) << " " + << node_port.at(1) << " size=" << node_port.size(); + } + auto node_it = node_maps.find(out_node_name); + if (node_it != node_maps.end()) { + tensorflow::Node* out_node = node_it->second; + int port = 0; + if (node_port.size() == 2) { + port = std::strtoul(node_port.at(1).c_str(), nullptr, 10); + out_types.push_back(out_node->output_type(port)); + } else { + out_types.push_back(out_node->output_type(0)); + } + for (auto out_edge : out_node->out_edges()) { + if (out_edge->src_output() == port) { + out_edges.push_back(out_edge); + break; + } + } + } else { + LOG(WARNING) << " couldn't find output node " << out_node_name; + } + } + VLOG(1) << "Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + } + auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto resmgr = trt_rm->getManager("TRTCalibOps"); + tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; + auto status = resmgr->Lookup(res_name, res_name, &calib_res); + if (!status.ok() || !calib_res->calibrator_) { + return tensorflow::errors::FailedPrecondition( + "You must run calibration" + " and inference conversion in the same proces"); + } + + calib_res->calibrator_->setDone(); + calib_res->thr_->join(); + delete calib_res->thr_; + if (!calib_res->engine_) { + LOG(FATAL) << "Calibration failed!, engine is nullptr. Did you run " + "calibration graph?"; + } + auto weight_rmgr = trt_rm->getManager("WeightStore"); + TF_CHECK_OK(weight_rmgr->Delete( + res_name, res_name)); + auto engine_plan = calib_res->engine_->serialize(); + calib_res->engine_->destroy(); + calib_res->network_->destroy(); + calib_res->builder_->destroy(); + calib_res->thr_ = nullptr; + calib_res->engine_ = nullptr; + calib_res->builder_ = nullptr; + tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); + std::vector income_edges; + for (const auto in_edge : c_node->in_edges()) { + auto src = in_edge->src(); + int dest_port = in_edge->dst_input(); + income_edges.emplace_back(src->name(), in_edge->src_output(), + c_node->input_type(dest_port)); + } + tensorflow::gtl::ArraySlice input_list( + income_edges); + op_builder.Input(input_list); + tensorflow::NodeDef engine_node; + const char* engine_plan_data = static_cast(engine_plan->data()); + string engine_plan_string(engine_plan_data, + engine_plan_data + engine_plan->size()); + status = op_builder.Attr("serialized_engine", engine_plan_string) + .Attr("input_nodes", input_names) + .Attr("output_nodes", output_nodes) + .Attr("OutT", out_types) + .Finalize(&engine_node); + if (!status.ok()) { + LOG(ERROR) << "Engine Node creation failed"; + return status; + } + auto trt_engine_node = graph.AddNode(engine_node, &status); + TF_CHECK_OK(status); + for (size_t i = 0; i < out_edges.size(); i++) { + VLOG(1) << "Connecting trt_engine_node output " << i << " with " + << out_edges.at(i)->dst()->name() << " port " + << out_edges.at(i)->dst_input(); + TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, + out_edges.at(i)->dst(), + out_edges.at(i)->dst_input())); + } + VLOG(1) << "Segment nodes:"; + for (auto& i : segment_nodes) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + auto it = node_maps.find(i); + if (it != node_maps.end()) { + graph.RemoveNode(it->second); + } + } + graph.RemoveNode(c_node); + return tensorflow::Status::OK(); +} + +tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { + // Visit nodes in reverse topological order and construct the TRT network. + + // Toposort + std::vector order_vec; + tensorflow::GetPostOrder(s.graph, &order_vec); + // Select just the subgraph + std::list order; + for (tensorflow::Node* node : order_vec) { + if (s.subgraph_node_ids.count(node->id())) { + order.push_front(node); // we want topological order to contstruct the + // network layer by layer + } + } + // topological order is needed to build TRT network + static int static_id = 0; + string subgraph_name_scope; + if (!order.empty()) { + subgraph_name_scope = order.front()->name(); + } + for (const tensorflow::Node* node : order) { + subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name()); + } + // TODO(sami,ben,jie): proper naming! + string calib_op_name = + StrCat(subgraph_name_scope, "my_trt_calib_op_", static_id); + string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id); + static_id++; + auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); + auto op_rmgr = trt_rmgr->getManager("TRTCalibOps"); + auto op_res = new tensorflow::tensorrt::TRTCalibrationResource(); + TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res)); + op_res->logger_ = new tensorflow::tensorrt::Logger(); + op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); + + if (!op_res->builder_) { + return tensorflow::errors::Internal( + "failed to create TensorRT builder object"); + } + + op_res->network_ = op_res->builder_->createNetwork(); + if (!op_res->network_) { + return tensorflow::errors::Internal( + "failed to create TensorRT network object"); + } + + // Build the network + auto weight_rmgr = trt_rmgr->getManager("WeightStore"); + auto ws = new tensorflow::tensorrt::TRTWeightStore(); + TF_CHECK_OK(weight_rmgr->Create(calib_op_name, calib_op_name, ws)); + Converter converter(op_res->network_, ws, s.precision_mode == FP16MODE); + std::vector input_names; + std::vector input_dtypes; + for (const std::pair& input : s.input_inds) { + VLOG(2) << "parsing input. Node id= " << input.first; + int node_id = input.first; + int output_idx = input.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + auto node_name = node->name(); + input_names.push_back(node_name); // insert original node name without port + // TODO(jie): alternative :) + if (!s.graph_properties.HasOutputProperties(node_name)) + return tensorflow::errors::Internal("failed to find input node: " + + node_name); + + auto op_info_vec = s.graph_properties.GetOutputProperties(node_name); + if (static_cast(op_info_vec.size()) < output_idx) + return tensorflow::errors::Internal( + "accessing output index of: ", output_idx, ", at node: ", node_name, + "with output entry from shape_map: ", op_info_vec.size()); + + auto op_info = op_info_vec.at(output_idx); + + tensorflow::DataType tf_dtype = op_info.dtype(); + input_dtypes.push_back(tf_dtype); + + nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); + TF_CHECK_OK(ConvertDType(tf_dtype, &dtype)); + + VLOG(2) << "accessing output index of: " << output_idx + << ", at node: " << node_name + << "with output entry from shape_map: " << op_info_vec.size(); + + // TODO(ben,jie): update TRT input format/dimension + nvinfer1::DimsCHW input_dim_psuedo_chw; + for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1; + + for (int i = 1; i < op_info.shape().dim_size(); i++) { + VLOG(2) << "dimension: " << i + << " , size: " << op_info.shape().dim(i).size(); + input_dim_psuedo_chw.d[i - 1] = op_info.shape().dim(i).size(); + } + + // TODO(ben,jie): proper way to restore input tensor name? + auto input_tensor_name = node_name; + if (output_idx != 0) input_tensor_name = StrCat(node_name, ":", output_idx); + + nvinfer1::ITensor* input_tensor = converter.network()->addInput( + input_tensor_name.c_str(), dtype, input_dim_psuedo_chw); + + if (!input_tensor) + return tensorflow::errors::InvalidArgument( + "Failed to create Input layer"); + VLOG(2) << "input tensor name :" << input_tensor_name; + + if (!converter.insert_input_tensor(input_tensor_name, input_tensor)) + return tensorflow::errors::AlreadyExists( + "output tensor already exists for op: " + input_tensor_name); + } + + VLOG(2) << "finished sorting"; + + for (const tensorflow::Node* node : order) { + const tensorflow::NodeDef& node_def = node->def(); + VLOG(2) << "converting node: " << node_def.name() << " , " << node_def.op(); + TF_RETURN_IF_ERROR(converter.convert_node(node_def)); + } + + VLOG(2) << "finished conversion"; + + // Gather output metadata + std::vector output_names; + std::vector output_dtypes; + int trt_engine_op_output_idx = 0; + for (const std::pair& output : s.output_inds) { + int node_id = output.first; + int output_idx = output.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + string op_name = node->name(); + string tensor_name = op_name; + + s.output_edge_map->insert( + {trt_engine_op_output_idx == 0 + ? engine_name + : StrCat(engine_name, ":", trt_engine_op_output_idx), + {output_idx, tensor_name}}); + trt_engine_op_output_idx++; + if (output_idx != 0) { + tensor_name = StrCat(tensor_name, ":", output_idx); + } + VLOG(1) << "output tensor name: " << tensor_name; + output_names.push_back(tensor_name); + auto tensor_or_weights = converter.get_tensor(tensor_name); + if (!tensor_or_weights.is_tensor()) { + return tensorflow::errors::InvalidArgument( + "Output node is weights not tensor"); + } + nvinfer1::ITensor* tensor = tensor_or_weights.tensor(); + if (!tensor) { + return tensorflow::errors::NotFound("Output tensor not found: " + + tensor_name); + } + converter.network()->markOutput(*tensor); + tensorflow::DataType tf_dtype = node->output_type(output_idx); + output_dtypes.push_back(tf_dtype); + nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT; + TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype)); + tensor->setType(trt_dtype); + } + + VLOG(2) << "finished output"; + + // Build the engine + op_res->builder_->setMaxBatchSize(s.max_batch_size); + op_res->builder_->setMaxWorkspaceSize(s.max_workspace_size_bytes); + + // Build the TRT op + // TODO(sami,ben,jie): proper naming! + tensorflow::NodeDefBuilder op_builder(calib_op_name, "TRTCalibOp"); + std::vector income_edges; + for (size_t i = 0; i < input_names.size(); ++i) { + int output_idx = s.input_inds.at(i).second; + // we wired up the input here already, it is redundant to do it again in + // ConvertSubGraphToTensorRT(convert_graph.cc) + auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut( + input_names.at(i), output_idx, input_dtypes.at(i)); + VLOG(1) << calib_op_name << " input " << i << " = " << input_names.at(i) + << ":" << output_idx + << " dType= " << tensorflow::DataTypeString(input_dtypes.at(i)); + income_edges.push_back(incoming_edge); + } + tensorflow::gtl::ArraySlice input_list( + income_edges); + op_builder.Input(input_list); + std::vector segment_names; + segment_names.reserve(s.subgraph_node_ids.size()); + for (int i : s.subgraph_node_ids) { + auto node = s.graph.FindNodeId(i); + segment_names.push_back(node->name()); + } + LOG(INFO) << "finished op preparation"; + + auto status = op_builder.Attr("segment_nodes", segment_names) + .Attr("input_names", input_names) + .Attr("segment_output_names", output_names) + .Attr("resource_name", calib_op_name) + .Finalize(s.trt_node); + + LOG(INFO) << status.ToString(); + LOG(INFO) << "finished op building"; + + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertSubGraphToTensorRTNodeDef( + tensorrt::convert::SubGraphParams& s) { + // Visit nodes in reverse topological order and construct the TRT network. + + // Toposort + std::vector order_vec; + tensorflow::GetPostOrder(s.graph, &order_vec); + // Select just the subgraph + std::list order; + for (tensorflow::Node* node : order_vec) { + if (s.subgraph_node_ids.count(node->id())) { + // We want topological order to contstruct the + // network layer by layer + order.push_front(node); + } + } + // Topological order is needed to build TRT network + + tensorflow::tensorrt::Logger trt_logger; + + auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger)); + if (!trt_builder) { + return tensorflow::errors::Internal( + "Failed to create TensorRT builder object"); + } + + auto trt_network = infer_object(trt_builder->createNetwork()); + if (!trt_network) { + return tensorflow::errors::Internal( + "Failed to create TensorRT network object"); + } + + string subgraph_name_scope; + if (!order.empty()) { + subgraph_name_scope = order.front()->name(); + } + for (const tensorflow::Node* node : order) { + subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name()); + } + static int static_id = 0; + // TODO(sami,ben,jie): proper naming! + string engine_name = StrCat(subgraph_name_scope, "my_trt_op"); + engine_name = StrCat(engine_name, static_id++); + auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); + auto weight_rmgr = trt_rmgr->getManager("WeightStore"); + auto ws = new tensorflow::tensorrt::TRTWeightStore(); + TF_CHECK_OK(weight_rmgr->Create(engine_name, engine_name, ws)); + + // Build the network + Converter converter(trt_network.get(), ws, s.precision_mode == FP16MODE); + + std::vector input_names; + std::vector input_dtypes; + for (const std::pair& input : s.input_inds) { + VLOG(2) << "parsing input!!!!!"; + int node_id = input.first; + int output_idx = input.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + auto node_name = node->name(); + // input_names should use the node name in the graph + // here it should be the input tensor name -> matching the binding + // insert original node name without port + auto tensor_name = node_name; + if (output_idx != 0) { + tensor_name = StrCat(tensor_name, ":", output_idx); + } + + VLOG(2) << "input name: " << node_name << " tensor_name: " << tensor_name + << " idx: " << output_idx; + + auto shape_inference_node_name = node_name; + auto shape_inference_output_idx = output_idx; + // rewire the shape inference to original node in the graph + if (s.output_edge_map->count(tensor_name)) { + shape_inference_node_name = s.output_edge_map->at(tensor_name).second; + shape_inference_output_idx = s.output_edge_map->at(tensor_name).first; + } + if (shape_inference_output_idx < 0) continue; + VLOG(2) << "shapeinference name: " << shape_inference_node_name + << " idx: " << shape_inference_output_idx; + + if (!s.graph_properties.HasOutputProperties(shape_inference_node_name)) + return tensorflow::errors::Internal("failed to find input node: " + + shape_inference_node_name); + + auto op_info_vec = + s.graph_properties.GetOutputProperties(shape_inference_node_name); + if (static_cast(op_info_vec.size()) <= shape_inference_output_idx) + return tensorflow::errors::Internal( + "accessing output index of: ", shape_inference_output_idx, + ", at node: ", shape_inference_node_name, + " with output entry from shape_map: ", op_info_vec.size()); + + auto op_info = op_info_vec.at(shape_inference_output_idx); + tensorflow::DataType tf_dtype = op_info.dtype(); + input_dtypes.push_back(tf_dtype); + + nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); + TF_CHECK_OK(ConvertDType(tf_dtype, &dtype)); + + VLOG(2) << "Accessing output index of: " << output_idx + << ", at node: " << node_name + << " with output entry from shape_map: " << op_info_vec.size(); + // TODO(ben,jie): update TRT input format/dimension + nvinfer1::DimsCHW input_dim_psuedo_chw; + for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1; + + // TODO(jie): TRT 3.x only support 4 dimensional input tensor. + // update the code once TRT 4.0 comes out. + if (op_info.shape().dim_size() != 4) + return tensorflow::errors::Unimplemented("require 4 dimensional input"); + + for (int i = 1; i < op_info.shape().dim_size(); i++) { + VLOG(2) << "dimension: " << i + << " , size: " << op_info.shape().dim(i).size(); + input_dim_psuedo_chw.d[i - 1] = op_info.shape().dim(i).size(); + } + + // TODO(ben,jie): proper way to restore input tensor name? + auto input_tensor_name = node_name; + if (output_idx != 0) { + input_tensor_name = StrCat(node_name, ":", output_idx); + } + + input_names.push_back(input_tensor_name); + nvinfer1::ITensor* input_tensor = converter.network()->addInput( + input_tensor_name.c_str(), dtype, input_dim_psuedo_chw); + + if (!input_tensor) + return tensorflow::errors::InvalidArgument( + "Failed to create Input layer"); + VLOG(2) << "Input tensor name :" << input_tensor_name; + + if (!converter.insert_input_tensor(input_tensor_name, input_tensor)) + return tensorflow::errors::AlreadyExists( + "Output tensor already exists for op: " + input_tensor_name); + } + + VLOG(2) << "Finished sorting"; + + for (const tensorflow::Node* node : order) { + const tensorflow::NodeDef& node_def = node->def(); + VLOG(2) << "Converting node: " << node_def.name() << " , " << node_def.op(); + TF_RETURN_IF_ERROR(converter.convert_node(node_def)); + } + + VLOG(2) << "Finished conversion"; + + // Gather output metadata + std::vector output_names; + std::vector output_dtypes; + int trt_engine_op_output_idx = 0; + for (const std::pair& output : s.output_inds) { + int node_id = output.first; + int output_idx = output.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + string op_name = node->name(); + string tensor_name = op_name; + + s.output_edge_map->insert( + {trt_engine_op_output_idx == 0 + ? engine_name + : StrCat(engine_name, ":", trt_engine_op_output_idx), + {output_idx, tensor_name}}); + trt_engine_op_output_idx++; + if (output_idx != 0) + tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); + VLOG(2) << "Output tensor name: " << tensor_name; + output_names.push_back(tensor_name); + auto tensor_or_weights = converter.get_tensor(tensor_name); + if (!tensor_or_weights.is_tensor()) { + return tensorflow::errors::InvalidArgument( + "Output node is weights not tensor"); + } + nvinfer1::ITensor* tensor = tensor_or_weights.tensor(); + if (!tensor) { + return tensorflow::errors::NotFound("Output tensor not found: " + + tensor_name); + } + converter.network()->markOutput(*tensor); + tensorflow::DataType tf_dtype = node->output_type(output_idx); + output_dtypes.push_back(tf_dtype); + nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT; + TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype)); + tensor->setType(trt_dtype); + } + + VLOG(2) << "Finished output"; + + // Build the engine + trt_builder->setMaxBatchSize(s.max_batch_size); + trt_builder->setMaxWorkspaceSize(s.max_workspace_size_bytes); + VLOG(0) << "Max batch size= " << s.max_batch_size + << " max workspace size= " << s.max_workspace_size_bytes; + if (s.precision_mode == FP16MODE) { + trt_builder->setHalf2Mode(true); + VLOG(0) << "Using FP16 precision mode"; + } + LOG(INFO) << "starting build engine"; + string engine_plan_string; + { + auto trt_engine = + infer_object(trt_builder->buildCudaEngine(*converter.network())); + VLOG(0) << "Built network"; + if (trt_engine.get() == nullptr) { + return tensorflow::errors::Internal("Engine building failure"); + } + auto engine_plan = infer_object(trt_engine->serialize()); + VLOG(0) << "Serialized engine"; + const char* engine_plan_data = + static_cast(engine_plan->data()); + engine_plan_string = + string(engine_plan_data, engine_plan_data + engine_plan->size()); + } + TF_RETURN_IF_ERROR(weight_rmgr->Delete( + engine_name, engine_name)); + LOG(INFO) << "finished engine " << engine_name; + + // Build the TRT op + tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); + std::vector income_edges; + VLOG(2) << "input edge size: " << input_names.size(); + for (size_t i = 0; i < input_names.size(); ++i) { + VLOG(2) << "input edges: " << i << " " << input_names.at(i); + int output_idx = s.input_inds.at(i).second; + // we wired up the input here already, it is redundant to do it again in + // ConvertSubGraphToTensorRT(convert_graph.cc) + auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut( + input_names.at(i), output_idx, input_dtypes.at(i)); + income_edges.push_back(incoming_edge); + } + tensorflow::gtl::ArraySlice input_list( + income_edges); + op_builder.Input(input_list); + + VLOG(0) << "Finished op preparation"; + + auto status = op_builder.Attr("serialized_engine", engine_plan_string) + .Attr("input_nodes", input_names) + .Attr("output_nodes", output_names) + .Attr("OutT", output_dtypes) + .Finalize(s.trt_node); + + VLOG(0) << status.ToString() << " finished op building"; + + return tensorflow::Status::OK(); +} + +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h new file mode 100644 index 0000000000000000000000000000000000000000..954a1e72f8604371fc00e088a67b4d411314dda6 --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -0,0 +1,87 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_ + +#include +#include +#include +#include +#include + +#include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/grappler/costs/graph_properties.h" +#include "tensorflow/core/lib/core/status.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { +namespace convert { + +const int FP32MODE = 0; +const int FP16MODE = 1; +const int INT8MODE = 2; + +struct SubGraphParams { + SubGraphParams( + tensorflow::Graph& inp_graph, + const std::set& subgraph_node_id_numbers, + const std::vector>& input_indices, + const std::vector>& output_indices, + size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, + const tensorflow::grappler::GraphProperties& current_graph_properties, + std::unordered_map>* output_edges, + tensorflow::NodeDef* constructed_trt_node, + int engine_precision_mode = FP32MODE) + : graph(inp_graph), + subgraph_node_ids(subgraph_node_id_numbers), + input_inds(input_indices), + output_inds(output_indices), + max_batch_size(max_supported_batch_size), + max_workspace_size_bytes(max_consumed_workspace_size_bytes), + graph_properties(current_graph_properties), + output_edge_map(output_edges), + trt_node(constructed_trt_node), + precision_mode(engine_precision_mode) {} + + tensorflow::Graph& graph; + const std::set& subgraph_node_ids; + const std::vector>& input_inds; // {node_id, output_idx} + const std::vector>& output_inds; // {node_id, output_idx} + size_t max_batch_size; + size_t max_workspace_size_bytes; + const tensorflow::grappler::GraphProperties& graph_properties; + std::unordered_map>* output_edge_map; + tensorflow::NodeDef* trt_node; + const int precision_mode; +}; + +// TODO(sami): Replace references with const reference or pointers +tensorflow::Status ConvertSubGraphToTensorRTNodeDef(SubGraphParams& params); +tensorflow::Status InjectCalibrationNode(SubGraphParams& params); +tensorflow::Status ConvertCalibrationNodeToEngineNode(tensorflow::Graph& graph, + tensorflow::Node* c_node); +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_ diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..aea44fd8a2fcc4c359a6cb0c98ae34711708326e --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc @@ -0,0 +1,136 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/kernels/trt_calib_op.h" +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/framework/tensor_types.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/platform/stream_executor.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +TRTCalibOp::TRTCalibOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("segment_nodes", &segment_nodes_)); + OP_REQUIRES_OK(context, context->GetAttr("input_names", &input_names_)); + OP_REQUIRES_OK(context, context->GetAttr("resource_name", &resource_name_)); +}; + +#define TYPECASE(dt, X, Y) \ + case dt: { \ + return (void*)X->flat::Type>().data(); \ + } + +void* GetTensorAddress(const Tensor* tensor_ptr) { + auto tensor_type = tensor_ptr->dtype(); + switch (tensor_type) { + TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr); + default: { + LOG(FATAL) << "Unsupported Data type " + << tensorflow::DataTypeString(tensor_type); + return nullptr; + } + } +} + +void TRTCalibOp::Compute(tensorflow::OpKernelContext* ctx) { + // TODO(aaroey): make sure ctx->resource_mgr() is used in future PR. + auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto res_mgr = trt_rm->getManager("TRTCalibOps"); + tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; + auto status = res_mgr->Lookup(resource_name_, resource_name_, &calib_res); + + if (!status.ok()) { + ctx->SetStatus(status); + return; + } + int num_inputs = ctx->num_inputs(); + // first run instantiate calibrator + if (calib_res->calibrator_ == nullptr) { + dev_tensors_.resize(num_inputs); + int batch_size = ctx->input(0).dim_size(0); + VLOG(1) << " Constructing calibrator"; + for (int i = 0; i < num_inputs; i++) { + // allocate workspace on device for inputs + const tensorflow::Tensor& t = ctx->input(i); + OP_REQUIRES_OK(ctx, + ctx->allocate_persistent(t.dtype(), t.shape(), + &dev_tensors_.at(i), nullptr)); + const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); + CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); + void* device_address = GetTensorAddress(device_tensor); + device_buffers_.emplace(input_names_.at(i), + std::pair( + device_address, device_tensor->TotalBytes())); + } + + calib_res->calibrator_ = + new TRTInt8Calibrator(device_buffers_, batch_size, resource_name_); + string label(resource_name_); + calib_res->thr_ = new std::thread([calib_res, label]() { + VLOG(1) << "Starting calibration thread, Calibration Resource @ " + << calib_res; + calib_res->builder_->setInt8Calibrator(calib_res->calibrator_); + calib_res->builder_->setInt8Mode(true); + calib_res->engine_ = calib_res->builder_->buildCudaEngine( + *calib_res->network_); // will loop until we terminate calibrator + VLOG(1) << "Calibration loop terminated " << label; + }); + VLOG(1) << "initialized calibrator resource"; + } // calibrator initialized + + // Pass input data to calibrator + std::unordered_map input_data; + for (int i = 0; i < num_inputs; i++) { + const Tensor& t = ctx->input(i); + void* data_address = GetTensorAddress(&t); + const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); + CHECK_EQ(t.TotalBytes(), + device_tensor->TotalBytes()); // use the tensor so FW keeps it + input_data.emplace(input_names_.at(i), data_address); + ctx->set_output(i, t); + } + VLOG(2) << "Filled map for sending"; + // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + const cudaStream_t* stream = CHECK_NOTNULL( + reinterpret_cast(ctx->op_device_context() + ->stream() + ->implementation() + ->CudaStreamMemberHack())); + calib_res->calibrator_->setBatch(input_data, *stream); + VLOG(2) << "Passed calibration data"; + // TODO(aaroey): make sure we wait for the completion of calibration on the + // last batch in future PR. +}; + +#undef TYPECASE + +REGISTER_KERNEL_BUILDER(Name("TRTCalibOp").Device(DEVICE_GPU), TRTCalibOp); + +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h new file mode 100644 index 0000000000000000000000000000000000000000..23df9db32f077a080eaff7479fcbe90d6a504c42 --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H +#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H + +#include +#include +#include +#include +#include +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +namespace tensorflow { +namespace tensorrt { +// TODO(sami): Convert this to async kernel! +class TRTCalibOp : public OpKernel { + public: + explicit TRTCalibOp(OpKernelConstruction* context); + + void Compute(OpKernelContext* context) override; + + private: + string resource_name_; + std::vector segment_nodes_; + std::vector input_names_; + std::vector shapes_; + std::unordered_map> device_buffers_; + std::vector dev_tensors_; +}; +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif +#endif // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..b32371b642f38b0851955a4a3beab97b86e1f6a0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -0,0 +1,157 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" + +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" + +namespace tensorflow { +static ::tensorflow::tensorrt::Logger logger; +namespace gpu = ::perftools::gputools; +using IRuntime = nvinfer1::IRuntime; +using Dims = nvinfer1::Dims; + +namespace tensorrt { + +TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { + // read serialized_engine + string serialized_engine; + OP_REQUIRES_OK(context, + context->GetAttr("serialized_engine", &serialized_engine)); + + // register input output node name in trt_sub_graph + OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_)); + OP_REQUIRES_OK(context, context->GetAttr("output_nodes", &output_nodes_)); + + // TODO(samikama) runtime should be taken from a resourcemanager as well. + // Only engine should be in the op and context and runtime should be taken + // from resourcemanager + // TODO(jie): cudaSetDevice make sure trt engine is allocated on the same + // gpu where the input/output is also located. + int gpu_id = context->device()->tensorflow_gpu_device_info()->gpu_id; + cudaSetDevice(gpu_id); + int device; + cudaGetDevice(&device); + if (gpu_id != device) LOG(FATAL) << "set device failed!"; + + // TODO(samikama) runtime should be taken from a resourcemanager as well. + // Only engine should be in the op and context and runtime should be taken + // from resourcemanager + + IRuntime* infer = nvinfer1::createInferRuntime(logger); + trt_engine_ptr_.reset(infer->deserializeCudaEngine( + serialized_engine.c_str(), serialized_engine.size(), nullptr)); + trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); + // Runtime is safe to delete after engine creation + infer->destroy(); +} + +void TRTEngineOp::Compute(OpKernelContext* context) { + int num_binding = context->num_inputs() + context->num_outputs(); + std::vector buffers(num_binding); + + size_t binding_index; + int num_batch = 0; + for (int i = 0; i < context->num_inputs(); i++) { + // Grab the input tensor + binding_index = trt_engine_ptr_->getBindingIndex(input_nodes_[i].c_str()); + + const Tensor& input_tensor = context->input(i); + const TensorShape& input_shape = input_tensor.shape(); + if (i == 0) { + num_batch = input_shape.dim_size(0); + if (num_batch > trt_engine_ptr_->getMaxBatchSize()) { + LOG(FATAL) << "input tensor batch larger than max_batch_size: " + << trt_engine_ptr_->getMaxBatchSize(); + } + } else if (num_batch != input_shape.dim_size(0)) { + LOG(FATAL) << "input data inconsistent batch size"; + break; + } + switch (trt_engine_ptr_->getBindingDataType(binding_index)) { + case nvinfer1::DataType::kFLOAT: + buffers[binding_index] = (void*)(input_tensor.flat().data()); + break; + case nvinfer1::DataType::kHALF: + LOG(FATAL) << "half size is not supported yet!"; + break; + case nvinfer1::DataType::kINT8: + LOG(FATAL) << "int8 is not supported yet!"; + break; + } + } + + for (int i = 0; i < static_cast(output_nodes_.size()); i++) { + // This is bad that we have to reallocate output buffer every run. + // Create an output tensor + binding_index = trt_engine_ptr_->getBindingIndex(output_nodes_[i].c_str()); + Tensor* output_tensor = nullptr; + + TensorShape output_shape; + if (binding_index != -1) { + auto dims = trt_engine_ptr_->getBindingDimensions(binding_index); + std::vector trt_shape(dims.nbDims + 1); + trt_shape[0] = num_batch; + for (int j = 0; j < dims.nbDims; j++) trt_shape[j + 1] = dims.d[j]; + OP_REQUIRES_OK(context, + TensorShapeUtils::MakeShape( + trt_shape.data(), trt_shape.size(), &output_shape)); + } else { + LOG(FATAL) << "output node not found, at " << output_nodes_[i]; + break; + } + + OP_REQUIRES_OK(context, + context->allocate_output(i, output_shape, &output_tensor)); + switch (trt_engine_ptr_->getBindingDataType(binding_index)) { + case nvinfer1::DataType::kFLOAT: + buffers[binding_index] = + reinterpret_cast(output_tensor->flat().data()); + break; + case nvinfer1::DataType::kHALF: + LOG(FATAL) << "half size is not supported yet!"; + break; + case nvinfer1::DataType::kINT8: + LOG(FATAL) << "int8 is not supported yet!"; + break; + } + } + // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + const cudaStream_t* stream = CHECK_NOTNULL( + reinterpret_cast(context->op_device_context() + ->stream() + ->implementation() + ->CudaStreamMemberHack())); + + // TODO(jie): trt enqueue does not return error + auto ret = trt_execution_context_ptr_->enqueue(num_batch, &buffers[0], + *stream, nullptr); + VLOG(2) << "enqueue returns: " << ret; + // sync should be done by TF. +} + +REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp); + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h new file mode 100644 index 0000000000000000000000000000000000000000..0964b4b18a781143fdd7884a2904321b9d14e354 --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -0,0 +1,62 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_ + +#include +#include +#include + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +class Logger; + +class TRTEngineOp : public OpKernel { + public: + explicit TRTEngineOp(OpKernelConstruction* context); + + void Compute(OpKernelContext* context) override; + + private: + template + struct Destroyer { + void operator()(T* d) { d->destroy(); } + }; + + template + using destroyed_ptr = std::unique_ptr>; + destroyed_ptr trt_engine_ptr_; + // TODO(samikama): context should go to a resource manager! + destroyed_ptr trt_execution_context_ptr_; + + std::vector input_nodes_; + std::vector output_nodes_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_ diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.cc b/tensorflow/contrib/tensorrt/log/trt_logger.cc new file mode 100644 index 0000000000000000000000000000000000000000..dda0dc9e712eb726800abfb6084f4f708d04825b --- /dev/null +++ b/tensorflow/contrib/tensorrt/log/trt_logger.cc @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/core/platform/logging.h" + +namespace tensorflow { +namespace tensorrt { + +// Use TF logging for TensorRT informations +void Logger::log(Severity severity, const char* msg) { + // Suppress info-level messages + switch (severity) { + case Severity::kINFO: { // Mark TRT info messages as debug! + VLOG(2) << name_ << " " << msg; + break; + } + case Severity::kWARNING: { + LOG(WARNING) << name_ << " " << msg; + break; + } + case Severity::kERROR: { + LOG(ERROR) << name_ << " " << msg; + break; + } + case Severity::kINTERNAL_ERROR: { + LOG(FATAL) << name_ << " " << msg; + break; + } + // This is useless for now. But would catch it in future if enum changes. It + // is always good to have default case! + default: { + LOG(FATAL) << name_ << "Got unknown severity level from TRT " << msg; + break; + } + } +} +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.h b/tensorflow/contrib/tensorrt/log/trt_logger.h new file mode 100644 index 0000000000000000000000000000000000000000..7f3544f8cfda8dce13881e1f8f4388b640e315f4 --- /dev/null +++ b/tensorflow/contrib/tensorrt/log/trt_logger.h @@ -0,0 +1,44 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_ + +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +// Logger for GIE info/warning/errors +class Logger : public nvinfer1::ILogger { + public: + Logger(string name = "DefaultLogger") : name_(name){}; + void log(nvinfer1::ILogger::Severity severity, const char* msg) override; + + private: + string name_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_ diff --git a/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..4835e5065068ec7a59995eb7f6126b31aecf6704 --- /dev/null +++ b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" +namespace tensorflow { + +REGISTER_OP("TRTCalibOp") + .Attr("segment_nodes: list(string)") // names of the ops in segment + .Attr("segment_output_names: list(string)") // names of the output ops in + // segment + .Attr("input_names: list(string)") // names of the inputs for + // passing into tensorrt + .Attr("resource_name: string") + .Attr("InT: list({int8, float16, float32})") + .Input("in_tensor: InT") + .Output("out_tensor: InT") + .SetShapeFn([](tensorflow::shape_inference::InferenceContext* c) { + for (int i = 0; i < c->num_inputs(); i++) { + c->set_output(i, c->input(i)); + } + return Status::OK(); + }); + +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..079d73f7bec3f9a9740e455b31a259cec287f849 --- /dev/null +++ b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc @@ -0,0 +1,43 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/tensor_shape.h" + +namespace tensorflow { + +namespace shape_inference { +extern Status TRTEngineOpShapeInference(InferenceContext* c); +} + +REGISTER_OP("TRTEngineOp") + .Attr("serialized_engine: string") + .Attr("input_nodes: list(string)") + .Attr("output_nodes: list(string)") + .Attr("InT: list({float32})") + .Attr("OutT: list({float32})") + .Input("in_tensor: InT") + .Output("out_tensor: OutT") + .SetShapeFn(shape_inference::TRTEngineOpShapeInference); + +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/python/__init__.py b/tensorflow/contrib/tensorrt/python/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0b2321b5fc7bcbd53c01d1c97cafcfcb229a83ef --- /dev/null +++ b/tensorflow/contrib/tensorrt/python/__init__.py @@ -0,0 +1,25 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Exposes the python wrapper for TensorRT graph transforms.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import,line-too-long +from tensorflow.contrib.tensorrt.python.ops import trt_engine_op +from tensorflow.contrib.tensorrt.python.trt_convert import calib_graph_to_infer_graph +from tensorflow.contrib.tensorrt.python.trt_convert import create_inference_graph +# pylint: enable=unused-import,line-too-long diff --git a/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py b/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py new file mode 100644 index 0000000000000000000000000000000000000000..31a313182be9a2fca7457a539670dbc911ccabb1 --- /dev/null +++ b/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py @@ -0,0 +1,34 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Exposes the Python wrapper of TRTEngineOp.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import platform + +if platform.system() != "Windows": + # pylint: disable=wildcard-import,unused-import,g-import-not-at-top + from tensorflow.contrib.tensorrt.ops.gen_trt_engine_op import * + + from tensorflow.contrib.util import loader + from tensorflow.python.platform import resource_loader + # pylint: enable=wildcard-import,unused-import,g-import-not-at-top + + _trt_engine_op = loader.load_op_library( + resource_loader.get_path_to_datafile("_trt_engine_op.so")) +else: + raise RuntimeError("Windows platforms are not supported") diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..666220d78c76960b2a3217486b308c17b912b467 --- /dev/null +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -0,0 +1,163 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Exposes the Python wrapper conversion to trt_graph.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import,line-too-long +import six as _six +from tensorflow.contrib.tensorrt.wrap_conversion import calib_convert +from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert +from tensorflow.core.framework import graph_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import errors +from tensorflow.python.framework import errors_impl as _impl +from tensorflow.python.framework import meta_graph +from tensorflow.python.framework import ops +from tensorflow.python.grappler import tf_optimizer +from tensorflow.python.util import compat +# pylint: enable=unused-import,line-too-long + + +# TODO(skama): get outputs from session when implemented as c++ +# optimization pass +def create_inference_graph(input_graph_def, + outputs, + max_batch_size=1, + max_workspace_size_bytes=2 << 20, + precision_mode="FP32", + minimum_segment_size=3): + """Python wrapper for the TRT transormation. + + Args: + input_graph_def: GraphDef object containing a model to be transformed. + outputs: list of tensors or node names for the model outputs. + max_batch_size: max size for the input batch + max_workspace_size_bytes: parameter to control memory allocation (in Bytes) + precision_mode: one of 'FP32', 'FP16' and 'INT8' + minimum_segment_size: the minimum number of nodes required for a subgraph to + be replaced by TRTEngineOp. + + Returns: + New GraphDef with TRTEngineOps placed in graph replacing subgraphs. + + Raises: + ValueError: if the provided precision mode is invalid. + RuntimeError: if the returned status message is malformed. + """ + supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2} + if precision_mode.upper() not in supported_precision_modes: + raise ValueError(("precision mode '{}' is not supported." + "It should be one of {}").format( + precision_mode, "{'FP32', 'FP16', 'INT8'}")) + mode = supported_precision_modes[precision_mode.upper()] + + def py2bytes(inp): + return inp + + def py3bytes(inp): + return inp.encode("utf-8", errors="surrogateescape") + + def py2string(inp): + return inp + + def py3string(inp): + return inp.decode("utf-8") + + if _six.PY2: + to_bytes = py2bytes + to_string = py2string + else: + to_bytes = py3bytes + to_string = py3string + + out_names = [] + for i in outputs: + if isinstance(i, ops.Tensor): + out_names.append(to_bytes(i.name)) + else: + out_names.append(to_bytes(i)) + + input_graph_def_str = input_graph_def.SerializeToString() + + # TODO(sami): Fix this when we can return status from C++ library + # There is a problem with the TF internal library setup that doesn't + # allow us to return a status object from C++. Thus we return a + # pair or strings where first one is encoded status and the second + # one is the transformed graphs protobuf string. + out = trt_convert(input_graph_def_str, out_names, max_batch_size, + max_workspace_size_bytes, mode, minimum_segment_size) + status = to_string(out[0]) + output_graph_def_string = out[1] + del input_graph_def_str # Save some memory + if len(status) < 2: + raise _impl.UnknownError(None, None, status) + if status[:2] != "OK": + msg = status.split(";") + if len(msg) == 1: + raise RuntimeError("Status message is malformed {}".format(status)) + # pylint: disable=protected-access + raise _impl._make_specific_exception(None, None, ";".join(msg[1:]), + int(msg[0])) + # pylint: enable=protected-access + output_graph_def = graph_pb2.GraphDef() + output_graph_def.ParseFromString(output_graph_def_string) + del output_graph_def_string # Save some memory + return output_graph_def + + +def calib_graph_to_infer_graph(calibration_graph_def): + """Convert an existing calibration graph to inference graph. + + Args: + calibration_graph_def: the calibration GraphDef object with calibration data + Returns: + New GraphDef with TRTEngineOps placed in graph replacing calibration nodes. + Raises: + RuntimeError: if the returned status message is malformed. + """ + + def py2string(inp): + return inp + + def py3string(inp): + return inp.decode("utf-8") + + if _six.PY2: + to_string = py2string + else: + to_string = py3string + + graph_str = calibration_graph_def.SerializeToString() + out = calib_convert(graph_str) + status = to_string(out[0]) + output_graph_def_string = out[1] + del graph_str # Save some memory + if len(status) < 2: + raise _impl.UnknownError(None, None, status) + if status[:2] != "OK": + msg = status.split(";") + if len(msg) == 1: + raise RuntimeError("Status message is malformed {}".format(status)) + # pylint: disable=protected-access + raise _impl._make_specific_exception(None, None, ";".join(msg[1:]), + int(msg[0])) + # pylint: enable=protected-access + output_graph_def = graph_pb2.GraphDef() + output_graph_def.ParseFromString(output_graph_def_string) + del output_graph_def_string # Save some memory + return output_graph_def diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc new file mode 100644 index 0000000000000000000000000000000000000000..1ae634722089363b87b31ad518f94dc9eef2ea03 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -0,0 +1,125 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" + +#include +#include +#include + +#include "tensorflow/core/platform/logging.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" + +namespace tensorflow { +namespace tensorrt { + +// set the batch size before constructing the thread to execute engine +int TRTInt8Calibrator::getBatchSize() const { return batch_size_; } + +TRTInt8Calibrator::TRTInt8Calibrator( + const std::unordered_map>& dev_buffers, + int batch_size, string engine_name) + : batch_size_(batch_size), + done_(false), + dev_buffers_(dev_buffers), + calib_running_(false), + batch_is_set_(false), + engine_name_(engine_name) {} + +bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, + const cudaStream_t stream) { + tensorflow::mutex_lock lock(cond_mtx_); + while ((calib_running_ || batch_is_set_) && + !done_) { // wait while calibration is running + cond_.wait(lock); + } + if (done_) return false; + CHECK(!calib_running_ && !batch_is_set_); + VLOG(1) << "Set Batch Waiting finished"; + for (const auto it : data) { + auto devptr = dev_buffers_.find(it.first); + if (devptr == dev_buffers_.end()) { + LOG(FATAL) << "FATAL " << engine_name_ << " input name '" << it.first + << "' does not match with the buffer names"; + } + const auto& d = devptr->second; + + // TODO(aaroey): we should not use sync copy on default stream. Make sure + // stream->ThenMemcpy() is used in future PRs. + // TODO(sami,aaroey): Need to figureout a way to ensure synchronization + // between stream, perhaps using a tensor? + auto status = cudaMemcpyAsync(d.first, it.second, d.second, + cudaMemcpyDeviceToDevice, stream); + if (status != cudaSuccess) { + LOG(FATAL) << "cudaMemcpy " << engine_name_ << " for '" << it.first + << "' failed with " << status; + } + } + // TODO(Sami, aaorey): Find an alternative way! + cudaStreamSynchronize( + stream); // we have to wait for the stream before returning! + batch_is_set_ = true; + cond_.notify_all(); + return true; +} + +bool TRTInt8Calibrator::getBatch(void** bindings, const char** names, + int num_bindings) { + tensorflow::mutex_lock lock(cond_mtx_); + calib_running_ = false; + cond_.notify_all(); + while ((!batch_is_set_ && !done_)) { // wait until new batch arrives + cond_.wait(lock); + } + if (done_) { + return false; + } + CHECK(!calib_running_ && batch_is_set_); + for (int i = 0; i < num_bindings; i++) { + auto it = dev_buffers_.find(names[i]); + if (it == dev_buffers_.end()) { + LOG(FATAL) << "Calibration engine asked for unknown tensor name '" + << names[i] << "' at position " << i; + } + + bindings[i] = it->second.first; + } + batch_is_set_ = false; + calib_running_ = true; + return true; +} + +const void* TRTInt8Calibrator::readCalibrationCache(std::size_t& length) { + return nullptr; +} +void TRTInt8Calibrator::setDone() { + tensorflow::mutex_lock lock(cond_mtx_); + done_ = true; + cond_.notify_all(); +} +void TRTInt8Calibrator::writeCalibrationCache(const void* ptr, + std::size_t length) {} +TRTInt8Calibrator::~TRTInt8Calibrator() { + VLOG(1) << "Destroying calibrator for " << engine_name_; +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h new file mode 100644 index 0000000000000000000000000000000000000000..4e7b74d6202ff437492d1098233fde5b0ba585be --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h @@ -0,0 +1,73 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ + +#include +#include +#include +#include +#include "tensorflow/core/platform/mutex.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +#include "cuda/include/cuda_runtime_api.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +// This class provides a 1 element queue to match TFs push model to +// TRTs pull model for calibration. When TRT implements a means for +// a push calibration This class should be updated accordingly + +struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator { + public: + TRTInt8Calibrator( + const std::unordered_map>& dev_buffers, + int batch_size, string engine_name); + int getBatchSize() const override; + bool getBatch(void* bindings[], const char* names[], + int num_bindings) override; + bool setBatch(const std::unordered_map& data, + const cudaStream_t stream); + void setDone(); + const void* readCalibrationCache(std::size_t& length) override; + void writeCalibrationCache(const void* ptr, std::size_t length) override; + ~TRTInt8Calibrator(); + + private: + const int batch_size_; + tensorflow::mutex cond_mtx_; // mutex for condition_variable + tensorflow::condition_variable cond_; // condition variable to implement + // producer-consumer queue for + // calibration + bool done_; + const std::unordered_map> + dev_buffers_; // map to keep tensorrt input buffers and sizes keyed with + // buffer names + bool calib_running_; + bool batch_is_set_; + string engine_name_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif +#endif + +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc new file mode 100644 index 0000000000000000000000000000000000000000..e663eed4dd6704e2f41bde1dfabd411e86669ecd --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/core/platform/logging.h" + +namespace tensorflow { +namespace tensorrt { + +std::shared_ptr +tensorflow::tensorrt::TRTResourceManager::getManager(const string& op_name) { + // mutex is held for lookup only. Most instantiations where mutex will be held + // longer will be during op creation and should be ok. + tensorflow::mutex_lock lock(map_mutex_); + auto s = managers_.find(op_name); + if (s == managers_.end()) { + auto it = managers_.emplace( + op_name, std::make_shared(op_name)); + VLOG(1) << "Returning a new manager " << op_name; + return it.first->second; + } + VLOG(1) << "Returning old manager " << op_name; + return s->second; +} + +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h new file mode 100644 index 0000000000000000000000000000000000000000..5f8ad491d3c13e8911b0b95c3e95e19afe4d59c0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h @@ -0,0 +1,49 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_ +#include + +#include +#include +#include "tensorflow/core/framework/resource_mgr.h" +#include "tensorflow/core/platform/mutex.h" + +namespace tensorflow { +namespace tensorrt { + +class TRTResourceManager { + TRTResourceManager() = default; + + public: + static std::shared_ptr instance() { + static std::shared_ptr instance_( + new TRTResourceManager); + return instance_; + } + // returns a manager for given op, if it doesn't exists it creates one + std::shared_ptr getManager(const string& op_name); + + private: + std::unordered_map> + managers_; + tensorflow::mutex map_mutex_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCE_TRT_RESOURCE_MANAGER_H_ diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h new file mode 100644 index 0000000000000000000000000000000000000000..3c85968ae7acf5c5fc567be6805a5d226b1094c7 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -0,0 +1,95 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_ + +#include +#include +#include +#include +#include +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/core/framework/resource_mgr.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +class TRTCalibrationResource : public tensorflow::ResourceBase { + public: + TRTCalibrationResource() + : calibrator_(nullptr), + builder_(nullptr), + network_(nullptr), + engine_(nullptr), + logger_(nullptr), + thr_(nullptr) {} + string DebugString() override { + std::stringstream oss; + oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl + << " Builder = " << std::hex << builder_ << std::dec << std::endl + << " Network = " << std::hex << network_ << std::dec << std::endl + << " Engine = " << std::hex << engine_ << std::dec << std::endl + << " Logger = " << std::hex << logger_ << std::dec << std::endl + << " Thread = " << std::hex << thr_ << std::dec << std::endl; + return oss.str(); + } + ~TRTCalibrationResource() { + VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString(); + } + TRTInt8Calibrator* calibrator_; + nvinfer1::IBuilder* builder_; + nvinfer1::INetworkDefinition* network_; + nvinfer1::ICudaEngine* engine_; + tensorflow::tensorrt::Logger* logger_; + // TODO(sami): Use threadpool threads! + std::thread* thr_; +}; + +class TRTWeightStore : public tensorflow::ResourceBase { + public: + TRTWeightStore() {} + std::list> store_; + string DebugString() override { + std::stringstream oss; + size_t lenBytes = 0; + for (const auto& v : store_) { + lenBytes += v.size() * sizeof(uint8_t); + } + oss << " Number of entries = " << store_.size() << std::endl + << " Total number of bytes = " + << store_.size() * sizeof(std::vector) + lenBytes << std::endl; + return oss.str(); + } + virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); } +}; + +class TRTEngineResource : public tensorflow::ResourceBase { + public: + TRTEngineResource() : runtime_(nullptr), ctx_(nullptr){}; + string DebugString() override { return string(""); } + nvinfer1::IRuntime* runtime_; + nvinfer1::IExecutionContext* ctx_; +}; + +} // namespace tensorrt +} // namespace tensorflow +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCEMGR_TRTRESOURCES_H_ +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc new file mode 100644 index 0000000000000000000000000000000000000000..6193f0b0a13f6985d5fc8dd4c6fc09b15f72f139 --- /dev/null +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -0,0 +1,253 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/segment/segment.h" + +#include +#include +#include + +#include "tensorflow/contrib/tensorrt/segment/union_find.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/types.h" + +namespace tensorflow { +namespace tensorrt { +namespace segment { + +namespace { + +bool CanContractEdge(const tensorflow::Edge* edge, + const tensorflow::Graph& graph) { + const tensorflow::Node* src = edge->src(); + const tensorflow::Node* dst = edge->dst(); + + // Can't contract edge if doing so would cause a cycle in the + // graph. So, if there is a directed path from 'src' to 'dst', other + // than 'edge' (or any other direct edge from 'src' to 'dst'), then + // combining 'src' and 'dst' will cause a cycle along that path. + // + // In practice, to avoid modifying the graph and to take advantage + // of existing graph functions, we perform an equivalent. + // 1. Get all nodes incoming to 'dst', excluding 'src' + // 2. Reverse DFS from those nodes + // 3. If reverse DFS reaches 'src' then we have a cycle + std::vector dfs_start_nodes; + for (tensorflow::Node* node : dst->in_nodes()) { + if (node != src) { + dfs_start_nodes.push_back(node); + } + } + + bool is_cycle = false; + if (!dfs_start_nodes.empty()) { + tensorflow::ReverseDFSFrom(graph, dfs_start_nodes, {}, + [&is_cycle, src](tensorflow::Node* node) { + if (node == src) { + is_cycle = true; + } + }); + } + + return !is_cycle; +} + +void ContractEdge(tensorflow::Edge* edge, tensorflow::Graph* graph, + std::vector* remove_edges) { + // Transfer all inputs and outputs of 'dst' to 'src' except edges + // connecting the two. + tensorflow::Node* src = edge->src(); + tensorflow::Node* dst = edge->dst(); + + // We can use '0' for input/output index because we don't need them + // to be accurate for the way we are using the graph. + std::vector in_edges(dst->in_edges().begin(), + dst->in_edges().end()); + for (const tensorflow::Edge* in_edge : in_edges) { + if (in_edge->src() != src) { + tensorflow::Edge* e = const_cast(in_edge); + if (e->src() == graph->source_node()) { + graph->AddEdge(e->src(), e->src_output(), src, + tensorflow::Graph::kControlSlot); + } else { + graph->AddEdge(e->src(), e->src_output(), src, 0 /* input index */); + } + } + } + + std::vector out_edges(dst->out_edges().begin(), + dst->out_edges().end()); + for (const tensorflow::Edge* out_edge : out_edges) { + tensorflow::Edge* e = const_cast(out_edge); + if (e->dst() == graph->sink_node()) { + graph->AddEdge(src, tensorflow::Graph::kControlSlot, e->dst(), + e->dst_input()); + } else { + graph->AddEdge(src, 0 /* output index */, e->dst(), e->dst_input()); + } + } + + // Return the edges that must be removed to disconnect 'dst' from + // the graph. We don't actually remove 'dst' since the caller holds + // references to all the nodes. + for (const auto& in_edge : dst->in_edges()) { + remove_edges->push_back(in_edge); + } + for (const auto& out_edge : dst->out_edges()) { + remove_edges->push_back(out_edge); + } +} + +} // namespace + +tensorflow::Status SegmentGraph( + const tensorflow::GraphDef& gdef, + const std::function& candidate_fn, + const SegmentOptions& options, SegmentNodesVector* segments) { + // Create a Graph representation of the GraphDef. + tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), + gdef.library()); + tensorflow::Graph graph(flib); + TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( + tensorflow::GraphConstructorOptions(), gdef, &graph)); + + // tensorflow::DumpGraph("Pre-Segment", &graph); + + // Use a union-find to collect the nodes that belong to the same + // segment. A node value of nullptr indicates that the node is not a + // candidate for TRT. + std::vector> node_segments; + for (int i = 0; i < graph.num_node_ids(); ++i) { + tensorflow::Node* node = graph.FindNodeId(i); + if (options.exclude_node_list.count(node->name()) != 0 || + !candidate_fn(node->def())) { + node = nullptr; + } + node_segments.emplace_back(node); + } + + // The segmentation algorithm below visits nodes in reverse + // topological order and attempts to merge nodes along output + // edges. That means that subgraphs grow from the output-side of the + // network towards the inputs. In general this is not guaranteed to + // produce a globally optimal segmentation. In the future if we have + // a measure of how beneficial it is to include a given node in a + // TRT subgraph then we can revisit this algorithm to take advantage + // of that information. + std::vector order; + tensorflow::GetPostOrder(graph, &order); + + for (const tensorflow::Node* node : order) { + // All output nodes of 'node' have been visited... + VLOG(2) << "Trying node " << node->name(); + + // 'node' must be a TRT candidate... + if (node_segments[node->id()].Value() == nullptr) { + VLOG(2) << "... not a TRT candidate"; + continue; + } + + // Contract output edges to combine 'node' with output + // nodes. Iterate since combining two nodes may unblock other + // combining. + while (true) { + std::set contract_edges; + for (const tensorflow::Edge* out_edge : node->out_edges()) { + VLOG(2) << "... out node " << out_edge->dst()->name(); + + // Out node must be TRT candidate... + if (node_segments[out_edge->dst()->id()].Value() == nullptr) { + VLOG(2) << "... ... not a TRT candidate"; + continue; + } + + if (CanContractEdge(out_edge, graph)) { + VLOG(2) << "... ... can contract"; + contract_edges.insert(out_edge); + } else { + VLOG(2) << "... ... cannot contract, would form cycle"; + } + } + + if (contract_edges.empty()) { + break; + } + + // Contract edges and collect the adjacent nodes into the same + // segment/subgraph. + while (!contract_edges.empty()) { + const tensorflow::Edge* contract_edge = *contract_edges.begin(); + const tensorflow::Node* src = contract_edge->src(); + const tensorflow::Node* dst = contract_edge->dst(); + + VLOG(2) << "Merge " << src->name() << " <- " << dst->name(); + node_segments[src->id()].Merge(&node_segments[dst->id()]); + + // Contracting the edge leaves disconnected graph edges. + // Remove these from the graph and from 'contract_edges' so we + // don't visit them again. + tensorflow::Edge* e = const_cast(contract_edge); + std::vector remove_edges; + ContractEdge(e, &graph, &remove_edges); + + for (const tensorflow::Edge* r : remove_edges) { + contract_edges.erase(r); + graph.RemoveEdge(r); + } + } + } + } + + // Collect the segments/subgraphs. Each subgraph is represented by a + // set of the names of the nodes in that subgraph. + std::unordered_map> sg_map; + for (auto& u : node_segments) { + if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) { + sg_map[u.ParentValue()->name()].insert(u.Value()->name()); + } + } + + // Convert the segments into the expected return format + for (const auto& itr : sg_map) { + const auto& segment_node_names = itr.second; + if (VLOG_IS_ON(1)) { + string s; + for (const auto& name : segment_node_names) { + s += " " + name; + } + VLOG(1) << "Segment " << segments->size() << ":" << s; + } + + // Don't use small segments. + if (static_cast(segment_node_names.size()) < + options.minimum_segment_size) { + VLOG(1) << "Segment " << segments->size() << " has only " + << segment_node_names.size() << " nodes, dropping"; + continue; + } + + segments->emplace_back(segment_node_names); + } + + return tensorflow::Status::OK(); +} + +} // namespace segment +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h new file mode 100644 index 0000000000000000000000000000000000000000..ee6e2b3ed26cd1fabc0e952d882d549046cd9a30 --- /dev/null +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -0,0 +1,56 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_ + +#include +#include + +#include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/types.h" + +namespace tensorflow { +namespace tensorrt { +namespace segment { + +using SegmentNodesVector = std::vector>; + +struct SegmentOptions { + // Segment must contain at least this many nodes. + int minimum_segment_size = 2; + std::set exclude_node_list; +}; + +// Get the subgraphs of a graph that can be handled by TensorRT. +// +// @param gdef The GraphDef describing the network +// @param candidate_fn A function that returns true for a NodeDef if +// that node can be handled by TensorRT. +// @param segments Returns the TensorRT segments/subgraphs. Each entry +// in the vector describes a subgraph by giving a set of the names of +// all the NodeDefs in that subgraph. +// @return the status. +tensorflow::Status SegmentGraph( + const tensorflow::GraphDef& gdef, + const std::function& candidate_fn, + const SegmentOptions& options, SegmentNodesVector* segments); + +} // namespace segment +} // namespace tensorrt +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_ diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..74cbc5f2b376b76324eed06d251767da6f928e3e --- /dev/null +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -0,0 +1,367 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/segment/segment.h" +#include "tensorflow/c/c_api.h" +#include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/framework/node_def.pb.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +namespace tensorflow { +namespace tensorrt { +namespace segment { +namespace test { + +class SegmentTest : public ::testing::Test { + public: + bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def); + + TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name); + TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name); + + std::function MakeCandidateFn( + const std::set& node_names); + + protected: + void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name, + TF_Operation** op); + void AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name, TF_Operation** op, bool check); + + SegmentOptions default_options_; +}; + +bool SegmentTest::GetGraphDef(TF_Graph* graph, + tensorflow::GraphDef* graph_def) { + TF_Status* s = TF_NewStatus(); + TF_Buffer* buffer = TF_NewBuffer(); + TF_GraphToGraphDef(graph, buffer, s); + bool ret = TF_GetCode(s) == TF_OK; + EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + if (ret) ret = graph_def->ParseFromArray(buffer->data, buffer->length); + TF_DeleteBuffer(buffer); + TF_DeleteStatus(s); + return ret; +} + +std::function SegmentTest::MakeCandidateFn( + const std::set& node_names) { + return [node_names](const NodeDef& node) -> bool { + return node_names.find(node.name()) != node_names.end(); + }; +} + +void SegmentTest::PlaceholderHelper(TF_Graph* graph, TF_Status* s, + const char* name, TF_Operation** op) { + TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name); + TF_SetAttrType(desc, "dtype", TF_INT32); + *op = TF_FinishOperation(desc, s); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + ASSERT_NE(*op, nullptr); +} + +TF_Operation* SegmentTest::Placeholder(TF_Graph* graph, TF_Status* s, + const char* name) { + TF_Operation* op; + PlaceholderHelper(graph, s, name, &op); + return op; +} + +void SegmentTest::AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name, TF_Operation** op, + bool check) { + TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name); + TF_Output add_inputs[2] = {{l, 0}, {r, 0}}; + TF_AddInputList(desc, add_inputs, 2); + *op = TF_FinishOperation(desc, s); + if (check) { + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + ASSERT_NE(*op, nullptr); + } +} + +TF_Operation* SegmentTest::Add(TF_Operation* l, TF_Operation* r, + TF_Graph* graph, TF_Status* s, + const char* name) { + TF_Operation* op; + AddHelper(l, r, graph, s, name, &op, true); + return op; +} + +TEST_F(SegmentTest, Empty) { + TF_Graph* graph = TF_NewGraph(); + + GraphDef graph_def; + ASSERT_TRUE(GetGraphDef(graph, &graph_def)); + + SegmentNodesVector segments; + ASSERT_EQ( + SegmentGraph(graph_def, MakeCandidateFn({}), default_options_, &segments), + tensorflow::Status::OK()); + + // Expect no segments/subgraphs. + EXPECT_TRUE(segments.empty()); + TF_DeleteGraph(graph); +} + +TEST_F(SegmentTest, Simple) { + TF_Status* s = TF_NewStatus(); + TF_Graph* graph = TF_NewGraph(); + + // feed + // // || + // add0 add1 + // | | / + // | add2 + // | / || + // add3 add4 + // | / + // + // + TF_Operation* feed = Placeholder(graph, s, "feed"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); + + TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); + TF_Operation* add4 = Add(add2, add2, graph, s, "add4"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); + + GraphDef graph_def; + ASSERT_TRUE(GetGraphDef(graph, &graph_def)); + + SegmentNodesVector segments; + ASSERT_EQ( + SegmentGraph(graph_def, + MakeCandidateFn({"add0", "add1", "add2", "add3", "add4"}), + default_options_, &segments), + tensorflow::Status::OK()); + + // Expect all Add operations to be collapsed into a single segment + ASSERT_EQ(segments.size(), 1); + std::vector expected{"add0", "add1", "add2", "add3", "add4"}; + for (const auto& ex : expected) { + EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + << "Missing expected node " << ex; + } + TF_DeleteGraph(graph); + TF_DeleteStatus(s); +} + +TEST_F(SegmentTest, AvoidCycle) { + TF_Status* s = TF_NewStatus(); + TF_Graph* graph = TF_NewGraph(); + + // add2 is not a TRT candidate so add0/add3 cannot be formed as a + // subgraph + // + // feed + // // || + // add0 add1 + // | | / + // | add2 + // | / || + // add3 add4 + // | / + // + // + TF_Operation* feed = Placeholder(graph, s, "feed"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); + + TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); + TF_Operation* add4 = Add(add2, add2, graph, s, "add4"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); + + GraphDef graph_def; + ASSERT_TRUE(GetGraphDef(graph, &graph_def)); + + SegmentNodesVector segments; + ASSERT_EQ( + SegmentGraph(graph_def, MakeCandidateFn({"add0", "add1", "add3", "add4"}), + default_options_, &segments), + tensorflow::Status::OK()); + + // Expect no subgraphs + EXPECT_EQ(segments.size(), 0); + TF_DeleteGraph(graph); + TF_DeleteStatus(s); +} + +TEST_F(SegmentTest, Multiple) { + TF_Status* s = TF_NewStatus(); + TF_Graph* graph = TF_NewGraph(); + + // add5 is not a TRT candidate so two subgraphs should be formed + // + // feed + // // || || + // add0 add1 add7 + // | | / / || + // | add2-----add5 add8 + // | / | | | | + // add3 add4 add6 + // | | / + // + // + TF_Operation* feed = Placeholder(graph, s, "feed"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); + + TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add7 = Add(feed, feed, graph, s, "add7"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add5 = Add(add2, add7, graph, s, "add5"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add8 = Add(add7, add7, graph, s, "add8"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); + TF_Operation* add4 = Add(add2, add5, graph, s, "add4"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); + TF_Operation* add6 = Add(add5, add8, graph, s, "add6"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add6"), string(TF_OperationName(add6))); + + GraphDef graph_def; + ASSERT_TRUE(GetGraphDef(graph, &graph_def)); + + SegmentNodesVector segments; + ASSERT_EQ(SegmentGraph(graph_def, + MakeCandidateFn({"add0", "add1", "add2", "add3", + "add4", "add6", "add7", "add8"}), + default_options_, &segments), + tensorflow::Status::OK()); + + // Expect two subgraphs + EXPECT_EQ(segments.size(), 2); + + std::vector expected0{"add0", "add1", "add2", "add3"}; + for (const auto& ex : expected0) { + EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + << "Missing expected node " << ex; + } + + std::vector expected1{"add6", "add8"}; + for (const auto& ex : expected1) { + EXPECT_TRUE(segments[1].find(ex) != segments[1].end()) + << "Missing expected node " << ex; + } + TF_DeleteGraph(graph); + TF_DeleteStatus(s); +} + +TEST_F(SegmentTest, BigIfElse) { + TF_Status* s = TF_NewStatus(); + TF_Graph* graph = TF_NewGraph(); + + // add2 is not a TRT candidate + // + // feed + // || + // add0 + // // || + // add1 add4 + // || || + // add2 add5 + // || || + // add3 add6 + // || // + // add7 + // || + // + // + TF_Operation* feed = Placeholder(graph, s, "feed"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); + + TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add1 = Add(add0, add0, graph, s, "add1"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add2 = Add(add1, add1, graph, s, "add2"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add3 = Add(add2, add2, graph, s, "add3"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add4 = Add(add0, add0, graph, s, "add4"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add5 = Add(add4, add4, graph, s, "add5"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add6 = Add(add5, add5, graph, s, "add6"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + TF_Operation* add7 = Add(add3, add6, graph, s, "add7"); + ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); + EXPECT_EQ(string("add7"), string(TF_OperationName(add7))); + + GraphDef graph_def; + ASSERT_TRUE(GetGraphDef(graph, &graph_def)); + + SegmentNodesVector segments; + ASSERT_EQ(SegmentGraph(graph_def, + MakeCandidateFn({"add0", "add1", "add3", "add4", + "add5", "add6", "add7"}), + default_options_, &segments), + tensorflow::Status::OK()); + + // Expect 2 subgraphs + EXPECT_EQ(segments.size(), 2); + + std::vector expected0{"add3", "add4", "add5", "add6", "add7"}; + for (const auto& ex : expected0) { + EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + << "Missing expected node " << ex; + } + + std::vector expected1{"add0", "add1"}; + for (const auto& ex : expected1) { + EXPECT_TRUE(segments[1].find(ex) != segments[1].end()) + << "Missing expected node " << ex; + } + TF_DeleteGraph(graph); + TF_DeleteStatus(s); +} + +} // namespace test +} // namespace segment +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/segment/union_find.h b/tensorflow/contrib/tensorrt/segment/union_find.h new file mode 100644 index 0000000000000000000000000000000000000000..1c64ebbb0ae532a4776ab8963515d19fd3b23b4c --- /dev/null +++ b/tensorflow/contrib/tensorrt/segment/union_find.h @@ -0,0 +1,79 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_ + +namespace tensorflow { +namespace tensorrt { +namespace segment { + +// Union-Find data structure. +// Each cluster has an associated value; when merging clusters we can control +// which value becomes the representative of the merged clusters. Values must be +// copyable. +template +class UnionFind { + public: + UnionFind() : size_(1), parent_(nullptr) {} + explicit UnionFind(const T& v) : size_(1), parent_(nullptr), value_(v) {} + + // Returns the number of elements in a cluster. + int Size() { return FindRoot()->size_; } + + // Merges this cluster with 'other'. This cluster's value becomes + // the value of the merged cluster; the value of 'other' is ignored. + void Merge(UnionFind* other); + + // Each cluster has an associated value. Retrieves the value associated + // with this cluster. + T& ParentValue() { return FindRoot()->value_; } + + // Get the original value of this node. + T& Value() { return value_; } + + private: + // Finds the root element of the cluster. Performs path compression. + UnionFind* FindRoot(); + + int size_; + UnionFind* parent_; + T value_; +}; + +template +void UnionFind::Merge(UnionFind* other) { + UnionFind* a = FindRoot(); + UnionFind* b = other->FindRoot(); + if (a == b) return; + + b->parent_ = a; + a->size_ += b->size_; +} + +template +UnionFind* UnionFind::FindRoot() { + if (!parent_) return this; + // Path compression: update intermediate nodes to point to the root of the + // equivalence class. + parent_ = parent_->FindRoot(); + return parent_; +} + +} // namespace segment +} // namespace tensorrt +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_ diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc new file mode 100644 index 0000000000000000000000000000000000000000..8b475177bc670ddae2b26b6a494f758eba20b2c3 --- /dev/null +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -0,0 +1,89 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h" + +#include +#include + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace shape_inference { + +tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { + tensorflow::tensorrt::Logger logger; + string serialized_engine; + TF_RETURN_IF_ERROR(context->GetAttr("serialized_engine", &serialized_engine)); + nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger); + nvinfer1::ICudaEngine* trt_engine = infer->deserializeCudaEngine( + serialized_engine.c_str(), serialized_engine.size(), nullptr); + + int num_batch = -1; + std::vector<::tensorflow::DataType> input_type; + TF_RETURN_IF_ERROR(context->GetAttr("InT", &input_type)); + for (size_t i = 0; i < context->num_inputs(); i++) { + // Check if input shape is legit + auto input_shape = context->input(i); + for (int j = 0; j < context->Rank(input_shape); j++) { + auto dim_handler = context->Dim(input_shape, j); + if (j == 0) { + if (i == 0) { + num_batch = context->Value(dim_handler); + } else if (num_batch != context->Value(dim_handler)) { + // TODO(jie): TensorRT engine requires consistent batch between inputs + // tensors. Segmenter should be aware of this. + LOG(FATAL) << "TensorRT engine requires consistent batch size"; + } + } + } + } + + // Arrange input here + std::vector input_nodes; + TF_RETURN_IF_ERROR(context->GetAttr("input_nodes", &input_nodes)); + + // Arrange output here + std::vector output_nodes; + TF_RETURN_IF_ERROR(context->GetAttr("output_nodes", &output_nodes)); + for (size_t i = 0; i < output_nodes.size(); i++) { + int binding_index = trt_engine->getBindingIndex(output_nodes[i].c_str()); + ShapeHandle output_shape; + std::vector dim_vec; + dim_vec.emplace_back(context->MakeDim(num_batch)); + if (binding_index != -1) { + auto dims = trt_engine->getBindingDimensions(binding_index); + for (int j = 0; j < dims.nbDims; j++) { + dim_vec.emplace_back(context->MakeDim(dims.d[j])); + } + } else { + LOG(FATAL) << "TensorRT engine cannot find binding: " << output_nodes[i]; + } + output_shape = context->MakeShape(dim_vec); + context->set_output(i, output_shape); + } + + return Status::OK(); +} + +} // namespace shape_inference +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h new file mode 100644 index 0000000000000000000000000000000000000000..4b50f66699f0965639e22169ee7d71e860314bf0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_ + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tensorflow { +namespace shape_inference { +Status TRTEngineOpShapeInference(InferenceContext* c); +} // namespace shape_inference +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_ diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py new file mode 100644 index 0000000000000000000000000000000000000000..0b661bd536c7c7f4566c89176420c3b40007bb74 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -0,0 +1,139 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +# normally we should do import tensorflow as tf and then +# tf.placeholder, tf.constant, tf.nn.conv2d etc but +# it looks like internal builds don't like it so +# importing every module individually + +from tensorflow.contrib import tensorrt as trt +from tensorflow.core.protobuf import config_pb2 as cpb2 +from tensorflow.python.client import session as csess +from tensorflow.python.framework import constant_op as cop +from tensorflow.python.framework import dtypes as dtypes +from tensorflow.python.framework import importer as importer +from tensorflow.python.framework import ops as ops +from tensorflow.python.ops import array_ops as aops +from tensorflow.python.ops import nn as nn +from tensorflow.python.ops import nn_ops as nn_ops + + +def get_simple_graph_def(): + """Create a simple graph and return its graph_def.""" + g = ops.Graph() + with g.as_default(): + a = aops.placeholder( + dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input") + e = cop.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtypes.float32) + conv = nn.conv2d( + input=a, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") + b = cop.constant( + [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtypes.float32) + t = nn.bias_add(conv, b, name="biasAdd") + relu = nn.relu(t, "relu") + idty = aops.identity(relu, "ID") + v = nn_ops.max_pool( + idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + aops.squeeze(v, name="output") + return g.as_graph_def() + + +def run_graph(gdef, dumm_inp): + """Run given graphdef once.""" + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + with csess.Session( + config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + val = sess.run(out, {inp: dumm_inp}) + return val + + +# Use real data that is representatitive of the inference dataset +# for calibration. For this test script it is random data. +def run_calibration(gdef, dumm_inp): + """Run given calibration graph multiple times.""" + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + with csess.Session( + config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + # run over real calibration data here, we are mimicking a calibration set of + # 30 different batches. Use as much calibration data as you want + for _ in range(30): + val = sess.run(out, {inp: dumm_inp}) + return val + + +if "__main__" in __name__: + inp_dims = (100, 24, 24, 2) + dummy_input = np.random.random_sample(inp_dims) + orig_graph = get_simple_graph_def() # use a frozen graph for inference + # Get optimized graph + trt_graph = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP32", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + o1 = run_graph(orig_graph, dummy_input) + o2 = run_graph(trt_graph, dummy_input) + o3 = run_graph(trt_graph, dummy_input) + assert np.array_equal(o1, o2) + assert np.array_equal(o3, o2) # sanity check + fp16_graph = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP16", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + int8_calib_gdef = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="INT8", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + o4 = run_graph(fp16_graph, dummy_input) + _ = run_calibration(int8_calib_gdef, dummy_input) + int8_graph = trt.calib_graph_to_infer_graph(int8_calib_gdef) + o5 = run_graph(int8_graph, dummy_input) + assert np.allclose(o1, o4) + assert np.allclose(o1, o5) + print("Pass") diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i new file mode 100644 index 0000000000000000000000000000000000000000..46480e99a113afb34702b0ecd71468d4bdc83f98 --- /dev/null +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -0,0 +1,186 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/* Wrap trt_conversion */ +%{ +#define SWIG_FILE_WITH_INIT +%} +%include "std_pair.i" +%include "tensorflow/python/platform/base.i" + +%{ +PyObject* pair_helper(std::pair* in) { + PyObject *first(nullptr), *second(nullptr), *tuple(nullptr); + first = PyBytes_FromStringAndSize(in->first.data(), in->first.length()); + if (!first) { + if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, "Pair conversion first argument failed"); + } + return NULL; + } + second = PyBytes_FromStringAndSize(in->second.data(), in->second.length()); + if (!second) { + if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "Pair conversion second argument failed"); + } + return NULL; + } + tuple = Py_BuildValue("(OO)", first, second); + if (!tuple) { + if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "Tuple creation from pair failed!"); + } + return NULL; + } + return tuple; +} +%} +%typemap(out) std::pair { + PyObject *tuple = pair_helper(&$1); + if (!tuple) SWIG_fail; + $result = tuple; +} +%{ +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/util/stat_summarizer.h" +#include "tensorflow/contrib/tensorrt/convert/convert_graph.h" +%} + +%ignoreall +%unignore tensorflow; +%unignore trt_convert; +%unignore calib_convert; + +%{ + +std::pair trt_convert( + string graph_def_string, // The serialized GraphDef string. + std::vector output_names, + size_t max_batch_size, + size_t max_workspace_size_bytes, + int precision_mode, + int minimum_segment_size + // Unfortunately we can't use TF_Status here since it + // is in c/c_api and brings in a lot of other libraries + // which in turn declare ops. These ops are included + // statically in our library and cause an abort when + // module is loaded due to double registration + // until Tensorflow properly exposes these headers + // we have to work around this by returning a string + // and converting it to exception on python side. + //,TF_Status* out_status) { +) { +#if GOOGLE_CUDA && GOOGLE_TENSORRT + string out_status; + + tensorflow::GraphDef graph_def; + if (!graph_def.ParseFromString(graph_def_string)) { + out_status = "InvalidArgument;Couldn't interpret input as a GraphDef"; + return std::pair{out_status, ""}; + } + + if(precision_mode < 0 || precision_mode > 2){ + out_status = "InvalidArgument;Invalid precision_mode"; + return std::pair{out_status, ""}; + } + if (!output_names.size()) { + out_status = "InvalidArgument;Size of the output_names vector is 0"; + return std::pair{out_status, ""}; + } + tensorflow::GraphDef outGraph; + tensorflow::Status conversion_status = + tensorflow::tensorrt::convert::ConvertGraphDefToTensorRT( + graph_def, output_names, max_batch_size, max_workspace_size_bytes, + &outGraph, precision_mode, minimum_segment_size); + if (!conversion_status.ok()) { + auto retCode = (int)conversion_status.code(); + char buff[2000]; + snprintf(buff, 2000, "%d;%s", retCode, + conversion_status.error_message().c_str()); + out_status = buff; + return std::pair{out_status, ""}; + } + string result; + if (!outGraph.SerializeToString(&result)) { + out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; + return std::pair{out_status, ""}; + } + out_status = "OK;All good!"; + return std::pair{out_status, result}; +#else + // Returns FAILED_PRECONDITION. + return std::pair{"9;TensorRT is not enabled!", ""}; +#endif // GOOGLE_CUDA && GOOGLE_TENSORRT +} + +std::pair calib_convert(string graph_def_string // const tensorflow::GraphDef& + // unfortunately we can't use TF_Status here since it + // is in c/c_api and brings in a lot of other libraries + // which in turn declare ops. These ops are included + // statically in our library and cause an abort when + // module is loaded due to double registration + // until Tensorflow properly exposes these headers + // we have to work around this by returning a string + // and converting it to exception on python side. + //,TF_Status* out_status) { +) { +#if GOOGLE_CUDA && GOOGLE_TENSORRT + string out_status; + + tensorflow::GraphDef graph_def; + if (!graph_def.ParseFromString(graph_def_string)) { + out_status = "InvalidArgument;Couldn't interpret input as a GraphDef"; + return std::pair{out_status, ""}; + } + + tensorflow::GraphDef outGraph; + tensorflow::Status conversion_status = + tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph(graph_def, + &outGraph); + if (!conversion_status.ok()) { + auto retCode = (int)conversion_status.code(); + char buff[2000]; + snprintf(buff, 2000, "%d;%s", retCode, + conversion_status.error_message().c_str()); + out_status = buff; + return std::pair{out_status, ""}; + } + string result; + if (!outGraph.SerializeToString(&result)) { + out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; + return std::pair{out_status, ""}; + } + out_status = "OK;All good!"; + return std::pair{out_status, result}; +#else + // Returns FAILED_PRECONDITION. + return std::pair{"9;TensorRT is not enabled!", ""}; +#endif // GOOGLE_CUDA && GOOGLE_TENSORRT +} +%} + +std::pair calib_convert(string graph_def_string); + +std::pair trt_convert(string graph_def_string, + std::vector output_names, + size_t max_batch_size, + size_t max_workspace_size_bytes, + int precision_mode, int minimum_segment_size); + + +%unignoreall diff --git a/tensorflow/contrib/testing/python/framework/fake_summary_writer.py b/tensorflow/contrib/testing/python/framework/fake_summary_writer.py index f2065c666255984c8ab770fc10f682b1eabad095..15a415df303df5be44e89c00005cb253ae2af286 100644 --- a/tensorflow/contrib/testing/python/framework/fake_summary_writer.py +++ b/tensorflow/contrib/testing/python/framework/fake_summary_writer.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.core.framework import summary_pb2 +from tensorflow.python.framework import test_util from tensorflow.python.summary.writer import writer from tensorflow.python.summary.writer import writer_cache @@ -85,7 +86,11 @@ class FakeSummaryWriter(object): if expected_added_graphs is not None: test_case.assertEqual(expected_added_graphs, self._added_graphs) if expected_added_meta_graphs is not None: - test_case.assertEqual(expected_added_meta_graphs, self._added_meta_graphs) + test_case.assertEqual(len(expected_added_meta_graphs), + len(self._added_meta_graphs)) + for expected, actual in zip(expected_added_meta_graphs, + self._added_meta_graphs): + test_util.assert_meta_graph_protos_equal(test_case, expected, actual) if expected_session_logs is not None: test_case.assertEqual(expected_session_logs, self._added_session_logs) diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD index bb86ecb2209f9bed3ad6c37f4b23bc7b361e1bd6..70bf67c7793ba96a8be0ced58421f778a5776d6c 100644 --- a/tensorflow/contrib/timeseries/examples/BUILD +++ b/tensorflow/contrib/timeseries/examples/BUILD @@ -25,7 +25,10 @@ py_test( srcs = ["predict_test.py"], data = ["data/period_trend.csv"], srcs_version = "PY2AND3", - tags = ["notsan"], # b/67513579 + tags = [ + "no_windows", # TODO: needs investigation on Windows + "notsan", # b/67513579 + ], deps = [ ":predict", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv index 02a60d1cf61765c7c916803fe918d8b7b186405e..9b15b4f0b26f11ac3281ca4206654872984628b6 100644 --- a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv +++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv @@ -1,100 +1,100 @@ -0,0.926906299771,1.99107237682,2.56546245685,3.07914768197,4.04839057867 -1,0.108010001864,1.41645361423,2.1686839775,2.94963962176,4.1263503303 -2,-0.800567600028,1.0172132907,1.96434754116,2.99885333086,4.04300485864 -3,0.0607042871898,0.719540073421,1.9765012584,2.89265588817,4.0951014426 -4,0.933712200629,0.28052120776,1.41018552514,2.69232603996,4.06481164223 -5,-0.171730652974,0.260054421028,1.48770816369,2.62199129293,4.44572807842 -6,-1.00180162933,0.333045158863,1.50006392277,2.88888309683,4.24755865606 -7,0.0580061875336,0.688929398826,1.56543458772,2.99840358953,4.52726873347 -8,0.764139447412,1.24704875327,1.77649279698,3.13578593851,4.63238922951 -9,-0.230331874785,1.47903998963,2.03547545751,3.20624030377,4.77980005228 -10,-1.03846045211,2.01133000781,2.31977503972,3.67951536251,5.09716775897 -11,0.188643592253,2.23285349038,2.68338482249,3.49817168611,5.24928239634 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b/tensorflow/contrib/timeseries/examples/known_anomaly.py index 7659dd308a7ee1b70d6688b85e4f6157ddee0540..c08c0b0acb917f527d7efa91874d6405b9220083 100644 --- a/tensorflow/contrib/timeseries/examples/known_anomaly.py +++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py @@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300): # Indicate the format of our exogenous feature, in this case a string # representing a boolean value. - string_feature = tf.contrib.layers.sparse_column_with_keys( - column_name="is_changepoint", keys=["no", "yes"]) + string_feature = tf.feature_column.categorical_column_with_vocabulary_list( + key="is_changepoint", vocabulary_list=["no", "yes"]) # Specify the way this feature is presented to the model, here using a one-hot # encoding. - one_hot_feature = tf.contrib.layers.one_hot_column( - sparse_id_column=string_feature) + one_hot_feature = tf.feature_column.indicator_column( + categorical_column=string_feature) estimator = tf.contrib.timeseries.StructuralEnsembleRegressor( periodicities=12, diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py index c834430b95d37f7c313c54c086b9c323cf3e921f..2eee878196bb64b523c491ca808ca8d6ff5dd36c 100644 --- a/tensorflow/contrib/timeseries/examples/lstm.py +++ b/tensorflow/contrib/timeseries/examples/lstm.py @@ -20,12 +20,14 @@ from __future__ import print_function import functools from os import path +import tempfile import numpy import tensorflow as tf from tensorflow.contrib.timeseries.python.timeseries import estimators as ts_estimators from tensorflow.contrib.timeseries.python.timeseries import model as ts_model +from tensorflow.contrib.timeseries.python.timeseries import state_management try: import matplotlib # pylint: disable=g-import-not-at-top @@ -46,7 +48,8 @@ _DATA_FILE = path.join(_MODULE_PATH, "data/multivariate_periods.csv") class _LSTMModel(ts_model.SequentialTimeSeriesModel): """A time series model-building example using an RNNCell.""" - def __init__(self, num_units, num_features, dtype=tf.float32): + def __init__(self, num_units, num_features, exogenous_feature_columns=None, + dtype=tf.float32): """Initialize/configure the model object. Note that we do not start graph building here. Rather, this object is a @@ -56,6 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): num_units: The number of units in the model's LSTMCell. num_features: The dimensionality of the time series (features per timestep). + exogenous_feature_columns: A list of `tf.feature_column`s representing + features which are inputs to the model but are not predicted by + it. These must then be present for training, evaluation, and + prediction. dtype: The floating point data type to use. """ super(_LSTMModel, self).__init__( @@ -63,6 +70,7 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): train_output_names=["mean"], predict_output_names=["mean"], num_features=num_features, + exogenous_feature_columns=exogenous_feature_columns, dtype=dtype) self._num_units = num_units # Filled in by initialize_graph() @@ -70,7 +78,7 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): self._lstm_cell_run = None self._predict_from_lstm_output = None - def initialize_graph(self, input_statistics): + def initialize_graph(self, input_statistics=None): """Save templates for components, which can then be used repeatedly. This method is called every time a new graph is created. It's safe to start @@ -102,6 +110,8 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): tf.zeros([], dtype=tf.int64), # The previous observation or prediction. tf.zeros([self.num_features], dtype=self.dtype), + # The most recently seen exogenous features. + tf.zeros(self._get_exogenous_embedding_shape(), dtype=self.dtype), # The state of the RNNCell (batch dimension removed since this parent # class will broadcast). [tf.squeeze(state_element, axis=0) @@ -129,7 +139,7 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): loss (note that we could also return other measures of goodness of fit, although only "loss" will be optimized). """ - state_from_time, prediction, lstm_state = state + state_from_time, prediction, exogenous, lstm_state = state with tf.control_dependencies( [tf.assert_equal(current_times, state_from_time)]): # Subtract the mean and divide by the variance of the series. Slightly @@ -141,16 +151,22 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): (prediction - transformed_values) ** 2, axis=-1) # Keep track of the new observation in model state. It won't be run # through the LSTM until the next _imputation_step. - new_state_tuple = (current_times, transformed_values, lstm_state) + new_state_tuple = (current_times, transformed_values, + exogenous, lstm_state) return (new_state_tuple, predictions) def _prediction_step(self, current_times, state): """Advance the RNN state using a previous observation or prediction.""" - _, previous_observation_or_prediction, lstm_state = state + _, previous_observation_or_prediction, exogenous, lstm_state = state + # Update LSTM state based on the most recent exogenous and endogenous + # features. + inputs = tf.concat([previous_observation_or_prediction, exogenous], + axis=-1) lstm_output, new_lstm_state = self._lstm_cell_run( - inputs=previous_observation_or_prediction, state=lstm_state) + inputs=inputs, state=lstm_state) next_prediction = self._predict_from_lstm_output(lstm_output) - new_state_tuple = (current_times, next_prediction, new_lstm_state) + new_state_tuple = (current_times, next_prediction, + exogenous, new_lstm_state) return new_state_tuple, {"mean": self._scale_back_data(next_prediction)} def _imputation_step(self, current_times, state): @@ -162,36 +178,85 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): def _exogenous_input_step( self, current_times, current_exogenous_regressors, state): - """Update model state based on exogenous regressors.""" - raise NotImplementedError( - "Exogenous inputs are not implemented for this example.") + """Save exogenous regressors in model state for use in _prediction_step.""" + state_from_time, prediction, _, lstm_state = state + return (state_from_time, prediction, + current_exogenous_regressors, lstm_state) def train_and_predict( - csv_file_name=_DATA_FILE, training_steps=200, estimator_config=None): + csv_file_name=_DATA_FILE, training_steps=200, estimator_config=None, + export_directory=None): """Train and predict using a custom time series model.""" # Construct an Estimator from our LSTM model. + categorical_column = tf.feature_column.categorical_column_with_hash_bucket( + key="categorical_exogenous_feature", hash_bucket_size=16) + exogenous_feature_columns = [ + # Exogenous features are not part of the loss, but can inform + # predictions. In this example the features have no extra information, but + # are included as an API example. + tf.feature_column.numeric_column( + "2d_exogenous_feature", shape=(2,)), + tf.feature_column.embedding_column( + categorical_column=categorical_column, dimension=10)] estimator = ts_estimators.TimeSeriesRegressor( - model=_LSTMModel(num_features=5, num_units=128), - optimizer=tf.train.AdamOptimizer(0.001), config=estimator_config) + model=_LSTMModel(num_features=5, num_units=128, + exogenous_feature_columns=exogenous_feature_columns), + optimizer=tf.train.AdamOptimizer(0.001), config=estimator_config, + # Set state to be saved across windows. + state_manager=state_management.ChainingStateManager()) reader = tf.contrib.timeseries.CSVReader( csv_file_name, column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,) - + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5)) + + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5 + + ("2d_exogenous_feature",) * 2 + + ("categorical_exogenous_feature",)), + # Data types other than for `times` need to be specified if they aren't + # float32. In this case one of our exogenous features has string dtype. + column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,))) train_input_fn = tf.contrib.timeseries.RandomWindowInputFn( reader, batch_size=4, window_size=32) estimator.train(input_fn=train_input_fn, steps=training_steps) evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader) evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1) # Predict starting after the evaluation + predict_exogenous_features = { + "2d_exogenous_feature": numpy.concatenate( + [numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])], + axis=-1), + "categorical_exogenous_feature": numpy.array( + ["strkey"] * 100)[None, :, None]} (predictions,) = tuple(estimator.predict( input_fn=tf.contrib.timeseries.predict_continuation_input_fn( - evaluation, steps=100))) + evaluation, steps=100, + exogenous_features=predict_exogenous_features))) times = evaluation["times"][0] observed = evaluation["observed"][0, :, :] predicted_mean = numpy.squeeze(numpy.concatenate( [evaluation["mean"][0], predictions["mean"]], axis=0)) all_times = numpy.concatenate([times, predictions["times"]], axis=0) + + # Export the model in SavedModel format. + if export_directory is None: + export_directory = tempfile.mkdtemp() + input_receiver_fn = estimator.build_raw_serving_input_receiver_fn() + export_location = estimator.export_savedmodel( + export_directory, input_receiver_fn) + # Predict using the SavedModel + with tf.Graph().as_default(): + with tf.Session() as session: + signatures = tf.saved_model.loader.load( + session, [tf.saved_model.tag_constants.SERVING], export_location) + saved_model_output = ( + tf.contrib.timeseries.saved_model_utils.predict_continuation( + continue_from=evaluation, signatures=signatures, + session=session, steps=100, + exogenous_features=predict_exogenous_features)) + # The exported model gives the same results as the Estimator.predict() + # call above. + numpy.testing.assert_allclose( + predictions["mean"], + numpy.squeeze(saved_model_output["mean"], axis=0)) return times, observed, all_times, predicted_mean diff --git a/tensorflow/contrib/timeseries/examples/lstm_test.py b/tensorflow/contrib/timeseries/examples/lstm_test.py index 3cace567266d497b12d836f44a335bbe5d916949..ca56e38ca079f71b38cf29605a295a50929945e8 100644 --- a/tensorflow/contrib/timeseries/examples/lstm_test.py +++ b/tensorflow/contrib/timeseries/examples/lstm_test.py @@ -36,7 +36,8 @@ class LSTMExampleTest(test.TestCase): def test_periodicity_learned(self): (observed_times, observed_values, all_times, predicted_values) = lstm.train_and_predict( - training_steps=100, estimator_config=_SeedRunConfig()) + training_steps=100, estimator_config=_SeedRunConfig(), + export_directory=self.get_temp_dir()) self.assertAllEqual([100], observed_times.shape) self.assertAllEqual([100, 5], observed_values.shape) self.assertAllEqual([200], all_times.shape) diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD index fff972c1f3277ad5d83673a202a50d1e6f7df210..64f5cd83575e1eeaa35360c4dc8c5a0d1f384066 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD @@ -140,11 +140,13 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", "//tensorflow/python:state_ops", + "//tensorflow/python:summary", "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/estimator:export", "//tensorflow/python/estimator:head", + "//tensorflow/python/estimator:metric_keys", ], ) @@ -154,9 +156,7 @@ py_test( "head_test.py", ], srcs_version = "PY2AND3", - tags = [ - "no_pip_gpu", # b/63391119 - ], + tags = ["no_pip_gpu"], # b/63391119 deps = [ ":feature_keys", ":head", @@ -425,6 +425,7 @@ py_test( srcs_version = "PY2AND3", tags = [ "no_pip_gpu", # b/63391119 + "no_windows", # TODO: needs investigation on Windows ], deps = [ ":feature_keys", diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py index 3738dfa154d4f39b9562446972443ed88f3fbe8b..8d13343e82340dae11b0be54e3bc3152060dca36 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py +++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py @@ -29,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin from tensorflow.python.estimator import estimator_lib from tensorflow.python.estimator.export import export_lib +from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.training import training as train @@ -72,15 +74,14 @@ class TimeSeriesRegressor(estimator_lib.Estimator): # tf.Example containing all features (times, values, any exogenous features) # and serialized model state (possibly also as a tf.Example). def build_raw_serving_input_receiver_fn(self, - exogenous_features=None, default_batch_size=None, default_series_length=None): """Build an input_receiver_fn for export_savedmodel which accepts arrays. + Automatically creates placeholders for exogenous `FeatureColumn`s passed to + the model. + Args: - exogenous_features: A dictionary mapping feature keys to exogenous - features (either Numpy arrays or Tensors). Used to determine the shapes - of placeholders for these features. default_batch_size: If specified, must be a scalar integer. Sets the batch size in the static shape information of all feature Tensors, which means only this batch size will be accepted by the exported model. If None @@ -94,9 +95,6 @@ class TimeSeriesRegressor(estimator_lib.Estimator): An input_receiver_fn which may be passed to the Estimator's export_savedmodel. """ - if exogenous_features is None: - exogenous_features = {} - def _serving_input_receiver_fn(): """A receiver function to be passed to export_savedmodel.""" placeholders = {} @@ -119,14 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator): dtype=self._model.dtype), shape=(default_batch_size, default_series_length, self._model.num_features))) - for feature_key, feature_value in exogenous_features.items(): - value_tensor = ops.convert_to_tensor(feature_value) - value_tensor.get_shape().with_rank_at_least(2) - feature_shape = value_tensor.get_shape().as_list() - feature_shape[0] = default_batch_size - feature_shape[1] = default_series_length - placeholders[feature_key] = array_ops.placeholder( - dtype=value_tensor.dtype, name=feature_key, shape=feature_shape) + if self._model.exogenous_feature_columns: + with ops.Graph().as_default(): + # Default placeholders have only an unknown batch dimension. Make them + # in a separate graph, then splice in the series length to the shapes + # and re-create them in the outer graph. + parsed_features = ( + feature_column.make_parse_example_spec( + self._model.exogenous_feature_columns)) + placeholder_features = parsing_ops.parse_example( + serialized=array_ops.placeholder( + shape=[None], dtype=dtypes.string), + features=parsed_features) + exogenous_feature_shapes = { + key: (value.get_shape(), value.dtype) for key, value + in placeholder_features.items()} + for feature_key, (batch_only_feature_shape, value_dtype) in ( + exogenous_feature_shapes.items()): + batch_only_feature_shape = ( + batch_only_feature_shape.with_rank_at_least(1).as_list()) + feature_shape = ([default_batch_size, default_series_length] + + batch_only_feature_shape[1:]) + placeholders[feature_key] = array_ops.placeholder( + dtype=value_dtype, name=feature_key, shape=feature_shape) # Models may not know the shape of their state without creating some # variables/ops. Avoid polluting the default graph by making a new one. We # use only static metadata from the returned Tensors. @@ -327,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor): determine the model size. Learning autoregressive coefficients typically requires more steps and a smaller step size than other components. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not part + of the series to be predicted. Passed to + `tf.feature_column.input_layer`. exogenous_update_condition: A function taking two Tensor arguments, `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]), and diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py index f0330bfbbd6e8067e5d085376acdf2e6bcaccb6a..1d96145e59ce80c2add6528da6e4e7ec9a1361f5 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head.py @@ -26,6 +26,7 @@ from tensorflow.contrib.timeseries.python.timeseries import feature_keys from tensorflow.python.estimator import estimator_lib from tensorflow.python.estimator.canned import head as head_lib +from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.export import export_lib from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -35,6 +36,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import nest +from tensorflow.python.summary import summary def time_series_regression_head(model, @@ -71,9 +73,32 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc self.input_statistics_generator = input_statistics_generator self._name = name + @property + def name(self): + return self._name + + # TODO(terrytangyuan): consolidate `model_outputs` and `_Head.LossSpec` + # once `_Head.create_loss` becomes extendable + def create_loss(self, features, mode, logits=None, labels=None): + """See `_Head`.""" + model_outputs = self.state_manager.define_loss( + self.model, features, mode) + summary.scalar( + head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), + model_outputs.loss) + return model_outputs + + @property + def logits_dimension(self): + """See `_Head`.""" + return 1 + def _train_ops(self, features): """Add training ops to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope( + "model", + # Use ResourceVariables to avoid race conditions. + use_resource=True): model_outputs = self.state_manager.define_loss( self.model, features, estimator_lib.ModeKeys.TRAIN) @@ -88,26 +113,9 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc mode=estimator_lib.ModeKeys.TRAIN, train_op=train_op) - # TODO(terrytangyuan): suffix summary and metrics keys by `"/" + name` - @property - def name(self): - return self._name - - # TODO(terrytangyuan): unused for now. Need to decouple - # `state_manager.define_loss` to satisfy the extendable return signature of - # `_Head.create_loss`. - def create_loss(self, features, mode, logits, labels): - """See `_Head`.""" - return None - - # TODO(terrytangyuan): check label dimension - @property - def logits_dimension(self): - return None - def _evaluate_ops(self, features): """Add ops for evaluation (aka filtering) to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): model_outputs = self.state_manager.define_loss( self.model, features, estimator_lib.ModeKeys.EVAL) metrics = {} @@ -128,7 +136,7 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _predict_ops(self, features): """Add ops for prediction to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): prediction = self.model.predict(features=features) prediction[feature_keys.PredictionResults.TIMES] = features[ feature_keys.PredictionFeatures.TIMES] @@ -137,12 +145,11 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _serving_ops(self, features): """Add ops for serving to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): prediction_outputs = self.model.predict(features=features) with variable_scope.variable_scope("model", reuse=True): - filtering_outputs = self.state_manager.define_loss( - self.model, features, estimator_lib.ModeKeys.EVAL) - + filtering_outputs = self.create_loss( + features, estimator_lib.ModeKeys.EVAL) return estimator_lib.EstimatorSpec( mode=estimator_lib.ModeKeys.PREDICT, export_outputs={ @@ -191,7 +198,7 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def create_estimator_spec(self, features, mode, labels=None): """Performs basic error checking and returns an EstimatorSpec.""" - with ops.name_scope("head"): + with ops.name_scope(self._name, "head"): if labels: raise ValueError( "The model received a `labels` dictionary, which is " diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py index d4ee59036624cffb216709e096981d362670e416..04225333b9377447f46d32663df76aece97a51e7 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py +++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py @@ -500,6 +500,41 @@ class CSVReader(ReaderBaseTimeSeriesParser): return features +class TFExampleReader(ReaderBaseTimeSeriesParser): + """Reads and parses `tf.Example`s from a TFRecords file.""" + + def __init__(self, + filenames, + features): + """Configure `tf.Example` parsing. + + Args: + filenames: A filename or list of filenames to read the time series + from. Each line must have columns corresponding to `column_names`. + features: A dictionary mapping from feature keys to `tf.FixedLenFeature` + objects. Must include `TrainEvalFeatures.TIMES` (scalar integer) and + `TrainEvalFeatures.VALUES` (floating point vector) features. + Raises: + ValueError: If required times/values features are not present. + """ + if feature_keys.TrainEvalFeatures.TIMES not in features: + raise ValueError("'{}' is a required column.".format( + feature_keys.TrainEvalFeatures.TIMES)) + if feature_keys.TrainEvalFeatures.VALUES not in features: + raise ValueError("'{}' is a required column.".format( + feature_keys.TrainEvalFeatures.VALUES)) + self._features = features + super(TFExampleReader, self).__init__(filenames=filenames) + + def _get_reader(self): + return io_ops.TFRecordReader() + + def _process_records(self, examples): + """Parse `tf.Example`s into `Tensors`.""" + return parsing_ops.parse_example( + serialized=examples, features=self._features) + + class TimeSeriesInputFn(object): """Base for classes which create batches of windows from a time series.""" diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py index ed78a835a4d451e9e7d18bb833d8ebed6c05a195..703537abf0fe3985aaf0434cc633cb410dd6bd4c 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py @@ -27,7 +27,11 @@ from tensorflow.contrib.timeseries.python.timeseries import input_pipeline from tensorflow.contrib.timeseries.python.timeseries import test_utils from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures +from tensorflow.core.example import example_pb2 +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.lib.io import tf_record +from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import coordinator as coordinator_lib @@ -52,6 +56,21 @@ def _make_csv_time_series(num_features, num_samples, test_tmpdir): return filename +def _make_tfexample_series(num_features, num_samples, test_tmpdir): + _, data_file = tempfile.mkstemp(dir=test_tmpdir) + with tf_record.TFRecordWriter(data_file) as writer: + for i in range(num_samples): + example = example_pb2.Example() + times = example.features.feature[TrainEvalFeatures.TIMES] + times.int64_list.value.append(i) + values = example.features.feature[TrainEvalFeatures.VALUES] + values.float_list.value.extend( + [float(i) * 2. + feature_number + for feature_number in range(num_features)]) + writer.write(example.SerializeToString()) + return data_file + + def _make_numpy_time_series(num_features, num_samples): times = numpy.arange(num_samples) values = times[:, None] * 2. + numpy.arange(num_features)[None, :] @@ -107,6 +126,19 @@ class RandomWindowInputFnTests(test.TestCase): time_series_reader = input_pipeline.CSVReader([filename]) self._test_out_of_order(time_series_reader, discard_out_of_order=False) + def test_tfexample_sort_out_of_order(self): + filename = _make_tfexample_series( + num_features=1, num_samples=50, + test_tmpdir=self.get_temp_dir()) + time_series_reader = input_pipeline.TFExampleReader( + [filename], + features={ + TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature( + shape=[], dtype=dtypes.int64), + TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature( + shape=[1], dtype=dtypes.float32)}) + self._test_out_of_order(time_series_reader, discard_out_of_order=False) + def test_numpy_sort_out_of_order(self): data = _make_numpy_time_series(num_features=1, num_samples=50) time_series_reader = input_pipeline.NumpyReader(data) @@ -183,6 +215,20 @@ class RandomWindowInputFnTests(test.TestCase): self._test_multivariate(time_series_reader=time_series_reader, num_features=2) + def test_tfexample_multivariate(self): + filename = _make_tfexample_series( + num_features=2, num_samples=50, + test_tmpdir=self.get_temp_dir()) + time_series_reader = input_pipeline.TFExampleReader( + [filename], + features={ + TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature( + shape=[], dtype=dtypes.int64), + TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature( + shape=[2], dtype=dtypes.float32)}) + self._test_multivariate(time_series_reader=time_series_reader, + num_features=2) + def test_numpy_multivariate(self): data = _make_numpy_time_series(num_features=3, num_samples=50) time_series_reader = input_pipeline.NumpyReader(data) @@ -248,6 +294,20 @@ class WholeDatasetInputFnTests(test.TestCase): self._whole_dataset_input_fn_test_template( time_series_reader=time_series_reader, num_features=1, num_samples=50) + def test_tfexample(self): + filename = _make_tfexample_series( + num_features=4, num_samples=100, + test_tmpdir=self.get_temp_dir()) + time_series_reader = input_pipeline.TFExampleReader( + [filename], + features={ + TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature( + shape=[], dtype=dtypes.int64), + TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature( + shape=[4], dtype=dtypes.float32)}) + self._whole_dataset_input_fn_test_template( + time_series_reader=time_series_reader, num_features=4, num_samples=100) + def test_numpy(self): data = _make_numpy_time_series(num_features=4, num_samples=100) time_series_reader = input_pipeline.NumpyReader(data) diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py index b32b5c5494ae14187954b900119678a5b53a3602..7644764a7459db3951fe9a2790389713dd412a8f 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/model.py +++ b/tensorflow/contrib/timeseries/python/timeseries/model.py @@ -21,17 +21,17 @@ from __future__ import print_function import abc import collections -from tensorflow.contrib import layers - from tensorflow.contrib.timeseries.python.timeseries import math_utils from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures +from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope @@ -65,11 +65,11 @@ class TimeSeriesModel(object): Args: num_features: Number of features for the time series - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not + part of the series to be predicted. Passed to + `tf.feature_column.input_layer`. dtype: The floating point datatype to use. """ if exogenous_feature_columns: @@ -83,6 +83,11 @@ class TimeSeriesModel(object): self._stats_means = None self._stats_sigmas = None + @property + def exogenous_feature_columns(self): + """`tf.feature_colum`s for features which are not predicted.""" + return self._exogenous_feature_columns + # TODO(allenl): Move more of the generic machinery for generating and # predicting into TimeSeriesModel, and possibly share it between generate() # and predict() @@ -250,6 +255,26 @@ class TimeSeriesModel(object): """ pass + def _get_exogenous_embedding_shape(self): + """Computes the shape of the vector returned by _process_exogenous_features. + + Returns: + The shape as a list. Does not include a batch dimension. + """ + if not self._exogenous_feature_columns: + return (0,) + with ops.Graph().as_default(): + parsed_features = ( + feature_column.make_parse_example_spec( + self._exogenous_feature_columns)) + placeholder_features = parsing_ops.parse_example( + serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string), + features=parsed_features) + embedded = feature_column.input_layer( + features=placeholder_features, + feature_columns=self._exogenous_feature_columns) + return embedded.get_shape().as_list()[1:] + def _process_exogenous_features(self, times, features): """Create a single vector from exogenous features. @@ -285,13 +310,13 @@ class TimeSeriesModel(object): # Avoid shape warnings when embedding "scalar" exogenous features (those # with only batch and window dimensions); input_from_feature_columns # expects input ranks to match the embedded rank. - if tensor.get_shape().ndims == 1: + if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string: exogenous_features_single_batch_dimension[name] = tensor[:, None] else: exogenous_features_single_batch_dimension[name] = tensor embedded_exogenous_features_single_batch_dimension = ( - layers.input_from_feature_columns( - columns_to_tensors=exogenous_features_single_batch_dimension, + feature_column.input_layer( + features=exogenous_features_single_batch_dimension, feature_columns=self._exogenous_feature_columns, trainable=True)) exogenous_regressors = array_ops.reshape( @@ -358,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel): may use _scale_back_data or _scale_back_variance to return predictions to the input scale. dtype: The floating point datatype to use. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects. See `TimeSeriesModel`. + exogenous_feature_columns: A list of `tf.feature_column`s objects. See + `TimeSeriesModel`. exogenous_update_condition: A function taking two Tensor arguments `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]) and returning a diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD index c86d06e9236962cbabbc56afa1cfe213e0c78bc0..07df7bc9a5cd45e0b54e78aaa6ea02fedc584cc3 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD @@ -40,6 +40,7 @@ py_test( timeout = "long", # Moderate but for asan srcs = ["state_space_model_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":state_space_model", "//tensorflow/contrib/layers:layers_py", diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py index 6257002647ed53bbde3ace11a6b45e4e2cdeb57d..951c6546d5fed77e0cfa98a4e774b804639d7dad 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py @@ -112,11 +112,11 @@ class StateSpaceModelConfiguration( exogenous_noise_decreases: If True, exogenous regressors can "set" model state, decreasing uncertainty. If both this parameter and exogenous_noise_increases are False, exogenous regressors are ignored. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not part + of the series to be predicted. Passed to + `tf.feature_column.input_layer`. exogenous_update_condition: A function taking two Tensor arguments `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]) and returning a diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py index 5980fc5d5deccc151b01c72fa19b734a7c485bdc..1fb4a3c121c8d7c1daf8fc4a3f59a8b8de38bf8f 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py @@ -187,9 +187,7 @@ class StateSpaceEquivalenceTests(test.TestCase): estimator.train(combined_input_fn, steps=1) export_location = estimator.export_savedmodel( self.get_temp_dir(), - estimator.build_raw_serving_input_receiver_fn( - exogenous_features={ - "exogenous": numpy.zeros((0, 0), dtype=numpy.float32)})) + estimator.build_raw_serving_input_receiver_fn()) with ops.Graph().as_default() as graph: random_model.initialize_graph() with self.test_session(graph=graph) as session: @@ -209,7 +207,7 @@ class StateSpaceEquivalenceTests(test.TestCase): features={ feature_keys.FilteringFeatures.TIMES: [1, 2], feature_keys.FilteringFeatures.VALUES: [1., 2.], - "exogenous": [-1., -2.]}) + "exogenous": [[-1.], [-2.]]}) second_split_filtering = saved_model_utils.filter_continuation( continue_from=first_split_filtering, signatures=signatures, @@ -217,7 +215,7 @@ class StateSpaceEquivalenceTests(test.TestCase): features={ feature_keys.FilteringFeatures.TIMES: [3, 4], feature_keys.FilteringFeatures.VALUES: [3., 4.], - "exogenous": [-3., -4.] + "exogenous": [[-3.], [-4.]] }) combined_filtering = saved_model_utils.filter_continuation( continue_from={ @@ -227,7 +225,7 @@ class StateSpaceEquivalenceTests(test.TestCase): features={ feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4], feature_keys.FilteringFeatures.VALUES: [1., 2., 3., 4.], - "exogenous": [-1., -2., -3., -4.] + "exogenous": [[-1.], [-2.], [-3.], [-4.]] }) split_predict = saved_model_utils.predict_continuation( continue_from=second_split_filtering, @@ -235,14 +233,14 @@ class StateSpaceEquivalenceTests(test.TestCase): session=session, steps=1, exogenous_features={ - "exogenous": [[-5.]]}) + "exogenous": [[[-5.]]]}) combined_predict = saved_model_utils.predict_continuation( continue_from=combined_filtering, signatures=signatures, session=session, steps=1, exogenous_features={ - "exogenous": [[-5.]]}) + "exogenous": [[[-5.]]]}) for state_key, combined_state_value in combined_filtering.items(): if state_key == feature_keys.FilteringResults.TIMES: continue diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index a7d54d8a0cc4598c26d1c7bd62f5b0aa1070701b..f9d433a45b12630ef46048dae4be4c922e84053e 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -24,6 +24,7 @@ cc_library( name = "all_ops", deps = [ ":cross_replica_ops_op_lib", + ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", ":replication_ops_op_lib", @@ -36,7 +37,9 @@ py_library( name = "tpu_estimator", srcs = [ "python/tpu/tpu_config.py", + "python/tpu/tpu_context.py", "python/tpu/tpu_estimator.py", + "python/tpu/tpu_system_metadata.py", "python/tpu/util.py", ], srcs_version = "PY2AND3", @@ -67,6 +70,7 @@ py_library( tf_gen_op_libs( op_lib_names = [ "cross_replica_ops", + "host_compute_ops", "infeed_ops", "outfeed_ops", "replication_ops", @@ -76,6 +80,7 @@ tf_gen_op_libs( deps = [ "//tensorflow/contrib/tpu/proto:tpu_embedding_config_proto_cc", "//tensorflow/core:lib_proto_parsing", + "//tensorflow/core:protos_all_cc", ], ) @@ -83,6 +88,7 @@ tf_custom_op_library( name = "python/ops/_tpu_ops.so", srcs = [ "ops/cross_replica_ops.cc", + "ops/host_compute_ops.cc", "ops/infeed_ops.cc", "ops/outfeed_ops.cc", "ops/replication_ops.cc", @@ -99,6 +105,7 @@ tf_gen_op_wrapper_py( name = "tpu_ops", deps = [ ":cross_replica_ops_op_lib", + ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", ":replication_ops_op_lib", @@ -161,6 +168,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":datasets", ":profiler", ":tpu_py", "//tensorflow/contrib/tpu/proto:topology_proto_py", @@ -179,6 +187,33 @@ py_library( ], ) +py_library( + name = "datasets", + srcs = [ + "python/tpu/datasets.py", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/data/python/ops:transformation_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:function", + "//tensorflow/python:functional_ops", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", + ], +) + +tf_py_test( + name = "datasets_test", + srcs = ["python/tpu/datasets_test.py"], + additional_deps = [ + "//tensorflow/python:client_testlib", + ":datasets", + ], + grpc_enabled = True, +) + tf_py_test( name = "tpu_test", size = "small", @@ -190,6 +225,7 @@ tf_py_test( "//tensorflow/python:framework", "//tensorflow/python:layers", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) tf_py_test( diff --git a/tensorflow/contrib/tpu/ops/host_compute_ops.cc b/tensorflow/contrib/tpu/ops/host_compute_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..48aeb81ac1311d3acd4972810f0a27a382f8b136 --- /dev/null +++ b/tensorflow/contrib/tpu/ops/host_compute_ops.cc @@ -0,0 +1,64 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("_XlaSendFromHost") + .Input("inputs: Tinputs") + .Input("dynamic_key: string") + .Attr("Tinputs: list(type) >= 0") + .Attr("key: string") + .Attr("device_ordinal: int") + .SetIsStateful() + .SetShapeFn(::tensorflow::shape_inference::NoOutputs) + .Doc(R"doc( +A placeholder op for multiple values that will be sent from TensorFlow to a +running XLA computation. + +inputs: A list of tensors that will be sent to the XLA computation. +dynamic_key: The key sent at runtime by the compile node to identify which +execution the transfer corresponds to. +Tinputs: The element types of each element in `inputs`. +key: A key that is unique in the computation and associates the send with the consumer in +the XLA computation. +device_ordinal: The device to use. +)doc"); + +REGISTER_OP("_XlaRecvAtHost") + .Input("dynamic_key: string") + .Output("outputs: Toutputs") + .Attr("Toutputs: list(type) >= 0") + .Attr("key: string") + .Attr("device_ordinal: int") + .SetIsStateful() + .SetShapeFn(::tensorflow::shape_inference::UnknownShape) + .Doc(R"doc( +A placeholder op for multiple values that will be sent to TensorFlow from a +running XLA computation. + +dynamic_key: The key sent at runtime by the compile node to identify which +execution the transfer corresponds to. +outputs: A list of tensors that will be received from the XLA computation. +Toutputs: The element types of each element in `outputs`. +key: A key that is unique in the computation and associates the send with the consumer in +the XLA computation. +device_ordinal: The device to use. +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/tpu/ops/infeed_ops.cc b/tensorflow/contrib/tpu/ops/infeed_ops.cc index 849c4a1102787870b372c35740cf0fe271efa078..efc546f9a6077de9cac5a5acefa3fc7206547fc6 100644 --- a/tensorflow/contrib/tpu/ops/infeed_ops.cc +++ b/tensorflow/contrib/tpu/ops/infeed_ops.cc @@ -41,6 +41,7 @@ REGISTER_OP("InfeedEnqueue") .Attr("dtype: type") .Attr("shape: shape = {}") .Attr("device_ordinal: int = -1") + .SetShapeFn(shape_inference::NoOutputs) .SetIsStateful() .Doc(R"doc( An op which feeds a single Tensor value into the computation. @@ -58,6 +59,7 @@ REGISTER_OP("InfeedEnqueueTuple") .Attr("dtypes: list(type)") .Attr("shapes: list(shape)") .Attr("device_ordinal: int = -1") + .SetShapeFn(shape_inference::NoOutputs) .SetIsStateful() .Doc(R"doc( An op which feeds multiple Tensor values into the computation as an XLA tuple. diff --git a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc index 28417b89e0d4e0c5b2ca4f4794d29ab8a31049d7..7bf5c21d0b526ee5e32448f75d39eca8add6d877 100644 --- a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc +++ b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc @@ -191,6 +191,7 @@ REGISTER_OP("ConfigureDistributedTPU") .Output("topology: string") .Attr("embedding_config: string = ''") .Attr("tpu_embedding_config: string = ''") + .Attr("is_global_init: bool = false") .SetIsStateful() .SetShapeFn(shape_inference::UnknownShape) .Doc(R"doc( @@ -202,6 +203,7 @@ topology. tpu_embedding_config: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program. embedding_config: Reserved. Do not use. +is_global_init: Reserved. Do not use. )doc"); REGISTER_OP("ShutdownDistributedTPU") @@ -212,4 +214,20 @@ An op that shuts down a running distributed TPU system. The Op returns an error if no system is running. )doc"); -} // namespace tensorflow +REGISTER_OP("SessionStatus") + .Input("fetch_start_timestamp: double") + .Output("status: string") + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Not for public usage. + +Returns messages from the current session as a serialized SessionStatusProto. + +This includes the current state of the compiler, along with any critical +logging or warning messages. + +fetch_start_timestamp: any messages earlier than this will be excluded from the +returned proto. +)doc"); + +} // end namespace tensorflow diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 78d237e6a201541b6095b101311db48b447cc477..a730d6142d890cc41f72176cf617ac0b0434192c 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -25,19 +25,34 @@ import sys import tensorflow as tf +# Cloud TPU Cluster Resolvers flags.DEFINE_string( - 'service_addr', None, 'Address of TPU profiler service e.g. ' - 'localhost:8466') + 'gcp_project', None, + 'Project name for the Cloud TPU-enabled project. If not specified, we ' + 'will attempt to automatically detect the GCE project from metadata.') +flags.DEFINE_string( + 'tpu_zone', + None, + help='GCE zone where the Cloud TPU is located in. If not specified, we ' + 'will attempt to automatically detect the GCE project from metadata.') +flags.DEFINE_string('tpu_name', None, + 'Name of the Cloud TPU for Cluster Resolvers. You must ' + 'specify either this flag or --master.') + +# Tool specific parameters flags.DEFINE_string( - 'logdir', None, 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' - 'gs://tb_bucket') + 'service_addr', None, 'Address of TPU profiler service e.g. ' + 'localhost:8466, you must specify either this flag or --tpu_name.') +flags.DEFINE_string('logdir', None, + 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' + 'gs://tb_bucket') flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') -flags.DEFINE_integer( - 'num_tracing_attempts', 3, 'Automatically retry N times when no trace ' - 'event is collected.') -flags.DEFINE_boolean( - 'include_dataset_ops', True, 'Set to false to profile longer TPU ' - 'device traces.') +flags.DEFINE_integer('num_tracing_attempts', 3, + 'Automatically retry N times when no trace ' + 'event is collected.') +flags.DEFINE_boolean('include_dataset_ops', True, + 'Set to false to profile longer TPU ' + 'device traces.') FLAGS = flags.FLAGS EXECUTABLE = 'data/capture_tpu_profile' @@ -48,16 +63,35 @@ def run_main(): def main(unused_argv=None): - if not FLAGS.service_addr or not FLAGS.logdir: - sys.exit('service_addr and logdir must be provided.') + tf.logging.set_verbosity(tf.logging.INFO) + + if FLAGS.service_addr is None and FLAGS.tpu_name is None: + sys.exit('You must specify either --service_addr or --tpu_name.') + + if FLAGS.service_addr is not None: + if FLAGS.tpu_name is not None: + tf.logging.warn('Both --service_addr and --tpu_name are set. Ignoring ' + '--tpu_name and using --service_addr.') + service_addr = FLAGS.service_addr + else: + tpu_cluster_resolver = ( + tf.contrib.cluster_resolver.TPUClusterResolver( + tpu_names=[FLAGS.tpu_name], + zone=FLAGS.tpu_zone, + project=FLAGS.gcp_project)) + service_addr = tpu_cluster_resolver.get_master() + service_addr = service_addr.replace('grpc://', '').replace(':8470', ':8466') + + if not FLAGS.logdir: + sys.exit('logdir must be provided.') executable_path = os.path.join(os.path.dirname(__file__), EXECUTABLE) logdir = os.path.expandvars(os.path.expanduser(FLAGS.logdir)) cmd = [executable_path] - cmd.append('--logdir='+logdir) - cmd.append('--service_addr='+FLAGS.service_addr) - cmd.append('--duration_ms='+str(FLAGS.duration_ms)) - cmd.append('--num_tracing_attempts='+str(FLAGS.num_tracing_attempts)) - cmd.append('--include_dataset_ops='+str(FLAGS.include_dataset_ops).lower()) + cmd.append('--logdir=' + logdir) + cmd.append('--service_addr=' + service_addr) + cmd.append('--duration_ms=' + str(FLAGS.duration_ms)) + cmd.append('--num_tracing_attempts=' + str(FLAGS.num_tracing_attempts)) + cmd.append('--include_dataset_ops=' + str(FLAGS.include_dataset_ops).lower()) subprocess.call(cmd) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index cb6198479908943a546710b94f059d27d9e41a84..8d99835b64152629c66607e6792495eb36319eb8 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,7 +20,7 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.6.0-rc0' +_VERSION = '1.6.0-rc1' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', @@ -47,20 +47,16 @@ setup( # 4 - Beta # 5 - Production/Stable 'Development Status :: 4 - Beta', - 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: Apache Software License', - 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', - 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Artificial Intelligence', @@ -69,4 +65,5 @@ setup( 'Topic :: Software Development :: Libraries :: Python Modules', ], license='Apache 2.0', - keywords='tensorflow performance tpu',) + keywords='tensorflow performance tpu', +) diff --git a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto index 2094294baad63ae73712c8648b588accd4551ef8..e5c798aa2f463a1a2d7cb041ba9b51569958f4fd 100644 --- a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto +++ b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto @@ -77,6 +77,8 @@ message StepInfoResult { // The infeed duration in picoseconds. // Can turn into a map if we want a variable number of ops. optional uint64 infeed_duration_ps = 3; + // The start time of this step in picoseconds. + optional uint64 begin_ps = 4; } // Result proto for a sequence of steps. @@ -155,6 +157,54 @@ message RunEnvironmentResult { repeated HostDependentJobInfoResult host_dependent_job_info = 6; } +// The types of host operations that are tracked. +enum HostOp { + // Invalid host op. + kINVALIDHostOp = 0; + // Each of host op type has two parts: + // (1) the stage where the op happens and (2) the op name. + // stage = Input Data Producer, op = Get Next Batch. + kInputDataProducerGetNextBatch = 1; + // stage = Input Data Producer, op = Session Run. + kInputDataProducerSessionRun = 2; + // stage = Input Data Producer, op = Forward Batch. + kInputDataProducerForwardBatch = 3; + // stage = Infeed Thread, op = Get Next Batch. + kInfeedThreadGetNextBatch = 4; + // stage = Infeed Thread, op = Session Run. + kInfeedThreadSessionRun = 5; + // stage = Infeed Thread, op = Forward Batch. + kInfeedThreadForwardBatch = 6; + // stage = Outfeed Thread, op = Get Next Batch. + kOutfeedThreadGetNextBatch = 7; + // stage = Outfeed Thread, op = Session Run. + kOutfeedThreadSessionRun = 8; + // stage = Outfeed Thread, op = Forward Batch. + kOutfeedThreadForwardBatch = 9; +} + +// Result proto for the host ops per TPU step. +message HostOpsPerTpuStep { + // Whether the data in this message is valid. + optional bool valid = 1 [default = false]; + // The current TPU step number. + optional uint32 tpu_step_num = 2; + // The beginning time of the current TPU step on the device in picoseconds. + optional uint64 tpu_step_begin_ps = 3; + // The ending time of the current TPU step on the device in picoseconds. + optional uint64 tpu_step_end_ps = 4; + // For each possible host operation, maps to the difference between the TPU + // step number that the host op targets and the current TPU step number. + // The key is HostOp, value is the step difference. + map step_diffs = 5; +} + +// Result proto for the host ops for all TPU steps. +message HostOpsResult { + // A sequence of HostOpsPerTpuStep (one for each TPU step) + repeated HostOpsPerTpuStep host_op_sequence = 1; +} + // Result proto for TfStatsHelper. message TfOpStats { // The result for the TF-metric database. @@ -171,4 +221,6 @@ message TfOpStats { optional double matrix_unit_utilization_percent = 6; // The run environment of this profiling session. optional RunEnvironmentResult run_environment = 7; + // The result for the host operations. + optional HostOpsResult host_ops = 8; } diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py index 1c970655d0e464912d9b0a514fca0523bc604726..14c63a79763300dcfe8d6c8e09b90f8e9c772358 100644 --- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py +++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py @@ -47,7 +47,8 @@ if platform.system() != "Windows": # types are supported. _SUPPORTED_INFEED_DTYPES = set([ - dtypes.bool, dtypes.int32, dtypes.bfloat16, dtypes.float32 + dtypes.bool, dtypes.int32, dtypes.int64, dtypes.bfloat16, dtypes.float32, + dtypes.complex64 ]) def infeed_dequeue(dtype, shape, name=None): diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..465c668fd8b42f150892f8e4b52de76c6fe13fa9 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -0,0 +1,184 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Library of Cloud TPU helper functions for data loading.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.ops import batching +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.data.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import function +from tensorflow.python.framework import ops +from tensorflow.python.ops import functional_ops + + +def _TextLineDataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = readers.TextLineDataset(filename, buffer_size=buffer_size) + return dataset + + +def _TFRecordDataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size) + return dataset + + +_FILETYPE_MAP = { + 'tfrecord': _TFRecordDataset, + 'textline': _TextLineDataset, + 'text': _TextLineDataset, +} + + +def StreamingFilesDataset(files, + filetype=None, + file_reader_job=None, + worker_job=None, + num_epochs=None, + filename_shuffle_buffer_size=None, + num_parallel_reads=None, + batch_transfer_size=None, + sloppy=None): + """StreamingFilesDataset constructs a dataset to stream from workers (GCE VM). + + Because Cloud TPUs are allocated over the network, a Cloud TPU cannot read + files local to your GCE VM. In order to train using files stored on your local + VM (e.g. on local SSD for extreme performance), use the StreamingFilesDataset + helper to generate a dataset to feed your Cloud TPU with files from your GCE + VM. + + The resulting dataset may return an OutOfRangeError if there are no files + found as a result of the fileglob expansion. + + Note: StreamingFilesDataset assumes that the session is using a + TPUClusterResolver and has therefore a worker and a coordinator job. File + loading will be done on the coordinator job. + + Args: + files: A string glob to match files, or a `tf.data.Dataset` generating file + names. + filetype: A string (one of 'tfrecord', or 'textline') or a single-argument + TensorFlow function that when given a filename returns a dataset. + file_reader_job: An optional string that corresponds to the job that should + perform the file reads. + worker_job: An optional string that corresponds to the job that should + process the tensors (i.e. your GPU or TPU worker). + num_epochs: The number of epochs through the training set that should be + generated. By default, it will repeat infinitely. + filename_shuffle_buffer_size: An optional integer whose value controls the + shuffling of the file names. If you would like to read from the files in + the same order, set to 0 or False. + num_parallel_reads: An optional integer controlling the number of files to + read from concurrently. (Set to 1 for no parallelism.) + batch_transfer_size: An optional integer controlling the batching used to + amortize the remote function invocation overhead. Set to a very large + number to increase throughput. Set to a very small number to reduce memory + consumption. Set to False to skip batching. + sloppy: (Optional.) If `False`, read input data while maintaining a + deterministic order. (This may have significant performance impacts.) + sloppy defaults to: True. + Returns: + A `tf.data.Dataset` with an infinite stream of elements generated by a + parallel interleaving of the set of files matched (or generated) by `files` + with a type is the output of the dataset specified by `filetype`. + + Raises: + ValueError: if any argument is not of the expected type. + """ + if filetype is None: + filetype = 'tfrecord' + + if isinstance(filetype, str): + if filetype not in _FILETYPE_MAP: + raise ValueError('Unexpected filetype: %s' % filetype) + reader_fn = _FILETYPE_MAP[filetype] + elif callable(filetype): + reader_fn = filetype + else: + raise ValueError('filetype should be a string or a callable') + + file_reader_job = file_reader_job or 'coordinator' + + worker_job = worker_job or 'worker' + + if filename_shuffle_buffer_size is None: + filename_shuffle_buffer_size = 4096 + + num_parallel_reads = num_parallel_reads or 8 + + if batch_transfer_size is None: + batch_transfer_size = 256 + + if sloppy is None: + sloppy = True + + with ops.device('/job:%s' % file_reader_job): + if isinstance(files, str): + source_dataset = dataset_ops.Dataset.list_files(files) + elif isinstance(files, dataset_ops.Dataset): + source_dataset = files + else: + raise ValueError('files was not a string or a dataset: %s' % files) + + if filename_shuffle_buffer_size: + source_dataset = source_dataset.shuffle( + buffer_size=filename_shuffle_buffer_size) + + # NOTE: We perform the `repeat` on the source dataset, because the output + # dataset does not currently have enough information to recreate an iterator + # over the source dataset when it reaches the end. + source_dataset = source_dataset.repeat(num_epochs) + + source_dataset = source_dataset.apply( + interleave_ops.parallel_interleave( + reader_fn, cycle_length=num_parallel_reads, sloppy=sloppy)) + + if batch_transfer_size: + source_dataset = source_dataset.batch(batch_transfer_size) + + source_dataset = source_dataset.prefetch(1) + + source_iterator = source_dataset.make_one_shot_iterator() + source_handle = source_iterator.string_handle() + + @function.Defun(dtypes.string) + def LoadingFunc(h): + remote_iterator = iterator_ops.Iterator.from_string_handle( + h, source_dataset.output_types, source_dataset.output_shapes) + return remote_iterator.get_next() + + def MapFn(unused_input): + return functional_ops.remote_call( + args=[source_handle], + Tout=[dtypes.string], + f=LoadingFunc, + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) + + with ops.device('/job:%s' % worker_job): + output_dataset = dataset_ops.Dataset.range(2).repeat().map( + MapFn, num_parallel_calls=4 if sloppy else None) + output_dataset = output_dataset.prefetch(1) + + if batch_transfer_size: + # Undo the batching used during the transfer. + output_dataset = output_dataset.apply(batching.unbatch()).prefetch(1) + + return output_dataset diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py new file mode 100644 index 0000000000000000000000000000000000000000..918cf0ed8e513de0d4207f7d2aac61ad886c8288 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -0,0 +1,181 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""TPU datasets tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.tpu.python.tpu import datasets +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import readers +from tensorflow.python.lib.io import python_io +from tensorflow.python.platform import test +from tensorflow.python.training import server_lib +from tensorflow.python.util import compat + +_NUM_FILES = 10 +_NUM_ENTRIES = 20 + + +class DatasetsTest(test.TestCase): + + def setUp(self): + super(DatasetsTest, self).setUp() + self._coord = server_lib.Server.create_local_server() + self._worker = server_lib.Server.create_local_server() + + self._cluster_def = cluster_pb2.ClusterDef() + worker_job = self._cluster_def.job.add() + worker_job.name = 'worker' + worker_job.tasks[0] = self._worker.target[len('grpc://'):] + coord_job = self._cluster_def.job.add() + coord_job.name = 'coordinator' + coord_job.tasks[0] = self._coord.target[len('grpc://'):] + + session_config = config_pb2.ConfigProto(cluster_def=self._cluster_def) + + self._sess = session.Session(self._worker.target, config=session_config) + + def testTextLineDataset(self): + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'text_line.%d.txt' % i) + contents = [] + for j in range(_NUM_ENTRIES): + contents.append(compat.as_bytes('%d: %d' % (i, j))) + with open(filename, 'wb') as f: + f.write(b'\n'.join(contents)) + all_contents.extend(contents) + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'text_line.*.txt'), filetype='text') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testTFRecordDataset(self): + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i) + writer = python_io.TFRecordWriter(filename) + for j in range(_NUM_ENTRIES): + record = compat.as_bytes('Record %d of file %d' % (j, i)) + writer.write(record) + all_contents.append(record) + writer.close() + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'tf_record*'), filetype='tfrecord') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testTFRecordDatasetFromDataset(self): + filenames = [] + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i) + filenames.append(filename) + writer = python_io.TFRecordWriter(filename) + for j in range(_NUM_ENTRIES): + record = compat.as_bytes('Record %d of file %d' % (j, i)) + writer.write(record) + all_contents.append(record) + writer.close() + + filenames = dataset_ops.Dataset.from_tensor_slices(filenames) + + dataset = datasets.StreamingFilesDataset(filenames, filetype='tfrecord') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testArbitraryReaderFunc(self): + + def MakeRecord(i, j): + return compat.as_bytes('%04d-%04d' % (i, j)) + + record_bytes = len(MakeRecord(10, 200)) + + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'fixed_length.%d' % i) + with open(filename, 'wb') as f: + for j in range(_NUM_ENTRIES): + record = MakeRecord(i, j) + f.write(record) + all_contents.append(record) + + def FixedLengthFile(filename): + return readers.FixedLengthRecordDataset(filename, record_bytes) + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'fixed_length*'), + filetype=FixedLengthFile) + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testUnexpectedFiletypeString(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), '*'), filetype='foo') + + def testUnexpectedFiletypeType(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), '*'), filetype=3) + + def testUnexpectedFilesType(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset(123, filetype='tfrecord') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/tpu/python/tpu/device_assignment.py b/tensorflow/contrib/tpu/python/tpu/device_assignment.py index ee202610a8a8a1406363b3010771e7806d5d84bf..bdd9b88af55fa4fb483ddbdbe5c51d7076cce675 100644 --- a/tensorflow/contrib/tpu/python/tpu/device_assignment.py +++ b/tensorflow/contrib/tpu/python/tpu/device_assignment.py @@ -87,6 +87,8 @@ class DeviceAssignment(object): core_assignment.shape)) self._core_assignment = core_assignment + self._task_and_cores_to_replicas = self._compute_task_and_cores_to_replicas( + self._core_assignment, self._topology_tasks) def _invert_topology(self, topology): """Inverts a [task,device,axis] topology to [x,y,z] -> task/device maps.""" @@ -100,6 +102,34 @@ class DeviceAssignment(object): devices[x, y, z] = device return tasks, devices + def _compute_task_and_cores_to_replicas(self, core_assignment, + topology_tasks): + """Computes a nested dict which maps task and logical core to replicas.""" + task_and_cores_to_replicas = {} + for replica in xrange(core_assignment.shape[0]): + for dx in xrange(core_assignment.shape[1]): + for dy in xrange(core_assignment.shape[2]): + for dz in xrange(core_assignment.shape[3]): + x, y, z = core_assignment[replica, dx, dy, dz, :] + task_id = topology_tasks[x, y, z] + if task_id not in task_and_cores_to_replicas: + task_and_cores_to_replicas[task_id] = {} + logical_core = (dx, dy, dz) + if logical_core not in task_and_cores_to_replicas[task_id]: + task_and_cores_to_replicas[task_id][logical_core] = set() + + task_and_cores_to_replicas[task_id][logical_core].add(replica) + + task_to_sorted_replica_id = {} + + for task, core_to_replicas in task_and_cores_to_replicas.items(): + core_to_sorted_replicas = {} + for core, replicas in core_to_replicas.items(): + core_to_sorted_replicas[core] = sorted(replicas) + + task_to_sorted_replica_id[task] = core_to_sorted_replicas + return task_to_sorted_replica_id + @property def topology(self): """A `Topology` that describes the TPU topology.""" @@ -119,6 +149,11 @@ class DeviceAssignment(object): """ return self._computation_shape + @property + def num_cores_per_replica(self): + """The number of cores per replica.""" + return np.prod(self.computation_shape) + @property def num_replicas(self): """The number of replicas of the computation.""" @@ -148,6 +183,26 @@ class DeviceAssignment(object): logical_offset = tuple([replica] + logical_core.tolist() + [slice(3)]) return tuple(self.core_assignment[logical_offset]) + def lookup_replicas(self, task_id, logical_core): + """Lookup replica ids by task number and logical core. + + Args: + task_id: TensorFlow task number. + logical_core: A tuple of three integers which represents a logical core. + Returns: + A sorted list of the replicas that are attached to that task and + loical_core. + Raises: + ValueError: If no replica exisis in the task which contains the logical + core. + """ + try: + return self._task_and_cores_to_replicas[task_id][logical_core] + except KeyError: + raise ValueError( + "Can not find any replica in task: {} contains logical_core: {} ". + format(task_id, logical_core)) + def tpu_ordinal(self, replica=0, logical_core=None): """Returns the ordinal of the TPU device assigned to a logical core.""" coordinates = self._coordinates(replica, logical_core) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py index 188db6e2f0d12ed441c043674df9e2e6bec7cc14..009326e3d0a22f995098de9541b5c54efea00807 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py @@ -23,7 +23,10 @@ import collections import json import os +import numpy as np + from tensorflow.contrib.tpu.python.tpu import util as util_lib +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.platform import tf_logging as logging @@ -31,26 +34,40 @@ from tensorflow.python.platform import tf_logging as logging _TF_CONFIG_ENV = run_config_lib._TF_CONFIG_ENV _SERVICE_KEY = run_config_lib._SERVICE_KEY _TPU_WORKER_JOB_NAME = 'tpu_worker_job_name' +_NUM_CORES_PER_HOST = 8 # pylint: enable=protected-access +# TODO(b/72511246) Provide a simplified api to configure model parallelism. class TPUConfig( collections.namedtuple('TPUConfig', [ 'iterations_per_loop', 'num_shards', + 'computation_shape', 'per_host_input_for_training', 'tpu_job_name', 'initial_infeed_sleep_secs', ])): - """TPU related configuration required by `TPUEstimator`. + r"""TPU related configuration required by `TPUEstimator`. Args: - iterations_per_loop: This is the number of train steps runnining in TPU + iterations_per_loop: This is the number of train steps running in TPU system before returning to CPU host for each `Session.run`. This means global step is increased `iterations_per_loop` times in one `Session.run`. It is recommended to be set as number of global steps for next checkpoint. - num_shards: The number of TPU shards in the system. + num_shards: (Deprecated, ignored by TPUEstimator). + The number of model replicas in the system. For non-model-parallelism + case, this number equals the total number of TPU cores. For + model-parallelism, the total number of TPU cores equals + product(computation_shape) * num_shards. + computation_shape: Defaults to `None`, which disables model parallelism. A + list of size 3 which describes the shape of a model replica's block of + cores. This is required by model-parallelism which enables partitioning + the model to multiple cores. For example, [2, 2, 1] means the model is + partitioned across 4 cores which span two cores in both x and y + coordinates. Please refer to @{tf.contrib.tpu.Topology} for the + geometry of a TPU mesh. per_host_input_for_training: If `True`, `input_fn` is invoked Per-Host rather than Per-Core. With Per-Host input pipeline deployment, `input_fn` is invoked once on each host. With Per-Core input pipeline deployment, it @@ -65,11 +82,15 @@ class TPUConfig( initial_infeed_sleep_secs: The number of seconds the infeed thread should wait before enqueueing the first batch. This helps avoid timeouts for models that require a long compilation time. + + Raises: + ValueError: If `computation_shape` or `computation_shape` are invalid. """ def __new__(cls, iterations_per_loop=2, - num_shards=2, + num_shards=None, + computation_shape=None, per_host_input_for_training=True, tpu_job_name=None, initial_infeed_sleep_secs=None): @@ -79,7 +100,22 @@ class TPUConfig( 'TPUConfig iterations_per_loop') # Check num_shards. - util_lib.check_positive_integer(num_shards, 'TPUConfig num_shards') + if num_shards is not None: + util_lib.check_positive_integer(num_shards, 'TPUConfig num_shards') + + # Check computation_shape + if computation_shape is not None and len(computation_shape) != 3: + raise ValueError( + 'computation_shape must be a list with length 3 or None; got {}'. + format(str(computation_shape))) + + if computation_shape is not None: + computation_shape_array = np.asarray(computation_shape, dtype=np.int32) + # This prevents any computation being replicated across multiple hosts, so + # that each host feeds the same number of computations. + if any(computation_shape_array < 1) or any(computation_shape_array > 2): + raise ValueError('computation_shape elements can only be 1 or 2; got ' + 'computation_shape={}'.format(computation_shape)) # Check initial_infeed_sleep_secs. if initial_infeed_sleep_secs: @@ -92,6 +128,7 @@ class TPUConfig( cls, iterations_per_loop=iterations_per_loop, num_shards=num_shards, + computation_shape=computation_shape, per_host_input_for_training=per_host_input_for_training, tpu_job_name=tpu_job_name, initial_infeed_sleep_secs=initial_infeed_sleep_secs) @@ -104,6 +141,7 @@ class RunConfig(run_config_lib.RunConfig): tpu_config=None, evaluation_master=None, master=None, + cluster=None, **kwargs): """Constructs a RunConfig. @@ -112,16 +150,26 @@ class RunConfig(run_config_lib.RunConfig): evaluation_master: a string. The address of the master to use for eval. Defaults to master if not set. master: a string. The address of the master to use for training. - tf_random_seed: an int. Sets the TensorFlow random seed. Defaults to None, - which initializes it randomly based on the environment. + cluster: a ClusterResolver + **kwargs: keyword config parameters. + + Raises: + ValueError: if cluster is not None and the provided session_config has a + cluster_def already. """ super(RunConfig, self).__init__(**kwargs) self._tpu_config = tpu_config or TPUConfig() + self._cluster = cluster # If user sets master and/or evaluation_master explicilty, including empty # string '', take it. Otherwise, take the values set by parent class. if master is not None: + if cluster is not None: + raise ValueError('Both master and cluster are set.') self._master = master + else: + if cluster: + self._master = cluster.master() if evaluation_master is not None: self._evaluation_master = evaluation_master @@ -135,6 +183,20 @@ class RunConfig(run_config_lib.RunConfig): # evaluation_master to master, unless user overwrites it. self._evaluation_master = self._master + # Set the ClusterSpec to use + if cluster: + self._cluster_spec = cluster.cluster_spec() + + # Merge the cluster_def into the ConfigProto. + if self._session_config is None: # pylint: disable=access-member-before-definition + self._session_config = config_pb2.ConfigProto(allow_soft_placement=True) + if self._session_config.HasField('cluster_def'): + raise ValueError( + 'You cannot provide a ClusterResolver and ' + 'session_config.cluster_def.') + self._session_config.cluster_def.CopyFrom( + self._cluster_spec.as_cluster_def()) + @property def evaluation_master(self): return self._evaluation_master @@ -147,6 +209,10 @@ class RunConfig(run_config_lib.RunConfig): def tpu_config(self): return self._tpu_config + @property + def cluster(self): + return self._cluster + def replace(self, **kwargs): if 'tpu_config' not in kwargs: return super(RunConfig, self).replace(**kwargs) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py index 60884aa32f932413b49ea2193a145828489ea04c..37ef3dbe1e66efe18b13ab9153ee346c08b9774a 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py @@ -43,6 +43,16 @@ class TPURunConfigTest(test.TestCase): tpu_config_lib.RunConfig( tpu_config=tpu_config_lib.TPUConfig(iterations_per_loop=0)) + def test_fail_with_invalid_computation_shape(self): + with self.assertRaisesRegexp(ValueError, + 'computation_shape must be a list with length' + ' 3 or None'): + tpu_config_lib.TPUConfig(computation_shape=[2, 1]) + + with self.assertRaisesRegexp(ValueError, + 'computation_shape elements can only be'): + tpu_config_lib.TPUConfig(computation_shape=[1, 3, 1]) + class TPURunConfigMasterTest(test.TestCase): diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py new file mode 100644 index 0000000000000000000000000000000000000000..c5c46ea741ea64ca37089431f8ed66cad7bc31fb --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -0,0 +1,510 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# =================================================================== +"""TPU system metdata and associated tooling.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from contextlib import contextmanager +import copy + +import numpy as np + +from tensorflow.contrib.tpu.python.tpu import device_assignment as tpu_device_assignment +from tensorflow.contrib.tpu.python.tpu import tpu_system_metadata as tpu_system_metadata_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.platform import tf_logging as logging + + +_DEFAULT_JOB_NAME = 'tpu_worker' +_DEFAULT_COORDINATOR_JOB_NAME = 'coordinator' +_LOCAL_MASTERS = ('', 'local') + + +class _TPUContext(object): + """A context holds immutable states of TPU computation. + + This immutable object holds TPUEstimator config, train/eval batch size, and + `TPUEstimator.use_tpu`, which is expected to be passed around. It also + provides utility functions, basded on the current state, to determine other + information commonly required by TPU computation, such as TPU device names, + TPU hosts, shard batch size, etc. + + N.B. As `mode` is not immutable state in Estimator, but essential to + distinguish between TPU training and evaluation, a common usage for + _TPUContext with `mode` is as follows: + ``` + with _ctx.with_mode(mode) as ctx: + if ctx.is_running_on_cpu(): + ... + ``` + """ + + def __init__(self, config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu): + self._config = config + self._train_batch_size = train_batch_size + self._eval_batch_size = eval_batch_size + self._predict_batch_size = predict_batch_size + self._use_tpu = use_tpu + self._model_parallelism_enabled = ( + use_tpu and config.tpu_config.computation_shape) + self._mode = None + + self._lazy_tpu_system_metadata_dict = {} # key by master address + self._lazy_device_assignment_dict = {} # key by master address + self._lazy_validation_dict = {} # key by ModeKeys + + def _assert_mode(self): + if self._mode is None: + raise RuntimeError( + '`mode` needs to be set via contextmanager `with_mode`.') + return self._mode + + @contextmanager + def with_mode(self, mode): + # NOTE(xiejw): Shallow copy is enough. It will share he lazy dictionaries, + # such as _lazy_tpu_system_metadata_dict between new copy and the original + # one. Note that all lazy states stored in properties _lazy_foo are sort of + # immutable as they should be same for the process lifetime. + new_ctx = copy.copy(self) + new_ctx._mode = mode # pylint: disable=protected-access + yield new_ctx + + @property + def mode(self): + return self._assert_mode() + + def _get_master_address(self): + mode = self._assert_mode() + config = self._config + master = ( + config.master + if mode != model_fn_lib.ModeKeys.EVAL else config.evaluation_master) + return master + + def _get_tpu_system_metadata(self): + """Gets the (maybe cached) TPU system metadata.""" + master = self._get_master_address() + tpu_system_metadata = self._lazy_tpu_system_metadata_dict.get(master) + if tpu_system_metadata is not None: + return tpu_system_metadata + + # pylint: disable=protected-access + tpu_system_metadata = ( + tpu_system_metadata_lib._query_tpu_system_metadata( + master, + run_config=self._config, + query_topology=self.model_parallelism_enabled)) + + self._lazy_tpu_system_metadata_dict[master] = tpu_system_metadata + return tpu_system_metadata + + def _get_device_assignment(self): + """Gets the (maybe cached) TPU device assignment.""" + master = self._get_master_address() + device_assignment = self._lazy_device_assignment_dict.get(master) + if device_assignment is not None: + return device_assignment + + tpu_system_metadata = self._get_tpu_system_metadata() + + device_assignment = tpu_device_assignment.device_assignment( + tpu_system_metadata.topology, + computation_shape=self._config.tpu_config.computation_shape, + num_replicas=self.num_replicas) + + logging.info('computation_shape: %s', + str(self._config.tpu_config.computation_shape)) + logging.info('num_replicas: %d', self.num_replicas) + logging.info('device_assignment.topology.device_coordinates: %s', + str(device_assignment.topology.device_coordinates)) + logging.info('device_assignment.core_assignment: %s', + str(device_assignment.core_assignment)) + + self._lazy_device_assignment_dict[master] = device_assignment + return device_assignment + + @property + def model_parallelism_enabled(self): + return self._model_parallelism_enabled + + @property + def device_assignment(self): + return (self._get_device_assignment() + if self._model_parallelism_enabled else None) + + @property + def num_of_cores_per_host(self): + metadata = self._get_tpu_system_metadata() + return metadata.num_of_cores_per_host + + @property + def num_cores(self): + metadata = self._get_tpu_system_metadata() + return metadata.num_cores + + @property + def num_of_replicas_per_host(self): + if self.model_parallelism_enabled: + return self.num_replicas // self.num_hosts + else: + return self.num_of_cores_per_host + + @property + def num_replicas(self): + num_cores_in_system = self.num_cores + + if self.model_parallelism_enabled: + computation_shape_array = np.asarray( + self._config.tpu_config.computation_shape, dtype=np.int32) + num_cores_per_replica = np.prod(computation_shape_array) + if num_cores_per_replica > num_cores_in_system: + raise ValueError( + 'The num of cores required by the model parallelism, specified by ' + 'TPUConfig.computation_shape, is larger than the total num of ' + 'TPU cores in the system. computation_shape: {}, num cores ' + 'in the system: {}'.format( + self._config.tpu_config.computation_shape, + num_cores_in_system)) + + if num_cores_in_system % num_cores_per_replica != 0: + raise RuntimeError( + 'The num of cores in the system ({}) is not divisible by the num ' + 'of cores ({}) required by the model parallelism, specified by ' + 'TPUConfig.computation_shape. This should never happen!'.format( + num_cores_in_system, num_cores_per_replica)) + + return num_cores_in_system // num_cores_per_replica + else: + return num_cores_in_system + + @property + def num_hosts(self): + metadata = self._get_tpu_system_metadata() + return metadata.num_hosts + + @property + def config(self): + return self._config + + def is_input_sharded_per_core(self): + """Return true if input_fn is invoked per-core (other than per-host).""" + mode = self._assert_mode() + return (mode == model_fn_lib.ModeKeys.TRAIN and + not self._config.tpu_config.per_host_input_for_training) + + def is_running_on_cpu(self, is_export_mode=False): + """Determines whether the input_fn and model_fn should be invoked on CPU. + + This API also validates user provided configuration, such as batch size, + according the lazy initialized TPU system metadata. + + Args: + is_export_mode: Indicates whether the current mode is for exporting the + model, when mode == PREDICT. Only with this bool, we could + tell whether user is calling the Estimator.predict or + Estimator.export_savedmodel, which are running on TPU and CPU + respectively. Parent class Estimator does not distingush these two. + + Returns: + bool, whether current input_fn or model_fn should be running on CPU. + + Raises: + ValueError: any configuration is invalid. + """ + + is_running_on_cpu = self._is_running_on_cpu(is_export_mode) + if not is_running_on_cpu: + self._validate_tpu_configuration() + return is_running_on_cpu + + def _is_running_on_cpu(self, is_export_mode): + """Determines whether the input_fn and model_fn should be invoked on CPU.""" + mode = self._assert_mode() + + if not self._use_tpu: + return True + + if mode != model_fn_lib.ModeKeys.PREDICT: + return False + + # There are actually 2 use cases when running with mode.PREDICT: prediction + # and saving the model. We run actual predictions on the TPU, but + # model export is run on the CPU. + if is_export_mode: + return True + + return False + + @property + def global_batch_size(self): + mode = self._assert_mode() + if mode == model_fn_lib.ModeKeys.TRAIN: + return self._train_batch_size + elif mode == model_fn_lib.ModeKeys.EVAL: + return self._eval_batch_size + elif mode == model_fn_lib.ModeKeys.PREDICT: + return self._predict_batch_size + else: + return None + + @property + def batch_size_for_input_fn(self): + """Returns the shard batch size for `input_fn`.""" + global_batch_size = self.global_batch_size + + if self.is_running_on_cpu(): + return global_batch_size + + # On TPU + if self.is_input_sharded_per_core(): + # We prohibit per core input sharding for the model parallelism case, + # therefore it is safe to use num_cores here. + return global_batch_size // self.num_cores + else: + return global_batch_size // self.num_hosts + + @property + def batch_size_for_model_fn(self): + """Returns the shard batch size for `model_fn`.""" + global_batch_size = self.global_batch_size + + if self.is_running_on_cpu(): + return global_batch_size + + # On TPU. always sharded per shard. + return global_batch_size // self.num_replicas + + @property + def master_job(self): + """Returns the job name to use to place TPU computations on. + + Returns: + A string containing the job name, or None if no job should be specified. + + Raises: + ValueError: If the user needs to specify a tpu_job_name, because we are + unable to infer the job name automatically, or if the user-specified job + names are inappropriate. + """ + run_config = self._config + # If the user specifies the tpu_job_name, use that. + if run_config.tpu_config.tpu_job_name: + return run_config.tpu_config.tpu_job_name + + # The tpu job is determined by the run_config. Right now, this method is + # required as tpu_config is not part of the RunConfig. + mode = self._assert_mode() + master = ( + run_config.evaluation_master + if mode == model_fn_lib.ModeKeys.EVAL else run_config.master) + if master in _LOCAL_MASTERS: + return None + + if (not run_config.session_config or + not run_config.session_config.cluster_def.job): + return _DEFAULT_JOB_NAME + cluster_def = run_config.session_config.cluster_def + job_names = set([job.name for job in cluster_def.job]) + if _DEFAULT_JOB_NAME in job_names: + # b/37868888 tracks allowing ClusterSpec propagation to reuse job names. + raise ValueError('Currently, tpu_worker is not an allowed job name.') + if len(job_names) == 1: + return cluster_def.job[0].name + if len(job_names) == 2: + if _DEFAULT_COORDINATOR_JOB_NAME in job_names: + job_names.remove(_DEFAULT_COORDINATOR_JOB_NAME) + return job_names.pop() + # TODO(b/67716447): Include more sophisticated heuristics. + raise ValueError( + 'Could not infer TPU job name. Please specify a tpu_job_name as part ' + 'of your TPUConfig.') + + @property + def tpu_host_placement_function(self): + """Returns the TPU host place function.""" + master = self.master_job + + def _placement_function(_sentinal=None, core_id=None, host_id=None): # pylint: disable=invalid-name + assert _sentinal is None + if core_id is not None and host_id is not None: + raise RuntimeError( + 'core_id and host_id can have only one non-None value.') + + if master is None: + return '/replica:0/task:0/device:CPU:0' + else: + if core_id is not None: + host_id = core_id / self.num_of_cores_per_host + return '/job:%s/task:%d/device:CPU:0' % (master, host_id) + + return _placement_function + + @property + def tpu_device_placement_function(self): + """Returns a TPU device placement Fn.""" + master = self.master_job + job_device = '' if master is None else ('/job:%s' % master) + + def _placement_function(i): + if self.model_parallelism_enabled: + return self.device_assignment.tpu_device(replica=i, job=master) + else: + num_of_cores_per_host = self.num_of_cores_per_host + host_id = i / num_of_cores_per_host + ordinal_id = i % num_of_cores_per_host + return '%s/task:%d/device:TPU:%d' % (job_device, host_id, ordinal_id) + + return _placement_function + + @property + def tpu_ordinal_function(self): + """Returns the TPU ordinal fn.""" + + def _tpu_ordinal_function(index): + """Return the TPU ordinal associated with a shard. + + Required because the enqueue ops are placed on CPU. + + Args: + index: the shard index + + Returns: + The ordinal of the TPU device the shard's infeed should be placed on. + """ + if self.model_parallelism_enabled: + return self.device_assignment.tpu_ordinal(replica=index) + else: + return index % self.num_of_cores_per_host + + return _tpu_ordinal_function + + def _validate_tpu_configuration(self): + """Validates the configuration based on the TPU system metadata.""" + mode = self._assert_mode() + if self._lazy_validation_dict.get(mode): + return + + # All following information is obtained from TPU system metadata. + num_cores = self.num_cores + num_replicas = self.num_replicas + num_hosts = self.num_hosts + + if not num_cores: + tpu_system_metadata = self._get_tpu_system_metadata() + raise RuntimeError( + 'Cannot find any TPU cores in the system. Please double check ' + 'Tensorflow master address and TPU worker(s). Available devices ' + 'are {}.'.format(tpu_system_metadata.devices)) + + if self._config.tpu_config.num_shards: + user_provided_num_replicas = self._config.tpu_config.num_shards + if user_provided_num_replicas != num_replicas: + message = ( + 'TPUConfig.num_shards is not set correctly. According to TPU ' + 'system metadata for Tensorflow master ({}): num_replicas should ' + 'be ({}), got ({}). For non-model-parallelism, num_replicas should ' + 'be the total num of TPU cores in the system. For ' + 'model-parallelism, the total number of TPU cores should be ' + 'product(computation_shape) * num_replicas. Please set it ' + 'accordingly or leave it as `None`'.format( + self._get_master_address(), num_replicas, + user_provided_num_replicas)) + + raise ValueError(message) + + if mode == model_fn_lib.ModeKeys.TRAIN: + if self._train_batch_size % num_replicas != 0: + raise ValueError( + 'train batch size {} must be divisible by number of replicas {}' + .format(self._train_batch_size, num_replicas)) + + elif mode == model_fn_lib.ModeKeys.EVAL: + if self._eval_batch_size is None: + raise ValueError( + 'eval_batch_size in TPUEstimator constructor cannot be `None`' + 'if .evaluate is running on TPU.') + if self._eval_batch_size % num_replicas != 0: + raise ValueError( + 'eval batch size {} must be divisible by number of replicas {}' + .format(self._eval_batch_size, num_replicas)) + if num_hosts > 1: + raise ValueError( + 'TPUEstimator.evaluate should be running on single TPU worker. ' + 'got {}.'.format(num_hosts)) + else: + assert mode == model_fn_lib.ModeKeys.PREDICT + if self._predict_batch_size is None: + raise ValueError( + 'predict_batch_size in TPUEstimator constructor should not be ' + '`None` if .predict is running on TPU.') + if self._predict_batch_size % num_replicas != 0: + raise ValueError( + 'predict batch size {} must be divisible by number of replicas {}' + .format(self._predict_batch_size, num_replicas)) + if num_hosts > 1: + raise ValueError( + 'TPUEstimator.predict should be running on single TPU worker. ' + 'got {}.'.format(num_hosts)) + + # Record the state "validated" into lazy dictionary. + self._lazy_validation_dict[mode] = True + + +class _OneCoreTPUContext(_TPUContext): + """Special _TPUContext for one core usage.""" + + def __init__(self, config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu): + + super(_OneCoreTPUContext, self).__init__( + config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu) + + def _get_tpu_system_metadata(self): + """Gets the (maybe cached) TPU system metadata.""" + master = self._get_master_address() + tpu_system_metadata = self._lazy_tpu_system_metadata_dict.get(master) + if tpu_system_metadata is not None: + return tpu_system_metadata + + tpu_system_metadata = ( + tpu_system_metadata_lib._TPUSystemMetadata( # pylint: disable=protected-access + num_cores=1, + num_hosts=1, + num_of_cores_per_host=1, + topology=None, + devices=[])) + + self._lazy_tpu_system_metadata_dict[master] = tpu_system_metadata + return tpu_system_metadata + + +def _get_tpu_context(config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu): + """Returns an instance of `_TPUContext`.""" + + if (config.tpu_config.num_shards == 1 and + config.tpu_config.computation_shape is None): + logging.warning( + 'Setting TPUConfig.num_shards==1 is an unsupported behavior. ' + 'Please fix as soon as possible (leaving num_shards as None.') + return _OneCoreTPUContext(config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu) + + return _TPUContext(config, train_batch_size, eval_batch_size, + predict_batch_size, use_tpu) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 56793f11d9c54d00c9cb2a535669de9aec5315e3..f3c2a510fd62aa3a49089c52587a763ac53bbd13 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import collections -from contextlib import contextmanager import copy import signal import threading @@ -34,13 +33,12 @@ from tensorflow.contrib.summary import summary_ops as contrib_summary from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_config +from tensorflow.contrib.tpu.python.tpu import tpu_context from tensorflow.contrib.tpu.python.tpu import tpu_feed from tensorflow.contrib.tpu.python.tpu import training_loop from tensorflow.contrib.tpu.python.tpu import util as util_lib - from tensorflow.core.framework.summary_pb2 import Summary from tensorflow.core.protobuf import config_pb2 - from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import model_fn as model_fn_lib @@ -71,6 +69,7 @@ _TPU_ESTIMATOR = 'tpu_estimator' _ITERATIONS_PER_LOOP_VAR = 'iterations_per_loop' _BATCH_SIZE_KEY = 'batch_size' _CROSS_REPLICA_SUM_OP = 'CrossReplicaSum' + _RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY] @@ -148,254 +147,6 @@ def _increase_eval_step_op(iterations_per_loop): use_locking=True) -_DEFAULT_JOB_NAME = 'tpu_worker' -_DEFAULT_COORDINATOR_JOB_NAME = 'coordinator' -_LOCAL_MASTERS = ('', 'local') - - -class _TPUContext(object): - """A context holds immutable states of TPU computation. - - This immutable object holds TPUEstimator config, train/eval batch size, and - `TPUEstimator.use_tpu`, which is expected to be passed around. It also - provides utility functions, basded on the current state, to determine other - information commonly required by TPU computation, such as TPU device names, - TPU hosts, shard batch size, etc. - - N.B. As `mode` is not immutable state in Estimator, but essential to - distinguish between TPU training and evaluation, a common usage for - _TPUContext with `mode` is as follows: - ``` - with _ctx.with_mode(mode) as ctx: - if ctx.is_running_on_cpu(): - ... - ``` - """ - - def __init__(self, config, train_batch_size, eval_batch_size, - predict_batch_size, use_tpu): - self._config = config - self._train_batch_size = train_batch_size - self._eval_batch_size = eval_batch_size - self._predict_batch_size = predict_batch_size - self._use_tpu = use_tpu - self._num_shards_or_none = self._config.tpu_config.num_shards - self._mode = None - - def _assert_mode(self): - if self._mode is None: - raise RuntimeError( - '`mode` needs to be set via contextmanager `with_mode`.') - return self._mode - - @property - def num_of_cores_per_host(self): - num_cores = self.num_cores - return min(num_cores, 8) - - @contextmanager - def with_mode(self, mode): - new_ctx = copy.copy(self) # Shallow copy is enough. - new_ctx._mode = mode # pylint: disable=protected-access - yield new_ctx - - @property - def mode(self): - return self._assert_mode() - - @property - def num_cores(self): - # TODO(xiejw): Adds lazy num_shards initialization. - return self._num_shards_or_none - - @property - def num_hosts(self): - return self.num_cores // self.num_of_cores_per_host - - @property - def config(self): - return self._config - - def is_input_sharded_per_core(self): - """Return true if input_fn is invoked per-core (other than per-host).""" - self._assert_mode() - return (self._mode == model_fn_lib.ModeKeys.TRAIN and - not self._config.tpu_config.per_host_input_for_training) - - def is_running_on_cpu(self, is_export_mode=False): - """Determines whether the input_fn and model_fn should be invoked on CPU. - - Args: - is_export_mode: Indicates whether the current mode is for exporting the - model, when mode == PREDICT. Only with this bool, we could - tell whether user is calling the Estimator.predict or - Estimator.export_savedmodel, which are running on TPU and CPU - respectively. Parent class Estimator does not distingush these two. - - Returns: - bool, whether current input_fn or model_fn should be running on CPU. - - Raises: - ValueError: any configuration is invalid. - """ - mode = self._assert_mode() - - if not self._use_tpu: - return True - - if mode != model_fn_lib.ModeKeys.PREDICT: - return False - - # There are actually 2 use cases when running with mode.PREDICT: prediction - # and saving the model. We run actual predictions on the TPU, but - # model export is run on the CPU. - if is_export_mode: - return True - - if self._predict_batch_size is None: - raise ValueError( - 'predict_batch_size in TPUEstimator constructor should not be ' - '`None` if .predict is running on TPU.') - if self.num_hosts > 1: - raise ValueError( - 'TPUEstimator.predict should be running on single host.') - - return False - - @property - def global_batch_size(self): - mode = self._assert_mode() - if mode == model_fn_lib.ModeKeys.TRAIN: - return self._train_batch_size - elif mode == model_fn_lib.ModeKeys.EVAL: - return self._eval_batch_size - elif mode == model_fn_lib.ModeKeys.PREDICT: - return self._predict_batch_size - else: - return None - - @property - def batch_size_for_input_fn(self): - """Returns the shard batch size for `input_fn`.""" - global_batch_size = self.global_batch_size - - if self.is_running_on_cpu(): - return global_batch_size - - # On TPU - if self.is_input_sharded_per_core(): - return global_batch_size // self.num_cores - else: - return global_batch_size // self.num_hosts - - @property - def batch_size_for_model_fn(self): - """Returns the shard batch size for `model_fn`.""" - global_batch_size = self.global_batch_size - - if self.is_running_on_cpu(): - return global_batch_size - - # On TPU. always sharded per core. - return global_batch_size // self.num_cores - - @property - def master_job(self): - """Returns the job name to use to place TPU computations on. - - Returns: - A string containing the job name, or None if no job should be specified. - - Raises: - ValueError: If the user needs to specify a tpu_job_name, because we are - unable to infer the job name automatically, or if the user-specified job - names are inappropriate. - """ - run_config = self._config - # If the user specifies the tpu_job_name, use that. - if run_config.tpu_config.tpu_job_name: - return run_config.tpu_config.tpu_job_name - - # The tpu job is determined by the run_config. Right now, this method is - # required as tpu_config is not part of the RunConfig. - mode = self._assert_mode() - master = ( - run_config.evaluation_master - if mode == model_fn_lib.ModeKeys.EVAL else run_config.master) - if master in _LOCAL_MASTERS: - return None - - if (not run_config.session_config or - not run_config.session_config.cluster_def.job): - return _DEFAULT_JOB_NAME - cluster_def = run_config.session_config.cluster_def - job_names = set([job.name for job in cluster_def.job]) - if _DEFAULT_JOB_NAME in job_names: - # b/37868888 tracks allowing ClusterSpec propagation to reuse job names. - raise ValueError('Currently, tpu_worker is not an allowed job name.') - if len(job_names) == 1: - return cluster_def.job[0].name - if len(job_names) == 2: - if _DEFAULT_COORDINATOR_JOB_NAME in job_names: - job_names.remove(_DEFAULT_COORDINATOR_JOB_NAME) - return job_names.pop() - # TODO(b/67716447): Include more sophisticated heuristics. - raise ValueError( - 'Could not infer TPU job name. Please specify a tpu_job_name as part ' - 'of your TPUConfig.') - - @property - def tpu_host_placement_function(self): - """Returns the TPU host place function.""" - master = self.master_job - - def _placement_function(_sentinal=None, core_id=None, host_id=None): # pylint: disable=invalid-name - assert _sentinal is None - if core_id is not None and host_id is not None: - raise RuntimeError( - 'core_id and host_id can have only one non-None value.') - - if master is None: - return '/replica:0/task:0/device:CPU:0' - else: - # This assumes that if using more than 8 shards, - # the job configuration varies 'task'. - if core_id is not None: - host_id = core_id / 8 - return '/job:%s/task:%d/device:CPU:0' % (master, host_id) - - return _placement_function - - @property - def tpu_device_placement_function(self): - master = self.master_job - job_device = '' if master is None else ('/job:%s' % master) - - def _placement_function(i): - return '%s/task:%d/device:TPU:%d' % (job_device, i / 8, i % 8) - - return _placement_function - - @property - def tpu_ordinal_function(self): - """Returns the TPU ordinal fn.""" - - def _tpu_ordinal_function(index): - """Return the TPU ordinal associated with a shard. - - Required because the enqueue ops are placed on CPU. - - Args: - index: the shard index - - Returns: - The ordinal of the TPU device the shard's infeed should be placed on. - """ - return index % 8 - - return _tpu_ordinal_function - - class _SIGNAL(object): """Signal used to control the thread of infeed/outfeed. @@ -485,7 +236,7 @@ class TPUEstimatorSpec( if self.eval_metrics is not None: host_calls['eval_metrics'] = self.eval_metrics if self.host_call is not None: - host_calls['host_call'] = wrap_hostcall_with_global_step(self.host_call) + host_calls['host_call'] = self.host_call host_call_ret = _OutfeedHostCall.create_cpu_hostcall(host_calls) eval_metric_ops = None if self.eval_metrics is not None: @@ -628,7 +379,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): # for TPU computation waits for the infeed enqueue forever. Close the # Session to cancel the main thread Session.run execution. # - # However, sleep for 2 minutes before explicit closing to give some time + # We sleep for a few seconds before closing to give some time # for the TPU compilation error, if any, propagating, from TPU to CPU # host. Compilation errors should be reported by the main thread so that # the program can be interrupted and users can take action. Due to a race @@ -641,7 +392,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): # If the main session is still running, the infeed/outfeed errors are # legitimate, and should be logged. - if not self._finished: + if not self._finished and self._feed_error: logging.error('Feed error: %s', self._feed_error) logging.error('Closing session. A RuntimeError should follow.') session.close() @@ -699,10 +450,12 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): name='OutfeedController', target=self._run_outfeed, args=(session,)) def before_run(self, run_context): - if self._feed_error: - logging.warning('Feed error occurred, terminating session.') - run_context.request_stop() - return + self._feed_error = None + + # Wait for the cancellation timer to complete before continuing. + if self._session_cancel_timer: + self._session_cancel_timer.join() + self._session_cancel_timer = None iterations = run_context.session.run(self._iterations_per_loop_var) @@ -908,7 +661,7 @@ def generate_per_core_enqueue_ops_fn_for_host(ctx, input_fn, def generate_per_host_enqueue_ops_fn_for_host( - ctx, input_fn, inputs_structure_recorder, batch_axis, device): + ctx, input_fn, inputs_structure_recorder, batch_axis, device, host_id): """Generates infeed enqueue ops for per-host input_fn on a single host.""" captured_infeed_queue = _CapturedObject() @@ -929,9 +682,23 @@ def generate_per_host_enqueue_ops_fn_for_host( if is_dataset: hooks.append(inputs.dataset_initializer_hook()) + # TODO(ylc): Refactoring the code to merge the tpu ordinal logic here and the + # _TPUContext.tpu_ordinal_function. We should either introduce another + # abstraction or a different helper method. + def _tpu_ordinal_function_impl(shard_index_in_host): + # We put both enqueue/dequeue op at tpu.core(0) in each replica. + replica = ctx.device_assignment.lookup_replicas( + host_id, (0, 0, 0))[shard_index_in_host] + return ctx.device_assignment.tpu_ordinal(replica=replica) + + if ctx.model_parallelism_enabled: + tpu_ordinal_function = _tpu_ordinal_function_impl + else: + tpu_ordinal_function = None + def enqueue_ops_fn(): with ops.device(device): - num_cores_per_host = ctx.num_of_cores_per_host + num_of_replicas_per_host = ctx.num_of_replicas_per_host # Convert user input to features and labels. If the user returns a # dataset, it is initialized and the features and labels extracted via # `dataset.iterator.get_next()` @@ -949,11 +716,12 @@ def generate_per_host_enqueue_ops_fn_for_host( tuple_shapes=[t.shape for t in unsharded_tensor_list], shard_dimensions=batch_axis) captured_infeed_queue.capture(infeed_queue) - infeed_queue.set_number_of_shards(num_cores_per_host) - + infeed_queue.set_number_of_shards(num_of_replicas_per_host) per_host_enqueue_ops = ( infeed_queue.split_inputs_and_generate_enqueue_ops( - unsharded_tensor_list, placement_function=lambda x: device)) + unsharded_tensor_list, + placement_function=lambda x: device, + tpu_ordinal_function=tpu_ordinal_function)) if signals is None: return per_host_enqueue_ops else: @@ -1016,7 +784,8 @@ class _InputPipeline(object): def _extract_key_names(tensor_or_dict): if tensor_or_dict is None: return [] - return tensor_or_dict.keys() if isinstance(tensor_or_dict, dict) else [] + return sorted(tensor_or_dict.keys()) if isinstance( + tensor_or_dict, dict) else [] # Extract structure. has_labels = labels is not None @@ -1152,7 +921,11 @@ class _InputPipeline(object): def dequeue_fn(): """dequeue_fn is used by TPU to retrieve the tensors.""" - values = self._infeed_queue.generate_dequeue_op() + # In the model-parallel case, both the host-side and device-side + # computations must agree on the core on which infeed takes place. We + # choose to perform infeed on logical core 0 of each replica. + with ops.device(tpu.core(0)): + values = self._infeed_queue.generate_dequeue_op() # The unflatten process uses the structure information recorded above. return self._inputs_structure_recorder.unflatten_features_and_labels( values) @@ -1199,7 +972,7 @@ class _InputPipeline(object): enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset = ( generate_per_host_enqueue_ops_fn_for_host( self._ctx, self._input_fn, self._inputs_structure_recorder, - self._batch_axis, host_device)) + self._batch_axis, host_device, host_id)) all_hooks.extend(hooks) # NOTE(xiejw): We dispatch here based on the return type of the @@ -1303,19 +1076,21 @@ class _ModelFnWrapper(object): self._call_model_fn(features, labels)) loss, train_op = estimator_spec.loss, estimator_spec.train_op - host_call_outfeed_ops = [] if isinstance(estimator_spec, TPUEstimatorSpec): captured_scaffold_fn.capture(estimator_spec.scaffold_fn) - if estimator_spec.host_call is not None: - host_call.record({ - 'host_call': wrap_hostcall_with_global_step( - estimator_spec.host_call)}) - host_call_outfeed_ops = host_call.create_enqueue_op() else: captured_scaffold_fn.capture(None) - with ops.control_dependencies([train_op] + host_call_outfeed_ops): - return array_ops.identity(loss) + # We must run train_op to update the variables prior to running the + # outfeed. + with ops.control_dependencies([train_op]): + host_call_outfeed_ops = [] + if (isinstance(estimator_spec, TPUEstimatorSpec) and + estimator_spec.host_call is not None): + host_call.record({'host_call': estimator_spec.host_call}) + host_call_outfeed_ops = host_call.create_enqueue_op() + with ops.control_dependencies(host_call_outfeed_ops): + return array_ops.identity(loss) return train_step, host_call, captured_scaffold_fn @@ -1574,7 +1349,9 @@ class _OutfeedHostCall(object): # TODO(jhseu): Consider deduping tensors. for name in self._names: tensors.extend(self._tensors[name]) - return [tpu_ops.outfeed_enqueue_tuple(tensors)] + + with ops.device(tpu.core(0)): + return [tpu_ops.outfeed_enqueue_tuple(tensors)] def create_tpu_hostcall(self): """Sends the tensors through outfeed and runs the host_fn on CPU. @@ -1607,10 +1384,11 @@ class _OutfeedHostCall(object): for shape in self._tensor_shapes[name]: tensor_shapes.append(shape) - # Outfeed ops execute on each JF node. Note: we must constraint it such that - # we have at most one outfeed dequeue and enqueue. + # Outfeed ops execute on each replica's first logical core. Note: we must + # constraint it such that we have at most one outfeed dequeue and enqueue + # per replica. tpu_device_placement_fn = self._ctx.tpu_device_placement_function - for i in xrange(self._ctx.num_cores): + for i in xrange(self._ctx.num_replicas): with ops.device(tpu_device_placement_fn(i)): outfeed_tensors = tpu_ops.outfeed_dequeue_tuple( dtypes=tensor_dtypes, shapes=tensor_shapes) @@ -1657,38 +1435,6 @@ class _OutfeedHostCall(object): return ret -def wrap_hostcall_with_global_step(hostcall): - """Wrap the hostcall so that we update the global step upon every call.""" - if hostcall is None: - return None - host_fn, tensors = hostcall - - def global_step_host_fn(_global_step, *args, **kwargs): # pylint: disable=invalid-name - # Note that we don't have any ordering here, so the graph may see a - # global_step that's off by 1. - state_ops.assign( - training.get_global_step(), - math_ops.cast(_global_step[0], dtypes.int64)) - return host_fn(*args, **kwargs) - # Give the global step tensor a batch dimension. Reshape is not supported for - # int64, so we cast it to int32. - # TODO(jhseu): Remove the cast once int64 is supported. - global_step_tensor = array_ops.reshape( - math_ops.cast(training.get_global_step(), dtypes.int32), [1]) - if isinstance(tensors, dict): - outfeed_tensors = {'_global_step': global_step_tensor} - outfeed_tensors.update(tensors) - return global_step_host_fn, outfeed_tensors - else: - fn_args = util.fn_args(host_fn) - if len(tensors) != len(fn_args): - raise RuntimeError( - 'In TPUEstimatorSpec.host_call, length of tensors {} does not match ' - 'method args of the function, which takes {}.'.format( - len(tensors), len(fn_args))) - return global_step_host_fn, [global_step_tensor] + list(tensors) - - class _OutfeedHostCallHook(session_run_hook.SessionRunHook): """Hook to run host calls when use_tpu=False.""" @@ -1867,12 +1613,12 @@ class TPUEstimator(estimator_lib.Estimator): train_batch_size: An int representing the global training batch size. TPUEstimator transforms this global batch size to a per-shard batch size, as params['batch_size'], when calling `input_fn` and `model_fn`. - Cannot be `None` if `use_tpu` is `True`. Must be divisible by - `config.tpu_config.num_shards`. + Cannot be `None` if `use_tpu` is `True`. + Must be divisible by total number of replicas. eval_batch_size: An int representing evaluation batch size. - Must be divisible by `config.tpu_config.num_shards`. + Must be divisible by total number of replicas. predict_batch_size: An int representing the prediction batch size. - Must be divisible by `config.tpu_config.num_shards`. + Must be divisible by total number of replicas. batch_axis: A python tuple of int values describing how each tensor produced by the Estimator `input_fn` should be split across the TPU compute shards. For example, if your input_fn produced (images, labels) @@ -1896,40 +1642,24 @@ class TPUEstimator(estimator_lib.Estimator): _RESERVED_PARAMS_KEYS, params)) if use_tpu: + # Perform some very basic validations. More validations will be found in + # _TPUContext. if train_batch_size is None: raise ValueError('`train_batch_size` cannot be `None`') - if not isinstance(train_batch_size, int): - raise ValueError('`train_batch_size` must be an int') - if train_batch_size < 1: - raise ValueError('`train_batch_size` must be positive') - - # The specified batch size is the batch size for the entire computation. - # The input_fn and model_fn are called per-shard, so we want to calculate - # the per-shard batch size and pass that. - if train_batch_size % config.tpu_config.num_shards != 0: + util_lib.check_positive_integer(train_batch_size, 'train_batch_size') + + if (not config.tpu_config.per_host_input_for_training and + config.tpu_config.computation_shape): raise ValueError( - 'train batch size {} must be divisible by number of shards {}' - .format(train_batch_size, config.tpu_config.num_shards)) + 'Model parallelism only supports per host input for training. ' + 'Please adjust TPURunconfig.per_host_input_for_training.') if eval_batch_size is not None: - if not isinstance(eval_batch_size, int): - raise ValueError('`eval_batch_size` must be an int') - if eval_batch_size < 1: - raise ValueError('`eval_batch_size` must be positive') - if eval_batch_size % config.tpu_config.num_shards != 0: - raise ValueError( - 'eval batch size {} must be divisible by number of shards {}' - .format(eval_batch_size, config.tpu_config.num_shards)) + util_lib.check_positive_integer(eval_batch_size, 'eval_batch_size') if predict_batch_size is not None: - if not isinstance(predict_batch_size, int): - raise ValueError('`predict_batch_size` must be an int') - if predict_batch_size < 1: - raise ValueError('`predict_batch_size` must be positive') - if predict_batch_size % config.tpu_config.num_shards != 0: - raise ValueError( - 'predict batch size {} must be divisible by number of shards {}' - .format(predict_batch_size, config.tpu_config.num_shards)) + util_lib.check_positive_integer(predict_batch_size, + 'predict_batch_size') # Verifies the model_fn signature according to Estimator framework. estimator_lib._verify_model_fn_args(model_fn, params) # pylint: disable=protected-access @@ -1949,8 +1679,11 @@ class TPUEstimator(estimator_lib.Estimator): self._config.tpu_config.iterations_per_loop) # All properties passed to _TPUContext are immutable. - self._ctx = _TPUContext(self._config, train_batch_size, eval_batch_size, - predict_batch_size, use_tpu) + # pylint: disable=protected-access + self._ctx = tpu_context._get_tpu_context( + self._config, train_batch_size, + eval_batch_size, predict_batch_size, + use_tpu) def _create_global_step(self, graph): """Creates a global step suitable for TPUs. @@ -1998,14 +1731,6 @@ class TPUEstimator(estimator_lib.Estimator): util_lib.check_positive_integer(steps, 'Eval steps') - if self._config.tpu_config.num_shards > 8: - raise NotImplementedError( - 'TPU evaluation is only supported with one host.') - - if self._ctx._eval_batch_size is None: # pylint: disable=protected-access - raise ValueError('`eval_batch_size` cannot be `None`' - 'if evaluate() is called on TPU.') - return [ evaluation._StopAfterNEvalsHook( # pylint: disable=protected-access num_evals=steps), @@ -2039,6 +1764,9 @@ class TPUEstimator(estimator_lib.Estimator): if 'config' in input_fn_args: kwargs['config'] = config + if 'mode' in input_fn_args: + kwargs['mode'] = mode + with self._ctx.with_mode(mode) as ctx: # Setting the batch size in params first. This helps user to have same # input_fn for use_tpu=True/False. @@ -2245,7 +1973,6 @@ class TPUEstimator(estimator_lib.Estimator): def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" - num_cores = ctx.num_cores iterations_per_loop_var = _create_or_get_iterations_per_loop() single_tpu_eval_step, host_calls, captured_scaffold_fn = ( @@ -2260,8 +1987,9 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): (loss,) = tpu.shard( multi_tpu_eval_steps_on_single_shard, inputs=[], - num_shards=num_cores, - outputs_from_all_shards=False) + num_shards=ctx.num_replicas, + outputs_from_all_shards=False, + device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) return loss, host_calls, scaffold @@ -2269,7 +1997,6 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" - num_cores = ctx.num_cores iterations_per_loop_var = _create_or_get_iterations_per_loop() single_tpu_train_step, host_call, captured_scaffold_fn = ( @@ -2284,8 +2011,9 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): (loss,) = tpu.shard( multi_tpu_train_steps_on_single_shard, inputs=[], - num_shards=num_cores, - outputs_from_all_shards=False) + num_shards=ctx.num_replicas, + outputs_from_all_shards=False, + device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) return loss, host_call, scaffold @@ -2581,6 +2309,11 @@ class _InputsWithStoppingSignals(_Inputs): """ def _map_fn(*args): + """The map fn to insert signals.""" + if len(args) == 1: + # Unpack the single Tensor/dict argument as features. This is required + # for the input_fn returns no labels. + args = args[0] features, labels = _Inputs._parse_inputs(args) new_input_dict = {} new_input_dict['features'] = features diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py b/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py index f8ba7d45e20b2f48e1409427665878df40a6db02..f5af03f33ca8f13af517007672e9ce0e12be6205 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py @@ -244,7 +244,8 @@ class ShardingPolicy(object): str(shapes), self.number_of_shards)) unsharded_shapes = [self._unshard_shape(s) for s in shapes] for i in xrange(self.number_of_shards - 1): - if unsharded_shapes[i] != unsharded_shapes[self.number_of_shards - 1]: + if not unsharded_shapes[i].is_compatible_with( + unsharded_shapes[self.number_of_shards - 1]): raise ValueError( "sharded shapes %s are not consistent shards of a full shape " "sharded %d ways along dimension %d" % ( diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..493d1848c072caa5254fc87c67badc2e99ec16ee --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py @@ -0,0 +1,155 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# =================================================================== +"""TPU system metadata and associated tooling.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import re + +from tensorflow.contrib.tpu.python.tpu import tpu +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session as session_lib +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.platform import tf_logging as logging + +_PINGING_MASTER_TIMEOUT_IN_MS = 60 * 1000 # 1 min +_RETRY_TIMES = 120 +_INITIAL_TPU_SYSTEM_TIMEOUT_IN_MS = 300 * 1000 # 5 mins + +_TPU_DEVICE_REG = re.compile(r'.*task:(\d+)/.*device:TPU:(\d+)$') + +# _TPUSystemMetadata is used by TPUEstimator to hold TPU configuration, +# including num_cores and num_hosts. +_TPUSystemMetadata = collections.namedtuple('_TPUSystemMetadata', [ + 'num_cores', + 'num_hosts', + 'num_of_cores_per_host', + 'topology', + 'devices', +]) + + +def _query_tpu_system_metadata(master_address, run_config, + query_topology=False): + """Automatically detects the TPU system metadata in the system.""" + tpu_core_count = 0 + devices = [] + device_dict = collections.defaultdict(list) + + retry_count = 1 + while True: + logging.info('Querying Tensorflow master (%s) for TPU system metadata.', + master_address) + try: + with ops.Graph().as_default(): + with session_lib.Session( + master_address, + config=_get_session_config_with_timeout( + _PINGING_MASTER_TIMEOUT_IN_MS, run_config)) as sess: + devices = sess.list_devices() + for device in devices: + match = _TPU_DEVICE_REG.match(device.name) + if match: + host_id = match.group(1) + core_id = match.group(2) + device_dict[host_id].append(core_id) + tpu_core_count += 1 + break + except errors.DeadlineExceededError: + msg = ('Fail to connect Tensorflow master. It could be the TPU worker is ' + 'not ready (still under scheduling) or Tensorflow ' + 'master address is correct: got (%s).' % + (master_address)) + + # TODO(xiejw): For local or grpc master we might not need retry logic + # here. + if retry_count <= _RETRY_TIMES: + logging.warning('%s', msg) + logging.warning('Retrying (%d/%d).', retry_count, _RETRY_TIMES) + retry_count += 1 + else: + raise ValueError(msg) + + num_of_cores_per_host = 0 + if tpu_core_count: + num_cores_per_host_set = set( + [len(core_ids) for core_ids in device_dict.values()]) + if len(num_cores_per_host_set) != 1: + raise RuntimeError( + 'TPU cores on each host is not same. This should not happen!. ' + 'devices: {}'.format(devices)) + num_of_cores_per_host = num_cores_per_host_set.pop() + + topology = None + if query_topology: + if not tpu_core_count: + raise RuntimeError( + 'Cannot find any TPU cores in the system (master address {}). ' + 'This usually means the master address is incorrect or the ' + 'TPU worker has some problems. Available devices: {}'.format( + master_address, devices)) + + topology = _obtain_topology(master_address, run_config) + + metadata = _TPUSystemMetadata( + num_cores=tpu_core_count, + num_hosts=len(device_dict), + num_of_cores_per_host=num_of_cores_per_host, + topology=topology, + devices=devices) + + if tpu_core_count: + logging.info('Found TPU system:') + logging.info('*** Num TPU Cores: %d', metadata.num_cores) + logging.info('*** Num TPU Workers: %d', metadata.num_hosts) + logging.info('*** Num TPU Cores Per Worker: %d', + metadata.num_of_cores_per_host) + logging.info('*** Available Devices: %s', metadata.devices) + else: + logging.info('Failed to find TPU: %s', metadata) + return metadata + + +def _obtain_topology(master_address, run_config): + try: + logging.info('Initializing TPU system (master: %s) to fetch topology ' + 'for model parallelism. This might take a while.', + master_address) + with ops.Graph().as_default(): + session_config = _get_session_config_with_timeout( + _INITIAL_TPU_SYSTEM_TIMEOUT_IN_MS, run_config) + with session_lib.Session( + master_address, config=session_config) as sess: + topology = sess.run(tpu.initialize_system()) + return topology + except errors.DeadlineExceededError: + raise ValueError( + 'Fail to initialize TPU system with master (%s). ' + 'Please double check the TPU system is functional.' % ( + master_address)) + + +def _get_session_config_with_timeout(timeout_in_secs, run_config): + cluster_def = None + if run_config.session_config and run_config.session_config.cluster_def.job: + cluster_def = run_config.session_config.cluster_def + + config = config_pb2.ConfigProto( + operation_timeout_in_ms=timeout_in_secs, cluster_def=cluster_def) + return config diff --git a/tensorflow/contrib/tpu/tpu_estimator.md b/tensorflow/contrib/tpu/tpu_estimator.md index ca1255b16b1575d291df51dfde696b36c38359ae..4ef8f9eebdb165e5fe221be8670276bf943159b3 100644 --- a/tensorflow/contrib/tpu/tpu_estimator.md +++ b/tensorflow/contrib/tpu/tpu_estimator.md @@ -231,7 +231,7 @@ Refer to this link for all [Cloud TPU documentation](https://cloud.google.com/tp ### Profiling -You can profile the `worker` by using instructions as spcified in the [Cloud TPU Tools](https://cloud.google.com/tpu/docs/cloud-tpu-tools). +You can profile the `worker` by using instructions as specified in the [Cloud TPU Tools](https://cloud.google.com/tpu/docs/cloud-tpu-tools). ### Is `int64` supported? diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py index fdfd27d6a414933b0bec824bae512c45dac24d3c..95e051e3b5bb9f8075e66891a45c64a27bca68d1 100644 --- a/tensorflow/contrib/training/python/training/hparam.py +++ b/tensorflow/contrib/training/python/training/hparam.py @@ -358,6 +358,8 @@ class HParams(object): ``` """ + _HAS_DYNAMIC_ATTRIBUTES = True # Required for pytype checks. + def __init__(self, hparam_def=None, model_structure=None, **kwargs): """Create an instance of `HParams` from keyword arguments. diff --git a/tensorflow/contrib/util/loader.py b/tensorflow/contrib/util/loader.py index f4283cd9ed6eb185ec56bdfef45f6ddc5253d128..dca01d26f4c6291074d855322c8215027cfd2ace 100644 --- a/tensorflow/contrib/util/loader.py +++ b/tensorflow/contrib/util/loader.py @@ -42,9 +42,10 @@ def load_op_library(path): plugin. """ if os.name == 'nt': - # To avoid makeing every user_ops aware of windows, re-write - # the file extension from .so to .dll. - path = re.sub(r'\.so$', '.dll', path) + # To avoid making every user_ops aware of windows, re-write + # the file extension from .so to .dll if .so file doesn't exist. + if not os.path.exists(path): + path = re.sub(r'\.so$', '.dll', path) # Currently we have only some user_ops as dlls on windows - don't try # to load them if the dll is not found. diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md index 1b99f4ce4f645d0c59b2552cf26f47495cbbba73..4b6104a8b4d542b1d8a9cb3e48eeed4950d791cd 100644 --- a/tensorflow/contrib/verbs/README.md +++ b/tensorflow/contrib/verbs/README.md @@ -25,9 +25,9 @@ The design is based on TensorFlow r1.0. An RDMA path is added between servers fo During the server setup, an RDMA manager is created to manage low-level RDMA components such as RDMA channel and RDMA adapter, an RDMA rendezvous manager is created to oversee send/recv operations between servers. Following the distributed TensorFlow design philosophy, the send operation is passive, i.e. merely placing a tensor in the local out-going table. It is the receive operation that actually initiates the tensor transfer. TensorFlow dynamically allocates memory for tensors that are to be sent or received. This causes difficulty for RDMA operations where pinned memory is required. Few remedies are possible: -1. The memory is pinned, transfered, then unpinned for each and every tensor to be transferred. This incurs significant operation overhead since pinning and unpinning memory for each dynamically generated tensor is slow. +1. The memory is pinned, transferred, then unpinned for each and every tensor to be transferred. This incurs significant operation overhead since pinning and unpinning memory for each dynamically generated tensor is slow. 2. Buffer is pre-allocated and pinned for each tensor. This incurs large memory overhead and extra copying from the tensor to its pinned buffer, but may still be faster than the former. -3. Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), there is a smart way to benefit from the TensorFlow allocation theme which is mostly pool based, i.e allocators pre-allocate a large memory block, and allocate the tensors from there. By attaching a custom Visitor to relevant alloactors, we can do a single registration of the entire memory block, which zeros the registration overhead. Once the block is registered, each new tensor allocated will be at a registred address, which will allow us to do direct RDMA writes to it. +3. Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), there is a smart way to benefit from the TensorFlow allocation theme which is mostly pool based, i.e allocators pre-allocate a large memory block, and allocate the tensors from there. By attaching a custom Visitor to relevant allocators, we can do a single registration of the entire memory block, which zeros the registration overhead. Once the block is registered, each new tensor allocated will be at a registered address, which will allow us to do direct RDMA writes to it. For best performance, we will adopt HKUST 0 copies approach in our solution. This means: @@ -77,7 +77,7 @@ When the receiver receives the **RDMA_MESSAGE_META_DATA_RESPONSE**, it will loca 1. Update the local meta-data cache. 2. Reallocate the result/proxy tensors. -3. Re-send the tensor request. For tracability, the new message has a different name: **RDMA_MESSAGE_TENSOR_RE_REQUEST**. +3. Re-send the tensor request. For traceability, the new message has a different name: **RDMA_MESSAGE_TENSOR_RE_REQUEST**. When the sender receives a **RDMA_MESSAGE_TENSOR_RE_REQUEST**, it will locate the relevant **RdmaTensorResponse** using the request index specified in the message, and invoke its **Resume()** method, which will RDMA write the contents of the tensor that was cloned earlier, to the new remote address specified in the re-request. @@ -93,7 +93,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen 1. When the sender receives a tensor request, the source tensor may or may not be ready yet. The situation is handled through a process of tag matching: * If the request arrives before the tensor is ready, then a callback is put in a local table, and will be invoked once the tensor arrives. - * If the tensor is ready before the request arives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediatly. + * If the tensor is ready before the request arrives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately. In code it is done by calling **RecvLocalAsync()**, which receives the tensor's key, step-id, and the callback. 2. When the callback is invoked, the relevant tensor is removed from the tag matching table. In the case where we need to send the tensor's meta-data, the **RdmaTensorResponse** will store a copy of the tensor until the re-request arrives. 3. The sending of protocol messages (**RDMA_MESSAGE_TENSOR_REQUEST**, **RDMA_MESSAGE_META_DATA_RESPONSE** and **RDMA_MESSAGE_TENSOR_RE_REQUEST**) is done by the class **RdmaMessageBuffer**. All messages are sent using RDMA writes from/to fixed messages buffers. This implies that we cannot send on a specific channel more than one message at a time. In order to synchronize the messages, the **RdmaMessageBuffer** holds the a local and remote buffer statuses which can be either busy or idle. When a write is issued, both statuses will be changed to busy. When the write-complete event is received, the local status is changed to idle. When the write is received on the remote side, the remote side will parse the message, and return an ACK back to the sending side on which the sending side will update the remote status to idle. When both the local and remote statuses are idle, the next message can be sent. @@ -115,7 +115,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen * Reallocate the result tensor (and proxy tensor if required). * Re-send the request to the remote side. * **RecvTensorContent()** - Receive tensor content from the remote side (RDMA write was completed). - * Decode proto if required and/or move to GPU if the content was not written to it directly (GPU direct is not avaliable). + * Decode proto if required and/or move to GPU if the content was not written to it directly (GPU direct is not available). * Invoke the done callback. * **class RdmaTensorResponse** - Holds and manages information for a single tensor response throughout the entire send cycle. API: * **Start()** - Start the response sequence. @@ -153,7 +153,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen * request_index - Request index. * is_dead/data_type/tensor_shape/tensor_bytes - The up-to-date meta-data. * checksum - In data validation mode, this will hold the checksum of the source tensor. -* **RDMA_MESSAGE_TENSOR_RE_REQUEST** - (receiver ==> sender) Tensor re-requset after meta-data update and reallocation of result/proxy tensors. +* **RDMA_MESSAGE_TENSOR_RE_REQUEST** - (receiver ==> sender) Tensor re-request after meta-data update and reallocation of result/proxy tensors. * type - The message type. * name (name_size) - Name of the requested tensor. * step_id - Step ID. diff --git a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md index 956b8f2147cf8154b6f1ade006d7bff194864c9b..da6fdd48e19e9d1503d1537926b1c464a0e77589 100644 --- a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md +++ b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md @@ -64,7 +64,7 @@ The protocol messages themselves will remain mostly unchanged at the first stage * type - The message type. * request_index - Request index. * is_dead/data_type/tensor_shape/tensor_bytes - The up-to-date meta-data. -* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-requset after meta-data update and reallocation of result/proxy tensors. +* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-request after meta-data update and reallocation of result/proxy tensors. * type - The message type. * name (name_size) - Name of the requested tensor. * step_id - Step ID. diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 4ba84b420f23849fd1e0bf8c13c0003175993666..619899ae951aa54a2f95f04cfc37760e1be9fc23 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -193,6 +193,7 @@ CORE_PROTO_SRCS = [ "protobuf/rewriter_config.proto", "protobuf/tensor_bundle.proto", "protobuf/saver.proto", + "util/event.proto", "util/memmapped_file_system.proto", "util/saved_tensor_slice.proto", ] @@ -211,7 +212,6 @@ ADDITIONAL_CORE_PROTO_SRCS = [ "protobuf/named_tensor.proto", "protobuf/saved_model.proto", "protobuf/tensorflow_server.proto", - "util/event.proto", "util/test_log.proto", ] @@ -339,6 +339,7 @@ cc_library( "lib/strings/strcat.h", "lib/strings/stringprintf.h", "platform/abi.h", + "platform/context.h", "platform/cpu_feature_guard.h", "platform/cpu_info.h", "platform/dynamic_annotations.h", @@ -376,6 +377,17 @@ cc_library( hdrs = ["platform/abi.h"], ) +cc_library( + name = "session_message", + srcs = ["util/session_message.cc"], + hdrs = ["util/session_message.h"], + deps = [ + ":framework", + ":lib", + ":protos_all_cc", + ], +) + cc_library( name = "stacktrace_handler", srcs = ["platform/stacktrace_handler.cc"], @@ -454,6 +466,7 @@ tf_cuda_library( "framework/reader_interface.h", "framework/reader_op_kernel.h", "framework/register_types.h", + "framework/register_types_traits.h", "framework/resource_mgr.h", "framework/resource_op_kernel.h", "framework/selective_registration.h", @@ -468,6 +481,7 @@ tf_cuda_library( "framework/type_index.h", "framework/type_traits.h", "framework/types.h", + "framework/visitable_allocator.h", "public/version.h", "util/activation_mode.h", "util/bcast.h", @@ -786,6 +800,7 @@ tf_cuda_library( "graph/graph.h", "graph/graph_constructor.h", "graph/graph_def_builder.h", + "graph/graph_def_builder_util.h", "graph/node_builder.h", "graph/validate.h", "graph/while_context.h", @@ -825,6 +840,7 @@ cc_library( "//tensorflow/core/kernels:dataset_ops", "//tensorflow/core/kernels:fake_quant_ops", "//tensorflow/core/kernels:function_ops", + "//tensorflow/core/kernels:functional_ops", "//tensorflow/core/kernels:histogram_op", "//tensorflow/core/kernels:image", "//tensorflow/core/kernels:io", @@ -974,22 +990,15 @@ filegroup( # Core sources for Android builds. filegroup( - name = "mobile_srcs", + name = "mobile_srcs_no_runtime", srcs = [ ":proto_text_srcs_all", - "//tensorflow/core/kernels:android_srcs", "//tensorflow/core/platform/default/build_config:android_srcs", - "//tensorflow/core/util/ctc:android_srcs", - "//tensorflow/core/util/tensor_bundle:android_srcs", ] + glob( [ "client/**/*.cc", - "common_runtime/**/*.h", - "common_runtime/**/*.cc", "framework/**/*.h", "framework/**/*.cc", - "graph/**/*.h", - "graph/**/*.cc", "lib/**/*.h", "lib/**/*.cc", "platform/**/*.h", @@ -1005,7 +1014,6 @@ filegroup( "**/*main.cc", "debug/**/*", "framework/op_gen_*", - "graph/dot.*", "lib/jpeg/**/*", "lib/png/**/*", "lib/gif/**/*", @@ -1022,6 +1030,10 @@ filegroup( "platform/stream_executor.*", "platform/windows/**/*", "user_ops/**/*.cu.cc", + "util/ctc/*.h", + "util/ctc/*.cc", + "util/tensor_bundle/*.h", + "util/tensor_bundle/*.cc", "common_runtime/gpu/**/*", "common_runtime/gpu_device_factory.*", ], @@ -1029,6 +1041,41 @@ filegroup( visibility = ["//visibility:public"], ) +filegroup( + name = "mobile_srcs_only_runtime", + srcs = [ + "//tensorflow/core/kernels:android_srcs", + "//tensorflow/core/util/ctc:android_srcs", + "//tensorflow/core/util/tensor_bundle:android_srcs", + ] + glob( + [ + "common_runtime/**/*.h", + "common_runtime/**/*.cc", + "graph/**/*.h", + "graph/**/*.cc", + ], + exclude = [ + "**/*test.*", + "**/*testutil*", + "**/*testlib*", + "**/*main.cc", + "common_runtime/gpu/**/*", + "common_runtime/gpu_device_factory.*", + "graph/dot.*", + ], + ), + visibility = ["//visibility:public"], +) + +filegroup( + name = "mobile_srcs", + srcs = [ + ":mobile_srcs_no_runtime", + ":mobile_srcs_only_runtime", + ], + visibility = ["//visibility:public"], +) + # Native library support for Android applications. Does not contain # operators, use :android_tensorflow_lib if you want full operator # support. @@ -1321,6 +1368,13 @@ tf_pyclif_proto_library( visibility = ["//visibility:public"], ) +tf_pyclif_proto_library( + name = "framework/kernel_def_pyclif", + proto_lib = ":protos_all_cc", + proto_srcfile = "framework/kernel_def.proto", + visibility = ["//visibility:public"], +) + tf_pyclif_proto_library( name = "framework/node_def_pyclif", proto_lib = ":protos_all_cc", @@ -1610,6 +1664,25 @@ cc_library( ], ) +cc_library( + name = "tflite_portable_logging", + srcs = [], + hdrs = [ + "lib/bfloat16/bfloat16.h", + "platform/default/integral_types.h", + "platform/default/logging.h", + "platform/logging.h", + "platform/macros.h", + "platform/platform.h", + "platform/types.h", + ], + copts = tf_copts(), + linkopts = ["-ldl"], + deps = [ + "//tensorflow/core/platform/default/build_config:logging", + ], +) + cc_library( name = "android_jpeg_internal", srcs = if_android([ @@ -1722,6 +1795,9 @@ FRAMEWORK_INTERNAL_PRIVATE_HEADERS = [ "platform/variant_coding.h", "graph/edgeset.h", "graph/graph.h", + "graph/graph_def_builder.h", + "graph/node_builder.h", + "graph/tensor_id.h", ] + glob( [ "example/**/*.h", @@ -1739,6 +1815,7 @@ FRAMEWORK_INTERNAL_PRIVATE_HEADERS = [ "framework/reader_base.*", "util/memmapped_file_system.*", "util/memmapped_file_system_writer.*", + "util/session_message.*", "util/version_info.cc", ], ) + select({ @@ -1756,6 +1833,7 @@ FRAMEWORK_INTERNAL_PUBLIC_HEADERS = [ "framework/tracking_allocator.h", # only needed for tests "framework/unique_tensor_references.h", "framework/variant.h", + "framework/visitable_allocator.h", "platform/variant_coding.h", "util/command_line_flags.h", "util/env_var.h", @@ -1796,6 +1874,13 @@ cc_header_only_library( ], ) +cc_header_only_library( + name = "core_cpu_headers_lib", + deps = [ + ":core_cpu_lib", + ], +) + tf_cuda_library( name = "framework_internal_impl", srcs = FRAMEWORK_INTERNAL_PRIVATE_HEADERS + [ @@ -1808,6 +1893,9 @@ tf_cuda_library( ] + [ "graph/edgeset.cc", "graph/graph.cc", + "graph/graph_def_builder.cc", + "graph/node_builder.cc", + "graph/tensor_id.cc", "graph/while_context.h", "graph/while_context.cc", ], @@ -1822,6 +1910,7 @@ tf_cuda_library( "framework/resource_handle.cc", "util/memmapped_file_system.*", "util/memmapped_file_system_writer.*", + "util/session_message.cc", "util/version_info.cc", ], ) + select({ @@ -1857,7 +1946,7 @@ tf_cuda_library( ) + if_mkl( [ "//third_party/mkl:intel_binary_blob", - "@mkl_dnn//:mkl_dnn", + "@mkl_dnn", ], ), alwayslink = 1, @@ -1910,7 +1999,6 @@ cc_library( tf_cuda_library( name = "cuda_device_functions", hdrs = ["util/cuda_device_functions.h"], - cuda_deps = ["//third_party_gpus/cuda:cuda_headers"], visibility = ["//visibility:public"], deps = [":framework_lite"], ) @@ -1937,6 +2025,7 @@ GRAPH_HDRS = [ "graph/graph.h", "graph/graph_constructor.h", # NOTE(mrry): Don't include the .cc since it depends on common_runtime. "graph/graph_def_builder.h", + "graph/graph_def_builder_util.h", "graph/graph_partition.h", "graph/mkl_layout_pass.h", "graph/mkl_tfconversion_pass.h", @@ -1957,12 +2046,9 @@ tf_cuda_library( "graph/colors.cc", "graph/control_flow.cc", "graph/costmodel.cc", - "graph/graph_def_builder.cc", "graph/graph_partition.cc", - "graph/node_builder.cc", "graph/optimizer_cse.cc", "graph/subgraph.cc", - "graph/tensor_id.cc", "graph/validate.cc", ], hdrs = GRAPH_HDRS, @@ -1979,6 +2065,7 @@ tf_cuda_library( CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ "common_runtime/device.h", + "common_runtime/eval_const_tensor.h", "common_runtime/graph_runner.h", "common_runtime/shape_refiner.h", "framework/versions.h", @@ -1987,10 +2074,12 @@ CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ tf_cuda_library( name = "core_cpu_base", srcs = [ + "common_runtime/eval_const_tensor.cc", "common_runtime/shape_refiner.cc", "common_runtime/shape_refiner.h", "framework/versions.h", "graph/graph_constructor.cc", # Depends on common_runtime. + "graph/graph_def_builder_util.cc", # Depends on common_runtime. "public/session.h", "public/session_options.h", "public/version.h", @@ -2049,7 +2138,6 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/stats_publisher_interface.h", "common_runtime/step_stats_collector.h", "common_runtime/threadpool_device.h", - "common_runtime/visitable_allocator.h", "graph/gradients.h", "graph/quantize_training.h", ] + if_mkl(["graph/mkl_graph_util.h"]) @@ -2075,6 +2163,7 @@ tf_cuda_library( "common_runtime/graph_runner.cc", "common_runtime/local_device.cc", "common_runtime/memory_types.cc", + "common_runtime/mkl_cpu_allocator.cc", "common_runtime/optimization_registry.cc", "common_runtime/parallel_concat_optimizer.cc", "common_runtime/placer.cc", @@ -2114,6 +2203,7 @@ tf_cuda_library( ] + if_mkl( [ "//third_party/mkl:intel_binary_blob", + "@mkl_dnn", ], ), alwayslink = 1, @@ -2158,14 +2248,12 @@ tf_cuda_library( ] + if_mkl( [ "//third_party/mkl:intel_binary_blob", - "@mkl_dnn//:mkl_dnn", + "@mkl_dnn", ], ) + tf_additional_core_deps() + if_static([":core_cpu_impl"]), alwayslink = 1, ) -# This library is deprecated and no longer publicly available. -# Do not add more uses of it. cc_library( name = "regexp_internal", hdrs = [ @@ -2235,12 +2323,25 @@ tf_cuda_library( ] + tf_additional_device_tracer_deps(), ) +cc_library( + name = "gpu_id", + srcs = ["common_runtime/gpu/gpu_id_manager.cc"], + hdrs = [ + "common_runtime/gpu/gpu_id.h", + "common_runtime/gpu/gpu_id_manager.h", + ], + deps = [ + ":lib", + ], +) + GPU_RUNTIME_HEADERS = [ "common_runtime/gpu/gpu_bfc_allocator.h", "common_runtime/gpu/gpu_cudamalloc_allocator.h", "common_runtime/gpu/gpu_debug_allocator.h", "common_runtime/gpu/gpu_device.h", "common_runtime/gpu/gpu_id.h", + "common_runtime/gpu/gpu_id_manager.h", "common_runtime/gpu/gpu_id_utils.h", "common_runtime/gpu/gpu_init.h", "common_runtime/gpu/gpu_managed_allocator.h", @@ -2259,7 +2360,6 @@ tf_cuda_library( "common_runtime/gpu/gpu_debug_allocator.cc", "common_runtime/gpu/gpu_device.cc", "common_runtime/gpu/gpu_device_factory.cc", - "common_runtime/gpu/gpu_id_utils.cc", "common_runtime/gpu/gpu_managed_allocator.cc", "common_runtime/gpu/gpu_stream_util.cc", "common_runtime/gpu/gpu_util.cc", @@ -2274,6 +2374,7 @@ tf_cuda_library( ":core_cpu_lib", ":framework", ":framework_internal", + ":gpu_id", ":gpu_init_impl", ":gpu_lib", ":graph", @@ -2863,6 +2964,7 @@ tf_cc_tests_gpu( linkstatic = tf_kernel_tests_linkstatic(), deps = [ ":gpu_headers_lib", + ":gpu_id", ":gpu_runtime", ":test", ], @@ -2874,7 +2976,7 @@ tf_cc_tests_gpu( srcs = glob(["user_ops/**/*_test.cc"]) + [ "common_runtime/gpu/gpu_bfc_allocator_test.cc", "common_runtime/gpu/gpu_device_test.cc", - "common_runtime/gpu/gpu_id_utils_test.cc", + "common_runtime/gpu/gpu_id_manager_test.cc", "common_runtime/gpu/gpu_event_mgr_test.cc", "common_runtime/gpu/pool_allocator_test.cc", ], @@ -2886,6 +2988,7 @@ tf_cc_tests_gpu( ":direct_session", ":framework", ":framework_internal", + ":gpu_id", ":gpu_runtime", ":lib", ":lib_internal", @@ -3281,6 +3384,7 @@ tf_cc_test_gpu( ":direct_session", ":framework", ":framework_internal", + ":gpu_id", ":gpu_runtime", ":lib", ":lib_internal", @@ -3441,6 +3545,7 @@ tf_cc_tests( "ops/parsing_ops_test.cc", "ops/random_ops_test.cc", "ops/set_ops_test.cc", + "ops/shape_function_test.cc", "ops/sparse_ops_test.cc", "ops/spectral_ops_test.cc", "ops/state_ops_test.cc", @@ -3599,6 +3704,18 @@ filegroup( visibility = ["//tensorflow:__subpackages__"], ) +alias( + name = "android_srcs_no_runtime", + actual = ":mobile_srcs_no_runtime", + visibility = ["//visibility:public"], +) + +alias( + name = "android_srcs_only_runtime", + actual = ":mobile_srcs_only_runtime", + visibility = ["//visibility:public"], +) + alias( name = "android_srcs", actual = ":mobile_srcs", diff --git a/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt b/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b9db8274dea5d904dbbc687927673e0c7f7fa649 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt @@ -0,0 +1,19 @@ +op { + graph_op_name: "ConsumeMutexLock" + in_arg { + name: "mutex_lock" + description: < [1, 2, 4, 7, 8] +idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +count ==> [2, 1, 3, 1, 2] +``` + +For an `2-D` tensor `x` with `axis = 0`: + +``` +# tensor 'x' is [[1, 0, 0], +# [1, 0, 0], +# [2, 0, 0]] +y, idx, count = unique_with_counts(x, axis=0) +y ==> [[1, 0, 0], + [2, 0, 0]] +idx ==> [0, 0, 1] +count ==> [2, 1] +``` + +For an `2-D` tensor `x` with `axis = 1`: + +``` +# tensor 'x' is [[1, 0, 0], +# [1, 0, 0], +# [2, 0, 0]] +y, idx, count = unique_with_counts(x, axis=1) +y ==> [[1, 0], + [1, 0], + [2, 0]] +idx ==> [0, 1, 1] +count ==> [1, 2] +``` +END +} diff --git a/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMax.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMax.pbtxt index 8298d62f253160847ee34bcdea5a81c7370e5124..4ca6780c95629de06db319db228f440219989793 100644 --- a/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMax.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMax.pbtxt @@ -14,20 +14,21 @@ Has same shape as data, except for dimension 0 which has size `num_segments`. END } - summary: "Computes the Max along segments of a tensor." + summary: "Computes the maximum along segments of a tensor." description: <::min()`. +If the maximum is empty for a given segment ID `i`, it outputs the smallest +possible value for the specific numeric type, +`output[i] = numeric_limits::lowest()`.

diff --git a/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMin.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMin.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..55ea69b5dd5f7fda5c877ca5771ec2cbb86e3a9a --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentMin.pbtxt @@ -0,0 +1,33 @@ +op { + graph_op_name: "UnsortedSegmentMin" + in_arg { + name: "segment_ids" + description: <::max()`. +END +} diff --git a/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentProd.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentProd.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..577ff53d60c5a174b4ba43a667885a6983b2dfb9 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnsortedSegmentProd.pbtxt @@ -0,0 +1,32 @@ +op { + graph_op_name: "UnsortedSegmentProd" + in_arg { + name: "segment_ids" + description: <
+ **From left to right, [*Iris setosa*](https://commons.wikimedia.org/w/index.php?curid=170298) (by [Radomil](https://commons.wikimedia.org/wiki/User:Radomil), CC BY-SA 3.0), @@ -90,11 +91,10 @@ a number. Here's the representation scheme: A **model** is the relationship between features and the label. For the Iris problem, the model defines the relationship -between the sepal and petal measurements and the Iris species. -Some simple models can be described with a few lines of algebra; -more complex machine learning models -contain such a large number of interlacing mathematical functions and -parameters that they become hard to summarize mathematically. +between the sepal and petal measurements and the predicted Iris species. Some +simple models can be described with a few lines of algebra, but complex machine +learning models have a large number of parameters that are difficult to +summarize. Could you determine the relationship between the four features and the Iris species *without* using machine learning? That is, could you use @@ -188,6 +188,7 @@ provides a programming stack consisting of multiple API layers:
+ **The TensorFlow Programming Environment.**

 

@@ -331,7 +332,7 @@ interpret data is such a rich topic that we devote an entire From a code perspective, you build a list of `feature_column` objects by calling functions from the @{tf.feature_column} module. Each object describes an input to the model. To tell the model to interpret data as a floating-point value, -call @{tf.feature_column.numeric_column). In `premade_estimator.py`, all +call @{tf.feature_column.numeric_column}. In `premade_estimator.py`, all four features should be interpreted as literal floating-point values, so the code to create a feature column looks as follows: @@ -380,6 +381,7 @@ fully connected neural network consisting of three hidden layers:
+ **A neural network with three hidden layers.**

 

@@ -568,6 +570,7 @@ of 0.5. The following suggests a more effective model: 5.5 2.5 4.0 1.3 1 1 + **A model that is 80% accurate.**

 

diff --git a/tensorflow/docs_src/get_started/premade_estimators.md b/tensorflow/docs_src/get_started/premade_estimators.md index 4f01f997c33c211e8cff81b6b268bb320aa794df..6bffd2e065548a42eb726df34542ecc7480ad38d 100644 --- a/tensorflow/docs_src/get_started/premade_estimators.md +++ b/tensorflow/docs_src/get_started/premade_estimators.md @@ -98,6 +98,7 @@ classifies Iris flowers into three different species based on the size of their alt="Petal geometry compared for three iris species: Iris setosa, Iris virginica, and Iris versicolor" src="../images/iris_three_species.jpg"> + **From left to right, [*Iris setosa*](https://commons.wikimedia.org/w/index.php?curid=170298) (by [Radomil](https://commons.wikimedia.org/wiki/User:Radomil), CC BY-SA 3.0), diff --git a/tensorflow/docs_src/install/index.md b/tensorflow/docs_src/install/index.md index 3c8488643f071c147dfbc4e0b4b4760b0a817718..4f85383925bbb8a03372b020e448a0e604f3b999 100644 --- a/tensorflow/docs_src/install/index.md +++ b/tensorflow/docs_src/install/index.md @@ -3,7 +3,7 @@ We've built and tested TensorFlow on the following 64-bit laptop/desktop operating systems: - * MacOS X 10.11 (El Capitan) or later. + * macOS 10.12.6 (Sierra) or later. * Ubuntu 16.04 or later * Windows 7 or later. diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index a783205b4a2d24182de6496e0173635990120185..0481c97885df97675553eccb08989846a1275948 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -15,7 +15,7 @@ instructions might also work on other variants, we have only tested following requirements: * Linux, 64-bit, x86 - * macOS X, Version 10.11 (El Capitan) or higher + * macOS X, Version 10.12.6 (Sierra) or higher ## Installation @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 5249e04615b506186a12807bb71ec4079db8156c..8f89898c92d00eaf0d637e78b3ec897a34df6b89 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -17,7 +17,7 @@ instructions might also work on other variants, we have only tested following requirements: * Linux, 64-bit, x86 - * macOS X, 10.11 (El Capitan) or higher + * macOS X, 10.12.6 (Sierra) or higher ## Installation @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 0c6c773e62483b2272cf3b80da0932b4b800bb71..0ee9c849e11448c4efc74263f6aacbb46056201d 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -18,7 +18,7 @@ instructions might also work on other variants, we have only tested following requirements: * Ubuntu 16.04 or higher; 64-bit, x86 - * macOS X 10.11 (El Capitan) or higher + * macOS 10.12.6 (Sierra) or higher * Windows 7 or higher; 64-bit, x86 The installation instructions for Android are in a separate @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.6.0-rc0 + 1.6.0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.6.0-rc0 + 1.6.0 @@ -123,12 +123,12 @@ instead: org.tensorflow libtensorflow - 1.6.0-rc0 + 1.6.0 org.tensorflow libtensorflow_jni_gpu - 1.6.0-rc0 + 1.6.0 ``` @@ -147,7 +147,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0.zip). 3. Extract this .zip file. @@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.6.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.6.0.jar HelloTF.java
### Running @@ -239,11 +239,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.6.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.6.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.6.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 105b225177315db07b1117c3ece4b77dd2b60cb2..3e8744bf9d1e01d00c703d9153db8ac48bce1d50 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -41,7 +41,8 @@ must be installed on your system: [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Ensure that you create the `CUDA_HOME` environment variable as described in the NVIDIA documentation. - * GPU card with CUDA Compute Capability 3.0 or higher. See + * GPU card with CUDA Compute Capability 3.0 or higher for building + from source and 3.5 or higher for our binaries. See [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. * The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. @@ -188,7 +189,7 @@ Take the following steps to install TensorFlow with Virtualenv: Virtualenv environment: 
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -293,7 +294,7 @@ take the following steps: 
      $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl
If this step fails, see @@ -356,24 +357,23 @@ where: to 6006. * TensorFlowCPUImage is required. It identifies the Docker container. Specify one of the following values: - * gcr.io/tensorflow/tensorflow, which is the TensorFlow CPU binary image. - * gcr.io/tensorflow/tensorflow:latest-devel, which is the latest + * tensorflow/tensorflow, which is the TensorFlow CPU binary image. + * tensorflow/tensorflow:latest-devel, which is the latest TensorFlow CPU Binary image plus source code. - * gcr.io/tensorflow/tensorflow:version, which is the + * tensorflow/tensorflow:version, which is the specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image. - * gcr.io/tensorflow/tensorflow:version-devel, which is + * tensorflow/tensorflow:version-devel, which is the specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary image plus source code. - gcr.io is the Google Container Registry. Note that some - TensorFlow images are also available at + TensorFlow images are available at [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/). For example, the following command launches the latest TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell: 
-$ docker run -it gcr.io/tensorflow/tensorflow bash
+$ docker run -it tensorflow/tensorflow bash
The following command also launches the latest TensorFlow CPU binary image in a @@ -381,7 +381,7 @@ Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook: 
-$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+$ docker run -it -p 8888:8888 tensorflow/tensorflow
Docker will download the TensorFlow binary image the first time you launch it. @@ -405,14 +405,14 @@ where: hostPort and containerPort to `8888`. * TensorFlowGPUImage specifies the Docker container. You must specify one of the following values: - * gcr.io/tensorflow/tensorflow:latest-gpu, which is the latest + * tensorflow/tensorflow:latest-gpu, which is the latest TensorFlow GPU binary image. - * gcr.io/tensorflow/tensorflow:latest-devel-gpu, which is + * tensorflow/tensorflow:latest-devel-gpu, which is the latest TensorFlow GPU Binary image plus source code. - * gcr.io/tensorflow/tensorflow:version-gpu, which is the + * tensorflow/tensorflow:version-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image. - * gcr.io/tensorflow/tensorflow:version-devel-gpu, which is + * tensorflow/tensorflow:version-devel-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image plus source code. @@ -421,7 +421,7 @@ following command launches the latest TensorFlow GPU binary image in a Docker container from which you can run TensorFlow programs in a shell: 
-$ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash
+$ nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash
The following command also launches the latest TensorFlow GPU binary image @@ -429,13 +429,13 @@ in a Docker container. In this Docker container, you can run TensorFlow programs in a Jupyter notebook: 
-$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
+$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
The following command installs an older TensorFlow version (0.12.1): 
-$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1-gpu
+$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:0.12.1-gpu
Docker will download the TensorFlow binary image the first time you launch it. @@ -480,8 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
- + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -506,7 +505,7 @@ If you installed through Docker, start a Docker container from which you can run bash. For example: 
-$ docker run -it gcr.io/tensorflow/tensorflow bash
+$ docker run -it tensorflow/tensorflow bash
@@ -648,14 +647,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -667,14 +666,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -686,14 +685,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp35-cp35m-linux_x86_64.whl
@@ -705,14 +704,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index a6ea548cfbdb3070c19b5c19ebc903ca76a4656a..94defcd18cab83c62fb79ffc473b7cb6139962f5 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -5,7 +5,11 @@ instructions might also work on other macOS variants, we have only tested (and we only support) these instructions on machines meeting the following requirements: - * macOS X 10.11 (El Capitan) or higher + * macOS 10.12.6 (Sierra) or higher + +Note: There are known, accuracy-affecting numerical issues before macOS 10.12.6 +(Sierra) that are described in +[GitHub#15933](https://github.com/tensorflow/tensorflow/issues/15933#issuecomment-366331383). Note: As of version 1.2, TensorFlow no longer provides GPU support on macOS. @@ -115,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -238,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -288,24 +292,23 @@ where: to 6006. * TensorFlowImage is required. It identifies the Docker container. You must specify one of the following values: - * gcr.io/tensorflow/tensorflow: TensorFlow binary image. - * gcr.io/tensorflow/tensorflow:latest-devel: TensorFlow + * tensorflow/tensorflow: TensorFlow binary image. + * tensorflow/tensorflow:latest-devel: TensorFlow Binary image plus source code. -gcr.io is the Google Container Registry. Note that some -TensorFlow images are also available at +The TensorFlow images are available at [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/). For example, the following command launches a TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell: -
$ docker run -it gcr.io/tensorflow/tensorflow bash
+
$ docker run -it tensorflow/tensorflow bash
The following command also launches a TensorFlow CPU binary image in a Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook: -
$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+
$ docker run -it -p 8888:8888 tensorflow/tensorflow
Docker will download the TensorFlow binary image the first time you launch it. @@ -347,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl @@ -372,7 +375,7 @@ do the following: If you installed through Docker, start a Docker container that runs bash. For example: -
$ docker run -it gcr.io/tensorflow/tensorflow bash
+
$ docker run -it tensorflow/tensorflow bash
@@ -520,7 +523,7 @@ This section documents the relevant values for Mac OS installations. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl
@@ -528,5 +531,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 0dbb15188e5074d48c8dc7fcdcdd33daf123c6d7..c09c9c2c0c6eaabe9066a2630a3aec39f05fcfec 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.6.0rc0 on Linux: +for TensorFlow 1.6.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0-py2-none-any.whl
## Validate your installation @@ -393,8 +393,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/get_started$Getting Started with -TensorFlow}. +If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). @@ -460,8 +459,8 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** - - + + @@ -479,7 +478,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.0N/AN/A
tensorflow_gpu-1.6.0rc0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.6.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.0N/AN/A
tensorflow_gpu-1.6.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.5.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.0N/AN/A
tensorflow_gpu-1.5.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow-1.4.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.5.4N/AN/A
- + @@ -493,8 +492,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.6.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.5.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.4.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.5.4N/AN/A
tensorflow-1.3.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.4.5N/AN/A
- - + + diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 86a111c2ec1ee1e126b3e96c83eae68da7da7e41..2413bc9cfbbfd577ebd583be4da82994e8551c9e 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -17,7 +17,7 @@ You must choose one of the following types of TensorFlow to install: NVIDIA® GPU, you must install this version. Note that this version of TensorFlow is typically much easier to install (typically, in 5 or 10 minutes), so even if you have an NVIDIA GPU, we recommend - installing this version first. + installing this version first. Prebuilt binaries will use AVX instructions. * **TensorFlow with GPU support**. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA® GPU meeting the prerequisites shown below @@ -41,13 +41,14 @@ installed on your system: Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your `%PATH%` environment variable. - * GPU card with CUDA Compute Capability 3.0 or higher. See + * GPU card with CUDA Compute Capability 3.0 or higher for building + from source and 3.5 or higher for our binaries. See [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. If you have a different version of one of the preceding packages, please change to the specified versions. In particular, the cuDNN version -must match exactly: TensorFlow will not load if it cannot find `cuDNN64_6.dll`. +must match exactly: TensorFlow will not load if it cannot find `cuDNN64_7.dll`. To use a different version of cuDNN, you must build from source. ## Determine how to install TensorFlow @@ -153,8 +154,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/get_started$Getting Started with -TensorFlow}. +If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/leftnav_files b/tensorflow/docs_src/install/leftnav_files index 0e8b5ae7a17eb43cffc76d40692c4f0042de44af..e523e06f67aad508238ee0965f34ebe16c77bf90 100644 --- a/tensorflow/docs_src/install/leftnav_files +++ b/tensorflow/docs_src/install/leftnav_files @@ -1,16 +1,16 @@ index.md ### Python -install_linux.md -install_mac.md -install_windows.md -install_sources.md +install_linux.md: Ubuntu +install_mac.md: MacOS +install_windows.md: Windows +install_sources.md: From source >>> migration.md ### Other Languages -install_java.md -install_go.md -install_c.md +install_java.md: Java +install_go.md: Go +install_c.md: C diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md index b5a1d5d7d1bf3b456ab24165e273969bdbd7bfca..08a5fbe41c87c88399682208c38bf7a892d8fc1a 100644 --- a/tensorflow/docs_src/mobile/android_build.md +++ b/tensorflow/docs_src/mobile/android_build.md @@ -90,8 +90,8 @@ using [ADB](https://developer.android.com/studio/command-line/adb.html). This requires some knowledge of build systems and Android developer tools, but we'll guide you through the basics here. -- First, follow our instructions for @{$install/install_sources$installing from - sources}. This will also guide you through installing Bazel and cloning the +- First, follow our instructions for @{$install/install_sources$installing from sources}. + This will also guide you through installing Bazel and cloning the TensorFlow code. - Download the Android [SDK](https://developer.android.com/studio/index.html) diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files index ac50f528ba468d8a830c059539d3399f413f39c8..4cf134cc3c2c323405d769a5ced5d5a68f188203 100644 --- a/tensorflow/docs_src/mobile/leftnav_files +++ b/tensorflow/docs_src/mobile/leftnav_files @@ -2,6 +2,7 @@ index.md ### TensorFlow Lite tflite/index.md tflite/demo_android.md +tflite/demo_ios.md >>> ### TensorFlow Mobile mobile_intro.md diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md index 17dbf1c3e6ad89768529864ba884274a51b3dfb2..69b63ae7d22ced9fd0299f17d1ae2d614c9a6be7 100644 --- a/tensorflow/docs_src/mobile/mobile_intro.md +++ b/tensorflow/docs_src/mobile/mobile_intro.md @@ -235,7 +235,7 @@ TensorFlow [on Github](https://github.com/tensorflow/models) that you can look through. Lean towards the simplest model you can find, and try to get started as soon as you have even a small amount of labelled data, since you’ll get the best results when you’re able to iterate quickly. The shorter the time it takes to -try training a model and running it in s real application, the better overall +try training a model and running it in its real application, the better overall results you’ll see. It’s common for an algorithm to get great training accuracy numbers but then fail to be useful within a real application because there’s a mismatch between the dataset and real usage. Prototype end-to-end usage as soon diff --git a/tensorflow/docs_src/mobile/optimizing.md b/tensorflow/docs_src/mobile/optimizing.md index 44cacff5dbbcb0685044c342184464b47a8ed090..ca9cb043e9282702044b0da26c27f5bf29141cae 100644 --- a/tensorflow/docs_src/mobile/optimizing.md +++ b/tensorflow/docs_src/mobile/optimizing.md @@ -290,8 +290,8 @@ run it on a 64-bit ARM device: You can interpret the results in exactly the same way as the desktop version above. If you have any trouble figuring out what the right input and output -names and types are, take a look at the @{$mobile/prepare_models$Preparing -models} page for details about detecting these for your model, and look at the +names and types are, take a look at the @{$mobile/prepare_models$Preparing models} +page for details about detecting these for your model, and look at the `summarize_graph` tool which may give you helpful information. diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 79b567897cb8a38ed2e27e73aa7e8fee95f718b8..c94b5597a673a7e68aed517b325b9719b3b73bbd 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -8,6 +8,9 @@ You'll need an Android device running Android 5.0 or higher to run the demo. To get you started working with TensorFlow Lite on Android, we'll walk you through building and deploying our TensorFlow demo app in Android Studio. +Note: For a more detailed guide see the +[TFLite Codelab](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/index.html#0) + It's also possible to build the demo app with Bazel, but we only recommend this for advanced users who are very familiar with the Bazel build environment. For more information on that, see our page [on Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite#building-tensorflow-lite-and-the-demo-app-from-source). diff --git a/tensorflow/docs_src/mobile/tflite/demo_ios.md b/tensorflow/docs_src/mobile/tflite/demo_ios.md new file mode 100644 index 0000000000000000000000000000000000000000..3ee9b1cbca6cfef98616bd33bbf91b756b4efa15 --- /dev/null +++ b/tensorflow/docs_src/mobile/tflite/demo_ios.md @@ -0,0 +1,68 @@ +# TensorFlow Lite Demo for iOS + +The TensorFlow Lite demo is a camera app that continuously classifies whatever +it sees from your device's back camera, using a quantized MobileNet model. These +instructions walk you through building and running the demo on an iOS device. + +## Prerequisites + +* You must have [Xcode](https://developer.apple.com/xcode/) installed and have a + valid Apple Developer ID, and have an iOS device set up and linked to your + developer account with all of the appropriate certificates. For these + instructions, we assume that you have already been able to build and deploy an + app to an iOS device with your current developer environment. + +* The demo app requires a camera and must be executed on a real iOS device. You + can build it and run with the iPhone Simulator but it won't have any camera + information to classify. + +* You don't need to build the entire TensorFlow library to run the demo, but you + will need to clone the TensorFlow repository if you haven't already: + + git clone https://github.com/tensorflow/tensorflow + +* You'll also need the Xcode command-line tools: + + xcode-select --install + + If this is a new install, you will need to run the Xcode application once to + agree to the license before continuing. + +## Building the iOS Demo App + +1. Install CocoaPods if you don't have it: + + sudo gem install cocoapods + +2. Download the model files used by the demo app (this is done from inside the + cloned directory): + + sh tensorflow/contrib/lite/examples/ios/download_models.sh + +3. Install the pod to generate the workspace file: + + cd tensorflow/contrib/lite/examples/ios/camera + pod install + + If you have installed this pod before and that command doesn't work, try + + pod update + + At the end of this step you should have a file called + `tflite_camera_example.xcworkspace`. + +4. Open the project in Xcode by typing this on the command line: + + open tflite_camera_example.xcworkspace + + This launches Xcode if it isn't open already and opens the + `tflite_camera_example` project. + +5. Build and run the app in Xcode. + + Note that as mentioned earlier, you must already have a device set up and + linked to your Apple Developer account in order to deploy the app on a + device. + +You'll have to grant permissions for the app to use the device's camera. Point +the camera at various objects and enjoy seeing how the model classifies things! diff --git a/tensorflow/docs_src/performance/datasets_performance.md b/tensorflow/docs_src/performance/datasets_performance.md index 4f95e17c3598c23645fad07441c267266e5ef34e..46b43b7673c561679e89fff0ae738b0e751fcff5 100644 --- a/tensorflow/docs_src/performance/datasets_performance.md +++ b/tensorflow/docs_src/performance/datasets_performance.md @@ -92,11 +92,11 @@ transform the data. Without pipelining, the CPU and the GPU/TPU sit idle much of the time: -![without pipelining](https://www.tensorflow.org/images/datasets_without_pipelining.png) +![without pipelining](/images/datasets_without_pipelining.png) With pipelining, idle time diminishes significantly: -![with pipelining](https://www.tensorflow.org/images/datasets_with_pipelining.png) +![with pipelining](/images/datasets_with_pipelining.png) The `tf.data` API provides a software pipelining mechanism through the @{tf.data.Dataset.prefetch} transformation, which can be used to decouple the @@ -139,7 +139,7 @@ multiple CPU cores. To make this possible, the `map` transformation provides the the following diagram illustrates the effect of setting `num_parallel_calls=2` to the `map` transformation: -![parallel map](https://www.tensorflow.org/images/datasets_parallel_map.png) +![parallel map](/images/datasets_parallel_map.png) Choosing the best value for the `num_parallel_calls` argument depends on your hardware, characteristics of your training data (such as its size and shape), @@ -213,7 +213,7 @@ number of datasets to overlap can be specified by the `cycle_length` argument. The following diagram illustrates the effect of supplying `cycle_length=2` to the `parallel_interleave` transformation: -![parallel io](https://www.tensorflow.org/images/datasets_parallel_io.png) +![parallel io](/images/datasets_parallel_io.png) To apply this change to our running example, change: diff --git a/tensorflow/docs_src/performance/leftnav_files b/tensorflow/docs_src/performance/leftnav_files index 316f023f43dcfe781c7819d1681335267ddd5f76..d11a7e5d07c3e6cfa092e7ac11189ce6c272c1ad 100644 --- a/tensorflow/docs_src/performance/leftnav_files +++ b/tensorflow/docs_src/performance/leftnav_files @@ -2,6 +2,7 @@ performance_guide.md datasets_performance.md performance_models.md benchmarks.md +quantization.md ### XLA xla/index.md @@ -11,6 +12,3 @@ xla/jit.md xla/operation_semantics.md xla/shapes.md xla/tfcompile.md - -### Quantization -quantization.md diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 544274cab68934419e8601a4d9714d80335fca28..411889cb1c616130f809e6228cc692ba3f951d48 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -1,226 +1,253 @@ -# How to Quantize Neural Networks with TensorFlow - -When modern neural networks were being developed, the biggest challenge was -getting them to work at all! That meant that accuracy and speed during training -were the top priorities. Using floating point arithmetic was the easiest way to -preserve accuracy, and GPUs were well-equipped to accelerate those calculations, -so it's natural that not much attention was paid to other numerical formats. - -These days, we actually have a lot of models being deployed in commercial -applications. The computation demands of training grow with the number of -researchers, but the cycles needed for inference expand in proportion to users. -That means pure inference efficiency has become a burning issue for a lot of -teams. - -That is where quantization comes in. It's an umbrella term that covers a lot of -different techniques to store numbers and perform calculations on them in more -compact formats than 32-bit floating point. I am going to focus on eight-bit -fixed point, for reasons I'll go into more detail on later. - -[TOC] - -## Why does Quantization Work? - -Training neural networks is done by applying many tiny nudges to the weights, -and these small increments typically need floating point precision to work -(though there are research efforts to use quantized representations here too). - -Taking a pre-trained model and running inference is very different. One of the -magical qualities of deep networks is that they tend to cope very well with high -levels of noise in their inputs. If you think about recognizing an object in a -photo you've just taken, the network has to ignore all the CCD noise, lighting -changes, and other non-essential differences between it and the training -examples it's seen before, and focus on the important similarities instead. This -ability means that they seem to treat low-precision calculations as just another -source of noise, and still produce accurate results even with numerical formats -that hold less information. - -## Why Quantize? - -Neural network models can take up a lot of space on disk, with the original -AlexNet being over 200 MB in float format for example. Almost all of that size -is taken up with the weights for the neural connections, since there are often -many millions of these in a single model. Because they're all slightly different -floating point numbers, simple compression formats like zip don't compress them -well. They are arranged in large layers though, and within each layer the -weights tend to be normally distributed within a certain range, for example -3.0 -to 6.0. - -The simplest motivation for quantization is to shrink file sizes by storing the -min and max for each layer, and then compressing each float value to an -eight-bit integer representing the closest real number in a linear set of 256 -within the range. For example with the -3.0 to 6.0 range, a 0 byte would -represent -3.0, a 255 would stand for 6.0, and 128 would represent about 1.5. -I'll go into the exact calculations later, since there's some subtleties, but -this means you can get the benefit of a file on disk that's shrunk by 75%, and -then convert back to float after loading so that your existing floating-point -code can work without any changes. - -Another reason to quantize is to reduce the computational resources you need to -do the inference calculations, by running them entirely with eight-bit inputs -and outputs. This is a lot more difficult since it requires changes everywhere -you do calculations, but offers a lot of potential rewards. Fetching eight-bit -values only requires 25% of the memory bandwidth of floats, so you'll make much -better use of caches and avoid bottlenecking on RAM access. You can also -typically use SIMD operations that do many more operations per clock cycle. In -some case you'll have a DSP chip available that can accelerate eight-bit -calculations too, which can offer a lot of advantages. - -Moving calculations over to eight bit will help you run your models faster, and -use less power (which is especially important on mobile devices). It also opens -the door to a lot of embedded systems that can't run floating point code -efficiently, so it can enable a lot of applications in the IoT world. - -## Why Not Train in Lower Precision Directly? - -There have been some experiments training at lower bit depths, but the results -seem to indicate that you need higher than eight bit to handle the back -propagation and gradients. That makes implementing the training more -complicated, and so starting with inference made sense. We also already have a -lot of float models already that we use and know well, so being able to convert -them directly is very convenient. - -## How Can You Quantize Your Models? - -TensorFlow has production-grade support for eight-bit calculations built in. It -also has a process for converting many models trained in floating-point over to -equivalent graphs using quantized calculations for inference. For example, -here's how you can translate the latest GoogLeNet model into a version that uses -eight-bit computations: - -```sh -curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" | - tar -C tensorflow/examples/label_image/data -xz -bazel build tensorflow/tools/graph_transforms:transform_graph -bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ - --in_graph=tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb \ - --out_graph=/tmp/quantized_graph.pb \ - --inputs=input \ - --outputs=InceptionV3/Predictions/Reshape_1 \ - --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3") - remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) - fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes - strip_unused_nodes sort_by_execution_order' +# Fixed Point Quantization + +Quantization techniques store and calculate numbers in more compact formats. +[TensorFlow Lite](/mobile/tflite/) adds quantization that uses an 8-bit fixed +point representation. + +Since a challenge for modern neural networks is optimizing for high accuracy, the +priority has been improving accuracy and speed during training. Using floating +point arithmetic is an easy way to preserve accuracy and GPUs are designed to +accelerate these calculations. + +However, as more machine learning models are deployed to mobile devices, +inference efficiency has become a critical issue. Where the computational demand +for *training* grows with the amount of models trained on different +architectures, the computational demand for *inference* grows in proportion to +the amount of users. + +## Quantization benefits + + +Using 8-bit calculations help your models run faster and use less power. This is +especially important for mobile devices and embedded applications that can't run +floating point code efficiently, for example, Internet of Things (IoT) and +robotics devices. There are additional opportunities to extend this support to +more backends and research lower precision networks. + +### Smaller file sizes {: .hide-from-toc} + +Neural network models require a lot of space on disk. For example, the original +AlexNet requires over 200 MB for the float format—almost all of that for the +model's millions of weights. Because the weights are slightly different +floating point numbers, simple compression formats perform poorly (like zip). + +Weights fall in large layers of numerical values. For each layer, weights tend to +be normally distributed within a range. Quantization can shrink file sizes by +storing the minimum and maximum weight for each layer, then compress each +weight's float value to an 8-bit integer representing the closest real number in +a linear set of 256 within the range. + +### Faster inference {: .hide-from-toc} + +Since calculations are run entirely on 8-bit inputs and outputs, quantization +reduces the computational resources needed for inference calculations. This is +more involved, requiring changes to all floating point calculations, but results +in a large speed-up for inference time. + +### Memory efficiency {: .hide-from-toc} + +Since fetching 8-bit values only requires 25% of the memory bandwidth of floats, +more efficient caches avoid bottlenecks for RAM access. In many cases, the power +consumption for running a neural network is dominated by memory access. The +savings from using fixed-point 8-bit weights and activations are significant. + +Typically, SIMD operations are available that run more operations per clock +cycle. In some cases, a DSP chip is available that accelerates 8-bit calculations +resulting in a massive speedup. + +## Fixed point quantization techniques + +The goal is to use the same precision for weights and activations during both +training and inference. But an important difference is that training consists of +a forward pass and a backward pass, while inference only uses a forward pass. +When we train the model with quantization in the loop, we ensure that the forward +pass matches precision for both training and inference. + +To minimize the loss in accuracy for fully fixed point models (weights and +activations), train the model with quantization in the loop. This simulates +quantization in the forward pass of a model so weights tend towards values that +perform better during quantized inference. The backward pass uses quantized +weights and activations and models quantization as a straight through estimator. +(See Bengio et al., [2013](https://arxiv.org/abs/1308.3432)) + +Additionally, the minimum and maximum values for activations are determined +during training. This allows a model trained with quantization in the loop to be +converted to a fixed point inference model with little effort, eliminating the +need for a separate calibration step. + +## Quantization training with TensorFlow + +TensorFlow can train models with quantization in the loop. Because training +requires small gradient adjustments, floating point values are still used. To +keep models as floating point while adding the quantization error in the training +loop, @{$array_ops#Fake_quantization$fake quantization} nodes simulate the +effect of quantization in the forward and backward passes. + +Since it's difficult to add these fake quantization operations to all the +required locations in the model, there's a function available that rewrites the +training graph. To create a fake quantized training graph: + +``` +# Build forward pass of model. +loss = tf.losses.get_total_loss() + +# Call the training rewrite which rewrites the graph in-place with +# FakeQuantization nodes and folds batchnorm for training. It is +# often needed to fine tune a floating point model for quantization +# with this training tool. When training from scratch, quant_delay +# can be used to activate quantization after training to converge +# with the float graph, effectively fine-tuning the model. +tf.contrib.quantize.create_training_graph(quant_delay=2000000) + +# Call backward pass optimizer as usual. +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +optimizer.minimize(loss) ``` -This will produce a new model that runs the same operations as the original, but -with eight bit calculations internally, and all weights quantized as well. If -you look at the file size, you'll see it's about a quarter of the original (23MB -versus 91MB). You can still run this model using exactly the same inputs and -outputs though, and you should get equivalent results. Here's an example: +The rewritten *eval graph* is non-trivially different from the *training graph* +since the quantization ops affect the batch normalization step. Because of this, +we've added a separate rewrite for the *eval graph*: -```sh -bazel build tensorflow/examples/label_image:label_image -bazel-bin/tensorflow/examples/label_image/label_image \ ---graph=/tmp/quantized_graph.pb \ +``` +# Build eval model +logits = tf.nn.softmax_cross_entropy_with_logits(...) + +# Call the eval rewrite which rewrites the graph in-place with +# FakeQuantization nodes and fold batchnorm for eval. +tf.contrib.quantize.create_eval_graph() + +# Save the checkpoint and eval graph proto to disk for freezing +# and providing to TFLite. +with open(eval_graph_file, ‘w’) as f: + f.write(str(g.as_graph_def())) +saver = tf.train.Saver() +saver.save(sess, checkpoint_name) +``` + +Methods to rewrite the training and eval graphs are an active area of research +and experimentation. Although rewrites and quantized training might not work or +improve performance for all models, we are working to generalize these +techniques. + +## Generating fully quantized models + +The previously demonstrated after-rewrite eval graph only *simulates* +quantization. To generate real fixed point computations from a trained +quantization model, convert it to a fixed point kernel. Tensorflow Lite supports +this conversion from the graph resulting from `create_eval_graph`. + +First, create a frozen graph that will be the input for the TensorFlow Lite +toolchain: + +``` +bazel build tensorflow/python/tools:freeze_graph && \ + bazel-bin/tensorflow/python/tools/freeze_graph \ + --input_graph=eval_graph_def.pb \ + --input_checkpoint=checkpoint \ + --output_graph=frozen_eval_graph.pb --output_node_names=outputs ``` -You'll see that this runs the newly-quantized graph, and outputs a very similar -answer to the original. - -You can run the same process on your own models saved out as GraphDefs, with the -input and output names adapted to those your network requires. I recommend that -you run them through the freeze_graph script first, to convert checkpoints into -constants stored in the file. - -## How Does the Quantization Process Work? - -We've implemented quantization by writing equivalent eight-bit versions of -operations that are commonly used during inference. These include convolution, -matrix multiplication, activation functions, pooling operations and -concatenation. The conversion script first replaces all the individual ops it -knows about with quantized equivalents. These are small sub-graphs that have -conversion functions before and after to move the data between float and -eight-bit. Below is an example of what they look like. First here's the original -Relu operation, with float inputs and outputs: - -![Relu Diagram](https://www.tensorflow.org/images/quantization0.png) - -Then, this is the equivalent converted subgraph, still with float inputs and -outputs, but with internal conversions so the calculations are done in eight -bit. - -![Converted Diagram](https://www.tensorflow.org/images/quantization1.png) - -The min and max operations actually look at the values in the input float -tensor, and then feeds them into the Dequantize operation that converts the -tensor into eight-bits. There are more details on how the quantized representation -works later on. - -Once the individual operations have been converted, the next stage is to remove -unnecessary conversions to and from float. If there are consecutive sequences of -operations that all have float equivalents, then there will be a lot of adjacent -Dequantize/Quantize ops. This stage spots that pattern, recognizes that they -cancel each other out, and removes them, like this: - -![Stripping Diagram](https://www.tensorflow.org/images/quantization2.png) - -Applied on a large scale to models where all of the operations have quantized -equivalents, this gives a graph where all of the tensor calculations are done in -eight bit, without having to convert to float. - -## What Representation is Used for Quantized Tensors? - -We approach converting floating-point arrays of numbers into eight-bit -representations as a compression problem. We know that the weights and -activation tensors in trained neural network models tend to have values that are -distributed across comparatively small ranges (for example you might have -15 to -+15 for weights, -500 to 1000 for activations on an image model, though the -exact numbers will vary). We also know from experiment that neural nets tend to -be very robust in the face of noise, and so the noise-like error produced by -quantizing down to a small set of values will not hurt the precision of the -overall results very much. We also want to pick a representation that's easy to -perform calculations on, especially the large matrix multiplications that form -the bulk of the work that's needed to run a model. - -These led us to pick a representation that has two floats to store the overall -minimum and maximum values that are represented by the lowest and highest -quantized value. Each entry in the quantized array represents a float value in -that range, distributed linearly between the minimum and maximum. For example, -if we have minimum = -10.0, and maximum = 30.0f, and an eight-bit array, here's -what the quantized values represent: +Provide this to the TensorFlow Lite Optimizing Converter (TOCO) to get a fully +quantized TensorFLow Lite model: ``` -Quantized | Float ---------- | ----- -0 | -10.0 -255 | 30.0 -128 | 10.0 +bazel build tensorflow/contrib/lite/toco:toco && \ + ./bazel-bin/third_party/tensorflow/contrib/lite/toco/toco \ + --input_file=frozen_eval_graph.pb \ + --output_file=tflite_model.tflite \ + --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \ + --inference_type=QUANTIZED_UINT8 \ + --input_shape="1,224, 224,3" \ + --input_array=input \ + --output_array=outputs \ + --std_value=127.5 --mean_value=127.5 ``` -The advantages of this format are that it can represent arbitrary magnitudes of -ranges, they don't have to be symmetrical, it can represent signed and unsigned -values, and the linear spread makes doing multiplications straightforward. There -are alternatives like [Song Han's code books](http://arxiv.org/pdf/1510.00149.pdf) -that can use lower bit depths by non-linearly distributing the float values -across the representation, but these tend to be more expensive to calculate on. - -The advantage of having a strong and clear definition of the quantized format is -that it's always possible to convert back and forth from float for operations -that aren't quantization-ready, or to inspect the tensors for debugging -purposes. One implementation detail in TensorFlow that we're hoping to improve -in the future is that the minimum and maximum float values need to be passed as -separate tensors to the one holding the quantized values, so graphs can get a -bit dense! - -The nice thing about the minimum and maximum ranges is that they can often be -pre-calculated. Weight parameters are constants known at load time, so their -ranges can also be stored as constants. We often know the ranges for inputs (for -examples images are usually RGB values in the range 0.0 to 255.0), and many -activation functions have known ranges too. This can avoid having to analyze the -outputs of an operation to determine the range, which we need to do for math ops -like convolution or matrix multiplication which produce 32-bit accumulated -results from 8-bit inputs. - -## What's Next? - -We've found that we can get extremely good performance on mobile and embedded -devices by using eight-bit arithmetic rather than floating-point. You can see -the framework we use to optimize matrix multiplications at -[gemmlowp](https://github.com/google/gemmlowp). We still need to apply all the -lessons we've learned to the TensorFlow ops to get maximum performance on -mobile, but we're actively working on that. Right now, this quantized -implementation is a reasonably fast and accurate reference implementation that -we're hoping will enable wider support for our eight-bit models on a wider -variety of devices. We also hope that this demonstration will encourage the -community to explore what's possible with low-precision neural networks. +See the documentation for @{tf.contrib.quantize} and +[TensorFlow Lite](/mobile/tflite/). + +## Quantized accuracy + +Fixed point [MobileNet](https://arxiv.org/abs/1704.0486) models are released with +8-bit weights and activations. Using the rewriters, these models achieve the +Top-1 accuracies listed in Table 1. For comparison, the floating point accuracies +are listed for the same models. The code used to generate these models +[is available](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) +along with links to all of the pretrained mobilenet_v1 models. + +
+
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.6.0rc0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.6.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.6.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.5.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.5.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.4.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
+ + + + + + + + + + + + + + + + + + + + + + +
Image SizeDepthTop-1 Accuracy:
Floating point		Top-1 Accuracy:
Fixed point: 8 bit weights and activations
1280.250.4150.399
1280.50.5630.549
1280.750.6210.598
12810.6520.64
1600.250.4550.435
1600.50.5910.577
1600.750.6530.639
16010.680.673
1920.250.4770.458
1920.50.6170.604
1920.750.6720.662
19210.70.69
2240.250.4980.482
2240.50.6330.622
2240.750.6840.679
22410.7090.697
+
+ Table 1: MobileNet Top-1 accuracy on Imagenet Validation dataset. +
+ + +## Representation for quantized tensors + +TensorFlow approaches the conversion of floating-point arrays of numbers into +8-bit representations as a compression problem. Since the weights and activation +tensors in trained neural network models tend to have values that are distributed +across comparatively small ranges (for example, -15 to +15 for weights or -500 to +1000 for image model activations). And since neural nets tend to be robust +handling noise, the error introduced by quantizing to a small set of values +maintains the precision of the overall results within an acceptable threshold. A +chosen representation must perform fast calculations, especially the large matrix +multiplications that comprise the bulk of the computations while running a model. + +This is represented with two floats that store the overall minimum and maximum +values corresponding to the lowest and highest quantized value. Each entry in the +quantized array represents a float value in that range, distributed linearly +between the minimum and maximum. For example, with a minimum of -10.0 and maximum +of 30.0f, and an 8-bit array, the quantized values represent the following: + +
+ + + + + +
QuantizedFloat
0-10.0
25530.0
12810.0
+
+ Table 2: Example quantized value range +
+
+ +The advantages of this representation format are: + +* It efficiently represents an arbitrary magnitude of ranges. +* The values don't have to be symmetrical. +* The format represents both signed and unsigned values. +* The linear spread makes multiplications straightforward. + +Alternative techniques use lower bit depths by non-linearly distributing the +float values across the representation, but currently are more expensive in terms +of computation time. (See Han et al., +[2016](https://arxiv.org/abs/1510.00149).) + +The advantage of having a clear definition of the quantized format is that it's +always possible to convert back and forth from fixed-point to floating-point for +operations that aren't quantization-ready, or to inspect the tensors for +debugging. diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index f865c30aa8bd1639a72a0f4641b883a8890f9c13..712d425331b9e75d4a1fe030a65ddff6be3d5292 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -45,27 +45,30 @@ feature dimension in `operand`), the operation calculates the gradients with respect to `operand`, `offset` and `scale` across all the other dimensions. The `feature_index` must be a valid index for the feature dimension in `operand`. -The three gradients are defined by the following formulas: +The three gradients are defined by the following formulas (Assuming a +4-dimensional tensor as `operand` and (l) is the index for feature dimension): -\\( \nabla x = \nabla y * \gamma * \sqrt{\sigma^2+\epsilon} \\) +\\( coef_l = \frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h (\nabla y_{ijkl} * (x_{ijkl} - \mu_l) / (\sigma^2_{l}+\epsilon)) \\) -\\( \nabla \gamma = sum(\nabla y * (x - \mu) * \sqrt{\sigma^2 + \epsilon}) \\) +\\( \nabla x_{ijkl} = \gamma_{l} * (1/\sqrt{\sigma^2_{l}+\epsilon}) * [\nabla y_{ijkl} - mean(\nabla y) - (x_{ijkl} - \mu_{l}) * coef_l] \\) -\\( \nabla \beta = sum(\nabla y) \\) +\\( \nabla \beta_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} \\) + +\\( \nabla \gamma_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} * ((x_{ijkl} - \mu_l) / \sqrt{\sigma^2_{l}+\epsilon}) \\) The inputs `mean` and `variance` represents moments value across batch and spatial dimensions. The output type is a tuple of three handles: -|Outputs | Type | Semantics | -|------------- | ----------------------- | ------------------------------------| -|`grad_operand`| `ComputationDataHandle` | gradient with respect to input | -: : : `operand` : -|`grad_scale` | `ComputationDataHandle` | gradient with respect to input | -: : : `scale` : -|`grad_offset` | `ComputationDataHandle` | gradient with respect to input | -: : : `offset` : +|Outputs | Type | Semantics | +|------------- | ----------------------- | ------------------------------------ | +|`grad_operand`| `ComputationDataHandle` | gradient with respect to input | +: : : `operand` (\\( \nabla x\\)) : +|`grad_scale` | `ComputationDataHandle` | gradient with respect to input | +: : : `scale` (\\( \nabla \gamma\\)) : +|`grad_offset` | `ComputationDataHandle` | gradient with respect to input | +: : : `offset`(\\( \nabla \beta\\)) : ## BatchNormInference @@ -119,11 +122,11 @@ Normalizes an array across batch and spatial dimensions. | Arguments | Type | Semantics | | --------------- | ----------------------- | -------------------------------- | | `operand` | `ComputationDataHandle` | n dimensional array to be | -: : : normalized : +: : : normalized (x) : | `scale` | `ComputationDataHandle` | 1 dimensional array | : : : (\\(\gamma\\)) : | `offset` | `ComputationDataHandle` | 1 dimensional array | -: : : (\\(\beta\\ ) : +: : : (\\(\beta\\)) : | `epsilon` | `float` | Epsilon value (\\(\epsilon\\)) | | `feature_index` | `int64` | Index to feature dimension | : : : in `operand` : @@ -135,8 +138,8 @@ element in `operand`. The `feature_index` must be a valid index for the feature dimension in `operand`. The algorithm goes as follows for each batch in `operand` \\(x\\) that -contains `m` elements with `w` and `h` as the size of spatial dimensions ( -assuming `operand` is an 4 dimensional array): +contains `m` elements with `w` and `h` as the size of spatial dimensions +(assuming `operand` is an 4 dimensional array): - Calculates batch mean \\(\mu_l\\) for each feature `l` in feature dimension: \\(\mu_l=\frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h x_{ijkl}\\) @@ -170,7 +173,7 @@ Similar to a `tf.bitcast` in TensorFlow, performs an element-wise bitcast operation from a data shape to a target shape. The dimensions must match, and the conversion is an element-wise one; e.g. `s32` elements become `f32` elements via bitcast routine. Bitcast is implemented as a low-level cast, so machines -with different floating point representations will give different results. +with different floating-point representations will give different results. `BitcastConvertType(operand, new_element_type)` @@ -351,7 +354,7 @@ each other) and contains the arguments in the order that they were specified. : : : concatenated between the `operands`. : With the exception of `dimension` all dimensions must be the same. This is -because XLA does not support "ragged" arrays Also note that rank-0 values +because XLA does not support "ragged" arrays. Also note that rank-0 values cannot be concatenated (as it's impossible to name the dimension along which the concatenation occurs). @@ -440,11 +443,13 @@ area and a computation is performed for each possible position of the window. | `lhs` | `ComputationDataHandle` | rank n+2 array of inputs | | `rhs` | `ComputationDataHandle` | rank n+2 array of kernel | : : : weights : -| `window_strides` | `ArraySlice` | n-d array of kernel strides | -| `padding` | `ArraySlice` | size n array of kernel strides| +| `padding` | `ArraySlice>` : padding : -| `lhs_dilation` | `ArraySlice` | n-d lhs dilation factor array | -| `rhs_dilation` | `ArraySlice` | n-d rhs dilation factor array | +| `lhs_dilation` | `ArraySlice` | size n lhs dilation factor | +: : : array | +| `rhs_dilation` | `ArraySlice` | size n rhs dilation factor +: : : array | Let n be the number of spatial dimensions. The `lhs` argument is a rank n+2 array describing the base area. This is called the input, even though of course @@ -468,7 +473,7 @@ filter/kernel/window. The dimensions are, in this order: window that moves across the base area. The `window_strides` argument specifies the stride of the convolutional window -in the spatial dimensions. For example, if the stride in a the first spatial +in the spatial dimensions. For example, if the stride in the first spatial dimension is 3, then the window can only be placed at coordinates where the first spatial index is divisible by 3. @@ -717,6 +722,7 @@ in 'dimension_numbers'. Associated contracting dimension numbers from the 'lhs' and 'rhs' do not need to be the same, but must be listed in the same order in both 'lhs/rhs_contracting_dimensions' arrays and have the same dimension sizes. +There must be exactly one contracting dimension on both 'lhs' and 'rhs'. Example with contracting dimension numbers: @@ -736,8 +742,9 @@ DotGeneral(lhs, rhs, dnums) -> { {6.0, 12.0}, ``` Associated batch dimension numbers from the 'lhs' and 'rhs' must have the same -dimension number, must be listed in the same order in both arrays, and must -have the same dimension sizes. +dimension number, must be listed in the same order in both arrays, must +have the same dimension sizes, and must be ordered before contracting and +non-contracting/non-batch dimension numbers. Example with batch dimension numbers (batch size 2, 2x2 matrices): @@ -769,6 +776,10 @@ DotGeneral(lhs, rhs, dnums) -> { { {1.0, 2.0}, | [b0, m, k] `dot` [b0, k, n] | [b0, m, n] | batch matmul | | [b0, b1, m, k] `dot` [b0, b1, k, n] | [b0, b1, m, n] | batch matmul | +It follows that the resulting dimension number starts with the batch dimension, +then the 'lhs' non-contracting/non-batch dimension, and finally the 'rhs' +non-contracting/non-batch dimension. + ## DynamicSlice See also @@ -936,7 +947,7 @@ expand the rank of the lower-rank operand up to the rank of the higher-rank operand. `broadcast_dimensions` maps the dimensions of the lower-rank shape to the dimensions of the higher-rank shape. The unmapped dimensions of the expanded shape are filled with dimensions of size one. Degenerate-dimension broadcasting -then broadcasts the shapes along these degenerate dimension to equalize the +then broadcasts the shapes along these degenerate dimensions to equalize the shapes of both operands. The semantics are described in detail on the @{$broadcasting$broadcasting page}. @@ -1021,6 +1032,213 @@ Arguments | Type | Semantics The function is applied to each element in the `operand` array, resulting in an array with the same shape. It is allowed for `operand` to be a scalar (rank 0). +## Gather + +The XLA gather operation stitches together several slices (each slice at a +potentially different runtime offset) of an input tensor into an output tensor. + +### General Semantics + +See also +[`ComputationBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h). +For a more intuitive description, see the "Informal Description" section below. + + `gather(operand, gather_indices, output_window_dims, elided_window_dims, window_bounds, gather_dims_to_operand_dims)` + +|Arguments | Type | Semantics | +|----------------- | ----------------------- | --------------------------------| +|`operand` | `ComputationDataHandle` | The tensor we’re gathering | +: : : from. : +|`gather_indices` | `ComputationDataHandle` | Tensor containing the starting | +: : : indices of the slices we're : +: : : we're stitching together into : +: : : the output tensor. : +|`index_vector_dim` | `int64` | The dimension in | +: : : `gather_indices` that contains : +: : : the starting indices. : +|`output_window_dims` | `ArraySlice` | The set of dimensions in the | +: : : output shape that are _window : +: : : dimensions_ (defined below). : +: : : Not all window dimensions may : +: : : be present in the output shape. : +|`elided_window_dims` | `ArraySlice` | The set of _window dimensions_ | +: : : that are not present in the output shape. : +: : : `window_bounds[i]` must be `1` for all `i` : +: : : in `elided_window_dims`. : +|`window_bounds` | `ArraySlice` | `window_bounds[i]` is the bounds | +: : : for window dimension `i`. This includes : +: : : both the window dimensions that are : +: : : explicitly part of the output shape (via : +: : : `output_window_dims`) and the window : +: : : dimensions that are elided (via : +: : : `elided_window_dims`). : +|`gather_dims_to_operand_dims` | `ArraySlice` | A dimension map (the | +: : : array is interpreted as mapping `i` to : +: : : `gather_dims_to_operand_dims[i]`) from : +: : : the gather indices in `gather_indices` to : +: : : the operand index space. It has to be : +: : : one-to-one and total. : + +For every index `Out` in the output tensor, we compute two things (more +precisely described later): + + - An index into `gather_indices.rank` - `1` dimensions of `gather_indices`, + which gives us a starting index of a slice, _operand slice_, in the operand + tensor. These `gather_indices.rank` - `1` dimensions are all the dimensions + in `gather_indices` except `index_vector_dim`. + + - A _window index_ that has the same rank as the operand. This index is + composed of the values in `Out` at dimensions `output_window_dims`, embedded + with zeroes according to `elided_window_dims`. + +The _window index_ is the relative index of the element in _operand slice_ that +should be present in the output at index `Out`. + +The output is a tensor of rank `output_window_dims.size` + `gather_indices.rank` +- `1`. Additionally, as a shorthand, we define `output_gather_dims` of type +`ArraySlice` as the set of dimensions in the output shape but not in +`output_window_dims`, in ascending order. E.g. if the output tensor has rank +`5`, `output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, +`3`} + +If `index_vector_dim` is equal to `gather_indices.rank` we implicitly +consider `gather_indices` to have a trailing `1` dimension (i.e. if +`gather_indices` was of shape `[6,7]` and `index_vector_dim` is `2` then +we implicitly consider the shape of `gather_indices` to be `[6,7,1]`). + +The bounds for the output tensor along dimension `i` is computed as follows: + + 1. If `i` is present in `output_gather_dims` (i.e. is equal to + `output_gather_dims[k]` for some `k`) then we pick the corresponding + dimension bounds out of `gather_indices.shape`, skipping + `index_vector_dim` (i.e. pick `gather_indices.shape.dims`[`k`] if `k` + < `index_vector_dim` and `gather_indices.shape.dims`[`k`+`1`] + otherwise). + 2. If `i` is present in `output_window_dims` (i.e. equal to + `output_window_dims`[`k`] for some `k`) then we pick the corresponding + bound out of `window_bounds` after accounting for `elided_window_dims` + (i.e. we pick `adjusted_window_bounds`[`k`] where `adjusted_window_bounds` + is `window_bounds` with the bounds at indices `elided_window_dims` + removed). + +The operand index `In` corresponding to an output index `Out` is computed as +follows: + + 1. Let `G` = { `Out`[`k`] for `k` in `output_gather_dims` }. Use `G` to slice + out vector `S` such that `S`[`i`] = `gather_indices`[Combine(`G`, `i`)] + where Combine(A, b) inserts b at position `index_vector_dim` into A. + Note that this is well defined even if `G` is empty -- if `G` is empty then + `S` = `gather_indices`. + 2. Create an index, `S``in`, into `operand` using `S` by + scattering `S` using the `gather_dims_to_operand_dims` map + (`S``in` is the starting indices for _operand slice_ mentioned + above). More precisely: + 1. `S``in`[`gather_dims_to_operand_dims`[`k`]] = `S`[`k`] if `k` < + `gather_dims_to_operand_dims.size`. + 2. `S``in`[`_`] = `0` otherwise. + 3. Create an index `W``in` into `operand` by scattering the indices + at the output window dimensions in `Out` according to + the `elided_window_dims` set (`W``in` is the _window index_ + mentioned above). More precisely: + 1. `W``in`[`window_dims_to_operand_dims`(`k`)] = `Out`[`k`] if + `k` < `output_window_dims.size` (`window_dims_to_operand_dims` is + defined below). + 2. `W``in`[`_`] = `0` otherwise. + 4. `In` is `W``in` + `S``in` where + is element-wise + addition. + +`window_dims_to_operand_dims` is the monotonic function with domain [`0`, +`output_window_dims.size`) and range [`0`, `operand.rank`) \ +`elided_window_dims`. So if, e.g., `output_window_dims.size` is `4`, +`operand.rank` is `6` and `elided_window_dims` is {`0`, `2`} then +`window_dims_to_operand_dims` is {`0`→`1`, `1`→`3`, `2`→`4`, `3`→`5`}. + +### Informal Description and Examples + +`index_vector_dim` is set to `gather_indices.rank` - `1` in all of the +examples that follow. More interesting values for `index_vector_dim` +does not change the operation fundamentally, but makes the visual representation +more cumbersome. + +To get an intuition on how all of the above fits together, let's look at an +example that gathers 5 slices of shape `[8,6]` from a `[16,11]` tensor. The +position of a slice into the `[16,11]` tensor can be represented as an index +vector of shape `S64[2]`, so the set of 5 positions can be represented as a +`S64[5,2]` tensor. + +The behavior of the gather operation can then be depicted as an index +transformation that takes [`G`,`W``0`,`W``1`], an index in +the output shape, and maps it to an element in the input tensor in the following +way: + +
+ +
+ +We first select an (`X`,`Y`) vector from the gather indices tensor using `G`. +The element in the output tensor at index +[`G`,`W``0`,`W``1`] is then the element in the input +tensor at index [`X`+`W``0`,`Y`+`W``1`]. + +`window_bounds` is `[8,6]`, which decides the range of W`0` and +W`1`, and this in turn decides the bounds of the slice. + +This gather operation acts as a batch dynamic slice with `G` as the batch +dimension. + +The gather indices may be multidimensional. For instance, a more general +version of the example above using a "gather indices" tensor of shape `[4,5,2]` +would translate indices like this: + +
+ +
+ +Again, this acts as a batch dynamic slice `G``0` and +`G``1` as the batch dimensions. The window bounds are still `[8,6]`. + +The gather operation in XLA generalizes the informal semantics outlined above in +the following ways: + + 1. We can configure which dimensions in the output shape are the window + dimensions (dimensions containing `W``0`, `W``1` in + the last example). The output gather dimensions (dimensions containing + `G``0`, `G``1` in the last example) are defined to be + the output dimensions that are not window dimensions. + + 2. The number of output window dimensions explicitly present in the output + shape may be smaller than the input rank. These "missing" dimensions, which + are listed explicitly as `elided_window_dims`, must have a window bound of + `1`. Since they have a window bound of `1` the only valid index for them is + `0` and eliding them does not introduce ambiguity. + + 3. The slice extracted from the "Gather Indices" tensor ((`X`, `Y`) in the last + example) may have fewer elements than the input tensor rank, and an explicit + mapping dictates how the index should be expanded to have the same rank as + the input. + +As a final example, we use (2) and (3) to implement `tf.gather_nd`: + +
+ +
+ +`G``0` and `G``1` are used to slice out a starting index +from the gather indices tensor as usual, except the starting index has only one +element, `X`. Similarly, there is only one output window index with the value +`W``0`. However, before being used as indices into the input tensor, +these are expanded in accordance to "Gather Index Mapping" +(`gather_dims_to_operand_dims` in the formal description) and "Window Mapping" +(`window_dims_to_operand_dims` in the formal description) into +[`0`,`W``0`] and [`X`,`0`] respectively, adding up to +[`X`,`W``0`]. In other words, the output index +[`G``0`,`G``1`,`W``0`] maps to the input index +[`GatherIndices`[`G``0`,`G``1`,`0`],`X`] which gives us +the semantics for `tf.gather_nd`. + +`window_bounds` for this case is `[1,11]`. Intuitively this means that every +index `X` in the gather indices tensor picks an entire row and the result is the +concatenation of all these rows. ## GetTupleElement @@ -1075,7 +1293,7 @@ result2 = while (condition, init = result1) { ``` Nested tuple shapes are not supported. For an empty tuple shape, the Infeed -operation is effectively a nop and proceeds without reading any data from the +operation is effectively a no-op and proceeds without reading any data from the Infeed of the device. > Note: We plan to allow multiple Infeed operations without a total order, in @@ -1138,7 +1356,7 @@ dimension. `PaddingConfig` is a repeated field of `PaddingConfigDimension`, which contains three fields for each dimension: `edge_padding_low`, `edge_padding_high`, and -`interior_padding`. `edge_padding_low` and `edge_padding_high` specifies the +`interior_padding`. `edge_padding_low` and `edge_padding_high` specify the amount of padding added at the low-end (next to index 0) and the high-end (next to the highest index) of each dimension respectively. The amount of edge padding can be negative -- the absolute value of negative padding indicates the number @@ -1147,8 +1365,8 @@ the amount of padding added between any two elements in each dimension. Interior padding occurs logically before edge padding, so in the case of negative edge padding elements are removed from the interior-padded operand. This operation is a no-op if the edge padding pairs are all (0, 0) and the interior padding values -are all 0. Figure below shows examples of different `edge_padding` and -`interior_padding` values for a two dimensional array. +are all 0. The figure below shows examples of different `edge_padding` and +`interior_padding` values for a two-dimensional array.
diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md index 9ede4ab83c1dcdb7370e83dfb9227fbb235d0689..9ccdbde627e6b2415835f7c0771eca1afa04f7f8 100644 --- a/tensorflow/docs_src/programmers_guide/datasets.md +++ b/tensorflow/docs_src/programmers_guide/datasets.md @@ -18,11 +18,11 @@ The `tf.data` API introduces two new abstractions to TensorFlow: tensors representing the image data and a label. There are two distinct ways to create a dataset: - * Creating a **source** (e.g. `Dataset.from_tensor_slices()`) constructs a + * Creating a **source** (e.g. `Dataset.from_tensor_slices()`) constructs a dataset from one or more `tf.Tensor` objects. - * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset + * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset from one or more `tf.data.Dataset` objects. * A `tf.data.Iterator` provides the main way to extract elements from a @@ -322,9 +322,39 @@ sess.run(iterator.initializer) next1, (next2, next3) = iterator.get_next() ``` -Note that evaluating *any* of `next1`, `next2`, or `next3` will advance the -iterator for all components. A typical consumer of an iterator will include all -components in a single expression. +Note that `next1`, `next2`, and `next3` are tensors produced by the +same op/node (created by `Iterator.get_next()`). Therefore, evaluating *any* of +these tensors will advance the iterator for all components. A typical consumer +of an iterator will include all components in a single expression. + +### Saving iterator state + +The @{tf.contrib.data.make_saveable_from_iterator} function creates a +`SaveableObject` from an iterator, which can be used to save and +restore the current state of the iterator (and, effectively, the whole input +pipeline). A saveable object thus created can be added to @{tf.train.Saver} +variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and +restoring in the same manner as a @{tf.Variable}. Refer to +@{$saved_model$Saving and Restoring} for details on how to save and restore +variables. + +```python +# Create saveable object from iterator. +saveable = tf.contrib.data.make_saveable_from_iterator(iterator) + +# Save the iterator state by adding it to the saveable objects collection. +tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable) +saver = tf.train.Saver() + +with tf.Session() as sess: + + if should_checkpoint: + saver.save(path_to_checkpoint) + +# Restore the iterator state. +with tf.Session() as sess: + saver.restore(sess, path_to_checkpoint) +``` ## Reading input data diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md index c1a90dee0a6e5e6bc9b51cd232718179f1511f61..5fb1c2da88c1634af28ae139bc77eeedde152c8b 100644 --- a/tensorflow/docs_src/programmers_guide/debugger.md +++ b/tensorflow/docs_src/programmers_guide/debugger.md @@ -1,4 +1,4 @@ -# Debugging TensorFlow Programs +# TensorFlow Debugger @@ -23,8 +23,13 @@ debuggers such as Python's `pdb` due to TensorFlow's computation-graph paradigm. > installed using `pip install .whl`, however curses on Windows > may not work as reliably as curses on Linux or Mac. -This tutorial demonstrates how to use the **tfdbg** command-line interface -(CLI) to debug the appearance of [`nan`s](https://en.wikipedia.org/wiki/NaN) +> NOTE: This guide focuses on the command-line interface (CLI) of tfdbg. For +> guide on how to use the graphical user interface (GUI) of tfdbg, i.e., the +> **TensorBoard Debugger Plugin**, please visit +> [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). + +This tutorial demonstrates how to use the **tfdbg** CLI to debug the appearance +of [`nan`s](https://en.wikipedia.org/wiki/NaN) and [`inf`s](https://en.wikipedia.org/wiki/Infinity), a frequently-encountered type of bug in TensorFlow model development. The following example is for users who use the low-level @@ -806,3 +811,13 @@ sess.run(b) the constant-folding would not occur and `tfdbg` should show the intermediate tensor dumps. + +**Q**: Is there a GUI for tfdbg? + +**A**: Yes, the **TensorBoard Debugger Plugin** is the GUI of tfdbg. + It offers features such as inspection of the computation graph, + real-time visualization of tensor values, continuation to tensor + and conditional breakpoints, and tying tensors to their + graph-construction source code, all in the browser environment. + To get started, please visit + [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md index 70931f2862de98cb1e934f85919d558a3b36304a..1548d438778afb897f6faf7cbe77fa41cfcc9ec3 100644 --- a/tensorflow/docs_src/programmers_guide/faq.md +++ b/tensorflow/docs_src/programmers_guide/faq.md @@ -159,8 +159,7 @@ available. These operations allow you to build sophisticated @{$reading_data$input pipelines}, at the cost of making the TensorFlow computation somewhat more complicated. See the how-to documentation for -@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using -`QueueRunner` objects to drive queues and readers} +@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using `QueueRunner` objects to drive queues and readers} for more information on how to use them. ## Variables diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md index 9049a5a9f3d44e255188c6c41cdb12a619464379..e69b717432e6a8fab0085eb419dcbc0991cd9d28 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/programmers_guide/graphs.md @@ -210,9 +210,8 @@ with tf.device("/device:GPU:0"): # Operations created in this context will be pinned to the GPU. result = tf.matmul(weights, img) ``` - -If you are deploying TensorFlow in a @{$deploy/distributed$typical distributed -configuration}, you might specify the job name and task ID to place variables on +If you are deploying TensorFlow in a @{$deploy/distributed$typical distributed configuration}, +you might specify the job name and task ID to place variables on a task in the parameter server job (`"/job:ps"`), and the other operations on task in the worker job (`"/job:worker"`): @@ -336,20 +335,20 @@ described below. controls the behavior of the session. For example, some of the configuration options include: - * `allow_soft_placement`. Set this to `True` to enable a "soft" device + * `allow_soft_placement`. Set this to `True` to enable a "soft" device placement algorithm, which ignores @{tf.device} annotations that attempt to place CPU-only operations on a GPU device, and places them on the CPU instead. - * `cluster_def`. When using distributed TensorFlow, this option allows you + * `cluster_def`. When using distributed TensorFlow, this option allows you to specify what machines to use in the computation, and provide a mapping between job names, task indices, and network addresses. See @{tf.train.ClusterSpec.as_cluster_def} for details. - * `graph_options.optimizer_options`. Provides control over the optimizations + * `graph_options.optimizer_options`. Provides control over the optimizations that TensorFlow performs on your graph before executing it. - * `gpu_options.allow_growth`. Set this to `True` to change the GPU memory + * `gpu_options.allow_growth`. Set this to `True` to change the GPU memory allocator so that it gradually increases the amount of memory allocated, rather than allocating most of the memory at startup. diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md index 7a5e90081d9145ca934929f0af11f2a40cb2dcae..e8c2fa6990c8ecfca1cfe76b3f813b4ae6917742 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/programmers_guide/index.md @@ -30,8 +30,12 @@ works. The units are as follows: can still be helpful. * @{$programmers_guide/saved_model}, which explains how to save and restore variables and models. + +## Accelerators + * @{$using_gpu} explains how TensorFlow assigns operations to devices and how you can change the arrangement manually. + * @{$using_tpu} explains how to modify `Estimator` programs to run on a TPU. ## ML Concepts diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/programmers_guide/leftnav_files index 38de3ccc3e474e6051976c810519212da8f5051e..3fe4cb2ddaee40d9d6c6470bee171dedb27ad890 100644 --- a/tensorflow/docs_src/programmers_guide/leftnav_files +++ b/tensorflow/docs_src/programmers_guide/leftnav_files @@ -10,7 +10,10 @@ tensors.md variables.md graphs.md saved_model.md + +### Accelerators using_gpu.md +using_tpu.md ### ML Concepts embedding.md @@ -19,9 +22,9 @@ embedding.md debugger.md ### TensorBoard -summaries_and_tensorboard.md -graph_viz.md -tensorboard_histograms.md +summaries_and_tensorboard.md: Visualizing Learning +graph_viz.md: Graphs +tensorboard_histograms.md: Histograms ### Misc version_compat.md diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/programmers_guide/low_level_intro.md index 8f6d3fbd46d8b76d6033d95fd51c1df45733f5a3..05709ad10a9275953d351e4a62cbf6d7fbffbbe3 100644 --- a/tensorflow/docs_src/programmers_guide/low_level_intro.md +++ b/tensorflow/docs_src/programmers_guide/low_level_intro.md @@ -286,6 +286,23 @@ while True: break ``` +If the `Dataset` depends on stateful operations you may need to +initialize the iterator before using it, as shown below: + +``` python +r = tf.random_normal([10,3]) +dataset = tf.data.Dataset.from_tensor_slices(r) +iterator = dataset.make_initializable_iterator() +next_row = iterator.get_next() + +sess.run(iterator.initializer) +while True: + try: + print(sess.run(next_row)) + except tf.errors.OutOfRangeError: + break +``` + For more details on Datasets and Iterators see: @{$programmers_guide/datasets}. ## Layers @@ -295,7 +312,7 @@ the same input. @{tf.layers$Layers} are the preferred way to add trainable parameters to a graph. Layers package together both the variables and the operations that act -on them, . For example a +on them. For example a [densely-connected layer](https://developers.google.com/machine-learning/glossary/#fully_connected_layer) performs a weighted sum across all inputs for each output and applies an optional @@ -478,7 +495,7 @@ good. Here's what we got; your own output will almost certainly differ: [ 0.10527515]] ``` -### loss +### Loss To optimize a model, you first need to define the loss. We'll use the mean square error, a standard loss for regression problems. @@ -504,7 +521,7 @@ TensorFlow provides [**optimizers**](https://developers.google.com/machine-learning/glossary/#optimizer) implementing standard optimization algorithms. These are implemented as sub-classes of @{tf.train.Optimizer}. They incrementally change each -variable in order to minimizethe loss. The simplest optimization algorithm is +variable in order to minimize the loss. The simplest optimization algorithm is [**gradient descent**](https://developers.google.com/machine-learning/glossary/#gradient_descent), implemented by @{tf.train.GradientDescentOptimizer}. It modifies each variable according to the magnitude of the derivative of loss with respect to diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md index f27a658342b8d33407e1c6ed5799a10c2305a74c..5dfe6ef7c80b7dc466b70cc6405f425b7a74ebba 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/programmers_guide/saved_model.md @@ -1,35 +1,33 @@ -# Saving and Restoring +# Save and Restore -This document explains how to save and restore -@{$variables$variables} and models. +The @{tf.train.Saver} class provides methods to save and restore models. The +@{tf.saved_model.simple_save} function is an easy way to build a +@{tf.saved_model$saved model} suitable for serving. +[Estimators](/programmers_guide/estimators) automatically save and restore +variables in the `model_dir`. +## Save and restore variables -## Saving and restoring variables +TensorFlow @{$variables} are the best way to represent shared, persistent state +manipulated by your program. The `tf.train.Saver` constructor adds `save` and +`restore` ops to the graph for all, or a specified list, of the variables in the +graph. The `Saver` object provides methods to run these ops, specifying paths +for the checkpoint files to write to or read from. -A TensorFlow variable provides the best way to represent shared, persistent -state manipulated by your program. (See @{$variables$Variables} for details.) -This section explains how to save and restore variables. -Note that Estimators automatically saves and restores variables -(in the `model_dir`). - -The `tf.train.Saver` class provides methods for saving and restoring models. -The `tf.train.Saver` constructor adds `save` and `restore` ops to the graph -for all, or a specified list, of the variables in the graph. The `Saver` -object provides methods to run these ops, specifying paths for the checkpoint -files to write to or read from. - -The saver will restore all variables already defined in your model. If you're +`Saver` restores all variables already defined in your model. If you're loading a model without knowing how to build its graph (for example, if you're writing a generic program to load models), then read the [Overview of saving and restoring models](#models) section later in this document. -TensorFlow saves variables in binary **checkpoint files** that, -roughly speaking, map variable names to tensor values. - +TensorFlow saves variables in binary *checkpoint files* that map variable +names to tensor values. +Caution: TensorFlow model files are code. Be careful with untrusted code. +See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) +for details. -### Saving variables +### Save variables Create a `Saver` with `tf.train.Saver()` to manage all variables in the model. For example, the following snippet demonstrates how to call the @@ -61,9 +59,7 @@ with tf.Session() as sess: print("Model saved in path: %s" % save_path) ``` - - -### Restoring variables +### Restore variables The `tf.train.Saver` object not only saves variables to checkpoint files, it also restores variables. Note that when you restore variables you do not have @@ -92,14 +88,11 @@ with tf.Session() as sess: print("v2 : %s" % v2.eval()) ``` -Notes: +Note: There is not a physical file called `/tmp/model.ckpt`. It is the *prefix* of +filenames created for the checkpoint. Users only interact with the prefix +instead of physical checkpoint files. -* There is not a physical file called "/tmp/model.ckpt". It is the **prefix** - of filenames created for the checkpoint. Users only interact with the - prefix instead of physical checkpoint files. - - -### Choosing which variables to save and restore +### Choose variables to save and restore If you do not pass any arguments to `tf.train.Saver()`, the saver handles all variables in the graph. Each variable is saved under the name that was passed @@ -198,29 +191,42 @@ chkp.print_tensors_in_checkpoint_file("/tmp/model.ckpt", tensor_name='v2', all_t -## Overview of saving and restoring models +## Save and restore models + +Use `SavedModel` to save and load your model—variables, the graph, and the +graph's metadata. This is a language-neutral, recoverable, hermetic +serialization format that enables higher-level systems and tools to produce, +consume, and transform TensorFlow models. TensorFlow provides several ways to +interact with `SavedModel`, including the @{tf.saved_model} APIs, +@{tf.estimator.Estimator}, and a command-line interface. -When you want to save and load variables, the graph, and the -graph's metadata--basically, when you want to save or restore -your model--we recommend using SavedModel. -**SavedModel** is a language-neutral, recoverable, hermetic -serialization format. SavedModel enables higher-level systems -and tools to produce, consume, and transform TensorFlow models. -TensorFlow provides several mechanisms for interacting with -SavedModel, including tf.saved_model APIs, Estimator APIs and a CLI. +## Build and load a SavedModel -## APIs to build and load a SavedModel +### Simple save -This section focuses on the APIs for building and loading a SavedModel, -particularly when using lower-level TensorFlow APIs. +The easiest way to create a `SavedModel` is to use the @{tf.saved_model.simple_save} +function: +```python +simple_save(session, + export_dir, + inputs={"x": x, "y": y}, + outputs={"z": z}) +``` -### Building a SavedModel +This configures the `SavedModel` so it can be loaded by +[TensorFlow serving](/serving/serving_basic) and supports the +[Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). +To access the classify, regress, or multi-inference APIs, use the manual +`SavedModel` builder APIs or an @{tf.estimator.Estimator}. -We provide a Python implementation of the SavedModel -@{tf.saved_model.builder$builder}. -The `SavedModelBuilder` class provides functionality to +### Manually build a SavedModel + +If your use case isn't covered by @{tf.saved_model.simple_save}, use the manual +@{tf.saved_model.builder$builder APIs} to create a `SavedModel`. + +The @{tf.saved_model.builder.SavedModelBuilder} class provides functionality to save multiple `MetaGraphDef`s. A **MetaGraph** is a dataflow graph, plus its associated variables, assets, and signatures. A **`MetaGraphDef`** is the protocol buffer representation of a MetaGraph. A **signature** is @@ -261,7 +267,7 @@ builder.save() ``` -### Loading a SavedModel in Python +### Load a SavedModel in Python The Python version of the SavedModel @{tf.saved_model.loader$loader} @@ -285,7 +291,7 @@ with tf.Session(graph=tf.Graph()) as sess: ``` -### Loading a SavedModel in C++ +### Load a SavedModel in C++ The C++ version of the SavedModel [loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h) @@ -303,7 +309,7 @@ LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain}, &bundle); ``` -### Loading and Serving a SavedModel in TensorFlow Serving +### Load and serve a SavedModel in TensorFlow serving You can easily load and serve a SavedModel with the TensorFlow Serving Model Server binary. See [instructions](https://www.tensorflow.org/serving/setup#installing_using_apt-get) @@ -371,7 +377,7 @@ SavedModel format. This section explains how to: * Serve the model from a local server and request predictions. -### Preparing serving inputs +### Prepare serving inputs During training, an @{$premade_estimators#input_fn$`input_fn()`} ingests data and prepares it for use by the model. At serving time, similarly, a @@ -445,7 +451,7 @@ to expect and how to map them to your model's expected inputs. By contrast, the *output* portion of the signature is determined by the model. -### Performing the export +### Perform the export To export your trained Estimator, call @{tf.estimator.Estimator.export_savedmodel} with the export base path and @@ -468,7 +474,7 @@ Session. > Note: It is your responsibility to garbage-collect old exports. > Otherwise, successive exports will accumulate under `export_dir_base`. -### Specifying the outputs of a custom model +### Specify the outputs of a custom model When writing a custom `model_fn`, you must populate the `export_outputs` element of the @{tf.estimator.EstimatorSpec} return value. This is a dict of @@ -500,7 +506,7 @@ indicating which `SignatureDef` will be served when an inference request does not specify one. -### Serving the exported model locally +### Serve the exported model locally For local deployment, you can serve your model using [TensorFlow Serving](https://github.com/tensorflow/serving), an open-source project that loads a @@ -519,7 +525,7 @@ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 - Now you have a server listening for inference requests via gRPC on port 9000! -### Requesting predictions from a local server +### Request predictions from a local server The server responds to gRPC requests according to the [PredictionService](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto#L15) @@ -612,7 +618,7 @@ passing in sample inputs in various formats (for example, Python expressions) and then fetching the output. -### Installing the SavedModel CLI +### Install the SavedModel CLI Broadly speaking, you can install TensorFlow in either of the following two ways: @@ -694,15 +700,15 @@ executing the computation graph later. For example: $ saved_model_cli show --dir \ /tmp/saved_model_dir --tag_set serve --signature_def serving_default The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 Method name is: tensorflow/serving/predict ``` @@ -714,32 +720,32 @@ $ saved_model_cli show --dir /tmp/saved_model_dir --all MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: signature_def['classify_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/classify ... signature_def['serving_default']: -The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/predict + The given SavedModel SignatureDef contains the following input(s): + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/predict ``` @@ -839,7 +845,7 @@ For example: `=[{"age":[22,24],"education":["BS","MS"]}]` ``` -#### Save Output +#### Save output By default, the SavedModel CLI writes output to stdout. If a directory is passed to `--outdir` option, the outputs will be saved as npy files named after @@ -848,7 +854,7 @@ output tensor keys under the given directory. Use `--overwrite` to overwrite existing output files. -#### TensorFlow Debugger (tfdbg) Integration +#### TensorFlow debugger (tfdbg) integration If `--tf_debug` option is set, the SavedModel CLI will use the TensorFlow Debugger (tfdbg) to watch the intermediate Tensors and runtime @@ -955,6 +961,3 @@ of checkpoints and assets: Each graph is associated with a specific set of tags, which enables identification during a load or restore operation. - - - diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md index 05dfdfdc4d2257fc680e7fa99b666ef86e3bef09..79280d246a8681a1716ba616073aca8066f27a3f 100644 --- a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md +++ b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md @@ -16,10 +16,17 @@ TensorBoard is fully configured, it looks like this:
-This tutorial is intended to get you started with simple TensorBoard usage. -There are other resources available as well! The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard) -has a lot more information on TensorBoard usage, including tips & tricks, and -debugging information. +This 30-minute tutorial is intended to get you started with simple TensorBoard +usage. It assumes a basic understanding of TensorFlow. + +There are other resources available as well! The [TensorBoard GitHub](https://github.com/tensorflow/tensorboard) +has a lot more information on using individual dashboards within TensorBoard +including tips & tricks and debugging information. + +## Setup + +[Install TensorFlow](https://www.tensorflow.org/install/). Installing TensorFlow +via pip should also automatically install TensorBoard. ## Serializing the data @@ -214,4 +221,5 @@ corner. Each tab represents a set of serialized data that can be visualized. For in depth information on how to use the *graph* tab to visualize your graph, see @{$graph_viz$TensorBoard: Graph Visualization}. -For more usage information on TensorBoard in general, see the [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard). +For more usage information on TensorBoard in general, see the +[TensorBoard GitHub](https://github.com/tensorflow/tensorboard). diff --git a/tensorflow/docs_src/api_guides/python/TPUEstimator.md b/tensorflow/docs_src/programmers_guide/using_tpu.md similarity index 100% rename from tensorflow/docs_src/api_guides/python/TPUEstimator.md rename to tensorflow/docs_src/programmers_guide/using_tpu.md diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md index 64250738056043e236b5eb236bcbf29375655260..e8cf7711552f4c83ed1e03e0753b580cc7505ddc 100644 --- a/tensorflow/docs_src/programmers_guide/variables.md +++ b/tensorflow/docs_src/programmers_guide/variables.md @@ -62,9 +62,10 @@ them. For this reason TensorFlow provides **collections**, which are named lists of tensors or other objects, such as `tf.Variable` instances. By default every `tf.Variable` gets placed in the following two collections: + * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across -multiple devices, - * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will + multiple devices, + * `tf.GraphKeys.TRAINABLE_VARIABLES` --- variables for which TensorFlow will calculate gradients. If you don't want a variable to be trainable, add it to the diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md index a28f1385c87c7a083ee96977c5ab268c6977e17e..e6613cc69f8aedf344fa25b6564889e34cd9bf53 100644 --- a/tensorflow/docs_src/programmers_guide/version_compat.md +++ b/tensorflow/docs_src/programmers_guide/version_compat.md @@ -60,7 +60,8 @@ patch versions. The public APIs consist of * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto) * [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto) -## What is *not* covered {not_covered} + +## What is *not* covered Some API functions are explicitly marked as "experimental" and can change in backward incompatible ways between minor releases. These include: diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md index df15bc0a9c3763aa51c2fc8cf36ce9fc3544ae68..246a420400a706387fc5d4a78672351f8fa48647 100644 --- a/tensorflow/docs_src/tutorials/image_retraining.md +++ b/tensorflow/docs_src/tutorials/image_retraining.md @@ -349,31 +349,32 @@ results, but if you intend to deploy your model on mobile devices or other resource-constrained environments you may want to trade off a little accuracy for much smaller file sizes or faster speeds. To help with that, the [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py) -supports 32 different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html). +supports different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html). These are a little less precise than Inception v3, but can result in far -smaller file sizes (down to less than a megabyte) and can be many times faster +smaller file sizes (a few megabytes) and can be many times faster to run. To train with one of these models, pass in the `--architecture` flag, for example: ``` python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos --architecture mobilenet_0.25_128_quantized + --image_dir ~/flower_photos --architecture mobilenet_0.25_128 ``` -This will create a 941KB model file in `/tmp/output_graph.pb`, with 25% of the -parameters of the full Mobilenet, taking 128x128 sized input images, and with -its weights quantized down to eight bits on disk. You can choose '1.0', '0.75', -'0.50', or '0.25' to control the number of weight parameters, and so the file -size (and to some extent the speed), '224', '192', '160', or '128' for the input -image size, with smaller sizes giving faster speeds, and an optional -'_quantized' at the end to indicate whether the file should contain 8-bit or -32-bit float weights. +This will create a 1.9MB model file in `/tmp/output_graph.pb`, with only 25% of +the number of neurons of the full Mobilenet, and trained to take 128x128 sized +input images. + +You can choose '1.0', '0.75', '0.50', or '0.25' to control the number of +neurons (activations of hidden layers); the number of weights (and hence to +some extent the file size and speed) shrinks like the square of that fraction. +You can choose '224', '192', '160', or '128' for the input image size, +with smaller sizes giving faster speeds. The speed and size advantages come at a loss to accuracy of course, but for many purposes this isn't critical. They can also be somewhat offset with improved training data. For example, training with distortions allows me to get above 80% -accuracy on the flower data set even with the 0.25/128/quantized graph above. +accuracy on the flower data set even with the 0.25/128 graph above. If you're going to be using the Mobilenet models in label_image or your own programs, you'll need to feed in an image of the specified size converted to a @@ -395,3 +396,9 @@ python tensorflow/examples/label_image/label_image.py \ --input_mean=128 --input_std=128 \ --image=$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg ``` + +For more information on deploying the retrained model to a mobile device, see +the [codelab version](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0) +of this tutorial, especially [part 2](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/#0), which describes +[TensorFlow Lite](/mobile/tflite/) and the additional optimizations it offers +(including quantization of model weights). diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md index 8c697e48e550c4e425db33bab7257532d209ac7a..af01d3eaa12157f82c981de005708509f6652cca 100644 --- a/tensorflow/docs_src/tutorials/index.md +++ b/tensorflow/docs_src/tutorials/index.md @@ -10,7 +10,7 @@ these tutorials. These tutorials cover different aspects of image recognition: - * @{$layers}, which introduces convolutional neural networks (CNNs) and + * @{$layers$MNIST}, which introduces convolutional neural networks (CNNs) and demonstrates how to build a CNN in TensorFlow. * @{$image_recognition}, which introduces the field of image recognition and uses a pre-trained model (Inception) for recognizing images. diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md index b898cbe29c2bac9ade341fe3b3566e42e133fc5b..ee03f440c9b4bcdf3b623137641a93859eeeedfc 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/layers.md @@ -625,8 +625,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`. > Note: If you don't explicitly assign a name to an operation via the `name` > argument, TensorFlow will assign a default name. A couple easy ways to > discover the names applied to operations are to visualize your graph on -> @{$graph_viz$TensorBoard}) or to enable the @{$debugger$TensorFlow Debugger -> (tfdbg)}. +> @{$graph_viz$TensorBoard}) or to enable the @{$debugger$TensorFlow Debugger (tfdbg)}. Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities @@ -635,7 +634,7 @@ should be logged after every 50 steps of training. ### Train the Model Now we're ready to train our model, which we can do by creating `train_input_fn` -ans calling `train()` on `mnist_classifier`. Add the following to `main()`: +and calling `train()` on `mnist_classifier`. Add the following to `main()`: ```python # Train the model diff --git a/tensorflow/docs_src/tutorials/leftnav_files b/tensorflow/docs_src/tutorials/leftnav_files index 41ffdc86010fb8407889df26eefa5fa59952c5da..888052428f951fa1a7cbd9c6d35497a056387097 100644 --- a/tensorflow/docs_src/tutorials/leftnav_files +++ b/tensorflow/docs_src/tutorials/leftnav_files @@ -1,22 +1,22 @@ index.md ### Images -layers.md -image_recognition.md -image_retraining.md +layers.md: MNIST +image_recognition.md: Image Recognition +image_retraining.md: Image Retraining deep_cnn.md ### Sequences recurrent.md -seq2seq.md -recurrent_quickdraw.md +seq2seq.md: Neural Machine Translation +recurrent_quickdraw.md: Drawing Classification audio_recognition.md ### Data Representation -wide.md -wide_and_deep.md +wide.md: Linear Models +wide_and_deep.md: Wide & Deep Learning word2vec.md -kernel_methods.md +kernel_methods.md: Kernel Methods ### Non-ML mandelbrot.md diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md index dba6f54c52ca5bf2569c66ad055329708de3991c..bf6b9d6cc6eacc3fda9a3fb240f57896607c4bac 100644 --- a/tensorflow/docs_src/tutorials/wide.md +++ b/tensorflow/docs_src/tutorials/wide.md @@ -82,7 +82,7 @@ Here's a list of columns available in the Census Income dataset: | hours_per_week | Continuous | Hours worked per week. | | native_country | Categorical | Country of origin of the | : : : individual. : -| income | Categorical | ">50K" or "<=50K", meaning | +| income_bracket | Categorical | ">50K" or "<=50K", meaning | : : : whether the person makes more : : : : than $50,000 annually. : @@ -247,7 +247,7 @@ hours_per_week = tf.feature_column.numeric_column('hours_per_week') ### Making Continuous Features Categorical through Bucketization Sometimes the relationship between a continuous feature and the label is not -linear. As an hypothetical example, a person's income may grow with age in the +linear. As a hypothetical example, a person's income may grow with age in the early stage of one's career, then the growth may slow at some point, and finally the income decreases after retirement. In this scenario, using the raw `age` as a real-valued feature column might not be a good choice because the model can @@ -361,6 +361,16 @@ The first line of the final output should be something like `accuracy: 0.83557522`, which means the accuracy is 83.6%. Feel free to try more features and transformations and see if you can do even better! +After the model is evaluated, we can use the model to predict whether an individual has an annual income of over +50,000 dollars given an individual's information input. +```python + pred_iter = model.predict(input_fn=lambda: input_fn(FLAGS.test_data, 1, False, 1)) + for pred in pred_iter: + print(pred['classes']) +``` + +The model prediction output would be like `[b'1']` or `[b'0']` which means whether corresponding individual has an annual income of over 50,000 dollars or not. + If you'd like to see a working end-to-end example, you can download our [example code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py) and set the `model_type` flag to `wide`. diff --git a/tensorflow/examples/android/res/animator/color_animation.xml b/tensorflow/examples/android/res/animator/color_animation.xml new file mode 100644 index 0000000000000000000000000000000000000000..891d8cc1d4f3e59d0371030fd763c5ad468e7887 --- /dev/null +++ b/tensorflow/examples/android/res/animator/color_animation.xml @@ -0,0 +1,30 @@ + + + + + diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java index bc0c738e53739a516bef268e6551cbb7741a6298..068c7b0d945669b8207097e81c03ade07bc7ca73 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java @@ -82,7 +82,7 @@ public class LegacyCameraConnectionFragment extends Fragment { try { Camera.Parameters parameters = camera.getParameters(); List focusModes = parameters.getSupportedFocusModes(); - if (focusModes != null + if (focusModes != null && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) { parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); } diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java index 184df1bdb42802bfe50b15429f09baeb5600e34f..1cddf3dc5568babb8c08c690fad143299f5ccca5 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java @@ -31,7 +31,8 @@ the RecognizeCommands helper class. package org.tensorflow.demo; -import android.animation.ValueAnimator; +import android.animation.AnimatorInflater; +import android.animation.AnimatorSet; import android.app.Activity; import android.content.pm.PackageManager; import android.media.AudioFormat; @@ -329,17 +330,13 @@ public class SpeechActivity extends Activity { labelIndex = i; } } - final View labelView = (View) labelsListView.getChildAt(labelIndex - 2); - ValueAnimator colorAnimation = - ValueAnimator.ofArgb(0x00b3ccff, 0xffb3ccff, 0x00b3ccff); - colorAnimation.setDuration(750); - colorAnimation.addUpdateListener( - new ValueAnimator.AnimatorUpdateListener() { - @Override - public void onAnimationUpdate(ValueAnimator animator) { - labelView.setBackgroundColor((int) animator.getAnimatedValue()); - } - }); + final View labelView = labelsListView.getChildAt(labelIndex - 2); + + AnimatorSet colorAnimation = + (AnimatorSet) + AnimatorInflater.loadAnimator( + SpeechActivity.this, R.animator.color_animation); + colorAnimation.setTarget(labelView); colorAnimation.start(); } } diff --git a/tensorflow/examples/get_started/regression/imports85.py b/tensorflow/examples/get_started/regression/imports85.py index 6bee556eb887a643b3a81691324736427ecc2707..4fdaceea9afee74550196031fe590c3a2abd20ed 100644 --- a/tensorflow/examples/get_started/regression/imports85.py +++ b/tensorflow/examples/get_started/regression/imports85.py @@ -131,11 +131,12 @@ def dataset(y_name="price", train_fraction=0.7): # booleans but we are dealing with symbolic tensors. return ~in_training_set(line) - base_dataset = (tf.contrib.data - # Get the lines from the file. - .TextLineDataset(path) - # drop lines with question marks. - .filter(has_no_question_marks)) + base_dataset = ( + tf.data + # Get the lines from the file. + .TextLineDataset(path) + # drop lines with question marks. + .filter(has_no_question_marks)) train = (base_dataset # Take only the training-set lines. diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py index 461fb1c5173f66278eb585d30bd8749a58fb6245..307eede5c03780e9244b035f020fc7846290d4d9 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -45,6 +45,7 @@ VALIDATION_FILE = 'validation.tfrecords' def decode(serialized_example): + """Parses an image and label from the given `serialized_example`.""" features = tf.parse_single_example( serialized_example, # Defaults are not specified since both keys are required. @@ -66,6 +67,7 @@ def decode(serialized_example): def augment(image, label): + """Placeholder for data augmentation.""" # OPTIONAL: Could reshape into a 28x28 image and apply distortions # here. Since we are not applying any distortions in this # example, and the next step expects the image to be flattened @@ -74,9 +76,8 @@ def augment(image, label): def normalize(image, label): - # Convert from [0, 255] -> [-0.5, 0.5] floats. + """Convert `image` from [0, 255] -> [-0.5, 0.5] floats.""" image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 - return image, label @@ -106,18 +107,23 @@ def inputs(train, batch_size, num_epochs): if train else VALIDATION_FILE) with tf.name_scope('input'): - # TFRecordDataset opens a protobuf and reads entries line by line - # could also be [list, of, filenames] + # TFRecordDataset opens a binary file and reads one record at a time. + # `filename` could also be a list of filenames, which will be read in order. dataset = tf.data.TFRecordDataset(filename) - dataset = dataset.repeat(num_epochs) - # map takes a python function and applies it to every sample + # The map transformation takes a function and applies it to every element + # of the dataset. dataset = dataset.map(decode) dataset = dataset.map(augment) dataset = dataset.map(normalize) - #the parameter is the queue size + # The shuffle transformation uses a finite-sized buffer to shuffle elements + # in memory. The parameter is the number of elements in the buffer. For + # completely uniform shuffling, set the parameter to be the same as the + # number of elements in the dataset. dataset = dataset.shuffle(1000 + 3 * batch_size) + + dataset = dataset.repeat(num_epochs) dataset = dataset.batch(batch_size) iterator = dataset.make_one_shot_iterator() @@ -153,7 +159,7 @@ def run_training(): sess.run(init_op) try: step = 0 - while True: #train until OutOfRangeError + while True: # Train until OutOfRangeError start_time = time.time() # Run one step of the model. The return values are diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py index 58c5f87884e5a091300f128403d00fb90bad59fe..99a71206acbd533ec8bc5a9644435eacad564cd4 100644 --- a/tensorflow/examples/image_retraining/retrain.py +++ b/tensorflow/examples/image_retraining/retrain.py @@ -41,7 +41,6 @@ The subfolder names are important, since they define what label is applied to each image, but the filenames themselves don't matter. Once your images are prepared, you can run the training with a command like this: - ```bash bazel build tensorflow/examples/image_retraining:retrain && \ bazel-bin/tensorflow/examples/image_retraining/retrain \ @@ -70,17 +69,22 @@ on resource-limited platforms, you can try the `--architecture` flag with a Mobilenet model. For example: Run floating-point version of mobilenet: + ```bash python tensorflow/examples/image_retraining/retrain.py \ --image_dir ~/flower_photos --architecture mobilenet_1.0_224 ``` -Run quantized version of mobilenet: +Run mobilenet, instrumented for quantization: + ```bash python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quantized + --image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quant ``` +These instrumented models can be converted to fully quantized mobile models via +TensorFlow Lite. + There are 32 different Mobilenet models to choose from, with a variety of file size and latency options. The first number can be '1.0', '0.75', '0.50', or '0.25' to control the size, and the second controls the input image size, either @@ -96,6 +100,12 @@ Visualize the summaries with this command: tensorboard --logdir /tmp/retrain_logs +To use with Tensorflow Serving: + +```bash +tensorflow_model_server --port=9000 --model_name=inception \ + --model_base_path=/tmp/saved_models/ +``` """ from __future__ import absolute_import from __future__ import division @@ -114,7 +124,6 @@ import numpy as np from six.moves import urllib import tensorflow as tf -from tensorflow.contrib.quantize.python import quant_ops from tensorflow.python.framework import graph_util from tensorflow.python.framework import tensor_shape from tensorflow.python.platform import gfile @@ -128,6 +137,9 @@ FLAGS = None # need to update these to reflect the values in the network you're using. MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M +# The location where variable checkpoints will be stored. +CHECKPOINT_NAME = '/tmp/_retrain_checkpoint' + def create_image_lists(image_dir, testing_percentage, validation_percentage): """Builds a list of training images from the file system. @@ -344,8 +356,8 @@ def maybe_download_and_extract(data_url): filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress) print() statinfo = os.stat(filepath) - tf.logging.info('Successfully downloaded %s %d bytes.', - filename, statinfo.st_size) + tf.logging.info('Successfully downloaded %s %d bytes.', filename, + statinfo.st_size) print('Extracting file from ', filepath) tarfile.open(filepath, 'r:gz').extractall(dest_directory) else: @@ -738,9 +750,9 @@ def variable_summaries(var): tf.summary.histogram('histogram', var) -def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, - bottleneck_tensor_size, quantize_layer): - """Adds a new softmax and fully-connected layer for training. +def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor, + bottleneck_tensor_size, quantize_layer, is_training): + """Adds a new softmax and fully-connected layer for training and eval. We need to retrain the top layer to identify our new classes, so this function adds the right operations to the graph, along with some variables to hold the @@ -756,7 +768,9 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, bottleneck_tensor: The output of the main CNN graph. bottleneck_tensor_size: How many entries in the bottleneck vector. quantize_layer: Boolean, specifying whether the newly added layer should be - quantized. + instrumented for quantized. + is_training: Boolean, specifying whether the newly add layer is for training + or eval. Returns: The tensors for the training and cross entropy results, and tensors for the @@ -771,50 +785,41 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, ground_truth_input = tf.placeholder( tf.int64, [None], name='GroundTruthInput') - # Organizing the following ops as `final_training_ops` so they're easier - # to see in TensorBoard - layer_name = 'final_training_ops' + # Organizing the following ops so they are easier to see in TensorBoard. + layer_name = 'final_retrain_ops' with tf.name_scope(layer_name): with tf.name_scope('weights'): initial_value = tf.truncated_normal( [bottleneck_tensor_size, class_count], stddev=0.001) layer_weights = tf.Variable(initial_value, name='final_weights') - if quantize_layer: - quantized_layer_weights = quant_ops.MovingAvgQuantize( - layer_weights, is_training=True) - variable_summaries(quantized_layer_weights) - variable_summaries(layer_weights) + with tf.name_scope('biases'): layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') - if quantize_layer: - quantized_layer_biases = quant_ops.MovingAvgQuantize( - layer_biases, is_training=True) - variable_summaries(quantized_layer_biases) - variable_summaries(layer_biases) with tf.name_scope('Wx_plus_b'): - if quantize_layer: - logits = tf.matmul(bottleneck_input, - quantized_layer_weights) + quantized_layer_biases - logits = quant_ops.MovingAvgQuantize( - logits, - init_min=-32.0, - init_max=32.0, - is_training=True, - num_bits=8, - narrow_range=False, - ema_decay=0.5) - tf.summary.histogram('pre_activations', logits) - else: - logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases - tf.summary.histogram('pre_activations', logits) + logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases + tf.summary.histogram('pre_activations', logits) final_tensor = tf.nn.softmax(logits, name=final_tensor_name) + # The tf.contrib.quantize functions rewrite the graph in place for + # quantization. The imported model graph has already been rewritten, so upon + # calling these rewrites, only the newly added final layer will be + # transformed. + if quantize_layer: + if is_training: + tf.contrib.quantize.create_training_graph() + else: + tf.contrib.quantize.create_eval_graph() + tf.summary.histogram('activations', final_tensor) + # If this is an eval graph, we don't need to add loss ops or an optimizer. + if not is_training: + return None, None, bottleneck_input, ground_truth_input, final_tensor + with tf.name_scope('cross_entropy'): cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy( labels=ground_truth_input, logits=logits) @@ -850,13 +855,91 @@ def add_evaluation_step(result_tensor, ground_truth_tensor): return evaluation_step, prediction -def save_graph_to_file(sess, graph, graph_file_name): +def run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor): + """Runs a final evaluation on an eval graph using the test data set. + + Args: + sess: Session for the train graph. + model_info: Model info dictionary from create_model_info() + class_count: Number of classes + image_lists: Dictionary of training images for each label. + jpeg_data_tensor: The layer to feed jpeg image data into. + decoded_image_tensor: The output of decoding and resizing the image. + resized_image_tensor: The input node of the recognition graph. + bottleneck_tensor: The bottleneck output layer of the CNN graph. + """ + (sess, bottleneck_input, ground_truth_input, evaluation_step, + prediction) = build_eval_session(model_info, class_count) + + test_bottlenecks, test_ground_truth, test_filenames = ( + get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size, + 'testing', FLAGS.bottleneck_dir, + FLAGS.image_dir, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor, FLAGS.architecture)) + test_accuracy, predictions = sess.run( + [evaluation_step, prediction], + feed_dict={ + bottleneck_input: test_bottlenecks, + ground_truth_input: test_ground_truth + }) + tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % + (test_accuracy * 100, len(test_bottlenecks))) + + if FLAGS.print_misclassified_test_images: + tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') + for i, test_filename in enumerate(test_filenames): + if predictions[i] != test_ground_truth[i]: + tf.logging.info('%70s %s' % (test_filename, + list(image_lists.keys())[predictions[i]])) + + +def build_eval_session(model_info, class_count): + """Builds an restored eval session without train operations for exporting. + + Args: + model_info: Model info dictionary from create_model_info() + class_count: Number of classes + + Returns: + Eval session containing the restored eval graph. + The bottleneck input, ground truth, eval step, and prediction tensors. + """ + # If quantized, we need to create the correct eval graph for exporting. + eval_graph, bottleneck_tensor, _ = create_model_graph(model_info) + + eval_sess = tf.Session(graph=eval_graph) + with eval_graph.as_default(): + # Add the new layer for exporting. + (_, _, bottleneck_input, + ground_truth_input, final_tensor) = add_final_retrain_ops( + class_count, FLAGS.final_tensor_name, bottleneck_tensor, + model_info['bottleneck_tensor_size'], model_info['quantize_layer'], + False) + + # Now we need to restore the values from the training graph to the eval + # graph. + tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME) + + evaluation_step, prediction = add_evaluation_step(final_tensor, + ground_truth_input) + + return (eval_sess, bottleneck_input, ground_truth_input, evaluation_step, + prediction) + + +def save_graph_to_file(graph, graph_file_name, model_info, class_count): + """Saves an graph to file, creating a valid quantized one if necessary.""" + sess, _, _, _, _ = build_eval_session(model_info, class_count) + graph = sess.graph + output_graph_def = graph_util.convert_variables_to_constants( sess, graph.as_graph_def(), [FLAGS.final_tensor_name]) with gfile.FastGFile(graph_file_name, 'wb') as f: f.write(output_graph_def.SerializeToString()) - return def prepare_file_system(): @@ -909,11 +992,10 @@ def create_model_info(architecture): return None version_string = parts[1] if (version_string != '1.0' and version_string != '0.75' and - version_string != '0.50' and version_string != '0.25'): + version_string != '0.5' and version_string != '0.25'): tf.logging.error( - """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', - but found '%s' for architecture '%s'""", - version_string, architecture) + """"The Mobilenet version should be '1.0', '0.75', '0.5', or '0.25', + but found '%s' for architecture '%s'""", version_string, architecture) return None size_string = parts[2] if (size_string != '224' and size_string != '192' and @@ -926,35 +1008,26 @@ def create_model_info(architecture): if len(parts) == 3: is_quantized = False else: - if parts[3] != 'quantized': + if parts[3] != 'quant': tf.logging.error( "Couldn't understand architecture suffix '%s' for '%s'", parts[3], architecture) return None is_quantized = True + data_url = 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/' + model_name = 'mobilenet_v1_' + version_string + '_' + size_string if is_quantized: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_quantized_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'Placeholder:0' - model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string + - '_quantized_frozen') - model_base_name = 'quantized_frozen_graph.pb' - - else: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'input:0' - model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string - model_base_name = 'frozen_graph.pb' + model_name += '_quant' + data_url += model_name + '.tgz' + bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' + resized_input_tensor_name = 'input:0' + model_file_name = model_name + '_frozen.pb' bottleneck_tensor_size = 1001 input_width = int(size_string) input_height = int(size_string) input_depth = 3 - model_file_name = os.path.join(model_dir_name, model_base_name) input_mean = 127.5 input_std = 127.5 else: @@ -1004,6 +1077,47 @@ def add_jpeg_decoding(input_width, input_height, input_depth, input_mean, return jpeg_data, mul_image +def export_model(model_info, class_count, saved_model_dir): + """Exports model for serving. + + Args: + model_info: The modelinfo for the current model. + class_count: The number of classes. + saved_model_dir: Directory in which to save exported model and variables. + """ + # The SavedModel should hold the eval graph. + sess, _, _, _, _ = build_eval_session(model_info, class_count) + graph = sess.graph + with graph.as_default(): + input_tensor = model_info['resized_input_tensor_name'] + in_image = sess.graph.get_tensor_by_name(input_tensor) + inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)} + + out_classes = sess.graph.get_tensor_by_name('final_result:0') + outputs = { + 'prediction': tf.saved_model.utils.build_tensor_info(out_classes) + } + + signature = tf.saved_model.signature_def_utils.build_signature_def( + inputs=inputs, + outputs=outputs, + method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME) + + legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') + + # Save out the SavedModel. + builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir) + builder.add_meta_graph_and_variables( + sess, [tf.saved_model.tag_constants.SERVING], + signature_def_map={ + tf.saved_model.signature_constants. + DEFAULT_SERVING_SIGNATURE_DEF_KEY: + signature + }, + legacy_init_op=legacy_init_op) + builder.save() + + def main(_): # Needed to make sure the logging output is visible. # See https://github.com/tensorflow/tensorflow/issues/3047 @@ -1018,11 +1132,6 @@ def main(_): tf.logging.error('Did not recognize architecture flag') return -1 - # Set up the pre-trained graph. - maybe_download_and_extract(model_info['data_url']) - graph, bottleneck_tensor, resized_image_tensor = ( - create_model_graph(model_info)) - # Look at the folder structure, and create lists of all the images. image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage, FLAGS.validation_percentage) @@ -1041,6 +1150,19 @@ def main(_): FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, FLAGS.random_brightness) + # Set up the pre-trained graph. + maybe_download_and_extract(model_info['data_url']) + graph, bottleneck_tensor, resized_image_tensor = ( + create_model_graph(model_info)) + + # Add the new layer that we'll be training. + with graph.as_default(): + (train_step, cross_entropy, bottleneck_input, + ground_truth_input, final_tensor) = add_final_retrain_ops( + class_count, FLAGS.final_tensor_name, bottleneck_tensor, + model_info['bottleneck_tensor_size'], model_info['quantize_layer'], + True) + with tf.Session(graph=graph) as sess: # Set up the image decoding sub-graph. jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding( @@ -1064,15 +1186,8 @@ def main(_): decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture) - # Add the new layer that we'll be training. - (train_step, cross_entropy, bottleneck_input, ground_truth_input, - final_tensor) = add_final_training_ops( - len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor, - model_info['bottleneck_tensor_size'], model_info['quantize_layer']) - # Create the operations we need to evaluate the accuracy of our new layer. - evaluation_step, prediction = add_evaluation_step( - final_tensor, ground_truth_input) + evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input) # Merge all the summaries and write them out to the summaries_dir merged = tf.summary.merge_all() @@ -1082,6 +1197,10 @@ def main(_): validation_writer = tf.summary.FileWriter( FLAGS.summaries_dir + '/validation') + # Create a train saver that is used to restore values into an eval graph + # when exporting models. + train_saver = tf.train.Saver() + # Set up all our weights to their initial default values. init = tf.global_variables_initializer() sess.run(init) @@ -1122,6 +1241,9 @@ def main(_): (datetime.now(), i, train_accuracy * 100)) tf.logging.info('%s: Step %d: Cross entropy = %f' % (datetime.now(), i, cross_entropy_value)) + # TODO(suharshs): Make this use an eval graph, to avoid quantization + # moving averages being updated by the validation set, though in + # practice this makes a negligable difference. validation_bottlenecks, validation_ground_truth, _ = ( get_random_cached_bottlenecks( sess, image_lists, FLAGS.validation_batch_size, 'validation', @@ -1144,41 +1266,33 @@ def main(_): if (intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0): + # If we want to do an intermediate save, save a checkpoint of the train + # graph, to restore into the eval graph. + train_saver.save(sess, CHECKPOINT_NAME) intermediate_file_name = (FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb') tf.logging.info('Save intermediate result to : ' + intermediate_file_name) - save_graph_to_file(sess, graph, intermediate_file_name) + save_graph_to_file(graph, intermediate_file_name, model_info, + class_count) + + # After training is complete, force one last save of the train checkpoint. + train_saver.save(sess, CHECKPOINT_NAME) # We've completed all our training, so run a final test evaluation on # some new images we haven't used before. - test_bottlenecks, test_ground_truth, test_filenames = ( - get_random_cached_bottlenecks( - sess, image_lists, FLAGS.test_batch_size, 'testing', - FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - FLAGS.architecture)) - test_accuracy, predictions = sess.run( - [evaluation_step, prediction], - feed_dict={bottleneck_input: test_bottlenecks, - ground_truth_input: test_ground_truth}) - tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % - (test_accuracy * 100, len(test_bottlenecks))) - - if FLAGS.print_misclassified_test_images: - tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') - for i, test_filename in enumerate(test_filenames): - if predictions[i] != test_ground_truth[i]: - tf.logging.info('%70s %s' % - (test_filename, - list(image_lists.keys())[predictions[i]])) + run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor) # Write out the trained graph and labels with the weights stored as # constants. - save_graph_to_file(sess, graph, FLAGS.output_graph) + save_graph_to_file(graph, FLAGS.output_graph, model_info, class_count) with gfile.FastGFile(FLAGS.output_labels, 'w') as f: f.write('\n'.join(image_lists.keys()) + '\n') + export_model(model_info, class_count, FLAGS.saved_model_dir) + if __name__ == '__main__': parser = argparse.ArgumentParser() @@ -1358,9 +1472,15 @@ if __name__ == '__main__': form 'mobilenet__[_quantized]'. For example, 'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224 pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much - less accurate, but smaller and faster network that's 920 KB on disk and - takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html + smaller and less accurate model, taking 128x128 images, and instrumented + for eventual quantization via TensorFlow Lite. + See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html for more information on Mobilenet.\ """) + parser.add_argument( + '--saved_model_dir', + type=str, + default='/tmp/saved_models/1/', + help='Where to save the exported graph.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py index 8b8dd45fd72e3d29bdb7f6291cc53b912adf3644..fb7324c58ac1be60baad840207f31a61ec6182be 100644 --- a/tensorflow/examples/image_retraining/retrain_test.py +++ b/tensorflow/examples/image_retraining/retrain_test.py @@ -67,22 +67,52 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0')) @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOps(self, flags_mock): + def testAddFinalRetrainOps(self, flags_mock): with tf.Graph().as_default(): with tf.Session() as sess: bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, False) + # Test creating final training op with quantization. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False, + False) self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOpsQuantized(self, flags_mock): - with tf.Graph().as_default(): + def testAddFinalRetrainOpsQuantized(self, flags_mock): + # Ensure that the training and eval graph for quantized models are correctly + # created. + with tf.Graph().as_default() as g: + with tf.Session() as sess: + bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') + # Test creating final training op with quantization, set is_training to + # true. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True) + self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) + found_fake_quant = 0 + for op in g.get_operations(): + if op.type == 'FakeQuantWithMinMaxVars': + found_fake_quant += 1 + # Ensure that the inputs of each FakeQuant operations has 2 Assign + # operations in the training graph (Assign[Min,Max]Last, + # Assign[Min,Max]Ema) + self.assertEqual(2, + len([i for i in op.inputs if 'Assign' in i.name])) + self.assertEqual(found_fake_quant, 2) + with tf.Graph().as_default() as g: with tf.Session() as sess: bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, True) + # Test creating final training op with quantization, set is_training to + # false. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False) self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) + found_fake_quant = 0 + for op in g.get_operations(): + if op.type == 'FakeQuantWithMinMaxVars': + found_fake_quant += 1 + for i in op.inputs: + # Ensure that no operations are Assign operation since this is the + # evaluation graph. + self.assertTrue('Assign' not in i.name) + self.assertEqual(found_fake_quant, 2) def testAddEvaluationStep(self): with tf.Graph().as_default(): diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py index 1c1bd57d715ae39539d9cec1347caa702a8261b9..fe5e0fc684abce08d3d7b7f3fa22bb5ba701c64a 100644 --- a/tensorflow/examples/label_image/label_image.py +++ b/tensorflow/examples/label_image/label_image.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function import argparse -import sys import numpy as np import tensorflow as tf diff --git a/tensorflow/examples/speech_commands/label_wav_dir.py b/tensorflow/examples/speech_commands/label_wav_dir.py new file mode 100644 index 0000000000000000000000000000000000000000..a34db512dda86be138e07a4ffaa1963fe00a5cea --- /dev/null +++ b/tensorflow/examples/speech_commands/label_wav_dir.py @@ -0,0 +1,136 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Runs a trained audio graph against WAVE files and reports the results. + +The model, labels and .wav files specified in the arguments will be loaded, and +then the predictions from running the model against the audio data will be +printed to the console. This is a useful script for sanity checking trained +models, and as an example of how to use an audio model from Python. + +Here's an example of running it: + +python tensorflow/examples/speech_commands/label_wav_dir.py \ +--graph=/tmp/my_frozen_graph.pb \ +--labels=/tmp/speech_commands_train/conv_labels.txt \ +--wav_dir=/tmp/speech_dataset/left + +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import glob +import sys + +import tensorflow as tf + +# pylint: disable=unused-import +from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio +# pylint: enable=unused-import + +FLAGS = None + + +def load_graph(filename): + """Unpersists graph from file as default graph.""" + with tf.gfile.FastGFile(filename, 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name='') + + +def load_labels(filename): + """Read in labels, one label per line.""" + return [line.rstrip() for line in tf.gfile.GFile(filename)] + + +def run_graph(wav_dir, labels, input_layer_name, output_layer_name, + num_top_predictions): + """Runs the audio data through the graph and prints predictions.""" + with tf.Session() as sess: + # Feed the audio data as input to the graph. + # predictions will contain a two-dimensional array, where one + # dimension represents the input image count, and the other has + # predictions per class + for wav_path in glob.glob(wav_dir + '/*.wav'): + if not wav_path or not tf.gfile.Exists(wav_path): + tf.logging.fatal('Audio file does not exist %s', wav_path) + + with open(wav_path, 'rb') as wav_file: + wav_data = wav_file.read() + + softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name) + predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data}) + + # Sort to show labels in order of confidence + print('\n%s' % (wav_path.split('/')[-1])) + top_k = predictions.argsort()[-num_top_predictions:][::-1] + for node_id in top_k: + human_string = labels[node_id] + score = predictions[node_id] + print('%s (score = %.5f)' % (human_string, score)) + + return 0 + + +def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels): + """Loads the model and labels, and runs the inference to print predictions.""" + if not labels or not tf.gfile.Exists(labels): + tf.logging.fatal('Labels file does not exist %s', labels) + + if not graph or not tf.gfile.Exists(graph): + tf.logging.fatal('Graph file does not exist %s', graph) + + labels_list = load_labels(labels) + + # load graph, which is stored in the default session + load_graph(graph) + + run_graph(wav_dir, labels_list, input_name, output_name, how_many_labels) + + +def main(_): + """Entry point for script, converts flags to arguments.""" + label_wav(FLAGS.wav_dir, FLAGS.labels, FLAGS.graph, FLAGS.input_name, + FLAGS.output_name, FLAGS.how_many_labels) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--wav_dir', type=str, default='', help='Audio file to be identified.') + parser.add_argument( + '--graph', type=str, default='', help='Model to use for identification.') + parser.add_argument( + '--labels', type=str, default='', help='Path to file containing labels.') + parser.add_argument( + '--input_name', + type=str, + default='wav_data:0', + help='Name of WAVE data input node in model.') + parser.add_argument( + '--output_name', + type=str, + default='labels_softmax:0', + help='Name of node outputting a prediction in the model.') + parser.add_argument( + '--how_many_labels', + type=int, + default=3, + help='Number of results to show.') + + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py index a4e80041f82191d7c58a3e52c929340eb604ec9d..f084931215261f183f1ecfc5517ea9a5126db039 100644 --- a/tensorflow/examples/speech_commands/train.py +++ b/tensorflow/examples/speech_commands/train.py @@ -357,12 +357,12 @@ if __name__ == '__main__': '--window_size_ms', type=float, default=30.0, - help='How long each spectrogram timeslice is',) + help='How long each spectrogram timeslice is.',) parser.add_argument( '--window_stride_ms', type=float, default=10.0, - help='How long each spectrogram timeslice is',) + help='How far to move in time between spectogram timeslices.',) parser.add_argument( '--dct_coefficient_count', type=int, diff --git a/tensorflow/examples/tutorials/mnist/input_data.py b/tensorflow/examples/tutorials/mnist/input_data.py index f1a7e1c4af57dba4f06326eb8b03c7eddae86b51..fa148ae3e6f44e140e3b4fb6a4204a601b6c0a24 100644 --- a/tensorflow/examples/tutorials/mnist/input_data.py +++ b/tensorflow/examples/tutorials/mnist/input_data.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +# pylint: disable=unused-import import gzip import os import tempfile @@ -27,3 +28,4 @@ from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets +# pylint: enable=unused-import diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index f6906b0f79b86910b5354bea420d00f62ff0caf8..14ae7fbf35836ad7f5d56101ae0fc33a3f3fb9ba 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -131,7 +131,7 @@ def generate_batch(batch_size, num_skips, skip_window): batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] - buffer = collections.deque(maxlen=span) + buffer = collections.deque(maxlen=span) # pylint: disable=redefined-builtin if data_index + span > len(data): data_index = 0 buffer.extend(data[data_index:data_index + span]) diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb index 18c456cad787b2ed5b39d5791de649874bbe7ae3..3b43d1fb55ee5d7f6a91754a221962755f04190c 100644 --- a/tensorflow/examples/udacity/5_word2vec.ipynb +++ b/tensorflow/examples/udacity/5_word2vec.ipynb @@ -455,7 +455,7 @@ " \n", " # Compute the similarity between minibatch examples and all embeddings.\n", " # We use the cosine distance:\n", - " norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n", + " norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n", " normalized_embeddings = embeddings / norm\n", " valid_embeddings = tf.nn.embedding_lookup(\n", " normalized_embeddings, valid_dataset)\n", diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index cb47651d7b3199cc804b2a3e89aaf2cead7b75c1..336df7c2f72ab1bbdc92ac00d097e04fdacc9fd5 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -220,174 +220,152 @@ func CreateSummaryFileWriter(scope *Scope, writer tf.Output, logdir tf.Output, m return scope.AddOperation(opspec) } -// Partitions `data` into `num_partitions` tensors using indices from `partitions`. -// -// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` -// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` -// are placed in `outputs[i]` in lexicographic order of `js`, and the first -// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. -// In detail, -// -// ```python -// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] -// -// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) -// ``` -// -// `data.shape` must start with `partitions.shape`. -// -// For example: +// FakeQuantWithMinMaxVarsPerChannelGradientAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannelGradient. +type FakeQuantWithMinMaxVarsPerChannelGradientAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsPerChannelGradientNumBits sets the optional num_bits attribute to value. // -// ```python -// # Scalar partitions. -// partitions = 1 -// num_partitions = 2 -// data = [10, 20] -// outputs[0] = [] # Empty with shape [0, 2] -// outputs[1] = [[10, 20]] +// value: The bitwidth of the quantization; between 2 and 8, inclusive. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsPerChannelGradientNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange sets the optional narrow_range attribute to value. // -// # Vector partitions. -// partitions = [0, 0, 1, 1, 0] -// num_partitions = 2 -// data = [10, 20, 30, 40, 50] -// outputs[0] = [10, 20, 50] -// outputs[1] = [30, 40] -// ``` +// value: Whether to quantize into 2^num_bits - 1 distinct values. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. // -// See `dynamic_stitch` for an example on how to merge partitions back. +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation, +// shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape +// same as `gradients`. +// min, max: Quantization interval, floats of shape `[d]`. // -//
-// -//
// -// Arguments: // -// partitions: Any shape. Indices in the range `[0, num_partitions)`. -// num_partitions: The number of partitions to output. -func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { +// Returns Backpropagated gradients w.r.t. inputs, shape same as +// `inputs`: +// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter, shape `[d]`: +// `sum_per_d(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter, shape `[d]`: +// `sum_per_d(gradients * (inputs > max))`. +func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_partitions": num_partitions} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "DynamicPartition", + Type: "FakeQuantWithMinMaxVarsPerChannelGradient", Input: []tf.Input{ - data, partitions, + gradients, inputs, min, max, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("DynamicPartition", err) - return - } - return outputs + return op.Output(0), op.Output(1), op.Output(2) } -// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2. -type MutableHashTableOfTensorsV2Attr func(optionalAttr) - -// MutableHashTableOfTensorsV2Container sets the optional container attribute to value. -// -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} +// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel. +type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr) -// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr { +// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["num_bits"] = value } } -// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value. // If not specified, defaults to false -func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value. -// If not specified, defaults to <> -func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr { +func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr { return func(m optionalAttr) { - m["value_shape"] = value + m["narrow_range"] = value } } -// Creates an empty hash table. +// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`, // -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a vector. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. +// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]` +// to 'outputs' tensor of same shape as `inputs`. // -// Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. // -// Returns Handle to a table. -func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) { +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableHashTableOfTensorsV2", - + Type: "FakeQuantWithMinMaxVarsPerChannel", + Input: []tf.Input{ + inputs, min, max, + }, Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. -type ResourceApplyProximalAdagradAttr func(optionalAttr) +// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient. +type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr) -// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value. // -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { +// value: The bitwidth of the quantization; between 2 and 8, inclusive. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["num_bits"] = value } } -// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. +// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value. // -// accum += grad * grad -// prox_v = var - lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// value: Whether to quantize into 2^num_bits - 1 distinct values. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxVars operation. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation. +// min, max: Quantization interval, scalar floats. // -// Returns the created operation. -func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { +// +// +// Returns Backpropagated gradients w.r.t. inputs: +// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter: +// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter: +// `sum(gradients * (inputs > max))`. +func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { if scope.Err() != nil { return } @@ -396,13 +374,14 @@ func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceApplyProximalAdagrad", + Type: "FakeQuantWithMinMaxVarsGradient", Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, + gradients, inputs, min, max, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } // MutableHashTableV2Attr is an optional argument to MutableHashTableV2. @@ -469,50 +448,74 @@ func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.Data return op.Output(0) } -// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. -type MapUnstageNoKeyAttr func(optionalAttr) +// Replaces the contents of the table with the specified keys and values. +// +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// values: Values to associate with keys. +// +// Returns the created operation. +func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableImportV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} -// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// MapPeekAttr is an optional argument to MapPeek. +type MapPeekAttr func(optionalAttr) + +// MapPeekCapacity sets the optional capacity attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { +func MapPeekCapacity(value int64) MapPeekAttr { return func(m optionalAttr) { m["capacity"] = value } } -// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// MapPeekMemoryLimit sets the optional memory_limit attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { +func MapPeekMemoryLimit(value int64) MapPeekAttr { return func(m optionalAttr) { m["memory_limit"] = value } } -// MapUnstageNoKeyContainer sets the optional container attribute to value. +// MapPeekContainer sets the optional container attribute to value. // If not specified, defaults to "" -func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { +func MapPeekContainer(value string) MapPeekAttr { return func(m optionalAttr) { m["container"] = value } } -// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// MapPeekSharedName sets the optional shared_name attribute to value. // If not specified, defaults to "" -func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { +func MapPeekSharedName(value string) MapPeekAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Op removes and returns a random (key, value) +// Op peeks at the values at the specified key. If the // -// from the underlying container. If the underlying container -// does not contain elements, the op will block until it does. -func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { +// underlying container does not contain this key +// this op will block until it does. +func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { if scope.Err() != nil { return } @@ -521,9 +524,9 @@ func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, opti a(attrs) } opspec := tf.OpSpec{ - Type: "MapUnstageNoKey", + Type: "MapPeek", Input: []tf.Input{ - indices, + key, indices, }, Attrs: attrs, } @@ -533,100 +536,11 @@ func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, opti } var idx int var err error - key = op.Output(idx) if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapUnstageNoKey", err) - return - } - return key, values -} - -// HashTableV2Attr is an optional argument to HashTableV2. -type HashTableV2Attr func(optionalAttr) - -// HashTableV2Container sets the optional container attribute to value. -// -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func HashTableV2Container(value string) HashTableV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// HashTableV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func HashTableV2SharedName(value string) HashTableV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// -// value: If true and shared_name is empty, the table is shared -// using the node name. -// If not specified, defaults to false -func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// Creates a non-initialized hash table. -// -// This op creates a hash table, specifying the type of its keys and values. -// Before using the table you will have to initialize it. After initialization the -// table will be immutable. -// -// Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. -// -// Returns Handle to a table. -func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "HashTableV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Replaces the contents of the table with the specified keys and values. -// -// The tensor `keys` must be of the same type as the keys of the table. -// The tensor `values` must be of the type of the table values. -// -// Arguments: -// table_handle: Handle to the table. -// keys: Any shape. Keys to look up. -// values: Values to associate with keys. -// -// Returns the created operation. -func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { - if scope.Err() != nil { + scope.UpdateErr("MapPeek", err) return } - opspec := tf.OpSpec{ - Type: "LookupTableImportV2", - Input: []tf.Input{ - table_handle, keys, values, - }, - } - return scope.AddOperation(opspec) + return values } // Returns (x - y)(x - y) element-wise. @@ -1515,61 +1429,6 @@ func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { return op.Output(0) } -// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient. -type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value. -// -// value: The bitwidth of the quantization; between 2 and 8, inclusive. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value. -// -// value: Whether to quantize into 2^num_bits - 1 distinct values. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Compute gradients for a FakeQuantWithMinMaxVars operation. -// -// Arguments: -// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation. -// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation. -// min, max: Quantization interval, scalar floats. -// -// -// -// Returns Backpropagated gradients w.r.t. inputs: -// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter: -// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter: -// `sum(gradients * (inputs > max))`. -func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVarsGradient", - Input: []tf.Input{ - gradients, inputs, min, max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler. type LogUniformCandidateSamplerAttr func(optionalAttr) @@ -2300,26 +2159,6 @@ func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units return op.Output(0) } -// Returns x / y element-wise for real types. -// -// If `x` and `y` are reals, this will return the floating-point division. -// -// *NOTE*: `Div` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RealDiv", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the log of the absolute value of `Gamma(x)` element-wise. func Lgamma(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -4327,34 +4166,6 @@ func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output return op.Output(0) } -// Fast Fourier transform. -// -// Computes the 1-dimensional discrete Fourier transform over the inner-most -// dimension of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its 1D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft -// @end_compatibility -func FFT(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // MaxPoolAttr is an optional argument to MaxPool. type MaxPoolAttr func(optionalAttr) @@ -4468,31 +4279,50 @@ func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax return op.Output(0) } -// CriticalSectionOpAttr is an optional argument to CriticalSectionOp. -type CriticalSectionOpAttr func(optionalAttr) +// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. +type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) -// CriticalSectionOpContainer sets the optional container attribute to value. -// -// value: the container this critical section is placed in. -// If not specified, defaults to "" -func CriticalSectionOpContainer(value string) CriticalSectionOpAttr { +// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["container"] = value + m["min"] = value } } -// CriticalSectionOpSharedName sets the optional shared_name attribute to value. -// -// value: the name by which this critical section is referred to. -// If not specified, defaults to "" -func CriticalSectionOpSharedName(value string) CriticalSectionOpAttr { +// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["max"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value } } -// Creates a handle to a CriticalSection resource. -func CriticalSectionOp(scope *Scope, optional ...CriticalSectionOpAttr) (resource tf.Output) { +// Compute gradients for a FakeQuantWithMinMaxArgs operation. +// +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. +// +// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: +// `gradients * (inputs >= min && inputs <= max)`. +func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { if scope.Err() != nil { return } @@ -4501,11 +4331,13 @@ func CriticalSectionOp(scope *Scope, optional ...CriticalSectionOpAttr) (resourc a(attrs) } opspec := tf.OpSpec{ - Type: "CriticalSectionOp", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) + Type: "FakeQuantWithMinMaxArgsGradient", + Input: []tf.Input{ + gradients, inputs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) return op.Output(0) } @@ -4814,6 +4646,78 @@ func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, stri return op.Output(0) } +// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2. +type MaxPoolGradV2Attr func(optionalAttr) + +// MaxPoolGradV2DataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients w.r.t. the input to `max_pool`. +func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradV2", + Input: []tf.Input{ + orig_input, orig_output, grad, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Restore a reader to a previously saved state. +// +// Not all Readers support being restored, so this can produce an +// Unimplemented error. +// +// Arguments: +// reader_handle: Handle to a Reader. +// state: Result of a ReaderSerializeState of a Reader with type +// matching reader_handle. +// +// Returns the created operation. +func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderRestoreStateV2", + Input: []tf.Input{ + reader_handle, state, + }, + } + return scope.AddOperation(opspec) +} + // TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. type TensorArrayGatherV3Attr func(optionalAttr) @@ -5486,111 +5390,6 @@ func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_f return op.Output(0), op.Output(1), op.Output(2) } -// SummaryWriterAttr is an optional argument to SummaryWriter. -type SummaryWriterAttr func(optionalAttr) - -// SummaryWriterSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func SummaryWriterSharedName(value string) SummaryWriterAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// SummaryWriterContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func SummaryWriterContainer(value string) SummaryWriterAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// Returns a handle to be used to access a summary writer. -// -// The summary writer is an in-graph resource which can be used by ops to write -// summaries to event files. -// -// Returns the summary writer resource. Scalar handle. -func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SummaryWriter", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes gradients for SparseSegmentMean. -// -// Returns tensor "output" with same shape as grad, except for dimension 0 whose -// value is output_dim0. -// -// Arguments: -// grad: gradient propagated to the SparseSegmentMean op. -// indices: indices passed to the corresponding SparseSegmentMean op. -// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. -// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. -func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentMeanGrad", - Input: []tf.Input{ - grad, indices, segment_ids, output_dim0, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Applies softmax to a batched N-D `SparseTensor`. -// -// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` -// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. -// -// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost -// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly -// zero elements do not participate*. Specifically, the algorithm is equivalent -// to the following: -// -// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix -// with shape `[B, C]`, along the size-C dimension; -// (2) Masks out the original implicitly-zero locations; -// (3) Renormalizes the remaining elements. -// -// Hence, the `SparseTensor` result has exactly the same non-zero indices and -// shape. -// -// Arguments: -// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a -// SparseTensor, in canonical ordering. -// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// -// Returns 1-D. The `NNZ` values for the result `SparseTensor`. -func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSoftmax", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // RandomPoissonAttr is an optional argument to RandomPoisson. type RandomPoissonAttr func(optionalAttr) @@ -5632,78 +5431,6 @@ func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...Ra return op.Output(0) } -// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2. -type MaxPoolGradV2Attr func(optionalAttr) - -// MaxPoolGradV2DataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of the maxpooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: 4-D. Gradients w.r.t. the output of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients w.r.t. the input to `max_pool`. -func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradV2", - Input: []tf.Input{ - orig_input, orig_output, grad, ksize, strides, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Restore a reader to a previously saved state. -// -// Not all Readers support being restored, so this can produce an -// Unimplemented error. -// -// Arguments: -// reader_handle: Handle to a Reader. -// state: Result of a ReaderSerializeState of a Reader with type -// matching reader_handle. -// -// Returns the created operation. -func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderRestoreStateV2", - Input: []tf.Input{ - reader_handle, state, - }, - } - return scope.AddOperation(opspec) -} - // ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2. type ResourceSparseApplyFtrlV2Attr func(optionalAttr) @@ -6941,6 +6668,225 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso return scope.AddOperation(opspec) } +// SummaryWriterAttr is an optional argument to SummaryWriter. +type SummaryWriterAttr func(optionalAttr) + +// SummaryWriterSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func SummaryWriterSharedName(value string) SummaryWriterAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// SummaryWriterContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func SummaryWriterContainer(value string) SummaryWriterAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// Returns a handle to be used to access a summary writer. +// +// The summary writer is an in-graph resource which can be used by ops to write +// summaries to event files. +// +// Returns the summary writer resource. Scalar handle. +func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SummaryWriter", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for SparseSegmentMean. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentMean op. +// indices: indices passed to the corresponding SparseSegmentMean op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. +func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMeanGrad", + Input: []tf.Input{ + grad, indices, segment_ids, output_dim0, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Applies softmax to a batched N-D `SparseTensor`. +// +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. +// +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: +// +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. +// +// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. +// +// Arguments: +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmax", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. +// +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, +// +// ```python +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] +// +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) +// ``` +// +// `data.shape` must start with `partitions.shape`. +// +// For example: +// +// ```python +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] +// ``` +// +// See `dynamic_stitch` for an example on how to merge partitions back. +// +//
+// +//
+// +// Arguments: +// +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_partitions": num_partitions} + opspec := tf.OpSpec{ + Type: "DynamicPartition", + Input: []tf.Input{ + data, partitions, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) + return + } + return outputs +} + +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the adagrad scheme. +// +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // CumprodAttr is an optional argument to Cumprod. type CumprodAttr func(optionalAttr) @@ -7825,78 +7771,21 @@ func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// StageSizeAttr is an optional argument to StageSize. -type StageSizeAttr func(optionalAttr) +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) -// StageSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. // -// REQUIRES: value >= 0 -func StageSizeCapacity(value int64) StageSizeAttr { +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { return func(m optionalAttr) { - m["capacity"] = value + m["iou_threshold"] = value } } -// StageSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageSizeMemoryLimit(value int64) StageSizeAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// StageSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StageSizeContainer(value string) StageSizeAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// StageSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StageSizeSharedName(value string) StageSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of elements in the underlying container. -func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StageSize", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. -type NonMaxSuppressionAttr func(optionalAttr) - -// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. -// -// value: A float representing the threshold for deciding whether boxes -// overlap too much with respect to IOU. -// If not specified, defaults to 0.5 -func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { - return func(m optionalAttr) { - m["iou_threshold"] = value - } -} - -// Greedily selects a subset of bounding boxes in descending order of score, +// Greedily selects a subset of bounding boxes in descending order of score, // // pruning away boxes that have high intersection-over-union (IOU) overlap // with previously selected boxes. Bounding boxes are supplied as @@ -8621,51 +8510,6 @@ func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Outp return op.Output(0) } -// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. -type ResourceApplyAdagradAttr func(optionalAttr) - -// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the adagrad scheme. -// -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdagrad", - Input: []tf.Input{ - var_, accum, lr, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - // Returns element-wise remainder of division. This emulates C semantics in that // // the result here is consistent with a truncating divide. E.g. `truncate(x / y) * @@ -9110,6 +8954,34 @@ func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, label return op.Output(0), op.Output(1) } +// Fast Fourier transform. +// +// Computes the 1-dimensional discrete Fourier transform over the inner-most +// dimension of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft +// @end_compatibility +func FFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. type ResourceSparseApplyAdagradDAAttr func(optionalAttr) @@ -9245,139 +9117,391 @@ func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { // If not specified, defaults to true func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { return func(m optionalAttr) { - m["clear_after_read"] = value + m["clear_after_read"] = value + } +} + +// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// +// value: If true (default is false), then all +// elements in the TensorArray will be expected to have have identical shapes. +// This allows certain behaviors, like dynamically checking for +// consistent shapes on write, and being able to fill in properly +// shaped zero tensors on stack -- even if the element_shape attribute +// is not fully defined. +// If not specified, defaults to false +func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["identical_element_shapes"] = value + } +} + +// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// +// value: Overrides the name used for the temporary tensor_array +// resource. Default value is the name of the 'TensorArray' op (which +// is guaranteed unique). +// If not specified, defaults to "" +func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { + return func(m optionalAttr) { + m["tensor_array_name"] = value + } +} + +// An array of Tensors of given size. +// +// Write data via Write and read via Read or Pack. +// +// Arguments: +// size: The size of the array. +// dtype: The type of the elements on the tensor_array. +// +// Returns The handle to the TensorArray.A scalar used to control gradient flow. +func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayV3", + Input: []tf.Input{ + size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPool3DAttr is an optional argument to MaxPool3D. +type MaxPool3DAttr func(optionalAttr) + +// MaxPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DDataFormat(value string) MaxPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D max pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradients of 3-D convolution with respect to the input. +// +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "Conv3DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. +type ResourceApplyProximalAdagradAttr func(optionalAttr) + +// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. +// +// accum += grad * grad +// prox_v = var - lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2. +type MutableHashTableOfTensorsV2Attr func(optionalAttr) + +// MutableHashTableOfTensorsV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// If not specified, defaults to false +func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value. +// If not specified, defaults to <> +func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["value_shape"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a vector. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableOfTensorsV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// HashTableV2Attr is an optional argument to HashTableV2. +type HashTableV2Attr func(optionalAttr) + +// HashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func HashTableV2Container(value string) HashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value } } -// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// HashTableV2SharedName sets the optional shared_name attribute to value. // -// value: If true (default is false), then all -// elements in the TensorArray will be expected to have have identical shapes. -// This allows certain behaviors, like dynamically checking for -// consistent shapes on write, and being able to fill in properly -// shaped zero tensors on stack -- even if the element_shape attribute -// is not fully defined. -// If not specified, defaults to false -func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func HashTableV2SharedName(value string) HashTableV2Attr { return func(m optionalAttr) { - m["identical_element_shapes"] = value + m["shared_name"] = value } } -// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. // -// value: Overrides the name used for the temporary tensor_array -// resource. Default value is the name of the 'TensorArray' op (which -// is guaranteed unique). -// If not specified, defaults to "" -func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { return func(m optionalAttr) { - m["tensor_array_name"] = value + m["use_node_name_sharing"] = value } } -// An array of Tensors of given size. +// Creates a non-initialized hash table. // -// Write data via Write and read via Read or Pack. +// This op creates a hash table, specifying the type of its keys and values. +// Before using the table you will have to initialize it. After initialization the +// table will be immutable. // // Arguments: -// size: The size of the array. -// dtype: The type of the elements on the tensor_array. +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. // -// Returns The handle to the TensorArray.A scalar used to control gradient flow. -func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { +// Returns Handle to a table. +func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TensorArrayV3", - Input: []tf.Input{ - size, - }, + Type: "HashTableV2", + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// MaxPool3DAttr is an optional argument to MaxPool3D. -type MaxPool3DAttr func(optionalAttr) +// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. +type MapUnstageNoKeyAttr func(optionalAttr) -// MaxPool3DDataFormat sets the optional data_format attribute to value. +// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DDataFormat(value string) MaxPool3DAttr { +// REQUIRES: value >= 0 +func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { return func(m optionalAttr) { - m["data_format"] = value + m["capacity"] = value } } -// Performs 3D max pooling on the input. +// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// Arguments: -// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. +// REQUIRES: value >= 0 +func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapUnstageNoKeyContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns a random (key, value) // -// Returns The max pooled output tensor. -func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { +// from the underlying container. If the underlying container +// does not contain elements, the op will block until it does. +func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPool3D", + Type: "MapUnstageNoKey", Input: []tf.Input{ - input, + indices, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the gradients of 3-D convolution with respect to the input. -// -// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. -// `in_channels` must match between `input` and `filter`. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "Conv3DBackpropInput", - Input: []tf.Input{ - input, filter, out_backprop, - }, - Attrs: attrs, + var idx int + var err error + key = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapUnstageNoKey", err) + return } - op := scope.AddOperation(opspec) - return op.Output(0) + return key, values } // Inverse 2D fast Fourier transform. @@ -11246,6 +11370,54 @@ func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf. return scope.AddOperation(opspec) } +// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad. +type MaxPoolGradGradAttr func(optionalAttr) + +// MaxPoolGradGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Returns the truth value of (x >= y) element-wise. // // *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting @@ -12013,15 +12185,72 @@ func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.D if scope.Err() != nil { return } - attrs := map[string]interface{}{"value_dtype": value_dtype} + attrs := map[string]interface{}{"value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableDenseHashTableV2", + Input: []tf.Input{ + empty_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StageSizeAttr is an optional argument to StageSize. +type StageSizeAttr func(optionalAttr) + +// StageSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeCapacity(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeMemoryLimit(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageSizeContainer(value string) StageSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageSizeSharedName(value string) StageSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableDenseHashTableV2", - Input: []tf.Input{ - empty_key, - }, + Type: "StageSize", + Attrs: attrs, } op := scope.AddOperation(opspec) @@ -12770,6 +12999,56 @@ func Neg(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// +// and `max` to 'outputs' tensor of same shape as `inputs`. +// +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVars", + Input: []tf.Input{ + inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Writes a `Summary` protocol buffer with a histogram. // // The generated @@ -14803,54 +15082,6 @@ func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { return scope.AddOperation(opspec) } -// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad. -type MaxPoolGradGradAttr func(optionalAttr) - -// MaxPoolGradGradDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes second-order gradients of the maxpooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients of gradients w.r.t. the input to `max_pool`. -func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradGrad", - Input: []tf.Input{ - orig_input, orig_output, grad, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // RandomUniformIntAttr is an optional argument to RandomUniformInt. type RandomUniformIntAttr func(optionalAttr) @@ -15121,57 +15352,6 @@ func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional return op.Output(0) } -// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel. -type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`, -// -// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]` -// to 'outputs' tensor of same shape as `inputs`. -// -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. -// -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVarsPerChannel", - Input: []tf.Input{ - inputs, min, max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // RandomShuffleAttr is an optional argument to RandomShuffle. type RandomShuffleAttr func(optionalAttr) @@ -17569,23 +17749,6 @@ func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backpr return op.Output(0) } -// Creates a dataset that contains the unique elements of `input_dataset`. -func UniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "UniqueDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2. type SelfAdjointEigV2Attr func(optionalAttr) @@ -19830,130 +19993,38 @@ func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, return op.Output(0) } -// Creates a dataset that concatenates `input_dataset` with `another_dataset`. -func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ConcatenateDataset", - Input: []tf.Input{ - input_dataset, another_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that contains the elements of `input_dataset` ignoring errors. -func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "IgnoreErrorsDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage. -type CropAndResizeGradImageAttr func(optionalAttr) - -// CropAndResizeGradImageMethod sets the optional method attribute to value. -// -// value: A string specifying the interpolation method. Only 'bilinear' is -// supported for now. -// If not specified, defaults to "bilinear" -func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr { - return func(m optionalAttr) { - m["method"] = value - } -} - -// Computes the gradient of the crop_and_resize op wrt the input image tensor. -// -// Arguments: -// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. -// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor -// specifies the coordinates of a box in the `box_ind[i]` image and is specified -// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of -// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the -// `[0, 1]` interval of normalized image height is mapped to -// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in -// which case the sampled crop is an up-down flipped version of the original -// image. The width dimension is treated similarly. Normalized coordinates -// outside the `[0, 1]` range are allowed, in which case we use -// `extrapolation_value` to extrapolate the input image values. -// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. -// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. -// image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` -// containing the original image size. Both `image_height` and `image_width` need -// to be positive. -// -// -// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`. -func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"T": T} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CropAndResizeGradImage", - Input: []tf.Input{ - grads, boxes, box_ind, image_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reads and outputs the entire contents of the input filename. -func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) { +// Returns x / y element-wise for real types. +// +// If `x` and `y` are reals, this will return the floating-point division. +// +// *NOTE*: `Div` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ReadFile", + Type: "RealDiv", Input: []tf.Input{ - filename, + x, y, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Concatenates tensors along one dimension. -// -// Arguments: -// values: List of `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// axis: 0-D. The dimension along which to concatenate. Must be in the -// range [-rank(values), rank(values)). -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) { +// Creates a dataset that concatenates `input_dataset` with `another_dataset`. +func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ConcatV2", + Type: "ConcatenateDataset", Input: []tf.Input{ - tf.OutputList(values), axis, + input_dataset, another_dataset, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -20680,68 +20751,6 @@ func TFRecordDataset(scope *Scope, filenames tf.Output, compression_type tf.Outp return op.Output(0) } -// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. -type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) - -// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. -// If not specified, defaults to -6 -func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["min"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. -// If not specified, defaults to 6 -func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["max"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Compute gradients for a FakeQuantWithMinMaxArgs operation. -// -// Arguments: -// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. -// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. -// -// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: -// `gradients * (inputs >= min && inputs <= max)`. -func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxArgsGradient", - Input: []tf.Input{ - gradients, inputs, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // BatchToSpace for 4-D tensors of type T. // // This is a legacy version of the more general BatchToSpaceND. @@ -22246,10 +22255,175 @@ func TensorArrayCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { return } opspec := tf.OpSpec{ - Type: "TensorArrayCloseV2", + Type: "TensorArrayCloseV2", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage. +type CropAndResizeGradImageAttr func(optionalAttr) + +// CropAndResizeGradImageMethod sets the optional method attribute to value. +// +// value: A string specifying the interpolation method. Only 'bilinear' is +// supported for now. +// If not specified, defaults to "bilinear" +func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// Computes the gradient of the crop_and_resize op wrt the input image tensor. +// +// Arguments: +// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` +// containing the original image size. Both `image_height` and `image_width` need +// to be positive. +// +// +// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResizeGradImage", + Input: []tf.Input{ + grads, boxes, box_ind, image_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reads and outputs the entire contents of the input filename. +func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReadFile", + Input: []tf.Input{ + filename, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates tensors along one dimension. +// +// Arguments: +// values: List of `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// axis: 0-D. The dimension along which to concatenate. Must be in the +// range [-rank(values), rank(values)). +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatV2", + Input: []tf.Input{ + tf.OutputList(values), axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Forwards the value of an available tensor from `inputs` to `output`. +// +// `Merge` waits for at least one of the tensors in `inputs` to become available. +// It is usually combined with `Switch` to implement branching. +// +// `Merge` forwards the first tensor to become available to `output`, and sets +// `value_index` to its index in `inputs`. +// +// Arguments: +// inputs: The input tensors, exactly one of which will become available. +// +// Returns Will be set to the available input tensor.The index of the chosen input tensor in `inputs`. +func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Merge", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// QueueCloseV2Attr is an optional argument to QueueCloseV2. +type QueueCloseV2Attr func(optionalAttr) + +// QueueCloseV2CancelPendingEnqueues sets the optional cancel_pending_enqueues attribute to value. +// +// value: If true, all pending enqueue requests that are +// blocked on the given queue will be canceled. +// If not specified, defaults to false +func QueueCloseV2CancelPendingEnqueues(value bool) QueueCloseV2Attr { + return func(m optionalAttr) { + m["cancel_pending_enqueues"] = value + } +} + +// Closes the given queue. +// +// This operation signals that no more elements will be enqueued in the +// given queue. Subsequent Enqueue(Many) operations will fail. +// Subsequent Dequeue(Many) operations will continue to succeed if +// sufficient elements remain in the queue. Subsequent Dequeue(Many) +// operations that would block will fail immediately. +// +// Arguments: +// handle: The handle to a queue. +// +// Returns the created operation. +func QueueCloseV2(scope *Scope, handle tf.Output, optional ...QueueCloseV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueCloseV2", Input: []tf.Input{ handle, }, + Attrs: attrs, } return scope.AddOperation(opspec) } @@ -24203,147 +24377,6 @@ func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output return scope.AddOperation(opspec) } -// MapPeekAttr is an optional argument to MapPeek. -type MapPeekAttr func(optionalAttr) - -// MapPeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekCapacity(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapPeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekMemoryLimit(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapPeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapPeekContainer(value string) MapPeekAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapPeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapPeekSharedName(value string) MapPeekAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op peeks at the values at the specified key. If the -// -// underlying container does not contain this key -// this op will block until it does. -func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapPeek", - Input: []tf.Input{ - key, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapPeek", err) - return - } - return values -} - -// QueueCloseV2Attr is an optional argument to QueueCloseV2. -type QueueCloseV2Attr func(optionalAttr) - -// QueueCloseV2CancelPendingEnqueues sets the optional cancel_pending_enqueues attribute to value. -// -// value: If true, all pending enqueue requests that are -// blocked on the given queue will be canceled. -// If not specified, defaults to false -func QueueCloseV2CancelPendingEnqueues(value bool) QueueCloseV2Attr { - return func(m optionalAttr) { - m["cancel_pending_enqueues"] = value - } -} - -// Closes the given queue. -// -// This operation signals that no more elements will be enqueued in the -// given queue. Subsequent Enqueue(Many) operations will fail. -// Subsequent Dequeue(Many) operations will continue to succeed if -// sufficient elements remain in the queue. Subsequent Dequeue(Many) -// operations that would block will fail immediately. -// -// Arguments: -// handle: The handle to a queue. -// -// Returns the created operation. -func QueueCloseV2(scope *Scope, handle tf.Output, optional ...QueueCloseV2Attr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QueueCloseV2", - Input: []tf.Input{ - handle, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Forwards the value of an available tensor from `inputs` to `output`. -// -// `Merge` waits for at least one of the tensors in `inputs` to become available. -// It is usually combined with `Switch` to implement branching. -// -// `Merge` forwards the first tensor to become available to `output`, and sets -// `value_index` to its index in `inputs`. -// -// Arguments: -// inputs: The input tensors, exactly one of which will become available. -// -// Returns Will be set to the available input tensor.The index of the chosen input tensor in `inputs`. -func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Merge", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - // MapUnstageAttr is an optional argument to MapUnstage. type MapUnstageAttr func(optionalAttr) @@ -28247,111 +28280,3 @@ func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf. op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) } - -// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. -type FakeQuantWithMinMaxVarsAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` -// -// and `max` to 'outputs' tensor of same shape as `inputs`. -// -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. -// -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", - Input: []tf.Input{ - inputs, min, max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FakeQuantWithMinMaxVarsPerChannelGradientAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannelGradient. -type FakeQuantWithMinMaxVarsPerChannelGradientAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsPerChannelGradientNumBits sets the optional num_bits attribute to value. -// -// value: The bitwidth of the quantization; between 2 and 8, inclusive. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsPerChannelGradientNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelGradientAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange sets the optional narrow_range attribute to value. -// -// value: Whether to quantize into 2^num_bits - 1 distinct values. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelGradientAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. -// -// Arguments: -// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation, -// shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`. -// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape -// same as `gradients`. -// min, max: Quantization interval, floats of shape `[d]`. -// -// -// -// Returns Backpropagated gradients w.r.t. inputs, shape same as -// `inputs`: -// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter, shape `[d]`: -// `sum_per_d(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter, shape `[d]`: -// `sum_per_d(gradients * (inputs > max))`. -func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVarsPerChannelGradient", - Input: []tf.Input{ - gradients, inputs, min, max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index a9ce5372aeb32b6957359fdcaa9da01c732c9f9f..1c84eae540d476ad3d3f5010be5c8aef48af58a5 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0 + 1.6.0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index fe34ca83ff30373fa4f3c4f345323bad40a8754e..cf1a7b6c9c51d2a6f08c2bd89d917af9d45212be 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0 + 1.6.0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 390152808eb0c4abebe093ed5db39faf37fcafe3..b202dcd5c79b253eb42929ef378392deaf83b8d9 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0 + 1.6.0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 524ec45f48bb91d09dfb5fca3cc19256d45587fb..606805ff33376f639740f324762290ac6732e19d 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.5.0 + 1.6.0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index 9cf3217f51f73184a02a58ead1a2735c5a44fd26..c6bba4e5365c7b400359d1d4005071cfec32978a 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0 + 1.6.0 ../ proto diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index d619f986a9a03ac67f5de6bbe80e686a05ce5d42..a22663f9f3ead8195b032e45c085ba02f337b594 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0 + 1.6.0 ../ tensorflow diff --git a/tensorflow/java/src/main/java/org/tensorflow/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/package-info.java index dd4859e1b14045e4123e7f15fbaff98e14d0b377..521c5c610c1f775cf9174664f5b786786ce1181d 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/package-info.java +++ b/tensorflow/java/src/main/java/org/tensorflow/package-info.java @@ -35,5 +35,9 @@ limitations under the License. *
  • Graph execution: Using a Session to execute the graphs and find the best label for an * image. * + * + *

    Additional examples can be found in the tensorflow/models + * GitHub repository. */ package org.tensorflow; diff --git a/tensorflow/java/src/main/native/tensor_jni.cc b/tensorflow/java/src/main/native/tensor_jni.cc index 745abec244d1528e918464473e5d3fb19ad5082c..7e3cf4a88aac5acd4721a07c8316d8d124dce001 100644 --- a/tensorflow/java/src/main/native/tensor_jni.cc +++ b/tensorflow/java/src/main/native/tensor_jni.cc @@ -400,7 +400,13 @@ size_t nonScalarTF_STRINGTensorSize(JNIEnv* env, jarray value, int num_dims) { for (jsize i = 0; i < len; ++i) { jarray elem = static_cast( env->GetObjectArrayElement(static_cast(value), i)); + if (elem == nullptr) { + throwException(env, kNullPointerException, + "null entries in provided array"); + return ret; + } ret += nonScalarTF_STRINGTensorSize(env, elem, num_dims - 1); + if (env->ExceptionCheck()) return ret; } return ret; } @@ -421,8 +427,8 @@ void fillNonScalarTF_STRINGTensorData(JNIEnv* env, jarray value, int num_dims, for (jsize i = 0; i < len; ++i) { jarray elem = static_cast( env->GetObjectArrayElement(static_cast(value), i)); - if (TF_GetCode(status) != TF_OK) return; fillNonScalarTF_STRINGTensorData(env, elem, num_dims - 1, writer, status); + if (TF_GetCode(status) != TF_OK) return; } } } // namespace @@ -444,6 +450,7 @@ JNIEXPORT jlong JNICALL Java_org_tensorflow_Tensor_allocateNonScalarBytes( } const size_t encoded_size = nonScalarTF_STRINGTensorSize(env, value, num_dims); + if (env->ExceptionCheck()) return 0; TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims, 8 * num_elements + encoded_size); if (t == nullptr) { diff --git a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java index 6538359d11a95eae698cc5aac8430e74ab1ed74c..1bd00a763ddff2f067183f57cfa80fdcbed84fd2 100644 --- a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java +++ b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java @@ -432,7 +432,7 @@ public class TensorTest { try (Tensor t = Tensor.create(vector, Integer.class)) { fail("Tensor.create() should fail because it was given an array of boxed values"); } catch (IllegalArgumentException e) { - // The expected exception + // The expected exception } } @@ -536,4 +536,15 @@ public class TensorTest { assertArrayEquals(matrix, cpy.copyTo(new float[2][3])); } } + + @Test + public void gracefullyFailCreationFromNullArrayForStringTensor() { + // Motivated by: https://github.com/tensorflow/tensorflow/issues/17130 + byte[][] array = new byte[1][]; + try { + Tensors.create(array); + } catch (NullPointerException e) { + // expected. + } + } } diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index f5cd7885e72f743713933db7ff754ee89bed2626..04e926ff16ce1eeff124cc9c73928e7f7c0bf43d 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1,5 +1,8 @@ # Description: # Python support for TensorFlow. +# +# Public targets: +# ":platform" - Low-level and platform-specific Python code. package( default_visibility = [ @@ -25,6 +28,7 @@ load("//tensorflow:tensorflow.bzl", "py_tests") load("//tensorflow:tensorflow.bzl", "tf_py_build_info_genrule") load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") load("//tensorflow:tensorflow.bzl", "tf_cc_shared_object") +load("//tensorflow:tensorflow.bzl", "tf_custom_op_library_additional_deps_impl") load("//tensorflow:tensorflow.bzl", "cuda_py_test") load("//tensorflow:tensorflow.bzl", "cuda_py_tests") load("//tensorflow/core:platform/default/build_config.bzl", "pyx_library") @@ -83,39 +87,39 @@ py_library( ":ops", ":platform", ":pywrap_tensorflow", + ":saver_test_utils", ":script_ops", ":session_ops", ":sets", ":sparse_ops", ":spectral_ops", + ":spectral_ops_test_util", ":standard_ops", ":state_ops", ":string_ops", + ":subscribe", ":summary", ":tensor_array_ops", - ":training", - ":saver_test_utils", - ":subscribe", ":test_ops", # TODO: Break testing code out into separate rule. - ":tf_item", ":tf_cluster", + ":tf_item", ":tf_optimizer", + ":training", ":util", ":weights_broadcast_ops", - "//third_party/py/numpy", + "//tensorflow/contrib:contrib_py", "//tensorflow/core:protos_all_py", "//tensorflow/python/data", "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//tensorflow/python/keras", - "//tensorflow/python/ops/losses", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/linalg", + "//tensorflow/python/ops/losses", "//tensorflow/python/profiler", "//tensorflow/python/saved_model", - ] + if_not_windows([ - "//tensorflow/contrib:contrib_py", - ]), + "//third_party/py/numpy", + ], ) tf_py_build_info_genrule() @@ -132,6 +136,7 @@ py_library( ], ) + ["platform/build_info.py"], srcs_version = "PY2AND3", + visibility = ["//visibility:public"], deps = [ ":lib", ":pywrap_tensorflow", @@ -761,6 +766,31 @@ py_library( ], ) +py_library( + name = "smart_cond", + srcs = ["framework/smart_cond.py"], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + ":tensor_util", + ], +) + +py_test( + name = "smart_cond_test", + size = "small", + srcs = ["framework/smart_cond_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":constant_op", + ":framework_ops", + ":math_ops", + ":session", + ":smart_cond", + ], +) + py_library( name = "sparse_tensor", srcs = ["framework/sparse_tensor.py"], @@ -917,7 +947,6 @@ py_test( srcs = ["framework/contrib_test.py"], main = "framework/contrib_test.py", srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ "//tensorflow:tensorflow_py", "//tensorflow/python:client_testlib", @@ -1282,7 +1311,6 @@ py_test( srcs = ["framework/dtypes_test.py"], main = "framework/dtypes_test.py", srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":framework_for_generated_wrappers", ":framework_test_lib", @@ -1624,7 +1652,6 @@ py_test( size = "small", srcs = ["ops/clip_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":client_testlib", ":clip_ops", @@ -1746,6 +1773,7 @@ py_library( py_library( name = "gradients", srcs = [ + "ops/custom_gradient.py", "ops/gradients.py", "ops/gradients_impl.py", ], @@ -1759,6 +1787,7 @@ py_library( ":control_flow_util", ":framework", ":framework_for_generated_wrappers", + ":framework_ops", ":functional_ops", ":image_grad", ":linalg_grad", @@ -1771,6 +1800,8 @@ py_library( ":platform", ":spectral_grad", ":util", + "//tensorflow/python/eager:context", + "//tensorflow/python/eager:tape", "//third_party/py/numpy", "@six_archive//:six", ], @@ -2514,6 +2545,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":array_ops", + ":checkpointable", ":control_flow_ops", ":dtypes", ":framework_ops", @@ -2683,7 +2715,6 @@ cuda_py_test( ], data = ["//tensorflow/core:image_testdata"], shard_count = 5, - tags = ["no_windows"], ) cuda_py_test( @@ -2847,6 +2878,30 @@ py_library( ], ) +py_library( + name = "checkpointable", + srcs = ["training/checkpointable.py"], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":dtypes", + ":io_ops_gen", + ":ops", + ":util", + "//tensorflow/python/eager:context", + ], +) + +py_test( + name = "checkpointable_test", + srcs = ["training/checkpointable_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":checkpointable", + ":client_testlib", + ], +) + py_test( name = "evaluation_test", size = "small", @@ -3197,6 +3252,11 @@ tf_py_wrap_cc( "util/transform_graph.i", "util/util.i", ], + # Use a DEF file to export symbols on Windows + win_def_file = select({ + "//tensorflow:windows": ":pywrap_tensorflow_filtered_def_file", + "//conditions:default": None, + }), deps = [ ":bfloat16_lib", ":cost_analyzer_lib", @@ -3240,6 +3300,65 @@ tf_py_wrap_cc( tf_additional_gdr_deps()), ) +# ** Targets for Windows build (start) ** +# We need the following targets to expose symbols from _pywrap_tensorflow.dll + +# Build a cc_binary from tf_custom_op_library_additional_deps_impl, +# it contains all object code from its dependencies. +cc_binary( + name = "tf_custom_op_library_additional_deps.so", + linkshared = 1, + linkstatic = 1, + deps = tf_custom_op_library_additional_deps_impl(), +) + +# Get a DEF file generated by parsing all object files +# of tf_custom_op_library_additional_deps.so +filegroup( + name = "pywrap_tensorflow_def_file", + srcs = [":tf_custom_op_library_additional_deps.so"], + output_group = "def_file", +) + +# Filter the DEF file to reduce the number of symbols to 64K or less. +# Note that we also write the name of the pyd file into DEF file so that +# the dynamic libraries of custom ops can find it at runtime. +genrule( + name = "pywrap_tensorflow_filtered_def_file", + srcs = [":pywrap_tensorflow_def_file"], + outs = ["pywrap_tensorflow_filtered_def_file.def"], + cmd = select({ + "//tensorflow:windows": """ + $(location @local_config_def_file_filter//:def_file_filter) \\ + --input $(location :pywrap_tensorflow_def_file) \\ + --output $@ \\ + --target _pywrap_tensorflow_internal.pyd + """, + "//conditions:default": "touch $@", # Just a placeholder for Unix platforms + }), + tools = ["@local_config_def_file_filter//:def_file_filter"], +) + +# Get the import library of _pywrap_tensorflow_internal.dll +filegroup( + name = "pywrap_tensorflow_import_lib_file", + srcs = [":_pywrap_tensorflow_internal.so"], + output_group = "interface_library", +) + +# Create a cc_import rule for the import library of _pywrap_tensorflow_internal.dll +# so that custom ops' dynamic libraries can link against it. +cc_import( + name = "pywrap_tensorflow_import_lib", + interface_library = select({ + "//tensorflow:windows": ":pywrap_tensorflow_import_lib_file", + "//conditions:default": "not_exsiting_on_unix.lib", # Just a placeholder for Unix platforms + }), + system_provided = 1, +) + +# ** Targets for Windows build (end) ** + py_library( name = "lib", srcs = [ @@ -3601,6 +3720,7 @@ py_test( ":framework_for_generated_wrappers", ":math_ops", ":state_ops_gen", + ":variable_scope", ":variables", "//tensorflow/core:protos_all_py", ], @@ -3611,7 +3731,6 @@ py_test( size = "small", srcs = ["lib/core/bfloat16_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":client_testlib", ":lib", @@ -3891,7 +4010,9 @@ py_test( size = "small", srcs = ["training/checkpoint_utils_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "no_cuda_on_cpu_tap", + ], deps = [ ":client", ":client_testlib", @@ -3900,6 +4021,7 @@ py_test( ":partitioned_variables", ":platform", ":pywrap_tensorflow", + ":resource_variable_ops", ":state_ops", ":training", ":variable_scope", @@ -3912,7 +4034,6 @@ py_test( size = "small", srcs = ["training/checkpoint_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":checkpoint_ops_gen", ":client", @@ -3934,10 +4055,7 @@ py_test( size = "medium", srcs = ["training/monitored_session_test.py"], srcs_version = "PY2AND3", - tags = [ - "no_windows", - "notsan", # b/67945581 - ], + tags = ["notsan"], # b/67945581 deps = [ ":array_ops", ":client_testlib", @@ -4063,6 +4181,7 @@ py_library( ":control_flow_ops", ":framework_for_generated_wrappers", ":platform", + ":smart_cond", ":tensor_util", ":util", ":variable_scope", @@ -4079,8 +4198,6 @@ py_library( "layers/convolutional.py", "layers/core.py", "layers/layers.py", - "layers/maxout.py", - "layers/network.py", "layers/normalization.py", "layers/pooling.py", ], @@ -4133,25 +4250,6 @@ py_test( ], ) -py_test( - name = "layers_network_test", - size = "small", - srcs = ["layers/network_test.py"], - main = "layers/network_test.py", - srcs_version = "PY2AND3", - deps = [ - ":array_ops", - ":client_testlib", - ":framework_for_generated_wrappers", - ":framework_test_lib", - ":layers", - ":layers_base", - ":sparse_ops", - "//tensorflow/python/eager:context", - "//third_party/py/numpy", - ], -) - py_test( name = "layers_core_test", size = "small", @@ -4190,22 +4288,6 @@ py_test( ], ) -py_test( - name = "layers_maxout_test", - size = "small", - srcs = ["layers/maxout_test.py"], - main = "layers/maxout_test.py", - srcs_version = "PY2AND3", - deps = [ - ":client_testlib", - ":framework_for_generated_wrappers", - ":layers", - ":math_ops", - ":nn_ops", - ":random_ops", - ], -) - py_test( name = "layers_utils_test", size = "small", @@ -4601,6 +4683,34 @@ py_test( ], ) +py_library( + name = "graph_placer", + srcs = [ + "grappler/controller.py", + "grappler/graph_placer.py", + "grappler/hierarchical_controller.py", + ], + deps = [ + ":python", + "//third_party/py/numpy", + ], +) + +py_test( + name = "graph_placer_test", + size = "large", + srcs = ["grappler/graph_placer_test.py"], + tags = [ + "grappler", + "no_pip", # graph_placer is not available in pip. + ], + deps = [ + ":client_testlib", + ":graph_placer", + "//tensorflow/python:math_ops", + ], +) + py_test( name = "memory_optimizer_test", size = "medium", @@ -4692,6 +4802,7 @@ py_test( srcs_version = "PY2AND3", tags = [ "grappler", + "no_cuda_on_cpu_tap", "no_pip", ], deps = [ diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index ea7604d30f127e51d573a5251420faa22cbb3468..5a9cd7531db6482313005feebaadf953c6da3bb4 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -60,7 +60,7 @@ from tensorflow.core.protobuf.tensorflow_server_pb2 import * from tensorflow.core.util.event_pb2 import * # Framework -from tensorflow.python.framework.framework_lib import * +from tensorflow.python.framework.framework_lib import * # pylint: disable=redefined-builtin from tensorflow.python.framework.versions import * from tensorflow.python.framework import errors from tensorflow.python.framework import graph_util @@ -116,6 +116,7 @@ from tensorflow.python.platform import test from tensorflow.python.util.all_util import remove_undocumented from tensorflow.python.util.all_util import make_all +from tensorflow.python.util.tf_export import tf_export # Import modules whose docstrings contribute, for use by remove_undocumented # below. @@ -138,6 +139,10 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import tensor_array_ops +# Eager execution +from tensorflow.python.eager.context import executing_eagerly +from tensorflow.python.framework.ops import enable_eager_execution + # Symbols whitelisted for export without documentation. # TODO(cwhipkey): review these and move to contrib, expose through # documentation, or remove. @@ -167,18 +172,39 @@ _allowed_symbols = [ 'TensorInfo', # Used for tf.saved_model functionality. ] +# Export protos +# pylint: disable=undefined-variable +tf_export('AttrValue')(AttrValue) +tf_export('ConfigProto')(ConfigProto) +tf_export('Event', 'summary.Event')(Event) +tf_export('GPUOptions')(GPUOptions) +tf_export('GraphDef')(GraphDef) +tf_export('GraphOptions')(GraphOptions) +tf_export('HistogramProto')(HistogramProto) +tf_export('LogMessage')(LogMessage) +tf_export('MetaGraphDef')(MetaGraphDef) +tf_export('NameAttrList')(NameAttrList) +tf_export('NodeDef')(NodeDef) +tf_export('OptimizerOptions')(OptimizerOptions) +tf_export('RunMetadata')(RunMetadata) +tf_export('RunOptions')(RunOptions) +tf_export('SessionLog', 'summary.SessionLog')(SessionLog) +tf_export('Summary', 'summary.Summary')(Summary) +tf_export('summary.SummaryDescription')(SummaryDescription) +tf_export('SummaryMetadata')(SummaryMetadata) +tf_export('summary.TaggedRunMetadata')(TaggedRunMetadata) +tf_export('TensorInfo')(TensorInfo) +# pylint: enable=undefined-variable + + # The following symbols are kept for compatibility. It is our plan # to remove them in the future. _allowed_symbols.extend([ 'arg_max', 'arg_min', - 'mul', # use tf.multiply instead. - 'neg', # use tf.negative instead. - 'sub', # use tf.subtract instead. 'create_partitioned_variables', 'deserialize_many_sparse', 'lin_space', - 'list_diff', # Use tf.listdiff instead. 'listdiff', # Use tf.listdiff instead. 'parse_single_sequence_example', 'serialize_many_sparse', @@ -268,6 +294,12 @@ _allowed_symbols.extend([ 'MONOLITHIC_BUILD', ]) +# Eager execution +_allowed_symbols.extend([ + 'enable_eager_execution', + 'executing_eagerly', +]) + # Remove all extra symbols that don't have a docstring or are not explicitly # referenced in the whitelist. remove_undocumented(__name__, _allowed_symbols, [ diff --git a/tensorflow/python/client/events_writer.i b/tensorflow/python/client/events_writer.i index de030fcb4282912475ed8853bae9d41cde2c085d..c72b76b8fa4a05588841466a836bc189bb64d154 100644 --- a/tensorflow/python/client/events_writer.i +++ b/tensorflow/python/client/events_writer.i @@ -23,6 +23,9 @@ limitations under the License. %nodefaultctor EventsWriter; +%ignore tensorflow::Status::operator=; +%include "tensorflow/core/lib/core/status.h" + %ignoreall %unignore tensorflow; %unignore tensorflow::EventsWriter; diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index f3c4fecdc0fde0436bea76cc774edaabe1bc07dd..924d62992a6f7c6e39b73752530eab70c6b5ce91 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -1085,7 +1085,10 @@ class BaseSession(SessionInterface): if isinstance(subfeed_val, ops.Tensor): raise TypeError('The value of a feed cannot be a tf.Tensor object. ' 'Acceptable feed values include Python scalars, ' - 'strings, lists, numpy ndarrays, or TensorHandles.') + 'strings, lists, numpy ndarrays, or TensorHandles.' + 'For reference, the tensor object was ' + + str(feed_val) + ' which was passed to the ' + 'feed with key ' + str(feed) + '.') subfeed_dtype = subfeed_t.dtype.as_numpy_dtype if isinstance(subfeed_val, int) and _convert_to_numpy_obj( @@ -1217,19 +1220,12 @@ class BaseSession(SessionInterface): compat.as_bytes(options.SerializeToString())) if options else None run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None try: - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, options_ptr, {}, fetch_list, target_list, - run_metadata_ptr, status) - else: - results = tf_session.TF_Run(self._session, options_ptr, {}, - fetch_list, target_list, status, - run_metadata_ptr) - if fetch_handler: - results = fetch_handler.build_results(self, results) - else: - results = results[0] if results else None + results = self._call_tf_sessionrun( + options_ptr, {}, fetch_list, target_list, run_metadata_ptr) + if fetch_handler: + results = fetch_handler.build_results(self, results) + else: + results = results[0] if results else None if run_metadata: proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) run_metadata.ParseFromString(compat.as_bytes(proto_data)) @@ -1250,13 +1246,7 @@ class BaseSession(SessionInterface): assert len(target_list) == 1 def _single_operation_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - tf_session.TF_SessionRun_wrapper(self._session, None, {}, [], - target_list, None, status) - else: - tf_session.TF_Run(self._session, None, {}, [], target_list, status, - None) + self._call_tf_sessionrun(None, {}, [], target_list, None) return _single_operation_run elif isinstance(fetches, ops.Tensor): @@ -1266,13 +1256,7 @@ class BaseSession(SessionInterface): assert not target_list def _single_tensor_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, None, {}, fetch_list, [], None, status) - else: - results = tf_session.TF_Run(self._session, None, {}, fetch_list, [], - status, None) + results = self._call_tf_sessionrun(None, {}, fetch_list, [], None) return results[0] return _single_tensor_run @@ -1280,13 +1264,8 @@ class BaseSession(SessionInterface): # In all other cases, we must use `fetch_handler` to build the # results for us. def _fetch_handler_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, None, {}, fetch_list, target_list, None, status) - else: - results = tf_session.TF_Run(self._session, None, {}, fetch_list, - target_list, status, None) + results = self._call_tf_sessionrun( + None, {}, fetch_list, target_list, None) return fetch_handler.build_results(self, results) return _fetch_handler_run @@ -1326,35 +1305,22 @@ class BaseSession(SessionInterface): fetches = _name_list(fetch_list) targets = _name_list(target_list) - def _run_fn(session, feed_dict, fetch_list, target_list, options, - run_metadata): + def _run_fn(feed_dict, fetch_list, target_list, options, run_metadata): # Ensure any changes to the graph are reflected in the runtime. self._extend_graph() - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - return tf_session.TF_SessionRun_wrapper(session, options, feed_dict, - fetch_list, target_list, - run_metadata, status) - else: - return tf_session.TF_Run(session, options, feed_dict, fetch_list, - target_list, status, run_metadata) + return self._call_tf_sessionrun( + options, feed_dict, fetch_list, target_list, run_metadata) - def _prun_fn(session, handle, feed_dict, fetch_list): + def _prun_fn(handle, feed_dict, fetch_list): if target_list: raise RuntimeError('partial_run() requires empty target_list.') - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - return tf_session.TF_SessionPRun_wrapper(session, handle, feed_dict, - fetch_list, status) - else: - return tf_session.TF_PRun(session, handle, feed_dict, fetch_list, - status) + return self._call_tf_sessionprun(handle, feed_dict, fetch_list) if handle is None: - return self._do_call(_run_fn, self._session, feeds, fetches, targets, - options, run_metadata) + return self._do_call(_run_fn, feeds, fetches, targets, options, + run_metadata) else: - return self._do_call(_prun_fn, self._session, handle, feeds, fetches) + return self._do_call(_prun_fn, handle, feeds, fetches) def _do_call(self, fn, *args): try: @@ -1374,23 +1340,23 @@ class BaseSession(SessionInterface): raise type(e)(node_def, op, message) def _extend_graph(self): - # Nothing to do if we're using the new session interface - # TODO(skyewm): remove this function altogether eventually if self._created_with_new_api: - return - - # Ensure any changes to the graph are reflected in the runtime. - with self._extend_lock: - if self._graph.version > self._current_version: - # pylint: disable=protected-access - graph_def, self._current_version = self._graph._as_graph_def( - from_version=self._current_version, add_shapes=self._add_shapes) - # pylint: enable=protected-access - + with self._graph._lock: # pylint: disable=protected-access with errors.raise_exception_on_not_ok_status() as status: - tf_session.TF_ExtendGraph(self._session, - graph_def.SerializeToString(), status) - self._opened = True + tf_session.ExtendSession(self._session, status) + else: + # Ensure any changes to the graph are reflected in the runtime. + with self._extend_lock: + if self._graph.version > self._current_version: + # pylint: disable=protected-access + graph_def, self._current_version = self._graph._as_graph_def( + from_version=self._current_version, add_shapes=self._add_shapes) + # pylint: enable=protected-access + + with errors.raise_exception_on_not_ok_status() as status: + tf_session.TF_ExtendGraph(self._session, + graph_def.SerializeToString(), status) + self._opened = True # The threshold to run garbage collection to delete dead tensors. _DEAD_HANDLES_THRESHOLD = 10 @@ -1441,6 +1407,27 @@ class BaseSession(SessionInterface): feed_dict[feed_tensor] = np_val return handles + def _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, + run_metadata): + with errors.raise_exception_on_not_ok_status() as status: + if self._created_with_new_api: + return tf_session.TF_SessionRun_wrapper( + self._session, options, feed_dict, fetch_list, target_list, + run_metadata, status) + else: + return tf_session.TF_Run( + self._session, options, feed_dict, fetch_list, target_list, + status, run_metadata) + + def _call_tf_sessionprun(self, handle, feed_dict, fetch_list): + with errors.raise_exception_on_not_ok_status() as status: + if self._created_with_new_api: + return tf_session.TF_SessionPRun_wrapper( + self._session, handle, feed_dict, fetch_list, status) + else: + return tf_session.TF_PRun( + self._session, handle, feed_dict, fetch_list, status) + @tf_export('Session') class Session(BaseSession): diff --git a/tensorflow/python/client/session_benchmark.py b/tensorflow/python/client/session_benchmark.py index 06e9a099267938e53e377e65ed975f7f4b8b966b..da74855193dbfe3019f23c542d86c5e493e9ac7a 100644 --- a/tensorflow/python/client/session_benchmark.py +++ b/tensorflow/python/client/session_benchmark.py @@ -22,7 +22,7 @@ import time import numpy as np -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index f12c0055115d41f1b90ba319ad79ec23378bebb1..442a66a68e2d795436ea3cb8809536a03442ae13 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -31,13 +31,13 @@ from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.core.lib.core import error_codes_pb2 from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.framework import common_shapes from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import function +from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_util @@ -47,8 +47,8 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gen_control_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import from tensorflow.python.ops import state_ops @@ -1054,6 +1054,43 @@ class SessionTest(test_util.TensorFlowTestCase): for t in threads: t.join() + def testParallelRunAndBuild(self): + with session.Session() as sess: + c = constant_op.constant(5.0) + stop = threading.Event() + + def run_loop(): + while not stop.is_set(): + self.assertEqual(sess.run(c), 5.0) + + threads = [self.checkedThread(target=run_loop) for _ in range(100)] + for t in threads: + t.start() + + # Do some graph construction. Try to exercise non-trivial paths. + graph = ops.get_default_graph() + gdef = None + for _ in range(10): + x = array_ops.placeholder(dtype=dtypes.float32) + with ops.colocate_with(x): + y = array_ops.placeholder(dtype=dtypes.float32) + with ops.device('/cpu:0'): + z = control_flow_ops.while_loop( + lambda x, y: x < 10, lambda x, y: (x + 1, x * y), [x, y]) + with graph._attr_scope({'_a': attr_value_pb2.AttrValue(b=False)}): + gradients_impl.gradients(z, [x, y]) + if gdef is None: + gdef = graph.as_graph_def() + else: + # NOTE(skyewm): import_graph_def breaks the running threads without + # the C API enabled. This is not a regression so I didn't fix it. + if ops._USE_C_API: + importer.import_graph_def(gdef, name='import') + + stop.set() + for t in threads: + t.join() + def testRunFeedDict(self): with session.Session() as s: x = array_ops.zeros([2]) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 1fd488e7b6388f7953a279dca8f93ab57a85f63d..e88fc0c01a8bb7534f47e2a0389965c102bbad7b 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -719,6 +719,11 @@ def TF_Reset(target, containers=None, config=None): $1 = &types_local; } +%unignore SetRequireShapeInferenceFns; +%unignore TF_TryEvaluateConstant_wrapper; +%noexception TF_TryEvaluateConstant_wrapper; +%unignore ExtendSession; + %include "tensorflow/python/client/tf_session_helper.h" %unignoreall diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc index 361dbc22b097a9bc82f656d7416b88c4a3a1ec2d..a8ab91749a86749a1eef25e2674634334682d0f3 100644 --- a/tensorflow/python/client/tf_session_helper.cc +++ b/tensorflow/python/client/tf_session_helper.cc @@ -493,4 +493,19 @@ std::vector TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( return input_strs; } +PyObject* TF_TryEvaluateConstant_wrapper(TF_Graph* graph, TF_Output output, + TF_Status* status) { + TF_Tensor* result_tensor; + bool evaluated = + TF_TryEvaluateConstant(graph, output, &result_tensor, status); + if (!evaluated || TF_GetCode(status) != TF_OK) Py_RETURN_NONE; + + Safe_TF_TensorPtr safe_result_tensor(result_tensor); + PyObject* out; + Status s = TF_TensorToPyArray(std::move(safe_result_tensor), &out); + Set_TF_Status_from_Status(status, s); + if (!s.ok()) Py_RETURN_NONE; + return out; +} + } // namespace tensorflow diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h index 29d5b28f40a7c07c199eec8c8cd85de626f6b068..83318dc178f6da3828a8dc41e81b7fc3e2e19e22 100644 --- a/tensorflow/python/client/tf_session_helper.h +++ b/tensorflow/python/client/tf_session_helper.h @@ -213,6 +213,11 @@ std::vector TF_GraphGetTensorShape_wrapper(TF_Graph* graph, std::vector TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( TF_ImportGraphDefResults* results); +// If evaluation was possible, returns the numpy ndarray of the evaluated +// result. Otherwise returns None. +PyObject* TF_TryEvaluateConstant_wrapper(TF_Graph* graph, TF_Output output, + TF_Status* status); + } // namespace tensorflow #endif // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_ diff --git a/tensorflow/python/data/kernel_tests/cache_dataset_op_test.py b/tensorflow/python/data/kernel_tests/cache_dataset_op_test.py index b71652c980f233ce116ea89544fcb38ad1d816d1..02720a2e985914d3a6774dc6f64d1316890c46bf 100644 --- a/tensorflow/python/data/kernel_tests/cache_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/cache_dataset_op_test.py @@ -28,6 +28,7 @@ from tensorflow.python.data.ops import iterator_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -202,44 +203,45 @@ class FilesystemCacheDatasetTest(test.TestCase): class MemoryCacheDatasetTest(test.TestCase): def testCacheDatasetPassthrough(self): - repeat_count = variables.Variable(constant_op.constant(10, dtypes.int64)) - dataset = dataset_ops.Dataset.range(3).flat_map( - lambda x: dataset_ops.Dataset.from_tensors(x).repeat(repeat_count)) + with ops.device("cpu:0"): + repeat_count = variables.Variable(constant_op.constant(10, dtypes.int64)) + dataset = dataset_ops.Dataset.range(3).flat_map( + lambda x: dataset_ops.Dataset.from_tensors(x).repeat(repeat_count)) - cached_dataset = dataset.cache().repeat(2) - uncached_dataset = dataset.repeat(2) + cached_dataset = dataset.cache().repeat(2) + uncached_dataset = dataset.repeat(2) - # Needs to be initializable to capture the variable. - cached_iterator = cached_dataset.make_initializable_iterator() - cached_next = cached_iterator.get_next() - uncached_iterator = uncached_dataset.make_initializable_iterator() - uncached_next = uncached_iterator.get_next() + # Needs to be initializable to capture the variable. + cached_iterator = cached_dataset.make_initializable_iterator() + cached_next = cached_iterator.get_next() + uncached_iterator = uncached_dataset.make_initializable_iterator() + uncached_next = uncached_iterator.get_next() - with self.test_session() as sess: + with self.test_session() as sess: - sess.run(repeat_count.initializer) - sess.run(cached_iterator.initializer) - sess.run(uncached_iterator.initializer) + sess.run(repeat_count.initializer) + sess.run(cached_iterator.initializer) + sess.run(uncached_iterator.initializer) - for i in range(3): - for _ in range(10): - self.assertEqual(sess.run(cached_next), i) - self.assertEqual(sess.run(uncached_next), i) + for i in range(3): + for _ in range(10): + self.assertEqual(sess.run(cached_next), i) + self.assertEqual(sess.run(uncached_next), i) - sess.run(repeat_count.assign(0)) + sess.run(repeat_count.assign(0)) - # The uncached iterator should now be empty. - with self.assertRaises(errors.OutOfRangeError): - sess.run(uncached_next) + # The uncached iterator should now be empty. + with self.assertRaises(errors.OutOfRangeError): + sess.run(uncached_next) - # The cached iterator replays from cache. - for i in range(3): - for _ in range(10): - self.assertEqual(sess.run(cached_next), i) + # The cached iterator replays from cache. + for i in range(3): + for _ in range(10): + self.assertEqual(sess.run(cached_next), i) - # The cached iterator should now be empty. - with self.assertRaises(errors.OutOfRangeError): - sess.run(cached_next) + # The cached iterator should now be empty. + with self.assertRaises(errors.OutOfRangeError): + sess.run(cached_next) def testEmptyCacheReading(self): components = (np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]), diff --git a/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py b/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py index 14627810b57f68fd96e3e3cc7b51b4fbf7365299..ea5b41e5d819743ad03f3148d654329aea51dab7 100644 --- a/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py +++ b/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py @@ -263,7 +263,7 @@ class DatasetConstructorTest(test.TestCase): for i in range(3): results = sess.run(get_next) for component, result_component in zip( - (zip(*components[:3])[i] + expected[i]), results): + (list(zip(*components[:3]))[i] + expected[i]), results): if sparse_tensor.is_sparse(component): self.assertSparseValuesEqual(component, result_component) else: diff --git a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py index f129d07b57b96b7869c84467aeb2276c93531ef8..6aabad2f574551cbdc152fe378eb9dc0f5f71995 100644 --- a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py +++ b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py @@ -21,9 +21,12 @@ import threading import numpy as np +from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.ops import script_ops from tensorflow.python.platform import test @@ -302,6 +305,89 @@ class DatasetConstructorTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testFromGeneratorStopShort(self): + + def generator(): + yield 0 + yield 1 + yield 2 + + iterator = ( + dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64).make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + self.assertAllEqual(0, sess.run(get_next)) + self.assertAllEqual(1, sess.run(get_next)) + + def testFromGeneratorDestructorCalled(self): + # Use an `Event` to signal that the generator has been deleted. + event = threading.Event() + + class GeneratorWrapper(object): + + def __iter__(self): + return self + + def next(self): + return self.__next__() + + def __next__(self): + return 42 + + def __del__(self): + event.set() + + iterator = dataset_ops.Dataset.from_generator( + GeneratorWrapper, + output_types=dtypes.int64).take(2).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with session.Session() as sess: + sess.run(init_op) + self.assertAllEqual(42, sess.run(get_next)) + self.assertAllEqual(42, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + # Test that `GeneratorWrapper` object is destroyed when the + # iterator terminates (and the generator iterator is deleted). + self.assertTrue(event.is_set()) + + def testGeneratorDatasetFinalizeFunctionCalled(self): + # NOTE(mrry): This test tests the internal `_GeneratorDataset`, + # which affords more control over what the finalize function can do than + # the `Dataset.from_generator()` wrapper. + + # Use an `Event` to signal that the generator has been deleted. + event = threading.Event() + + def finalize_fn(_): + def finalize_py_func(): + event.set() + return 0 + return script_ops.py_func(finalize_py_func, [], [dtypes.int64], + stateful=True) + + dummy = constant_op.constant(37) + iterator = (dataset_ops._GeneratorDataset(dummy, lambda x: x, + lambda x: x, finalize_fn) + .take(2) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + self.assertAllEqual(37, sess.run(get_next)) + self.assertAllEqual(37, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + self.assertTrue(event.is_set()) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py b/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py index b9258b720edd4ecd620c61eed18f6f975cb7f439..2c71723167e0a64650f672c311de40da7acafa79 100644 --- a/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py @@ -17,11 +17,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import time + import numpy as np +from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import functional_ops @@ -156,6 +160,57 @@ class FilterDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testReturnComponent(self): + iterator = ( + dataset_ops.Dataset.zip( + (dataset_ops.Dataset.range(10), + dataset_ops.Dataset.from_tensors(True).repeat(None))) + .filter(lambda x, y: y).make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + for i in range(10): + self.assertEqual((i, True), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + +class FilterDatasetBenchmark(test.Benchmark): + + def _benchmark(self, predicate, name): + with ops.Graph().as_default(): + dataset = ( + dataset_ops.Dataset.from_tensors(True).repeat(None).filter(predicate)) + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + with session.Session() as sess: + for _ in range(5): + sess.run(next_element.op) + deltas = [] + for _ in range(100): + start = time.time() + for _ in range(100): + sess.run(next_element.op) + end = time.time() + deltas.append(end - start) + + median_wall_time = np.median(deltas) / 100 + print("Filter dataset using %s. Median wall time: %f" % + (name, median_wall_time)) + self.report_benchmark( + iters=100, + wall_time=median_wall_time, + name="benchmark_filter_dataset_%s" % name) + + def benchmarkSimpleFunction(self): + self._benchmark(array_ops.identity, "simple_function") + + def benchmarkReturnComponentOptimization(self): + self._benchmark(lambda x: x, "return_component") + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/kernel_tests/interleave_dataset_op_test.py b/tensorflow/python/data/kernel_tests/interleave_dataset_op_test.py index 28cb50c00208f95e64bb11ae80656383b1f41e1e..7dbf7268d74a2a18af551de64ced03daab264799 100644 --- a/tensorflow/python/data/kernel_tests/interleave_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/interleave_dataset_op_test.py @@ -201,6 +201,20 @@ class InterleaveDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testEmptyInput(self): + iterator = ( + dataset_ops.Dataset.from_tensor_slices([]) + .repeat(None) + .interleave(dataset_ops.Dataset.from_tensors, cycle_length=2) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py index 4e7691ee8144a19a62476281d86fb5df46dd3e4b..6442eb9ff554e61829796fb904342072d1846a32 100644 --- a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py @@ -46,8 +46,9 @@ class ListFilesDatasetOpTest(test.TestCase): dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) with self.test_session() as sess: itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) + sess.run(next_element) def testSimpleDirectory(self): filenames = ['a', 'b', 'c'] @@ -56,13 +57,14 @@ class ListFilesDatasetOpTest(test.TestCase): dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) with self.test_session() as sess: itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() full_filenames = [] produced_filenames = [] for filename in filenames: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): sess.run(itr.get_next()) @@ -73,12 +75,13 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) + sess.run(next_element) def testSimpleDirectoryInitializer(self): filenames = ['a', 'b', 'c'] @@ -89,6 +92,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) @@ -98,7 +102,7 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) @@ -114,6 +118,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py')}) @@ -123,7 +128,7 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames[1:-1]: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): @@ -138,6 +143,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py*')}) @@ -147,13 +153,44 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames[1:]: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): sess.run(itr.get_next()) + def testNoShuffle(self): + filenames = ['a', 'b', 'c'] + self._touchTempFiles(filenames) + + # Repeat the list twice and ensure that the order is the same each time. + # NOTE(mrry): This depends on an implementation detail of `list_files()`, + # which is that the list of files is captured when the iterator is + # initialized. Otherwise, or if e.g. the iterator were initialized more than + # once, it's possible that the non-determinism of `tf.matching_files()` + # would cause this test to fail. However, it serves as a useful confirmation + # that the `shuffle=False` argument is working as intended. + # TODO(b/73959787): Provide some ordering guarantees so that this test is + # more meaningful. + dataset = dataset_ops.Dataset.list_files( + path.join(self.tmp_dir, '*'), shuffle=False).repeat(2) + with self.test_session() as sess: + itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() + + full_filenames = [] + produced_filenames = [] + for filename in filenames * 2: + full_filenames.append( + compat.as_bytes(path.join(self.tmp_dir, filename))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) + with self.assertRaises(errors.OutOfRangeError): + sess.run(itr.get_next()) + self.assertItemsEqual(full_filenames, produced_filenames) + self.assertEqual(produced_filenames[:len(filenames)], + produced_filenames[len(filenames):]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py index 04d1abdb254feea1df6f1b8cfc5a512802107224..0791c614fa88700fdf2d0d673e168fc9784731a5 100644 --- a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py @@ -602,6 +602,28 @@ class MapDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testParallelMapOutOfRangeError(self): + def raising_py_func(i): + if i == 100: + raise StopIteration() + else: + return i + + iterator = ( + dataset_ops.Dataset.range(105) + .map(lambda x: script_ops.py_func(raising_py_func, [x], dtypes.int64), + num_parallel_calls=2) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + for i in range(100): + self.assertEqual(i, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + class MapDatasetBenchmark(test.Benchmark): diff --git a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py index d7140088c310767d40bd2cf3413c899375acab15..1ddedfda4e1c9d6b6949f796be1870f167435763 100644 --- a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py @@ -21,6 +21,7 @@ import gzip import os import zlib +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import readers from tensorflow.python.framework import constant_op @@ -736,12 +737,43 @@ class TFRecordDatasetTest(test.TestCase): one_mebibyte = 2**20 d = readers.TFRecordDataset(self.test_filenames, buffer_size=one_mebibyte) iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() with self.test_session() as sess: for j in range(self._num_files): for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(iterator.get_next())) + self.assertAllEqual(self._record(j, i), sess.run(next_element)) with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) + sess.run(next_element) + + def testReadFromDatasetOfFiles(self): + files = dataset_ops.Dataset.from_tensor_slices(self.test_filenames) + d = readers.TFRecordDataset(files) + iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + for j in range(self._num_files): + for i in range(self._num_records): + self.assertAllEqual(self._record(j, i), sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testReadTenEpochsFromDatasetOfFilesInParallel(self): + files = dataset_ops.Dataset.from_tensor_slices( + self.test_filenames).repeat(10) + d = readers.TFRecordDataset(files, num_parallel_reads=4) + iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() + expected = [] + actual = [] + with self.test_session() as sess: + for _ in range(10): + for j in range(self._num_files): + for i in range(self._num_records): + expected.append(self._record(j, i)) + actual.append(sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + self.assertEqual(sorted(expected), sorted(actual)) if __name__ == "__main__": diff --git a/tensorflow/python/data/kernel_tests/sequence_dataset_op_test.py b/tensorflow/python/data/kernel_tests/sequence_dataset_op_test.py index ae08032e191487c38d73876374b24e8f6eefbc80..1d27b036eb804aa301b916b7ed0b7884f75e1a0f 100644 --- a/tensorflow/python/data/kernel_tests/sequence_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/sequence_dataset_op_test.py @@ -201,9 +201,7 @@ class SequenceDatasetTest(test.TestCase): with self.test_session() as sess: sess.run(init_op) - with self.assertRaisesRegexp( - errors.OutOfRangeError, - "Attempted to repeat an empty dataset infinitely."): + with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) diff --git a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py index c089fb08c1082c1cf74d492796550980d6755591..5fcc48831f3ca744e015c92760f12ea4dbef2ff7 100644 --- a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py @@ -132,6 +132,33 @@ class ShuffleDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testSeedZero(self): + """Test for same behavior when the seed is a Python or Tensor zero.""" + iterator = ( + dataset_ops.Dataset.range(10).shuffle(10, seed=0) + .make_one_shot_iterator()) + get_next = iterator.get_next() + + elems = [] + with self.test_session() as sess: + for _ in range(10): + elems.append(sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + seed_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) + iterator = ( + dataset_ops.Dataset.range(10).shuffle(10, seed=seed_placeholder) + .make_initializable_iterator()) + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer, feed_dict={seed_placeholder: 0}) + for elem in elems: + self.assertEqual(elem, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + def testDefaultArguments(self): components = [0, 1, 2, 3, 4] iterator = (dataset_ops.Dataset.from_tensor_slices(components).shuffle(5) diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD index f12b358a7dc35c18338171e489fa88ba1a82d11b..a8f2154db8c1555dcba07229edf33d9c581ddad1 100644 --- a/tensorflow/python/data/ops/BUILD +++ b/tensorflow/python/data/ops/BUILD @@ -23,6 +23,7 @@ py_library( "//tensorflow/python:tensor_util", "//tensorflow/python:util", "//tensorflow/python/data/util:nest", + "//tensorflow/python/data/util:random_seed", "//tensorflow/python/data/util:sparse", "//third_party/py/numpy", ], @@ -34,6 +35,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":dataset_ops", + "//tensorflow/python:array_ops", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index c4b7e4919bbbdb4c2096f124b54c264fa62e3fab..e0d63b5ebc7078c3c24ad18258c725efab609820 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -26,16 +26,17 @@ import six from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import math_ops @@ -90,7 +91,7 @@ class Dataset(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "dataset.make_initializable_iterator is not supported when eager " "execution is enabled.") @@ -122,7 +123,7 @@ class Dataset(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "dataset.make_one_shot_iterator is not supported when eager " "execution is enabled.") @@ -331,10 +332,10 @@ class Dataset(object): generator_state = Dataset._GeneratorState(generator) - def get_iterator_id_map_fn(unused_dummy): + def get_iterator_id_fn(unused_dummy): """Creates a unique `iterator_id` for each pass over the dataset. - The "iterator_id" disambiguates between multiple concurrently + The returned `iterator_id` disambiguates between multiple concurrently existing iterators. Args: @@ -347,7 +348,7 @@ class Dataset(object): return script_ops.py_func( generator_state.get_next_id, [], dtypes.int64, stateful=True) - def generator_map_fn(iterator_id_t): + def generator_next_fn(iterator_id_t): """Generates the next element from iterator with ID `iterator_id_t`. We map this function across an infinite repetition of the @@ -363,11 +364,9 @@ class Dataset(object): def generator_py_func(iterator_id): """A `py_func` that will be called to invoke the iterator.""" - try: - values = next(generator_state.get_iterator(iterator_id)) - except StopIteration: - generator_state.iterator_completed(iterator_id) - raise StopIteration("Iteration finished.") + # `next()` raises `StopIteration` when there are no more + # elements remaining to be generated. + values = next(generator_state.get_iterator(iterator_id)) # Use the same _convert function from the py_func() implementation to # convert the returned values to arrays early, so that we can inspect @@ -408,17 +407,31 @@ class Dataset(object): return nest.pack_sequence_as(output_types, flat_values) + def finalize_fn(iterator_id_t): + """Releases host-side state for the iterator with ID `iterator_id_t`.""" + + def finalize_py_func(iterator_id): + generator_state.iterator_completed(iterator_id) + # We return a dummy value so that the `finalize_fn` has a valid + # signature. + # NOTE(mrry): Explicitly create an array of `np.int64` because implicit + # casting in `py_func()` will create an array of `np.int32` on Windows, + # leading to a runtime error. + return np.array(0, dtype=np.int64) + + return script_ops.py_func( + finalize_py_func, [iterator_id_t], dtypes.int64, stateful=True) + # This function associates each traversal of `generator` with a unique # iterator ID. - def flat_map_fn(iterator_id_t): - # First, generate an infinite dataset containing the iterator ID repeated - # forever. - repeated_id = Dataset.from_tensors(iterator_id_t).repeat(None) - - # The `generator_map_fn` gets the next element from the iterator with the - # relevant ID, and raises StopIteration when that iterator contains no + def flat_map_fn(dummy_arg): + # The `get_iterator_id_fn` gets a unique ID for the current instance of + # of the generator. + # The `generator_next_fn` gets the next element from the iterator with the + # given ID, and raises StopIteration when that iterator contains no # more elements. - return repeated_id.map(generator_map_fn) + return _GeneratorDataset(dummy_arg, get_iterator_id_fn, generator_next_fn, + finalize_fn) # A single-element dataset that, each time it is evaluated, contains a # freshly-generated and unique (for the returned dataset) int64 @@ -426,7 +439,7 @@ class Dataset(object): # is encapsulated in `generator_state`, and captured in # `get_iterator_id_map_fn`. dummy = 0 - id_dataset = Dataset.from_tensors(dummy).map(get_iterator_id_map_fn) + id_dataset = Dataset.from_tensors(dummy) # A dataset that contains all of the elements generated by a # single iterator created from `generator`, identified by the @@ -545,7 +558,7 @@ class Dataset(object): return PrefetchDataset(self, buffer_size) @staticmethod - def list_files(file_pattern): + def list_files(file_pattern, shuffle=None): """A dataset of all files matching a pattern. Example: @@ -558,16 +571,31 @@ class Dataset(object): - /path/to/dir/b.py - /path/to/dir/c.py - NOTE: The order of the file names returned can be non-deterministic. + NOTE: The order of the file names returned can be non-deterministic even + when `shuffle` is `False`. Args: file_pattern: A string or scalar string `tf.Tensor`, representing the filename pattern that will be matched. + shuffle: (Optional.) If `True`, the file names will be shuffled randomly. + Defaults to `True`. Returns: Dataset: A `Dataset` of strings corresponding to file names. """ - return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern)) + # TODO(b/73959787): Add a `seed` argument and make the `shuffle=False` + # behavior deterministic (e.g. by sorting the filenames). + if shuffle is None: + shuffle = True + matching_files = gen_io_ops.matching_files(file_pattern) + dataset = Dataset.from_tensor_slices(matching_files) + if shuffle: + # NOTE(mrry): The shuffle buffer size must be greater than zero, but the + # list of files might be empty. + buffer_size = math_ops.maximum( + array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1) + dataset = dataset.shuffle(buffer_size) + return dataset def repeat(self, count=None): """Repeats this dataset `count` times. @@ -746,11 +774,31 @@ class Dataset(object): def padded_batch(self, batch_size, padded_shapes, padding_values=None): """Combines consecutive elements of this dataset into padded batches. - Like `Dataset.dense_to_sparse_batch()`, this method combines - multiple consecutive elements of this dataset, which might have - different shapes, into a single element. The tensors in the - resulting element have an additional outer dimension, and are - padded to the respective shape in `padded_shapes`. + This transformation combines multiple consecutive elements of the input + dataset into a single element. Like @{tf.data.Dataset.batch}, the tensors + in the resulting element have an additional outer dimension, which will be + `batch_size` for all but the last element, and `N % batch_size` for the + last element (where `N` is the number of elements in this dataset). Unlike + @{tf.data.Dataset.batch}, the elements may have different shapes for some + of their components, and this transformation will pad each component to + the respective shape in `padding_shapes`. The `padding_shapes` argument + determines the resulting shape for each dimension of each component in an + output element: + + * If the dimension is a constant (e.g. `tf.Dimension(37)`), the component + will be padded out to that length in that dimension. + * If the dimension is unknown (e.g. `tf.Dimension(None)`), the component + will be padded out to the maximum length of all elements in that + dimension. + + NOTE: If the number of elements (`N`) in this dataset is not an exact + multiple of `batch_size`, the final batch contain smaller tensors with + shape `N % batch_size` in the batch dimension. If your program depends on + the batches having the same shape, consider using the + @{tf.contrib.data.padded_batch_and_drop_remainder} transformation instead. + + See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements + that may have different shapes into a @{tf.SparseTensor}. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of @@ -773,7 +821,7 @@ class Dataset(object): return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values) def map(self, map_func, num_parallel_calls=None): - """Maps `map_func` across this datset. + """Maps `map_func` across this dataset. Args: map_func: A function mapping a nested structure of tensors (having @@ -1033,6 +1081,196 @@ class SparseTensorSliceDataset(Dataset): return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64) +class _GeneratorDataset(Dataset): + """A `Dataset` that generates elements by invoking a function.""" + + def __init__(self, init_args, init_func, next_func, finalize_func): + """Constructs a `_GeneratorDataset`. + + Args: + init_args: A nested structure representing the arguments to `init_func`. + init_func: A TensorFlow function that will be called on `init_args` each + time a C++ iterator over this dataset is constructed. Returns a nested + structure representing the "state" of the dataset. + next_func: A TensorFlow function that will be called on the result of + `init_func` to produce each element, and that raises `OutOfRangeError` + to terminate iteration. + finalize_func: A TensorFlow function that will be called on the result of + `init_func` immediately before a C++ iterator over this dataset is + destroyed. The return value is ignored. + """ + super(_GeneratorDataset, self).__init__() + # These members will be initialized by `tf_init_func`. + self._state_classes = None + self._state_shapes = None + self._state_types = None + + self._init_args = init_args + + init_args_classes = sparse.get_classes(init_args) + init_args_shapes = nest.pack_sequence_as( + init_args, [t.get_shape() for t in nest.flatten(init_args)]) + init_args_types = nest.pack_sequence_as( + init_args, [t.dtype for t in nest.flatten(init_args)]) + + @function.Defun(*nest.flatten( + sparse.as_dense_types(init_args_types, init_args_classes))) + def tf_init_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + dense_shapes = sparse.as_dense_shapes(init_args_shapes, init_args_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(init_args_classes, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, init_args_types, init_args_shapes, init_args_classes) + if _should_unpack_args(nested_args): + ret = init_func(*nested_args) + else: + ret = init_func(nested_args) + + # If `init_func` returns a list of tensors, `nest.flatten()` and + # `ops.convert_to_tensor()` would conspire to attempt to stack + # those tensors into a single tensor, because the customized + # version of `nest.flatten()` does not recurse into lists. Since + # it is more likely that the list arose from returning the + # result of an operation (such as `tf.py_func()`) that returns a + # list of not-necessarily-stackable tensors, we treat the + # returned value is a `tuple` instead. A user wishing to pack + # the return value into a single tensor can use an explicit + # `tf.stack()` before returning. + if isinstance(ret, list): + ret = tuple(ret) + + # Convert any `SparseTensorValue`s to `SparseTensor`s. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor_lib.SparseTensor.from_value(t) + if sparse_tensor_lib.is_sparse(t) else t for t in nest.flatten(ret) + ]) + + self._state_classes = sparse.get_classes(ret) + self._state_shapes = nest.pack_sequence_as( + ret, [t.get_shape() for t in nest.flatten(ret)]) + self._state_types = nest.pack_sequence_as( + ret, [t.dtype for t in nest.flatten(ret)]) + + # Serialize any sparse tensors and convert result to tensors. + ret = nest.pack_sequence_as(ret, [ + ops.convert_to_tensor(t) + for t in nest.flatten(sparse.serialize_sparse_tensors(ret)) + ]) + return nest.flatten(ret) + + self._init_func = tf_init_func + self._init_func.add_to_graph(ops.get_default_graph()) + + # These members will be initialized by `tf_next_func`. + self._output_classes = None + self._output_shapes = None + self._output_types = None + + @function.Defun(*nest.flatten( + sparse.as_dense_types(self._state_types, self._state_classes))) + def tf_next_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + # Pass in shape information from the input_dataset. + dense_shapes = sparse.as_dense_shapes(self._state_shapes, + self._state_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(self._state_classes, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, self._state_types, self._state_shapes, + self._state_classes) + if _should_unpack_args(nested_args): + ret = next_func(*nested_args) + else: + ret = next_func(nested_args) + + # If `next_func` returns a list of tensors, `nest.flatten()` and + # `ops.convert_to_tensor()` would conspire to attempt to stack + # those tensors into a single tensor, because the customized + # version of `nest.flatten()` does not recurse into lists. Since + # it is more likely that the list arose from returning the + # result of an operation (such as `tf.py_func()`) that returns a + # list of not-necessarily-stackable tensors, we treat the + # returned value is a `tuple` instead. A user wishing to pack + # the return value into a single tensor can use an explicit + # `tf.stack()` before returning. + if isinstance(ret, list): + ret = tuple(ret) + + # Convert any `SparseTensorValue`s to `SparseTensor`s. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor_lib.SparseTensor.from_value(t) + if sparse_tensor_lib.is_sparse(t) else t for t in nest.flatten(ret) + ]) + + self._output_classes = sparse.get_classes(ret) + self._output_shapes = nest.pack_sequence_as( + ret, [t.get_shape() for t in nest.flatten(ret)]) + self._output_types = nest.pack_sequence_as( + ret, [t.dtype for t in nest.flatten(ret)]) + + # Serialize any sparse tensors and convert result to tensors. + ret = nest.pack_sequence_as(ret, [ + ops.convert_to_tensor(t) + for t in nest.flatten(sparse.serialize_sparse_tensors(ret)) + ]) + return nest.flatten(ret) + + self._next_func = tf_next_func + self._next_func.add_to_graph(ops.get_default_graph()) + + @function.Defun(*nest.flatten( + sparse.as_dense_types(self._state_types, self._state_classes))) + def tf_finalize_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + # Pass in shape information from the state. + dense_shapes = sparse.as_dense_shapes(self._state_shapes, + self._state_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(self._state_classes, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, self._state_types, self._state_shapes, + self._state_classes) + if _should_unpack_args(nested_args): + return finalize_func(*nested_args) + else: + return finalize_func(nested_args) + + self._finalize_func = tf_finalize_func + self._finalize_func.add_to_graph(ops.get_default_graph()) + + def _as_variant_tensor(self): + return gen_dataset_ops.generator_dataset( + nest.flatten(self._init_args) + self._init_func.captured_inputs, + self._next_func.captured_inputs, + self._finalize_func.captured_inputs, + init_func=self._init_func, + next_func=self._next_func, + finalize_func=self._finalize_func, + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + class ZipDataset(Dataset): """A `Dataset` that zips its inputs together.""" @@ -1282,16 +1520,7 @@ class ShuffleDataset(Dataset): self._input_dataset = input_dataset self._buffer_size = ops.convert_to_tensor( buffer_size, dtype=dtypes.int64, name="buffer_size") - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) if reshuffle_each_iteration is None: self._reshuffle_each_iteration = True else: diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index e573fe01928b77dea55a782e4e86a00873346f07..4756ec74820bace5bea4e1f41ebe214420fe5c3d 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -44,8 +44,9 @@ GET_NEXT_CALL_WARNING_MESSAGE = ( "This often indicates that `Iterator.get_next()` is being called inside " "a training loop, which will cause gradual slowdown and eventual resource " "exhaustion. If this is the case, restructure your code to call " - "`next_element = iterator.get_next() once outside the loop, and use " - "`next_element` inside the loop.") + "`next_element = iterator.get_next()` once outside the loop, and use " + "`next_element` as the input to some computation that is invoked inside " + "the loop.") @tf_export("data.Iterator") @@ -303,7 +304,42 @@ class Iterator(object): dataset._as_variant_tensor(), self._iterator_resource, name=name) # pylint: disable=protected-access def get_next(self, name=None): - """Returns a nested structure of `tf.Tensor`s containing the next element. + """Returns a nested structure of `tf.Tensor`s representing the next element. + + In graph mode, you should typically call this method *once* and use its + result as the input to another computation. A typical loop will then call + @{tf.Session.run} on the result of that computation. The loop will terminate + when the `Iterator.get_next()` operation raises + @{tf.errors.OutOfRangeError}. The following skeleton shows how to use + this method when building a training loop: + + ```python + dataset = ... # A `tf.data.Dataset` object. + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() + + # Build a TensorFlow graph that does something with each element. + loss = model_function(next_element) + optimizer = ... # A `tf.train.Optimizer` object. + train_op = optimizer.minimize(loss) + + with tf.Session() as sess: + try: + while True: + sess.run(train_op) + except tf.errors.OutOfRangeError: + pass + ``` + + NOTE: It is legitimate to call `Iterator.get_next()` multiple times, e.g. + when you are distributing different elements to multiple devices in a single + step. However, a common pitfall arises when users call `Iterator.get_next()` + in each iteration of their training loop. `Iterator.get_next()` adds ops to + the graph, and executing each op allocates resources (including threads); as + a consequence, invoking it in every iteration of a training loop causes + slowdown and eventual resource exhaustion. To guard against this outcome, we + log a warning when the number of uses crosses a fixed threshold of + suspiciousness. Args: name: (Optional.) A name for the created operation. diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index fa7601741b11f018e9b53ed3b77a7561be50d3f4..6c493d8163b051b2e724335923d7b4c721523083 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -17,11 +17,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.data.ops.dataset_ops import Dataset +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import convert +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes +from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.util.tf_export import tf_export @@ -31,7 +35,7 @@ _DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB @tf_export("data.TextLineDataset") -class TextLineDataset(Dataset): +class TextLineDataset(dataset_ops.Dataset): """A `Dataset` comprising lines from one or more text files.""" def __init__(self, filenames, compression_type=None, buffer_size=None): @@ -73,8 +77,7 @@ class TextLineDataset(Dataset): return dtypes.string -@tf_export("data.TFRecordDataset") -class TFRecordDataset(Dataset): +class _TFRecordDataset(dataset_ops.Dataset): """A `Dataset` comprising records from one or more TFRecord files.""" def __init__(self, filenames, compression_type=None, buffer_size=None): @@ -87,7 +90,7 @@ class TFRecordDataset(Dataset): buffer_size: (Optional.) A `tf.int64` scalar representing the number of bytes in the read buffer. 0 means no buffering. """ - super(TFRecordDataset, self).__init__() + super(_TFRecordDataset, self).__init__() # Force the type to string even if filenames is an empty list. self._filenames = ops.convert_to_tensor( filenames, dtypes.string, name="filenames") @@ -118,8 +121,159 @@ class TFRecordDataset(Dataset): return dtypes.string +class ParallelInterleaveDataset(dataset_ops.Dataset): + """A `Dataset` that maps a function over its input and flattens the result.""" + + def __init__(self, input_dataset, map_func, cycle_length, block_length, + sloppy, buffer_output_elements, prefetch_input_elements): + """See `tf.contrib.data.parallel_interleave()` for details.""" + super(ParallelInterleaveDataset, self).__init__() + self._input_dataset = input_dataset + + @function.Defun(*nest.flatten( + sparse.as_dense_types(input_dataset.output_types, + input_dataset.output_classes))) + def tf_map_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + # Pass in shape information from the input_dataset. + dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, + input_dataset.output_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(input_dataset.output_types, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, input_dataset.output_types, input_dataset.output_shapes, + input_dataset.output_classes) + if dataset_ops._should_unpack_args(nested_args): # pylint: disable=protected-access + dataset = map_func(*nested_args) + else: + dataset = map_func(nested_args) + + if not isinstance(dataset, dataset_ops.Dataset): + raise TypeError("`map_func` must return a `Dataset` object.") + + self._output_classes = dataset.output_classes + self._output_types = dataset.output_types + self._output_shapes = dataset.output_shapes + + return dataset._as_variant_tensor() # pylint: disable=protected-access + + self._map_func = tf_map_func + self._map_func.add_to_graph(ops.get_default_graph()) + + self._cycle_length = ops.convert_to_tensor( + cycle_length, dtype=dtypes.int64, name="cycle_length") + self._block_length = ops.convert_to_tensor( + block_length, dtype=dtypes.int64, name="block_length") + self._sloppy = ops.convert_to_tensor( + sloppy, dtype=dtypes.bool, name="sloppy") + self._buffer_output_elements = convert.optional_param_to_tensor( + "buffer_output_elements", + buffer_output_elements, + argument_default=2 * block_length) + self._prefetch_input_elements = convert.optional_param_to_tensor( + "prefetch_input_elements", + prefetch_input_elements, + argument_default=2 * cycle_length) + + def _as_variant_tensor(self): + return gen_dataset_ops.parallel_interleave_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + self._map_func.captured_inputs, + self._cycle_length, + self._block_length, + self._sloppy, + self._buffer_output_elements, + self._prefetch_input_elements, + f=self._map_func, + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + +@tf_export("data.TFRecordDataset") +class TFRecordDataset(dataset_ops.Dataset): + """A `Dataset` comprising records from one or more TFRecord files.""" + + def __init__(self, filenames, compression_type=None, buffer_size=None, + num_parallel_reads=None): + """Creates a `TFRecordDataset` to read for one or more TFRecord files. + + NOTE: The `num_parallel_reads` argument can be used to improve performance + when reading from a remote filesystem. + + Args: + filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or + more filenames. + compression_type: (Optional.) A `tf.string` scalar evaluating to one of + `""` (no compression), `"ZLIB"`, or `"GZIP"`. + buffer_size: (Optional.) A `tf.int64` scalar representing the number of + bytes in the read buffer. 0 means no buffering. + num_parallel_reads: (Optional.) A `tf.int64` scalar representing the + number of files to read in parallel. Defaults to reading files + sequentially. + + Raises: + TypeError: If any argument does not have the expected type. + ValueError: If any argument does not have the expected shape. + """ + super(TFRecordDataset, self).__init__() + if isinstance(filenames, dataset_ops.Dataset): + if filenames.output_types != dtypes.string: + raise TypeError( + "`filenames` must be a `tf.data.Dataset` of `tf.string` elements.") + if not filenames.output_shapes.is_compatible_with(tensor_shape.scalar()): + raise ValueError( + "`filenames` must be a `tf.data.Dataset` of scalar `tf.string` " + "elements.") + else: + filenames = ops.convert_to_tensor(filenames, dtype=dtypes.string) + filenames = array_ops.reshape(filenames, [-1], name="flat_filenames") + filenames = dataset_ops.Dataset.from_tensor_slices(filenames) + + def read_one_file(filename): + return _TFRecordDataset(filename, compression_type, buffer_size) + + if num_parallel_reads is None: + self._impl = filenames.flat_map(read_one_file) + else: + self._impl = ParallelInterleaveDataset( + filenames, read_one_file, cycle_length=num_parallel_reads, + block_length=1, sloppy=False, buffer_output_elements=None, + prefetch_input_elements=None) + + def _as_variant_tensor(self): + return self._impl._as_variant_tensor() # pylint: disable=protected-access + + @property + def output_classes(self): + return self._impl.output_classes + + @property + def output_shapes(self): + return self._impl.output_shapes + + @property + def output_types(self): + return self._impl.output_types + + @tf_export("data.FixedLengthRecordDataset") -class FixedLengthRecordDataset(Dataset): +class FixedLengthRecordDataset(dataset_ops.Dataset): """A `Dataset` of fixed-length records from one or more binary files.""" def __init__(self, diff --git a/tensorflow/python/data/util/BUILD b/tensorflow/python/data/util/BUILD index e32c7b54a48dd887c2748897c3ce3661aab9f497..b1bdbdab37b63667b475c732df7a47d9e57f2b19 100644 --- a/tensorflow/python/data/util/BUILD +++ b/tensorflow/python/data/util/BUILD @@ -86,6 +86,30 @@ py_test( ], ) +py_library( + name = "random_seed", + srcs = ["random_seed.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework", + ], +) + +py_test( + name = "random_seed_test", + size = "small", + srcs = ["random_seed_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":random_seed", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:util", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/python/data/util/random_seed.py b/tensorflow/python/data/util/random_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..e2c9d8672f94587fd3164f25f97b44a97526be07 --- /dev/null +++ b/tensorflow/python/data/util/random_seed.py @@ -0,0 +1,58 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for generating Tensor-valued random seeds.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import random_seed +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops + + +def get_seed(seed): + """Returns the local seeds an operation should use given an op-specific seed. + + See @{tf.get_seed} for more details. This wrapper adds support for the case + where `seed` may be a tensor. + + Args: + seed: An integer or a @{tf.int64} scalar tensor. + + Returns: + A tuple of two @{tf.int64} scalar tensors that should be used for the local + seed of the calling dataset. + """ + seed, seed2 = random_seed.get_seed(seed) + if seed is None: + seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") + else: + seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") + if seed2 is None: + seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") + else: + with ops.name_scope("seed2") as scope: + seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64) + seed2 = array_ops.where( + math_ops.logical_and( + math_ops.equal(seed, 0), math_ops.equal(seed2, 0)), + constant_op.constant(2**31 - 1, dtype=dtypes.int64), + seed2, + name=scope) + return seed, seed2 diff --git a/tensorflow/python/data/util/random_seed_test.py b/tensorflow/python/data/util/random_seed_test.py new file mode 100644 index 0000000000000000000000000000000000000000..33227e82afe6fe1c748693d107d4e9844abb8e09 --- /dev/null +++ b/tensorflow/python/data/util/random_seed_test.py @@ -0,0 +1,83 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for utilities working with arbitrarily nested structures.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.util import random_seed as data_random_seed +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test + + +class RandomSeedTest(test.TestCase): + + @test_util.run_in_graph_and_eager_modes() + def testRandomSeed(self): + zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero') + one_t = constant_op.constant(1, dtype=dtypes.int64, name='one') + intmax_t = constant_op.constant( + 2**31 - 1, dtype=dtypes.int64, name='intmax') + test_cases = [ + # Each test case is a tuple with input to get_seed: + # (input_graph_seed, input_op_seed) + # and output from get_seed: + # (output_graph_seed, output_op_seed) + ((None, None), (0, 0)), + ((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)), + ((1, 1), (1, 1)), + ((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output + ((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either + ((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument + # Once more, with tensor-valued arguments + ((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)), + ((1, one_t), (1, 1)), + ((0, zero_t), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output + ((2**31 - 1, zero_t), (0, 2**31 - 1)), # Don't wrap to (0, 0) either + ((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument + ] + for tc in test_cases: + tinput, toutput = tc[0], tc[1] + random_seed.set_random_seed(tinput[0]) + g_seed, op_seed = data_random_seed.get_seed(tinput[1]) + g_seed = self.evaluate(g_seed) + op_seed = self.evaluate(op_seed) + msg = 'test_case = {0}, got {1}, want {2}'.format( + tinput, (g_seed, op_seed), toutput) + self.assertEqual((g_seed, op_seed), toutput, msg=msg) + random_seed.set_random_seed(None) + + if not context.executing_eagerly(): + random_seed.set_random_seed(1) + tinput = (1, None) + toutput = (1, ops.get_default_graph()._last_id) # pylint: disable=protected-access + random_seed.set_random_seed(tinput[0]) + g_seed, op_seed = data_random_seed.get_seed(tinput[1]) + g_seed = self.evaluate(g_seed) + op_seed = self.evaluate(op_seed) + msg = 'test_case = {0}, got {1}, want {2}'.format(1, (g_seed, op_seed), + toutput) + self.assertEqual((g_seed, op_seed), toutput, msg=msg) + random_seed.set_random_seed(None) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index f0e90f67772d114142ccc218ed9f42b723a1b556..b3abbf21e9302de7e1ac3e7cbf3bc5324dc0f62f 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -913,6 +913,7 @@ cuda_py_test( "//tensorflow/python:util", "//tensorflow/python:variables", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) py_test( @@ -957,7 +958,7 @@ cuda_py_test( cuda_py_test( name = "session_debug_grpc_test", - size = "medium", + size = "large", srcs = ["lib/session_debug_grpc_test.py"], additional_deps = [ ":debug_data", diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py index a0fe6066acd1462a94e93d6091db237d01cfede3..dea019fef58015fbd7982a81319dcabe4e5f4930 100644 --- a/tensorflow/python/debug/cli/cli_shared.py +++ b/tensorflow/python/debug/cli/cli_shared.py @@ -175,7 +175,7 @@ def format_tensor(tensor, include_numeric_summary: Whether a text summary of the numeric values (if applicable) will be included. write_path: A path to save the tensor value (after any slicing) to - (optinal). `numpy.save()` is used to save the value. + (optional). `numpy.save()` is used to save the value. Returns: An instance of `debugger_cli_common.RichTextLines` representing the diff --git a/tensorflow/python/debug/cli/tensor_format.py b/tensorflow/python/debug/cli/tensor_format.py index e0759a8bc1ab271906fc4ec75b55529f8a0d2b74..9ba84e3f2261de277361d503e9189583494a5084 100644 --- a/tensorflow/python/debug/cli/tensor_format.py +++ b/tensorflow/python/debug/cli/tensor_format.py @@ -134,7 +134,7 @@ def format_tensor(tensor, if include_metadata: lines.append(" dtype: %s" % str(tensor.dtype)) - lines.append(" shape: %s" % str(tensor.shape)) + lines.append(" shape: %s" % str(tensor.shape).replace("L", "")) if lines: lines.append("") diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py index 16f51a4b32f711b97077643cec669bb8970e0b21..589a13db7f798aef3bb82dfbd442deabfbcf2a41 100644 --- a/tensorflow/python/debug/lib/debug_gradients.py +++ b/tensorflow/python/debug/lib/debug_gradients.py @@ -156,11 +156,12 @@ class GradientsDebugger(object): # TODO(cais): Implement value_stack. grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid) # pylint: disable=protected-access - identity_op = (gen_array_ops._debug_gradient_ref_identity - if input_tensor.dtype._is_ref_dtype - else gen_array_ops._debug_gradient_identity) - debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name) + identity_op = ( + gen_array_ops.debug_gradient_ref_identity + if input_tensor.dtype._is_ref_dtype else + gen_array_ops.debug_gradient_identity) # pylint: enable=protected-access + debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name) assert debug_grad_identity.dtype == input_tensor.dtype if debug_grad_identity.op.name != grad_debug_op_name: raise ValueError( diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py index c1e9869d978e4f5ddfd3cd5f1abd7f5c97b7ca88..01867fc69d0782b34edb1e8eb873b19f5dfc8529 100644 --- a/tensorflow/python/debug/lib/debug_gradients_test.py +++ b/tensorflow/python/debug/lib/debug_gradients_test.py @@ -40,6 +40,7 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): def setUp(self): rewriter_config = rewriter_config_pb2.RewriterConfig( + disable_model_pruning=True, dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) config = config_pb2.ConfigProto(graph_options=graph_options) @@ -117,8 +118,8 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(self): grad_debugger = debug_gradients.GradientsDebugger() grad_debugger.identify_gradient(self.w) - with self.assertRaisesRegexp( - ValueError, "The graph already contains an op named .*"): + with self.assertRaisesRegexp(ValueError, + "The graph already contains an op named .*"): grad_debugger.identify_gradient(self.w) def testIdentifyGradientWorksOnMultipleLosses(self): @@ -144,10 +145,10 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsNot(dz1_dy, dz2_dy) self.sess.run(variables.global_variables_initializer()) - self.assertAllClose(5.0 ** 2, self.sess.run(z1)) - self.assertAllClose(5.0 ** 0.5, self.sess.run(z2)) + self.assertAllClose(5.0**2, self.sess.run(z1)) + self.assertAllClose(5.0**0.5, self.sess.run(z2)) self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy)) - self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy)) + self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy)) def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self): grad_debugger_1 = debug_gradients.GradientsDebugger() @@ -259,8 +260,8 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.sess.run(variables.global_variables_initializer()) self.assertAllClose(3.0, self.sess.run(u_grad)) self.assertAllClose(2.0, self.sess.run(v_grad)) - self.assertAllClose( - 3.0, self.sess.run(grad_debugger.gradient_tensor("u:0"))) + self.assertAllClose(3.0, self.sess.run( + grad_debugger.gradient_tensor("u:0"))) def testWatchGradientsWorksOnMultipleTensors(self): y = math_ops.add(self.w, -1.0, name="y") @@ -277,10 +278,10 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(grad_debugger.gradient_tensor("w:0"), ops.Tensor) self.sess.run(variables.global_variables_initializer()) - self.assertAllClose( - 1.0, self.sess.run(grad_debugger.gradient_tensor("w:0"))) - self.assertAllClose( - 3.0, self.sess.run(grad_debugger.gradient_tensor("u:0"))) + self.assertAllClose(1.0, self.sess.run( + grad_debugger.gradient_tensor("w:0"))) + self.assertAllClose(3.0, self.sess.run( + grad_debugger.gradient_tensor("u:0"))) def testWatchGradientsByXTensorsWorks(self): y = math_ops.add(self.w, -1.0, name="foo/y") @@ -290,8 +291,8 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): # But we can still get the gradient tensors by using # watch_gradients_by_x_tensors(). grad_debugger = debug_gradients.GradientsDebugger() - with grad_debugger.watch_gradients_by_tensors( - self.sess.graph, [self.w, self.u, y]): + with grad_debugger.watch_gradients_by_tensors(self.sess.graph, + [self.w, self.u, y]): gradient_descent.GradientDescentOptimizer(0.1).minimize(z) self.assertEqual(3, len(grad_debugger.gradient_tensors())) @@ -324,18 +325,18 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsNot(dz1_dy, dz2_dy) self.sess.run(variables.global_variables_initializer()) - self.assertAllClose(5.0 ** 2, self.sess.run(z1)) - self.assertAllClose(5.0 ** 0.5, self.sess.run(z2)) + self.assertAllClose(5.0**2, self.sess.run(z1)) + self.assertAllClose(5.0**0.5, self.sess.run(z2)) self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy)) - self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy)) + self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy)) def testGradientsValuesFromDumpWorks(self): y = math_ops.add(self.w, -1.0, name="y") z = math_ops.square(y, name="z") grad_debugger = debug_gradients.GradientsDebugger() - with grad_debugger.watch_gradients_by_tensors( - self.sess.graph, [self.w, self.u, y]): + with grad_debugger.watch_gradients_by_tensors(self.sess.graph, + [self.w, self.u, y]): train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(z) self.sess.run(variables.global_variables_initializer()) @@ -343,10 +344,7 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): run_options = config_pb2.RunOptions(output_partition_graphs=True) dump_dir = tempfile.mkdtemp() debug_url = "file://" + dump_dir - debug_utils.watch_graph( - run_options, - self.sess.graph, - debug_urls=debug_url) + debug_utils.watch_graph(run_options, self.sess.graph, debug_urls=debug_url) run_metadata = config_pb2.RunMetadata() self.assertAllClose(2.0, self.sess.run(self.u)) self.sess.run(train_op, options=run_options, run_metadata=run_metadata) diff --git a/tensorflow/python/debug/lib/session_debug_testlib.py b/tensorflow/python/debug/lib/session_debug_testlib.py index 20a40018bf9c67c5b743963489c8fc5616efa2db..f4fac1401918ccacd38aae5ad2ef8d686c9204b9 100644 --- a/tensorflow/python/debug/lib/session_debug_testlib.py +++ b/tensorflow/python/debug/lib/session_debug_testlib.py @@ -988,7 +988,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase): def testWatchingVariableUpdateOpsSeesUpdatedValues(self): """Watch output slots on Variable-updating ops, with no emitted edges.""" - with session.Session() as sess: + with session.Session(config=no_rewrite_session_config()) as sess: u_init = constant_op.constant(10.0) u = variables.Variable(u_init, name="gdo/u") v_init = constant_op.constant(20.0) diff --git a/tensorflow/python/debug/wrappers/grpc_wrapper.py b/tensorflow/python/debug/wrappers/grpc_wrapper.py index 74d7c2b9e242f947a33c0bdb6508847808d69c0b..fb9494f57636e46e54ef230cf4803dbb6ccad0c7 100644 --- a/tensorflow/python/debug/wrappers/grpc_wrapper.py +++ b/tensorflow/python/debug/wrappers/grpc_wrapper.py @@ -17,6 +17,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import signal +import sys import traceback # Google-internal import(s). @@ -137,6 +139,29 @@ class GrpcDebugWrapperSession(framework.NonInteractiveDebugWrapperSession): if not address.startswith(common.GRPC_URL_PREFIX) else address) +def _signal_handler(unused_signal, unused_frame): + try: + input_func = raw_input + except NameError: + # Python 3 does not have raw_input. + input_func = input + + while True: + response = input_func("\nSIGINT received. Quit program? (Y/n): ").strip() + if response in ("", "Y", "y"): + sys.exit(0) + elif response in ("N", "n"): + break + + +def register_signal_handler(): + try: + signal.signal(signal.SIGINT, _signal_handler) + except ValueError: + # This can happen if we are not in the MainThread. + pass + + class TensorBoardDebugWrapperSession(GrpcDebugWrapperSession): """A tfdbg Session wrapper that can be used with TensorBoard Debugger Plugin. @@ -185,6 +210,8 @@ class TensorBoardDebugWrapperSession(GrpcDebugWrapperSession): # sent to the debug servers. self._sent_graph_version = -1 + register_signal_handler() + def run(self, fetches, feed_dict=None, diff --git a/tensorflow/python/debug/wrappers/hooks.py b/tensorflow/python/debug/wrappers/hooks.py index 0204254ccab109f4844f077df78902872d1156d5..6705cd31e291d2eab7aa8179e9b2b829f8970c18 100644 --- a/tensorflow/python/debug/wrappers/hooks.py +++ b/tensorflow/python/debug/wrappers/hooks.py @@ -345,6 +345,7 @@ class TensorBoardDebugHook(GrpcDebugHook): self._grpc_debug_server_addresses = grpc_debug_server_addresses self._send_traceback_and_source_code = send_traceback_and_source_code self._sent_graph_version = -1 + grpc_wrapper.register_signal_handler() def before_run(self, run_context): if self._send_traceback_and_source_code: diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index ab81d40148476735492890f608315b19eaa0a33f..5bedf9c6fdfcf630a72ef8a34bca38417b738d4f 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -42,7 +42,6 @@ py_library( ":backprop", ":context", ":core", - ":custom_gradient", ":execute", ":function", ":graph_callable", @@ -103,7 +102,6 @@ cuda_py_test( additional_deps = [ ":backprop", ":context", - ":custom_gradient", ":test", "//tensorflow/python:embedding_ops", "//tensorflow/python:array_ops", @@ -206,21 +204,6 @@ cc_library( ], ) -py_library( - name = "custom_gradient", - srcs = ["custom_gradient.py"], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:internal"], - deps = [ - ":context", - ":tape", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python:util", - ], -) - py_library( name = "graph_only_ops", srcs = ["graph_only_ops.py"], @@ -364,7 +347,6 @@ py_test( deps = [ ":backprop", ":context", - ":custom_gradient", ":test", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index d79d1fc0a6400a894293f3254d5cac5a10661e13..88de1a951ffd85b4ca97a084c8616d804dc10b45 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -116,110 +116,6 @@ _gradient_functions_lock = threading.Lock() _tracing = False -# TODO(apassos) replace this with a mechanism which can happen at the op -# gradient function registration site, to be less error-prone -# TODO(apassos) add ops other than those in nn_grad and math_grad -_ops_which_dont_need_outputs = set([ - "Identity", - "MatMul", - "Conv2DBackpropInput", - "Conv2DBackpropFilter", - "Conv3D", - "Conv3DBackpropInputV2", - "AvgPool3D", - "AvgPool3DGrad", - "MaxPool3D", - "MaxPool3DGrad", - "MaxPool3DGradGrad", - "BiasAdd", - "BiasAddV1", - "BiasAddGrad", - "Relu6", - "Softplus", - "SoftplusGrad", - "Softsign", - "ReluGrad", - "Conv2D", - "DepthwiseConv2dNative", - "Dilation2D", - "AvgPool", - "AvgPoolGrad", - "BatchNormWithGlobalNormalization", - "L2Loss", - "Sum", - "Prod", - "SegmentSum", - "SegmentMean", - "SparseSegmentSum", - "SparseSegmentMean", - "SparseSegmentSqrtN", - "SegmentMin", - "SegmentMax", - "UnsortedSegmentSum", - "UnsortedSegmentMax", - "Abs", - "Neg", - "ReciprocalGrad", - "Square", - "Expm1", - "Log", - "Log1p", - "TanhGrad", - "SigmoidGrad", - "Sign", - "Sin", - "Cos", - "Tan", - "Add", - "Sub", - "Mul", - "Div", - "RealDiv", - "Maximum", - "Minimum", - "SquaredDifference", - "Select", - "SparseMatMul", - "BatchMatMul", - "Complex", - "Real", - "Imag", - "Angle", - "Conj", - "Cast", - "Cross", - "Cumsum", - "Cumprod", - "ReadVariableOp", - "VarHandleOp", - "Shape", -]) - -_ops_which_dont_need_inputs = set([ - "Identity", - "Softmax", - "LogSoftmax", - "BiasAdd", - "Relu", - "Elu", - "Selu", - "SparseSoftmaxCrossEntropyWithLogits", - "Neg", - "Inv", - "Reciprocal", - "Sqrt", - "Exp", - "Tanh", - "Sigmoid", - "Real", - "Imag", - "Conj", - "ReadVariableOp", - "VarHandleOp", - "Shape", -]) - - # TODO(agarwal): use an automatic mechanism for handling None arguments to # gradient functions. # Some gradient functions can accept None arguments for gradients. The following @@ -238,57 +134,25 @@ _grad_fn_accepts_none_for_indices = { } -def _record_gradient(op_name, inputs, attrs, results, name): - """Records gradients for a TensorFlow operation. - - Args: - op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to - execute. - inputs: A flat list of Tensor object inputs to the operation. - attrs: A tuple with alternating string attr names and attr values for this - operation. - results: The results of the operation (as a flat list). - name: Customized name for the operation. - - Returns: - A list of maybe-wrapped results. Either Tensors or TensorNodes. - - Raises: - An exception on error. - """ - if not tape.could_possibly_record(): - return - - if op_name in _ops_which_dont_need_outputs: - op_outputs = None - else: - # TODO(apassos) this line creates a weak circular reference where the - # backprop function keeps an output alive which in turn keeps the tape entry - # alive which keeps the backprop function alive. Figure out how to break - # this up without breaking second derivatives of ops like Exp whose - # gradients depend only on the outputs. - op_outputs = results - - if op_name in _ops_which_dont_need_inputs: - op_inputs = None - else: - op_inputs = inputs - - num_inputs = len(inputs) +def _get_backward_fn(op_name, attrs, num_inputs, op_inputs, op_outputs): def grad_fn(*orig_outputs): - """Generated gradient function.""" result = _magic_gradient_function(op_name, attrs, num_inputs, op_inputs, op_outputs, orig_outputs) if _tracing: - print("Gradient for", (name if name else op_name), "inputs", op_inputs, - "output_grads", orig_outputs, "gradients", result) + print("Gradient for", op_name, "inputs", op_inputs, "output_grads", + orig_outputs, "gradients", result) return nest.flatten(result) - tape.record_operation(op_name, results, inputs, grad_fn) - if _tracing: - print("Computed op", (name if name else op_name), "inputs", inputs, - "outputs", results) + return grad_fn + + +pywrap_tensorflow.TFE_Py_RegisterBackwardFunctionGetter(_get_backward_fn) + + +def _record_gradient(op_name, inputs, attrs, results, name): + return pywrap_tensorflow.TFE_Py_RecordGradient(op_name, inputs, attrs, + results, name) execute.record_gradient = _record_gradient @@ -355,6 +219,7 @@ def implicit_val_and_grad(f): tape.pop_tape(this_tape) # Sorting variables by id, which is monotonically increasing in construction # order. This ensures unique order across executions. + # TODO(josh11b): Move the sort to the C++ implementation in pywrap_tfe_src.cc. variables = list(sorted(this_tape.watched_variables(), key=lambda v: v.handle._id)) # pylint: disable=protected-access sources = [x.handle for x in variables] @@ -616,7 +481,7 @@ def val_and_grad_function(f, params=None): return decorated -def make_vjp(f, params=None): +def make_vjp(f, params=None, persistent=True): """Returns a function that computes f and is vjp w.r.t. params. The term "vjp" here is an abbreviation for vector-jacobian product. @@ -625,6 +490,8 @@ def make_vjp(f, params=None): f: the function to be differentiated. params: the parameters (numbers or names) to differentiate with respect to. A value of None will differentiate with respect to all parameters. + persistent: Boolean controlling whether the VJP function can be re-used. + Must be True or False. Returns: A function, which when called, returns a tuple (value, vjp), where: @@ -652,7 +519,7 @@ def make_vjp(f, params=None): """Computes the value and gradient of the decorated function.""" parameter_positions = _get_arg_spec(f, params, args) assert not kwds, "The gradient function can't take keyword arguments." - this_tape = tape.push_new_tape() + this_tape = tape.push_new_tape(persistent=persistent) try: sources = [] args = [ @@ -734,8 +601,7 @@ def _num_elements(grad): raise ValueError("`grad` not a Tensor or IndexedSlices.") -_last_zero_shape_dtype = [None, None] -_last_zero = [None] +_zeros_cache = context._TensorCache() # pylint: disable=protected-access def _fast_fill(value, shape, dtype): @@ -744,14 +610,17 @@ def _fast_fill(value, shape, dtype): def _zeros(shape, dtype): """Wraps array_ops.zeros to cache last zero for a given shape and dtype.""" + device = context.context().device_name if dtype == dtypes.variant: # TODO(apassos): need to save enough information about variant tensors to do # a zeros return None - if [shape, dtype] != _last_zero_shape_dtype: - _last_zero_shape_dtype[:] = [shape, dtype] - _last_zero[0] = _fast_fill(0, shape, dtype) - return _last_zero[0] + cache_key = shape, dtype, device + cached = _zeros_cache.get(cache_key) + if cached is None: + cached = _fast_fill(0, shape, dtype) + _zeros_cache.put(cache_key, cached) + return cached def _ones(shape, dtype): @@ -858,13 +727,22 @@ class GradientTape(object): t = t.handle tape.watch(t) - def gradient(self, target, sources): + def watched_variables(self): + # Sorting variables by id, which is monotonically increasing in construction + # order. This ensures unique order across executions. + # TODO(josh11b): Move the sort to the C++ implementation in pywrap_tfe_src.cc. + return list(sorted(self._tape.watched_variables(), + key=lambda v: v.handle._id)) # pylint: disable=protected-access + + def gradient(self, target, sources, output_gradients=None): """Computes the gradient using information traced by the tape. Args: target: the tensor to be differentiated. sources: a list of Tensors or Variables, the target will be differentiated with respect to the sources. + output_gradients: a list of gradients, one for each element of + target. Defaults to None. Returns: a list of Tensors (or IndexedSlices, or None), one for each element in @@ -882,7 +760,8 @@ class GradientTape(object): else x for x in sources] grad = imperative_grad.imperative_grad( - _default_vspace, self._tape, [target], sources) + _default_vspace, self._tape, [target], sources, + output_gradients=output_gradients) if not self._persistent: self._tape = None return grad diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index a12113893ab3eac671e8138472bc95e9d8b89499..07a2155d24bd9e00340273b3af36712216f6e8be 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -23,7 +23,6 @@ import numpy as np from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import custom_gradient from tensorflow.python.eager import tape from tensorflow.python.eager import test from tensorflow.python.framework import constant_op @@ -32,6 +31,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops @@ -115,6 +115,19 @@ class BackpropTest(test.TestCase): with self.assertRaises(RuntimeError): backprop.gradients_function(f)(constant_op.constant(1.0)) + def testGradientsFunctionInCustomGradient(self): + + @custom_gradient.custom_gradient + def f(x): + (y,) = backprop.gradients_function(lambda x: x * x)(x) + + def grad(dy): + return [2 * dy] + + return y, grad + + self.assertAllEqual(f(1.0), 2.0) + def testImplicitGradOverEmbeddingLookup(self): batch_size = 8 embedding_size = 512 @@ -205,11 +218,22 @@ class BackpropTest(test.TestCase): def f(x): return x * x - wrapped_fn = backprop.make_vjp(f) + wrapped_fn = backprop.make_vjp(f, persistent=False) result, vjp = wrapped_fn(constant_op.constant(3.0)) self.assertAllEqual(result, 9.0) self.assertAllEqual(vjp(2.0)[0], 12.0) + def testPersistentMakeVJP(self): + + def f(x): + return x * x + + wrapped_fn = backprop.make_vjp(f, persistent=True) + _, vjp = wrapped_fn(constant_op.constant(3.0)) + vjp_result1 = vjp(2.0)[0] + vjp_result2 = vjp(2.0)[0] + self.assertAllEqual(vjp_result1, vjp_result2, 12.0) + @test_util.assert_no_new_tensors def testGradGrad(self): diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py index 75526ba9c139e78dfe9e3de271f1316924539371..4255677a68836decce1aaf8d4d7d2df2bf86315b 100644 --- a/tensorflow/python/eager/benchmarks_test.py +++ b/tensorflow/python/eager/benchmarks_test.py @@ -28,11 +28,13 @@ from __future__ import print_function import time import numpy as np +import six from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop # pylint: disable=unused-import from tensorflow.python.eager import context +from tensorflow.python.eager import core from tensorflow.python.eager import function from tensorflow.python.eager import test from tensorflow.python.framework import dtypes @@ -41,24 +43,31 @@ from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops import resource_variable_ops CPU = "/device:CPU:0" GPU = "/device:GPU:0" -def record_gradient_callback(inputs, attrs, results): - return backprop._record_gradient("MatMul", inputs, attrs, results, None) - - -def c_tfe_py_fastpath_execute(a, b, transpose_a=False, transpose_b=False): +def c_tfe_py_fastpath_execute(a, + b, + transpose_a=False, + transpose_b=False, + name=None): ctx = context.context() - assert not ctx.in_graph_mode( + assert ctx.in_eager_mode( ), "The prototype doesn't contain C code for graph construction" - ctx_handle = ctx._handle # pylint: disable=protected-access - - return pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, None, "MatMul", record_gradient_callback, a, b, - "transpose_a", transpose_a, "transpose_b", transpose_b)[0] + try: + return pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", name, + ctx._post_execution_callbacks, a, b, "transpose_a", transpose_a, + "transpose_b", transpose_b) + except core._NotOkStatusException as e: + if name is not None: + message = e.message + " name: " + name + else: + message = e.message + six.raise_from(core._status_to_exception(e.code, message), None) class MicroBenchmarks(test.Benchmark): @@ -233,7 +242,8 @@ class MicroBenchmarks(test.Benchmark): def _benchmark_gen_math_ops_matmul(self, m, transpose_b, num_iters): def func(): - gen_math_ops._mat_mul(m, m, transpose_b=transpose_b) + gen_math_ops.mat_mul(m, m, transpose_b=transpose_b) + self._run(func, num_iters) def _benchmark_tfe_py_fastpath_execute_matmul(self, m, transpose_b, @@ -262,6 +272,24 @@ class MicroBenchmarks(test.Benchmark): func = lambda: f(m, m, transpose_b) self._run(func, num_iters) + def _benchmark_read_variable(self, m, num_iters): + self._run(m.value, num_iters) + + def _benchmark_matmul_read_variable(self, m, num_iters): + self._benchmark_gen_math_ops_matmul( + m, transpose_b=False, num_iters=num_iters) + + def _benchmark_matmul_read_variable_with_tape(self, m, num_iters): + with backprop.GradientTape() as tape: + tape.watch(m) + self._benchmark_gen_math_ops_matmul( + m, transpose_b=False, num_iters=num_iters) + + def _benchmark_read_variable_with_tape(self, m, num_iters): + with backprop.GradientTape() as tape: + tape.watch(m) + self._run(m.value, num_iters) + # Benchmarks for A^2, A of dimension 2 by 2. def benchmark_np_matmul_2_by_2(self): self._benchmark_np_matmul( @@ -398,6 +426,43 @@ class MicroBenchmarks(test.Benchmark): self._benchmark_defun_matmul( m, transpose_b=True, num_iters=self._num_iters_100_by_784) + def benchmark_matmul_read_variable_op_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_matmul_read_variable(m, num_iters=self._num_iters_2_by_2) + + def benchmark_matmul_read_variable_op_with_tape_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_matmul_read_variable_with_tape( + m, num_iters=self._num_iters_2_by_2) + + def benchmark_read_variable_op_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_read_variable(m, num_iters=self._num_iters_2_by_2) + + def benchmark_read_variable_op_2_by_2_GPU(self): + if not context.num_gpus(): + return + with context.device(GPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2.gpu()) + self._benchmark_read_variable(m, num_iters=self._num_iters_2_by_2) + + def benchmark_read_variable_op_with_tape_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_read_variable_with_tape( + m, num_iters=self._num_iters_2_by_2) + + def benchmark_read_variable_op_with_tape_2_by_2_GPU(self): + if not context.num_gpus(): + return + with context.device(GPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2.gpu()) + self._benchmark_read_variable_with_tape( + m, num_iters=self._num_iters_2_by_2) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index b6c7d823237231a138f6a25bb9d03954b69d58d9..87d3ed880a88e040f02b957da99a094ec2d783ef 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -30,7 +30,9 @@ from tensorflow.python.framework import c_api_util from tensorflow.python.framework import device as pydev from tensorflow.python.framework import errors from tensorflow.python.util import compat +from tensorflow.python.util import is_in_graph_mode from tensorflow.python.util import tf_contextlib +from tensorflow.python.util.tf_export import tf_export GRAPH_MODE = 0 EAGER_MODE = 1 @@ -53,6 +55,26 @@ DEVICE_PLACEMENT_SILENT_FOR_INT32 = ( pywrap_tensorflow.TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32) +class _TensorCache(object): + """Simple cache which evicts items based on length in a FIFO manner.""" + + def __init__(self, max_items=256): + self._data = collections.OrderedDict() + self._max_items = max_items if max_items else 256 + + def put(self, key, value): + self._data[key] = value + + if len(self._data) > self._max_items: + self._data.popitem(last=False) + + def get(self, key): + return self._data.get(key, None) + + def flush(self): + self._data = {} + + # TODO(agarwal): better name ? class _EagerContext(threading.local): """Thread local eager context.""" @@ -66,6 +88,7 @@ class _EagerContext(threading.local): self.recording_summaries = False self.summary_writer_resource = None self.scalar_cache = {} + self.ones_rank_cache = _TensorCache() ContextStackEntry = collections.namedtuple( @@ -238,18 +261,18 @@ class Context(object): if mode == EAGER_MODE: context_stack.pop() - def in_graph_mode(self): - """Returns True if current thread is in GRAPH mode.""" - return self._eager_context.mode == GRAPH_MODE - - def in_eager_mode(self): - """Returns True if current thread is in EAGER mode.""" + def executing_eagerly(self): + """Returns True if current thread has eager executing enabled.""" return self._eager_context.mode == EAGER_MODE def scalar_cache(self): """Per-device cache for scalars.""" return self._eager_context.scalar_cache + def ones_rank_cache(self): + """Per-device cache for scalars.""" + return self._eager_context.ones_rank_cache + @property def scope_name(self): """Returns scope name for the current thread.""" @@ -496,23 +519,29 @@ def internal_operation_seed(): return context()._internal_operation_seed() # pylint: disable=protected-access -def in_graph_mode(): - """Returns True if current thread is in GRAPH mode for default context.""" - return context().in_graph_mode() +@tf_export("executing_eagerly") +def executing_eagerly(): + """Returns True if the current thread has eager execution enabled. + + Eager execution is typically enabled via @{tf.enable_eager_execution}, + but may also be enabled within the context of a Python function via + tf.contrib.eager.py_func. + """ + return context().executing_eagerly() def in_eager_mode(): - """Returns True if current thread is in EAGER mode for default context.""" - return context().in_eager_mode() + """Use executing_eagerly() instead. This function will be removed.""" + return executing_eagerly() def graph_mode(): - """Context-manager to enable GRAPH mode for current thread.""" + """Context-manager to disable eager execution for the current thread.""" return context()._mode(GRAPH_MODE) # pylint: disable=protected-access def eager_mode(): - """Context-manager to enable EAGER mode for current thread.""" + """Context-manager to enable eager execution for the current thread.""" return context()._mode(EAGER_MODE) # pylint: disable=protected-access @@ -599,3 +628,14 @@ def export_run_metadata(): A RunMetadata protocol buffer. """ return context().export_run_metadata() + + +# Not every user creates a Context via context.context() +# (for example, enable_eager_execution in python/framework/ops.py), +# but they do all import this file. Note that IS_IN_GRAPH_MODE and +# in_graph_mode are both parameterless functions. +def _tmp_in_graph_mode(): + return not executing_eagerly() + + +is_in_graph_mode.IS_IN_GRAPH_MODE = _tmp_in_graph_mode diff --git a/tensorflow/python/eager/core.py b/tensorflow/python/eager/core.py index 483b7172107838a0069831f2347b0c644c05c000..8fb69300209d74a164c38654d737432cdfb7884a 100644 --- a/tensorflow/python/eager/core.py +++ b/tensorflow/python/eager/core.py @@ -47,3 +47,17 @@ class _NotOkStatusException(Exception): pywrap_tensorflow.TFE_Py_RegisterExceptionClass(_NotOkStatusException) + + +class _FallbackException(Exception): + """Exception class to handle fallback from the fastpath. + + The fastpath that we refer to here is the one implemented to reduce per-op + overheads (TFE_Py_FastPathExecute_C). If the conditions for executing the op + on the fastpath are not met, we fallback to a safer (and more complete) + slowpath, and this Exception is raised to signal that transition. + """ + pass + + +pywrap_tensorflow.TFE_Py_RegisterFallbackExceptionClass(_FallbackException) diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py index ee3c10633e1cb849e319f2f5490e5beb5dd15c80..d504ca0b057b75aaf4dd261bd1876d9f90ef5d69 100644 --- a/tensorflow/python/eager/core_test.py +++ b/tensorflow/python/eager/core_test.py @@ -33,7 +33,10 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import resource_variable_ops def execute(op_name, num_outputs, inputs, attrs=None): @@ -54,8 +57,7 @@ class TFETest(test_util.TensorFlowTestCase): def testContext(self): ctx = context.Context() - self.assertFalse(ctx.in_graph_mode()) - self.assertTrue(ctx.in_eager_mode()) + self.assertTrue(ctx.executing_eagerly()) self.assertEqual('', ctx.scope_name) ctx.scope_name = 'foo' @@ -99,6 +101,18 @@ class TFETest(test_util.TensorFlowTestCase): self.assertEqual(len(cpu_stats.node_stats), 1) self.assertEqual(cpu_stats.node_stats[0].node_name, 'Add') + def testShouldCopy(self): + if not context.context().num_gpus(): + self.skipTest('No devices other than CPUs found') + with ops.device('gpu:0'): + x = constant_op.constant(1.0) + y = array_ops.identity(x) + # The value we're testing y.device against will depend on what the behavior + # of not explicitly specifying a device in the context is. This behavior is + # subject to change (for example, in the future we may want to use GPUs, if + # available, when no device is explicitly provided) + self.assertEqual(y.device, '/job:localhost/replica:0/task:0/device:CPU:0') + def testContextStackContainsEagerMode(self): # Eager execution has been enabled, and no other context # switch has occurred, so `context_stack` should contain @@ -135,9 +149,9 @@ class TFETest(test_util.TensorFlowTestCase): def get_context_values(ctx): return [ - ctx.in_graph_mode(), - ctx.in_eager_mode(), ctx.scope_name, ctx.summary_writer_resource, - ctx.device_name, ctx.num_gpus() + ctx.executing_eagerly(), ctx.scope_name, ctx.summary_writer_resource, + ctx.device_name, + ctx.num_gpus() ] def get_values(ctx, values): @@ -168,6 +182,18 @@ class TFETest(test_util.TensorFlowTestCase): attrs=('T', x.dtype.as_datatype_enum))[0].cpu().numpy() self.assertEqual(3, result) + def testResourceTensorPlacement(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + + with context.device('gpu:0'): + v = resource_variable_ops.ResourceVariable(1.0) + with context.device('cpu:0'): + # Check that even though we specified the cpu device we'll run the read op + # in the device where the handle is. + self.assertAllEqual( + gen_resource_variable_ops.read_variable_op(v.handle, v.dtype), 1.0) + def testCopyBetweenDevices(self): if not context.context().num_gpus(): self.skipTest('No GPUs found') @@ -519,5 +545,61 @@ class TFETest(test_util.TensorFlowTestCase): self.assertIsInstance(t, ops.EagerTensor) +class SendRecvTest(test_util.TensorFlowTestCase): + + cpu_device = '/job:localhost/replica:0/task:0/device:CPU:0' + + def _send(self, tensor, tensor_name, to_device): + return execute( + b'_Send', num_outputs=0, inputs=[tensor], + attrs=('T', tensor.dtype.as_datatype_enum, + 'tensor_name', tensor_name, + 'send_device', tensor.device, + 'send_device_incarnation', 0, + 'recv_device', to_device, + 'client_terminated', True)) + + def _recv(self, dtype, tensor_name, from_device): + device_name = context.context().device_name + if not device_name: + device_name = self.cpu_device + return execute( + b'_Recv', num_outputs=1, inputs=[], + attrs=('tensor_type', dtype.as_datatype_enum, + 'tensor_name', tensor_name, + 'send_device', from_device, + 'send_device_incarnation', 0, + 'recv_device', device_name, + 'client_terminated', False))[0] + + def testBasic(self): + t0 = constant_op.constant(1.0) + t1 = constant_op.constant(2.0) + self._send(t0, 't0', self.cpu_device) + self._send(t1, 't1', self.cpu_device) + self.assertAllEqual( + self._recv(dtypes.float32, 't0', self.cpu_device), + 1.0) + self.assertAllEqual( + self._recv(dtypes.float32, 't1', self.cpu_device), + 2.0) + + def testLocalCrossDevice(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + gpu_device_name = '/job:localhost/replica:0/task:0/device:GPU:0' + with ops.device('GPU:0'): + t0 = constant_op.constant(1.0) + self._send(t0, 't0', self.cpu_device) + self.assertAllEqual( + self._recv(dtypes.float32, 't0', gpu_device_name), + 1.0) + self._send(constant_op.constant(2.0), 't1', gpu_device_name) + with ops.device('GPU:0'): + self.assertAllEqual( + self._recv(dtypes.float32, 't1', self.cpu_device), + 2.0) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/eager/custom_gradient.py b/tensorflow/python/eager/custom_gradient.py deleted file mode 100644 index 05460ff9968312528d87f5fc2ad0495b4da2ad1a..0000000000000000000000000000000000000000 --- a/tensorflow/python/eager/custom_gradient.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Decorator to overrides the gradient for a function.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.python.eager import context -from tensorflow.python.eager import tape -from tensorflow.python.framework import ops as tf_ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gen_array_ops -from tensorflow.python.util import nest -from tensorflow.python.util import tf_decorator - - -def custom_gradient(f): - """Decorator to define a function with a custom gradient. - - The input function is expected to return the tuple - (results, gradient_function). - - The output function will return results while possibly recording the - gradient_function and inputs in the tape. - - Args: - f: function to be decorated. - - Returns: - decorated function. - """ - - def decorated(*args, **kwargs): - """Decorated function with custom gradient.""" - if context.in_graph_mode(): - if kwargs: - raise ValueError( - "custom_gradient in graph mode doesn't support keyword arguments.") - name = "CustomGradient-%s" % tf_ops.uid() - args = [tf_ops.convert_to_tensor(x) for x in args] - result, grad_fn = f(*args) - flat_result = nest.flatten(result) - all_tensors = flat_result + args - - @tf_ops.RegisterGradient(name) - def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable - gradients = nest.flatten(grad_fn(*result_grads[:len(flat_result)])) - # Need to return one value per input to the IdentityN, so pad the - # gradients of the inputs of the custom_gradient function with the - # gradients of the outputs as well. - return ([None] * len(flat_result)) + gradients - - with tf_ops.get_default_graph().gradient_override_map( - {"IdentityN": name}): - all_tensors = array_ops.identity_n(all_tensors) - return nest.pack_sequence_as( - structure=result, flat_sequence=all_tensors[:len(flat_result)]) - - input_tensors = [tf_ops.convert_to_tensor(x) for x in args] - - with tape.stop_recording(): - result, grad_fn = f(*args, **kwargs) - flat_result = nest.flatten(result) - # TODO(apassos) consider removing the identity below. - flat_result = [gen_array_ops.identity(x) for x in flat_result] - - def actual_grad_fn(*outputs): - return nest.flatten(grad_fn(*outputs)) - - tape.record_operation( - f.__name__, - flat_result, - input_tensors, - actual_grad_fn) - flat_result = list(flat_result) - return nest.pack_sequence_as(result, flat_result) - - return tf_decorator.make_decorator(f, decorated) diff --git a/tensorflow/python/eager/execute.py b/tensorflow/python/eager/execute.py index 306cf07aabe1c214d02da5f077a57043cc1f4089..2ff5b8d8f489731c14d8abb81652a17026ed4935 100644 --- a/tensorflow/python/eager/execute.py +++ b/tensorflow/python/eager/execute.py @@ -72,7 +72,7 @@ def execute_with_callbacks(op_name, num_outputs, inputs, attrs, ctx, name=None): """Monkey-patch to execute to enable execution callbacks.""" tensors = quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) for callback in ctx.post_execution_callbacks: - callback(op_name, name, attrs, inputs, tensors) + callback(op_name, inputs, attrs, tensors, name) return tensors diff --git a/tensorflow/python/eager/execution_callbacks.py b/tensorflow/python/eager/execution_callbacks.py index 988442c971f7bf978f1848278fd4955d79428fc5..535361498a9dd33003d0479051e97d7ff2553067 100644 --- a/tensorflow/python/eager/execution_callbacks.py +++ b/tensorflow/python/eager/execution_callbacks.py @@ -104,10 +104,10 @@ class InfOrNanError(Exception): def inf_nan_callback(op_type, - op_name, - attrs, inputs, + attrs, outputs, + op_name, check_inf=True, check_nan=True, action=_DEFAULT_CALLBACK_ACTION): @@ -121,14 +121,14 @@ def inf_nan_callback(op_type, Args: op_type: Name of the TFE operation type (e.g., `MatMul`). - op_name: Name of the TFE operation. This name is set by client and can be - `None` if it unset. - attrs: Attributes of the TFE operation, as a tuple of alternating attribute - names and attribute values. inputs: The `list` of input tensors to the operation, currently unused by this callback. + attrs: Attributes of the TFE operation, as a tuple of alternating attribute + names and attribute values. outputs: The `list` of output tensors from the operation, checked by this callback for `inf` and `nan` values. + op_name: Name of the TFE operation. This name is set by client and can be + `None` if it unset. check_inf: (`bool`) Whether this callback should check for `inf` values in the output tensor values. check_nan: (`bool`) Whether this callback should check for `nan` values in @@ -187,26 +187,38 @@ def inf_nan_callback(op_type, def inf_callback(op_type, - op_name, - attrs, inputs, + attrs, outputs, + op_name, action=_DEFAULT_CALLBACK_ACTION): """A specialization of `inf_nan_callback` that checks for `inf`s only.""" inf_nan_callback( - op_type, op_name, attrs, inputs, outputs, check_inf=True, check_nan=False, + op_type, + inputs, + attrs, + outputs, + op_name, + check_inf=True, + check_nan=False, action=action) def nan_callback(op_type, - op_name, - attrs, inputs, + attrs, outputs, + op_name, action=_DEFAULT_CALLBACK_ACTION): """A specialization of `inf_nan_callback` that checks for `nan`s only.""" inf_nan_callback( - op_type, op_name, attrs, inputs, outputs, check_inf=False, check_nan=True, + op_type, + inputs, + attrs, + outputs, + op_name, + check_inf=False, + check_nan=True, action=action) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 246df9afefbf7665fa100bb601efcf3df717458d..343012e552592a6f8bb1255118add3e938aa443c 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -36,6 +36,8 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.util import compat from tensorflow.python.util import nest @@ -110,7 +112,7 @@ def _convert_to_graph_tensor(value, dtype=None, name=None, as_ref=False): """ del as_ref # Unused. - if context.in_eager_mode(): + if context.executing_eagerly(): return value default_graph = ops.get_default_graph() @@ -161,31 +163,15 @@ class CapturingGraph(ops.Graph): op_def=None, compute_shapes=True, compute_device=True): - # TODO(apassos) probably control flow has to be handled delicately here as - # in if a resource is accessed inside a control flow context we need the - # control dependency to point to something outside the context which is - # guaranteed to happen after the access. - # # TODO(apassos) this should do some form of alias analysis as ops which # forward the resources such as Identity and Switch can cause serialization # to fail. - resource_inputs = set() - control_inputs = set() for i, inp in enumerate(inputs): if inp.graph is not self: inputs[i] = capture_value(self.captures, inp, inp.dtype, inp.op.name) - inp = inputs[i] - if inp.dtype == dtypes_module.resource: - if inp.name in self._last_op_using_resource_tensor: - control_inputs.add(self._last_op_using_resource_tensor[inp.name]) - resource_inputs.add(inp.name) - with self.control_dependencies(list(control_inputs)): - op = super(CapturingGraph, self).create_op( - op_type, inputs, dtypes, input_types, name, attrs, op_def, - compute_shapes, compute_device) - for name in resource_inputs: - self._last_op_using_resource_tensor[name] = op - return op + return super(CapturingGraph, self).create_op( + op_type, inputs, dtypes, input_types, name, attrs, op_def, + compute_shapes, compute_device) # TODO(apassos): it'd be really nice if we could scope this registration. @@ -195,33 +181,66 @@ ops.register_tensor_conversion_function( ops.EagerTensor, _convert_to_graph_tensor, priority=-1) -class _CapturingContext(object): - """Tracks references to Tensors outside this context while it is active.""" +# pylint: disable=invalid-name +class HelperContext(object): + """ControlFlowContext with a customizable AddOp method.""" - def __init__(self): - # known_ops are ops which are created while this context is active - self.known_ops = set() + def __init__(self, add_op_internal): + self._add_op_internal = add_op_internal + self._values = set() # control flow code sometimes updates this. + + def _AddOpInternal(self, op): + self._add_op_internal(op) + + @property + def outer_context(self): + return self._outer_context + + def GetWhileContext(self): + if self._outer_context: + return self._outer_context.GetWhileContext() - # captured_tensors are all tensors referenced to by ops in this context but - # not produced in it - self.captured_tensors = set() + def IsWhileContext(self): + return False + + def IsCondContext(self): + return False + + def IsXLAContext(self): + return False def AddOp(self, op): # pylint: disable=invalid-name - if op.type in ["Variable", "VariableV2", "VarHandleOp"]: - raise ValueError("tfe.defun cannot capture variables created without " - "using tf.get_variable. Op: %s" % op) - self.known_ops.add(op) - for i in op.inputs: - if i.op not in self.known_ops: - self.captured_tensors.add(i) + self._AddOpInternal(op) + if self._outer_context: + self._outer_context.AddOp(op) + + def AddName(self, _): + pass + + def AddInnerOp(self, op): + self._AddOpInternal(op) + if self._outer_context: + self._outer_context.AddInnerOp(op) + + def AddValue(self, val): + if self._outer_context: + return self._outer_context.AddValue(val) + else: + return val def __enter__(self): + # pylint: disable=protected-access self._g = ops.get_default_graph() - self._old = self._g._get_control_flow_context() # pylint: disable=protected-access - self._g._set_control_flow_context(self) # pylint: disable=protected-access + self._outer_context = self._g._get_control_flow_context() + self._g._set_control_flow_context(self) + self._nested_contexts = ( + self._outer_context._nested_contexts + if self._outer_context is not None else None) + # pylint: enable=protected-access - def __exit__(self, _, __, ___): # pylint: disable=invalid-name - self._g._set_control_flow_context(self._old) # pylint: disable=protected-access + def __exit__(self, *_): + self._g._set_control_flow_context(self._outer_context) # pylint: disable=protected-access +# pylint: enable=invalid-name def _forward_name(n): @@ -276,7 +295,7 @@ class _EagerDefinedFunction(object): proto_data = pywrap_tensorflow.TF_GetBuffer(buffer_) function_def = function_pb2.FunctionDef() function_def.ParseFromString(compat.as_bytes(proto_data)) - if context.in_eager_mode(): + if context.executing_eagerly(): _register(fn) self.definition = function_def self.name = function_def.signature.name @@ -367,7 +386,20 @@ class GraphModeFunction(object): def _construct_backprop_function(self): """Constructs the backprop function object for this function.""" with self._graph.as_default(), context.graph_mode(): - c = _CapturingContext() + c_known_ops = set() + c_captured_tensors = set() + + def add_op_internal(op): + if op.type in ["Variable", "VariableV2", "VarHandleOp"]: + raise ValueError("tfe.defun cannot capture variables created without " + "using tf.get_variable. Op: %s" % op) + c_known_ops.add(op) + for i in op.inputs: + if i.op not in c_known_ops: + c_captured_tensors.add(i) + + c = HelperContext(add_op_internal) + with c: filtered_outputs = [x for x in self._returns if x is not None] self._out_grad_placeholders = [ @@ -381,7 +413,7 @@ class GraphModeFunction(object): grad for grad in _flatten(in_gradients) if grad is not None) output_shapes = tuple(grad.shape for grad in backward_outputs) - captures = list(sorted(c.captured_tensors, key=lambda x: x.name)) + captures = list(sorted(c_captured_tensors, key=lambda x: x.name)) forward_name = _forward_name(self._func_name) self._forward_fdef = _EagerDefinedFunction( forward_name, self._graph, self._ops, self._input_placeholders, @@ -394,7 +426,7 @@ class GraphModeFunction(object): # means rerunning the function-defining code will always define the same # function, which is useful if we serialize this etc. function_def_ops = tuple(x - for x in sorted(c.known_ops, key=lambda x: x.name) + for x in sorted(c_known_ops, key=lambda x: x.name) if x not in all_ignored_ops) bname = _backward_name(self._func_name) self._backward_function = GraphModeFunction( @@ -406,7 +438,14 @@ class GraphModeFunction(object): all_args = args + self._extra_inputs signature = self._forward_fdef.signature ctx = context.context() - if ctx.in_graph_mode(): + if ctx.executing_eagerly(): + outputs = execute.execute( + str(signature.name), + num_outputs=len(signature.output_arg), + inputs=all_args, + attrs=None, + ctx=ctx) + else: g = ops.get_default_graph() g._add_function(self._forward_fdef) # pylint: disable=protected-access op = g.create_op( @@ -421,13 +460,6 @@ class GraphModeFunction(object): outputs, (ops.Tensor, type(None))) else list(outputs) for i, s in enumerate(self._output_shapes): outputs[i].set_shape(s) - else: - outputs = execute.execute( - str(signature.name), - num_outputs=len(signature.output_arg), - inputs=all_args, - attrs=None, - ctx=ctx) real_outputs = outputs[:len(self._returns)] side_outputs = outputs[len(self._returns):] @@ -498,7 +530,14 @@ class GraphModeFunction(object): return self._backprop_call(tensor_inputs) ctx = context.context() - if ctx.in_graph_mode(): + if ctx.executing_eagerly(): + result = execute.execute( + str(self._func_name), + num_outputs=self._num_outputs, + inputs=tensor_inputs + self._extra_inputs, + attrs=None, + ctx=ctx) + else: g = ops.get_default_graph() self.add_to_graph(g) signature = self._function_def.definition.signature @@ -515,13 +554,6 @@ class GraphModeFunction(object): return op for i, s in enumerate(self._output_shapes): result[i].set_shape(s) - else: - result = execute.execute( - str(self._func_name), - num_outputs=self._num_outputs, - inputs=tensor_inputs + self._extra_inputs, - attrs=None, - ctx=ctx) return self._build_call_outputs(result) @@ -589,13 +621,21 @@ def _defun_internal(name, func, args, kwds): for collection in curr_graph.collections: tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection( collection) - with tmp_graph.as_default(): + with tmp_graph.as_default(), AutomaticControlDependencies() as a: func_inputs = _get_defun_inputs(args) + def convert(x): + if x is None: + return None + x = ops.convert_to_tensor_or_indexed_slices(x) + x = a.mark_as_return(x) + return x + with capture_tensors(captures): this_tape = tape.push_new_tape() try: func_outputs = func(*func_inputs, **kwds) + func_outputs = nest.map_structure(convert, func_outputs) finally: tape.pop_tape(this_tape) variables = this_tape.watched_variables() @@ -626,7 +666,7 @@ def _defun_internal(name, func, args, kwds): if x not in all_ignored_ops) # Register any other functions defined in the graph # TODO(ashankar): Oh lord, forgive me for this lint travesty. - if context.in_eager_mode(): + if context.executing_eagerly(): for f in tmp_graph._functions.values(): # pylint: disable=protected-access # TODO(ashankar): What about the gradient registry? _register(f._c_func) # pylint: disable=protected-access @@ -756,7 +796,11 @@ def defun(func): or more Tensor objects). """ # TODO(apassos): deal with captured global state. Deal with control flow. - return tf_decorator.make_decorator(func, named_defun(func, func.__name__)) + try: + name = func.__name__ + except AttributeError: + name = "function" + return tf_decorator.make_decorator(func, named_defun(func, name)) def make_defun_op(func, *args, **kwds): @@ -809,3 +853,238 @@ def make_defun_op(func, *args, **kwds): if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): raise ValueError("Tensor keyword arguments are not supported.") return _defun_internal(name, func, args, kwds) + + +class AutomaticControlDependencies(object): + """Context manager to automatically add control dependencies. + + Code under this context manager will act as if a sensible set of control + dependencies were present. More specifically: + 1. All stateful ops in the scope will execute + 2. Stateful ops which modify the same resource will execute in program order + + Note: creating variables in an automatic control dependencies context is not + supported (the value of the variables will never change as they will keep + getting reinitialized). + + NOT THREAD SAFE + """ + + def __init__(self): + self._returned_tensors = set() + + def mark_as_return(self, tensor): + """Acts like identity but marks the `Tensor` as a return value. + + This will possibly return a copy of the `Tensor`. Usage: + + ``` + with AutomaticControlDependencies() as a: + ... + t = a.mark_as_return(t) + _ = ...(t...) # i.e. it's safe to use t here + ``` + + Args: + tensor: the `Tensor` to be marked + + Returns: + a copy of the `Tensor`. + """ + if isinstance(tensor, ops.IndexedSlices): + values = array_ops.identity(tensor.values) + indices = array_ops.identity(tensor.indices) + self._returned_tensors.add(indices) + self._returned_tensors.add(values) + return ops.IndexedSlices(values, indices, dense_shape=tensor.dense_shape) + # We want to make the return values depend on the stateful operations, but + # we don't want to introduce a cycle, so we make the return value the result + # of a new identity operation that the stateful operations definitely don't + # depend on. + tensor = array_ops.identity(tensor) + self._returned_tensors.add(tensor) + return tensor + + def __enter__(self): + if context.executing_eagerly(): + return self + # This code assumes no other thread is adding ops to the graph while + # we're adding ops to the graph. + # TODO(apassos): Fix this by locking the graph or using a temporary + # graph (but that would mess up devices and collections at least, + # probably other things as well). + self._graph = ops.get_default_graph() + self._n_operations = len(self._graph.get_operations()) + return self + + def _process_switch(self, switch_op, ops_which_must_run, + last_op_using_resource_tensor, merge_for_resource): + """Processes a switch node for a resource input. + + When tensorflow creates a cond, it creates a control flow context for each + branch of the cond. Each external tensor accessed by that branch is routed + through a switch op, which gets created in the graph _after_ the op which + uses that tensor get created. + + If the resource comes from another switch op we process that one first. + + _process_switch creates a corresponding merge node for the switch node. This + merge node is added to the outer control flow context of the switch + node. We also ensure that: + + 1. The switch node executes after the previous op which used the resource + tensor + + 2. Any op which uses a resource output of the switch node executes before + the merge for the switch node. + + 3. The next op which uses the input resource to the switch node (which + might be another switch node for the other branch of the conditional) + will execute after the merge node is done. + + 4. The merge node is marked as must_run so it will run even if no + subsequent operation uses the resource. + + Args: + switch_op: the switch op to be processed + ops_which_must_run: the set of ops which must run + last_op_using_resource_tensor: map from resource tensor to last op using + it + merge_for_resource: map from resource tensor to merge which must follow + all usages of it. + """ + inp = switch_op.inputs[0] + if inp.dtype == dtypes_module.resource and inp.op.type == "Switch": + self._process_switch(inp.op, ops_which_must_run, + last_op_using_resource_tensor, merge_for_resource) + if switch_op.outputs[0] in merge_for_resource: + return + new_merge = control_flow_ops.merge(switch_op.outputs, + name="artificial_merge") + new_merge[0].op._control_flow_context = ( # pylint: disable=protected-access + switch_op._control_flow_context.outer_context) # pylint: disable=protected-access + # Ensures the merge always runs + ops_which_must_run.add(new_merge[0].op) + if inp in last_op_using_resource_tensor: + # Ensures the switch exectutes after the previous op using the resource. + switch_op._add_control_input(last_op_using_resource_tensor[inp]) # pylint: disable=protected-access + # Ensure the next op outside the cond happens after the merge. + last_op_using_resource_tensor[inp] = new_merge[0].op + if inp in merge_for_resource: + merge_for_resource[inp]._add_control_input(new_merge[0].op) # pylint: disable=protected-access + for o in switch_op.outputs: + # Ensures the merge will execute after all ops inside the cond + merge_for_resource[o] = new_merge[0].op + + def __exit__(self, unused_type, unused_value, unused_traceback): + if context.executing_eagerly(): + return + + if self._graph is not ops.get_default_graph(): + raise RuntimeError( + "Graph changed while trying to add control dependencies.") + + # map from resource tensor to the last op which used it + last_op_using_resource_tensor = {} + # set of conditional and loop exits + ops_which_must_run = set() + # merge which must depend on ops which use this resource + merge_for_resource = {} + + new_operations = self._graph.get_operations()[self._n_operations:] + + # Ensures that uses of resource tensors get serialized properly and all + # execute. This is done by keeping a map from resource tensor to the last op + # in graph-construction order which used it (last_op_using_resource_tensor). + # + # Conditionals are written in TensorFlow such that every external tensor + # accessed in the conditional goes through a switch op and every return + # tensor (it's guaranteed that there will be at least one) goes through a + # merge op. + # + # To handle conditionals, switches are handled in a special way (see + # comments for _process_switch). Merge nodes created by TF's conditional + # logic (as opposed to by _process_switch) are forced to run and also get a + # control dependency added to them to ensure all stateful ops inside their + # control flow context run. + # + # We also ensure that if an op is using a resource output by a switch node + # (that is, a resource tensor for which there's a value in + # merge_for_resource) this op will run before the merge for that resource. + # + # We try to add control inputs to nodes respecting their control flow + # contexts to avoid dead nodes propagating everywhere and leading to + # "retval[0] doesn't have value" errors. If a node gets a control dependency + # on a dead node (i.e. a note from an untaken control flow branch) that node + # will be marked as dead unless it's a merge node. + # + # TODO(apassos): serialize non-resource-taking stateful ops as well, and + # test that it works. Support while loops. Support init_scope escaping from + # this. + for op in new_operations: + control_inputs = set() + # Ensure stateful ops run + if (op.type not in self._graph._registered_ops # pylint: disable=protected-access + or self._graph._registered_ops[op.type].is_stateful): # pylint: disable=protected-access + ops_which_must_run.add(op) + # Ignore switches (they're handled separately) + if op.type == "Switch" and op.inputs[0].dtype == dtypes_module.resource: + continue + # Make merges trigger all other computation which must run + if op.type == "Merge": + for o in ops_which_must_run: + op._add_control_input(o) # pylint: disable=protected-access + for inp in o.inputs: + if inp in last_op_using_resource_tensor: + last_op_using_resource_tensor[inp] = op + ops_which_must_run = set([op]) + continue + for inp in op.inputs: + if inp.dtype == dtypes_module.resource: + # Deal with switches, finally. + if inp.op.type == "Switch": + self._process_switch(inp.op, ops_which_must_run, + last_op_using_resource_tensor, + merge_for_resource) + # Ensure uses of resources are serialized + if inp in last_op_using_resource_tensor: + if (last_op_using_resource_tensor[inp]._control_flow_context # pylint: disable=protected-access + is op._control_flow_context): # pylint: disable=protected-access + control_inputs.add(last_op_using_resource_tensor[inp]) + # Ensure merges happen after the closing of a cond block + if inp in merge_for_resource: + merge_for_resource[inp]._add_control_input(op) # pylint: disable=protected-access + last_op_using_resource_tensor[inp] = op + control_inputs = [c for c in control_inputs + if c._control_flow_context is op._control_flow_context] # pylint: disable=protected-access + op._add_control_inputs(control_inputs) # pylint: disable=protected-access + + # Ensure all ops which must run do run + for r in self._returned_tensors: + if ops_which_must_run: + r.op._add_control_inputs( # pylint: disable=protected-access + [o for o in ops_which_must_run + if o._control_flow_context is r.op._control_flow_context]) # pylint: disable=protected-access + + +def automatic_control_dependencies(f): + """Wraps f to automatically insert control dependencies. + + The inserted dependencies ensure that: + 1. All stateful ops in f run when the result of f runs + 2. Updates to the same resources happen in order. + + Args: + f: the function to be wrapped. + + Returns: + The wrapped function. + """ + + def wrapper(*args, **kwds): + with AutomaticControlDependencies() as a: + result = f(*args, **kwds) + result_flat = [a.mark_as_return(t) for t in nest.flatten(result)] + return nest.pack_sequence_as(result, result_flat) + + return tf_decorator.make_decorator(f, wrapper) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 3e8e67ac7e242887e1c4f7d89a2e2bc395db22fe..b9cde16867d498d73715535f028a5eb2bea97ea6 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope @@ -595,5 +596,172 @@ class FunctionTest(test.TestCase): create_variable() +class AutomaticControlDependenciesTest(test.TestCase): + + def testBasic(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + with function.AutomaticControlDependencies() as c: + v.assign(v + 1) + v.assign(2 * v) + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(), 4.0) + + def testCondMustRun(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + + def true_fn(): + v.assign(v + 1) + return 0.0 + + def false_fn(): + v.assign(v + 4) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) + self.assertAllEqual(val.eval(feed_dict={p: True}), 6.0) + + def testCondMustRunSeparateRead(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + + def true_fn(): + v.assign(v + 1) + return 0.0 + + def false_fn(): + v.assign(v + 4) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + one = constant_op.constant(1.0) + one = c.mark_as_return(one) + one.eval(feed_dict={p: False}) + self.assertAllEqual(v.read_value().eval(), 5.0) + one.eval(feed_dict={p: True}) + self.assertAllEqual(v.read_value().eval(), 6.0) + + def testCondNested(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + q = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + + def true_fn(): + v.assign(v + 1, name='true') + return 1.0 + + def false_fn(): + + def inner_true_fn(): + v.assign(v * 2, name='false_true') + return 2.0 + + def inner_false_fn(): + v.assign(v * 3, name='false_false') + return 3.0 + + control_flow_ops.cond(q, inner_true_fn, inner_false_fn) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + with ops.name_scope('final'): + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(feed_dict={p: False, q: False}), 3.0) + self.assertAllEqual(val.eval(feed_dict={p: False, q: True}), 6.0) + self.assertAllEqual(val.eval(feed_dict={p: True, q: True}), 7.0) + self.assertAllEqual(val.eval(feed_dict={p: True, q: False}), 8.0) + + def testCondOneBranch(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + + def true_fn(): + return 0.0 + + def false_fn(): + v.assign(v + 4) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) + self.assertAllEqual(val.eval(feed_dict={p: True}), 5.0) + + def testCondOneBranchUpdateBefore(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + v.assign(v * 2) + + def true_fn(): + return 0.0 + + def false_fn(): + v.assign(v + 4) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(feed_dict={p: False}), 6.0) + self.assertAllEqual(val.eval(feed_dict={p: True}), 12.0) + + def testCondOneBranchUpdateAfter(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + p = array_ops.placeholder(dtype=dtypes.bool) + with function.AutomaticControlDependencies() as c: + + def true_fn(): + return 0.0 + + def false_fn(): + v.assign(v + 4) + return 1.0 + + control_flow_ops.cond(p, true_fn, false_fn) + v.assign(v * 2) + val = v.read_value() + val = c.mark_as_return(val) + self.assertAllEqual(val.eval(feed_dict={p: False}), 10.0) + self.assertAllEqual(val.eval(feed_dict={p: True}), 20.0) + + def testDecorator(self): + with context.graph_mode(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + variables.global_variables_initializer().run() + + @function.automatic_control_dependencies + def f(): + v.assign(v + 1) + v.assign(2 * v) + return v.read_value() + + self.assertAllEqual(f().eval(), 4.0) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py index 62106bf0e2809e3c056e4a357f3d05251b7dca68..ee5d87f0835a8e70e0ce14537a51ea5418db41b9 100644 --- a/tensorflow/python/eager/graph_callable.py +++ b/tensorflow/python/eager/graph_callable.py @@ -279,9 +279,12 @@ def _graph_callable_internal(func, shape_and_dtypes): # scope's view of which variables exist. variable_captures = _VariableCapturingScope() with variable_captures.initializing_scope(), function.capture_tensors( - captures): + captures), function.AutomaticControlDependencies() as a: func_outputs = func(*func_inputs) - outputs_list = nest.flatten(func_outputs) + outputs_list = nest.flatten(func_outputs) + for i, x in enumerate(outputs_list): + if x is not None: + outputs_list[i] = a.mark_as_return(x) if len(outputs_list) == 1 and outputs_list[0] is None: outputs_list = [] output_shapes = [x.shape for x in outputs_list] @@ -294,9 +297,12 @@ def _graph_callable_internal(func, shape_and_dtypes): # knows about all variables. tmp_graph.clear_resource_control_flow_state() with variable_captures.capturing_scope(), function.capture_tensors( - captures): + captures), function.AutomaticControlDependencies() as a: captured_outputs = func(*func_inputs) captured_outlist = nest.flatten(captured_outputs) + for i, x in enumerate(captured_outlist): + if x is not None: + captured_outlist[i] = a.mark_as_return(x) capturing_operations = tmp_graph.get_operations()[ len(initializing_operations):] @@ -400,7 +406,7 @@ def graph_callable(shape_and_dtypes): A callable graph object. """ # TODO(alive,apassos): support initialized_value and friends from tf.Variable. - assert context.in_eager_mode(), ( + assert context.executing_eagerly(), ( "graph_callable can only be used when Eager execution is enabled.") def decorator(func): return tf_decorator.make_decorator(func, diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py index f2e70341d975fb06bce7f2ce6cba7d8c3bc9826c..f70c7544d6c9e8095e95d0629b94384bc1cbe35b 100644 --- a/tensorflow/python/eager/ops_test.py +++ b/tensorflow/python/eager/ops_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import numpy as np +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import test @@ -130,8 +131,12 @@ class OpsTest(test_util.TensorFlowTestCase): dtype=dtypes.int64) values = constant_op.constant([2, 3, 5, 7, 11]) shape = constant_op.constant([2, 7], dtype=dtypes.int64) - result = sparse_ops.gen_sparse_ops._sparse_split( # pylint: disable=protected-access - split_dim, indices, values, shape, num_split=2) + result = sparse_ops.gen_sparse_ops.sparse_split( + split_dim, + indices, + values, + shape, + num_split=2) output_indices, output_values, output_shape = result self.assertEqual(2, len(output_indices)) self.assertEqual(2, len(output_values)) @@ -277,6 +282,25 @@ class OpsTest(test_util.TensorFlowTestCase): context._context = context.Context() # pylint: enable=protected-access + def testSoftPlacement(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + # Temporarily replace the context + # pylint: disable=protected-access + del context._context + try: + context._context = context.Context( + device_policy=context.DEVICE_PLACEMENT_SILENT, + config=config_pb2.ConfigProto(allow_soft_placement=True)) + cpu_tensor = constant_op.constant(1.0) + result = cpu_tensor + cpu_tensor + self.assertEqual(result.device, + '/job:localhost/replica:0/task:0/device:GPU:0') + finally: + del context._context + context._context = context.Context() + # pylint: enable=protected-access + def testRandomUniform(self): scalar_shape = constant_op.constant([], dtype=dtypes.int32) diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc index 0f18f28c9556c52c7f0d571dbdfa932dcb7e2721..c2ce8efd7f70c6ba93b6d444f88ddbb9aa51ccdb 100644 --- a/tensorflow/python/eager/python_eager_op_gen.cc +++ b/tensorflow/python/eager/python_eager_op_gen.cc @@ -42,6 +42,8 @@ namespace { const int kRightMargin = 78; +constexpr char kEagerFallbackSuffix[] = "_eager_fallback"; + string AttrVarName(const string& attr_name, std::unordered_map* attr_expressions) { const string var = strings::StrCat("_attr_", attr_name); @@ -49,11 +51,12 @@ string AttrVarName(const string& attr_name, return var; } -void AddInferredAttr(const string& attr_name, const string& value_expression, - string* result, +void AddInferredAttr(const string& indentation, const string& attr_name, + const string& value_expression, string* result, std::unordered_map* attr_expressions) { - strings::StrAppend(result, " ", AttrVarName(attr_name, attr_expressions), - " = ", value_expression, "\n"); + strings::StrAppend(result, indentation, + AttrVarName(attr_name, attr_expressions), " = ", + value_expression, "\n"); } string VectorToTuple(const std::vector& l) { @@ -121,11 +124,33 @@ class GenEagerPythonOp : public python_op_gen_internal::GenPythonOp { string Code() override; protected: - void ExpectListArg(const string& arg_name); - void AddEagerInferredAttrs(); - void AddEagerInputCasts(); - void AddEagerAttrs(); - void AddEagerExecute(const string& num_outputs_expr); + void HandleGraphMode(const string& function_setup); + + string GetEagerNotAllowedError(); + void ExpectListArg(const string& indentation, const string& arg_name, + string* output); + bool GetEagerFunctionSetup(const string& indentation, string* function_setup); + void GetOutputSizesAndNumOutputsExpr(std::vector* output_sizes, + string* num_outputs_expr); + + void AddEagerFunctionTeardown(const string& indentation, + const std::vector& output_sizes, + bool execute_record_gradient); + + bool AddEagerFastPathAndGraphCode(const string& parameters, + const std::vector& output_sizes, + const string& eager_not_allowed_error); + bool AddEagerFallbackCode(const string& parameters, + const std::vector& output_sizes, + const string& num_outputs_expr, + const string& eager_not_allowed_error); + void AddEagerFastPathExecute(); + + void AddEagerInferredAttrs(const string& indentation); + void AddEagerInputCasts(const string& indentation); + void AddEagerAttrs(const string& indentation); + void AddEagerExecute(const string& indentation, + const string& num_outputs_expr); void AddAttrForArg(const string& attr, int arg_index) { gtl::InsertIfNotPresent(&inferred_attrs_, attr, @@ -148,6 +173,13 @@ class GenEagerPythonOp : public python_op_gen_internal::GenPythonOp { typedef std::unordered_map> AttrToArgMap; AttrToArgMap attr_to_args_; std::unordered_map attr_expressions_; + // This has all the input args followed by those attrs that don't have + // defaults. + std::vector params_no_default_; + // The parameters with defaults (these have to be listed after those without). + // No input args are included, just attrs. + std::vector> + params_with_default_; }; string GetEagerPythonOp(const OpDef& op_def, const ApiDef& api_def, @@ -207,18 +239,12 @@ string GenEagerPythonOp::Code() { if (api_def_.visibility() == ApiDef::SKIP) { return ""; } - // This has all the input args followed by those attrs that don't have - // defaults. - std::vector params_no_default; - // The parameters with defaults (these have to be listed after those without). - // No input args are included, just attrs. - std::vector> - params_with_default; for (int i = 0; i < api_def_.arg_order_size(); ++i) { const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_); const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_); - params_no_default.emplace_back(api_def_arg.name(), api_def_arg.rename_to()); + params_no_default_.emplace_back(api_def_arg.name(), + api_def_arg.rename_to()); if (!arg.type_attr().empty()) { AddAttrForArg(arg.type_attr(), i); } else if (!arg.type_list_attr().empty()) { @@ -235,7 +261,7 @@ string GenEagerPythonOp::Code() { if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) { if (api_def_attr.has_default_value()) { if (attr.type() == "tensor") { - params_with_default.emplace_back( + params_with_default_.emplace_back( python_op_gen_internal::ParamNames(api_def_attr.name(), api_def_attr.rename_to()), strings::StrCat( @@ -247,22 +273,22 @@ string GenEagerPythonOp::Code() { for (const auto& pb : api_def_attr.default_value().list().tensor()) { pbtxt.emplace_back(TensorPBString(pb)); } - params_with_default.emplace_back( + params_with_default_.emplace_back( python_op_gen_internal::ParamNames(api_def_attr.name(), api_def_attr.rename_to()), strings::StrCat("[_execute.make_tensor(_pb, \"", api_def_attr.rename_to(), "\") for _pb in ", VectorToTuple(pbtxt), "]")); } else { - params_with_default.emplace_back( + params_with_default_.emplace_back( python_op_gen_internal::ParamNames(api_def_attr.name(), api_def_attr.rename_to()), python_op_gen_internal::AttrValueToPython( attr.type(), api_def_attr.default_value(), "_dtypes.")); } } else { - params_no_default.emplace_back(api_def_attr.name(), - api_def_attr.rename_to()); + params_no_default_.emplace_back(api_def_attr.name(), + api_def_attr.rename_to()); } } } @@ -270,29 +296,29 @@ string GenEagerPythonOp::Code() { // Save the list of attr parameters (attrs that won't be inferred), // those with defaults go at the end. // Get the attrs in the order we want by taking the attrs without defaults - // from the end of params_no_default, and adding params_no_default. - attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() + - params_with_default.size()); - for (int i = op_def_.input_arg_size(); i < params_no_default.size(); ++i) { - attrs_.push_back(params_no_default[i].GetName()); + // from the end of params_no_default_, and adding params_no_default_. + attrs_.reserve(params_no_default_.size() - op_def_.input_arg_size() + + params_with_default_.size()); + for (int i = op_def_.input_arg_size(); i < params_no_default_.size(); ++i) { + attrs_.push_back(params_no_default_[i].GetName()); } - for (const auto& p : params_with_default) { + for (const auto& p : params_with_default_) { attrs_.push_back(p.first.GetName()); } - param_names_.reserve(params_no_default.size() + params_with_default.size()); - param_names_.insert(param_names_.begin(), params_no_default.begin(), - params_no_default.end()); - for (const auto& param_and_default : params_with_default) { + param_names_.reserve(params_no_default_.size() + params_with_default_.size()); + param_names_.insert(param_names_.begin(), params_no_default_.begin(), + params_no_default_.end()); + for (const auto& param_and_default : params_with_default_) { param_names_.push_back(param_and_default.first); } string parameters; - for (const auto& param : params_no_default) { + for (const auto& param : params_no_default_) { if (!parameters.empty()) strings::StrAppend(¶meters, ", "); strings::StrAppend(¶meters, param.GetRenameTo()); } - for (const auto& param_and_default : params_with_default) { + for (const auto& param_and_default : params_with_default_) { if (!parameters.empty()) strings::StrAppend(¶meters, ", "); strings::StrAppend(¶meters, param_and_default.first.GetRenameTo(), "=", param_and_default.second); @@ -300,19 +326,125 @@ string GenEagerPythonOp::Code() { if (!parameters.empty()) strings::StrAppend(¶meters, ", "); strings::StrAppend(¶meters, "name=None"); - AddExport(); - AddDefLine(parameters); - AddDocStringDescription(); - AddDocStringArgs(); - AddDocStringInputs(); - AddDocStringAttrs(); - AddDocStringNameArg(); - AddOutputGlobals(); - AddDocStringOutputs(); - strings::StrAppend(&result_, " \"\"\"\n"); + // Add attr_expressions_ for attrs that are params. + for (int i = 0; i < attrs_.size(); ++i) { + const string& attr_name = attrs_[i]; + const string& attr_api_name = + param_names_[i + op_def_.input_arg_size()].GetRenameTo(); + attr_expressions_[attr_name] = attr_api_name; + } + // Add attr_expressions_ for attrs that are inferred. + for (int i = 0; i < op_def_.attr_size(); ++i) { + const auto& attr(op_def_.attr(i)); + if (attr.type() == "int") { + auto arg_list = attr_to_args_.find(attr.name()); + if (arg_list != attr_to_args_.end()) { + AttrVarName(attr.name(), &attr_expressions_); + } + } + } + + string num_outputs_expr; + std::vector output_sizes(num_outs_); + GetOutputSizesAndNumOutputsExpr(&output_sizes, &num_outputs_expr); + + string eager_not_allowed_error = GetEagerNotAllowedError(); + + if (!AddEagerFastPathAndGraphCode(parameters, output_sizes, + eager_not_allowed_error)) { + return result_; + } - // Function body. + if (!AddEagerFallbackCode(parameters, output_sizes, num_outputs_expr, + eager_not_allowed_error)) { + return result_; + } + return prelude_ + result_; +} + +void GenEagerPythonOp::HandleGraphMode(const string& function_setup) { + // Handle graph-mode case + strings::StrAppend(&result_, + " _ctx = _context.context()\n" + " if not _ctx.executing_eagerly():\n", + function_setup, + " _, _, _op = _op_def_lib._apply_op_helper(\n"); + AddBodyNoReturn(" "); + if (num_outs_ > 0) { + strings::StrAppend(&result_, " _result = _op.outputs[:]\n"); + // Special case handling for stateful op with single list output + // that might be empty. + if (num_outs_ == 1 && op_def_.is_stateful() && + (!op_def_.output_arg(0).number_attr().empty() || + !op_def_.output_arg(0).type_list_attr().empty())) { + // TODO(josh11b): Can skip this if the number_attr/type_list_attr has + // a constraint indicating that this can never be empty. + strings::StrAppend(&result_, + " if not _result:\n" + " return _op\n"); + } + strings::StrAppend(&result_, " _inputs_flat = _op.inputs\n"); + + // Compute graph-mode attrs. + if (op_def_.attr_size() > 0) { + string attr_values; + for (int i = 0; i < op_def_.attr_size(); ++i) { + if (i > 0) strings::StrAppend(&attr_values, ", "); + const auto& attr_name(op_def_.attr(i).name()); + strings::StrAppend(&attr_values, "\"", attr_name, "\", _op.get_attr(\"", + attr_name, "\")"); + } + strings::StrAppend(&attr_values, ")"); + strings::StrAppend(&result_, + WordWrap(" _attrs = (", attr_values, kRightMargin), + "\n"); + } else { + strings::StrAppend(&result_, " _attrs = None\n"); + } + } else { + strings::StrAppend(&result_, " return _op\n"); + } +} + +string GenEagerPythonOp::GetEagerNotAllowedError() { + bool eager_allowed = true; + string ref_arg; + for (int i = 0; i < op_def_.input_arg_size(); ++i) { + const auto& arg = op_def_.input_arg(i); + if (arg.is_ref()) { + eager_allowed = false; + DCHECK_EQ(op_def_.input_arg(i).name(), api_def_.in_arg(i).name()); + ref_arg = api_def_.in_arg(i).rename_to(); + } + } + for (int i = 0; i < op_def_.output_arg_size(); ++i) { + const auto& arg = op_def_.output_arg(i); + if (arg.is_ref()) { + eager_allowed = false; + DCHECK_EQ(op_def_.output_arg(i).name(), api_def_.out_arg(i).name()); + ref_arg = api_def_.out_arg(i).rename_to(); + } + } + + if (eager_allowed) return ""; + + return strings::StrCat("raise RuntimeError(\"", op_name_, + " op does not support eager execution. ", "Arg '", + ref_arg, "' is a ref.\")\n"); +} + +void GenEagerPythonOp::ExpectListArg(const string& indentation, + const string& arg_name, string* output) { + strings::StrAppend(output, indentation, "if not isinstance(", arg_name, + ", (list, tuple)):\n", indentation, " raise TypeError(\n", + indentation, " \"Expected list for '", arg_name, + "' argument to \"\n", indentation, " \"'", op_name_, + "' Op, not %r.\" % ", arg_name, ")\n"); +} + +bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation, + string* function_setup) { // Validate list inputs, infer length attrs. for (int i = 0; i < op_def_.attr_size(); ++i) { const auto& attr(op_def_.attr(i)); @@ -324,32 +456,27 @@ string GenEagerPythonOp::Code() { for (auto iter = arg_list->second.begin(); iter != arg_list->second.end(); ++iter) { const string& arg_api_name = param_names_[*iter].GetRenameTo(); - ExpectListArg(arg_api_name); + ExpectListArg(indentation, arg_api_name, function_setup); if (iter == arg_list->second.begin()) { - AddInferredAttr(attr.name(), + AddInferredAttr(indentation, attr.name(), strings::StrCat("len(", arg_api_name, ")"), - &result_, &attr_expressions_); + function_setup, &attr_expressions_); } else { const auto& attr_var = attr_expressions_[attr.name()]; - strings::StrAppend(&result_, " if len(", arg_api_name, - ") != ", attr_var, - ":\n" - " raise ValueError(\n" - " \"List argument '", - arg_api_name, "' to '", op_name_, - "' Op with length %d \"\n" - " \"must match length %d of argument '", - inferred_attrs_[attr.name()], - "'.\" %\n" - " (len(", - arg_api_name, "), ", attr_var, "))\n"); + strings::StrAppend( + function_setup, indentation, "if len(", arg_api_name, + ") != ", attr_var, ":\n", indentation, " raise ValueError(\n", + indentation, " \"List argument '", arg_api_name, "' to '", + op_name_, "' Op with length %d \"\n", indentation, + " \"must match length %d of argument '", + inferred_attrs_[attr.name()], "'.\" %\n", indentation, + " (len(", arg_api_name, "), ", attr_var, "))\n"); } } } } } - // Values for non-inferred attrs. for (int i = 0; i < attrs_.size(); ++i) { const string& attr_name = attrs_[i]; const auto& param = param_names_[i + op_def_.input_arg_size()]; @@ -357,241 +484,300 @@ string GenEagerPythonOp::Code() { const string& attr_api_name = param.GetRenameTo(); StringPiece attr_type = attr.type(); attr_expressions_[attr_name] = attr_api_name; - const int default_index = i - (attrs_.size() - params_with_default.size()); + const int default_index = i - (attrs_.size() - params_with_default_.size()); if (default_index >= 0) { - const string& default_value = params_with_default[default_index].second; - strings::StrAppend(&result_, " if ", attr_api_name, " is None:\n"); - strings::StrAppend(&result_, " ", attr_api_name, " = ", default_value, - "\n"); + const string& default_value = params_with_default_[default_index].second; + strings::StrAppend(function_setup, indentation, "if ", attr_api_name, + " is None:\n"); + strings::StrAppend(function_setup, indentation, " ", attr_api_name, + " = ", default_value, "\n"); } if (attr_type.starts_with("list(")) { - ExpectListArg(attr_api_name); + ExpectListArg(indentation, attr_api_name, function_setup); } if (attr_type == "string") { - strings::StrAppend(&result_, " ", attr_api_name, " = _execute.make_str(", - attr_api_name, ", \"", attr_api_name, "\")\n"); + strings::StrAppend(function_setup, indentation, attr_api_name, + " = _execute.make_str(", attr_api_name, ", \"", + attr_api_name, "\")\n"); } else if (attr_type == "list(string)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_str(_s, \"", attr_api_name, "\") for _s in ", attr_api_name, "]\n"); } else if (attr_type == "int") { - strings::StrAppend(&result_, " ", attr_api_name, " = _execute.make_int(", - attr_api_name, ", \"", attr_api_name, "\")\n"); + strings::StrAppend(function_setup, indentation, attr_api_name, + " = _execute.make_int(", attr_api_name, ", \"", + attr_api_name, "\")\n"); } else if (attr_type == "list(int)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_int(_i, \"", attr_api_name, "\") for _i in ", attr_api_name, "]\n"); } else if (attr_type == "float") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = _execute.make_float(", attr_api_name, ", \"", attr_api_name, "\")\n"); } else if (attr_type == "list(float)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_float(_f, \"", attr_api_name, "\") for _f in ", attr_api_name, "]\n"); } else if (attr_type == "bool") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = _execute.make_bool(", attr_api_name, ", \"", attr_api_name, "\")\n"); } else if (attr_type == "list(bool)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_bool(_b, \"", attr_api_name, "\") for _b in ", attr_api_name, "]\n"); } else if (attr_type == "type") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = _execute.make_type(", attr_api_name, ", \"", attr_api_name, "\")\n"); } else if (attr_type == "list(type)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_type(_t, \"", attr_api_name, "\") for _t in ", attr_api_name, "]\n"); } else if (attr_type == "shape") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = _execute.make_shape(", attr_api_name, ", \"", attr_api_name, "\")\n"); } else if (attr_type == "list(shape)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_shape(_s, \"", attr_api_name, "\") for _s in ", attr_api_name, "]\n"); } else if (attr_type == "tensor") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = _execute.make_tensor(", attr_api_name, ", \"", attr_api_name, "\")\n"); } else if (attr_type == "list(tensor)") { - strings::StrAppend(&result_, " ", attr_api_name, + strings::StrAppend(function_setup, indentation, attr_api_name, " = [_execute.make_tensor(_t, \"", attr_api_name, "\") for _t in ", attr_api_name, "]\n"); } else if (attr_type != "func") { - return strings::StrCat("# No definition for ", function_name_, - " since we don't support attrs with type\n" - "# '", - attr_type, "' right now.\n\n"); + *function_setup = + strings::StrCat("# No definition for ", function_name_, + " since we don't support attrs with type\n" + "# '", + attr_type, "' right now.\n\n"); + return false; } } + return true; +} - // Figure out the list of inputs. - const string inputs = FlattenInputs(nullptr, nullptr); - - // Handle graph-mode case - strings::StrAppend(&result_, - " _ctx = _context.context()\n" - - " if _ctx.in_graph_mode():\n" - " _, _, _op = _op_def_lib._apply_op_helper(\n"); - AddBodyNoReturn(" "); - if (num_outs_ > 0) { - strings::StrAppend(&result_, " _result = _op.outputs[:]\n"); - // Special case handling for stateful op with single list output - // that might be empty. - if (num_outs_ == 1 && op_def_.is_stateful() && - (!op_def_.output_arg(0).number_attr().empty() || - !op_def_.output_arg(0).type_list_attr().empty())) { - // TODO(josh11b): Can skip this if the number_attr/type_list_attr has - // a constraint indicating that this can never be empty. - strings::StrAppend(&result_, - " if not _result:\n" - " return _op\n"); - } - strings::StrAppend(&result_, " _inputs_flat = _op.inputs\n"); - - // Compute graph-mode attrs. - if (op_def_.attr_size() > 0) { - string attr_values; - for (int i = 0; i < op_def_.attr_size(); ++i) { - if (i > 0) strings::StrAppend(&attr_values, ", "); - const auto& attr_name(op_def_.attr(i).name()); - strings::StrAppend(&attr_values, "\"", attr_name, "\", _op.get_attr(\"", - attr_name, "\")"); - } - strings::StrAppend(&attr_values, ")"); - strings::StrAppend(&result_, - WordWrap(" _attrs = (", attr_values, kRightMargin), - "\n"); - } else { - strings::StrAppend(&result_, " _attrs = None\n"); - } - } else { - strings::StrAppend(&result_, " return _op\n"); - } - - // Handle eager-mode case - strings::StrAppend(&result_, " else:\n"); - +// If output i is list output, output_sizes[i] will be set to a +// string with the python expression that will evaluate to its +// length. output_sizes[i] is empty for non-list outputs. +void GenEagerPythonOp::GetOutputSizesAndNumOutputsExpr( + std::vector* output_sizes, string* num_outputs_expr) { // Expression representing the number of outputs. int num_fixed_outputs = 0; - string num_outputs_expr; - // If output i is list output, output_sizes[i] will be set to a - // string with the python expression that will evaluate to its - // length. output_sizes[i] is empty for non-list outputs. - std::vector output_sizes(num_outs_); for (int i = 0; i < num_outs_; ++i) { const auto& arg(op_def_.output_arg(i)); if (!arg.number_attr().empty()) { - if (!num_outputs_expr.empty()) { - strings::StrAppend(&num_outputs_expr, " + "); + if (!num_outputs_expr->empty()) { + strings::StrAppend(num_outputs_expr, " + "); } - output_sizes[i] = attr_expressions_[arg.number_attr()]; - strings::StrAppend(&num_outputs_expr, output_sizes[i]); + (*output_sizes)[i] = attr_expressions_[arg.number_attr()]; + strings::StrAppend(num_outputs_expr, (*output_sizes)[i]); } else if (!arg.type_list_attr().empty()) { - if (!num_outputs_expr.empty()) { - strings::StrAppend(&num_outputs_expr, " + "); + if (!num_outputs_expr->empty()) { + strings::StrAppend(num_outputs_expr, " + "); } // Have to be careful to use an expression that works in both // graph and eager paths here. const auto iter = inferred_attrs_.find(arg.type_list_attr()); if (iter == inferred_attrs_.end()) { - output_sizes[i] = strings::StrCat( + (*output_sizes)[i] = strings::StrCat( "len(", attr_expressions_[arg.type_list_attr()], ")"); } else { - output_sizes[i] = strings::StrCat("len(", iter->second, ")"); + (*output_sizes)[i] = strings::StrCat("len(", iter->second, ")"); } - strings::StrAppend(&num_outputs_expr, output_sizes[i]); + strings::StrAppend(num_outputs_expr, (*output_sizes)[i]); } else { ++num_fixed_outputs; } } if (num_fixed_outputs > 0) { - if (!num_outputs_expr.empty()) { - strings::StrAppend(&num_outputs_expr, " + "); - } - strings::StrAppend(&num_outputs_expr, num_fixed_outputs); - } else if (num_outputs_expr.empty()) { - num_outputs_expr = "0"; - } - - bool eager_allowed = true; - string ref_arg; - for (int i = 0; i < op_def_.input_arg_size(); ++i) { - const auto& arg = op_def_.input_arg(i); - if (arg.is_ref()) { - eager_allowed = false; - DCHECK_EQ(op_def_.input_arg(i).name(), api_def_.in_arg(i).name()); - ref_arg = api_def_.in_arg(i).rename_to(); - } - } - for (int i = 0; i < op_def_.output_arg_size(); ++i) { - const auto& arg = op_def_.output_arg(i); - if (arg.is_ref()) { - eager_allowed = false; - DCHECK_EQ(op_def_.output_arg(i).name(), api_def_.out_arg(i).name()); - ref_arg = api_def_.out_arg(i).rename_to(); + if (!num_outputs_expr->empty()) { + strings::StrAppend(num_outputs_expr, " + "); } + strings::StrAppend(num_outputs_expr, num_fixed_outputs); + } else if (num_outputs_expr->empty()) { + *num_outputs_expr = "0"; } +} - if (eager_allowed) { - AddEagerInferredAttrs(); - AddEagerInputCasts(); - strings::StrAppend(&result_, " _inputs_flat = ", inputs, "\n"); - AddEagerAttrs(); - AddEagerExecute(num_outputs_expr); - } else { - strings::StrAppend(&result_, - " raise RuntimeError(\n" - " \"", - op_name_, " op does not support eager execution. ", - "Arg '", ref_arg, "'' is a ref.\")\n"); - } - +void GenEagerPythonOp::AddEagerFunctionTeardown( + const string& indentation, const std::vector& output_sizes, + bool execute_record_gradient) { if (num_outs_ > 0) { - strings::StrAppend(&result_, " _execute.record_gradient(\n", " \"", - op_def_.name(), - "\", _inputs_flat, _attrs, _result, name)\n"); + if (execute_record_gradient) { + strings::StrAppend(&result_, indentation, "_execute.record_gradient(\n", + " \"", op_def_.name(), + "\", _inputs_flat, _attrs, _result, name)\n"); + } if (num_outs_ == 1 && !output_sizes[0].empty()) { // Single list result. } else if (num_outs_ == 1) { // Execute returns a single-element list which we need to destructure. - strings::StrAppend(&result_, " _result, = _result\n"); + strings::StrAppend(&result_, indentation, "_result, = _result\n"); } else { // Have multiple outputs, so we will need to reformat the return // value of execute() to be a list with one entry per op output // (that entry will be a list of tensors if that output is of list // type). // For list outputs, convert the right subrange of _result into a list. - Unflatten(" ", output_sizes, "_result", &result_); + Unflatten(indentation, output_sizes, "_result", &result_); // Convert to a named tuple. - strings::StrAppend(&result_, " _result = _", op_def_.name(), + strings::StrAppend(&result_, indentation, "_result = _", op_def_.name(), "Output._make(_result)\n"); } } else { - strings::StrAppend(&result_, " _result = None\n"); + strings::StrAppend(&result_, indentation, "_result = None\n"); } - strings::StrAppend(&result_, " return _result\n\n"); - return prelude_ + result_; + strings::StrAppend(&result_, indentation, "return _result\n\n"); +} + +bool GenEagerPythonOp::AddEagerFastPathAndGraphCode( + const string& parameters, const std::vector& output_sizes, + const string& eager_not_allowed_error) { + AddExport(); + AddDefLine(function_name_, parameters); + AddDocStringDescription(); + AddDocStringArgs(); + AddDocStringInputs(); + AddDocStringAttrs(); + AddDocStringNameArg(); + AddOutputGlobals(); // Added to prelude_ + AddDocStringOutputs(); + strings::StrAppend(&result_, " \"\"\"\n"); + + // Handle graph-mode case + string function_setup; + if (!GetEagerFunctionSetup(" ", &function_setup)) { + result_ = function_setup; + return false; + } + HandleGraphMode(function_setup); + AddEagerFunctionTeardown(" ", output_sizes, + true /* execute_record_gradient */); + + // Handle eager-mode case + strings::StrAppend(&result_, " else:\n"); + + if (eager_not_allowed_error.empty()) { + AddEagerFastPathExecute(); + } else { + strings::StrAppend(&result_, " ", eager_not_allowed_error); + } + + strings::StrAppend(&result_, "\n\n"); + return true; +} + +bool GenEagerPythonOp::AddEagerFallbackCode( + const string& parameters, const std::vector& output_sizes, + const string& num_outputs_expr, const string& eager_not_allowed_error) { + if (!eager_not_allowed_error.empty()) { + strings::StrAppend(&result_, " ", eager_not_allowed_error); + return true; + } + + AddDefLine(strings::StrCat(function_name_, kEagerFallbackSuffix), parameters); + strings::StrAppend( + &result_, " r\"\"\"This is the slowpath function for Eager mode.\n"); + strings::StrAppend(&result_, " This is for function ", function_name_, + "\n \"\"\"\n"); + + strings::StrAppend(&result_, " _ctx = _context.context()\n"); + + string function_setup; + if (!GetEagerFunctionSetup(" ", &function_setup)) { + result_ = function_setup; + return false; + } + strings::StrAppend(&result_, function_setup); + + AddEagerInferredAttrs(" "); + AddEagerInputCasts(" "); + strings::StrAppend( + &result_, " _inputs_flat = ", FlattenInputs(nullptr, nullptr), "\n"); + AddEagerAttrs(" "); + AddEagerExecute(" ", num_outputs_expr); + + AddEagerFunctionTeardown(" ", output_sizes, + true /* execute_record_gradient */); + + return true; } -void GenEagerPythonOp::ExpectListArg(const string& arg_name) { - strings::StrAppend(&result_, " if not isinstance(", arg_name, - ", (list, tuple)):\n" - " raise TypeError(\n" - " \"Expected list for '", - arg_name, - "' argument to \"\n" - " \"'", - op_name_, "' Op, not %r.\" % ", arg_name, ")\n"); +void GenEagerPythonOp::AddEagerFastPathExecute() { + string fastpath_execute_params = + strings::StrCat("_ctx._handle, _ctx.device_name, \"", op_def_.name(), + "\", ", "name, _ctx._post_execution_callbacks"); + string fallback_params; + + for (int i = 0; i < api_def_.in_arg_size(); i++) { + const string param_name = param_names_[i].GetRenameTo(); + strings::StrAppend(&fastpath_execute_params, ", ", param_name); + if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", "); + strings::StrAppend(&fallback_params, param_name); + } + + for (const auto& attr : api_def_.attr()) { + if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) { + strings::StrAppend(&fastpath_execute_params, ", \"", attr.name(), "\", ", + attr.rename_to()); + + if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", "); + strings::StrAppend(&fallback_params, attr.rename_to(), "=", + attr.rename_to()); + } + } + + if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", "); + strings::StrAppend(&fallback_params, "name=name"); + + strings::StrAppend(&result_, " try:\n"); + strings::StrAppend( + &result_, " ", + "_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n", + WordWrap(strings::StrCat(" "), + strings::StrCat(fastpath_execute_params, ")"), kRightMargin), + "\n"); + + if (op_def_.output_arg_size() > 1) { + const string output_tuple_name = + strings::StrCat("_", op_def_.name(), "Output"); + strings::StrAppend(&result_, " ", "_result = ", output_tuple_name, + "._make(_result)\n"); + } + strings::StrAppend(&result_, " ", "return _result\n"); + + // Handle fallback. + strings::StrAppend(&result_, " ", "except _core._FallbackException:\n"); + strings::StrAppend( + &result_, " ", "return ", function_name_, kEagerFallbackSuffix, + "(\n", + WordWrap(strings::StrCat(" "), + strings::StrCat(fallback_params, ")"), kRightMargin), + "\n"); + + // Any errors thrown from execute need to be unwrapped from + // _NotOkStatusException. + strings::StrAppend(&result_, " ", + "except _core._NotOkStatusException as e:\n"); + strings::StrAppend(&result_, " ", "if name is not None:\n"); + strings::StrAppend(&result_, " ", + "message = e.message + \" name: \" + name\n"); + strings::StrAppend(&result_, " ", "else:\n"); + strings::StrAppend(&result_, " ", "message = e.message\n"); + strings::StrAppend( + &result_, " ", + "_six.raise_from(_core._status_to_exception(e.code, message), None)\n"); } -void GenEagerPythonOp::AddEagerInferredAttrs() { +void GenEagerPythonOp::AddEagerInferredAttrs(const string& indentation) { // Figure out values for inferred attrs, and cast to eager tensors. for (int i = 0; i < op_def_.attr_size(); ++i) { const auto& attr(op_def_.attr(i)); @@ -618,24 +804,24 @@ void GenEagerPythonOp::AddEagerInferredAttrs() { const string inputs_var = param_names_[arg_list->second.front()].GetRenameTo(); if (output_sizes.front().empty()) { - strings::StrAppend(&result_, " ", var_name, ", (", inputs_var, - ",) = ", conversion, "\n"); + strings::StrAppend(&result_, indentation, var_name, ", (", + inputs_var, ",) = ", conversion, "\n"); } else { - strings::StrAppend(&result_, " ", var_name, ", ", inputs_var, - " = ", conversion, "\n"); + strings::StrAppend(&result_, indentation, var_name, ", ", + inputs_var, " = ", conversion, "\n"); } } else { const string inputs_var = strings::StrCat("_inputs_", attr.name()); - strings::StrAppend(&result_, " ", var_name, ", ", inputs_var, + strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var, " = ", conversion, "\n"); // Convert from a flat list of eager tensors back to the // parameter variables. - Unflatten(" ", output_sizes, inputs_var, &result_); + Unflatten(indentation, output_sizes, inputs_var, &result_); std::vector p; for (int j : arg_list->second) { p.emplace_back(param_names_[j].GetRenameTo()); } - strings::StrAppend(&result_, " ", VectorToTuple(p), " = ", + strings::StrAppend(&result_, indentation, VectorToTuple(p), " = ", inputs_var, "\n"); } } else if (attr.type() == "list(type)") { @@ -662,14 +848,14 @@ void GenEagerPythonOp::AddEagerInferredAttrs() { inputs_var = param_names_[arg_list->second.front()].GetRenameTo(); conversion = "_execute.convert_to_mixed_eager_tensors"; } - strings::StrAppend(&result_, " ", var_name, ", ", inputs_var, " = ", - conversion, "(", inputs_var, ", _ctx)\n"); + strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var, + " = ", conversion, "(", inputs_var, ", _ctx)\n"); } } } } -void GenEagerPythonOp::AddEagerInputCasts() { +void GenEagerPythonOp::AddEagerInputCasts(const string& indentation) { // Cast remaining args to eager tensors for (int i = 0; i < op_def_.input_arg_size(); ++i) { const auto& arg(op_def_.input_arg(i)); @@ -678,12 +864,12 @@ void GenEagerPythonOp::AddEagerInputCasts() { const string fn = arg.number_attr().empty() ? "" : "n_"; const string dtype = python_op_gen_internal::DataTypeToPython(arg.type(), "_dtypes."); - strings::StrAppend(&result_, " ", param, " = _ops.convert_", fn, + strings::StrAppend(&result_, indentation, param, " = _ops.convert_", fn, "to_tensor(", param, ", ", dtype, ")\n"); } } -void GenEagerPythonOp::AddEagerAttrs() { +void GenEagerPythonOp::AddEagerAttrs(const string& indentation) { // Compute eager attrs if (op_def_.attr_size() > 0) { string attr_values; @@ -695,14 +881,19 @@ void GenEagerPythonOp::AddEagerAttrs() { } strings::StrAppend(&attr_values, ")"); strings::StrAppend( - &result_, WordWrap(" _attrs = (", attr_values, kRightMargin), "\n"); + &result_, + WordWrap(indentation, strings::StrCat("_attrs = (", attr_values), + kRightMargin), + "\n"); } else { - strings::StrAppend(&result_, " _attrs = None\n"); + strings::StrAppend(&result_, indentation, "_attrs = None\n"); } } -void GenEagerPythonOp::AddEagerExecute(const string& num_outputs_expr) { - const string return_prefix = " _result = _execute.execute("; +void GenEagerPythonOp::AddEagerExecute(const string& indentation, + const string& num_outputs_expr) { + const string return_prefix = + strings::StrCat(indentation, "_result = _execute.execute("); const string return_args = strings::StrCat( "b\"", op_def_.name(), "\", ", num_outputs_expr, ", inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)"); @@ -723,8 +914,8 @@ string GetEagerPythonOps(const OpList& ops, const ApiDefMap& api_defs, This file is MACHINE GENERATED! Do not edit. )"); - // Mention the original source file so someone tracing back through generated - // Python code will know where to look next. + // Mention the original source file so someone tracing back through + // generated Python code will know where to look next. if (!source_file_name.empty()) { strings::StrAppend(&result, "Original C++ source file: "); strings::StrAppend(&result, source_file_name); @@ -734,11 +925,14 @@ This file is MACHINE GENERATED! Do not edit. strings::StrAppend(&result, R"(""" import collections as _collections +import six as _six -from tensorflow.python.eager import execute as _execute +from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core +from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes +from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 @@ -761,10 +955,10 @@ from tensorflow.python.util.tf_export import tf_export if (api_def->visibility() == ApiDef::SKIP) { continue; } - // An op is hidden if either its ApiDef visibility is HIDDEN // or it is in the hidden_ops list. bool is_hidden = api_def->visibility() == ApiDef::HIDDEN; + bool hidden_by_api_def = is_hidden; if (!is_hidden) { for (const string& hidden : hidden_ops) { if (op_def.name() == hidden) { @@ -777,13 +971,22 @@ from tensorflow.python.util.tf_export import tf_export string function_name; python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(), &function_name); - if (is_hidden) function_name = strings::StrCat("_", function_name); - - // When users create custom python wrappers, they may link in the - // default op registry by accident, and because they can't - // enumerate all 'hidden' symbols, this guard is to prevent - // instantiating a python reserved word in their wrapper. - if (python_op_gen_internal::IsPythonReserved(function_name)) { + bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name); + + // Prefix an op with underscore if the op is listed in hidden_ops or + // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix. + // Do not add underscores to ops set to HIDDEN in ApiDef otherwise. + // TODO(annarev): don't prefix with underscores even if op is in hidden_ops. + if (is_hidden) { + if (!hidden_by_api_def || is_reserved || + python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) { + function_name = strings::StrCat("_", function_name); + } + } else if (is_reserved) { + // When users create custom python wrappers, they may link in the + // default op registry by accident, and because they can't + // enumerate all 'hidden' symbols, this guard is to prevent + // instantiating a python reserved word in their wrapper. continue; } diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc index 6fa076507d11ab9c88891cbeb0a4fb3959e4e99d..8338bc43432ef8b214cfc43cb9819a2c29c957dc 100644 --- a/tensorflow/python/eager/pywrap_tensor.cc +++ b/tensorflow/python/eager/pywrap_tensor.cc @@ -185,6 +185,16 @@ typedef struct EagerTensor { // This stores `_keras_mask` object and is set by Tensorflow layers. PyObject* keras_mask; + + // This stores `_tensor_shape`, a cached `TensorShape` object, and is set the + // first time that `_EagerTensorBase`'s `shape` property is called. + PyObject* tensor_shape; + + // We store a status object here as an optimization to avoid allocating a new + // Status objects on different functions that operate on EagerTensor and need + // to use a TF_Status object. However note that accesses to `status` are not + // thread-safe. + TF_Status* status; } EagerTensor; // tp_init for EagerTensor. @@ -195,6 +205,9 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { self->handle_data = Py_None; Py_INCREF(Py_None); self->keras_mask = Py_None; + Py_INCREF(Py_None); + self->tensor_shape = Py_None; + self->status = TF_NewStatus(); PyObject* value; PyObject* context = nullptr; PyObject* device = nullptr; @@ -269,17 +282,17 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { } TF_DataType handle_dtype = TFE_TensorHandleDataType(handle.get()); if (desired_dtype >= 0 && desired_dtype != handle_dtype) { - auto out_status = tensorflow::make_safe(TF_NewStatus()); handle = tensorflow::make_safe( EagerCast(GetContext(context), handle.get(), handle_dtype, - static_cast(desired_dtype), out_status.get())); - if (TF_GetCode(out_status.get()) != TF_OK) { - PyErr_SetString( - PyExc_ValueError, - tensorflow::strings::StrCat("Error while casting from DataType ", - handle_dtype, " to ", desired_dtype, ". ", - TF_Message(out_status.get())) - .c_str()); + static_cast(desired_dtype), self->status)); + if (TF_GetCode(self->status) != TF_OK) { + PyErr_SetString(PyExc_ValueError, + tensorflow::strings::StrCat( + "Error while casting from DataType ", handle_dtype, + " to ", desired_dtype, ". ", TF_Message(self->status)) + .c_str()); + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); return -1; } handle_dtype = TFE_TensorHandleDataType(handle.get()); @@ -323,8 +336,10 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { // tp_dealloc for EagerTensor. void EagerTensor_dealloc(EagerTensor* self) { + TF_DeleteStatus(self->status); Py_DECREF(self->handle_data); Py_DECREF(self->keras_mask); + Py_DECREF(self->tensor_shape); TFE_DeleteTensorHandle(self->handle); self->handle = nullptr; // We have the global interpreter lock, so use this chance to perform delayed @@ -348,12 +363,21 @@ static PyObject* EagerTensor_datatype_enum(EagerTensor* self) { // Getter for `_shape_tuple`. static PyObject* EagerTensor_shape_tuple(EagerTensor* self) { auto handle = self->handle; - int n = TFE_TensorHandleNumDims(handle); + int n = TFE_TensorHandleNumDims(handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } PyObject* shape = PyTuple_New(n); if (PyErr_Occurred()) return nullptr; for (int i = 0; i < n; ++i) { - PyObject* dim = PyLong_FromLongLong(TFE_TensorHandleDim(handle, i)); - if (dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) { + PyObject* dim = + PyLong_FromLongLong(TFE_TensorHandleDim(handle, i, self->status)); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError) || + dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); Py_DECREF(shape); if (dim != nullptr) Py_DECREF(dim); PyErr_SetString(PyExc_RuntimeError, "Error while creating shape"); @@ -365,10 +389,16 @@ static PyObject* EagerTensor_shape_tuple(EagerTensor* self) { // Getter for `_rank`. static PyObject* EagerTensor_rank(EagerTensor* self) { + int num_dims = TFE_TensorHandleNumDims(self->handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } #if PY_MAJOR_VERSION < 3 - return PyInt_FromLong(TFE_TensorHandleNumDims(self->handle)); + return PyInt_FromLong(num_dims); #else - return PyLong_FromLong(TFE_TensorHandleNumDims(self->handle)); + return PyLong_FromLong(num_dims); #endif } @@ -397,6 +427,19 @@ static int EagerTensor_setkeras_mask(EagerTensor* self, PyObject* value, self->keras_mask = value; return 0; } + +static PyObject* EagerTensor_tensor_shape(EagerTensor* self, void* unused) { + Py_INCREF(self->tensor_shape); + return self->tensor_shape; +} + +static int EagerTensor_settensor_shape(EagerTensor* self, PyObject* value, + void* unused) { + Py_DECREF(self->tensor_shape); + Py_INCREF(value); + self->tensor_shape = value; + return 0; +} // Function `_copy_to_device`. static PyObject* EagerTensor_copy_to_device(EagerTensor* self, PyObject* args, PyObject* kwds) { @@ -437,10 +480,16 @@ static PyObject* EagerTensor_numpy(EagerTensor* self) { // Getter `device`. static PyObject* EagerTensor_device(EagerTensor* self) { + const char* device = TFE_TensorHandleDeviceName(self->handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } #if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromString(TFE_TensorHandleDeviceName(self->handle)); + return PyUnicode_FromString(device); #else - return PyBytes_FromString(TFE_TensorHandleDeviceName(self->handle)); + return PyBytes_FromString(device); #endif } @@ -455,6 +504,9 @@ static PyGetSetDef EagerTensor_getseters[] = { {const_cast("_keras_mask"), (getter)EagerTensor_keras_mask, (setter)EagerTensor_setkeras_mask, const_cast("_keras_mask"), nullptr}, + {const_cast("_tensor_shape"), (getter)EagerTensor_tensor_shape, + (setter)EagerTensor_settensor_shape, const_cast("_tensor_shape"), + nullptr}, {nullptr} /* Sentinel */ }; @@ -491,16 +543,11 @@ PyTypeObject* EagerTensorType = nullptr; #if PY_MAJOR_VERSION >= 3 static PyType_Slot EagerTensor_Type_slots[] = { - Py_tp_dealloc, - reinterpret_cast(EagerTensor_dealloc), - Py_tp_methods, - reinterpret_cast(EagerTensor_methods), - Py_tp_getset, - reinterpret_cast(EagerTensor_getseters), - Py_tp_init, - reinterpret_cast(EagerTensor_init), - 0, - nullptr, + {Py_tp_dealloc, reinterpret_cast(EagerTensor_dealloc)}, + {Py_tp_methods, reinterpret_cast(EagerTensor_methods)}, + {Py_tp_getset, reinterpret_cast(EagerTensor_getseters)}, + {Py_tp_init, reinterpret_cast(EagerTensor_init)}, + {0, nullptr}, }; PyType_Spec EagerTensor_Type_spec = {"EagerTensor", sizeof(EagerTensor), 0, @@ -575,7 +622,10 @@ PyObject* EagerTensorFromHandle(TFE_TensorHandle* handle) { t->handle_data = Py_None; Py_INCREF(Py_None); t->keras_mask = Py_None; + Py_INCREF(Py_None); + t->tensor_shape = Py_None; t->handle = handle; + t->status = TF_NewStatus(); } return reinterpret_cast(t); } @@ -673,6 +723,7 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) { auto tensor = tensorflow::make_safe(TF_AllocateTensor( TF_INT32, &num_tensors_int, /*num_dims=*/1, /*len=*/4 * num_tensors_int)); int32_t* data = reinterpret_cast(TF_TensorData(tensor.get())); + auto status = tensorflow::make_safe(TF_NewStatus()); for (Py_ssize_t i = 0; i < num_tensors; ++i) { PyObject* tensor_obj = PyList_GET_ITEM(tensor_list, i); if (!EagerTensor_CheckExact(tensor_obj)) { @@ -687,21 +738,27 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) { EagerTensor* t = reinterpret_cast(tensor_obj); TFE_TensorHandle* handle = t->handle; - if (slice_dim >= TFE_TensorHandleNumDims(handle)) { - PyErr_SetString(PyExc_IndexError, - tensorflow::strings::StrCat( - "Slice dimension (", slice_dim, - ") must be smaller than rank of all " - "tensors, but tensor at index ", - i, " has rank ", TFE_TensorHandleNumDims(handle)) - .c_str()); + int num_dims = TFE_TensorHandleNumDims(handle, status.get()); + if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) { + return nullptr; + } + if (slice_dim >= num_dims) { + PyErr_SetString( + PyExc_IndexError, + tensorflow::strings::StrCat("Slice dimension (", slice_dim, + ") must be smaller than rank of all " + "tensors, but tensor at index ", + i, " has rank ", num_dims) + .c_str()); + return nullptr; + } + int64_t dim = TFE_TensorHandleDim(handle, slice_dim, status.get()); + if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) { return nullptr; } - int64_t dim = TFE_TensorHandleDim(handle, slice_dim); data[i] = dim; } - auto status = tensorflow::make_safe(TF_NewStatus()); TFE_TensorHandle* handle = TFE_NewTensorHandle(tensor.get(), status.get()); if (TF_GetCode(status.get()) != TF_OK) { PyErr_SetString( diff --git a/tensorflow/python/eager/pywrap_tfe.h b/tensorflow/python/eager/pywrap_tfe.h index 4aea134fa9df845fe2a84f32d56a17a8766bde9b..32d731d0f68910b8e41a57cb32ae60c3ea6742f7 100644 --- a/tensorflow/python/eager/pywrap_tfe.h +++ b/tensorflow/python/eager/pywrap_tfe.h @@ -47,8 +47,34 @@ void TFE_Py_Execute(TFE_Context* ctx, const char* device_name, // Registers e as the Exception class for handling not ok Status. Returns // Py_None if registration succeeds, else throws a TypeError and returns NULL. +// +// This function is not thread-safe. PyObject* TFE_Py_RegisterExceptionClass(PyObject* e); +// Registers e as the type of the ResourceVariable class. +// Returns Py_None if registration succeeds, else throws a TypeError and returns +// NULL. +// +// This function is not thread-safe. +PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e); + +// Registers e as the Exception to be raised when the conditions of +// TFE_Py_FastPathExecute_C have not been met. When this exception is set, it +// is a signal to the calling code that it should fall back to the safer (and +// more complete) code path. +// +// This function is not thread-safe. +PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e); + +// Registers e as the backward_function_getter. +// The registered function creates a backward function (a function that can +// return the gradient of the inputs an op given the gradient of it's outputs). +// The registered function will be passed the following arguments: +// op_name, attrs, num_inputs, op_inputs, op_outputs +// +// This function is not thread-safe. +PyObject* TFE_Py_RegisterBackwardFunctionGetter(PyObject* e); + // Returns 0 if 'status' is TF_OK. Otherwise, raises an exception (using // `exception` if not nullptr, else using the class registered via // TFE_Py_RegisterExceptionClass), and returns -1. @@ -141,11 +167,10 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, // Item 2: device_name: Name of the device on which to execute the operation, // or NULL for automatic selection. // Item 3: op_name: Name of the TensorFlow op to execute. -// Item 4: record_gradient_callback: Callback that records the gradient of the -// result. -// The callback takes (inputs, attrs, result) - all sequences and -// records the gradient. -// Item 5 onwards: inputs - This is a list of inputs followed by a list of +// Item 4: name: An optional name for the operation. +// Item 5: List representing all callbacks to execute after successful +// op execute. +// Item 6 onwards: inputs - This is a list of inputs followed by a list of // attrs. It is not necessary for type attrs to be present. // // This is named _C since there doesn't seem to be any way to make it visible @@ -153,6 +178,11 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, // directive. PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args); +// Record the gradient for a given op. +PyObject* TFE_Py_RecordGradient(PyObject* op_name, PyObject* inputs, + PyObject* attrs, PyObject* results, + PyObject* name); + // Returns the set of variables watched by the given tape. PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape); diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index d927f3abedb88deddabd4c4d931d12053005a3ff..9146e2bb950735dc0f9e018f5fe4e2d9038c5c2b 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -23,17 +23,38 @@ limitations under the License. #include "tensorflow/c/eager/tape.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/gtl/compactptrset.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/python/eager/pywrap_tensor.h" +#include "tensorflow/python/lib/core/safe_ptr.h" using tensorflow::string; using tensorflow::strings::Printf; namespace { +struct FastPathOpExecInfo { + TFE_Context* ctx; + const char* device_name; + // The op def of the main op being executed. + const tensorflow::OpDef* op_def; + + bool run_callbacks; + bool run_post_exec_callbacks; + bool run_gradient_callback; + + // The op name of the main op being executed. + PyObject* name; + // The op type name of the main op being executed. + PyObject* op_name; + PyObject* callbacks; +}; + #define PARSE_VALUE(fn_name, type, check_fn, parse_fn) \ bool fn_name(const string& key, PyObject* py_value, TF_Status* status, \ type* value) { \ @@ -56,10 +77,50 @@ PARSE_VALUE(ParseInt64Value, int64_t, PyLong_Check, PyLong_AsLong) #else PARSE_VALUE(ParseIntValue, int, PyInt_Check, PyInt_AsLong) PARSE_VALUE(ParseInt64Value, int64_t, PyInt_Check, PyInt_AsLong) +PARSE_VALUE(ParseInt64LongValue, int64_t, PyLong_Check, PyLong_AsLong) #endif PARSE_VALUE(ParseFloatValue, float, PyFloat_Check, PyFloat_AsDouble) #undef PARSE_VALUE +Py_ssize_t TensorShapeNumDims(PyObject* value) { + const auto size = PySequence_Size(value); + if (size == -1) { + // TensorShape.__len__ raises an error in the scenario where the shape is an + // unknown, which needs to be cleared. + // TODO(nareshmodi): ensure that this is actually a TensorShape. + PyErr_Clear(); + } + return size; +} + +bool IsInteger(PyObject* py_value) { +#if PY_MAJOR_VERSION >= 3 + return PyLong_Check(py_value); +#else + return PyInt_Check(py_value); +#endif +} + +bool ParseDimensionValue(const string& key, PyObject* py_value, + TF_Status* status, int64_t* value) { + if (IsInteger(py_value)) { + return ParseInt64Value(key, py_value, status, value); + } + + tensorflow::Safe_PyObjectPtr dimension_value( + PyObject_GetAttrString(py_value, "_value")); + if (dimension_value == nullptr) { + TF_SetStatus( + status, TF_INVALID_ARGUMENT, + tensorflow::strings::StrCat("Expecting a Dimension for attr ", key, + ", got ", py_value->ob_type->tp_name) + .c_str()); + return false; + } + + return ParseInt64Value(key, dimension_value.get(), status, value); +} + bool ParseStringValue(const string& key, PyObject* py_value, TF_Status* status, const char** value) { if (PyBytes_Check(py_value)) { @@ -86,8 +147,32 @@ bool ParseBoolValue(const string& key, PyObject* py_value, TF_Status* status, return true; } -bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, - TF_AttrType type, TF_Status* status) { +// The passed in py_value is expected to be an object of the python type +// dtypes.DType or an int. +bool ParseTypeValue(const string& key, PyObject* py_value, TF_Status* status, + int* value) { + if (IsInteger(py_value)) { + return ParseIntValue(key, py_value, status, value); + } + + tensorflow::Safe_PyObjectPtr py_type_enum( + PyObject_GetAttrString(py_value, "_type_enum")); + if (py_type_enum == nullptr) { + TF_SetStatus( + status, TF_INVALID_ARGUMENT, + tensorflow::strings::StrCat("Expecting a DType.dtype for attr ", key, + ", got ", py_value->ob_type->tp_name) + .c_str()); + return false; + } + + return ParseIntValue(key, py_type_enum.get(), status, value); +} + +bool SetOpAttrList( + TFE_Op* op, const char* key, PyObject* py_list, TF_AttrType type, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status) { if (!PySequence_Check(py_list)) { TF_SetStatus( status, TF_INVALID_ARGUMENT, @@ -97,6 +182,7 @@ bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, return false; } const int num_values = PySequence_Size(py_list); + if (attr_list_sizes != nullptr) (*attr_list_sizes)[key] = num_values; #define PARSE_LIST(c_type, parse_fn) \ std::unique_ptr values(new c_type[num_values]); \ @@ -118,7 +204,7 @@ bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, PARSE_LIST(unsigned char, ParseBoolValue); TFE_OpSetAttrBoolList(op, key, values.get(), num_values); } else if (type == TF_ATTR_TYPE) { - PARSE_LIST(int, ParseIntValue); + PARSE_LIST(int, ParseTypeValue); TFE_OpSetAttrTypeList(op, key, reinterpret_cast(values.get()), num_values); @@ -138,8 +224,10 @@ bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, .c_str()); return false; } - const auto size = PySequence_Size(py_value); - total_dims += size; + const auto size = TensorShapeNumDims(py_value); + if (size >= 0) { + total_dims += size; + } } } // Allocate a buffer that can fit all of the dims together. @@ -155,14 +243,20 @@ bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, dims[i] = nullptr; num_dims[i] = -1; } else { - const auto size = PySequence_Size(py_value); + const auto size = TensorShapeNumDims(py_value); + if (size == -1) { + dims[i] = nullptr; + num_dims[i] = -1; + continue; + } dims[i] = offset; num_dims[i] = size; for (int j = 0; j < size; ++j) { auto inner_py_value = PySequence_ITEM(py_value, j); if (inner_py_value == Py_None) { *offset = -1; - } else if (!ParseInt64Value(key, inner_py_value, status, offset)) { + } else if (!ParseDimensionValue(key, inner_py_value, status, + offset)) { return false; } ++offset; @@ -183,8 +277,123 @@ bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, return true; } -bool SetOpAttrScalar(TFE_Context* ctx, TFE_Op* op, const char* key, - PyObject* py_value, TF_AttrType type, TF_Status* status) { +// This is only declared here since GetFunc makes a recursive call to +// SetOpAttrScalarDefault. +void SetOpAttrScalarDefault( + TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, + const char* attr_name, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status); + +TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func, + TF_Status* status) { + TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status); + for (const auto& attr : func.attr()) { + if (TF_GetCode(status) != TF_OK) return nullptr; + SetOpAttrScalarDefault(ctx, func_op, attr.second, attr.first.data(), + nullptr, status); + if (TF_GetCode(status) != TF_OK) return nullptr; + } + return func_op; +} + +void SetOpAttrListDefault( + TFE_Context* ctx, TFE_Op* op, const tensorflow::OpDef::AttrDef& attr, + const char* key, TF_AttrType type, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status) { + if (type == TF_ATTR_STRING) { + int num_values = attr.default_value().list().s_size(); + std::unique_ptr values(new const char*[num_values]); + (*attr_list_sizes)[key] = num_values; + for (int i = 0; i < num_values; i++) { + values[i] = attr.default_value().list().s(i).data(); + } + TFE_OpSetAttrStringList(op, key, values.get(), num_values); + } else if (type == TF_ATTR_INT) { + int num_values = attr.default_value().list().i_size(); + std::unique_ptr values(new int64_t[num_values]); + (*attr_list_sizes)[key] = num_values; + for (int i = 0; i < num_values; i++) { + values[i] = attr.default_value().list().i(i); + } + TFE_OpSetAttrIntList(op, key, values.get(), num_values); + } else if (type == TF_ATTR_FLOAT) { + int num_values = attr.default_value().list().f_size(); + std::unique_ptr values(new float[num_values]); + (*attr_list_sizes)[key] = num_values; + for (int i = 0; i < num_values; i++) { + values[i] = attr.default_value().list().f(i); + } + TFE_OpSetAttrFloatList(op, key, values.get(), num_values); + } else if (type == TF_ATTR_BOOL) { + int num_values = attr.default_value().list().b_size(); + std::unique_ptr values(new unsigned char[num_values]); + (*attr_list_sizes)[key] = num_values; + for (int i = 0; i < num_values; i++) { + values[i] = attr.default_value().list().b(i); + } + TFE_OpSetAttrBoolList(op, key, values.get(), num_values); + } else if (type == TF_ATTR_TYPE) { + int num_values = attr.default_value().list().type_size(); + std::unique_ptr values(new int[num_values]); + (*attr_list_sizes)[key] = num_values; + for (int i = 0; i < num_values; i++) { + values[i] = attr.default_value().list().type(i); + } + TFE_OpSetAttrTypeList(op, key, + reinterpret_cast(values.get()), + attr.default_value().list().type_size()); + } else if (type == TF_ATTR_SHAPE) { + int num_values = attr.default_value().list().shape_size(); + (*attr_list_sizes)[key] = num_values; + int total_dims = 0; + for (int i = 0; i < num_values; ++i) { + if (!attr.default_value().list().shape(i).unknown_rank()) { + total_dims += attr.default_value().list().shape(i).dim_size(); + } + } + // Allocate a buffer that can fit all of the dims together. + std::unique_ptr buffer(new int64_t[total_dims]); + // Copy the input dims into the buffer and set dims to point to + // the start of each list's dims. + std::unique_ptr dims(new const int64_t*[num_values]); + std::unique_ptr num_dims(new int[num_values]); + int64_t* offset = buffer.get(); + for (int i = 0; i < num_values; ++i) { + const auto& shape = attr.default_value().list().shape(i); + if (shape.unknown_rank()) { + dims[i] = nullptr; + num_dims[i] = -1; + } else { + for (int j = 0; j < shape.dim_size(); j++) { + *offset = shape.dim(j).size(); + ++offset; + } + } + } + TFE_OpSetAttrShapeList(op, key, dims.get(), num_dims.get(), num_values, + status); + } else if (type == TF_ATTR_FUNC) { + int num_values = attr.default_value().list().func_size(); + (*attr_list_sizes)[key] = num_values; + std::unique_ptr funcs(new const TFE_Op*[num_values]); + for (int i = 0; i < num_values; i++) { + funcs[i] = GetFunc(ctx, attr.default_value().list().func(i), status); + } + TFE_OpSetAttrFunctionList(op, key, funcs.get(), num_values); + } else { + TF_SetStatus(status, TF_UNIMPLEMENTED, + "Lists of tensors are not yet implemented for default valued " + "attributes for an operation."); + } +} + +bool SetOpAttrScalar( + TFE_Context* ctx, TFE_Op* op, const char* key, PyObject* py_value, + TF_AttrType type, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status) { if (type == TF_ATTR_STRING) { const char* value; if (!ParseStringValue(key, py_value, status, &value)) return false; @@ -193,6 +402,10 @@ bool SetOpAttrScalar(TFE_Context* ctx, TFE_Op* op, const char* key, int64_t value; if (!ParseInt64Value(key, py_value, status, &value)) return false; TFE_OpSetAttrInt(op, key, value); + // attr_list_sizes is set for all int attributes (since at this point we are + // not aware if that attribute might be used to calculate the size of an + // output list or not). + if (attr_list_sizes != nullptr) (*attr_list_sizes)[key] = value; } else if (type == TF_ATTR_FLOAT) { float value; if (!ParseFloatValue(key, py_value, status, &value)) return false; @@ -203,7 +416,7 @@ bool SetOpAttrScalar(TFE_Context* ctx, TFE_Op* op, const char* key, TFE_OpSetAttrBool(op, key, value); } else if (type == TF_ATTR_TYPE) { int value; - if (!ParseIntValue(key, py_value, status, &value)) return false; + if (!ParseTypeValue(key, py_value, status, &value)) return false; TFE_OpSetAttrType(op, key, static_cast(value)); } else if (type == TF_ATTR_SHAPE) { if (py_value == Py_None) { @@ -217,13 +430,18 @@ bool SetOpAttrScalar(TFE_Context* ctx, TFE_Op* op, const char* key, .c_str()); return false; } - const auto num_dims = PySequence_Size(py_value); + const auto num_dims = TensorShapeNumDims(py_value); + if (num_dims == -1) { + TFE_OpSetAttrShape(op, key, nullptr, -1, status); + return true; + } std::unique_ptr dims(new int64_t[num_dims]); for (int i = 0; i < num_dims; ++i) { auto inner_py_value = PySequence_ITEM(py_value, i); if (inner_py_value == Py_None) { dims[i] = -1; - } else if (!ParseInt64Value(key, inner_py_value, status, &dims[i])) { + } else if (!ParseDimensionValue(key, inner_py_value, status, + &dims[i])) { return false; } } @@ -269,6 +487,65 @@ bool SetOpAttrScalar(TFE_Context* ctx, TFE_Op* op, const char* key, return true; } +void SetOpAttrScalarDefault( + TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, + const char* attr_name, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status) { + switch (default_value.value_case()) { + case tensorflow::AttrValue::kS: + TFE_OpSetAttrString(op, attr_name, default_value.s().data()); + break; + case tensorflow::AttrValue::kI: + TFE_OpSetAttrInt(op, attr_name, static_cast(default_value.i())); + (*attr_list_sizes)[attr_name] = default_value.i(); + break; + case tensorflow::AttrValue::kF: + TFE_OpSetAttrFloat(op, attr_name, default_value.f()); + break; + case tensorflow::AttrValue::kB: + TFE_OpSetAttrBool(op, attr_name, default_value.b()); + break; + case tensorflow::AttrValue::kType: + TFE_OpSetAttrType(op, attr_name, + static_cast(default_value.type())); + break; + case tensorflow::AttrValue::kShape: { + const auto& tensor_shape = default_value.shape(); + if (tensor_shape.unknown_rank()) { + TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status); + } else { + const auto num_dims = tensor_shape.dim_size(); + std::unique_ptr dims(new int64_t[num_dims]); + for (int i = 0; i < num_dims; ++i) { + dims[i] = tensor_shape.dim(i).size(); + } + TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status); + } + } break; + case tensorflow::AttrValue::kFunc: { + const auto func_op = GetFunc(ctx, default_value.func(), status); + if (TF_GetCode(status) != TF_OK) return; + // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList + // require TFE_Op* and just convert it internally a NameAttrValue, so + // consider adding an overload to the C API to make this case easier. + TFE_OpSetAttrFunction(op, attr_name, func_op); + } break; + case tensorflow::AttrValue::kList: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::kTensor: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::kPlaceholder: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::VALUE_NOT_SET: + TF_SetStatus( + status, TF_UNIMPLEMENTED, + tensorflow::strings::StrCat("Unable to get setfor default value: ", + default_value.DebugString()) + .data()); + } +} + // start_index is the index at which the Tuple/List attrs will start getting // processed. void SetOpAttrs(TFE_Context* ctx, TFE_Op* op, PyObject* attrs, int start_index, @@ -294,9 +571,40 @@ void SetOpAttrs(TFE_Context* ctx, TFE_Op* op, PyObject* attrs, int start_index, const TF_AttrType type = TFE_OpGetAttrType(op, key, &is_list, out_status); if (TF_GetCode(out_status) != TF_OK) return; if (is_list != 0) { - if (!SetOpAttrList(op, key, py_value, type, out_status)) return; + if (!SetOpAttrList(op, key, py_value, type, nullptr, out_status)) return; + } else { + if (!SetOpAttrScalar(ctx, op, key, py_value, type, nullptr, out_status)) + return; + } + } +} + +// This function will set the op attrs required. If an attr has the value of +// None, then it will read the AttrDef to get the default value and set that +// instead. Any failure in this function will simply fall back to the slow +// path. +void SetOpAttrWithDefaults( + TFE_Context* ctx, TFE_Op* op, const tensorflow::OpDef::AttrDef& attr, + const char* attr_name, PyObject* attr_value, + tensorflow::gtl::FlatMap* attr_list_sizes, + TF_Status* status) { + unsigned char is_list = 0; + const TF_AttrType type = TFE_OpGetAttrType(op, attr_name, &is_list, status); + if (TF_GetCode(status) != TF_OK) return; + if (attr_value == Py_None) { + if (is_list != 0) { + SetOpAttrListDefault(ctx, op, attr, attr_name, type, attr_list_sizes, + status); + } else { + SetOpAttrScalarDefault(ctx, op, attr.default_value(), attr_name, + attr_list_sizes, status); + } + } else { + if (is_list != 0) { + SetOpAttrList(op, attr_name, attr_value, type, attr_list_sizes, status); } else { - if (!SetOpAttrScalar(ctx, op, key, py_value, type, out_status)) return; + SetOpAttrScalar(ctx, op, attr_name, attr_value, type, attr_list_sizes, + status); } } } @@ -305,6 +613,14 @@ void SetOpAttrs(TFE_Context* ctx, TFE_Op* op, PyObject* attrs, int start_index, tensorflow::mutex exception_class_mutex(tensorflow::LINKER_INITIALIZED); PyObject* exception_class GUARDED_BY(exception_class_mutex) = nullptr; +// Python subclass of Exception that is created to signal fallback. +PyObject* fallback_exception_class = nullptr; + +// Python function that returns a backward_function. +PyObject* backward_function_getter = nullptr; + +PyTypeObject* resource_variable_type = nullptr; + tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED); tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0; @@ -353,13 +669,78 @@ PyObject* TFE_Py_RegisterExceptionClass(PyObject* e) { "TFE_Py_RegisterExceptionClass: " "Registered class should be subclass of Exception."); return nullptr; + } + + Py_INCREF(e); + exception_class = e; + Py_RETURN_NONE; +} + +PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e) { + if (!PyType_Check(e)) { + PyErr_SetString( + PyExc_TypeError, + "TFE_Py_RegisterResourceVariableType: Need to register a type."); + return nullptr; + } + + if (resource_variable_type != nullptr) { + Py_DECREF(resource_variable_type); + } + + Py_INCREF(e); + resource_variable_type = reinterpret_cast(e); + Py_RETURN_NONE; +} + +PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e) { + if (fallback_exception_class != nullptr) { + Py_DECREF(fallback_exception_class); + } + if (PyObject_IsSubclass(e, PyExc_Exception) <= 0) { + fallback_exception_class = nullptr; + PyErr_SetString(PyExc_TypeError, + "TFE_Py_RegisterFallbackExceptionClass: " + "Registered class should be subclass of Exception."); + return nullptr; + } else { + Py_INCREF(e); + fallback_exception_class = e; + Py_RETURN_NONE; + } +} + +PyObject* TFE_Py_RegisterBackwardFunctionGetter(PyObject* e) { + if (backward_function_getter != nullptr) { + Py_DECREF(backward_function_getter); + } + if (!PyCallable_Check(e)) { + backward_function_getter = nullptr; + PyErr_SetString(PyExc_TypeError, + "TFE_Py_RegisterBackwardFunctionGetter: " + "Registered object should be function."); + return nullptr; } else { Py_INCREF(e); - exception_class = e; + backward_function_getter = e; Py_RETURN_NONE; } } +void RaiseFallbackException(const char* message) { + if (fallback_exception_class != nullptr) { + PyErr_SetObject(fallback_exception_class, Py_BuildValue("s", message)); + return; + } + + PyErr_SetString( + PyExc_RuntimeError, + tensorflow::strings::StrCat( + "Fallback exception type not set, attempting to fallback due to ", + message) + .data()); +} + int MaybeRaiseExceptionFromTFStatus(TF_Status* status, PyObject* exception) { if (TF_GetCode(status) == TF_OK) return 0; const char* msg = TF_Message(status); @@ -761,16 +1142,10 @@ PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) { return result; } -void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, - PyObject* input_tensors, - PyObject* backward_function) { - if (GetTapeSet()->empty() || *ThreadTapeIsStopped()) { - return; - } - std::vector input_ids = MakeTensorIDList(input_tensors); - if (PyErr_Occurred()) { - return; - } +namespace { +void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, + const std::vector& input_ids, + PyObject* backward_function) { std::vector output_info; PyObject* seq = PySequence_Fast(output_tensors, "expected a sequence of integer tensor ids"); @@ -809,6 +1184,19 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, [backward_function]() { Py_DECREF(backward_function); }); } } +} // namespace + +void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, + PyObject* input_tensors, + PyObject* backward_function) { + if (GetTapeSet()->empty() || *ThreadTapeIsStopped()) { + return; + } + std::vector input_ids = MakeTensorIDList(input_tensors); + if (PyErr_Occurred()) return; + + TapeSetRecordOperation(op_type, output_tensors, input_ids, backward_function); +} void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) { for (TFE_Py_Tape* tape : SafeTapeSet()) { @@ -1031,17 +1419,320 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, } return py_result; } - Py_INCREF(Py_None); - return Py_None; + return PyList_New(0); } namespace { -static const int kFastPathExecuteInputStartIndex = 4; +static const int kFastPathExecuteInputStartIndex = 5; -bool CheckEagerTensors(PyObject* seq, int start_index, int num_to_check) { - for (int i = start_index; i < start_index + num_to_check; i++) { - PyObject* item = PyTuple_GET_ITEM(seq, i); - if (!EagerTensor_CheckExact(item)) return false; +PyObject* GetPythonObjectFromString(const char* s) { +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromString(s); +#else + return PyBytes_FromString(s); +#endif +} + +bool CheckResourceVariable(PyObject* item) { + return PyObject_TypeCheck(item, resource_variable_type); +} + +bool CheckInputsOk(PyObject* seq, int start_index, + const tensorflow::OpDef& op_def) { + for (int i = 0; i < op_def.input_arg_size(); i++) { + PyObject* item = PyTuple_GET_ITEM(seq, i + start_index); + if (!op_def.input_arg(i).number_attr().empty() || + !op_def.input_arg(i).type_list_attr().empty()) { + // This item should be a list input. + if (!PyList_Check(item)) return false; + for (Py_ssize_t j = 0; j < PyList_Size(item); j++) { + PyObject* inner_item = PyList_GET_ITEM(item, j); + if (!EagerTensor_CheckExact(inner_item) && + !CheckResourceVariable(inner_item)) { + return false; + } + } + } else if (!EagerTensor_CheckExact(item) && !CheckResourceVariable(item)) { + return false; + } + } + + return true; +} + +bool OpDoesntRequireOutput(const string& op_name) { + static tensorflow::gtl::FlatSet* ops_that_dont_require_outputs = + new tensorflow::gtl::FlatSet({ + "Identity", + "MatMul", + "Conv2DBackpropInput", + "Conv2DBackpropFilter", + "Conv3D", + "Conv3DBackpropInputV2", + "AvgPool3D", + "AvgPool3DGrad", + "MaxPool3D", + "MaxPool3DGrad", + "MaxPool3DGradGrad", + "BiasAdd", + "BiasAddV1", + "BiasAddGrad", + "Relu6", + "Softplus", + "SoftplusGrad", + "Softsign", + "ReluGrad", + "Conv2D", + "DepthwiseConv2dNative", + "Dilation2D", + "AvgPool", + "AvgPoolGrad", + "BatchNormWithGlobalNormalization", + "L2Loss", + "Sum", + "Prod", + "SegmentSum", + "SegmentMean", + "SparseSegmentSum", + "SparseSegmentMean", + "SparseSegmentSqrtN", + "SegmentMin", + "SegmentMax", + "UnsortedSegmentSum", + "UnsortedSegmentMax", + "Abs", + "Neg", + "ReciprocalGrad", + "Square", + "Expm1", + "Log", + "Log1p", + "TanhGrad", + "SigmoidGrad", + "Sign", + "Sin", + "Cos", + "Tan", + "Add", + "Sub", + "Mul", + "Div", + "RealDiv", + "Maximum", + "Minimum", + "SquaredDifference", + "Select", + "SparseMatMul", + "BatchMatMul", + "Complex", + "Real", + "Imag", + "Angle", + "Conj", + "Cast", + "Cross", + "Cumsum", + "Cumprod", + "ReadVariableOp", + "VarHandleOp", + "Shape", + }); + + return ops_that_dont_require_outputs->find(op_name) != + ops_that_dont_require_outputs->end(); +} + +bool OpDoesntRequireInput(const string& op_name) { + static tensorflow::gtl::FlatSet* ops_that_dont_require_inputs = + new tensorflow::gtl::FlatSet({ + "Identity", + "Softmax", + "LogSoftmax", + "BiasAdd", + "Relu", + "Elu", + "Selu", + "SparseSoftmaxCrossEntropyWithLogits", + "Neg", + "Inv", + "Reciprocal", + "Sqrt", + "Exp", + "Tanh", + "Sigmoid", + "Real", + "Imag", + "Conj", + "ReadVariableOp", + "VarHandleOp", + "Shape", + }); + + return ops_that_dont_require_inputs->find(op_name) != + ops_that_dont_require_inputs->end(); +} + +PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs, + PyObject* results, PyObject* name) { + std::vector input_ids = MakeTensorIDList(inputs); + if (PyErr_Occurred()) return nullptr; + + bool should_record = false; + for (TFE_Py_Tape* tape : SafeTapeSet()) { + if (tape->tape->ShouldRecord(input_ids)) { + should_record = true; + break; + } + } + if (!should_record) Py_RETURN_NONE; + + string c_op_name = TFE_GetPythonString(op_name); + PyObject* op_outputs; + if (OpDoesntRequireOutput(c_op_name)) { + op_outputs = Py_None; + } else { + op_outputs = results; + } + + PyObject* op_inputs; + if (OpDoesntRequireInput(c_op_name)) { + op_inputs = Py_None; + } else { + op_inputs = inputs; + } + + PyObject* num_inputs = PyLong_FromLong(PySequence_Size(inputs)); + PyObject* callback_args = + Py_BuildValue("OOOOO", op_name, attrs, num_inputs, op_inputs, op_outputs); + + PyObject* backward_function = + PyObject_CallObject(backward_function_getter, callback_args); + Py_DECREF(callback_args); + if (backward_function == nullptr) return nullptr; + + TapeSetRecordOperation(op_name, results, input_ids, backward_function); + + Py_DECREF(backward_function); + + Py_RETURN_NONE; +} + +void MaybeWatchVariable(PyObject* input) { + DCHECK(CheckResourceVariable(input)); + DCHECK(PyObject_HasAttrString(input, "_trainable")); + + tensorflow::Safe_PyObjectPtr trainable( + PyObject_GetAttrString(input, "_trainable")); + if (trainable.get() == Py_False) return; + TFE_Py_TapeSetWatchVariable(input); +} + +bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info, + PyObject* input, tensorflow::Safe_PyObjectPtr* output, + TF_Status* status) { + MaybeWatchVariable(input); + + TFE_Op* op = TFE_NewOp(parent_op_exec_info.ctx, "ReadVariableOp", status); + auto cleaner = tensorflow::gtl::MakeCleanup([op] { TFE_DeleteOp(op); }); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Set dtype + DCHECK(PyObject_HasAttrString(input, "_dtype")); + tensorflow::Safe_PyObjectPtr dtype(PyObject_GetAttrString(input, "_dtype")); + int value; + if (!ParseTypeValue("_dtype", dtype.get(), status, &value)) { + return false; + } + TFE_OpSetAttrType(op, "dtype", static_cast(value)); + + TFE_OpSetDevice(op, parent_op_exec_info.device_name, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Get handle + tensorflow::Safe_PyObjectPtr handle(PyObject_GetAttrString(input, "_handle")); + if (!EagerTensor_CheckExact(handle.get())) return false; + TFE_OpAddInput(op, EagerTensor_Handle(handle.get()), status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + int num_retvals = 1; + TFE_TensorHandle* output_handle; + TFE_Execute(op, &output_handle, &num_retvals, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Always create the py object (and correctly DECREF it) from the returned + // value, else the data will leak. + output->reset(EagerTensorFromHandle(output_handle)); + + // TODO(nareshmodi): Should we run post exec callbacks here? + if (parent_op_exec_info.run_gradient_callback) { + tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(1)); + PyTuple_SET_ITEM(inputs.get(), 0, handle.release()); + + tensorflow::Safe_PyObjectPtr outputs(PyTuple_New(1)); + Py_INCREF(output->get()); // stay alive after since tuple steals. + PyTuple_SET_ITEM(outputs.get(), 0, output->get()); + + if (!RecordGradient(GetPythonObjectFromString("ReadVariableOp"), + inputs.get(), Py_None, outputs.get(), Py_None)) { + return false; + } + } + + return true; +} + +// Supports only 2 cases at the moment: +// i) input is an EagerTensor +// ii) input is a ResourceVariable - in this case, the is_variable param is set +// to true. +bool ConvertToTensor(const FastPathOpExecInfo& op_exec_info, PyObject* input, + tensorflow::Safe_PyObjectPtr* output_handle, + TF_Status* status) { + if (CheckResourceVariable(input)) { + return ReadVariableOp(op_exec_info, input, output_handle, status); + } + + Py_INCREF(input); + output_handle->reset(input); + + return true; +} + +// Adds input and type attr to the op, and to the list of flattened +// inputs/attrs. +bool AddInputToOp(const FastPathOpExecInfo& op_exec_info, PyObject* input, + const tensorflow::OpDef::ArgDef* input_arg, + std::vector* flattened_attrs, + std::vector* flattened_inputs, + TFE_Op* op, TF_Status* status) { + // py_eager_tensor's ownership is transferred to flattened_inputs if it is + // required, else the object is destroyed and DECREF'd when the object goes + // out of scope in this function. + tensorflow::Safe_PyObjectPtr py_eager_tensor = nullptr; + + if (!ConvertToTensor(op_exec_info, input, &py_eager_tensor, status)) { + return false; + } + + TFE_TensorHandle* input_handle = EagerTensor_Handle(py_eager_tensor.get()); + + if (input_arg != nullptr && !input_arg->type_attr().empty()) { + auto dtype = TFE_TensorHandleDataType(input_handle); + TFE_OpSetAttrType(op, input_arg->type_attr().data(), dtype); + if (flattened_attrs != nullptr) { + flattened_attrs->emplace_back( + GetPythonObjectFromString(input_arg->type_attr().data())); + flattened_attrs->emplace_back(PyLong_FromLong(dtype)); + } + } + + if (flattened_inputs != nullptr) { + flattened_inputs->emplace_back(std::move(py_eager_tensor)); + } + + TFE_OpAddInput(op, input_handle, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { + return false; } return true; @@ -1073,59 +1764,89 @@ const char* GetDeviceName(PyObject* py_device_name) { return nullptr; } -bool MaybeRunRecordGradientCallback(const tensorflow::OpDef* op_def, - PyObject* args, PyObject* result, - PyObject* record_gradient_callback) { - if (*ThreadTapeIsStopped() || GetTapeSet()->empty() || - record_gradient_callback == Py_None) { - return true; - } - if (!PyCallable_Check(record_gradient_callback)) { - PyErr_SetString( - PyExc_TypeError, - Printf( - "expected a function for record_gradient_callback, got %s instead", - record_gradient_callback->ob_type->tp_name) - .c_str()); +bool RaiseIfNotPyList(PyObject* list, const string& attr_name) { + if (!PyList_Check(list)) { + PyErr_SetString(PyExc_TypeError, + Printf("expected a list for attr %s, got %s instead", + attr_name.data(), list->ob_type->tp_name) + .data()); + return false; } + return true; +} - PyObject* inputs = PyTuple_New(op_def->input_arg_size()); - for (int i = 0; i < op_def->input_arg_size(); i++) { - auto* input = PyTuple_GET_ITEM(args, kFastPathExecuteInputStartIndex + i); +bool RunCallbacks( + const FastPathOpExecInfo& op_exec_info, PyObject* args, + const std::vector& flattened_inputs, + const std::vector& flattened_attrs, + PyObject* flattened_result) { + if (!op_exec_info.run_callbacks) return true; + + tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(flattened_inputs.size())); + for (int i = 0; i < flattened_inputs.size(); i++) { + PyObject* input = flattened_inputs[i].get(); Py_INCREF(input); - PyTuple_SET_ITEM(inputs, i, input); + PyTuple_SET_ITEM(inputs.get(), i, input); } - int args_size = PyTuple_GET_SIZE(args); - int num_attrs = - args_size - op_def->input_arg_size() - kFastPathExecuteInputStartIndex; - PyObject* attrs = PyTuple_New(num_attrs); - for (int i = 0; i < num_attrs; i++) { - auto* attr = PyTuple_GET_ITEM( - args, kFastPathExecuteInputStartIndex + op_def->input_arg_size() + i); + int num_non_inferred_attrs = PyTuple_GET_SIZE(args) - + op_exec_info.op_def->input_arg_size() - + kFastPathExecuteInputStartIndex; + int num_attrs = flattened_attrs.size() + num_non_inferred_attrs; + tensorflow::Safe_PyObjectPtr attrs(PyTuple_New(num_attrs)); + + for (int i = 0; i < num_non_inferred_attrs; i++) { + auto* attr = + PyTuple_GET_ITEM(args, kFastPathExecuteInputStartIndex + + op_exec_info.op_def->input_arg_size() + i); Py_INCREF(attr); - PyTuple_SET_ITEM(attrs, i, attr); + PyTuple_SET_ITEM(attrs.get(), i, attr); + } + for (int i = num_non_inferred_attrs; i < num_attrs; i++) { + PyObject* attr_or_name = + flattened_attrs.at(i - num_non_inferred_attrs).get(); + Py_INCREF(attr_or_name); + PyTuple_SET_ITEM(attrs.get(), i, attr_or_name); } - PyObject* callback_args = Py_BuildValue("OOO", inputs, attrs, result); - PyObject_CallObject(record_gradient_callback, callback_args); + if (op_exec_info.run_gradient_callback) { + if (!RecordGradient(op_exec_info.op_name, inputs.get(), attrs.get(), + flattened_result, op_exec_info.name)) { + return false; + } + } + + if (op_exec_info.run_post_exec_callbacks) { + tensorflow::Safe_PyObjectPtr callback_args( + Py_BuildValue("OOOOO", op_exec_info.op_name, inputs.get(), attrs.get(), + flattened_result, op_exec_info.name)); + for (Py_ssize_t i = 0; i < PyList_Size(op_exec_info.callbacks); i++) { + PyObject* callback_fn = PyList_GET_ITEM(op_exec_info.callbacks, i); + if (!PyCallable_Check(callback_fn)) { + PyErr_SetString( + PyExc_TypeError, + Printf("expected a function for " + "post execution callback in index %ld, got %s instead", + i, callback_fn->ob_type->tp_name) + .c_str()); + return false; + } + PyObject* callback_result = + PyObject_CallObject(callback_fn, callback_args.get()); + if (!callback_result) { + return false; + } + Py_DECREF(callback_result); + } + } - Py_DECREF(inputs); - Py_DECREF(callback_args); - Py_DECREF(attrs); return true; } + } // namespace PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { - TFE_Context* ctx = reinterpret_cast( - PyCapsule_GetPointer(PyTuple_GET_ITEM(args, 0), nullptr)); - const tensorflow::OpDef* op_def = GetOpDef(PyTuple_GET_ITEM(args, 2)); - if (op_def == nullptr) return nullptr; - const char* device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1)); - PyObject* record_gradient_callback = PyTuple_GET_ITEM(args, 3); - Py_ssize_t args_size = PyTuple_GET_SIZE(args); if (args_size < kFastPathExecuteInputStartIndex) { PyErr_SetString( @@ -1136,6 +1857,31 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } + FastPathOpExecInfo op_exec_info; + + op_exec_info.ctx = reinterpret_cast( + PyCapsule_GetPointer(PyTuple_GET_ITEM(args, 0), nullptr)); + op_exec_info.device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1)); + op_exec_info.op_name = PyTuple_GET_ITEM(args, 2); + op_exec_info.op_def = GetOpDef(op_exec_info.op_name); + if (op_exec_info.op_def == nullptr) return nullptr; + op_exec_info.name = PyTuple_GET_ITEM(args, 3); + op_exec_info.callbacks = PyTuple_GET_ITEM(args, 4); + + const tensorflow::OpDef* op_def = op_exec_info.op_def; + + // TODO(nareshmodi): Add a benchmark for the fast-path with gradient callbacks + // (similar to benchmark_tf_gradient_function_*). Also consider using an + // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks + // point out problems with heap allocs. + op_exec_info.run_gradient_callback = + !*ThreadTapeIsStopped() && !GetTapeSet()->empty(); + op_exec_info.run_post_exec_callbacks = + op_exec_info.callbacks != Py_None && + PyList_Size(op_exec_info.callbacks) > 0; + op_exec_info.run_callbacks = op_exec_info.run_gradient_callback || + op_exec_info.run_post_exec_callbacks; + if (args_size < kFastPathExecuteInputStartIndex + op_def->input_arg_size()) { PyErr_SetString( PyExc_ValueError, @@ -1147,18 +1893,15 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } - if (!CheckEagerTensors(args, kFastPathExecuteInputStartIndex, - op_def->input_arg_size())) { - // TODO(nareshmodi): Maybe some other way of signalling that this should - // fall back? - PyErr_SetString(PyExc_NotImplementedError, - "This function does not handle the case of the path where " - "all inputs are not already EagerTensors."); + if (!CheckInputsOk(args, kFastPathExecuteInputStartIndex, *op_def)) { + RaiseFallbackException( + "This function does not handle the case of the path where " + "all inputs are not already EagerTensors."); return nullptr; } TF_Status* status = TF_NewStatus(); - TFE_Op* op = TFE_NewOp(ctx, op_def->name().c_str(), status); + TFE_Op* op = TFE_NewOp(op_exec_info.ctx, op_def->name().c_str(), status); auto cleaner = tensorflow::gtl::MakeCleanup([status, op] { TF_DeleteStatus(status); TFE_DeleteOp(op); @@ -1167,62 +1910,250 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } - TFE_OpSetDevice(op, device_name, status); - if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { - return nullptr; + // Mapping of attr name to size - used to calculate the number of values + // to be expected by the TFE_Execute run. + tensorflow::gtl::FlatMap attr_list_sizes; + + // Set non-inferred attrs, including setting defaults if the attr is passed in + // as None. + for (int i = kFastPathExecuteInputStartIndex + op_def->input_arg_size(); + i < args_size; i += 2) { + PyObject* py_attr_name = PyTuple_GET_ITEM(args, i); + const tensorflow::StringPiece attr_name(TFE_GetPythonString(py_attr_name)); + PyObject* py_attr_value = PyTuple_GET_ITEM(args, i + 1); + + // Not creating an index since most of the time there are not more than a + // few attrs. + // TODO(nareshmodi): Maybe include the index as part of the + // OpRegistrationData. + for (const auto& attr : op_def->attr()) { + if (attr_name == attr.name()) { + SetOpAttrWithDefaults(op_exec_info.ctx, op, attr, attr_name.data(), + py_attr_value, &attr_list_sizes, status); + + if (TF_GetCode(status) != TF_OK) { + RaiseFallbackException(TF_Message(status)); + return nullptr; + } + + break; + } + } } - // Add non-type attrs. - SetOpAttrs(ctx, op, args, - kFastPathExecuteInputStartIndex + op_def->input_arg_size(), - status); + TFE_OpSetDevice(op, op_exec_info.device_name, status); if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { return nullptr; } - // Add type attrs and inputs. + // Flat attrs and inputs as required by the record_gradient call. The attrs + // here only contain inferred attrs (non-inferred attrs are added directly + // from the input args). + // All items in flattened_attrs and flattened_inputs contain + // Safe_PyObjectPtr - any time something steals a reference to this, it must + // INCREF. + // TODO(nareshmodi): figure out why PyList_New/PyList_Append don't work + // directly. + std::unique_ptr> flattened_attrs = + nullptr; + std::unique_ptr> flattened_inputs = + nullptr; + + // TODO(nareshmodi): Encapsulate callbacks information into a struct. + if (op_exec_info.run_callbacks) { + flattened_attrs.reset(new std::vector); + flattened_inputs.reset(new std::vector); + } + + // Add inferred attrs and inputs. + // The following code might set duplicate type attrs. This will result in + // the CacheKey for the generated AttrBuilder possibly differing from + // those where the type attrs are correctly set. Inconsistent CacheKeys + // for ops means that there might be unnecessarily duplicated kernels. + // TODO(nareshmodi): Fix this. for (int i = 0; i < op_def->input_arg_size(); i++) { const auto& input_arg = op_def->input_arg(i); PyObject* input = PyTuple_GET_ITEM(args, kFastPathExecuteInputStartIndex + i); - TFE_TensorHandle* input_handle = EagerTensor_Handle(input); - - // The following code might set duplicate type attrs. This will result in - // the CacheKey for the generated AttrBuilder possibly differing from those - // where the type attrs are correctly set. Inconsistent CacheKeys for ops - // means that there might be unnecessarily duplicated kernels. - // TODO(nareshmodi): Fix this. - if (!input_arg.type_attr().empty()) { - TFE_OpSetAttrType(op, input_arg.type_attr().data(), - TFE_TensorHandleDataType(input_handle)); + if (!input_arg.number_attr().empty()) { + // The item is a homogeneous list. + if (!RaiseIfNotPyList(input, input_arg.number_attr())) return nullptr; + Py_ssize_t len = PyList_Size(input); + + TFE_OpSetAttrInt(op, input_arg.number_attr().data(), len); + if (op_exec_info.run_callbacks) { + flattened_attrs->emplace_back( + GetPythonObjectFromString(input_arg.number_attr().data())); + flattened_attrs->emplace_back(PyLong_FromLong(len)); + } + attr_list_sizes[input_arg.number_attr()] = len; + + if (len > 0) { + // First item adds the type attr. + if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, 0), &input_arg, + flattened_attrs.get(), flattened_inputs.get(), op, + status)) { + return nullptr; + } + + for (Py_ssize_t j = 1; j < len; j++) { + // Since the list is homogeneous, we don't need to re-add the attr. + if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, j), + nullptr /* input_arg */, + nullptr /* flattened_attrs */, + flattened_inputs.get(), op, status)) { + return nullptr; + } + } + } + } else if (!input_arg.type_list_attr().empty()) { + // The item is a heterogeneous list. + if (!RaiseIfNotPyList(input, input_arg.type_list_attr())) return nullptr; + const string& attr_name = input_arg.type_list_attr(); + Py_ssize_t len = PyList_Size(input); + tensorflow::gtl::InlinedVector attr_value(len); + PyObject* py_attr_value = nullptr; + if (op_exec_info.run_callbacks) { + py_attr_value = PyTuple_New(len); + } + for (Py_ssize_t j = 0; j < len; j++) { + PyObject* py_input = PyList_GET_ITEM(input, j); + tensorflow::Safe_PyObjectPtr py_eager_tensor; + if (!ConvertToTensor(op_exec_info, py_input, &py_eager_tensor, + status)) { + return nullptr; + } + + TFE_TensorHandle* input_handle = + EagerTensor_Handle(py_eager_tensor.get()); + + attr_value[j] = TFE_TensorHandleDataType(input_handle); + + TFE_OpAddInput(op, input_handle, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { + return nullptr; + } + + if (op_exec_info.run_callbacks) { + flattened_inputs->emplace_back(std::move(py_eager_tensor)); + + PyTuple_SET_ITEM(py_attr_value, j, PyLong_FromLong(attr_value[j])); + } + } + if (op_exec_info.run_callbacks) { + flattened_attrs->emplace_back( + GetPythonObjectFromString(attr_name.data())); + flattened_attrs->emplace_back(py_attr_value); + } + TFE_OpSetAttrTypeList(op, attr_name.data(), attr_value.data(), + attr_value.size()); + attr_list_sizes[attr_name] = len; + } else { + // The item is a single item. + if (!AddInputToOp(op_exec_info, input, &input_arg, flattened_attrs.get(), + flattened_inputs.get(), op, status)) { + return nullptr; + } } + } - TFE_OpAddInput(op, input_handle, status); - if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { - return nullptr; + int num_retvals = 0; + for (int i = 0; i < op_def->output_arg_size(); i++) { + const auto& output_arg = op_def->output_arg(i); + if (!output_arg.number_attr().empty()) { + num_retvals += attr_list_sizes[output_arg.number_attr()]; + } else if (!output_arg.type_list_attr().empty()) { + num_retvals += attr_list_sizes[output_arg.type_list_attr()]; + } else { + num_retvals++; } } - int num_retvals = op_def->output_arg_size(); tensorflow::gtl::InlinedVector retvals(num_retvals); Py_BEGIN_ALLOW_THREADS; TFE_Execute(op, retvals.data(), &num_retvals, status); Py_END_ALLOW_THREADS; - if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { + + if (TF_GetCode(status) != TF_OK) { + // Augment the status with the op_name for easier debugging similar to + // TFE_Py_Execute. + TF_SetStatus(status, TF_GetCode(status), + tensorflow::strings::StrCat( + TF_Message(status), + " [Op:", TFE_GetPythonString(op_exec_info.op_name), "]") + .c_str()); + + MaybeRaiseExceptionFromTFStatus(status, nullptr); return nullptr; } - PyObject* result = PyTuple_New(num_retvals); + PyObject* flat_result = PyList_New(num_retvals); for (int i = 0; i < num_retvals; ++i) { - PyTuple_SET_ITEM(result, i, EagerTensorFromHandle(retvals[i])); + PyList_SET_ITEM(flat_result, i, EagerTensorFromHandle(retvals[i])); } - if (!MaybeRunRecordGradientCallback(op_def, args, result, - record_gradient_callback)) { + if (!RunCallbacks(op_exec_info, args, *flattened_inputs, *flattened_attrs, + flat_result)) { return nullptr; } + // Unflatten results. + if (op_def->output_arg_size() == 0) { + Py_RETURN_NONE; + } + + if (op_def->output_arg_size() == 1) { + if (!op_def->output_arg(0).number_attr().empty() || + !op_def->output_arg(0).type_list_attr().empty()) { + return flat_result; + } else { + auto* result = PyList_GET_ITEM(flat_result, 0); + Py_INCREF(result); + Py_DECREF(flat_result); + return result; + } + } + + // Correctly output the results that are made into a namedtuple. + PyObject* result = PyList_New(op_def->output_arg_size()); + int flat_result_index = 0; + for (int i = 0; i < op_def->output_arg_size(); i++) { + if (!op_def->output_arg(i).number_attr().empty()) { + int list_length = attr_list_sizes[op_def->output_arg(i).number_attr()]; + PyObject* inner_list = PyList_New(list_length); + for (int j = 0; j < list_length; j++) { + PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + Py_INCREF(obj); + PyList_SET_ITEM(inner_list, j, obj); + } + PyList_SET_ITEM(result, i, inner_list); + } else if (!op_def->output_arg(i).type_list_attr().empty()) { + int list_length = attr_list_sizes[op_def->output_arg(i).type_list_attr()]; + PyObject* inner_list = PyList_New(list_length); + for (int j = 0; j < list_length; j++) { + PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + Py_INCREF(obj); + PyList_SET_ITEM(inner_list, j, obj); + } + PyList_SET_ITEM(result, i, inner_list); + } else { + PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + Py_INCREF(obj); + PyList_SET_ITEM(result, i, obj); + } + } + Py_DECREF(flat_result); return result; } + +PyObject* TFE_Py_RecordGradient(PyObject* op_name, PyObject* inputs, + PyObject* attrs, PyObject* results, + PyObject* name) { + if (*ThreadTapeIsStopped() || GetTapeSet()->empty()) { + Py_RETURN_NONE; + } + + return RecordGradient(op_name, inputs, attrs, results, name); +} diff --git a/tensorflow/python/eager/pywrap_tfe_test.py b/tensorflow/python/eager/pywrap_tfe_test.py index d4f4ed592fb99e475af4652a33e5364d9abeea1a..faaae40b3f1ef02984a7a75c23ae4acae65ac335 100644 --- a/tensorflow/python/eager/pywrap_tfe_test.py +++ b/tensorflow/python/eager/pywrap_tfe_test.py @@ -24,23 +24,10 @@ from tensorflow.python.eager import context from tensorflow.python.eager import test from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops - - -def record_gradient_callback(inputs, attrs, results): - return backprop._record_gradient("MatMul", inputs, attrs, results, None) - - -def c_tfe_py_fastpath_execute(a, b, transpose_a=False, transpose_b=False): - ctx = context.context() - assert not ctx.in_graph_mode( - ), "The prototype doesn't contain C code for graph construction" - ctx_handle = ctx._handle # pylint: disable=protected-access - - return pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, ctx.device_name, "MatMul", record_gradient_callback, a, b, - "transpose_a", transpose_a, "transpose_b", transpose_b)[0] +from tensorflow.python.ops import resource_variable_ops class Tests(test.TestCase): @@ -54,55 +41,154 @@ class Tests(test.TestCase): a_100_by_784 = random_ops.random_uniform((100, 784)) b_100_by_784 = random_ops.random_uniform((100, 784)) + ctx = context.context() + self.assertAllClose( math_ops.matmul(a_2_by_2, b_2_by_2), - c_tfe_py_fastpath_execute(a_2_by_2, b_2_by_2)) + pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, + b_2_by_2, "transpose_a", False, "transpose_b", False)) self.assertAllClose( math_ops.matmul(a_100_by_784, b_100_by_784, transpose_b=True), - c_tfe_py_fastpath_execute(a_100_by_784, b_100_by_784, transpose_b=True)) + pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, a_100_by_784, + b_100_by_784, "transpose_a", False, "transpose_b", True)) + + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_ResourceVariableMatMulCorrectResponse(self): + ctx = context.context() + a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) + m = resource_variable_ops.ResourceVariable(a_2_by_2) + x = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a", + False, "transpose_b", False) + y = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, a_2_by_2, + "transpose_a", False, "transpose_b", False) + + self.assertAllEqual(x, y) @test_util.assert_no_new_tensors @test_util.assert_no_garbage_created def testFastpathExecute_TapeWrite(self): + ctx = context.context() with backprop.GradientTape(persistent=True) as tape: a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) tape.watch(a_2_by_2) - z = c_tfe_py_fastpath_execute(a_2_by_2, a_2_by_2) + z = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, + a_2_by_2, "transpose_a", False, "transpose_b", False) dz_dy = tape.gradient(z, [a_2_by_2])[0] self.assertAllEqual(dz_dy.numpy(), constant_op.constant(4.0, shape=[2, 2]).numpy()) @test_util.assert_no_new_tensors @test_util.assert_no_garbage_created - def testFastpathExecute_MatMulSlowPath(self): - a_2_by_2 = random_ops.random_uniform((2, 2)).cpu().numpy() + def testFastpathExecute_ResourceVariableTapeWrite(self): + ctx = context.context() + with backprop.GradientTape(persistent=True) as tape: + a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) + m = resource_variable_ops.ResourceVariable(a_2_by_2) + tape.watch(m) + z = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, m, m, + "transpose_a", False, "transpose_b", False) + dz_dy = tape.gradient(z, [m])[0] + self.assertAllEqual(dz_dy.numpy(), + constant_op.constant(4.0, shape=[2, 2]).numpy()) - with self.assertRaises(NotImplementedError): - c_tfe_py_fastpath_execute(a_2_by_2, a_2_by_2) + # Tests homogeneous list op + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_AddNCorrectResponse(self): + ctx = context.context() + a_2_by_2 = random_ops.random_uniform((2, 2)) + b_2_by_2 = random_ops.random_uniform((2, 2)) + + self.assertAllClose( + math_ops.add_n([a_2_by_2, b_2_by_2]), + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, + "AddN", None, None, + [a_2_by_2, b_2_by_2])) + + # Tests homogeneous list op + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_AddNTapeWrite(self): + ctx = context.context() + a_2_by_2 = random_ops.random_uniform((2, 2)) + b_2_by_2 = random_ops.random_uniform((2, 2)) + + with backprop.GradientTape(persistent=True) as tape: + tape.watch(a_2_by_2) + tape.watch(b_2_by_2) + z1 = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "AddN", None, None, + [a_2_by_2, b_2_by_2]) + z2 = math_ops.add_n([a_2_by_2, b_2_by_2]) + dz1_dy = tape.gradient(z1, [a_2_by_2])[0] + dz2_dy = tape.gradient(z2, [a_2_by_2])[0] + self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy()) + + # Tests heterogeneous list op + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_IdentityNCorrectResponse(self): + ctx = context.context() + a_2_by_2 = random_ops.random_uniform((2, 2)) + b_2_by_2 = random_ops.random_uniform((2, 2)) + + self.assertAllClose( + array_ops.identity_n([a_2_by_2, b_2_by_2]), + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, + "IdentityN", None, None, + [a_2_by_2, b_2_by_2])) + + # Tests heterogeneous list op + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_IdentityNTapeWrite(self): + ctx = context.context() + a_2_by_2 = random_ops.random_uniform((2, 2)) + b_2_by_2 = random_ops.random_uniform((2, 2)) + + with backprop.GradientTape(persistent=True) as tape: + tape.watch(a_2_by_2) + tape.watch(b_2_by_2) + z1 = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "IdentityN", None, None, + [a_2_by_2, b_2_by_2]) + z2 = array_ops.identity_n([a_2_by_2, b_2_by_2]) + dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0] + dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0] + self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy()) @test_util.assert_no_new_tensors @test_util.assert_no_garbage_created def testFastpathExecute_InvalidInputs(self): a_2_by_2 = random_ops.random_uniform((2, 2)) ctx = context.context() - assert not ctx.in_graph_mode( + assert ctx.executing_eagerly( ), "The prototype doesn't contain C code for graph construction" ctx_handle = ctx._handle # pylint: disable=protected-access + # Not enough base params with self.assertRaisesRegexp(ValueError, - "at least 4 items in the input tuple"): + "at least 5 items in the input tuple"): pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, "Identity") + # Not enough inputs with self.assertRaisesRegexp(ValueError, - "Expected to be at least 5, was 4"): - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, ctx_handle, "Identity", record_gradient_callback) + "Expected to be at least 6, was 5"): + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx_handle, + "Identity", None, []) + # Bad type with self.assertRaisesRegexp(TypeError, "expected a string for op_name"): - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, ctx.device_name, ctx_handle, record_gradient_callback, - a_2_by_2) + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, + ctx_handle, None, [], a_2_by_2) if __name__ == "__main__": diff --git a/tensorflow/python/eager/tape_test.py b/tensorflow/python/eager/tape_test.py index b490bac66db03b0a61a8852f45f1f558cccaf121..4326d5efa3d362e883815eb2d3dafb27df25afd4 100644 --- a/tensorflow/python/eager/tape_test.py +++ b/tensorflow/python/eager/tape_test.py @@ -21,11 +21,11 @@ from __future__ import print_function from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import custom_gradient from tensorflow.python.eager import test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops # Importing nn_grad for the registration functions. @@ -165,21 +165,6 @@ class TapeTest(test.TestCase): g, = backprop.gradients_function(fn, [0])(t) self.assertAllEqual(g, 1.0) - def testCustomGradientGraphMode(self): - with context.graph_mode(), self.test_session(): - - @custom_gradient.custom_gradient - def f(x): - - def grad(dresult): - return dresult * 10.0 - - return x, grad - - inp = constant_op.constant(1.0) - grad = gradients_impl.gradients(f(inp), inp) - self.assertAllEqual(grad[0].eval(), 10.0) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py index 138152ac1c6b2d7e399218208dd7bdf2d8136f5e..3e92a77543e3d2162497e9f995f3adc2a01cb4dd 100644 --- a/tensorflow/python/estimator/canned/baseline.py +++ b/tensorflow/python/estimator/canned/baseline.py @@ -59,6 +59,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops.losses import losses from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export # The default learning rate of 0.3 is a historical artifact of the initial # implementation, but seems a reasonable choice. @@ -173,6 +174,7 @@ def _baseline_model_fn(features, labels, mode, head, optimizer, train_op_fn=train_op_fn) +@tf_export('estimator.BaselineClassifier') class BaselineClassifier(estimator.Estimator): """A classifier that can establish a simple baseline. @@ -275,6 +277,7 @@ class BaselineClassifier(estimator.Estimator): config=config) +@tf_export('estimator.BaselineRegressor') class BaselineRegressor(estimator.Estimator): """A regressor that can establish a simple baseline. diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py index 0f274a23c03426fc431c15ac0a14617a4a65bb79..7043da8de036e5be27d223271c37e065d9ffbcdd 100644 --- a/tensorflow/python/estimator/canned/dnn.py +++ b/tensorflow/python/estimator/canned/dnn.py @@ -33,6 +33,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops.losses import losses from tensorflow.python.summary import summary from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export # The default learning rate of 0.05 is a historical artifact of the initial # implementation, but seems a reasonable choice. @@ -149,9 +150,7 @@ def _dnn_model_fn(features, config: `RunConfig` object to configure the runtime settings. Returns: - predictions: A dict of `Tensor` objects. - loss: A scalar containing the loss of the step. - train_op: The op for training. + An `EstimatorSpec` instance. Raises: ValueError: If features has the wrong type. @@ -198,6 +197,7 @@ def _dnn_model_fn(features, logits=logits) +@tf_export('estimator.DNNClassifier') class DNNClassifier(estimator.Estimator): """A classifier for TensorFlow DNN models. @@ -358,6 +358,7 @@ class DNNClassifier(estimator.Estimator): warm_start_from=warm_start_from) +@tf_export('estimator.DNNRegressor') class DNNRegressor(estimator.Estimator): """A regressor for TensorFlow DNN models. diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index 1a0f4c5c3931a6b41026470f30e7bdd381e5b37a..6d0fb96057ee93964ee3571bae3b878faad88882 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -37,6 +37,7 @@ from tensorflow.python.ops.losses import losses from tensorflow.python.summary import summary from tensorflow.python.training import sync_replicas_optimizer from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export # The default learning rates are a historical artifact of the initial # implementation. @@ -116,7 +117,7 @@ def _dnn_linear_combined_model_fn(features, config: `RunConfig` object to configure the runtime settings. Returns: - `ModelFnOps` + An `EstimatorSpec` instance. Raises: ValueError: If both `linear_feature_columns` and `dnn_features_columns` @@ -225,6 +226,7 @@ def _dnn_linear_combined_model_fn(features, logits=logits) +@tf_export('estimator.DNNLinearCombinedClassifier') class DNNLinearCombinedClassifier(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined classification models. @@ -405,6 +407,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator): warm_start_from=warm_start_from) +@tf_export('estimator.DNNLinearCombinedRegressor') class DNNLinearCombinedRegressor(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined models for regression. diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py index cb9e3fc6ca116ac0f48a37cea92fa4119754f324..8d742a2c6147e86619d4c0aad59b69459384bd4d 100644 --- a/tensorflow/python/estimator/canned/head.py +++ b/tensorflow/python/estimator/canned/head.py @@ -1156,6 +1156,7 @@ def _regression_head_with_mean_squared_error_loss( label_dimension=1, loss_reduction=losses.Reduction.SUM, loss_fn=None, + inverse_link_fn=None, name=None): """Creates a `_Head` for regression using the `mean_squared_error` loss. @@ -1174,10 +1175,16 @@ def _regression_head_with_mean_squared_error_loss( `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or `[D0, D1, ... DN, label_dimension]`. - Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + Supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or `(labels, logits, features)` as arguments and returns unreduced loss with shape `[D0, D1, ... DN, label_dimension]`. + Also supports custom `inverse_link_fn`, also known as 'mean function'. + `inverse_link_fn` takes `logits` as argument and returns predicted values. + This function is the inverse of the link function defined in + https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function + Namely, for poisson regression, set `inverse_link_fn=tf.exp`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -1188,7 +1195,9 @@ def _regression_head_with_mean_squared_error_loss( `[batch_size, label_dimension]`). loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. - loss_fn: Optional loss function. + loss_fn: Optional loss function. Defaults to `mean_squared_error`. + inverse_link_fn: Optional inverse link function, also known as 'mean + function'. Defaults to identity. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -1208,6 +1217,7 @@ def _regression_head_with_mean_squared_error_loss( label_dimension=label_dimension, loss_reduction=loss_reduction, loss_fn=loss_fn, + inverse_link_fn=inverse_link_fn, name=name) @@ -1220,6 +1230,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): weight_column=None, loss_reduction=losses.Reduction.SUM, loss_fn=None, + inverse_link_fn=None, name=None): """`Head` for regression.""" if label_dimension < 1: @@ -1228,6 +1239,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): self._weight_column = weight_column self._loss_reduction = loss_reduction self._loss_fn = loss_fn + self._inverse_link_fn = inverse_link_fn self._name = name @property @@ -1294,9 +1306,19 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): # Predict. with ops.name_scope(self._name, 'head'): logits = _check_logits_final_dim(logits, self._logits_dimension) - predictions = {prediction_keys.PredictionKeys.PREDICTIONS: logits} + if self._inverse_link_fn: + predicted_value = self._inverse_link_fn(logits) + predictions = { + prediction_keys.PredictionKeys.PREDICTIONS: predicted_value, + prediction_keys.PredictionKeys.LOGITS: logits, + } + else: + predicted_value = logits + predictions = { + prediction_keys.PredictionKeys.PREDICTIONS: predicted_value} if mode == model_fn.ModeKeys.PREDICT: - regression_output = export_output.RegressionOutput(value=logits) + regression_output = export_output.RegressionOutput( + value=predicted_value) return model_fn.EstimatorSpec( mode=model_fn.ModeKeys.PREDICT, predictions=predictions, diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py index 3a03770af498981a054c3df9155e83a60c7f0350..b40758f8fef13bad939b3fc2f296e848ade46f63 100644 --- a/tensorflow/python/estimator/canned/head_test.py +++ b/tensorflow/python/estimator/canned/head_test.py @@ -300,7 +300,12 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): features = {'x': values_2x3} # Static shape. - with self.assertRaisesRegexp(ValueError, 'Dimensions must be equal'): + with self.assertRaisesRegexp( + ValueError, + r'Shape mismatch: The shape of labels \(received \(3,\)\) should equal ' + r'the shape of logits except for the last dimension ' + r'\(received \(2, 3\)\)\.' + ): head.create_loss( features=features, mode=model_fn.ModeKeys.EVAL, @@ -2703,10 +2708,9 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase): self.assertIsNone(spec.loss) self.assertEqual({}, spec.eval_metric_ops) self.assertIsNone(spec.train_op) + default_serving_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY self.assertItemsEqual( - (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, - 'predict', - 'regression'), + (default_serving_key, 'predict', 'regression'), spec.export_outputs.keys()) _assert_no_hooks(self, spec) @@ -2714,6 +2718,54 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase): with self.test_session(): _initialize_variables(self, spec.scaffold) self.assertAllClose(logits, spec.predictions[prediction_key].eval()) + self.assertAllClose( + logits, spec.export_outputs[default_serving_key].value.eval()) + self.assertAllClose( + logits, spec.export_outputs['regression'].value.eval()) + self.assertAllClose( + logits, spec.export_outputs['predict'].outputs['predictions'].eval()) + + def test_predict_with_inverse_link_fn(self): + def _inverse_link_fn(logits): + return logits - 10. + head = head_lib._regression_head_with_mean_squared_error_loss( + inverse_link_fn=_inverse_link_fn) + + # Create estimator spec. + logits = np.array(((45,), (41,),), dtype=np.int32) + expected_predictions = np.array(((35,), (31,),), dtype=np.int32) + spec = head.create_estimator_spec( + features={'x': np.array(((42.,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.PREDICT, + logits=logits) + + # Assert spec contains expected tensors. + keys = prediction_keys.PredictionKeys + self.assertItemsEqual( + (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys()) + self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype) + self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype) + default_serving_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + self.assertItemsEqual( + (default_serving_key, 'predict', 'regression'), + spec.export_outputs.keys()) + + # Assert predictions. + with self.test_session(): + _initialize_variables(self, spec.scaffold) + self.assertAllClose( + expected_predictions, spec.predictions[keys.PREDICTIONS].eval()) + self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval()) + self.assertAllClose( + expected_predictions, + spec.export_outputs[default_serving_key].value.eval()) + self.assertAllClose( + expected_predictions, spec.export_outputs['regression'].value.eval()) + self.assertAllClose( + expected_predictions, + spec.export_outputs['predict'].outputs['predictions'].eval()) + self.assertAllClose( + logits, spec.export_outputs['predict'].outputs['logits'].eval()) def test_eval_create_loss(self): head = head_lib._regression_head_with_mean_squared_error_loss() diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index a5b1172e729240a2ea02fa1d4330420786c2686c..a2f24ef27044680fe93b176b5207593165d0d109 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -34,6 +34,7 @@ from tensorflow.python.ops.losses import losses from tensorflow.python.summary import summary from tensorflow.python.training import ftrl from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export # The default learning rate of 0.2 is a historical artifact of the initial @@ -170,6 +171,7 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, logits=logits) +@tf_export('estimator.LinearClassifier') class LinearClassifier(estimator.Estimator): """Linear classifier model. @@ -322,6 +324,7 @@ class LinearClassifier(estimator.Estimator): warm_start_from=warm_start_from) +@tf_export('estimator.LinearRegressor') class LinearRegressor(estimator.Estimator): """An estimator for TensorFlow Linear regression problems. diff --git a/tensorflow/python/estimator/canned/parsing_utils.py b/tensorflow/python/estimator/canned/parsing_utils.py index f153272947ca427b25b00e6df4741d7ada5790df..74e5e5a1bed80229c68daa3ff33ee7af4004bf47 100644 --- a/tensorflow/python/estimator/canned/parsing_utils.py +++ b/tensorflow/python/estimator/canned/parsing_utils.py @@ -23,8 +23,10 @@ import six from tensorflow.python.feature_column import feature_column as fc from tensorflow.python.framework import dtypes from tensorflow.python.ops import parsing_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('estimator.classifier_parse_example_spec') def classifier_parse_example_spec(feature_columns, label_key, label_dtype=dtypes.int64, @@ -164,6 +166,7 @@ def classifier_parse_example_spec(feature_columns, return parsing_spec +@tf_export('estimator.regressor_parse_example_spec') def regressor_parse_example_spec(feature_columns, label_key, label_dtype=dtypes.float32, diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 78d74b63d3ebea29e2dc3ab8f655efcc7ab8e130..8ed3e4cd19d97355a59945b778f5d19d94b1aabf 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -49,6 +49,7 @@ from tensorflow.python.saved_model import builder as saved_model_builder from tensorflow.python.saved_model import tag_constants from tensorflow.python.summary import summary from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import device_setter from tensorflow.python.training import evaluation from tensorflow.python.training import monitored_session from tensorflow.python.training import saver @@ -57,12 +58,14 @@ from tensorflow.python.training import training_util from tensorflow.python.util import compat from tensorflow.python.util import compat_internal from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export _VALID_MODEL_FN_ARGS = set( ['features', 'labels', 'mode', 'params', 'self', 'config']) +@tf_export('estimator.Estimator') class Estimator(object): """Estimator class to train and evaluate TensorFlow models. @@ -163,7 +166,7 @@ class Estimator(object): ValueError: if this is called via a subclass and if that class overrides a member of `Estimator`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Estimators are not supported when eager execution is enabled.') @@ -425,7 +428,8 @@ class Estimator(object): input_fn, predict_keys=None, hooks=None, - checkpoint_path=None): + checkpoint_path=None, + yield_single_examples=True): """Yields predictions for given features. Args: @@ -451,13 +455,18 @@ class Estimator(object): inside the prediction call. checkpoint_path: Path of a specific checkpoint to predict. If `None`, the latest checkpoint in `model_dir` is used. + yield_single_examples: If False, yield the whole batch as returned by the + model_fn instead of decomposing the batch into individual elements. This + is useful if model_fn return some tensor with first dimension not + equal to the batch size Yields: Evaluated values of `predictions` tensors. Raises: ValueError: Could not find a trained model in model_dir. - ValueError: if batch length of predictions are not same. + ValueError: if batch length of predictions are not same and + yield_single_examples is True. ValueError: If there is a conflict between `predict_keys` and `predictions`. For example if `predict_keys` is not `None` but `EstimatorSpec.predictions` is not a `dict`. @@ -490,7 +499,9 @@ class Estimator(object): hooks=all_hooks) as mon_sess: while not mon_sess.should_stop(): preds_evaluated = mon_sess.run(predictions) - if not isinstance(predictions, dict): + if not yield_single_examples: + yield preds_evaluated + elif not isinstance(predictions, dict): for pred in preds_evaluated: yield pred else: @@ -502,9 +513,11 @@ class Estimator(object): def _assert_members_are_not_overridden(self): """Asserts members of `Estimator` are not overridden.""" - allowed_overrides = set(['_call_input_fn', '_create_global_step', - '_convert_train_steps_to_hooks', - '_convert_eval_steps_to_hooks']) + allowed_overrides = set([ + '_call_input_fn', '_create_global_step', + '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', + '_tf_api_names' + ]) estimator_members = set([m for m in Estimator.__dict__.keys() if not m.startswith('__')]) subclass_members = set(self.__class__.__dict__.keys()) @@ -558,7 +571,7 @@ class Estimator(object): export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels. serving_input_receiver_fn: A function that takes no argument and - returns a `ServingInputReceiver`. + returns a `ServingInputReceiver` or `TensorServingInputReceiver`. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or `None` if no extra assets are needed. as_text: whether to write the SavedModel proto in text format. @@ -613,7 +626,6 @@ class Estimator(object): sharded=True) saver_for_restore.restore(session, checkpoint_path) - # TODO(b/36111876): replace legacy_init_op with main_op mechanism # pylint: disable=protected-access local_init_op = ( estimator_spec.scaffold.local_init_op or @@ -709,7 +721,7 @@ class Estimator(object): """Creates the global step tensor in graph. The global step tensor must be an integer type with name 'global_step' and - be added to the collection ${tf.GraphKeys.GLOBAL_STEP}. + be added to the collection @{tf.GraphKeys.GLOBAL_STEP}. Args: graph: The graph in which to create the global step tensor. @@ -996,13 +1008,6 @@ def _get_replica_device_setter(config): Returns: A replica device setter, or None. """ - ps_ops = [ - 'Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable', - 'MutableHashTableV2', 'MutableHashTableOfTensors', - 'MutableHashTableOfTensorsV2', 'MutableDenseHashTable', - 'MutableDenseHashTableV2' - ] - if config.task_type: worker_device = '/job:%s/task:%d' % (config.task_type, config.task_id) else: @@ -1013,7 +1018,7 @@ def _get_replica_device_setter(config): ps_tasks=config.num_ps_replicas, worker_device=worker_device, merge_devices=True, - ps_ops=ps_ops, + ps_ops=list(device_setter.STANDARD_PS_OPS), cluster=config.cluster_spec) else: return None @@ -1103,7 +1108,7 @@ def _write_dict_to_summary(output_dir, isinstance(dictionary[key], np.int32) or isinstance(dictionary[key], int)): summary_proto.value.add(tag=key, simple_value=int(dictionary[key])) - elif isinstance(dictionary[key], six.string_types): + elif isinstance(dictionary[key], six.binary_type): try: summ = summary_pb2.Summary.FromString(dictionary[key]) for i, _ in enumerate(summ.value): diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 39a5b998ebdcccfbeddf0fc96dab44dc91a289fa..ac0ff41dd260ce76e214af740f9334b58e725563 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -48,6 +48,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import state_ops @@ -80,18 +81,18 @@ def dummy_model_fn(features, labels, params): _, _, _ = features, labels, params -def check_eventfile_for_keyword(keyword, est): +def check_eventfile_for_keyword(keyword, dir_): """Checks event files for the keyword.""" writer_cache.FileWriterCache.clear() # Get last Event written. - event_paths = glob.glob(os.path.join(est.model_dir, 'events*')) + event_paths = glob.glob(os.path.join(dir_, 'events*')) last_event = None for last_event in summary_iterator.summary_iterator(event_paths[-1]): if last_event.summary is not None: - if last_event.summary.value: - if keyword in last_event.summary.value[0].tag: + for value in last_event.summary.value: + if keyword in value.tag: return True return False @@ -610,7 +611,7 @@ class EstimatorTrainTest(test.TestCase): # Make sure nothing is stuck in limbo. writer_cache.FileWriterCache.clear() - if check_eventfile_for_keyword('loss', est): + if check_eventfile_for_keyword('loss', est.model_dir): return self.fail('{} should be part of reported summaries.'.format('loss')) @@ -1290,8 +1291,9 @@ class EstimatorEvaluateTest(test.TestCase): # Make sure nothing is stuck in limbo. writer_cache.FileWriterCache.clear() - # Get last Event written. - if check_eventfile_for_keyword('image', est): + # Get last evaluation Event written. + if check_eventfile_for_keyword('image', os.path.join(est.model_dir, + 'eval')): return self.fail('{} should be part of reported summaries.'.format('image')) @@ -1472,6 +1474,27 @@ class EstimatorPredictTest(test.TestCase): 'Batch length of predictions should be same'): next(est.predict(dummy_input_fn)) + def test_iterate_batches(self): + + def _model_fn(features, labels, mode): + _, _ = features, labels + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(0.), + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions={ + # First dim is different but the prediction should still work + 'y1': array_ops.zeros(shape=[3]), + 'y2': array_ops.zeros(shape=[5, 3]) + }) + + est = estimator.Estimator(model_fn=_model_fn) + est.train(dummy_input_fn, steps=1) + + predictions = next(est.predict(dummy_input_fn, yield_single_examples=False)) + self.assertAllEqual(predictions['y1'].shape, [3]) + self.assertAllEqual(predictions['y2'].shape, [5, 3]) + def test_predict_keys_defined_for_tensor(self): def _model_fn(features, labels, mode): @@ -1914,6 +1937,60 @@ class EstimatorExportTest(test.TestCase): # cleanup gfile.DeleteRecursively(tmpdir) + def test_export_savedmodel_tensor_features(self): + """Test that models accepting a single raw Tensor can be exported. + + See https://github.com/tensorflow/tensorflow/issues/11674 + + If the model_fn and receiver_fn accept raw tensors rather than dictionaries + as input, export_savedmodel should be okay with that, too. + + """ + + tmpdir = tempfile.mkdtemp() + + def _input_fn_tensor_features(): + t = array_ops.constant([1, 2, 3], dtype=dtypes.float32, shape=[1, 3]) + return (t, None) + + def _model_fn_tensor_features(features, labels, mode): + _ = labels + prediction = math_ops.matmul(features, features, transpose_b=True) + + return model_fn_lib.EstimatorSpec( + mode, + predictions=prediction, + loss=constant_op.constant(1.), + train_op=state_ops.assign_add(training.get_global_step(), 1), + export_outputs={ + 'test': export_output.PredictOutput({'prediction': prediction}) + }) + + def _serving_input_receiver_fn(): + feat = array_ops.placeholder(dtype=dtypes.float32) + return export.TensorServingInputReceiver( + features=feat, receiver_tensors=feat) + + est = estimator.Estimator(model_fn=_model_fn_tensor_features) + est.train(input_fn=_input_fn_tensor_features, steps=1) + + # Perform the export. + export_dir_base = os.path.join( + compat.as_bytes(tmpdir), compat.as_bytes('export')) + export_dir = est.export_savedmodel( + export_dir_base, _serving_input_receiver_fn) + + # Restore, to validate that the export was well-formed. + with ops.Graph().as_default() as graph: + with session.Session(graph=graph) as sess: + loader.load(sess, [tag_constants.SERVING], export_dir) + graph_ops = [x.name.lower() for x in graph.get_operations()] + self.assertTrue('const' in graph_ops) + self.assertTrue('matmul' in graph_ops) + + # Clean up. + gfile.DeleteRecursively(tmpdir) + def test_scaffold_is_used_for_saver(self): tmpdir = tempfile.mkdtemp() diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index 83251c79fc561e16ebddb638668b92b3c69b8af4..f240e11478bac4071fde87a07e6168b5a4a7b286 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -120,6 +120,62 @@ class ServingInputReceiver(collections.namedtuple( receiver_tensors_alternatives=receiver_tensors_alternatives) +@tf_export('estimator.export.TensorServingInputReceiver') +class TensorServingInputReceiver(collections.namedtuple( + 'TensorServingInputReceiver', + ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])): + """A return type for a serving_input_receiver_fn. + + This is for use with models that expect a single `Tensor` or `SparseTensor` + as an input feature, as opposed to a dict of features. + + The normal `ServingInputReceiver` always returns a feature dict, even if it + contains only one entry, and so can be used only with models that accept such + a dict. For models that accept only a single raw feature, the + `serving_input_receiver_fn` provided to `Estimator.export_savedmodel()` should + return this `TensorServingInputReceiver` instead. See: + https://github.com/tensorflow/tensorflow/issues/11674 + + Note that the receiver_tensors and receiver_tensor_alternatives arguments + will be automatically converted to the dict representation in either case, + because the SavedModel format requires each input `Tensor` to have a name + (provided by the dict key). + + The expected return values are: + features: A single `Tensor` or `SparseTensor`, representing the feature + to be passed to the model. + receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying + input nodes where this receiver expects to be fed by default. Typically, + this is a single placeholder expecting serialized `tf.Example` protos. + receiver_tensors_alternatives: a dict of string to additional + groups of receiver tensors, each of which may be a `Tensor` or a dict of + string to `Tensor`. These named receiver tensor alternatives generate + additional serving signatures, which may be used to feed inputs at + different points within the input receiver subgraph. A typical usage is + to allow feeding raw feature `Tensor`s *downstream* of the + tf.parse_example() op. Defaults to None. + """ + + def __new__(cls, features, receiver_tensors, + receiver_tensors_alternatives=None): + if features is None: + raise ValueError('features must be defined.') + if not (isinstance(features, ops.Tensor) + or isinstance(features, sparse_tensor.SparseTensor)): + raise ValueError('feature must be a Tensor or SparseTensor.') + + receiver = ServingInputReceiver( + features=features, + receiver_tensors=receiver_tensors, + receiver_tensors_alternatives=receiver_tensors_alternatives) + + return super(TensorServingInputReceiver, cls).__new__( + cls, + features=receiver.features[_SINGLE_FEATURE_DEFAULT_NAME], + receiver_tensors=receiver.receiver_tensors, + receiver_tensors_alternatives=receiver.receiver_tensors_alternatives) + + @tf_export('estimator.export.build_parsing_serving_input_receiver_fn') def build_parsing_serving_input_receiver_fn(feature_spec, default_batch_size=None): diff --git a/tensorflow/python/estimator/export/export_lib.py b/tensorflow/python/estimator/export/export_lib.py index 99cd81d678bc04e7ed52de721a1fdf799c116795..226fc97fd3a3aefe61c4b88088873ce7489168c7 100644 --- a/tensorflow/python/estimator/export/export_lib.py +++ b/tensorflow/python/estimator/export/export_lib.py @@ -22,6 +22,7 @@ from __future__ import print_function from tensorflow.python.estimator.export.export import build_parsing_serving_input_receiver_fn from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn from tensorflow.python.estimator.export.export import ServingInputReceiver +from tensorflow.python.estimator.export.export import TensorServingInputReceiver from tensorflow.python.estimator.export.export_output import ClassificationOutput from tensorflow.python.estimator.export.export_output import ExportOutput from tensorflow.python.estimator.export.export_output import PredictOutput @@ -34,6 +35,7 @@ _allowed_symbols = [ 'build_parsing_serving_input_receiver_fn', 'build_raw_serving_input_receiver_fn', 'ServingInputReceiver', + 'TensorServingInputReceiver', 'ClassificationOutput', 'ExportOutput', 'PredictOutput', diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py index 8442bf04accbd0bc15f5958069bf3060debd42bc..eb9688bc973666554b6057f5f546b9a2d18461d6 100644 --- a/tensorflow/python/estimator/export/export_test.py +++ b/tensorflow/python/estimator/export/export_test.py @@ -385,5 +385,67 @@ class ExportTest(test_util.TensorFlowTestCase): self.assertTrue(int(time_2) < int(time_3)) +class TensorServingReceiverTest(test_util.TensorFlowTestCase): + + def test_tensor_serving_input_receiver_constructor(self): + features = constant_op.constant([0]) + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + r = export.TensorServingInputReceiver(features, receiver_tensors) + self.assertTrue(isinstance(r.features, ops.Tensor)) + self.assertTrue(isinstance(r.receiver_tensors, dict)) + + def test_tensor_serving_input_receiver_sparse(self): + features = sparse_tensor.SparseTensor( + indices=[[0, 0]], values=[1], dense_shape=[1, 1]) + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + r = export.TensorServingInputReceiver(features, receiver_tensors) + self.assertTrue(isinstance(r.features, sparse_tensor.SparseTensor)) + self.assertTrue(isinstance(r.receiver_tensors, dict)) + + def test_serving_input_receiver_features_invalid(self): + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + + with self.assertRaisesRegexp(ValueError, "features must be defined"): + export.TensorServingInputReceiver( + features=None, + receiver_tensors=receiver_tensors) + + with self.assertRaisesRegexp(ValueError, "feature must be a Tensor"): + export.TensorServingInputReceiver( + features={"1": constant_op.constant([1])}, + receiver_tensors=receiver_tensors) + + def test_serving_input_receiver_receiver_tensors_invalid(self): + features = constant_op.constant([0]) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensors must be defined"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors=None) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensors keys must be strings"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors={ + 1: array_ops.placeholder(dtypes.string, name="example0")}) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensor example1 must be a Tensor"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors={"example1": [1]}) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index ba522f396d0eda1bb3d13b21acfddcc3d593e21b..a3f04626d1e5ed7ca7fb09a5dcc2457a0cf5ab82 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -25,8 +25,10 @@ from tensorflow.python.estimator import gc from tensorflow.python.framework import errors_impl from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging +from tensorflow.python.util.tf_export import tf_export +@tf_export('estimator.Exporter') class Exporter(object): """A class representing a type of model export.""" @@ -123,6 +125,7 @@ class _SavedModelExporter(Exporter): return export_result +@tf_export('estimator.FinalExporter') class FinalExporter(Exporter): """This class exports the serving graph and checkpoints in the end. @@ -174,6 +177,7 @@ class FinalExporter(Exporter): is_the_final_export) +@tf_export('estimator.LatestExporter') class LatestExporter(Exporter): """This class regularly exports the serving graph and checkpoints. diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index b08f83fc569b1bb1ea6e5c93c57be7b5bb96f0a5..8111ab564c017175b3f7bc1020d850db74587958 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -31,8 +31,10 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import monitored_session from tensorflow.python.training import session_run_hook from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export +@tf_export('estimator.ModeKeys') class ModeKeys(object): """Standard names for model modes. @@ -52,6 +54,7 @@ LOSS_METRIC_KEY = 'loss' AVERAGE_LOSS_METRIC_KEY = 'average_loss' +@tf_export('estimator.EstimatorSpec') class EstimatorSpec( collections.namedtuple('EstimatorSpec', [ 'mode', 'predictions', 'loss', 'train_op', 'eval_metric_ops', diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py index e446b3e03a262e0d4abe69df73cb8604b0dab9f9..62f035bce558f57c5fd39d60b44cf8eb0130ce38 100644 --- a/tensorflow/python/estimator/run_config.py +++ b/tensorflow/python/estimator/run_config.py @@ -27,8 +27,8 @@ import six from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib -from tensorflow.python.util import compat from tensorflow.python.util import compat_internal +from tensorflow.python.util.tf_export import tf_export _USE_DEFAULT = object() @@ -287,6 +287,7 @@ class TaskType(object): EVALUATOR = 'evaluator' +@tf_export('estimator.RunConfig') class RunConfig(object): """This class specifies the configurations for an `Estimator` run.""" @@ -344,7 +345,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'worker', 'index': 1}}) - config = ClusterConfig() + config = RunConfig() assert config.master == 'host4:2222' assert config.task_id == 1 assert config.num_ps_replicas == 2 @@ -362,7 +363,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'chief', 'index': 0}}) - config = ClusterConfig() + config = RunConfig() assert config.master == 'host0:2222' assert config.task_id == 0 assert config.num_ps_replicas == 2 @@ -380,7 +381,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'evaluator', 'index': 0}}) - config = ClusterConfig() + config = RunConfig() assert config.master == '' assert config.evaluator_master == '' assert config.task_id == 0 diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 2e84c5014f6e17b34b38d2dfe5711b5b654553bb..e38b765da52a7b6957a4fb8a02087c5d1fd5a781 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -35,6 +35,7 @@ from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import server_lib from tensorflow.python.training import session_run_hook from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export _MAX_DELAY_SECS = 60 _DELAY_SECS_PER_WORKER = 5 @@ -114,6 +115,7 @@ def _is_google_env(): return tf_config.get(_ENVIRONMENT_KEY) == _ENVIRONMENT_GOOGLE_VALUE +@tf_export('estimator.TrainSpec') class TrainSpec( collections.namedtuple('TrainSpec', ['input_fn', 'max_steps', 'hooks'])): """Configuration for the "train" part for the `train_and_evaluate` call. @@ -126,9 +128,16 @@ class TrainSpec( """Creates a validated `TrainSpec` instance. Args: - input_fn: Training input function returning a tuple of: - features - `Tensor` or dictionary of string feature name to `Tensor`. - labels - `Tensor` or dictionary of `Tensor` with labels. + input_fn: A function that provides input data for training as minibatches. + See @{$get_started/premade_estimators#create_input_functions} for more + information. The function should construct and return one of + the following: + * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a + tuple (features, labels) with same constraints as below. + * A tuple (features, labels): Where features is a `Tensor` or a + dictionary of string feature name to `Tensor` and labels is a + `Tensor` or a dictionary of string label name to `Tensor`. + max_steps: Int. Positive number of total steps for which to train model. If `None`, train forever. The training `input_fn` is not expected to generate `OutOfRangeError` or `StopIteration` exceptions. See the @@ -158,6 +167,7 @@ class TrainSpec( cls, input_fn=input_fn, max_steps=max_steps, hooks=hooks) +@tf_export('estimator.EvalSpec') class EvalSpec( collections.namedtuple('EvalSpec', [ 'input_fn', 'steps', 'name', 'hooks', 'exporters', 'start_delay_secs', @@ -182,9 +192,16 @@ class EvalSpec( """Creates a validated `EvalSpec` instance. Args: - input_fn: Evaluation input function returning a tuple of: - features - `Tensor` or dictionary of string feature name to `Tensor`. - labels - `Tensor` or dictionary of `Tensor` with labels. + input_fn: A function that constructs the input data for evaluation. + See @{$get_started/premade_estimators#create_input_functions} for more + information. The function should construct and return one of + the following: + * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a + tuple (features, labels) with same constraints as below. + * A tuple (features, labels): Where features is a `Tensor` or a + dictionary of string feature name to `Tensor` and labels is a + `Tensor` or a dictionary of string label name to `Tensor`. + steps: Int. Positive number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception. See `Estimator.evaluate` for details. @@ -246,6 +263,7 @@ class EvalSpec( throttle_secs=throttle_secs) +@tf_export('estimator.train_and_evaluate') def train_and_evaluate(estimator, train_spec, eval_spec): """Train and evaluate the `estimator`. @@ -451,15 +469,21 @@ class _TrainingExecutor(object): train_hooks=None, continuous_eval_listener=None): if not isinstance(estimator, estimator_lib.Estimator): - raise TypeError('`estimator` must have type `tf.estimator.Estimator`.') + raise TypeError( + '`estimator` must have type `tf.estimator.Estimator`. ' + 'Got: {}'.format(type(estimator))) self._estimator = estimator if not isinstance(train_spec, TrainSpec): - raise TypeError('`train_spec` must have type `tf.estimator.TrainSpec`.') + raise TypeError( + '`train_spec` must have type `tf.estimator.TrainSpec`. ' + 'Got: {}'.format(type(train_spec))) self._train_spec = train_spec if not isinstance(eval_spec, EvalSpec): - raise TypeError('`eval_spec` must have type `tf.estimator.EvalSpec`.') + raise TypeError( + '`eval_spec` must have type `tf.estimator.EvalSpec`. ' + 'Got: {}'.format(type(eval_spec))) self._eval_spec = eval_spec self._train_hooks = _validate_hooks(train_hooks) diff --git a/tensorflow/python/estimator/util.py b/tensorflow/python/estimator/util.py index b7ba76d8714e6b13551bb3e18083f45e53d2afc3..3ce8eea84b6bf601ce89dfaa7d8e3a5d193468b3 100644 --- a/tensorflow/python/estimator/util.py +++ b/tensorflow/python/estimator/util.py @@ -21,10 +21,12 @@ from __future__ import print_function import functools +from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect def _is_bounded_method(fn): + _, fn = tf_decorator.unwrap(fn) return tf_inspect.ismethod(fn) and (fn.__self__ is not None) diff --git a/tensorflow/python/estimator/warm_starting_util.py b/tensorflow/python/estimator/warm_starting_util.py index 48110ef57fcba897cc495323973b2f6761c3add4..adb013f5c653c4967a743047fef4e805946e0f59 100644 --- a/tensorflow/python/estimator/warm_starting_util.py +++ b/tensorflow/python/estimator/warm_starting_util.py @@ -30,8 +30,10 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_ops from tensorflow.python.training import checkpoint_utils from tensorflow.python.training import saver +from tensorflow.python.util.tf_export import tf_export +@tf_export("estimator.VocabInfo") class VocabInfo( collections.namedtuple("VocabInfo", [ "new_vocab", @@ -81,6 +83,7 @@ class VocabInfo( ) +@tf_export("estimator.WarmStartSettings") class WarmStartSettings( collections.namedtuple("WarmStartSettings", [ "ckpt_to_initialize_from", @@ -117,21 +120,13 @@ class WarmStartSettings( ws = WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000") ``` - Warm-start only the embeddings (input layer) and their accumulator variables: + Warm-start only the embeddings (input layer): ``` ws = WarmStartSettings(ckpt_to_initialize_from="/tmp", vars_to_warm_start=".*input_layer.*") ``` - Warm-start everything except the optimizer accumulator variables - (DNN defaults to Adagrad): - - ``` - ws = WarmStartSettings(ckpt_to_initialize_from="/tmp", - vars_to_warm_start="^(?!.*(Adagrad))") - ``` - Warm-start all weights but the embedding parameters corresponding to `sc_vocab_file` have a different vocab from the one used in the current model: @@ -423,6 +418,8 @@ def _warm_start(warm_start_settings): # Both warm_start_settings.vars_to_warm_start = '.*' and # warm_start_settings.vars_to_warm_start = None will match everything here. for v in ops.get_collection( + # TODO(eddz): Allow for different collections here (to support + # warm-starting accumulators). ops.GraphKeys.TRAINABLE_VARIABLES, scope=warm_start_settings.vars_to_warm_start): if not isinstance(v, list): diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD index a758f8a4fc4898713772c4e919acda48b0f6ad0b..238a90b67d9d0039c25a6f3800aad25a2db9e36f 100644 --- a/tensorflow/python/feature_column/BUILD +++ b/tensorflow/python/feature_column/BUILD @@ -74,7 +74,10 @@ py_test( srcs = ["feature_column_test.py"], data = [":vocabulary_testdata"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_cuda_on_cpu_tap", + "no_pip", + ], deps = [ ":feature_column", ":feature_column_py", diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index 5947d8f6e2348b12dae8f8ee05c26ecd9e342fcd..85971c91bf1af7de892865e82d6f9c7128c43971 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -16,7 +16,7 @@ FeatureColumns provide a high level abstraction for ingesting and representing features. FeatureColumns are also the primary way of encoding features for -canned ${tf.estimator.Estimator}s. +canned @{tf.estimator.Estimator}s. When using FeatureColumns with `Estimators`, the type of feature column you should choose depends on (1) the feature type and (2) the model type. @@ -158,6 +158,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import tf_export def _internal_input_layer(features, @@ -511,6 +512,7 @@ def make_parse_example_spec(feature_columns): ```python # Define features and transformations + feature_a = categorical_column_with_vocabulary_file(...) feature_b = numeric_column(...) feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...) feature_a_x_feature_c = crossed_column( @@ -662,6 +664,7 @@ def embedding_column( trainable=trainable) +@tf_export('feature_column.shared_embedding_columns') def shared_embedding_columns( categorical_columns, dimension, combiner='mean', initializer=None, shared_embedding_collection_name=None, ckpt_to_load_from=None, @@ -1623,7 +1626,7 @@ class _FeatureColumn(object): It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other - supported objects. Please check documentation of ${tf.parse_example} for all + supported objects. Please check documentation of @{tf.parse_example} for all supported spec objects. Let's say a Feature column depends on raw feature ('raw') and another @@ -1674,7 +1677,7 @@ class _DenseColumn(_FeatureColumn): weight_collections: List of graph collections to which Variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.Variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.Variable}). Returns: `Tensor` of shape [batch_size] + `_variable_shape`. @@ -1732,7 +1735,7 @@ class _CategoricalColumn(_FeatureColumn): WARNING: Do not subclass this layer unless you know what you are doing: the API is subject to future changes. - A categorical feature typically handled with a ${tf.SparseTensor} of IDs. + A categorical feature typically handled with a @{tf.SparseTensor} of IDs. """ __metaclass__ = abc.ABCMeta @@ -1767,7 +1770,7 @@ class _CategoricalColumn(_FeatureColumn): weight_collections: List of graph collections to which variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.get_variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.get_variable}). """ pass diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py index 3b1092f923112dbd9a081942d40162ae384bf167..3c5aebbce8af117aa1e216f1ef07ded181c997ea 100644 --- a/tensorflow/python/framework/common_shapes.py +++ b/tensorflow/python/framework/common_shapes.py @@ -34,7 +34,7 @@ def scalar_shape(unused_op): def unchanged_shape(op): - """Shape function for ops that output an tensor like their first input.""" + """Shape function for ops that output a tensor like their first input.""" return [op.inputs[0].get_shape()] diff --git a/tensorflow/python/framework/constant_op.py b/tensorflow/python/framework/constant_op.py index d3d8c9c154fbfcc9613acce4e1bdab7df2e7d56d..782b505d6c1d0b576b7734f088c4d2c9625f4be2 100644 --- a/tensorflow/python/framework/constant_op.py +++ b/tensorflow/python/framework/constant_op.py @@ -181,7 +181,7 @@ def constant(value, dtype=None, shape=None, name="Const", verify_shape=False): TypeError: if shape is incorrectly specified or unsupported. """ ctx = context.context() - if not ctx.in_graph_mode(): + if ctx.executing_eagerly(): t = convert_to_eager_tensor(value, ctx, dtype) if shape is None: return t diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py index c825114483697f63163d5a33365049b7c2b539a3..99ae8b24f11c4955379ae532ba7b921ebec63385 100644 --- a/tensorflow/python/framework/dtypes.py +++ b/tensorflow/python/framework/dtypes.py @@ -238,9 +238,9 @@ class DType(object): min, max : tuple Lower and upper intensity limits. """ - min, max = dtype_range[self.as_numpy_dtype] + min, max = dtype_range[self.as_numpy_dtype] # pylint: disable=redefined-builtin if clip_negative: - min = 0 + min = 0 # pylint: disable=redefined-builtin return min, max def is_compatible_with(self, other): @@ -356,7 +356,7 @@ complex128 = DType(types_pb2.DT_COMPLEX128) tf_export("complex128").export_constant(__name__, "complex128") int64 = DType(types_pb2.DT_INT64) tf_export("int64").export_constant(__name__, "int64") -bool = DType(types_pb2.DT_BOOL) +bool = DType(types_pb2.DT_BOOL) # pylint: disable=redefined-builtin tf_export("bool").export_constant(__name__, "bool") qint8 = DType(types_pb2.DT_QINT8) tf_export("qint8").export_constant(__name__, "qint8") diff --git a/tensorflow/python/framework/framework_lib.py b/tensorflow/python/framework/framework_lib.py index d16fe979e6ef9a41063c3a2b3e8a3e18de2aa9d7..3172f3c2c3d259d2c3f2b340b101aef043d0fc33 100644 --- a/tensorflow/python/framework/framework_lib.py +++ b/tensorflow/python/framework/framework_lib.py @@ -118,7 +118,7 @@ from tensorflow.python.framework.ops import register_tensor_conversion_function # go/tf-wildcard-import # pylint: disable=wildcard-import -from tensorflow.python.framework.dtypes import * +from tensorflow.python.framework.dtypes import * # pylint: disable=redefined-builtin # Load a TensorFlow plugin from tensorflow.python.framework.load_library import * diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index cba225e749d88a45c43266e45172a7335a8e0b71..14d72d8a3de7e22bee4f9961c2f66044c217f641 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -353,8 +353,10 @@ class _DefinedFunction(object): outputs = (outputs,) if any([_ is None for _ in outputs]): raise ValueError("Function can not return None.") - # Ensures each output is a Tensor. - outputs = [ops.convert_to_tensor(_) for _ in outputs] + # Ensures each output is a Tensor in the function graph. + outputs = [ops.convert_to_tensor(t) for t in outputs] + outputs = [temp_graph.capture(t) if t.graph is not temp_graph else t + for t in outputs] self._extra_inputs = temp_graph.extra_inputs inputs.extend(temp_graph.extra_args) # pylint: disable=protected-access @@ -487,10 +489,10 @@ class _DefinedFunction(object): # Adds this function into 'g'. # pylint: disable=protected-access - if context.in_graph_mode(): - g._add_function(self) - else: + if context.executing_eagerly(): context.context().add_function_def(self.definition) + else: + g._add_function(self) # pylint: enable=protected-access # Ensures related sub-routines are defined in 'g', too. @@ -683,28 +685,34 @@ class _FuncGraph(ops.Graph): def create_op(self, op_type, inputs, data_types, **kwargs): for i, x in enumerate(inputs): if isinstance(x, ops.EagerTensor) or x.graph is not self: - # Referring to a tensor from other graph. - if x in self._captured: - # Captured already. - inputs[i] = self._captured[x] - elif self._capture_by_value: - inputs[i] = self._add_tensor_and_parents(x) - else: - # Substitute with a placeholder. - self.extra_inputs.append(x) - # Hoist the new input placeholder out of any control flow context - # we're currently in. - with ops.control_dependencies(None): - ph = array_ops.placeholder(x.dtype, shape=x.get_shape()) - # pylint: disable=protected-access - ph._handle_data = x._handle_data - # pylint: enable=protected-access - inputs[i] = ph - self._captured[x] = ph - self.extra_args.append(ph) + inputs[i] = self.capture(x) return super(_FuncGraph, self).create_op(op_type, inputs, data_types, **kwargs) + def capture(self, tensor): + """Adds the given tensor to this graph and returns the captured tensor.""" + if tensor in self._captured: + # Captured already. + return self._captured[tensor] + elif self._capture_by_value: + return self._add_tensor_and_parents(tensor) + else: + return self._capture_tensor_as_extra_input(tensor) + + def _capture_tensor_as_extra_input(self, tensor): + # Substitute with a placeholder. + self.extra_inputs.append(tensor) + # Hoist the new input placeholder out of any control flow context + # we're currently in. + with ops.control_dependencies(None): + ph = array_ops.placeholder(tensor.dtype, shape=tensor.get_shape()) + # pylint: disable=protected-access + ph._handle_data = tensor._handle_data + # pylint: enable=protected-access + self._captured[tensor] = ph + self.extra_args.append(ph) + return ph + def _add_tensor_and_parents(self, tensor): op = self._add_op_and_parents(tensor.op) return op.outputs[tensor.value_index] diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index b35cee0111266fd7b744161d8a7e75664cbda122..65ca801cbe922b36e3bc72bc2fbcd88f66aa5290 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -193,7 +193,7 @@ class FunctionTest(test.TestCase): @function.Defun(dtypes.float32, dtypes.float32) def XSquarePlusOneGrad(x, dy): - dx = functional_ops._symbolic_gradient( + dx = functional_ops.symbolic_gradient( input=[x, dy], Tout=[dtypes.float32], f="XSquarePlusOneFn", name="dx") return dx @@ -295,7 +295,7 @@ class FunctionTest(test.TestCase): # gradient function is (x, y, dz) -> (dx, dy). dx's shape # should be the same as x's; and dy's shape should be the same # as y's. - dx, dy = functional_ops._symbolic_gradient( + dx, dy = functional_ops.symbolic_gradient( input=[x, y, dz], Tout=[dtypes.float32] * 2, f="Foo") self.assertEqual(x.get_shape(), dx.get_shape()) self.assertEqual(y.get_shape(), dy.get_shape()) @@ -725,9 +725,16 @@ class FunctionTest(test.TestCase): y = Foo(constant_op.constant([[10.]])) + @function.Defun() + def Bar(): + return w + + z = Bar() + with self.test_session(graph=g): variables.global_variables_initializer().run() self.assertAllEqual(y.eval(), [[12.0]]) + self.assertAllEqual(z.eval(), [[1.0]]) def testCaptureControls(self): g = ops.Graph() @@ -1458,7 +1465,7 @@ class FunctionInlineControlTest(test.TestCase): def Cell(v): # If v is a vector [n, 1], x is a big square matrix. x = math_ops.tanh(v + array_ops.transpose(v, [1, 0])) - return math_ops.reduce_sum(x, 1, keep_dims=True) + return math_ops.reduce_sum(x, 1, keepdims=True) @function.Defun(dtype) def Forward(x): diff --git a/tensorflow/python/framework/graph_util_impl.py b/tensorflow/python/framework/graph_util_impl.py index 5a543317e665a940841714fd72d834a430f8406a..910364364c8be84b1a629dbdaae5e69443d07e75 100644 --- a/tensorflow/python/framework/graph_util_impl.py +++ b/tensorflow/python/framework/graph_util_impl.py @@ -235,7 +235,7 @@ def convert_variables_to_constants(sess, variable_names = [] variable_dict_names = [] for node in inference_graph.node: - if node.op in ["Variable", "VariableV2"]: + if node.op in ["Variable", "VariableV2", "VarHandleOp"]: variable_name = node.name if ((variable_names_whitelist is not None and variable_name not in variable_names_whitelist) or @@ -243,7 +243,10 @@ def convert_variables_to_constants(sess, variable_name in variable_names_blacklist)): continue variable_dict_names.append(variable_name) - variable_names.append(variable_name + ":0") + if node.op == "VarHandleOp": + variable_names.append(variable_name + "/Read/ReadVariableOp:0") + else: + variable_names.append(variable_name + ":0") if variable_names: returned_variables = sess.run(variable_names) else: @@ -266,6 +269,17 @@ def convert_variables_to_constants(sess, tensor=tensor_util.make_tensor_proto( data, dtype=dtype.type, shape=data.shape))) how_many_converted += 1 + elif input_node.op == "ReadVariableOp" and ( + input_node.input[0] in found_variables): + # The preceding branch converts all VarHandleOps of ResourceVariables to + # constants, so we need to convert the associated ReadVariableOps to + # Identity ops. + output_node.op = "Identity" + output_node.name = input_node.name + output_node.input.extend([input_node.input[0]]) + output_node.attr["T"].CopyFrom(input_node.attr["dtype"]) + if "_class" in input_node.attr: + output_node.attr["_class"].CopyFrom(input_node.attr["_class"]) else: output_node.CopyFrom(input_node) output_graph_def.node.extend([output_node]) diff --git a/tensorflow/python/framework/graph_util_test.py b/tensorflow/python/framework/graph_util_test.py index 0421837d49de753d642aed59d1524619a243dcb8..b618152b0256fd043dc7259960d867278ba55b0a 100644 --- a/tensorflow/python/framework/graph_util_test.py +++ b/tensorflow/python/framework/graph_util_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import gen_state_ops from tensorflow.python.ops import math_ops # pylint: disable=unused-import from tensorflow.python.ops import math_ops as math_ops_lib +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -47,46 +48,46 @@ class DeviceFunctionsTest(test.TestCase): def testTwoDeviceFunctions(self): with ops.Graph().as_default() as g: - var_0 = gen_state_ops._variable( + var_0 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_0", container="", shared_name="") with g.device(test_device_func_pin_variable_to_cpu): - var_1 = gen_state_ops._variable( + var_1 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_1", container="", shared_name="") - var_2 = gen_state_ops._variable( + var_2 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_2", container="", shared_name="") - var_3 = gen_state_ops._variable( + var_3 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_3", container="", shared_name="") with g.device(test_device_func_pin_variable_to_cpu): - var_4 = gen_state_ops._variable( + var_4 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_4", container="", shared_name="") with g.device("/device:GPU:0"): - var_5 = gen_state_ops._variable( + var_5 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_5", container="", shared_name="") - var_6 = gen_state_ops._variable( + var_6 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_6", @@ -226,52 +227,62 @@ class DeviceFunctionsTest(test.TestCase): constant_graph_def.library) def testConvertVariablesToConsts(self): - with ops.Graph().as_default(): - variable_node = variables.Variable(1.0, name="variable_node") - _ = variables.Variable(1.0, name="unused_variable_node") - output_node = math_ops_lib.multiply( - variable_node, 2.0, name="output_node") - with session.Session() as sess: - init = variables.initialize_variables([variable_node]) - sess.run(init) - output = sess.run(output_node) - self.assertNear(2.0, output, 0.00001) - variable_graph_def = sess.graph.as_graph_def() - # First get the constant_graph_def when variable_names_whitelist is set, - # note that if variable_names_whitelist is not set an error will be - # thrown because unused_variable_node is not initialized. - constant_graph_def = graph_util.convert_variables_to_constants( - sess, - variable_graph_def, ["output_node"], - variable_names_whitelist=set(["variable_node"])) + self._test_variable_to_const_conversion(use_resource=False) - # Then initialize the unused variable, and get another - # constant_graph_def when variable_names_whitelist is not set. - sess.run(variables.global_variables_initializer()) - constant_graph_def_without_variable_whitelist = ( - graph_util.convert_variables_to_constants(sess, variable_graph_def, - ["output_node"])) - - # The unused variable should be cleared so the two graphs should be - # equivalent. - self.assertEqual( - str(constant_graph_def), - str(constant_graph_def_without_variable_whitelist)) - - # Test variable name black list. This should result in the variable not - # being a const. - sess.run(variables.global_variables_initializer()) - constant_graph_def_with_blacklist = ( - graph_util.convert_variables_to_constants( - sess, - variable_graph_def, ["output_node"], - variable_names_blacklist=set(["variable_node"]))) - variable_node = None - for node in constant_graph_def_with_blacklist.node: - if node.name == "variable_node": - variable_node = node - self.assertIsNotNone(variable_node) - self.assertEqual(variable_node.op, "VariableV2") + def testConvertResourceVariablesToConsts(self): + self._test_variable_to_const_conversion(use_resource=True) + + def _test_variable_to_const_conversion(self, use_resource): + with ops.Graph().as_default(): + with variable_scope.variable_scope("", use_resource=use_resource): + variable_node = variable_scope.get_variable( + "variable_node", initializer=1.0) + another_variable = variable_scope.get_variable( + "unused_variable_node", initializer=1.0) + output_node = math_ops_lib.multiply( + variable_node, 2.0, name="output_node") + with session.Session() as sess: + sess.run(variable_node.initializer) + output = sess.run(output_node) + self.assertNear(2.0, output, 0.00001) + variable_graph_def = sess.graph.as_graph_def() + # First get the constant_graph_def when variable_names_whitelist is + # set, note that if variable_names_whitelist is not set an error will + # be thrown because unused_variable_node is not initialized. + constant_graph_def = graph_util.convert_variables_to_constants( + sess, + variable_graph_def, ["output_node"], + variable_names_whitelist=set(["variable_node"])) + + # Then initialize the unused variable, and get another + # constant_graph_def when variable_names_whitelist is not set. + sess.run(another_variable.initializer) + constant_graph_def_without_variable_whitelist = ( + graph_util.convert_variables_to_constants( + sess, variable_graph_def, ["output_node"])) + + # The unused variable should be cleared so the two graphs should be + # equivalent. + self.assertEqual( + str(constant_graph_def), + str(constant_graph_def_without_variable_whitelist)) + + # Test variable name black list. This should result in the variable + # not being a const. + constant_graph_def_with_blacklist = ( + graph_util.convert_variables_to_constants( + sess, + variable_graph_def, ["output_node"], + variable_names_blacklist=set(["variable_node"]))) + variable_node = None + for node in constant_graph_def_with_blacklist.node: + if node.name == "variable_node": + variable_node = node + self.assertIsNotNone(variable_node) + if use_resource: + self.assertEqual(variable_node.op, "VarHandleOp") + else: + self.assertEqual(variable_node.op, "VariableV2") # Now we make sure the variable is now a constant, and that the graph still # produces the expected result. @@ -279,8 +290,9 @@ class DeviceFunctionsTest(test.TestCase): _ = importer.import_graph_def(constant_graph_def, name="") self.assertEqual(4, len(constant_graph_def.node)) for node in constant_graph_def.node: - self.assertNotEqual("Variable", node.op) - self.assertNotEqual("VariableV2", node.op) + self.assertNotIn( + node.op, + ["Variable", "VariableV2", "VarHandleOp", "ReadVariableOp"]) with session.Session() as sess: output_node = sess.graph.get_tensor_by_name("output_node:0") output = sess.run(output_node) diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index c26644362c86798012b5812f3554662a6d1d680b..783e9259ad1fae4eb05fc7beeb5be83ae3f7fdce 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -150,7 +150,7 @@ def _MaybeDevice(device): yield -def _ProcessGraphDefParam(graph_def): +def _ProcessGraphDefParam(graph_def, op_dict): """Type-checks and possibly canonicalizes `graph_def`.""" if not isinstance(graph_def, graph_pb2.GraphDef): # `graph_def` could be a dynamically-created message, so try a duck-typed @@ -161,6 +161,22 @@ def _ProcessGraphDefParam(graph_def): graph_def.MergeFrom(old_graph_def) except TypeError: raise TypeError('graph_def must be a GraphDef proto.') + else: + # If we're using the graph_def provided by the caller, modify graph_def + # in-place to add attr defaults to the NodeDefs (this is visible to the + # caller). + # NOTE(skyewm): this is undocumented behavior that at least meta_graph.py + # depends on. It might make sense to move this to meta_graph.py and have + # import_graph_def not modify the graph_def argument (we'd have to make sure + # this doesn't break anything else.) + for node in graph_def.node: + if node.op not in op_dict: + # Assume unrecognized ops are functions for now. TF_ImportGraphDef will + # report an error if the op is actually missing. + continue + op_def = op_dict[node.op] + _SetDefaultAttrValues(node, op_def) + return graph_def @@ -254,7 +270,6 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map, """Populates the TF_ImportGraphDefOptions `options`.""" c_api.TF_ImportGraphDefOptionsSetPrefix(options, prefix) c_api.TF_ImportGraphDefOptionsSetUniquifyNames(options, True) - c_api.TF_ImportGraphDefOptionsSetUniquifyPrefix(options, True) for input_src, input_dst in input_map.items(): input_src = compat.as_str(input_src) @@ -369,6 +384,17 @@ def _GatherReturnElements(requested_return_elements, graph, results): return combined_return_elements +def _SetDefaultAttrValues(node_def, op_def): + """Set any default attr values in `node_def` that aren't present.""" + assert node_def.op == op_def.name + for attr_def in op_def.attr: + key = attr_def.name + if attr_def.HasField('default_value'): + value = node_def.attr[key] + if value is None or value.WhichOneof('value') is None: + node_def.attr[key].CopyFrom(attr_def.default_value) + + @tf_export('import_graph_def') @deprecated_args(None, 'Please file an issue at ' 'https://github.com/tensorflow/tensorflow/issues if you depend' @@ -420,12 +446,12 @@ def import_graph_def(graph_def, do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ - graph_def = _ProcessGraphDefParam(graph_def) + op_dict = op_def_registry.get_registered_ops() + + graph_def = _ProcessGraphDefParam(graph_def, op_dict) input_map = _ProcessInputMapParam(input_map) return_elements = _ProcessReturnElementsParam(return_elements) - op_dict = op_def_registry.get_registered_ops() - if producer_op_list is not None: # TODO(skyewm): make a copy of graph_def so we're not mutating the argument? _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def) @@ -449,16 +475,21 @@ def import_graph_def(graph_def, _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) - with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: - try: - with errors.raise_exception_on_not_ok_status() as status: - results = c_api.TF_GraphImportGraphDefWithResults( - graph._c_graph, serialized, options, status) # pylint: disable=protected-access - except errors.InvalidArgumentError as e: - # Convert to ValueError for backwards compatibility. - raise ValueError(str(e)) + # _ProcessNewOps mutates the new operations. _lock ensures a Session.run + # call cannot occur between creating the TF_Operations in the + # TF_GraphImportGraphDefWithResults call and mutating the them in + # _ProcessNewOps. + with graph._lock: # pylint: disable=protected-access + with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: + try: + with errors.raise_exception_on_not_ok_status() as status: + results = c_api.TF_GraphImportGraphDefWithResults( + graph._c_graph, serialized, options, status) # pylint: disable=protected-access + except errors.InvalidArgumentError as e: + # Convert to ValueError for backwards compatibility. + raise ValueError(str(e)) - _ProcessNewOps(graph) + _ProcessNewOps(graph) # Create _DefinedFunctions for any imported functions. # @@ -535,16 +566,9 @@ def import_graph_def(graph_def, # Check to see if this op's name matches a previously seen op if node.name in name_to_op: raise ValueError('Duplicate name \'%s\' in GraphDef.' % node.name) - # Set any default attr values that aren't present. if node.op not in op_dict: raise ValueError('No op named %s in defined operations.' % node.op) op_def = op_dict[node.op] - for attr_def in op_def.attr: - key = attr_def.name - if attr_def.HasField('default_value'): - value = node.attr[key] - if value is None or value.WhichOneof('value') is None: - node.attr[key].CopyFrom(attr_def.default_value) output_types = _OutputTypes(node, op_dict) name_to_op[node.name] = g.create_op( diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py index acaec37f810cb00daa9bae17ffbcb675648b9fe1..bf5d9fe0936882c242198bdc7118f9f3a4e79260 100644 --- a/tensorflow/python/framework/importer_test.py +++ b/tensorflow/python/framework/importer_test.py @@ -154,6 +154,25 @@ class ImportGraphDefTest(test.TestCase): self.assertEqual(b3.name, "A_3/B") self.assertEqual(list(b3.inputs), [a3.outputs[0]]) + # Import with an already-used name but with a '/' to indicate an + # "absolute" name scope (see the Graph.name_scope docstring). + a_a, a_b = importer.import_graph_def( + graph_def, + return_elements=["A", "B"], + name="A/") + self.assertEqual(a_a.name, "A/A") + self.assertEqual(a_b.name, "A/B") + self.assertEqual(list(a_b.inputs), [a_a.outputs[0]]) + + # Repeat the same import. + a_a1, a_b1 = importer.import_graph_def( + graph_def, + return_elements=["A", "B"], + name="A/") + self.assertEqual(a_a1.name, "A/A_1") + self.assertEqual(a_b1.name, "A/B_1") + self.assertEqual(list(a_b1.inputs), [a_a1.outputs[0]]) + # Import with existing de-duped node names a1_1, b1_1 = importer.import_graph_def( self._MakeGraphDef(""" diff --git a/tensorflow/python/framework/load_library.py b/tensorflow/python/framework/load_library.py index c997ead829855f33efdb3efe947c3f59b5dbe76c..1f2aa264c110930b318f30e3a24010a96ebce47e 100644 --- a/tensorflow/python/framework/load_library.py +++ b/tensorflow/python/framework/load_library.py @@ -21,10 +21,10 @@ from __future__ import print_function import hashlib import imp import sys -import threading +import threading # pylint: disable=unused-import from tensorflow.core.framework import op_def_pb2 -from tensorflow.core.lib.core import error_codes_pb2 +from tensorflow.core.lib.core import error_codes_pb2 # pylint: disable=unused-import from tensorflow.python import pywrap_tensorflow as py_tf from tensorflow.python.framework import errors_impl from tensorflow.python.util import compat diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py index 8c03a5f19dee31a6609590e46d608af9a686c5fe..4bb9941bb778ae9b4022ef9d376cb031223ddb1c 100644 --- a/tensorflow/python/framework/meta_graph.py +++ b/tensorflow/python/framework/meta_graph.py @@ -695,7 +695,7 @@ def import_scoped_meta_graph(meta_graph_or_file, Raises: ValueError: If the graph_def contains unbound inputs. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "eager execution is enabled.") if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef): @@ -741,6 +741,7 @@ def import_scoped_meta_graph(meta_graph_or_file, producer_op_list=producer_op_list) # Restores all the other collections. + variable_objects = {} for key, col_def in sorted(meta_graph_def.collection_def.items()): # Don't add unbound_inputs to the new graph. if key == unbound_inputs_col_name: @@ -756,11 +757,23 @@ def import_scoped_meta_graph(meta_graph_or_file, from_proto = ops.get_from_proto_function(key) if from_proto and kind == "bytes_list": proto_type = ops.get_collection_proto_type(key) - for value in col_def.bytes_list.value: - proto = proto_type() - proto.ParseFromString(value) - graph.add_to_collection( - key, from_proto(proto, import_scope=scope_to_prepend_to_names)) + if key in ops.GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access + for value in col_def.bytes_list.value: + variable = variable_objects.get(value, None) + if variable is None: + proto = proto_type() + proto.ParseFromString(value) + variable = from_proto( + proto, import_scope=scope_to_prepend_to_names) + variable_objects[value] = variable + graph.add_to_collection(key, variable) + else: + for value in col_def.bytes_list.value: + proto = proto_type() + proto.ParseFromString(value) + graph.add_to_collection( + key, from_proto( + proto, import_scope=scope_to_prepend_to_names)) else: field = getattr(col_def, kind) if key in _COMPAT_COLLECTION_LIST: @@ -843,7 +856,7 @@ def export_scoped_meta_graph(filename=None, Raises: ValueError: When the `GraphDef` is larger than 2GB. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "Eager Execution is enabled.") graph = graph or ops.get_default_graph() diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py index f2f1e83da15eacdbb4f194967b51559d279ae1a4..21963d0beee398da8e90c2c829b2d4607ec6cc42 100644 --- a/tensorflow/python/framework/meta_graph_test.py +++ b/tensorflow/python/framework/meta_graph_test.py @@ -261,6 +261,29 @@ class SimpleMetaGraphTest(test.TestCase): self.assertEqual(node_def.attr["attr_1"].i, 1) self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs) + def testVariableObjectsAreSharedAmongCollections(self): + with ops.Graph().as_default() as graph1: + v = variables.Variable(3.0) + # A single instance of Variable is shared among the collections: + global_vars = graph1.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + trainable_vars = graph1.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) + self.assertEqual(len(global_vars), 1) + self.assertEqual(len(trainable_vars), 1) + self.assertIs(global_vars[0], trainable_vars[0]) + self.assertIs(v, global_vars[0]) + + orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(graph=graph1) + del graph1 # To avoid accidental references in code involving graph2. + + with ops.Graph().as_default() as graph2: + meta_graph.import_scoped_meta_graph(orig_meta_graph) + global_vars = graph2.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + trainable_vars = graph2.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) + self.assertEqual(len(global_vars), 1) + self.assertEqual(len(trainable_vars), 1) + # A single instance of Variable is shared among the collections: + self.assertIs(global_vars[0], trainable_vars[0]) + @test_util.with_c_api class ScopedMetaGraphTest(test.TestCase): @@ -882,22 +905,12 @@ class ExportImportAcrossScopesTest(test.TestCase): with variable_scope.variable_scope("importA/keepA"): graph_fn(use_resource=use_resource) - if use_resource: - # Bringing in a collection that contains ResourceVariables adds ops - # to the graph, so mimic the same behavior. - for collection_key in sorted([ - ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.TRAINABLE_VARIABLES, - ]): - for var in expected_graph.get_collection(collection_key): - var._read_variable_op() - result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0] expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0] if use_resource: # Clear all shared_name attributes before comparing, since they are - # supposed to be orthogonal to scopes. + # orthogonal to scopes and are not updated on export/import. for meta_graph_def in [result, expected]: for node in meta_graph_def.graph_def.node: shared_name_attr = "shared_name" diff --git a/tensorflow/python/framework/op_def_library_test.py b/tensorflow/python/framework/op_def_library_test.py index 817007ce6c18e11d19038e09d77a8f27bd7eca91..84ca062ade3b32c37212ba2d5b7eb9c64fb1dfa5 100644 --- a/tensorflow/python/framework/op_def_library_test.py +++ b/tensorflow/python/framework/op_def_library_test.py @@ -42,7 +42,7 @@ class OpDefLibraryTest(test_util.TensorFlowTestCase): def setUp(self): self._lib = test_ops._op_def_lib - def _add_op(self, ascii): + def _add_op(self, ascii): # pylint: disable=redefined-builtin op_def = op_def_pb2.OpDef() text_format.Merge(ascii, op_def) self._lib.add_op(op_def) @@ -1336,7 +1336,7 @@ class OpDefLibraryGraphTest(test_util.TensorFlowTestCase): def setUp(self): self._lib = test_ops._op_def_lib - def _add_op(self, ascii): + def _add_op(self, ascii): # pylint: disable=redefined-builtin op_def = op_def_pb2.OpDef() text_format.Merge(ascii, op_def) self._lib.add_op(op_def) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index ea589cc4d401607621d916f775f30986bdffba8f..f5dde3a35840a531ddd77b70284a39852137941e 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -368,8 +368,8 @@ class Tensor(_TensorLike): A `TensorShape` representing the shape of this tensor. """ - if _USE_C_API: - graph = self._op._graph._c_graph # pylint: disable=protected-access + graph = self._op._graph._c_graph # pylint: disable=protected-access + if graph: with errors.raise_exception_on_not_ok_status() as status: num_dims = c_api.TF_GraphGetTensorNumDims(graph, self._as_tf_output(), status) @@ -395,10 +395,10 @@ class Tensor(_TensorLike): "Tensor._shape cannot be assigned, use Tensor.set_shape instead.") def __iter__(self): - if context.in_graph_mode(): + if not context.executing_eagerly(): raise TypeError( - "`Tensor` objects are not iterable when eager execution is not " - "enabled. To iterate over this tensor use `tf.map_fn`.") + "Tensor objects are not iterable when eager execution is not " + "enabled. To iterate over this tensor use tf.map_fn.") shape = self._shape_tuple() if shape is None: raise TypeError("Cannot iterate over a tensor with unknown shape.") @@ -466,7 +466,7 @@ class Tensor(_TensorLike): ValueError: If `shape` is not compatible with the current shape of this tensor. """ - if not _USE_C_API: + if not self._op._graph._c_graph: # pylint: disable=protected-access # ASIM self._shape_val = self._shape_val.merge_with(shape) return if not isinstance(shape, tensor_shape.TensorShape): @@ -772,7 +772,7 @@ class _EagerTensorBase(Tensor): six.raise_from(core._status_to_exception(e.code, e.message), None) # Record the copy on tape and define backprop copy as well. - if not context.in_graph_mode(): + if context.executing_eagerly(): self_device = self.device def grad_fun(dresult): return [dresult._copy(device_name=self_device)] @@ -782,7 +782,11 @@ class _EagerTensorBase(Tensor): @property def shape(self): - return tensor_shape.TensorShape(self._shape_tuple()) + if self._tensor_shape is None: # pylint: disable=access-member-before-definition + # `_tensor_shape` is declared and defined in the definition of + # `EagerTensor`, in C. + self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple()) + return self._tensor_shape def get_shape(self): """Alias of Tensor.shape.""" @@ -989,7 +993,7 @@ def internal_convert_to_tensor(value, """ if ctx is None: ctx = context.context() - if ctx.in_eager_mode(): + if ctx.executing_eagerly(): # Fast path for EagerTensors that don't need any conversion. if isinstance(value, EagerTensor): # Note that we don't check that value's dtype matches the dtype @@ -1618,7 +1622,7 @@ class Operation(object): for i, x in zip(inputs, input_types)): raise TypeError("In op '%s', input types (%s) are not compatible " "with expected types (%s)" % - (self.node_def.name, [i.dtype for i in inputs], + (node_def.name, [i.dtype for i in inputs], input_types)) # Build the list of control inputs. @@ -1657,7 +1661,7 @@ class Operation(object): self._c_op = c_op elif self._graph._c_graph: # pylint: disable=protected-access if op_def is None: - op_def = self._graph._registered_ops[node_def.op] + op_def = self._graph._get_op_def(node_def.op) # TODO(skyewm): op_def_library.apply_op() flattens the incoming inputs. # Refactor so we don't have to do this here. grouped_inputs = self._reconstruct_sequence_inputs( @@ -2164,16 +2168,7 @@ class Operation(object): """ # pylint: enable=line-too-long if self._c_op: - with c_api_util.tf_buffer() as buf: - with errors.raise_exception_on_not_ok_status() as status: - # pylint: disable=protected-access - c_api.TF_GraphGetOpDef(self._graph._c_graph, - compat.as_bytes(self.type), buf, status) - # pylint: enable=protected-access - data = c_api.TF_GetBuffer(buf) - op_def = op_def_pb2.OpDef() - op_def.ParseFromString(compat.as_bytes(data)) - return op_def + return self._graph._get_op_def(self.type) else: return self._op_def_val @@ -2699,15 +2694,20 @@ class Graph(object): def __init__(self): """Creates a new, empty Graph.""" - # Protects the core state that may be accessed by multiple readers. - # Only state that can be returned via public accessors (`as_graph_def()`, - # `get_operations()`, `as_graph_element()`, `get_collection()`, and - # `get_collection_ref()`) is by the lock. Thread-safety is provided on a - # best-effort basis to support buggy programs, and is not guaranteed by the - # public `tf.Graph` API. + # Protects core state that can be returned via public accessors, as well as + # synchronizes Session.run calls with methods that create and mutate ops + # (e.g. Graph.create_op()). This synchronization is necessary because it's + # illegal to modify an operation after it's been run. Thread-safety is + # provided on a best-effort basis to support buggy programs, and is not + # guaranteed by the public `tf.Graph` API. + # + # The lock must be reentrant because create_op can be called recursively due + # to control flow. Without a reentrant lock, many methods would also need a + # "locked" version or parameter (including generated code). + # # NOTE(mrry): This does not protect the various stacks. A warning will # be reported if these are used from multiple threads - self._lock = threading.Lock() + self._lock = threading.RLock() self._nodes_by_id = dict() # GUARDED_BY(self._lock) self._next_id_counter = 0 # GUARDED_BY(self._lock) self._nodes_by_name = dict() # GUARDED_BY(self._lock) @@ -2716,15 +2716,21 @@ class Graph(object): self._name_stack = "" # Maps a name used in the graph to the next id to use for that name. self._names_in_use = {} + self._stack_state_is_thread_local = False + self._thread_local = threading.local() # Functions that will be applied to choose a device if none is specified. - self._device_function_stack = [] + # After switch_to_thread_local(), self._thread_local._device_function_stack + # is used instead. + self._graph_device_function_stack = [] # Default original_op applied to new ops. self._default_original_op = None # Current control flow context. It could be either CondContext or # WhileContext defined in ops/control_flow_ops.py self._control_flow_context = None # A new node will depend of the union of all of the nodes in the stack. - self._control_dependencies_stack = [] + # After switch_to_thread_local(), + # self._thread_local._control_dependencies_stack is used instead. + self._graph_control_dependencies_stack = [] # Arbitrary collections of objects. self._collections = {} # The graph-level random seed @@ -2746,8 +2752,9 @@ class Graph(object): producer=versions.GRAPH_DEF_VERSION, min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER) self._building_function = False - # Stack of colocate_with ops - self._colocation_stack = [] + # Stack of colocate_with ops. After switch_to_thread_local(), + # self._thread_local._colocation_stack is used instead. + self._graph_colocation_stack = [] # Set of tensors that are dangerous to feed! self._unfeedable_tensors = set() # Set of operations that are dangerous to fetch! @@ -2770,16 +2777,19 @@ class Graph(object): # TODO(skyewm): fold as much of the above as possible into the C # implementation - if _USE_C_API or self._use_c_api_hack(): + if self._use_c_api_hack(): self._scoped_c_graph = c_api_util.ScopedTFGraph() + # The C API requires all ops to have shape functions. Disable this + # requirement (many custom ops do not have shape functions, and we don't + # want to break these existing cases). + c_api.SetRequireShapeInferenceFns(self._c_graph, False) else: self._scoped_c_graph = None - self._variable_creator_stack = [] # TODO(apassos) remove once the C API is used by default. def _use_c_api_hack(self): """Temporary hack; can be overridden to force C API usage.""" - return False + return _USE_C_API def _convert_stack(self, stack, include_func_start_lineno=False): """Converts a stack extracted using _extract_stack() to a traceback stack. @@ -2815,17 +2825,26 @@ class Graph(object): # frozen, and this functionality is still not ready for public visibility. @tf_contextlib.contextmanager def _variable_creator_scope(self, creator): + # This step makes a copy of the existing stack, and it also initializes + # self._thread_local._variable_creator_stack if it doesn't exist yet. old = list(self._variable_creator_stack) - self._variable_creator_stack.append(creator) + self._thread_local._variable_creator_stack.append(creator) try: yield finally: - self._variable_creator_stack = old + self._thread_local._variable_creator_stack = old # Note: this method is private because the API of tf.Graph() is public and # frozen, and this functionality is still not ready for public visibility. - def _get_variable_creator_stack(self): - return list(self._variable_creator_stack) + @property + def _variable_creator_stack(self): + if not hasattr(self._thread_local, "_variable_creator_stack"): + self._thread_local._variable_creator_stack = [] + return list(self._thread_local._variable_creator_stack) + + @_variable_creator_stack.setter + def _variable_creator_stack(self, variable_creator_stack): + self._thread_local._variable_creator_stack = variable_creator_stack def _extract_stack(self): """A lightweight, extensible re-implementation of traceback.extract_stack. @@ -3039,7 +3058,7 @@ class Graph(object): """ # pylint: enable=line-too-long - if _USE_C_API: + if self._c_graph: with self._lock: with c_api_util.tf_buffer() as buf: with errors.raise_exception_on_not_ok_status() as status: @@ -3257,17 +3276,20 @@ class Graph(object): input_ops = set([t.op for t in inputs]) control_inputs = self._control_dependencies_for_inputs(input_ops) - ret = Operation( - node_def, - self, - inputs=inputs, - output_types=dtypes, - control_inputs=control_inputs, - input_types=input_types, - original_op=self._default_original_op, - op_def=op_def) - self._create_op_helper(ret, compute_shapes=compute_shapes, - compute_device=compute_device) + # _create_op_helper mutates the new Operation. _lock ensures a Session.run + # call cannot occur between creating and mutating the op. + with self._lock: + ret = Operation( + node_def, + self, + inputs=inputs, + output_types=dtypes, + control_inputs=control_inputs, + input_types=input_types, + original_op=self._default_original_op, + op_def=op_def) + self._create_op_helper(ret, compute_shapes=compute_shapes, + compute_device=compute_device) return ret def _create_op_from_tf_operation(self, c_op, compute_device=True): @@ -3359,9 +3381,9 @@ class Graph(object): if (op.device and pydev.canonical_name(op.device) != pydev.canonical_name(colocation_op.device)): logging.warning("Tried to colocate %s with an op %s that had " - "a different device: %s vs %s. " - "Ignoring colocation property.", op.name, - colocation_op.name, op.device, + "a different device: %s vs %s. Postponing " + "error-checking until all devices are assigned.", + op.name, colocation_op.name, op.device, colocation_op.device) else: op._set_device(colocation_op.device) # pylint: disable=protected-access @@ -3377,8 +3399,8 @@ class Graph(object): # (2) "is_stateful" is set in OpDef # (3) "container" attribute is in OpDef # (4) "container" attribute is None - if (self._container and op.type in self._registered_ops and - self._registered_ops[op.type].is_stateful): + # TODO(skyewm): remove op.op_def check when _USE_C_API is removed. + if self._container and op.op_def and op.op_def.is_stateful: try: container_attr = op.get_attr("container") except ValueError: @@ -3658,6 +3680,22 @@ class Graph(object): def _last_id(self): return self._next_id_counter + def _get_op_def(self, type): # pylint: disable=redefined-builtin + """Returns the `OpDef` proto for `type`. `type` is a string.""" + if self._c_graph: + with c_api_util.tf_buffer() as buf: + with errors.raise_exception_on_not_ok_status() as status: + # pylint: disable=protected-access + c_api.TF_GraphGetOpDef(self._c_graph, + compat.as_bytes(type), buf, status) + # pylint: enable=protected-access + data = c_api.TF_GetBuffer(buf) + op_def = op_def_pb2.OpDef() + op_def.ParseFromString(compat.as_bytes(data)) + return op_def + else: + return self._registered_ops[type] + def as_default(self): """Returns a context manager that makes this `Graph` the default graph. @@ -4662,6 +4700,79 @@ class Graph(object): else: return tensor_or_op not in self._unfetchable_ops + def switch_to_thread_local(self): + """Make device, colocation and dependencies stacks thread-local. + + Device, colocation and dependencies stacks are not thread-local be default. + If multiple threads access them, then the state is shared. This means that + one thread may affect the behavior of another thread. + + After this method is called, the stacks become thread-local. If multiple + threads access them, then the state is not shared. Each thread uses its own + value; a thread doesn't affect other threads by mutating such a stack. + + The initial value for every thread's stack is set to the current value + of the stack when `switch_to_thread_local()` was first called. + """ + if not self._stack_state_is_thread_local: + self._stack_state_is_thread_local = True + + @property + def _device_function_stack(self): + if self._stack_state_is_thread_local: + # This may be called from a thread where device_function_stack doesn't yet + # exist. + if not hasattr(self._thread_local, "_device_function_stack"): + self._thread_local._device_function_stack = ( + self._graph_device_function_stack[:]) + return self._thread_local._device_function_stack + else: + return self._graph_device_function_stack + + @_device_function_stack.setter + def _device_function_stack(self, device_function_stack): + if self._stack_state_is_thread_local: + self._thread_local._device_function_stack = device_function_stack + else: + self._graph_device_function_stack = device_function_stack + + @property + def _colocation_stack(self): + if self._stack_state_is_thread_local: + # This may be called from a thread where colocation_stack doesn't yet + # exist. + if not hasattr(self._thread_local, "_colocation_stack"): + self._thread_local._colocation_stack = self._graph_colocation_stack[:] + return self._thread_local._colocation_stack + else: + return self._graph_colocation_stack + + @_colocation_stack.setter + def _colocation_stack(self, colocation_stack): + if self._stack_state_is_thread_local: + self._thread_local._colocation_stack = colocation_stack + else: + self._graph_colocation_stack = colocation_stack + + @property + def _control_dependencies_stack(self): + if self._stack_state_is_thread_local: + # This may be called from a thread where control_dependencies_stack + # doesn't yet exist. + if not hasattr(self._thread_local, "_control_dependencies_stack"): + self._thread_local._control_dependencies_stack = ( + self._graph_control_dependencies_stack[:]) + return self._thread_local._control_dependencies_stack + else: + return self._graph_control_dependencies_stack + + @_control_dependencies_stack.setter + def _control_dependencies_stack(self, control_dependencies): + if self._stack_state_is_thread_local: + self._thread_local._control_dependencies_stack = control_dependencies + else: + self._graph_control_dependencies_stack = control_dependencies + # TODO(agarwal): currently device directives in an outer eager scope will not # apply to inner graph mode code. Fix that. @@ -4686,15 +4797,15 @@ def device(device_name_or_function): Raises: RuntimeError: If eager execution is enabled and a function is passed in. """ - if context.in_graph_mode(): - return get_default_graph().device(device_name_or_function) - else: + if context.executing_eagerly(): # TODO(agarwal): support device functions in EAGER mode. if callable(device_name_or_function): raise RuntimeError( "tf.device does not support functions when eager execution " "is enabled.") return context.device(device_name_or_function) + else: + return get_default_graph().device(device_name_or_function) @tf_export("container") @@ -4713,13 +4824,20 @@ def container(container_name): @tf_export("colocate_with") def colocate_with(op, ignore_existing=False): - if context.in_graph_mode(): - return get_default_graph().colocate_with(op, ignore_existing) - else: + if context.executing_eagerly(): if op is not None: return device(op.device) else: return _NullContextmanager() + else: + default_graph = get_default_graph() + if isinstance(op, EagerTensor): + if default_graph.building_function: + op = internal_convert_to_tensor(op) + else: + raise ValueError("Encountered an Eager-defined Tensor during graph " + "construction, but a function was not being built.") + return default_graph.colocate_with(op, ignore_existing) @tf_export("control_dependencies") @@ -4739,10 +4857,10 @@ def control_dependencies(control_inputs): A context manager that specifies control dependencies for all operations constructed within the context. """ - if context.in_graph_mode(): - return get_default_graph().control_dependencies(control_inputs) - else: + if context.executing_eagerly(): return _NullContextmanager() + else: + return get_default_graph().control_dependencies(control_inputs) class _DefaultStack(threading.local): @@ -5005,75 +5123,106 @@ def init_scope(): """ # pylint: enable=g-doc-return-or-yield,line-too-long - in_graph_mode = context.in_graph_mode() - # Retrieve the active name scope: entering an `init_scope` preserves - # the name scope of the current context. - if in_graph_mode: + if context.executing_eagerly(): + # Fastpath. + with tape.stop_recording(): + yield + else: + # Retrieve the active name scope: entering an `init_scope` preserves + # the name scope of the current context. default_graph = get_default_graph() scope = default_graph.get_name_scope() - else: - scope = context.context().scope_name - if scope and scope[-1] != '/': - # Names that end with trailing slashes are treated by `name_scope` as - # absolute. - scope = scope + '/' - - outer_context = None - if in_graph_mode and not _default_graph_stack.stack: - outer_context = default_graph.as_default - else: - for stack_entry in reversed(context.context_stack.stack): - if not stack_entry.is_building_function: - outer_context = stack_entry.enter_context_fn - break + if scope and scope[-1] != '/': + # Names that end with trailing slashes are treated by `name_scope` as + # absolute. + scope = scope + '/' + + outer_context = None + if not _default_graph_stack.stack: + # If the default graph stack is empty, then we cannot be building a + # function. Install the global graph (which, in this case, is also the + # default graph) as the outer context. + if default_graph.building_function: + raise RuntimeError("The global graph is building a function.") + outer_context = default_graph.as_default + else: + # Find a context that is not building a function. + for stack_entry in reversed(context.context_stack.stack): + if not stack_entry.is_building_function: + outer_context = stack_entry.enter_context_fn + break + + if outer_context is None: + # As a last resort, obtain the global default graph; this graph doesn't + # necessarily live on the graph stack (and hence it doesn't necessarily + # live on the context stack), but it is stored in the graph stack's + # encapsulating object. + outer_context = _default_graph_stack._GetGlobalDefaultGraph().as_default # pylint: disable=protected-access - if outer_context is None: - raise AssertionError("All graphs are building functions, and no " + if outer_context is None: + # Sanity check; this shouldn't be triggered. + raise RuntimeError("All graphs are building functions, and no " "eager context was previously active.") - try: with outer_context(), name_scope(scope), control_dependencies( None), tape.stop_recording(): yield - finally: - pass +@tf_export("enable_eager_execution") def enable_eager_execution(config=None, device_policy=None): - """Enables, for the rest of the lifetime of this program, eager execution. + """Enables eager execution for the lifetime of this program. - If not called immediately on startup risks creating breakage and bugs. + Eager execution provides an imperative interface to TensorFlow. With eager + execution enabled, TensorFlow functions execute operations immediately (as + opposed to adding to a graph to be executed later in a @{tf.Session}) and + return concrete values (as opposed to symbolic references to a node in a + computational graph). - Example: + For example: ```python - tfe.enable_eager_execution() + tf.enable_eager_execution() # After eager execution is enabled, operations are executed as they are - # defined and `Tensor`s hold concrete values, which can be accessed as - # `numpy.ndarray`s through the `numpy()` method. + # defined and Tensor objects hold concrete values, which can be accessed as + # numpy.ndarray`s through the numpy() method. assert tf.multiply(6, 7).numpy() == 42 ``` + Eager execution cannot be enabled after TensorFlow APIs have been used to + create or execute graphs. It is typically recommended to invoke this function + at program startup and not in a library (as most libraries should be usable + both with and without eager execution). + Args: - config: (Optional.) A `ConfigProto` protocol buffer with configuration - options for the Context. Note that a lot of these options may be - currently unimplemented or irrelevant when eager execution is enabled. - device_policy: (Optional.) What policy to use when trying to run an - operation on a device with inputs which are not on that device. + config: (Optional.) A @{tf.ConfigProto} to use to configure the environment + in which operations are executed. Note that @{tf.ConfigProto} is also + used to configure graph execution (via @{tf.Session}) and many options + within `tf.ConfigProto` are not implemented (or are irrelevant) when + eager execution is enabled. + device_policy: (Optional.) Policy controlling how operations requiring + inputs on a specific device (e.g., a GPU 0) handle inputs on a different + device (e.g. GPU 1 or CPU). Valid values: - tfe.DEVICE_PLACEMENT_EXPLICIT: raises an error if the placement is not - correct. - tfe.DEVICE_PLACEMENT_WARN: copies the tensors which are not on the - right device but raises a warning. - tfe.DEVICE_PLACEMENT_SILENT: silently copies the tensors. This might - hide performance problems. - tfe.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies int32 tensors, - raising errors on the other ones. + + - tf.contrib.eager.DEVICE_PLACEMENT_EXPLICIT: raises an error if the + placement is not correct. + + - tf.contrib.eager.DEVICE_PLACEMENT_WARN: copies the tensors which are not + on the right device but logs a warning. + + - tf.contrib.eager.DEVICE_PLACEMENT_SILENT: silently copies the tensors. + Note that this may hide performance problems as there is no notification + provided when operations are blocked on the tensor being copied between + devices. + + - tf.contrib.eager.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies + int32 tensors, raising errors on the other ones. Raises: - ValueError: If trying to create a context after using graph operations - or if trying to create a context with nontrivial options which differ - from those of the existing context. + ValueError: If eager execution is enabled after creating/executing a + TensorFlow graph, or if options provided conflict with a previous call + to this function. """ if config is not None and not isinstance(config, config_pb2.ConfigProto): raise TypeError( @@ -5083,7 +5232,7 @@ def enable_eager_execution(config=None, device_policy=None): context.DEVICE_PLACEMENT_SILENT, context.DEVICE_PLACEMENT_SILENT_FOR_INT32): raise ValueError( - "device_policy must be one of None, tfe.DEVICE_PLACEMENT_*" + "device_policy must be one of None, tf.contrib.eager.DEVICE_PLACEMENT_*" ) # pylint: disable=protected-access if context._default_mode == context.GRAPH_MODE: @@ -5092,7 +5241,7 @@ def enable_eager_execution(config=None, device_policy=None): _default_graph_stack._global_default_graph is not None) if graph_mode_has_been_used: raise ValueError( - "tfe.enable_eager_execution has to be called at program startup.") + "tf.enable_eager_execution must be called at program startup.") context._default_mode = context.EAGER_MODE if context._context is None: context._context = context.Context(config=config, @@ -5115,7 +5264,7 @@ def enable_eager_execution(config=None, device_policy=None): context._context._device_policy)) else: raise ValueError( - "tfe.enable_eager_execution has to be called at program startup.") + "tf.enable_eager_execution must be called at program startup.") def eager_run(main=None, argv=None): @@ -5530,6 +5679,9 @@ def get_all_collection_keys(): return get_default_graph().get_all_collection_keys() +name_scope_cache = {} + + # Named like a function for backwards compatibility with the # @tf_contextlib.contextmanager version, which was switched to a class to avoid # some object creation overhead. @@ -5572,7 +5724,7 @@ class name_scope(object): # pylint: disable=invalid-name self._default_name = default_name self._values = values self._ctx = context.context() - self._in_eager_mode = self._ctx.in_eager_mode() + self._in_eager_mode = self._ctx.executing_eagerly() def __enter__(self): """Start the scope block. @@ -5589,7 +5741,11 @@ class name_scope(object): # pylint: disable=invalid-name if not self._name: scope_name = "" else: - if self._name[-1] == "/": + cache_key = self._name, self._old_name, self._default_name + if cache_key in name_scope_cache: + self._ctx.scope_name = name_scope_cache[cache_key] + return self._ctx.scope_name + elif self._name[-1] == "/": # A trailing slash breaks out of nested name scopes, indicating a # fully specified scope name, for compatibility with Graph.name_scope. scope_name = self._name @@ -5598,6 +5754,7 @@ class name_scope(object): # pylint: disable=invalid-name scope_name = ( self._old_name + name_with_trailing_slash if self._old_name else name_with_trailing_slash) + name_scope_cache[cache_key] = scope_name self._ctx.scope_name = scope_name return scope_name else: @@ -5746,7 +5903,7 @@ def get_from_proto_function(collection_name): def _assert_collection_is_ok(collection_name): - if context.in_eager_mode(): + if context.executing_eagerly(): if collection_name in GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access raise ValueError("When Eager Execution is enabled, variable " "collections are not supported.") diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index c5e177d521013417d28fb01a975208ba5e4cf90b..c294f830bc334738d66824a60ca535da4a8f3b2b 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import gc +import threading import weakref from tensorflow.core.framework import attr_value_pb2 @@ -916,7 +917,6 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): op = g.get_operation_by_name("myloop/myop") self.assertIsNotNone(op) - self.assertEqual(len(op.control_inputs), 1) # External control dep is removed and replaced with internal control dep self.assertNotEqual(op.control_inputs[0], c.op) self.assertIsNotNone(op.control_inputs[0]._get_control_flow_context()) @@ -1382,6 +1382,180 @@ class DeviceTest(test_util.TensorFlowTestCase): """, gd) +@test_util.with_c_api +class MultithreadedGraphStateTest(test_util.TensorFlowTestCase): + + class TestThread(threading.Thread): + + def __init__(self, graph, replica_id): + super(MultithreadedGraphStateTest.TestThread, self).__init__() + self._graph = graph + self._replica_id = replica_id + # This thread sets this event when it mutated the graph. The caller can + # wait for that. + self.has_mutated_graph = threading.Event() + # This thread waits for when it should continue. The caller can set this + # event. + self.should_continue = threading.Event() + + def run(self): + # Mutate a graph's stack, then set `has_mutated_graph`, then wait for + # `should_continue`, then add an op to the graph affected by the graph's + # stack. + raise NotImplementedError("must be implemented in descendants") + + def testDeviceFunctionStack(self): + + class DeviceSettingThread(self.TestThread): + + def run(self): + with g.device("/job:worker/replica:{}".format(self._replica_id)): + self.has_mutated_graph.set() + self.should_continue.wait() + self.should_continue.clear() + g.create_op( + "FloatOutput", [], [dtypes.float32], + name="FloatOutput_{}".format(self._replica_id)) + + g = ops.Graph() + # If `switch_to_thread` isn't called, then device placement of the ops + # below is not deterministic. + g.switch_to_thread_local() + threads = [DeviceSettingThread(g, i) for i in range(3)] + for t in threads: + t.start() + t.has_mutated_graph.wait() + t.has_mutated_graph.clear() + for t in threads: + t.should_continue.set() + t.join() + + gd = g.as_graph_def() + self.assertProtoEqualsVersion(""" + node { name: "FloatOutput_0" op: "FloatOutput" + device: "/job:worker/replica:0" } + node { name: "FloatOutput_1" op: "FloatOutput" + device: "/job:worker/replica:1" } + node { name: "FloatOutput_2" op: "FloatOutput" + device: "/job:worker/replica:2" } + """, gd) + + def testColocateWith(self): + + class ColocatingThread(self.TestThread): + + def __init__(self, graph, replica_id, op_to_colocate_with): + super(ColocatingThread, self).__init__(graph, replica_id) + self._op_to_colocate_with = op_to_colocate_with + + def run(self): + with g.colocate_with(self._op_to_colocate_with): + self.has_mutated_graph.set() + self.should_continue.wait() + self.should_continue.clear() + g.create_op( + "FloatOutput", [], [dtypes.float32], + name="FloatOutput_{}".format(self._replica_id)) + + g = ops.Graph() + ops_to_colocate_with = [] + for i in range(3): + with g.device("/job:worker/replica:{}".format(i)): + ops_to_colocate_with.append( + g.create_op( + "FloatOutput", [], [dtypes.float32], + name="ColocateWithMe_{}".format(i))) + + # If `switch_to_thread` isn't called, then `device` and `attr` values for + # the ops below are not deterministic. + g.switch_to_thread_local() + threads = [ + ColocatingThread(g, i, ops_to_colocate_with[i]) for i in range(3) + ] + for t in threads: + t.start() + t.has_mutated_graph.wait() + t.has_mutated_graph.clear() + for t in threads: + t.should_continue.set() + t.join() + + gd = g.as_graph_def() + self.assertProtoEqualsVersion(""" + node { name: "ColocateWithMe_0" op: "FloatOutput" + device: "/job:worker/replica:0" } + node { name: "ColocateWithMe_1" op: "FloatOutput" + device: "/job:worker/replica:1" } + node { name: "ColocateWithMe_2" op: "FloatOutput" + device: "/job:worker/replica:2" } + node { name: "FloatOutput_0" op: "FloatOutput" + device: "/job:worker/replica:0" + attr { key: "_class" + value { list { + s: "loc:@ColocateWithMe_0"}}}} + node { name: "FloatOutput_1" op: "FloatOutput" + device: "/job:worker/replica:1" + attr { key: "_class" + value { list { + s: "loc:@ColocateWithMe_1"}}}} + node { name: "FloatOutput_2" op: "FloatOutput" + device: "/job:worker/replica:2" + attr { key: "_class" + value { list { + s: "loc:@ColocateWithMe_2"}}}} + """, gd) + + def testControlDependencies(self): + + class DependingThread(self.TestThread): + + def __init__(self, graph, replica_id, dependency_op): + super(DependingThread, self).__init__(graph, replica_id) + self._dependency_op = dependency_op + + def run(self): + with g.control_dependencies([self._dependency_op]): + self.has_mutated_graph.set() + self.should_continue.wait() + self.should_continue.clear() + g.create_op( + "FloatOutput", [], [dtypes.float32], + name="FloatOutput_{}".format(self._replica_id)) + + g = ops.Graph() + dependency_ops = [] + for i in range(3): + dependency_ops.append( + g.create_op( + "FloatOutput", [], [dtypes.float32], + name="ColocateWithMe_{}".format(i))) + + # If `switch_to_thread` isn't called, then `input` values for the ops below + # are not deterministic. + g.switch_to_thread_local() + threads = [DependingThread(g, i, dependency_ops[i]) for i in range(3)] + for t in threads: + t.start() + t.has_mutated_graph.wait() + t.has_mutated_graph.clear() + for t in threads: + t.should_continue.set() + t.join() + + gd = g.as_graph_def() + self.assertProtoEqualsVersion(""" + node { name: "ColocateWithMe_0" op: "FloatOutput" } + node { name: "ColocateWithMe_1" op: "FloatOutput" } + node { name: "ColocateWithMe_2" op: "FloatOutput" } + node { name: "FloatOutput_0" op: "FloatOutput" + input: "^ColocateWithMe_0" } + node { name: "FloatOutput_1" op: "FloatOutput" + input: "^ColocateWithMe_1" } + node { name: "FloatOutput_2" op: "FloatOutput" + input: "^ColocateWithMe_2" } + """, gd) + + @test_util.with_c_api class ObjectWithName(object): @@ -1589,7 +1763,13 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): return constant_op.constant(2.0) future.calls = 0 - if context.in_graph_mode(): + if context.executing_eagerly(): + a = constant_op.constant(1.0) + b = future() + with ops.control_dependencies([a, b]): + c = constant_op.constant(3.0) + self.assertEqual(future.calls, 1) + else: g = ops.Graph() with g.as_default(): a = constant_op.constant(1.0) @@ -1598,12 +1778,6 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): c = constant_op.constant(3.0) self.assertEqual(c.op.control_inputs, [a.op, b.op]) self.assertEqual(future.calls, 1) - else: - a = constant_op.constant(1.0) - b = future() - with ops.control_dependencies([a, b]): - c = constant_op.constant(3.0) - self.assertEqual(future.calls, 1) def testBasicWithConversion(self): g = ops.Graph() @@ -1976,19 +2150,11 @@ class InitScopeTest(test_util.TensorFlowTestCase): with ops.init_scope(): # Because g is building a function, init_scope should # escape out to the eager context. - self.assertTrue(context.in_eager_mode()) + self.assertTrue(context.executing_eagerly()) # g should be reinstated as the default graph, and the # graph context should be re-entered. self.assertIs(g, ops.get_default_graph()) - self.assertTrue(context.in_graph_mode()) - - def testAllGraphsBuildingFunctionsRaisesError(self): - g = ops.Graph() - g._building_function = True # pylint: disable=protected-access - with g.as_default(): - with self.assertRaises(AssertionError): - with ops.init_scope(): - pass + self.assertFalse(context.executing_eagerly()) def testStaysInEagerWhenOnlyEagerContextActive(self): with context.eager_mode(): @@ -2067,6 +2233,29 @@ class InitScopeTest(test_util.TensorFlowTestCase): self.assertEqual(4, int(compiled_outer(inner=compiled_inner))) self.assertEqual(7, int(compiled_outer(inner=compiled_inner))) + def testFallsBackToGlobalGraphWhenAllGraphsAreBuildingFunctions(self): + with context.graph_mode(): + ops.reset_default_graph() + # This doesn't push anything onto the graph stack, but it does + # set the stack's global graph. + global_graph = ops.get_default_graph() + fn_graph = ops.Graph() + + # pylint: disable=protected-access + fn_graph._building_function = True + self.assertEqual(len(ops._default_graph_stack.stack), 0) + with fn_graph.as_default(): + self.assertEqual(len(ops._default_graph_stack.stack), 1) + with ops.init_scope(): + self.assertGreater(len(ops._default_graph_stack.stack), 1) + dummy = constant_op.constant(1.0) + self.assertEqual(len(ops._default_graph_stack.stack), 1) + # Note that the global graph is _not_ on the graph stack. + self.assertEqual(len(ops._default_graph_stack.stack), 0) + # Ensure that `dummy` was added to the global graph. + self.assertEqual(global_graph, dummy.graph) + # pylint: enable=protected-access + def testInstallsDefaultGraphWhenGraphStackIsEmptyInGraphMode(self): with context.graph_mode(): # pylint: disable=protected-access @@ -2088,12 +2277,13 @@ class InitScopeTest(test_util.TensorFlowTestCase): with context.eager_mode(): def foo(): with ops.name_scope("inner"), ops.init_scope(): - if context.in_graph_mode(): - self.assertEqual(ops.get_name_scope(), "inner") - else: + if context.executing_eagerly(): # A trailing slash is always appended when eager execution is # enabled. self.assertEqual(context.context().scope_name, "inner/") + else: + self.assertEqual(ops.get_name_scope(), "inner") + foo() self.assertEqual(ops.get_name_scope(), "") foo_compiled = eager_function.defun(foo) @@ -2703,7 +2893,7 @@ class OutputTypesTest(test_util.TensorFlowTestCase): with g.as_default(): x = constant_op.constant([1, 1, 2, 4, 4, 4, 7, 8, 8], dtype=dtypes.double) - y, _ = gen_array_ops._unique(x) + y, _ = gen_array_ops.unique(x) self.assertEqual([types_pb2.DT_DOUBLE, types_pb2.DT_INT32], y.op._output_types) # pylint: disable=protected-access diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc index 85cba59be4d8337322d6057d818341428649ecba..64d214a07fdf8997d57363c47b2636619756e3bf 100644 --- a/tensorflow/python/framework/python_op_gen.cc +++ b/tensorflow/python/framework/python_op_gen.cc @@ -75,6 +75,36 @@ bool IsPythonReserved(const string& s) { return kPythonReserved->count(s) > 0; } +bool IsOpWithUnderscorePrefix(const string& s) { + static const std::set* const kUnderscoreOps = new std::set( + {// Lowercase built-in functions and types in Python, from: + // [x for x in dir(__builtins__) if x[0].islower()] + // These need to be excluded so they don't conflict with actual built-in + // functions since we use '*' imports. + "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray", + "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile", + "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod", + "enumerate", "eval", "execfile", "exit", "file", "filter", "float", + "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help", + "hex", "id", "input", "int", "intern", "isinstance", "issubclass", + "iter", "len", "license", "list", "locals", "long", "map", "max", + "memoryview", "min", "next", "object", "oct", "open", "ord", "pow", + "print", "property", "quit", "range", "raw_input", "reduce", "reload", + "repr", "reversed", "round", "set", "setattr", "slice", "sorted", + "staticmethod", "str", "sum", "super", "tuple", "type", "unichr", + "unicode", "vars", "xrange", "zip", + // These have the same name as ops defined in Python and might be used + // incorrectly depending on order of '*' imports. + // TODO(annarev): reduce usage of '*' imports and remove these from the + // list. + "fused_batch_norm", "histogram_fixed_width", "stack", + "batch_norm_with_global_normalization", + // TODO(annarev): replace these ops in the next change. + "broadcast_gradient_args", "concat", "enter", "histogram_summary", + "ref_enter", "ref_identity", "scalar_summary"}); + return kUnderscoreOps->count(s) > 0; +} + string AvoidPythonReserved(const string& s) { if (IsPythonReserved(s)) return strings::StrCat(s, "_"); return s; @@ -580,8 +610,13 @@ void GenPythonOp::AddExport() { strings::StrAppend(&result_, ")\n"); } +void GenPythonOp::AddDefLine(const string& function_name, + const string& parameters) { + strings::StrAppend(&result_, "def ", function_name, "(", parameters, "):\n"); +} + void GenPythonOp::AddDefLine(const string& parameters) { - strings::StrAppend(&result_, "def ", function_name_, "(", parameters, "):\n"); + AddDefLine(function_name_, parameters); } void GenPythonOp::AddDocStringDescription() { @@ -811,6 +846,7 @@ from tensorflow.python.util.tf_export import tf_export // An op is hidden if either its ApiDef visibility is HIDDEN // or it is in the hidden_ops list. bool is_hidden = api_def->visibility() == ApiDef::HIDDEN; + bool hidden_by_api_def = is_hidden; if (!is_hidden) { for (const string& hidden : hidden_ops) { if (op_def.name() == hidden) { @@ -823,13 +859,22 @@ from tensorflow.python.util.tf_export import tf_export string function_name; python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(), &function_name); - if (is_hidden) function_name = strings::StrCat("_", function_name); - - // When users create custom python wrappers, they may link in the - // default op registry by accident, and because they can't - // enumerate all 'hidden' symbols, this guard is to prevent - // instantiating a python reserved word in their wrapper. - if (python_op_gen_internal::IsPythonReserved(function_name)) { + bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name); + + // Prefix an op with underscore if the op is listed in hidden_ops or + // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix. + // Do not add underscores to ops set to HIDDEN in ApiDef otherwise. + // TODO(annarev): don't prefix with underscores even if op is in hidden_ops. + if (is_hidden) { + if (!hidden_by_api_def || is_reserved || + python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) { + function_name = strings::StrCat("_", function_name); + } + } else if (is_reserved) { + // When users create custom python wrappers, they may link in the + // default op registry by accident, and because they can't + // enumerate all 'hidden' symbols, this guard is to prevent + // instantiating a python reserved word in their wrapper. continue; } diff --git a/tensorflow/python/framework/python_op_gen_internal.h b/tensorflow/python/framework/python_op_gen_internal.h index d09b36a3e8247241420649c6a4a950be6edc3c00..e0cfb05f4bdf8afd09957c62a9ba3af1fd0882a6 100644 --- a/tensorflow/python/framework/python_op_gen_internal.h +++ b/tensorflow/python/framework/python_op_gen_internal.h @@ -29,6 +29,9 @@ namespace python_op_gen_internal { // Returns true if s is a Python keyword or built-in. bool IsPythonReserved(const string& s); +// Whether the op should be prefixed with underscore. +bool IsOpWithUnderscorePrefix(const string& s); + // Add a _ to the end of s if necessary to avoid a Python keyword or built-in. string AvoidPythonReserved(const string& s); @@ -73,6 +76,7 @@ class GenPythonOp { protected: // Print: def Function(parameters): + void AddDefLine(const string& function_name, const string& parameters); void AddDefLine(const string& parameters); // Format the Op's descriptions so that it can be a Python docstring. diff --git a/tensorflow/python/framework/random_seed.py b/tensorflow/python/framework/random_seed.py index 1e74a790a3fb0c72b7c0fb1127ffac95f386d85e..b724432e00b0d11de86a0fff9ff31758ad36479f 100644 --- a/tensorflow/python/framework/random_seed.py +++ b/tensorflow/python/framework/random_seed.py @@ -52,20 +52,20 @@ def get_seed(op_seed): A tuple of two integers that should be used for the local seed of this operation. """ - is_graph_mode = context.in_graph_mode() + eager = context.executing_eagerly() - if is_graph_mode: - global_seed = ops.get_default_graph().seed - else: + if eager: global_seed = context.global_seed() + else: + global_seed = ops.get_default_graph().seed if global_seed is not None: if op_seed is None: # pylint: disable=protected-access - if is_graph_mode: - op_seed = ops.get_default_graph()._last_id - else: + if eager: op_seed = context.internal_operation_seed() + else: + op_seed = ops.get_default_graph()._last_id seeds = _truncate_seed(global_seed), _truncate_seed(op_seed) else: @@ -176,7 +176,7 @@ def set_random_seed(seed): Args: seed: integer. """ - if context.in_graph_mode(): - ops.get_default_graph().seed = seed - else: + if context.executing_eagerly(): context.set_global_seed(seed) + else: + ops.get_default_graph().seed = seed diff --git a/tensorflow/python/framework/random_seed_test.py b/tensorflow/python/framework/random_seed_test.py index b4c98ab8b289c850c6171425167bb17606a4162d..194492268631abfa911bd45f13a302c09a2c8bda 100644 --- a/tensorflow/python/framework/random_seed_test.py +++ b/tensorflow/python/framework/random_seed_test.py @@ -40,13 +40,13 @@ class RandomSeedTest(test.TestCase): ((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either ((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument ] - if context.in_graph_mode(): - # 0 will be the default_graph._lastid. - test_cases.append(((1, None), (1, 0))) - else: + if context.executing_eagerly(): # operation seed is random number generated based on global seed. # it's not tested due to possibility of platform or version difference. pass + else: + # 0 will be the default_graph._lastid. + test_cases.append(((1, None), (1, 0))) for tc in test_cases: tinput, toutput = tc[0], tc[1] random_seed.set_random_seed(tinput[0]) diff --git a/tensorflow/python/framework/smart_cond.py b/tensorflow/python/framework/smart_cond.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd9f47d5a337b5284eeb85296216c0de390bcba --- /dev/null +++ b/tensorflow/python/framework/smart_cond.py @@ -0,0 +1,94 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""smart_cond and related utilties.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python import pywrap_tensorflow as c_api +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import control_flow_ops + + +def smart_cond(pred, true_fn=None, false_fn=None, name=None): + """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. + + If `pred` is a bool or has a constant value, we return either `true_fn()` + or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. + + Arguments: + pred: A scalar determining whether to return the result of `true_fn` or + `false_fn`. + true_fn: The callable to be performed if pred is true. + false_fn: The callable to be performed if pred is false. + name: Optional name prefix when using `tf.cond`. + + Returns: + Tensors returned by the call to either `true_fn` or `false_fn`. + + Raises: + TypeError: If `true_fn` or `false_fn` is not callable. + """ + if not callable(true_fn): + raise TypeError("`true_fn` must be callable.") + if not callable(false_fn): + raise TypeError("`false_fn` must be callable.") + + pred_value = smart_constant_value(pred) + if pred_value is not None: + if pred_value: + return true_fn() + else: + return false_fn() + else: + return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn, + name=name) + + +def smart_constant_value(pred): + """Return the bool value for `pred`, or None if `pred` had a dynamic value. + + Arguments: + pred: A scalar, either a Python bool or tensor. + + Returns: + True or False if `pred` has a constant boolean value, None otherwise. + + Raises: + TypeError: If `pred` is not a Tensor or bool. + """ + if pred in {0, 1}: # Accept 1/0 as valid boolean values + pred_value = bool(pred) + elif isinstance(pred, bool): + pred_value = pred + elif isinstance(pred, ops.Tensor): + pred_value = tensor_util.constant_value(pred) + # TODO(skyewm): consider folding this into tensor_util.constant_value when + # _USE_C_API is removed (there may be performance and correctness bugs, so I + # wanted to limit the change hidden behind _USE_C_API). + # pylint: disable=protected-access + if pred_value is None and ops._USE_C_API: + with errors.raise_exception_on_not_ok_status() as status: + pred_value = c_api.TF_TryEvaluateConstant_wrapper( + pred.graph._c_graph, pred._as_tf_output(), status) + # pylint: enable=protected-access + + else: + raise TypeError("`pred` must be a Tensor, or a Python bool, or 1 or 0. " + "Found instead: %s" % pred) + return pred_value diff --git a/tensorflow/python/framework/smart_cond_test.py b/tensorflow/python/framework/smart_cond_test.py new file mode 100644 index 0000000000000000000000000000000000000000..30703559802dabcbd7feb0bf0dee4258828773b5 --- /dev/null +++ b/tensorflow/python/framework/smart_cond_test.py @@ -0,0 +1,102 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.client import session +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond +from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import googletest + + +@test_util.with_c_api +class SmartCondTest(test_util.TensorFlowTestCase): + + def testTrue(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(2) + y = constant_op.constant(5) + z = smart_cond.smart_cond(True, lambda: math_ops.multiply(x, 16), + lambda: math_ops.multiply(y, 5)) + self.assertEqual(z.eval(), 32) + + def testFalse(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(4) + y = constant_op.constant(3) + z = smart_cond.smart_cond(False, lambda: math_ops.multiply(x, 16), + lambda: math_ops.multiply(y, 3)) + self.assertEqual(z.eval(), 9) + + def testUnknown(self): + with ops.Graph().as_default(): + with session.Session(): + x = array_ops.placeholder(dtype=dtypes.int32) + y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1), + lambda: constant_op.constant(2)) + self.assertEqual(y.eval(feed_dict={x: 1}), 1) + self.assertEqual(y.eval(feed_dict={x: -1}), 2) + + def testEval(self): + # Constant expression evaluation only works with the C API enabled. + if not ops._USE_C_API: return + + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + y = constant_op.constant(2) + # x * y > 0 can be evaluated at graph construction time, so the false + # branch shouldn't be evaluated at all. + def raise_exception(): + raise RuntimeError("did not expect to be called") + z = smart_cond.smart_cond(x * y > 0, lambda: constant_op.constant(1), + raise_exception) + self.assertEqual(z.eval(feed_dict={x: 1}), 1) + + def testPlaceholderWithDefault(self): + with ops.Graph().as_default(): + with session.Session(): + x = array_ops.placeholder_with_default(1, shape=()) + y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1), + lambda: constant_op.constant(2)) + self.assertEqual(y.eval(), 1) + self.assertEqual(y.eval(feed_dict={x: -1}), 2) + + def testMissingArg1(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + with self.assertRaises(TypeError): + smart_cond.smart_cond(True, false_fn=lambda: x) + + def testMissingArg2(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + with self.assertRaises(TypeError): + smart_cond.smart_cond(True, lambda: x) + + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py index 222071cb9e87aa0fdd9788d1c72df4c66ea61547..6f2ab8408edce20d73f8447a4490db0749ef3aa1 100644 --- a/tensorflow/python/framework/tensor_shape.py +++ b/tensorflow/python/framework/tensor_shape.py @@ -456,6 +456,7 @@ class TensorShape(object): else: # Got a list of dimensions self._dims = [as_dimension(d) for d in dims_iter] + self._ndims = None def __repr__(self): return "TensorShape(%r)" % self._dims @@ -473,19 +474,26 @@ class TensorShape(object): """Returns a list of Dimensions, or None if the shape is unspecified.""" return self._dims + @dims.setter + def dims(self, dims): + self._dims = dims + self._ndims = None + @property def ndims(self): """Returns the rank of this shape, or None if it is unspecified.""" if self._dims is None: return None else: - return len(self._dims) + if self._ndims is None: + self._ndims = len(self._dims) + return self._ndims def __len__(self): """Returns the rank of this shape, or raises ValueError if unspecified.""" if self._dims is None: raise ValueError("Cannot take the length of Shape with unknown rank.") - return len(self._dims) + return self.ndims def __bool__(self): """Returns True if this shape contains non-zero information.""" diff --git a/tensorflow/python/framework/tensor_spec.py b/tensorflow/python/framework/tensor_spec.py index a0411bc3d9b4b2b87e5a31e9f201154f28ccf1cc..27a9ab8c606e2fe6f74e849d977543da1fb1dca6 100644 --- a/tensorflow/python/framework/tensor_spec.py +++ b/tensorflow/python/framework/tensor_spec.py @@ -166,16 +166,8 @@ class BoundedTensorSpec(TensorSpec): @classmethod def from_spec(cls, spec): dtype = dtypes.as_dtype(spec.dtype) - if dtype in [dtypes.float64, dtypes.float32]: - # Avoid under/over-flow for `dtype.maximum - dtype.minimum`. - low = dtype.min / 2 - high = dtype.max / 2 - else: - low = dtype.min - high = dtype.max - - minimum = getattr(spec, "minimum", low) - maximum = getattr(spec, "maximum", high) + minimum = getattr(spec, "minimum", dtype.min) + maximum = getattr(spec, "maximum", dtype.max) return BoundedTensorSpec(spec.shape, dtype, minimum, maximum, spec.name) @property diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index 0e5f696111ae7f74b41f8af21a5190fc2617e51a..984bcecdfe05efd79bdf218197c410b14abe3516 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -557,17 +557,18 @@ def MakeNdarray(tensor): dtype = tensor_dtype.as_numpy_dtype if tensor.tensor_content: - return np.fromstring(tensor.tensor_content, dtype=dtype).reshape(shape) - elif tensor_dtype == dtypes.float16: + return (np.frombuffer(tensor.tensor_content, dtype=dtype).copy() + .reshape(shape)) + elif tensor_dtype == dtypes.float16 or tensor_dtype == dtypes.bfloat16: # the half_val field of the TensorProto stores the binary representation # of the fp16: we need to reinterpret this as a proper float16 if len(tensor.half_val) == 1: tmp = np.array(tensor.half_val[0], dtype=np.uint16) - tmp.dtype = np.float16 + tmp.dtype = tensor_dtype.as_numpy_dtype return np.repeat(tmp, num_elements).reshape(shape) else: tmp = np.fromiter(tensor.half_val, dtype=np.uint16) - tmp.dtype = np.float16 + tmp.dtype = tensor_dtype.as_numpy_dtype return tmp.reshape(shape) elif tensor_dtype == dtypes.float32: if len(tensor.float_val) == 1: @@ -585,8 +586,7 @@ def MakeNdarray(tensor): return np.fromiter(tensor.double_val, dtype=dtype).reshape(shape) elif tensor_dtype in [ dtypes.int32, dtypes.uint8, dtypes.uint16, dtypes.int16, dtypes.int8, - dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16, - dtypes.bfloat16 + dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16 ]: if len(tensor.int_val) == 1: return np.repeat(np.array(tensor.int_val[0], dtype=dtype), @@ -828,7 +828,7 @@ def constant_value_as_shape(tensor): # pylint: disable=invalid-name Returns: A `TensorShape` based on the constant value of the given `tensor`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return tensor_shape.as_shape( [dim if dim != -1 else None for dim in tensor.numpy()]) diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py index f2de69e159646b4a085645fa1bfef7782e78cd59..35fff80c61b98e7603d3b7b5df3cabdb59059a72 100644 --- a/tensorflow/python/framework/tensor_util_test.py +++ b/tensorflow/python/framework/tensor_util_test.py @@ -199,6 +199,25 @@ class TensorUtilTest(test.TestCase): dtype=nptype), a) + def testFloatMutateArray(self): + t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], dtype=dtypes.float32) + a = tensor_util.MakeNdarray(t) + a[0] = 5.0 + self.assertEquals(np.float32, a.dtype) + self.assertAllClose(np.array([5.0, 20.0, 30.0], dtype=np.float32), a) + if sys.byteorder == "big": + self.assertProtoEquals(""" + dtype: DT_FLOAT + tensor_shape { dim { size: 3 } } + tensor_content: "A \000\000A\240\000\000A\360\000\000" + """, t) + else: + self.assertProtoEquals(""" + dtype: DT_FLOAT + tensor_shape { dim { size: 3 } } + tensor_content: "\000\000 A\000\000\240A\000\000\360A" + """, t) + def testHalf(self): t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=np.float16)) self.assertProtoEquals(""" @@ -216,6 +235,26 @@ class TensorUtilTest(test.TestCase): self.assertEquals(np.float16, a.dtype) self.assertAllClose(np.array([10.0, 20.0], dtype=np.float16), a) + def testBfloat16(self): + test_type = dtypes.bfloat16.as_numpy_dtype + t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=test_type)) + # 10.0: 16672 = 010000010(130) 0100000: (1+0/2+1/4) * 2^(130-127) + # 20.0: 16800 = 010000011(131) 0100000: (1+0/2+1/4) * 2^(131-127) + self.assertProtoEquals(""" + dtype: DT_BFLOAT16 + tensor_shape { + dim { + size: 2 + } + } + half_val: 16672 + half_val: 16800 + """, t) + + a = tensor_util.MakeNdarray(t) + self.assertEquals(test_type, a.dtype) + self.assertAllClose(np.array([10.0, 20.0], dtype=test_type), a) + def testInt(self): t = tensor_util.make_tensor_proto(10) self.assertProtoEquals(""" @@ -749,7 +788,7 @@ class ConstantValueTest(test.TestCase): self.assertAllClose(np_val, tensor_util.constant_value(tf_val)) def testUnknown(self): - tf_val = gen_state_ops._variable( + tf_val = gen_state_ops.variable( shape=[3, 4, 7], dtype=dtypes.float32, name="tf_val", diff --git a/tensorflow/python/framework/test_ops.cc b/tensorflow/python/framework/test_ops.cc index c6c6c2233c9a81467f57abe2d42f0df9b7ce7106..070b5ac11f563443a97b304ddcdaabd2f4338445 100644 --- a/tensorflow/python/framework/test_ops.cc +++ b/tensorflow/python/framework/test_ops.cc @@ -76,6 +76,11 @@ REGISTER_OP("TestStringOutput") .Output("output2: string") .SetShapeFn(shape_inference::UnknownShape); +REGISTER_OP("TestAttr") + .Output("out: T") + .Attr("T: {float, double}") + .SetShapeFn(shape_inference::UnknownShape); + namespace { enum KernelLabel { DEFAULT_LABEL, OVERLOAD_1_LABEL, OVERLOAD_2_LABEL }; } // namespace @@ -188,6 +193,20 @@ class ResourceUsingOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("ResourceUsingOp").Device(DEVICE_CPU), ResourceUsingOp); +class TestAttrOp : public OpKernel { + public: + explicit TestAttrOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + Tensor* output; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &output)); + output->scalar()() = 1.0; + } +}; + +REGISTER_KERNEL_BUILDER( + Name("TestAttr").Device(DEVICE_CPU).TypeConstraint("T"), TestAttrOp); + // Various test ops without kernels. These are used to test graph construction. REGISTER_OP("A") diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 15e8f5a38d6a1cbfa46c305f9f5c24e9d2dbc1d7..1b76e7d23ed8ace9a3a06bb3e9b5b253d921b147 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -49,7 +49,7 @@ from tensorflow.python.client import device_lib from tensorflow.python.client import session from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import tape +from tensorflow.python.eager import tape # pylint: disable=unused-import from tensorflow.python.framework import device as pydev from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -200,11 +200,14 @@ def _strip_checkpoint_v2_randomized(graph_def): def IsGoogleCudaEnabled(): return pywrap_tensorflow.IsGoogleCudaEnabled() - def CudaSupportsHalfMatMulAndConv(): return pywrap_tensorflow.CudaSupportsHalfMatMulAndConv() +def IsMklEnabled(): + return pywrap_tensorflow.IsMklEnabled() + + def InstallStackTraceHandler(): pywrap_tensorflow.InstallStacktraceHandler() @@ -419,6 +422,11 @@ def with_c_api(cls): Returns: cls with new test methods added """ + # If the C API is already enabled, don't do anything. Some tests break if the + # same test is run twice, so this allows us to turn on the C API by default + # without breaking these tests. + if ops._USE_C_API: return cls + for name, value in cls.__dict__.copy().items(): if callable(value) and name.startswith("test"): setattr(cls, name + "WithCApi", enable_c_api(value)) @@ -463,8 +471,8 @@ def assert_no_new_tensors(f): f(self, **kwargs) # Make an effort to clear caches, which would otherwise look like leaked # Tensors. - backprop._last_zero = [None] - backprop._shape_dtype = [None, None] + backprop._zeros_cache.flush() + context.get_default_context().ones_rank_cache().flush() context.get_default_context().scalar_cache().clear() gc.collect() tensors_after = [ @@ -502,6 +510,30 @@ def assert_no_garbage_created(f): previous_garbage = len(gc.garbage) f(self, **kwargs) gc.collect() + if len(gc.garbage) > previous_garbage: + logging.error( + "The decorated test created work for Python's garbage collector, " + "likely due to a reference cycle. New objects in cycle(s):") + for i, obj in enumerate(gc.garbage[previous_garbage:]): + try: + logging.error( + "Object %d of %d" % (i, len(gc.garbage) - previous_garbage)) + def _safe_object_str(obj): + return "<%s %d>" % (obj.__class__.__name__, id(obj)) + logging.error(" Object type: %s" % (_safe_object_str(obj),)) + logging.error(" Referrer types: %s" % ( + ', '.join([_safe_object_str(ref) + for ref in gc.get_referrers(obj)]),)) + logging.error(" Referent types: %s" % ( + ', '.join([_safe_object_str(ref) + for ref in gc.get_referents(obj)]),)) + logging.error(" Object attribute names: %s" % (dir(obj),)) + logging.error(" Object __str__:") + logging.error(obj) + logging.error(" Object __repr__:") + logging.error(repr(obj)) + except Exception: + logging.error("(Exception while printing object)") # This will fail if any garbage has been created, typically because of a # reference cycle. self.assertEqual(previous_garbage, len(gc.garbage)) @@ -560,6 +592,7 @@ def run_in_graph_and_eager_modes(__unused__=None, # This decorator runs the wrapped test twice. # Reset the test environment between runs. self.tearDown() + self._tempdir = None self.setUp() def run_eager_mode(self, **kwargs): @@ -692,7 +725,7 @@ class TensorFlowTestCase(googletest.TestCase): self._tempdir = tempfile.mkdtemp(dir=googletest.GetTempDir()) return self._tempdir - def _AssertProtoEquals(self, a, b): + def _AssertProtoEquals(self, a, b, msg=None): """Asserts that a and b are the same proto. Uses ProtoEq() first, as it returns correct results @@ -702,11 +735,12 @@ class TensorFlowTestCase(googletest.TestCase): Args: a: a proto. b: another proto. + msg: Optional message to report on failure. """ if not compare.ProtoEq(a, b): - compare.assertProtoEqual(self, a, b, normalize_numbers=True) + compare.assertProtoEqual(self, a, b, normalize_numbers=True, msg=msg) - def assertProtoEquals(self, expected_message_maybe_ascii, message): + def assertProtoEquals(self, expected_message_maybe_ascii, message, msg=None): """Asserts that message is same as parsed expected_message_ascii. Creates another prototype of message, reads the ascii message into it and @@ -715,8 +749,9 @@ class TensorFlowTestCase(googletest.TestCase): Args: expected_message_maybe_ascii: proto message in original or ascii form. message: the message to validate. + msg: Optional message to report on failure. """ - + msg = msg if msg else "" if isinstance(expected_message_maybe_ascii, type(message)): expected_message = expected_message_maybe_ascii self._AssertProtoEquals(expected_message, message) @@ -726,20 +761,21 @@ class TensorFlowTestCase(googletest.TestCase): expected_message_maybe_ascii, expected_message, descriptor_pool=descriptor_pool.Default()) - self._AssertProtoEquals(expected_message, message) + self._AssertProtoEquals(expected_message, message, msg=msg) else: - assert False, ("Can't compare protos of type %s and %s" % - (type(expected_message_maybe_ascii), type(message))) + assert False, ("Can't compare protos of type %s and %s. %s" % + (type(expected_message_maybe_ascii), type(message), msg)) def assertProtoEqualsVersion( self, expected, actual, producer=versions.GRAPH_DEF_VERSION, - min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER): + min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER, + msg=None): expected = "versions { producer: %d min_consumer: %d };\n%s" % ( producer, min_consumer, expected) - self.assertProtoEquals(expected, actual) + self.assertProtoEquals(expected, actual, msg=msg) def assertStartsWith(self, actual, expected_start, msg=None): """Assert that actual.startswith(expected_start) is True. @@ -780,7 +816,7 @@ class TensorFlowTestCase(googletest.TestCase): Returns: tensors numpy values. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return self._eval_helper(tensors) else: sess = ops.get_default_session() @@ -1029,7 +1065,7 @@ class TensorFlowTestCase(googletest.TestCase): "%f != %f +/- %f%s" % (f1, f2, err, " (%s)" % msg if msg is not None else "")) - def assertArrayNear(self, farray1, farray2, err): + def assertArrayNear(self, farray1, farray2, err, msg=None): """Asserts that two float arrays are near each other. Checks that for all elements of farray1 and farray2 @@ -1039,23 +1075,25 @@ class TensorFlowTestCase(googletest.TestCase): farray1: a list of float values. farray2: a list of float values. err: a float value. + msg: Optional message to report on failure. """ - self.assertEqual(len(farray1), len(farray2)) + self.assertEqual(len(farray1), len(farray2), msg=msg) for f1, f2 in zip(farray1, farray2): - self.assertNear(float(f1), float(f2), err) + self.assertNear(float(f1), float(f2), err, msg=msg) def _NDArrayNear(self, ndarray1, ndarray2, err): return np.linalg.norm(ndarray1 - ndarray2) < err - def assertNDArrayNear(self, ndarray1, ndarray2, err): + def assertNDArrayNear(self, ndarray1, ndarray2, err, msg=None): """Asserts that two numpy arrays have near values. Args: ndarray1: a numpy ndarray. ndarray2: a numpy ndarray. err: a float. The maximum absolute difference allowed. + msg: Optional message to report on failure. """ - self.assertTrue(self._NDArrayNear(ndarray1, ndarray2, err)) + self.assertTrue(self._NDArrayNear(ndarray1, ndarray2, err), msg=msg) def _GetNdArray(self, a): if not isinstance(a, np.ndarray): @@ -1097,9 +1135,16 @@ class TensorFlowTestCase(googletest.TestCase): np.testing.assert_allclose( a, b, rtol=rtol, atol=atol, err_msg=msg, equal_nan=True) - def _assertAllCloseRecursive(self, a, b, rtol=1e-6, atol=1e-6, path=None): + def _assertAllCloseRecursive(self, + a, + b, + rtol=1e-6, + atol=1e-6, + path=None, + msg=None): path = path or [] path_str = (("[" + "][".join([str(p) for p in path]) + "]") if path else "") + msg = msg if msg else "" # Check if a and/or b are namedtuples. if hasattr(a, "_asdict"): @@ -1108,18 +1153,18 @@ class TensorFlowTestCase(googletest.TestCase): b = b._asdict() a_is_dict = isinstance(a, dict) if a_is_dict != isinstance(b, dict): - raise ValueError("Can't compare dict to non-dict, a%s vs b%s." % - (path_str, path_str)) + raise ValueError("Can't compare dict to non-dict, a%s vs b%s. %s" % + (path_str, path_str, msg)) if a_is_dict: self.assertItemsEqual( a.keys(), b.keys(), - msg="mismatched keys: a%s has keys %s, but b%s has keys %s" % - (path_str, a.keys(), path_str, b.keys())) + msg="mismatched keys: a%s has keys %s, but b%s has keys %s. %s" % + (path_str, a.keys(), path_str, b.keys(), msg)) for k in a: path.append(k) self._assertAllCloseRecursive( - a[k], b[k], rtol=rtol, atol=atol, path=path) + a[k], b[k], rtol=rtol, atol=atol, path=path, msg=msg) del path[-1] elif isinstance(a, (list, tuple)): # Try to directly compare a, b as ndarrays; if not work, then traverse @@ -1132,29 +1177,35 @@ class TensorFlowTestCase(googletest.TestCase): b_as_ndarray, rtol=rtol, atol=atol, - msg="Mismatched value: a%s is different from b%s." % (path_str, - path_str)) + msg="Mismatched value: a%s is different from b%s. %s" % + (path_str, path_str, msg)) except (ValueError, TypeError) as e: if len(a) != len(b): raise ValueError( - "Mismatched length: a%s has %d items, but b%s has %d items" % - (path_str, len(a), path_str, len(b))) + "Mismatched length: a%s has %d items, but b%s has %d items. %s" % + (path_str, len(a), path_str, len(b), msg)) for idx, (a_ele, b_ele) in enumerate(zip(a, b)): path.append(str(idx)) self._assertAllCloseRecursive( - a_ele, b_ele, rtol=rtol, atol=atol, path=path) + a_ele, b_ele, rtol=rtol, atol=atol, path=path, msg=msg) del path[-1] # a and b are ndarray like objects else: - self._assertArrayLikeAllClose( - a, - b, - rtol=rtol, - atol=atol, - msg="Mismatched value: a%s is different from b%s." % (path_str, - path_str)) - - def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6): + try: + self._assertArrayLikeAllClose( + a, + b, + rtol=rtol, + atol=atol, + msg="Mismatched value: a%s is different from b%s." % (path_str, + path_str)) + except TypeError as e: + msg = "Error: a%s has %s, but b%s has %s" % ( + path_str, type(a), path_str, type(b)) + e.args = ((e.args[0] + ' : ' + msg,) + e.args[1:]) + raise + + def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6, msg=None): """Asserts that two structures of numpy arrays, have near values. `a` and `b` can be arbitrarily nested structures. A layer of a nested @@ -1167,6 +1218,7 @@ class TensorFlowTestCase(googletest.TestCase): numpy `ndarray`, or any arbitrarily nested of structure of these. rtol: relative tolerance. atol: absolute tolerance. + msg: Optional message to report on failure. Raises: ValueError: if only one of `a[p]` and `b[p]` is a dict or @@ -1174,7 +1226,7 @@ class TensorFlowTestCase(googletest.TestCase): to the nested structure, e.g. given `a = [(1, 1), {'d': (6, 7)}]` and `[p] = [1]['d']`, then `a[p] = (6, 7)`. """ - self._assertAllCloseRecursive(a, b, rtol=rtol, atol=atol) + self._assertAllCloseRecursive(a, b, rtol=rtol, atol=atol, msg=msg) def assertAllCloseAccordingToType(self, a, @@ -1186,7 +1238,8 @@ class TensorFlowTestCase(googletest.TestCase): half_rtol=1e-3, half_atol=1e-3, bfloat16_rtol=1e-2, - bfloat16_atol=1e-2): + bfloat16_atol=1e-2, + msg=None): """Like assertAllClose, but also suitable for comparing fp16 arrays. In particular, the tolerance is reduced to 1e-3 if at least @@ -1203,6 +1256,7 @@ class TensorFlowTestCase(googletest.TestCase): half_atol: absolute tolerance for float16. bfloat16_rtol: relative tolerance for bfloat16. bfloat16_atol: absolute tolerance for bfloat16. + msg: Optional message to report on failure. """ a = self._GetNdArray(a) b = self._GetNdArray(b) @@ -1219,19 +1273,21 @@ class TensorFlowTestCase(googletest.TestCase): rtol = max(rtol, bfloat16_rtol) atol = max(atol, bfloat16_atol) - self.assertAllClose(a, b, rtol=rtol, atol=atol) + self.assertAllClose(a, b, rtol=rtol, atol=atol, msg=msg) - def assertAllEqual(self, a, b): + def assertAllEqual(self, a, b, msg=None): """Asserts that two numpy arrays have the same values. Args: a: the expected numpy ndarray or anything can be converted to one. b: the actual numpy ndarray or anything can be converted to one. + msg: Optional message to report on failure. """ + msg = msg if msg else "" a = self._GetNdArray(a) b = self._GetNdArray(b) - self.assertEqual(a.shape, b.shape, "Shape mismatch: expected %s, got %s." % - (a.shape, b.shape)) + self.assertEqual(a.shape, b.shape, "Shape mismatch: expected %s, got %s." + " %s" % (a.shape, b.shape, msg)) same = (a == b) if a.dtype == np.float32 or a.dtype == np.float64: @@ -1248,7 +1304,7 @@ class TensorFlowTestCase(googletest.TestCase): x, y = a, b print("not equal lhs = ", x) print("not equal rhs = ", y) - np.testing.assert_array_equal(a, b) + np.testing.assert_array_equal(a, b, err_msg=msg) # pylint: disable=g-doc-return-or-yield @contextlib.contextmanager @@ -1298,12 +1354,13 @@ class TensorFlowTestCase(googletest.TestCase): return self.assertRaisesWithPredicateMatch(errors.OpError, expected_err_re_or_predicate) - def assertShapeEqual(self, np_array, tf_tensor): + def assertShapeEqual(self, np_array, tf_tensor, msg=None): """Asserts that a Numpy ndarray and a TensorFlow tensor have the same shape. Args: np_array: A Numpy ndarray or Numpy scalar. tf_tensor: A Tensor. + msg: Optional message to report on failure. Raises: TypeError: If the arguments have the wrong type. @@ -1312,19 +1369,22 @@ class TensorFlowTestCase(googletest.TestCase): raise TypeError("np_array must be a Numpy ndarray or Numpy scalar") if not isinstance(tf_tensor, ops.Tensor): raise TypeError("tf_tensor must be a Tensor") - self.assertAllEqual(np_array.shape, tf_tensor.get_shape().as_list()) + self.assertAllEqual( + np_array.shape, tf_tensor.get_shape().as_list(), msg=msg) - def assertDeviceEqual(self, device1, device2): + def assertDeviceEqual(self, device1, device2, msg=None): """Asserts that the two given devices are the same. Args: device1: A string device name or TensorFlow `DeviceSpec` object. device2: A string device name or TensorFlow `DeviceSpec` object. + msg: Optional message to report on failure. """ device1 = pydev.canonical_name(device1) device2 = pydev.canonical_name(device2) self.assertEqual(device1, device2, - "Devices %s and %s are not equal" % (device1, device2)) + "Devices %s and %s are not equal. %s" % + (device1, device2, msg)) # Fix Python 3 compatibility issues if six.PY3: diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index a717eb39513ac3369ae133b6090ff82597f12eb7..20d816050ffdcf39b389e114a92b9ed566df83d4 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -82,6 +82,14 @@ class TestUtilTest(test_util.TensorFlowTestCase): else: print("GoogleCuda is disabled") + def testIsMklEnabled(self): + # This test doesn't assert anything. + # It ensures the py wrapper function is generated correctly. + if test_util.IsMklEnabled(): + print("MKL is enabled") + else: + print("MKL is disabled") + def testAssertProtoEqualsStr(self): graph_str = "node { name: 'w1' op: 'params' }" diff --git a/tensorflow/python/framework/versions.py b/tensorflow/python/framework/versions.py index bdcbc15af63c57d712abfac97537f86b3bbe1737..06955b885852a641bc814f88c99838effe03bfd4 100644 --- a/tensorflow/python/framework/versions.py +++ b/tensorflow/python/framework/versions.py @@ -35,7 +35,9 @@ tf_export("GIT_VERSION").export_constant(__name__, "GIT_VERSION") COMPILER_VERSION = __compiler_version__ tf_export("COMPILER_VERSION").export_constant(__name__, "COMPILER_VERSION") CXX11_ABI_FLAG = __cxx11_abi_flag__ +tf_export("CXX11_ABI_FLAG").export_constant(__name__, "CXX11_ABI_FLAG") MONOLITHIC_BUILD = __monolithic_build__ +tf_export("MONOLITHIC_BUILD").export_constant(__name__, "MONOLITHIC_BUILD") GRAPH_DEF_VERSION = pywrap_tensorflow.GRAPH_DEF_VERSION tf_export("GRAPH_DEF_VERSION").export_constant(__name__, "GRAPH_DEF_VERSION") diff --git a/tensorflow/python/grappler/cluster.i b/tensorflow/python/grappler/cluster.i index 0c8d04ff29518d587079a76e3fee3b2e327c6c5c..067c8213d4741936e4c28aaedf4f30639b8cdc41 100644 --- a/tensorflow/python/grappler/cluster.i +++ b/tensorflow/python/grappler/cluster.i @@ -140,6 +140,7 @@ static GCluster TF_NewCluster(bool allow_soft_placement, timeout_s, num_cpu_cores, num_gpus); cluster_->DisableDetailedStats(disable_detailed_stats); cluster_->AllowSoftPlacement(allow_soft_placement); + cluster_->SetNumWarmupSteps(10); tensorflow::Status status = cluster_->Provision(); tensorflow::Set_TF_Status_from_Status(out_status, status); return GCluster(cluster_); @@ -205,7 +206,7 @@ static PyObject* TF_ListDevices(GCluster cluster) { return result; } -static std::vector TF_ListAvailableOps() { +static PyObject* TF_ListAvailableOps() { tensorflow::OpRegistry* registry = tensorflow::OpRegistry::Global(); std::vector ops; registry->GetRegisteredOps(&ops); @@ -214,7 +215,14 @@ static std::vector TF_ListAvailableOps() { op_names.push_back(op.name()); } std::sort(op_names.begin(), op_names.end()); - return op_names; + + PyGILState_STATE gstate = PyGILState_Ensure(); + PyObject* result = PyList_New(op_names.size()); + for (int i = 0; i < op_names.size(); ++i) { + PyList_SetItem(result, i, PyString_FromString(op_names[i].c_str())); + } + PyGILState_Release(gstate); + return result; } static PyObject* TF_GetSupportedDevices(GCluster cluster, GItem item) { @@ -431,7 +439,7 @@ static GCluster TF_NewVirtualCluster( TF_Status* out_status); static void TF_ShutdownCluster(GCluster cluster); static PyObject* TF_ListDevices(GCluster cluster); -static std::vector TF_ListAvailableOps(); +static PyObject* TF_ListAvailableOps(); static PyObject* TF_GetSupportedDevices(GCluster cluster, GItem item); static float TF_EstimatePerformance(const tensorflow::NamedDevice& device); static PyObject* TF_MeasureCosts( diff --git a/tensorflow/python/grappler/cluster_test.py b/tensorflow/python/grappler/cluster_test.py index 10d515a36458d4025060cf4900251cd493f40795..a3c4c2bbeba7c4ee5d00268c0e475e11a31fa7eb 100644 --- a/tensorflow/python/grappler/cluster_test.py +++ b/tensorflow/python/grappler/cluster_test.py @@ -45,7 +45,7 @@ class ClusterTest(test.TestCase): op_perfs, run_time, step_stats = grappler_cluster.MeasureCosts( grappler_item) self.assertTrue(run_time > 0) - self.assertEqual(len(op_perfs), 7) + self.assertEqual(len(op_perfs), 8) self.assertTrue(step_stats.dev_stats) def testNoDetailedStats(self): @@ -125,14 +125,14 @@ class ClusterTest(test.TestCase): disable_detailed_stats=False, disable_timeline=False) as gcluster: op_perfs, run_time, step_stats = gcluster.MeasureCosts(grappler_item) self.assertTrue(run_time > 0) - self.assertEqual(len(op_perfs), 7) + self.assertEqual(len(op_perfs), 8) self.assertTrue(step_stats.dev_stats) def testAvailableOps(self): with cluster.Provision() as gcluster: op_names = gcluster.ListAvailableOps() - self.assertTrue(b'Add' in op_names) - self.assertTrue(b'MatMul' in op_names) + self.assertTrue('Add' in op_names) + self.assertTrue('MatMul' in op_names) self.assertEqual(op_names, sorted(op_names)) def testSupportDevices(self): diff --git a/tensorflow/python/grappler/controller.py b/tensorflow/python/grappler/controller.py new file mode 100644 index 0000000000000000000000000000000000000000..5677f4f52310dd68dc80c87275b50be95ba86b60 --- /dev/null +++ b/tensorflow/python/grappler/controller.py @@ -0,0 +1,142 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Controller Class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import defaultdict + + +class Controller(object): + """Controller class.""" + + def __init__(self, item, cluster): + """Controller class initializer. + + Args: + item: The metagraph to place wrapped in a cluster. + cluster: A cluster of devices on which to place the item. + """ + self.item = item + + self._node = {} + for node in item.metagraph.graph_def.node: + self._node[node.name] = node + + self._fanout = defaultdict(lambda: []) + for node in item.metagraph.graph_def.node: + for fanin in self._get_node_fanin(node): + self._fanout[fanin.name].append(node) + + important_op_names = item.IdentifyImportantOps(sort_topologically=True) + + # List of important ops (these are the ops to place) sorted in topological + # order. The order of this collection is deterministic. + self.important_ops = [] + for name in important_op_names: + self.important_ops.append(self._node[name]) + + self.node_properties = item.GetOpProperties() + + self.cluster = cluster + self.devices = cluster.ListDevices() + + self.colocation_constraints = item.GetColocationGroups() + + self.placement_constraints = cluster.GetSupportedDevices(item) + for node_name, dev in self.placement_constraints.items(): + if len(dev) == 1: + # Place the node on the supported device + node = self._node[node_name] + node.device = dev[0] + fanout = self.get_node_fanout(node) + # Update the fanout of the fanin to bypass the node + for fanin in self._get_node_fanin(node): + fanout_of_fanin = self.get_node_fanout(fanin) + fanout_of_fanin += fanout + fanout_of_fanin.remove(node) + # Remove node from the list of important ops since we don't need to + # place the node. + if node in self.important_ops: + self.important_ops.remove(node) + important_op_names.remove(node.name) + + # List of important op names, in non deterministic order. + self.important_op_names = frozenset(important_op_names) + + @property + def input_graph_def(self): + return self.item.metagraph.graph_def + + @property + def num_devices(self): + return len(self.devices) + + def get_node_by_name(self, node_name): + return self._node[node_name] + + def get_node_fanout(self, node): + return self._fanout[node.name] + + def get_placements(self, *args, **kwargs): + """Returns: Two TF ops. + + Args: + *args: "". + **kwargs: "". + + Returns: + y_preds: tensor of size [batch_size, num_ops] + log_probs: python dict of at least two fields: "sample", "target" each + containing a tensor of size [batch_size], corresponding to the log_probs. + """ + raise NotImplementedError + + def eval_placement(self, sess, *args, **kwargs): + """At this time, this method evaluates ONLY ONE placement. + + Args: + sess: a tf.Session() object used to retrieve cached assignment info. + *args: "". + **kwargs: "". + + Returns: + run_time: scalar + """ + raise NotImplementedError + + def export_placement(self, metagraph): + """Annotate the placement onto the specified metagraph. + + Args: + metagraph: the metagraph to annotate with the placement. + """ + for node in metagraph.graph_def.node: + if node.name in self.important_op_names: + node.device = self.get_node_by_name(node.name).device + + # Get the nodes in the immediate fanin of node. + # Beware: this doesn't take into account the nodes that may be skipped + # since placement constraints force their placement. + def _get_node_fanin(self, node): + input_ops = [] + for fanin_name in node.input: + if fanin_name[0] == "^": + fanin_name = fanin_name[1:] + fanin_name = fanin_name.split(":")[0] + input_ops.append(self.get_node_by_name(fanin_name)) + return input_ops diff --git a/tensorflow/python/grappler/cost_analyzer.cc b/tensorflow/python/grappler/cost_analyzer.cc index 88bf900dca6d97773959eb309a4a3c5931fdcb88..b474e19894957d01c7c8978282c547df81a9b2b3 100644 --- a/tensorflow/python/grappler/cost_analyzer.cc +++ b/tensorflow/python/grappler/cost_analyzer.cc @@ -30,11 +30,12 @@ CostAnalyzer::CostAnalyzer(const GrapplerItem& item, Cluster* cluster, analytical_estimator_(cluster, false), suffix_(suffix) {} -Status CostAnalyzer::GenerateReport(std::ostream& os, bool per_node_report) { +Status CostAnalyzer::GenerateReport(std::ostream& os, bool per_node_report, + bool verbose) { GatherCosts(); PreprocessCosts(); AnalyzeCosts(); - PrintAnalysis(os, per_node_report); + PrintAnalysis(os, per_node_report, verbose); return Status::OK(); } @@ -158,7 +159,8 @@ void CostAnalyzer::AnalyzeCosts() { } } -void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report) const { +void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report, + bool verbose) const { os << std::endl; os << std::left << std::setw(50) << "Total time measured in ns (serialized): " << std::right @@ -227,10 +229,55 @@ void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report) const { os << std::endl; if (per_node_report) { - os << "Below is the per-node report:" << std::endl; - os << op_perf_.DebugString(); + if (verbose) { + os << "Below is the full per-node report:" << std::endl; + os << op_perf_.DebugString(); + } else { + os << "Below is the per-node report summary:" << std::endl; + int width = 35; + int width_narrow = 15; + int width_wide = 20; + os << std::setw(width + 1) << "Op,"; + os << std::setw(width_wide + 1) << "Measured time (ns),"; + os << std::setw(width_wide + 1) << "Compute time (ns),"; + os << std::setw(width_wide + 1) << "Memory time (ns),"; + os << std::setw(width_narrow + 2) << "Compute eff,"; + os << std::setw(width_narrow + 2) << "Memory eff,"; + os << " Inputs" << std::endl; + for (int i = 0; i < op_perf_.op_performance_size(); i++) { + const auto& perf = op_perf_.op_performance(i); + string op_name = perf.op().op(); + os << std::setw(width) << op_name << ","; + os << std::setw(width_wide) << perf.compute_cost() << ","; + os << std::setw(width_wide) << perf.compute_time() << ","; + os << std::setw(width_wide) << perf.memory_time() << ","; + os << std::setw(width_narrow) << std::setprecision(2) + << perf.compute_efficiency() * 100 << "%,"; + os << std::setw(width_narrow) << std::setprecision(2) + << perf.memory_efficiency() * 100 << "%,"; + os << " ["; + for (int j = 0; j < perf.op().inputs_size(); j++) { + const auto& shape = perf.op().inputs(j).shape(); + if (shape.dim_size() > 0) { + os << "("; + std::vector dims; + for (int k = 0; k < shape.dim_size(); k++) { + os << shape.dim(k).size(); + if (k < shape.dim_size() - 1) { + os << ", "; + } + } + os << ")"; + if (j < perf.op().inputs_size() - 1) { + os << ", "; + } + } + } + os << "]" << std::endl; + } + os << std::endl; + } } } - } // end namespace grappler } // end namespace tensorflow diff --git a/tensorflow/python/grappler/cost_analyzer.h b/tensorflow/python/grappler/cost_analyzer.h index 0e860e0fee9923510292d3cf1a8069435787476f..b5364aa37ab2fbbeb0a33e6764539cca795f2fa6 100644 --- a/tensorflow/python/grappler/cost_analyzer.h +++ b/tensorflow/python/grappler/cost_analyzer.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/core/framework/cost_graph.pb.h" #include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/grappler/clusters/cluster.h" #include "tensorflow/core/grappler/costs/analytical_cost_estimator.h" #include "tensorflow/core/grappler/costs/cost_estimator.h" @@ -50,7 +51,7 @@ class CostAnalyzer { public: explicit CostAnalyzer(const GrapplerItem& item, Cluster* cluster, const string& suffix); - Status GenerateReport(std::ostream& os, bool per_node_report); + Status GenerateReport(std::ostream& os, bool per_node_report, bool verbose); private: void PredictCosts(CostEstimator* cost_estimator, CostGraphDef* cost_graph, @@ -59,7 +60,8 @@ class CostAnalyzer { void PreprocessCosts(); void AnalyzeCosts(); void SortOpsByTime(std::map ops); - void PrintAnalysis(std::ostream& os, bool per_node_report) const; + void PrintAnalysis(std::ostream& os, bool per_node_report, + bool verbose) const; const GrapplerItem* item_; MeasuringCostEstimator measure_estimator_; diff --git a/tensorflow/python/grappler/cost_analyzer.i b/tensorflow/python/grappler/cost_analyzer.i index 4c0953435ba3fa6423bbc869fcca909d0c2ccb25..8f7fdb47f267bea582e371eb9ea6982b6e9341ad 100644 --- a/tensorflow/python/grappler/cost_analyzer.i +++ b/tensorflow/python/grappler/cost_analyzer.i @@ -44,7 +44,7 @@ limitations under the License. %{ string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_node_report, - GCluster cluster) { + bool verbose, GCluster cluster) { tensorflow::grappler::ItemConfig cfg; cfg.apply_optimizations = false; std::unique_ptr item = @@ -57,11 +57,11 @@ string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_no tensorflow::grappler::CostAnalyzer analyzer(*item, cluster.get(), suffix); std::stringstream os; - analyzer.GenerateReport(os, per_node_report); + analyzer.GenerateReport(os, per_node_report, verbose); return os.str(); } %} string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_node_report, - GCluster cluster); + bool verbose, GCluster cluster); diff --git a/tensorflow/python/grappler/cost_analyzer.py b/tensorflow/python/grappler/cost_analyzer.py index a1ff915c61ba14d9a899d7f6c9a2c49855969b00..6a4690e91ba981706eed0d9fdfae2e64359d0416 100644 --- a/tensorflow/python/grappler/cost_analyzer.py +++ b/tensorflow/python/grappler/cost_analyzer.py @@ -24,7 +24,10 @@ from tensorflow.python.grappler import cluster as gcluster from tensorflow.python.grappler import item as gitem -def GenerateCostReport(metagraph, per_node_report=False, cluster=None): +def GenerateCostReport(metagraph, + per_node_report=False, + verbose=False, + cluster=None): """Analyze the cost of each TensorFlow op and node in the provided metagraph. Args: @@ -32,6 +35,7 @@ def GenerateCostReport(metagraph, per_node_report=False, cluster=None): per_node_report: by default the report contains stats aggregated on a per op type basis, setting per_node_report to True adds results for each individual node to the report. + verbose: Prints out the entire operation proto instead of a summary table. cluster: Analyze the costs using the specified cluster, or the local machine if no cluster was specified. @@ -42,8 +46,9 @@ def GenerateCostReport(metagraph, per_node_report=False, cluster=None): cluster = gcluster.Cluster(disable_detailed_stats=False) with errors.raise_exception_on_not_ok_status(): - ret_from_swig = tf_wrap.GenerateCostReport( - metagraph.SerializeToString(), per_node_report, cluster.tf_cluster) + ret_from_swig = tf_wrap.GenerateCostReport(metagraph.SerializeToString(), + per_node_report, verbose, + cluster.tf_cluster) return ret_from_swig diff --git a/tensorflow/python/grappler/cost_analyzer_test.py b/tensorflow/python/grappler/cost_analyzer_test.py index 511908c79ce47d6849bf97d11bc42f2f1bb13f18..b8225b81a52f1a2ee10663544d54f1c9bd7ee785 100644 --- a/tensorflow/python/grappler/cost_analyzer_test.py +++ b/tensorflow/python/grappler/cost_analyzer_test.py @@ -48,7 +48,7 @@ class CostAnalysisTest(test.TestCase): train_op.append(d) mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph()) - report = cost_analyzer.GenerateCostReport(mg) + report = cost_analyzer.GenerateCostReport(mg, per_node_report=True) # Check the report headers self.assertTrue(b"Total time measured in ns (serialized):" in report) @@ -57,6 +57,26 @@ class CostAnalysisTest(test.TestCase): self.assertTrue(b"Total time analytical in ns (lower bound):" in report) self.assertTrue(b"Overall efficiency (analytical upper/actual):" in report) self.assertTrue(b"Overall efficiency (analytical lower/actual):" in report) + self.assertTrue(b"Below is the per-node report summary:" in report) + + # Also print the report to make it easier to debug + print("{}".format(report)) + + def testVerbose(self): + """Make sure the full report is generated with verbose=True.""" + a = constant_op.constant(10, name="a") + b = constant_op.constant(20, name="b") + c = math_ops.add_n([a, b], name="c") + d = math_ops.add_n([b, c], name="d") + train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) + train_op.append(d) + mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph()) + + report = cost_analyzer.GenerateCostReport( + mg, per_node_report=True, verbose=True) + + # Check the report headers + self.assertTrue(b"Below is the full per-node report:" in report) # Also print the report to make it easier to debug print("{}".format(report)) diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py index ac251f2bbd9a30d2da9303bcbd20e84e5f5fc772..0853db252406966cec36b63efafec9ec755c7e87 100644 --- a/tensorflow/python/grappler/cost_analyzer_tool.py +++ b/tensorflow/python/grappler/cost_analyzer_tool.py @@ -35,11 +35,20 @@ from tensorflow.python.platform import gfile from tensorflow.python.training import saver -def main(_): +def get_metagraph(): + """Constructs and returns a MetaGraphDef from the input file.""" if FLAGS.metagraphdef: with gfile.GFile(FLAGS.metagraphdef) as meta_file: metagraph = meta_graph_pb2.MetaGraphDef() - metagraph.ParseFromString(meta_file.read()) + if FLAGS.metagraphdef.endswith(".pbtxt"): + text_format.Merge(meta_file.read(), metagraph) + else: + metagraph.ParseFromString(meta_file.read()) + if FLAGS.fetch is not None: + fetch_collection = meta_graph_pb2.CollectionDef() + for fetch in FLAGS.fetch.split(","): + fetch_collection.node_list.value.append(fetch) + metagraph.collection_def["train_op"].CopyFrom(fetch_collection) else: with gfile.GFile(FLAGS.graphdef) as graph_file: graph_def = graph_pb2.GraphDef() @@ -49,19 +58,28 @@ def main(_): graph_def.ParseFromString(graph_file.read()) importer.import_graph_def(graph_def, name="") graph = ops.get_default_graph() - fetch = graph.get_operation_by_name(FLAGS.fetch) - graph.add_to_collection("train_op", fetch) + for fetch in FLAGS.fetch.split(","): + fetch_op = graph.get_operation_by_name(fetch) + graph.add_to_collection("train_op", fetch_op) metagraph = saver.export_meta_graph( graph_def=graph.as_graph_def(), graph=graph) + return metagraph + +def main(_): + metagraph = get_metagraph() + rewriter_config = rewriter_config_pb2.RewriterConfig() if FLAGS.rewriter_config is not None: - rewriter_config = rewriter_config_pb2.RewriterConfig() text_format.Merge(FLAGS.rewriter_config, rewriter_config) - optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph) - metagraph.graph_def.CopyFrom(optimized_graph) + optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph) + metagraph.graph_def.CopyFrom(optimized_graph) - report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report) + report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report, + FLAGS.verbose) print(report) + if FLAGS.memory_report: + report = cost_analyzer.GenerateMemoryReport(metagraph) + print(report) if __name__ == "__main__": @@ -76,16 +94,11 @@ if __name__ == "__main__": type=str, default=None, help="Input .pb GraphDef file path.") - # Consider making flag fetch work together with flag metagraphdef. As some - # MetaGraphDef files don't have collection train_op. parser.add_argument( "--fetch", type=str, default=None, - help= - "The name of the fetch node. This flag is ignored if flag " - "metagraphdef is used." - ) + help="The names of the fetch node delimited by comma.") parser.add_argument( "--rewriter_config", type=str, @@ -101,5 +114,13 @@ if __name__ == "__main__": help="Generate per-node report. By default the report contains stats " "aggregated on a per op type basis, per_node_report adds results " "for each individual node to the report.") + parser.add_argument( + "--memory_report", + action="store_true", + help="Generate memory usage report.") + parser.add_argument( + "--verbose", + action="store_true", + help="Generate verbose reports. By default, succinct reports are used.") FLAGS, unparsed = parser.parse_known_args() app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/grappler/graph_placer.py b/tensorflow/python/grappler/graph_placer.py new file mode 100644 index 0000000000000000000000000000000000000000..1cd51df4d962583555e08ae973ab43d15ba01997 --- /dev/null +++ b/tensorflow/python/grappler/graph_placer.py @@ -0,0 +1,120 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Graph Placer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops as tf_ops +from tensorflow.python.grappler import cluster as gcluster +from tensorflow.python.grappler import hierarchical_controller +from tensorflow.python.grappler import item as gitem +from tensorflow.python.grappler import tf_optimizer +from tensorflow.python.training import training + + +def PlaceGraph(metagraph, + cluster=None, + allotted_time=3600, + hparams=None, + verbose=False): + """Place the provided metagraph. + + Args: + metagraph: the metagraph to place. + cluster: an optional set of hardware resource to optimize the placement for. + If none is specified, we'll optimize the placement for the hardware + available on the local machine. + allotted_time: the maximum amount to time in seconds to spend optimizing + the placement. + hparams: hyperparameters used to fine tune the placer. + verbose: prints debug information if True. + + Returns: + The placed metagraph. + """ + if cluster is None: + cluster = gcluster.Cluster() + + # Optimize the metagraph to speedup the placement + rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.optimizers.append("pruning") + rewriter_config.optimizers.append("constfold") + rewriter_config.optimizers.append("arithmetic") + rewriter_config.optimizers.append("dependency") + rewriter_config.optimizers.append("pruning") + optimized_graph = tf_optimizer.OptimizeGraph( + rewriter_config, metagraph, verbose=verbose, cluster=cluster) + optimized_metagraph = meta_graph_pb2.MetaGraphDef() + optimized_metagraph.CopyFrom(metagraph) + optimized_metagraph.graph_def.CopyFrom(optimized_graph) + + item = gitem.Item(optimized_metagraph) + + # Measure the runtime achievable with the original placement. + try: + _, original_run_time, _ = cluster.MeasureCosts(item) + if verbose: + print("Runtime for original placement: " + str(original_run_time)) + except errors.OpError as e: + if verbose: + print("Original placement isn't feasible: " + str(e)) + original_run_time = hparams.failing_signal + + if hparams is None: + hparams = hierarchical_controller.hierarchical_controller_hparams() + # We run with a single child + hparams.num_children = 1 + + with tf_ops.Graph().as_default(): + # Place all the nodes of the controller on the CPU. We don't want them to + # fight for accelerator memory with the model to optimize. + with tf_ops.device("/device:CPU:0"): + model = hierarchical_controller.HierarchicalController( + hparams, item, cluster) + ops = model.build_controller() + session_creator = training.ChiefSessionCreator() + with training.MonitoredSession(session_creator=session_creator) as sess: + start_time = time.time() + current_time = start_time + while current_time - start_time < allotted_time: + grouping_actions = model.generate_grouping(sess) + input_to_seq2seq = model.create_group_embeddings( + grouping_actions, verbose=verbose) + model.generate_placement(input_to_seq2seq, sess) + try: + run_time = model.eval_placement( + sess, + verbose=verbose) + except errors.OpError as e: + if verbose: + print("Failed to run graph:" + str(e)) + run_time = hparams.failing_signal + updated = model.update_reward(sess, run_time, verbose=verbose) + if updated and run_time < original_run_time: + if verbose: + print("Found better placement, with runtime " + str(run_time)) + model.export_placement(metagraph) + + model.process_reward(sess) + + current_time = time.time() + + return metagraph diff --git a/tensorflow/python/grappler/graph_placer_test.py b/tensorflow/python/grappler/graph_placer_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9eabe3cd5437022eb3b98010d0f384cc9f6bac2a --- /dev/null +++ b/tensorflow/python/grappler/graph_placer_test.py @@ -0,0 +1,140 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests the graph placer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from tensorflow.core.protobuf import device_properties_pb2 +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import meta_graph +from tensorflow.python.framework import ops as tf_ops +from tensorflow.python.grappler import cluster +from tensorflow.python.grappler import graph_placer +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test + + +class GraphPlacerTest(test.TestCase): + + @staticmethod + def _buildMnist(batch_size=128, + input_size=256, + num_classes=1024, + num_layers=10, + hidden_size=256, + name='mnist'): + g = tf_ops.get_default_graph() + with g.as_default(): + ops = {} + x = random_ops.random_uniform( + [batch_size, input_size], -0.1, 0.1, dtype=dtypes.float32) + for layer_id in range(num_layers): + with variable_scope.variable_scope('layer_{}'.format(layer_id)): + a = input_size if layer_id == 0 else hidden_size + b = hidden_size if layer_id < num_layers - 1 else num_classes + w = variable_scope.get_variable('w', [a, b]) + x = math_ops.matmul(x, w) + x = nn_ops.relu(x) + ops['y_preds'] = math_ops.argmax(x, axis=1) + + train_op = g.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP) + train_op.append(ops['y_preds']) + return g + + @staticmethod + def _buildCluster(num_cpus=1, num_gpus=1): + devices = [] + if num_gpus > 0: + device_properties = device_properties_pb2.DeviceProperties( + type='GPU', + vendor='NVidia', + model='GeForce GTX TITAN X', + frequency=1076, + num_cores=24, + environment={'architecture': '5.2', + 'cuda': '8000', + 'cudnn': '6021'}, + num_registers=65536, + l1_cache_size=24576, + l2_cache_size=3145728, + shared_memory_size_per_multiprocessor=98304, + memory_size=12783648768, + bandwidth=336480000) + for i in range(num_gpus): + devices.append( + device_properties_pb2.NamedDevice( + properties=device_properties, name='/GPU:' + str(i))) + + assert num_cpus > 0 + device_properties = device_properties_pb2.DeviceProperties( + type='CPU', + frequency=2000, + num_cores=4, + l1_cache_size=32768, + l2_cache_size=262144, + l3_cache_size=12582912) + for i in range(num_cpus): + devices.append( + device_properties_pb2.NamedDevice( + properties=device_properties, name='/CPU:' + str(i))) + + return cluster.Cluster(devices=devices) + + def testBasic(self): + """Place a trivial graph.""" + a = constant_op.constant(10, name='a') + b = constant_op.constant(20, name='b') + c = math_ops.add_n([a, b], name='c') + d = math_ops.add_n([b, c], name='d') + train_op = tf_ops.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP) + train_op.append(d) + mg = meta_graph.create_meta_graph_def(graph=tf_ops.get_default_graph()) + + gcluster = cluster.Cluster() + placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster) + + self.assertEqual(4, len(placed_mg.graph_def.node)) + self.assertItemsEqual([node.name for node in placed_mg.graph_def.node], + [node.name for node in mg.graph_def.node]) + + available_devices = [device.name for device in gcluster.ListDevices()] + for node in placed_mg.graph_def.node: + # The constant nodes are optimized away before the placer is run, and + # therefore won't be placed. + self.assertTrue(not node.device or node.device in available_devices) + + def testMNIST(self): + graph = GraphPlacerTest._buildMnist() + mg = meta_graph.create_meta_graph_def(graph=graph) + gcluster = GraphPlacerTest._buildCluster(num_gpus=1) + # Spend 15 seconds trying to optimize the placement of the model. This + # should give us enough time to exercise the code, but not enough to find + # a good placement, so we'll just check for legality. + placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster) + self.assertEqual(len(placed_mg.graph_def.node), len(mg.graph_def.node)) + self.assertItemsEqual([node.name for node in placed_mg.graph_def.node], + [node.name for node in mg.graph_def.node]) + available_devices = [device.name for device in gcluster.ListDevices()] + for node in placed_mg.graph_def.node: + self.assertTrue(not node.device or node.device in available_devices) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/grappler/hierarchical_controller.py b/tensorflow/python/grappler/hierarchical_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..c0866c1069ac7f7e25cbd12cb5a490e2ed5e4bec --- /dev/null +++ b/tensorflow/python/grappler/hierarchical_controller.py @@ -0,0 +1,1117 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""HierarchicalController Class. + +The HierarchicalController encompasses the entire lifecycle of training the +device placement policy, including generating op embeddings, getting groups for +each op, placing those groups and running the predicted placements. + +Different assignment models can inherit from this class. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import numpy as np +import six +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops as tf_ops +from tensorflow.python.grappler.controller import Controller +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import clip_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import embedding_ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.summary import summary +from tensorflow.python.training import adam +from tensorflow.python.training import gradient_descent +from tensorflow.python.training import learning_rate_decay +from tensorflow.python.training import training_util + + +class PlacerParams(object): + """Class to hold a set of placement parameters as name-value pairs. + + A typical usage is as follows: + + ```python + # Create a PlacerParams object specifying names and values of the model + # parameters: + params = PlacerParams(hidden_size=128, decay_steps=50) + + # The parameters are available as attributes of the PlacerParams object: + hparams.hidden_size ==> 128 + hparams.decay_steps ==> 50 + ``` + + """ + + def __init__(self, **kwargs): + """Create an instance of `PlacerParams` from keyword arguments. + + The keyword arguments specify name-values pairs for the parameters. + The parameter types are inferred from the type of the values passed. + + The parameter names are added as attributes of `PlacerParams` object, + and they can be accessed directly with the dot notation `params._name_`. + + Example: + + ```python + # Define 1 parameter: 'hidden_size' + params = PlacerParams(hidden_size=128) + params.hidden_size ==> 128 + ``` + + Args: + **kwargs: Key-value pairs where the key is the parameter name and + the value is the value for the parameter. + """ + for name, value in six.iteritems(kwargs): + self.add_param(name, value) + + def add_param(self, name, value): + """Adds {name, value} pair to hyperparameters. + + Args: + name: Name of the hyperparameter. + value: Value of the hyperparameter. Can be one of the following types: + int, float, string, int list, float list, or string list. + + Raises: + ValueError: if one of the arguments is invalid. + """ + # Keys in kwargs are unique, but 'name' could be the name of a pre-existing + # attribute of this object. In that case we refuse to use it as a + # parameter name. + if getattr(self, name, None) is not None: + raise ValueError("Parameter name is reserved: %s" % name) + setattr(self, name, value) + + +def hierarchical_controller_hparams(): + """Hyperparameters for hierarchical planner.""" + return PlacerParams( + hidden_size=512, + forget_bias_init=1.0, + temperature=1.0, + logits_std_noise=0.5, + stop_noise_step=750, + decay_steps=50, + max_num_outputs=5, + max_output_size=5, + tanh_constant=1.0, + adj_embed_dim=20, + grouping_hidden_size=64, + num_groups=None, + bi_lstm=True, + failing_signal=100, + stop_sampling=500, + start_with_failing_signal=True, + always_update_baseline=False, + bl_dec=0.9, + grad_bound=1.0, + lr=0.1, + lr_dec=0.95, + start_decay_step=400, + optimizer_type="adam", + stop_updating_after_steps=1000, + name="hierarchical_controller", + keep_prob=1.0, + reward_function="sqrt", + seed=1234, + # distributed training params + num_children=1) + + +class HierarchicalController(Controller): + """HierarchicalController class.""" + + def __init__(self, hparams, item, cluster, controller_id=0): + """HierarchicalController class initializer. + + Args: + hparams: All hyper-parameters. + item: The metagraph to place. + cluster: The cluster of hardware devices to optimize for. + controller_id: the id of the controller in a multi-controller setup. + """ + super(HierarchicalController, self).__init__(item, cluster) + self.ctrl_id = controller_id + self.hparams = hparams + + if self.hparams.num_groups is None: + self.num_groups = min(256, 20 * self.num_devices) + else: + self.num_groups = self.hparams.num_groups + + # creates self.op_embeddings and self.type_dict + self.create_op_embeddings(verbose=False) + # TODO(azalia) clean up embedding/group_embedding_size names + self.group_emb_size = ( + 2 * self.num_groups + len(self.type_dict) + + self.hparams.max_num_outputs * self.hparams.max_output_size) + self.embedding_size = self.group_emb_size + self.initializer = init_ops.glorot_uniform_initializer( + seed=self.hparams.seed) + + with variable_scope.variable_scope( + self.hparams.name, + initializer=self.initializer, + reuse=variable_scope.AUTO_REUSE): + # define parameters of feedforward + variable_scope.get_variable("w_grouping_ff", [ + 1 + self.hparams.max_num_outputs * self.hparams.max_output_size + + self.hparams.adj_embed_dim, self.hparams.grouping_hidden_size + ]) + variable_scope.get_variable( + "w_grouping_softmax", + [self.hparams.grouping_hidden_size, self.num_groups]) + if self.hparams.bi_lstm: + variable_scope.get_variable("encoder_lstm_forward", [ + self.embedding_size + self.hparams.hidden_size / 2, + 2 * self.hparams.hidden_size + ]) + variable_scope.get_variable("encoder_lstm_backward", [ + self.embedding_size + self.hparams.hidden_size / 2, + 2 * self.hparams.hidden_size + ]) + variable_scope.get_variable( + "device_embeddings", [self.num_devices, self.hparams.hidden_size]) + variable_scope.get_variable( + "decoder_lstm", + [2 * self.hparams.hidden_size, 4 * self.hparams.hidden_size]) + variable_scope.get_variable( + "device_softmax", [2 * self.hparams.hidden_size, self.num_devices]) + variable_scope.get_variable("device_go_embedding", + [1, self.hparams.hidden_size]) + variable_scope.get_variable( + "encoder_forget_bias", + shape=1, + dtype=dtypes.float32, + initializer=init_ops.constant_initializer( + self.hparams.forget_bias_init)) + variable_scope.get_variable( + "decoder_forget_bias", + shape=1, + dtype=dtypes.float32, + initializer=init_ops.constant_initializer( + self.hparams.forget_bias_init)) + variable_scope.get_variable( + "attn_w_1", [self.hparams.hidden_size, self.hparams.hidden_size]) + variable_scope.get_variable( + "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size]) + variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1]) + + else: + variable_scope.get_variable("encoder_lstm", [ + self.embedding_size + self.hparams.hidden_size, + 4 * self.hparams.hidden_size + ]) + variable_scope.get_variable( + "device_embeddings", [self.num_devices, self.hparams.hidden_size]) + variable_scope.get_variable( + "decoder_lstm", + [2 * self.hparams.hidden_size, 4 * self.hparams.hidden_size]) + variable_scope.get_variable( + "device_softmax", [2 * self.hparams.hidden_size, self.num_devices]) + variable_scope.get_variable("device_go_embedding", + [1, self.hparams.hidden_size]) + variable_scope.get_variable( + "encoder_forget_bias", + shape=1, + dtype=dtypes.float32, + initializer=init_ops.constant_initializer( + self.hparams.forget_bias_init)) + variable_scope.get_variable( + "decoder_forget_bias", + shape=1, + dtype=dtypes.float32, + initializer=init_ops.constant_initializer( + self.hparams.forget_bias_init)) + variable_scope.get_variable( + "attn_w_1", [self.hparams.hidden_size, self.hparams.hidden_size]) + variable_scope.get_variable( + "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size]) + variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1]) + seq2seq_input_layer = array_ops.placeholder_with_default( + array_ops.zeros([self.hparams.num_children, + self.num_groups, + self.group_emb_size], + dtypes.float32), + shape=(self.hparams.num_children, self.num_groups, self.group_emb_size)) + self.seq2seq_input_layer = seq2seq_input_layer + + def compute_reward(self, run_time): + if self.hparams.reward_function == "id": + reward = run_time + elif self.hparams.reward_function == "sqrt": + reward = math.sqrt(run_time) + elif self.hparams.reward_function == "log": + reward = math.log1p(run_time) + else: + raise NotImplementedError( + "Unrecognized reward function '%s', consider your " + "--reward_function flag value." % self.hparams.reward_function) + return reward + + def build_controller(self): + """RL optimization interface. + + Returns: + ops: A dictionary holding handles of the model used for training. + """ + + self._global_step = training_util.get_or_create_global_step() + ops = {} + ops["loss"] = 0 + + failing_signal = self.compute_reward(self.hparams.failing_signal) + + ctr = {} + + with tf_ops.name_scope("controller_{}".format(self.ctrl_id)): + with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)): + ctr["reward"] = {"value": [], "ph": [], "update": []} + ctr["ready"] = {"value": [], "ph": [], "update": []} + ctr["best_reward"] = {"value": [], "update": []} + for i in range(self.hparams.num_children): + reward_value = variable_scope.get_local_variable( + "reward_{}".format(i), + initializer=0.0, + dtype=dtypes.float32, + trainable=False) + reward_ph = array_ops.placeholder( + dtypes.float32, shape=(), name="reward_ph_{}".format(i)) + reward_update = state_ops.assign( + reward_value, reward_ph, use_locking=True) + ctr["reward"]["value"].append(reward_value) + ctr["reward"]["ph"].append(reward_ph) + ctr["reward"]["update"].append(reward_update) + best_reward = variable_scope.get_local_variable( + "best_reward_{}".format(i), + initializer=failing_signal, + dtype=dtypes.float32, + trainable=False) + ctr["best_reward"]["value"].append(best_reward) + ctr["best_reward"]["update"].append( + state_ops.assign(best_reward, + math_ops.minimum(best_reward, reward_update))) + + ready_value = variable_scope.get_local_variable( + "ready_{}".format(i), + initializer=True, + dtype=dtypes.bool, + trainable=False) + ready_ph = array_ops.placeholder( + dtypes.bool, shape=(), name="ready_ph_{}".format(i)) + ready_update = state_ops.assign( + ready_value, ready_ph, use_locking=True) + ctr["ready"]["value"].append(ready_value) + ctr["ready"]["ph"].append(ready_ph) + ctr["ready"]["update"].append(ready_update) + + ctr["grouping_y_preds"], ctr["grouping_log_probs"] = self.get_groupings() + summary.histogram( + "grouping_actions", + array_ops.slice(ctr["grouping_y_preds"]["sample"], [0, 0], + [1, array_ops.shape(self.op_embeddings)[0]])) + + with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)): + ctr["baseline"] = variable_scope.get_local_variable( + "baseline", + initializer=failing_signal + if self.hparams.start_with_failing_signal else 0.0, + dtype=dtypes.float32, + trainable=False) + + new_baseline = self.hparams.bl_dec * ctr["baseline"] + ( + 1 - self.hparams.bl_dec) * math_ops.reduce_mean( + ctr["reward"]["value"]) + if not self.hparams.always_update_baseline: + baseline_mask = math_ops.less(ctr["reward"]["value"], failing_signal) + selected_reward = array_ops.boolean_mask(ctr["reward"]["value"], + baseline_mask) + selected_baseline = control_flow_ops.cond( + math_ops.reduce_any(baseline_mask), + lambda: math_ops.reduce_mean(selected_reward), + lambda: constant_op.constant(0, dtype=dtypes.float32)) + ctr["pos_reward"] = selected_baseline + pos_ = math_ops.less( + constant_op.constant(0, dtype=dtypes.float32), selected_baseline) + selected_baseline = self.hparams.bl_dec * ctr["baseline"] + ( + 1 - self.hparams.bl_dec) * selected_baseline + selected_baseline = control_flow_ops.cond( + pos_, lambda: selected_baseline, lambda: ctr["baseline"]) + new_baseline = control_flow_ops.cond( + math_ops.less(self.global_step, + self.hparams.stop_updating_after_steps), + lambda: new_baseline, lambda: selected_baseline) + ctr["baseline_update"] = state_ops.assign( + ctr["baseline"], new_baseline, use_locking=True) + + ctr["y_preds"], ctr["log_probs"] = self.get_placements() + summary.histogram("actions", ctr["y_preds"]["sample"]) + mask = math_ops.less(ctr["reward"]["value"], failing_signal) + ctr["loss"] = ctr["reward"]["value"] - ctr["baseline"] + ctr["loss"] *= ( + ctr["log_probs"]["sample"] + ctr["grouping_log_probs"]["sample"]) + + selected_loss = array_ops.boolean_mask(ctr["loss"], mask) + selected_loss = control_flow_ops.cond( + math_ops.reduce_any(mask), + lambda: math_ops.reduce_mean(-selected_loss), + lambda: constant_op.constant(0, dtype=dtypes.float32)) + + ctr["loss"] = control_flow_ops.cond( + math_ops.less(self.global_step, + self.hparams.stop_updating_after_steps), + lambda: math_ops.reduce_mean(-ctr["loss"]), lambda: selected_loss) + + ctr["reward_s"] = math_ops.reduce_mean(ctr["reward"]["value"]) + summary.scalar("loss", ctr["loss"]) + summary.scalar("avg_reward", ctr["reward_s"]) + summary.scalar("best_reward_so_far", best_reward) + summary.scalar( + "advantage", + math_ops.reduce_mean(ctr["reward"]["value"] - ctr["baseline"])) + + with variable_scope.variable_scope( + "optimizer", reuse=variable_scope.AUTO_REUSE): + (ctr["train_op"], ctr["lr"], ctr["grad_norm"], + ctr["grad_norms"]) = self._get_train_ops( + ctr["loss"], + tf_ops.get_collection(tf_ops.GraphKeys.TRAINABLE_VARIABLES), + self.global_step, + grad_bound=self.hparams.grad_bound, + lr_init=self.hparams.lr, + lr_dec=self.hparams.lr_dec, + start_decay_step=self.hparams.start_decay_step, + decay_steps=self.hparams.decay_steps, + optimizer_type=self.hparams.optimizer_type) + + summary.scalar("gradnorm", ctr["grad_norm"]) + summary.scalar("lr", ctr["lr"]) + ctr["summary"] = summary.merge_all() + ops["controller"] = ctr + + self.ops = ops + return ops + + @property + def global_step(self): + return self._global_step + + def create_op_embeddings(self, verbose=False): + if verbose: + print("process input graph for op embeddings") + self.num_ops = len(self.important_ops) + # topological sort of important nodes + topo_order = [op.name for op in self.important_ops] + + # create index to name for topologicaly sorted important nodes + name_to_topo_order_index = {} + for idx, x in enumerate(topo_order): + name_to_topo_order_index[x] = idx + self.name_to_topo_order_index = name_to_topo_order_index + + # create adj matrix + adj_dict = {} + for idx, op in enumerate(self.important_ops): + for output_op in self.get_node_fanout(op): + output_op_name = output_op.name + if output_op_name in self.important_op_names: + if name_to_topo_order_index[op.name] not in adj_dict: + adj_dict[name_to_topo_order_index[op.name]] = [] + adj_dict[name_to_topo_order_index[op.name]].extend( + [name_to_topo_order_index[output_op_name], 1]) + if output_op_name not in adj_dict: + adj_dict[name_to_topo_order_index[output_op_name]] = [] + adj_dict[name_to_topo_order_index[output_op_name]].extend( + [name_to_topo_order_index[op.name], -1]) + + # get op_type op_output_shape, and adj info + output_embed_dim = (self.hparams.max_num_outputs * + self.hparams.max_output_size) + + # TODO(bsteiner): don't filter based on used ops so that we can generalize + # to models that use other types of ops. + used_ops = set() + for node in self.important_ops: + op_type = str(node.op) + used_ops.add(op_type) + + self.type_dict = {} + for op_type in self.cluster.ListAvailableOps(): + if op_type in used_ops: + self.type_dict[op_type] = len(self.type_dict) + + op_types = np.zeros([self.num_ops], dtype=np.int32) + op_output_shapes = np.full( + [self.num_ops, output_embed_dim], -1.0, dtype=np.float32) + for idx, node in enumerate(self.important_ops): + op_types[idx] = self.type_dict[node.op] + # output shape + op_name = node.name + for i, output_prop in enumerate(self.node_properties[op_name]): + if output_prop.shape.__str__() == "": + continue + shape = output_prop.shape + for j, dim in enumerate(shape.dim): + if dim.size >= 0: + if i * self.hparams.max_output_size + j >= output_embed_dim: + break + op_output_shapes[idx, + i * self.hparams.max_output_size + j] = dim.size + # adj for padding + op_adj = np.full( + [self.num_ops, self.hparams.adj_embed_dim], 0, dtype=np.float32) + for idx in adj_dict: + neighbors = adj_dict[int(idx)] + min_dim = min(self.hparams.adj_embed_dim, len(neighbors)) + padding_size = self.hparams.adj_embed_dim - min_dim + neighbors = neighbors[:min_dim] + [0] * padding_size + op_adj[int(idx)] = neighbors + + # op_embedding starts here + op_embeddings = np.zeros( + [ + self.num_ops, + 1 + self.hparams.max_num_outputs * self.hparams.max_output_size + + self.hparams.adj_embed_dim + ], + dtype=np.float32) + for idx, op_name in enumerate(topo_order): + op_embeddings[idx] = np.concatenate( + (np.array([op_types[idx]]), op_output_shapes[idx], op_adj[int(idx)])) + self.op_embeddings = constant_op.constant( + op_embeddings, dtype=dtypes.float32) + if verbose: + print("num_ops = {}".format(self.num_ops)) + print("num_types = {}".format(len(self.type_dict))) + + def get_groupings(self, *args, **kwargs): + num_children = self.hparams.num_children + with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)): + grouping_actions_cache = variable_scope.get_local_variable( + "grouping_actions_cache", + initializer=init_ops.zeros_initializer, + dtype=dtypes.int32, + shape=[num_children, self.num_ops], + trainable=False) + input_layer = self.op_embeddings + input_layer = array_ops.expand_dims(input_layer, 0) + feed_ff_input_layer = array_ops.tile(input_layer, [num_children, 1, 1]) + grouping_actions, grouping_log_probs = {}, {} + grouping_actions["sample"], grouping_log_probs[ + "sample"] = self.make_grouping_predictions(feed_ff_input_layer) + + grouping_actions["sample"] = state_ops.assign(grouping_actions_cache, + grouping_actions["sample"]) + self.grouping_actions_cache = grouping_actions_cache + + return grouping_actions, grouping_log_probs + + def make_grouping_predictions(self, input_layer, reuse=None): + """model that predicts grouping (grouping_actions). + + Args: + input_layer: group_input_layer + reuse: reuse + + Returns: + grouping_actions: actions + grouping_log_probs: log probabilities corresponding to actions + """ + with variable_scope.variable_scope(self.hparams.name, reuse=True): + # input_layer: tensor of size [1, num_ops, hidden_size] + w_grouping_ff = variable_scope.get_variable("w_grouping_ff") + w_grouping_softmax = variable_scope.get_variable("w_grouping_softmax") + + batch_size = array_ops.shape(input_layer)[0] + embedding_dim = array_ops.shape(input_layer)[2] + + reshaped = array_ops.reshape(input_layer, + [batch_size * self.num_ops, embedding_dim]) + ff_output = math_ops.matmul(reshaped, w_grouping_ff) + logits = math_ops.matmul(ff_output, w_grouping_softmax) + if self.hparams.logits_std_noise > 0: + num_in_logits = math_ops.cast( + array_ops.size(logits), dtype=dtypes.float32) + avg_norm = math_ops.divide( + linalg_ops.norm(logits), math_ops.sqrt(num_in_logits)) + logits_noise = random_ops.random_normal( + array_ops.shape(logits), + stddev=self.hparams.logits_std_noise * avg_norm) + logits = control_flow_ops.cond( + self.global_step > self.hparams.stop_noise_step, lambda: logits, + lambda: logits + logits_noise) + logits = array_ops.reshape(logits, + [batch_size * self.num_ops, self.num_groups]) + actions = random_ops.multinomial(logits, 1, seed=self.hparams.seed) + actions = math_ops.to_int32(actions) + actions = array_ops.reshape(actions, [batch_size, self.num_ops]) + action_label = array_ops.reshape(actions, [-1]) + log_probs = nn_ops.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=action_label) + log_probs = array_ops.reshape(log_probs, [batch_size, -1]) + log_probs = math_ops.reduce_sum(log_probs, 1) + grouping_actions = actions + grouping_log_probs = log_probs + return grouping_actions, grouping_log_probs + + def create_group_embeddings(self, grouping_actions, verbose=False): + """Approximating the blocks of a TF graph from a graph_def. + + Args: + grouping_actions: grouping predictions. + verbose: print stuffs. + + Returns: + groups: list of groups. + """ + groups = [ + self._create_group_embeddings(grouping_actions, i, verbose) for + i in range(self.hparams.num_children) + ] + return np.stack(groups, axis=0) + + def _create_group_embeddings(self, grouping_actions, child_id, verbose=False): + """Approximating the blocks of a TF graph from a graph_def for each child. + + Args: + grouping_actions: grouping predictions. + child_id: child_id for the group. + verbose: print stuffs. + + Returns: + groups: group embedding for the child_id. + """ + if verbose: + print("Processing input_graph") + + # TODO(azalia): Build inter-adjacencies dag matrix. + # record dag_matrix + dag_matrix = np.zeros([self.num_groups, self.num_groups], dtype=np.float32) + for op in self.important_ops: + topo_op_index = self.name_to_topo_order_index[op.name] + group_index = grouping_actions[child_id][topo_op_index] + for output_op in self.get_node_fanout(op): + if output_op.name not in self.important_op_names: + continue + output_group_index = ( + grouping_actions[child_id][self.name_to_topo_order_index[ + output_op.name]]) + dag_matrix[group_index, output_group_index] += 1.0 + num_connections = np.sum(dag_matrix) + num_intra_group_connections = dag_matrix.trace() + num_inter_group_connections = num_connections - num_intra_group_connections + if verbose: + print("grouping evaluation metric") + print(("num_connections={} num_intra_group_connections={} " + "num_inter_group_connections={}").format( + num_connections, num_intra_group_connections, + num_inter_group_connections)) + self.dag_matrix = dag_matrix + + # output_shape + op_output_shapes = np.zeros( + [ + len(self.important_ops), + self.hparams.max_num_outputs * self.hparams.max_output_size + ], + dtype=np.float32) + + for idx, op in enumerate(self.important_ops): + for i, output_properties in enumerate(self.node_properties[op.name]): + if output_properties.shape.__str__() == "": + continue + if i > self.hparams.max_num_outputs: + break + shape = output_properties.shape + for j, dim in enumerate(shape.dim): + if dim.size > 0: + k = i * self.hparams.max_output_size + j + if k >= self.hparams.max_num_outputs * self.hparams.max_output_size: + break + op_output_shapes[idx, k] = dim.size + + # group_embedding + group_embedding = np.zeros( + [ + self.num_groups, len(self.type_dict) + + self.hparams.max_num_outputs * self.hparams.max_output_size + ], + dtype=np.float32) + for op_index, op in enumerate(self.important_ops): + group_index = grouping_actions[child_id][ + self.name_to_topo_order_index[op.name]] + type_name = str(op.op) + type_index = self.type_dict[type_name] + group_embedding[group_index, type_index] += 1 + group_embedding[group_index, :self.hparams.max_num_outputs * self.hparams. + max_output_size] += ( + op_output_shapes[op_index]) + grouping_adjacencies = np.concatenate( + [dag_matrix, np.transpose(dag_matrix)], axis=1) + group_embedding = np.concatenate( + [grouping_adjacencies, group_embedding], axis=1) + group_normalizer = np.amax(group_embedding, axis=1, keepdims=True) + group_embedding /= (group_normalizer + 1.0) + if verbose: + print("Finished Processing Input Graph") + return group_embedding + + def get_placements(self, *args, **kwargs): + num_children = self.hparams.num_children + with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)): + actions_cache = variable_scope.get_local_variable( + "actions_cache", + initializer=init_ops.zeros_initializer, + dtype=dtypes.int32, + shape=[num_children, self.num_groups], + trainable=False) + + x = self.seq2seq_input_layer + last_c, last_h, attn_mem = self.encode(x) + actions, log_probs = {}, {} + actions["sample"], log_probs["sample"] = ( + self.decode( + x, last_c, last_h, attn_mem, mode="sample")) + actions["target"], log_probs["target"] = ( + self.decode( + x, + last_c, + last_h, + attn_mem, + mode="target", + y=actions_cache)) + actions["greedy"], log_probs["greedy"] = ( + self.decode( + x, last_c, last_h, attn_mem, mode="greedy")) + actions["sample"] = control_flow_ops.cond( + self.global_step < self.hparams.stop_sampling, + lambda: state_ops.assign(actions_cache, actions["sample"]), + lambda: state_ops.assign(actions_cache, actions["target"])) + self.actions_cache = actions_cache + + return actions, log_probs + + def encode(self, x): + """Encoder using LSTM. + + Args: + x: tensor of size [num_children, num_groups, embedding_size] + + Returns: + last_c, last_h: tensors of size [num_children, hidden_size], the final + LSTM states + attn_mem: tensor of size [num_children, num_groups, hidden_size], the + attention + memory, i.e. concatenation of all hidden states, linearly transformed by + an attention matrix attn_w_1 + """ + if self.hparams.bi_lstm: + with variable_scope.variable_scope(self.hparams.name, reuse=True): + w_lstm_forward = variable_scope.get_variable("encoder_lstm_forward") + w_lstm_backward = variable_scope.get_variable("encoder_lstm_backward") + forget_bias = variable_scope.get_variable("encoder_forget_bias") + attn_w_1 = variable_scope.get_variable("attn_w_1") + else: + with variable_scope.variable_scope(self.hparams.name, reuse=True): + w_lstm = variable_scope.get_variable("encoder_lstm") + forget_bias = variable_scope.get_variable("encoder_forget_bias") + attn_w_1 = variable_scope.get_variable("attn_w_1") + + embedding_size = array_ops.shape(x)[2] + + signals = array_ops.split(x, self.num_groups, axis=1) + for i in range(len(signals)): + signals[i] = array_ops.reshape( + signals[i], [self.hparams.num_children, embedding_size]) + + if self.hparams.bi_lstm: + + def body(i, prev_c_forward, prev_h_forward, prev_c_backward, + prev_h_backward): + """while loop for LSTM.""" + signal_forward = signals[i] + next_c_forward, next_h_forward = lstm(signal_forward, prev_c_forward, + prev_h_forward, w_lstm_forward, + forget_bias) + + signal_backward = signals[self.num_groups - 1 - i] + next_c_backward, next_h_backward = lstm( + signal_backward, prev_c_backward, prev_h_backward, w_lstm_backward, + forget_bias) + + next_h = array_ops.concat([next_h_forward, next_h_backward], axis=1) + all_h.append(next_h) + + return (next_c_forward, next_h_forward, next_c_backward, + next_h_backward) + + c_forward = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size / 2], + dtype=dtypes.float32) + h_forward = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size / 2], + dtype=dtypes.float32) + + c_backward = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size / 2], + dtype=dtypes.float32) + h_backward = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size / 2], + dtype=dtypes.float32) + all_h = [] + + for i in range(0, self.num_groups): + c_forward, h_forward, c_backward, h_backward = body( + i, c_forward, h_forward, c_backward, h_backward) + + last_c = array_ops.concat([c_forward, c_backward], axis=1) + last_h = array_ops.concat([h_forward, h_backward], axis=1) + attn_mem = array_ops.stack(all_h) + + else: + + def body(i, prev_c, prev_h): + signal = signals[i] + next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias) + all_h.append(next_h) + return next_c, next_h + + c = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size], + dtype=dtypes.float32) + h = array_ops.zeros( + [self.hparams.num_children, self.hparams.hidden_size], + dtype=dtypes.float32) + all_h = [] + + for i in range(0, self.num_groups): + c, h = body(i, c, h) + + last_c = c + last_h = h + attn_mem = array_ops.stack(all_h) + + attn_mem = array_ops.transpose(attn_mem, [1, 0, 2]) + attn_mem = array_ops.reshape( + attn_mem, + [self.hparams.num_children * self.num_groups, self.hparams.hidden_size]) + attn_mem = math_ops.matmul(attn_mem, attn_w_1) + attn_mem = array_ops.reshape( + attn_mem, + [self.hparams.num_children, self.num_groups, self.hparams.hidden_size]) + + return last_c, last_h, attn_mem + + def decode(self, + x, + last_c, + last_h, + attn_mem, + mode="target", + y=None): + """Decoder using LSTM. + + Args: + x: tensor of size [num_children, num_groups, embedding_size]. + last_c: tensor of size [num_children, hidden_size], the final LSTM states + computed by self.encoder. + last_h: same as last_c. + attn_mem: tensor of size [num_children, num_groups, hidden_size]. + mode: "target" or "sample". + y: tensor of size [num_children, num_groups], the device placements. + + Returns: + actions: tensor of size [num_children, num_groups], the placements of + devices + """ + with variable_scope.variable_scope(self.hparams.name, reuse=True): + w_lstm = variable_scope.get_variable("decoder_lstm") + forget_bias = variable_scope.get_variable("decoder_forget_bias") + device_embeddings = variable_scope.get_variable("device_embeddings") + device_softmax = variable_scope.get_variable("device_softmax") + device_go_embedding = variable_scope.get_variable("device_go_embedding") + attn_w_2 = variable_scope.get_variable("attn_w_2") + attn_v = variable_scope.get_variable("attn_v") + + actions = tensor_array_ops.TensorArray( + dtypes.int32, + size=self.num_groups, + infer_shape=False, + clear_after_read=False) + + # pylint: disable=unused-argument + def condition(i, *args): + return math_ops.less(i, self.num_groups) + + # pylint: disable=missing-docstring + def body(i, prev_c, prev_h, actions, log_probs): + # pylint: disable=g-long-lambda + signal = control_flow_ops.cond( + math_ops.equal(i, 0), + lambda: array_ops.tile(device_go_embedding, + [self.hparams.num_children, 1]), + lambda: embedding_ops.embedding_lookup(device_embeddings, + actions.read(i - 1)) + ) + if self.hparams.keep_prob is not None: + signal = nn_ops.dropout(signal, self.hparams.keep_prob) + next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias) + query = math_ops.matmul(next_h, attn_w_2) + query = array_ops.reshape( + query, [self.hparams.num_children, 1, self.hparams.hidden_size]) + query = math_ops.tanh(query + attn_mem) + query = array_ops.reshape(query, [ + self.hparams.num_children * self.num_groups, self.hparams.hidden_size + ]) + query = math_ops.matmul(query, attn_v) + query = array_ops.reshape(query, + [self.hparams.num_children, self.num_groups]) + query = nn_ops.softmax(query) + query = array_ops.reshape(query, + [self.hparams.num_children, self.num_groups, 1]) + query = math_ops.reduce_sum(attn_mem * query, axis=1) + query = array_ops.concat([next_h, query], axis=1) + logits = math_ops.matmul(query, device_softmax) + logits /= self.hparams.temperature + if self.hparams.tanh_constant > 0: + logits = math_ops.tanh(logits) * self.hparams.tanh_constant + if self.hparams.logits_std_noise > 0: + num_in_logits = math_ops.cast( + array_ops.size(logits), dtype=dtypes.float32) + avg_norm = math_ops.divide( + linalg_ops.norm(logits), math_ops.sqrt(num_in_logits)) + logits_noise = random_ops.random_normal( + array_ops.shape(logits), + stddev=self.hparams.logits_std_noise * avg_norm) + logits = control_flow_ops.cond( + self.global_step > self.hparams.stop_noise_step, lambda: logits, + lambda: logits + logits_noise) + + if mode == "sample": + next_y = random_ops.multinomial(logits, 1, seed=self.hparams.seed) + elif mode == "greedy": + next_y = math_ops.argmax(logits, 1) + elif mode == "target": + next_y = array_ops.slice(y, [0, i], [-1, 1]) + else: + raise NotImplementedError + next_y = math_ops.to_int32(next_y) + next_y = array_ops.reshape(next_y, [self.hparams.num_children]) + actions = actions.write(i, next_y) + log_probs += nn_ops.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=next_y) + return i + 1, next_c, next_h, actions, log_probs + + loop_vars = [ + constant_op.constant(0, dtype=dtypes.int32), last_c, last_h, actions, + array_ops.zeros([self.hparams.num_children], dtype=dtypes.float32) + ] + loop_outputs = control_flow_ops.while_loop(condition, body, loop_vars) + + last_c = loop_outputs[-4] + last_h = loop_outputs[-3] + actions = loop_outputs[-2].stack() + actions = array_ops.transpose(actions, [1, 0]) + log_probs = loop_outputs[-1] + return actions, log_probs + + def eval_placement(self, + sess, + child_id=0, + verbose=False): + grouping_actions, actions = sess.run([ + self.grouping_actions_cache, + self.actions_cache + ]) + grouping_actions = grouping_actions[child_id] + actions = actions[child_id] + if verbose: + global_step = sess.run(self.global_step) + if global_step % 100 == 0: + log_string = "op group assignments: " + for a in grouping_actions: + log_string += "{} ".format(a) + print(log_string[:-1]) + log_string = "group device assignments: " + for a in actions: + log_string += "{} ".format(a) + print(log_string[:-1]) + + for op in self.important_ops: + topo_order_index = self.name_to_topo_order_index[op.name] + group_index = grouping_actions[topo_order_index] + op.device = self.devices[actions[group_index]].name + try: + _, run_time, _ = self.cluster.MeasureCosts(self.item) + except errors.ResourceExhaustedError: + run_time = self.hparams.failing_signal + return run_time + + def update_reward(self, + sess, + run_time, + child_id=0, + verbose=False): + reward = self.compute_reward(run_time) + controller_ops = self.ops["controller"] + _, best_reward = sess.run( + [ + controller_ops["reward"]["update"][child_id], + controller_ops["best_reward"]["update"][child_id] + ], + feed_dict={ + controller_ops["reward"]["ph"][child_id]: reward, + }) + if verbose: + print(("run_time={:<.5f} reward={:<.5f} " + "best_reward={:<.5f}").format(run_time, reward, best_reward)) + + # Reward is a double, best_reward a float: allow for some slack in the + # comparison. + updated = abs(best_reward - reward) < 1e-6 + return updated + + def generate_grouping(self, sess): + controller_ops = self.ops["controller"] + grouping_actions = sess.run(controller_ops["grouping_y_preds"]["sample"]) + return grouping_actions + + def generate_placement(self, grouping, sess): + controller_ops = self.ops["controller"] + feed_seq2seq_input_dict = {} + feed_seq2seq_input_dict[self.seq2seq_input_layer] = grouping + sess.run( + controller_ops["y_preds"]["sample"], feed_dict=feed_seq2seq_input_dict) + + def process_reward(self, sess): + controller_ops = self.ops["controller"] + run_ops = [ + controller_ops["loss"], controller_ops["lr"], + controller_ops["grad_norm"], controller_ops["grad_norms"], + controller_ops["train_op"] + ] + sess.run(run_ops) + sess.run(controller_ops["baseline_update"]) + + def _get_train_ops(self, + loss, + tf_variables, + global_step, + grad_bound=1.25, + lr_init=1e-3, + lr_dec=0.9, + start_decay_step=10000, + decay_steps=100, + optimizer_type="adam"): + """Loss optimizer. + + Args: + loss: scalar tf tensor + tf_variables: list of training variables, typically + tf.trainable_variables() + global_step: global_step + grad_bound: max gradient norm + lr_init: initial learning rate + lr_dec: leaning rate decay coefficient + start_decay_step: start decaying learning rate after this many steps + decay_steps: apply decay rate factor at this step intervals + optimizer_type: optimizer type should be either adam or sgd + + Returns: + train_op: training op + learning_rate: scalar learning rate tensor + grad_norm: l2 norm of the gradient vector + all_grad_norms: l2 norm of each component + """ + lr_gstep = global_step - start_decay_step + + def f1(): + return constant_op.constant(lr_init) + + def f2(): + return learning_rate_decay.exponential_decay(lr_init, lr_gstep, + decay_steps, lr_dec, True) + + learning_rate = control_flow_ops.cond( + math_ops.less(global_step, start_decay_step), + f1, + f2, + name="learning_rate") + + if optimizer_type == "adam": + opt = adam.AdamOptimizer(learning_rate) + elif optimizer_type == "sgd": + opt = gradient_descent.GradientDescentOptimizer(learning_rate) + grads_and_vars = opt.compute_gradients(loss, tf_variables) + grad_norm = clip_ops.global_norm([g for g, v in grads_and_vars]) + all_grad_norms = {} + clipped_grads = [] + clipped_rate = math_ops.maximum(grad_norm / grad_bound, 1.0) + for g, v in grads_and_vars: + if g is not None: + if isinstance(g, tf_ops.IndexedSlices): + clipped = g.values / clipped_rate + norm_square = math_ops.reduce_sum(clipped * clipped) + clipped = tf_ops.IndexedSlices(clipped, g.indices) + else: + clipped = g / clipped_rate + norm_square = math_ops.reduce_sum(clipped * clipped) + all_grad_norms[v.name] = math_ops.sqrt(norm_square) + clipped_grads.append((clipped, v)) + + train_op = opt.apply_gradients(clipped_grads, global_step) + return train_op, learning_rate, grad_norm, all_grad_norms + + +def lstm(x, prev_c, prev_h, w_lstm, forget_bias): + """LSTM cell. + + Args: + x: tensors of size [num_children, hidden_size]. + prev_c: tensors of size [num_children, hidden_size]. + prev_h: same as prev_c. + w_lstm: . + forget_bias: . + + Returns: + next_c: + next_h: + """ + ifog = math_ops.matmul(array_ops.concat([x, prev_h], axis=1), w_lstm) + i, f, o, g = array_ops.split(ifog, 4, axis=1) + i = math_ops.sigmoid(i) + f = math_ops.sigmoid(f + forget_bias) + o = math_ops.sigmoid(o) + g = math_ops.tanh(g) + next_c = i * g + f * prev_c + next_h = o * math_ops.tanh(next_c) + return next_c, next_h diff --git a/tensorflow/python/grappler/item.i b/tensorflow/python/grappler/item.i index d0fc1a04f220e0a053257e0206bb07b25f3767c6..9a84c60b04029a64ed35a01f045a6eec5e492504 100644 --- a/tensorflow/python/grappler/item.i +++ b/tensorflow/python/grappler/item.i @@ -96,10 +96,10 @@ static GItem TF_NewItem( return GItem(item.release()); } -static std::vector TF_IdentifyImportantOps(GItem item, bool sort_topologically, +static PyObject* TF_IdentifyImportantOps(GItem item, bool sort_topologically, TF_Status* status) { if (item.is_none()) { - return {}; + Py_RETURN_NONE; } std::vector main_ops = item->MainOpsFanin(); @@ -132,7 +132,13 @@ static std::vector TF_IdentifyImportantOps(GItem item, bool sort_topolog } } - return ops; + PyGILState_STATE gstate = PyGILState_Ensure(); + PyObject* result = PyList_New(ops.size()); + for (int i = 0; i < ops.size(); ++i) { + PyList_SetItem(result, i, PyString_FromString(ops[i].c_str())); + } + PyGILState_Release(gstate); + return result; } static PyObject* TF_GetOpProperties(GItem item) { @@ -305,7 +311,7 @@ static PyObject* TF_GetColocationGroups(GItem item) { static GItem TF_NewItem( const tensorflow::MetaGraphDef& meta_graph, bool ignore_colocation, bool ignore_user_placement, TF_Status* out_status); -static std::vector TF_IdentifyImportantOps(GItem item, bool sort_topologically, - TF_Status* status); +static PyObject* TF_IdentifyImportantOps(GItem item, bool sort_topologically, + TF_Status* status); static PyObject* TF_GetOpProperties(GItem item); static PyObject* TF_GetColocationGroups(GItem item); diff --git a/tensorflow/python/grappler/item_test.py b/tensorflow/python/grappler/item_test.py index cd70e2fdecc74f9d99240ac566f3c28e900a06c2..7c3efd6249cbdaa2675632f7fc8e25fb88658a24 100644 --- a/tensorflow/python/grappler/item_test.py +++ b/tensorflow/python/grappler/item_test.py @@ -56,7 +56,7 @@ class ItemTest(test.TestCase): mg = meta_graph.create_meta_graph_def(graph=g) grappler_item = item.Item(mg) op_list = grappler_item.IdentifyImportantOps() - self.assertItemsEqual([b'Const', b'Const_1', b'add'], op_list) + self.assertItemsEqual(['Const', 'Const_1', 'add'], op_list) def testOpProperties(self): with ops.Graph().as_default() as g: diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index 5bc9e4b8030d69530a2427172badb7331ad58155..5a84b16a23f567fba6d08aaefd3b816a76907735 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -256,7 +256,7 @@ class LayoutOptimizerTest(test.TestCase): x = random_ops.truncated_normal([1, 784], seed=0) output = _two_layer_model(x) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -293,7 +293,7 @@ class LayoutOptimizerTest(test.TestCase): add = bn0[0] + bn1[0] output = array_ops.identity(add) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={dim: 3}) with session.Session(config=_get_config()) as sess: @@ -321,11 +321,11 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) dim = array_ops.placeholder(dtype='int32') sizes = constant_op.constant([50, 10, 4], shape=[3]) - split = gen_array_ops._split_v( + split = gen_array_ops.split_v( value=conv, size_splits=sizes, axis=dim, num_split=3) output = math_ops.reduce_sum(split[0]) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={dim: 3}) with session.Session(config=_get_config()) as sess: @@ -359,7 +359,7 @@ class LayoutOptimizerTest(test.TestCase): pad = array_ops.pad(conv, paddings) output = array_ops.identity(pad) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -390,7 +390,7 @@ class LayoutOptimizerTest(test.TestCase): reduce_sum = math_ops.reduce_sum(conv) output = array_ops.identity(reduce_sum) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -419,7 +419,7 @@ class LayoutOptimizerTest(test.TestCase): cast = math_ops.cast(conv, dtype='bool') output = array_ops.identity(cast) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -450,7 +450,67 @@ class LayoutOptimizerTest(test.TestCase): squeeze = array_ops.squeeze(reduce_sum) output = array_ops.identity(squeeze) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run(output, run_metadata=metadata) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + # Three transposes were initially added in the Expand phase of + # LayoutOptimizer; two of them are cancelled out in the Collapse phase. + expected_num_transposes = 1 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + + def testSqueezeAlongHW(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2], keep_dims=True) + squeeze = array_ops.squeeze(reduce_sum, axis=[1, 2]) + output = array_ops.identity(squeeze) + + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run(output, run_metadata=metadata) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + # Three transposes were initially added in the Expand phase of + # LayoutOptimizer; two of them are cancelled out in the Collapse phase. + expected_num_transposes = 1 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + + def testSqueezeAlongNHW(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2], keep_dims=True) + squeeze = array_ops.squeeze(reduce_sum, axis=[0, 1, 2]) + output = array_ops.identity(squeeze) + + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -479,7 +539,7 @@ class LayoutOptimizerTest(test.TestCase): reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2, 3]) output = array_ops.identity(reduce_sum) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -508,7 +568,7 @@ class LayoutOptimizerTest(test.TestCase): reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2]) output = array_ops.identity(reduce_sum) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -537,7 +597,7 @@ class LayoutOptimizerTest(test.TestCase): reduce_sum = math_ops.reduce_sum(conv, axis=[3]) output = array_ops.identity(reduce_sum) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -558,6 +618,94 @@ class LayoutOptimizerTest(test.TestCase): self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) + def testReduceSumAlongCKeepDims(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + reduce_sum = math_ops.reduce_sum(conv, axis=[3], keep_dims=True) + output = array_ops.identity(reduce_sum) + + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run(output, run_metadata=metadata) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + # Four transposes were initially added in the Expand phase of + # LayoutOptimizer; two of them are cancelled out in the Collapse phase. + expected_num_transposes = 2 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self._assert_trans_nchw_to_nhwc('Sum-0-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + + def testReduceSumAlongHKeepDims(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + reduce_sum = math_ops.reduce_sum(conv, axis=[2], keep_dims=True) + output = array_ops.identity(reduce_sum) + + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run(output, run_metadata=metadata) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + # Four transposes were initially added in the Expand phase of + # LayoutOptimizer; two of them are cancelled out in the Collapse phase. + expected_num_transposes = 2 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + + def testReduceSumAlongWCKeepDims(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + reduce_sum = math_ops.reduce_sum(conv, axis=[2, 3], keep_dims=True) + output = array_ops.identity(reduce_sum) + + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run(output, run_metadata=metadata) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + # Four transposes were initially added in the Expand phase of + # LayoutOptimizer; two of them are cancelled out in the Collapse phase. + expected_num_transposes = 2 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + def testConcatWithControlDependency(self): if test.is_gpu_available(cuda_only=True): random_seed.set_random_seed(0) @@ -570,7 +718,7 @@ class LayoutOptimizerTest(test.TestCase): concat = array_ops.concat([conv, conv], axis) output = array_ops.identity(concat) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -604,7 +752,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(fill) x_val = [3.4] * 784 - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={x: x_val}) with session.Session(config=_get_config()) as sess: @@ -646,7 +794,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(tile) multiple_val = [2, 3, 4, 1] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={multiple: multiple_val}) with session.Session(config=_get_config()) as sess: @@ -681,7 +829,7 @@ class LayoutOptimizerTest(test.TestCase): reverse = array_ops.reverse(conv, dims) output = array_ops.identity(reverse) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -714,7 +862,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(reverse) dims_val = [2, 3] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={dims: dims_val}) with session.Session(config=_get_config()) as sess: @@ -748,10 +896,10 @@ class LayoutOptimizerTest(test.TestCase): add = math_ops.add(conv, conv) mean = math_ops.reduce_mean(conv) condition = math_ops.less(conv, mean) - select = gen_math_ops._select(condition, conv, add) + select = gen_math_ops.select(condition, conv, add) output = array_ops.identity(select) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -771,6 +919,37 @@ class LayoutOptimizerTest(test.TestCase): self._assert_trans_nchw_to_nhwc('Select-0-0', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) + def testSelectOpConditionUnknownShape(self): + if test.is_gpu_available(cuda_only=True): + random_seed.set_random_seed(0) + x = random_ops.truncated_normal([1, 784], seed=0) + conv = _two_layer_model(x) + add = math_ops.add(conv, conv) + condition = array_ops.placeholder(dtype='bool') + select = gen_math_ops.select(condition, conv, add) + output = array_ops.identity(select) + + condition_val = np.zeros((1, 7, 7, 64)) + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output, feed_dict={condition: condition_val}) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run( + output, run_metadata=metadata, feed_dict={condition: condition_val}) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + expected_num_transposes = 3 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) + def testSelectOpScalarCondition(self): if test.is_gpu_available(cuda_only=True): random_seed.set_random_seed(0) @@ -778,10 +957,10 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) add = math_ops.add(conv, conv) condition = constant_op.constant(True) - select = gen_math_ops._select(condition, conv, add) + select = gen_math_ops.select(condition, conv, add) output = array_ops.identity(select) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -811,7 +990,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(pad) paddings_val = [[1, 2], [3, 4], [5, 6], [7, 8]] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={paddings: paddings_val}) with session.Session(config=_get_config()) as sess: @@ -844,11 +1023,11 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) ksize = constant_op.constant([1, 2, 3, 1], shape=[4]) strides = array_ops.placeholder(dtype='int32', shape=[4]) - max_pool = gen_nn_ops._max_pool_v2(conv, ksize, strides, 'VALID') + max_pool = gen_nn_ops.max_pool_v2(conv, ksize, strides, 'VALID') output = array_ops.identity(max_pool) strides_val = [1, 3, 2, 1] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={strides: strides_val}) with session.Session(config=_get_config()) as sess: @@ -885,7 +1064,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(max_pool_grad) strides_val = [1, 3, 2, 1] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={strides: strides_val}) with session.Session(config=_get_config()) as sess: @@ -920,7 +1099,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(s) size_val = [1, 2, 3, 4] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={size: size_val}) with session.Session(config=_get_config()) as sess: @@ -956,7 +1135,7 @@ class LayoutOptimizerTest(test.TestCase): output = array_ops.identity(s) end_val = [1, 2, 3, 4] - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={end: end_val}) with session.Session(config=_get_config()) as sess: @@ -994,7 +1173,7 @@ class LayoutOptimizerTest(test.TestCase): s = conv[:, :, 1:-1, :] output = array_ops.identity(s) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -1029,7 +1208,7 @@ class LayoutOptimizerTest(test.TestCase): s = conv[:, :, :, 1:-1] output = array_ops.identity(s) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -1068,7 +1247,7 @@ class LayoutOptimizerTest(test.TestCase): [1, 2, 3, 1], s) output = array_ops.identity(s_grad) - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={end: end_val}) with session.Session(config=_get_config()) as sess: @@ -1104,7 +1283,7 @@ class LayoutOptimizerTest(test.TestCase): output = math_ops.add(shapen[0], shapen[1]) x_val = [1.7] * 784 - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output, feed_dict={x: x_val}) with session.Session(config=_get_config()) as sess: @@ -1127,11 +1306,42 @@ class LayoutOptimizerTest(test.TestCase): self._assert_vec_nchw_to_nhwc('ShapeN-0-0', nodes) self.assertAllEqual(output_val_ref, output_val) + def testShapeNFollowedByNotConvertibleNodeReshape(self): + if test.is_gpu_available(cuda_only=True): + x = array_ops.placeholder(dtype='float32') + conv = _two_layer_model(x) + conv_reshape = array_ops.reshape(conv, [1, 1, 1, -1]) + shapen = array_ops.shape_n([conv, conv_reshape]) + shape = array_ops.identity(shapen[1]) + ones = array_ops.ones(shape) + output = math_ops.add_n([conv_reshape, ones]) + + x_val = [1.7] * 784 + with session.Session(config=_get_config(False)) as sess: + output_val_ref = sess.run(output, feed_dict={x: x_val}) + + with session.Session(config=_get_config()) as sess: + metadata = config_pb2.RunMetadata() + output_val = sess.run( + output, run_metadata=metadata, feed_dict={x: x_val}) + + nodes = [] + num_transposes = 0 + for node in metadata.cost_graph.node: + if _is_transpose(node.name): + num_transposes += 1 + nodes.append(node.name) + + expected_num_transposes = 2 + self.assertEqual(expected_num_transposes, num_transposes) + self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) + self.assertAllEqual(output_val_ref, output_val) + def testLoop(self): if test.is_gpu_available(cuda_only=True): output = _loop() - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -1158,7 +1368,7 @@ class LayoutOptimizerTest(test.TestCase): if test.is_gpu_available(cuda_only=True): output = _loop_with_branch() - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -1182,7 +1392,7 @@ class LayoutOptimizerTest(test.TestCase): if test.is_gpu_available(cuda_only=True): output = _loop_with_vec_and_4d() - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: @@ -1206,7 +1416,7 @@ class LayoutOptimizerTest(test.TestCase): if test.is_gpu_available(cuda_only=True): output = _model_with_second_port() - with session.Session() as sess: + with session.Session(config=_get_config(False)) as sess: output_val_ref = sess.run(output) with session.Session(config=_get_config()) as sess: diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 948911f099674af4c6dd19bfdac75e5fc1f75c78..4df959ce04169395589aeebaef9e3e7839e2300c 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -162,7 +162,8 @@ class MemoryOptimizerRecomputeTest(test.TestCase): arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, - memory_optimizer_target_node_name_prefix='optimizer/gradients/'), + # Checks that name scope "gradients/" also match sub-scope. + memory_optimizer_target_node_name_scope='gradients/'), original_metagraph) self.assertGreater( len(rewritten_graph_def.node), @@ -176,6 +177,35 @@ class MemoryOptimizerRecomputeTest(test.TestCase): len([node for node in rewritten_graph_def.node if 'Recomputed/' in node.name])) + def testRewritingNameScopedGradientNamesScope(self): + """Tests that rewriting occurs with non-standard gradient names.""" + (original_metagraph, _, _, + _) = self._GetMetaGraph(optimizer_scope_name='foo/bar') + rewritten_graph_def = tf_optimizer.OptimizeGraph( + rewriter_config_pb2.RewriterConfig( + disable_model_pruning=True, + constant_folding=rewriter_config_pb2.RewriterConfig.OFF, + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, + layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, + arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + memory_optimization=rewriter_config_pb2.RewriterConfig. + RECOMPUTATION_HEURISTICS, + # This should not match anything. + memory_optimizer_target_node_name_scope='r/gradients/'), + original_metagraph) + self.assertEqual( + len(rewritten_graph_def.node), len(original_metagraph.graph_def.node)) + self.assertEqual(0, + len([ + node for node in original_metagraph.graph_def.node + if 'Recomputed/' in node.name + ])) + self.assertEqual(0, + len([ + node for node in rewritten_graph_def.node + if 'Recomputed/' in node.name + ])) + def _GetMemoryOptimizerSessionConfig(self): rewrite_options = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, diff --git a/tensorflow/python/grappler/tf_optimizer.i b/tensorflow/python/grappler/tf_optimizer.i index 1b657983a4690dd0ddb7f569ce514b08cb10400a..de9326ccfc1653c2afd0833dcdca2cc4bfdabed5 100644 --- a/tensorflow/python/grappler/tf_optimizer.i +++ b/tensorflow/python/grappler/tf_optimizer.i @@ -100,6 +100,7 @@ PyObject* TF_OptimizeGraph( tensorflow::grappler::ItemConfig item_config; item_config.inline_functions = false; item_config.apply_optimizations = false; + item_config.ignore_user_placement = false; std::unique_ptr grappler_item = tensorflow::grappler::GrapplerItemFromMetaGraphDef(graph_id, metagraph, item_config); diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py index 55dcbe2071f74204e0bbdd141560f33cefdf174d..3ee4d7807ea5677a742514eb56267b94c6b92bba 100644 --- a/tensorflow/python/grappler/tf_optimizer_test.py +++ b/tensorflow/python/grappler/tf_optimizer_test.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.grappler import tf_optimizer from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -48,6 +49,31 @@ class PyWrapOptimizeGraphTest(test.TestCase): self.assertEqual(len(graph.node), 1) self.assertItemsEqual([node.name for node in graph.node], ['d']) + def testKeepNodes(self): + g = ops.Graph() + with g.as_default(): + a1 = variables.Variable( + 1.0) # Must be preserved since it's in the collection 'variables'. + a2 = constant_op.constant(0, shape=[50, 50], name='keep') + ops.add_to_collection('a2', a2) # Explicitly add to collection. + b = constant_op.constant(1, shape=[100, 10]) + c = constant_op.constant(0, shape=[10, 30]) + d = math_ops.matmul(b, c) + ops.add_to_collection('train_op', d) # d is the fetch node. + + # Optimize the graph. + mg = meta_graph.create_meta_graph_def(graph=g) + rewriter_config = rewriter_config_pb2.RewriterConfig() + optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) + + # Check that the nodes referenced in various collections have been preserved + self.assertEqual(len(optimized_graph.node), 5) + self.assertEqual(d.op.name, optimized_graph.node[0].name) + self.assertEqual(a1.op.name, optimized_graph.node[1].name) + self.assertEqual('Variable/initial_value', optimized_graph.node[2].name) + self.assertEqual(a2.op.name, optimized_graph.node[3].name) + self.assertEqual('Variable/Assign', optimized_graph.node[4].name) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index fdac22bb53cc7e78d854d4b5ff756a190c9c62b6..f47be178864a12249bbe6440fc02f2381903776d 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -3,10 +3,17 @@ licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE"]) + package(default_visibility = ["//visibility:public"]) load("//tensorflow:tensorflow.bzl", "py_test") +config_setting( + name = "empty_condition", + values = {"define": "UNUSED=unused"}, +) + py_library( name = "keras", srcs = [ @@ -37,9 +44,16 @@ py_library( "_impl/keras/datasets/mnist.py", "_impl/keras/datasets/reuters.py", "_impl/keras/engine/__init__.py", - "_impl/keras/engine/topology.py", + "_impl/keras/engine/base_layer.py", + "_impl/keras/engine/input_layer.py", + "_impl/keras/engine/network.py", + "_impl/keras/engine/saving.py", + "_impl/keras/engine/sequential.py", "_impl/keras/engine/training.py", + "_impl/keras/engine/training_arrays.py", "_impl/keras/engine/training_eager.py", + "_impl/keras/engine/training_generator.py", + "_impl/keras/engine/training_utils.py", "_impl/keras/estimator.py", "_impl/keras/initializers.py", "_impl/keras/layers/__init__.py", @@ -72,8 +86,8 @@ py_library( "_impl/keras/utils/generic_utils.py", "_impl/keras/utils/io_utils.py", "_impl/keras/utils/layer_utils.py", + "_impl/keras/utils/multi_gpu_utils.py", "_impl/keras/utils/np_utils.py", - "_impl/keras/utils/training_utils.py", "_impl/keras/utils/vis_utils.py", "_impl/keras/wrappers/__init__.py", "_impl/keras/wrappers/scikit_learn.py", @@ -117,7 +131,11 @@ py_library( ], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = [ + deps = select({ + ":empty_condition": [], + "//conditions:default": [], + }) + [ + "@six_archive//:six", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:check_ops", @@ -156,7 +174,6 @@ py_library( "//tensorflow/python/estimator", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/saved_model", - "@six_archive//:six", ], ) @@ -254,6 +271,11 @@ py_test( size = "small", srcs = ["_impl/keras/metrics_test.py"], srcs_version = "PY2AND3", + tags = [ + "manual", + "no_oss", + "notap", + ], deps = [ ":keras", "//tensorflow/python:client_testlib", @@ -393,7 +415,7 @@ py_test( py_test( name = "convolutional_test", - size = "medium", + size = "large", srcs = ["_impl/keras/layers/convolutional_test.py"], srcs_version = "PY2AND3", tags = [ @@ -614,7 +636,10 @@ py_test( size = "small", srcs = ["_impl/keras/utils/io_utils_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "no_windows", # TODO: needs investigation on Windows + "notsan", + ], deps = [ ":keras", "//tensorflow/python:client_testlib", @@ -635,9 +660,9 @@ py_test( ) py_test( - name = "training_utils_test", + name = "multi_gpu_utils_test", size = "medium", - srcs = ["_impl/keras/utils/training_utils_test.py"], + srcs = ["_impl/keras/utils/multi_gpu_utils_test.py"], srcs_version = "PY2AND3", tags = ["multi_gpu"], deps = [ @@ -734,6 +759,19 @@ py_test( ], ) +py_test( + name = "model_subclassing_test", + size = "medium", + srcs = ["_impl/keras/model_subclassing_test.py"], + srcs_version = "PY2AND3", + tags = ["notsan"], + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + py_test( name = "topology_test", size = "small", @@ -741,9 +779,31 @@ py_test( srcs_version = "PY2AND3", deps = [ ":keras", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", + "//third_party/py/numpy", + ], +) + +py_test( + name = "saving_test", + size = "small", + srcs = ["_impl/keras/engine/saving_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + +py_test( + name = "sequential_test", + size = "small", + srcs = ["_impl/keras/engine/sequential_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", "//third_party/py/numpy", ], ) @@ -764,7 +824,7 @@ py_test( py_test( name = "estimator_test", - size = "medium", + size = "large", srcs = ["_impl/keras/estimator_test.py"], srcs_version = "PY2AND3", tags = ["notsan"], diff --git a/tensorflow/python/keras/_impl/keras/__init__.py b/tensorflow/python/keras/_impl/keras/__init__.py index 73113539329c5493141db243b85254062f7b8f88..b63907b2e60acfc80ee411b9193b2829f0224c3e 100644 --- a/tensorflow/python/keras/_impl/keras/__init__.py +++ b/tensorflow/python/keras/_impl/keras/__init__.py @@ -40,4 +40,4 @@ from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.models import Sequential -__version__ = '2.1.3-tf' +__version__ = '2.1.4-tf' diff --git a/tensorflow/python/keras/_impl/keras/activations.py b/tensorflow/python/keras/_impl/keras/activations.py index 4852b8c36ae5f475b33f12b7c7f21ae424ba242e..236e17653e1b762e1e6962f453b714d1bf7bcbf7 100644 --- a/tensorflow/python/keras/_impl/keras/activations.py +++ b/tensorflow/python/keras/_impl/keras/activations.py @@ -24,8 +24,10 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object from tensorflow.python.layers.base import Layer from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.activations.softmax') def softmax(x, axis=-1): """Softmax activation function. @@ -50,10 +52,12 @@ def softmax(x, axis=-1): raise ValueError('Cannot apply softmax to a tensor that is 1D') +@tf_export('keras.activations.elu') def elu(x, alpha=1.0): return K.elu(x, alpha) +@tf_export('keras.activations.selu') def selu(x): """Scaled Exponential Linear Unit. (Klambauer et al., 2017). @@ -73,38 +77,47 @@ def selu(x): return scale * K.elu(x, alpha) +@tf_export('keras.activations.softplus') def softplus(x): return K.softplus(x) +@tf_export('keras.activations.softsign') def softsign(x): return K.softsign(x) +@tf_export('keras.activations.relu') def relu(x, alpha=0., max_value=None): return K.relu(x, alpha=alpha, max_value=max_value) +@tf_export('keras.activations.tanh') def tanh(x): return K.tanh(x) +@tf_export('keras.activations.sigmoid') def sigmoid(x): return K.sigmoid(x) +@tf_export('keras.activations.hard_sigmoid') def hard_sigmoid(x): return K.hard_sigmoid(x) +@tf_export('keras.activations.linear') def linear(x): return x +@tf_export('keras.activations.serialize') def serialize(activation): return activation.__name__ +@tf_export('keras.activations.deserialize') def deserialize(name, custom_objects=None): return deserialize_keras_object( name, @@ -113,6 +126,7 @@ def deserialize(name, custom_objects=None): printable_module_name='activation function') +@tf_export('keras.activations.get') def get(identifier): if identifier is None: return linear diff --git a/tensorflow/python/keras/_impl/keras/applications/densenet.py b/tensorflow/python/keras/_impl/keras/applications/densenet.py index 9e40d3493024c5472afca636ffc1510fad9b52d1..ca83e8691237216e799f2ca738dcb6822506e2cb 100644 --- a/tensorflow/python/keras/_impl/keras/applications/densenet.py +++ b/tensorflow/python/keras/_impl/keras/applications/densenet.py @@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization @@ -45,6 +45,7 @@ from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.layers import ZeroPadding2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export DENSENET121_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels.h5' @@ -298,6 +299,8 @@ def DenseNet(blocks, return model +@tf_export('keras.applications.DenseNet121', + 'keras.applications.densenet.DenseNet121') def DenseNet121(include_top=True, weights='imagenet', input_tensor=None, @@ -308,6 +311,8 @@ def DenseNet121(include_top=True, input_shape, pooling, classes) +@tf_export('keras.applications.DenseNet169', + 'keras.applications.densenet.DenseNet169') def DenseNet169(include_top=True, weights='imagenet', input_tensor=None, @@ -318,6 +323,8 @@ def DenseNet169(include_top=True, input_shape, pooling, classes) +@tf_export('keras.applications.DenseNet201', + 'keras.applications.densenet.DenseNet201') def DenseNet201(include_top=True, weights='imagenet', input_tensor=None, @@ -328,6 +335,7 @@ def DenseNet201(include_top=True, input_shape, pooling, classes) +@tf_export('keras.applications.densenet.preprocess_input') def preprocess_input(x, data_format=None): """Preprocesses a numpy array encoding a batch of images. diff --git a/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py b/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py index f1f20f12a8da2cb9dcb918838a4df55cdcd04602..c26a28ed4087e30968585ec8ac0b64b51513bcae 100644 --- a/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py +++ b/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py @@ -25,6 +25,7 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export CLASS_INDEX = None @@ -162,6 +163,9 @@ def _preprocess_symbolic_input(x, data_format, mode): return x +@tf_export('keras.applications.resnet50.preprocess_input', + 'keras.applications.vgg19.preprocess_input', + 'keras.applications.vgg16.preprocess_input') def preprocess_input(x, data_format=None, mode='caffe'): """Preprocesses a tensor or Numpy array encoding a batch of images. @@ -193,6 +197,15 @@ def preprocess_input(x, data_format=None, mode='caffe'): return _preprocess_symbolic_input(x, data_format=data_format, mode=mode) +@tf_export('keras.applications.nasnet.decode_predictions', + 'keras.applications.resnet50.decode_predictions', + 'keras.applications.vgg19.decode_predictions', + 'keras.applications.vgg16.decode_predictions', + 'keras.applications.inception_resnet_v2.decode_predictions', + 'keras.applications.inception_v3.decode_predictions', + 'keras.applications.densenet.decode_predictions', + 'keras.applications.mobilenet.decode_predictions', + 'keras.applications.xception.decode_predictions') def decode_predictions(preds, top=5): """Decodes the prediction of an ImageNet model. @@ -221,7 +234,8 @@ def decode_predictions(preds, top=5): CLASS_INDEX_PATH, cache_subdir='models', file_hash='c2c37ea517e94d9795004a39431a14cb') - CLASS_INDEX = json.load(open(fpath)) + with open(fpath) as f: + CLASS_INDEX = json.load(f) results = [] for pred in preds: top_indices = pred.argsort()[-top:][::-1] diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py index 1dc15b5b3437718e285d694eb058ef124ee52c0b..17e407dd58460e6d6802a3e137a96faf38a6f576 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py @@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization @@ -46,11 +46,13 @@ from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/' +@tf_export('keras.applications.inception_resnet_v2.preprocess_input') def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. @@ -190,6 +192,8 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): return x +@tf_export('keras.applications.InceptionResNetV2', + 'keras.applications.inception_resnet_v2.InceptionResNetV2') def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py index ff57116f2dabed58e6993320425384ed55fde65b..2897c6058eb445ceacc34084b53dc89f556e3e9c 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py @@ -37,7 +37,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization @@ -50,6 +50,7 @@ from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5' @@ -101,6 +102,8 @@ def conv2d_bn(x, return x +@tf_export('keras.applications.InceptionV3', + 'keras.applications.inception_v3.InceptionV3') def InceptionV3(include_top=True, weights='imagenet', input_tensor=None, @@ -399,6 +402,8 @@ def InceptionV3(include_top=True, return model +@tf_export('keras.applications.nasnet.preprocess_input', + 'keras.applications.inception_v3.preprocess_input') def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. diff --git a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py index 790bf8ceade6039bbd651fce1960c04f9c51c63e..ad96b53a4528d99a014a0214b52a78d6a60076f8 100644 --- a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py +++ b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py @@ -79,8 +79,8 @@ from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D @@ -93,6 +93,7 @@ from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/' @@ -102,6 +103,7 @@ def relu6(x): return K.relu(x, max_value=6) +@tf_export('keras.applications.mobilenet.preprocess_input') def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. @@ -303,6 +305,8 @@ class DepthwiseConv2D(Conv2D): return config +@tf_export('keras.applications.MobileNet', + 'keras.applications.mobilenet.MobileNet') def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, @@ -557,7 +561,7 @@ def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)): and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value. filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. @@ -623,7 +627,7 @@ def _depthwise_conv_block(inputs, (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the pointwise convolution). + (i.e. the number of output filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. diff --git a/tensorflow/python/keras/_impl/keras/applications/nasnet.py b/tensorflow/python/keras/_impl/keras/applications/nasnet.py index 5dd038c096f09422e381ba96006c3ebcd88481d9..dd33230a7eb9272f8fc60daee63e1f92574cf5e3 100644 --- a/tensorflow/python/keras/_impl/keras/applications/nasnet.py +++ b/tensorflow/python/keras/_impl/keras/applications/nasnet.py @@ -49,7 +49,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.inception_v3 import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import add from tensorflow.python.keras._impl.keras.layers import AveragePooling2D @@ -67,6 +67,7 @@ from tensorflow.python.keras._impl.keras.layers import ZeroPadding2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export NASNET_MOBILE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile.h5' @@ -323,6 +324,8 @@ def NASNet(input_shape=None, return model +@tf_export('keras.applications.NASNetLarge', + 'keras.applications.nasnet.NASNetLarge') def NASNetLarge(input_shape=None, include_top=True, weights='imagenet', @@ -390,6 +393,8 @@ def NASNetLarge(input_shape=None, default_size=331) +@tf_export('keras.applications.NASNetMobile', + 'keras.applications.nasnet.NASNetMobile') def NASNetMobile(input_shape=None, include_top=True, weights='imagenet', diff --git a/tensorflow/python/keras/_impl/keras/applications/resnet50.py b/tensorflow/python/keras/_impl/keras/applications/resnet50.py index 5705b3481afac4fe8f50d91c3099db5d4fb63878..46c0e635578c7f4707b027247943d75b16d703ad 100644 --- a/tensorflow/python/keras/_impl/keras/applications/resnet50.py +++ b/tensorflow/python/keras/_impl/keras/applications/resnet50.py @@ -34,7 +34,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization @@ -49,6 +49,7 @@ from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5' @@ -146,6 +147,8 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, return x +@tf_export('keras.applications.ResNet50', + 'keras.applications.resnet50.ResNet50') def ResNet50(include_top=True, weights='imagenet', input_tensor=None, diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg16.py b/tensorflow/python/keras/_impl/keras/applications/vgg16.py index c91c24e6fbed100c55d38432b2338a51213f64d2..cefb25063e30505c9c34b49fd2df6eb7210d7ca8 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg16.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg16.py @@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense from tensorflow.python.keras._impl.keras.layers import Flatten @@ -44,12 +44,14 @@ from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' +@tf_export('keras.applications.VGG16', 'keras.applications.vgg16.VGG16') def VGG16(include_top=True, weights='imagenet', input_tensor=None, diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg19.py b/tensorflow/python/keras/_impl/keras/applications/vgg19.py index 223cd79d7bfb660098273e46444d9588ae10f7a3..dadaf4fdf0cc5922752c6867720c5d8cdbcab19a 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg19.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg19.py @@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense from tensorflow.python.keras._impl.keras.layers import Flatten @@ -44,12 +44,14 @@ from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5' WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5' +@tf_export('keras.applications.VGG19', 'keras.applications.vgg19.VGG19') def VGG19(include_top=True, weights='imagenet', input_tensor=None, diff --git a/tensorflow/python/keras/_impl/keras/applications/xception.py b/tensorflow/python/keras/_impl/keras/applications/xception.py index 0a6eb4953a7ab82635f5b99ca021898876c02052..971063a16d1f5ba0e25189f1ef2f6c24eb5f8d61 100644 --- a/tensorflow/python/keras/_impl/keras/applications/xception.py +++ b/tensorflow/python/keras/_impl/keras/applications/xception.py @@ -44,7 +44,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D @@ -57,12 +57,15 @@ from tensorflow.python.keras._impl.keras.layers import SeparableConv2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5' TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5' +@tf_export('keras.applications.Xception', + 'keras.applications.xception.Xception') def Xception(include_top=True, weights='imagenet', input_tensor=None, @@ -328,6 +331,7 @@ def Xception(include_top=True, return model +@tf_export('keras.applications.xception.preprocess_input') def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py index 098ea063f951ef86c2a474aa50d6239b514cc699..688dc070e6a07ae9081efce1eba9da656ecbd6f6 100644 --- a/tensorflow/python/keras/_impl/keras/backend.py +++ b/tensorflow/python/keras/_impl/keras/backend.py @@ -48,14 +48,16 @@ from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variables as variables_module from tensorflow.python.training import moving_averages +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_inspect - +from tensorflow.python.util.tf_export import tf_export py_all = all py_sum = sum @@ -97,6 +99,7 @@ _IMAGE_DATA_FORMAT = 'channels_last' _LOCAL_DEVICES = None +@tf_export('keras.backend.backend') def backend(): """Publicly accessible method for determining the current backend. @@ -108,6 +111,7 @@ def backend(): return 'tensorflow' +@tf_export('keras.backend.epsilon') def epsilon(): """Returns the value of the fuzz factor used in numeric expressions. @@ -123,6 +127,7 @@ def epsilon(): return _EPSILON +@tf_export('keras.backend.set_epsilon') def set_epsilon(value): """Sets the value of the fuzz factor used in numeric expressions. @@ -143,6 +148,7 @@ def set_epsilon(value): _EPSILON = value +@tf_export('keras.backend.floatx') def floatx(): """Returns the default float type, as a string. @@ -160,6 +166,7 @@ def floatx(): return _FLOATX +@tf_export('keras.backend.set_floatx') def set_floatx(value): """Sets the default float type. @@ -185,6 +192,7 @@ def set_floatx(value): _FLOATX = str(value) +@tf_export('keras.backend.cast_to_floatx') def cast_to_floatx(x): """Cast a Numpy array to the default Keras float type. @@ -212,6 +220,7 @@ def cast_to_floatx(x): return np.asarray(x, dtype=_FLOATX) +@tf_export('keras.backend.image_data_format') def image_data_format(): """Returns the default image data format convention. @@ -227,6 +236,7 @@ def image_data_format(): return _IMAGE_DATA_FORMAT +@tf_export('keras.backend.set_image_data_format') def set_image_data_format(data_format): """Sets the value of the image data format convention. @@ -248,10 +258,11 @@ def set_image_data_format(data_format): """ global _IMAGE_DATA_FORMAT if data_format not in {'channels_last', 'channels_first'}: - raise ValueError('Unknown data_format:', data_format) + raise ValueError('Unknown data_format: ' + str(data_format)) _IMAGE_DATA_FORMAT = str(data_format) +@tf_export('keras.backend.get_uid') def get_uid(prefix=''): """Associates a string prefix with an integer counter in a TensorFlow graph. @@ -279,6 +290,7 @@ def get_uid(prefix=''): return layer_name_uids[prefix] +@tf_export('keras.backend.reset_uids') def reset_uids(): per_graph_layer_name_uids = tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS keys = list(per_graph_layer_name_uids.keys()) @@ -286,6 +298,7 @@ def reset_uids(): del per_graph_layer_name_uids[key] +@tf_export('keras.backend.clear_session') def clear_session(): """Destroys the current TF graph and creates a new one. @@ -302,6 +315,7 @@ def clear_session(): _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = phase +@tf_export('keras.backend.manual_variable_initialization') def manual_variable_initialization(value): """Sets the manual variable initialization flag. @@ -318,6 +332,7 @@ def manual_variable_initialization(value): _MANUAL_VAR_INIT = value +@tf_export('keras.backend.learning_phase') def learning_phase(): """Returns the learning phase flag. @@ -327,13 +342,11 @@ def learning_phase(): Returns: Learning phase (scalar integer tensor or Python integer). - - Raises: - ValueError: If called when Eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): if 'eager' not in _GRAPH_LEARNING_PHASES: - raise ValueError('No learning phase set in Eager mode.') + # Fallback to inference mode as default. + return 0 return _GRAPH_LEARNING_PHASES['eager'] graph = ops.get_default_graph() @@ -344,6 +357,7 @@ def learning_phase(): return _GRAPH_LEARNING_PHASES[graph] +@tf_export('keras.backend.set_learning_phase') def set_learning_phase(value): """Sets the learning phase to a fixed value. @@ -355,13 +369,43 @@ def set_learning_phase(value): """ global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned if value not in {0, 1}: - raise ValueError('Expected learning phase to be ' '0 or 1.') - if context.in_eager_mode(): + raise ValueError('Expected learning phase to be 0 or 1.') + if context.executing_eagerly(): _GRAPH_LEARNING_PHASES['eager'] = value else: _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = value +@tf_contextlib.contextmanager +def learning_phase_scope(value): + """Provides a scope within which the learning phase is equal to `value`. + + The learning phase gets restored to its original value upon exiting the scope. + + Arguments: + value: Learning phase value, either 0 or 1 (integers). + + Yields: + The provided value. + + Raises: + ValueError: if `value` is neither `0` nor `1`. + """ + if value not in {0, 1}: + raise ValueError('Expected learning phase to be 0 or 1.') + previous_value = learning_phase() + try: + set_learning_phase(value) + yield value + finally: + # Restore learning phase to initial value. + if context.executing_eagerly(): + _GRAPH_LEARNING_PHASES['eager'] = previous_value + else: + _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = previous_value + + +@tf_export('keras.backend.get_session') def get_session(): """Returns the TF session to be used by the backend. @@ -397,6 +441,7 @@ def get_session(): return session +@tf_export('keras.backend.set_session') def set_session(session): """Sets the global TensorFlow session. @@ -470,7 +515,7 @@ def _get_available_gpus(): def _has_nchw_support(): """Check whether the current scope supports NCHW ops. - Tensorflow does not support NCHW on CPU. Therefore we check if we are not + TensorFlow does not support NCHW on CPU. Therefore we check if we are not explicitly put on CPU, and have GPUs available. In this case there will be soft-placing on the GPU device. @@ -499,6 +544,7 @@ def _to_tensor(x, dtype): return ops.convert_to_tensor(x, dtype=dtype) +@tf_export('keras.backend.is_sparse') def is_sparse(tensor): """Returns whether a tensor is a sparse tensor. @@ -522,6 +568,7 @@ def is_sparse(tensor): return isinstance(tensor, sparse_tensor.SparseTensor) +@tf_export('keras.backend.to_dense') def to_dense(tensor): """Converts a sparse tensor into a dense tensor and returns it. @@ -551,6 +598,7 @@ def to_dense(tensor): name_scope = ops.name_scope +@tf_export('keras.backend.variable') def variable(value, dtype=None, name=None, constraint=None): """Instantiates a variable and returns it. @@ -589,7 +637,7 @@ def variable(value, dtype=None, name=None, constraint=None): v._keras_shape = sparse_coo.shape v._uses_learning_phase = False return v - v = variables_module.Variable( + v = resource_variable_ops.ResourceVariable( value, dtype=dtypes_module.as_dtype(dtype), name=name, @@ -623,6 +671,7 @@ def _initialize_variables(session): session.run(variables_module.variables_initializer(uninitialized_vars)) +@tf_export('keras.backend.constant') def constant(value, dtype=None, shape=None, name=None): """Creates a constant tensor. @@ -691,6 +740,7 @@ def is_keras_tensor(x): return hasattr(x, '_keras_history') +@tf_export('keras.backend.placeholder') def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None): """Instantiates a placeholder tensor and returns it. @@ -743,6 +793,7 @@ def is_placeholder(x): return False +@tf_export('keras.backend.shape') def shape(x): """Returns the symbolic shape of a tensor or variable. @@ -775,6 +826,7 @@ def shape(x): return array_ops.shape(x) +@tf_export('keras.backend.int_shape') def int_shape(x): """Returns the shape of tensor or variable as a tuple of int or None entries. @@ -802,6 +854,7 @@ def int_shape(x): return None +@tf_export('keras.backend.ndim') def ndim(x): """Returns the number of axes in a tensor, as an integer. @@ -829,6 +882,7 @@ def ndim(x): return None +@tf_export('keras.backend.dtype') def dtype(x): """Returns the dtype of a Keras tensor or variable, as a string. @@ -859,6 +913,7 @@ def dtype(x): return x.dtype.base_dtype.name +@tf_export('keras.backend.eval') def eval(x): """Evaluates the value of a variable. @@ -880,6 +935,7 @@ def eval(x): return to_dense(x).eval(session=get_session()) +@tf_export('keras.backend.zeros') def zeros(shape, dtype=None, name=None): """Instantiates an all-zeros variable and returns it. @@ -912,6 +968,7 @@ def zeros(shape, dtype=None, name=None): return v +@tf_export('keras.backend.ones') def ones(shape, dtype=None, name=None): """Instantiates an all-ones variable and returns it. @@ -944,6 +1001,7 @@ def ones(shape, dtype=None, name=None): return v +@tf_export('keras.backend.eye') def eye(size, dtype=None, name=None): """Instantiate an identity matrix and returns it. @@ -972,6 +1030,7 @@ def eye(size, dtype=None, name=None): return variable(linalg_ops.eye(size, dtype=tf_dtype), dtype, name) +@tf_export('keras.backend.zeros_like') def zeros_like(x, dtype=None, name=None): """Instantiates an all-zeros variable of the same shape as another tensor. @@ -997,6 +1056,7 @@ def zeros_like(x, dtype=None, name=None): return array_ops.zeros_like(x, dtype=dtype, name=name) +@tf_export('keras.backend.ones_like') def ones_like(x, dtype=None, name=None): """Instantiates an all-ones variable of the same shape as another tensor. @@ -1035,6 +1095,7 @@ def identity(x, name=None): return array_ops.identity(x, name=name) +@tf_export('keras.backend.random_uniform_variable') def random_uniform_variable(shape, low, high, dtype=None, name=None, seed=None): """Instantiates a variable with values drawn from a uniform distribution. @@ -1071,6 +1132,7 @@ def random_uniform_variable(shape, low, high, dtype=None, name=None, seed=None): return variable(value, dtype=dtype, name=name) +@tf_export('keras.backend.random_normal_variable') def random_normal_variable(shape, mean, scale, dtype=None, name=None, seed=None): """Instantiates a variable with values drawn from a normal distribution. @@ -1108,6 +1170,7 @@ def random_normal_variable(shape, mean, scale, dtype=None, name=None, return variable(value, dtype=dtype, name=name) +@tf_export('keras.backend.count_params') def count_params(x): """Returns the static number of elements in a variable or tensor. @@ -1130,6 +1193,7 @@ def count_params(x): return np.prod(x.get_shape().as_list()) +@tf_export('keras.backend.cast') def cast(x, dtype): """Casts a tensor to a different dtype and returns it. @@ -1165,10 +1229,12 @@ def cast(x, dtype): # UPDATES OPS +@tf_export('keras.backend.update') def update(x, new_x): return state_ops.assign(x, new_x) +@tf_export('keras.backend.update_add') def update_add(x, increment): """Update the value of `x` by adding `increment`. @@ -1182,6 +1248,7 @@ def update_add(x, increment): return state_ops.assign_add(x, increment) +@tf_export('keras.backend.update_sub') def update_sub(x, decrement): """Update the value of `x` by subtracting `decrement`. @@ -1195,6 +1262,7 @@ def update_sub(x, decrement): return state_ops.assign_sub(x, decrement) +@tf_export('keras.backend.moving_average_update') def moving_average_update(x, value, momentum): """Compute the moving average of a variable. @@ -1213,6 +1281,7 @@ def moving_average_update(x, value, momentum): # LINEAR ALGEBRA +@tf_export('keras.backend.dot') def dot(x, y): """Multiplies 2 tensors (and/or variables) and returns a *tensor*. @@ -1284,6 +1353,7 @@ def dot(x, y): return out +@tf_export('keras.backend.batch_dot') def batch_dot(x, y, axes=None): """Batchwise dot product. @@ -1376,6 +1446,7 @@ def batch_dot(x, y, axes=None): return out +@tf_export('keras.backend.transpose') def transpose(x): """Transposes a tensor and returns it. @@ -1411,6 +1482,7 @@ def transpose(x): return array_ops.transpose(x) +@tf_export('keras.backend.gather') def gather(reference, indices): """Retrieves the elements of indices `indices` in the tensor `reference`. @@ -1427,6 +1499,7 @@ def gather(reference, indices): # ELEMENT-WISE OPERATIONS +@tf_export('keras.backend.max') def max(x, axis=None, keepdims=False): """Maximum value in a tensor. @@ -1444,6 +1517,7 @@ def max(x, axis=None, keepdims=False): return math_ops.reduce_max(x, axis, keepdims) +@tf_export('keras.backend.min') def min(x, axis=None, keepdims=False): """Minimum value in a tensor. @@ -1461,6 +1535,7 @@ def min(x, axis=None, keepdims=False): return math_ops.reduce_min(x, axis, keepdims) +@tf_export('keras.backend.sum') def sum(x, axis=None, keepdims=False): """Sum of the values in a tensor, alongside the specified axis. @@ -1478,6 +1553,7 @@ def sum(x, axis=None, keepdims=False): return math_ops.reduce_sum(x, axis, keepdims) +@tf_export('keras.backend.prod') def prod(x, axis=None, keepdims=False): """Multiplies the values in a tensor, alongside the specified axis. @@ -1521,6 +1597,7 @@ def cumprod(x, axis=0): return math_ops.cumprod(x, axis=axis) +@tf_export('keras.backend.var') def var(x, axis=None, keepdims=False): """Variance of a tensor, alongside the specified axis. @@ -1543,6 +1620,7 @@ def var(x, axis=None, keepdims=False): devs_squared, axis, keepdims) +@tf_export('keras.backend.std') def std(x, axis=None, keepdims=False): """Standard deviation of a tensor, alongside the specified axis. @@ -1560,6 +1638,7 @@ def std(x, axis=None, keepdims=False): return math_ops.sqrt(var(x, axis=axis, keepdims=keepdims)) +@tf_export('keras.backend.mean') def mean(x, axis=None, keepdims=False): """Mean of a tensor, alongside the specified axis. @@ -1579,6 +1658,7 @@ def mean(x, axis=None, keepdims=False): return math_ops.reduce_mean(x, axis, keepdims) +@tf_export('keras.backend.any') def any(x, axis=None, keepdims=False): """Bitwise reduction (logical OR). @@ -1594,6 +1674,7 @@ def any(x, axis=None, keepdims=False): return math_ops.reduce_any(x, axis, keepdims) +@tf_export('keras.backend.all') def all(x, axis=None, keepdims=False): """Bitwise reduction (logical AND). @@ -1609,6 +1690,7 @@ def all(x, axis=None, keepdims=False): return math_ops.reduce_all(x, axis, keepdims) +@tf_export('keras.backend.argmax') def argmax(x, axis=-1): """Returns the index of the maximum value along an axis. @@ -1622,6 +1704,7 @@ def argmax(x, axis=-1): return math_ops.argmax(x, axis) +@tf_export('keras.backend.argmin') def argmin(x, axis=-1): """Returns the index of the minimum value along an axis. @@ -1635,6 +1718,7 @@ def argmin(x, axis=-1): return math_ops.argmin(x, axis) +@tf_export('keras.backend.square') def square(x): """Element-wise square. @@ -1647,6 +1731,7 @@ def square(x): return math_ops.square(x) +@tf_export('keras.backend.abs') def abs(x): """Element-wise absolute value. @@ -1659,6 +1744,7 @@ def abs(x): return math_ops.abs(x) +@tf_export('keras.backend.sqrt') def sqrt(x): """Element-wise square root. @@ -1674,6 +1760,7 @@ def sqrt(x): return math_ops.sqrt(x) +@tf_export('keras.backend.exp') def exp(x): """Element-wise exponential. @@ -1686,6 +1773,7 @@ def exp(x): return math_ops.exp(x) +@tf_export('keras.backend.log') def log(x): """Element-wise log. @@ -1719,6 +1807,7 @@ def logsumexp(x, axis=None, keepdims=False): return math_ops.reduce_logsumexp(x, axis, keepdims) +@tf_export('keras.backend.round') def round(x): """Element-wise rounding to the closest integer. @@ -1733,6 +1822,7 @@ def round(x): return math_ops.round(x) +@tf_export('keras.backend.sign') def sign(x): """Element-wise sign. @@ -1745,6 +1835,7 @@ def sign(x): return math_ops.sign(x) +@tf_export('keras.backend.pow') def pow(x, a): """Element-wise exponentiation. @@ -1758,6 +1849,7 @@ def pow(x, a): return math_ops.pow(x, a) +@tf_export('keras.backend.clip') def clip(x, min_value, max_value): """Element-wise value clipping. @@ -1778,6 +1870,7 @@ def clip(x, min_value, max_value): return clip_ops.clip_by_value(x, min_value, max_value) +@tf_export('keras.backend.equal') def equal(x, y): """Element-wise equality between two tensors. @@ -1791,6 +1884,7 @@ def equal(x, y): return math_ops.equal(x, y) +@tf_export('keras.backend.not_equal') def not_equal(x, y): """Element-wise inequality between two tensors. @@ -1804,6 +1898,7 @@ def not_equal(x, y): return math_ops.not_equal(x, y) +@tf_export('keras.backend.greater') def greater(x, y): """Element-wise truth value of (x > y). @@ -1817,6 +1912,7 @@ def greater(x, y): return math_ops.greater(x, y) +@tf_export('keras.backend.greater_equal') def greater_equal(x, y): """Element-wise truth value of (x >= y). @@ -1830,6 +1926,7 @@ def greater_equal(x, y): return math_ops.greater_equal(x, y) +@tf_export('keras.backend.less') def less(x, y): """Element-wise truth value of (x < y). @@ -1843,6 +1940,7 @@ def less(x, y): return math_ops.less(x, y) +@tf_export('keras.backend.less_equal') def less_equal(x, y): """Element-wise truth value of (x <= y). @@ -1856,6 +1954,7 @@ def less_equal(x, y): return math_ops.less_equal(x, y) +@tf_export('keras.backend.maximum') def maximum(x, y): """Element-wise maximum of two tensors. @@ -1869,6 +1968,7 @@ def maximum(x, y): return math_ops.maximum(x, y) +@tf_export('keras.backend.minimum') def minimum(x, y): """Element-wise minimum of two tensors. @@ -1882,6 +1982,7 @@ def minimum(x, y): return math_ops.minimum(x, y) +@tf_export('keras.backend.sin') def sin(x): """Computes sin of x element-wise. @@ -1894,6 +1995,7 @@ def sin(x): return math_ops.sin(x) +@tf_export('keras.backend.cos') def cos(x): """Computes cos of x element-wise. @@ -2008,6 +2110,7 @@ def _fused_normalize_batch_in_training(x, x, gamma, beta, epsilon=epsilon, data_format=tf_data_format) +@tf_export('keras.backend.normalize_batch_in_training') def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. @@ -2037,6 +2140,7 @@ def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): x, gamma, beta, reduction_axes, epsilon=epsilon) +@tf_export('keras.backend.batch_normalization') def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): """Applies batch normalization on x given mean, var, beta and gamma. @@ -2060,6 +2164,7 @@ def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): # SHAPE OPERATIONS +@tf_export('keras.backend.concatenate') def concatenate(tensors, axis=-1): """Concatenates a list of tensors alongside the specified axis. @@ -2083,6 +2188,7 @@ def concatenate(tensors, axis=-1): return array_ops.concat([to_dense(x) for x in tensors], axis) +@tf_export('keras.backend.reshape') def reshape(x, shape): """Reshapes a tensor to the specified shape. @@ -2096,6 +2202,7 @@ def reshape(x, shape): return array_ops.reshape(x, shape) +@tf_export('keras.backend.permute_dimensions') def permute_dimensions(x, pattern): """Permutes axes in a tensor. @@ -2110,6 +2217,7 @@ def permute_dimensions(x, pattern): return array_ops.transpose(x, perm=pattern) +@tf_export('keras.backend.resize_images') def resize_images(x, height_factor, width_factor, data_format): """Resizes the images contained in a 4D tensor. @@ -2151,9 +2259,10 @@ def resize_images(x, height_factor, width_factor, data_format): if original_shape[2] is not None else None, None)) return x else: - raise ValueError('Invalid data_format:', data_format) + raise ValueError('Invalid data_format: ' + str(data_format)) +@tf_export('keras.backend.resize_volumes') def resize_volumes(x, depth_factor, height_factor, width_factor, data_format): """Resizes the volume contained in a 5D tensor. @@ -2182,9 +2291,10 @@ def resize_volumes(x, depth_factor, height_factor, width_factor, data_format): output = repeat_elements(output, width_factor, axis=3) return output else: - raise ValueError('Invalid data_format:', data_format) + raise ValueError('Invalid data_format: ' + str(data_format)) +@tf_export('keras.backend.repeat_elements') def repeat_elements(x, rep, axis): """Repeats the elements of a tensor along an axis, like `np.repeat`. @@ -2237,6 +2347,7 @@ def repeat_elements(x, rep, axis): return x_rep +@tf_export('keras.backend.repeat') def repeat(x, n): """Repeats a 2D tensor. @@ -2256,12 +2367,13 @@ def repeat(x, n): return array_ops.tile(x, pattern) +@tf_export('keras.backend.arange') def arange(start, stop=None, step=1, dtype='int32'): """Creates a 1D tensor containing a sequence of integers. The function arguments use the same convention as Theano's arange: if only one argument is provided, - it is in fact the "stop" argument. + it is in fact the "stop" argument and "start" is 0. The default type of the returned tensor is `'int32'` to match TensorFlow's default. @@ -2276,7 +2388,7 @@ def arange(start, stop=None, step=1, dtype='int32'): An integer tensor. """ - # Match the behavior of numpy and Theano by returning an empty seqence. + # Match the behavior of numpy and Theano by returning an empty sequence. if stop is None and start < 0: start = 0 result = math_ops.range(start, limit=stop, delta=step, name='arange') @@ -2301,6 +2413,7 @@ def tile(x, n): return array_ops.tile(x, n) +@tf_export('keras.backend.flatten') def flatten(x): """Flatten a tensor. @@ -2313,6 +2426,7 @@ def flatten(x): return array_ops.reshape(x, [-1]) +@tf_export('keras.backend.batch_flatten') def batch_flatten(x): """Turn a nD tensor into a 2D tensor with same 0th dimension. @@ -2328,6 +2442,7 @@ def batch_flatten(x): return x +@tf_export('keras.backend.expand_dims') def expand_dims(x, axis=-1): """Adds a 1-sized dimension at index "axis". @@ -2341,6 +2456,7 @@ def expand_dims(x, axis=-1): return array_ops.expand_dims(x, axis) +@tf_export('keras.backend.squeeze') def squeeze(x, axis): """Removes a 1-dimension from the tensor at index "axis". @@ -2354,6 +2470,7 @@ def squeeze(x, axis): return array_ops.squeeze(x, [axis]) +@tf_export('keras.backend.temporal_padding') def temporal_padding(x, padding=(1, 1)): """Pads the middle dimension of a 3D tensor. @@ -2370,6 +2487,7 @@ def temporal_padding(x, padding=(1, 1)): return array_ops.pad(x, pattern) +@tf_export('keras.backend.spatial_2d_padding') def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None): """Pads the 2nd and 3rd dimensions of a 4D tensor. @@ -2391,7 +2509,7 @@ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None): if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) if data_format == 'channels_first': pattern = [[0, 0], [0, 0], list(padding[0]), list(padding[1])] @@ -2400,6 +2518,7 @@ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None): return array_ops.pad(x, pattern) +@tf_export('keras.backend.spatial_3d_padding') def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None): """Pads 5D tensor with zeros along the depth, height, width dimensions. @@ -2431,7 +2550,7 @@ def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None): if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) if data_format == 'channels_first': pattern = [[0, 0], [0, 0], [padding[0][0], padding[0][1]], @@ -2443,6 +2562,7 @@ def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None): return array_ops.pad(x, pattern) +@tf_export('keras.backend.stack') def stack(x, axis=0): """Stacks a list of rank `R` tensors into a rank `R+1` tensor. @@ -2456,6 +2576,7 @@ def stack(x, axis=0): return array_ops.stack(x, axis=axis) +@tf_export('keras.backend.one_hot') def one_hot(indices, num_classes): """Computes the one-hot representation of an integer tensor. @@ -2474,6 +2595,7 @@ def one_hot(indices, num_classes): return array_ops.one_hot(indices, depth=num_classes, axis=-1) +@tf_export('keras.backend.reverse') def reverse(x, axes): """Reverse a tensor along the specified axes. @@ -2493,6 +2615,7 @@ def reverse(x, axes): # VALUE MANIPULATION +@tf_export('keras.backend.get_value') def get_value(x): """Returns the value of a variable. @@ -2502,9 +2625,12 @@ def get_value(x): Returns: A Numpy array. """ + if context.executing_eagerly(): + return x.numpy() return x.eval(session=get_session()) +@tf_export('keras.backend.batch_get_value') def batch_get_value(tensors): """Returns the value of more than one tensor variable. @@ -2514,12 +2640,15 @@ def batch_get_value(tensors): Returns: A list of Numpy arrays. """ + if context.executing_eagerly(): + return [x.numpy() for x in tensors] if tensors: return get_session().run(tensors) else: return [] +@tf_export('keras.backend.set_value') def set_value(x, value): """Sets the value of a variable, from a Numpy array. @@ -2529,18 +2658,22 @@ def set_value(x, value): (of the same shape). """ value = np.asarray(value, dtype=dtype(x)) - tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0]) - if hasattr(x, '_assign_placeholder'): - assign_placeholder = x._assign_placeholder - assign_op = x._assign_op + if context.executing_eagerly(): + x.assign(value) else: - assign_placeholder = array_ops.placeholder(tf_dtype, shape=value.shape) - assign_op = x.assign(assign_placeholder) - x._assign_placeholder = assign_placeholder - x._assign_op = assign_op - get_session().run(assign_op, feed_dict={assign_placeholder: value}) + tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0]) + if hasattr(x, '_assign_placeholder'): + assign_placeholder = x._assign_placeholder + assign_op = x._assign_op + else: + assign_placeholder = array_ops.placeholder(tf_dtype, shape=value.shape) + assign_op = x.assign(assign_placeholder) + x._assign_placeholder = assign_placeholder + x._assign_op = assign_op + get_session().run(assign_op, feed_dict={assign_placeholder: value}) +@tf_export('keras.backend.batch_set_value') def batch_set_value(tuples): """Sets the values of many tensor variables at once. @@ -2548,25 +2681,31 @@ def batch_set_value(tuples): tuples: a list of tuples `(tensor, value)`. `value` should be a Numpy array. """ - if tuples: - assign_ops = [] - feed_dict = {} + if context.executing_eagerly(): for x, value in tuples: - value = np.asarray(value, dtype=dtype(x)) - tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0]) - if hasattr(x, '_assign_placeholder'): - assign_placeholder = x._assign_placeholder - assign_op = x._assign_op - else: - assign_placeholder = array_ops.placeholder(tf_dtype, shape=value.shape) - assign_op = x.assign(assign_placeholder) - x._assign_placeholder = assign_placeholder - x._assign_op = assign_op - assign_ops.append(assign_op) - feed_dict[assign_placeholder] = value - get_session().run(assign_ops, feed_dict=feed_dict) + x.assign(np.asarray(value, dtype=dtype(x))) + else: + if tuples: + assign_ops = [] + feed_dict = {} + for x, value in tuples: + value = np.asarray(value, dtype=dtype(x)) + tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0]) + if hasattr(x, '_assign_placeholder'): + assign_placeholder = x._assign_placeholder + assign_op = x._assign_op + else: + assign_placeholder = array_ops.placeholder(tf_dtype, + shape=value.shape) + assign_op = x.assign(assign_placeholder) + x._assign_placeholder = assign_placeholder + x._assign_op = assign_op + assign_ops.append(assign_op) + feed_dict[assign_placeholder] = value + get_session().run(assign_ops, feed_dict=feed_dict) +@tf_export('keras.backend.print_tensor') def print_tensor(x, message=''): """Prints `message` and the tensor value when evaluated. @@ -2639,7 +2778,7 @@ class Function(object): self.updates_op = control_flow_ops.group(*updates_ops) self.name = name # additional tensor substitutions - self.feed_dict = session_kwargs.pop('feed_dict', {}) + self.feed_dict = session_kwargs.pop('feed_dict', None) # additional operations self.fetches = session_kwargs.pop('fetches', []) if not isinstance(self.fetches, list): @@ -2649,8 +2788,15 @@ class Function(object): def __call__(self, inputs): if not isinstance(inputs, (list, tuple)): raise TypeError('`inputs` should be a list or tuple.') - feed_dict = self.feed_dict.copy() + + if self.feed_dict: + feed_dict = self.feed_dict.copy() + else: + feed_dict = {} + for tensor, value in zip(self.inputs, inputs): + if value is None: + continue if is_sparse(tensor): sparse_coo = value.tocoo() indices = np.concatenate((np.expand_dims(sparse_coo.row, 1), @@ -2664,6 +2810,7 @@ class Function(object): return updated[:len(self.outputs)] +@tf_export('keras.backend.function') def function(inputs, outputs, updates=None, **kwargs): """Instantiates a Keras function. @@ -2683,12 +2830,13 @@ def function(inputs, outputs, updates=None, **kwargs): for key in kwargs: if (key not in tf_inspect.getargspec(session_module.Session.run)[0] and key not in tf_inspect.getargspec(Function.__init__)[0]): - msg = ('Invalid argument "%s" passed to K.function with Tensorflow ' + msg = ('Invalid argument "%s" passed to K.function with TensorFlow ' 'backend') % key raise ValueError(msg) return Function(inputs, outputs, updates=updates, **kwargs) +@tf_export('keras.backend.gradients') def gradients(loss, variables): """Returns the gradients of `variables` w.r.t. `loss`. @@ -2703,6 +2851,7 @@ def gradients(loss, variables): loss, variables, colocate_gradients_with_ops=True) +@tf_export('keras.backend.stop_gradient') def stop_gradient(variables): """Returns `variables` but with zero gradient w.r.t. every other variable. @@ -2723,6 +2872,7 @@ def stop_gradient(variables): # CONTROL FLOW +@tf_export('keras.backend.rnn') def rnn(step_function, inputs, initial_states, @@ -2781,6 +2931,7 @@ def rnn(step_function, ndim = len(inputs.get_shape()) if ndim < 3: raise ValueError('Input should be at least 3D.') + inputs_shape = inputs.get_shape() axes = [1, 0] + list(range(2, ndim)) inputs = array_ops.transpose(inputs, (axes)) @@ -2799,7 +2950,7 @@ def rnn(step_function, if unroll: if not inputs.get_shape()[0]: - raise ValueError('Unrolling requires a ' 'fixed number of timesteps.') + raise ValueError('Unrolling requires a fixed number of timesteps.') states = initial_states successive_states = [] successive_outputs = [] @@ -2965,10 +3116,19 @@ def rnn(step_function, axes = [1, 0] + list(range(2, len(outputs.get_shape()))) outputs = array_ops.transpose(outputs, axes) - last_output._uses_learning_phase = uses_learning_phase + + # Static shape inference: (samples, time, ...) + outputs_shape = outputs.get_shape().as_list() + outputs_shape[0] = inputs_shape[0] + outputs_shape[1] = inputs_shape[1] + outputs.set_shape(outputs_shape) + + if not context.executing_eagerly(): + last_output._uses_learning_phase = uses_learning_phase return last_output, outputs, new_states +@tf_export('keras.backend.switch') def switch(condition, then_expression, else_expression): """Switches between two operations depending on a scalar value. @@ -3032,6 +3192,7 @@ def switch(condition, then_expression, else_expression): return x +@tf_export('keras.backend.in_train_phase') def in_train_phase(x, alt, training=None): """Selects `x` in train phase, and `alt` otherwise. @@ -3075,6 +3236,7 @@ def in_train_phase(x, alt, training=None): return x +@tf_export('keras.backend.in_test_phase') def in_test_phase(x, alt, training=None): """Selects `x` in test phase, and `alt` otherwise. @@ -3098,6 +3260,7 @@ def in_test_phase(x, alt, training=None): # NN OPERATIONS +@tf_export('keras.backend.relu') def relu(x, alpha=0., max_value=None): """Rectified linear unit. @@ -3124,6 +3287,7 @@ def relu(x, alpha=0., max_value=None): return x +@tf_export('keras.backend.elu') def elu(x, alpha=1.): """Exponential linear unit. @@ -3141,6 +3305,7 @@ def elu(x, alpha=1.): return array_ops.where(x > 0, res, alpha * res) +@tf_export('keras.backend.softmax') def softmax(x): """Softmax of a tensor. @@ -3153,6 +3318,7 @@ def softmax(x): return nn.softmax(x) +@tf_export('keras.backend.softplus') def softplus(x): """Softplus of a tensor. @@ -3165,6 +3331,7 @@ def softplus(x): return nn.softplus(x) +@tf_export('keras.backend.softsign') def softsign(x): """Softsign of a tensor. @@ -3177,6 +3344,7 @@ def softsign(x): return nn.softsign(x) +@tf_export('keras.backend.categorical_crossentropy') def categorical_crossentropy(target, output, from_logits=False): """Categorical crossentropy between an output tensor and a target tensor. @@ -3195,7 +3363,7 @@ def categorical_crossentropy(target, output, from_logits=False): # expects logits, Keras expects probabilities. if not from_logits: # scale preds so that the class probas of each sample sum to 1 - output /= math_ops.reduce_sum( + output = output / math_ops.reduce_sum( # pylint: disable=g-no-augmented-assignment output, len(output.get_shape()) - 1, True) # manual computation of crossentropy epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype) @@ -3207,6 +3375,7 @@ def categorical_crossentropy(target, output, from_logits=False): return nn.softmax_cross_entropy_with_logits(labels=target, logits=output) +@tf_export('keras.backend.sparse_categorical_crossentropy') def sparse_categorical_crossentropy(target, output, from_logits=False): """Categorical crossentropy with integer targets. @@ -3240,6 +3409,7 @@ def sparse_categorical_crossentropy(target, output, from_logits=False): return res +@tf_export('keras.backend.binary_crossentropy') def binary_crossentropy(target, output, from_logits=False): """Binary crossentropy between an output tensor and a target tensor. @@ -3263,6 +3433,7 @@ def binary_crossentropy(target, output, from_logits=False): return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output) +@tf_export('keras.backend.sigmoid') def sigmoid(x): """Element-wise sigmoid. @@ -3275,6 +3446,7 @@ def sigmoid(x): return nn.sigmoid(x) +@tf_export('keras.backend.hard_sigmoid') def hard_sigmoid(x): """Segment-wise linear approximation of sigmoid. @@ -3295,6 +3467,7 @@ def hard_sigmoid(x): return x +@tf_export('keras.backend.tanh') def tanh(x): """Element-wise tanh. @@ -3307,6 +3480,7 @@ def tanh(x): return nn.tanh(x) +@tf_export('keras.backend.dropout') def dropout(x, level, noise_shape=None, seed=None): """Sets entries in `x` to zero at random, while scaling the entire tensor. @@ -3329,6 +3503,7 @@ def dropout(x, level, noise_shape=None, seed=None): return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed) +@tf_export('keras.backend.l2_normalize') def l2_normalize(x, axis=None): """Normalizes a tensor wrt the L2 norm alongside the specified axis. @@ -3342,6 +3517,7 @@ def l2_normalize(x, axis=None): return nn.l2_normalize(x, dim=axis) +@tf_export('keras.backend.in_top_k') def in_top_k(predictions, targets, k): """Returns whether the `targets` are in the top `k` `predictions`. @@ -3419,7 +3595,7 @@ def _preprocess_conv3d_input(x, data_format): def _preprocess_padding(padding): - """Convert keras' padding to tensorflow's padding. + """Convert keras' padding to TensorFlow's padding. Arguments: padding: string, one of 'same' , 'valid' @@ -3435,10 +3611,11 @@ def _preprocess_padding(padding): elif padding == 'valid': padding = 'VALID' else: - raise ValueError('Invalid padding:', padding) + raise ValueError('Invalid padding: ' + str(padding)) return padding +@tf_export('keras.backend.conv1d') def conv1d(x, kernel, strides=1, @@ -3465,7 +3642,7 @@ def conv1d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) kernel_shape = kernel.get_shape().as_list() if padding == 'causal': @@ -3488,6 +3665,7 @@ def conv1d(x, return x +@tf_export('keras.backend.conv2d') def conv2d(x, kernel, strides=(1, 1), @@ -3516,7 +3694,7 @@ def conv2d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv2d_input(x, data_format) padding = _preprocess_padding(padding) @@ -3532,6 +3710,7 @@ def conv2d(x, return x +@tf_export('keras.backend.conv2d_transpose') def conv2d_transpose(x, kernel, output_shape, @@ -3562,7 +3741,7 @@ def conv2d_transpose(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) if isinstance(output_shape, (tuple, list)): output_shape = array_ops.stack(output_shape) @@ -3621,16 +3800,18 @@ def separable_conv1d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv1d_input(x, data_format) padding = _preprocess_padding(padding) + if not isinstance(strides, tuple): + strides = tuple(strides) if tf_data_format == 'NHWC': spatial_start_dim = 1 - strides = (1, 1) + strides + (1,) + strides = (1,) + strides * 2 + (1,) else: spatial_start_dim = 2 - strides = (1, 1, 1) + strides + strides = (1, 1) + strides * 2 x = array_ops.expand_dims(x, spatial_start_dim) depthwise_kernel = array_ops.expand_dims(depthwise_kernel, 0) pointwise_kernel = array_ops.expand_dims(pointwise_kernel, 0) @@ -3653,6 +3834,7 @@ def separable_conv1d(x, return x +@tf_export('keras.backend.separable_conv2d') def separable_conv2d(x, depthwise_kernel, pointwise_kernel, @@ -3682,10 +3864,12 @@ def separable_conv2d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv2d_input(x, data_format) padding = _preprocess_padding(padding) + if not isinstance(strides, tuple): + strides = tuple(strides) if tf_data_format == 'NHWC': strides = (1,) + strides + (1,) else: @@ -3731,7 +3915,7 @@ def depthwise_conv2d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv2d_input(x, data_format) padding = _preprocess_padding(padding) @@ -3752,6 +3936,7 @@ def depthwise_conv2d(x, return x +@tf_export('keras.backend.conv3d') def conv3d(x, kernel, strides=(1, 1, 1), @@ -3780,7 +3965,7 @@ def conv3d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv3d_input(x, data_format) padding = _preprocess_padding(padding) @@ -3826,7 +4011,7 @@ def conv3d_transpose(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) if isinstance(output_shape, (tuple, list)): output_shape = array_ops.stack(output_shape) @@ -3857,6 +4042,7 @@ def conv3d_transpose(x, return x +@tf_export('keras.backend.pool2d') def pool2d(x, pool_size, strides=(1, 1), @@ -3884,7 +4070,7 @@ def pool2d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv2d_input(x, data_format) padding = _preprocess_padding(padding) @@ -3902,13 +4088,14 @@ def pool2d(x, x = nn.avg_pool( x, pool_size, strides, padding=padding, data_format=tf_data_format) else: - raise ValueError('Invalid pooling mode:', pool_mode) + raise ValueError('Invalid pooling mode: ' + str(pool_mode)) if data_format == 'channels_first' and tf_data_format == 'NHWC': x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW return x +@tf_export('keras.backend.pool3d') def pool3d(x, pool_size, strides=(1, 1, 1), @@ -3936,7 +4123,7 @@ def pool3d(x, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) x, tf_data_format = _preprocess_conv3d_input(x, data_format) padding = _preprocess_padding(padding) @@ -3954,7 +4141,7 @@ def pool3d(x, x = nn.avg_pool3d( x, pool_size, strides, padding=padding, data_format=tf_data_format) else: - raise ValueError('Invalid pooling mode:', pool_mode) + raise ValueError('Invalid pooling mode: ' + str(pool_mode)) if data_format == 'channels_first' and tf_data_format == 'NDHWC': x = array_ops.transpose(x, (0, 4, 1, 2, 3)) @@ -3985,7 +4172,7 @@ def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None): if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) stride = strides[0] kernel_shape = int_shape(kernel) @@ -4041,7 +4228,7 @@ def local_conv2d(inputs, if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) stride_row, stride_col = strides output_row, output_col = output_shape @@ -4072,6 +4259,7 @@ def local_conv2d(inputs, return output +@tf_export('keras.backend.bias_add') def bias_add(x, bias, data_format=None): """Adds a bias vector to a tensor. @@ -4093,56 +4281,59 @@ def bias_add(x, bias, data_format=None): if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) + raise ValueError('Unknown data_format: ' + str(data_format)) bias_shape = int_shape(bias) if len(bias_shape) != 1 and len(bias_shape) != ndim(x) - 1: raise ValueError( 'Unexpected bias dimensions %d, expect to be 1 or %d dimensions' % (len(bias_shape), ndim(x))) + # pylint: disable=g-no-augmented-assignment if ndim(x) == 5: if data_format == 'channels_first': if len(bias_shape) == 1: - x += reshape(bias, (1, bias_shape[0], 1, 1, 1)) + x = x + reshape(bias, (1, bias_shape[0], 1, 1, 1)) else: - x += reshape(bias, (1, bias_shape[3]) + bias_shape[:3]) + x = x + reshape(bias, (1, bias_shape[3]) + bias_shape[:3]) elif data_format == 'channels_last': if len(bias_shape) == 1: - x += reshape(bias, (1, 1, 1, bias_shape[0])) + x = x + reshape(bias, (1, 1, 1, bias_shape[0])) else: - x += reshape(bias, (1,) + bias_shape) + x = x + reshape(bias, (1,) + bias_shape) elif ndim(x) == 4: if data_format == 'channels_first': if len(bias_shape) == 1: if _has_nchw_support(): x = nn.bias_add(x, bias, data_format='NCHW') else: - x += reshape(bias, (1, bias_shape[0], 1, 1)) + x = x + reshape(bias, (1, bias_shape[0], 1, 1)) else: - x += reshape(bias, (1, bias_shape[2]) + bias_shape[:2]) + x = x + reshape(bias, (1, bias_shape[2]) + bias_shape[:2]) elif data_format == 'channels_last': if len(bias_shape) == 1: x = nn.bias_add(x, bias, data_format='NHWC') else: - x += reshape(bias, (1,) + bias_shape) + x = x + reshape(bias, (1,) + bias_shape) elif ndim(x) == 3: if data_format == 'channels_first': if len(bias_shape) == 1: - x += reshape(bias, (1, bias_shape[0], 1)) + x = x + reshape(bias, (1, bias_shape[0], 1)) else: - x += reshape(bias, (1, bias_shape[1], bias_shape[0])) + x = x + reshape(bias, (1, bias_shape[1], bias_shape[0])) elif data_format == 'channels_last': if len(bias_shape) == 1: - x += reshape(bias, (1, 1, bias_shape[0])) + x = x + reshape(bias, (1, 1, bias_shape[0])) else: - x += reshape(bias, (1,) + bias_shape) + x = x + reshape(bias, (1,) + bias_shape) else: x = nn.bias_add(x, bias) + # pylint: enable=g-no-augmented-assignment return x # RANDOMNESS +@tf_export('keras.backend.random_normal') def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): """Returns a tensor with normal distribution of values. @@ -4165,6 +4356,7 @@ def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed) +@tf_export('keras.backend.random_uniform') def random_uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None): """Returns a tensor with uniform distribution of values. @@ -4188,6 +4380,7 @@ def random_uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None): shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed) +@tf_export('keras.backend.random_binomial') def random_binomial(shape, p=0.0, dtype=None, seed=None): """Returns a tensor with random binomial distribution of values. @@ -4209,6 +4402,7 @@ def random_binomial(shape, p=0.0, dtype=None, seed=None): array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype)) +@tf_export('keras.backend.truncated_normal') def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): """Returns a tensor with truncated random normal distribution of values. @@ -4242,6 +4436,7 @@ def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): # in TensorFlow's CTC implementation +@tf_export('keras.backend.ctc_label_dense_to_sparse') def ctc_label_dense_to_sparse(labels, label_lengths): """Converts CTC labels from dense to sparse. @@ -4286,6 +4481,7 @@ def ctc_label_dense_to_sparse(labels, label_lengths): math_ops.to_int64(indices), vals_sparse, math_ops.to_int64(label_shape)) +@tf_export('keras.backend.ctc_batch_cost') def ctc_batch_cost(y_true, y_pred, input_length, label_length): """Runs CTC loss algorithm on each batch element. @@ -4315,6 +4511,7 @@ def ctc_batch_cost(y_true, y_pred, input_length, label_length): inputs=y_pred, labels=sparse_labels, sequence_length=input_length), 1) +@tf_export('keras.backend.ctc_decode') def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1): """Decodes the output of a softmax. @@ -4366,6 +4563,7 @@ def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1): # HIGH ORDER FUNCTIONS +@tf_export('keras.backend.map_fn') def map_fn(fn, elems, name=None, dtype=None): """Map the function fn over the elements elems and return the outputs. @@ -4381,6 +4579,7 @@ def map_fn(fn, elems, name=None, dtype=None): return functional_ops.map_fn(fn, elems, name=name, dtype=dtype) +@tf_export('keras.backend.foldl') def foldl(fn, elems, initializer=None, name=None): """Reduce elems using fn to combine them from left to right. @@ -4397,6 +4596,7 @@ def foldl(fn, elems, initializer=None, name=None): return functional_ops.foldl(fn, elems, initializer=initializer, name=name) +@tf_export('keras.backend.foldr') def foldr(fn, elems, initializer=None, name=None): """Reduce elems using fn to combine them from right to left. diff --git a/tensorflow/python/keras/_impl/keras/backend_test.py b/tensorflow/python/keras/_impl/keras/backend_test.py index 27833e368d1e1c6938b83d08553fca9f4c3669a2..fb4b2a0e1dc06c904d4b93038840dbf688d42ed4 100644 --- a/tensorflow/python/keras/_impl/keras/backend_test.py +++ b/tensorflow/python/keras/_impl/keras/backend_test.py @@ -128,6 +128,22 @@ class BackendUtilsTest(test.TestCase): sess.run(variables.global_variables_initializer()) sess.run(y, feed_dict={x: np.random.random((2, 3))}) + def test_learning_phase_scope(self): + with self.test_session(): + initial_learning_phase = keras.backend.learning_phase() + with keras.backend.learning_phase_scope(1) as lp: + self.assertEqual(lp, 1) + self.assertEqual(keras.backend.learning_phase(), 1) + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + with keras.backend.learning_phase_scope(0) as lp: + self.assertEqual(lp, 0) + self.assertEqual(keras.backend.learning_phase(), 0) + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + with self.assertRaises(ValueError): + with keras.backend.learning_phase_scope(None): + pass + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + def test_int_shape(self): x = keras.backend.placeholder(shape=(3, 4)) self.assertEqual(keras.backend.int_shape(x), (3, 4)) @@ -915,6 +931,15 @@ class BackendNNOpsTest(test.TestCase): last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs, initial_states, **kwargs) + # check static shape inference + self.assertEquals(last_output.get_shape().as_list(), + [num_samples, output_dim]) + self.assertEquals(outputs.get_shape().as_list(), + [num_samples, timesteps, output_dim]) + for state in new_states: + self.assertEquals(state.get_shape().as_list(), + [num_samples, output_dim]) + last_output_list[i].append(keras.backend.eval(last_output)) outputs_list[i].append(keras.backend.eval(outputs)) self.assertEqual(len(new_states), 1) diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py index f0d9e0b0f522d344d9e04365f04655f00eea787f..deb1e8867dba3d52816ebda02bd9a3bf2ec7bc09 100644 --- a/tensorflow/python/keras/_impl/keras/callbacks.py +++ b/tensorflow/python/keras/_impl/keras/callbacks.py @@ -35,6 +35,7 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary as tf_summary +from tensorflow.python.util.tf_export import tf_export try: @@ -159,10 +160,11 @@ class CallbackList(object): return iter(self.callbacks) +@tf_export('keras.callbacks.Callback') class Callback(object): """Abstract base class used to build new callbacks. - # Properties + Attributes: params: dict. Training parameters (eg. verbosity, batch size, number of epochs...). model: instance of `keras.models.Model`. @@ -215,12 +217,23 @@ class Callback(object): pass +@tf_export('keras.callbacks.BaseLogger') class BaseLogger(Callback): """Callback that accumulates epoch averages of metrics. This callback is automatically applied to every Keras model. + + Arguments: + stateful_metrics: Iterable of string names of metrics that + should *not* be averaged over an epoch. + Metrics in this list will be logged as-is in `on_epoch_end`. + All others will be averaged in `on_epoch_end`. """ + def __init__(self, stateful_metrics=None): + super(BaseLogger, self).__init__() + self.stateful_metrics = set(stateful_metrics or []) + def on_epoch_begin(self, epoch, logs=None): self.seen = 0 self.totals = {} @@ -231,19 +244,26 @@ class BaseLogger(Callback): self.seen += batch_size for k, v in logs.items(): - if k in self.totals: - self.totals[k] += v * batch_size + if k in self.stateful_metrics: + self.totals[k] = v else: - self.totals[k] = v * batch_size + if k in self.totals: + self.totals[k] += v * batch_size + else: + self.totals[k] = v * batch_size def on_epoch_end(self, epoch, logs=None): if logs is not None: for k in self.params['metrics']: if k in self.totals: # Make value available to next callbacks. - logs[k] = self.totals[k] / self.seen + if k in self.stateful_metrics: + logs[k] = self.totals[k] + else: + logs[k] = self.totals[k] / self.seen +@tf_export('keras.callbacks.TerminateOnNaN') class TerminateOnNaN(Callback): """Callback that terminates training when a NaN loss is encountered. """ @@ -260,6 +280,7 @@ class TerminateOnNaN(Callback): self.model.stop_training = True +@tf_export('keras.callbacks.ProgbarLogger') class ProgbarLogger(Callback): """Callback that prints metrics to stdout. @@ -267,12 +288,16 @@ class ProgbarLogger(Callback): count_mode: One of "steps" or "samples". Whether the progress bar should count samples seen or steps (batches) seen. + stateful_metrics: Iterable of string names of metrics that + should *not* be averaged over an epoch. + Metrics in this list will be logged as-is. + All others will be averaged over time (e.g. loss, etc). Raises: ValueError: In case of invalid `count_mode`. """ - def __init__(self, count_mode='samples'): + def __init__(self, count_mode='samples', stateful_metrics=None): super(ProgbarLogger, self).__init__() if count_mode == 'samples': self.use_steps = False @@ -280,6 +305,7 @@ class ProgbarLogger(Callback): self.use_steps = True else: raise ValueError('Unknown `count_mode`: ' + str(count_mode)) + self.stateful_metrics = set(stateful_metrics or []) def on_train_begin(self, logs=None): self.verbose = self.params['verbose'] @@ -293,7 +319,10 @@ class ProgbarLogger(Callback): else: target = self.params['samples'] self.target = target - self.progbar = Progbar(target=self.target, verbose=self.verbose) + self.progbar = Progbar( + target=self.target, + verbose=self.verbose, + stateful_metrics=self.stateful_metrics) self.seen = 0 def on_batch_begin(self, batch, logs=None): @@ -323,9 +352,10 @@ class ProgbarLogger(Callback): if k in logs: self.log_values.append((k, logs[k])) if self.verbose: - self.progbar.update(self.seen, self.log_values, force=True) + self.progbar.update(self.seen, self.log_values) +@tf_export('keras.callbacks.History') class History(Callback): """Callback that records events into a `History` object. @@ -345,6 +375,7 @@ class History(Callback): self.history.setdefault(k, []).append(v) +@tf_export('keras.callbacks.ModelCheckpoint') class ModelCheckpoint(Callback): """Save the model after every epoch. @@ -448,6 +479,7 @@ class ModelCheckpoint(Callback): self.model.save(filepath, overwrite=True) +@tf_export('keras.callbacks.EarlyStopping') class EarlyStopping(Callback): """Stop training when a monitored quantity has stopped improving. @@ -531,6 +563,7 @@ class EarlyStopping(Callback): print('Epoch %05d: early stopping' % (self.stopped_epoch + 1)) +@tf_export('keras.callbacks.RemoteMonitor') class RemoteMonitor(Callback): """Callback used to stream events to a server. @@ -575,6 +608,7 @@ class RemoteMonitor(Callback): 'root server at ' + str(self.root)) +@tf_export('keras.callbacks.LearningRateScheduler') class LearningRateScheduler(Callback): """Learning rate scheduler. @@ -603,6 +637,7 @@ class LearningRateScheduler(Callback): 'rate to %s.' % (epoch + 1, lr)) +@tf_export('keras.callbacks.TensorBoard') class TensorBoard(Callback): # pylint: disable=line-too-long """Tensorboard basic visualizations. @@ -743,16 +778,24 @@ class TensorBoard(Callback): while i < val_size: step = min(self.batch_size, val_size - i) batch_val = [] - batch_val.append(val_data[0][i:i + step]) - batch_val.append(val_data[1][i:i + step]) - batch_val.append(val_data[2][i:i + step]) + batch_val.append(val_data[0][i:i + step] + if val_data[0] is not None else None) + batch_val.append(val_data[1][i:i + step] + if val_data[1] is not None else None) + batch_val.append(val_data[2][i:i + step] + if val_data[2] is not None else None) if self.model.uses_learning_phase: # do not slice the learning phase - batch_val = [x[i:i + step] for x in val_data[:-1]] + batch_val = [x[i:i + step] if x is not None else None + for x in val_data[:-1]] batch_val.append(val_data[-1]) else: - batch_val = [x[i:i + step] for x in val_data] - feed_dict = dict(zip(tensors, batch_val)) + batch_val = [x[i:i + step] if x is not None else None + for x in val_data] + feed_dict = {} + for key, val in zip(tensors, batch_val): + if val is not None: + feed_dict[key] = val result = self.sess.run([self.merged], feed_dict=feed_dict) summary_str = result[0] self.writer.add_summary(summary_str, epoch) @@ -772,6 +815,7 @@ class TensorBoard(Callback): self.writer.close() +@tf_export('keras.callbacks.ReduceLROnPlateau') class ReduceLROnPlateau(Callback): """Reduce learning rate when a metric has stopped improving. @@ -891,6 +935,7 @@ class ReduceLROnPlateau(Callback): return self.cooldown_counter > 0 +@tf_export('keras.callbacks.CSVLogger') class CSVLogger(Callback): """Callback that streams epoch results to a csv file. @@ -971,6 +1016,7 @@ class CSVLogger(Callback): self.writer = None +@tf_export('keras.callbacks.LambdaCallback') class LambdaCallback(Callback): r"""Callback for creating simple, custom callbacks on-the-fly. diff --git a/tensorflow/python/keras/_impl/keras/constraints.py b/tensorflow/python/keras/_impl/keras/constraints.py index 4b051c93f3aaca18e10df666966c66b4dce28111..271fbbb63d3dfd50507837e190860d48315a14f2 100644 --- a/tensorflow/python/keras/_impl/keras/constraints.py +++ b/tensorflow/python/keras/_impl/keras/constraints.py @@ -24,8 +24,10 @@ import six from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.constraints.Constraint') class Constraint(object): def __call__(self, w): @@ -35,6 +37,7 @@ class Constraint(object): return {} +@tf_export('keras.constraints.MaxNorm', 'keras.constraints.max_norm') class MaxNorm(Constraint): """MaxNorm weight constraint. @@ -64,22 +67,22 @@ class MaxNorm(Constraint): def __call__(self, w): norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True)) desired = K.clip(norms, 0, self.max_value) - w *= (desired / (K.epsilon() + norms)) - return w + return w * (desired / (K.epsilon() + norms)) def get_config(self): return {'max_value': self.max_value, 'axis': self.axis} +@tf_export('keras.constraints.NonNeg', 'keras.constraints.non_neg') class NonNeg(Constraint): """Constrains the weights to be non-negative. """ def __call__(self, w): - w *= K.cast(K.greater_equal(w, 0.), K.floatx()) - return w + return w * K.cast(K.greater_equal(w, 0.), K.floatx()) +@tf_export('keras.constraints.UnitNorm', 'keras.constraints.unit_norm') class UnitNorm(Constraint): """Constrains the weights incident to each hidden unit to have unit norm. @@ -108,6 +111,7 @@ class UnitNorm(Constraint): return {'axis': self.axis} +@tf_export('keras.constraints.MinMaxNorm', 'keras.constraints.min_max_norm') class MinMaxNorm(Constraint): """MinMaxNorm weight constraint. @@ -148,8 +152,7 @@ class MinMaxNorm(Constraint): desired = ( self.rate * K.clip(norms, self.min_value, self.max_value) + (1 - self.rate) * norms) - w *= (desired / (K.epsilon() + norms)) - return w + return w * (desired / (K.epsilon() + norms)) def get_config(self): return { @@ -173,10 +176,12 @@ nonneg = non_neg unitnorm = unit_norm +@tf_export('keras.constraints.serialize') def serialize(constraint): return serialize_keras_object(constraint) +@tf_export('keras.constraints.deserialize') def deserialize(config, custom_objects=None): return deserialize_keras_object( config, @@ -185,6 +190,7 @@ def deserialize(config, custom_objects=None): printable_module_name='constraint') +@tf_export('keras.constraints.get') def get(identifier): if identifier is None: return None @@ -196,4 +202,5 @@ def get(identifier): elif callable(identifier): return identifier else: - raise ValueError('Could not interpret constraint identifier:', identifier) + raise ValueError('Could not interpret constraint identifier: ' + + str(identifier)) diff --git a/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py b/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py index cfd7df61d5ea47b810776ac8da1bdfcff77d177f..13fa9aed2b8da124af4e9f68c779e08d3094cb5d 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py +++ b/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py @@ -21,8 +21,10 @@ from __future__ import print_function import numpy as np from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.boston_housing.load_data') def load_data(path='boston_housing.npz', test_split=0.2, seed=113): """Loads the Boston Housing dataset. diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar.py b/tensorflow/python/keras/_impl/keras/datasets/cifar.py index 7ada3340a59e114d73095068ec476da5973b67fb..02344897f774723d0ad690ae641952cb63022bdf 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar.py @@ -34,17 +34,16 @@ def load_batch(fpath, label_key='labels'): Returns: A tuple `(data, labels)`. """ - f = open(fpath, 'rb') - if sys.version_info < (3,): - d = cPickle.load(f) - else: - d = cPickle.load(f, encoding='bytes') - # decode utf8 - d_decoded = {} - for k, v in d.items(): - d_decoded[k.decode('utf8')] = v - d = d_decoded - f.close() + with open(fpath, 'rb') as f: + if sys.version_info < (3,): + d = cPickle.load(f) + else: + d = cPickle.load(f, encoding='bytes') + # decode utf8 + d_decoded = {} + for k, v in d.items(): + d_decoded[k.decode('utf8')] = v + d = d_decoded data = d['data'] labels = d[label_key] diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar10.py b/tensorflow/python/keras/_impl/keras/datasets/cifar10.py index fb9d98d42cca9c98d6e9ea46782d1e3d31a4d7dc..6b772433822474c06efcce1701226a4a67abe361 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar10.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar10.py @@ -25,8 +25,10 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.datasets.cifar import load_batch from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.cifar10.load_data') def load_data(): """Loads CIFAR10 dataset. diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar100.py b/tensorflow/python/keras/_impl/keras/datasets/cifar100.py index 95aace599a721618eaae51c89f05de01653c682d..28d74116a50979abab207dbec88e384210dfc070 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar100.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar100.py @@ -25,8 +25,10 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.datasets.cifar import load_batch from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.cifar100.load_data') def load_data(label_mode='fine'): """Loads CIFAR100 dataset. diff --git a/tensorflow/python/keras/_impl/keras/datasets/imdb.py b/tensorflow/python/keras/_impl/keras/datasets/imdb.py index 880c9c821b832caed4a3072c094d72a3171f7a63..7467bb24646227705972262381aa5cf1de809f1c 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/imdb.py +++ b/tensorflow/python/keras/_impl/keras/datasets/imdb.py @@ -25,8 +25,10 @@ import numpy as np from tensorflow.python.keras._impl.keras.preprocessing.sequence import _remove_long_seq from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.imdb.load_data') def load_data(path='imdb.npz', num_words=None, skip_top=0, @@ -128,6 +130,7 @@ def load_data(path='imdb.npz', return (x_train, y_train), (x_test, y_test) +@tf_export('keras.datasets.imdb.get_word_index') def get_word_index(path='imdb_word_index.json'): """Retrieves the dictionary mapping word indices back to words. @@ -141,7 +144,5 @@ def get_word_index(path='imdb_word_index.json'): path, origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json', file_hash='bfafd718b763782e994055a2d397834f') - f = open(path) - data = json.load(f) - f.close() - return data + with open(path) as f: + return json.load(f) diff --git a/tensorflow/python/keras/_impl/keras/datasets/mnist.py b/tensorflow/python/keras/_impl/keras/datasets/mnist.py index ec12a31dcf07cad6ec076aad9bd1f671236cf457..e30691373e9aafad61b101476e21d6860527ce98 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/mnist.py +++ b/tensorflow/python/keras/_impl/keras/datasets/mnist.py @@ -21,8 +21,10 @@ from __future__ import print_function import numpy as np from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.mnist.load_data') def load_data(path='mnist.npz'): """Loads the MNIST dataset. diff --git a/tensorflow/python/keras/_impl/keras/datasets/reuters.py b/tensorflow/python/keras/_impl/keras/datasets/reuters.py index 95cf8852a9c6b1866573231a833f9a95af7b0d55..b711696b5eecf9ba07a66cef25c1811c182b3b60 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/reuters.py +++ b/tensorflow/python/keras/_impl/keras/datasets/reuters.py @@ -25,8 +25,10 @@ import numpy as np from tensorflow.python.keras._impl.keras.preprocessing.sequence import _remove_long_seq from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.reuters.load_data') def load_data(path='reuters.npz', num_words=None, skip_top=0, @@ -112,6 +114,7 @@ def load_data(path='reuters.npz', return (x_train, y_train), (x_test, y_test) +@tf_export('keras.datasets.reuters.get_word_index') def get_word_index(path='reuters_word_index.json'): """Retrieves the dictionary mapping word indices back to words. diff --git a/tensorflow/python/keras/_impl/keras/engine/__init__.py b/tensorflow/python/keras/_impl/keras/engine/__init__.py index 31f624f9af65cac60b6466d4eb5753cbdee984c6..1bc533ab8f7ba37948d82bc69fe1c9bfe00d6834 100644 --- a/tensorflow/python/keras/_impl/keras/engine/__init__.py +++ b/tensorflow/python/keras/_impl/keras/engine/__init__.py @@ -18,13 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs -from tensorflow.python.keras._impl.keras.engine.topology import Input -from tensorflow.python.keras._impl.keras.engine.topology import InputLayer -from tensorflow.python.keras._impl.keras.engine.topology import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import Layer +from tensorflow.python.keras._impl.keras.engine.base_layer import InputSpec +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.engine.training import Model - - -# Note: topology.Node is an internal class, -# it isn't meant to be used by Keras users. diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..5615241ae3077102ef40f9c0619161964a62a335 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py @@ -0,0 +1,505 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Base layer code (`Layer`). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from six.moves import zip # pylint: disable=redefined-builtin + +from tensorflow.python.eager import context +from tensorflow.python.framework import tensor_shape +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import constraints +from tensorflow.python.keras._impl.keras import initializers +from tensorflow.python.keras._impl.keras import regularizers +from tensorflow.python.keras._impl.keras.utils import generic_utils +from tensorflow.python.layers import base as tf_base_layers +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + + +# pylint: disable=invalid-name +InputSpec = tf_base_layers.InputSpec +Node = tf_base_layers.Node +TFBaseLayer = tf_base_layers.Layer +# pylint: enable=invalid-name + + +@tf_export('keras.layers.Layer') +class Layer(tf_base_layers.Layer): + """Abstract base layer class. + + # Properties + name: String, must be unique within a model. + input_spec: List of InputSpec class instances + each entry describes one required input: + - ndim + - dtype + A layer with `n` input tensors must have + an `input_spec` of length `n`. + trainable: Boolean, whether the layer weights + will be updated during training. + uses_learning_phase: Whether any operation + of the layer uses `K.in_training_phase()` + or `K.in_test_phase()`. + input_shape: Shape tuple. Provided for convenience, + but note that there may be cases in which this + attribute is ill-defined (e.g. a shared layer + with multiple input shapes), in which case + requesting `input_shape` will raise an Exception. + Prefer using `layer.get_input_shape_for(input_shape)`, + or `layer.get_input_shape_at(node_index)`. + output_shape: Shape tuple. See above. + inbound_nodes: List of nodes. + outbound_nodes: List of nodes. + input, output: Input/output tensor(s). Note that if the layer is used + more than once (shared layer), this is ill-defined + and will raise an exception. In such cases, use + `layer.get_input_at(node_index)`. + input_mask, output_mask: Same as above, for masks. + trainable_weights: List of variables. + non_trainable_weights: List of variables. + weights: The concatenation of the lists trainable_weights and + non_trainable_weights (in this order). + + # Methods + call(x, mask=None): Where the layer's logic lives. + __call__(x, mask=None): Wrapper around the layer logic (`call`). + If x is a Keras tensor: + - Connect current layer with last layer from tensor: + `self._add_inbound_node(last_layer)` + - Add layer to tensor history + If layer is not built: + - Build from inputs shape + get_weights() + set_weights(weights) + get_config() + count_params() + compute_output_shape(input_shape) + compute_mask(x, mask) + get_input_at(node_index) + get_output_at(node_index) + get_input_shape_at(node_index) + get_output_shape_at(node_index) + get_input_mask_at(node_index) + get_output_mask_at(node_index) + + # Class Methods + from_config(config) + + # Internal methods: + build(input_shape) + _add_inbound_node(layer, index=0) + """ + + def __init__(self, **kwargs): + # These properties should be set by the user via keyword arguments. + # note that 'dtype', 'input_shape' and 'batch_input_shape' + # are only applicable to input layers: do not pass these keywords + # to non-input layers. + allowed_kwargs = { + 'activity_regularizer', + 'input_shape', + 'batch_input_shape', + 'batch_size', + 'dtype', + 'name', + 'trainable', + 'weights', + } + # Validate optional keyword arguments. + for kwarg in kwargs: + if kwarg not in allowed_kwargs: + raise TypeError('Keyword argument not understood:', kwarg) + + # Get layer name. + name = kwargs.get('name') + + # Get `trainable` status. + trainable = kwargs.get('trainable', True) + + # Get `dtype`. + dtype = kwargs.get('dtype') + if dtype is None: + dtype = K.floatx() + + # Call super, which will set all properties common to Keras layers + # and core TF layers. + super(Layer, self).__init__( + name=name, dtype=dtype, trainable=trainable, + activity_regularizer=kwargs.get('activity_regularizer')) + + # Add properties that are Keras-only for now. + self.supports_masking = False + + # Manage input shape information if passed. + if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: + # In this case we will later create an input layer + # to insert before the current layer + if 'batch_input_shape' in kwargs: + batch_input_shape = tuple(kwargs['batch_input_shape']) + elif 'input_shape' in kwargs: + if 'batch_size' in kwargs: + batch_size = kwargs['batch_size'] + else: + batch_size = None + batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) + self._batch_input_shape = batch_input_shape + + # Manage initial weight values if passed. + if 'weights' in kwargs: + self._initial_weights = kwargs['weights'] + else: + self._initial_weights = None + + def add_weight(self, + name, + shape, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + constraint=None): + """Adds a weight variable to the layer. + + Arguments: + name: String, the name for the weight variable. + shape: The shape tuple of the weight. + dtype: The dtype of the weight. + initializer: An Initializer instance (callable). + regularizer: An optional Regularizer instance. + trainable: A boolean, whether the weight should + be trained via backprop or not (assuming + that the layer itself is also trainable). + constraint: An optional Constraint instance. + + Returns: + The created weight variable. + """ + if dtype is None: + dtype = K.floatx() + weight = self.add_variable(name, shape, + dtype=dtype, + initializer=initializers.get(initializer), + regularizer=regularizers.get(regularizer), + constraint=constraints.get(constraint), + trainable=trainable) + return weight + + def call(self, inputs, **kwargs): # pylint: disable=unused-argument + """This is where the layer's logic lives. + + Arguments: + inputs: Input tensor, or list/tuple of input tensors. + **kwargs: Additional keyword arguments. + + Returns: + A tensor or list/tuple of tensors. + """ + return inputs + + def __call__(self, inputs, **kwargs): + """Wrapper around self.call(), for handling internal references. + + If a Keras tensor is passed: + - We call self._add_inbound_node(). + - If necessary, we `build` the layer to match + the shape of the input(s). + - We update the _keras_history of the output tensor(s) + with the current layer. + This is done as part of _add_inbound_node(). + + Arguments: + inputs: Can be a tensor or list/tuple of tensors. + **kwargs: Additional keyword arguments to be passed to `call()`. + + Returns: + Output of the layer's `call` method. + + Raises: + ValueError: in case the layer is missing shape information + for its `build` call. + """ + # Actually call the layer (optionally building it). + output = super(Layer, self).__call__(inputs, **kwargs) + if context.executing_eagerly(): + return output + + if hasattr(self, '_symbolic_set_inputs') and not self.inputs: + # Subclassed network: explicitly set metadata normally set by a call to + # self._set_inputs(). + self._symbolic_set_inputs(inputs, output) + + # Update learning phase info. + output_tensors = generic_utils.to_list(output) + uses_lp = any( + [getattr(x, '_uses_learning_phase', False) + for x in generic_utils.to_list(inputs)]) + uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp + for i in range(len(output_tensors)): + output_tensors[i]._uses_learning_phase = getattr( + output_tensors[i], '_uses_learning_phase', False) or uses_lp + + # Optionally load weight values that were specified at layer instantiation. + if hasattr(self, '_initial_weights') and self._initial_weights is not None: + self.set_weights(self._initial_weights) + del self._initial_weights + return output + + def compute_output_shape(self, input_shape): + """Computes the output shape of the layer. + + Assumes that the layer will be built + to match that input shape provided. + + Arguments: + input_shape: Shape tuple (tuple of integers) + or list of shape tuples (one per output tensor of the layer). + Shape tuples can include None for free dimensions, + instead of an integer. + + Returns: + An input shape tuple. + """ + logging.warning( + 'All custom layers should implement the ' + '`compute_output_shape` method. This layer (' + self.name + ') ' + 'is relying on the base `Layer.compute_output_shape` implementation, ' + 'which will start raising a `NotImplementedError` ' + 'as of July 1st, 2018.') + return input_shape + + def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument + """Computes an output mask tensor. + + Arguments: + inputs: Tensor or list of tensors. + mask: Tensor or list of tensors. + + Returns: + None or a tensor (or list of tensors, + one per output tensor of the layer). + """ + if not self.supports_masking: + if mask is not None: + if isinstance(mask, list): + if any(m is not None for m in mask): + raise TypeError('Layer ' + self.name + ' does not support masking, ' + 'but was passed an input_mask: ' + str(mask)) + else: + raise TypeError('Layer ' + self.name + ' does not support masking, ' + 'but was passed an input_mask: ' + str(mask)) + # masking not explicitly supported: return None as mask + return None + # if masking is explicitly supported, by default + # carry over the input mask + return mask + + def get_input_mask_at(self, node_index): + """Retrieves the input mask tensor(s) of a layer at a given node. + + Arguments: + node_index: Integer, index of the node + from which to retrieve the attribute. + E.g. `node_index=0` will correspond to the + first time the layer was called. + + Returns: + A mask tensor + (or list of tensors if the layer has multiple inputs). + """ + inputs = self.get_input_at(node_index) + if isinstance(inputs, list): + return [getattr(x, '_keras_mask', None) for x in inputs] + else: + return getattr(inputs, '_keras_mask', None) + + def get_output_mask_at(self, node_index): + """Retrieves the output mask tensor(s) of a layer at a given node. + + Arguments: + node_index: Integer, index of the node + from which to retrieve the attribute. + E.g. `node_index=0` will correspond to the + first time the layer was called. + + Returns: + A mask tensor + (or list of tensors if the layer has multiple outputs). + """ + output = self.get_output_at(node_index) + if isinstance(output, list): + return [getattr(x, '_keras_mask', None) for x in output] + else: + return getattr(output, '_keras_mask', None) + + @property + def input_mask(self): + """Retrieves the input mask tensor(s) of a layer. + + Only applicable if the layer has exactly one inbound node, + i.e. if it is connected to one incoming layer. + + Returns: + Input mask tensor (potentially None) or list of input + mask tensors. + + Raises: + AttributeError: if the layer is connected to + more than one incoming layers. + """ + inputs = self.input + if isinstance(inputs, list): + return [getattr(x, '_keras_mask', None) for x in inputs] + else: + return getattr(inputs, '_keras_mask', None) + + @property + def output_mask(self): + """Retrieves the output mask tensor(s) of a layer. + + Only applicable if the layer has exactly one inbound node, + i.e. if it is connected to one incoming layer. + + Returns: + Output mask tensor (potentially None) or list of output + mask tensors. + + Raises: + AttributeError: if the layer is connected to + more than one incoming layers. + """ + output = self.output + if isinstance(output, list): + return [getattr(x, '_keras_mask', None) for x in output] + else: + return getattr(output, '_keras_mask', None) + + def set_weights(self, weights): + """Sets the weights of the layer, from Numpy arrays. + + Arguments: + weights: a list of Numpy arrays. The number + of arrays and their shape must match + number of the dimensions of the weights + of the layer (i.e. it should match the + output of `get_weights`). + + Raises: + ValueError: If the provided weights list does not match the + layer's specifications. + """ + params = self.weights + if len(params) != len(weights): + raise ValueError('You called `set_weights(weights)` on layer "' + + self.name + '" with a weight list of length ' + + str(len(weights)) + ', but the layer was expecting ' + + str(len(params)) + ' weights. Provided weights: ' + + str(weights)[:50] + '...') + if not params: + return + weight_value_tuples = [] + param_values = K.batch_get_value(params) + for pv, p, w in zip(param_values, params, weights): + if pv.shape != w.shape: + raise ValueError('Layer weight shape ' + str(pv.shape) + + ' not compatible with ' + 'provided weight shape ' + str(w.shape)) + weight_value_tuples.append((p, w)) + K.batch_set_value(weight_value_tuples) + + def get_weights(self): + """Returns the current weights of the layer. + + Returns: + Weights values as a list of numpy arrays. + """ + params = self.weights + return K.batch_get_value(params) + + def get_config(self): + """Returns the config of the layer. + + A layer config is a Python dictionary (serializable) + containing the configuration of a layer. + The same layer can be reinstantiated later + (without its trained weights) from this configuration. + + The config of a layer does not include connectivity + information, nor the layer class name. These are handled + by `Network` (one layer of abstraction above). + + Returns: + Python dictionary. + """ + config = {'name': self.name, 'trainable': self.trainable} + if hasattr(self, '_batch_input_shape'): + config['batch_input_shape'] = self._batch_input_shape + if hasattr(self, 'dtype'): + config['dtype'] = self.dtype + return config + + @classmethod + def from_config(cls, config): + """Creates a layer from its config. + + This method is the reverse of `get_config`, + capable of instantiating the same layer from the config + dictionary. It does not handle layer connectivity + (handled by Network), nor weights (handled by `set_weights`). + + Arguments: + config: A Python dictionary, typically the + output of get_config. + + Returns: + A layer instance. + """ + return cls(**config) + + @tf_base_layers.Layer.activity_regularizer.setter + def activity_regularizer(self, activity_regularizer): + self._activity_regularizer = activity_regularizer + + +def shape_type_conversion(fn): + """Decorator that handles tuple/TensorShape conversion. + + Used in `compute_output_shape` and `build`. + + Arguments: + fn: function to wrap. + + Returns: + Wrapped function. + """ + + def wrapper(instance, input_shape): + if input_shape is not None: + if isinstance(input_shape, list): + input_shape = [ + tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape] + else: + input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) + output_shape = fn(instance, input_shape) + if output_shape is not None: + if isinstance(output_shape, list): + return [tensor_shape.TensorShape(x) for x in output_shape] + return tensor_shape.TensorShape(output_shape) + + return wrapper diff --git a/tensorflow/python/keras/_impl/keras/engine/input_layer.py b/tensorflow/python/keras/_impl/keras/engine/input_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..29a17555e0290e7a1664c55cffdb3dd0ddc30bc3 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/input_layer.py @@ -0,0 +1,230 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Input layer code (`Input` and `InputLayer`). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.framework import tensor_shape +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.layers import base as tf_base_layers +from tensorflow.python.ops import array_ops +from tensorflow.python.util.tf_export import tf_export + + +class InputLayer(base_layer.Layer): + """Layer to be used as an entry point into a Network (a graph of layers). + + It can either wrap an existing tensor (pass an `input_tensor` argument) + or create its a placeholder tensor (pass arguments `input_shape`, and + optionally, `dtype`). + + It is generally recommend to use the functional layer API via `Input`, + (which creates an `InputLayer`) without directly using `InputLayer`. + + Arguments: + input_shape: Shape tuple (not including the batch axis), or `TensorShape` + instance (not including the batch axis). + batch_size: Optional input batch size (integer or None). + dtype: Datatype of the input. + input_tensor: Optional tensor to use as layer input + instead of creating a placeholder. + sparse: Boolean, whether the placeholder created + is meant to be sparse. + name: Name of the layer (string). + """ + + def __init__(self, + input_shape=None, + batch_size=None, + dtype=None, + input_tensor=None, + sparse=False, + name=None, + **kwargs): + if 'batch_input_shape' in kwargs: + batch_input_shape = kwargs.pop('batch_input_shape') + if input_shape and batch_input_shape: + raise ValueError('Only provide the input_shape OR ' + 'batch_input_shape argument to ' + 'InputLayer, not both at the same time.') + batch_size = batch_input_shape[0] + input_shape = batch_input_shape[1:] + if kwargs: + raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) + + if not name: + prefix = 'input' + name = prefix + '_' + str(K.get_uid(prefix)) + + if not dtype: + if input_tensor is None: + dtype = K.floatx() + else: + dtype = K.dtype(input_tensor) + super(InputLayer, self).__init__(dtype=dtype, name=name) + self.built = True + self.sparse = sparse + self.batch_size = batch_size + + if isinstance(input_shape, tensor_shape.TensorShape): + input_shape = tuple(input_shape.as_list()) + + if input_tensor is None: + if input_shape is not None: + batch_input_shape = (batch_size,) + tuple(input_shape) + else: + batch_input_shape = None + + if context.executing_eagerly(): + # In eager mode, create a temporary placeholder to call the layer on. + input_tensor = tf_base_layers._DeferredTensor( # pylint: disable=protected-access + shape=batch_input_shape, + dtype=dtype, + name=self.name) + else: + # In graph mode, create a graph placeholder to call the layer on. + if sparse: + input_tensor = array_ops.sparse_placeholder( + shape=batch_input_shape, + dtype=dtype, + name=self.name) + else: + input_tensor = array_ops.placeholder( + shape=batch_input_shape, + dtype=dtype, + name=self.name) + + # For compatibility with Keras API. + self.is_placeholder = True + self._batch_input_shape = batch_input_shape + else: + # For compatibility with Keras API. + self.is_placeholder = False + self._batch_input_shape = tuple(input_tensor.get_shape().as_list()) + + # Create an input node to add to self.outbound_node + # and set output_tensors' _keras_history. + input_tensor._keras_history = (self, 0, 0) # pylint: disable=protected-access + tf_base_layers.Node( + self, + inbound_layers=[], + node_indices=[], + tensor_indices=[], + input_tensors=[input_tensor], + output_tensors=[input_tensor]) + + def get_config(self): + config = { + 'batch_input_shape': self._batch_input_shape, + 'dtype': self.dtype, + 'sparse': self.sparse, + 'name': self.name + } + return config + + +@tf_export('keras.layers.Input', 'keras.Input') +def Input( # pylint: disable=invalid-name + shape=None, + batch_size=None, + name=None, + dtype=None, + sparse=False, + tensor=None, + **kwargs): + """`Input()` is used to instantiate a Keras tensor. + + A Keras tensor is a tensor object from the underlying backend + (Theano or TensorFlow), which we augment with certain + attributes that allow us to build a Keras model + just by knowing the inputs and outputs of the model. + + For instance, if a, b and c are Keras tensors, + it becomes possible to do: + `model = Model(input=[a, b], output=c)` + + The added Keras attribute is: + `_keras_history`: Last layer applied to the tensor. + the entire layer graph is retrievable from that layer, + recursively. + + Arguments: + shape: A shape tuple (integers), not including the batch size. + For instance, `shape=(32,)` indicates that the expected input + will be batches of 32-dimensional vectors. + batch_size: optional static batch size (integer). + name: An optional name string for the layer. + Should be unique in a model (do not reuse the same name twice). + It will be autogenerated if it isn't provided. + dtype: The data type expected by the input, as a string + (`float32`, `float64`, `int32`...) + sparse: A boolean specifying whether the placeholder + to be created is sparse. + tensor: Optional existing tensor to wrap into the `Input` layer. + If set, the layer will not create a placeholder tensor. + **kwargs: deprecated arguments support. + + Returns: + A tensor. + + Example: + + ```python + # this is a logistic regression in Keras + x = Input(shape=(32,)) + y = Dense(16, activation='softmax')(x) + model = Model(x, y) + ``` + + Raises: + ValueError: in case of invalid arguments. + """ + if 'batch_shape' in kwargs: + batch_shape = kwargs.pop('batch_shape') + if shape and batch_shape: + raise ValueError('Only provide the shape OR ' + 'batch_shape argument to ' + 'Input, not both at the same time.') + batch_size = batch_shape[0] + shape = batch_shape[1:] + if kwargs: + raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) + + if dtype is None: + dtype = K.floatx() + if not shape and tensor is None: + raise ValueError('Please provide to Input either a `shape`' + ' or a `tensor` argument. Note that ' + '`shape` does not include the batch ' + 'dimension.') + input_layer = InputLayer( + input_shape=shape, + batch_size=batch_size, + name=name, + dtype=dtype, + sparse=sparse, + input_tensor=tensor) + # Return tensor including `_keras_history`. + # Note that in this case train_output and test_output are the same pointer. + outputs = input_layer._inbound_nodes[0].output_tensors + if len(outputs) == 1: + return outputs[0] + else: + return outputs diff --git a/tensorflow/python/keras/_impl/keras/engine/network.py b/tensorflow/python/keras/_impl/keras/engine/network.py new file mode 100644 index 0000000000000000000000000000000000000000..bde16cdeb07f4142d86d5d22523d55f50e5448a9 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/network.py @@ -0,0 +1,1515 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""A `Network` is way to compose layers: the topological form of a `Model`. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import json +import os + +import numpy as np +from six.moves import zip # pylint: disable=redefined-builtin + +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.utils import generic_utils +from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite +from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary +from tensorflow.python.layers import base as tf_base_layers +from tensorflow.python.layers import utils as tf_layers_util +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable +from tensorflow.python.util import nest +from tensorflow.python.util import tf_inspect + + +# pylint: disable=g-import-not-at-top +try: + import h5py +except ImportError: + h5py = None + +try: + import yaml +except ImportError: + yaml = None +# pylint: enable=g-import-not-at-top + + +class Network(base_layer.Layer): + """A `Network` is a composition of layers. + + It is the topological form of a "model". A `Model` + is simply a `Network` with added training routines. + """ + + def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called + # Signature detection + if (len(args) == 2 or + len(args) == 1 and 'outputs' in kwargs or + 'inputs' in kwargs and 'outputs' in kwargs): + # Graph network + self._init_graph_network(*args, **kwargs) + else: + # Subclassed network + self._init_subclassed_network(**kwargs) + + def _base_init(self, name=None): + # The following are implemented as property functions: + # self.trainable_weights + # self.non_trainable_weights + # self.input_spec + # self.losses + # self.updates + + self._init_set_name(name) + self._activity_regularizer = None + # This acts just like the `trainable` attribute of any layer instance. + # It does not affect users of the underlying layers, only users of the + # Network instance. + self.trainable = True + self._is_compiled = False + self._expects_training_arg = False + + self.supports_masking = False + self.optimizer = None + + # Private attributes to implement compatibility with Layer. + self._updates = [] # Used in symbolic mode only. + self._losses = [] # Used in symbolic mode only. + self._scope = None # Never used. + self._reuse = None # Never used. + if context.executing_eagerly(): + self._graph = None + else: + self._graph = ops.get_default_graph() # Used in symbolic mode only. + # A Network does not create weights of its own, thus has no dtype. + self._dtype = None + + # All layers in order of horizontal graph traversal. + # Entries are unique. Includes input and output layers. + self._layers = [] + + # Used in symbolic mode only, only in conjonction with graph-networks + self._outbound_nodes = [] + self._inbound_nodes = [] + + def _init_graph_network(self, inputs, outputs, name=None): + # Normalize and set self.inputs, self.outputs. + if isinstance(inputs, (list, tuple)): + self.inputs = list(inputs) # Tensor or list of tensors. + else: + self.inputs = [inputs] + if isinstance(outputs, (list, tuple)): + self.outputs = list(outputs) + else: + self.outputs = [outputs] + + # User-prodived argument validation. + if context.executing_eagerly(): + # Check that all inputs/outputs are DeferredTensors. + for tensor in self.inputs: + if not isinstance(tensor, tf_base_layers._DeferredTensor): # pylint: disable=protected-access + raise TypeError('When eager execution is enabled, ' + 'inputs must come from a call to ' + '`tf.keras.Input` (called after ' + 'tfe.enable_eager_execution()). ' + 'Received invalid input: ' + str(tensor)) + for tensor in self.outputs: + if not isinstance(tensor, tf_base_layers._DeferredTensor): # pylint: disable=protected-access + raise TypeError('When eager execution is enabled, ' + 'outputs must come from a call to ' + 'a layer (called after ' + 'tfe.enable_eager_execution()). ' + 'Received invalid output: ' + str(tensor)) + # Check for redundancy in inputs. + if len(set(self.inputs)) != len(self.inputs): + raise ValueError('The list of inputs passed to the model ' + 'is redundant. ' + 'All inputs should only appear once.' + ' Found: ' + str(self.inputs)) + for x in self.inputs: + # Check that x has appropriate `_keras_history` metadata. + if not hasattr(x, '_keras_history'): + cls_name = self.__class__.__name__ + raise ValueError('Input tensors to a ' + cls_name + ' ' + + 'must come from `tf.layers.Input`. ' + 'Received: ' + str(x) + + ' (missing previous layer metadata).') + # Check that x is an input tensor. + # pylint: disable=protected-access + layer, node_index, tensor_index = x._keras_history + if len(layer._inbound_nodes) > 1 or ( + layer._inbound_nodes and layer._inbound_nodes[0].inbound_layers): + cls_name = self.__class__.__name__ + logging.warning(cls_name + ' inputs must come from ' + '`tf.layers.Input` (thus holding past layer metadata), ' + 'they cannot be the output of ' + 'a previous non-Input layer. ' + 'Here, a tensor specified as ' + 'input to "' + self.name + '" was not an Input tensor, ' + 'it was generated by layer ' + layer.name + '.\n' + 'Note that input tensors are ' + 'instantiated via `tensor = tf.layers.Input(shape)`.\n' + 'The tensor that caused the issue was: ' + str(x.name)) + for x in self.outputs: + if not hasattr(x, '_keras_history'): + cls_name = self.__class__.__name__ + raise ValueError('Output tensors to a ' + cls_name + ' must be ' + 'the output of a TensorFlow `Layer` ' + '(thus holding past layer metadata). Found: ' + str(x)) + + self._base_init(name=name) + self._compute_previous_mask = ( + 'mask' in tf_inspect.getargspec(self.call).args or + hasattr(self, 'compute_mask')) + # A Network does not create weights of its own, thus it is already + # built. + self.built = True + self._is_graph_network = True + + # # List of initial layers (1 to 1 mapping with self.inputs, + # # hence the same layer might appear twice) + # self._input_layers = [] + # self._input_layers_node_indices = [] + # self._input_layers_tensor_indices = [] + # # list of layers (1 to 1 mapping with self.inputs, + # # hence the same layer might appear twice) + # self._output_layers = [] + # self._output_layers_node_indices = [] + # self._output_layers_tensor_indices = [] + + self._input_layers = [] + self._output_layers = [] + self._input_coordinates = [] + self._output_coordinates = [] + + # This is for performance optimization when calling the Network on new + # inputs. Every time the Network is called on a set on input tensors, + # we compute the output tensors, output masks and output shapes in one pass, + # then cache them here. When any of these outputs is queried later, we + # retrieve it from there instead of recomputing it. + self._output_mask_cache = {} + self._output_tensor_cache = {} + self._output_shape_cache = {} + + # Build self._output_layers: + for x in self.outputs: + layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access + self._output_layers.append(layer) + self._output_coordinates.append((layer, node_index, tensor_index)) + + # Build self._input_layers: + for x in self.inputs: + layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access + # It's supposed to be an input layer, so only one node + # and one tensor output. + assert node_index == 0 + assert tensor_index == 0 + self._input_layers.append(layer) + self._input_coordinates.append((layer, node_index, tensor_index)) + + # Keep track of the network's nodes and layers. + nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network( + self.inputs, self.outputs) + self._network_nodes = nodes + self._nodes_by_depth = nodes_by_depth + self._layers = layers + self._layers_by_depth = layers_by_depth + + # Create the node linking internal inputs to internal outputs. + tf_base_layers.Node( + outbound_layer=self, + inbound_layers=[], + node_indices=[], + tensor_indices=[], + input_tensors=self.inputs, + output_tensors=self.outputs) + + # Fill in the output mask cache. + masks = [] + for x in self.inputs: + mask = x._keras_mask if hasattr(x, '_keras_mask') else None # pylint: disable=protected-access + masks.append(mask) + mask_cache_key = (tf_layers_util.object_list_uid(self.inputs) + '_' + + tf_layers_util.object_list_uid(masks)) + masks = [] + for x in self.outputs: + mask = x._keras_mask if hasattr(x, '_keras_mask') else None # pylint: disable=protected-access + masks.append(mask) + if len(masks) == 1: + mask = masks[0] + else: + mask = masks + self._output_mask_cache[mask_cache_key] = mask + + # Build self.input_names and self.output_names. + self.input_names = [] + self.output_names = [] + self._feed_input_names = [] + self._feed_inputs = [] + self._feed_input_shapes = [] + for i, layer in enumerate(self._input_layers): + self.input_names.append(layer.name) + if layer.is_placeholder: + self._feed_input_names.append(layer.name) + self._feed_input_shapes.append(K.int_shape(self.inputs[i])) + # layer.input gives an error in eager mode + if not context.executing_eagerly(): + self._feed_inputs.append(layer.input) + for layer in self._output_layers: + self.output_names.append(layer.name) + + def _init_subclassed_network(self, name=None): + self._base_init(name=name) + self._is_graph_network = False + if 'training' in tf_inspect.getargspec(self.call).args: + self._expects_training_arg = True + else: + self._expects_training_arg = False + + self.outputs = None + self.inputs = None + self.built = False + + def __setattr__(self, name, value): + if isinstance(value, (tf_base_layers.Layer, Network)): + try: + is_graph_network = self._is_graph_network + except AttributeError: + raise RuntimeError('It looks like you are subclassing `Model` and you ' + 'forgot to call `super(YourClass, self).__init__()`.' + ' Always start with this line.') + if not is_graph_network: + if value not in self._layers: + self._layers.append(value) + if isinstance(value, checkpointable.CheckpointableBase): + # Layer (and therefore Network/Model) inherit from CheckpointableBase + # rather than Checkpointable, which means there is no Checkpointable + # __setattr__ override (it would be a performance issue for functional + # layers). Therefore Model tracks Checkpointable objects itself. + self._track_checkpointable( + checkpointable=value, name=name, overwrite=True) + super(Network, self).__setattr__(name, value) + + def add_variable(self, name, shape, dtype=None, initializer=None, + regularizer=None, trainable=True, constraint=None): + raise NotImplementedError('`add_variable` is not supported on Networks.') + + def add_loss(self, *args, **kwargs): + if context.executing_eagerly(): + raise NotImplementedError('`add_loss` is not supported on Networks ' + 'when eager execution is enabled.') + super(Network, self).add_loss(*args, **kwargs) + + @property + def uses_learning_phase(self): + return any( + [getattr(x, '_uses_learning_phase', False) for x in self.outputs]) + + @property + def stateful(self): + return any([(hasattr(layer, 'stateful') and layer.stateful) + for layer in self.layers]) + + def reset_states(self): + for layer in self.layers: + if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False): + layer.reset_states() + + @property + def state_updates(self): + """Returns the `updates` from all layers that are stateful. + + This is useful for separating training updates and + state updates, e.g. when we need to update a layer's internal state + during prediction. + + Returns: + A list of update ops. + """ + state_updates = [] + for layer in self.layers: + if getattr(layer, 'stateful', False): + if hasattr(layer, 'updates'): + state_updates += layer.updates + return state_updates + + def get_weights(self): + """Retrieves the weights of the model. + + Returns: + A flat list of Numpy arrays. + """ + weights = [] + for layer in self.layers: + weights += layer.weights + return K.batch_get_value(weights) + + def set_weights(self, weights): + """Sets the weights of the model. + + Arguments: + weights: A list of Numpy arrays with shapes and types matching + the output of `model.get_weights()`. + """ + tuples = [] + for layer in self.layers: + num_param = len(layer.weights) + layer_weights = weights[:num_param] + for sw, w in zip(layer.weights, layer_weights): + tuples.append((sw, w)) + weights = weights[num_param:] + K.batch_set_value(tuples) + + def compute_mask(self, inputs, mask): + if not self._is_graph_network: + return None + + inputs = generic_utils.to_list(inputs) + if mask is None: + masks = [None for _ in range(len(inputs))] + else: + masks = generic_utils.to_list(mask) + cache_key = (tf_layers_util.object_list_uid(inputs) + + '_' + tf_layers_util.object_list_uid(masks)) + if cache_key in self._output_mask_cache: + return self._output_mask_cache[cache_key] + else: + _, output_masks = self._run_internal_graph(inputs, mask=masks) + return output_masks + + @property + def layers(self): + return self._layers + + def get_layer(self, name=None, index=None): + """Retrieves a layer based on either its name (unique) or index. + + Indices are based on order of horizontal graph traversal (bottom-up). + + Arguments: + name: String, name of layer. + index: Integer, index of layer. + + Returns: + A layer instance. + + Raises: + ValueError: In case of invalid layer name or index. + """ + # TODO(fchollet): We could build a dictionary based on layer names + # since they are constant, but we have not done that yet. + if index is not None: + if len(self.layers) <= index: + raise ValueError('Was asked to retrieve layer at index ' + str(index) + + ' but model only has ' + str(len(self.layers)) + + ' layers.') + else: + return self.layers[index] + else: + if not name: + raise ValueError('Provide either a layer name or layer index.') + for layer in self.layers: + if layer.name == name: + return layer + raise ValueError('No such layer: ' + name) + + @property + def updates(self): + """Retrieve the network's updates. + + Will only include updates that are either + unconditional, or conditional on inputs to this model + (e.g. will not include updates that were created by layers of this model + outside of the model). + + Effectively, `network.updates` behaves like `layer.updates`. + + Concrete example: + + ```python + bn = keras.layers.BatchNormalization() + x1 = keras.layers.Input(shape=(10,)) + _ = bn(x1) # This creates 2 updates. + + x2 = keras.layers.Input(shape=(10,)) + y2 = bn(x2) # This creates 2 more updates. + + # The BN layer has now 4 updates. + self.assertEqual(len(bn.updates), 4) + + # Let's create a model from x2 to y2. + model = keras.models.Model(x2, y2) + + # The model does not list all updates from its underlying layers, + # but only the updates that are relevant to it. Updates created by layers + # outside of the model are discarded. + self.assertEqual(len(model.updates), 2) + + # If you keep calling the model, you append to its updates, just like + # what happens for a layer. + x3 = keras.layers.Input(shape=(10,)) + y3 = model(x3) + self.assertEqual(len(model.updates), 4) + + # But if you call the inner BN layer independently, you don't affect + # the model's updates. + x4 = keras.layers.Input(shape=(10,)) + _ = bn(x4) + self.assertEqual(len(model.updates), 4) + ``` + + Returns: + A list of update ops. + """ + if context.executing_eagerly(): + return [] + + if not self.trainable and not self.stateful: + return [] + + updates = [] + for layer in self.layers: + updates += layer.updates + + # `updates` might contain irrelevant updates, so it needs to be filtered + # with respect to inputs the model has been called on. + if self.inputs: + relevant_inputs = self.inputs[:] + else: + relevant_inputs = [] + for i in range(1, len(self._inbound_nodes)): + inputs = self.get_input_at(i) + if isinstance(inputs, list): + relevant_inputs += inputs + else: + relevant_inputs.append(inputs) + reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs, + updates) + relevant_conditional_updates = [x for x in updates if x in reachable] + unconditional_updates = [ + x for x in updates if x._unconditional_update] # pylint: disable=protected-access + # A layer could be used multiple times in a nested structure, + # so the updates list must be de-duped. + return list(set( + relevant_conditional_updates + unconditional_updates + self._updates)) + + @property + def losses(self): + """Retrieve the network's losses. + + Will only include losses that are either + unconditional, or conditional on inputs to this model + (e.g. will not include losses that depend on tensors + that aren't inputs to this model). + + Returns: + A list of loss tensors. + """ + losses = [] + for layer in self.layers: + losses += layer.losses + if context.executing_eagerly(): + return losses + + if self.inputs: + relevant_inputs = self.inputs[:] + else: + relevant_inputs = [] + for i in range(1, len(self._inbound_nodes)): + inputs = self.get_input_at(i) + if isinstance(inputs, list): + relevant_inputs += inputs + else: + relevant_inputs.append(inputs) + reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs, + losses) + relevant_conditional_losses = [x for x in losses if x in reachable] + unconditional_losses = [ + x for x in losses if x._unconditional_loss] # pylint: disable=protected-access + return list(set( + relevant_conditional_losses + unconditional_losses + self._losses)) + + @property + def trainable_weights(self): + if not self.trainable: + return [] + weights = [] + for layer in self.layers: + weights += layer.trainable_weights + return weights + + @property + def non_trainable_weights(self): + weights = [] + for layer in self.layers: + weights += layer.non_trainable_weights + if not self.trainable: + trainable_weights = [] + for layer in self.layers: + trainable_weights += layer.trainable_weights + return trainable_weights + weights + return weights + + @property + def input_spec(self): + """Gets the network's input specs. + + Returns: + A list of `InputSpec` instances (one per input to the model) + or a single instance if the model has only one input. + """ + # If not a graph network, can't assume anything. + if not self._is_graph_network: + return None + + specs = [] + for layer in self._input_layers: + if layer.input_spec is None: + specs.append(None) + else: + if not isinstance(layer.input_spec, list): + raise TypeError('Layer ' + layer.name + + ' has an input_spec attribute that ' + 'is not a list. We expect a list. ' + 'Found input_spec = ' + str(layer.input_spec)) + specs += layer.input_spec + if len(specs) == 1: + return specs[0] + return specs + + def call(self, inputs, training=None, mask=None): + """Call the model on new inputs. + + In this case `call` just reapplies + all ops in the graph to the new inputs + (e.g. build a new computational graph from the provided inputs). + + Arguments: + inputs: A tensor or list of tensors. + training: Boolean or boolean scalar tensor, indicating whether to run + the `Network` in training mode or inference mode. + mask: A mask or list of masks. A mask can be + either a tensor or None (no mask). + + Returns: + A tensor if there is a single output, or + a list of tensors if there are more than one outputs. + """ + inputs = nest.flatten(inputs) + if mask is None: + masks = [None for _ in range(len(inputs))] + else: + masks = nest.flatten(mask) + + if not context.executing_eagerly(): + # Try to retrieve cached outputs if the layer has already been called + # on these exact inputs. + cache_key = (tf_layers_util.object_list_uid(inputs) + + '_' + tf_layers_util.object_list_uid(masks)) + if cache_key in self._output_tensor_cache: + # Cache hit. + return self._output_tensor_cache[cache_key] + # Actually apply the network graph to the new inputs. + outputs, _ = self._run_internal_graph(inputs, + training=training, + mask=masks) + return outputs + + def compute_output_shape(self, input_shape): + if not self._is_graph_network: + raise NotImplementedError + + if isinstance(input_shape, list): + input_shapes = [] + for shape in input_shape: + if shape is not None: + input_shapes.append(tuple(tensor_shape.TensorShape(shape).as_list())) + else: + input_shapes.append(None) + else: + if input_shape is not None: + input_shapes = [tuple(tensor_shape.TensorShape(input_shape).as_list())] + else: + input_shapes = [None] + + if len(input_shapes) != len(self._input_layers): + raise ValueError('Invalid input_shape argument ' + str(input_shape) + + ': model has ' + str(len(self._input_layers)) + + ' tensor inputs.') + + cache_key = tf_layers_util.object_list_uid(input_shapes) + if cache_key not in self._output_shape_cache: + # Cache miss. We have to run the network graph manually (recursive calls + # to `compute_output_shape`). + layers_to_output_shapes = {} + for i in range(len(input_shapes)): + layer = self._input_layers[i] + input_shape = input_shapes[i] + # It's an input layer: then `compute_output_shape` is identity, + # and there is only one node and one tensor output. + shape_key = layer.name + '_0_0' + layers_to_output_shapes[shape_key] = input_shape + + depth_keys = list(self._nodes_by_depth.keys()) + depth_keys.sort(reverse=True) + # Iterate over nodes, by depth level. + if len(depth_keys) > 1: + for depth in depth_keys: + nodes = self._nodes_by_depth[depth] + for node in nodes: + # This is always a single layer, never a list. + layer = node.outbound_layer + if layer in self._input_layers: + # We've already covered the input layers + # a few lines above. + continue + # Potentially redundant list, + # same size as node.input_tensors. + input_shapes = [] + for j in range(len(node.inbound_layers)): + inbound_layer = node.inbound_layers[j] + node_index = node.node_indices[j] + tensor_index = node.tensor_indices[j] + shape_key = inbound_layer.name + '_%s_%s' % (node_index, + tensor_index) + input_shape = layers_to_output_shapes[shape_key] + input_shapes.append(input_shape) + + if len(input_shapes) == 1: + output_shape = layer.compute_output_shape(input_shapes[0]) + else: + output_shape = layer.compute_output_shape(input_shapes) + if isinstance(output_shape, list): + output_shapes = [ + tuple(tensor_shape.TensorShape(shape).as_list()) + for shape in output_shape + ] + else: + output_shapes = [ + tuple(tensor_shape.TensorShape(output_shape).as_list()) + ] + + node_index = layer._inbound_nodes.index(node) # pylint: disable=protected-access + for j in range(len(output_shapes)): + shape_key = layer.name + '_%s_%s' % (node_index, j) + layers_to_output_shapes[shape_key] = output_shapes[j] + + # Read final output shapes from layers_to_output_shapes. + output_shapes = [] + for i in range(len(self._output_layers)): + layer, node_index, tensor_index = self._output_coordinates[i] + shape_key = layer.name + '_%s_%s' % (node_index, tensor_index) + output_shapes.append(layers_to_output_shapes[shape_key]) + # Store in cache. + self._output_shape_cache[cache_key] = output_shapes + else: + # Cache hit. + output_shapes = self._output_shape_cache[cache_key] + + if isinstance(output_shapes, list): + if len(output_shapes) == 1: + return tensor_shape.TensorShape(output_shapes[0]) + else: + return [tensor_shape.TensorShape(shape) for shape in output_shapes] + else: + return tensor_shape.TensorShape(output_shapes) + + def _run_internal_graph(self, inputs, training=None, mask=None): + """Computes output tensors for new inputs. + + # Note: + - Expects `inputs` to be a list (potentially with 1 element). + - Can be run on non-Keras tensors. + + Arguments: + inputs: List of tensors + training: Boolean learning phase. + mask: List of masks (tensors or None). + + Returns: + Three lists: output_tensors, output_masks, output_shapes + """ + # Note: masking support is relevant mainly for Keras. + # It cannot be factored out without having the fully reimplement the network + # calling logic on the Keras side. We choose to incorporate it in + # Network because 1) it may be useful to fully support in tf.layers in + # the future and 2) Keras is a major user of Network. If you don't + # use masking, it does not interfere with regular behavior at all and you + # can ignore it. + if mask is None: + masks = [None for _ in range(len(inputs))] + else: + masks = mask + + # Dictionary mapping reference tensors to tuples + # (computed tensor, compute mask) + # we assume a 1:1 mapping from tensor to mask + # TODO(fchollet): raise exception when a `.compute_mask()` call + # does not return a list the same size as `call` + tensor_map = {} + for x, y, mask in zip(self.inputs, inputs, masks): + tensor_map[str(id(x))] = (y, mask) + + depth_keys = list(self._nodes_by_depth.keys()) + depth_keys.sort(reverse=True) + for depth in depth_keys: + nodes = self._nodes_by_depth[depth] + for node in nodes: + # This is always a single layer, never a list. + layer = node.outbound_layer + reference_input_tensors = node.input_tensors + reference_output_tensors = node.output_tensors + + # If all previous input tensors are available in tensor_map, + # then call node.inbound_layer on them. + computed_data = [] # List of tuples (input, mask). + for x in reference_input_tensors: + if str(id(x)) in tensor_map: + computed_data.append(tensor_map[str(id(x))]) + + if len(computed_data) == len(reference_input_tensors): + # Call layer (reapplying ops to new inputs). + with ops.name_scope(layer.name): + if node.arguments: + kwargs = node.arguments + else: + kwargs = {} + if len(computed_data) == 1: + computed_tensor, computed_mask = computed_data[0] + # Ensure mask propagation if applicable. + if 'mask' in tf_inspect.getargspec(layer.call).args: + kwargs.setdefault('mask', computed_mask) + if 'training' in tf_inspect.getargspec(layer.call).args: + kwargs.setdefault('training', training) + + output_tensors = nest.flatten( + layer.call(computed_tensor, **kwargs)) + if hasattr(layer, 'compute_mask'): + output_masks = nest.flatten( + layer.compute_mask(computed_tensor, computed_mask)) + else: + output_masks = [None for _ in range(len(output_tensors))] + computed_tensors = [computed_tensor] + computed_masks = [computed_mask] + else: + computed_tensors = [x[0] for x in computed_data] + computed_masks = [x[1] for x in computed_data] + if 'mask' in tf_inspect.getargspec(layer.call).args: + kwargs.setdefault('mask', computed_masks) + if 'training' in tf_inspect.getargspec(layer.call).args: + kwargs.setdefault('training', training) + + output_tensors = nest.flatten( + layer.call(computed_tensors, **kwargs)) + if hasattr(layer, 'compute_mask'): + output_masks = nest.flatten( + layer.compute_mask(computed_tensors, computed_masks)) + else: + output_masks = [None for _ in range(len(output_tensors))] + + if not context.executing_eagerly(): + if layer.activity_regularizer is not None: + regularization_losses = [ + layer.activity_regularizer(x) for x in output_tensors + ] + # Apply activity regularizer if any: + layer.add_loss(regularization_losses, computed_tensors) + + # Update tensor_map. + for x, y, mask in zip(reference_output_tensors, output_tensors, + output_masks): + tensor_map[str(id(x))] = (y, mask) + + output_tensors = [] + output_masks = [] + output_shapes = [] + for x in self.outputs: + assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x) + tensor, mask = tensor_map[str(id(x))] + output_shapes.append(tf_layers_util.static_shape(x)) + output_tensors.append(tensor) + output_masks.append(mask) + + if len(output_tensors) == 1: + output_tensors = output_tensors[0] + if output_shapes is not None: + output_shapes = output_shapes[0] + if output_masks is not None: + output_masks = output_masks[0] + + if not context.executing_eagerly(): + # Update cache; + # keys are based on ids on input tensors and inputs masks. + cache_key = (tf_layers_util.object_list_uid(inputs) + + '_' + tf_layers_util.object_list_uid(masks)) + self._output_tensor_cache[cache_key] = output_tensors + self._output_mask_cache[cache_key] = output_masks + + if output_shapes is not None: + input_shapes = [tf_layers_util.static_shape(x) for x in inputs] + cache_key = tf_layers_util.object_list_uid(input_shapes) + self._output_shape_cache[cache_key] = output_shapes + + return output_tensors, output_masks + + def get_config(self): + if not self._is_graph_network: + raise NotImplementedError + + config = { + 'name': self.name, + } + node_conversion_map = {} + for layer in self.layers: + if issubclass(layer.__class__, Network): + # Networks start with a pre-existing node + # linking their input to output. + kept_nodes = 1 + else: + kept_nodes = 0 + for original_node_index, node in enumerate(layer._inbound_nodes): + node_key = _make_node_key(layer.name, original_node_index) + if node_key in self._network_nodes: + node_conversion_map[node_key] = kept_nodes + kept_nodes += 1 + layer_configs = [] + for layer in self.layers: # From the earliest layers on. + layer_class_name = layer.__class__.__name__ + layer_config = layer.get_config() + filtered_inbound_nodes = [] + for original_node_index, node in enumerate(layer._inbound_nodes): + node_key = _make_node_key(layer.name, original_node_index) + if node_key in self._network_nodes: + # The node is relevant to the model: + # add to filtered_inbound_nodes. + if node.arguments: + try: + json.dumps(node.arguments) + kwargs = node.arguments + except TypeError: + logging.warning( + 'Layer ' + layer.name + + ' was passed non-serializable keyword arguments: ' + + str(node.arguments) + '. They will not be included ' + 'in the serialized model (and thus will be missing ' + 'at deserialization time).') + kwargs = {} + else: + kwargs = {} + if node.inbound_layers: + node_data = [] + for i in range(len(node.inbound_layers)): + inbound_layer = node.inbound_layers[i] + node_index = node.node_indices[i] + tensor_index = node.tensor_indices[i] + node_key = _make_node_key(inbound_layer.name, node_index) + new_node_index = node_conversion_map.get(node_key, 0) + node_data.append( + [inbound_layer.name, new_node_index, tensor_index, kwargs]) + filtered_inbound_nodes.append(node_data) + layer_configs.append({ + 'name': layer.name, + 'class_name': layer_class_name, + 'config': layer_config, + 'inbound_nodes': filtered_inbound_nodes, + }) + config['layers'] = layer_configs + + # Gather info about inputs and outputs. + model_inputs = [] + for i in range(len(self._input_layers)): + layer, node_index, tensor_index = self._input_coordinates[i] + node_key = _make_node_key(layer.name, node_index) + if node_key not in self._network_nodes: + continue + new_node_index = node_conversion_map[node_key] + model_inputs.append([layer.name, new_node_index, tensor_index]) + config['input_layers'] = model_inputs + model_outputs = [] + for i in range(len(self._output_layers)): + layer, node_index, tensor_index = self._output_coordinates[i] + node_key = _make_node_key(layer.name, node_index) + if node_key not in self._network_nodes: + continue + new_node_index = node_conversion_map[node_key] + model_outputs.append([layer.name, new_node_index, tensor_index]) + config['output_layers'] = model_outputs + return copy.deepcopy(config) + + @classmethod + def from_config(cls, config, custom_objects=None): + """Instantiates a Model from its config (output of `get_config()`). + + Arguments: + config: Model config dictionary. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A model instance. + + Raises: + ValueError: In case of improperly formatted config dict. + """ + # Layer instances created during + # the graph reconstruction process + created_layers = {} + + # Dictionary mapping layer instances to + # node data that specifies a layer call. + # It acts as a queue that maintains any unprocessed + # layer call until it becomes possible to process it + # (i.e. until the input tensors to the call all exist). + unprocessed_nodes = {} + + def add_unprocessed_node(layer, node_data): + if layer not in unprocessed_nodes: + unprocessed_nodes[layer] = [node_data] + else: + unprocessed_nodes[layer].append(node_data) + + def process_node(layer, node_data): + """Deserialize a node. + + Arguments: + layer: layer instance. + node_data: node config dict. + + Raises: + ValueError: In case of improperly formatted `node_data` dict. + """ + input_tensors = [] + for input_data in node_data: + inbound_layer_name = input_data[0] + inbound_node_index = input_data[1] + inbound_tensor_index = input_data[2] + if len(input_data) == 3: + kwargs = {} + elif len(input_data) == 4: + kwargs = input_data[3] + else: + raise ValueError('Improperly formatted model config.') + if inbound_layer_name not in created_layers: + add_unprocessed_node(layer, node_data) + return + inbound_layer = created_layers[inbound_layer_name] + if len(inbound_layer._inbound_nodes) <= inbound_node_index: + add_unprocessed_node(layer, node_data) + return + inbound_node = inbound_layer._inbound_nodes[inbound_node_index] + input_tensors.append(inbound_node.output_tensors[inbound_tensor_index]) + # Call layer on its inputs, thus creating the node + # and building the layer if needed. + if input_tensors: + if len(input_tensors) == 1: + layer(input_tensors[0], **kwargs) + else: + layer(input_tensors, **kwargs) + + def process_layer(layer_data): + """Deserialize a layer, then call it on appropriate inputs. + + Arguments: + layer_data: layer config dict. + + Raises: + ValueError: In case of improperly formatted `layer_data` dict. + """ + layer_name = layer_data['name'] + + # Instantiate layer. + from tensorflow.python.keras._impl.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top + + layer = deserialize_layer(layer_data, custom_objects=custom_objects) + created_layers[layer_name] = layer + + # Gather layer inputs. + inbound_nodes_data = layer_data['inbound_nodes'] + for node_data in inbound_nodes_data: + # We don't process nodes (i.e. make layer calls) + # on the fly because the inbound node may not yet exist, + # in case of layer shared at different topological depths + # (e.g. a model such as A(B(A(B(x))))) + add_unprocessed_node(layer, node_data) + + # First, we create all layers and enqueue nodes to be processed + for layer_data in config['layers']: + process_layer(layer_data) + # Then we process nodes in order of layer depth. + # Nodes that cannot yet be processed (if the inbound node + # does not yet exist) are re-enqueued, and the process + # is repeated until all nodes are processed. + while unprocessed_nodes: + for layer_data in config['layers']: + layer = created_layers[layer_data['name']] + if layer in unprocessed_nodes: + for node_data in unprocessed_nodes.pop(layer): + process_node(layer, node_data) + + name = config.get('name') + input_tensors = [] + output_tensors = [] + for layer_data in config['input_layers']: + layer_name, node_index, tensor_index = layer_data + assert layer_name in created_layers + layer = created_layers[layer_name] + layer_output_tensors = layer._inbound_nodes[node_index].output_tensors + input_tensors.append(layer_output_tensors[tensor_index]) + for layer_data in config['output_layers']: + layer_name, node_index, tensor_index = layer_data + assert layer_name in created_layers + layer = created_layers[layer_name] + layer_output_tensors = layer._inbound_nodes[node_index].output_tensors + output_tensors.append(layer_output_tensors[tensor_index]) + return cls(inputs=input_tensors, outputs=output_tensors, name=name) + + def save(self, filepath, overwrite=True, include_optimizer=True): + """Save the model to a single HDF5 file. + + The savefile includes: + - The model architecture, allowing to re-instantiate the model. + - The model weights. + - The state of the optimizer, allowing to resume training + exactly where you left off. + + This allows you to save the entirety of the state of a model + in a single file. + + Saved models can be reinstantiated via `keras.models.load_model`. + The model returned by `load_model` + is a compiled model ready to be used (unless the saved model + was never compiled in the first place). + + Arguments: + filepath: String, path to the file to save the weights to. + overwrite: Whether to silently overwrite any existing file at the + target location, or provide the user with a manual prompt. + include_optimizer: If True, save optimizer's state together. + + Example: + + ```python + from keras.models import load_model + + model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' + del model # deletes the existing model + + # returns a compiled model + # identical to the previous one + model = load_model('my_model.h5') + ``` + """ + if not self._is_graph_network: + raise NotImplementedError + + from tensorflow.python.keras._impl.keras.models import save_model # pylint: disable=g-import-not-at-top + save_model(self, filepath, overwrite, include_optimizer) + + def save_weights(self, filepath, overwrite=True): + """Dumps all layer weights to a HDF5 file. + + The weight file has: + - `layer_names` (attribute), a list of strings + (ordered names of model layers). + - For every layer, a `group` named `layer.name` + - For every such layer group, a group attribute `weight_names`, + a list of strings + (ordered names of weights tensor of the layer). + - For every weight in the layer, a dataset + storing the weight value, named after the weight tensor. + + Arguments: + filepath: String, path to the file to save the weights to. + overwrite: Whether to silently overwrite any existing file at the + target location, or provide the user with a manual prompt. + + Raises: + ImportError: If h5py is not available. + """ + if h5py is None: + raise ImportError('`save_weights` requires h5py.') + # If file exists and should not be overwritten: + if not overwrite and os.path.isfile(filepath): + proceed = ask_to_proceed_with_overwrite(filepath) + if not proceed: + return + with h5py.File(filepath, 'w') as f: + saving.save_weights_to_hdf5_group(f, self.layers) + + def load_weights(self, filepath, by_name=False): + """Loads all layer weights from a HDF5 save file. + + If `by_name` is False (default) weights are loaded + based on the network's topology, meaning the architecture + should be the same as when the weights were saved. + Note that layers that don't have weights are not taken + into account in the topological ordering, so adding or + removing layers is fine as long as they don't have weights. + + If `by_name` is True, weights are loaded into layers + only if they share the same name. This is useful + for fine-tuning or transfer-learning models where + some of the layers have changed. + + Arguments: + filepath: String, path to the weights file to load. + by_name: Boolean, whether to load weights by name + or by topological order. + + Raises: + ImportError: If h5py is not available. + """ + if h5py is None: + raise ImportError('`load_weights` requires h5py.') + with h5py.File(filepath, 'r') as f: + if 'layer_names' not in f.attrs and 'model_weights' in f: + f = f['model_weights'] + if by_name: + saving.load_weights_from_hdf5_group_by_name(f, self.layers) + else: + saving.load_weights_from_hdf5_group(f, self.layers) + + def _updated_config(self): + """Util hared between different serialization methods. + + Returns: + Model config with Keras version information added. + """ + from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top + + config = self.get_config() + model_config = { + 'class_name': self.__class__.__name__, + 'config': config, + 'keras_version': keras_version, + 'backend': K.backend() + } + return model_config + + def to_json(self, **kwargs): + """Returns a JSON string containing the network configuration. + + To load a network from a JSON save file, use + `keras.models.model_from_json(json_string, custom_objects={})`. + + Arguments: + **kwargs: Additional keyword arguments + to be passed to `json.dumps()`. + + Returns: + A JSON string. + """ + if not self._is_graph_network: + raise NotImplementedError + + def get_json_type(obj): + # If obj is any numpy type + if type(obj).__module__ == np.__name__: + return obj.item() + + # If obj is a python 'type' + if type(obj).__name__ == type.__name__: + return obj.__name__ + + raise TypeError('Not JSON Serializable:', obj) + + model_config = self._updated_config() + return json.dumps(model_config, default=get_json_type, **kwargs) + + def to_yaml(self, **kwargs): + """Returns a yaml string containing the network configuration. + + To load a network from a yaml save file, use + `keras.models.model_from_yaml(yaml_string, custom_objects={})`. + + `custom_objects` should be a dictionary mapping + the names of custom losses / layers / etc to the corresponding + functions / classes. + + Arguments: + **kwargs: Additional keyword arguments + to be passed to `yaml.dump()`. + + Returns: + A YAML string. + + Raises: + ImportError: if yaml module is not found. + """ + if not self._is_graph_network: + raise NotImplementedError + + if yaml is None: + raise ImportError('Requires yaml module installed.') + return yaml.dump(self._updated_config(), **kwargs) + + def summary(self, line_length=None, positions=None, print_fn=None): + """Prints a string summary of the network. + + Arguments: + line_length: Total length of printed lines + (e.g. set this to adapt the display to different + terminal window sizes). + positions: Relative or absolute positions of log elements + in each line. If not provided, + defaults to `[.33, .55, .67, 1.]`. + print_fn: Print function to use. Defaults to `print`. + It will be called on each line of the summary. + You can set it to a custom function + in order to capture the string summary. + """ + print_layer_summary(self, + line_length=line_length, + positions=positions, + print_fn=print_fn) + + +def get_source_inputs(tensor, layer=None, node_index=None): + """Returns the list of input tensors necessary to compute `tensor`. + + Output will always be a list of tensors + (potentially with 1 element). + + Arguments: + tensor: The tensor to start from. + layer: Origin layer of the tensor. Will be + determined via tensor._keras_history if not provided. + node_index: Origin node index of the tensor. + + Returns: + List of input tensors. + """ + if not hasattr(tensor, '_keras_history'): + return tensor + + if layer is None or node_index: + layer, node_index, _ = tensor._keras_history + if not layer._inbound_nodes: + return [tensor] + else: + node = layer._inbound_nodes[node_index] + if not node.inbound_layers: + # Reached an Input layer, stop recursion. + return node.input_tensors + else: + source_tensors = [] + for i in range(len(node.inbound_layers)): + x = node.input_tensors[i] + layer = node.inbound_layers[i] + node_index = node.node_indices[i] + previous_sources = get_source_inputs(x, layer, node_index) + # Avoid input redundancy. + for x in previous_sources: + if x not in source_tensors: + source_tensors.append(x) + return source_tensors + + +def _make_node_key(layer_name, node_index): + return layer_name + '_ib-' + str(node_index) + + +def _map_graph_network(inputs, outputs): + """Validate a network's topology and gather its layers and nodes. + + Arguments: + inputs: List of input tensors. + outputs: List of outputs tensors. + + Returns: + A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`. + - nodes: list of Node instances. + - nodes_by_depth: dict mapping ints (depth) to lists of node instances. + - layers: list of Layer instances. + - layers_by_depth: dict mapping ints (depth) to lists of layer instances. + + Raises: + ValueError: In case the network is not valid (e.g. disconnected graph). + """ + # Network_nodes: set of nodes included in the graph of layers + # (not all nodes included in the layers are relevant to the current graph). + network_nodes = set() # ids of all nodes relevant to the Network + nodes_depths = {} # dict {node: depth value} + layers_depths = {} # dict {layer: depth value} + layer_indices = {} # dict {layer: index in traversal} + nodes_in_decreasing_depth = [] + + def build_map(tensor, + finished_nodes, + nodes_in_progress, + layer, + node_index, + tensor_index): + """Builds a map of the graph of layers. + + This recursively updates the map `layer_indices`, + the list `nodes_in_decreasing_depth` and the set `network_nodes`. + + Arguments: + tensor: Some tensor in a graph. + finished_nodes: Set of nodes whose subgraphs have been traversed + completely. Useful to prevent duplicated work. + nodes_in_progress: Set of nodes that are currently active on the + recursion stack. Useful to detect cycles. + layer: Layer from which `tensor` comes from. If not provided, + will be obtained from `tensor._keras_history`. + node_index: Node index from which `tensor` comes from. + tensor_index: Tensor_index from which `tensor` comes from. + + Raises: + ValueError: if a cycle is detected. + """ + node = layer._inbound_nodes[node_index] # pylint: disable=protected-access + + # Prevent cycles. + if node in nodes_in_progress: + raise ValueError('The tensor ' + str(tensor) + ' at layer "' + + layer.name + '" is part of a cycle.') + + # Don't repeat work for shared subgraphs + if node in finished_nodes: + return + + node_key = _make_node_key(layer.name, node_index) + # Update network_nodes. + network_nodes.add(node_key) + + # Store the traversal order for layer sorting. + if layer not in layer_indices: + layer_indices[layer] = len(layer_indices) + + nodes_in_progress.add(node) + + # Propagate to all previous tensors connected to this node. + for i in range(len(node.inbound_layers)): + x = node.input_tensors[i] + layer = node.inbound_layers[i] + node_index = node.node_indices[i] + tensor_index = node.tensor_indices[i] + build_map(x, finished_nodes, nodes_in_progress, layer, + node_index, tensor_index) + + finished_nodes.add(node) + nodes_in_progress.remove(node) + nodes_in_decreasing_depth.append(node) + + finished_nodes = set() + nodes_in_progress = set() + for x in outputs: + layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access + build_map(x, finished_nodes, nodes_in_progress, + layer=layer, + node_index=node_index, + tensor_index=tensor_index) + + for node in reversed(nodes_in_decreasing_depth): + # If the depth is not set, the node has no outbound nodes (depth 0). + depth = nodes_depths.setdefault(node, 0) + + # Update the depth of the corresponding layer + previous_depth = layers_depths.get(node.outbound_layer, 0) + # If we've seen this layer before at a higher depth, + # we should use that depth instead of the node depth. + # This is necessary for shared layers that have inputs at different + # depth levels in the graph. + depth = max(depth, previous_depth) + layers_depths[node.outbound_layer] = depth + nodes_depths[node] = depth + + # Update the depth of inbound nodes. + # The "depth" of a node is the max of the depths + # of all layers it is connected to. + for i in range(len(node.inbound_layers)): + inbound_layer = node.inbound_layers[i] + node_index = node.node_indices[i] + inbound_node = inbound_layer._inbound_nodes[node_index] # pylint: disable=protected-access + previous_depth = nodes_depths.get(inbound_node, 0) + nodes_depths[inbound_node] = max(depth + 1, previous_depth) + + # Build a dict {depth: list of nodes with this depth} + nodes_by_depth = {} + for node, depth in nodes_depths.items(): + if depth not in nodes_by_depth: + nodes_by_depth[depth] = [] + nodes_by_depth[depth].append(node) + + # Build a dict {depth: list of layers with this depth} + layers_by_depth = {} + for layer, depth in layers_depths.items(): + if depth not in layers_by_depth: + layers_by_depth[depth] = [] + layers_by_depth[depth].append(layer) + + # Get sorted list of layer depths. + depth_keys = list(layers_by_depth.keys()) + depth_keys.sort(reverse=True) + + # Set self.layers and self._layers_by_depth. + layers = [] + for depth in depth_keys: + layers_for_depth = layers_by_depth[depth] + # Network.layers needs to have a deterministic order: + # here we order them by traversal order. + layers_for_depth.sort(key=lambda x: layer_indices[x]) + layers.extend(layers_for_depth) + + # Get sorted list of node depths. + depth_keys = list(nodes_by_depth.keys()) + depth_keys.sort(reverse=True) + + # Check that all tensors required are computable. + # computable_tensors: all tensors in the graph + # that can be computed from the inputs provided. + computable_tensors = [] + for x in inputs: + computable_tensors.append(x) + + layers_with_complete_input = [] # To provide a better error msg. + for depth in depth_keys: + for node in nodes_by_depth[depth]: + layer = node.outbound_layer + if layer: + for x in node.input_tensors: + if x not in computable_tensors: + raise ValueError('Graph disconnected: ' + 'cannot obtain value for tensor ' + str(x) + + ' at layer "' + layer.name + '". ' + 'The following previous layers ' + 'were accessed without issue: ' + + str(layers_with_complete_input)) + for x in node.output_tensors: + computable_tensors.append(x) + layers_with_complete_input.append(layer.name) + + # Ensure name unicity, which will be crucial for serialization + # (since serialized nodes refer to layers by their name). + all_names = [layer.name for layer in layers] + for name in all_names: + if all_names.count(name) != 1: + raise ValueError('The name "' + name + '" is used ' + + str(all_names.count(name)) + ' times in the model. ' + 'All layer names should be unique.') + return network_nodes, nodes_by_depth, layers, layers_by_depth diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py new file mode 100644 index 0000000000000000000000000000000000000000..52522e693511b010d0501651e594d346984c41e3 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/saving.py @@ -0,0 +1,671 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Model saving utilities. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json +import os + +import numpy as np +from six.moves import zip # pylint: disable=redefined-builtin + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.utils import conv_utils +from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + +# pylint: disable=g-import-not-at-top +try: + import h5py +except ImportError: + h5py = None + +try: + import yaml +except ImportError: + yaml = None +# pylint: enable=g-import-not-at-top + + +@tf_export('keras.models.save_model') +def save_model(model, filepath, overwrite=True, include_optimizer=True): + """Save a model to a HDF5 file. + + The saved model contains: + - the model's configuration (topology) + - the model's weights + - the model's optimizer's state (if any) + + Thus the saved model can be reinstantiated in + the exact same state, without any of the code + used for model definition or training. + + Arguments: + model: Keras model instance to be saved. + filepath: String, path where to save the model. + overwrite: Whether we should overwrite any existing + model at the target location, or instead + ask the user with a manual prompt. + include_optimizer: If True, save optimizer's state together. + + Raises: + ImportError: if h5py is not available. + """ + + if h5py is None: + raise ImportError('`save_model` requires h5py.') + + def get_json_type(obj): + """Serialize any object to a JSON-serializable structure. + + Arguments: + obj: the object to serialize + + Returns: + JSON-serializable structure representing `obj`. + + Raises: + TypeError: if `obj` cannot be serialized. + """ + # if obj is a serializable Keras class instance + # e.g. optimizer, layer + if hasattr(obj, 'get_config'): + return {'class_name': obj.__class__.__name__, 'config': obj.get_config()} + + # if obj is any numpy type + if type(obj).__module__ == np.__name__: + if isinstance(obj, np.ndarray): + return {'type': type(obj), 'value': obj.tolist()} + else: + return obj.item() + + # misc functions (e.g. loss function) + if callable(obj): + return obj.__name__ + + # if obj is a python 'type' + if type(obj).__name__ == type.__name__: + return obj.__name__ + + raise TypeError('Not JSON Serializable:', obj) + + from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top + + # If file exists and should not be overwritten. + if not overwrite and os.path.isfile(filepath): + proceed = ask_to_proceed_with_overwrite(filepath) + if not proceed: + return + + with h5py.File(filepath, mode='w') as f: + f.attrs['keras_version'] = str(keras_version).encode('utf8') + f.attrs['backend'] = K.backend().encode('utf8') + f.attrs['model_config'] = json.dumps( + { + 'class_name': model.__class__.__name__, + 'config': model.get_config() + }, + default=get_json_type).encode('utf8') + + model_weights_group = f.create_group('model_weights') + model_layers = model.layers + save_weights_to_hdf5_group(model_weights_group, model_layers) + + if include_optimizer and hasattr(model, 'optimizer'): + if isinstance(model.optimizer, optimizers.TFOptimizer): + logging.warning( + 'TensorFlow optimizers do not ' + 'make it possible to access ' + 'optimizer attributes or optimizer state ' + 'after instantiation. ' + 'As a result, we cannot save the optimizer ' + 'as part of the model save file.' + 'You will have to compile your model again after loading it. ' + 'Prefer using a Keras optimizer instead ' + '(see keras.io/optimizers).') + else: + f.attrs['training_config'] = json.dumps( + { + 'optimizer_config': { + 'class_name': model.optimizer.__class__.__name__, + 'config': model.optimizer.get_config() + }, + 'loss': model.loss, + 'metrics': model.metrics, + 'sample_weight_mode': model.sample_weight_mode, + 'loss_weights': model.loss_weights, + }, + default=get_json_type).encode('utf8') + + # Save optimizer weights. + symbolic_weights = getattr(model.optimizer, 'weights') + if symbolic_weights: + optimizer_weights_group = f.create_group('optimizer_weights') + weight_values = K.batch_get_value(symbolic_weights) + weight_names = [] + for w, val in zip(symbolic_weights, weight_values): + name = str(w.name) + weight_names.append(name.encode('utf8')) + optimizer_weights_group.attrs['weight_names'] = weight_names + for name, val in zip(weight_names, weight_values): + param_dset = optimizer_weights_group.create_dataset( + name, val.shape, dtype=val.dtype) + if not val.shape: + # scalar + param_dset[()] = val + else: + param_dset[:] = val + f.flush() + + +@tf_export('keras.models.load_model') +def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin + """Loads a model saved via `save_model`. + + Arguments: + filepath: String, path to the saved model. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + compile: Boolean, whether to compile the model + after loading. + + Returns: + A Keras model instance. If an optimizer was found + as part of the saved model, the model is already + compiled. Otherwise, the model is uncompiled and + a warning will be displayed. When `compile` is set + to False, the compilation is omitted without any + warning. + + Raises: + ImportError: if h5py is not available. + ValueError: In case of an invalid savefile. + """ + if h5py is None: + raise ImportError('`load_model` requires h5py.') + + if not custom_objects: + custom_objects = {} + + def convert_custom_objects(obj): + """Handles custom object lookup. + + Arguments: + obj: object, dict, or list. + + Returns: + The same structure, where occurrences + of a custom object name have been replaced + with the custom object. + """ + if isinstance(obj, list): + deserialized = [] + for value in obj: + deserialized.append(convert_custom_objects(value)) + return deserialized + if isinstance(obj, dict): + deserialized = {} + for key, value in obj.items(): + deserialized[key] = convert_custom_objects(value) + return deserialized + if obj in custom_objects: + return custom_objects[obj] + return obj + + with h5py.File(filepath, mode='r') as f: + # instantiate model + model_config = f.attrs.get('model_config') + if model_config is None: + raise ValueError('No model found in config file.') + model_config = json.loads(model_config.decode('utf-8')) + model = model_from_config(model_config, custom_objects=custom_objects) + + # set weights + load_weights_from_hdf5_group(f['model_weights'], model.layers) + + # Early return if compilation is not required. + if not compile: + return model + + # instantiate optimizer + training_config = f.attrs.get('training_config') + if training_config is None: + logging.warning('No training configuration found in save file: ' + 'the model was *not* compiled. Compile it manually.') + return model + training_config = json.loads(training_config.decode('utf-8')) + optimizer_config = training_config['optimizer_config'] + optimizer = optimizers.deserialize( + optimizer_config, custom_objects=custom_objects) + + # Recover loss functions and metrics. + loss = convert_custom_objects(training_config['loss']) + metrics = convert_custom_objects(training_config['metrics']) + sample_weight_mode = training_config['sample_weight_mode'] + loss_weights = training_config['loss_weights'] + + # Compile model. + model.compile( + optimizer=optimizer, + loss=loss, + metrics=metrics, + loss_weights=loss_weights, + sample_weight_mode=sample_weight_mode) + + # Set optimizer weights. + if 'optimizer_weights' in f: + # Build train function (to get weight updates). + model._make_train_function() + optimizer_weights_group = f['optimizer_weights'] + optimizer_weight_names = [ + n.decode('utf8') + for n in optimizer_weights_group.attrs['weight_names'] + ] + optimizer_weight_values = [ + optimizer_weights_group[n] for n in optimizer_weight_names + ] + try: + model.optimizer.set_weights(optimizer_weight_values) + except ValueError: + logging.warning('Error in loading the saved optimizer ' + 'state. As a result, your model is ' + 'starting with a freshly initialized ' + 'optimizer.') + return model + + +@tf_export('keras.models.model_from_config') +def model_from_config(config, custom_objects=None): + """Instantiates a Keras model from its config. + + Arguments: + config: Configuration dictionary. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + + Raises: + TypeError: if `config` is not a dictionary. + """ + if isinstance(config, list): + raise TypeError('`model_from_config` expects a dictionary, not a list. ' + 'Maybe you meant to use ' + '`Sequential.from_config(config)`?') + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +@tf_export('keras.models.model_from_yaml') +def model_from_yaml(yaml_string, custom_objects=None): + """Parses a yaml model configuration file and returns a model instance. + + Arguments: + yaml_string: YAML string encoding a model configuration. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + + Raises: + ImportError: if yaml module is not found. + """ + if yaml is None: + raise ImportError('Requires yaml module installed.') + config = yaml.load(yaml_string) + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +@tf_export('keras.models.model_from_json') +def model_from_json(json_string, custom_objects=None): + """Parses a JSON model configuration file and returns a model instance. + + Arguments: + json_string: JSON string encoding a model configuration. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + """ + config = json.loads(json_string) + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +def save_weights_to_hdf5_group(f, layers): + from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top + + f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers] + f.attrs['backend'] = K.backend().encode('utf8') + f.attrs['keras_version'] = str(keras_version).encode('utf8') + + for layer in layers: + g = f.create_group(layer.name) + symbolic_weights = layer.weights + weight_values = K.batch_get_value(symbolic_weights) + weight_names = [] + for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)): + if hasattr(w, 'name') and w.name: + name = str(w.name) + else: + name = 'param_' + str(i) + weight_names.append(name.encode('utf8')) + g.attrs['weight_names'] = weight_names + for name, val in zip(weight_names, weight_values): + param_dset = g.create_dataset(name, val.shape, dtype=val.dtype) + if not val.shape: + # scalar + param_dset[()] = val + else: + param_dset[:] = val + + +def preprocess_weights_for_loading(layer, + weights, + original_keras_version=None, + original_backend=None): + """Converts layers weights from Keras 1 format to Keras 2. + + Arguments: + layer: Layer instance. + weights: List of weights values (Numpy arrays). + original_keras_version: Keras version for the weights, as a string. + original_backend: Keras backend the weights were trained with, + as a string. + + Returns: + A list of weights values (Numpy arrays). + """ + if layer.__class__.__name__ == 'Bidirectional': + num_weights_per_layer = len(weights) // 2 + forward_weights = preprocess_weights_for_loading( + layer.forward_layer, weights[:num_weights_per_layer], + original_keras_version, original_backend) + backward_weights = preprocess_weights_for_loading( + layer.backward_layer, weights[num_weights_per_layer:], + original_keras_version, original_backend) + weights = forward_weights + backward_weights + + if original_keras_version == '1': + if layer.__class__.__name__ == 'TimeDistributed': + weights = preprocess_weights_for_loading( + layer.layer, weights, original_keras_version, original_backend) + + if layer.__class__.__name__ == 'Conv1D': + shape = weights[0].shape + # Handle Keras 1.1 format + if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters: + # Legacy shape: + # (filters, input_dim, filter_length, 1) + assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0], + 1) + weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) + weights[0] = weights[0][:, 0, :, :] + + if layer.__class__.__name__ == 'Conv2D': + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, stack_size, filters) + weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) + + if layer.__class__.__name__ == 'Conv2DTranspose': + if layer.data_format == 'channels_last': + # old: (kernel_rows, kernel_cols, stack_size, filters) + # new: (kernel_rows, kernel_cols, filters, stack_size) + weights[0] = np.transpose(weights[0], (0, 1, 3, 2)) + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, filters, stack_size) + weights[0] = np.transpose(weights[0], (2, 3, 0, 1)) + + if layer.__class__.__name__ == 'Conv3D': + if layer.data_format == 'channels_first': + # old: (filters, stack_size, ...) + # new: (..., stack_size, filters) + weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0)) + + if layer.__class__.__name__ == 'GRU': + if len(weights) == 9: + kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[4], weights[7]], axis=-1) + bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ == 'LSTM': + if len(weights) == 12: + # old: i, c, f, o + # new: i, f, c, o + kernel = np.concatenate( + [weights[0], weights[6], weights[3], weights[9]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[7], weights[4], weights[10]], axis=-1) + bias = np.concatenate( + [weights[2], weights[8], weights[5], weights[11]], axis=-1) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ == 'ConvLSTM2D': + if len(weights) == 12: + kernel = np.concatenate( + [weights[0], weights[6], weights[3], weights[9]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[7], weights[4], weights[10]], axis=-1) + bias = np.concatenate( + [weights[2], weights[8], weights[5], weights[11]], axis=-1) + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, stack_size, filters) + kernel = np.transpose(kernel, (2, 3, 1, 0)) + recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0)) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ in ['Model', 'Sequential']: + new_weights = [] + # trainable weights + for sublayer in layer.layers: + num_weights = len(sublayer.trainable_weights) + if num_weights > 0: + new_weights.extend( + preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + + # non-trainable weights + for sublayer in layer.layers: + num_weights = len([ + l for l in sublayer.weights if l not in sublayer.trainable_weights + ]) + if num_weights > 0: + new_weights.extend( + preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + weights = new_weights + + conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] + if layer.__class__.__name__ in conv_layers: + if original_backend == 'theano': + weights[0] = conv_utils.convert_kernel(weights[0]) + if layer.__class__.__name__ == 'ConvLSTM2D': + weights[1] = conv_utils.convert_kernel(weights[1]) + if K.int_shape(layer.weights[0]) != weights[0].shape: + weights[0] = np.transpose(weights[0], (3, 2, 0, 1)) + if layer.__class__.__name__ == 'ConvLSTM2D': + weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) + + # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM + if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: + # Determine if loading a CuDNNLSTM layer from the number of bias weights: + # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) + # if there's no bias weight in the file, skip this conversion + units = weights[1].shape[0] + bias = weights[2] + if len(bias) == units * 8: + # reshape the kernels + kernels = np.split(weights[0], 4, axis=1) + kernels = [ + kernel.reshape(-1).reshape(kernel.shape, order='F') + for kernel in kernels + ] + weights[0] = np.concatenate(kernels, axis=1) + + # transpose the recurrent kernels + recurrent_kernels = np.split(weights[1], 4, axis=1) + recurrent_kernels = [kernel.T for kernel in recurrent_kernels] + weights[1] = np.concatenate(recurrent_kernels, axis=1) + + # split the bias into half and merge + weights[2] = bias[:units * 4] + bias[units * 4:] + + return weights + + +def load_weights_from_hdf5_group(f, layers): + """Implements topological (order-based) weight loading. + + Arguments: + f: A pointer to a HDF5 group. + layers: a list of target layers. + + Raises: + ValueError: in case of mismatch between provided layers + and weights file. + """ + if 'keras_version' in f.attrs: + original_keras_version = f.attrs['keras_version'].decode('utf8') + else: + original_keras_version = '1' + if 'backend' in f.attrs: + original_backend = f.attrs['backend'].decode('utf8') + else: + original_backend = None + + filtered_layers = [] + for layer in layers: + weights = layer.weights + if weights: + filtered_layers.append(layer) + + layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] + filtered_layer_names = [] + for name in layer_names: + g = f[name] + weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] + if weight_names: + filtered_layer_names.append(name) + layer_names = filtered_layer_names + if len(layer_names) != len(filtered_layers): + raise ValueError('You are trying to load a weight file ' + 'containing ' + str(len(layer_names)) + + ' layers into a model with ' + str(len(filtered_layers)) + + ' layers.') + + # We batch weight value assignments in a single backend call + # which provides a speedup in TensorFlow. + weight_value_tuples = [] + for k, name in enumerate(layer_names): + g = f[name] + weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] + weight_values = [g[weight_name] for weight_name in weight_names] + layer = filtered_layers[k] + symbolic_weights = layer.weights + weight_values = preprocess_weights_for_loading( + layer, weight_values, original_keras_version, original_backend) + if len(weight_values) != len(symbolic_weights): + raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + + '" in the current model) was found to ' + 'correspond to layer ' + name + ' in the save file. ' + 'However the new layer ' + layer.name + ' expects ' + + str(len(symbolic_weights)) + + ' weights, but the saved weights have ' + + str(len(weight_values)) + ' elements.') + weight_value_tuples += zip(symbolic_weights, weight_values) + K.batch_set_value(weight_value_tuples) + + +def load_weights_from_hdf5_group_by_name(f, layers): + """Implements name-based weight loading. + + (instead of topological weight loading). + + Layers that have no matching name are skipped. + + Arguments: + f: A pointer to a HDF5 group. + layers: a list of target layers. + + Raises: + ValueError: in case of mismatch between provided layers + and weights file. + """ + if 'keras_version' in f.attrs: + original_keras_version = f.attrs['keras_version'].decode('utf8') + else: + original_keras_version = '1' + if 'backend' in f.attrs: + original_backend = f.attrs['backend'].decode('utf8') + else: + original_backend = None + + # New file format. + layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] + + # Reverse index of layer name to list of layers with name. + index = {} + for layer in layers: + if layer.name: + index.setdefault(layer.name, []).append(layer) + + # We batch weight value assignments in a single backend call + # which provides a speedup in TensorFlow. + weight_value_tuples = [] + for k, name in enumerate(layer_names): + g = f[name] + weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] + weight_values = [g[weight_name] for weight_name in weight_names] + + for layer in index.get(name, []): + symbolic_weights = layer.weights + weight_values = preprocess_weights_for_loading( + layer, weight_values, original_keras_version, original_backend) + if len(weight_values) != len(symbolic_weights): + raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + + '") expects ' + str(len(symbolic_weights)) + + ' weight(s), but the saved weights' + ' have ' + + str(len(weight_values)) + ' element(s).') + # Set values. + for i in range(len(weight_values)): + weight_value_tuples.append((symbolic_weights[i], weight_values[i])) + K.batch_set_value(weight_value_tuples) diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb17641b0d26bc227b142d9302dc1da9637c506 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py @@ -0,0 +1,375 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#,============================================================================ +"""Tests for model saving.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import shutil +import tempfile + +import numpy as np + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test +from tensorflow.python.training import training as training_module + +try: + import h5py # pylint:disable=g-import-not-at-top +except ImportError: + h5py = None + + +class TestWeightSavingAndLoading(test.TestCase): + + def test_weight_loading(self): + with self.test_session(): + a = keras.layers.Input(shape=(2,)) + x = keras.layers.Dense(3)(a) + b = keras.layers.Dense(1)(x) + model = keras.models.Model(a, b) + + x = np.random.random((3, 2)) + ref_y = model.predict(x) + weights = model.get_weights() + model.set_weights(weights) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + with self.assertRaises(ValueError): + model.set_weights(weights[1:]) + with self.assertRaises(ValueError): + model.set_weights(weights[::-1]) + + if h5py is None: + return # Skip rest of test if H5py isn't available. + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + h5_path = os.path.join(temp_dir, 'test.h5') + model.save_weights(h5_path) + model.load_weights(h5_path) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + model.load_weights(h5_path, by_name=True) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + def test_weight_preprocessing(self): + input_dim = 3 + output_dim = 3 + size = 2 + cases = [ + [ + (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))), + [np.random.random((2, 1)), np.random.random((2, 1))], + (None, 3, 2), + ], + [ + (keras.layers.TimeDistributed(keras.layers.Dense(1))), + [np.random.random((2, 1)), np.random.random((1,))], + (None, 3, 2), + ], + [ + (keras.layers.Conv1D(output_dim, size, use_bias=False)), + [np.random.random((output_dim, input_dim, size, 1))], + (None, 4, input_dim), + ], + [ + (keras.layers.Conv2D(output_dim, size, + use_bias=False, data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size))], + (None, input_dim, 4, 4), + ], + [ + (keras.layers.Conv2DTranspose(output_dim, size, + use_bias=False, + data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size))], + (None, input_dim, 4, 4), + ], + [ + (keras.layers.Conv2DTranspose(output_dim, size, + use_bias=False, + data_format='channels_last')), + [np.random.random((size, size, input_dim, output_dim))], + (None, 4, 4, input_dim), + ], + [ + (keras.layers.Conv3D(output_dim, size, + use_bias=False, data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size, size))], + (None, input_dim, 4, 4, 4), + ], + [ + (keras.layers.GRU(output_dim)), + [np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,))], + (None, 4, input_dim), + ], + [ + (keras.layers.LSTM(output_dim)), + [np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,))], + (None, 4, input_dim), + ], + ] + for layer, weights, input_shape in cases: + layer.build(input_shape) + _ = keras.engine.saving.preprocess_weights_for_loading( + layer, weights, original_keras_version='1') + + model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)]) + _ = keras.engine.saving.preprocess_weights_for_loading( + model, model.weights, original_keras_version='1') + + x = keras.Input((2,)) + y = keras.layers.Dense(2)(x) + model = keras.models.Model(x, y) + _ = keras.engine.saving.preprocess_weights_for_loading( + model, model.weights, original_keras_version='1') + + def test_sequential_weight_loading(self): + if h5py is None: + return + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') + + num_hidden = 5 + input_dim = 3 + batch_size = 5 + num_classes = 2 + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + + x = np.random.random((batch_size, input_dim)) + ref_y = model.predict(x) + + model.save_weights(h5_path) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + model.load_weights(h5_path) + y = model.predict(x) + + self.assertAllClose(y, ref_y) + + +class TestWholeModelSaving(test.TestCase): + + def test_sequential_model_saving(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + new_model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + out2 = new_model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + # test that new updates are the same with both models + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + new_model.train_on_batch(x, y) + out = model.predict(x) + out2 = new_model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_sequential_model_saving_2(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + # test with custom optimizer, loss + + class CustomOp(keras.optimizers.RMSprop): + pass + + def custom_loss(y_true, y_pred): + return keras.losses.mse(y_true, y_pred) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc']) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model( + fname, + custom_objects={'CustomOp': CustomOp, + 'custom_loss': custom_loss}) + os.close(fd) + os.remove(fname) + + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_functional_model_saving(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + inputs = keras.layers.Input(shape=(3,)) + x = keras.layers.Dense(2)(inputs) + output = keras.layers.Dense(3)(x) + + model = keras.models.Model(inputs, output) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_saving_without_compilation(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_with_tf_optimizer(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', + optimizer=training_module.AdadeltaOptimizer(0.1), + metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_right_after_compilation(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + model.model._make_train_function() + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_lambda_numpy_array_arguments(self): + if h5py is None: + return # Skip test if models cannot be saved. + + mean = np.random.random((4, 2, 3)) + std = np.abs(np.random.random((4, 2, 3))) + 1e-5 + inputs = keras.layers.Input(shape=(4, 2, 3)) + output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, + arguments={'mu': mean, 'std': std})(inputs) + model = keras.models.Model(inputs, output) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + self.assertAllClose(mean, model.layers[1].arguments['mu']) + self.assertAllClose(std, model.layers[1].arguments['std']) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential.py b/tensorflow/python/keras/_impl/keras/engine/sequential.py new file mode 100644 index 0000000000000000000000000000000000000000..db5e7754bc22ba360dbf635f1bd80334f58e8509 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/sequential.py @@ -0,0 +1,997 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Home of the `Sequential` model. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import os + +from tensorflow.python.framework import ops +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import layers as layer_module +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.keras._impl.keras.engine import network +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.engine.training import Model +from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + +try: + import h5py # pylint: disable=g-import-not-at-top +except ImportError: + h5py = None + + +@tf_export('keras.models.Sequential', 'keras.Sequential') +class Sequential(Model): + """Linear stack of layers. + + Arguments: + layers: list of layers to add to the model. + + # Note + The first layer passed to a Sequential model + should have a defined input shape. What that + means is that it should have received an `input_shape` + or `batch_input_shape` argument, + or for some type of layers (recurrent, Dense...) + an `input_dim` argument. + + Example: + + ```python + model = Sequential() + # first layer must have a defined input shape + model.add(Dense(32, input_dim=500)) + # afterwards, Keras does automatic shape inference + model.add(Dense(32)) + + # also possible (equivalent to the above): + model = Sequential() + model.add(Dense(32, input_shape=(500,))) + model.add(Dense(32)) + + # also possible (equivalent to the above): + model = Sequential() + # here the batch dimension is None, + # which means any batch size will be accepted by the model. + model.add(Dense(32, batch_input_shape=(None, 500))) + model.add(Dense(32)) + ``` + """ + + def __init__(self, layers=None, name=None): + self._is_graph_network = True + self._is_compiled = False + self._layers = [] # Stack of layers. + self.model = None # Internal Model instance. + self.inputs = [] # List of input tensors + self.outputs = [] # List of length 1: the output tensor (unique). + self._trainable = True + self._initial_weights = None + self._input_layers = [] + + # Model attributes. + self._inbound_nodes = [] + self._outbound_nodes = [] + self.built = False + + # Set model name. + if not name: + prefix = 'sequential_' + name = prefix + str(K.get_uid(prefix)) + self._name = name + + # Used by Layer base class. + self._dtype = None + self._activity_regularizer = None + + # The following properties are not actually used by Keras; + # they exist for compatibility with TF's variable scoping mechanism. + self._updates = [] + self._losses = [] + self._scope = None + self._reuse = None + self._base_name = name + self._graph = ops.get_default_graph() + + # Add to the model any layers passed to the constructor. + if layers: + for layer in layers: + self.add(layer) + + def add(self, layer): + """Adds a layer instance on top of the layer stack. + + Arguments: + layer: layer instance. + + Raises: + TypeError: If `layer` is not a layer instance. + ValueError: In case the `layer` argument does not + know its input shape. + ValueError: In case the `layer` argument has + multiple output tensors, or is already connected + somewhere else (forbidden in `Sequential` models). + """ + if not isinstance(layer, (base_layer.Layer, base_layer.TFBaseLayer)): + raise TypeError('The added layer must be ' + 'an instance of class Layer. ' + 'Found: ' + str(layer)) + if not self.outputs: + # First layer in model: check that it is an input layer. + if not isinstance(layer, InputLayer): + # Create an input layer. + # First, we need to infer its expected input shape and dtype. + if isinstance(layer, (Model, Sequential)): + # We were passed a model as first layer. + # This requires a specific way to figure out the + # input shape and dtype. + if not layer.layers: + raise ValueError('Cannot add an empty model ' + 'to a `Sequential` model.') + # In case of nested models: recover the first layer + # of the deepest model to infer input shape and dtype. + first_layer = layer.layers[0] + while isinstance(first_layer, (Model, Sequential)): + first_layer = first_layer.layers[0] + batch_shape = first_layer._batch_input_shape + dtype = first_layer.dtype + else: + # We were passed a regular layer, and it should + # know about its input shape. Otherwise, that's an error. + if not hasattr(layer, '_batch_input_shape'): + raise ValueError('The first layer in a ' + 'Sequential model must ' + 'get an `input_shape` argument.') + batch_shape = layer._batch_input_shape + dtype = layer.dtype + # Instantiate the input layer. + x = Input( + batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input') + # This will build the current layer + # and create the node connecting the current layer + # to the input layer we just created. + layer(x) + + if len(layer._inbound_nodes[-1].output_tensors) != 1: + raise ValueError('All layers in a Sequential model ' + 'should have a single output tensor. ' + 'For multi-output layers, ' + 'use the functional API.') + + self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] + self.inputs = network.get_source_inputs(self.outputs[0]) + + # We create an input node, which we will keep updated + # as we add more layers + base_layer.Node( + outbound_layer=self, + inbound_layers=[], + node_indices=[], + tensor_indices=[], + input_tensors=self.inputs, + output_tensors=self.outputs) + else: + output_tensor = layer(self.outputs[0]) + if isinstance(output_tensor, list): + raise TypeError('All layers in a Sequential model ' + 'should have a single output tensor. ' + 'For multi-output layers, ' + 'use the functional API.') + self.outputs = [output_tensor] + # update self._inbound_nodes + self._inbound_nodes[0].output_tensors = self.outputs + self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] + + self._layers.append(layer) + self.built = False + + def pop(self): + """Removes the last layer in the model. + + Raises: + TypeError: if there are no layers in the model. + """ + if not self.layers: + raise TypeError('There are no layers in the model.') + + self.layers.pop() + if not self.layers: + self.outputs = [] + self._inbound_nodes = [] + self._outbound_nodes = [] + else: + self.layers[-1]._outbound_nodes = [] + self.outputs = [self.layers[-1].output] + # update self._inbound_nodes + self._inbound_nodes[0].output_tensors = self.outputs + self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] + self.built = False + + def get_layer(self, name=None, index=None): + """Retrieve a layer that is part of the model. + + Returns a layer based on either its name (unique) + or its index in the graph. Indices are based on + order of horizontal graph traversal (bottom-up). + + Arguments: + name: string, name of layer. + index: integer, index of layer. + + Returns: + A layer instance. + """ + if not self.built: + self.build() + return self.model.get_layer(name, index) + + def call(self, inputs, **kwargs): + if not self.built: + self.build() + return self.model.call(inputs, **kwargs) + + def build(self, input_shape=None): + if not self.inputs or not self.outputs: + raise TypeError('Sequential model cannot be built: model is empty.' + ' Add some layers first.') + # actually create the model + self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model') + self.model.trainable = self.trainable + + # mirror model attributes + self.supports_masking = self.model.supports_masking + self._output_mask_cache = self.model._output_mask_cache + self._output_tensor_cache = self.model._output_tensor_cache + self._output_shape_cache = self.model._output_shape_cache + self._input_layers = self.model._input_layers + self._output_layers = self.model._output_layers + self._input_coordinates = self.model._input_coordinates + self._output_coordinates = self.model._output_coordinates + self._nodes_by_depth = self.model._nodes_by_depth + self._network_nodes = self.model._network_nodes + self.output_names = self.model.output_names + self.input_names = self.model.input_names + self._feed_input_names = self.model._feed_input_names + self._feed_inputs = self.model._feed_inputs + + # Make sure child model callbacks + # will call the parent Sequential model. + self.model.callback_model = self + + self.built = True + + @property + def uses_learning_phase(self): + if not self.built: + self.build() + return self.model.uses_learning_phase + + def _gather_list_attr(self, attr): + all_attrs = [] + for layer in self.layers: + all_attrs += getattr(layer, attr, []) + return all_attrs + + def _make_train_function(self): + self.model._make_train_function() + + def _make_test_function(self): + self.model._make_test_function() + + def _make_predict_function(self): + self.model._make_predict_function() + + @property + def trainable(self): + return self._trainable + + @trainable.setter + def trainable(self, value): + if self.model: + self.model.trainable = value + self._trainable = value + + @property + def trainable_weights(self): + if not self.trainable: + return [] + return self._gather_list_attr('trainable_weights') + + @property + def non_trainable_weights(self): + weights = self._gather_list_attr('non_trainable_weights') + if not self.trainable: + trainable_weights = self._gather_list_attr('trainable_weights') + return trainable_weights + weights + return weights + + @property + def regularizers(self): + if not self.built: + self.build() + return self.model.regularizers + + def get_weights(self): + """Retrieves the weights of the model. + + Returns: + A flat list of Numpy arrays + (one array per model weight). + """ + if not self.built: + self.build() + return self.model.get_weights() + + def set_weights(self, weights): + """Sets the weights of the model. + + Arguments: + weights: Should be a list + of Numpy arrays with shapes and types matching + the output of `model.get_weights()`. + """ + if not self.built: + self.build() + self.model.set_weights(weights) + + def load_weights(self, filepath, by_name=False): + if h5py is None: + raise ImportError('`load_weights` requires h5py.') + f = h5py.File(filepath, mode='r') + if 'layer_names' not in f.attrs and 'model_weights' in f: + f = f['model_weights'] + layers = self.layers + if by_name: + saving.load_weights_from_hdf5_group_by_name(f, layers) + else: + saving.load_weights_from_hdf5_group(f, layers) + if hasattr(f, 'close'): + f.close() + + def save_weights(self, filepath, overwrite=True): + if h5py is None: + raise ImportError('`save_weights` requires h5py.') + # If file exists and should not be overwritten: + if not overwrite and os.path.isfile(filepath): + proceed = ask_to_proceed_with_overwrite(filepath) + if not proceed: + return + layers = self.layers + f = h5py.File(filepath, 'w') + saving.save_weights_to_hdf5_group(f, layers) + f.flush() + f.close() + + def compile(self, + optimizer, + loss, + metrics=None, + sample_weight_mode=None, + weighted_metrics=None, + target_tensors=None, + **kwargs): + """Configures the model for training. + + Arguments: + optimizer: String (name of optimizer) or optimizer object. + See [optimizers](/optimizers). + loss: String (name of objective function) or objective function. + See [losses](/losses). + If the model has multiple outputs, you can use a different loss + on each output by passing a dictionary or a list of losses. + The loss value that will be minimized by the model + will then be the sum of all individual losses. + metrics: List of metrics to be evaluated by the model + during training and testing. + Typically you will use `metrics=['accuracy']`. + To specify different metrics for different outputs of a + multi-output model, you could also pass a dictionary, + such as `metrics={'output_a': 'accuracy'}`. + sample_weight_mode: If you need to do timestep-wise + sample weighting (2D weights), set this to `"temporal"`. + `None` defaults to sample-wise weights (1D). + If the model has multiple outputs, you can use a different + `sample_weight_mode` on each output by passing a + dictionary or a list of modes. + weighted_metrics: list of metrics to be evaluated and weighted + by `sample_weight` or `class_weight` during training and testing. + target_tensors: By default, Keras will create a placeholder for the + model's target, which will be fed with the target data during + training. If instead you would like to use your own + target tensor (in turn, Keras will not expect external + Numpy data for these targets at training time), you + can specify them via the `target_tensors` argument. + It should be a single tensor + (for a single-output `Sequential` model). + **kwargs: These arguments are passed into `tf.Session.run`. + + Example: + ```python + model = Sequential() + model.add(Dense(32, input_shape=(500,))) + model.add(Dense(10, activation='softmax')) + model.compile(optimizer='rmsprop', + loss='categorical_crossentropy', + metrics=['accuracy']) + ``` + """ + # create the underlying model + self.build() + # call compile method of Model class + self.model.compile( + optimizer, + loss, + metrics=metrics, + sample_weight_mode=sample_weight_mode, + weighted_metrics=weighted_metrics, + target_tensors=target_tensors, + **kwargs) + self.optimizer = self.model.optimizer + self.loss = self.model.loss + self.metrics = self.model.metrics + self.loss_weights = self.model.loss_weights + self.sample_weight_mode = self.model.sample_weight_mode + self.weighted_metrics = self.model.weighted_metrics + self.targets = self.model.targets + self.metrics_tensors = self.model.metrics_tensors + self.metrics_names = self.model.metrics_names + self.sample_weights = self.model.sample_weights + self.total_loss = self.model.total_loss + + def fit(self, + x=None, + y=None, + batch_size=None, + epochs=1, + verbose=1, + callbacks=None, + validation_split=0., + validation_data=None, + shuffle=True, + class_weight=None, + sample_weight=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None, + **kwargs): + """Trains the model for a fixed number of epochs. + + Arguments: + x: Numpy array of training data. + If the input layer in the model is named, you can also pass a + dictionary mapping the input name to a Numpy array. + `x` can be `None` (default) if feeding from + TensorFlow data tensors. + y: Numpy array of target (label) data. + If the output layer in the model is named, you can also pass a + dictionary mapping the output name to a Numpy array. + `y` can be `None` (default) if feeding from + TensorFlow data tensors. + batch_size: Integer or `None`. + Number of samples per gradient update. + If unspecified, it will default to 32. + epochs: Integer. Number of epochs to train the model. + An epoch is an iteration over the entire `x` and `y` + data provided. + Note that in conjunction with `initial_epoch`, + `epochs` is to be understood as "final epoch". + The model is not trained for a number of iterations + given by `epochs`, but merely until the epoch + of index `epochs` is reached. + verbose: 0, 1, or 2. Verbosity mode. + 0 = silent, 1 = progress bar, 2 = one line per epoch. + callbacks: List of `keras.callbacks.Callback` instances. + List of callbacks to apply during training. + See [callbacks](/callbacks). + validation_split: Float between 0 and 1: + Fraction of the training data to be used as validation data. + The model will set apart this fraction of the training data, + will not train on it, and will evaluate + the loss and any model metrics + on this data at the end of each epoch. + The validation data is selected from the last samples + in the `x` and `y` data provided, before shuffling. + validation_data: tuple `(x_val, y_val)` or tuple + `(x_val, y_val, val_sample_weights)` on which to evaluate + the loss and any model metrics at the end of each epoch. + The model will not be trained on this data. + This will override `validation_split`. + shuffle: Boolean (whether to shuffle the training data + before each epoch) or str (for 'batch'). + 'batch' is a special option for dealing with the + limitations of HDF5 data; it shuffles in batch-sized chunks. + Has no effect when `steps_per_epoch` is not `None`. + class_weight: Optional dictionary mapping class indices (integers) + to a weight (float) value, used for weighting the loss function + (during training only). + This can be useful to tell the model to + "pay more attention" to samples from + an under-represented class. + sample_weight: Optional Numpy array of weights for + the training samples, used for weighting the loss function + (during training only). You can either pass a flat (1D) + Numpy array with the same length as the input samples + (1:1 mapping between weights and samples), + or in the case of temporal data, + you can pass a 2D array with shape + `(samples, sequence_length)`, + to apply a different weight to every timestep of every sample. + In this case you should make sure to specify + `sample_weight_mode="temporal"` in `compile()`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run). + steps_per_epoch: Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. When training with input tensors such as + TensorFlow data tensors, the default `None` is equal to + the number of unique samples in your dataset divided by + the batch size, or 1 if that cannot be determined. + validation_steps: Only relevant if `steps_per_epoch` + is specified. Total number of steps (batches of samples) + to validate before stopping. + **kwargs: Used for backwards compatibility support. + + Returns: + A `History` object. Its `History.history` attribute is + a record of training loss values and metrics values + at successive epochs, as well as validation loss values + and validation metrics values (if applicable). + + Raises: + RuntimeError: If the model was never compiled. + ValueError: In case of mismatch between the provided input data + and what the model expects. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.fit( + x, + y, + batch_size=batch_size, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + validation_split=validation_split, + validation_data=validation_data, + shuffle=shuffle, + class_weight=class_weight, + sample_weight=sample_weight, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) + + def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None): + """Computes the loss on some input data, batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + batch_size: integer. Number of samples per gradient update. + verbose: verbosity mode, 0 or 1. + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.evaluate( + x, + y, + batch_size=batch_size, + verbose=verbose, + sample_weight=sample_weight) + + def predict(self, x, batch_size=32, verbose=0): + """Generates output predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: the input data, as a Numpy array. + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A Numpy array of predictions. + """ + if not self.built: + self.build() + return self.model.predict(x, batch_size=batch_size, verbose=verbose) + + def predict_on_batch(self, x): + """Returns predictions for a single batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + + Returns: + A Numpy array of predictions. + """ + if not self.built: + self.build() + return self.model.predict_on_batch(x) + + def train_on_batch(self, x, y, class_weight=None, sample_weight=None): + """Single gradient update over one batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + class_weight: dictionary mapping classes to a weight value, + used for scaling the loss function (during training only). + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar training loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.train_on_batch( + x, y, sample_weight=sample_weight, class_weight=class_weight) + + def test_on_batch(self, x, y, sample_weight=None): + """Evaluates the model over a single batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.test_on_batch(x, y, sample_weight=sample_weight) + + def predict_proba(self, x, batch_size=32, verbose=0): + """Generates class probability predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A Numpy array of probability predictions. + """ + preds = self.predict(x, batch_size, verbose) + if preds.min() < 0. or preds.max() > 1.: + logging.warning('Network returning invalid probability values. ' + 'The last layer might not normalize predictions ' + 'into probabilities ' + '(like softmax or sigmoid would).') + return preds + + def predict_classes(self, x, batch_size=32, verbose=0): + """Generate class predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A numpy array of class predictions. + """ + proba = self.predict(x, batch_size=batch_size, verbose=verbose) + if proba.shape[-1] > 1: + return proba.argmax(axis=-1) + else: + return (proba > 0.5).astype('int32') + + def fit_generator(self, + generator, + steps_per_epoch=None, + epochs=1, + verbose=1, + callbacks=None, + validation_data=None, + validation_steps=None, + class_weight=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + shuffle=True, + initial_epoch=0, + **kwargs): + """Fits the model on data generated batch-by-batch by a Python generator. + + The generator is run in parallel to the model, for efficiency. + For instance, this allows you to do real-time data augmentation + on images on CPU in parallel to training your model on GPU. + + Arguments: + generator: A generator. + The output of the generator must be either + - a tuple (inputs, targets) + - a tuple (inputs, targets, sample_weights). + All arrays should contain the same number of samples. + The generator is expected to loop over its data + indefinitely. An epoch finishes when `steps_per_epoch` + batches have been seen by the model. + steps_per_epoch: Total number of steps (batches of samples) + to yield from `generator` before declaring one epoch + finished and starting the next epoch. It should typically + be equal to the number of samples of your dataset + divided by the batch size. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + epochs: Integer, total number of iterations on the data. + Note that in conjunction with initial_epoch, the parameter + epochs is to be understood as "final epoch". The model is + not trained for n steps given by epochs, but until the + epoch epochs is reached. + verbose: Verbosity mode, 0, 1, or 2. + callbacks: List of callbacks to be called during training. + validation_data: This can be either + - A generator for the validation data + - A tuple (inputs, targets) + - A tuple (inputs, targets, sample_weights). + validation_steps: Only relevant if `validation_data` + is a generator. + Number of steps to yield from validation generator + at the end of every epoch. It should typically + be equal to the number of samples of your + validation dataset divided by the batch size. + Optional for `Sequence`: if unspecified, will use + the `len(validation_data)` as a number of steps. + class_weight: Dictionary mapping class indices to a weight + for the class. + max_queue_size: Maximum size for the generator queue + workers: Maximum number of processes to spin up + use_multiprocessing: If True, use process based threading. + Note that because + this implementation relies on multiprocessing, + you should not pass + non picklable arguments to the generator + as they can't be passed + easily to children processes. + shuffle: Whether to shuffle the order of the batches at + the beginning of each epoch. Only used with instances + of `Sequence` (keras.utils.Sequence). + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + **kwargs: support for legacy arguments. + + Returns: + A `History` object. + + Raises: + RuntimeError: if the model was never compiled. + ValueError: In case the generator yields + data in an invalid format. + + Example: + + ```python + def generate_arrays_from_file(path): + while 1: + f = open(path) + for line in f: + # create Numpy arrays of input data + # and labels, from each line in the file + x, y = process_line(line) + yield (x, y) + f.close() + + model.fit_generator(generate_arrays_from_file('/my_file.txt'), + steps_per_epoch=1000, epochs=10) + ``` + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.fit_generator( + generator, + steps_per_epoch, + epochs, + verbose=verbose, + callbacks=callbacks, + validation_data=validation_data, + validation_steps=validation_steps, + class_weight=class_weight, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle, + initial_epoch=initial_epoch) + + def evaluate_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + **kwargs): + """Evaluates the model on a data generator. + + The generator should return the same kind of data + as accepted by `test_on_batch`. + + Arguments: + generator: Generator yielding tuples (inputs, targets) + or (inputs, targets, sample_weights) + steps: Total number of steps (batches of samples) + to yield from `generator` before stopping. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + max_queue_size: maximum size for the generator queue + workers: maximum number of processes to spin up + use_multiprocessing: if True, use process based threading. + Note that because this implementation + relies on multiprocessing, you should not pass + non picklable arguments to the generator + as they can't be passed easily to children processes. + **kwargs: support for legacy arguments. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + ValueError: In case the generator yields + data in an invalid format. + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.evaluate_generator( + generator, + steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) + + def predict_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0, + **kwargs): + """Generates predictions for the input samples from a data generator. + + The generator should return the same kind of data as accepted by + `predict_on_batch`. + + Arguments: + generator: generator yielding batches of input samples. + steps: Total number of steps (batches of samples) + to yield from `generator` before stopping. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + max_queue_size: maximum size for the generator queue + workers: maximum number of processes to spin up + use_multiprocessing: if True, use process based threading. + Note that because this implementation + relies on multiprocessing, you should not pass + non picklable arguments to the generator + as they can't be passed easily to children processes. + verbose: verbosity mode, 0 or 1. + **kwargs: support for legacy arguments. + + Returns: + A Numpy array of predictions. + + Raises: + ValueError: In case the generator yields + data in an invalid format. + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + self.build() + return self.model.predict_generator( + generator, + steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) + + def get_config(self): + config = [] + for layer in self.layers: + config.append({ + 'class_name': layer.__class__.__name__, + 'config': layer.get_config() + }) + return copy.deepcopy(config) + + @classmethod + def from_config(cls, config, custom_objects=None): + model = cls() + for conf in config: + layer = layer_module.deserialize(conf, custom_objects=custom_objects) + model.add(layer) + return model diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential_test.py b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py new file mode 100644 index 0000000000000000000000000000000000000000..166634bd8219b831ce212ba983a4ab695b00c3b7 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py @@ -0,0 +1,152 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests specific to `Sequential` model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test + + +class TestSequential(test.TestCase): + """Most Sequential model API tests are covered in `training_test.py`. + """ + + def test_basic_methods(self): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1, input_dim=2)) + model.add(keras.layers.Dropout(0.3, name='dp')) + model.add(keras.layers.Dense(2, kernel_regularizer='l2', + kernel_constraint='max_norm')) + model.build() + self.assertEqual(model.state_updates, model.model.state_updates) + self.assertEqual(model.get_layer(name='dp').name, 'dp') + + def test_sequential_pop(self): + num_hidden = 5 + input_dim = 3 + batch_size = 5 + num_classes = 2 + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + model.compile(loss='mse', optimizer='sgd') + x = np.random.random((batch_size, input_dim)) + y = np.random.random((batch_size, num_classes)) + model.fit(x, y, epochs=1) + model.pop() + self.assertEqual(len(model.layers), 1) + self.assertEqual(model.output_shape, (None, num_hidden)) + model.compile(loss='mse', optimizer='sgd') + y = np.random.random((batch_size, num_hidden)) + model.fit(x, y, epochs=1) + + # Test popping single-layer model + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.pop() + self.assertEqual(len(model.layers), 0) + self.assertEqual(len(model.outputs), 0) + + # Invalid use case + model = keras.models.Sequential() + with self.assertRaises(TypeError): + model.pop() + + def test_invalid_use_cases(self): + with self.test_session(): + # Added objects must be layer instances + with self.assertRaises(TypeError): + model = keras.models.Sequential() + model.add(None) + + # Added layers must have an inputs shape + with self.assertRaises(ValueError): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1)) + + # Added layers cannot have multiple outputs + class MyLayer(keras.layers.Layer): + + def call(self, inputs): + return [3 * inputs, 2 * inputs] + + def compute_output_shape(self, input_shape): + return [input_shape, input_shape] + + with self.assertRaises(ValueError): + model = keras.models.Sequential() + model.add(MyLayer(input_shape=(3,))) + with self.assertRaises(TypeError): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1, input_dim=1)) + model.add(MyLayer()) + + # Building empty model + model = keras.models.Sequential() + with self.assertRaises(TypeError): + model.build() + + def test_nested_sequential_trainability(self): + input_dim = 20 + num_units = 10 + num_classes = 2 + + inner_model = keras.models.Sequential() + inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) + + model = keras.models.Sequential() + model.add(inner_model) + model.add(keras.layers.Dense(num_classes)) + + self.assertEqual(len(model.trainable_weights), 4) + inner_model.trainable = False + self.assertEqual(len(model.trainable_weights), 2) + inner_model.trainable = True + self.assertEqual(len(model.trainable_weights), 4) + + def test_sequential_update_disabling(self): + val_a = np.random.random((10, 4)) + val_out = np.random.random((10, 4)) + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.BatchNormalization(input_shape=(4,))) + + model.trainable = False + assert not model.updates + + model.compile('sgd', 'mse') + assert not model.updates + assert not model.model.updates + + x1 = model.predict(val_a) + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + self.assertAllClose(x1, x2, atol=1e-7) + + model.trainable = True + model.compile('sgd', 'mse') + assert model.updates + assert model.model.updates + + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + assert np.abs(np.sum(x1 - x2)) > 1e-5 diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py deleted file mode 100644 index 8354a2b8fd7c0182c1daaa7e8fa8390da6038d0b..0000000000000000000000000000000000000000 --- a/tensorflow/python/keras/_impl/keras/engine/topology.py +++ /dev/null @@ -1,1603 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -# pylint: disable=protected-access -"""Base layer code and base model (Network) code. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import copy -import json -import os - -import numpy as np -from six.moves import zip # pylint: disable=redefined-builtin - -from tensorflow.python.eager import context -from tensorflow.python.framework import tensor_shape -from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import constraints -from tensorflow.python.keras._impl.keras import initializers -from tensorflow.python.keras._impl.keras import regularizers -from tensorflow.python.keras._impl.keras.utils import conv_utils -from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite -from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary -from tensorflow.python.layers import base as tf_base_layers -from tensorflow.python.layers import network as tf_network -from tensorflow.python.layers import utils as tf_layers_util -from tensorflow.python.platform import tf_logging as logging - - -# pylint: disable=g-import-not-at-top -try: - import h5py -except ImportError: - h5py = None - -try: - import yaml -except ImportError: - yaml = None -# pylint: enable=g-import-not-at-top - -# pylint: disable=invalid-name -InputSpec = tf_base_layers.InputSpec -Node = tf_base_layers.Node -TFBaseLayer = tf_base_layers.Layer -# pylint: enable=invalid-name - - -class Layer(tf_base_layers.Layer): - """Abstract base layer class. - - # Properties - name: String, must be unique within a model. - input_spec: List of InputSpec class instances - each entry describes one required input: - - ndim - - dtype - A layer with `n` input tensors must have - an `input_spec` of length `n`. - trainable: Boolean, whether the layer weights - will be updated during training. - uses_learning_phase: Whether any operation - of the layer uses `K.in_training_phase()` - or `K.in_test_phase()`. - input_shape: Shape tuple. Provided for convenience, - but note that there may be cases in which this - attribute is ill-defined (e.g. a shared layer - with multiple input shapes), in which case - requesting `input_shape` will raise an Exception. - Prefer using `layer.get_input_shape_for(input_shape)`, - or `layer.get_input_shape_at(node_index)`. - output_shape: Shape tuple. See above. - inbound_nodes: List of nodes. - outbound_nodes: List of nodes. - input, output: Input/output tensor(s). Note that if the layer is used - more than once (shared layer), this is ill-defined - and will raise an exception. In such cases, use - `layer.get_input_at(node_index)`. - input_mask, output_mask: Same as above, for masks. - trainable_weights: List of variables. - non_trainable_weights: List of variables. - weights: The concatenation of the lists trainable_weights and - non_trainable_weights (in this order). - - # Methods - call(x, mask=None): Where the layer's logic lives. - __call__(x, mask=None): Wrapper around the layer logic (`call`). - If x is a Keras tensor: - - Connect current layer with last layer from tensor: - `self._add_inbound_node(last_layer)` - - Add layer to tensor history - If layer is not built: - - Build from inputs shape - get_weights() - set_weights(weights) - get_config() - count_params() - compute_output_shape(input_shape) - compute_mask(x, mask) - get_input_at(node_index) - get_output_at(node_index) - get_input_shape_at(node_index) - get_output_shape_at(node_index) - get_input_mask_at(node_index) - get_output_mask_at(node_index) - - # Class Methods - from_config(config) - - # Internal methods: - build(input_shape) - _add_inbound_node(layer, index=0) - """ - - def __init__(self, **kwargs): - # These properties should be set by the user via keyword arguments. - # note that 'dtype', 'input_shape' and 'batch_input_shape' - # are only applicable to input layers: do not pass these keywords - # to non-input layers. - allowed_kwargs = { - 'activity_regularizer', - 'input_shape', - 'batch_input_shape', - 'batch_size', - 'dtype', - 'name', - 'trainable', - 'weights', - } - # Validate optional keyword arguments. - for kwarg in kwargs: - if kwarg not in allowed_kwargs: - raise TypeError('Keyword argument not understood:', kwarg) - - # Get layer name. - name = kwargs.get('name') - - # Get `trainable` status. - trainable = kwargs.get('trainable', True) - - # Get `dtype`. - dtype = kwargs.get('dtype') - if dtype is None: - dtype = K.floatx() - - # Call super, which will set all properties common to Keras layers - # and core TF layers. - super(Layer, self).__init__( - name=name, dtype=dtype, trainable=trainable, - activity_regularizer=kwargs.get('activity_regularizer')) - - # Add properties that are Keras-only for now. - self.supports_masking = False - - # Manage input shape information if passed. - if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: - # In this case we will later create an input layer - # to insert before the current layer - if 'batch_input_shape' in kwargs: - batch_input_shape = tuple(kwargs['batch_input_shape']) - elif 'input_shape' in kwargs: - if 'batch_size' in kwargs: - batch_size = kwargs['batch_size'] - else: - batch_size = None - batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) - self._batch_input_shape = batch_input_shape - - # Manage initial weight values if passed. - if 'weights' in kwargs: - self._initial_weights = kwargs['weights'] - else: - self._initial_weights = None - - def add_weight(self, - name, - shape, - dtype=None, - initializer=None, - regularizer=None, - trainable=True, - constraint=None): - """Adds a weight variable to the layer. - - Arguments: - name: String, the name for the weight variable. - shape: The shape tuple of the weight. - dtype: The dtype of the weight. - initializer: An Initializer instance (callable). - regularizer: An optional Regularizer instance. - trainable: A boolean, whether the weight should - be trained via backprop or not (assuming - that the layer itself is also trainable). - constraint: An optional Constraint instance. - - Returns: - The created weight variable. - """ - if dtype is None: - dtype = K.floatx() - weight = self.add_variable(name, shape, - dtype=dtype, - initializer=initializers.get(initializer), - regularizer=regularizers.get(regularizer), - constraint=constraints.get(constraint), - trainable=trainable) - return weight - - def call(self, inputs, **kwargs): # pylint: disable=unused-argument - """This is where the layer's logic lives. - - Arguments: - inputs: Input tensor, or list/tuple of input tensors. - **kwargs: Additional keyword arguments. - - Returns: - A tensor or list/tuple of tensors. - """ - return inputs - - def __call__(self, inputs, **kwargs): - """Wrapper around self.call(), for handling internal references. - - If a Keras tensor is passed: - - We call self._add_inbound_node(). - - If necessary, we `build` the layer to match - the shape of the input(s). - - We update the _keras_history of the output tensor(s) - with the current layer. - This is done as part of _add_inbound_node(). - - Arguments: - inputs: Can be a tensor or list/tuple of tensors. - **kwargs: Additional keyword arguments to be passed to `call()`. - - Returns: - Output of the layer's `call` method. - - Raises: - ValueError: in case the layer is missing shape information - for its `build` call. - """ - # Actually call the layer (optionally building it). - output = super(Layer, self).__call__(inputs, **kwargs) - if context.in_eager_mode(): - return output - - # Update learning phase info. - output_tensors = _to_list(output) - uses_lp = any( - [getattr(x, '_uses_learning_phase', False) for x in _to_list(inputs)]) - uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp - for i in range(len(output_tensors)): - output_tensors[i]._uses_learning_phase = getattr( - output_tensors[i], '_uses_learning_phase', False) or uses_lp - - # Optionally load weight values that were specified at layer instantiation. - if hasattr(self, '_initial_weights') and self._initial_weights is not None: - self.set_weights(self._initial_weights) - del self._initial_weights - return output - - def compute_output_shape(self, input_shape): - """Computes the output shape of the layer. - - Assumes that the layer will be built - to match that input shape provided. - - Arguments: - input_shape: Shape tuple (tuple of integers) - or list of shape tuples (one per output tensor of the layer). - Shape tuples can include None for free dimensions, - instead of an integer. - - Returns: - An input shape tuple. - """ - logging.warning( - 'All custom layers should implement the ' - '`compute_output_shape` method. This layer (' + self.name + ') ' - 'is relying on the base `Layer.compute_output_shape` implementation, ' - 'which will start raising a `NotImplementedError` ' - 'as of July 1st, 2018.') - return input_shape - - def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument - """Computes an output mask tensor. - - Arguments: - inputs: Tensor or list of tensors. - mask: Tensor or list of tensors. - - Returns: - None or a tensor (or list of tensors, - one per output tensor of the layer). - """ - if not self.supports_masking: - if mask is not None: - if isinstance(mask, list): - if any(m is not None for m in mask): - raise TypeError('Layer ' + self.name + ' does not support masking, ' - 'but was passed an input_mask: ' + str(mask)) - else: - raise TypeError('Layer ' + self.name + ' does not support masking, ' - 'but was passed an input_mask: ' + str(mask)) - # masking not explicitly supported: return None as mask - return None - # if masking is explicitly supported, by default - # carry over the input mask - return mask - - def get_input_mask_at(self, node_index): - """Retrieves the input mask tensor(s) of a layer at a given node. - - Arguments: - node_index: Integer, index of the node - from which to retrieve the attribute. - E.g. `node_index=0` will correspond to the - first time the layer was called. - - Returns: - A mask tensor - (or list of tensors if the layer has multiple inputs). - """ - inputs = self.get_input_at(node_index) - if isinstance(inputs, list): - return [getattr(x, '_keras_mask', None) for x in inputs] - else: - return getattr(inputs, '_keras_mask', None) - - def get_output_mask_at(self, node_index): - """Retrieves the output mask tensor(s) of a layer at a given node. - - Arguments: - node_index: Integer, index of the node - from which to retrieve the attribute. - E.g. `node_index=0` will correspond to the - first time the layer was called. - - Returns: - A mask tensor - (or list of tensors if the layer has multiple outputs). - """ - output = self.get_output_at(node_index) - if isinstance(output, list): - return [getattr(x, '_keras_mask', None) for x in output] - else: - return getattr(output, '_keras_mask', None) - - @property - def input_mask(self): - """Retrieves the input mask tensor(s) of a layer. - - Only applicable if the layer has exactly one inbound node, - i.e. if it is connected to one incoming layer. - - Returns: - Input mask tensor (potentially None) or list of input - mask tensors. - - Raises: - AttributeError: if the layer is connected to - more than one incoming layers. - """ - inputs = self.input - if isinstance(inputs, list): - return [getattr(x, '_keras_mask', None) for x in inputs] - else: - return getattr(inputs, '_keras_mask', None) - - @property - def output_mask(self): - """Retrieves the output mask tensor(s) of a layer. - - Only applicable if the layer has exactly one inbound node, - i.e. if it is connected to one incoming layer. - - Returns: - Output mask tensor (potentially None) or list of output - mask tensors. - - Raises: - AttributeError: if the layer is connected to - more than one incoming layers. - """ - output = self.output - if isinstance(output, list): - return [getattr(x, '_keras_mask', None) for x in output] - else: - return getattr(output, '_keras_mask', None) - - def set_weights(self, weights): - """Sets the weights of the layer, from Numpy arrays. - - Arguments: - weights: a list of Numpy arrays. The number - of arrays and their shape must match - number of the dimensions of the weights - of the layer (i.e. it should match the - output of `get_weights`). - - Raises: - ValueError: If the provided weights list does not match the - layer's specifications. - """ - params = self.weights - if len(params) != len(weights): - raise ValueError('You called `set_weights(weights)` on layer "' + - self.name + '" with a weight list of length ' + - str(len(weights)) + ', but the layer was expecting ' + - str(len(params)) + ' weights. Provided weights: ' + - str(weights)[:50] + '...') - if not params: - return - weight_value_tuples = [] - param_values = K.batch_get_value(params) - for pv, p, w in zip(param_values, params, weights): - if pv.shape != w.shape: - raise ValueError('Layer weight shape ' + str(pv.shape) + - ' not compatible with ' - 'provided weight shape ' + str(w.shape)) - weight_value_tuples.append((p, w)) - K.batch_set_value(weight_value_tuples) - - def get_weights(self): - """Returns the current weights of the layer. - - Returns: - Weights values as a list of numpy arrays. - """ - params = self.weights - return K.batch_get_value(params) - - def get_config(self): - """Returns the config of the layer. - - A layer config is a Python dictionary (serializable) - containing the configuration of a layer. - The same layer can be reinstantiated later - (without its trained weights) from this configuration. - - The config of a layer does not include connectivity - information, nor the layer class name. These are handled - by `Network` (one layer of abstraction above). - - Returns: - Python dictionary. - """ - config = {'name': self.name, 'trainable': self.trainable} - if hasattr(self, '_batch_input_shape'): - config['batch_input_shape'] = self._batch_input_shape - if hasattr(self, 'dtype'): - config['dtype'] = self.dtype - return config - - @classmethod - def from_config(cls, config): - """Creates a layer from its config. - - This method is the reverse of `get_config`, - capable of instantiating the same layer from the config - dictionary. It does not handle layer connectivity - (handled by Network), nor weights (handled by `set_weights`). - - Arguments: - config: A Python dictionary, typically the - output of get_config. - - Returns: - A layer instance. - """ - return cls(**config) - - @tf_base_layers.Layer.activity_regularizer.setter - def activity_regularizer(self, activity_regularizer): - self._activity_regularizer = activity_regularizer - - -class InputLayer(tf_network.InputLayer, Layer): - """Layer to be used as an entry point into a graph. - - It can either wrap an existing tensor (pass an `input_tensor` argument) - or create its a placeholder tensor (pass argument `input_shape`. - - Arguments: - input_shape: Shape tuple, not including the batch axis. - batch_size: Optional input batch size (integer or None). - dtype: Datatype of the input. - input_tensor: Optional tensor to use as layer input - instead of creating a placeholder. - sparse: Boolean, whether the placeholder created - is meant to be sparse. - name: Name of the layer (string). - """ - - def __init__(self, - input_shape=None, - batch_size=None, - dtype=None, - input_tensor=None, - sparse=False, - name=None, - **kwargs): - if 'batch_input_shape' in kwargs: - batch_input_shape = kwargs.pop('batch_input_shape') - if input_shape and batch_input_shape: - raise ValueError('Only provide the input_shape OR ' - 'batch_input_shape argument to ' - 'InputLayer, not both at the same time.') - batch_size = batch_input_shape[0] - input_shape = batch_input_shape[1:] - if kwargs: - raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) - - if not name: - prefix = 'input' - name = prefix + '_' + str(K.get_uid(prefix)) - - if not dtype: - if input_tensor is None: - dtype = K.floatx() - else: - dtype = K.dtype(input_tensor) - super(InputLayer, self).__init__(input_shape=input_shape, - batch_size=batch_size, - dtype=dtype, - input_tensor=input_tensor, - sparse=sparse, - name=name) - - def get_config(self): - config = { - 'batch_input_shape': self._batch_input_shape, - 'dtype': self.dtype, - 'sparse': self.sparse, - 'name': self.name - } - return config - - -def Input( # pylint: disable=invalid-name - shape=None, - batch_size=None, - name=None, - dtype=None, - sparse=False, - tensor=None, - **kwargs): - """`Input()` is used to instantiate a Keras tensor. - - A Keras tensor is a tensor object from the underlying backend - (Theano or TensorFlow), which we augment with certain - attributes that allow us to build a Keras model - just by knowing the inputs and outputs of the model. - - For instance, if a, b and c are Keras tensors, - it becomes possible to do: - `model = Model(input=[a, b], output=c)` - - The added Keras attribute is: - `_keras_history`: Last layer applied to the tensor. - the entire layer graph is retrievable from that layer, - recursively. - - Arguments: - shape: A shape tuple (integers), not including the batch size. - For instance, `shape=(32,)` indicates that the expected input - will be batches of 32-dimensional vectors. - batch_size: optional static batch size (integer). - name: An optional name string for the layer. - Should be unique in a model (do not reuse the same name twice). - It will be autogenerated if it isn't provided. - dtype: The data type expected by the input, as a string - (`float32`, `float64`, `int32`...) - sparse: A boolean specifying whether the placeholder - to be created is sparse. - tensor: Optional existing tensor to wrap into the `Input` layer. - If set, the layer will not create a placeholder tensor. - **kwargs: deprecated arguments support. - - Returns: - A tensor. - - Example: - - ```python - # this is a logistic regression in Keras - x = Input(shape=(32,)) - y = Dense(16, activation='softmax')(x) - model = Model(x, y) - ``` - - Raises: - ValueError: in case of invalid arguments. - """ - if 'batch_shape' in kwargs: - batch_shape = kwargs.pop('batch_shape') - if shape and batch_shape: - raise ValueError('Only provide the shape OR ' - 'batch_shape argument to ' - 'Input, not both at the same time.') - batch_size = batch_shape[0] - shape = batch_shape[1:] - if kwargs: - raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) - - if dtype is None: - dtype = K.floatx() - if not shape and tensor is None: - raise ValueError('Please provide to Input either a `shape`' - ' or a `tensor` argument. Note that ' - '`shape` does not include the batch ' - 'dimension.') - input_layer = InputLayer( - input_shape=shape, - batch_size=batch_size, - name=name, - dtype=dtype, - sparse=sparse, - input_tensor=tensor) - # Return tensor including `_keras_history`. - # Note that in this case train_output and test_output are the same pointer. - outputs = input_layer._inbound_nodes[0].output_tensors - if len(outputs) == 1: - return outputs[0] - else: - return outputs - - -class Network(tf_network.GraphNetwork, Layer): - """A Network is a directed acyclic graph of layers. - - It is the topological form of a "model". A Model - is simply a Network with added training routines. - - # Properties - name - inputs - outputs - input_layers - output_layers - input_spec (list of class instances) - each entry describes one required input: - - ndim - - dtype - trainable (boolean) - input_shape - output_shape - inbound_nodes: list of nodes - outbound_nodes: list of nodes - trainable_weights (list of variables) - non_trainable_weights (list of variables) - - # Methods - summary - get_layer - get_weights - set_weights - get_config - compute_output_shape - - # Class Methods - from_config - """ - - def __init__(self, inputs, outputs, name=None): - super(Network, self).__init__(inputs, outputs, name=name) - - self.supports_masking = False - # Fill in the output mask cache. - masks = [] - for x in self.inputs: - mask = x._keras_mask if hasattr(x, '_keras_mask') else None - masks.append(mask) - mask_cache_key = (tf_layers_util.object_list_uid(self.inputs) + '_' + - tf_layers_util.object_list_uid(masks)) - masks = [] - for x in self.outputs: - mask = x._keras_mask if hasattr(x, '_keras_mask') else None - masks.append(mask) - if len(masks) == 1: - mask = masks[0] - else: - mask = masks - self._output_mask_cache[mask_cache_key] = mask - - # Build self.input_names and self.output_names. - self.input_names = [] - self.output_names = [] - self._feed_input_names = [] - self._feed_inputs = [] - self._feed_input_shapes = [] - for i, layer in enumerate(self._input_layers): - self.input_names.append(layer.name) - if layer.is_placeholder: - self._feed_input_names.append(layer.name) - self._feed_input_shapes.append(K.int_shape(self.inputs[i])) - # layer.input gives an error in eager mode - if context.in_graph_mode(): - self._feed_inputs.append(layer.input) - for layer in self._output_layers: - self.output_names.append(layer.name) - - self._internal_input_shapes = [K.int_shape(x) for x in self.inputs] - self._internal_output_shapes = [K.int_shape(x) for x in self.outputs] - - @property - def uses_learning_phase(self): - return any( - [getattr(x, '_uses_learning_phase', False) for x in self.outputs]) - - @property - def stateful(self): - return any([(hasattr(layer, 'stateful') and layer.stateful) - for layer in self.layers]) - - def reset_states(self): - for layer in self.layers: - if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False): - layer.reset_states() - - @property - def state_updates(self): - """Returns the `updates` from all layers that are stateful. - - This is useful for separating training updates and - state updates, e.g. when we need to update a layer's internal state - during prediction. - - Returns: - A list of update ops. - """ - state_updates = [] - for layer in self.layers: - if getattr(layer, 'stateful', False): - if hasattr(layer, 'updates'): - state_updates += layer.updates - return state_updates - - def get_weights(self): - """Retrieves the weights of the model. - - Returns: - A flat list of Numpy arrays. - """ - weights = [] - for layer in self.layers: - weights += layer.weights - return K.batch_get_value(weights) - - def set_weights(self, weights): - """Sets the weights of the model. - - Arguments: - weights: A list of Numpy arrays with shapes and types matching - the output of `model.get_weights()`. - """ - tuples = [] - for layer in self.layers: - num_param = len(layer.weights) - layer_weights = weights[:num_param] - for sw, w in zip(layer.weights, layer_weights): - tuples.append((sw, w)) - weights = weights[num_param:] - K.batch_set_value(tuples) - - def compute_mask(self, inputs, mask): - inputs = _to_list(inputs) - if mask is None: - masks = [None for _ in range(len(inputs))] - else: - masks = _to_list(mask) - cache_key = ','.join([str(id(x)) for x in inputs]) - cache_key += '_' + ','.join([str(id(x)) for x in masks]) - if cache_key in self._output_mask_cache: - return self._output_mask_cache[cache_key] - else: - _, output_masks = self._run_internal_graph(inputs, masks) - return output_masks - - def get_config(self): - config = { - 'name': self.name, - } - node_conversion_map = {} - for layer in self.layers: - if issubclass(layer.__class__, Network): - # Networks start with a pre-existing node - # linking their input to output. - kept_nodes = 1 - else: - kept_nodes = 0 - for original_node_index, node in enumerate(layer._inbound_nodes): - node_key = tf_network._make_node_key(layer.name, - original_node_index) - if node_key in self._network_nodes: - node_conversion_map[node_key] = kept_nodes - kept_nodes += 1 - layer_configs = [] - for layer in self.layers: # From the earliest layers on. - layer_class_name = layer.__class__.__name__ - layer_config = layer.get_config() - filtered_inbound_nodes = [] - for original_node_index, node in enumerate(layer._inbound_nodes): - node_key = tf_network._make_node_key(layer.name, - original_node_index) - if node_key in self._network_nodes: - # The node is relevant to the model: - # add to filtered_inbound_nodes. - if node.arguments: - try: - json.dumps(node.arguments) - kwargs = node.arguments - except TypeError: - logging.warning( - 'Layer ' + layer.name + - ' was passed non-serializable keyword arguments: ' + - str(node.arguments) + '. They will not be included ' - 'in the serialized model (and thus will be missing ' - 'at deserialization time).') - kwargs = {} - else: - kwargs = {} - if node.inbound_layers: - node_data = [] - for i in range(len(node.inbound_layers)): - inbound_layer = node.inbound_layers[i] - node_index = node.node_indices[i] - tensor_index = node.tensor_indices[i] - node_key = tf_network._make_node_key(inbound_layer.name, - node_index) - new_node_index = node_conversion_map.get(node_key, 0) - node_data.append( - [inbound_layer.name, new_node_index, tensor_index, kwargs]) - filtered_inbound_nodes.append(node_data) - layer_configs.append({ - 'name': layer.name, - 'class_name': layer_class_name, - 'config': layer_config, - 'inbound_nodes': filtered_inbound_nodes, - }) - config['layers'] = layer_configs - - # Gather info about inputs and outputs. - model_inputs = [] - for i in range(len(self._input_layers)): - layer, node_index, tensor_index = self._input_coordinates[i] - node_key = tf_network._make_node_key(layer.name, - node_index) - if node_key not in self._network_nodes: - continue - new_node_index = node_conversion_map[node_key] - model_inputs.append([layer.name, new_node_index, tensor_index]) - config['input_layers'] = model_inputs - model_outputs = [] - for i in range(len(self._output_layers)): - layer, node_index, tensor_index = self._output_coordinates[i] - node_key = tf_network._make_node_key(layer.name, - node_index) - if node_key not in self._network_nodes: - continue - new_node_index = node_conversion_map[node_key] - model_outputs.append([layer.name, new_node_index, tensor_index]) - config['output_layers'] = model_outputs - return copy.deepcopy(config) - - @classmethod - def from_config(cls, config, custom_objects=None): - """Instantiates a Model from its config (output of `get_config()`). - - Arguments: - config: Model config dictionary. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A model instance. - - Raises: - ValueError: In case of improperly formatted config dict. - """ - # Layer instances created during - # the graph reconstruction process - created_layers = {} - - # Dictionary mapping layer instances to - # node data that specifies a layer call. - # It acts as a queue that maintains any unprocessed - # layer call until it becomes possible to process it - # (i.e. until the input tensors to the call all exist). - unprocessed_nodes = {} - - def add_unprocessed_node(layer, node_data): - if layer not in unprocessed_nodes: - unprocessed_nodes[layer] = [node_data] - else: - unprocessed_nodes[layer].append(node_data) - - def process_node(layer, node_data): - """Deserialize a node. - - Arguments: - layer: layer instance. - node_data: node config dict. - - Raises: - ValueError: In case of improperly formatted `node_data` dict. - """ - input_tensors = [] - for input_data in node_data: - inbound_layer_name = input_data[0] - inbound_node_index = input_data[1] - inbound_tensor_index = input_data[2] - if len(input_data) == 3: - kwargs = {} - elif len(input_data) == 4: - kwargs = input_data[3] - else: - raise ValueError('Improperly formatted model config.') - if inbound_layer_name not in created_layers: - add_unprocessed_node(layer, node_data) - return - inbound_layer = created_layers[inbound_layer_name] - if len(inbound_layer._inbound_nodes) <= inbound_node_index: - add_unprocessed_node(layer, node_data) - return - inbound_node = inbound_layer._inbound_nodes[inbound_node_index] - input_tensors.append(inbound_node.output_tensors[inbound_tensor_index]) - # Call layer on its inputs, thus creating the node - # and building the layer if needed. - if input_tensors: - if len(input_tensors) == 1: - layer(input_tensors[0], **kwargs) - else: - layer(input_tensors, **kwargs) - - def process_layer(layer_data): - """Deserialize a layer, then call it on appropriate inputs. - - Arguments: - layer_data: layer config dict. - - Raises: - ValueError: In case of improperly formatted `layer_data` dict. - """ - layer_name = layer_data['name'] - - # Instantiate layer. - from tensorflow.python.keras._impl.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top - - layer = deserialize_layer(layer_data, custom_objects=custom_objects) - created_layers[layer_name] = layer - - # Gather layer inputs. - inbound_nodes_data = layer_data['inbound_nodes'] - for node_data in inbound_nodes_data: - # We don't process nodes (i.e. make layer calls) - # on the fly because the inbound node may not yet exist, - # in case of layer shared at different topological depths - # (e.g. a model such as A(B(A(B(x))))) - add_unprocessed_node(layer, node_data) - - # First, we create all layers and enqueue nodes to be processed - for layer_data in config['layers']: - process_layer(layer_data) - # Then we process nodes in order of layer depth. - # Nodes that cannot yet be processed (if the inbound node - # does not yet exist) are re-enqueued, and the process - # is repeated until all nodes are processed. - while unprocessed_nodes: - for layer_data in config['layers']: - layer = created_layers[layer_data['name']] - if layer in unprocessed_nodes: - for node_data in unprocessed_nodes.pop(layer): - process_node(layer, node_data) - - name = config.get('name') - input_tensors = [] - output_tensors = [] - for layer_data in config['input_layers']: - layer_name, node_index, tensor_index = layer_data - assert layer_name in created_layers - layer = created_layers[layer_name] - layer_output_tensors = layer._inbound_nodes[node_index].output_tensors - input_tensors.append(layer_output_tensors[tensor_index]) - for layer_data in config['output_layers']: - layer_name, node_index, tensor_index = layer_data - assert layer_name in created_layers - layer = created_layers[layer_name] - layer_output_tensors = layer._inbound_nodes[node_index].output_tensors - output_tensors.append(layer_output_tensors[tensor_index]) - return cls(inputs=input_tensors, outputs=output_tensors, name=name) - - def save(self, filepath, overwrite=True, include_optimizer=True): - """Save the model to a single HDF5 file. - - The savefile includes: - - The model architecture, allowing to re-instantiate the model. - - The model weights. - - The state of the optimizer, allowing to resume training - exactly where you left off. - - This allows you to save the entirety of the state of a model - in a single file. - - Saved models can be reinstantiated via `keras.models.load_model`. - The model returned by `load_model` - is a compiled model ready to be used (unless the saved model - was never compiled in the first place). - - Arguments: - filepath: String, path to the file to save the weights to. - overwrite: Whether to silently overwrite any existing file at the - target location, or provide the user with a manual prompt. - include_optimizer: If True, save optimizer's state together. - - Example: - - ```python - from keras.models import load_model - - model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' - del model # deletes the existing model - - # returns a compiled model - # identical to the previous one - model = load_model('my_model.h5') - ``` - """ - from tensorflow.python.keras._impl.keras.models import save_model # pylint: disable=g-import-not-at-top - save_model(self, filepath, overwrite, include_optimizer) - - def save_weights(self, filepath, overwrite=True): - """Dumps all layer weights to a HDF5 file. - - The weight file has: - - `layer_names` (attribute), a list of strings - (ordered names of model layers). - - For every layer, a `group` named `layer.name` - - For every such layer group, a group attribute `weight_names`, - a list of strings - (ordered names of weights tensor of the layer). - - For every weight in the layer, a dataset - storing the weight value, named after the weight tensor. - - Arguments: - filepath: String, path to the file to save the weights to. - overwrite: Whether to silently overwrite any existing file at the - target location, or provide the user with a manual prompt. - - Raises: - ImportError: If h5py is not available. - """ - if h5py is None: - raise ImportError('`save_weights` requires h5py.') - # If file exists and should not be overwritten: - if not overwrite and os.path.isfile(filepath): - proceed = ask_to_proceed_with_overwrite(filepath) - if not proceed: - return - f = h5py.File(filepath, 'w') - save_weights_to_hdf5_group(f, self.layers) - f.flush() - f.close() - - def load_weights(self, filepath, by_name=False): - """Loads all layer weights from a HDF5 save file. - - If `by_name` is False (default) weights are loaded - based on the network's topology, meaning the architecture - should be the same as when the weights were saved. - Note that layers that don't have weights are not taken - into account in the topological ordering, so adding or - removing layers is fine as long as they don't have weights. - - If `by_name` is True, weights are loaded into layers - only if they share the same name. This is useful - for fine-tuning or transfer-learning models where - some of the layers have changed. - - Arguments: - filepath: String, path to the weights file to load. - by_name: Boolean, whether to load weights by name - or by topological order. - - Raises: - ImportError: If h5py is not available. - """ - if h5py is None: - raise ImportError('`load_weights` requires h5py.') - f = h5py.File(filepath, mode='r') - if 'layer_names' not in f.attrs and 'model_weights' in f: - f = f['model_weights'] - if by_name: - load_weights_from_hdf5_group_by_name(f, self.layers) - else: - load_weights_from_hdf5_group(f, self.layers) - - if hasattr(f, 'close'): - f.close() - - def _updated_config(self): - """Util hared between different serialization methods. - - Returns: - Model config with Keras version information added. - """ - from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top - - config = self.get_config() - model_config = { - 'class_name': self.__class__.__name__, - 'config': config, - 'keras_version': keras_version, - 'backend': K.backend() - } - return model_config - - def to_json(self, **kwargs): - """Returns a JSON string containing the network configuration. - - To load a network from a JSON save file, use - `keras.models.model_from_json(json_string, custom_objects={})`. - - Arguments: - **kwargs: Additional keyword arguments - to be passed to `json.dumps()`. - - Returns: - A JSON string. - """ - - def get_json_type(obj): - # If obj is any numpy type - if type(obj).__module__ == np.__name__: - return obj.item() - - # If obj is a python 'type' - if type(obj).__name__ == type.__name__: - return obj.__name__ - - raise TypeError('Not JSON Serializable:', obj) - - model_config = self._updated_config() - return json.dumps(model_config, default=get_json_type, **kwargs) - - def to_yaml(self, **kwargs): - """Returns a yaml string containing the network configuration. - - To load a network from a yaml save file, use - `keras.models.model_from_yaml(yaml_string, custom_objects={})`. - - `custom_objects` should be a dictionary mapping - the names of custom losses / layers / etc to the corresponding - functions / classes. - - Arguments: - **kwargs: Additional keyword arguments - to be passed to `yaml.dump()`. - - Returns: - A YAML string. - - Raises: - ImportError: if yaml module is not found. - """ - if yaml is None: - raise ImportError('Requires yaml module installed.') - return yaml.dump(self._updated_config(), **kwargs) - - def summary(self, line_length=None, positions=None, print_fn=None): - """Prints a string summary of the network. - - Arguments: - line_length: Total length of printed lines - (e.g. set this to adapt the display to different - terminal window sizes). - positions: Relative or absolute positions of log elements - in each line. If not provided, - defaults to `[.33, .55, .67, 1.]`. - print_fn: Print function to use. Defaults to `print`. - It will be called on each line of the summary. - You can set it to a custom function - in order to capture the string summary. - """ - print_layer_summary(self, - line_length=line_length, - positions=positions, - print_fn=print_fn) - - -def get_source_inputs(tensor, layer=None, node_index=None): - """Returns the list of input tensors necessary to compute `tensor`. - - Output will always be a list of tensors - (potentially with 1 element). - - Arguments: - tensor: The tensor to start from. - layer: Origin layer of the tensor. Will be - determined via tensor._keras_history if not provided. - node_index: Origin node index of the tensor. - - Returns: - List of input tensors. - """ - if not hasattr(tensor, '_keras_history'): - return tensor - - if layer is None or node_index: - layer, node_index, _ = tensor._keras_history - if not layer._inbound_nodes: - return [tensor] - else: - node = layer._inbound_nodes[node_index] - if not node.inbound_layers: - # Reached an Input layer, stop recursion. - return node.input_tensors - else: - source_tensors = [] - for i in range(len(node.inbound_layers)): - x = node.input_tensors[i] - layer = node.inbound_layers[i] - node_index = node.node_indices[i] - previous_sources = get_source_inputs(x, layer, node_index) - # Avoid input redundancy. - for x in previous_sources: - if x not in source_tensors: - source_tensors.append(x) - return source_tensors - - -def _to_list(x): - """Normalizes a list/tensor into a list. - - If a tensor is passed, we return - a list of size 1 containing the tensor. - - Arguments: - x: target object to be normalized. - - Returns: - A list. - """ - if isinstance(x, list): - return x - return [x] - - -def save_weights_to_hdf5_group(f, layers): - from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top - - f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers] - f.attrs['backend'] = K.backend().encode('utf8') - f.attrs['keras_version'] = str(keras_version).encode('utf8') - - for layer in layers: - g = f.create_group(layer.name) - symbolic_weights = layer.weights - weight_values = K.batch_get_value(symbolic_weights) - weight_names = [] - for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)): - if hasattr(w, 'name') and w.name: - name = str(w.name) - else: - name = 'param_' + str(i) - weight_names.append(name.encode('utf8')) - g.attrs['weight_names'] = weight_names - for name, val in zip(weight_names, weight_values): - param_dset = g.create_dataset(name, val.shape, dtype=val.dtype) - if not val.shape: - # scalar - param_dset[()] = val - else: - param_dset[:] = val - - -def preprocess_weights_for_loading(layer, - weights, - original_keras_version=None, - original_backend=None): - """Converts layers weights from Keras 1 format to Keras 2. - - Arguments: - layer: Layer instance. - weights: List of weights values (Numpy arrays). - original_keras_version: Keras version for the weights, as a string. - original_backend: Keras backend the weights were trained with, - as a string. - - Returns: - A list of weights values (Numpy arrays). - """ - if layer.__class__.__name__ == 'Bidirectional': - num_weights_per_layer = len(weights) // 2 - forward_weights = preprocess_weights_for_loading( - layer.forward_layer, weights[:num_weights_per_layer], - original_keras_version, original_backend) - backward_weights = preprocess_weights_for_loading( - layer.backward_layer, weights[num_weights_per_layer:], - original_keras_version, original_backend) - weights = forward_weights + backward_weights - - if original_keras_version == '1': - if layer.__class__.__name__ == 'TimeDistributed': - weights = preprocess_weights_for_loading( - layer.layer, weights, original_keras_version, original_backend) - - if layer.__class__.__name__ == 'Conv1D': - shape = weights[0].shape - # Handle Keras 1.1 format - if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters: - # Legacy shape: - # (filters, input_dim, filter_length, 1) - assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0], - 1) - weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) - weights[0] = weights[0][:, 0, :, :] - - if layer.__class__.__name__ == 'Conv2D': - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, stack_size, filters) - weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) - - if layer.__class__.__name__ == 'Conv2DTranspose': - if layer.data_format == 'channels_last': - # old: (kernel_rows, kernel_cols, stack_size, filters) - # new: (kernel_rows, kernel_cols, filters, stack_size) - weights[0] = np.transpose(weights[0], (0, 1, 3, 2)) - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, filters, stack_size) - weights[0] = np.transpose(weights[0], (2, 3, 0, 1)) - - if layer.__class__.__name__ == 'Conv3D': - if layer.data_format == 'channels_first': - # old: (filters, stack_size, ...) - # new: (..., stack_size, filters) - weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0)) - - if layer.__class__.__name__ == 'GRU': - if len(weights) == 9: - kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[4], weights[7]], axis=-1) - bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ == 'LSTM': - if len(weights) == 12: - # old: i, c, f, o - # new: i, f, c, o - kernel = np.concatenate( - [weights[0], weights[6], weights[3], weights[9]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[7], weights[4], weights[10]], axis=-1) - bias = np.concatenate( - [weights[2], weights[8], weights[5], weights[11]], axis=-1) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ == 'ConvLSTM2D': - if len(weights) == 12: - kernel = np.concatenate( - [weights[0], weights[6], weights[3], weights[9]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[7], weights[4], weights[10]], axis=-1) - bias = np.concatenate( - [weights[2], weights[8], weights[5], weights[11]], axis=-1) - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, stack_size, filters) - kernel = np.transpose(kernel, (2, 3, 1, 0)) - recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0)) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ in ['Model', 'Sequential']: - new_weights = [] - # trainable weights - for sublayer in layer.layers: - num_weights = len(sublayer.trainable_weights) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - - # non-trainable weights - for sublayer in layer.layers: - num_weights = len([ - l for l in sublayer.weights if l not in sublayer.trainable_weights - ]) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - weights = new_weights - - conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] - if layer.__class__.__name__ in conv_layers: - if original_backend == 'theano': - weights[0] = conv_utils.convert_kernel(weights[0]) - if layer.__class__.__name__ == 'ConvLSTM2D': - weights[1] = conv_utils.convert_kernel(weights[1]) - if K.int_shape(layer.weights[0]) != weights[0].shape: - weights[0] = np.transpose(weights[0], (3, 2, 0, 1)) - if layer.__class__.__name__ == 'ConvLSTM2D': - weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) - - # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM - if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: - # Determine if loading a CuDNNLSTM layer from the number of bias weights: - # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) - # if there's no bias weight in the file, skip this conversion - units = weights[1].shape[0] - bias = weights[2] - if len(bias) == units * 8: - # reshape the kernels - kernels = np.split(weights[0], 4, axis=1) - kernels = [ - kernel.reshape(-1).reshape(kernel.shape, order='F') - for kernel in kernels - ] - weights[0] = np.concatenate(kernels, axis=1) - - # transpose the recurrent kernels - recurrent_kernels = np.split(weights[1], 4, axis=1) - recurrent_kernels = [kernel.T for kernel in recurrent_kernels] - weights[1] = np.concatenate(recurrent_kernels, axis=1) - - # split the bias into half and merge - weights[2] = bias[:units * 4] + bias[units * 4:] - - return weights - - -def load_weights_from_hdf5_group(f, layers): - """Implements topological (order-based) weight loading. - - Arguments: - f: A pointer to a HDF5 group. - layers: a list of target layers. - - Raises: - ValueError: in case of mismatch between provided layers - and weights file. - """ - if 'keras_version' in f.attrs: - original_keras_version = f.attrs['keras_version'].decode('utf8') - else: - original_keras_version = '1' - if 'backend' in f.attrs: - original_backend = f.attrs['backend'].decode('utf8') - else: - original_backend = None - - filtered_layers = [] - for layer in layers: - weights = layer.weights - if weights: - filtered_layers.append(layer) - - layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] - filtered_layer_names = [] - for name in layer_names: - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - if weight_names: - filtered_layer_names.append(name) - layer_names = filtered_layer_names - if len(layer_names) != len(filtered_layers): - raise ValueError('You are trying to load a weight file ' - 'containing ' + str(len(layer_names)) + - ' layers into a model with ' + str(len(filtered_layers)) + - ' layers.') - - # We batch weight value assignments in a single backend call - # which provides a speedup in TensorFlow. - weight_value_tuples = [] - for k, name in enumerate(layer_names): - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - weight_values = [g[weight_name] for weight_name in weight_names] - layer = filtered_layers[k] - symbolic_weights = layer.weights - weight_values = preprocess_weights_for_loading( - layer, weight_values, original_keras_version, original_backend) - if len(weight_values) != len(symbolic_weights): - raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + - '" in the current model) was found to ' - 'correspond to layer ' + name + ' in the save file. ' - 'However the new layer ' + layer.name + ' expects ' + - str(len(symbolic_weights)) + - ' weights, but the saved weights have ' + - str(len(weight_values)) + ' elements.') - weight_value_tuples += zip(symbolic_weights, weight_values) - K.batch_set_value(weight_value_tuples) - - -def load_weights_from_hdf5_group_by_name(f, layers): - """Implements name-based weight loading. - - (instead of topological weight loading). - - Layers that have no matching name are skipped. - - Arguments: - f: A pointer to a HDF5 group. - layers: a list of target layers. - - Raises: - ValueError: in case of mismatch between provided layers - and weights file. - """ - if 'keras_version' in f.attrs: - original_keras_version = f.attrs['keras_version'].decode('utf8') - else: - original_keras_version = '1' - if 'backend' in f.attrs: - original_backend = f.attrs['backend'].decode('utf8') - else: - original_backend = None - - # New file format. - layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] - - # Reverse index of layer name to list of layers with name. - index = {} - for layer in layers: - if layer.name: - index.setdefault(layer.name, []).append(layer) - - # We batch weight value assignments in a single backend call - # which provides a speedup in TensorFlow. - weight_value_tuples = [] - for k, name in enumerate(layer_names): - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - weight_values = [g[weight_name] for weight_name in weight_names] - - for layer in index.get(name, []): - symbolic_weights = layer.weights - weight_values = preprocess_weights_for_loading( - layer, weight_values, original_keras_version, original_backend) - if len(weight_values) != len(symbolic_weights): - raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + - '") expects ' + str(len(symbolic_weights)) + - ' weight(s), but the saved weights' + ' have ' + - str(len(weight_values)) + ' element(s).') - # Set values. - for i in range(len(weight_values)): - weight_value_tuples.append((symbolic_weights[i], weight_values[i])) - K.batch_set_value(weight_value_tuples) - - -def shape_type_conversion(fn): - """Decorator that handles tuple/TensorShape conversion. - - Used in `compute_output_shape` and `build`. - - Arguments: - fn: function to wrap. - - Returns: - Wrapped function. - """ - - def wrapper(instance, input_shape): - if input_shape is not None: - if isinstance(input_shape, list): - input_shape = [ - tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape] - else: - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) - output_shape = fn(instance, input_shape) - if output_shape is not None: - if isinstance(output_shape, list): - return [tensor_shape.TensorShape(x) for x in output_shape] - return tensor_shape.TensorShape(output_shape) - - return wrapper diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py index 479ee877fd2471a67b5b5b81e8fbf338ce755a7b..b50277c8fff917d77694903c989fd02ea98b1711 100644 --- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py @@ -18,14 +18,17 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os -import shutil - import numpy as np +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import test_util from tensorflow.python.keras._impl import keras +from tensorflow.python.layers import base as tf_base_layers from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops from tensorflow.python.platform import test try: @@ -33,31 +36,255 @@ try: except ImportError: yaml = None -try: - import h5py # pylint:disable=g-import-not-at-top -except ImportError: - h5py = None - class TopologyConstructionTest(test.TestCase): - def test_get_updates_for(self): - a = keras.layers.Input(shape=(2,)) - dense_layer = keras.layers.Dense(1) - dense_layer.add_update(0, inputs=a) - dense_layer.add_update(1, inputs=None) + def test_get_updates(self): + + class MyLayer(keras.layers.Layer): + + def build(self, input_shape): + self.a = self.add_variable('a', + (1, 1), + 'float32', + trainable=False) + self.b = self.add_variable('b', + (1, 1), + 'float32', + trainable=False) + self.add_update(state_ops.assign_add(self.a, [[1.]])) + self.built = True + + def call(self, inputs): + self.add_update(state_ops.assign_add(self.a, inputs), + inputs=True) + return inputs + 1 + + x1 = keras.Input(shape=(1,)) + layer = MyLayer() + _ = layer.apply(x1) + + self.assertEqual(len(layer.updates), 2) + self.assertEqual(len(layer.get_updates_for(x1)), 1) + self.assertEqual(len(layer.get_updates_for(None)), 1) + + x2 = keras.Input(shape=(1,)) + y2 = layer.apply(x2) + + self.assertEqual(len(layer.updates), 3) + self.assertEqual(len(layer.get_updates_for(x1)), 1) + self.assertEqual(len(layer.get_updates_for(x2)), 1) + self.assertEqual(len(layer.get_updates_for(None)), 1) + + network = keras.engine.Network(x2, y2) + self.assertEqual(len(network.updates), 2) + self.assertEqual(len(network.get_updates_for(x1)), 0) + self.assertEqual(len(network.get_updates_for(x2)), 1) + self.assertEqual(len(network.get_updates_for(None)), 1) + + x3 = keras.Input(shape=(1,)) + _ = layer.apply(x3) + self.assertEqual(len(network.updates), 2) + + x4 = keras.Input(shape=(1,)) + _ = network(x4) + self.assertEqual(len(network.updates), 3) + self.assertEqual(len(network.get_updates_for(x2)), 1) + self.assertEqual(len(network.get_updates_for(x4)), 1) + self.assertEqual(len(network.get_updates_for(None)), 1) + + network.add_update(state_ops.assign_add(layer.a, [[1]])) + self.assertEqual(len(network.updates), 4) + self.assertEqual(len(network.get_updates_for(None)), 2) + + network.add_update(state_ops.assign_add(layer.a, x4), inputs=True) + self.assertEqual(len(network.updates), 5) + self.assertEqual(len(network.get_updates_for(x4)), 2) + + def test_get_losses(self): + + class MyLayer(keras.layers.Layer): + + def build(self, input_shape): + self.a = self.add_variable('a', + (1, 1), + 'float32', + trainable=False) + self.b = self.add_variable('b', + (1, 1), + 'float32', + trainable=False) + self.add_loss(math_ops.reduce_sum(self.a)) + self.built = True + + def call(self, inputs): + self.add_loss(math_ops.reduce_sum(inputs), + inputs=True) + return inputs + 1 + + x1 = keras.Input(shape=(1,)) + layer = MyLayer() + _ = layer.apply(x1) + + self.assertEqual(len(layer.losses), 2) + self.assertEqual(len(layer.get_losses_for(x1)), 1) + self.assertEqual(len(layer.get_losses_for(None)), 1) + + x2 = keras.Input(shape=(1,)) + y2 = layer.apply(x2) + + self.assertEqual(len(layer.losses), 3) + self.assertEqual(len(layer.get_losses_for(x1)), 1) + self.assertEqual(len(layer.get_losses_for(x2)), 1) + self.assertEqual(len(layer.get_losses_for(None)), 1) + + network = keras.engine.Network(x2, y2) + self.assertEqual(len(network.losses), 2) + self.assertEqual(len(network.get_losses_for(x1)), 0) + self.assertEqual(len(network.get_losses_for(x2)), 1) + self.assertEqual(len(network.get_losses_for(None)), 1) + + x3 = keras.Input(shape=(1,)) + _ = layer.apply(x3) + self.assertEqual(len(network.losses), 2) + + x4 = keras.Input(shape=(1,)) + _ = network(x4) + self.assertEqual(len(network.losses), 3) + self.assertEqual(len(network.get_losses_for(x2)), 1) + self.assertEqual(len(network.get_losses_for(x4)), 1) + self.assertEqual(len(network.get_losses_for(None)), 1) + + network.add_loss(math_ops.reduce_sum(layer.a)) + self.assertEqual(len(network.losses), 4) + self.assertEqual(len(network.get_losses_for(None)), 2) + + network.add_loss(math_ops.reduce_sum(x4), inputs=True) + self.assertEqual(len(network.losses), 5) + self.assertEqual(len(network.get_losses_for(x4)), 2) + + def testTopologicalAttributes(self): + # test layer attributes / methods related to cross-layer connectivity. + a = keras.Input(shape=(32,), name='input_a') + b = keras.Input(shape=(32,), name='input_b') + + # test input, output, input_shape, output_shape + test_layer = keras.layers.Dense(16, name='test_layer') + a_test = test_layer(a) + self.assertEqual(test_layer.input, a) + self.assertEqual(test_layer.output, a_test) + self.assertEqual(test_layer.input_shape, (None, 32)) + self.assertEqual(test_layer.output_shape, (None, 16)) - self.assertListEqual(dense_layer.get_updates_for(a), [0]) - self.assertListEqual(dense_layer.get_updates_for(None), [1]) + # test `get_*_at` methods + dense = keras.layers.Dense(16, name='dense_1') + a_2 = dense(a) + b_2 = dense(b) - def test_get_losses_for(self): - a = keras.layers.Input(shape=(2,)) - dense_layer = keras.layers.Dense(1) - dense_layer.add_loss(0, inputs=a) - dense_layer.add_loss(1, inputs=None) + self.assertEqual(dense.get_input_at(0), a) + self.assertEqual(dense.get_input_at(1), b) + self.assertEqual(dense.get_output_at(0), a_2) + self.assertEqual(dense.get_output_at(1), b_2) + self.assertEqual(dense.get_input_shape_at(0), (None, 32)) + self.assertEqual(dense.get_input_shape_at(1), (None, 32)) + self.assertEqual(dense.get_output_shape_at(0), (None, 16)) + self.assertEqual(dense.get_output_shape_at(1), (None, 16)) + + # Test invalid value for attribute retrieval. + with self.assertRaises(ValueError): + dense.get_input_at(2) + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + _ = new_dense.input + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + _ = new_dense.output + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + _ = new_dense.output_shape + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + _ = new_dense.input_shape + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + a = keras.Input(shape=(3, 32)) + a = keras.Input(shape=(5, 32)) + a_2 = dense(a) + b_2 = dense(b) + _ = new_dense.input_shape + with self.assertRaises(AttributeError): + new_dense = keras.layers.Dense(16) + a = keras.Input(shape=(3, 32)) + a = keras.Input(shape=(5, 32)) + a_2 = dense(a) + b_2 = dense(b) + _ = new_dense.output_shape + + def testTopologicalAttributesMultiOutputLayer(self): + + class PowersLayer(keras.layers.Layer): - self.assertListEqual(dense_layer.get_losses_for(a), [0]) - self.assertListEqual(dense_layer.get_losses_for(None), [1]) + def call(self, inputs): + return [inputs**2, inputs**3] + + x = keras.Input(shape=(32,)) + test_layer = PowersLayer() + p1, p2 = test_layer(x) # pylint: disable=not-callable + + self.assertEqual(test_layer.input, x) + self.assertEqual(test_layer.output, [p1, p2]) + self.assertEqual(test_layer.input_shape, (None, 32)) + self.assertEqual(test_layer.output_shape, [(None, 32), (None, 32)]) + + def testTopologicalAttributesMultiInputLayer(self): + + class AddLayer(keras.layers.Layer): + + def call(self, inputs): + assert len(inputs) == 2 + return inputs[0] + inputs[1] + + a = keras.Input(shape=(32,)) + b = keras.Input(shape=(32,)) + test_layer = AddLayer() + y = test_layer([a, b]) # pylint: disable=not-callable + + self.assertEqual(test_layer.input, [a, b]) + self.assertEqual(test_layer.output, y) + self.assertEqual(test_layer.input_shape, [(None, 32), (None, 32)]) + self.assertEqual(test_layer.output_shape, (None, 32)) + + def testBasicNetwork(self): + # minimum viable network + x = keras.Input(shape=(32,)) + dense = keras.layers.Dense(2) + y = dense(x) + network = keras.engine.Network(x, y, name='dense_network') + + # test basic attributes + self.assertEqual(network.name, 'dense_network') + self.assertEqual(len(network.layers), 2) # InputLayer + Dense + self.assertEqual(network.layers[1], dense) + self.assertEqual(network.weights, dense.weights) + self.assertEqual(network.trainable_weights, dense.trainable_weights) + self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights) + + # test callability on Input + x_2 = keras.Input(shape=(32,)) + y_2 = network(x_2) + self.assertEqual(y_2.get_shape().as_list(), [None, 2]) + + # test callability on regular tensor + x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32)) + y_2 = network(x_2) + self.assertEqual(y_2.get_shape().as_list(), [None, 2]) + + # test network `trainable` attribute + network.trainable = False + self.assertEqual(network.weights, dense.weights) + self.assertEqual(network.trainable_weights, []) + self.assertEqual(network.non_trainable_weights, + dense.trainable_weights + dense.non_trainable_weights) def test_trainable_weights(self): a = keras.layers.Input(shape=(2,)) @@ -100,41 +327,6 @@ class TopologyConstructionTest(test.TestCase): self.assertListEqual(model.trainable_weights, []) self.assertListEqual(model.non_trainable_weights, weights) - def test_weight_loading(self): - with self.test_session(): - a = keras.layers.Input(shape=(2,)) - x = keras.layers.Dense(3)(a) - b = keras.layers.Dense(1)(x) - model = keras.models.Model(a, b) - - x = np.random.random((3, 2)) - ref_y = model.predict(x) - weights = model.get_weights() - model.set_weights(weights) - y = model.predict(x) - self.assertAllClose(ref_y, y) - - with self.assertRaises(ValueError): - model.set_weights(weights[1:]) - with self.assertRaises(ValueError): - model.set_weights(weights[::-1]) - - if h5py is None: - return # Skip rest of test if H5py isn't available. - - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - - h5_path = os.path.join(temp_dir, 'test.h5') - model.save_weights(h5_path) - model.load_weights(h5_path) - y = model.predict(x) - self.assertAllClose(ref_y, y) - - model.load_weights(h5_path, by_name=True) - y = model.predict(x) - self.assertAllClose(ref_y, y) - def test_learning_phase(self): with self.test_session(): a = keras.layers.Input(shape=(32,), name='input_a') @@ -302,7 +494,7 @@ class TopologyConstructionTest(test.TestCase): self.assertListEqual([x.shape for x in fn_outputs], [(10, 64), (10, 5)]) # test get_source_inputs - self.assertListEqual(keras.engine.topology.get_source_inputs(c), [a, b]) + self.assertListEqual(keras.engine.network.get_source_inputs(c), [a, b]) # serialization / deserialization json_config = model.to_json() @@ -339,7 +531,10 @@ class TopologyConstructionTest(test.TestCase): e = keras.layers.Input(shape=(32,), name='input_e') f = keras.layers.Input(shape=(32,), name='input_f') + self.assertEqual(len(model.inputs), 2) g, h = model([e, f]) + self.assertEqual(len(model.inputs), 2) + self.assertEqual(g.name, 'model/dense_2/BiasAdd:0') self.assertListEqual(g.get_shape().as_list(), c.get_shape().as_list()) self.assertListEqual(h.get_shape().as_list(), d.get_shape().as_list()) @@ -520,7 +715,9 @@ class TopologyConstructionTest(test.TestCase): j = keras.layers.Input(shape=(32,), name='input_j') k = keras.layers.Input(shape=(32,), name='input_k') + self.assertEqual(len(model.inputs), 2) m, n = model([j, k]) + self.assertEqual(len(model.inputs), 2) tf_model = keras.models.Model([j, k], [m, n]) j_tf = array_ops.placeholder(dtype=dtypes.float32, shape=(None, 32)) @@ -546,96 +743,63 @@ class TopologyConstructionTest(test.TestCase): model = keras.models.Model(a, b) self.assertEqual(model.output_mask.get_shape().as_list(), [None, 10]) - def test_weight_preprocessing(self): - input_dim = 3 - output_dim = 3 - size = 2 - cases = [ - [ - (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))), - [np.random.random((2, 1)), np.random.random((2, 1))], - (None, 3, 2), - ], - [ - (keras.layers.TimeDistributed(keras.layers.Dense(1))), - [np.random.random((2, 1)), np.random.random((1,))], - (None, 3, 2), - ], - [ - (keras.layers.Conv1D(output_dim, size, use_bias=False)), - [np.random.random((output_dim, input_dim, size, 1))], - (None, 4, input_dim), - ], - [ - (keras.layers.Conv2D(output_dim, size, - use_bias=False, data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size))], - (None, input_dim, 4, 4), - ], - [ - (keras.layers.Conv2DTranspose(output_dim, size, - use_bias=False, - data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size))], - (None, input_dim, 4, 4), - ], - [ - (keras.layers.Conv2DTranspose(output_dim, size, - use_bias=False, - data_format='channels_last')), - [np.random.random((size, size, input_dim, output_dim))], - (None, 4, 4, input_dim), - ], - [ - (keras.layers.Conv3D(output_dim, size, - use_bias=False, data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size, size))], - (None, input_dim, 4, 4, 4), - ], - [ - (keras.layers.GRU(output_dim)), - [np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,))], - (None, 4, input_dim), - ], - [ - (keras.layers.LSTM(output_dim)), - [np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,))], - (None, 4, input_dim), - ], - ] - for layer, weights, input_shape in cases: - layer.build(input_shape) - _ = keras.engine.topology.preprocess_weights_for_loading( - layer, weights, original_keras_version='1') - - model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)]) - _ = keras.engine.topology.preprocess_weights_for_loading( - model, model.weights, original_keras_version='1') - - x = keras.Input((2,)) - y = keras.layers.Dense(2)(x) - model = keras.models.Model(x, y) - _ = keras.engine.topology.preprocess_weights_for_loading( - model, model.weights, original_keras_version='1') + def testMaskingSingleInput(self): + + class MaskedLayer(keras.layers.Layer): + + def call(self, inputs, mask=None): + if mask is not None: + return inputs * mask + return inputs + + def compute_mask(self, inputs, mask=None): + return array_ops.ones_like(inputs) + + if context.executing_eagerly(): + a = constant_op.constant([2] * 32) + mask = constant_op.constant([0, 1] * 16) + a._keras_mask = mask + b = MaskedLayer().apply(a) + self.assertTrue(hasattr(b, '_keras_mask')) + self.assertAllEqual( + self.evaluate(array_ops.ones_like(mask)), + self.evaluate(getattr(b, '_keras_mask'))) + self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b)) + else: + x = keras.Input(shape=(32,)) + y = MaskedLayer()(x) # pylint: disable=not-callable + network = keras.engine.Network(x, y) + + # test callability on Input + x_2 = keras.Input(shape=(32,)) + y_2 = network(x_2) + self.assertEqual(y_2.get_shape().as_list(), [None, 32]) + + # test callability on regular tensor + x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32)) + y_2 = network(x_2) + self.assertEqual(y_2.get_shape().as_list(), [None, 32]) + + def test_activity_regularization_with_model_composition(self): + + def reg(x): + return keras.backend.sum(x) + + net_a_input = keras.Input((2,)) + net_a = net_a_input + net_a = keras.layers.Dense(2, kernel_initializer='ones', + use_bias=False, + activity_regularizer=reg)(net_a) + model_a = keras.Model([net_a_input], [net_a]) + + net_b_input = keras.Input((2,)) + net_b = model_a(net_b_input) + model_b = keras.Model([net_b_input], [net_b]) + + model_b.compile(optimizer='sgd', loss=None) + x = np.ones((1, 2)) + loss = model_b.evaluate(x) + self.assertEqual(loss, 4.) def test_layer_sharing_at_heterogenous_depth(self): with self.test_session(): @@ -685,5 +849,92 @@ class TopologyConstructionTest(test.TestCase): output_val_2 = m2.predict(x_val) self.assertAllClose(output_val, output_val_2, atol=1e-6) + def test_explicit_training_argument(self): + with self.test_session(): + a = keras.layers.Input(shape=(2,)) + b = keras.layers.Dropout(0.5)(a) + base_model = keras.models.Model(a, b) + + a = keras.layers.Input(shape=(2,)) + b = base_model(a, training=False) + model = keras.models.Model(a, b) + + x = np.ones((100, 2)) + y = np.ones((100, 2)) + model.compile(optimizer='sgd', loss='mse') + loss = model.train_on_batch(x, y) + self.assertEqual(loss, 0) # In inference mode, output is equal to input. + + a = keras.layers.Input(shape=(2,)) + b = base_model(a, training=True) + model = keras.models.Model(a, b) + preds = model.predict(x) + self.assertEqual(np.min(preds), 0.) # At least one unit was dropped. + + +class DeferredModeTest(test.TestCase): + + def testDeferredTensorAttributes(self): + x = tf_base_layers._DeferredTensor(shape=(None, 2), + dtype='float32', + name='x') + self.assertEqual(str(x), + 'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)') + self.assertEqual(repr(x), + '<_DeferredTensor \'x\' shape=(?, 2) dtype=float32>') + + @test_util.run_in_graph_and_eager_modes() + def testSimpleNetworkBuilding(self): + inputs = keras.engine.Input(shape=(32,)) + if context.executing_eagerly(): + self.assertIsInstance(inputs, tf_base_layers._DeferredTensor) + self.assertEqual(inputs.dtype.name, 'float32') + self.assertEqual(inputs.shape.as_list(), [None, 32]) + + x = keras.layers.Dense(2)(inputs) + if context.executing_eagerly(): + self.assertIsInstance(x, tf_base_layers._DeferredTensor) + self.assertEqual(x.dtype.name, 'float32') + self.assertEqual(x.shape.as_list(), [None, 2]) + + outputs = keras.layers.Dense(4)(x) + network = keras.engine.Network(inputs, outputs) + self.assertIsInstance(network, keras.engine.Network) + + if context.executing_eagerly(): + # It should be possible to call such a network on EagerTensors. + inputs = constant_op.constant( + np.random.random((10, 32)).astype('float32')) + outputs = network(inputs) + self.assertEqual(outputs.shape.as_list(), [10, 4]) + + @test_util.run_in_graph_and_eager_modes() + def testMultiIONetworkbuilding(self): + input_a = keras.engine.Input(shape=(32,)) + input_b = keras.engine.Input(shape=(16,)) + a = keras.layers.Dense(16)(input_a) + + class AddLayer(keras.layers.Layer): + + def call(self, inputs): + return inputs[0] + inputs[1] + + def compute_output_shape(self, input_shape): + return input_shape[0] + + c = AddLayer()([a, input_b]) # pylint: disable=not-callable + c = keras.layers.Dense(2)(c) + + network = keras.engine.Network([input_a, input_b], [a, c]) + if context.executing_eagerly(): + a_val = constant_op.constant( + np.random.random((10, 32)).astype('float32')) + b_val = constant_op.constant( + np.random.random((10, 16)).astype('float32')) + outputs = network([a_val, b_val]) + self.assertEqual(len(outputs), 2) + self.assertEqual(outputs[0].shape.as_list(), [10, 16]) + self.assertEqual(outputs[1].shape.as_list(), [10, 2]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py index 43d95b1f194e1d30cbdae726fdf5979bd7065d25..8b82c0b313cd2f5a07df76ef9ec6fee5bdde7342 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training.py +++ b/tensorflow/python/keras/_impl/keras/engine/training.py @@ -18,558 +18,95 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy - import numpy as np from tensorflow.python.eager import context from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import callbacks as cbks from tensorflow.python.keras._impl.keras import losses from tensorflow.python.keras._impl.keras import metrics as metrics_module from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.engine import training_arrays from tensorflow.python.keras._impl.keras.engine import training_eager -from tensorflow.python.keras._impl.keras.engine.topology import Network -from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer -from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer -from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence -from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.keras._impl.keras.engine import training_generator +from tensorflow.python.keras._impl.keras.engine import training_utils +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.network import Network +from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays +from tensorflow.python.layers.base import _DeferredTensor +from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer as tf_optimizer_module +from tensorflow.python.util.tf_export import tf_export -try: - from scipy.sparse import issparse # pylint: disable=g-import-not-at-top -except ImportError: - issparse = None - - -def _standardize_input_data(data, - names, - shapes=None, - check_batch_axis=True, - exception_prefix=''): - """Normalizes inputs and targets provided by users. - - Users may pass data as a list of arrays, dictionary of arrays, - or as a single array. We normalize this to an ordered list of - arrays (same order as `names`), while checking that the provided - arrays have shapes that match the network's expectations. - - Arguments: - data: User-provided input data (polymorphic). - names: List of expected array names. - shapes: Optional list of expected array shapes. - check_batch_axis: Boolean; whether to check that - the batch axis of the arrays matches the expected - value found in `shapes`. - exception_prefix: String prefix used for exception formatting. - - Returns: - List of standardized input arrays (one array per model input). - - Raises: - ValueError: in case of improperly formatted user-provided data. - """ - if not names: - if data is not None and hasattr(data, '__len__') and len(data): - raise ValueError('Error when checking model ' + exception_prefix + ': ' - 'expected no data, but got:', data) - return [] - if data is None: - return [None for _ in range(len(names))] - - if isinstance(data, dict): - try: - data = [ - data[x].values - if data[x].__class__.__name__ == 'DataFrame' else data[x] - for x in names - ] - except KeyError as e: - raise ValueError('No data provided for "' + e.args[0] + '". Need data ' - 'for each key in: ' + str(names)) - elif isinstance(data, list): - if isinstance(data[0], list): - data = [np.asarray(d) for d in data] - elif len(names) == 1 and isinstance(data[0], (float, int)): - data = [np.asarray(data)] - else: - data = [ - x.values if x.__class__.__name__ == 'DataFrame' else x for x in data - ] - else: - data = data.values if data.__class__.__name__ == 'DataFrame' else data - data = [data] - data = [ - np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data - ] - - if len(data) != len(names): - if data and hasattr(data[0], 'shape'): - raise ValueError('Error when checking model ' + exception_prefix + - ': the list of Numpy arrays that you are passing to ' - 'your model is not the size the model expected. ' - 'Expected to see ' + str(len(names)) + ' array(s), ' - 'but instead got the following list of ' + - str(len(data)) + ' arrays: ' + str(data)[:200] + '...') - elif len(names) > 1: - raise ValueError( - 'Error when checking model ' + exception_prefix + - ': you are passing a list as input to your model, ' - 'but the model expects a list of ' + str(len(names)) + - ' Numpy arrays instead. The list you passed was: ' + str(data)[:200]) - elif len(data) == 1 and not hasattr(data[0], 'shape'): - raise TypeError('Error when checking model ' + exception_prefix + - ': data should be a Numpy array, or list/dict of ' - 'Numpy arrays. Found: ' + str(data)[:200] + '...') - elif len(names) == 1: - data = [np.asarray(data)] - - # Check shapes compatibility. - if shapes: - for i in range(len(names)): - if shapes[i] is not None: - data_shape = data[i].shape - shape = shapes[i] - if data[i].ndim != len(shape): - raise ValueError('Error when checking ' + exception_prefix + - ': expected ' + names[i] + ' to have ' + - str(len(shape)) + ' dimensions, but got array ' - 'with shape ' + str(data_shape)) - if not check_batch_axis: - data_shape = data_shape[1:] - shape = shape[1:] - for dim, ref_dim in zip(data_shape, shape): - if ref_dim != dim and ref_dim: - raise ValueError( - 'Error when checking ' + exception_prefix + ': expected ' + - names[i] + ' to have shape ' + str(shape) + - ' but got array with shape ' + str(data_shape)) - return data +@tf_export('keras.models.Model', 'keras.Model') +class Model(Network): + """`Model` groups layers into an object with training and inference features. -def _standardize_sample_or_class_weights(x_weight, output_names, weight_type): - """Maps `sample_weight` or `class_weight` to model outputs. + There are two ways to instantiate a `Model`: - Arguments: - x_weight: User-provided `sample_weight` or `class_weight` argument. - output_names: List of output names (strings) in the model. - weight_type: A string used purely for exception printing. + 1 - With the "functional API", where you start from `Input`, + you chain layer calls to specify the model's forward pass, + and finally you create your model from inputs and outputs: - Returns: - A list of `sample_weight` or `class_weight` where there are exactly - one element per model output. + ```python + import tensorflow as tf - Raises: - ValueError: In case of invalid user-provided argument. - """ - if x_weight is None or len(x_weight) == 0: # pylint: disable=g-explicit-length-test - return [None for _ in output_names] - if len(output_names) == 1: - if isinstance(x_weight, list) and len(x_weight) == 1: - return x_weight - if isinstance(x_weight, dict) and output_names[0] in x_weight: - return [x_weight[output_names[0]]] - else: - return [x_weight] - if isinstance(x_weight, list): - if len(x_weight) != len(output_names): - raise ValueError('Provided `' + weight_type + '` was a list of ' + - str(len(x_weight)) + ' elements, but the model has ' + - str(len(output_names)) + ' outputs. ' - 'You should provide one `' + weight_type + '`' - 'array per model output.') - return x_weight - if isinstance(x_weight, dict): - x_weights = [] - for name in output_names: - x_weights.append(x_weight.get(name)) - return x_weights - else: - raise TypeError( - 'The model has multiple outputs, so `' + weight_type + '` ' - 'should be either a list or a dict. ' - 'Provided `' + weight_type + '` type not understood: ' + str(x_weight)) - - -def _standardize_class_weights(class_weight, output_names): - return _standardize_sample_or_class_weights(class_weight, output_names, - 'class_weight') - - -def _standardize_sample_weights(sample_weight, output_names): - return _standardize_sample_or_class_weights(sample_weight, output_names, - 'sample_weight') - - -def _check_array_lengths(inputs, targets, weights=None): - """Does user input validation for numpy arrays. - - Arguments: - inputs: list of Numpy arrays of inputs. - targets: list of Numpy arrays of targets. - weights: list of Numpy arrays of sample weights. - - Raises: - ValueError: in case of incorrectly formatted data. - """ + inputs = tf.keras.Input(shape=(3,)) + x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs) + outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x) + model = tf.keras.Model(inputs=inputs, outputs=outputs) + ``` - def set_of_lengths(x): - # return a set with the variation between - # different shapes, with None => 0 - if x is None: - return {0} - else: - return set([0 if y is None else y.shape[0] for y in x]) - - set_x = set_of_lengths(inputs) - set_y = set_of_lengths(targets) - set_w = set_of_lengths(weights) - if len(set_x) > 1: - raise ValueError('All input arrays (x) should have ' - 'the same number of samples. Got array shapes: ' + - str([x.shape for x in inputs])) - if len(set_y) > 1: - raise ValueError('All target arrays (y) should have ' - 'the same number of samples. Got array shapes: ' + - str([y.shape for y in targets])) - if set_x and set_y and list(set_x)[0] != list(set_y)[0]: - raise ValueError('Input arrays should have ' - 'the same number of samples as target arrays. ' - 'Found ' + str(list(set_x)[0]) + ' input samples ' - 'and ' + str(list(set_y)[0]) + ' target samples.') - if len(set_w) > 1: - raise ValueError('All sample_weight arrays should have ' - 'the same number of samples. Got array shapes: ' + - str([w.shape for w in weights])) - if set_y and set_w and list(set_y)[0] != list(set_w)[0]: - raise ValueError('Sample_weight arrays should have ' - 'the same number of samples as target arrays. Got ' + - str(list(set_y)[0]) + ' input samples and ' + - str(list(set_w)[0]) + ' target samples.') - - -def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes): - """Does validation on the compatibility of targets and loss functions. - - This helps prevent users from using loss functions incorrectly. - - Arguments: - targets: list of Numpy arrays of targets. - loss_fns: list of loss functions. - output_shapes: list of shapes of model outputs. - - Raises: - ValueError: if a loss function or target array - is incompatible with an output. - """ - key_losses = { - losses.mean_squared_error, losses.binary_crossentropy, - losses.categorical_crossentropy - } - for y, loss, shape in zip(targets, loss_fns, output_shapes): - if loss is None: - continue - if loss is losses.categorical_crossentropy: - if y.shape[-1] == 1: - raise ValueError('You are passing a target array of shape ' + str( - y.shape) + ' while using as loss `categorical_crossentropy`. ' - '`categorical_crossentropy` expects ' - 'targets to be binary matrices (1s and 0s) ' - 'of shape (samples, classes). ' - 'If your targets are integer classes, ' - 'you can convert them to the expected format via:\n' - '```\n' - 'from keras.utils import to_categorical\n' - 'y_binary = to_categorical(y_int)\n' - '```\n' - '\n' - 'Alternatively, you can use the loss function ' - '`sparse_categorical_crossentropy` instead, ' - 'which does expect integer targets.') - if loss in key_losses: - for target_dim, out_dim in zip(y.shape[1:], shape[1:]): - if out_dim is not None and target_dim != out_dim: - raise ValueError('A target array with shape ' + str(y.shape) + - ' was passed for an output of shape ' + str(shape) + - ' while using as loss `' + loss.__name__ + '`. ' - 'This loss expects ' - 'targets to have the same shape ' - 'as the output.') - - -def _collect_metrics(metrics, output_names): - """Maps metric functions to model outputs. - - Arguments: - metrics: a list or dict of metric functions. - output_names: a list of the names (strings) of model outputs. - - Returns: - A list (one entry per model output) of lists of metric functions. - For instance, if the model has 2 outputs, and for the first output - we want to compute "binary_accuracy" and "binary_crossentropy", - and just "binary_accuracy" for the second output, - the list would look like: - `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]` - - Raises: - TypeError: if an incorrect type is passed for the `metrics` argument. - """ - if not metrics: - return [[] for _ in output_names] - if isinstance(metrics, list): - # we then apply all metrics to all outputs. - return [copy.copy(metrics) for _ in output_names] - elif isinstance(metrics, dict): - nested_metrics = [] - for name in output_names: - output_metrics = metrics.get(name, []) - if not isinstance(output_metrics, list): - output_metrics = [output_metrics] - nested_metrics.append(output_metrics) - return nested_metrics - else: - raise TypeError('Type of `metrics` argument not understood. ' - 'Expected a list or dictionary, found: ' + str(metrics)) - - -def _batch_shuffle(index_array, batch_size): - """Shuffles an array in a batch-wise fashion. - - Useful for shuffling HDF5 arrays - (where one cannot access arbitrary indices). - - Arguments: - index_array: array of indices to be shuffled. - batch_size: integer. - - Returns: - The `index_array` array, shuffled in a batch-wise fashion. - """ - batch_count = int(len(index_array) / batch_size) - # to reshape we need to be cleanly divisible by batch size - # we stash extra items and reappend them after shuffling - last_batch = index_array[batch_count * batch_size:] - index_array = index_array[:batch_count * batch_size] - index_array = index_array.reshape((batch_count, batch_size)) - np.random.shuffle(index_array) - index_array = index_array.flatten() - return np.append(index_array, last_batch) - - -def _make_batches(size, batch_size): - """Returns a list of batch indices (tuples of indices). - - Arguments: - size: Integer, total size of the data to slice into batches. - batch_size: Integer, batch size. - - Returns: - A list of tuples of array indices. - """ - num_batches = (size + batch_size - 1) // batch_size # round up - return [(i * batch_size, min(size, (i + 1) * batch_size)) - for i in range(num_batches)] + 2 - By subclassing the `Model` class: in that case, you should define your + layers in `__init__` and you should implement the model's forward pass + in `call`. + ```python + import tensorflow as tf -def _slice_arrays(arrays, start=None, stop=None): - """Slice an array or list of arrays. + class MyModel(tf.keras.Model): - This takes an array-like, or a list of - array-likes, and outputs: - - arrays[start:stop] if `arrays` is an array-like - - [x[start:stop] for x in arrays] if `arrays` is a list + def __init__(self): + self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) + self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) - Can also work on list/array of indices: `_slice_arrays(x, indices)` + def call(self, inputs): + x = self.dense1(inputs) + return self.dense2(x) - Arguments: - arrays: Single array or list of arrays. - start: can be an integer index (start index) - or a list/array of indices - stop: integer (stop index); should be None if - `start` was a list. - - Returns: - A slice of the array(s). - """ - if arrays is None: - return [None] - elif isinstance(arrays, list): - if hasattr(start, '__len__'): - # hdf5 datasets only support list objects as indices - if hasattr(start, 'shape'): - start = start.tolist() - return [None if x is None else x[start] for x in arrays] - else: - return [None if x is None else x[start:stop] for x in arrays] - else: - if hasattr(start, '__len__'): - if hasattr(start, 'shape'): - start = start.tolist() - return arrays[start] - elif hasattr(start, '__getitem__'): - return arrays[start:stop] - else: - return [None] + model = MyModel() + ``` + If you subclass `Model`, you can optionally have + a `training` argument (boolean) in `call`, which you can use to specify + a different behavior in training and inference: -def _weighted_masked_objective(fn): - """Adds support for masking and sample-weighting to an objective function. + ```python + import tensorflow as tf - It transforms an objective function `fn(y_true, y_pred)` - into a sample-weighted, cost-masked objective function - `fn(y_true, y_pred, weights, mask)`. - - Arguments: - fn: The objective function to wrap, - with signature `fn(y_true, y_pred)`. - - Returns: - A function with signature `fn(y_true, y_pred, weights, mask)`. - """ - if fn is None: - return None + class MyModel(tf.keras.Model): - def weighted(y_true, y_pred, weights, mask=None): - """Wrapper function. + def __init__(self): + self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) + self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) + self.dropout = tf.keras.layers.Dropout(0.5) - Arguments: - y_true: `y_true` argument of `fn`. - y_pred: `y_pred` argument of `fn`. - weights: Weights tensor. - mask: Mask tensor. + def call(self, inputs, training=False): + x = self.dense1(inputs) + if training: + x = self.dropout(x, training=training) + return self.dense2(x) - Returns: - Scalar tensor. - """ - # score_array has ndim >= 2 - score_array = fn(y_true, y_pred) - if mask is not None: - # Cast the mask to floatX to avoid float64 upcasting in theano - mask = K.cast(mask, K.floatx()) - # mask should have the same shape as score_array - score_array *= mask - # the loss per batch should be proportional - # to the number of unmasked samples. - score_array /= K.mean(mask) - - # apply sample weighting - if weights is not None: - # reduce score_array to same ndim as weight array - ndim = K.ndim(score_array) - weight_ndim = K.ndim(weights) - score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim))) - score_array *= weights - score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx())) - return K.mean(score_array) - - return weighted - - -def _standardize_weights(y, - sample_weight=None, - class_weight=None, - sample_weight_mode=None): - """Performs sample weight validation and standardization. - - Everything gets normalized to a single sample-wise (or timestep-wise) - weight array. - - Arguments: - y: Numpy array of model targets to be weighted. - sample_weight: User-provided `sample_weight` argument. - class_weight: User-provided `class_weight` argument. - sample_weight_mode: One of `None` or `"temporal"`. - `"temporal"` indicated that we expect 2D weight data - that will be applied to the last 2 dimensions of - the targets (i.e. we are weighting timesteps, not samples). - - Returns: - A numpy array of target weights, one entry per sample to weight. - - Raises: - ValueError: In case of invalid user-provided arguments. - """ - if sample_weight_mode is not None: - if sample_weight_mode != 'temporal': - raise ValueError('"sample_weight_mode ' - 'should be None or "temporal". ' - 'Found: ' + str(sample_weight_mode)) - if len(y.shape) < 3: - raise ValueError('Found a sample_weight array for ' - 'an input with shape ' + str(y.shape) + '. ' - 'Timestep-wise sample weighting (use of ' - 'sample_weight_mode="temporal") is restricted to ' - 'outputs that are at least 3D, i.e. that have ' - 'a time dimension.') - if sample_weight is not None and len(sample_weight.shape) != 2: - raise ValueError('Found a sample_weight array with shape ' + - str(sample_weight.shape) + '. ' - 'In order to use timestep-wise sample weighting, ' - 'you should pass a 2D sample_weight array.') - else: - if sample_weight is not None and len(sample_weight.shape) != 1: - raise ValueError('Found a sample_weight array with shape ' + - str(sample_weight.shape) + '. ' - 'In order to use timestep-wise sample weights, ' - 'you should specify ' - 'sample_weight_mode="temporal" ' - 'in compile(). If you just mean to use ' - 'sample-wise weights, make sure your ' - 'sample_weight array is 1D.') - - if sample_weight is not None: - if len(sample_weight.shape) > len(y.shape): - raise ValueError( - 'Found a sample_weight with shape' + str(sample_weight.shape) + '.' - 'Expected sample_weight with rank ' - 'less than or equal to ' + str(len(y.shape))) - - if y.shape[:sample_weight.ndim] != sample_weight.shape: - raise ValueError( - 'Found a sample_weight array with shape ' + str(sample_weight.shape) + - ' for an input with shape ' + str(y.shape) + '. ' - 'sample_weight cannot be broadcast.') - return sample_weight - elif isinstance(class_weight, dict): - if len(y.shape) > 2: - raise ValueError('`class_weight` not supported for ' - '3+ dimensional targets.') - if y.shape[1] > 1: - y_classes = y.argmax(axis=1) - elif y.shape[1] == 1: - y_classes = np.reshape(y, y.shape[0]) - else: - y_classes = y - - weights = np.asarray( - [class_weight[cls] for cls in y_classes if cls in class_weight]) - - if len(weights) != len(y_classes): - # subtract the sets to pick all missing classes - existing_classes = set(y_classes) - existing_class_weight = set(class_weight.keys()) - raise ValueError('`class_weight` must contain all classes in the data.' - ' The classes %s exist in the data but not in ' - '`class_weight`.' % - (existing_classes - existing_class_weight)) - return weights - else: - if sample_weight_mode is None: - return np.ones((y.shape[0],), dtype=K.floatx()) - else: - return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx()) - - -class Model(Network): - """The `Model` class adds training & evaluation routines to a `Network`. + model = MyModel() + ``` """ def compile(self, optimizer, - loss, + loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, @@ -625,16 +162,30 @@ class Model(Network): `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ loss = loss or {} - if context.in_eager_mode() and not isinstance( - optimizer, tf_optimizer_module.Optimizer): + if context.executing_eagerly() and not isinstance( + optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): raise ValueError('Only TF native optimizers are supported in Eager mode.') self.optimizer = optimizers.get(optimizer) self.loss = loss + self.metrics = metrics or [] self.loss_weights = loss_weights - if context.in_eager_mode() and sample_weight_mode is not None: + if context.executing_eagerly() and sample_weight_mode is not None: raise ValueError('sample_weight_mode is not supported in Eager mode.') self.sample_weight_mode = sample_weight_mode + if context.executing_eagerly() and weighted_metrics is not None: + raise ValueError('weighted_metrics is not supported in Eager mode.') + self.weighted_metrics = weighted_metrics + if context.executing_eagerly() and target_tensors is not None: + raise ValueError('target_tensors is not supported in Eager mode.') + self.target_tensors = target_tensors + + if not self.built: + # Model is not compilable because it does not know its number of inputs + # and outputs, nor their shapes and names. We will compile after the first + # time the model gets called on training data. + return + self._is_compiled = True # Prepare loss functions. if isinstance(loss, dict): @@ -665,7 +216,8 @@ class Model(Network): loss_functions = [loss_function for _ in range(len(self.outputs))] self.loss_functions = loss_functions - weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions] + weighted_losses = [training_utils.weighted_masked_objective(fn) + for fn in loss_functions] skip_target_indices = [] skip_target_weighing_indices = [] self._feed_outputs = [] @@ -678,7 +230,7 @@ class Model(Network): skip_target_weighing_indices.append(i) # Prepare output masks. - if context.in_graph_mode(): + if not context.executing_eagerly(): masks = self.compute_mask(self.inputs, mask=None) if masks is None: masks = [None for _ in self.outputs] @@ -712,34 +264,32 @@ class Model(Network): self.loss_weights_list = loss_weights_list # initialization for Eager mode execution - if context.in_eager_mode(): + if context.executing_eagerly(): if target_tensors is not None: raise ValueError('target_tensors are not currently supported in Eager' 'mode.') self.total_loss = None - self.metrics = metrics - self.weighted_metrics = weighted_metrics self.metrics_tensors = [] self.metrics_names = ['loss'] for i in range(len(self.outputs)): if len(self.outputs) > 1: self.metrics_names.append(self.output_names[i] + '_loss') - self.nested_metrics = _collect_metrics(metrics, self.output_names) + self.nested_metrics = training_utils.collect_metrics(metrics, + self.output_names) self._feed_sample_weight_modes = [] for i in range(len(self.outputs)): self._feed_sample_weight_modes.append(None) self.sample_weights = [] self.targets = [] - self._collected_trainable_weights = self.trainable_weights for i in range(len(self.outputs)): self._feed_output_names.append(self.output_names[i]) - + self._collected_trainable_weights = self.trainable_weights return # Prepare targets of model. self.targets = [] self._feed_targets = [] - if target_tensors is not None: + if target_tensors not in (None, []): if isinstance(target_tensors, list): if len(target_tensors) != len(self.outputs): raise ValueError( @@ -766,9 +316,9 @@ class Model(Network): if i in skip_target_indices: self.targets.append(None) else: - shape = self._internal_output_shapes[i] + shape = K.int_shape(self.outputs[i]) name = self.output_names[i] - if target_tensors is not None: + if target_tensors not in (None, []): target = target_tensors[i] else: target = None @@ -842,12 +392,12 @@ class Model(Network): sample_weights.append(None) else: if sample_weight_mode == 'temporal': - sample_weights.append( - K.placeholder(ndim=2, name=name + '_sample_weights')) + sample_weights.append(array_ops.placeholder_with_default( + [[1.]], shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: - sample_weights.append( - K.placeholder(ndim=1, name=name + '_sample_weights')) + sample_weights.append(array_ops.placeholder_with_default( + [1.], shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] @@ -856,7 +406,6 @@ class Model(Network): self._feed_sample_weight_modes.append(self.sample_weight_modes[i]) # Prepare metrics. - self.metrics = metrics self.weighted_metrics = weighted_metrics self.metrics_names = ['loss'] self.metrics_tensors = [] @@ -896,17 +445,11 @@ class Model(Network): # List of same size as output_names. # contains tuples (metrics for output, names of metrics). - nested_metrics = _collect_metrics(metrics, self.output_names) - nested_weighted_metrics = _collect_metrics(weighted_metrics, - self.output_names) - - def append_metric(layer_index, metric_name, metric_tensor): - """Helper function used in loop below.""" - if len(self.output_names) > 1: - metric_name = self.output_names[layer_index] + '_' + metric_name - self.metrics_names.append(metric_name) - self.metrics_tensors.append(metric_tensor) - + nested_metrics = training_utils.collect_metrics(metrics, self.output_names) + nested_weighted_metrics = training_utils.collect_metrics(weighted_metrics, + self.output_names) + self.metrics_updates = [] + self.stateful_metric_names = [] with K.name_scope('metrics'): for i in range(len(self.outputs)): if i in skip_target_indices: @@ -925,42 +468,67 @@ class Model(Network): if metric in ('accuracy', 'acc', 'crossentropy', 'ce'): # custom handling of accuracy/crossentropy # (because of class mode duality) - output_shape = self._internal_output_shapes[i] + output_shape = self.outputs[i].get_shape().as_list() if (output_shape[-1] == 1 or self.loss_functions[i] == losses.binary_crossentropy): # case: binary accuracy/crossentropy if metric in ('accuracy', 'acc'): - acc_fn = metrics_module.binary_accuracy + metric_fn = metrics_module.binary_accuracy elif metric in ('crossentropy', 'ce'): - acc_fn = metrics_module.binary_crossentropy + metric_fn = metrics_module.binary_crossentropy elif self.loss_functions[ i] == losses.sparse_categorical_crossentropy: # case: categorical accuracy/crossentropy with sparse targets if metric in ('accuracy', 'acc'): - acc_fn = metrics_module.sparse_categorical_accuracy + metric_fn = metrics_module.sparse_categorical_accuracy elif metric in ('crossentropy', 'ce'): - acc_fn = metrics_module.sparse_categorical_crossentropy + metric_fn = metrics_module.sparse_categorical_crossentropy else: # case: categorical accuracy/crossentropy if metric in ('accuracy', 'acc'): - acc_fn = metrics_module.categorical_accuracy + metric_fn = metrics_module.categorical_accuracy elif metric in ('crossentropy', 'ce'): - acc_fn = metrics_module.categorical_crossentropy + metric_fn = metrics_module.categorical_crossentropy if metric in ('accuracy', 'acc'): suffix = 'acc' elif metric in ('crossentropy', 'ce'): suffix = 'ce' - weighted_metric_fn = _weighted_masked_objective(acc_fn) + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) metric_name = metric_name_prefix + suffix else: metric_fn = metrics_module.get(metric) - weighted_metric_fn = _weighted_masked_objective(metric_fn) - metric_name = metric_name_prefix + metric_fn.__name__ + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) + # Get metric name as string + if hasattr(metric_fn, 'name'): + metric_name = metric_fn.name + else: + metric_name = metric_fn.__name__ + metric_name = metric_name_prefix + metric_name with K.name_scope(metric_name): metric_result = weighted_metric_fn( y_true, y_pred, weights=weights, mask=masks[i]) - append_metric(i, metric_name, metric_result) + + # Append to self.metrics_names, self.metric_tensors, + # self.stateful_metric_names + if len(self.output_names) > 1: + metric_name = '%s_%s' % (self.output_names[i], metric_name) + # Dedupe name + j = 1 + base_metric_name = metric_name + while metric_name in self.metrics_names: + metric_name = '%s_%d' % (base_metric_name, j) + j += 1 + self.metrics_names.append(metric_name) + self.metrics_tensors.append(metric_result) + + # Keep track of state updates created by + # stateful metrics (i.e. metrics layers). + if isinstance(metric_fn, Layer): + self.stateful_metric_names.append(metric_name) + self.metrics_updates += metric_fn.updates handle_metrics(output_metrics) handle_metrics(output_weighted_metrics, weights=weights) @@ -1018,10 +586,15 @@ class Model(Network): with K.name_scope('training'): with K.name_scope(self.optimizer.__class__.__name__): - training_updates = self.optimizer.get_updates( + # Training updates + updates = self.optimizer.get_updates( params=self._collected_trainable_weights, loss=self.total_loss) - - updates = self.updates + training_updates + # Unconditional updates + updates += self.get_updates_for(None) + # Conditional updates relevant to this model + updates += self.get_updates_for(self._feed_inputs) + # Stateful metrics updates + updates += self.metrics_updates # Gets loss and metrics. Updates weights at each call. self.train_function = K.function( inputs, [self.total_loss] + self.metrics_tensors, @@ -1042,7 +615,7 @@ class Model(Network): # Does update the network states. self.test_function = K.function( inputs, [self.total_loss] + self.metrics_tensors, - updates=self.state_updates, + updates=self.state_updates + self.metrics_updates, name='test_function', **self._function_kwargs) @@ -1064,467 +637,215 @@ class Model(Network): name='predict_function', **kwargs) - def _check_num_samples(self, - ins, - batch_size=None, - steps=None, - steps_name='steps'): - """Determine the number of samples provided for training and evaluation. - - The number of samples is not defined when running with `steps`, - in which case the number of samples is set to `None`. - - Arguments: - ins: List of tensors to be fed to the Keras function. - batch_size: Integer batch size or `None` if not defined. - steps: Total number of steps (batches of samples) - before declaring `_predict_loop` finished. - Ignored with the default value of `None`. - steps_name: The public API's parameter name for `steps`. - - Raises: - ValueError: when `steps` is `None` and the attribute `ins.shape` - does not exist. Also raises ValueError when `steps` is not `None` - and `batch_size` is not `None` because they are mutually - exclusive. - - Returns: - When steps is `None`, returns the number of samples to be - processed based on the size of the first dimension of the - first input numpy array. When steps is not `None` and - `batch_size` is `None`, returns `None`. - - Raises: - ValueError: In case of invalid arguments. - """ - if steps is not None: - num_samples = None - if batch_size is not None: - raise ValueError( - 'If ' + steps_name + ' is set, the `batch_size` must be None.') - elif ins and hasattr(ins[0], 'shape'): - num_samples = ins[0].shape[0] - else: - raise ValueError( - 'Either the input data should have ' - 'a defined shape, or ' + steps_name + ' should be specified.') - return num_samples - - def _fit_loop(self, - f, - ins, - out_labels=None, - batch_size=None, - epochs=100, - verbose=1, - callbacks=None, - val_f=None, - val_ins=None, - shuffle=True, - callback_metrics=None, - initial_epoch=0, - steps_per_epoch=None, - validation_steps=None): - """Abstract fit function for `f(ins)`. - - Assume that f returns a list, labeled by out_labels. - - Arguments: - f: Keras function returning a list of tensors - ins: List of tensors to be fed to `f` - out_labels: List of strings, display names of - the outputs of `f` - batch_size: Integer batch size or None if unknown. - epochs: Number of times to iterate over the data - verbose: Verbosity mode, 0, 1 or 2 - callbacks: List of callbacks to be called during training - val_f: Keras function to call for validation - val_ins: List of tensors to be fed to `val_f` - shuffle: Whether to shuffle the data at the beginning of each epoch - callback_metrics: List of strings, the display names of the metrics - passed to the callbacks. They should be the - concatenation of list the display names of the outputs of - `f` and the list of display names of the outputs of `f_val`. - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run) - steps_per_epoch: Total number of steps (batches of samples) - before declaring one epoch finished and starting the - next epoch. Ignored with the default value of `None`. - validation_steps: Number of steps to run validation for - (only if doing validation from data tensors). - Ignored with the default value of `None`. + def _standardize_user_data(self, + x, + y=None, + sample_weight=None, + class_weight=None, + batch_size=None): + """Runs validation checks on input and target data passed by the user. + + Also standardizes the data to lists of arrays, in order. + + Also builds and compiles the model on the fly if it is a subclassed model + that has never been called before (and thus has no inputs/outputs). + + This is a purely internal method, subject to refactoring at any time. + + Args: + x: An array or list of arrays, to be used as input data. If the model + has known, named inputs, this could also be a dict mapping input names + to the corresponding array. + y: An array or list of arrays, to be used as target data. If the model + has known, named outputs, this could also be a dict mapping output names + to the corresponding array. + sample_weight: An optional sample-weight array passed by the user to + weight the importance of each sample in `x`. + class_weight: An optional class-weight array by the user to + weight the importance of samples in `x` based on the class they belong + to, as conveyed by `y`. + batch_size: Integer batch size. If provided, it is used to run additional + validation checks on stateful models. Returns: - `History` object. + A tuple of 3 lists: input arrays, target arrays, sample-weight arrays. + If the model's input and targets are symbolic, these lists are empty + (since the model takes no user-provided data, instead the data comes + from the symbolic inputs/targets). Raises: - ValueError: in case of invalid arguments. + ValueError: In case of invalid user-provided data. + RuntimeError: If the model was never compiled. """ - do_validation = False - if val_f and val_ins: - do_validation = True - if verbose and ins and hasattr(ins[0], 'shape') and hasattr( - val_ins[0], 'shape'): - print('Train on %d samples, validate on %d samples' % - (ins[0].shape[0], val_ins[0].shape[0])) - if validation_steps: - do_validation = True - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` ' - 'when doing step-wise ' - 'training, i.e. `steps_per_epoch` ' - 'must be set.') - - num_train_samples = self._check_num_samples( - ins, batch_size, steps_per_epoch, 'steps_per_epoch') - if num_train_samples is not None: - index_array = np.arange(num_train_samples) - - self.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history] - if verbose: - if steps_per_epoch is not None: - count_mode = 'steps' + # First, we build/compile the model on the fly if necessary. + all_inputs = [] + if not self.built: + # We need to use `x` to set the model inputs. + # We type-check that `x` and `y` are either single arrays + # or lists of arrays. + if isinstance(x, (list, tuple)): + if not all(isinstance(v, np.ndarray) or + tensor_util.is_tensor(v) for v in x): + raise ValueError('Please provide as model inputs either a single ' + 'array or a list of arrays. You passed: x=' + str(x)) + all_inputs += list(x) + elif isinstance(x, dict): + raise ValueError('Please do not pass a dictionary as model inputs.') else: - count_mode = 'samples' - callbacks += [cbks.ProgbarLogger(count_mode)] - callbacks = cbks.CallbackList(callbacks) - out_labels = out_labels or [] - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(self, 'callback_model') and self.callback_model: - callback_model = self.callback_model + if not isinstance(x, np.ndarray) and not tensor_util.is_tensor(x): + raise ValueError('Please provide as model inputs either a single ' + 'array or a list of arrays. You passed: x=' + str(x)) + all_inputs.append(x) + + # Build the model using the retrieved inputs (value or symbolic). + # If values, then in symbolic-mode placeholders will be created + # to match the value shapes. + if not self.inputs: + self._set_inputs(x) + + if y is not None: + if not self.optimizer: + raise RuntimeError('You must compile a model before ' + 'training/testing. ' + 'Use `model.compile(optimizer, loss)`.') + if not self._is_compiled: + # On-the-fly compilation of the model. + # We need to use `y` to set the model targets. + if isinstance(y, (list, tuple)): + if not all(isinstance(v, np.ndarray) or + tensor_util.is_tensor(v) for v in y): + raise ValueError('Please provide as model targets either a single ' + 'array or a list of arrays. ' + 'You passed: y=' + str(y)) + elif isinstance(y, dict): + raise ValueError('Please do not pass a dictionary as model targets.') + else: + if not isinstance(y, np.ndarray) and not tensor_util.is_tensor(y): + raise ValueError('Please provide as model targets either a single ' + 'array or a list of arrays. ' + 'You passed: y=' + str(y)) + + # Typecheck that all inputs are *either* value *or* symbolic. + # TODO(fchollet): this check could be removed in Eager mode? + if y is not None: + if isinstance(y, (list, tuple)): + all_inputs += list(y) + else: + all_inputs.append(y) + if any(tensor_util.is_tensor(v) for v in all_inputs): + if not all(tensor_util.is_tensor(v) for v in all_inputs): + raise ValueError('Do not pass inputs that mix Numpy arrays and ' + 'TensorFlow tensors. ' + 'You passed: x=' + str(x) + '; y=' + str(y)) + + if context.executing_eagerly(): + target_tensors = None + else: + # Handle target tensors if any passed. + if not isinstance(y, (list, tuple)): + y = [y] + target_tensors = [v for v in y if tensor_util.is_tensor(v)] + self.compile(optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, + loss_weights=self.loss_weights, + target_tensors=target_tensors) + + # If `x` and `y` were all symbolic, then no model should not be fed any + # inputs and targets. + # Note: in this case, `any` and `all` are equivalent since we disallow + # mixed symbolic/value inputs. + if any(tensor_util.is_tensor(v) for v in all_inputs): + return [], [], [] + + # What follows is input validation and standardization to list format, + # in the case where all inputs are value arrays. + + if context.executing_eagerly(): + # In eager mode, do not do shape validation. + feed_input_names = self.input_names + feed_input_shapes = None + elif not self._is_graph_network: + # Case: symbolic-mode subclassed network. Do not do shape validation. + feed_input_names = self._feed_input_names + feed_input_shapes = None else: - callback_model = self - - callbacks.set_model(callback_model) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - callbacks.on_train_begin() - callback_model.stop_training = False - for cbk in callbacks: - cbk.validation_data = val_ins - - # To prevent a slowdown, we find beforehand the arrays that need conversion. - feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights - indices_for_conversion_to_dense = [] - for i in range(len(feed)): - if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): - indices_for_conversion_to_dense.append(i) - - for epoch in range(initial_epoch, epochs): - callbacks.on_epoch_begin(epoch) - epoch_logs = {} - if steps_per_epoch is not None: - for step_index in range(steps_per_epoch): - batch_logs = {} - batch_logs['batch'] = step_index - batch_logs['size'] = 1 - callbacks.on_batch_begin(step_index, batch_logs) - outs = f(ins) - - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(step_index, batch_logs) - if callback_model.stop_training: - break - - if do_validation: - val_outs = self._test_loop( - val_f, - val_ins, - batch_size=batch_size, - steps=validation_steps, - verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - else: - if shuffle == 'batch': - index_array = _batch_shuffle(index_array, batch_size) - elif shuffle: - np.random.shuffle(index_array) - - batches = _make_batches(num_train_samples, batch_size) - - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - try: - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - except TypeError: - raise TypeError('TypeError while preparing batch. ' - 'If using HDF5 input data, ' - 'pass shuffle="batch".') - batch_logs = {} - batch_logs['batch'] = batch_index - batch_logs['size'] = len(batch_ids) - callbacks.on_batch_begin(batch_index, batch_logs) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - outs = f(ins_batch) - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(batch_index, batch_logs) - if callback_model.stop_training: - break - - if batch_index == len(batches) - 1: # Last batch. - if do_validation: - val_outs = self._test_loop( - val_f, val_ins, batch_size=batch_size, verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: - break - callbacks.on_train_end() - return self.history - - def _predict_loop(self, f, ins, batch_size=32, verbose=0, steps=None): - """Abstract method to loop over some data in batches. + # Case: symbolic-mode graph network. + # In this case, we run extensive shape validation checks. + feed_input_names = self._feed_input_names + feed_input_shapes = self._feed_input_shapes - Arguments: - f: Keras function returning a list of tensors. - ins: list of tensors to be fed to `f`. - batch_size: integer batch size. - verbose: verbosity mode. - steps: Total number of steps (batches of samples) - before declaring `_predict_loop` finished. - Ignored with the default value of `None`. + # Standardize the inputs. + x = training_utils.standardize_input_data( + x, + feed_input_names, + feed_input_shapes, + check_batch_axis=False, # Don't enforce the batch size. + exception_prefix='input') - Returns: - Array of predictions (if the model has a single output) - or list of arrays of predictions - (if the model has multiple outputs). - """ - num_samples = self._check_num_samples(ins, batch_size, steps, 'steps') - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps) + if y is not None: + if context.executing_eagerly(): + feed_output_names = self.output_names + feed_output_shapes = None + # Sample weighting not supported in this case. + # TODO(fchollet): consider supporting it. + feed_sample_weight_modes = [None for _ in self.outputs] + elif not self._is_graph_network: + feed_output_names = self._feed_output_names + feed_output_shapes = None + # Sample weighting not supported in this case. + # TODO(fchollet): consider supporting it. + feed_sample_weight_modes = [None for _ in self.outputs] else: - progbar = Progbar(target=num_samples) - - indices_for_conversion_to_dense = [] - for i in range(len(self._feed_inputs)): - if (issparse is not None and issparse(ins[i]) and - not K.is_sparse(self._feed_inputs[i])): - indices_for_conversion_to_dense.append(i) - - if steps is not None: - # Step-based predictions. - # Since we do not know how many samples - # we will see, we cannot pre-allocate - # the returned Numpy arrays. - # Instead, we store one array per batch seen - # and concatenate them upon returning. - unconcatenated_outs = [] - for step in range(steps): - batch_outs = f(ins) - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if step == 0: - for batch_out in batch_outs: - unconcatenated_outs.append([]) - for i, batch_out in enumerate(batch_outs): - unconcatenated_outs[i].append(batch_out) - if verbose == 1: - progbar.update(step + 1) - if len(unconcatenated_outs) == 1: - return np.concatenate(unconcatenated_outs[0], axis=0) - return [ - np.concatenate(unconcatenated_outs[i], axis=0) - for i in range(len(unconcatenated_outs)) + feed_output_names = self._feed_output_names + feed_sample_weight_modes = self._feed_sample_weight_modes + feed_output_shapes = [] + for output_shape, loss_fn in zip(self._feed_output_shapes, + self._feed_loss_fns): + if loss_fn is losses.sparse_categorical_crossentropy: + feed_output_shapes.append(output_shape[:-1] + (1,)) + elif (not hasattr(loss_fn, '__name__') or + getattr(losses, loss_fn.__name__, None) is None): + # If `loss_fn` is not a function (e.g. callable class) + # or if it not in the `losses` module, then + # it is a user-defined loss and we make no assumptions + # about it. + feed_output_shapes.append(None) + else: + feed_output_shapes.append(output_shape) + + # Standardize the outputs. + y = training_utils.standardize_input_data( + y, + feed_output_names, + feed_output_shapes, + check_batch_axis=False, # Don't enforce the batch size. + exception_prefix='target') + + # Generate sample-wise weight values given the `sample_weight` and + # `class_weight` arguments. + sample_weights = training_utils.standardize_sample_weights( + sample_weight, feed_output_names) + class_weights = training_utils.standardize_class_weights( + class_weight, feed_output_names) + sample_weights = [ + training_utils.standardize_weights(ref, sw, cw, mode) + for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, + feed_sample_weight_modes) ] + # Check that all arrays have the same length. + training_utils.check_array_lengths(x, y, sample_weights) + if self._is_graph_network and not context.executing_eagerly(): + # Additional checks to avoid users mistakenly using improper loss fns. + training_utils.check_loss_and_target_compatibility( + y, self._feed_loss_fns, feed_output_shapes) else: - # Sample-based predictions. - outs = [] - batches = _make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if ins and isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - batch_outs = f(ins_batch) - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if batch_index == 0: - # Pre-allocate the results arrays. - for batch_out in batch_outs: - shape = (num_samples,) + batch_out.shape[1:] - outs.append(np.zeros(shape, dtype=batch_out.dtype)) - for i, batch_out in enumerate(batch_outs): - outs[i][batch_start:batch_end] = batch_out - if verbose == 1: - progbar.update(batch_end) - if len(outs) == 1: - return outs[0] - return outs - - def _test_loop(self, f, ins, batch_size=None, verbose=0, steps=None): - """Abstract method to loop over some data in batches. - - Arguments: - f: Keras function returning a list of tensors. - ins: list of tensors to be fed to `f`. - batch_size: integer batch size or `None`. - verbose: verbosity mode. - steps: Total number of steps (batches of samples) - before declaring predictions finished. - Ignored with the default value of `None`. - - Returns: - Scalar loss (if the model has a single output and no metrics) - or list of scalars (if the model has multiple outputs - and/or metrics). The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - """ - num_samples = self._check_num_samples(ins, batch_size, steps, 'steps') - outs = [] - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps) - else: - progbar = Progbar(target=num_samples) - - # To prevent a slowdown, we find beforehand the arrays that need conversion. - feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights - indices_for_conversion_to_dense = [] - for i in range(len(feed)): - if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): - indices_for_conversion_to_dense.append(i) - - if steps is not None: - for step in range(steps): - batch_outs = f(ins) - if isinstance(batch_outs, list): - if step == 0: - for _ in enumerate(batch_outs): - outs.append(0.) - for i, batch_out in enumerate(batch_outs): - outs[i] += batch_out - else: - if step == 0: - outs.append(0.) - outs[0] += batch_outs - if verbose == 1: - progbar.update(step + 1) - for i in range(len(outs)): - outs[i] /= steps - else: - batches = _make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - batch_outs = f(ins_batch) - - if isinstance(batch_outs, list): - if batch_index == 0: - for batch_out in enumerate(batch_outs): - outs.append(0.) - for i, batch_out in enumerate(batch_outs): - outs[i] += batch_out * len(batch_ids) - else: - if batch_index == 0: - outs.append(0.) - outs[0] += batch_outs * len(batch_ids) - if verbose == 1: - progbar.update(batch_end) - for i in range(len(outs)): - outs[i] /= num_samples - if len(outs) == 1: - return outs[0] - return outs + y = [] + sample_weights = [] - def _standardize_user_data(self, - x, - y, - sample_weight=None, - class_weight=None, - check_batch_axis=True, - batch_size=None): - if not hasattr(self, 'optimizer'): - raise RuntimeError('You must compile a model before ' - 'training/testing. ' - 'Use `model.compile(optimizer, loss)`.') - - output_shapes = [] - for output_shape, loss_fn in zip(self._feed_output_shapes, - self._feed_loss_fns): - if loss_fn is losses.sparse_categorical_crossentropy: - output_shapes.append(output_shape[:-1] + (1,)) - elif (not hasattr(loss_fn, '__name__') or - getattr(losses, loss_fn.__name__, None) is None): - # If `loss_fn` is not a function (e.g. callable class) - # or if it not in the `losses` module, then - # it is a user-defined loss and we make no assumptions - # about it. - output_shapes.append(None) - else: - output_shapes.append(output_shape) - x = _standardize_input_data( - x, - self._feed_input_names, - self._feed_input_shapes, - check_batch_axis=False, - exception_prefix='input') - y = _standardize_input_data( - y, - self._feed_output_names, - output_shapes, - check_batch_axis=False, - exception_prefix='target') - sample_weights = _standardize_sample_weights(sample_weight, - self._feed_output_names) - class_weights = _standardize_class_weights(class_weight, - self._feed_output_names) - sample_weights = [ - _standardize_weights(ref, sw, cw, mode) - for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, - self._feed_sample_weight_modes) - ] - _check_array_lengths(x, y, sample_weights) - _check_loss_and_target_compatibility(y, self._feed_loss_fns, - self._feed_output_shapes) if self.stateful and batch_size: + # Check that for stateful networks, number of samples is a multiple + # of the static batch size. if x[0].shape[0] % batch_size != 0: raise ValueError('In a stateful network, ' 'you should only pass inputs with ' @@ -1533,19 +854,164 @@ class Model(Network): str(x[0].shape[0]) + ' samples') return x, y, sample_weights - def _get_deduped_metrics_names(self): - out_labels = self.metrics_names + def _set_inputs(self, inputs, training=None): + """Set model's input and output specs based on the input data received. + + This is to be used for Model subclasses, which do not know at instantiation + time what their inputs look like. + + Args: + inputs: Single array, or list of arrays. The arrays could be placeholders, + Numpy arrays, or data tensors. + - if placeholders: the model is built on top of these placeholders, + and we expect Numpy data to be fed for them when calling `fit`/etc. + - if Numpy data: we create placeholders matching the shape of the Numpy + arrays. We expect Numpy data to be fed for these placeholders + when calling `fit`/etc. + - if data tensors: the model is built on top of these tensors. + We do not expect any Numpy data to be provided when calling `fit`/etc. + training: Boolean or None. Only relevant in symbolic mode. Specifies + whether to build the model's graph in inference mode (False), training + mode (True), or using the Keras learning phase (None). + """ + if context.executing_eagerly(): + self._eager_set_inputs(inputs) + else: + self._symbolic_set_inputs(inputs, training=training) + + def _set_scope(self, scope=None): + """Modify the Layer scope creation logic to create ResourceVariables.""" + super(Model, self)._set_scope(scope=scope) + # Subclassed Models create ResourceVariables by default. This makes it + # easier to use Models in an eager/graph agnostic way (since eager execution + # always uses ResourceVariables). + if not self._is_graph_network: + self._scope.set_use_resource(True) + + def _eager_set_inputs(self, inputs): + """Set model's input and output specs based on the input data received. - # Rename duplicated metrics name - # (can happen with an output layer shared among multiple dataflows). - deduped_out_labels = [] - for i, label in enumerate(out_labels): - new_label = label - if out_labels.count(label) > 1: - dup_idx = out_labels[:i].count(label) - new_label += '_' + str(dup_idx + 1) - deduped_out_labels.append(new_label) - return deduped_out_labels + This is to be used for Model subclasses, which do not know at instantiation + time what their inputs look like. + + We assume the number and ndim of outputs + does not change over different calls. + + Args: + inputs: Argument `x` (input data) passed by the user upon first model use. + + Raises: + ValueError: If the model's inputs are already set. + """ + assert context.executing_eagerly() + if self.inputs: + raise ValueError('Model inputs are already set.') + # On-the-fly setting of model inputs/outputs as DeferredTensors, + # to keep track of number of inputs and outputs and their ndim. + if isinstance(inputs, (list, tuple)): + dummy_output_values = self.call( + [ops.convert_to_tensor(v, dtype=K.floatx()) for v in inputs]) + dummy_input_values = list(inputs) + else: + dummy_output_values = self.call( + ops.convert_to_tensor(inputs, dtype=K.floatx())) + dummy_input_values = [inputs] + if isinstance(dummy_output_values, (list, tuple)): + dummy_output_values = list(dummy_output_values) + else: + dummy_output_values = [dummy_output_values] + self.outputs = [ + _DeferredTensor(shape=(None for _ in v.shape), + dtype=v.dtype) for v in dummy_output_values] + self.inputs = [ + _DeferredTensor(shape=(None for _ in v.shape), + dtype=v.dtype) for v in dummy_input_values] + self.input_names = [ + 'input_%d' % (i + 1) for i in range(len(dummy_input_values))] + self.output_names = [ + 'output_%d' % (i + 1) for i in range(len(dummy_output_values))] + self.built = True + + def _symbolic_set_inputs(self, inputs, outputs=None, training=None): + """Set model's inputs and output specs based. + + This is to be used for Model subclasses, which do not know at instantiation + time what their inputs look like. + + Args: + inputs: Argument `x` (input data) passed by the user upon first model use. + outputs: None, a data tensor, or a list of data tensors. If None, the + outputs will be determined by invoking self.call(), otherwise the + provided value will be used. + training: Boolean or None. Only relevant in symbolic mode. Specifies + whether to build the model's graph in inference mode (False), training + mode (True), or using the Keras learning phase (None). + + Raises: + ValueError: If the model's inputs are already set. + """ + assert not context.executing_eagerly() + if self.inputs: + raise ValueError('Model inputs are already set.') + + # On-the-fly setting of symbolic model inputs (either by using the tensor + # provided, or by creating a placeholder if Numpy data was provided). + self.inputs = [] + self.input_names = [] + self._feed_inputs = [] + self._feed_input_names = [] + self._feed_input_shapes = [] + if isinstance(inputs, (list, tuple)): + inputs = list(inputs) + else: + inputs = [inputs] + + for i, v in enumerate(inputs): + name = 'input_%d' % (i + 1) + self.input_names.append(name) + if isinstance(v, list): + v = np.asarray(v) + if v.ndim == 1: + v = np.expand_dims(v, 1) + if isinstance(v, (np.ndarray)): + # We fix the placeholder shape except the batch size. + # This is suboptimal, but it is the best we can do with the info + # we have. The user should call `model._set_inputs(placeholders)` + # to specify custom placeholders if the need arises. + shape = (None,) + v.shape[1:] + placeholder = K.placeholder(shape=shape, name=name) + self.inputs.append(placeholder) + self._feed_inputs.append(placeholder) + self._feed_input_names.append(name) + self._feed_input_shapes.append(shape) + else: + # Assumed tensor - TODO(fchollet) additional type check? + self.inputs.append(v) + if K.is_placeholder(v): + self._feed_inputs.append(v) + self._feed_input_names.append(name) + self._feed_input_shapes.append(K.int_shape(v)) + + if outputs is None: + # Obtain symbolic outputs by calling the model. + if len(self.inputs) == 1: + if self._expects_training_arg: + outputs = self.call(self.inputs[0], training=training) + else: + outputs = self.call(self.inputs[0]) + else: + if self._expects_training_arg: + outputs = self.call(self.inputs, training=training) + else: + outputs = self.call(self.inputs) + if isinstance(outputs, (list, tuple)): + outputs = list(outputs) + else: + outputs = [outputs] + self.outputs = outputs + self.output_names = [ + 'output_%d' % (i + 1) for i in range(len(self.outputs))] + self.built = True def fit(self, x=None, @@ -1656,6 +1122,9 @@ class Model(Network): ValueError: In case of mismatch between the provided input data and what the model expects. """ + # TODO(fchollet): this method may be creating reference cycles, which would + # lead to accumulating garbage in memory when called in a loop. Investigate. + # Backwards compatibility if batch_size is None and steps_per_epoch is None: batch_size = 32 @@ -1671,19 +1140,16 @@ class Model(Network): raise ValueError('If fitting from data tensors, ' 'you should specify the `steps_per_epoch` ' 'argument.') + # Validate user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight, - check_batch_axis=False, batch_size=batch_size) # Prepare validation data. - do_validation = False - val_ins = [] if validation_data: - do_validation = True if len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence val_sample_weight = None @@ -1700,94 +1166,55 @@ class Model(Network): val_x, val_y, sample_weight=val_sample_weight, - check_batch_axis=False, batch_size=batch_size) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = val_x + val_y + val_sample_weights + [0.] - else: - val_ins = val_x + val_y + val_sample_weights elif validation_split and 0. < validation_split < 1.: - do_validation = True if hasattr(x[0], 'shape'): split_at = int(x[0].shape[0] * (1. - validation_split)) else: split_at = int(len(x[0]) * (1. - validation_split)) - x, val_x = (_slice_arrays(x, 0, split_at), _slice_arrays(x, split_at)) - y, val_y = (_slice_arrays(y, 0, split_at), _slice_arrays(y, split_at)) - sample_weights, val_sample_weights = (_slice_arrays( - sample_weights, 0, split_at), _slice_arrays(sample_weights, split_at)) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = val_x + val_y + val_sample_weights + [0.] - else: - val_ins = val_x + val_y + val_sample_weights - + x, val_x = (slice_arrays(x, 0, split_at), slice_arrays(x, split_at)) + y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at)) + sample_weights, val_sample_weights = (slice_arrays( + sample_weights, 0, split_at), slice_arrays(sample_weights, split_at)) elif validation_steps: - do_validation = True - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = [0.] - - # Prepare input arrays and training function. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [1.] + val_x = [] + val_y = [] + val_sample_weights = [] else: - ins = x + y + sample_weights - - # Prepare display labels. - out_labels = self._get_deduped_metrics_names() - - if context.in_eager_mode(): - if do_validation: - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - callback_metrics = copy.copy(out_labels) + val_x = None + val_y = None + val_sample_weights = None + if context.executing_eagerly(): return training_eager.fit_loop( self, - ins, - out_labels=out_labels, + inputs=x, + targets=y, + sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, - val_ins=val_ins, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, shuffle=shuffle, - callback_metrics=callback_metrics, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) else: - self._make_train_function() - f = self.train_function - - if do_validation: - if context.in_graph_mode(): - self._make_test_function() - val_f = self.test_function - else: - val_f = None - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - val_f = None - callback_metrics = copy.copy(out_labels) - - # Delegate logic to `_fit_loop`. - return self._fit_loop( - f, - ins, - out_labels=out_labels, + return training_arrays.fit_loop( + self, x, y, + sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, - val_f=val_f, - val_ins=val_ins, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, shuffle=shuffle, - callback_metrics=callback_metrics, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) @@ -1854,27 +1281,22 @@ class Model(Network): raise ValueError('If evaluating from data tensors, ' 'you should specify the `steps` ' 'argument.') + # Validate user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, - check_batch_axis=False, batch_size=batch_size) - # Prepare inputs, delegate logic to `_test_loop`. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [0.] - else: - ins = x + y + sample_weights - if context.in_eager_mode(): + if context.executing_eagerly(): return training_eager.test_loop( - self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + self, inputs=x, targets=y, sample_weights=sample_weights, + batch_size=batch_size, verbose=verbose, steps=steps) else: - self._make_test_function() - f = self.test_function - return self._test_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + return training_arrays.test_loop( + self, inputs=x, targets=y, sample_weights=sample_weights, + batch_size=batch_size, verbose=verbose, steps=steps) def predict(self, x, batch_size=None, verbose=0, steps=None): """Generates output predictions for the input samples. @@ -1906,36 +1328,14 @@ class Model(Network): raise ValueError('If predicting from data tensors, ' 'you should specify the `steps` ' 'argument.') - # Validate user data. - x = _standardize_input_data( - x, - self._feed_input_names, - self._feed_input_shapes, - check_batch_axis=False) - if self.stateful: - if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0: - raise ValueError('In a stateful network, ' - 'you should only pass inputs with ' - 'a number of samples that can be ' - 'divided by the batch size. Found: ' + - str(x[0].shape[0]) + ' samples. ' - 'Batch size: ' + str(batch_size) + '.') - - # Prepare inputs, delegate logic to `_predict_loop`. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + [0.] - else: - ins = x + x, _, _ = self._standardize_user_data(x) - if context.in_eager_mode(): + if context.executing_eagerly(): return training_eager.predict_loop( - self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + self, x, batch_size=batch_size, verbose=verbose, steps=steps) else: - self._make_predict_function() - f = self.predict_function - - return self._predict_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + return training_arrays.predict_loop( + self, x, batch_size=batch_size, verbose=verbose, steps=steps) def train_on_batch(self, x, y, sample_weight=None, class_weight=None): """Runs a single gradient update on a single batch of data. @@ -1972,27 +1372,30 @@ class Model(Network): and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. + Raises: + ValueError: In case of invalid user-provided arguments. """ x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, - class_weight=class_weight, - check_batch_axis=True) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [1.] - else: - ins = x + y + sample_weights + class_weight=class_weight) - if context.in_eager_mode(): - return training_eager.train_on_batch(self, ins) + if context.executing_eagerly(): + outputs = training_eager.train_on_batch( + self, x, y, sample_weights=sample_weights) + else: + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + y + sample_weights + [1] + else: + ins = x + y + sample_weights - if context.in_graph_mode(): self._make_train_function() outputs = self.train_function(ins) - if len(outputs) == 1: - return outputs[0] - return outputs + + if len(outputs) == 1: + return outputs[0] + return outputs def test_on_batch(self, x, y, sample_weight=None): """Test the model on a single batch of samples. @@ -2023,24 +1426,25 @@ class Model(Network): the display labels for the scalar outputs. Raises: - ValueError: in case of invalid arguments. + ValueError: In case of invalid user-provided arguments. """ x, y, sample_weights = self._standardize_user_data( - x, y, sample_weight=sample_weight, check_batch_axis=True) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [0.] - else: - ins = x + y + sample_weights + x, y, sample_weight=sample_weight) - if context.in_eager_mode(): - return training_eager.test_on_batch(self, ins) - - if context.in_graph_mode(): + if context.executing_eagerly(): + outputs = training_eager.test_on_batch( + self, x, y, sample_weights=sample_weights) + else: + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + y + sample_weights + [0] + else: + ins = x + y + sample_weights self._make_test_function() outputs = self.test_function(ins) - if len(outputs) == 1: - return outputs[0] - return outputs + + if len(outputs) == 1: + return outputs[0] + return outputs def predict_on_batch(self, x): """Returns predictions for a single batch of samples. @@ -2052,28 +1456,21 @@ class Model(Network): Numpy array(s) of predictions. """ - x = _standardize_input_data(x, self._feed_input_names, - self._feed_input_shapes) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + [0.] - else: - ins = x + x, _, _ = self._standardize_user_data(x) - if context.in_eager_mode(): - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + if context.executing_eagerly(): + inputs = [ops.convert_to_tensor(val, dtype=K.floatx()) for val in x] + return self(inputs) # pylint: disable=not-callable - eager_model_inputs = [] - for i in range(len(self.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - outs = self(eager_model_inputs) # pylint: disable=not-callable - return outs + if not context.executing_eagerly(): + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + [0] + else: + ins = x - if context.in_graph_mode(): self._make_predict_function() outputs = self.predict_function(ins) + if len(outputs) == 1: return outputs[0] return outputs @@ -2185,213 +1582,25 @@ class Model(Network): ValueError: In case the generator yields data in an invalid format. """ - wait_time = 0.01 # in seconds - epoch = initial_epoch - - do_validation = bool(validation_data) - self._make_train_function() - if do_validation: - self._make_test_function() - - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps_per_epoch is None: - if is_sequence: - steps_per_epoch = len(generator) - else: - raise ValueError('`steps_per_epoch=None` is only valid for a' - ' generator based on the `keras.utils.Sequence`' - ' class. Please specify `steps_per_epoch` or use' - ' the `keras.utils.Sequence` class.') - - # python 2 has 'next', 3 has '__next__' - # avoid any explicit version checks - val_gen = ( - hasattr(validation_data, 'next') or - hasattr(validation_data, '__next__') or - isinstance(validation_data, Sequence)) - if (val_gen and not isinstance(validation_data, Sequence) and - not validation_steps): - raise ValueError('`validation_steps=None` is only valid for a' - ' generator based on the `keras.utils.Sequence`' - ' class. Please specify `validation_steps` or use' - ' the `keras.utils.Sequence` class.') - - # Prepare display labels. - out_labels = self._get_deduped_metrics_names() - callback_metrics = out_labels + ['val_' + n for n in out_labels] - - # prepare callbacks - self.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history] - if verbose: - callbacks += [cbks.ProgbarLogger(count_mode='steps')] - callbacks = cbks.CallbackList(callbacks) - - # it's possible to callback a different model than self: - if hasattr(self, 'callback_model') and self.callback_model: - callback_model = self.callback_model - else: - callback_model = self - callbacks.set_model(callback_model) - callbacks.set_params({ - 'epochs': epochs, - 'steps': steps_per_epoch, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics, - }) - callbacks.on_train_begin() - - enqueuer = None - val_enqueuer = None - - try: - if do_validation: - if val_gen: - if workers > 0: - if isinstance(validation_data, Sequence): - val_enqueuer = OrderedEnqueuer( - validation_data, use_multiprocessing=use_multiprocessing) - if validation_steps is None: - validation_steps = len(validation_data) - else: - val_enqueuer = GeneratorEnqueuer( - validation_data, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - val_enqueuer.start(workers=workers, max_queue_size=max_queue_size) - validation_generator = val_enqueuer.get() - else: - validation_generator = validation_data - else: - if len(validation_data) == 2: - val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence - val_sample_weight = None - elif len(validation_data) == 3: - val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence - else: - raise ValueError( - '`validation_data` should be a tuple ' - '`(val_x, val_y, val_sample_weight)` ' - 'or `(val_x, val_y)`. Found: ' + str(validation_data)) - val_x, val_y, val_sample_weights = self._standardize_user_data( - val_x, val_y, val_sample_weight) - val_data = val_x + val_y + val_sample_weights - if self.uses_learning_phase and not isinstance( - K.learning_phase(), int): - val_data += [0.] - for cbk in callbacks: - cbk.validation_data = val_data - - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - shuffle=shuffle) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - callback_model.stop_training = False - # Construct epoch logs. - epoch_logs = {} - while epoch < epochs: - callbacks.on_epoch_begin(epoch) - steps_done = 0 - batch_index = 0 - while steps_done < steps_per_epoch: - generator_output = next(output_generator) - - if not hasattr(generator_output, '__len__'): - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - - if len(generator_output) == 2: - x, y = generator_output - sample_weight = None - elif len(generator_output) == 3: - x, y, sample_weight = generator_output - else: - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - # build batch logs - batch_logs = {} - if isinstance(x, list): - batch_size = x[0].shape[0] - elif isinstance(x, dict): - batch_size = list(x.values())[0].shape[0] - else: - batch_size = x.shape[0] - batch_logs['batch'] = batch_index - batch_logs['size'] = batch_size - callbacks.on_batch_begin(batch_index, batch_logs) - - outs = self.train_on_batch( - x, y, sample_weight=sample_weight, class_weight=class_weight) - - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(batch_index, batch_logs) - - batch_index += 1 - steps_done += 1 - - # Epoch finished. - if steps_done >= steps_per_epoch and do_validation: - if val_gen: - val_outs = self.evaluate_generator( - validation_generator, validation_steps, workers=0) - else: - # No need for try/except because - # data has already been validated. - val_outs = self.evaluate( - val_x, - val_y, - batch_size=batch_size, - sample_weight=val_sample_weights, - verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - - if callback_model.stop_training: - break - - callbacks.on_epoch_end(epoch, epoch_logs) - epoch += 1 - if callback_model.stop_training: - break - - finally: - try: - if enqueuer is not None: - enqueuer.stop() - finally: - if val_enqueuer is not None: - val_enqueuer.stop() - - callbacks.on_train_end() - return self.history + if not self._is_graph_network: + raise NotImplementedError( + '`fit_generator` is not yet enabled for Model subclasses') + + return training_generator.fit_generator( + self, + generator, + steps_per_epoch=steps_per_epoch, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + validation_data=validation_data, + validation_steps=validation_steps, + class_weight=class_weight, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle, + initial_epoch=initial_epoch) def evaluate_generator(self, generator, @@ -2440,87 +1649,17 @@ class Model(Network): ValueError: In case the generator yields data in an invalid format. """ - self._make_test_function() - - steps_done = 0 - wait_time = 0.01 - all_outs = [] - batch_sizes = [] - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps is None: - if is_sequence: - steps = len(generator) - else: - raise ValueError('`steps=None` is only valid for a generator' - ' based on the `keras.utils.Sequence` class.' - ' Please specify `steps` or use the' - ' `keras.utils.Sequence` class.') - enqueuer = None - - try: - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, use_multiprocessing=use_multiprocessing) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - while steps_done < steps: - generator_output = next(output_generator) - if not hasattr(generator_output, '__len__'): - raise ValueError('Output of generator should be a tuple ' - '(x, y, sample_weight) ' - 'or (x, y). Found: ' + str(generator_output)) - if len(generator_output) == 2: - x, y = generator_output - sample_weight = None - elif len(generator_output) == 3: - x, y, sample_weight = generator_output - else: - raise ValueError('Output of generator should be a tuple ' - '(x, y, sample_weight) ' - 'or (x, y). Found: ' + str(generator_output)) - outs = self.test_on_batch(x, y, sample_weight=sample_weight) - - if isinstance(x, list): - batch_size = x[0].shape[0] - elif isinstance(x, dict): - batch_size = list(x.values())[0].shape[0] - else: - batch_size = x.shape[0] - if batch_size == 0: - raise ValueError('Received an empty batch. ' - 'Batches should at least contain one item.') - all_outs.append(outs) - - steps_done += 1 - batch_sizes.append(batch_size) - - finally: - if enqueuer is not None: - enqueuer.stop() - - if not isinstance(outs, list): - return np.average(np.asarray(all_outs), weights=batch_sizes) - else: - averages = [] - for i in range(len(outs)): - averages.append( - np.average([out[i] for out in all_outs], weights=batch_sizes)) - return averages + if not self._is_graph_network: + raise NotImplementedError( + '`evaluate_generator` is not yet enabled for Model subclasses') + + return training_generator.evaluate_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) def predict_generator(self, generator, @@ -2564,88 +1703,15 @@ class Model(Network): ValueError: In case the generator yields data in an invalid format. """ - self._make_predict_function() - - steps_done = 0 - wait_time = 0.01 - all_outs = [] - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps is None: - if is_sequence: - steps = len(generator) - else: - raise ValueError('`steps=None` is only valid for a generator' - ' based on the `keras.utils.Sequence` class.' - ' Please specify `steps` or use the' - ' `keras.utils.Sequence` class.') - enqueuer = None - - try: - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, use_multiprocessing=use_multiprocessing) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - if verbose == 1: - progbar = Progbar(target=steps) - - while steps_done < steps: - generator_output = next(output_generator) - if isinstance(generator_output, tuple): - # Compatibility with the generators - # used for training. - if len(generator_output) == 2: - x, _ = generator_output - elif len(generator_output) == 3: - x, _, _ = generator_output - else: - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - else: - # Assumes a generator that only - # yields inputs (not targets and sample weights). - x = generator_output - - outs = self.predict_on_batch(x) - if not isinstance(outs, list): - outs = [outs] - - if not all_outs: - for out in outs: - all_outs.append([]) - - for i, out in enumerate(outs): - all_outs[i].append(out) - steps_done += 1 - if verbose == 1: - progbar.update(steps_done) - - finally: - if enqueuer is not None: - enqueuer.stop() - - if len(all_outs) == 1: - if steps_done == 1: - return all_outs[0][0] - else: - return np.concatenate(all_outs[0]) - if steps_done == 1: - return [out[0] for out in all_outs] - else: - return [np.concatenate(out) for out in all_outs] + if not self._is_graph_network: + raise NotImplementedError( + '`predict_generator` is not yet enabled for Model subclasses') + + return training_generator.predict_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) diff --git a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py new file mode 100644 index 0000000000000000000000000000000000000000..9291ef5fe6005b616a6a8e038c586c043de595ca --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py @@ -0,0 +1,495 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Part of the Keras training engine related to plain array data. +""" +# pylint: disable=protected-access +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy + +import numpy as np + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import callbacks as cbks +from tensorflow.python.keras._impl.keras.engine import training_utils +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches +from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays + +try: + from scipy.sparse import issparse # pylint: disable=g-import-not-at-top +except ImportError: + issparse = None + + +def fit_loop(model, + inputs, + targets, + sample_weights=None, + batch_size=None, + epochs=100, + verbose=1, + callbacks=None, + val_inputs=None, + val_targets=None, + val_sample_weights=None, + shuffle=True, + callback_metrics=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None): + """Abstract fit function for arrays of data. + + Arguments: + model: Keras Model instance. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + batch_size: Integer batch size or None if unknown. + epochs: Number of times to iterate over the data + verbose: Verbosity mode, 0, 1 or 2 + callbacks: List of callbacks to be called during training + val_inputs: List of input arrays. + val_targets: List of target arrays. + val_sample_weights: Optional list of sample weight arrays. + shuffle: Whether to shuffle the data at the beginning of each epoch + callback_metrics: List of strings, the display names of the metrics + passed to the callbacks. They should be the + concatenation of list the display names of the outputs of + `f` and the list of display names of the outputs of `f_val`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + steps_per_epoch: Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. Ignored with the default value of `None`. + validation_steps: Number of steps to run validation for + (only if doing validation from data tensors). + Ignored with the default value of `None`. + + Returns: + `History` object. + + Raises: + ValueError: in case of invalid arguments. + """ + model._make_train_function() + f = model.train_function + + sample_weights = sample_weights or [] + val_sample_weights = val_sample_weights or [] + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + targets + sample_weights + [1] + if val_inputs: + val_ins = val_inputs + val_targets + val_sample_weights + [1] + else: + ins = inputs + targets + sample_weights + if val_inputs: + val_ins = val_inputs + val_targets + val_sample_weights + if not val_inputs: + val_ins = [] + + do_validation = False + if val_inputs: + do_validation = True + if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr( + val_inputs[0], 'shape'): + print('Train on %d samples, validate on %d samples' % + (inputs[0].shape[0], val_inputs[0].shape[0])) + if validation_steps: + do_validation = True + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` ' + 'when doing step-wise ' + 'training, i.e. `steps_per_epoch` ' + 'must be set.') + + out_labels = model.metrics_names + if do_validation: + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] + else: + callback_metrics = copy.copy(out_labels) + + num_train_samples = training_utils.check_num_samples( + ins, batch_size, steps_per_epoch, 'steps_per_epoch') + if num_train_samples is not None: + index_array = np.arange(num_train_samples) + + model.history = cbks.History() + all_callbacks = [cbks.BaseLogger( + stateful_metrics=model.stateful_metric_names)] + if verbose: + if steps_per_epoch is not None: + count_mode = 'steps' + else: + count_mode = 'samples' + all_callbacks.append( + cbks.ProgbarLogger( + count_mode, stateful_metrics=model.stateful_metric_names)) + all_callbacks += (callbacks or []) + [model.history] + callbacks = cbks.CallbackList(all_callbacks) + out_labels = out_labels or [] + + # it's possible to callback a different model than self + # (used by Sequential models) + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + + callbacks.set_model(callback_model) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + callbacks.on_train_begin() + callback_model.stop_training = False + for cbk in callbacks: + cbk.validation_data = val_ins + + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + + for epoch in range(initial_epoch, epochs): + # Reset stateful metrics + for m in model.metrics: + if isinstance(m, Layer): + m.reset_states() + # Update callbacks + callbacks.on_epoch_begin(epoch) + epoch_logs = {} + if steps_per_epoch is not None: + for step_index in range(steps_per_epoch): + batch_logs = {} + batch_logs['batch'] = step_index + batch_logs['size'] = 1 + callbacks.on_batch_begin(step_index, batch_logs) + outs = f(ins) + + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(step_index, batch_logs) + if callback_model.stop_training: + break + + if do_validation: + val_outs = test_loop( + model, + val_inputs, + val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + steps=validation_steps, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + else: + if shuffle == 'batch': + index_array = training_utils.batch_shuffle(index_array, batch_size) + elif shuffle: + np.random.shuffle(index_array) + + batches = make_batches(num_train_samples, batch_size) + + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + try: + if isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + except TypeError: + raise TypeError('TypeError while preparing batch. ' + 'If using HDF5 input data, ' + 'pass shuffle="batch".') + batch_logs = {} + batch_logs['batch'] = batch_index + batch_logs['size'] = len(batch_ids) + callbacks.on_batch_begin(batch_index, batch_logs) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + outs = f(ins_batch) + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(batch_index, batch_logs) + if callback_model.stop_training: + break + + if batch_index == len(batches) - 1: # Last batch. + if do_validation: + val_outs = test_loop( + model, + val_inputs, + val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + callbacks.on_epoch_end(epoch, epoch_logs) + if callback_model.stop_training: + break + callbacks.on_train_end() + return model.history + + +def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: Keras Model instance. + inputs: list of tensors to be fed to `f`. + batch_size: integer batch size. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring `_predict_loop` finished. + Ignored with the default value of `None`. + + Returns: + Array of predictions (if the model has a single output) + or list of arrays of predictions + (if the model has multiple outputs). + """ + model._make_predict_function() + f = model.predict_function + + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + [0] + else: + ins = inputs + + if hasattr(model, 'metrics'): + for m in model.metrics: + if isinstance(m, Layer): + m.reset_states() + + num_samples = training_utils.check_num_samples( + inputs, batch_size, steps, 'steps') + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps, + stateful_metrics=model.stateful_metric_names) + else: + progbar = Progbar(target=num_samples, + stateful_metrics=model.stateful_metric_names) + + indices_for_conversion_to_dense = [] + for i in range(len(model._feed_inputs)): + if (issparse is not None and issparse(inputs[i]) and + not K.is_sparse(model._feed_inputs[i])): + indices_for_conversion_to_dense.append(i) + + if steps is not None: + # Step-based predictions. + # Since we do not know how many samples + # we will see, we cannot pre-allocate + # the returned Numpy arrays. + # Instead, we store one array per batch seen + # and concatenate them upon returning. + unconcatenated_outs = [] + for step in range(steps): + batch_outs = f(ins) + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if step == 0: + for batch_out in batch_outs: + unconcatenated_outs.append([]) + for i, batch_out in enumerate(batch_outs): + unconcatenated_outs[i].append(batch_out) + if verbose == 1: + progbar.update(step + 1) + if len(unconcatenated_outs) == 1: + return np.concatenate(unconcatenated_outs[0], axis=0) + return [ + np.concatenate(unconcatenated_outs[i], axis=0) + for i in range(len(unconcatenated_outs)) + ] + else: + # Sample-based predictions. + outs = [] + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if ins and isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + batch_outs = f(ins_batch) + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if batch_index == 0: + # Pre-allocate the results arrays. + for batch_out in batch_outs: + shape = (num_samples,) + batch_out.shape[1:] + outs.append(np.zeros(shape, dtype=batch_out.dtype)) + for i, batch_out in enumerate(batch_outs): + outs[i][batch_start:batch_end] = batch_out + if verbose == 1: + progbar.update(batch_end) + if len(outs) == 1: + return outs[0] + return outs + + +def test_loop(model, inputs, targets, + sample_weights=None, + batch_size=None, + verbose=0, + steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: Keras Model instance. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + batch_size: integer batch size or `None`. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring predictions finished. + Ignored with the default value of `None`. + + Returns: + Scalar loss (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + """ + model._make_test_function() + f = model.test_function + + sample_weights = sample_weights or [] + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + targets + sample_weights + [0] + else: + ins = inputs + targets + sample_weights + + if hasattr(model, 'metrics'): + for m in model.metrics: + if isinstance(m, Layer): + m.reset_states() + stateful_metric_indices = [ + i for i, name in enumerate(model.metrics_names) + if str(name) in model.stateful_metric_names + ] + else: + stateful_metric_indices = [] + + num_samples = training_utils.check_num_samples( + ins, batch_size, steps, 'steps') + outs = [] + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) + + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + + if steps is not None: + for step in range(steps): + batch_outs = f(ins) + if isinstance(batch_outs, list): + if step == 0: + for _ in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + if i in stateful_metric_indices: + outs[i] = batch_out + else: + outs[i] += batch_out + else: + if step == 0: + outs.append(0.) + outs[0] += batch_outs + if verbose == 1: + progbar.update(step + 1) + for i in range(len(outs)): + if i not in stateful_metric_indices: + outs[i] /= steps + else: + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + batch_outs = f(ins_batch) + + if isinstance(batch_outs, list): + if batch_index == 0: + for batch_out in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + if i in stateful_metric_indices: + outs[i] = batch_out + else: + outs[i] += batch_out * len(batch_ids) + else: + if batch_index == 0: + outs.append(0.) + outs[0] += batch_outs * len(batch_ids) + if verbose == 1: + progbar.update(batch_end) + for i in range(len(outs)): + if i not in stateful_metric_indices: + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py index 0a115969ca614d8d50a60f8980fa49bf404cc66f..67858a578c5c95b3099e1e6713f3287748fc861f 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py @@ -12,83 +12,29 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras training and evaluation routines. +"""Keras training and evaluation routines for eager execution. """ # pylint: disable=protected-access from __future__ import absolute_import from __future__ import division from __future__ import print_function + +import copy + import numpy as np + from tensorflow.python.eager.backprop import GradientTape from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util -from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import backend from tensorflow.python.keras._impl.keras import callbacks as cbks from tensorflow.python.keras._impl.keras import losses from tensorflow.python.keras._impl.keras import metrics as metrics_module +from tensorflow.python.keras._impl.keras.engine import training_utils +from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar - - -def _make_batches(size, batch_size): - """Returns a list of batch indices (tuples of indices). - - Arguments: - size: Integer, total size of the data to slice into batches. - batch_size: Integer, batch size. - - Returns: - A list of tuples of array indices. - """ - num_batches = int(np.ceil(size / float(batch_size))) - return [(i * batch_size, min(size, (i + 1) * batch_size)) - for i in range(0, num_batches)] - - -def _slice_arrays(arrays, start=None, stop=None): - """Slice an array or list of arrays. - - This takes an array-like, or a list of - array-likes, and outputs: - - arrays[start:stop] if `arrays` is an array-like - - [x[start:stop] for x in arrays] if `arrays` is a list - - Can also work on list/array of indices: `_slice_arrays(x, indices)` - - Arguments: - arrays: Single array or list of arrays. - start: can be an integer index (start index) - or a list/array of indices - stop: integer (stop index); should be None if - `start` was a list. - - Returns: - A slice of the array(s). - - Raises: - ValueError: If the value of start is a list and stop is not None. - """ - if arrays is None: - return [None] - if isinstance(start, list) and stop is not None: - raise ValueError('The stop argument has to be None if the value of start is' - 'a list.') - elif isinstance(arrays, list): - if hasattr(start, '__len__'): - # hdf5 datasets only support list objects as indices - if hasattr(start, 'shape'): - start = start.tolist() - return [None if x is None else x[start] for x in arrays] - else: - return [None if x is None else x[start:stop] for x in arrays] - else: - if hasattr(start, '__len__'): - if hasattr(start, 'shape'): - start = start.tolist() - return arrays[start] - elif hasattr(start, '__getitem__'): - return arrays[start:stop] - else: - return [None] +from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays +from tensorflow.python.platform import tf_logging as logging def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None): @@ -114,7 +60,7 @@ def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None): def _eager_loss_fn(outputs, targets, loss_fn, output_name): - with K.name_scope(output_name + '_loss'): + with backend.name_scope(output_name + '_loss'): loss = loss_fn(targets, outputs) return loss @@ -142,7 +88,7 @@ def _eager_metrics_fn(model, outputs, targets): output_metrics = model.nested_metrics[i] for nested_output_metric in output_metrics: metric_name, metric_fn = _get_metrics_info( - nested_output_metric, model._internal_output_shapes[i], + nested_output_metric, backend.int_shape(model.outputs[i]), model.loss_functions[i]) if len(model.output_names) > 1: @@ -150,22 +96,23 @@ def _eager_metrics_fn(model, outputs, targets): if metric_name not in model.metrics_names: model.metrics_names.append(metric_name) - with K.name_scope(metric_name): + with backend.name_scope(metric_name): metric_result = metric_fn(outputs[i], targets[i]) metric_names.append(metric_name) - metric_results.append(K.mean(metric_result)) + metric_results.append(backend.mean(metric_result)) return metric_names, metric_results -def _model_loss(model, inputs, targets): +def _model_loss(model, inputs, targets, sample_weights=None, training=False): """Calculates the loss for a given model. Arguments: - model: The model on which metrics are being calculated. - inputs: The inputs of the given model. This is typically the mini batch of - data that is fed to the model. - targets: The predictions or targets of the given model. + model: The model on which metrics are being calculated. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + training: Whether the model should be run in inference or training mode. Returns: Returns the model output, total loss and loss value calculated using the @@ -173,7 +120,16 @@ def _model_loss(model, inputs, targets): applies masking and sample weighting to the loss value. """ total_loss = 0 - outs = model(inputs) + if len(inputs) == 1: + if model._expects_training_arg: + outs = model.call(inputs[0], training=training) + else: + outs = model.call(inputs[0]) + else: + if model._expects_training_arg: + outs = model.call(inputs, training=training) + else: + outs = model.call(inputs) if not isinstance(outs, list): outs = [outs] @@ -181,33 +137,22 @@ def _model_loss(model, inputs, targets): targets = [targets] loss_metrics = [] - with K.name_scope('loss'): + with backend.name_scope('loss'): for i, loss_fn in enumerate(model.loss_functions): - # compute the loss - output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn, - model.output_names[i]) - loss_metrics.append(K.mean(output_loss)) + if sample_weights: + weights = sample_weights[i] + else: + weights = None + # TODO(fchollet): support masking; in practice `_keras_mask` is never + # set in this context currently. mask = outs[i]._keras_mask - # adapted from weighted_loss_fn - if mask is not None: - # mask should have the same shape as output_loss - output_loss *= mask - # the loss per batch should be proportional - # to the number of unmasked samples. - output_loss /= K.mean(mask) - - # adapted from weighted_loss_fn - # apply sample weighting - if model.sample_weights: - # reduce score_array to same ndim as weight array - ndim = K.ndim(output_loss) - weight_ndim = K.ndim(model.sample_weights) - output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim))) - output_loss *= model.sample_weights - output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0), - K.floatx())) - output_loss = K.mean(output_loss) + + weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn) + with backend.name_scope(model.output_names[i] + '_loss'): + output_loss = weighted_masked_fn( + outs[i], targets[i], weights, mask=mask) + loss_metrics.append(backend.mean(output_loss)) loss_weight = model.loss_weights_list[i] if total_loss is None: @@ -215,7 +160,7 @@ def _model_loss(model, inputs, targets): else: total_loss += loss_weight * output_loss - total_loss = K.mean(total_loss) + total_loss = backend.mean(total_loss) # Add regularization losses custom_losses = [] for layer in model.layers: @@ -228,16 +173,20 @@ def _model_loss(model, inputs, targets): return outs, total_loss, loss_metrics -def _process_single_batch(eager_model_inputs, eager_model_outputs, model, - training=True): +def _process_single_batch(model, + inputs, + targets, + sample_weights=None, + training=False): """Calculate the loss and gradient for one input batch. The model weights are updated if training is set to True. Arguments: - eager_model_inputs: Input batch data. - eager_model_outputs: Output batch data. model: Model whose loss has to be calculated. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. training: The boolean represents if the weights of the model are updated. 'fit' methods will set this to True while 'evaluate' methods will set this to False. @@ -246,82 +195,83 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model, output of the model, total loss and the loss associated with each output. Raises: - ValueError: If the model loss is 0 or if the trainable weights list is - empty when the trainable parameter is set to True. + ValueError: If the model has no loss to optimize. """ - K.set_learning_phase(training) - with GradientTape() as tape: - outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, - eager_model_outputs) - if loss is None: - raise ValueError('The model cannot be run ' - 'because it has no loss to optimize.') - if training: - if not model._collected_trainable_weights: - raise ValueError('The list of trainable weights is empty. Make sure that ' - 'you are not setting model.trainable to False before ' - 'compiling the model.') - grads = tape.gradient(loss, model._collected_trainable_weights) - model.optimizer.apply_gradients(zip(grads, - model._collected_trainable_weights)) - return outs, loss, loss_metrics - - -def train_on_batch(model, ins): + with backend.learning_phase_scope(1 if training else 0): + with GradientTape() as tape: + outs, loss, loss_metrics = _model_loss(model, inputs, targets, + sample_weights=sample_weights, + training=training) + if loss is None: + raise ValueError('The model cannot be run ' + 'because it has no loss to optimize.') + if training: + if not model._collected_trainable_weights: + logging.warning('The list of trainable weights is empty. Make sure that' + ' you are not setting model.trainable to False before ' + 'compiling the model.') + else: + grads = tape.gradient(loss, model._collected_trainable_weights) + model.optimizer.apply_gradients(zip(grads, + model._collected_trainable_weights)) + return outs, loss, loss_metrics + + +def train_on_batch(model, inputs, targets, sample_weights=None): """Calculates the loss and gradient updates for one input batch. Arguments: - model: Given model on which loss and gradients are calculated. - ins: Input and output batch numpy arrays. + model: Model whose loss has to be calculated. + inputs: Input batch data. + targets: Target batch data. + sample_weights: Sample weight batch data. Returns: total loss and the loss associated with each output. """ - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) + inputs = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs] + targets = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets] + sample_weights = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None for val in sample_weights] outs, loss, _ = _process_single_batch( - eager_model_inputs, eager_model_outputs, model) + model, inputs, targets, sample_weights=sample_weights, training=True) if not isinstance(outs, list): outs = [outs] _, metrics_results = _eager_metrics_fn( - model, outs, eager_model_outputs) + model, outs, targets) if not isinstance(loss, list): loss = [loss] return loss + metrics_results -def test_on_batch(model, ins): +def test_on_batch(model, inputs, targets, sample_weights=None): """Calculates the loss for one input batch. Arguments: - model: Given model on which loss is calculated. - ins: Input and output batch numpy arrays. + model: Model whose loss has to be calculated. + inputs: Input batch data. + targets: Target batch data. + sample_weights: Sample weight batch data. Returns: total loss, loss and metrics associated with each output. """ - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) + inputs = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs] + targets = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets] + sample_weights = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None for val in sample_weights] outs, loss, loss_metrics = _process_single_batch( - eager_model_inputs, eager_model_outputs, model, training=False) + model, inputs, targets, sample_weights=sample_weights, training=False) if not isinstance(outs, list): outs = [outs] metric_names, metrics_results = _eager_metrics_fn( - model, outs, eager_model_outputs) + model, outs, targets) model.metrics_names.append(metric_names) if not isinstance(loss, list): loss = [loss] @@ -330,32 +280,35 @@ def test_on_batch(model, ins): def fit_loop( model, - ins, - out_labels=None, + inputs, + targets, + sample_weights=None, + val_inputs=None, + val_targets=None, + val_sample_weights=None, batch_size=None, epochs=100, verbose=1, callbacks=None, - val_ins=None, shuffle=True, callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): - """Abstract fit function for `f(ins)`. - - Assume that f returns a list, labeled by out_labels. + """Abstract fit function for eager execution. Arguments: model: Instance of the model that is being executed in Eager mode. - ins: List of tensors to be fed to `f` - out_labels: List of strings, display names of - the outputs of `f` + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + val_inputs: Input data for validation. + val_targets: Target data for validation. + val_sample_weights: Sample weight data for validation. batch_size: Integer batch size or None if unknown. epochs: Number of times to iterate over the data verbose: Verbosity mode, 0, 1 or 2 callbacks: List of callbacks to be called during training - val_ins: List of tensors to be fed to `val_f` shuffle: Whether to shuffle the data at the beginning of each epoch callback_metrics: List of strings, the display names of the metrics passed to the callbacks. They should be the @@ -376,164 +329,196 @@ def fit_loop( ValueError: In case of invalid argument values. """ # Required for Eager mode - K.set_learning_phase(True) - - do_validation = False - if val_ins: - do_validation = True - if (verbose and ins and hasattr(ins[0], 'shape') and - hasattr(val_ins[0], 'shape')): - print('Train on %d samples, validate on %d samples' % - (ins[0].shape[0], val_ins[0].shape[0])) - if validation_steps: - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` when doing step-wise ' - 'training, i.e. `steps_per_epoch` must be set.') - do_validation = True - - num_train_samples = model._check_num_samples( - ins, batch_size, steps_per_epoch, 'steps_per_epoch') - - if num_train_samples is not None: - index_array = np.arange(num_train_samples) - - model.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] - if verbose: - if steps_per_epoch is not None: - count_mode = 'steps' + with backend.learning_phase_scope(1): + do_validation = False + if val_inputs: + do_validation = True + if (verbose and inputs and hasattr(inputs[0], 'shape') and + hasattr(val_inputs[0], 'shape')): + print('Train on %d samples, validate on %d samples' % + (inputs[0].shape[0], val_inputs[0].shape[0])) + if validation_steps: + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` when doing step-wise ' + 'training, i.e. `steps_per_epoch` must be set.') + do_validation = True + + out_labels = model.metrics_names + if do_validation: + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] else: - count_mode = 'samples' - callbacks += [cbks.ProgbarLogger(count_mode)] - callbacks = cbks.CallbackList(callbacks) - out_labels = out_labels or [] - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(model, 'callback_model') and model.callback_model: - callback_model = model.callback_model - else: - callback_model = model - - callbacks.set_model(callback_model) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - callbacks.on_train_begin() - callback_model.stop_training = False - for cbk in callbacks: - cbk.validation_data = val_ins - - for epoch in range(initial_epoch, epochs): - callbacks.on_epoch_begin(epoch) - epoch_logs = {} - if shuffle == 'batch': - index_array = model._batch_shuffle(index_array, batch_size) - elif shuffle: - np.random.shuffle(index_array) - - batches = _make_batches(num_train_samples, batch_size) + callback_metrics = copy.copy(out_labels) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - try: - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - except TypeError: - raise TypeError('TypeError while preparing batch. ' - 'If using HDF5 input data, ' - 'pass shuffle="batch".') - batch_logs = {} - batch_logs['batch'] = batch_index - batch_logs['size'] = len(batch_ids) - - callbacks.on_batch_begin(batch_index, batch_logs) - - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) - - outs, loss, loss_metrics = _process_single_batch(eager_model_inputs, - eager_model_outputs, - model) - - if not isinstance(outs, list): - outs = [outs] - - for l, o in zip(out_labels, outs): - batch_logs[l] = o - # Required for Eager mode - metrics_names, metrics_results = _eager_metrics_fn(model, outs, - eager_model_outputs) - batch_logs['loss'] = tensor_util.constant_value(K.mean(loss)) - - # TODO(anjalisridhar): Move this to compile to avoid duplicate code. - # In graph mode we set the metric names in compile. However in - # Eager mode we calculate the metrics for each batch in fit_loop. - # We could calculate the metric names and functions in compile. - # This would avoid setting the callback parameters separately. - # We need to do this for the first iteration alone - for m in metrics_names: - if m not in callback_metrics: - callback_metrics.append(m) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - - for k, v in zip(model.metrics_names, - [K.mean(loss)] + loss_metrics + metrics_results): - batch_logs[k] = tensor_util.constant_value(v) - - callbacks.on_batch_end(batch_index, batch_logs) + if sample_weights: + feed_data = inputs + targets + sample_weights + else: + feed_data = inputs + targets + num_train_samples = training_utils.check_num_samples( + feed_data, + batch_size=batch_size, + steps=steps_per_epoch, + steps_name='steps_per_epoch') + + if num_train_samples is not None: + index_array = np.arange(num_train_samples) + + model.history = cbks.History() + callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] + if verbose: + if steps_per_epoch is not None: + count_mode = 'steps' + else: + count_mode = 'samples' + callbacks += [cbks.ProgbarLogger(count_mode)] + callbacks = cbks.CallbackList(callbacks) + + # it's possible to callback a different model than self + # (used by Sequential models) + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + + callbacks.set_model(callback_model) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + callbacks.on_train_begin() + callback_model.stop_training = False + for cbk in callbacks: + if not val_inputs: + cbk.validation_data = [] + elif val_sample_weights: + cbk.validation_data = val_inputs + val_targets + val_sample_weights + else: + cbk.validation_data = val_inputs + val_targets + + for epoch in range(initial_epoch, epochs): + callbacks.on_epoch_begin(epoch) + epoch_logs = {} + if shuffle == 'batch': + index_array = model._batch_shuffle(index_array, batch_size) + elif shuffle: + np.random.shuffle(index_array) + + batches = make_batches(num_train_samples, batch_size) + + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + try: + inputs_batch = slice_arrays(inputs, batch_ids) + targets_batch = slice_arrays(targets, batch_ids) + if sample_weights: + sample_weights_batch = slice_arrays(sample_weights, batch_ids) + else: + sample_weights_batch = None + except TypeError: + raise TypeError('TypeError while preparing batch. ' + 'If using HDF5 input data, ' + 'pass shuffle="batch".') + batch_logs = {} + batch_logs['batch'] = batch_index + batch_logs['size'] = len(batch_ids) + + callbacks.on_batch_begin(batch_index, batch_logs) + + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] + targets_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in targets_batch] + if sample_weights: + sample_weights_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None + for val in sample_weights_batch] + + outs, loss, loss_metrics = _process_single_batch( + model, + inputs_batch, + targets_batch, + sample_weights=sample_weights_batch, + training=True) + + if not isinstance(outs, list): + outs = [outs] + + for l, o in zip(out_labels, outs): + batch_logs[l] = o + # Required for Eager mode + metrics_names, metrics_results = _eager_metrics_fn( + model, outs, targets_batch) + batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss)) + + # TODO(anjalisridhar): Move this to compile to avoid duplicate code. + # In graph mode we set the metric names in compile. However in + # Eager mode we calculate the metrics for each batch in fit_loop. + # We could calculate the metric names and functions in compile. + # This would avoid setting the callback parameters separately. + # We need to do this for the first iteration alone + for m in metrics_names: + if m not in callback_metrics: + callback_metrics.append(m) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + + for k, v in zip(model.metrics_names, + [backend.mean(loss)] + loss_metrics + metrics_results): + batch_logs[k] = tensor_util.constant_value(v) + + callbacks.on_batch_end(batch_index, batch_logs) + if callback_model.stop_training: + break + + if batch_index == len(batches) - 1: # Last batch. + if do_validation: + val_outs = test_loop( + model, val_inputs, val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + callbacks.on_epoch_end(epoch, epoch_logs) if callback_model.stop_training: break + callbacks.on_train_end() + return model.history - if batch_index == len(batches) - 1: # Last batch. - if do_validation: - val_outs = test_loop( - model, val_ins, batch_size=batch_size, verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: - break - callbacks.on_train_end() - return model.history - - -def test_loop(model, ins, batch_size=None, verbose=0, steps=None): + +def test_loop(model, inputs, targets, + sample_weights=None, + batch_size=None, + verbose=0, + steps=None): """Abstract method to loop over some data in batches. Arguments: model: Model instance that is being evaluated in Eager mode. - ins: list of tensors to be fed to `f`. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. batch_size: integer batch size or `None`. verbose: verbosity mode. steps: Total number of steps (batches of samples) @@ -546,68 +531,79 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None): and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. """ - K.set_learning_phase(False) - num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') - outs = [] - if verbose == 1: - progbar = Progbar(target=num_samples) - batches = _make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) - - loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, - eager_model_outputs) - _, metrics_results = _eager_metrics_fn(model, loss_outs, - eager_model_outputs) - batch_outs = [] - for _, v in zip(model.metrics_names, - [K.mean(loss)] + loss_metrics + metrics_results): - batch_outs.append(tensor_util.constant_value(v)) - - if isinstance(batch_outs, list): - if batch_index == 0: - for batch_out in enumerate(batch_outs): + with backend.learning_phase_scope(0): + feed_data = inputs + targets + if sample_weights: + feed_data += sample_weights + num_samples = training_utils.check_num_samples( + feed_data, batch_size=batch_size, steps=steps, steps_name='steps') + outs = [] + if verbose == 1: + progbar = Progbar(target=num_samples) + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + inputs_batch = slice_arrays(inputs, batch_ids) + targets_batch = slice_arrays(targets, batch_ids) + if sample_weights: + sample_weights_batch = slice_arrays(sample_weights, batch_ids) + else: + sample_weights_batch = None + + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] + targets_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in targets_batch] + if sample_weights: + sample_weights_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None + for val in sample_weights_batch] + + loss_outs, loss, loss_metrics = _model_loss( + model, + inputs_batch, + targets_batch, + sample_weights=sample_weights_batch, + training=False) + _, metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch) + batch_outs = [] + for _, v in zip(model.metrics_names, + [backend.mean(loss)] + loss_metrics + metrics_results): + batch_outs.append(tensor_util.constant_value(v)) + + if isinstance(batch_outs, list): + if batch_index == 0: + for batch_out in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + outs[i] += batch_out * len(batch_ids) + else: + if batch_index == 0: outs.append(0.) - for i, batch_out in enumerate(batch_outs): - outs[i] += batch_out * len(batch_ids) - else: - if batch_index == 0: - outs.append(0.) - outs[0] += batch_outs * len(batch_ids) + outs[0] += batch_outs * len(batch_ids) - if verbose == 1: - progbar.update(batch_end) - for i in range(len(outs)): - outs[i] /= num_samples - if len(outs) == 1: - return outs[0] - return outs + if verbose == 1: + progbar.update(batch_end) + for i in range(len(outs)): + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs -def predict_loop(model, ins, batch_size=32, verbose=0, steps=None): +def predict_loop(model, inputs, + batch_size=32, + verbose=0, + steps=None): """Abstract method to loop over some data in batches. Arguments: model: - ins: list of tensors to be fed to `f`. + inputs: List of input arrays. batch_size: integer batch size. verbose: verbosity mode. steps: Total number of steps (batches of samples) @@ -619,48 +615,50 @@ def predict_loop(model, ins, batch_size=32, verbose=0, steps=None): or list of arrays of predictions (if the model has multiple outputs). """ - K.set_learning_phase(False) - num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps) - else: - progbar = Progbar(target=num_samples) - - outs = [] - batches = _make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if ins and isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = _slice_arrays(ins, batch_ids) - - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - - eager_model_inputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - batch_outs = model(eager_model_inputs) - - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if batch_index == 0: - # Pre-allocate the results arrays. - for batch_out in batch_outs: - dims = batch_out.shape[1:].dims - dims_list = [d.value for d in dims] - shape = (num_samples,) + tuple(dims_list) - outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype)) - for i, batch_out in enumerate(batch_outs): - outs[i][batch_start:batch_end] = batch_out + with backend.learning_phase_scope(0): + num_samples = training_utils.check_num_samples( + inputs, batch_size, steps, 'steps') if verbose == 1: - progbar.update(batch_end) - if len(outs) == 1: - return outs[0] - return outs + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) + + outs = [] + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + inputs_batch = slice_arrays(inputs, batch_ids) + + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] + + if len(inputs_batch) == 1: + if model._expects_training_arg: + batch_outs = model.call(inputs_batch[0], training=False) + else: + batch_outs = model.call(inputs_batch[0]) + else: + if model._expects_training_arg: + batch_outs = model.call(inputs_batch, training=False) + else: + batch_outs = model.call(inputs_batch) + + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if batch_index == 0: + # Pre-allocate the results arrays. + for batch_out in batch_outs: + dims = batch_out.shape[1:].dims + dims_list = [d.value for d in dims] + shape = (num_samples,) + tuple(dims_list) + outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype)) + for i, batch_out in enumerate(batch_outs): + outs[i][batch_start:batch_end] = batch_out + if verbose == 1: + progbar.update(batch_end) + if len(outs) == 1: + return outs[0] + return outs diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py index 81e2f7a5145a586f6a4cc34f54033723fae6a6e9..8848b393d5e602e564cb357c32a937eaabd68203 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py @@ -314,10 +314,9 @@ class LossWeightingTest(test.TestCase): def test_class_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 weighted_class = 3 - train_samples = 3000 - test_samples = 3000 + train_samples = 300 + test_samples = 300 input_dim = 5 model = keras.models.Sequential() @@ -342,16 +341,16 @@ class LossWeightingTest(test.TestCase): test_ids = np.where(int_y_test == np.array(weighted_class))[0] class_weight = dict([(i, 1.) for i in range(num_classes)]) - class_weight[weighted_class] = 2. + class_weight[weighted_class] = 4. sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = 4. model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, class_weight=class_weight, validation_data=(x_train, y_train, sample_weight)) @@ -359,14 +358,14 @@ class LossWeightingTest(test.TestCase): x_train, y_train, batch_size=batch_size, - epochs=epochs // 2, + epochs=2, verbose=0, class_weight=class_weight) model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 2, + epochs=2, verbose=0, class_weight=class_weight, validation_split=0.1) @@ -381,10 +380,9 @@ class LossWeightingTest(test.TestCase): def test_sample_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 weighted_class = 3 - train_samples = 3000 - test_samples = 3000 + train_samples = 300 + test_samples = 300 input_dim = 5 model = keras.models.Sequential() @@ -396,36 +394,32 @@ class LossWeightingTest(test.TestCase): optimizer=RMSPropOptimizer(learning_rate=0.001)) np.random.seed(43) - (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( + (x_train, y_train), _ = testing_utils.get_test_data( train_samples=train_samples, test_samples=test_samples, input_shape=(input_dim,), num_classes=num_classes) - int_y_test = y_test.copy() int_y_train = y_train.copy() - # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) - y_test = keras.utils.to_categorical(y_test, num_classes) - test_ids = np.where(int_y_test == np.array(weighted_class))[0] class_weight = dict([(i, 1.) for i in range(num_classes)]) - class_weight[weighted_class] = 2. + class_weight[weighted_class] = 4. sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = 4. model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, sample_weight=sample_weight) model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, sample_weight=sample_weight, validation_split=0.1) @@ -538,209 +532,6 @@ class LossWeightingTest(test.TestCase): model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) -class TestDynamicTrainability(test.TestCase): - - def test_trainable_warning(self): - x = np.random.random((5, 3)) - y = np.random.random((5, 2)) - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=3)) - model.trainable = False - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - model.trainable = True - with self.assertRaises(ValueError): - model.train_on_batch(x, y) - - def test_trainable_argument(self): - x = np.random.random((5, 3)) - y = np.random.random((5, 2)) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=3, trainable=False)) - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - out = model.predict(x) - with self.assertRaises(ValueError): - model.train_on_batch(x, y) - out_2 = model.predict(x) - self.assertAllClose(out, out_2) - - # test with nesting - inputs = keras.layers.Input(shape=(3,)) - output = model(inputs) - model = keras.models.Model(inputs, output) - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - out = model.predict(x) - with self.assertRaises(ValueError): - model.train_on_batch(x, y) - out_2 = model.predict(x) - self.assertAllClose(out, out_2) - - def test_layer_trainability_switch(self): - # with constructor argument, in Sequential - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, trainable=False, input_dim=1)) - self.assertListEqual(model.trainable_weights, []) - - # by setting the `trainable` argument, in Sequential - model = keras.models.Sequential() - layer = keras.layers.Dense(2, input_dim=1) - model.add(layer) - self.assertListEqual(model.trainable_weights, layer.trainable_weights) - layer.trainable = False - self.assertListEqual(model.trainable_weights, []) - - # with constructor argument, in Model - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2, trainable=False)(x) - model = keras.models.Model(x, y) - self.assertListEqual(model.trainable_weights, []) - - # by setting the `trainable` argument, in Model - x = keras.layers.Input(shape=(1,)) - layer = keras.layers.Dense(2) - y = layer(x) - model = keras.models.Model(x, y) - self.assertListEqual(model.trainable_weights, layer.trainable_weights) - layer.trainable = False - self.assertListEqual(model.trainable_weights, []) - - def test_model_trainability_switch(self): - # a non-trainable model has no trainable weights - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - model = keras.models.Model(x, y) - model.trainable = False - self.assertListEqual(model.trainable_weights, []) - - # same for Sequential - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=1)) - model.trainable = False - self.assertListEqual(model.trainable_weights, []) - - def test_nested_model_trainability(self): - - # a Sequential inside a Model - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(2, input_dim=1)) - - x = keras.layers.Input(shape=(1,)) - y = inner_model(x) - outer_model = keras.models.Model(x, y) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Sequential inside a Sequential - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(2, input_dim=1)) - outer_model = keras.models.Sequential() - outer_model.add(inner_model) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Model inside a Model - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - inner_model = keras.models.Model(x, y) - x = keras.layers.Input(shape=(1,)) - y = inner_model(x) - outer_model = keras.models.Model(x, y) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Model inside a Sequential - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - inner_model = keras.models.Model(x, y) - outer_model = keras.models.Sequential() - outer_model.add(inner_model) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - -class TestTrainingUtils(test.TestCase): - - def test_check_array_lengths(self): - keras.engine.training._check_array_lengths(None, None, None) - a_np = np.random.random((4, 3, 3)) - keras.engine.training._check_array_lengths(a_np, a_np, a_np) - keras.engine.training._check_array_lengths( - [a_np, a_np], [a_np, a_np], [a_np, a_np]) - keras.engine.training._check_array_lengths([None], [None], [None]) - - b_np = np.random.random((3, 4)) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, None, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, a_np, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [None], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [b_np], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], None, [b_np]) - - def test_slice_arrays(self): - input_a = np.random.random((10, 3)) - keras.engine.training._slice_arrays(None) - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) - input_a = [None, [1, 1], None, [1, 1]] - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) - input_a = [None] - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) - input_a = None - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) - - def test_fit_with_BatchNorm(self): - model = keras.models.Sequential() - model.add(keras.layers.Dense(10, input_dim=4)) - model.add(keras.layers.BatchNormalization()) - model.add(keras.layers.Activation('tanh')) - model.add(keras.layers.Dropout(0.2)) - - input_a_np = np.random.random((10, 4)) - output_b_np = np.random.random((10, 10)) - - model.compile(loss='binary_crossentropy', optimizer=RMSPropOptimizer(0.001)) - model.fit(input_a_np, output_b_np, epochs=1, batch_size=5, verbose=0) - - def test_fit_with_regularization(self): - model = keras.models.Sequential() - with self.assertRaises(ValueError): - model.add( - keras.layers.Dense(4, input_dim=3, - kernel_regularizer=keras.regularizers.l2(0.01), - activity_regularizer=keras.regularizers.l1(0.01))) - - if __name__ == '__main__': # Bazel sets these environment variables to very long paths. # Tempfile uses them to create long paths, and in turn multiprocessing diff --git a/tensorflow/python/keras/_impl/keras/engine/training_generator.py b/tensorflow/python/keras/_impl/keras/engine/training_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..4af62c85d573f0985bb1428c76ff4bc413dd9253 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_generator.py @@ -0,0 +1,439 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Part of the Keras training engine related to Python generators of array data. +""" +# pylint: disable=protected-access +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import callbacks as cbks +from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer +from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer +from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence +from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.platform import tf_logging as logging + + +def fit_generator(model, + generator, + steps_per_epoch=None, + epochs=1, + verbose=1, + callbacks=None, + validation_data=None, + validation_steps=None, + class_weight=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + shuffle=True, + initial_epoch=0): + """See docstring for `Model.fit_generator`.""" + wait_time = 0.01 # in seconds + epoch = initial_epoch + + do_validation = bool(validation_data) + model._make_train_function() + if do_validation: + model._make_test_function() + + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps_per_epoch is None: + if is_sequence: + steps_per_epoch = len(generator) + else: + raise ValueError('`steps_per_epoch=None` is only valid for a' + ' generator based on the `keras.utils.Sequence`' + ' class. Please specify `steps_per_epoch` or use' + ' the `keras.utils.Sequence` class.') + + # python 2 has 'next', 3 has '__next__' + # avoid any explicit version checks + val_gen = ( + hasattr(validation_data, 'next') or + hasattr(validation_data, '__next__') or + isinstance(validation_data, Sequence)) + if (val_gen and not isinstance(validation_data, Sequence) and + not validation_steps): + raise ValueError('`validation_steps=None` is only valid for a' + ' generator based on the `keras.utils.Sequence`' + ' class. Please specify `validation_steps` or use' + ' the `keras.utils.Sequence` class.') + + # Prepare display labels. + out_labels = model.metrics_names + callback_metrics = out_labels + ['val_%s' % n for n in out_labels] + + # prepare callbacks + model.history = cbks.History() + callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] + if verbose: + callbacks += [cbks.ProgbarLogger(count_mode='steps')] + callbacks = cbks.CallbackList(callbacks) + + # it's possible to callback a different model than self: + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + callbacks.set_model(callback_model) + callbacks.set_params({ + 'epochs': epochs, + 'steps': steps_per_epoch, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics, + }) + callbacks.on_train_begin() + + enqueuer = None + val_enqueuer = None + + try: + if do_validation: + if val_gen: + if workers > 0: + if isinstance(validation_data, Sequence): + val_enqueuer = OrderedEnqueuer( + validation_data, use_multiprocessing=use_multiprocessing) + if validation_steps is None: + validation_steps = len(validation_data) + else: + val_enqueuer = GeneratorEnqueuer( + validation_data, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + val_enqueuer.start(workers=workers, max_queue_size=max_queue_size) + validation_generator = val_enqueuer.get() + else: + validation_generator = validation_data + else: + if len(validation_data) == 2: + val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence + val_sample_weight = None + elif len(validation_data) == 3: + val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence + else: + raise ValueError( + '`validation_data` should be a tuple ' + '`(val_x, val_y, val_sample_weight)` ' + 'or `(val_x, val_y)`. Found: ' + str(validation_data)) + val_x, val_y, val_sample_weights = model._standardize_user_data( + val_x, val_y, val_sample_weight) + val_data = val_x + val_y + val_sample_weights + if model.uses_learning_phase and not isinstance( + K.learning_phase(), int): + val_data += [0] + for cbk in callbacks: + cbk.validation_data = val_data + + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + output_generator = generator + + callback_model.stop_training = False + # Construct epoch logs. + epoch_logs = {} + while epoch < epochs: + callbacks.on_epoch_begin(epoch) + steps_done = 0 + batch_index = 0 + while steps_done < steps_per_epoch: + generator_output = next(output_generator) + + if not hasattr(generator_output, '__len__'): + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + + if len(generator_output) == 2: + x, y = generator_output + sample_weight = None + elif len(generator_output) == 3: + x, y, sample_weight = generator_output + else: + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + # build batch logs + batch_logs = {} + if isinstance(x, list): + batch_size = x[0].shape[0] + elif isinstance(x, dict): + batch_size = list(x.values())[0].shape[0] + else: + batch_size = x.shape[0] + batch_logs['batch'] = batch_index + batch_logs['size'] = batch_size + callbacks.on_batch_begin(batch_index, batch_logs) + + outs = model.train_on_batch( + x, y, sample_weight=sample_weight, class_weight=class_weight) + + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(batch_index, batch_logs) + + batch_index += 1 + steps_done += 1 + + # Epoch finished. + if steps_done >= steps_per_epoch and do_validation: + if val_gen: + val_outs = evaluate_generator( + model, validation_generator, validation_steps, workers=0) + else: + # No need for try/except because + # data has already been validated. + val_outs = model.evaluate( + val_x, + val_y, + batch_size=batch_size, + sample_weight=val_sample_weights, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + + if callback_model.stop_training: + break + + callbacks.on_epoch_end(epoch, epoch_logs) + epoch += 1 + if callback_model.stop_training: + break + + finally: + try: + if enqueuer is not None: + enqueuer.stop() + finally: + if val_enqueuer is not None: + val_enqueuer.stop() + + callbacks.on_train_end() + return model.history + + +def evaluate_generator(model, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False): + """See docstring for `Model.evaluate_generator`.""" + model._make_test_function() + + steps_done = 0 + wait_time = 0.01 + all_outs = [] + batch_sizes = [] + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps is None: + if is_sequence: + steps = len(generator) + else: + raise ValueError('`steps=None` is only valid for a generator' + ' based on the `keras.utils.Sequence` class.' + ' Please specify `steps` or use the' + ' `keras.utils.Sequence` class.') + enqueuer = None + + try: + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, use_multiprocessing=use_multiprocessing) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + output_generator = generator + + while steps_done < steps: + generator_output = next(output_generator) + if not hasattr(generator_output, '__len__'): + raise ValueError('Output of generator should be a tuple ' + '(x, y, sample_weight) ' + 'or (x, y). Found: ' + str(generator_output)) + if len(generator_output) == 2: + x, y = generator_output + sample_weight = None + elif len(generator_output) == 3: + x, y, sample_weight = generator_output + else: + raise ValueError('Output of generator should be a tuple ' + '(x, y, sample_weight) ' + 'or (x, y). Found: ' + str(generator_output)) + outs = model.test_on_batch(x, y, sample_weight=sample_weight) + + if isinstance(x, list): + batch_size = x[0].shape[0] + elif isinstance(x, dict): + batch_size = list(x.values())[0].shape[0] + else: + batch_size = x.shape[0] + if batch_size == 0: + raise ValueError('Received an empty batch. ' + 'Batches should at least contain one item.') + all_outs.append(outs) + + steps_done += 1 + batch_sizes.append(batch_size) + + finally: + if enqueuer is not None: + enqueuer.stop() + + if not isinstance(outs, list): + return np.average(np.asarray(all_outs), weights=batch_sizes) + else: + averages = [] + for i in range(len(outs)): + averages.append( + np.average([out[i] for out in all_outs], weights=batch_sizes)) + return averages + + +def predict_generator(model, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0): + """See docstring for `Model.predict_generator`.""" + model._make_predict_function() + + steps_done = 0 + wait_time = 0.01 + all_outs = [] + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps is None: + if is_sequence: + steps = len(generator) + else: + raise ValueError('`steps=None` is only valid for a generator' + ' based on the `keras.utils.Sequence` class.' + ' Please specify `steps` or use the' + ' `keras.utils.Sequence` class.') + enqueuer = None + + try: + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, use_multiprocessing=use_multiprocessing) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + output_generator = generator + + if verbose == 1: + progbar = Progbar(target=steps) + + while steps_done < steps: + generator_output = next(output_generator) + if isinstance(generator_output, tuple): + # Compatibility with the generators + # used for training. + if len(generator_output) == 2: + x, _ = generator_output + elif len(generator_output) == 3: + x, _, _ = generator_output + else: + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + else: + # Assumes a generator that only + # yields inputs (not targets and sample weights). + x = generator_output + + outs = model.predict_on_batch(x) + if not isinstance(outs, list): + outs = [outs] + + if not all_outs: + for out in outs: + all_outs.append([]) + + for i, out in enumerate(outs): + all_outs[i].append(out) + steps_done += 1 + if verbose == 1: + progbar.update(steps_done) + + finally: + if enqueuer is not None: + enqueuer.stop() + + if len(all_outs) == 1: + if steps_done == 1: + return all_outs[0][0] + else: + return np.concatenate(all_outs[0]) + if steps_done == 1: + return [out[0] for out in all_outs] + else: + return [np.concatenate(out) for out in all_outs] diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py index b380238e4e2bb3bccbfc5efdc0db213d86910fe5..38ba0f0eaea69486d279c18451ed29dbd8617ec7 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py @@ -25,7 +25,8 @@ import numpy as np from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils -from tensorflow.python.keras._impl.keras.engine.training import _weighted_masked_objective +from tensorflow.python.keras._impl.keras.engine.training_utils import weighted_masked_objective +from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays from tensorflow.python.platform import test try: @@ -704,7 +705,7 @@ class LossMaskingTest(test.TestCase): def test_loss_masking(self): with self.test_session(): - weighted_loss = _weighted_masked_objective(keras.losses.get('mae')) + weighted_loss = weighted_masked_objective(keras.losses.get('mae')) shape = (3, 4, 2) x = np.arange(24).reshape(shape) y = 2 * x @@ -1036,43 +1037,35 @@ class TestGeneratorMethods(test.TestCase): class TestTrainingUtils(test.TestCase): def test_check_array_lengths(self): - keras.engine.training._check_array_lengths(None, None, None) + keras.engine.training_utils.check_array_lengths(None, None, None) a_np = np.random.random((4, 3, 3)) - keras.engine.training._check_array_lengths(a_np, a_np, a_np) - keras.engine.training._check_array_lengths( + keras.engine.training_utils.check_array_lengths(a_np, a_np, a_np) + keras.engine.training_utils.check_array_lengths( [a_np, a_np], [a_np, a_np], [a_np, a_np]) - keras.engine.training._check_array_lengths([None], [None], [None]) + keras.engine.training_utils.check_array_lengths([None], [None], [None]) b_np = np.random.random((3, 4)) with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, None, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, a_np, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [None], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [b_np], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], None, [b_np]) + keras.engine.training_utils.check_array_lengths([a_np], [b_np], None) def test_slice_arrays(self): input_a = np.random.random((10, 3)) - keras.engine.training._slice_arrays(None) - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) + slice_arrays(input_a, 0) + slice_arrays(None) + slice_arrays(input_a, 0, 1) + slice_arrays(input_a, stop=2) input_a = [None, [1, 1], None, [1, 1]] - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) + slice_arrays(input_a, 0) + slice_arrays(input_a, 0, 1) + slice_arrays(input_a, stop=2) input_a = [None] - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) + slice_arrays(input_a, 0) + slice_arrays(input_a, 0, 1) + slice_arrays(input_a, stop=2) input_a = None - keras.engine.training._slice_arrays(input_a, 0) - keras.engine.training._slice_arrays(input_a, 0, 1) - keras.engine.training._slice_arrays(input_a, stop=2) + slice_arrays(input_a, 0) + slice_arrays(input_a, 0, 1) + slice_arrays(input_a, stop=2) class TestTrainingWithDataTensors(test.TestCase): diff --git a/tensorflow/python/keras/_impl/keras/engine/training_utils.py b/tensorflow/python/keras/_impl/keras/engine/training_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..105638ce1087e8668b49b6653a847667e8f9157d --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_utils.py @@ -0,0 +1,534 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Training-related utilities. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy + +import numpy as np + +from tensorflow.python.framework import tensor_util +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import losses + + +def check_num_samples(ins, + batch_size=None, + steps=None, + steps_name='steps'): + """Determine the number of samples provided for training and evaluation. + + The number of samples is not defined when running with `steps`, + in which case the number of samples is set to `None`. + + Arguments: + ins: List of tensors to be fed to the Keras function. + batch_size: Integer batch size or `None` if not defined. + steps: Total number of steps (batches of samples) + before declaring `_predict_loop` finished. + Ignored with the default value of `None`. + steps_name: The public API's parameter name for `steps`. + + Raises: + ValueError: when `steps` is `None` and the attribute `ins.shape` + does not exist. Also raises ValueError when `steps` is not `None` + and `batch_size` is not `None` because they are mutually + exclusive. + + Returns: + When steps is `None`, returns the number of samples to be + processed based on the size of the first dimension of the + first input numpy array. When steps is not `None` and + `batch_size` is `None`, returns `None`. + + Raises: + ValueError: In case of invalid arguments. + """ + if steps is not None: + num_samples = None + if batch_size is not None: + raise ValueError( + 'If ' + steps_name + ' is set, the `batch_size` must be None.') + elif ins and hasattr(ins[0], 'shape'): + num_samples = ins[0].shape[0] + else: + raise ValueError( + 'Either the input data should have ' + 'a defined shape, or ' + steps_name + ' should be specified.') + return num_samples + + +def standardize_input_data(data, + names, + shapes=None, + check_batch_axis=True, + exception_prefix=''): + """Normalizes inputs and targets provided by users. + + Users may pass data as a list of arrays, dictionary of arrays, + or as a single array. We normalize this to an ordered list of + arrays (same order as `names`), while checking that the provided + arrays have shapes that match the network's expectations. + + Arguments: + data: User-provided input data (polymorphic). + names: List of expected array names. + shapes: Optional list of expected array shapes. + check_batch_axis: Boolean; whether to check that + the batch axis of the arrays matches the expected + value found in `shapes`. + exception_prefix: String prefix used for exception formatting. + + Returns: + List of standardized input arrays (one array per model input). + + Raises: + ValueError: in case of improperly formatted user-provided data. + """ + if not names: + if data is not None and hasattr(data, '__len__') and len(data): + raise ValueError('Error when checking model ' + exception_prefix + ': ' + 'expected no data, but got:', data) + return [] + if data is None: + return [None for _ in range(len(names))] + + if isinstance(data, dict): + try: + data = [ + data[x].values + if data[x].__class__.__name__ == 'DataFrame' else data[x] + for x in names + ] + except KeyError as e: + raise ValueError('No data provided for "' + e.args[0] + '". Need data ' + 'for each key in: ' + str(names)) + elif isinstance(data, list): + if isinstance(data[0], list): + data = [np.asarray(d) for d in data] + elif len(names) == 1 and isinstance(data[0], (float, int)): + data = [np.asarray(data)] + else: + data = [ + x.values if x.__class__.__name__ == 'DataFrame' else x for x in data + ] + else: + data = data.values if data.__class__.__name__ == 'DataFrame' else data + data = [data] + data = [ + np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data + ] + + if len(data) != len(names): + if data and hasattr(data[0], 'shape'): + raise ValueError('Error when checking model ' + exception_prefix + + ': the list of Numpy arrays that you are passing to ' + 'your model is not the size the model expected. ' + 'Expected to see ' + str(len(names)) + ' array(s), ' + 'but instead got the following list of ' + + str(len(data)) + ' arrays: ' + str(data)[:200] + '...') + elif len(names) > 1: + raise ValueError( + 'Error when checking model ' + exception_prefix + + ': you are passing a list as input to your model, ' + 'but the model expects a list of ' + str(len(names)) + + ' Numpy arrays instead. The list you passed was: ' + str(data)[:200]) + elif len(data) == 1 and not hasattr(data[0], 'shape'): + raise TypeError('Error when checking model ' + exception_prefix + + ': data should be a Numpy array, or list/dict of ' + 'Numpy arrays. Found: ' + str(data)[:200] + '...') + elif len(names) == 1: + data = [np.asarray(data)] + + # Check shapes compatibility. + if shapes: + for i in range(len(names)): + if shapes[i] is not None: + data_shape = data[i].shape + shape = shapes[i] + if data[i].ndim != len(shape): + raise ValueError('Error when checking ' + exception_prefix + + ': expected ' + names[i] + ' to have ' + + str(len(shape)) + ' dimensions, but got array ' + 'with shape ' + str(data_shape)) + if not check_batch_axis: + data_shape = data_shape[1:] + shape = shape[1:] + for dim, ref_dim in zip(data_shape, shape): + if ref_dim != dim and ref_dim: + raise ValueError( + 'Error when checking ' + exception_prefix + ': expected ' + + names[i] + ' to have shape ' + str(shape) + + ' but got array with shape ' + str(data_shape)) + return data + + +def standardize_sample_or_class_weights(x_weight, output_names, weight_type): + """Maps `sample_weight` or `class_weight` to model outputs. + + Arguments: + x_weight: User-provided `sample_weight` or `class_weight` argument. + output_names: List of output names (strings) in the model. + weight_type: A string used purely for exception printing. + + Returns: + A list of `sample_weight` or `class_weight` where there are exactly + one element per model output. + + Raises: + ValueError: In case of invalid user-provided argument. + """ + if x_weight is None or len(x_weight) == 0: # pylint: disable=g-explicit-length-test + return [None for _ in output_names] + if len(output_names) == 1: + if isinstance(x_weight, list) and len(x_weight) == 1: + return x_weight + if isinstance(x_weight, dict) and output_names[0] in x_weight: + return [x_weight[output_names[0]]] + else: + return [x_weight] + if isinstance(x_weight, list): + if len(x_weight) != len(output_names): + raise ValueError('Provided `' + weight_type + '` was a list of ' + + str(len(x_weight)) + ' elements, but the model has ' + + str(len(output_names)) + ' outputs. ' + 'You should provide one `' + weight_type + '`' + 'array per model output.') + return x_weight + if isinstance(x_weight, dict): + x_weights = [] + for name in output_names: + x_weights.append(x_weight.get(name)) + return x_weights + else: + raise TypeError( + 'The model has multiple outputs, so `' + weight_type + '` ' + 'should be either a list or a dict. ' + 'Provided `' + weight_type + '` type not understood: ' + str(x_weight)) + + +def standardize_class_weights(class_weight, output_names): + return standardize_sample_or_class_weights(class_weight, output_names, + 'class_weight') + + +def standardize_sample_weights(sample_weight, output_names): + return standardize_sample_or_class_weights(sample_weight, output_names, + 'sample_weight') + + +def check_array_lengths(inputs, targets, weights=None): + """Does user input validation for numpy arrays. + + Arguments: + inputs: list of Numpy arrays of inputs. + targets: list of Numpy arrays of targets. + weights: list of Numpy arrays of sample weights. + + Raises: + ValueError: in case of incorrectly formatted data. + """ + + def set_of_lengths(x): + # return a set with the variation between + # different shapes, with None => 0 + if x is None: + return {} + else: + return set([y.shape[0] for y in x if y is not None]) + + set_x = set_of_lengths(inputs) + set_y = set_of_lengths(targets) + set_w = set_of_lengths(weights) + if len(set_x) > 1: + raise ValueError('All input arrays (x) should have ' + 'the same number of samples. Got array shapes: ' + + str([x.shape for x in inputs])) + if len(set_y) > 1: + raise ValueError('All target arrays (y) should have ' + 'the same number of samples. Got array shapes: ' + + str([y.shape for y in targets])) + if set_x and set_y and list(set_x)[0] != list(set_y)[0]: + raise ValueError('Input arrays should have ' + 'the same number of samples as target arrays. ' + 'Found ' + str(list(set_x)[0]) + ' input samples ' + 'and ' + str(list(set_y)[0]) + ' target samples.') + if len(set_w) > 1: + raise ValueError('All sample_weight arrays should have ' + 'the same number of samples. Got array shapes: ' + + str([w.shape for w in weights])) + if set_y and set_w and list(set_y)[0] != list(set_w)[0]: + raise ValueError('Sample_weight arrays should have ' + 'the same number of samples as target arrays. Got ' + + str(list(set_y)[0]) + ' input samples and ' + + str(list(set_w)[0]) + ' target samples.') + + +def check_loss_and_target_compatibility(targets, loss_fns, output_shapes): + """Does validation on the compatibility of targets and loss functions. + + This helps prevent users from using loss functions incorrectly. This check + is purely for UX purposes. + + Arguments: + targets: list of Numpy arrays of targets. + loss_fns: list of loss functions. + output_shapes: list of shapes of model outputs. + + Raises: + ValueError: if a loss function or target array + is incompatible with an output. + """ + key_losses = { + losses.mean_squared_error, losses.binary_crossentropy, + losses.categorical_crossentropy + } + for y, loss, shape in zip(targets, loss_fns, output_shapes): + if y is None or loss is None or tensor_util.is_tensor(y): + continue + if loss is losses.categorical_crossentropy: + if y.shape[-1] == 1: + raise ValueError('You are passing a target array of shape ' + str( + y.shape) + ' while using as loss `categorical_crossentropy`. ' + '`categorical_crossentropy` expects ' + 'targets to be binary matrices (1s and 0s) ' + 'of shape (samples, classes). ' + 'If your targets are integer classes, ' + 'you can convert them to the expected format via:\n' + '```\n' + 'from keras.utils import to_categorical\n' + 'y_binary = to_categorical(y_int)\n' + '```\n' + '\n' + 'Alternatively, you can use the loss function ' + '`sparse_categorical_crossentropy` instead, ' + 'which does expect integer targets.') + if loss in key_losses: + for target_dim, out_dim in zip(y.shape[1:], shape[1:]): + if out_dim is not None and target_dim != out_dim: + raise ValueError('A target array with shape ' + str(y.shape) + + ' was passed for an output of shape ' + str(shape) + + ' while using as loss `' + loss.__name__ + '`. ' + 'This loss expects ' + 'targets to have the same shape ' + 'as the output.') + + +def collect_metrics(metrics, output_names): + """Maps metric functions to model outputs. + + Arguments: + metrics: a list or dict of metric functions. + output_names: a list of the names (strings) of model outputs. + + Returns: + A list (one entry per model output) of lists of metric functions. + For instance, if the model has 2 outputs, and for the first output + we want to compute "binary_accuracy" and "binary_crossentropy", + and just "binary_accuracy" for the second output, + the list would look like: + `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]` + + Raises: + TypeError: if an incorrect type is passed for the `metrics` argument. + """ + if not metrics: + return [[] for _ in output_names] + if isinstance(metrics, list): + # we then apply all metrics to all outputs. + return [copy.copy(metrics) for _ in output_names] + elif isinstance(metrics, dict): + nested_metrics = [] + for name in output_names: + output_metrics = metrics.get(name, []) + if not isinstance(output_metrics, list): + output_metrics = [output_metrics] + nested_metrics.append(output_metrics) + return nested_metrics + else: + raise TypeError('Type of `metrics` argument not understood. ' + 'Expected a list or dictionary, found: ' + str(metrics)) + + +def batch_shuffle(index_array, batch_size): + """Shuffles an array in a batch-wise fashion. + + Useful for shuffling HDF5 arrays + (where one cannot access arbitrary indices). + + Arguments: + index_array: array of indices to be shuffled. + batch_size: integer. + + Returns: + The `index_array` array, shuffled in a batch-wise fashion. + """ + batch_count = int(len(index_array) / batch_size) + # to reshape we need to be cleanly divisible by batch size + # we stash extra items and reappend them after shuffling + last_batch = index_array[batch_count * batch_size:] + index_array = index_array[:batch_count * batch_size] + index_array = index_array.reshape((batch_count, batch_size)) + np.random.shuffle(index_array) + index_array = index_array.flatten() + return np.append(index_array, last_batch) + + +def weighted_masked_objective(fn): + """Adds support for masking and sample-weighting to an objective function. + + It transforms an objective function `fn(y_true, y_pred)` + into a sample-weighted, cost-masked objective function + `fn(y_true, y_pred, weights, mask)`. + + Arguments: + fn: The objective function to wrap, + with signature `fn(y_true, y_pred)`. + + Returns: + A function with signature `fn(y_true, y_pred, weights, mask)`. + """ + if fn is None: + return None + + def weighted(y_true, y_pred, weights, mask=None): + """Wrapper function. + + Arguments: + y_true: `y_true` argument of `fn`. + y_pred: `y_pred` argument of `fn`. + weights: Weights tensor. + mask: Mask tensor. + + Returns: + Scalar tensor. + """ + # score_array has ndim >= 2 + score_array = fn(y_true, y_pred) + if mask is not None: + # Cast the mask to floatX to avoid float64 upcasting in theano + mask = K.cast(mask, K.floatx()) + # mask should have the same shape as score_array + score_array *= mask + # the loss per batch should be proportional + # to the number of unmasked samples. + score_array /= K.mean(mask) + + # apply sample weighting + if weights is not None: + # reduce score_array to same ndim as weight array + ndim = K.ndim(score_array) + weight_ndim = K.ndim(weights) + score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim))) + score_array *= weights + score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx())) + return K.mean(score_array) + + return weighted + + +def standardize_weights(y, + sample_weight=None, + class_weight=None, + sample_weight_mode=None): + """Performs sample weight validation and standardization. + + Everything gets normalized to a single sample-wise (or timestep-wise) + weight array. + + Arguments: + y: Numpy array of model targets to be weighted. + sample_weight: User-provided `sample_weight` argument. + class_weight: User-provided `class_weight` argument. + sample_weight_mode: One of `None` or `"temporal"`. + `"temporal"` indicated that we expect 2D weight data + that will be applied to the last 2 dimensions of + the targets (i.e. we are weighting timesteps, not samples). + + Returns: + A numpy array of target weights, one entry per sample to weight. + + Raises: + ValueError: In case of invalid user-provided arguments. + """ + if sample_weight_mode is not None: + if sample_weight_mode != 'temporal': + raise ValueError('"sample_weight_mode ' + 'should be None or "temporal". ' + 'Found: ' + str(sample_weight_mode)) + if len(y.shape) < 3: + raise ValueError('Found a sample_weight array for ' + 'an input with shape ' + str(y.shape) + '. ' + 'Timestep-wise sample weighting (use of ' + 'sample_weight_mode="temporal") is restricted to ' + 'outputs that are at least 3D, i.e. that have ' + 'a time dimension.') + if sample_weight is not None and len(sample_weight.shape) != 2: + raise ValueError('Found a sample_weight array with shape ' + + str(sample_weight.shape) + '. ' + 'In order to use timestep-wise sample weighting, ' + 'you should pass a 2D sample_weight array.') + else: + if sample_weight is not None and len(sample_weight.shape) != 1: + raise ValueError('Found a sample_weight array with shape ' + + str(sample_weight.shape) + '. ' + 'In order to use timestep-wise sample weights, ' + 'you should specify ' + 'sample_weight_mode="temporal" ' + 'in compile(). If you just mean to use ' + 'sample-wise weights, make sure your ' + 'sample_weight array is 1D.') + + if sample_weight is not None: + if len(sample_weight.shape) > len(y.shape): + raise ValueError( + 'Found a sample_weight with shape' + str(sample_weight.shape) + '.' + 'Expected sample_weight with rank ' + 'less than or equal to ' + str(len(y.shape))) + + if y.shape[:sample_weight.ndim] != sample_weight.shape: + raise ValueError( + 'Found a sample_weight array with shape ' + str(sample_weight.shape) + + ' for an input with shape ' + str(y.shape) + '. ' + 'sample_weight cannot be broadcast.') + return sample_weight + elif isinstance(class_weight, dict): + if len(y.shape) > 2: + raise ValueError('`class_weight` not supported for ' + '3+ dimensional targets.') + if y.shape[1] > 1: + y_classes = np.argmax(y, axis=1) + elif y.shape[1] == 1: + y_classes = np.reshape(y, y.shape[0]) + else: + y_classes = y + + weights = np.asarray( + [class_weight[cls] for cls in y_classes if cls in class_weight]) + + if len(weights) != len(y_classes): + # subtract the sets to pick all missing classes + existing_classes = set(y_classes) + existing_class_weight = set(class_weight.keys()) + raise ValueError('`class_weight` must contain all classes in the data.' + ' The classes %s exist in the data but not in ' + '`class_weight`.' % + (existing_classes - existing_class_weight)) + return weights + else: + return None diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py index 624e92a04b8860d9a3974f2edb4a443482958259..081f25e91452582afabee1e43f6e15f7e5d0013d 100644 --- a/tensorflow/python/keras/_impl/keras/estimator.py +++ b/tensorflow/python/keras/_impl/keras/estimator.py @@ -25,11 +25,15 @@ from tensorflow.python.client import session from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import export as export_lib from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import models +from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.utils.generic_utils import CustomObjectScope from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_module @@ -37,6 +41,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY @@ -49,36 +54,174 @@ def _cast_tensor_to_floatx(x): return math_ops.cast(x, K.floatx()) -def _create_ordered_io(keras_model, estimator_io_dict, is_input=True): +def _create_ordered_io(keras_model, estimator_io, is_input=True): """Create a list of tensors from IO dictionary based on Keras IO order. Args: - keras_model: an instance of compiled keras model. - estimator_io_dict: features or labels dictionary from model_fn. + keras_model: An instance of compiled keras model. + estimator_io: The features or labels (dict or plain array) from model_fn. is_input: True if dictionary is for inputs. Returns: - a list of tensors based on Keras IO order. + A list of tensors based on Keras IO order. Raises: ValueError: if dictionary keys cannot be found in Keras model input_names or output_names. """ - if is_input: - keras_io_names = keras_model.input_names + if isinstance(estimator_io, (list, tuple)): + # Case currently not supported by most built-in input_fn, + # but it's good to have for sanity + return [_cast_tensor_to_floatx(x) for x in estimator_io] + elif isinstance(estimator_io, dict): + if is_input: + if keras_model._is_graph_network: + keras_io_names = keras_model.input_names + else: + keras_io_names = [ + 'input_%d' % i for i in range(1, len(estimator_io) + 1)] + else: + if keras_model._is_graph_network: + keras_io_names = keras_model.output_names + else: + keras_io_names = [ + 'output_%d' % i for i in range(1, len(estimator_io) + 1)] + + for key in estimator_io: + if key not in keras_io_names: + raise ValueError( + 'Cannot find %s with name "%s" in Keras Model. ' + 'It needs to match one ' + 'of the following: %s' % ('input' if is_input else 'output', key, + ', '.join(keras_io_names))) + tensors = [_cast_tensor_to_floatx(estimator_io[io_name]) + for io_name in keras_io_names] + return tensors else: - keras_io_names = keras_model.output_names + # Plain array. + return _cast_tensor_to_floatx(estimator_io) - for key in estimator_io_dict: - if key not in keras_io_names: - raise ValueError( - 'Cannot find %s with name "%s" in Keras Model. It needs to match ' - 'one of the following: %s' % ('input' if is_input else 'output', key, - ', '.join(keras_io_names))) - tensors = [] - for io_name in keras_io_names: - tensors.append(_cast_tensor_to_floatx(estimator_io_dict[io_name])) - return tensors + +def _in_place_subclassed_model_reset(model): + """Substitute for model cloning that works for subclassed models. + + Subclassed models cannot be cloned because their topology is not serializable. + To "instantiate" an identical model in a new TF graph, we reuse the original + model object, but we clear its state. + + After calling this function on a model intance, you can use the model instance + as if it were a model clone (in particular you can use it in a new graph). + + This method clears the state of the input model. It is thus destructive. + However the original state can be restored fully by calling + `_in_place_subclassed_model_state_restoration`. + + Args: + model: Instance of a Keras model created via subclassing. + + Raises: + ValueError: In case the model uses a subclassed model as inner layer. + """ + assert not model._is_graph_network # Only makes sense for subclassed networks + # Retrieve all layers tracked by the model as well as their attribute names + attributes_cache = {} + for name in dir(model): + try: + value = getattr(model, name) + except (AttributeError, ValueError, TypeError): + continue + if isinstance(value, Layer): + attributes_cache[name] = value + assert value in model._layers + elif isinstance(value, (list, tuple)) and name not in ('layers', '_layers'): + # Handle case: list/tuple of layers (also tracked by the Network API). + if value and all(isinstance(val, Layer) for val in value): + raise ValueError('We do not support the use of list-of-layers ' + 'attributes in subclassed models used with ' + '`model_to_estimator` at this time. Found list ' + 'model: %s' % name) + + # Replace layers on the model with fresh layers + layers_to_names = {value: key for key, value in attributes_cache.items()} + original_layers = model._layers[:] + model._layers = [] + for layer in original_layers: # We preserve layer order. + config = layer.get_config() + # This will not work for nested subclassed models used as layers. + # This would be theoretically possible to support, but would add complexity. + # Only do it if users complain. + if isinstance(layer, Network) and not layer._is_graph_network: + raise ValueError('We do not support the use of nested subclassed models ' + 'in `model_to_estimator` at this time. Found nested ' + 'model: %s' % layer) + fresh_layer = layer.__class__.from_config(config) + name = layers_to_names[layer] + setattr(model, name, fresh_layer) + + # Cache original model build attributes (in addition to layers) + if (not hasattr(model, '_original_attributes_cache') or + model._original_attributes_cache is None): + if model.built: + attributes_to_cache = [ + 'inputs', + 'outputs', + '_feed_outputs', + '_feed_output_names', + '_feed_output_shapes', + '_feed_loss_fns', + 'loss_weights_list', + 'targets', + '_feed_targets', + 'sample_weight_modes', + 'weighted_metrics', + 'metrics_names', + 'metrics_tensors', + 'metrics_updates', + 'stateful_metric_names', + 'total_loss', + 'sample_weights', + '_feed_sample_weights', + 'train_function', + 'test_function', + 'predict_function', + '_collected_trainable_weights', + '_feed_inputs', + '_feed_input_names', + '_feed_input_shapes', + 'optimizer', + ] + for name in attributes_to_cache: + attributes_cache[name] = getattr(model, name) + model._original_attributes_cache = attributes_cache + + # Reset built state + model.built = False + model.inputs = None + model.outputs = None + + +def _in_place_subclassed_model_state_restoration(model): + """Restores the original state of a model after it was "reset". + + This undoes this action of `_in_place_subclassed_model_reset`. + + Args: + model: Instance of a Keras model created via subclassing, on which + `_in_place_subclassed_model_reset` was previously called. + """ + assert not model._is_graph_network + # Restore layers and build attributes + if (hasattr(model, '_original_attributes_cache') and + model._original_attributes_cache is not None): + model._layers = [] + for name, value in model._original_attributes_cache.items(): + setattr(model, name, value) + model._original_attributes_cache = None + else: + # Restore to the state of a never-called model. + model.built = False + model.inputs = None + model.outputs = None def _clone_and_build_model(mode, @@ -92,8 +235,8 @@ def _clone_and_build_model(mode, mode: training mode. keras_model: an instance of compiled keras model. custom_objects: Dictionary for custom objects. - features: - labels: + features: Dict of tensors. + labels: Dict of tensors, or single tensor instance. Returns: The newly built model. @@ -101,33 +244,49 @@ def _clone_and_build_model(mode, # Set to True during training, False for inference. K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN) - # Clone keras model. - input_tensors = None if features is None else _create_ordered_io( - keras_model, features) - if custom_objects: - with CustomObjectScope(custom_objects): + # Get list of inputs. + if features is None: + input_tensors = None + else: + input_tensors = _create_ordered_io(keras_model, + estimator_io=features, + is_input=True) + # Get list of outputs. + if labels is None: + target_tensors = None + elif isinstance(labels, dict): + target_tensors = _create_ordered_io(keras_model, + estimator_io=labels, + is_input=False) + else: + target_tensors = [ + _cast_tensor_to_floatx( + sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)) + ] + + if keras_model._is_graph_network: + if custom_objects: + with CustomObjectScope(custom_objects): + model = models.clone_model(keras_model, input_tensors=input_tensors) + else: model = models.clone_model(keras_model, input_tensors=input_tensors) else: - model = models.clone_model(keras_model, input_tensors=input_tensors) + model = keras_model + _in_place_subclassed_model_reset(model) + if input_tensors is not None: + model._set_inputs(input_tensors) # Compile/Build model - if mode is model_fn_lib.ModeKeys.PREDICT and not model.built: - model.build() + if mode is model_fn_lib.ModeKeys.PREDICT: + if isinstance(model, models.Sequential): + model.build() else: - optimizer_config = keras_model.optimizer.get_config() - optimizer = keras_model.optimizer.__class__.from_config(optimizer_config) - optimizer.iterations = training_util.get_or_create_global_step() - - # Get list of outputs. - if labels is None: - target_tensors = None - elif isinstance(labels, dict): - target_tensors = _create_ordered_io(keras_model, labels, is_input=False) + if isinstance(keras_model.optimizer, optimizers.TFOptimizer): + optimizer = keras_model.optimizer else: - target_tensors = [ - _cast_tensor_to_floatx( - sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)) - ] + optimizer_config = keras_model.optimizer.get_config() + optimizer = keras_model.optimizer.__class__.from_config(optimizer_config) + optimizer.iterations = training_util.get_or_create_global_step() model.compile( optimizer, @@ -167,10 +326,14 @@ def _create_keras_model_fn(keras_model, custom_objects=None): # Set loss and metric only during train and evaluate. if mode is not model_fn_lib.ModeKeys.PREDICT: - model._make_train_function() # pylint: disable=protected-access + if mode is model_fn_lib.ModeKeys.TRAIN: + model._make_train_function() # pylint: disable=protected-access + else: + model._make_test_function() # pylint: disable=protected-access loss = model.total_loss if model.metrics: + # TODO(fchollet): support stateful metrics eval_metric_ops = {} # When each metric maps to an output if isinstance(model.metrics, dict): @@ -194,6 +357,10 @@ def _create_keras_model_fn(keras_model, custom_objects=None): if mode is model_fn_lib.ModeKeys.TRAIN: train_op = model.train_function.updates_op + if not model._is_graph_network: + # Reset model state to original state, + # to avoid `model_fn` being destructive for the initial model argument. + _in_place_subclassed_model_state_restoration(keras_model) return model_fn_lib.EstimatorSpec( mode=mode, predictions=predictions, @@ -221,18 +388,18 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects, Returns: The model_fn for a keras Estimator. """ - with ops.Graph().as_default() as g, g.device(estimator._device_fn): - random_seed.set_random_seed(estimator.config.tf_random_seed) - training_util.create_global_step() - model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model, - custom_objects) - - if isinstance(model, models.Sequential): - model = model.model - # Load weights and save to checkpoint if there is no checkpoint - latest_path = saver_lib.latest_checkpoint(estimator.model_dir) - if not latest_path: - with session.Session() as sess: + # Load weights and save to checkpoint if there is no checkpoint + latest_path = saver_lib.latest_checkpoint(estimator.model_dir) + if not latest_path: + with ops.Graph().as_default(): + random_seed.set_random_seed(estimator.config.tf_random_seed) + training_util.create_global_step() + model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model, + custom_objects) + if isinstance(model, models.Sequential): + model = model.model + # save to checkpoint + with session.Session(config=estimator._session_config) as sess: model.set_weights(keras_weights) # Make update ops and initialize all variables. if not model.train_function: @@ -244,6 +411,7 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects, saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt')) +@tf_export('keras.estimator.model_to_estimator') def model_to_estimator(keras_model=None, keras_model_path=None, custom_objects=None, @@ -272,10 +440,11 @@ def model_to_estimator(keras_model=None, """ if (not keras_model) and (not keras_model_path): raise ValueError( - 'Either keras_model or keras_model_path needs to be provided.') + 'Either `keras_model` or `keras_model_path` needs to be provided.') if keras_model and keras_model_path: raise ValueError( - 'Please specity either keras_model or keras_model_path but not both.') + 'Please specity either `keras_model` or `keras_model_path`, ' + 'but not both.') if not keras_model: if keras_model_path.startswith( @@ -286,18 +455,42 @@ def model_to_estimator(keras_model=None, logging.info('Loading models from %s', keras_model_path) keras_model = models.load_model(keras_model_path) else: - logging.info('Using the Keras model from memory.') + logging.info('Using the Keras model provided.') keras_model = keras_model - if not hasattr(keras_model, 'optimizer'): + if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer: raise ValueError( - 'Given keras model has not been compiled yet. Please compile first ' - 'before creating the estimator.') + 'The given keras model has not been compiled yet. Please compile first ' + 'before calling `model_to_estimator`.') + + if isinstance(config, dict): + config = run_config_lib.RunConfig(**config) - keras_weights = keras_model.get_weights() keras_model_fn = _create_keras_model_fn(keras_model, custom_objects) - est = estimator_lib.Estimator( + estimator = estimator_lib.Estimator( keras_model_fn, model_dir=model_dir, config=config) - # TODO(yifeif): move checkpoint initialization to scaffold.init_fn - _save_first_checkpoint(keras_model, est, custom_objects, keras_weights) - return est + + # Pass the config into keras backend's default session. + with session.Session(config=estimator._session_config) as sess: + K.set_session(sess) + + keras_weights = keras_model.get_weights() + if keras_model._is_graph_network: + # TODO(yifeif): move checkpoint initialization to scaffold.init_fn + _save_first_checkpoint(keras_model, + estimator, + custom_objects, + keras_weights) + elif keras_model.built: + logging.warning('You are creating an Estimator from a Keras model ' + 'manually subclassed from `Model`, that was ' + 'already called on some inputs (and thus already had ' + 'weights). We are currently unable to preserve ' + 'the model\'s state (its weights) ' + 'as part of the estimator ' + 'in this case. Be warned that the estimator ' + 'has been created using ' + 'a freshly initialized version of your model.\n' + 'Note that this doesn\'t affect the state of the ' + 'model instance you passed as `keras_model` argument.') + return estimator diff --git a/tensorflow/python/keras/_impl/keras/estimator_test.py b/tensorflow/python/keras/_impl/keras/estimator_test.py index 9fc48b4117e7ee2c717d5418754254aa02b82869..e076dc25b16900636313f0ddd85a61b8d917fc91 100644 --- a/tensorflow/python/keras/_impl/keras/estimator_test.py +++ b/tensorflow/python/keras/_impl/keras/estimator_test.py @@ -17,12 +17,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import json from math import log10 import os import tempfile import numpy as np +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import test_util @@ -32,6 +34,7 @@ from tensorflow.python.keras._impl.keras.applications import mobilenet from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import rmsprop try: @@ -62,12 +65,42 @@ def simple_functional_model(): return model -def get_resource_for_simple_model(is_sequential, is_evaluate): - model = simple_sequential_model( - ) if is_sequential else simple_functional_model() - if is_sequential: +def simple_subclassed_model(): + + class SimpleModel(keras.Model): + + def __init__(self): + super(SimpleModel, self).__init__() + self.dense1 = keras.layers.Dense(16, activation='relu') + self.dp = keras.layers.Dropout(0.1) + self.dense2 = keras.layers.Dense(_NUM_CLASS, activation='softmax') + + def call(self, inputs): + x = self.dense1(inputs) + x = self.dp(x) + return self.dense2(x) + + return SimpleModel() + + +def get_resource_for_simple_model(model_type='sequential', + is_evaluate=False,): + if model_type == 'sequential': + model = simple_sequential_model() model.build() - input_name = model.input_names[0] + elif model_type == 'subclass': + model = simple_subclassed_model() + else: + assert model_type == 'functional' + model = simple_functional_model() + + if model_type == 'subclass': + input_name = 'input_1' + output_name = 'output_1' + else: + input_name = model.input_names[0] + output_name = model.output_names[0] + np.random.seed(_RANDOM_SEED) (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( train_samples=_TRAIN_SIZE, @@ -78,17 +111,19 @@ def get_resource_for_simple_model(is_sequential, is_evaluate): y_test = keras.utils.to_categorical(y_test) train_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_train}, - y=y_train, + x=randomize_io_type(x_train, input_name), + y=randomize_io_type(y_train, output_name), shuffle=False, num_epochs=None, batch_size=16) evaluate_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_test}, y=y_test, num_epochs=1, shuffle=False) + x=randomize_io_type(x_test, input_name), + y=randomize_io_type(y_test, output_name), + num_epochs=1, shuffle=False) predict_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_test}, num_epochs=1, shuffle=False) + x=randomize_io_type(x_test, input_name), num_epochs=1, shuffle=False) inference_input_fn = evaluate_input_fn if is_evaluate else predict_input_fn @@ -96,6 +131,14 @@ def get_resource_for_simple_model(is_sequential, is_evaluate): y_test), train_input_fn, inference_input_fn +def randomize_io_type(array, name): + switch = np.random.random() + if switch > 0.5: + return array + else: + return {name: array} + + def multi_inputs_multi_outputs_model(): # test multi-input layer a = keras.layers.Input(shape=(16,), name='input_a') @@ -132,10 +175,10 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): gfile.DeleteRecursively(self._base_dir) def test_train(self): - for is_sequential in [True, False]: + for model_type in ['sequential', 'functional']: keras_model, (_, _), ( _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( - is_sequential=is_sequential, is_evaluate=True) + model_type=model_type, is_evaluate=True) keras_model.compile( loss='categorical_crossentropy', optimizer='rmsprop', @@ -153,10 +196,87 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + def test_train_with_tf_optimizer(self): + for model_type in ['sequential', 'functional']: + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type=model_type, is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, + # Also use dict config argument to get test coverage for that line. + config={ + 'tf_random_seed': _RANDOM_SEED, + 'model_dir': self._base_dir, + }) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + writer_cache.FileWriterCache.clear() + gfile.DeleteRecursively(self._config.model_dir) + + def test_train_with_subclassed_model(self): + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='subclass', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + def test_train_with_subclassed_model_with_existing_state(self): + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='subclass', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + # Create state + keras_model.train_on_batch(np.random.random((10,) + _INPUT_SIZE), + np.random.random((10, _NUM_CLASS))) + original_preds = keras_model.predict(np.ones((10,) + _INPUT_SIZE)) + + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + # Check that original model state was not altered + preds = keras_model.predict(np.ones((10,) + _INPUT_SIZE)) + self.assertAllClose(original_preds, preds, atol=1e-5) + # Check that the original model compilation did not break + keras_model.train_on_batch(np.random.random((10,) + _INPUT_SIZE), + np.random.random((10, _NUM_CLASS))) + def test_evaluate(self): keras_model, (x_train, y_train), ( x_test, y_test), _, eval_input_fn = get_resource_for_simple_model( - is_sequential=False, is_evaluate=True) + model_type='functional', is_evaluate=True) with self.test_session(): metrics = [ @@ -198,7 +318,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): # Check that predict on a pretrained model yield the same result. keras_model, (x_train, y_train), ( x_test, _), _, pred_input_fn = get_resource_for_simple_model( - is_sequential=True, is_evaluate=False) + model_type='sequential', is_evaluate=False) with self.test_session(): keras_model.compile( @@ -260,7 +380,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): keras_model, (x_train, y_train), ( x_test, _), _, pred_input_fn = get_resource_for_simple_model( - is_sequential=False, is_evaluate=False) + model_type='functional', is_evaluate=False) with self.test_session(): keras_model.compile( @@ -352,6 +472,46 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): model_dir=tempfile.mkdtemp(dir=self._base_dir), custom_objects=custom_objects) + def test_tf_config(self): + keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + tf_config = json.dumps({ + 'cluster': { + run_config_lib.TaskType.PS: ['localhost:1234'], + run_config_lib.TaskType.WORKER: ['localhost:1236'], + run_config_lib.TaskType.MASTER: ['localhost:1238'] + }, + 'task': { + 'type': run_config_lib.TaskType.MASTER, + 'index': 0 + } + }) + with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}): + with self.test_session(): + keras.estimator.model_to_estimator( + keras_model=keras_model, + model_dir=tempfile.mkdtemp(dir=self._base_dir)) + + def test_gpu_config(self): + keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3) + sess_config = config_pb2.ConfigProto(gpu_options=gpu_options) + self._config._session_config = sess_config + keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + self.assertEqual(keras.backend.get_session() + ._config.gpu_options.per_process_gpu_memory_fraction, + gpu_options.per_process_gpu_memory_fraction) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/initializers.py b/tensorflow/python/keras/_impl/keras/initializers.py index 8752faa534a3d6094ce530e490571ff939f86dbb..300bed5e1437074d010760c427c14f68e58ac363 100644 --- a/tensorflow/python/keras/_impl/keras/initializers.py +++ b/tensorflow/python/keras/_impl/keras/initializers.py @@ -32,8 +32,10 @@ from tensorflow.python.ops.init_ops import RandomUniform from tensorflow.python.ops.init_ops import TruncatedNormal from tensorflow.python.ops.init_ops import VarianceScaling from tensorflow.python.ops.init_ops import Zeros +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.initializers.lecun_normal') def lecun_normal(seed=None): """LeCun normal initializer. @@ -56,6 +58,7 @@ def lecun_normal(seed=None): scale=1., mode='fan_in', distribution='normal', seed=seed) +@tf_export('keras.initializers.lecun_uniform') def lecun_uniform(seed=None): """LeCun uniform initializer. @@ -77,6 +80,7 @@ def lecun_uniform(seed=None): scale=1., mode='fan_in', distribution='uniform', seed=seed) +@tf_export('keras.initializers.glorot_normal') def glorot_normal(seed=None): """Glorot normal initializer, also called Xavier normal initializer. @@ -99,6 +103,7 @@ def glorot_normal(seed=None): scale=1., mode='fan_avg', distribution='normal', seed=seed) +@tf_export('keras.initializers.glorot_uniform') def glorot_uniform(seed=None): """Glorot uniform initializer, also called Xavier uniform initializer. @@ -121,6 +126,7 @@ def glorot_uniform(seed=None): scale=1., mode='fan_avg', distribution='uniform', seed=seed) +@tf_export('keras.initializers.he_normal') def he_normal(seed=None): """He normal initializer. @@ -141,6 +147,7 @@ def he_normal(seed=None): scale=2., mode='fan_in', distribution='normal', seed=seed) +@tf_export('keras.initializers.he_uniform') def he_uniform(seed=None): """He uniform variance scaling initializer. @@ -178,10 +185,12 @@ orthogonal = Orthogonal # Utility functions +@tf_export('keras.initializers.serialize') def serialize(initializer): return serialize_keras_object(initializer) +@tf_export('keras.initializers.deserialize') def deserialize(config, custom_objects=None): return deserialize_keras_object( config, @@ -190,6 +199,7 @@ def deserialize(config, custom_objects=None): printable_module_name='initializer') +@tf_export('keras.initializers.get') def get(identifier): if isinstance(identifier, dict): return deserialize(identifier) @@ -199,4 +209,5 @@ def get(identifier): elif callable(identifier): return identifier else: - raise ValueError('Could not interpret initializer identifier:', identifier) + raise ValueError('Could not interpret initializer identifier: ' + + str(identifier)) diff --git a/tensorflow/python/keras/_impl/keras/integration_test.py b/tensorflow/python/keras/_impl/keras/integration_test.py index 15c3d14727a44c9726a1c2c86f47640bcc490e70..280f7ed1b11e2026ac196eb319f7d5da8301f060 100644 --- a/tensorflow/python/keras/_impl/keras/integration_test.py +++ b/tensorflow/python/keras/_impl/keras/integration_test.py @@ -23,7 +23,6 @@ import numpy as np from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.layers import core as tf_core_layers -from tensorflow.python.layers import network as tf_network_layers from tensorflow.python.ops import nn from tensorflow.python.platform import test @@ -275,10 +274,10 @@ class KerasIntegrationTest(test.TestCase): y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test) - inputs = tf_network_layers.Input(shape=(10,)) + inputs = keras.Input(shape=(10,)) x = tf_core_layers.Dense(32, activation=nn.relu)(inputs) outputs = tf_core_layers.Dense(2, activation=nn.softmax)(x) - model = keras.models.Model(inputs, outputs) + model = keras.Model(inputs, outputs) model.summary() model.compile(loss='categorical_crossentropy', diff --git a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py index ffbf77c4b8e4fa4454bfa82e473522ee7a316222..c40ee109aaea7dacea72e095b1d8cea3ed2e9bf8 100644 --- a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py @@ -25,9 +25,11 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.LeakyReLU') class LeakyReLU(Layer): """Leaky version of a Rectified Linear Unit. @@ -66,6 +68,7 @@ class LeakyReLU(Layer): return input_shape +@tf_export('keras.layers.PReLU') class PReLU(Layer): """Parametric Rectified Linear Unit. @@ -163,6 +166,7 @@ class PReLU(Layer): return input_shape +@tf_export('keras.layers.ELU') class ELU(Layer): """Exponential Linear Unit. @@ -201,6 +205,7 @@ class ELU(Layer): return input_shape +@tf_export('keras.layers.ThresholdedReLU') class ThresholdedReLU(Layer): """Thresholded Rectified Linear Unit. @@ -239,6 +244,7 @@ class ThresholdedReLU(Layer): return input_shape +@tf_export('keras.layers.Softmax') class Softmax(Layer): """Softmax activation function. diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional.py b/tensorflow/python/keras/_impl/keras/layers/convolutional.py index 2ee07327751f9f7dcc87aaa83e18bcc1b5991d5c..162ae6c28f1afae1dd8aaf70213b808d9ad9598f 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional.py @@ -38,8 +38,10 @@ from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling3D # pylint: enable=unused-import from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.layers import convolutional as tf_convolutional_layers +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.Conv1D', 'keras.layers.Convolution1D') class Conv1D(tf_convolutional_layers.Conv1D, Layer): """1D convolution layer (e.g. temporal convolution). @@ -58,7 +60,7 @@ class Conv1D(tf_convolutional_layers.Conv1D, Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, @@ -153,6 +155,7 @@ class Conv1D(tf_convolutional_layers.Conv1D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Conv2D', 'keras.layers.Convolution2D') class Conv2D(tf_convolutional_layers.Conv2D, Layer): """2D convolution layer (e.g. spatial convolution over images). @@ -170,7 +173,7 @@ class Conv2D(tf_convolutional_layers.Conv2D, Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for @@ -286,6 +289,7 @@ class Conv2D(tf_convolutional_layers.Conv2D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Conv3D', 'keras.layers.Convolution3D') class Conv3D(tf_convolutional_layers.Conv3D, Layer): """3D convolution layer (e.g. spatial convolution over volumes). @@ -304,7 +308,7 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for @@ -426,6 +430,8 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Conv2DTranspose', + 'keras.layers.Convolution2DTranspose') class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer): """Transposed convolution layer (sometimes called Deconvolution). @@ -563,6 +569,8 @@ class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Conv3DTranspose', + 'keras.layers.Convolution3DTranspose') class Conv3DTranspose(tf_convolutional_layers.Conv3DTranspose, Layer): """Transposed convolution layer (sometimes called Deconvolution). @@ -711,6 +719,8 @@ class Conv3DTranspose(tf_convolutional_layers.Conv3DTranspose, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.SeparableConv1D', + 'keras.layers.SeparableConvolution1D') class SeparableConv1D(tf_convolutional_layers.SeparableConv1D, Layer): """Depthwise separable 1D convolution. @@ -849,6 +859,8 @@ class SeparableConv1D(tf_convolutional_layers.SeparableConv1D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.SeparableConv2D', + 'keras.layers.SeparableConvolution2D') class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer): """Depthwise separable 2D convolution. @@ -865,7 +877,7 @@ class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for @@ -1012,6 +1024,7 @@ class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.UpSampling1D') class UpSampling1D(Layer): """Upsampling layer for 1D inputs. @@ -1047,6 +1060,7 @@ class UpSampling1D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.UpSampling2D') class UpSampling2D(Layer): """Upsampling layer for 2D inputs. @@ -1114,6 +1128,7 @@ class UpSampling2D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.UpSampling3D') class UpSampling3D(Layer): """Upsampling layer for 3D inputs. @@ -1186,6 +1201,7 @@ class UpSampling3D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.ZeroPadding1D') class ZeroPadding1D(Layer): """Zero-padding layer for 1D input (e.g. temporal sequence). @@ -1226,6 +1242,7 @@ class ZeroPadding1D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.ZeroPadding2D') class ZeroPadding2D(Layer): """Zero-padding layer for 2D input (e.g. picture). @@ -1327,6 +1344,7 @@ class ZeroPadding2D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.ZeroPadding3D') class ZeroPadding3D(Layer): """Zero-padding layer for 3D data (spatial or spatio-temporal). @@ -1444,6 +1462,7 @@ class ZeroPadding3D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Cropping1D') class Cropping1D(Layer): """Cropping layer for 1D input (e.g. temporal sequence). @@ -1488,6 +1507,7 @@ class Cropping1D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Cropping2D') class Cropping2D(Layer): """Cropping layer for 2D input (e.g. picture). @@ -1619,6 +1639,7 @@ class Cropping2D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Cropping3D') class Cropping3D(Layer): """Cropping layer for 3D data (e.g. diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py index 565db19e41fd4ebf334ea950566c8edf6896deaf..d95a0942452afa82e277c358be5c3b2ba061ac98 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py @@ -26,9 +26,10 @@ from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.layers.recurrent import Recurrent from tensorflow.python.keras._impl.keras.utils import conv_utils +from tensorflow.python.util.tf_export import tf_export class ConvRecurrent2D(Recurrent): @@ -38,7 +39,7 @@ class ConvRecurrent2D(Recurrent): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window. strides: An integer or tuple/list of n integers, @@ -190,6 +191,7 @@ class ConvRecurrent2D(Recurrent): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.ConvLSTM2D') class ConvLSTM2D(ConvRecurrent2D): """Convolutional LSTM. @@ -198,7 +200,7 @@ class ConvLSTM2D(ConvRecurrent2D): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window. strides: An integer or tuple/list of n integers, diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py index 39c9d4f0fb2751b0eef3b28f6d5b8cb0a93e22e5..f4a134b96cec0385cb24a208f3403db944b68edc 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py @@ -18,8 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import copy + import numpy as np +from tensorflow.python.eager import context +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -27,45 +31,40 @@ from tensorflow.python.platform import test class Convolution1DTest(test.TestCase): - def test_dilated_conv1d(self): - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Conv1D, - input_data=np.reshape(np.arange(4, dtype='float32'), (1, 4, 1)), - kwargs={ - 'filters': 1, - 'kernel_size': 2, - 'dilation_rate': 1, - 'padding': 'valid', - 'kernel_initializer': 'ones', - 'use_bias': False, - }, - expected_output=[[[1], [3], [5]]]) - - def test_conv_1d(self): - batch_size = 2 - steps = 8 - input_dim = 2 - kernel_size = 3 - filters = 3 + def _run_test(self, kwargs, arg, values): + num_samples = 2 + stack_size = 3 + length = 7 - for padding in ['valid', 'same']: - for strides in [1, 2]: - if padding == 'same' and strides != 1: - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.Conv1D, + kwargs=test_kwargs, + input_shape=(num_samples, length, stack_size)) + + @tf_test_util.run_in_graph_and_eager_modes() + def test_conv1d(self): + kwargs = { + 'filters': 2, + 'kernel_size': 3, + } + + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [2]) + self._run_test(kwargs, 'dilation_rate', [2]) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Conv1D, - kwargs={ - 'filters': filters, - 'kernel_size': kernel_size, - 'padding': padding, - 'strides': strides - }, - input_shape=(batch_size, steps, input_dim)) - - def test_conv_1d_regularizers(self): + kwargs = { + 'filters': 2, + 'kernel_size': 3, + 'padding': 'same', + } + self._run_test(kwargs, 'dilation_rate', [2]) + self._run_test(kwargs, 'dilation_rate', [3]) + + def test_conv1d_regularizers(self): kwargs = { 'filters': 3, 'kernel_size': 3, @@ -82,7 +81,7 @@ class Convolution1DTest(test.TestCase): layer(keras.backend.variable(np.ones((1, 5, 2)))) self.assertEqual(len(layer.losses), 3) - def test_conv_1d_constraints(self): + def test_conv1d_constraints(self): k_constraint = lambda x: x b_constraint = lambda x: x @@ -103,35 +102,44 @@ class Convolution1DTest(test.TestCase): class Conv2DTest(test.TestCase): - def test_convolution_2d(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 2 stack_size = 3 - kernel_size = (3, 2) num_row = 7 num_col = 6 - for padding in ['valid', 'same']: - for strides in [(1, 1), (2, 2)]: - if padding == 'same' and strides != (1, 1): - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.SeparableConv2D, + kwargs=test_kwargs, + input_shape=(num_samples, num_row, num_col, stack_size)) + + @tf_test_util.run_in_graph_and_eager_modes() + def test_conv2d(self): + kwargs = { + 'filters': 2, + 'kernel_size': (3, 3), + } + + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [(2, 2)]) + if test.is_gpu_available(cuda_only=True): + # Only runs on GPU with CUDA, channels_first is not supported on CPU. + # TODO(b/62340061): Support channels_first on CPU. + self._run_test(kwargs, 'data_format', ['channels_first']) + self._run_test(kwargs, 'dilation_rate', [(2, 2)]) - with self.test_session(use_gpu=True): - # Only runs on GPU with CUDA, channels_first is not supported on CPU. - # TODO(b/62340061): Support channels_first on CPU. - if test.is_gpu_available(cuda_only=True): - testing_utils.layer_test( - keras.layers.Conv2D, - kwargs={ - 'filters': filters, - 'kernel_size': kernel_size, - 'padding': padding, - 'strides': strides, - 'data_format': 'channels_first' - }, - input_shape=(num_samples, stack_size, num_row, num_col)) - - def test_convolution_2d_regularizers(self): + kwargs = { + 'filters': 2, + 'kernel_size': 3, + 'padding': 'same', + } + self._run_test(kwargs, 'dilation_rate', [2]) + + def test_conv2d_regularizers(self): kwargs = { 'filters': 3, 'kernel_size': 3, @@ -148,7 +156,7 @@ class Conv2DTest(test.TestCase): layer(keras.backend.variable(np.ones((1, 5, 5, 2)))) self.assertEqual(len(layer.losses), 3) - def test_convolution_2d_constraints(self): + def test_conv2d_constraints(self): k_constraint = lambda x: x b_constraint = lambda x: x @@ -166,51 +174,35 @@ class Conv2DTest(test.TestCase): self.assertEqual(layer.kernel.constraint, k_constraint) self.assertEqual(layer.bias.constraint, b_constraint) - def test_dilated_conv_2d(self): - num_samples = 2 - filters = 2 - stack_size = 3 - kernel_size = (3, 2) - num_row = 7 - num_col = 6 - - # Test dilation - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Conv2D, - kwargs={ - 'filters': filters, - 'kernel_size': kernel_size, - 'dilation_rate': (2, 2) - }, - input_shape=(num_samples, num_row, num_col, stack_size)) - class Conv2DTransposeTest(test.TestCase): - def test_conv2d_transpose(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 2 stack_size = 3 - num_row = 5 + num_row = 7 num_col = 6 - for padding in ['valid', 'same']: - for strides in [(1, 1), (2, 2)]: - if padding == 'same' and strides != (1, 1): - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.Conv2DTranspose, + kwargs=test_kwargs, + input_shape=(num_samples, num_row, num_col, stack_size)) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Conv2DTranspose, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'padding': padding, - 'strides': strides, - 'data_format': 'channels_last' - }, - input_shape=(num_samples, num_row, num_col, stack_size)) + @tf_test_util.run_in_graph_and_eager_modes() + def test_conv2dtranspose(self): + kwargs = { + 'filters': 2, + 'kernel_size': (3, 3), + } + + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [(2, 2)]) + if test.is_gpu_available(cuda_only=True): + self._run_test(kwargs, 'data_format', ['channels_first']) def test_conv2dtranspose_regularizers(self): kwargs = { @@ -250,30 +242,33 @@ class Conv2DTransposeTest(test.TestCase): class Conv3DTransposeTest(test.TestCase): - def test_conv3d_transpose(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 2 stack_size = 3 - num_row = 5 + num_row = 7 num_col = 6 - depth = 4 + depth = 5 - for padding in ['valid', 'same']: - for strides in [(1, 1, 1), (2, 2, 2)]: - if padding == 'same' and strides != (1, 1, 1): - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.Conv3DTranspose, + kwargs=test_kwargs, + input_shape=(num_samples, depth, num_row, num_col, stack_size)) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Conv3DTranspose, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'padding': padding, - 'strides': strides, - 'data_format': 'channels_last' - }, - input_shape=(num_samples, depth, num_row, num_col, stack_size)) + @tf_test_util.run_in_graph_and_eager_modes() + def test_conv3dtranspose(self): + kwargs = { + 'filters': 2, + 'kernel_size': (3, 3, 3), + } + + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [(2, 2, 2)]) + if test.is_gpu_available(cuda_only=True): + self._run_test(kwargs, 'data_format', ['channels_first']) def test_conv3dtranspose_regularizers(self): kwargs = { @@ -313,29 +308,38 @@ class Conv3DTransposeTest(test.TestCase): class SeparableConv1DTest(test.TestCase): - def test_separable_conv_1d(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 6 stack_size = 3 length = 7 - strides = 1 - for padding in ['valid', 'same']: - for multiplier in [1, 2]: - if padding == 'same' and strides != 1: - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.SeparableConv1D, + kwargs=test_kwargs, + input_shape=(num_samples, length, stack_size)) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.SeparableConv1D, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'padding': padding, - 'strides': strides, - 'depth_multiplier': multiplier - }, - input_shape=(num_samples, length, stack_size)) + @tf_test_util.run_in_graph_and_eager_modes() + def test_separable_conv1d(self): + kwargs = { + 'filters': 2, + 'kernel_size': 3, + } + + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [2]) + self._run_test(kwargs, 'dilation_rate', [2]) + self._run_test(kwargs, 'depth_multiplier', [2]) + + kwargs = { + 'filters': 2, + 'kernel_size': 3, + 'padding': 'same', + } + self._run_test(kwargs, 'dilation_rate', [2]) def test_separable_conv1d_regularizers(self): kwargs = { @@ -379,30 +383,41 @@ class SeparableConv1DTest(test.TestCase): class SeparableConv2DTest(test.TestCase): - def test_separable_conv_2d(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 6 stack_size = 3 num_row = 7 num_col = 6 - for padding in ['valid', 'same']: - for strides in [(1, 1), (2, 2)]: - for multiplier in [1, 2]: - if padding == 'same' and strides != (1, 1): - continue + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.SeparableConv2D, + kwargs=test_kwargs, + input_shape=(num_samples, num_row, num_col, stack_size)) + + @tf_test_util.run_in_graph_and_eager_modes() + def test_separable_conv2d(self): + kwargs = { + 'filters': 2, + 'kernel_size': 3, + } - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.SeparableConv2D, - kwargs={ - 'filters': filters, - 'kernel_size': (3, 3), - 'padding': padding, - 'strides': strides, - 'depth_multiplier': multiplier - }, - input_shape=(num_samples, num_row, num_col, stack_size)) + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [2]) + if test.is_gpu_available(cuda_only=True): + self._run_test(kwargs, 'data_format', ['channels_first']) + self._run_test(kwargs, 'dilation_rate', [2]) + self._run_test(kwargs, 'depth_multiplier', [2]) + + kwargs = { + 'filters': 2, + 'kernel_size': 3, + 'padding': 'same', + } + self._run_test(kwargs, 'dilation_rate', [2]) def test_separable_conv2d_regularizers(self): kwargs = { @@ -446,33 +461,36 @@ class SeparableConv2DTest(test.TestCase): class Conv3DTest(test.TestCase): - def test_convolution_3d(self): + def _run_test(self, kwargs, arg, values): num_samples = 2 - filters = 2 stack_size = 3 + num_row = 7 + num_col = 6 + depth = 5 - input_len_dim1 = 9 - input_len_dim2 = 8 - input_len_dim3 = 8 + test_kwargs = copy.copy(kwargs) + for value in values: + test_kwargs[arg] = value + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.Conv3D, + kwargs=test_kwargs, + input_shape=(num_samples, depth, num_row, num_col, stack_size)) - for padding in ['valid', 'same']: - for strides in [(1, 1, 1), (2, 2, 2)]: - if padding == 'same' and strides != (1, 1, 1): - continue + @tf_test_util.run_in_graph_and_eager_modes() + def test_conv3d(self): + kwargs = { + 'filters': 2, + 'kernel_size': (3, 3, 3), + } - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.Convolution3D, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'padding': padding, - 'strides': strides - }, - input_shape=(num_samples, input_len_dim1, input_len_dim2, - input_len_dim3, stack_size)) - - def test_convolution_3d_regularizers(self): + self._run_test(kwargs, 'padding', ['valid', 'same']) + self._run_test(kwargs, 'strides', [(2, 2, 2)]) + self._run_test(kwargs, 'dilation_rate', [(2, 2, 2)]) + if test.is_gpu_available(cuda_only=True): + self._run_test(kwargs, 'data_format', ['channels_first']) + + def test_conv3d_regularizers(self): kwargs = { 'filters': 3, 'kernel_size': 3, @@ -490,7 +508,7 @@ class Conv3DTest(test.TestCase): layer(keras.backend.variable(np.ones((1, 5, 5, 5, 2)))) self.assertEqual(len(layer.losses), 3) - def test_convolution_3d_constraints(self): + def test_conv3d_constraints(self): k_constraint = lambda x: x b_constraint = lambda x: x @@ -511,6 +529,7 @@ class Conv3DTest(test.TestCase): class ZeroPaddingTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_zero_padding_1d(self): num_samples = 2 input_dim = 2 @@ -534,7 +553,10 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding1D(padding=2) layer.build(shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) for offset in [0, 1, -1, -2]: np.testing.assert_allclose(np_output[:, offset, :], 0.) np.testing.assert_allclose(np_output[:, 2:-2, :], 1.) @@ -542,7 +564,10 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding1D(padding=(1, 2)) layer.build(shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) for left_offset in [0]: np.testing.assert_allclose(np_output[:, left_offset, :], 0.) for right_offset in [-1, -2]: @@ -556,6 +581,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding1D(padding=None) + @tf_test_util.run_in_graph_and_eager_modes() def test_zero_padding_2d(self): num_samples = 2 stack_size = 2 @@ -584,7 +610,10 @@ class ZeroPaddingTest(test.TestCase): padding=(2, 2), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) if data_format == 'channels_last': for offset in [0, 1, -1, -2]: np.testing.assert_allclose(np_output[:, offset, :, :], 0.) @@ -600,7 +629,10 @@ class ZeroPaddingTest(test.TestCase): padding=((1, 2), (3, 4)), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) if data_format == 'channels_last': for top_offset in [0]: np.testing.assert_allclose(np_output[:, top_offset, :, :], 0.) @@ -628,6 +660,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding2D(padding=None) + @tf_test_util.run_in_graph_and_eager_modes() def test_zero_padding_3d(self): num_samples = 2 stack_size = 2 @@ -650,7 +683,10 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding3D(padding=(2, 2, 2)) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) for offset in [0, 1, -1, -2]: np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.) np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.) @@ -666,11 +702,13 @@ class ZeroPaddingTest(test.TestCase): class UpSamplingTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_upsampling_1d(self): with self.test_session(use_gpu=True): testing_utils.layer_test( keras.layers.UpSampling1D, kwargs={'size': 2}, input_shape=(3, 5, 4)) + @tf_test_util.run_in_graph_and_eager_modes() def test_upsampling_2d(self): num_samples = 2 stack_size = 2 @@ -699,7 +737,10 @@ class UpSamplingTest(test.TestCase): size=(length_row, length_col), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) if data_format == 'channels_first': assert np_output.shape[2] == length_row * input_num_row assert np_output.shape[3] == length_col * input_num_col @@ -717,6 +758,7 @@ class UpSamplingTest(test.TestCase): np.testing.assert_allclose(np_output, expected_out) + @tf_test_util.run_in_graph_and_eager_modes() def test_upsampling_3d(self): num_samples = 2 stack_size = 2 @@ -748,7 +790,10 @@ class UpSamplingTest(test.TestCase): data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) if data_format == 'channels_first': assert np_output.shape[2] == length_dim1 * input_len_dim1 assert np_output.shape[3] == length_dim2 * input_len_dim2 @@ -773,6 +818,7 @@ class UpSamplingTest(test.TestCase): class CroppingTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_cropping_1d(self): num_samples = 2 time_length = 4 @@ -791,6 +837,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping1D(cropping=None) + @tf_test_util.run_in_graph_and_eager_modes() def test_cropping_2d(self): num_samples = 2 stack_size = 2 @@ -818,7 +865,10 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) # compare with numpy if data_format == 'channels_first': expected_out = inputs[:, :, cropping[0][0]:-cropping[0][1], cropping[ @@ -842,7 +892,10 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) # compare with input np.testing.assert_allclose(np_output, inputs) @@ -852,6 +905,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping2D(cropping=None) + @tf_test_util.run_in_graph_and_eager_modes() def test_cropping_3d(self): num_samples = 2 stack_size = 2 @@ -883,7 +937,10 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - np_output = keras.backend.eval(output) + if context.executing_eagerly(): + np_output = output.numpy() + else: + np_output = keras.backend.eval(output) # compare with numpy if data_format == 'channels_first': expected_out = inputs[:, :, @@ -897,7 +954,7 @@ class CroppingTest(test.TestCase): cropping[2][0]:-cropping[2][1], :] np.testing.assert_allclose(np_output, expected_out) - # test incorrect use + # test incorrect use with self.assertRaises(ValueError): keras.layers.Cropping3D(cropping=(1, 1)) with self.assertRaises(ValueError): diff --git a/tensorflow/python/keras/_impl/keras/layers/core.py b/tensorflow/python/keras/_impl/keras/layers/core.py index ea2d3f2f04a591ab97f09dd0a43829fe9f75fc9e..73e4f15f7e259211c892fdc663e14dcb14aec58d 100644 --- a/tensorflow/python/keras/_impl/keras/layers/core.py +++ b/tensorflow/python/keras/_impl/keras/layers/core.py @@ -37,8 +37,10 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import func_dump from tensorflow.python.keras._impl.keras.utils.generic_utils import func_load from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.layers import core as tf_core_layers +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.Masking') class Masking(Layer): """Masks a sequence by using a mask value to skip timesteps. @@ -89,6 +91,7 @@ class Masking(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Dropout') class Dropout(tf_core_layers.Dropout, Layer): """Applies Dropout to the input. @@ -121,7 +124,7 @@ class Dropout(tf_core_layers.Dropout, Layer): training = K.learning_phase() output = super(Dropout, self).call(inputs, training=training) # EagerTensor object has no attribute _uses_learning_phase - if not context.in_eager_mode() and training is K.learning_phase(): + if not context.executing_eagerly() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output @@ -135,6 +138,7 @@ class Dropout(tf_core_layers.Dropout, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.SpatialDropout1D') class SpatialDropout1D(Dropout): """Spatial 1D version of Dropout. @@ -171,6 +175,7 @@ class SpatialDropout1D(Dropout): return noise_shape +@tf_export('keras.layers.SpatialDropout2D') class SpatialDropout2D(Dropout): """Spatial 2D version of Dropout. @@ -224,6 +229,7 @@ class SpatialDropout2D(Dropout): return (input_shape[0], 1, 1, input_shape[3]) +@tf_export('keras.layers.SpatialDropout3D') class SpatialDropout3D(Dropout): """Spatial 3D version of Dropout. @@ -276,6 +282,7 @@ class SpatialDropout3D(Dropout): return (input_shape[0], 1, 1, 1, input_shape[4]) +@tf_export('keras.layers.Activation') class Activation(Layer): """Applies an activation function to an output. @@ -309,6 +316,7 @@ class Activation(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Reshape') class Reshape(Layer): """Reshapes an output to a certain shape. @@ -414,6 +422,7 @@ class Reshape(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Permute') class Permute(Layer): """Permutes the dimensions of the input according to a given pattern. @@ -466,6 +475,7 @@ class Permute(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Flatten') class Flatten(tf_core_layers.Flatten, Layer): """Flattens the input. Does not affect the batch size. @@ -485,6 +495,7 @@ class Flatten(tf_core_layers.Flatten, Layer): pass +@tf_export('keras.layers.RepeatVector') class RepeatVector(Layer): """Repeats the input n times. @@ -528,6 +539,7 @@ class RepeatVector(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Lambda') class Lambda(Layer): """Wraps arbitrary expression as a `Layer` object. @@ -709,6 +721,7 @@ class Lambda(Layer): return cls(**config) +@tf_export('keras.layers.Dense') class Dense(tf_core_layers.Dense, Layer): """Just your regular densely-connected NN layer. @@ -813,6 +826,7 @@ class Dense(tf_core_layers.Dense, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.ActivityRegularization') class ActivityRegularization(Layer): """Layer that applies an update to the cost function based input activity. diff --git a/tensorflow/python/keras/_impl/keras/layers/core_test.py b/tensorflow/python/keras/_impl/keras/layers/core_test.py index bdb99c91c289cf808fec7b891376dbfcf5504aca..2ca816adbdcecaf371776d99f3da60d0d8790832 100644 --- a/tensorflow/python/keras/_impl/keras/layers/core_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/core_test.py @@ -20,11 +20,9 @@ from __future__ import print_function import numpy as np -from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils -from tensorflow.python.ops import init_ops from tensorflow.python.platform import test @@ -52,146 +50,134 @@ class CoreLayersTest(test.TestCase): dropout = keras.layers.Dropout(0.5) self.assertEqual(True, dropout.supports_masking) - with self.test_session(): - testing_utils.layer_test( - keras.layers.SpatialDropout1D, - kwargs={'rate': 0.5}, - input_shape=(2, 3, 4)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.SpatialDropout2D, - kwargs={'rate': 0.5}, - input_shape=(2, 3, 4, 5)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.SpatialDropout2D, - kwargs={'rate': 0.5, 'data_format': 'channels_first'}, - input_shape=(2, 3, 4, 5)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.SpatialDropout3D, - kwargs={'rate': 0.5}, - input_shape=(2, 3, 4, 4, 5)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.SpatialDropout3D, - kwargs={'rate': 0.5, 'data_format': 'channels_first'}, - input_shape=(2, 3, 4, 4, 5)) - + @tf_test_util.run_in_graph_and_eager_modes() + def test_spatial_dropout(self): + testing_utils.layer_test( + keras.layers.SpatialDropout1D, + kwargs={'rate': 0.5}, + input_shape=(2, 3, 4)) + + testing_utils.layer_test( + keras.layers.SpatialDropout2D, + kwargs={'rate': 0.5}, + input_shape=(2, 3, 4, 5)) + + testing_utils.layer_test( + keras.layers.SpatialDropout2D, + kwargs={'rate': 0.5, 'data_format': 'channels_first'}, + input_shape=(2, 3, 4, 5)) + + testing_utils.layer_test( + keras.layers.SpatialDropout3D, + kwargs={'rate': 0.5}, + input_shape=(2, 3, 4, 4, 5)) + + testing_utils.layer_test( + keras.layers.SpatialDropout3D, + kwargs={'rate': 0.5, 'data_format': 'channels_first'}, + input_shape=(2, 3, 4, 4, 5)) + + @tf_test_util.run_in_graph_and_eager_modes() def test_activation(self): # with string argument - with self.test_session(): - testing_utils.layer_test( - keras.layers.Activation, - kwargs={'activation': 'relu'}, - input_shape=(3, 2)) + testing_utils.layer_test( + keras.layers.Activation, + kwargs={'activation': 'relu'}, + input_shape=(3, 2)) # with function argument - with self.test_session(): - testing_utils.layer_test( - keras.layers.Activation, - kwargs={'activation': keras.backend.relu}, - input_shape=(3, 2)) + testing_utils.layer_test( + keras.layers.Activation, + kwargs={'activation': keras.backend.relu}, + input_shape=(3, 2)) + @tf_test_util.run_in_graph_and_eager_modes() def test_reshape(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Reshape, - kwargs={'target_shape': (8, 1)}, - input_shape=(3, 2, 4)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.Reshape, - kwargs={'target_shape': (-1, 1)}, - input_shape=(3, 2, 4)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.Reshape, - kwargs={'target_shape': (1, -1)}, - input_shape=(3, 2, 4)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.Reshape, - kwargs={'target_shape': (-1, 1)}, - input_shape=(None, None, 2)) - + testing_utils.layer_test( + keras.layers.Reshape, + kwargs={'target_shape': (8, 1)}, + input_shape=(3, 2, 4)) + + testing_utils.layer_test( + keras.layers.Reshape, + kwargs={'target_shape': (-1, 1)}, + input_shape=(3, 2, 4)) + + testing_utils.layer_test( + keras.layers.Reshape, + kwargs={'target_shape': (1, -1)}, + input_shape=(3, 2, 4)) + + testing_utils.layer_test( + keras.layers.Reshape, + kwargs={'target_shape': (-1, 1)}, + input_shape=(None, None, 2)) + + @tf_test_util.run_in_graph_and_eager_modes() def test_permute(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) + testing_utils.layer_test( + keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) + @tf_test_util.run_in_graph_and_eager_modes() def test_flatten(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) + testing_utils.layer_test( + keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) + @tf_test_util.run_in_graph_and_eager_modes() def test_repeat_vector(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2)) + testing_utils.layer_test( + keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2)) + @tf_test_util.run_in_graph_and_eager_modes() def test_lambda(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Lambda, - kwargs={'function': lambda x: x + 1}, - input_shape=(3, 2)) - - with self.test_session(): - testing_utils.layer_test( - keras.layers.Lambda, - kwargs={ - 'function': lambda x, a, b: x * a + b, - 'arguments': { - 'a': 0.6, - 'b': 0.4 - } - }, - input_shape=(3, 2)) - - with self.test_session(): - # test serialization with function - def f(x): - return x + 1 - - ld = keras.layers.Lambda(f) - config = ld.get_config() - ld = keras.layers.deserialize({ - 'class_name': 'Lambda', - 'config': config - }) - - # test with lambda - ld = keras.layers.Lambda( - lambda x: keras.backend.concatenate([keras.backend.square(x), x])) - config = ld.get_config() - ld = keras.layers.Lambda.from_config(config) - + testing_utils.layer_test( + keras.layers.Lambda, + kwargs={'function': lambda x: x + 1}, + input_shape=(3, 2)) + + testing_utils.layer_test( + keras.layers.Lambda, + kwargs={ + 'function': lambda x, a, b: x * a + b, + 'arguments': { + 'a': 0.6, + 'b': 0.4 + } + }, + input_shape=(3, 2)) + + # test serialization with function + def f(x): + return x + 1 + + ld = keras.layers.Lambda(f) + config = ld.get_config() + ld = keras.layers.deserialize({ + 'class_name': 'Lambda', + 'config': config + }) + + # test with lambda + ld = keras.layers.Lambda( + lambda x: keras.backend.concatenate([keras.backend.square(x), x])) + config = ld.get_config() + ld = keras.layers.Lambda.from_config(config) + + @tf_test_util.run_in_graph_and_eager_modes() def test_dense(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2)) + testing_utils.layer_test( + keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2)) - with self.test_session(): - testing_utils.layer_test( - keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 2)) + testing_utils.layer_test( + keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 2)) - with self.test_session(): - testing_utils.layer_test( - keras.layers.Dense, kwargs={'units': 3}, input_shape=(None, None, 2)) + testing_utils.layer_test( + keras.layers.Dense, kwargs={'units': 3}, input_shape=(None, None, 2)) - with self.test_session(): - testing_utils.layer_test( - keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2)) + testing_utils.layer_test( + keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2)) - # Test regularization + def test_dense_regularization(self): with self.test_session(): layer = keras.layers.Dense( 3, @@ -202,7 +188,7 @@ class CoreLayersTest(test.TestCase): layer(keras.backend.variable(np.ones((2, 4)))) self.assertEqual(3, len(layer.losses)) - # Test constraints + def test_dense_constraints(self): with self.test_session(): k_constraint = keras.constraints.max_norm(0.01) b_constraint = keras.constraints.max_norm(0.01) @@ -212,12 +198,6 @@ class CoreLayersTest(test.TestCase): self.assertEqual(layer.kernel.constraint, k_constraint) self.assertEqual(layer.bias.constraint, b_constraint) - def test_eager_dense(self): - with context.eager_mode(): - l = keras.layers.Dense(units=3, - kernel_initializer=init_ops.zeros_initializer()) - self.assertAllEqual(l(constant_op.constant([[1.0]])), [[0., 0., 0.]]) - def test_activity_regularization(self): with self.test_session(): layer = keras.layers.ActivityRegularization(l1=0.1) diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings.py b/tensorflow/python/keras/_impl/keras/layers/embeddings.py index f8e31068f8910ff44889963c8c301c39844d85cb..006ecd3135be25d43133daed1603734ecd1be955 100644 --- a/tensorflow/python/keras/_impl/keras/layers/embeddings.py +++ b/tensorflow/python/keras/_impl/keras/layers/embeddings.py @@ -23,9 +23,11 @@ from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.Embedding') class Embedding(Layer): """Turns positive integers (indexes) into dense vectors of fixed size. diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py b/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py index 1712111b877cf1fee4353c5542f33a973a26de95..26fd1f1c114587c2f1b3e0155f1259dd5f0dcf60 100644 --- a/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -25,47 +26,44 @@ from tensorflow.python.platform import test class EmbeddingTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_embedding(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.Embedding, - kwargs={'output_dim': 4, - 'input_dim': 10, - 'input_length': 2}, - input_shape=(3, 2), - input_dtype='int32', - expected_output_dtype='float32') + testing_utils.layer_test( + keras.layers.Embedding, + kwargs={'output_dim': 4, + 'input_dim': 10, + 'input_length': 2}, + input_shape=(3, 2), + input_dtype='int32', + expected_output_dtype='float32') - with self.test_session(): - testing_utils.layer_test( - keras.layers.Embedding, - kwargs={'output_dim': 4, - 'input_dim': 10, - 'mask_zero': True}, - input_shape=(3, 2), - input_dtype='int32', - expected_output_dtype='float32') + testing_utils.layer_test( + keras.layers.Embedding, + kwargs={'output_dim': 4, + 'input_dim': 10, + 'mask_zero': True}, + input_shape=(3, 2), + input_dtype='int32', + expected_output_dtype='float32') - with self.test_session(): - testing_utils.layer_test( - keras.layers.Embedding, - kwargs={'output_dim': 4, - 'input_dim': 10, - 'mask_zero': True}, - input_shape=(3, 4, 2), - input_dtype='int32', - expected_output_dtype='float32') + testing_utils.layer_test( + keras.layers.Embedding, + kwargs={'output_dim': 4, + 'input_dim': 10, + 'mask_zero': True}, + input_shape=(3, 4, 2), + input_dtype='int32', + expected_output_dtype='float32') - with self.test_session(): - testing_utils.layer_test( - keras.layers.Embedding, - kwargs={'output_dim': 4, - 'input_dim': 10, - 'mask_zero': True, - 'input_length': (None, 2)}, - input_shape=(3, 4, 2), - input_dtype='int32', - expected_output_dtype='float32') + testing_utils.layer_test( + keras.layers.Embedding, + kwargs={'output_dim': 4, + 'input_dim': 10, + 'mask_zero': True, + 'input_length': (None, 2)}, + input_shape=(3, 4, 2), + input_dtype='int32', + expected_output_dtype='float32') if __name__ == '__main__': diff --git a/tensorflow/python/keras/_impl/keras/layers/gru_test.py b/tensorflow/python/keras/_impl/keras/layers/gru_test.py index c57fbac41cc43995ef3249414ed03928e7ffd044..48e7e14f5ab73b534ab0d1c765ad2572b2930b2b 100644 --- a/tensorflow/python/keras/_impl/keras/layers/gru_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/gru_test.py @@ -20,64 +20,66 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer class GRULayerTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_return_sequences_GRU(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.GRU, - kwargs={'units': units, - 'return_sequences': True}, - input_shape=(num_samples, timesteps, embedding_dim)) + testing_utils.layer_test( + keras.layers.GRU, + kwargs={'units': units, + 'return_sequences': True}, + input_shape=(num_samples, timesteps, embedding_dim)) + @tf_test_util.run_in_graph_and_eager_modes() def test_dynamic_behavior_GRU(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - layer = keras.layers.GRU(units, input_shape=(None, embedding_dim)) - model = keras.models.Sequential() - model.add(layer) - model.compile('sgd', 'mse') - x = np.random.random((num_samples, timesteps, embedding_dim)) - y = np.random.random((num_samples, units)) - model.train_on_batch(x, y) - + layer = keras.layers.GRU(units, input_shape=(None, embedding_dim)) + model = keras.models.Sequential() + model.add(layer) + model.compile(RMSPropOptimizer(0.01), 'mse') + x = np.random.random((num_samples, timesteps, embedding_dim)) + y = np.random.random((num_samples, units)) + model.train_on_batch(x, y) + + @tf_test_util.run_in_graph_and_eager_modes() def test_dropout_GRU(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.GRU, - kwargs={'units': units, - 'dropout': 0.1, - 'recurrent_dropout': 0.1}, - input_shape=(num_samples, timesteps, embedding_dim)) - + testing_utils.layer_test( + keras.layers.GRU, + kwargs={'units': units, + 'dropout': 0.1, + 'recurrent_dropout': 0.1}, + input_shape=(num_samples, timesteps, embedding_dim)) + + @tf_test_util.run_in_graph_and_eager_modes() def test_implementation_mode_GRU(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - for mode in [0, 1, 2]: - testing_utils.layer_test( - keras.layers.GRU, - kwargs={'units': units, - 'implementation': mode}, - input_shape=(num_samples, timesteps, embedding_dim)) + for mode in [0, 1, 2]: + testing_utils.layer_test( + keras.layers.GRU, + kwargs={'units': units, + 'implementation': mode}, + input_shape=(num_samples, timesteps, embedding_dim)) def test_statefulness_GRU(self): num_samples = 2 diff --git a/tensorflow/python/keras/_impl/keras/layers/local.py b/tensorflow/python/keras/_impl/keras/layers/local.py index b844b071e02b4f8f217a09a7d412609f6e2cadeb..13d96e939220c11a4090cf535e3efa4365fe8b62 100644 --- a/tensorflow/python/keras/_impl/keras/layers/local.py +++ b/tensorflow/python/keras/_impl/keras/layers/local.py @@ -25,10 +25,12 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils import conv_utils +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.LocallyConnected1D') class LocallyConnected1D(Layer): """Locally-connected layer for 1D inputs. @@ -51,7 +53,7 @@ class LocallyConnected1D(Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, @@ -193,6 +195,7 @@ class LocallyConnected1D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.LocallyConnected2D') class LocallyConnected2D(Layer): """Locally-connected layer for 2D inputs. @@ -219,7 +222,7 @@ class LocallyConnected2D(Layer): Arguments: filters: Integer, the dimensionality of the output space - (i.e. the number output of filters in the convolution). + (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for diff --git a/tensorflow/python/keras/_impl/keras/layers/local_test.py b/tensorflow/python/keras/_impl/keras/layers/local_test.py index a815a0fadc8215c00f3db4749e323f96e44b66f3..93741d24b9a74cf9e8a83069f7c4235b1f489818 100644 --- a/tensorflow/python/keras/_impl/keras/layers/local_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/local_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -27,6 +28,7 @@ from tensorflow.python.platform import test class LocallyConnectedLayersTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_locallyconnected_1d(self): num_samples = 2 num_steps = 8 @@ -39,16 +41,15 @@ class LocallyConnectedLayersTest(test.TestCase): if padding == 'same' and strides != 1: continue - with self.test_session(): - testing_utils.layer_test( - keras.layers.LocallyConnected1D, - kwargs={ - 'filters': filters, - 'kernel_size': filter_length, - 'padding': padding, - 'strides': strides - }, - input_shape=(num_samples, num_steps, input_dim)) + testing_utils.layer_test( + keras.layers.LocallyConnected1D, + kwargs={ + 'filters': filters, + 'kernel_size': filter_length, + 'padding': padding, + 'strides': strides + }, + input_shape=(num_samples, num_steps, input_dim)) def test_locallyconnected_1d_regularization(self): num_samples = 2 @@ -86,6 +87,7 @@ class LocallyConnectedLayersTest(test.TestCase): self.assertEqual(layer.kernel.constraint, k_constraint) self.assertEqual(layer.bias.constraint, b_constraint) + @tf_test_util.run_in_graph_and_eager_modes() def test_locallyconnected_2d(self): num_samples = 8 filters = 3 @@ -98,20 +100,18 @@ class LocallyConnectedLayersTest(test.TestCase): if padding == 'same' and strides != (1, 1): continue - with self.test_session(): - testing_utils.layer_test( - keras.layers.LocallyConnected2D, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'padding': padding, - 'kernel_regularizer': 'l2', - 'bias_regularizer': 'l2', - 'activity_regularizer': 'l2', - 'strides': strides, - 'data_format': 'channels_last' - }, - input_shape=(num_samples, num_row, num_col, stack_size)) + testing_utils.layer_test( + keras.layers.LocallyConnected2D, + kwargs={ + 'filters': filters, + 'kernel_size': 3, + 'padding': padding, + 'kernel_regularizer': 'l2', + 'bias_regularizer': 'l2', + 'strides': strides, + 'data_format': 'channels_last' + }, + input_shape=(num_samples, num_row, num_col, stack_size)) def test_locallyconnected_2d_channels_first(self): num_samples = 8 diff --git a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py index 8d359bf17cdb80c98aeeed6d69e301962609ce59..11a5e0aeaacfa7520361ae41ac3d40607e8a9050 100644 --- a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py @@ -20,64 +20,81 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer class LSTMLayerTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_return_sequences_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.LSTM, - kwargs={'units': units, - 'return_sequences': True}, - input_shape=(num_samples, timesteps, embedding_dim)) + testing_utils.layer_test( + keras.layers.LSTM, + kwargs={'units': units, + 'return_sequences': True}, + input_shape=(num_samples, timesteps, embedding_dim)) + + def test_static_shape_inference_LSTM(self): + # Github issue: 15165 + timesteps = 3 + embedding_dim = 4 + units = 2 + + model = keras.models.Sequential() + inputs = keras.layers.Dense(embedding_dim, + input_shape=(timesteps, embedding_dim)) + model.add(inputs) + layer = keras.layers.LSTM(units, return_sequences=True) + model.add(layer) + outputs = model.layers[-1].output + self.assertEquals(outputs.get_shape().as_list(), [None, timesteps, units]) + @tf_test_util.run_in_graph_and_eager_modes() def test_dynamic_behavior_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - layer = keras.layers.LSTM(units, input_shape=(None, embedding_dim)) - model = keras.models.Sequential() - model.add(layer) - model.compile('sgd', 'mse') - x = np.random.random((num_samples, timesteps, embedding_dim)) - y = np.random.random((num_samples, units)) - model.train_on_batch(x, y) - + layer = keras.layers.LSTM(units, input_shape=(None, embedding_dim)) + model = keras.models.Sequential() + model.add(layer) + model.compile(RMSPropOptimizer(0.001), 'mse') + x = np.random.random((num_samples, timesteps, embedding_dim)) + y = np.random.random((num_samples, units)) + model.train_on_batch(x, y) + + @tf_test_util.run_in_graph_and_eager_modes() def test_dropout_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.LSTM, - kwargs={'units': units, - 'dropout': 0.1, - 'recurrent_dropout': 0.1}, - input_shape=(num_samples, timesteps, embedding_dim)) - + testing_utils.layer_test( + keras.layers.LSTM, + kwargs={'units': units, + 'dropout': 0.1, + 'recurrent_dropout': 0.1}, + input_shape=(num_samples, timesteps, embedding_dim)) + + @tf_test_util.run_in_graph_and_eager_modes() def test_implementation_mode_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - for mode in [0, 1, 2]: - testing_utils.layer_test( - keras.layers.LSTM, - kwargs={'units': units, - 'implementation': mode}, - input_shape=(num_samples, timesteps, embedding_dim)) + for mode in [0, 1, 2]: + testing_utils.layer_test( + keras.layers.LSTM, + kwargs={'units': units, + 'implementation': mode}, + input_shape=(num_samples, timesteps, embedding_dim)) def test_statefulness_LSTM(self): num_samples = 2 diff --git a/tensorflow/python/keras/_impl/keras/layers/merge.py b/tensorflow/python/keras/_impl/keras/layers/merge.py index 38b0b302972a31fb29b516680470c587d75a0781..c660cbd449b11a139f64cfa8b3a35310a597491c 100644 --- a/tensorflow/python/keras/_impl/keras/layers/merge.py +++ b/tensorflow/python/keras/_impl/keras/layers/merge.py @@ -21,8 +21,9 @@ from __future__ import division from __future__ import print_function from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras.engine.topology import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.util.tf_export import tf_export class _Merge(Layer): @@ -210,6 +211,7 @@ class _Merge(Layer): return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False) +@tf_export('keras.layers.Add') class Add(_Merge): """Layer that adds a list of inputs. @@ -279,6 +281,7 @@ class Subtract(_Merge): return inputs[0] - inputs[1] +@tf_export('keras.layers.Multiply') class Multiply(_Merge): """Layer that multiplies (element-wise) a list of inputs. @@ -294,6 +297,7 @@ class Multiply(_Merge): return output +@tf_export('keras.layers.Average') class Average(_Merge): """Layer that averages a list of inputs. @@ -309,6 +313,7 @@ class Average(_Merge): return output / len(inputs) +@tf_export('keras.layers.Maximum') class Maximum(_Merge): """Layer that computes the maximum (element-wise) a list of inputs. @@ -339,6 +344,7 @@ class Minimum(_Merge): return output +@tf_export('keras.layers.Concatenate') class Concatenate(_Merge): """Layer that concatenates a list of inputs. @@ -429,6 +435,7 @@ class Concatenate(_Merge): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.Dot') class Dot(_Merge): """Layer that computes a dot product between samples in two tensors. @@ -543,6 +550,7 @@ class Dot(_Merge): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.add') def add(inputs, **kwargs): """Functional interface to the `Add` layer. @@ -599,6 +607,7 @@ def subtract(inputs, **kwargs): return Subtract(**kwargs)(inputs) +@tf_export('keras.layers.multiply') def multiply(inputs, **kwargs): """Functional interface to the `Multiply` layer. @@ -612,6 +621,7 @@ def multiply(inputs, **kwargs): return Multiply(**kwargs)(inputs) +@tf_export('keras.layers.average') def average(inputs, **kwargs): """Functional interface to the `Average` layer. @@ -625,6 +635,7 @@ def average(inputs, **kwargs): return Average(**kwargs)(inputs) +@tf_export('keras.layers.maximum') def maximum(inputs, **kwargs): """Functional interface to the `Maximum` layer. @@ -651,6 +662,7 @@ def minimum(inputs, **kwargs): return Minimum(**kwargs)(inputs) +@tf_export('keras.layers.concatenate') def concatenate(inputs, axis=-1, **kwargs): """Functional interface to the `Concatenate` layer. @@ -665,6 +677,7 @@ def concatenate(inputs, axis=-1, **kwargs): return Concatenate(axis=axis, **kwargs)(inputs) +@tf_export('keras.layers.dot') def dot(inputs, axes, normalize=False, **kwargs): """Functional interface to the `Dot` layer. diff --git a/tensorflow/python/keras/_impl/keras/layers/merge_test.py b/tensorflow/python/keras/_impl/keras/layers/merge_test.py index bb03dda1fc645222c1ced97cfce8d459586dd89d..b2fe06f93e33ed63d6a2aa29522ecb552f582440 100644 --- a/tensorflow/python/keras/_impl/keras/layers/merge_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/merge_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -27,24 +28,25 @@ from tensorflow.python.platform import test class MergeLayersTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_add(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4, 5)) - i2 = keras.layers.Input(shape=(4, 5)) - i3 = keras.layers.Input(shape=(4, 5)) + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + i3 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.add([i1, i2, i3]) - self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) - model = keras.models.Model([i1, i2, i3], o) + o = keras.layers.add([i1, i2, i3]) + self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) + model = keras.models.Model([i1, i2, i3], o) - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - x3 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2, x3]) - self.assertEqual(out.shape, (2, 4, 5)) - self.assertAllClose(out, x1 + x2 + x3, atol=1e-4) + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + x3 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2, x3]) + self.assertEqual(out.shape, (2, 4, 5)) + self.assertAllClose(out, x1 + x2 + x3, atol=1e-4) - # test masking + def test_merge_add_masking(self): + with self.test_session(): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) m1 = keras.layers.Masking()(i1) @@ -54,11 +56,13 @@ class MergeLayersTest(test.TestCase): mask = layer.output_mask self.assertListEqual(mask.get_shape().as_list(), [None, 4]) - # test missing shape + def test_merge_add_dynamic_shape(self): + with self.test_session(): i1 = array_ops.placeholder(shape=(4, None), dtype='float32') i2 = array_ops.placeholder(shape=(4, 5), dtype='float32') layer = keras.layers.Add() o = layer([i1, i2]) + self.assertListEqual(o.get_shape().as_list(), [4, 5]) def test_merge_elementwise_errors(self): i1 = keras.layers.Input(shape=(4, 5)) @@ -72,79 +76,82 @@ class MergeLayersTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.add([i1]) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_multiply(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4, 5)) - i2 = keras.layers.Input(shape=(4, 5)) - i3 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.multiply([i1, i2, i3]) - self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) - model = keras.models.Model([i1, i2, i3], o) - - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - x3 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2, x3]) - self.assertEqual(out.shape, (2, 4, 5)) - self.assertAllClose(out, x1 * x2 * x3, atol=1e-4) - + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + i3 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.multiply([i1, i2, i3]) + self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) + model = keras.models.Model([i1, i2, i3], o) + + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + x3 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2, x3]) + self.assertEqual(out.shape, (2, 4, 5)) + self.assertAllClose(out, x1 * x2 * x3, atol=1e-4) + + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_average(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4, 5)) - i2 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.average([i1, i2]) - self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) - model = keras.models.Model([i1, i2], o) + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.average([i1, i2]) + self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) + model = keras.models.Model([i1, i2], o) - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 4, 5)) - self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4) + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 4, 5)) + self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_maximum(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4, 5)) - i2 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.maximum([i1, i2]) - self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) - model = keras.models.Model([i1, i2], o) + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.maximum([i1, i2]) + self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) + model = keras.models.Model([i1, i2], o) - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 4, 5)) - self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4) + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 4, 5)) + self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_minimum(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4, 5)) - i2 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.minimum([i1, i2]) - self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) - model = keras.models.Model([i1, i2], o) + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.minimum([i1, i2]) + self.assertListEqual(o.get_shape().as_list(), [None, 4, 5]) + model = keras.models.Model([i1, i2], o) - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 4, 5)) - self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4) + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 4, 5)) + self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_concatenate(self): + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.concatenate([i1, i2], axis=1) + self.assertListEqual(o.get_shape().as_list(), [None, 8, 5]) + model = keras.models.Model([i1, i2], o) + + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 8, 5)) + self.assertAllClose(out, np.concatenate([x1, x2], axis=1), atol=1e-4) + + def test_merge_concatenate_masking(self): with self.test_session(): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) - o = keras.layers.concatenate([i1, i2], axis=1) - self.assertListEqual(o.get_shape().as_list(), [None, 8, 5]) - model = keras.models.Model([i1, i2], o) - - x1 = np.random.random((2, 4, 5)) - x2 = np.random.random((2, 4, 5)) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 8, 5)) - self.assertAllClose(out, np.concatenate([x1, x2], axis=1), atol=1e-4) - - # test masking m1 = keras.layers.Masking()(i1) layer = keras.layers.Concatenate() o = layer([m1, i2]) @@ -162,35 +169,35 @@ class MergeLayersTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'called on a list'): keras.layers.concatenate([i1], axis=-1) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_dot(self): - with self.test_session(): - i1 = keras.layers.Input(shape=(4,)) - i2 = keras.layers.Input(shape=(4,)) - o = keras.layers.dot([i1, i2], axes=1) - self.assertListEqual(o.get_shape().as_list(), [None, 1]) - model = keras.models.Model([i1, i2], o) - _ = keras.layers.Dot(axes=1).get_config() - - x1 = np.random.random((2, 4)) - x2 = np.random.random((2, 4)) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 1)) - expected = np.zeros((2, 1)) - expected[0, 0] = np.dot(x1[0], x2[0]) - expected[1, 0] = np.dot(x1[1], x2[1]) - self.assertAllClose(out, expected, atol=1e-4) - - # Test with negative tuple of axes. - o = keras.layers.dot([i1, i2], axes=(-1, -1)) - self.assertListEqual(o.get_shape().as_list(), [None, 1]) - model = keras.models.Model([i1, i2], o) - out = model.predict([x1, x2]) - self.assertEqual(out.shape, (2, 1)) - self.assertAllClose(out, expected, atol=1e-4) - - # test compute_output_shape - layer = keras.layers.Dot(axes=-1) - self.assertEqual(layer.compute_output_shape([(4, 5), (4, 5)]), (4, 1)) + i1 = keras.layers.Input(shape=(4,)) + i2 = keras.layers.Input(shape=(4,)) + o = keras.layers.dot([i1, i2], axes=1) + self.assertListEqual(o.get_shape().as_list(), [None, 1]) + model = keras.models.Model([i1, i2], o) + _ = keras.layers.Dot(axes=1).get_config() + + x1 = np.random.random((2, 4)) + x2 = np.random.random((2, 4)) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 1)) + expected = np.zeros((2, 1)) + expected[0, 0] = np.dot(x1[0], x2[0]) + expected[1, 0] = np.dot(x1[1], x2[1]) + self.assertAllClose(out, expected, atol=1e-4) + + # Test with negative tuple of axes. + o = keras.layers.dot([i1, i2], axes=(-1, -1)) + self.assertListEqual(o.get_shape().as_list(), [None, 1]) + model = keras.models.Model([i1, i2], o) + out = model.predict([x1, x2]) + self.assertEqual(out.shape, (2, 1)) + self.assertAllClose(out, expected, atol=1e-4) + + # test compute_output_shape + layer = keras.layers.Dot(axes=-1) + self.assertEqual(layer.compute_output_shape([(4, 5), (4, 5)]), (4, 1)) def test_dot_errors(self): i1 = keras.layers.Input(shape=(4, 5)) @@ -208,6 +215,7 @@ class MergeLayersTest(test.TestCase): dot = keras.layers.Dot(1) dot.compute_output_shape(1) + @tf_test_util.run_in_graph_and_eager_modes() def test_merge_subtract(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) diff --git a/tensorflow/python/keras/_impl/keras/layers/noise.py b/tensorflow/python/keras/_impl/keras/layers/noise.py index 04fffcc384cc4d868937d92bbd5a8f6505ca1770..e309d160e5a9be97ff5f5356dad9dfaf85430233 100644 --- a/tensorflow/python/keras/_impl/keras/layers/noise.py +++ b/tensorflow/python/keras/_impl/keras/layers/noise.py @@ -22,9 +22,11 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.GaussianNoise') class GaussianNoise(Layer): """Apply additive zero-centered Gaussian noise. @@ -70,6 +72,7 @@ class GaussianNoise(Layer): return input_shape +@tf_export('keras.layers.GaussianDropout') class GaussianDropout(Layer): """Apply multiplicative 1-centered Gaussian noise. @@ -116,6 +119,7 @@ class GaussianDropout(Layer): return input_shape +@tf_export('keras.layers.AlphaDropout') class AlphaDropout(Layer): """Applies Alpha Dropout to the input. diff --git a/tensorflow/python/keras/_impl/keras/layers/noise_test.py b/tensorflow/python/keras/_impl/keras/layers/noise_test.py index f9b4d9cd090ffec1a5acd9118ea6a65798bd72a6..af4f031ec95bb56b72c1f1018e0e529d8ff55564 100644 --- a/tensorflow/python/keras/_impl/keras/layers/noise_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/noise_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -39,12 +40,12 @@ class NoiseLayersTest(test.TestCase): kwargs={'rate': 0.5}, input_shape=(3, 2, 3)) + @tf_test_util.run_in_graph_and_eager_modes() def test_AlphaDropout(self): - with self.test_session(): - testing_utils.layer_test( - keras.layers.AlphaDropout, - kwargs={'rate': 0.2}, - input_shape=(3, 2, 3)) + testing_utils.layer_test( + keras.layers.AlphaDropout, + kwargs={'rate': 0.2}, + input_shape=(3, 2, 3)) if __name__ == '__main__': diff --git a/tensorflow/python/keras/_impl/keras/layers/normalization.py b/tensorflow/python/keras/_impl/keras/layers/normalization.py index eecb14ceaa38968d54ea6702e534ee29b6e180d5..3b44b20bf822429351002c0f81fe8f9596d595d3 100644 --- a/tensorflow/python/keras/_impl/keras/layers/normalization.py +++ b/tensorflow/python/keras/_impl/keras/layers/normalization.py @@ -25,8 +25,10 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import Layer from tensorflow.python.layers import normalization as tf_normalization_layers +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.BatchNormalization') class BatchNormalization(tf_normalization_layers.BatchNormalization, Layer): """Batch normalization layer (Ioffe and Szegedy, 2014). @@ -109,7 +111,7 @@ class BatchNormalization(tf_normalization_layers.BatchNormalization, Layer): if training is None: training = K.learning_phase() output = super(BatchNormalization, self).call(inputs, training=training) - if context.in_graph_mode() and training is K.learning_phase(): + if not context.executing_eagerly() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output diff --git a/tensorflow/python/keras/_impl/keras/layers/normalization_test.py b/tensorflow/python/keras/_impl/keras/layers/normalization_test.py index 39a90e597089b30d110f26f074eba5d6895e52df..2b3628c3f1023612297465bdf3286246261992a2 100644 --- a/tensorflow/python/keras/_impl/keras/layers/normalization_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/normalization_test.py @@ -132,13 +132,19 @@ class NormalizationLayersTest(test.TestCase): model.compile('sgd', 'mse') model.train_on_batch(x, x) - assert len(model.updates) == 2 + self.assertEqual(len(bn.updates), 4) + self.assertEqual(len(model.updates), 2) + self.assertEqual(len(model.get_updates_for(x1)), 0) + self.assertEqual(len(model.get_updates_for(x2)), 2) # Test model-level reuse x3 = keras.layers.Input(shape=(10,)) y3 = model(x3) - new_model = keras.models.Model(x3, y3) - assert len(model.updates) == 2 + new_model = keras.models.Model(x3, y3, name='new_model') + + self.assertEqual(len(new_model.updates), 2) + self.assertEqual(len(model.updates), 4) + self.assertEqual(len(new_model.get_updates_for(x3)), 2) new_model.compile('sgd', 'mse') new_model.train_on_batch(x, x) diff --git a/tensorflow/python/keras/_impl/keras/layers/pooling.py b/tensorflow/python/keras/_impl/keras/layers/pooling.py index b133e2dfaf1bcacd055f6a597bd557f696469ffc..15d53379769d8142f5b2755a07479f60751346d2 100644 --- a/tensorflow/python/keras/_impl/keras/layers/pooling.py +++ b/tensorflow/python/keras/_impl/keras/layers/pooling.py @@ -24,8 +24,10 @@ from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.layers import pooling as tf_pooling_layers +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.MaxPool1D', 'keras.layers.MaxPooling1D') class MaxPooling1D(tf_pooling_layers.MaxPooling1D, Layer): """Max pooling operation for temporal data. @@ -58,6 +60,7 @@ class MaxPooling1D(tf_pooling_layers.MaxPooling1D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.AveragePooling1D', 'keras.layers.AvgPool1D') class AveragePooling1D(tf_pooling_layers.AveragePooling1D, Layer): """Average pooling for temporal data. @@ -91,6 +94,7 @@ class AveragePooling1D(tf_pooling_layers.AveragePooling1D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.MaxPool2D', 'keras.layers.MaxPooling2D') class MaxPooling2D(tf_pooling_layers.MaxPooling2D, Layer): """Max pooling operation for spatial data. @@ -156,6 +160,7 @@ class MaxPooling2D(tf_pooling_layers.MaxPooling2D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.AveragePooling2D', 'keras.layers.AvgPool2D') class AveragePooling2D(tf_pooling_layers.AveragePooling2D, Layer): """Average pooling operation for spatial data. @@ -221,6 +226,7 @@ class AveragePooling2D(tf_pooling_layers.AveragePooling2D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.MaxPool3D', 'keras.layers.MaxPooling3D') class MaxPooling3D(tf_pooling_layers.MaxPooling3D, Layer): """Max pooling operation for 3D data (spatial or spatio-temporal). @@ -282,6 +288,7 @@ class MaxPooling3D(tf_pooling_layers.MaxPooling3D, Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.AveragePooling3D', 'keras.layers.AvgPool3D') class AveragePooling3D(tf_pooling_layers.AveragePooling3D, Layer): """Average pooling operation for 3D data (spatial or spatio-temporal). @@ -359,6 +366,8 @@ class _GlobalPooling1D(Layer): raise NotImplementedError +@tf_export('keras.layers.GlobalAveragePooling1D', + 'keras.layers.GlobalAvgPool1D') class GlobalAveragePooling1D(_GlobalPooling1D): """Global average pooling operation for temporal data. @@ -374,6 +383,7 @@ class GlobalAveragePooling1D(_GlobalPooling1D): return K.mean(inputs, axis=1) +@tf_export('keras.layers.GlobalMaxPool1D', 'keras.layers.GlobalMaxPooling1D') class GlobalMaxPooling1D(_GlobalPooling1D): """Global max pooling operation for temporal data. @@ -414,6 +424,8 @@ class _GlobalPooling2D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.GlobalAveragePooling2D', + 'keras.layers.GlobalAvgPool2D') class GlobalAveragePooling2D(_GlobalPooling2D): """Global average pooling operation for spatial data. @@ -449,6 +461,7 @@ class GlobalAveragePooling2D(_GlobalPooling2D): return K.mean(inputs, axis=[2, 3]) +@tf_export('keras.layers.GlobalMaxPool2D', 'keras.layers.GlobalMaxPooling2D') class GlobalMaxPooling2D(_GlobalPooling2D): """Global max pooling operation for spatial data. @@ -509,6 +522,8 @@ class _GlobalPooling3D(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.GlobalAveragePooling3D', + 'keras.layers.GlobalAvgPool3D') class GlobalAveragePooling3D(_GlobalPooling3D): """Global Average pooling operation for 3D data. @@ -544,6 +559,7 @@ class GlobalAveragePooling3D(_GlobalPooling3D): return K.mean(inputs, axis=[2, 3, 4]) +@tf_export('keras.layers.GlobalMaxPool3D', 'keras.layers.GlobalMaxPooling3D') class GlobalMaxPooling3D(_GlobalPooling3D): """Global Max pooling operation for 3D data. diff --git a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py index ec0a5ae560f49ee39ecffb64f4ac65d3e800024c..bb003c1dddf80e2a745c1268a3a7d045f4e8b036 100644 --- a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -25,81 +27,85 @@ from tensorflow.python.platform import test class GlobalPoolingTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_globalpooling_1d(self): - with self.test_session(use_gpu=True): - testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D, - input_shape=(3, 4, 5)) - testing_utils.layer_test( - keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5)) + testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D, + input_shape=(3, 4, 5)) + testing_utils.layer_test( + keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5)) + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_globalpooling_2d(self): - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.pooling.GlobalMaxPooling2D, - kwargs={'data_format': 'channels_first'}, - input_shape=(3, 4, 5, 6)) - testing_utils.layer_test( - keras.layers.pooling.GlobalMaxPooling2D, - kwargs={'data_format': 'channels_last'}, - input_shape=(3, 5, 6, 4)) - testing_utils.layer_test( - keras.layers.pooling.GlobalAveragePooling2D, - kwargs={'data_format': 'channels_first'}, - input_shape=(3, 4, 5, 6)) - testing_utils.layer_test( - keras.layers.pooling.GlobalAveragePooling2D, - kwargs={'data_format': 'channels_last'}, - input_shape=(3, 5, 6, 4)) - + testing_utils.layer_test( + keras.layers.pooling.GlobalMaxPooling2D, + kwargs={'data_format': 'channels_first'}, + input_shape=(3, 4, 5, 6)) + testing_utils.layer_test( + keras.layers.pooling.GlobalMaxPooling2D, + kwargs={'data_format': 'channels_last'}, + input_shape=(3, 5, 6, 4)) + testing_utils.layer_test( + keras.layers.pooling.GlobalAveragePooling2D, + kwargs={'data_format': 'channels_first'}, + input_shape=(3, 4, 5, 6)) + testing_utils.layer_test( + keras.layers.pooling.GlobalAveragePooling2D, + kwargs={'data_format': 'channels_last'}, + input_shape=(3, 5, 6, 4)) + + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_globalpooling_3d(self): - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.pooling.GlobalMaxPooling3D, - kwargs={'data_format': 'channels_first'}, - input_shape=(3, 4, 3, 4, 3)) - testing_utils.layer_test( - keras.layers.pooling.GlobalMaxPooling3D, - kwargs={'data_format': 'channels_last'}, - input_shape=(3, 4, 3, 4, 3)) - testing_utils.layer_test( - keras.layers.pooling.GlobalAveragePooling3D, - kwargs={'data_format': 'channels_first'}, - input_shape=(3, 4, 3, 4, 3)) - testing_utils.layer_test( - keras.layers.pooling.GlobalAveragePooling3D, - kwargs={'data_format': 'channels_last'}, - input_shape=(3, 4, 3, 4, 3)) + testing_utils.layer_test( + keras.layers.pooling.GlobalMaxPooling3D, + kwargs={'data_format': 'channels_first'}, + input_shape=(3, 4, 3, 4, 3)) + testing_utils.layer_test( + keras.layers.pooling.GlobalMaxPooling3D, + kwargs={'data_format': 'channels_last'}, + input_shape=(3, 4, 3, 4, 3)) + testing_utils.layer_test( + keras.layers.pooling.GlobalAveragePooling3D, + kwargs={'data_format': 'channels_first'}, + input_shape=(3, 4, 3, 4, 3)) + testing_utils.layer_test( + keras.layers.pooling.GlobalAveragePooling3D, + kwargs={'data_format': 'channels_last'}, + input_shape=(3, 4, 3, 4, 3)) class Pooling2DTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_maxpooling_2d(self): pool_size = (3, 3) - with self.test_session(use_gpu=True): - for strides in [(1, 1), (2, 2)]: - testing_utils.layer_test( - keras.layers.MaxPooling2D, - kwargs={ - 'strides': strides, - 'padding': 'valid', - 'pool_size': pool_size - }, - input_shape=(3, 5, 6, 4)) - - def test_averagepooling_2d(self): - with self.test_session(use_gpu=True): + for strides in [(1, 1), (2, 2)]: testing_utils.layer_test( - keras.layers.AveragePooling2D, - kwargs={'strides': (2, 2), - 'padding': 'same', - 'pool_size': (2, 2)}, - input_shape=(3, 5, 6, 4)) - testing_utils.layer_test( - keras.layers.AveragePooling2D, - kwargs={'strides': (2, 2), - 'padding': 'valid', - 'pool_size': (3, 3)}, + keras.layers.MaxPooling2D, + kwargs={ + 'strides': strides, + 'padding': 'valid', + 'pool_size': pool_size + }, input_shape=(3, 5, 6, 4)) + + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) + def test_averagepooling_2d(self): + testing_utils.layer_test( + keras.layers.AveragePooling2D, + kwargs={'strides': (2, 2), + 'padding': 'same', + 'pool_size': (2, 2)}, + input_shape=(3, 5, 6, 4)) + testing_utils.layer_test( + keras.layers.AveragePooling2D, + kwargs={'strides': (2, 2), + 'padding': 'valid', + 'pool_size': (3, 3)}, + input_shape=(3, 5, 6, 4)) + + # This part of the test can only run on GPU but doesn't appear + # to be properly assigned to a GPU when running in eager mode. + if not context.executing_eagerly(): # Only runs on GPU with CUDA, channels_first is not supported on CPU. # TODO(b/62340061): Support channels_first on CPU. if test.is_gpu_available(cuda_only=True): @@ -116,66 +122,66 @@ class Pooling2DTest(test.TestCase): class Pooling3DTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_maxpooling_3d(self): pool_size = (3, 3, 3) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.MaxPooling3D, - kwargs={'strides': 2, - 'padding': 'valid', - 'pool_size': pool_size}, - input_shape=(3, 11, 12, 10, 4)) - testing_utils.layer_test( - keras.layers.MaxPooling3D, - kwargs={ - 'strides': 3, - 'padding': 'valid', - 'data_format': 'channels_first', - 'pool_size': pool_size - }, - input_shape=(3, 4, 11, 12, 10)) - + testing_utils.layer_test( + keras.layers.MaxPooling3D, + kwargs={'strides': 2, + 'padding': 'valid', + 'pool_size': pool_size}, + input_shape=(3, 11, 12, 10, 4)) + testing_utils.layer_test( + keras.layers.MaxPooling3D, + kwargs={ + 'strides': 3, + 'padding': 'valid', + 'data_format': 'channels_first', + 'pool_size': pool_size + }, + input_shape=(3, 4, 11, 12, 10)) + + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_averagepooling_3d(self): pool_size = (3, 3, 3) - with self.test_session(use_gpu=True): - testing_utils.layer_test( - keras.layers.AveragePooling3D, - kwargs={'strides': 2, - 'padding': 'valid', - 'pool_size': pool_size}, - input_shape=(3, 11, 12, 10, 4)) - testing_utils.layer_test( - keras.layers.AveragePooling3D, - kwargs={ - 'strides': 3, - 'padding': 'valid', - 'data_format': 'channels_first', - 'pool_size': pool_size - }, - input_shape=(3, 4, 11, 12, 10)) + testing_utils.layer_test( + keras.layers.AveragePooling3D, + kwargs={'strides': 2, + 'padding': 'valid', + 'pool_size': pool_size}, + input_shape=(3, 11, 12, 10, 4)) + testing_utils.layer_test( + keras.layers.AveragePooling3D, + kwargs={ + 'strides': 3, + 'padding': 'valid', + 'data_format': 'channels_first', + 'pool_size': pool_size + }, + input_shape=(3, 4, 11, 12, 10)) class Pooling1DTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_maxpooling_1d(self): - with self.test_session(use_gpu=True): - for padding in ['valid', 'same']: - for stride in [1, 2]: - testing_utils.layer_test( - keras.layers.MaxPooling1D, - kwargs={'strides': stride, - 'padding': padding}, - input_shape=(3, 5, 4)) + for padding in ['valid', 'same']: + for stride in [1, 2]: + testing_utils.layer_test( + keras.layers.MaxPooling1D, + kwargs={'strides': stride, + 'padding': padding}, + input_shape=(3, 5, 4)) + @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True) def test_averagepooling_1d(self): - with self.test_session(use_gpu=True): - for padding in ['valid', 'same']: - for stride in [1, 2]: - testing_utils.layer_test( - keras.layers.AveragePooling1D, - kwargs={'strides': stride, - 'padding': padding}, - input_shape=(3, 5, 4)) + for padding in ['valid', 'same']: + for stride in [1, 2]: + testing_utils.layer_test( + keras.layers.AveragePooling1D, + kwargs={'strides': stride, + 'padding': padding}, + input_shape=(3, 5, 4)) if __name__ == '__main__': diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py index 1b0f6cb6cf20fc55d3edefc9684d423cf25b7e0e..29107198079472517c0264c583ad92996580a32b 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py @@ -19,8 +19,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numbers import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K @@ -29,11 +31,13 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.StackedRNNCells') class StackedRNNCells(Layer): """Wrapper allowing a stack of RNN cells to behave as a single cell. @@ -85,7 +89,7 @@ class StackedRNNCells(Layer): state_size.append(cell.state_size) return tuple(state_size) - def call(self, inputs, states, **kwargs): + def call(self, inputs, states, constants=None, **kwargs): # Recover per-cell states. nested_states = [] for cell in self.cells[::-1]: @@ -100,7 +104,12 @@ class StackedRNNCells(Layer): # Call the cells in order and store the returned states. new_nested_states = [] for cell, states in zip(self.cells, nested_states): - inputs, states = cell.call(inputs, states, **kwargs) + if has_arg(cell.call, 'constants'): + inputs, states = cell.call(inputs, states, constants=constants, + **kwargs) + else: + inputs, states = cell.call(inputs, states, **kwargs) + new_nested_states.append(states) # Format the new states as a flat list @@ -112,9 +121,15 @@ class StackedRNNCells(Layer): @shape_type_conversion def build(self, input_shape): + if isinstance(input_shape, list): + constants_shape = input_shape[1:] + input_shape = input_shape[0] for cell in self.cells: if isinstance(cell, Layer): - cell.build(input_shape) + if has_arg(cell.call, 'constants'): + cell.build([input_shape] + constants_shape) + else: + cell.build(input_shape) if hasattr(cell.state_size, '__len__'): output_dim = cell.state_size[0] else: @@ -200,19 +215,19 @@ class StackedRNNCells(Layer): losses = [] for cell in self.cells: if isinstance(cell, Layer): - cell_losses = cell.losses - losses += cell_losses - return losses + losses += cell.losses + return losses + self._losses - def get_losses_for(self, inputs=None): - losses = [] + @property + def updates(self): + updates = [] for cell in self.cells: if isinstance(cell, Layer): - cell_losses = cell.get_losses_for(inputs) - losses += cell_losses - return losses + updates += cell.updates + return updates + self._updates +@tf_export('keras.layers.RNN') class RNN(Layer): """Base class for recurrent layers. @@ -400,7 +415,7 @@ class RNN(Layer): @property def states(self): if self._states is None: - if isinstance(self.cell.state_size, int): + if isinstance(self.cell.state_size, numbers.Integral): num_states = 1 else: num_states = len(self.cell.state_size) @@ -525,12 +540,14 @@ class RNN(Layer): self._num_constants = len(constants) additional_specs += self.constants_spec # at this point additional_inputs cannot be empty - is_keras_tensor = hasattr(additional_inputs[0], '_keras_history') + is_keras_tensor = K.is_keras_tensor(additional_inputs[0]) for tensor in additional_inputs: - if hasattr(tensor, '_keras_history') != is_keras_tensor: + if K.is_keras_tensor(tensor) != is_keras_tensor: raise ValueError('The initial state or constants of an RNN' ' layer cannot be specified with a mix of' - ' Keras tensors and non-Keras tensors') + ' Keras tensors and non-Keras tensors' + '(a "Keras tensor" is a tensor that was' + 'returned by a Keras layer, or by `Input`)') if is_keras_tensor: # Compute the full input spec, including state and constants @@ -614,7 +631,7 @@ class RNN(Layer): if self.stateful: updates = [] for i in range(len(states)): - updates.append((self.states[i], states[i])) + updates.append(K.update(self.states[i], states[i])) self.add_update(updates, inputs) if self.return_sequences: @@ -774,24 +791,28 @@ class RNN(Layer): @property def losses(self): + losses = [] if isinstance(self.cell, Layer): - return self.cell.losses - return [] + losses += self.cell.losses + return losses + self._losses - def get_losses_for(self, inputs=None): + @property + def updates(self): + updates = [] if isinstance(self.cell, Layer): - cell_losses = self.cell.get_losses_for(inputs) - return cell_losses + super(RNN, self).get_losses_for(inputs) - return super(RNN, self).get_losses_for(inputs) + updates += self.cell.updates + return updates + self._updates +@tf_export('keras.layers.SimpleRNNCell') class SimpleRNNCell(Layer): """Cell class for SimpleRNN. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. - If you pass None, no activation is applied + Default: hyperbolic tangent (`tanh`). + If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, @@ -915,7 +936,9 @@ class SimpleRNNCell(Layer): # Properly set learning phase on output tensor. if 0 < self.dropout + self.recurrent_dropout: - if training is None: + if training is None and not context.executing_eagerly(): + # This would be harmless to set in eager mode, but eager tensors + # disallow setting arbitrary attributes. output._uses_learning_phase = True return output, [output] @@ -954,12 +977,14 @@ class SimpleRNNCell(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.SimpleRNN') class SimpleRNN(RNN): """Fully-connected RNN where the output is to be fed back to input. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. + Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. @@ -1163,16 +1188,21 @@ class SimpleRNN(RNN): return cls(**config) +@tf_export('keras.layers.GRUCell') class GRUCell(Layer): """Cell class for the GRU layer. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. + Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. + Default: hard sigmoid (`hard_sigmoid`). + If you pass `None`, no activation is applied + (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. @@ -1272,23 +1302,6 @@ class GRUCell(Layer): constraint=self.bias_constraint) else: self.bias = None - - self.kernel_z = self.kernel[:, :self.units] - self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units] - self.kernel_r = self.kernel[:, self.units:self.units * 2] - self.recurrent_kernel_r = self.recurrent_kernel[:, self.units: - self.units * 2] - self.kernel_h = self.kernel[:, self.units * 2:] - self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:] - - if self.use_bias: - self.bias_z = self.bias[:self.units] - self.bias_r = self.bias[self.units:self.units * 2] - self.bias_h = self.bias[self.units * 2:] - else: - self.bias_z = None - self.bias_r = None - self.bias_h = None self.built = True def call(self, inputs, states, training=None): @@ -1323,13 +1336,13 @@ class GRUCell(Layer): inputs_z = inputs inputs_r = inputs inputs_h = inputs - x_z = K.dot(inputs_z, self.kernel_z) - x_r = K.dot(inputs_r, self.kernel_r) - x_h = K.dot(inputs_h, self.kernel_h) + x_z = K.dot(inputs_z, self.kernel[:, :self.units]) + x_r = K.dot(inputs_r, self.kernel[:, self.units:self.units * 2]) + x_h = K.dot(inputs_h, self.kernel[:, self.units * 2:]) if self.use_bias: - x_z = K.bias_add(x_z, self.bias_z) - x_r = K.bias_add(x_r, self.bias_r) - x_h = K.bias_add(x_h, self.bias_h) + x_z = K.bias_add(x_z, self.bias[:self.units]) + x_r = K.bias_add(x_r, self.bias[self.units:self.units * 2]) + x_h = K.bias_add(x_h, self.bias[self.units * 2:]) if 0. < self.recurrent_dropout < 1.: h_tm1_z = h_tm1 * rec_dp_mask[0] @@ -1340,11 +1353,14 @@ class GRUCell(Layer): h_tm1_r = h_tm1 h_tm1_h = h_tm1 z = self.recurrent_activation( - x_z + K.dot(h_tm1_z, self.recurrent_kernel_z)) + x_z + K.dot(h_tm1_z, self.recurrent_kernel[:, :self.units])) r = self.recurrent_activation( - x_r + K.dot(h_tm1_r, self.recurrent_kernel_r)) + x_r + K.dot(h_tm1_r, self.recurrent_kernel[:, self.units: + self.units * 2])) - hh = self.activation(x_h + K.dot(r * h_tm1_h, self.recurrent_kernel_h)) + hh = self.activation(x_h + K.dot(r * h_tm1_h, + self.recurrent_kernel[:, + self.units * 2:])) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] @@ -1368,49 +1384,40 @@ class GRUCell(Layer): hh = self.activation(x_h + recurrent_h) h = z * h_tm1 + (1 - z) * hh if 0 < self.dropout + self.recurrent_dropout: - if training is None: + if training is None and not context.executing_eagerly(): + # This would be harmless to set in eager mode, but eager tensors + # disallow setting arbitrary attributes. h._uses_learning_phase = True return h, [h] def get_config(self): config = { - 'units': - self.units, - 'activation': - activations.serialize(self.activation), + 'units': self.units, + 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), - 'use_bias': - self.use_bias, - 'kernel_initializer': - initializers.serialize(self.kernel_initializer), + 'use_bias': self.use_bias, + 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), - 'bias_initializer': - initializers.serialize(self.bias_initializer), - 'kernel_regularizer': - regularizers.serialize(self.kernel_regularizer), + 'bias_initializer': initializers.serialize(self.bias_initializer), + 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), - 'bias_regularizer': - regularizers.serialize(self.bias_regularizer), - 'kernel_constraint': - constraints.serialize(self.kernel_constraint), + 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), - 'bias_constraint': - constraints.serialize(self.bias_constraint), - 'dropout': - self.dropout, - 'recurrent_dropout': - self.recurrent_dropout, - 'implementation': - self.implementation + 'bias_constraint': constraints.serialize(self.bias_constraint), + 'dropout': self.dropout, + 'recurrent_dropout': self.recurrent_dropout, + 'implementation': self.implementation } base_config = super(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.GRU') class GRU(RNN): """Gated Recurrent Unit - Cho et al. @@ -1419,10 +1426,14 @@ class GRU(RNN): Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. - If you pass None, no activation is applied + Default: hyperbolic tangent (`tanh`). + If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. + Default: hard sigmoid (`hard_sigmoid`). + If you pass `None`, no activation is applied + (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. @@ -1646,16 +1657,21 @@ class GRU(RNN): return cls(**config) +@tf_export('keras.layers.LSTMCell') class LSTMCell(Layer): """Cell class for the LSTM layer. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. - If you pass None, no activation is applied + Default: hyperbolic tangent (`tanh`). + If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. + Default: hard sigmoid (`hard_sigmoid`). + If you pass `None`, no activation is applied + (ie. "linear" activation: `a(x) = x`).x use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. @@ -1772,29 +1788,6 @@ class LSTMCell(Layer): constraint=self.bias_constraint) else: self.bias = None - - self.kernel_i = self.kernel[:, :self.units] - self.kernel_f = self.kernel[:, self.units:self.units * 2] - self.kernel_c = self.kernel[:, self.units * 2:self.units * 3] - self.kernel_o = self.kernel[:, self.units * 3:] - - self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units] - self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: - self.units * 2] - self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: - self.units * 3] - self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:] - - if self.use_bias: - self.bias_i = self.bias[:self.units] - self.bias_f = self.bias[self.units:self.units * 2] - self.bias_c = self.bias[self.units * 2:self.units * 3] - self.bias_o = self.bias[self.units * 3:] - else: - self.bias_i = None - self.bias_f = None - self.bias_c = None - self.bias_o = None self.built = True def call(self, inputs, states, training=None): @@ -1832,15 +1825,15 @@ class LSTMCell(Layer): inputs_f = inputs inputs_c = inputs inputs_o = inputs - x_i = K.dot(inputs_i, self.kernel_i) - x_f = K.dot(inputs_f, self.kernel_f) - x_c = K.dot(inputs_c, self.kernel_c) - x_o = K.dot(inputs_o, self.kernel_o) + x_i = K.dot(inputs_i, self.kernel[:, :self.units]) + x_f = K.dot(inputs_f, self.kernel[:, self.units:self.units * 2]) + x_c = K.dot(inputs_c, self.kernel[:, self.units * 2:self.units * 3]) + x_o = K.dot(inputs_o, self.kernel[:, self.units * 3:]) if self.use_bias: - x_i = K.bias_add(x_i, self.bias_i) - x_f = K.bias_add(x_f, self.bias_f) - x_c = K.bias_add(x_c, self.bias_c) - x_o = K.bias_add(x_o, self.bias_o) + x_i = K.bias_add(x_i, self.bias[:self.units]) + x_f = K.bias_add(x_f, self.bias[self.units:self.units * 2]) + x_c = K.bias_add(x_c, self.bias[self.units * 2:self.units * 3]) + x_o = K.bias_add(x_o, self.bias[self.units * 3:]) if 0 < self.recurrent_dropout < 1.: h_tm1_i = h_tm1 * rec_dp_mask[0] @@ -1853,13 +1846,15 @@ class LSTMCell(Layer): h_tm1_c = h_tm1 h_tm1_o = h_tm1 i = self.recurrent_activation( - x_i + K.dot(h_tm1_i, self.recurrent_kernel_i)) + x_i + K.dot(h_tm1_i, self.recurrent_kernel[:, :self.units])) f = self.recurrent_activation( - x_f + K.dot(h_tm1_f, self.recurrent_kernel_f)) + x_f + K.dot(h_tm1_f, + self.recurrent_kernel[:, self.units: self.units * 2])) c = f * c_tm1 + i * self.activation( - x_c + K.dot(h_tm1_c, self.recurrent_kernel_c)) + x_c + K.dot(h_tm1_c, + self.recurrent_kernel[:, self.units * 2: self.units * 3])) o = self.recurrent_activation( - x_o + K.dot(h_tm1_o, self.recurrent_kernel_o)) + x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:])) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] @@ -1882,7 +1877,9 @@ class LSTMCell(Layer): h = o * self.activation(c) if 0 < self.dropout + self.recurrent_dropout: - if training is None: + if training is None and not context.executing_eagerly(): + # This would be harmless to set in eager mode, but eager tensors + # disallow setting arbitrary attributes. h._uses_learning_phase = True return h, [h, c] @@ -1927,16 +1924,21 @@ class LSTMCell(Layer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.layers.LSTM') class LSTM(RNN): """Long-Short Term Memory layer - Hochreiter 1997. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. - If you pass None, no activation is applied + Default: hyperbolic tangent (`tanh`). + If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. + Default: hard sigmoid (`hard_sigmoid`). + If you pass `None`, no activation is applied + (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. @@ -2454,7 +2456,7 @@ class Recurrent(Layer): if self.stateful: updates = [] for i in range(len(states)): - updates.append((self.states[i], states[i])) + updates.append(K.update(self.states[i], states[i])) self.add_update(updates, inputs) # Properly set learning phase diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py b/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py index a1407a24ea895976cbf95f0ea5c7ba98335af329..de022153f6f07240a0dff70e5faeed5b6d4a8c5f 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py @@ -253,7 +253,7 @@ class RNNTest(test.TestCase): self.assertAllClose(y_np, y_np_2, atol=1e-4) with self.test_session(): - # test flat list inputs + # test flat list inputs. with keras.utils.CustomObjectScope(custom_objects): layer = keras.layers.RNN.from_config(config.copy()) y = layer([x, c]) @@ -262,6 +262,35 @@ class RNNTest(test.TestCase): y_np_3 = model.predict([x_np, c_np]) self.assertAllClose(y_np, y_np_3, atol=1e-4) + with self.test_session(): + # Test stacking. + cells = [keras.layers.recurrent.GRUCell(8), + RNNCellWithConstants(12), + RNNCellWithConstants(32)] + layer = keras.layers.recurrent.RNN(cells) + y = layer(x, constants=c) + model = keras.models.Model([x, c], y) + model.compile(optimizer='rmsprop', loss='mse') + model.train_on_batch( + [np.zeros((6, 5, 5)), np.zeros((6, 3))], + np.zeros((6, 32)) + ) + + with self.test_session(): + # Test stacked RNN serialization + x_np = np.random.random((6, 5, 5)) + c_np = np.random.random((6, 3)) + y_np = model.predict([x_np, c_np]) + weights = model.get_weights() + config = layer.get_config() + with keras.utils.CustomObjectScope(custom_objects): + layer = keras.layers.recurrent.RNN.from_config(config.copy()) + y = layer(x, constants=c) + model = keras.models.Model([x, c], y) + model.set_weights(weights) + y_np_2 = model.predict([x_np, c_np]) + self.assertAllClose(y_np, y_np_2, atol=1e-4) + def test_rnn_cell_with_constants_layer_passing_initial_state(self): class RNNCellWithConstants(keras.layers.Layer): @@ -353,13 +382,10 @@ class RNNTest(test.TestCase): self.assertAllClose(y_np, y_np_3, atol=1e-4) def test_stacked_rnn_attributes(self): - cells = [keras.layers.LSTMCell(3), - keras.layers.LSTMCell(3, kernel_regularizer='l2')] + cells = [keras.layers.LSTMCell(1), + keras.layers.LSTMCell(1)] layer = keras.layers.RNN(cells) - layer.build((None, None, 5)) - - # Test regularization losses - self.assertEqual(len(layer.losses), 1) + layer.build((None, None, 1)) # Test weights self.assertEqual(len(layer.trainable_weights), 6) @@ -367,11 +393,32 @@ class RNNTest(test.TestCase): self.assertEqual(len(layer.trainable_weights), 3) self.assertEqual(len(layer.non_trainable_weights), 3) - # Test `get_losses_for` - x = keras.Input((None, 5)) - y = keras.backend.sum(x) - cells[0].add_loss(y, inputs=x) - self.assertEqual(layer.get_losses_for(x), [y]) + # Test `get_losses_for` and `losses` + x = keras.Input((None, 1)) + loss_1 = keras.backend.sum(x) + loss_2 = keras.backend.sum(cells[0].kernel) + cells[0].add_loss(loss_1, inputs=x) + cells[0].add_loss(loss_2) + self.assertEqual(len(layer.losses), 2) + self.assertEqual(layer.get_losses_for(None), [loss_2]) + self.assertEqual(layer.get_losses_for(x), [loss_1]) + + # Test `get_updates_for` and `updates` + cells = [keras.layers.LSTMCell(1), + keras.layers.LSTMCell(1)] + layer = keras.layers.RNN(cells) + layer.build((None, None, 1)) + + x = keras.Input((None, 1)) + update_1 = keras.backend.update_add( + cells[0].kernel, x[0, 0, 0] * cells[0].kernel) + update_2 = keras.backend.update_add( + cells[0].kernel, keras.backend.ones_like(cells[0].kernel)) + cells[0].add_update(update_1, inputs=x) + cells[0].add_update(update_2) + self.assertEqual(len(layer.updates), 2) + self.assertEqual(layer.get_updates_for(None), [update_2]) + self.assertEqual(layer.get_updates_for(x), [update_1]) def test_rnn_dynamic_trainability(self): layer_class = keras.layers.SimpleRNN diff --git a/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py b/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py index 7edebdacd07d74fe6b5a982d12645fb5556bdf75..8c7189cd4718450a85c015e08ab3a58cc5d86531 100644 --- a/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py @@ -20,64 +20,66 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer class SimpleRNNLayerTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_return_sequences_SimpleRNN(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.SimpleRNN, - kwargs={'units': units, - 'return_sequences': True}, - input_shape=(num_samples, timesteps, embedding_dim)) + testing_utils.layer_test( + keras.layers.SimpleRNN, + kwargs={'units': units, + 'return_sequences': True}, + input_shape=(num_samples, timesteps, embedding_dim)) + @tf_test_util.run_in_graph_and_eager_modes() def test_dynamic_behavior_SimpleRNN(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim)) - model = keras.models.Sequential() - model.add(layer) - model.compile('sgd', 'mse') - x = np.random.random((num_samples, timesteps, embedding_dim)) - y = np.random.random((num_samples, units)) - model.train_on_batch(x, y) - + layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim)) + model = keras.models.Sequential() + model.add(layer) + model.compile(RMSPropOptimizer(0.01), 'mse') + x = np.random.random((num_samples, timesteps, embedding_dim)) + y = np.random.random((num_samples, units)) + model.train_on_batch(x, y) + + @tf_test_util.run_in_graph_and_eager_modes() def test_dropout_SimpleRNN(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - testing_utils.layer_test( - keras.layers.SimpleRNN, - kwargs={'units': units, - 'dropout': 0.1, - 'recurrent_dropout': 0.1}, - input_shape=(num_samples, timesteps, embedding_dim)) - + testing_utils.layer_test( + keras.layers.SimpleRNN, + kwargs={'units': units, + 'dropout': 0.1, + 'recurrent_dropout': 0.1}, + input_shape=(num_samples, timesteps, embedding_dim)) + + @tf_test_util.run_in_graph_and_eager_modes() def test_implementation_mode_SimpleRNN(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - with self.test_session(): - for mode in [0, 1, 2]: - testing_utils.layer_test( - keras.layers.SimpleRNN, - kwargs={'units': units, - 'implementation': mode}, - input_shape=(num_samples, timesteps, embedding_dim)) + for mode in [0, 1, 2]: + testing_utils.layer_test( + keras.layers.SimpleRNN, + kwargs={'units': units, + 'implementation': mode}, + input_shape=(num_samples, timesteps, embedding_dim)) def test_statefulness_SimpleRNN(self): num_samples = 2 diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py index 3667956f8049b8f5e9cc9ab7a9e0037c260a8ce1..76ddd9299dd669da35d89a6fe8fc521ce4c26337 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py @@ -25,11 +25,13 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.layers import utils as tf_layers_util +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.layers.Wrapper') class Wrapper(Layer): """Abstract wrapper base class. @@ -59,6 +61,14 @@ class Wrapper(Layer): else: return None + @property + def trainable(self): + return self.layer.trainable + + @trainable.setter + def trainable(self, value): + self.layer.trainable = value + @property def trainable_weights(self): return self.layer.trainable_weights @@ -69,34 +79,11 @@ class Wrapper(Layer): @property def updates(self): - if hasattr(self.layer, 'updates'): - return self.layer.updates - return [] - - def get_updates_for(self, inputs=None): - # If the wrapper modifies the inputs, use the modified inputs to - # get the updates from the inner layer. - inner_inputs = inputs - if inputs is not None: - uid = tf_layers_util.object_list_uid(inputs) - if uid in self._input_map: - inner_inputs = self._input_map[uid] - - updates = self.layer.get_updates_for(inner_inputs) - updates += super(Wrapper, self).get_updates_for(inputs) - return updates + return self.layer.updates + self._updates @property def losses(self): - if hasattr(self.layer, 'losses'): - return self.layer.losses - return [] - - def get_losses_for(self, inputs=None): - if inputs is None: - losses = self.layer.get_losses_for(None) - return losses + super(Wrapper, self).get_losses_for(None) - return super(Wrapper, self).get_losses_for(inputs) + return self.layer.losses + self._losses def get_weights(self): return self.layer.get_weights() @@ -122,6 +109,7 @@ class Wrapper(Layer): return cls(layer, **config) +@tf_export('keras.layers.TimeDistributed') class TimeDistributed(Wrapper): """This wrapper allows to apply a layer to every temporal slice of an input. @@ -246,6 +234,7 @@ class TimeDistributed(Wrapper): return y +@tf_export('keras.layers.Bidirectional') class Bidirectional(Wrapper): """Bidirectional wrapper for RNNs. @@ -274,7 +263,6 @@ class Bidirectional(Wrapper): """ def __init__(self, layer, merge_mode='concat', weights=None, **kwargs): - super(Bidirectional, self).__init__(layer, **kwargs) if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]: raise ValueError('Invalid merge mode. ' 'Merge mode should be one of ' @@ -294,6 +282,19 @@ class Bidirectional(Wrapper): self.return_sequences = layer.return_sequences self.return_state = layer.return_state self.supports_masking = True + self._trainable = True + super(Bidirectional, self).__init__(layer, **kwargs) + self.input_spec = layer.input_spec + + @property + def trainable(self): + return self._trainable + + @trainable.setter + def trainable(self, value): + self._trainable = value + self.forward_layer.trainable = value + self.backward_layer.trainable = value def get_weights(self): return self.forward_layer.get_weights() + self.backward_layer.get_weights() @@ -324,6 +325,61 @@ class Bidirectional(Wrapper): return [output_shape] + state_shape + copy.copy(state_shape) return output_shape + def __call__(self, inputs, initial_state=None, **kwargs): + if isinstance(inputs, list): + if len(inputs) > 1: + initial_state = inputs[1:] + inputs = inputs[0] + + if initial_state is None: + return super(Bidirectional, self).__call__(inputs, **kwargs) + + # Standardize `initial_state` into list + if isinstance(initial_state, tuple): + initial_state = list(initial_state) + elif not isinstance(initial_state, list): + initial_state = [initial_state] + + # Check if `initial_state` can be splitted into half + num_states = len(initial_state) + if num_states % 2 > 0: + raise ValueError( + 'When passing `initial_state` to a Bidirectional RNN, the state ' + 'should be a list containing the states of the underlying RNNs. ' + 'Found: ' + str(initial_state)) + + # Applies the same workaround as in `RNN.__call__`, without handling + # constants + kwargs['initial_state'] = initial_state + additional_inputs = initial_state + additional_specs = [InputSpec(shape=K.int_shape(state)) + for state in initial_state] + self.forward_layer.state_spec = additional_specs[:num_states // 2] + self.backward_layer.state_spec = additional_specs[num_states // 2:] + + is_keras_tensor = K.is_keras_tensor(additional_inputs[0]) + for tensor in additional_inputs: + if K.is_keras_tensor(tensor) != is_keras_tensor: + raise ValueError('The initial state of a Bidirectional' + ' layer cannot be specified with a mix of' + ' Keras tensors and non-Keras tensors' + ' (a "Keras tensor" is a tensor that was' + ' returned by a Keras layer, or by `Input`)') + + if is_keras_tensor: + # Compute the full input spec, including state + full_input = [inputs] + additional_inputs + full_input_spec = self.input_spec + additional_specs + + # Perform the call with temporarily replaced input_spec + original_input_spec = self.input_spec + self.input_spec = full_input_spec + output = super(Bidirectional, self).__call__(full_input, **kwargs) + self.input_spec = original_input_spec + return output + else: + return super(Bidirectional, self).__call__(inputs, **kwargs) + def call(self, inputs, training=None, mask=None, initial_state=None): kwargs = {} if has_arg(self.layer.call, 'training'): @@ -332,11 +388,6 @@ class Bidirectional(Wrapper): kwargs['mask'] = mask if initial_state is not None and has_arg(self.layer.call, 'initial_state'): - if not isinstance(initial_state, list): - raise ValueError( - 'When passing `initial_state` to a Bidirectional RNN, the state ' - 'should be a list containing the states of the underlying RNNs. ' - 'Found: ' + str(initial_state)) forward_state = initial_state[:len(initial_state) // 2] backward_state = initial_state[len(initial_state) // 2:] y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs) diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py index f48c8919a148403874758b618aaa9a662e511240..8fcf66e90ff1289a06a996768ae5de2f1548a27c 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py @@ -20,44 +20,43 @@ from __future__ import print_function import numpy as np +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer class TimeDistributedTest(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes() def test_timedistributed_dense(self): - # first, test with Dense layer - with self.test_session(): - model = keras.models.Sequential() - model.add( - keras.layers.TimeDistributed( - keras.layers.Dense(2), input_shape=(3, 4))) - model.compile(optimizer='rmsprop', loss='mse') - model.fit( - np.random.random((10, 3, 4)), - np.random.random((10, 3, 2)), - epochs=1, - batch_size=10) - - # test config - model.get_config() + model = keras.models.Sequential() + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(2), input_shape=(3, 4))) + model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse') + model.fit( + np.random.random((10, 3, 4)), + np.random.random((10, 3, 2)), + epochs=1, + batch_size=10) + + # test config + model.get_config() def test_timedistributed_static_batch_size(self): - with self.test_session(): - model = keras.models.Sequential() - model.add( - keras.layers.TimeDistributed( - keras.layers.Dense(2), input_shape=(3, 4), batch_size=10)) - model.compile(optimizer='rmsprop', loss='mse') - model.fit( - np.random.random((10, 3, 4)), - np.random.random((10, 3, 2)), - epochs=1, - batch_size=10) + model = keras.models.Sequential() + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(2), input_shape=(3, 4), batch_size=10)) + model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse') + model.fit( + np.random.random((10, 3, 4)), + np.random.random((10, 3, 2)), + epochs=1, + batch_size=10) def test_timedistributed_conv2d(self): - # test with Conv2D with self.test_session(): model = keras.models.Sequential() model.add( @@ -73,7 +72,6 @@ class TimeDistributedTest(test.TestCase): model.summary() def test_timedistributed_stacked(self): - # test stacked layers with self.test_session(): model = keras.models.Sequential() model.add( @@ -133,6 +131,20 @@ class TimeDistributedTest(test.TestCase): # Verify input_map has one mapping from inputs to reshaped inputs. self.assertEqual(len(td._input_map.keys()), 1) + def test_TimeDistributed_trainable(self): + # test layers that need learning_phase to be set + x = keras.layers.Input(shape=(3, 2)) + layer = keras.layers.TimeDistributed(keras.layers.BatchNormalization()) + _ = layer(x) + assert len(layer.updates) == 2 + assert len(layer.trainable_weights) == 2 + layer.trainable = False + assert not layer.updates + assert not layer.trainable_weights + layer.trainable = True + assert len(layer.updates) == 2 + assert len(layer.trainable_weights) == 2 + class BidirectionalTest(test.TestCase): @@ -153,7 +165,7 @@ class BidirectionalTest(test.TestCase): model.add( keras.layers.Bidirectional( rnn(output_dim), merge_mode=mode, input_shape=(timesteps, dim))) - model.compile(loss='mse', optimizer='sgd') + model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse') model.fit(x, y, epochs=1, batch_size=1) # test compute output shape @@ -338,23 +350,38 @@ class BidirectionalTest(test.TestCase): units = 3 with self.test_session(): - inputs = keras.Input((timesteps, dim)) + input1 = keras.layers.Input((timesteps, dim)) layer = keras.layers.Bidirectional( rnn(units, return_state=True, return_sequences=True)) - outputs = layer(inputs) - output, state = outputs[0], outputs[1:] + state = layer(input1)[1:] # test passing invalid initial_state: passing a tensor + input2 = keras.layers.Input((timesteps, dim)) with self.assertRaises(ValueError): output = keras.layers.Bidirectional( - rnn(units))(output, initial_state=state[0]) + rnn(units))(input2, initial_state=state[0]) # test valid usage: passing a list - output = keras.layers.Bidirectional( - rnn(units))(output, initial_state=state) - model = keras.Model(inputs, output) - inputs = np.random.rand(samples, timesteps, dim) - outputs = model.predict(inputs) + output = keras.layers.Bidirectional(rnn(units))(input2, + initial_state=state) + model = keras.models.Model([input1, input2], output) + assert len(model.layers) == 4 + assert isinstance(model.layers[-1].input, list) + inputs = [np.random.rand(samples, timesteps, dim), + np.random.rand(samples, timesteps, dim)] + model.predict(inputs) + + def test_Bidirectional_trainable(self): + # test layers that need learning_phase to be set + with self.test_session(): + x = keras.layers.Input(shape=(3, 2)) + layer = keras.layers.Bidirectional(keras.layers.SimpleRNN(3)) + _ = layer(x) + assert len(layer.trainable_weights) == 6 + layer.trainable = False + assert not layer.trainable_weights + layer.trainable = True + assert len(layer.trainable_weights) == 6 def _to_list(ls): diff --git a/tensorflow/python/keras/_impl/keras/losses.py b/tensorflow/python/keras/_impl/keras/losses.py index fe0ef54360608db5a7ae95b1ec89e32640db0931..1576ed7b999f65992f46b357c8ebeda8935c68d0 100644 --- a/tensorflow/python/keras/_impl/keras/losses.py +++ b/tensorflow/python/keras/_impl/keras/losses.py @@ -24,41 +24,54 @@ import six from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.metrics.mean_squared_error', + 'keras.losses.mean_squared_error') def mean_squared_error(y_true, y_pred): return K.mean(K.square(y_pred - y_true), axis=-1) +@tf_export('keras.metrics.mean_absolute_error', + 'keras.losses.mean_absolute_error') def mean_absolute_error(y_true, y_pred): return K.mean(K.abs(y_pred - y_true), axis=-1) +@tf_export('keras.metrics.mean_absolute_percentage_error', + 'keras.losses.mean_absolute_percentage_error') def mean_absolute_percentage_error(y_true, y_pred): diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None)) return 100. * K.mean(diff, axis=-1) +@tf_export('keras.metrics.mean_squared_logarithmic_error', + 'keras.losses.mean_squared_logarithmic_error') def mean_squared_logarithmic_error(y_true, y_pred): first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.) second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.) return K.mean(K.square(first_log - second_log), axis=-1) +@tf_export('keras.metrics.squared_hinge', 'keras.losses.squared_hinge') def squared_hinge(y_true, y_pred): return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1) +@tf_export('keras.metrics.hinge', 'keras.losses.hinge') def hinge(y_true, y_pred): return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1) +@tf_export('keras.losses.categorical_hinge') def categorical_hinge(y_true, y_pred): pos = K.sum(y_true * y_pred, axis=-1) neg = K.max((1. - y_true) * y_pred, axis=-1) return K.maximum(0., neg - pos + 1.) +@tf_export('keras.losses.logcosh') def logcosh(y_true, y_pred): """Logarithm of the hyperbolic cosine of the prediction error. @@ -81,28 +94,38 @@ def logcosh(y_true, y_pred): return K.mean(_logcosh(y_pred - y_true), axis=-1) +@tf_export('keras.metrics.categorical_crossentropy', + 'keras.losses.categorical_crossentropy') def categorical_crossentropy(y_true, y_pred): return K.categorical_crossentropy(y_true, y_pred) +@tf_export('keras.metrics.sparse_categorical_crossentropy', + 'keras.losses.sparse_categorical_crossentropy') def sparse_categorical_crossentropy(y_true, y_pred): return K.sparse_categorical_crossentropy(y_true, y_pred) +@tf_export('keras.metrics.binary_crossentropy', + 'keras.losses.binary_crossentropy') def binary_crossentropy(y_true, y_pred): return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1) +@tf_export('keras.metrics.kullback_leibler_divergence', + 'keras.losses.kullback_leibler_divergence') def kullback_leibler_divergence(y_true, y_pred): y_true = K.clip(y_true, K.epsilon(), 1) y_pred = K.clip(y_pred, K.epsilon(), 1) return K.sum(y_true * K.log(y_true / y_pred), axis=-1) +@tf_export('keras.metrics.poisson', 'keras.losses.poisson') def poisson(y_true, y_pred): return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1) +@tf_export('keras.metrics.cosine_proximity', 'keras.losses.cosine_proximity') def cosine_proximity(y_true, y_pred): y_true = K.l2_normalize(y_true, axis=-1) y_pred = K.l2_normalize(y_pred, axis=-1) @@ -119,10 +142,12 @@ kld = KLD = kullback_leibler_divergence cosine = cosine_proximity +@tf_export('keras.losses.serialize') def serialize(loss): return serialize_keras_object(loss) +@tf_export('keras.losses.deserialize') def deserialize(name, custom_objects=None): return deserialize_keras_object( name, @@ -131,6 +156,7 @@ def deserialize(name, custom_objects=None): printable_module_name='loss function') +@tf_export('keras.losses.get') def get(identifier): if identifier is None: return None diff --git a/tensorflow/python/keras/_impl/keras/metrics.py b/tensorflow/python/keras/_impl/keras/metrics.py index 3c18e6826076b553136f5457f59e8bfe081bdb40..82778a3dc4fbdc13bb6682d01e28ff68882b6dd9 100644 --- a/tensorflow/python/keras/_impl/keras/metrics.py +++ b/tensorflow/python/keras/_impl/keras/metrics.py @@ -36,12 +36,16 @@ from tensorflow.python.keras._impl.keras.losses import poisson from tensorflow.python.keras._impl.keras.losses import sparse_categorical_crossentropy from tensorflow.python.keras._impl.keras.losses import squared_hinge from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object +from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.metrics.binary_accuracy') def binary_accuracy(y_true, y_pred): return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1) +@tf_export('keras.metrics.categorical_accuracy') def categorical_accuracy(y_true, y_pred): return K.cast( K.equal(K.argmax(y_true, axis=-1), K.argmax(y_pred, axis=-1)), K.floatx()) @@ -54,10 +58,12 @@ def sparse_categorical_accuracy(y_true, y_pred): K.floatx())), K.floatx()) +@tf_export('keras.metrics.top_k_categorical_accuracy') def top_k_categorical_accuracy(y_true, y_pred, k=5): return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1) +@tf_export('keras.metrics.sparse_top_k_categorical_accuracy') def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5): return K.mean( K.in_top_k(y_pred, K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1) @@ -72,24 +78,29 @@ msle = MSLE = mean_squared_logarithmic_error cosine = cosine_proximity +@tf_export('keras.metrics.serialize') def serialize(metric): - return metric.__name__ + return serialize_keras_object(metric) -def deserialize(name, custom_objects=None): +@tf_export('keras.metrics.deserialize') +def deserialize(config, custom_objects=None): return deserialize_keras_object( - name, + config, module_objects=globals(), custom_objects=custom_objects, printable_module_name='metric function') +@tf_export('keras.metrics.get') def get(identifier): - if isinstance(identifier, six.string_types): - identifier = str(identifier) - return deserialize(identifier) + if isinstance(identifier, dict): + config = {'class_name': str(identifier), 'config': {}} + return deserialize(config) + elif isinstance(identifier, six.string_types): + return deserialize(str(identifier)) elif callable(identifier): return identifier else: raise ValueError('Could not interpret ' - 'metric function identifier:', identifier) + 'metric function identifier: %s' % identifier) diff --git a/tensorflow/python/keras/_impl/keras/metrics_test.py b/tensorflow/python/keras/_impl/keras/metrics_test.py index f4792f3543cc5ca8e5e7ad03d9906bbfadd1fb04..44289ea02abf5ae5f8befbe515552aea3d4b231e 100644 --- a/tensorflow/python/keras/_impl/keras/metrics_test.py +++ b/tensorflow/python/keras/_impl/keras/metrics_test.py @@ -72,6 +72,77 @@ class KerasMetricsTest(test.TestCase): keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=1)) self.assertEqual(result, 0.) + def test_stateful_metrics(self): + np.random.seed(1334) + + class BinaryTruePositives(keras.layers.Layer): + """Stateful Metric to count the total true positives over all batches. + + Assumes predictions and targets of shape `(samples, 1)`. + + Arguments: + threshold: Float, lower limit on prediction value that counts as a + positive class prediction. + name: String, name for the metric. + """ + + def __init__(self, name='true_positives', **kwargs): + super(BinaryTruePositives, self).__init__(name=name, **kwargs) + self.true_positives = keras.backend.variable(value=0, dtype='int32') + + def reset_states(self): + keras.backend.set_value(self.true_positives, 0) + + def __call__(self, y_true, y_pred): + """Computes the number of true positives in a batch. + + Args: + y_true: Tensor, batch_wise labels + y_pred: Tensor, batch_wise predictions + + Returns: + The total number of true positives seen this epoch at the + completion of the batch. + """ + y_true = keras.backend.cast(y_true, 'int32') + y_pred = keras.backend.cast(keras.backend.round(y_pred), 'int32') + correct_preds = keras.backend.cast( + keras.backend.equal(y_pred, y_true), 'int32') + true_pos = keras.backend.cast( + keras.backend.sum(correct_preds * y_true), 'int32') + current_true_pos = self.true_positives * 1 + self.add_update(keras.backend.update_add(self.true_positives, + true_pos), + inputs=[y_true, y_pred]) + return current_true_pos + true_pos + + metric_fn = BinaryTruePositives() + config = keras.metrics.serialize(metric_fn) + metric_fn = keras.metrics.deserialize( + config, custom_objects={'BinaryTruePositives': BinaryTruePositives}) + + # Test on simple model + inputs = keras.Input(shape=(2,)) + outputs = keras.layers.Dense(1, activation='sigmoid')(inputs) + model = keras.Model(inputs, outputs) + model.compile(optimizer='sgd', + loss='binary_crossentropy', + metrics=['acc', metric_fn]) + + # Test fit, evaluate + samples = 1000 + x = np.random.random((samples, 2)) + y = np.random.randint(2, size=(samples, 1)) + model.fit(x, y, epochs=1, batch_size=10) + outs = model.evaluate(x, y, batch_size=10) + preds = model.predict(x) + + def ref_true_pos(y_true, y_pred): + return np.sum(np.logical_and(y_pred > 0.5, y_true == 1)) + + # Test correctness (e.g. updates should have been run) + self.assertAllClose(outs[2], ref_true_pos(y, preds), atol=1e-5) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py new file mode 100644 index 0000000000000000000000000000000000000000..58b144365be6cd8ea5b2ea82e275eacdee6b6c84 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py @@ -0,0 +1,578 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Model subclassing.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import tempfile + +import numpy as np + +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util +from tensorflow.python.keras._impl import keras +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer + +try: + import h5py # pylint:disable=g-import-not-at-top +except ImportError: + h5py = None + + +class SimpleTestModel(keras.Model): + + def __init__(self, use_bn=False, use_dp=False, num_classes=10): + super(SimpleTestModel, self).__init__(name='test_model') + self.use_bn = use_bn + self.use_dp = use_dp + self.num_classes = num_classes + + self.dense1 = keras.layers.Dense(32, activation='relu') + self.dense2 = keras.layers.Dense(num_classes, activation='softmax') + if self.use_dp: + self.dp = keras.layers.Dropout(0.5) + if self.use_bn: + self.bn = keras.layers.BatchNormalization(axis=-1) + + def call(self, inputs): + x = self.dense1(inputs) + if self.use_dp: + x = self.dp(x) + if self.use_bn: + x = self.bn(x) + return self.dense2(x) + + +class MultiIOTestModel(keras.Model): + + def __init__(self, use_bn=False, use_dp=False, num_classes=(2, 3)): + super(MultiIOTestModel, self).__init__(name='test_model') + self.use_bn = use_bn + self.use_dp = use_dp + self.num_classes = num_classes + + self.dense1 = keras.layers.Dense(32, activation='relu') + self.dense2 = keras.layers.Dense(num_classes[0], activation='softmax') + self.dense3 = keras.layers.Dense(num_classes[1], activation='softmax') + if use_dp: + self.dp = keras.layers.Dropout(0.5) + if use_bn: + self.bn = keras.layers.BatchNormalization() + + def call(self, inputs): + x1, x2 = inputs + x1 = self.dense1(x1) + x2 = self.dense1(x2) + if self.use_dp: + x1 = self.dp(x1) + if self.use_bn: + x2 = self.bn(x2) + return [self.dense2(x1), self.dense3(x2)] + + +class NestedTestModel1(keras.Model): + """A model subclass nested inside a model subclass. + """ + + def __init__(self, num_classes=2): + super(NestedTestModel1, self).__init__(name='nested_model_1') + self.num_classes = num_classes + self.dense1 = keras.layers.Dense(32, activation='relu') + self.dense2 = keras.layers.Dense(num_classes, activation='relu') + self.bn = keras.layers.BatchNormalization() + self.test_net = SimpleTestModel(num_classes=4, + use_bn=True, + use_dp=True) + + def call(self, inputs): + x = self.dense1(inputs) + x = self.bn(x) + x = self.test_net(x) # pylint: disable=not-callable + return self.dense2(x) + + +def get_functional_graph_model(input_dim, num_classes): + # A simple functional-API model (a.k.a. graph network) + inputs = keras.Input(shape=(input_dim,)) + x = keras.layers.Dense(32, activation='relu')(inputs) + x = keras.layers.BatchNormalization()(x) + outputs = keras.layers.Dense(num_classes)(x) + return keras.Model(inputs, outputs) + + +class NestedTestModel2(keras.Model): + """A model subclass with a functional-API graph network inside. + """ + + def __init__(self, num_classes=2): + super(NestedTestModel2, self).__init__(name='nested_model_2') + self.num_classes = num_classes + self.dense1 = keras.layers.Dense(32, activation='relu') + self.dense2 = keras.layers.Dense(num_classes, activation='relu') + self.bn = self.bn = keras.layers.BatchNormalization() + self.test_net = get_functional_graph_model(32, 4) + + def call(self, inputs): + x = self.dense1(inputs) + x = self.bn(x) + x = self.test_net(x) + return self.dense2(x) + + +def get_nested_model_3(input_dim, num_classes): + # A functional-API model with a subclassed model inside. + # NOTE: this requires the inner subclass to implement `compute_output_shape`. + + inputs = keras.Input(shape=(input_dim,)) + x = keras.layers.Dense(32, activation='relu')(inputs) + x = keras.layers.BatchNormalization()(x) + + class Inner(keras.Model): + + def __init__(self): + super(Inner, self).__init__() + self.dense1 = keras.layers.Dense(32, activation='relu') + self.dense2 = keras.layers.Dense(5, activation='relu') + self.bn = keras.layers.BatchNormalization() + + def call(self, inputs): + x = self.dense1(inputs) + x = self.dense2(x) + return self.bn(x) + + def compute_output_shape(self, input_shape): + return tensor_shape.TensorShape((input_shape[0], 5)) + + test_model = Inner() + x = test_model(x) # pylint: disable=not-callable + outputs = keras.layers.Dense(num_classes)(x) + return keras.Model(inputs, outputs, name='nested_model_3') + + +class ModelSubclassingTest(test.TestCase): + + @test_util.run_in_graph_and_eager_modes() + def test_single_io_workflow_with_np_arrays(self): + num_classes = 2 + num_samples = 100 + input_dim = 50 + + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) + + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) + + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) + + @test_util.run_in_graph_and_eager_modes() + def test_multi_io_workflow_with_np_arrays(self): + num_classes = (2, 3) + num_samples = 1000 + input_dim = 50 + + model = MultiIOTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + _ = model.evaluate([x1, x2], [y1, y2], verbose=0) + + def test_single_io_workflow_with_tensors(self): + + num_classes = 2 + num_samples = 10 + input_dim = 50 + + with self.test_session(): + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + + x = array_ops.ones((num_samples, input_dim)) + y = array_ops.zeros((num_samples, num_classes)) + + model.fit(x, y, epochs=2, steps_per_epoch=10, verbose=0) + _ = model.evaluate(steps=10, verbose=0) + + def test_multi_io_workflow_with_tensors(self): + + num_classes = (2, 3) + num_samples = 10 + input_dim = 50 + + with self.test_session(): + model = MultiIOTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + + x1 = array_ops.ones((num_samples, input_dim)) + x2 = array_ops.ones((num_samples, input_dim)) + y1 = array_ops.zeros((num_samples, num_classes[0])) + y2 = array_ops.zeros((num_samples, num_classes[1])) + + model.fit([x1, x2], [y1, y2], epochs=2, steps_per_epoch=10, verbose=0) + _ = model.evaluate(steps=10, verbose=0) + + def test_multi_io_workflow_with_numpy_arrays_and_custom_placeholders(self): + + num_classes = (2, 3) + num_samples = 1000 + input_dim = 50 + + with self.test_session(): + model = MultiIOTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + x2_placeholder = array_ops.placeholder( + dtype='float32', shape=(None, input_dim)) + model._set_inputs([x1, x2_placeholder]) + + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + _ = model.evaluate([x1, x2], [y1, y2], verbose=0) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_attributes(self): + # layers, weights, trainable_weights, non_trainable_weights, inputs, outputs + + num_classes = (2, 3) + num_samples = 100 + input_dim = 50 + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + self.assertEqual(model.name, 'test_model') + self.assertEqual(model.built, False) + self.assertEqual(len(model.weights), 0) + + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.train_on_batch([x1, x2], [y1, y2]) + + self.assertEqual(model.built, True) + self.assertEqual(len(model.layers), 4) + self.assertEqual(len(model.weights), 10) + self.assertEqual(len(model.trainable_weights), 8) + self.assertEqual(len(model.non_trainable_weights), 2) + self.assertEqual(len(model.inputs), 2) + self.assertEqual(len(model.outputs), 2) + + @test_util.run_in_graph_and_eager_modes() + def test_updates(self): + # test that updates get run during training + num_samples = 100 + input_dim = 50 + + class BNNet(keras.Model): + + def __init__(self): + super(BNNet, self).__init__() + self.bn = keras.layers.BatchNormalization(beta_initializer='ones', + gamma_initializer='ones') + + def call(self, inputs): + return self.bn(inputs) + + x = np.ones((num_samples, input_dim)) + y = np.ones((num_samples, input_dim)) + + model = BNNet() + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + y_ref = model.predict(x) + + model.train_on_batch(x, y) + y_new = model.predict(x) + self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1) + + @test_util.run_in_graph_and_eager_modes() + def test_training_and_inference_behavior(self): + # test that dropout is applied in training and not inference + + num_samples = 100 + input_dim = 50 + + class DPNet(keras.Model): + + def __init__(self): + super(DPNet, self).__init__() + self.dp = keras.layers.Dropout(0.5) + self.dense = keras.layers.Dense(1, + use_bias=False, + kernel_initializer='ones') + + def call(self, inputs): + x = self.dp(inputs) + return self.dense(x) + + model = DPNet() + x = np.ones((num_samples, input_dim)) + y = model.predict(x) + self.assertEqual(np.sum(y), np.sum(x)) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + loss = model.train_on_batch(x, y) + self.assertGreater(loss, 0.1) + + @test_util.run_in_graph_and_eager_modes() + def test_training_methods(self): + # test fit, train_on_batch + # on different input types: list, dict + + num_classes = (2, 3) + num_samples = 100 + input_dim = 50 + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + model.fit({'input_1': x1, 'input_2': x2}, + {'output_1': y1, 'output_2': y2}, + epochs=2, batch_size=32) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0, + validation_data=([x1, x2], [y1, y2])) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.train_on_batch([x1, x2], [y1, y2]) + model.train_on_batch({'input_1': x1, 'input_2': x2}, + {'output_1': y1, 'output_2': y2}) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_inference_methods(self): + # test predict, evaluate, test_on_batch, predict_on_batch + # on different input types: list, dict + num_classes = (2, 3) + num_samples = 100 + input_dim = 50 + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.evaluate([x1, x2], [y1, y2]) + model.test_on_batch([x1, x2], [y1, y2]) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.predict([x1, x2]) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.predict_on_batch([x1, x2]) + + @test_util.run_in_graph_and_eager_modes() + def test_trainable_mutation(self): + # test that you can change `trainable` on a model or layer, and that + # it freezes the model state during training + # TODO(fchollet): add test after we unify BN behavior in eager and symbolic. + pass + + @test_util.run_in_graph_and_eager_modes() + def test_saving(self): + if h5py is None: + return # Skip test if models cannot be saved. + + num_classes = (2, 3) + num_samples = 100 + input_dim = 50 + + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + y_ref_1, y_ref_2 = model.predict([x1, x2]) + + fd, fname = tempfile.mkstemp('.h5') + model.save_weights(fname) + + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + # need to build the model before loading weights + # (otherwise no weights to load) + model._set_inputs([x1, x2]) + model.load_weights(fname) + + y1, y2 = model.predict([x1, x2]) + self.assertAllClose(y_ref_1, y1, atol=1e-5) + self.assertAllClose(y_ref_2, y2, atol=1e-5) + os.close(fd) + os.remove(fname) + + @test_util.run_in_graph_and_eager_modes() + def test_summary(self): + + class ToString(object): + + def __init__(self): + self.contents = '' + + def __call__(self, msg): + self.contents += msg + '\n' + + # Single-io + model = SimpleTestModel(num_classes=4, use_bn=True, use_dp=True) + model._set_inputs(np.ones((3, 4))) # need to build model first + print_fn = ToString() + model.summary(print_fn=print_fn) + self.assertTrue('Trainable params: 356' in print_fn.contents) + + # Multi-io + model = MultiIOTestModel(num_classes=(5, 6), use_bn=True, use_dp=True) + model._set_inputs([np.ones((3, 4)), + np.ones((3, 4))]) # need to build model first + print_fn = ToString() + model.summary(print_fn=print_fn) + self.assertTrue('Trainable params: 587' in print_fn.contents) + + @test_util.run_in_graph_and_eager_modes() + def test_subclass_nested_in_subclass(self): + num_classes = 2 + num_samples = 100 + input_dim = 50 + + model = NestedTestModel1(num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) + + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) + + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) + + self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) + self.assertEqual(len(model.non_trainable_weights), + 2 + len(model.test_net.non_trainable_weights)) + self.assertEqual(len(model.trainable_weights), + 6 + len(model.test_net.trainable_weights)) + + @test_util.run_in_graph_and_eager_modes() + def test_graph_nested_in_subclass(self): + num_classes = 2 + num_samples = 100 + input_dim = 50 + + model = NestedTestModel2(num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) + + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) + + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) + + self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) + self.assertEqual(len(model.non_trainable_weights), + 2 + len(model.test_net.non_trainable_weights)) + self.assertEqual(len(model.trainable_weights), + 6 + len(model.test_net.trainable_weights)) + + @test_util.run_in_graph_and_eager_modes() + def test_subclass_nested_in_graph(self): + num_classes = 2 + num_samples = 100 + input_dim = 50 + + model = get_nested_model_3(input_dim=input_dim, num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) + + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) + + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) + + self.assertEqual(len(model.weights), 16) + self.assertEqual( + len(model.non_trainable_weights), 4) + self.assertEqual(len(model.trainable_weights), 12) + + @test_util.run_in_graph_and_eager_modes() + def test_support_for_manual_training_arg(self): + # In most cases, the `training` argument is left unspecified, in which + # case it defaults to value corresponding to the Model method being used + # (fit -> True, predict -> False, etc). + # If the user writes their model `call` method to take + # an explicit `training` argument, we must check that the correct value + # is being passed to the model for each method call. + + class DPNet(keras.Model): + + def __init__(self): + super(DPNet, self).__init__() + self.dp = keras.layers.Dropout(0.5) + self.dense = keras.layers.Dense(1, + use_bias=False, + kernel_initializer='ones') + + def call(self, inputs, training=False): + x = self.dp(inputs, training=training) + return self.dense(x) + + model = DPNet() + x = np.ones((10, 10)) + y = model.predict(x) + self.assertEqual(np.sum(y), np.sum(x)) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + loss = model.train_on_batch(x, y) + self.assertGreater(loss, 0.1) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py index 9cd547200d89184bb7c2aad28b59d2793f093205..9602e7ba39b290f33c7ca9d0d1b5b35838667531 100644 --- a/tensorflow/python/keras/_impl/keras/models.py +++ b/tensorflow/python/keras/_impl/keras/models.py @@ -13,1326 +13,30 @@ # limitations under the License. # ============================================================================== # pylint: disable=protected-access -"""Home of the Sequential model, and the `save_model`/`load_model` functions. +"""Code for model cloning, plus model-related API entries. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy -import json -import os - -import numpy as np - -from tensorflow.python.framework import ops from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import layers as layer_module -from tensorflow.python.keras._impl.keras import optimizers -from tensorflow.python.keras._impl.keras.engine import topology -from tensorflow.python.keras._impl.keras.engine.topology import Input -from tensorflow.python.keras._impl.keras.engine.topology import InputLayer -from tensorflow.python.keras._impl.keras.engine.topology import Layer -from tensorflow.python.keras._impl.keras.engine.topology import TFBaseLayer -from tensorflow.python.keras._impl.keras.engine.training import Model +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.engine import sequential +from tensorflow.python.keras._impl.keras.engine import training +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.utils import generic_utils from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg -from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite -from tensorflow.python.platform import tf_logging as logging - - -# pylint: disable=g-import-not-at-top -try: - import h5py -except ImportError: - h5py = None - -try: - import yaml -except ImportError: - yaml = None -# pylint: enable=g-import-not-at-top - - -def save_model(model, filepath, overwrite=True, include_optimizer=True): - """Save a model to a HDF5 file. - - The saved model contains: - - the model's configuration (topology) - - the model's weights - - the model's optimizer's state (if any) - - Thus the saved model can be reinstantiated in - the exact same state, without any of the code - used for model definition or training. - - Arguments: - model: Keras model instance to be saved. - filepath: String, path where to save the model. - overwrite: Whether we should overwrite any existing - model at the target location, or instead - ask the user with a manual prompt. - include_optimizer: If True, save optimizer's state together. - - Raises: - ImportError: if h5py is not available. - """ - - if h5py is None: - raise ImportError('`save_model` requires h5py.') - - def get_json_type(obj): - """Serialize any object to a JSON-serializable structure. - - Arguments: - obj: the object to serialize - - Returns: - JSON-serializable structure representing `obj`. - - Raises: - TypeError: if `obj` cannot be serialized. - """ - # if obj is a serializable Keras class instance - # e.g. optimizer, layer - if hasattr(obj, 'get_config'): - return {'class_name': obj.__class__.__name__, 'config': obj.get_config()} - - # if obj is any numpy type - if type(obj).__module__ == np.__name__: - if isinstance(obj, np.ndarray): - return {'type': type(obj), 'value': obj.tolist()} - else: - return obj.item() - - # misc functions (e.g. loss function) - if callable(obj): - return obj.__name__ - - # if obj is a python 'type' - if type(obj).__name__ == type.__name__: - return obj.__name__ - - raise TypeError('Not JSON Serializable:', obj) - - from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top - - # If file exists and should not be overwritten. - if not overwrite and os.path.isfile(filepath): - proceed = ask_to_proceed_with_overwrite(filepath) - if not proceed: - return - - with h5py.File(filepath, mode='w') as f: - f.attrs['keras_version'] = str(keras_version).encode('utf8') - f.attrs['backend'] = K.backend().encode('utf8') - f.attrs['model_config'] = json.dumps( - { - 'class_name': model.__class__.__name__, - 'config': model.get_config() - }, - default=get_json_type).encode('utf8') - - model_weights_group = f.create_group('model_weights') - model_layers = model.layers - topology.save_weights_to_hdf5_group(model_weights_group, model_layers) - - if include_optimizer and hasattr(model, 'optimizer'): - if isinstance(model.optimizer, optimizers.TFOptimizer): - logging.warning( - 'TensorFlow optimizers do not ' - 'make it possible to access ' - 'optimizer attributes or optimizer state ' - 'after instantiation. ' - 'As a result, we cannot save the optimizer ' - 'as part of the model save file.' - 'You will have to compile your model again after loading it. ' - 'Prefer using a Keras optimizer instead ' - '(see keras.io/optimizers).') - else: - f.attrs['training_config'] = json.dumps( - { - 'optimizer_config': { - 'class_name': model.optimizer.__class__.__name__, - 'config': model.optimizer.get_config() - }, - 'loss': model.loss, - 'metrics': model.metrics, - 'sample_weight_mode': model.sample_weight_mode, - 'loss_weights': model.loss_weights, - }, - default=get_json_type).encode('utf8') - - # Save optimizer weights. - symbolic_weights = getattr(model.optimizer, 'weights') - if symbolic_weights: - optimizer_weights_group = f.create_group('optimizer_weights') - weight_values = K.batch_get_value(symbolic_weights) - weight_names = [] - for w, val in zip(symbolic_weights, weight_values): - name = str(w.name) - weight_names.append(name.encode('utf8')) - optimizer_weights_group.attrs['weight_names'] = weight_names - for name, val in zip(weight_names, weight_values): - param_dset = optimizer_weights_group.create_dataset( - name, val.shape, dtype=val.dtype) - if not val.shape: - # scalar - param_dset[()] = val - else: - param_dset[:] = val - f.flush() - - -def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin - """Loads a model saved via `save_model`. - - Arguments: - filepath: String, path to the saved model. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - compile: Boolean, whether to compile the model - after loading. - - Returns: - A Keras model instance. If an optimizer was found - as part of the saved model, the model is already - compiled. Otherwise, the model is uncompiled and - a warning will be displayed. When `compile` is set - to False, the compilation is omitted without any - warning. - - Raises: - ImportError: if h5py is not available. - ValueError: In case of an invalid savefile. - """ - if h5py is None: - raise ImportError('`load_model` requires h5py.') - - if not custom_objects: - custom_objects = {} - - def convert_custom_objects(obj): - """Handles custom object lookup. - - Arguments: - obj: object, dict, or list. - - Returns: - The same structure, where occurrences - of a custom object name have been replaced - with the custom object. - """ - if isinstance(obj, list): - deserialized = [] - for value in obj: - deserialized.append(convert_custom_objects(value)) - return deserialized - if isinstance(obj, dict): - deserialized = {} - for key, value in obj.items(): - deserialized[key] = convert_custom_objects(value) - return deserialized - if obj in custom_objects: - return custom_objects[obj] - return obj - - with h5py.File(filepath, mode='r') as f: - # instantiate model - model_config = f.attrs.get('model_config') - if model_config is None: - raise ValueError('No model found in config file.') - model_config = json.loads(model_config.decode('utf-8')) - model = model_from_config(model_config, custom_objects=custom_objects) - - # set weights - topology.load_weights_from_hdf5_group(f['model_weights'], model.layers) - - # Early return if compilation is not required. - if not compile: - return model - - # instantiate optimizer - training_config = f.attrs.get('training_config') - if training_config is None: - logging.warning('No training configuration found in save file: ' - 'the model was *not* compiled. Compile it manually.') - return model - training_config = json.loads(training_config.decode('utf-8')) - optimizer_config = training_config['optimizer_config'] - optimizer = optimizers.deserialize( - optimizer_config, custom_objects=custom_objects) - - # Recover loss functions and metrics. - loss = convert_custom_objects(training_config['loss']) - metrics = convert_custom_objects(training_config['metrics']) - sample_weight_mode = training_config['sample_weight_mode'] - loss_weights = training_config['loss_weights'] - - # Compile model. - model.compile( - optimizer=optimizer, - loss=loss, - metrics=metrics, - loss_weights=loss_weights, - sample_weight_mode=sample_weight_mode) - - # Set optimizer weights. - if 'optimizer_weights' in f: - # Build train function (to get weight updates). - if isinstance(model, Sequential): - model.model._make_train_function() - else: - model._make_train_function() - optimizer_weights_group = f['optimizer_weights'] - optimizer_weight_names = [ - n.decode('utf8') - for n in optimizer_weights_group.attrs['weight_names'] - ] - optimizer_weight_values = [ - optimizer_weights_group[n] for n in optimizer_weight_names - ] - try: - model.optimizer.set_weights(optimizer_weight_values) - except ValueError: - logging.warning('Error in loading the saved optimizer ' - 'state. As a result, your model is ' - 'starting with a freshly initialized ' - 'optimizer.') - return model - - -def model_from_config(config, custom_objects=None): - """Instantiates a Keras model from its config. - - Arguments: - config: Configuration dictionary. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - - Raises: - TypeError: if `config` is not a dictionary. - """ - if isinstance(config, list): - raise TypeError('`model_from_config` expects a dictionary, not a list. ' - 'Maybe you meant to use ' - '`Sequential.from_config(config)`?') - return layer_module.deserialize(config, custom_objects=custom_objects) - - -def model_from_yaml(yaml_string, custom_objects=None): - """Parses a yaml model configuration file and returns a model instance. - - Arguments: - yaml_string: YAML string encoding a model configuration. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - - Raises: - ImportError: if yaml module is not found. - """ - if yaml is None: - raise ImportError('Requires yaml module installed.') - config = yaml.load(yaml_string) - return layer_module.deserialize(config, custom_objects=custom_objects) - - -def model_from_json(json_string, custom_objects=None): - """Parses a JSON model configuration file and returns a model instance. - - Arguments: - json_string: JSON string encoding a model configuration. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - """ - config = json.loads(json_string) - return layer_module.deserialize(config, custom_objects=custom_objects) - - -class Sequential(Model): - """Linear stack of layers. - - Arguments: - layers: list of layers to add to the model. - - # Note - The first layer passed to a Sequential model - should have a defined input shape. What that - means is that it should have received an `input_shape` - or `batch_input_shape` argument, - or for some type of layers (recurrent, Dense...) - an `input_dim` argument. - - Example: - - ```python - model = Sequential() - # first layer must have a defined input shape - model.add(Dense(32, input_dim=500)) - # afterwards, Keras does automatic shape inference - model.add(Dense(32)) - - # also possible (equivalent to the above): - model = Sequential() - model.add(Dense(32, input_shape=(500,))) - model.add(Dense(32)) - - # also possible (equivalent to the above): - model = Sequential() - # here the batch dimension is None, - # which means any batch size will be accepted by the model. - model.add(Dense(32, batch_input_shape=(None, 500))) - model.add(Dense(32)) - ``` - """ - - def __init__(self, layers=None, name=None): - self.layers = [] # Stack of layers. - self.model = None # Internal Model instance. - self.inputs = [] # List of input tensors - self.outputs = [] # List of length 1: the output tensor (unique). - self._trainable = True - self._initial_weights = None - self._input_layers = [] - - # Model attributes. - self._inbound_nodes = [] - self._outbound_nodes = [] - self.built = False - - # Set model name. - if not name: - prefix = 'sequential_' - name = prefix + str(K.get_uid(prefix)) - self._name = name - - # Used by Layer base class. - self._dtype = None - self._activity_regularizer = None - self._per_input_losses = {} - self._per_input_updates = {} - - # The following properties are not actually used by Keras; - # they exist for compatibility with TF's variable scoping mechanism. - self._updates = [] - self._losses = [] - self._scope = None - self._reuse = None - self._base_name = name - self._graph = ops.get_default_graph() - - # Add to the model any layers passed to the constructor. - if layers: - for layer in layers: - self.add(layer) - - def add(self, layer): - """Adds a layer instance on top of the layer stack. - - Arguments: - layer: layer instance. - - Raises: - TypeError: If `layer` is not a layer instance. - ValueError: In case the `layer` argument does not - know its input shape. - ValueError: In case the `layer` argument has - multiple output tensors, or is already connected - somewhere else (forbidden in `Sequential` models). - """ - if not isinstance(layer, (Layer, TFBaseLayer)): - raise TypeError('The added layer must be ' - 'an instance of class Layer. ' - 'Found: ' + str(layer)) - if not self.outputs: - # First layer in model: check that it is an input layer. - if not isinstance(layer, InputLayer): - # Create an input layer. - # First, we need to infer its expected input shape and dtype. - if isinstance(layer, (Model, Sequential)): - # We were passed a model as first layer. - # This requires a specific way to figure out the - # input shape and dtype. - if not layer.layers: - raise ValueError('Cannot add an empty model ' - 'to a `Sequential` model.') - # In case of nested models: recover the first layer - # of the deepest model to infer input shape and dtype. - first_layer = layer.layers[0] - while isinstance(first_layer, (Model, Sequential)): - first_layer = first_layer.layers[0] - batch_shape = first_layer._batch_input_shape - dtype = first_layer.dtype - else: - # We were passed a regular layer, and it should - # know about its input shape. Otherwise, that's an error. - if not hasattr(layer, '_batch_input_shape'): - raise ValueError('The first layer in a ' - 'Sequential model must ' - 'get an `input_shape` argument.') - batch_shape = layer._batch_input_shape - dtype = layer.dtype - # Instantiate the input layer. - x = Input( - batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input') - # This will build the current layer - # and create the node connecting the current layer - # to the input layer we just created. - layer(x) - - if len(layer._inbound_nodes[-1].output_tensors) != 1: - raise ValueError('All layers in a Sequential model ' - 'should have a single output tensor. ' - 'For multi-output layers, ' - 'use the functional API.') - - self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] - self.inputs = topology.get_source_inputs(self.outputs[0]) - - # We create an input node, which we will keep updated - # as we add more layers - topology.Node( - outbound_layer=self, - inbound_layers=[], - node_indices=[], - tensor_indices=[], - input_tensors=self.inputs, - output_tensors=self.outputs) - else: - output_tensor = layer(self.outputs[0]) - if isinstance(output_tensor, list): - raise TypeError('All layers in a Sequential model ' - 'should have a single output tensor. ' - 'For multi-output layers, ' - 'use the functional API.') - self.outputs = [output_tensor] - # update self._inbound_nodes - self._inbound_nodes[0].output_tensors = self.outputs - self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] - - self.layers.append(layer) - self.built = False - - def pop(self): - """Removes the last layer in the model. - - Raises: - TypeError: if there are no layers in the model. - """ - if not self.layers: - raise TypeError('There are no layers in the model.') - - self.layers.pop() - if not self.layers: - self.outputs = [] - self._inbound_nodes = [] - self._outbound_nodes = [] - else: - self.layers[-1]._outbound_nodes = [] - self.outputs = [self.layers[-1].output] - # update self._inbound_nodes - self._inbound_nodes[0].output_tensors = self.outputs - self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] - self.built = False - - def get_layer(self, name=None, index=None): - """Retrieve a layer that is part of the model. - - Returns a layer based on either its name (unique) - or its index in the graph. Indices are based on - order of horizontal graph traversal (bottom-up). - - Arguments: - name: string, name of layer. - index: integer, index of layer. - - Returns: - A layer instance. - """ - if not self.built: - self.build() - return self.model.get_layer(name, index) - - def call(self, inputs, mask=None): - if not self.built: - self.build() - return self.model.call(inputs, mask) - - def build(self, input_shape=None): - if not self.inputs or not self.outputs: - raise TypeError('Sequential model cannot be built: model is empty.' - ' Add some layers first.') - # actually create the model - self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model') - self.model.trainable = self.trainable - - # mirror model attributes - self.supports_masking = self.model.supports_masking - self._output_mask_cache = self.model._output_mask_cache - self._output_tensor_cache = self.model._output_tensor_cache - self._output_shape_cache = self.model._output_shape_cache - self._input_layers = self.model._input_layers - self._output_layers = self.model._output_layers - self._input_coordinates = self.model._input_coordinates - self._output_coordinates = self.model._output_coordinates - self._nodes_by_depth = self.model._nodes_by_depth - self._network_nodes = self.model._network_nodes - self.output_names = self.model.output_names - self.input_names = self.model.input_names - self._feed_input_names = self.model._feed_input_names - self._feed_inputs = self.model._feed_inputs - - # Make sure child model callbacks - # will call the parent Sequential model. - self.model.callback_model = self - - self.built = True - - @property - def uses_learning_phase(self): - if not self.built: - self.build() - return self.model.uses_learning_phase - - def _gather_list_attr(self, attr): - all_attrs = [] - for layer in self.layers: - all_attrs += getattr(layer, attr, []) - return all_attrs - - @property - def trainable(self): - return self._trainable - - @trainable.setter - def trainable(self, value): - if self.model: - self.model.trainable = value - self._trainable = value - - @property - def trainable_weights(self): - if not self.trainable: - return [] - return self._gather_list_attr('trainable_weights') - - @property - def non_trainable_weights(self): - weights = self._gather_list_attr('non_trainable_weights') - if not self.trainable: - trainable_weights = self._gather_list_attr('trainable_weights') - return trainable_weights + weights - return weights - - @property - def updates(self): - if not self.built: - self.build() - return self.model.updates - - @property - def state_updates(self): - if not self.built: - self.build() - return self.model.state_updates - - def get_updates_for(self, inputs): - if not self.built: - self.build() - return self.model.get_updates_for(inputs) - - @property - def losses(self): - if not self.built: - self.build() - return self.model.losses - - def get_losses_for(self, inputs): - if not self.built: - self.build() - return self.model.get_losses_for(inputs) - - @property - def regularizers(self): - if not self.built: - self.build() - return self.model.regularizers - - def get_weights(self): - """Retrieves the weights of the model. - - Returns: - A flat list of Numpy arrays - (one array per model weight). - """ - if not self.built: - self.build() - return self.model.get_weights() - - def set_weights(self, weights): - """Sets the weights of the model. - - Arguments: - weights: Should be a list - of Numpy arrays with shapes and types matching - the output of `model.get_weights()`. - """ - if not self.built: - self.build() - self.model.set_weights(weights) - - def load_weights(self, filepath, by_name=False): - if h5py is None: - raise ImportError('`load_weights` requires h5py.') - f = h5py.File(filepath, mode='r') - if 'layer_names' not in f.attrs and 'model_weights' in f: - f = f['model_weights'] - layers = self.layers - if by_name: - topology.load_weights_from_hdf5_group_by_name(f, layers) - else: - topology.load_weights_from_hdf5_group(f, layers) - if hasattr(f, 'close'): - f.close() - - def save_weights(self, filepath, overwrite=True): - if h5py is None: - raise ImportError('`save_weights` requires h5py.') - # If file exists and should not be overwritten: - if not overwrite and os.path.isfile(filepath): - proceed = ask_to_proceed_with_overwrite(filepath) - if not proceed: - return - layers = self.layers - f = h5py.File(filepath, 'w') - topology.save_weights_to_hdf5_group(f, layers) - f.flush() - f.close() - - def compile(self, - optimizer, - loss, - metrics=None, - sample_weight_mode=None, - weighted_metrics=None, - target_tensors=None, - **kwargs): - """Configures the model for training. - - Arguments: - optimizer: String (name of optimizer) or optimizer object. - See [optimizers](/optimizers). - loss: String (name of objective function) or objective function. - See [losses](/losses). - If the model has multiple outputs, you can use a different loss - on each output by passing a dictionary or a list of losses. - The loss value that will be minimized by the model - will then be the sum of all individual losses. - metrics: List of metrics to be evaluated by the model - during training and testing. - Typically you will use `metrics=['accuracy']`. - To specify different metrics for different outputs of a - multi-output model, you could also pass a dictionary, - such as `metrics={'output_a': 'accuracy'}`. - sample_weight_mode: If you need to do timestep-wise - sample weighting (2D weights), set this to `"temporal"`. - `None` defaults to sample-wise weights (1D). - If the model has multiple outputs, you can use a different - `sample_weight_mode` on each output by passing a - dictionary or a list of modes. - weighted_metrics: list of metrics to be evaluated and weighted - by `sample_weight` or `class_weight` during training and testing. - target_tensors: By default, Keras will create a placeholder for the - model's target, which will be fed with the target data during - training. If instead you would like to use your own - target tensor (in turn, Keras will not expect external - Numpy data for these targets at training time), you - can specify them via the `target_tensors` argument. - It should be a single tensor - (for a single-output `Sequential` model). - **kwargs: These arguments are passed into `tf.Session.run`. - - Example: - ```python - model = Sequential() - model.add(Dense(32, input_shape=(500,))) - model.add(Dense(10, activation='softmax')) - model.compile(optimizer='rmsprop', - loss='categorical_crossentropy', - metrics=['accuracy']) - ``` - """ - # create the underlying model - self.build() - # call compile method of Model class - self.model.compile( - optimizer, - loss, - metrics=metrics, - sample_weight_mode=sample_weight_mode, - weighted_metrics=weighted_metrics, - target_tensors=target_tensors, - **kwargs) - self.optimizer = self.model.optimizer - self.loss = self.model.loss - self.metrics = self.model.metrics - self.loss_weights = self.model.loss_weights - self.sample_weight_mode = self.model.sample_weight_mode - self.weighted_metrics = self.model.weighted_metrics - self.targets = self.model.targets - self.metrics_tensors = self.model.metrics_tensors - self.metrics_names = self.model.metrics_names - self.sample_weights = self.model.sample_weights - self.total_loss = self.model.total_loss - - def fit(self, - x=None, - y=None, - batch_size=None, - epochs=1, - verbose=1, - callbacks=None, - validation_split=0., - validation_data=None, - shuffle=True, - class_weight=None, - sample_weight=None, - initial_epoch=0, - steps_per_epoch=None, - validation_steps=None, - **kwargs): - """Trains the model for a fixed number of epochs. - - Arguments: - x: Numpy array of training data. - If the input layer in the model is named, you can also pass a - dictionary mapping the input name to a Numpy array. - `x` can be `None` (default) if feeding from - TensorFlow data tensors. - y: Numpy array of target (label) data. - If the output layer in the model is named, you can also pass a - dictionary mapping the output name to a Numpy array. - `y` can be `None` (default) if feeding from - TensorFlow data tensors. - batch_size: Integer or `None`. - Number of samples per gradient update. - If unspecified, it will default to 32. - epochs: Integer. Number of epochs to train the model. - An epoch is an iteration over the entire `x` and `y` - data provided. - Note that in conjunction with `initial_epoch`, - `epochs` is to be understood as "final epoch". - The model is not trained for a number of iterations - given by `epochs`, but merely until the epoch - of index `epochs` is reached. - verbose: 0, 1, or 2. Verbosity mode. - 0 = silent, 1 = progress bar, 2 = one line per epoch. - callbacks: List of `keras.callbacks.Callback` instances. - List of callbacks to apply during training. - See [callbacks](/callbacks). - validation_split: Float between 0 and 1: - Fraction of the training data to be used as validation data. - The model will set apart this fraction of the training data, - will not train on it, and will evaluate - the loss and any model metrics - on this data at the end of each epoch. - The validation data is selected from the last samples - in the `x` and `y` data provided, before shuffling. - validation_data: tuple `(x_val, y_val)` or tuple - `(x_val, y_val, val_sample_weights)` on which to evaluate - the loss and any model metrics at the end of each epoch. - The model will not be trained on this data. - This will override `validation_split`. - shuffle: Boolean (whether to shuffle the training data - before each epoch) or str (for 'batch'). - 'batch' is a special option for dealing with the - limitations of HDF5 data; it shuffles in batch-sized chunks. - Has no effect when `steps_per_epoch` is not `None`. - class_weight: Optional dictionary mapping class indices (integers) - to a weight (float) value, used for weighting the loss function - (during training only). - This can be useful to tell the model to - "pay more attention" to samples from - an under-represented class. - sample_weight: Optional Numpy array of weights for - the training samples, used for weighting the loss function - (during training only). You can either pass a flat (1D) - Numpy array with the same length as the input samples - (1:1 mapping between weights and samples), - or in the case of temporal data, - you can pass a 2D array with shape - `(samples, sequence_length)`, - to apply a different weight to every timestep of every sample. - In this case you should make sure to specify - `sample_weight_mode="temporal"` in `compile()`. - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run). - steps_per_epoch: Total number of steps (batches of samples) - before declaring one epoch finished and starting the - next epoch. When training with input tensors such as - TensorFlow data tensors, the default `None` is equal to - the number of unique samples in your dataset divided by - the batch size, or 1 if that cannot be determined. - validation_steps: Only relevant if `steps_per_epoch` - is specified. Total number of steps (batches of samples) - to validate before stopping. - **kwargs: Used for backwards compatibility support. - - Returns: - A `History` object. Its `History.history` attribute is - a record of training loss values and metrics values - at successive epochs, as well as validation loss values - and validation metrics values (if applicable). - - Raises: - RuntimeError: If the model was never compiled. - ValueError: In case of mismatch between the provided input data - and what the model expects. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.fit( - x, - y, - batch_size=batch_size, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - validation_split=validation_split, - validation_data=validation_data, - shuffle=shuffle, - class_weight=class_weight, - sample_weight=sample_weight, - initial_epoch=initial_epoch, - steps_per_epoch=steps_per_epoch, - validation_steps=validation_steps) - - def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None): - """Computes the loss on some input data, batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - batch_size: integer. Number of samples per gradient update. - verbose: verbosity mode, 0 or 1. - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.evaluate( - x, - y, - batch_size=batch_size, - verbose=verbose, - sample_weight=sample_weight) - - def predict(self, x, batch_size=32, verbose=0): - """Generates output predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: the input data, as a Numpy array. - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A Numpy array of predictions. - """ - if not self.built: - self.build() - return self.model.predict(x, batch_size=batch_size, verbose=verbose) - - def predict_on_batch(self, x): - """Returns predictions for a single batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - - Returns: - A Numpy array of predictions. - """ - if not self.built: - self.build() - return self.model.predict_on_batch(x) - - def train_on_batch(self, x, y, class_weight=None, sample_weight=None): - """Single gradient update over one batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - class_weight: dictionary mapping classes to a weight value, - used for scaling the loss function (during training only). - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar training loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.train_on_batch( - x, y, sample_weight=sample_weight, class_weight=class_weight) - - def test_on_batch(self, x, y, sample_weight=None): - """Evaluates the model over a single batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.test_on_batch(x, y, sample_weight=sample_weight) - - def predict_proba(self, x, batch_size=32, verbose=0): - """Generates class probability predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A Numpy array of probability predictions. - """ - preds = self.predict(x, batch_size, verbose) - if preds.min() < 0. or preds.max() > 1.: - logging.warning('Network returning invalid probability values. ' - 'The last layer might not normalize predictions ' - 'into probabilities ' - '(like softmax or sigmoid would).') - return preds - - def predict_classes(self, x, batch_size=32, verbose=0): - """Generate class predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A numpy array of class predictions. - """ - proba = self.predict(x, batch_size=batch_size, verbose=verbose) - if proba.shape[-1] > 1: - return proba.argmax(axis=-1) - else: - return (proba > 0.5).astype('int32') - - def fit_generator(self, - generator, - steps_per_epoch=None, - epochs=1, - verbose=1, - callbacks=None, - validation_data=None, - validation_steps=None, - class_weight=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - shuffle=True, - initial_epoch=0, - **kwargs): - """Fits the model on data generated batch-by-batch by a Python generator. - - The generator is run in parallel to the model, for efficiency. - For instance, this allows you to do real-time data augmentation - on images on CPU in parallel to training your model on GPU. - - Arguments: - generator: A generator. - The output of the generator must be either - - a tuple (inputs, targets) - - a tuple (inputs, targets, sample_weights). - All arrays should contain the same number of samples. - The generator is expected to loop over its data - indefinitely. An epoch finishes when `steps_per_epoch` - batches have been seen by the model. - steps_per_epoch: Total number of steps (batches of samples) - to yield from `generator` before declaring one epoch - finished and starting the next epoch. It should typically - be equal to the number of samples of your dataset - divided by the batch size. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - epochs: Integer, total number of iterations on the data. - Note that in conjunction with initial_epoch, the parameter - epochs is to be understood as "final epoch". The model is - not trained for n steps given by epochs, but until the - epoch epochs is reached. - verbose: Verbosity mode, 0, 1, or 2. - callbacks: List of callbacks to be called during training. - validation_data: This can be either - - A generator for the validation data - - A tuple (inputs, targets) - - A tuple (inputs, targets, sample_weights). - validation_steps: Only relevant if `validation_data` - is a generator. - Number of steps to yield from validation generator - at the end of every epoch. It should typically - be equal to the number of samples of your - validation dataset divided by the batch size. - Optional for `Sequence`: if unspecified, will use - the `len(validation_data)` as a number of steps. - class_weight: Dictionary mapping class indices to a weight - for the class. - max_queue_size: Maximum size for the generator queue - workers: Maximum number of processes to spin up - use_multiprocessing: If True, use process based threading. - Note that because - this implementation relies on multiprocessing, - you should not pass - non picklable arguments to the generator - as they can't be passed - easily to children processes. - shuffle: Whether to shuffle the order of the batches at - the beginning of each epoch. Only used with instances - of `Sequence` (keras.utils.Sequence). - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run) - **kwargs: support for legacy arguments. - - Returns: - A `History` object. - - Raises: - RuntimeError: if the model was never compiled. - ValueError: In case the generator yields - data in an invalid format. - - Example: - - ```python - def generate_arrays_from_file(path): - while 1: - f = open(path) - for line in f: - # create Numpy arrays of input data - # and labels, from each line in the file - x, y = process_line(line) - yield (x, y) - f.close() - - model.fit_generator(generate_arrays_from_file('/my_file.txt'), - steps_per_epoch=1000, epochs=10) - ``` - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.fit_generator( - generator, - steps_per_epoch, - epochs, - verbose=verbose, - callbacks=callbacks, - validation_data=validation_data, - validation_steps=validation_steps, - class_weight=class_weight, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - shuffle=shuffle, - initial_epoch=initial_epoch) - - def evaluate_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - **kwargs): - """Evaluates the model on a data generator. - - The generator should return the same kind of data - as accepted by `test_on_batch`. - - Arguments: - generator: Generator yielding tuples (inputs, targets) - or (inputs, targets, sample_weights) - steps: Total number of steps (batches of samples) - to yield from `generator` before stopping. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - max_queue_size: maximum size for the generator queue - workers: maximum number of processes to spin up - use_multiprocessing: if True, use process based threading. - Note that because this implementation - relies on multiprocessing, you should not pass - non picklable arguments to the generator - as they can't be passed easily to children processes. - **kwargs: support for legacy arguments. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - ValueError: In case the generator yields - data in an invalid format. - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.evaluate_generator( - generator, - steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing) - - def predict_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - verbose=0, - **kwargs): - """Generates predictions for the input samples from a data generator. - - The generator should return the same kind of data as accepted by - `predict_on_batch`. - - Arguments: - generator: generator yielding batches of input samples. - steps: Total number of steps (batches of samples) - to yield from `generator` before stopping. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - max_queue_size: maximum size for the generator queue - workers: maximum number of processes to spin up - use_multiprocessing: if True, use process based threading. - Note that because this implementation - relies on multiprocessing, you should not pass - non picklable arguments to the generator - as they can't be passed easily to children processes. - verbose: verbosity mode, 0 or 1. - **kwargs: support for legacy arguments. - - Returns: - A Numpy array of predictions. - - Raises: - ValueError: In case the generator yields - data in an invalid format. - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - self.build() - return self.model.predict_generator( - generator, - steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - verbose=verbose) - def get_config(self): - config = [] - for layer in self.layers: - config.append({ - 'class_name': layer.__class__.__name__, - 'config': layer.get_config() - }) - return copy.deepcopy(config) - @classmethod - def from_config(cls, config, custom_objects=None): - model = cls() - for conf in config: - layer = layer_module.deserialize(conf, custom_objects=custom_objects) - model.add(layer) - return model +# API entries importable from `keras.models`: +Model = training.Model # pylint: disable=invalid-name +Sequential = sequential.Sequential # pylint: disable=invalid-name +save_model = saving.save_model +load_model = saving.load_model +model_from_config = saving.model_from_config +model_from_yaml = saving.model_from_yaml +model_from_json = saving.model_from_json def _clone_functional_model(model, input_tensors=None): @@ -1386,7 +90,7 @@ def _clone_functional_model(model, input_tensors=None): else: # Make sure that all input tensors come from a Keras layer. # If tensor comes from an input layer: cache the input layer. - input_tensors = topology._to_list(input_tensors) + input_tensors = generic_utils.to_list(input_tensors) input_tensors_ = [] for i, x in enumerate(input_tensors): if not K.is_keras_tensor(x): @@ -1423,7 +127,7 @@ def _clone_functional_model(model, input_tensors=None): # Reuse previously cloned layer. layer = layer_map[layer] # Don't call InputLayer multiple times. - if isinstance(layer, topology.InputLayer): + if isinstance(layer, InputLayer): continue # Gather inputs to call the new layer. @@ -1448,8 +152,9 @@ def _clone_functional_model(model, input_tensors=None): if has_arg(layer.call, 'mask'): if 'mask' not in kwargs: kwargs['mask'] = computed_mask - output_tensors = topology._to_list(layer(computed_tensor, **kwargs)) - output_masks = topology._to_list( + output_tensors = generic_utils.to_list(layer(computed_tensor, + **kwargs)) + output_masks = generic_utils.to_list( layer.compute_mask(computed_tensor, computed_mask)) computed_tensors = [computed_tensor] computed_masks = [computed_mask] @@ -1459,8 +164,9 @@ def _clone_functional_model(model, input_tensors=None): if has_arg(layer.call, 'mask'): if 'mask' not in kwargs: kwargs['mask'] = computed_masks - output_tensors = topology._to_list(layer(computed_tensors, **kwargs)) - output_masks = topology._to_list( + output_tensors = generic_utils.to_list(layer(computed_tensors, + **kwargs)) + output_masks = generic_utils.to_list( layer.compute_mask(computed_tensors, computed_masks)) # Update tensor_map. for x, y, mask in zip(reference_output_tensors, output_tensors, @@ -1510,14 +216,14 @@ def _clone_sequential_model(model, input_tensors=None): if input_tensors is None: return Sequential(layers=layers, name=model.name) else: - if len(topology._to_list(input_tensors)) != 1: + if len(generic_utils.to_list(input_tensors)) != 1: raise ValueError('To clone a `Sequential` model, we expect ' ' at most one tensor ' 'as part of `input_tensors`.') - x = topology._to_list(input_tensors)[0] + x = generic_utils.to_list(input_tensors)[0] if K.is_keras_tensor(x): origin_layer = x._keras_history[0] - if isinstance(origin_layer, topology.InputLayer): + if isinstance(origin_layer, InputLayer): return Sequential(layers=[origin_layer] + layers, name=model.name) else: raise ValueError('Cannot clone a `Sequential` model on top ' diff --git a/tensorflow/python/keras/_impl/keras/models_test.py b/tensorflow/python/keras/_impl/keras/models_test.py index 04017e4b28b27e52f88a7746fc44510c29edffce..5978ddd987c63b9d87a31be6837172f08512ef73 100644 --- a/tensorflow/python/keras/_impl/keras/models_test.py +++ b/tensorflow/python/keras/_impl/keras/models_test.py @@ -12,362 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for training routines.""" +"""Tests for `models.py` (model cloning, mainly).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os -import shutil -import tempfile - import numpy as np from tensorflow.python.keras._impl import keras from tensorflow.python.platform import test -from tensorflow.python.training import training as training_module - -try: - import h5py # pylint:disable=g-import-not-at-top -except ImportError: - h5py = None - - -class TestModelSaving(test.TestCase): - - def test_sequential_model_saving(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.RepeatVector(3)) - model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy], - sample_weight_mode='temporal') - x = np.random.random((1, 3)) - y = np.random.random((1, 3, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - new_model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - out2 = new_model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - # test that new updates are the same with both models - x = np.random.random((1, 3)) - y = np.random.random((1, 3, 3)) - model.train_on_batch(x, y) - new_model.train_on_batch(x, y) - out = model.predict(x) - out2 = new_model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_sequential_model_saving_2(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - # test with custom optimizer, loss - - class CustomOp(keras.optimizers.RMSprop): - pass - - def custom_loss(y_true, y_pred): - return keras.losses.mse(y_true, y_pred) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc']) - - x = np.random.random((1, 3)) - y = np.random.random((1, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model( - fname, - custom_objects={'CustomOp': CustomOp, - 'custom_loss': custom_loss}) - os.close(fd) - os.remove(fname) - - out2 = model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_functional_model_saving(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - inputs = keras.layers.Input(shape=(3,)) - x = keras.layers.Dense(2)(inputs) - output = keras.layers.Dense(3)(x) - - model = keras.models.Model(inputs, output) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy]) - x = np.random.random((1, 3)) - y = np.random.random((1, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - out2 = model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_saving_without_compilation(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_with_tf_optimizer(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', - optimizer=training_module.AdadeltaOptimizer(0.1), - metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_right_after_compilation(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - model.model._make_train_function() - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_lambda_numpy_array_arguments(self): - if h5py is None: - return # Skip test if models cannot be saved. - - mean = np.random.random((4, 2, 3)) - std = np.abs(np.random.random((4, 2, 3))) + 1e-5 - inputs = keras.layers.Input(shape=(4, 2, 3)) - output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, - arguments={'mu': mean, 'std': std})(inputs) - model = keras.models.Model(inputs, output) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - self.assertAllClose(mean, model.layers[1].arguments['mu']) - self.assertAllClose(std, model.layers[1].arguments['std']) - - -class TestSequential(test.TestCase): - """Most Sequential model API tests are covered in `training_test.py`. - """ - - def test_basic_methods(self): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1, input_dim=2)) - model.add(keras.layers.Dropout(0.3, name='dp')) - model.add(keras.layers.Dense(2, kernel_regularizer='l2', - kernel_constraint='max_norm')) - model.build() - self.assertEqual(model.state_updates, model.model.state_updates) - self.assertEqual(model.get_layer(name='dp').name, 'dp') - - def test_sequential_pop(self): - num_hidden = 5 - input_dim = 3 - batch_size = 5 - num_classes = 2 - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - model.compile(loss='mse', optimizer='sgd') - x = np.random.random((batch_size, input_dim)) - y = np.random.random((batch_size, num_classes)) - model.fit(x, y, epochs=1) - model.pop() - self.assertEqual(len(model.layers), 1) - self.assertEqual(model.output_shape, (None, num_hidden)) - model.compile(loss='mse', optimizer='sgd') - y = np.random.random((batch_size, num_hidden)) - model.fit(x, y, epochs=1) - - # Test popping single-layer model - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.pop() - self.assertEqual(len(model.layers), 0) - self.assertEqual(len(model.outputs), 0) - - # Invalid use case - model = keras.models.Sequential() - with self.assertRaises(TypeError): - model.pop() - - def test_sequential_weight_loading(self): - if h5py is None: - return - - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - h5_path = os.path.join(temp_dir, 'test.h5') - - num_hidden = 5 - input_dim = 3 - batch_size = 5 - num_classes = 2 - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - - x = np.random.random((batch_size, input_dim)) - ref_y = model.predict(x) - - model.save_weights(h5_path) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - model.load_weights(h5_path) - y = model.predict(x) - - self.assertAllClose(y, ref_y) - - def test_invalid_use_cases(self): - with self.test_session(): - # Added objects must be layer instances - with self.assertRaises(TypeError): - model = keras.models.Sequential() - model.add(None) - - # Added layers must have an inputs shape - with self.assertRaises(ValueError): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1)) - - # Added layers cannot have multiple outputs - class MyLayer(keras.layers.Layer): - - def call(self, inputs): - return [3 * inputs, 2 * inputs] - - def compute_output_shape(self, input_shape): - return [input_shape, input_shape] - - with self.assertRaises(ValueError): - model = keras.models.Sequential() - model.add(MyLayer(input_shape=(3,))) - with self.assertRaises(TypeError): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1, input_dim=1)) - model.add(MyLayer()) - - # Building empty model - model = keras.models.Sequential() - with self.assertRaises(TypeError): - model.build() - - def test_nested_sequential_trainability(self): - input_dim = 20 - num_units = 10 - num_classes = 2 - - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) - - model = keras.models.Sequential() - model.add(inner_model) - model.add(keras.layers.Dense(num_classes)) - - self.assertEqual(len(model.trainable_weights), 4) - inner_model.trainable = False - self.assertEqual(len(model.trainable_weights), 2) - inner_model.trainable = True - self.assertEqual(len(model.trainable_weights), 4) - - def test_sequential_update_disabling(self): - val_a = np.random.random((10, 4)) - val_out = np.random.random((10, 4)) - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.BatchNormalization(input_shape=(4,))) - - model.trainable = False - assert not model.updates - - model.compile('sgd', 'mse') - assert not model.updates - assert not model.model.updates - - x1 = model.predict(val_a) - model.train_on_batch(val_a, val_out) - x2 = model.predict(val_a) - self.assertAllClose(x1, x2, atol=1e-7) - - model.trainable = True - model.compile('sgd', 'mse') - assert model.updates - assert model.model.updates - - model.train_on_batch(val_a, val_out) - x2 = model.predict(val_a) - assert np.abs(np.sum(x1 - x2)) > 1e-5 class TestModelCloning(test.TestCase): diff --git a/tensorflow/python/keras/_impl/keras/optimizers.py b/tensorflow/python/keras/_impl/keras/optimizers.py index a55a5e39a69c4286f0a002474b5ad543c04bf256..6520128c5b65451aef20ec9626245fba5ef29927 100644 --- a/tensorflow/python/keras/_impl/keras/optimizers.py +++ b/tensorflow/python/keras/_impl/keras/optimizers.py @@ -24,7 +24,6 @@ import copy import six from six.moves import zip # pylint: disable=redefined-builtin -from tensorflow.python.eager import context from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import ops from tensorflow.python.keras._impl.keras import backend as K @@ -33,6 +32,7 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_ke from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer as tf_optimizer_module +from tensorflow.python.util.tf_export import tf_export def clip_norm(g, c, n): @@ -66,6 +66,7 @@ def clip_norm(g, c, n): return g +@tf_export('keras.optimizers.Optimizer') class Optimizer(object): """Abstract optimizer base class. @@ -150,6 +151,7 @@ class Optimizer(object): return cls(**config) +@tf_export('keras.optimizers.SGD') class SGD(Optimizer): """Stochastic gradient descent optimizer. @@ -180,8 +182,9 @@ class SGD(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) # momentum shapes = [K.int_shape(p) for p in params] moments = [K.zeros(shape) for shape in shapes] @@ -213,6 +216,7 @@ class SGD(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.RMSprop') class RMSprop(Optimizer): """RMSProp optimizer. @@ -251,8 +255,9 @@ class RMSprop(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) for p, g, a in zip(params, grads, accumulators): # update accumulator @@ -278,6 +283,7 @@ class RMSprop(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.Adagrad') class Adagrad(Optimizer): """Adagrad optimizer. @@ -311,8 +317,9 @@ class Adagrad(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) for p, g, a in zip(params, grads, accumulators): new_a = a + K.square(g) # update accumulator @@ -336,6 +343,7 @@ class Adagrad(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.Adadelta') class Adadelta(Optimizer): """Adadelta optimizer. @@ -373,8 +381,9 @@ class Adadelta(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators): # update accumulator @@ -407,6 +416,7 @@ class Adadelta(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.Adam') class Adam(Optimizer): """Adam optimizer. @@ -451,8 +461,9 @@ class Adam(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) t = K.cast(self.iterations, K.floatx()) + 1 lr_t = lr * ( @@ -500,6 +511,7 @@ class Adam(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.Adamax') class Adamax(Optimizer): """Adamax optimizer from Adam paper's Section 7. @@ -539,8 +551,9 @@ class Adamax(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / - (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) + lr = lr * (1. / # pylint: disable=g-no-augmented-assignment + (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) t = K.cast(self.iterations, K.floatx()) + 1 lr_t = lr / (1. - K.pow(self.beta_1, t)) @@ -581,6 +594,7 @@ class Adamax(Optimizer): return dict(list(base_config.items()) + list(config.items())) +@tf_export('keras.optimizers.Nadam') class Nadam(Optimizer): """Nesterov Adam optimizer. @@ -681,8 +695,7 @@ class TFOptimizer(Optimizer): def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer with K.name_scope(self.__class__.__name__): - if context.in_graph_mode(): - self.iterations = K.variable(0, dtype='int64', name='iterations') + self.iterations = K.variable(0, dtype='int64', name='iterations') def apply_gradients(self, grads): self.optimizer.apply_gradients(grads) @@ -691,8 +704,10 @@ class TFOptimizer(Optimizer): return self.optimizer.compute_gradients(loss, params) def get_updates(self, loss, params): - grads = self.optimizer.compute_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] + if not params: + return self.updates + grads = self.optimizer.compute_gradients(loss, params) opt_update = self.optimizer.apply_gradients( grads, global_step=self.iterations) self.updates.append(opt_update) @@ -720,10 +735,12 @@ adamax = Adamax nadam = Nadam +@tf_export('keras.optimizers.serialize') def serialize(optimizer): return serialize_keras_object(optimizer) +@tf_export('keras.optimizers.deserialize') def deserialize(config, custom_objects=None): """Inverse of the `serialize` function. @@ -757,6 +774,7 @@ def deserialize(config, custom_objects=None): printable_module_name='optimizer') +@tf_export('keras.optimizers.get') def get(identifier): """Retrieves a Keras Optimizer instance. diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/image.py b/tensorflow/python/keras/_impl/keras/preprocessing/image.py index db1fdd4e6b0fd39536f4f69ab396c7e5552710ea..d12f10863921ee7d635930f34e8bc701c89864e8 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/image.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/image.py @@ -32,6 +32,7 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export try: from scipy import linalg @@ -62,6 +63,7 @@ if pil_image is not None: _PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS +@tf_export('keras.preprocessing.image.random_rotation') def random_rotation(x, rg, row_axis=1, @@ -96,6 +98,7 @@ def random_rotation(x, return x +@tf_export('keras.preprocessing.image.random_shift') def random_shift(x, wrg, hrg, @@ -132,6 +135,7 @@ def random_shift(x, return x +@tf_export('keras.preprocessing.image.random_shear') def random_shear(x, intensity, row_axis=1, @@ -166,6 +170,7 @@ def random_shear(x, return x +@tf_export('keras.preprocessing.image.random_zoom') def random_zoom(x, zoom_range, row_axis=1, @@ -209,6 +214,7 @@ def random_zoom(x, return x +@tf_export('keras.preprocessing.image.random_channel_shift') def random_channel_shift(x, intensity, channel_axis=0): x = np.rollaxis(x, channel_axis, 0) min_x, max_x = np.min(x), np.max(x) @@ -230,6 +236,7 @@ def transform_matrix_offset_center(matrix, x, y): return transform_matrix +@tf_export('keras.preprocessing.image.apply_transform') def apply_transform(x, transform_matrix, channel_axis=0, @@ -267,6 +274,7 @@ def apply_transform(x, return x +@tf_export('keras.preprocessing.image.flip_axis') def flip_axis(x, axis): x = np.asarray(x).swapaxes(axis, 0) x = x[::-1, ...] @@ -274,6 +282,7 @@ def flip_axis(x, axis): return x +@tf_export('keras.preprocessing.image.array_to_img') def array_to_img(x, data_format=None, scale=True): """Converts a 3D Numpy array to a PIL Image instance. @@ -324,6 +333,7 @@ def array_to_img(x, data_format=None, scale=True): raise ValueError('Unsupported channel number: ', x.shape[2]) +@tf_export('keras.preprocessing.image.img_to_array') def img_to_array(img, data_format=None): """Converts a PIL Image instance to a Numpy array. @@ -358,6 +368,7 @@ def img_to_array(img, data_format=None): return x +@tf_export('keras.preprocessing.image.load_img') def load_img(path, grayscale=False, target_size=None, interpolation='nearest'): """Loads an image into PIL format. @@ -411,6 +422,7 @@ def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'): ] +@tf_export('keras.preprocessing.image.ImageDataGenerator') class ImageDataGenerator(object): """Generate minibatches of image data with real-time data augmentation. @@ -824,6 +836,7 @@ class ImageDataGenerator(object): vt.T / np.sqrt(s_expand**2 + self.zca_epsilon)).dot(vt) +@tf_export('keras.preprocessing.image.Iterator') class Iterator(Sequence): """Base class for image data iterators. @@ -913,6 +926,7 @@ class Iterator(Sequence): raise NotImplementedError +@tf_export('keras.preprocessing.image.NumpyArrayIterator') class NumpyArrayIterator(Iterator): """Iterator yielding data from a Numpy array. @@ -1094,6 +1108,7 @@ def _list_valid_filenames_in_directory(directory, white_list_formats, return classes, filenames +@tf_export('keras.preprocessing.image.DirectoryIterator') class DirectoryIterator(Iterator): """Iterator capable of reading images from a directory on disk. diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py index 4d59250af03cd5e264fb8a36ba70311840bd68b0..a423d96d3d8578df347b7ee36fb53dfd335e0d65 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py @@ -22,8 +22,10 @@ import random import numpy as np from six.moves import range # pylint: disable=redefined-builtin +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.preprocessing.sequence.pad_sequences') def pad_sequences(sequences, maxlen=None, dtype='int32', @@ -104,6 +106,7 @@ def pad_sequences(sequences, return x +@tf_export('keras.preprocessing.sequence.make_sampling_table') def make_sampling_table(size, sampling_factor=1e-5): """Generates a word rank-based probabilistic sampling table. @@ -137,6 +140,7 @@ def make_sampling_table(size, sampling_factor=1e-5): return np.minimum(1., f / np.sqrt(f)) +@tf_export('keras.preprocessing.sequence.skipgrams') def skipgrams(sequence, vocabulary_size, window_size=4, diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/text.py b/tensorflow/python/keras/_impl/keras/preprocessing/text.py index 8f7f25dc0a3e6fd234abb5fc97b3441ddcf16a4e..1e3828ccf1e3bf9c443691e1c1da5697bedb4653 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/text.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/text.py @@ -28,6 +28,7 @@ from six.moves import range # pylint: disable=redefined-builtin from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export if sys.version_info < (3,): @@ -36,6 +37,7 @@ else: maketrans = str.maketrans +@tf_export('keras.preprocessing.text.text_to_word_sequence') def text_to_word_sequence(text, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, @@ -64,6 +66,7 @@ def text_to_word_sequence(text, return [i for i in seq if i] +@tf_export('keras.preprocessing.text.one_hot') def one_hot(text, n, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', @@ -129,6 +132,7 @@ def hashing_trick(text, return [(hash_function(w) % (n - 1) + 1) for w in seq] +@tf_export('keras.preprocessing.text.Tokenizer') class Tokenizer(object): """Text tokenization utility class. diff --git a/tensorflow/python/keras/_impl/keras/regularizers.py b/tensorflow/python/keras/_impl/keras/regularizers.py index c53ee8a1aeccbf862324d1e91235ce1a00adb457..2c30844647acdb78d1ca31d052ec7e5ecc6dcc2a 100644 --- a/tensorflow/python/keras/_impl/keras/regularizers.py +++ b/tensorflow/python/keras/_impl/keras/regularizers.py @@ -23,8 +23,10 @@ import six from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.regularizers.Regularizer') class Regularizer(object): """Regularizer base class. """ @@ -37,6 +39,7 @@ class Regularizer(object): return cls(**config) +@tf_export('keras.regularizers.L1L2') class L1L2(Regularizer): """Regularizer for L1 and L2 regularization. @@ -64,22 +67,27 @@ class L1L2(Regularizer): # Aliases. +@tf_export('keras.regularizers.l1') def l1(l=0.01): return L1L2(l1=l) +@tf_export('keras.regularizers.l2') def l2(l=0.01): return L1L2(l2=l) +@tf_export('keras.regularizers.l1_l2') def l1_l2(l1=0.01, l2=0.01): # pylint: disable=redefined-outer-name return L1L2(l1=l1, l2=l2) +@tf_export('keras.regularizers.serialize') def serialize(regularizer): return serialize_keras_object(regularizer) +@tf_export('keras.regularizers.deserialize') def deserialize(config, custom_objects=None): return deserialize_keras_object( config, @@ -88,6 +96,7 @@ def deserialize(config, custom_objects=None): printable_module_name='regularizer') +@tf_export('keras.regularizers.get') def get(identifier): if identifier is None: return None diff --git a/tensorflow/python/keras/_impl/keras/testing_utils.py b/tensorflow/python/keras/_impl/keras/testing_utils.py index b889e311b37d48732641205a90ca83af34ea4489..60799ee1e038b4466351248bb5de7c8fc0de02a2 100644 --- a/tensorflow/python/keras/_impl/keras/testing_utils.py +++ b/tensorflow/python/keras/_impl/keras/testing_utils.py @@ -22,6 +22,7 @@ import numpy as np from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl import keras +from tensorflow.python.training.rmsprop import RMSPropOptimizer from tensorflow.python.util import tf_inspect @@ -105,8 +106,14 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, # test in functional API x = keras.layers.Input(shape=input_shape[1:], dtype=input_dtype) y = layer(x) - assert keras.backend.dtype(y) == expected_output_dtype - + if keras.backend.dtype(y) != expected_output_dtype: + raise AssertionError('When testing layer %s, for input %s, found output ' + 'dtype=%s but expected to find %s.\nFull kwargs: %s' % + (layer_cls.__name__, + x, + keras.backend.dtype(y), + expected_output_dtype, + kwargs)) # check shape inference model = keras.models.Model(x, y) expected_output_shape = tuple( @@ -117,7 +124,15 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, for expected_dim, actual_dim in zip(expected_output_shape, actual_output_shape): if expected_dim is not None: - assert expected_dim == actual_dim + if expected_dim != actual_dim: + raise AssertionError( + 'When testing layer %s, for input %s, found output_shape=' + '%s but expected to find %s.\nFull kwargs: %s' % + (layer_cls.__name__, + x, + actual_output_shape, + expected_output_shape, + kwargs)) if expected_output is not None: np.testing.assert_allclose(actual_output, expected_output, rtol=1e-3) @@ -131,7 +146,7 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, np.testing.assert_allclose(output, actual_output, rtol=1e-3) # test training mode (e.g. useful for dropout tests) - model.compile('rmsprop', 'mse') + model.compile(RMSPropOptimizer(0.01), 'mse') model.train_on_batch(input_data, actual_output) # test as first layer in Sequential API @@ -146,7 +161,15 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, for expected_dim, actual_dim in zip(expected_output_shape, actual_output_shape): if expected_dim is not None: - assert expected_dim == actual_dim + if expected_dim != actual_dim: + raise AssertionError( + 'When testing layer %s, for input %s, found output_shape=' + '%s but expected to find %s.\nFull kwargs: %s' % + (layer_cls.__name__, + x, + actual_output_shape, + expected_output_shape, + kwargs)) if expected_output is not None: np.testing.assert_allclose(actual_output, expected_output, rtol=1e-3) @@ -159,9 +182,5 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, output = recovered_model.predict(input_data) np.testing.assert_allclose(output, actual_output, rtol=1e-3) - # test training mode (e.g. useful for dropout tests) - model.compile('rmsprop', 'mse') - model.train_on_batch(input_data, actual_output) - # for further checks in the caller function return actual_output diff --git a/tensorflow/python/keras/_impl/keras/utils/__init__.py b/tensorflow/python/keras/_impl/keras/utils/__init__.py index 370ae0dd0f0d00059f1b0cc79459abe75c8ca494..0c9f19a0c8dcf3bf929e102b31679a03b27728f7 100644 --- a/tensorflow/python/keras/_impl/keras/utils/__init__.py +++ b/tensorflow/python/keras/_impl/keras/utils/__init__.py @@ -31,8 +31,8 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_ke from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary +from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.np_utils import normalize from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical -from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model diff --git a/tensorflow/python/keras/_impl/keras/utils/data_utils.py b/tensorflow/python/keras/_impl/keras/utils/data_utils.py index fcee9fbcc32d9356f5776b6f53cae6c125313d62..e87c8f48ef0967d561db1ab841a669d783f9b1ec 100644 --- a/tensorflow/python/keras/_impl/keras/utils/data_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/data_utils.py @@ -40,6 +40,7 @@ from six.moves.urllib.error import URLError from six.moves.urllib.request import urlopen from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.util.tf_export import tf_export try: @@ -138,6 +139,7 @@ def _extract_archive(file_path, path='.', archive_format='auto'): return False +@tf_export('keras.utils.get_file') def get_file(fname, origin, untar=False, @@ -318,6 +320,7 @@ def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535): return False +@tf_export('keras.utils.Sequence') class Sequence(object): """Base object for fitting to a sequence of data, such as a dataset. @@ -414,6 +417,7 @@ def get_index(uid, i): return _SHARED_SEQUENCES[uid][i] +@tf_export('keras.utils.SequenceEnqueuer') class SequenceEnqueuer(object): """Base class to enqueue inputs. @@ -613,6 +617,7 @@ class OrderedEnqueuer(SequenceEnqueuer): _SHARED_SEQUENCES[self.uid] = None +@tf_export('keras.utils.GeneratorEnqueuer') class GeneratorEnqueuer(SequenceEnqueuer): """Builds a queue out of a data generator. diff --git a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py index adbe6c3288a3eabb858e78267577ddff6d798972..5196bf17400c33d876daa430a9d3d5b4f4b491a1 100644 --- a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py @@ -291,55 +291,73 @@ class Progbar(object): Arguments: target: Total number of steps expected, None if unknown. + width: Progress bar width on screen. + verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose) + stateful_metrics: Iterable of string names of metrics that + should *not* be averaged over time. Metrics in this list + will be displayed as-is. All others will be averaged + by the progbar before display. interval: Minimum visual progress update interval (in seconds). """ - def __init__(self, target, width=30, verbose=1, interval=0.05): - self.width = width - if target is None: - target = -1 + def __init__(self, target, width=30, verbose=1, interval=0.05, + stateful_metrics=None): self.target = target - self.sum_values = {} - self.unique_values = [] - self.start = time.time() - self.last_update = 0 - self.interval = interval - self.total_width = 0 - self.seen_so_far = 0 + self.width = width self.verbose = verbose + self.interval = interval + if stateful_metrics: + self.stateful_metrics = set(stateful_metrics) + else: + self.stateful_metrics = set() + self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()) or - 'ipykernel' in sys.modules) - - def update(self, current, values=None, force=False): + 'ipykernel' in sys.modules or + 'posix' in sys.modules) + self._total_width = 0 + self._seen_so_far = 0 + # We use a dict + list to avoid garbage collection + # issues found in OrderedDict + self._values = {} + self._values_order = [] + self._start = time.time() + self._last_update = 0 + + def update(self, current, values=None): """Updates the progress bar. Arguments: current: Index of current step. - values: List of tuples (name, value_for_last_step). - The progress bar will display averages for these values. - force: Whether to force visual progress update. + values: List of tuples: + `(name, value_for_last_step)`. + If `name` is in `stateful_metrics`, + `value_for_last_step` will be displayed as-is. + Else, an average of the metric over time will be displayed. """ values = values or [] for k, v in values: - if k not in self.sum_values: - self.sum_values[k] = [ - v * (current - self.seen_so_far), current - self.seen_so_far - ] - self.unique_values.append(k) + if k not in self._values_order: + self._values_order.append(k) + if k not in self.stateful_metrics: + if k not in self._values: + self._values[k] = [v * (current - self._seen_so_far), + current - self._seen_so_far] + else: + self._values[k][0] += v * (current - self._seen_so_far) + self._values[k][1] += (current - self._seen_so_far) else: - self.sum_values[k][0] += v * (current - self.seen_so_far) - self.sum_values[k][1] += (current - self.seen_so_far) - self.seen_so_far = current + self._values[k] = v + self._seen_so_far = current now = time.time() - info = ' - %.0fs' % (now - self.start) + info = ' - %.0fs' % (now - self._start) if self.verbose == 1: - if (not force and (now - self.last_update) < self.interval and - current < self.target): + if (now - self._last_update < self.interval and + self.target is not None and current < self.target): return - prev_total_width = self.total_width + prev_total_width = self._total_width if self._dynamic_display: sys.stdout.write('\b' * prev_total_width) sys.stdout.write('\r') @@ -360,22 +378,21 @@ class Progbar(object): bar += '=' bar += ('.' * (self.width - prog_width)) bar += ']' - sys.stdout.write(bar) - self.total_width = len(bar) else: bar = '%7d/Unknown' % current - self.total_width = len(bar) + self._total_width = len(bar) sys.stdout.write(bar) if current: - time_per_unit = (now - self.start) / current + time_per_unit = (now - self._start) / current else: time_per_unit = 0 if self.target is not None and current < self.target: eta = time_per_unit * (self.target - current) if eta > 3600: - eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, + eta_format = '%d:%02d:%02d' % (eta // 3600, + (eta % 3600) // 60, eta % 60) elif eta > 60: eta_format = '%d:%02d' % (eta // 60, eta % 60) @@ -391,35 +408,32 @@ class Progbar(object): else: info += ' %.0fus/step' % (time_per_unit * 1e6) - for k in self.unique_values: + for k in self._values_order: info += ' - %s:' % k - if isinstance(self.sum_values[k], list): - avg = np.mean(self.sum_values[k][0] / max(1, self.sum_values[k][1])) + if isinstance(self._values[k], list): + avg = np.mean(self._values[k][0] / max(1, self._values[k][1])) if abs(avg) > 1e-3: info += ' %.4f' % avg else: info += ' %.4e' % avg else: - info += ' %s' % self.sum_values[k] + info += ' %s' % self._values[k] + + self._total_width += len(info) + if prev_total_width > self._total_width: + info += (' ' * (prev_total_width - self._total_width)) - self.total_width += len(info) - if prev_total_width > self.total_width: - info += (' ' * (prev_total_width - self.total_width)) if self.target is not None and current >= self.target: info += '\n' sys.stdout.write(info) sys.stdout.flush() - if current >= self.target: - sys.stdout.write('\n') - elif self.verbose == 2: if self.target is None or current >= self.target: - for k in self.unique_values: + for k in self._values_order: info += ' - %s:' % k - avg = np.mean( - self.sum_values[k][0] / max(1, self.sum_values[k][1])) + avg = np.mean(self._values[k][0] / max(1, self._values[k][1])) if avg > 1e-3: info += ' %.4f' % avg else: @@ -429,7 +443,86 @@ class Progbar(object): sys.stdout.write(info) sys.stdout.flush() - self.last_update = now + self._last_update = now def add(self, n, values=None): - self.update(self.seen_so_far + n, values) + self.update(self._seen_so_far + n, values) + + +def make_batches(size, batch_size): + """Returns a list of batch indices (tuples of indices). + + Arguments: + size: Integer, total size of the data to slice into batches. + batch_size: Integer, batch size. + + Returns: + A list of tuples of array indices. + """ + num_batches = int(np.ceil(size / float(batch_size))) + return [(i * batch_size, min(size, (i + 1) * batch_size)) + for i in range(0, num_batches)] + + +def slice_arrays(arrays, start=None, stop=None): + """Slice an array or list of arrays. + + This takes an array-like, or a list of + array-likes, and outputs: + - arrays[start:stop] if `arrays` is an array-like + - [x[start:stop] for x in arrays] if `arrays` is a list + + Can also work on list/array of indices: `slice_arrays(x, indices)` + + Arguments: + arrays: Single array or list of arrays. + start: can be an integer index (start index) + or a list/array of indices + stop: integer (stop index); should be None if + `start` was a list. + + Returns: + A slice of the array(s). + + Raises: + ValueError: If the value of start is a list and stop is not None. + """ + if arrays is None: + return [None] + if isinstance(start, list) and stop is not None: + raise ValueError('The stop argument has to be None if the value of start is' + 'a list.') + elif isinstance(arrays, list): + if hasattr(start, '__len__'): + # hdf5 datasets only support list objects as indices + if hasattr(start, 'shape'): + start = start.tolist() + return [None if x is None else x[start] for x in arrays] + else: + return [None if x is None else x[start:stop] for x in arrays] + else: + if hasattr(start, '__len__'): + if hasattr(start, 'shape'): + start = start.tolist() + return arrays[start] + elif hasattr(start, '__getitem__'): + return arrays[start:stop] + else: + return [None] + + +def to_list(x): + """Normalizes a list/tensor into a list. + + If a tensor is passed, we return + a list of size 1 containing the tensor. + + Arguments: + x: target object to be normalized. + + Returns: + A list. + """ + if isinstance(x, list): + return x + return [x] diff --git a/tensorflow/python/keras/_impl/keras/utils/io_utils.py b/tensorflow/python/keras/_impl/keras/utils/io_utils.py index b36c769843d13a910efa6cf8c0d5309e3333f69b..bbf1d2a3d9c3948271780ec3fad3316b4e6d53c3 100644 --- a/tensorflow/python/keras/_impl/keras/utils/io_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/io_utils.py @@ -22,6 +22,7 @@ from collections import defaultdict import sys import numpy as np +from tensorflow.python.util.tf_export import tf_export try: @@ -30,6 +31,7 @@ except ImportError: h5py = None +@tf_export('keras.utils.HDF5Matrix') class HDF5Matrix(object): """Representation of HDF5 dataset to be used instead of a Numpy array. diff --git a/tensorflow/python/keras/_impl/keras/utils/layer_utils.py b/tensorflow/python/keras/_impl/keras/utils/layer_utils.py index a2d32424b51ea3160d530a5162ccc269e5815e57..4c8009dfd80e1aec457fa03687f2840c7fe4607b 100644 --- a/tensorflow/python/keras/_impl/keras/utils/layer_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/layer_utils.py @@ -23,6 +23,7 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.conv_utils import convert_kernel +from tensorflow.python.util.tf_export import tf_export def count_params(weights): @@ -58,6 +59,10 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): if model.__class__.__name__ == 'Sequential': sequential_like = True + elif not model._is_graph_network: + # We treat subclassed models as a simple sequence of layers, for logging + # purposes. + sequential_like = True else: sequential_like = True nodes_by_depth = model._nodes_by_depth.values() @@ -117,17 +122,24 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): print_fn('=' * line_length) def print_layer_summary(layer): + """Prints a summary for a single layer. + + Arguments: + layer: target layer. + """ try: output_shape = layer.output_shape except AttributeError: output_shape = 'multiple' + except RuntimeError: # output_shape unknown in Eager mode. + output_shape = '?' name = layer.name cls_name = layer.__class__.__name__ fields = [name + ' (' + cls_name + ')', output_shape, layer.count_params()] print_row(fields, positions) def print_layer_summary_with_connections(layer): - """Prints a summary for a single layer. + """Prints a summary for a single layer (including topological connections). Arguments: layer: target layer. @@ -190,6 +202,7 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): print_fn('_' * line_length) +@tf_export('keras.utils.convert_all_kernels_in_model') def convert_all_kernels_in_model(model): """Converts all convolution kernels in a model from Theano to TensorFlow. diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py similarity index 98% rename from tensorflow/python/keras/_impl/keras/utils/training_utils.py rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py index ce7402e9d279278eaaf5aab58a3973eec6de8e99..231ace2a0b4a4f25cebf06a5216cf3d30aadc49b 100644 --- a/tensorflow/python/keras/_impl/keras/utils/training_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py @@ -125,7 +125,7 @@ def multi_gpu_model(model, gpus): if gpus <= 1: raise ValueError('For multi-gpu usage to be effective, ' 'call `multi_gpu_model` with `gpus >= 2`. ' - 'Received: `gpus=%d`' % gpus) + 'Received: `gpus=%s`' % gpus) num_gpus = gpus target_gpu_ids = range(num_gpus) @@ -136,7 +136,7 @@ def multi_gpu_model(model, gpus): ] for device in target_devices: if device not in available_devices: - raise ValueError('To call `multi_gpu_model` with `gpus=%d`, ' + raise ValueError('To call `multi_gpu_model` with `gpus=%s`, ' 'we expect the following devices to be available: %s. ' 'However this machine only has: %s. ' 'Try reducing `gpus`.' % (gpus, target_devices, diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils_test.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py similarity index 64% rename from tensorflow/python/keras/_impl/keras/utils/training_utils_test.py rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py index 12354c49ca72cddc0f395bcfcfabab18c1189227..0a38d6b5228fe791ce14adc7e37e0b7a6926fadf 100644 --- a/tensorflow/python/keras/_impl/keras/utils/training_utils_test.py +++ b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py @@ -19,21 +19,34 @@ from __future__ import print_function import numpy as np - +from tensorflow.python import data from tensorflow.python.keras._impl import keras from tensorflow.python.platform import test +def check_if_compatible_devices(gpus=2): + available_devices = [ + keras.utils.multi_gpu_utils._normalize_device_name(name) + for name in keras.utils.multi_gpu_utils._get_available_devices() + ] + if '/gpu:%d' % (gpus - 1) not in available_devices: + return False + return True + + class TestMultiGPUModel(test.TestCase): - def multi_gpu_test_simple_model(self): + def test_multi_gpu_test_simple_model(self): gpus = 2 num_samples = 1000 input_dim = 10 output_dim = 1 hidden_dim = 10 epochs = 2 - target_gpu_id = [0, 2, 4] + target_gpu_id = [0, 1] + + if not check_if_compatible_devices(gpus=gpus): + return with self.test_session(): model = keras.models.Sequential() @@ -47,12 +60,11 @@ class TestMultiGPUModel(test.TestCase): parallel_model = keras.utils.multi_gpu_model(model, gpus=gpus) parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit(x, y, epochs=epochs) - parallel_model = keras.utils.multi_gpu_model(model, gpus=target_gpu_id) parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit(x, y, epochs=epochs) - def multi_gpu_test_multi_io_model(self): + def test_multi_gpu_test_multi_io_model(self): gpus = 2 num_samples = 1000 input_dim_a = 10 @@ -61,7 +73,10 @@ class TestMultiGPUModel(test.TestCase): output_dim_b = 2 hidden_dim = 10 epochs = 2 - target_gpu_id = [0, 2, 4] + target_gpu_id = [0, 1] + + if not check_if_compatible_devices(gpus=gpus): + return with self.test_session(): input_a = keras.Input((input_dim_a,)) @@ -86,7 +101,10 @@ class TestMultiGPUModel(test.TestCase): parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit([a_x, b_x], [a_y, b_y], epochs=epochs) - def multi_gpu_test_invalid_devices(self): + def test_multi_gpu_test_invalid_devices(self): + if not check_if_compatible_devices(gpus=2): + return + with self.test_session(): input_shape = (1000, 10) model = keras.models.Sequential() @@ -115,3 +133,53 @@ class TestMultiGPUModel(test.TestCase): with self.assertRaises(ValueError): parallel_model = keras.utils.multi_gpu_model(model, gpus=[0]) parallel_model.fit(x, y, epochs=2) + + def test_nested_model_with_tensor_input(self): + gpus = 2 + input_dim = 10 + shape = (input_dim,) + num_samples = 16 + num_classes = 10 + + if not check_if_compatible_devices(gpus=gpus): + return + + with self.test_session(): + input_shape = (num_samples,) + shape + x_train = np.random.randint(0, 255, input_shape) + y_train = np.random.randint(0, num_classes, (input_shape[0],)) + keras.backend.set_learning_phase(True) + + y_train = keras.utils.to_categorical(y_train, num_classes) + + x_train = x_train.astype('float32') + y_train = y_train.astype('float32') + + dataset = data.Dataset.from_tensor_slices((x_train, y_train)) + dataset = dataset.repeat() + dataset = dataset.batch(4) + iterator = dataset.make_one_shot_iterator() + + inputs, targets = iterator.get_next() + + input_tensor = keras.layers.Input(tensor=inputs) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(3, + input_shape=(input_dim,))) + model.add(keras.layers.Dense(num_classes)) + + output = model(input_tensor) + outer_model = keras.Model(input_tensor, output) + parallel_model = keras.utils.multi_gpu_model(outer_model, gpus=gpus) + + parallel_model.compile( + loss='categorical_crossentropy', + optimizer=keras.optimizers.RMSprop(lr=0.0001, decay=1e-6), + metrics=['accuracy'], + target_tensors=[targets]) + parallel_model.fit(epochs=1, steps_per_epoch=3) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/utils/np_utils.py b/tensorflow/python/keras/_impl/keras/utils/np_utils.py index 231833e7760f1824df673ce84bc5d77df91721b0..a611be08aaed824ebb278b4b28ef52ea1872563b 100644 --- a/tensorflow/python/keras/_impl/keras/utils/np_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/np_utils.py @@ -18,8 +18,10 @@ from __future__ import division from __future__ import print_function import numpy as np +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.utils.to_categorical') def to_categorical(y, num_classes=None): """Converts a class vector (integers) to binary class matrix. @@ -48,6 +50,7 @@ def to_categorical(y, num_classes=None): return categorical +@tf_export('keras.utils.normalize') def normalize(x, axis=-1, order=2): """Normalizes a Numpy array. diff --git a/tensorflow/python/keras/_impl/keras/utils/vis_utils.py b/tensorflow/python/keras/_impl/keras/utils/vis_utils.py index 0c5f2c19c79d5007882dcbc5d14a4cc8dd57ce3d..45c1b92075c50956fee004409e98898411e83d27 100644 --- a/tensorflow/python/keras/_impl/keras/utils/vis_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/vis_utils.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import os +from tensorflow.python.util.tf_export import tf_export try: @@ -127,6 +128,7 @@ def model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB'): return dot +@tf_export('keras.utils.plot_model') def plot_model(model, to_file='model.png', show_shapes=False, diff --git a/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py b/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py index 223ceac3deae643fa07594d10e551bea581eb641..2884dc84cc5d99511947e6f0f97b0bf8a505221f 100644 --- a/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py +++ b/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py @@ -26,6 +26,7 @@ import numpy as np from tensorflow.python.keras._impl.keras.models import Sequential from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical +from tensorflow.python.util.tf_export import tf_export class BaseWrapper(object): @@ -187,6 +188,7 @@ class BaseWrapper(object): return res +@tf_export('keras.wrappers.scikit_learn.KerasClassifier') class KerasClassifier(BaseWrapper): """Implementation of the scikit-learn classifier API for Keras. """ @@ -309,6 +311,7 @@ class KerasClassifier(BaseWrapper): 'the `model.compile()` method.') +@tf_export('keras.wrappers.scikit_learn.KerasRegressor') class KerasRegressor(BaseWrapper): """Implementation of the scikit-learn regressor API for Keras. """ diff --git a/tensorflow/python/keras/utils/__init__.py b/tensorflow/python/keras/utils/__init__.py index 91cc8607274a80a14dd27a64274da7f8f0aafab1..2f74cf031d0520c8d874b7269c52e3b9e1b9931b 100644 --- a/tensorflow/python/keras/utils/__init__.py +++ b/tensorflow/python/keras/utils/__init__.py @@ -30,9 +30,9 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model +from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.np_utils import normalize from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical -from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model del absolute_import diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index d4ceb2e489c8a20d26eaf9d89b12992d2b8673d7..f27ca5c2051be81eb94488ba388c11bcfebafc4b 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -295,7 +295,6 @@ tf_py_test( "//tensorflow/python:nn_grad", ], data = ["//tensorflow/core:image_testdata"], - tags = ["no_windows"], ) tf_py_test( @@ -712,6 +711,18 @@ cuda_py_test( ], ) +tf_py_test( + name = "regex_replace_op_test", + size = "small", + srcs = ["regex_replace_op_test.py"], + additional_deps = [ + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:string_ops", + ], +) + tf_py_test( name = "save_restore_ops_test", size = "small", @@ -1075,6 +1086,7 @@ cuda_py_test( tags = [ "no_windows", "noasan", + "notap", ], ) @@ -1126,7 +1138,6 @@ tf_py_test( "//tensorflow/python:variables", ], data = ["//tensorflow/core:lmdb_testdata"], - tags = ["no_windows"], ) cuda_py_test( @@ -1559,12 +1570,15 @@ cuda_py_test( "//third_party/py/numpy", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:layers", "//tensorflow/python:framework", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:init_ops", + "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", "//tensorflow/python:nn_ops", "//tensorflow/python:partitioned_variables", + "//tensorflow/python:random_ops", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", ], @@ -2313,7 +2327,6 @@ cuda_py_test( "//tensorflow/python:variables", ], shard_count = 4, - tags = ["no_windows"], ) cuda_py_test( @@ -2444,7 +2457,6 @@ cuda_py_test( "//tensorflow/python/eager:context", ], shard_count = 10, - tags = ["no_windows"], ) cuda_py_test( @@ -2892,6 +2904,40 @@ tf_py_test( ], ) +tf_py_test( + name = "accumulate_n_test", + size = "small", + srcs = ["accumulate_n_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:variables", + ], +) + +tf_py_test( + name = "accumulate_n_eager_test", + size = "small", + srcs = ["accumulate_n_eager_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python/eager:backprop", + "//tensorflow/python/eager:context", + "//tensorflow/python/eager:tape", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py similarity index 67% rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py rename to tensorflow/python/kernel_tests/accumulate_n_eager_test.py index 8f44698da851b48abf831e957c80fa1643a58bda..dc11b7deceb9040584aca1f629f4d003aef39428 100644 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py +++ b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py @@ -12,53 +12,41 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for new version of accumulate_n op that will eventually go into -`ops.math_ops`. - -These test cases spefically exercise the `eager` APIs. They need to be in a -separate file from the remaining tests because eager mode is currently something -you can turn on but can't turn off for the lifetime of the current process.""" +"""Tests for new version of accumulate_n op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2 - from tensorflow.python.eager import backprop -from tensorflow.python.eager import context as eager_context -from tensorflow.python.eager import tape from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import ops from tensorflow.python.framework import test_util -from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test - class AccumulateNV2EagerTest(test_util.TensorFlowTestCase): - """Tests of the new, differentiable version of accumulate_n""" + """Tests of the new, differentiable version of accumulate_n.""" def testMinimalEagerMode(self): forty = constant_op.constant(40) two = constant_op.constant(2) - answer = av2.accumulate_n_v2([forty, two]) + answer = math_ops.accumulate_n([forty, two]) self.assertEqual(42, answer.numpy()) - def testFloat(self): np.random.seed(12345) x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).numpy()) - self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).numpy()) + self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).numpy()) + self.assertAllClose(x[0] * 5, + math_ops.accumulate_n([tf_x[0]] * 5).numpy()) def testGrad(self): np.random.seed(42) @@ -70,16 +58,14 @@ class AccumulateNV2EagerTest(test_util.TensorFlowTestCase): ] def fn(first, second, third): - return av2.accumulate_n_v2([first, second, third]) + return math_ops.accumulate_n([first, second, third]) grad_fn = backprop.gradients_function(fn) grad = grad_fn(input_vars[0], input_vars[1], input_vars[2]) - self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 + self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 [elem.numpy() for elem in grad]) - if __name__ == "__main__": ops.enable_eager_execution() test.main() - diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py b/tensorflow/python/kernel_tests/accumulate_n_test.py similarity index 78% rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py rename to tensorflow/python/kernel_tests/accumulate_n_test.py index 6f65fe771eb77c10d0914faa90886b587adae68c..0a6d4aea370eb788de0c65b4758a3210a7d2944d 100644 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py +++ b/tensorflow/python/kernel_tests/accumulate_n_test.py @@ -12,43 +12,42 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for new version of accumulate_n op that will eventually go into -`ops.math_ops`.""" +"""Tests for new version of accumulate_n op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2 - -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import gradients +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest class AccumulateNV2Test(test_util.TensorFlowTestCase): - """Tests of the new, differentiable version of accumulate_n""" + """Tests of the new, differentiable version of accumulate_n.""" def testFloat(self): np.random.seed(12345) x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval()) - self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval()) + self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval()) + self.assertAllClose(x[0] * 5, + math_ops.accumulate_n([tf_x[0]] * 5).eval()) def testInt(self): np.random.seed(54321) x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval()) - self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval()) + self.assertAllEqual(sum(x), math_ops.accumulate_n(tf_x).eval()) + self.assertAllEqual(x[0] * 6, + math_ops.accumulate_n([tf_x[0]] * 6).eval()) def testGrad(self): np.random.seed(42) @@ -56,9 +55,9 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True) as sess: input_vars = [ variables.Variable(10.0 * np.random.random()) - for i in range(0, num_inputs) + for _ in range(0, num_inputs) ] - accum_n = av2.accumulate_n_v2(input_vars) + accum_n = math_ops.accumulate_n(input_vars) sess.run(variables.global_variables_initializer()) accum_n_grad = gradients.gradients(accum_n, input_vars) self.assertAllEqual( @@ -78,7 +77,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): ops.convert_to_tensor(x, dtype=dtypes_lib.float32) for x in random_arrays ] - tf_val = av2.accumulate_n_v2(random_tensors) + tf_val = math_ops.accumulate_n(random_tensors) np_val = random_arrays[0] for random_array in random_arrays[1:]: np_val += random_array @@ -87,7 +86,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): def testZeroArgs(self): with self.test_session(): with self.assertRaises(ValueError): - tf_val = av2.accumulate_n_v2([]) + tf_val = math_ops.accumulate_n([]) tf_val.eval() def testWrongShape(self): @@ -95,28 +94,28 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): a = variables.Variable(0.2) b = variables.Variable(0.1) - tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2]) # Should be shape=[] + math_ops.accumulate_n([a, b], shape=[2, 2]) # Should be shape=[] def testIncompatibleShapes(self): with self.test_session(): with self.assertRaises(ValueError): a = variables.Variable(np.array([0.1, 0.2])) b = variables.Variable(np.array([[0.3], [0.4]])) - tf_val = av2.accumulate_n_v2([a, b]) + math_ops.accumulate_n([a, b]) def testWrongType(self): with self.test_session(): with self.assertRaises(TypeError): a = variables.Variable(0.2, dtype=np.float32) b = variables.Variable(0.1, dtype=np.float32) - tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32) + math_ops.accumulate_n([a, b], tensor_dtype=np.int32) def testWrongTypeOneInput(self): # Scenario that used to trigger a bug, even when testWrongType() worked with self.test_session(): with self.assertRaises(TypeError): a = variables.Variable(0.2, dtype=np.float32) - tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32) + math_ops.accumulate_n([a], tensor_dtype=np.int32) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index ee7a5621e0c860cf710dd619ba85465c717a5196..d35f62b18601a6e33b1981e6b4d564d7e43da4b7 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function import time +import unittest import numpy as np @@ -497,7 +498,7 @@ class StridedSliceTest(test_util.TensorFlowTestCase): def test_basic_slice(self): for tensor_type in STRIDED_SLICE_TYPES: - with self.test_session(use_gpu=True): + with self.test_session(use_gpu=not tensor_type.is_integer): checker = StridedSliceChecker( self, StridedSliceChecker.REF_TENSOR, tensor_type=tensor_type) _ = checker[:, :, :] @@ -883,7 +884,8 @@ class StridedSliceAssignChecker(object): if self.tensor_type.is_complex: value -= 1j * value - with self.test.test_session(use_gpu=True) as sess: + with self.test.test_session( + use_gpu=not self.tensor_type.is_integer) as sess: if self._use_resource: var = resource_variable_ops.ResourceVariable(self.x) else: @@ -973,9 +975,7 @@ class SliceAssignTest(test_util.TensorFlowTestCase): errors.InvalidArgumentError, "l-value dtype int32 does not match r-value dtype int64"): sess.run(v[:].assign(too_large_val)) - with self.assertRaisesRegexp( - errors.InvalidArgumentError, - "l-value dtype int32 does not match r-value dtype int8"): + with self.assertRaises(errors.InvalidArgumentError): sess.run(v[:].assign(too_small_val)) @@ -1161,8 +1161,11 @@ class InvertPermutationTest(test_util.TensorFlowTestCase): self.assertAllEqual(y.get_shape(), [5]) self.assertAllEqual(y.eval(), [2, 4, 3, 0, 1]) + class UnravelIndexTest(test_util.TensorFlowTestCase): + # TODO(b/73086570): Reenable test. + @unittest.skip("Test does not pass internally.") def testUnravelIndex(self): with self.test_session(): for dtype in [dtypes.int32, dtypes.int64]: @@ -1220,7 +1223,7 @@ class SnapshotOpTest(test_util.TensorFlowTestCase): for dtype in [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]: with self.test_session(use_gpu=True): x = constant_op.constant([0, 1, 2, 3], dtype=dtype) - y = gen_array_ops._snapshot(x) + y = gen_array_ops.snapshot(x) self.assertAllEqual(y.eval(), [0, 1, 2, 3]) diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 2d1b3d9b7e836591646a2d0e59742bf6139446d1..0ef08581c9f931b991ef0c1218dc503345e248c2 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -83,14 +83,14 @@ class AtrousConvolutionTest(test.TestCase): checks = [] def add_check(check, *args, **kwargs): - if context.in_eager_mode(): + if context.executing_eagerly(): args_val, kwargs_val = self.evaluate([args, kwargs]) check(*args_val, **kwargs_val) else: checks.append((check, args, kwargs)) yield add_check - if context.in_graph_mode(): + if not context.executing_eagerly(): all_values = self.evaluate([[args, kwargs] for _, args, kwargs in checks]) for (check, _, _), (args, kwargs) in zip(checks, all_values): check(*args, **kwargs) diff --git a/tensorflow/python/kernel_tests/batchtospace_op_test.py b/tensorflow/python/kernel_tests/batchtospace_op_test.py index 0c802476a0e788aff3de84ab736fa8f1de5daab4..6143cd3baa6317fc512d80f94b494710037d4082 100644 --- a/tensorflow/python/kernel_tests/batchtospace_op_test.py +++ b/tensorflow/python/kernel_tests/batchtospace_op_test.py @@ -44,7 +44,7 @@ class CppOpImpl(object): @staticmethod def batch_to_space(*args, **kwargs): - return gen_array_ops._batch_to_space(*args, **kwargs) + return gen_array_ops.batch_to_space(*args, **kwargs) class BatchToSpaceDepthToSpace(test.TestCase, PythonOpImpl): diff --git a/tensorflow/python/kernel_tests/bcast_ops_test.py b/tensorflow/python/kernel_tests/bcast_ops_test.py index 9e512346053a4c3af089170f47313606c4a307c2..cb46fcb0076c1ca437089f5b9d87100667e2a404 100644 --- a/tensorflow/python/kernel_tests/bcast_ops_test.py +++ b/tensorflow/python/kernel_tests/bcast_ops_test.py @@ -20,8 +20,8 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.ops.gen_array_ops import _broadcast_args from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args +from tensorflow.python.ops.gen_array_ops import broadcast_args from tensorflow.python.platform import test @@ -29,7 +29,7 @@ class BcastOpsTest(test.TestCase): def _GetBroadcastShape(self, xs, ys): with self.test_session() as sess: - return sess.run(_broadcast_args(xs, ys)) + return sess.run(broadcast_args(xs, ys)) def _GetGradientArgs(self, xs, ys): with self.test_session() as sess: diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py index 2e94603a3f3d4ca9074320cfb4e9bf06b6640e82..26d3df9e635ea49c6f25e3bb1df25ea72df27304 100644 --- a/tensorflow/python/kernel_tests/check_ops_test.py +++ b/tensorflow/python/kernel_tests/check_ops_test.py @@ -102,17 +102,15 @@ class AssertEqualTest(test.TestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(static_big, static_small, message="fail") - # Dynamic check - if context.in_graph_mode(): - with self.test_session(): - small = array_ops.placeholder(dtypes.int32, name="small") - big = array_ops.placeholder(dtypes.int32, name="big") - with ops.control_dependencies( - [check_ops.assert_equal( - big, small, message="fail")]): - out = array_ops.identity(small) - with self.assertRaisesOpError("fail.*big.*small"): - out.eval(feed_dict={small: [1, 2], big: [3, 4]}) + def test_raises_when_greater_dynamic(self): + with self.test_session(): + small = array_ops.placeholder(dtypes.int32, name="small") + big = array_ops.placeholder(dtypes.int32, name="big") + with ops.control_dependencies( + [check_ops.assert_equal(big, small, message="fail")]): + out = array_ops.identity(small) + with self.assertRaisesOpError("fail.*big.*small"): + out.eval(feed_dict={small: [1, 2], big: [3, 4]}) def test_error_message_eager(self): expected_error_msg_full = r"""big does not equal small @@ -182,15 +180,14 @@ First 2 elements of y: with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(static_big, static_small, message="fail") - # Dynamic check - if context.in_graph_mode(): - with self.test_session(): - small = array_ops.placeholder(dtypes.int32, name="small") - big = array_ops.placeholder(dtypes.int32, name="big") - with ops.control_dependencies([check_ops.assert_equal(small, big)]): - out = array_ops.identity(small) - with self.assertRaisesOpError("small.*big"): - out.eval(feed_dict={small: [3, 1], big: [4, 2]}) + def test_raises_when_less_dynamic(self): + with self.test_session(): + small = array_ops.placeholder(dtypes.int32, name="small") + big = array_ops.placeholder(dtypes.int32, name="big") + with ops.control_dependencies([check_ops.assert_equal(small, big)]): + out = array_ops.identity(small) + with self.assertRaisesOpError("small.*big"): + out.eval(feed_dict={small: [3, 1], big: [4, 2]}) @test_util.run_in_graph_and_eager_modes() def test_doesnt_raise_when_equal_and_broadcastable_shapes(self): diff --git a/tensorflow/python/kernel_tests/checkpoint_ops_test.py b/tensorflow/python/kernel_tests/checkpoint_ops_test.py index a786d0a47e569f71812086fb93c21dc12660a2a5..7f147ba53a71539962f424158731e359724f664f 100644 --- a/tensorflow/python/kernel_tests/checkpoint_ops_test.py +++ b/tensorflow/python/kernel_tests/checkpoint_ops_test.py @@ -50,7 +50,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_no_vocab_changes(self): """Tests where vocab does not change at all.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.old_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -63,7 +63,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_shifted_vocab(self): """Tests where vocab is the same, but shifted / ordered differently.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -76,7 +76,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_offset(self): """Tests offset and num_new_vocab logic.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=1, @@ -89,7 +89,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_old_vocab_size(self): """Tests where old_vocab_size is specified.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -132,7 +132,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # No column remapping, new weight matrix has second row, then first row. row_remapping = [1, 0] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -147,7 +147,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # No row remapping, new weight matrix has third col, then first col. row_remapping = list(range(self.old_num_rows)) col_remapping = [2, 0] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -162,7 +162,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # Both row and column remappings. row_remapping = [1, 0, 4] col_remapping = [1, 15] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -177,7 +177,7 @@ class LoadAndRemapMatrixTest(test.TestCase): def test_load_and_remap_with_init(self): """Tests the op's load and remap where there are missing entries.""" init_val = 42 - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -196,7 +196,7 @@ class LoadAndRemapMatrixTest(test.TestCase): """Tests when all the rows are missing and need to be initialized.""" num_rows = 7 initializing_values = [42] * num_rows * self.old_num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[-1] * num_rows, @@ -214,7 +214,7 @@ class LoadAndRemapMatrixTest(test.TestCase): num_rows = 7 num_cols = 4 initializing_values = [42] * num_rows * num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[-1] * num_rows, @@ -235,7 +235,7 @@ class LoadAndRemapMatrixTest(test.TestCase): invalid_remapping = [1, 0, 0, 0, 1, 2] # Invalid row remapping. - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=invalid_remapping, @@ -247,7 +247,7 @@ class LoadAndRemapMatrixTest(test.TestCase): remapped_matrix.eval() # Invalid column remapping. - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=list(range(self.old_num_rows)), @@ -260,7 +260,7 @@ class LoadAndRemapMatrixTest(test.TestCase): def test_load_and_remap_incorrect_initializing_values(self): """Tests that errors are raised with incorrect number of init values.""" - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -275,7 +275,7 @@ class LoadAndRemapMatrixTest(test.TestCase): with self.test_session(), self.assertRaises(errors.InvalidArgumentError): remapped_matrix.eval() - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -314,7 +314,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): num_rows, num_cols = np_value.shape # Tests loading the entire tensor (except reversed). - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Simply reverses the rows of the matrix. @@ -332,7 +332,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): self.assertGreater(num_rows, 2) prefix_rows = 2 suffix_rows = 3 - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Reverses the rows of the matrix, then prepends and appends @@ -353,7 +353,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): # Tests when everything is taken from initializing_values. new_rows = 7 initializing_values = [42] * new_rows * num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Nothing is loaded from the old tensor. diff --git a/tensorflow/python/kernel_tests/concat_op_test.py b/tensorflow/python/kernel_tests/concat_op_test.py index a5fd3bc3345f41d9d3f07278dc7979c1103b597f..81c6a4aa6e6edc4b49338a0bfd354302b1dfac0b 100644 --- a/tensorflow/python/kernel_tests/concat_op_test.py +++ b/tensorflow/python/kernel_tests/concat_op_test.py @@ -495,9 +495,9 @@ class ConcatOpTest(test.TestCase): p = [] shape = np.array([7, 13]) if test.is_gpu_available(): - num_tensors = 10000 + num_tensors = 5000 else: - num_tensors = 1000 + num_tensors = 500 for i in np.arange(num_tensors): input_shape = shape placeholder = array_ops.placeholder(dtypes.float32, shape=input_shape) @@ -526,7 +526,7 @@ class ConcatOpTest(test.TestCase): with self.test_session(use_gpu=True): t1 = [] t2 = [] - output = gen_array_ops._concat_v2([t1, t2], 0).eval() + output = gen_array_ops.concat_v2([t1, t2], 0).eval() self.assertFalse(output) # Checks that output is empty def testConcatInvalidAxis(self): @@ -534,20 +534,20 @@ class ConcatOpTest(test.TestCase): with self.test_session(use_gpu=True): t1 = [1] t2 = [2] - gen_array_ops._concat_v2([t1, t2], 1).eval() + gen_array_ops.concat_v2([t1, t2], 1).eval() def testConcatNegativeAxis(self): with self.test_session(use_gpu=True): t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] - c = gen_array_ops._concat_v2([t1, t2], -2) + c = gen_array_ops.concat_v2([t1, t2], -2) self.assertEqual([4, 3], c.get_shape().as_list()) output = c.eval() self.assertAllEqual([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], output) - c = gen_array_ops._concat_v2([t1, t2], -1) + c = gen_array_ops.concat_v2([t1, t2], -1) self.assertEqual([2, 6], c.get_shape().as_list()) output = c.eval() self.assertAllEqual([[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]], output) @@ -615,7 +615,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) @@ -624,7 +624,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([[2, 3, 5]], dtypes.int32) s1 = constant_op.constant([[2, 7, 5]], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"should be a vector"): sess.run(off) @@ -634,7 +634,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(4, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"Concat dim is out of range: 4 vs. 3"): sess.run(off) @@ -644,7 +644,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5, 10], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"should contain 3 elem"): sess.run(off) @@ -654,7 +654,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 10], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp( errors_impl.InvalidArgumentError, r"All dimensions except 1 must match. Input 1 has shape \[2 7 10\] " @@ -667,7 +667,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) @@ -675,7 +675,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([1, 3, 5], dtypes.int32) s2 = constant_op.constant([3, 3, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]]) diff --git a/tensorflow/python/kernel_tests/constant_op_test.py b/tensorflow/python/kernel_tests/constant_op_test.py index 576bb68ba49cf5a5c7618131ad8a567672cb08d8..16e56349c45dd56a335f6f881826d975e24bd110 100644 --- a/tensorflow/python/kernel_tests/constant_op_test.py +++ b/tensorflow/python/kernel_tests/constant_op_test.py @@ -465,9 +465,8 @@ class ZerosLikeTest(test.TestCase): def testZerosLikeGPU(self): for dtype in [ dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64, - dtypes_lib.int32, dtypes_lib.int64, - dtypes_lib.complex64, dtypes_lib.complex128, - dtypes_lib.bool + dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.complex64, + dtypes_lib.complex128, dtypes_lib.bool ]: self._compareZeros(dtype, fully_defined_shape=False, use_gpu=True) self._compareZeros(dtype, fully_defined_shape=True, use_gpu=True) diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 4fafc36014e65318c72610949bfdfb092293c95a..b429fa5c423effce0f0ccb0ad34875dab2808777 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -552,7 +552,7 @@ class ControlFlowTest(test.TestCase): def testCondRef(self): with self.test_session(): - x = gen_state_ops._variable( + x = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="x", @@ -580,7 +580,7 @@ class ControlFlowTest(test.TestCase): def testUninitializedRefIdentity(self): with self.test_session() as sess: - v = gen_state_ops._variable( + v = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="v", @@ -704,6 +704,36 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [n], parallel_iterations=20) self.assertEqual(10000, r.eval()) + def testWhileExternalControlDependencies(self): + with self.test_session(): + v = variables.Variable(0.0) + v.initializer.run() + increment = v.assign_add(1.0) + + def body_fn(i): + with ops.control_dependencies([increment]): + return i + i + + result = control_flow_ops.while_loop(cond=lambda i: i < 1, + body=body_fn, loop_vars=[1]) + result.eval() + self.assertAllEqual(v.eval(), 1.0) + + def testWhileExternalControlDependenciesNoInput(self): + with self.test_session(): + v = variables.Variable(0.0) + v.initializer.run() + increment = v.assign_add(1.0) + + def body_fn(unused_i): + with ops.control_dependencies([increment]): + return constant_op.constant(5, name="five") + + result = control_flow_ops.while_loop(cond=lambda i: i < 5, + body=body_fn, loop_vars=[0]) + result.eval() + self.assertAllEqual(v.eval(), 1.0) + def testWhileWithRefs_1(self): with self.test_session() as sess: x = variables.Variable(0)._ref() # pylint: disable=protected-access @@ -1590,7 +1620,7 @@ class ControlFlowTest(test.TestCase): def testWhileStack_1(self): with self.test_session(): - s = gen_data_flow_ops._stack_v2(-1, dtypes.int32, stack_name="foo") + s = gen_data_flow_ops.stack_v2(-1, dtypes.int32, stack_name="foo") i = constant_op.constant(0) def c(i): @@ -1599,7 +1629,7 @@ class ControlFlowTest(test.TestCase): def b(i): ni = math_ops.add(i, 1) ni = control_flow_ops.with_dependencies( - [gen_data_flow_ops._stack_push_v2(s, i)], ni) + [gen_data_flow_ops.stack_push_v2(s, i)], ni) return ni r = control_flow_ops.while_loop(c, b, [i], parallel_iterations=1) @@ -1611,7 +1641,7 @@ class ControlFlowTest(test.TestCase): def b1(i, x): ni = math_ops.subtract(i, 1) - nx = x + gen_data_flow_ops._stack_pop_v2(s, dtypes.int32) + nx = x + gen_data_flow_ops.stack_pop_v2(s, dtypes.int32) return [ni, nx] _, rx = control_flow_ops.while_loop( @@ -1810,6 +1840,23 @@ class ControlFlowTest(test.TestCase): [tensor_shape.unknown_shape()]) self.assertAllClose(9.0, r.eval(feed_dict={x: 1.0})) + def testCondGradInNestedWhiles(self): + def outer_body(i, x): + _, x = control_flow_ops.while_loop( + lambda j, x: j < 3, inner_body, [0, 0.0]) + return i + 1, x + + def inner_body(j, x): + y = control_flow_ops.cond(math_ops.less(x, 1), lambda: 2 * x, lambda: x) + return j + 1, gradients_impl.gradients(y, x)[0] + + i, x = control_flow_ops.while_loop(lambda i, x: i < 3, outer_body, [0, 0.0]) + + with self.test_session() as sess: + i_val, x_val = sess.run([i, x]) + self.assertEqual(i_val, 3) + self.assertAllClose(x_val, 1.0) + def testWhile_NestedInput(self): with self.test_session() as sess: named = collections.namedtuple("named", ("a", "b")) diff --git a/tensorflow/python/kernel_tests/conv2d_transpose_test.py b/tensorflow/python/kernel_tests/conv2d_transpose_test.py index 7d0bc54b6993daff0298f9d76e9e67dfcbfa5711..b692d3da609fd97a55b8f5fce3334b8e9d97c827 100644 --- a/tensorflow/python/kernel_tests/conv2d_transpose_test.py +++ b/tensorflow/python/kernel_tests/conv2d_transpose_test.py @@ -21,7 +21,6 @@ from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.python.client import device_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -175,7 +174,7 @@ class Conv2DTransposeTest(test.TestCase): self.assertLess(err, err_tolerance) def testConv2DTransposeSingleStrideNCHW(self): - # `NCHW` data fomat is only supported for CUDA device. + # `NCHW` data format is only supported for CUDA device. if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): strides = [1, 1, 1, 1] @@ -210,7 +209,7 @@ class Conv2DTransposeTest(test.TestCase): self.assertAllClose(target, value[n, k, h, w]) def testConv2DTransposeSameNCHW(self): - # `NCHW` data fomat is only supported for CUDA device. + # `NCHW` data format is only supported for CUDA device. if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): strides = [1, 1, 2, 2] @@ -246,7 +245,7 @@ class Conv2DTransposeTest(test.TestCase): self.assertAllClose(target, value[n, k, h, w]) def testConv2DTransposeValidNCHW(self): - # `NCHW` data fomat is only supported for CUDA device. + # `NCHW` data format is only supported for CUDA device. if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): strides = [1, 1, 2, 2] diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index c5446326ba1763b962b32866235251d773e069f2..f4fe01f868da25660171c614bbf84390aead3ade 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -24,7 +24,7 @@ import time import numpy as np -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import layers from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op @@ -302,25 +302,20 @@ class Conv2DTest(test.TestCase): padding, dilations): expected_results = [] computed_results = [] - default_dilations = (dilations[0] == 1 and dilations[1] == 1) for data_format, use_gpu in GetTestConfigs(): - # If any dilation rate is larger than 1, only do test on the GPU - # because we currently do not have a CPU implementation for arbitrary - # dilation rates. - if default_dilations or use_gpu: - expected, computed = self._ComputeReferenceDilatedConv( - tensor_in_sizes, filter_in_sizes, strides, dilations, padding, - data_format, use_gpu) - expected_results.append(expected) - computed_results.append(computed) - tolerance = 1e-2 if use_gpu else 1e-5 - expected_values = self.evaluate(expected_results) - computed_values = self.evaluate(computed_results) - for e_value, c_value in zip(expected_values, computed_values): - print("expected = ", e_value) - print("actual = ", c_value) - self.assertAllClose( - e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) + expected, computed = self._ComputeReferenceDilatedConv( + tensor_in_sizes, filter_in_sizes, strides, dilations, padding, + data_format, use_gpu) + expected_results.append(expected) + computed_results.append(computed) + tolerance = 1e-2 if use_gpu else 1e-5 + expected_values = self.evaluate(expected_results) + computed_values = self.evaluate(computed_results) + for e_value, c_value in zip(expected_values, computed_values): + print("expected = ", e_value) + print("actual = ", c_value) + self.assertAllClose( + e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, strides, padding, expected): @@ -365,13 +360,12 @@ class Conv2DTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testConv2D2x2Filter2x1Dilation(self): - if test.is_gpu_available(cuda_only=True): - self._VerifyDilatedConvValues( - tensor_in_sizes=[1, 4, 4, 1], - filter_in_sizes=[2, 2, 1, 1], - strides=[1, 1], - dilations=[2, 1], - padding="VALID") + self._VerifyDilatedConvValues( + tensor_in_sizes=[1, 4, 4, 1], + filter_in_sizes=[2, 2, 1, 1], + strides=[1, 1], + dilations=[2, 1], + padding="VALID") @test_util.run_in_graph_and_eager_modes() def testConv2DEmpty(self): @@ -385,13 +379,12 @@ class Conv2DTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testConv2DEmptyDilation(self): - if test.is_gpu_available(cuda_only=True): - self._VerifyDilatedConvValues( - tensor_in_sizes=[0, 2, 3, 3], - filter_in_sizes=[1, 1, 3, 3], - strides=[1, 1], - dilations=[2, 1], - padding="VALID") + self._VerifyDilatedConvValues( + tensor_in_sizes=[0, 2, 3, 3], + filter_in_sizes=[1, 1, 3, 3], + strides=[1, 1], + dilations=[2, 1], + padding="VALID") @test_util.run_in_graph_and_eager_modes() def testConv2D2x2Filter(self): @@ -406,13 +399,12 @@ class Conv2DTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testConv2D2x2FilterDilation(self): - if test.is_gpu_available(cuda_only=True): - self._VerifyDilatedConvValues( - tensor_in_sizes=[1, 2, 3, 3], - filter_in_sizes=[2, 2, 3, 3], - strides=[1, 1], - dilations=[1, 2], - padding="VALID") + self._VerifyDilatedConvValues( + tensor_in_sizes=[1, 2, 3, 3], + filter_in_sizes=[2, 2, 3, 3], + strides=[1, 1], + dilations=[1, 2], + padding="VALID") @test_util.run_in_graph_and_eager_modes() def testConv2D1x2Filter(self): @@ -430,13 +422,12 @@ class Conv2DTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testConv2D1x2FilterDilation(self): - if test.is_gpu_available(cuda_only=True): - self._VerifyDilatedConvValues( - tensor_in_sizes=[1, 2, 3, 3], - filter_in_sizes=[1, 2, 3, 3], - strides=[1, 1], - dilations=[2, 1], - padding="VALID") + self._VerifyDilatedConvValues( + tensor_in_sizes=[1, 2, 3, 3], + filter_in_sizes=[1, 2, 3, 3], + strides=[1, 1], + dilations=[2, 1], + padding="VALID") @test_util.run_in_graph_and_eager_modes() def testConv2D2x2FilterStride2(self): @@ -512,15 +503,14 @@ class Conv2DTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testConv2DKernelSizeMatchesInputSizeDilation(self): - if test.is_gpu_available(cuda_only=True): - self._VerifyDilatedConvValues( - tensor_in_sizes=[1, 3, 3, 1], - filter_in_sizes=[2, 2, 1, 2], - strides=[1, 1], - dilations=[2, 2], - padding="VALID") + self._VerifyDilatedConvValues( + tensor_in_sizes=[1, 3, 3, 1], + filter_in_sizes=[2, 2, 1, 2], + strides=[1, 1], + dilations=[2, 2], + padding="VALID") - # TODO this currently fails. + # TODO(yzhwang): this currently fails. # self._VerifyValues(tensor_in_sizes=[1, 8, 8, 1], # filter_in_sizes=[2, 2, 1, 1], # strides=[4, 4], padding="SAME", @@ -1523,36 +1513,6 @@ class Conv2DTest(test.TestCase): strides=[1, 1, 1, 1], padding="VALID")) - def testCPUConv2DNCHWUnimplemented(self): - with self.test_session(use_gpu=False): - with self.assertRaisesRegexp(errors_impl.UnimplementedError, - "NHWC tensor format for now"): - conv = self._SetupValuesForDevice( - tensor_in_sizes=[1, 4, 4, 1], - filter_in_sizes=[2, 2, 1, 1], - dilations=[1, 1], - strides=[1, 1], - padding="VALID", - data_format="NCHW", - dtype=dtypes.float32, - use_gpu=False) - self.evaluate(conv) - - def testCPUConv2DDilatedUnimplemented(self): - with self.test_session(use_gpu=False): - with self.assertRaisesRegexp(errors_impl.UnimplementedError, - "dilated rate of 1 for now"): - conv = self._SetupValuesForDevice( - tensor_in_sizes=[1, 4, 4, 1], - filter_in_sizes=[2, 2, 1, 1], - dilations=[2, 1], - strides=[1, 1], - padding="VALID", - data_format="NHWC", - dtype=dtypes.float32, - use_gpu=False) - self.evaluate(conv) - class DepthwiseConv2DTest(test.TestCase): @@ -1887,7 +1847,7 @@ def GetInceptionFwdTest(input_size, filter_size, stride, padding, def GetInceptionFwdDilatedConvTest(input_size, filter_size, stride, padding): def Test(self): - if test.is_gpu_available(cuda_only=True) and stride == 1: + if stride == 1: tf_logging.info("Testing InceptionFwd with dilations %s", (input_size, filter_size, stride, padding)) self._VerifyDilatedConvValues( diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index 0d9b46c30dbbed20dd940e0427fbf6f6d5415106..8db0bb6f0dc495e7be2cd717787acf87156f42af 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -495,11 +495,11 @@ class UnaryOpTest(test.TestCase): dtype_tols = [(np.float32, 5e-4), (np.float64, 1e-6), (np.complex64, 5e-4), (np.complex128, 1e-6)] op_range = [ - (gen_math_ops._reciprocal_grad, [-2, 2]), - (gen_math_ops._rsqrt_grad, [0.1, 3]), - (gen_math_ops._sigmoid_grad, [-2, 2]), - (gen_math_ops._sqrt_grad, [0.1, 3]), - (gen_math_ops._tanh_grad, [-2, 2]), + (gen_math_ops.reciprocal_grad, [-2, 2]), + (gen_math_ops.rsqrt_grad, [0.1, 3]), + (gen_math_ops.sigmoid_grad, [-2, 2]), + (gen_math_ops.sqrt_grad, [0.1, 3]), + (gen_math_ops.tanh_grad, [-2, 2]), ] def rand(dtype): diff --git a/tensorflow/python/kernel_tests/decode_bmp_op_test.py b/tensorflow/python/kernel_tests/decode_bmp_op_test.py index c67c26b7be0777587eb6d7c49119ad6cd2e22953..35f8f76991a679e4164da4c63bacbe79fb5cd2c2 100644 --- a/tensorflow/python/kernel_tests/decode_bmp_op_test.py +++ b/tensorflow/python/kernel_tests/decode_bmp_op_test.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors_impl from tensorflow.python.ops import array_ops from tensorflow.python.ops import image_ops from tensorflow.python.platform import test diff --git a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py index 89fd26c544b5f2e8c15cec1b2d21a8c95fd503db..510daf79dc4252c3e2943e2ba23c1012370bf456 100644 --- a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py +++ b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import os import time -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops diff --git a/tensorflow/python/kernel_tests/decode_raw_op_test.py b/tensorflow/python/kernel_tests/decode_raw_op_test.py index 0c7025f54e672bb09e601715a58864673a670d12..122a9ed46967fc9c02c59ea3047216cb73a72293 100644 --- a/tensorflow/python/kernel_tests/decode_raw_op_test.py +++ b/tensorflow/python/kernel_tests/decode_raw_op_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import numpy as np -import sys from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops diff --git a/tensorflow/python/kernel_tests/depthtospace_op_test.py b/tensorflow/python/kernel_tests/depthtospace_op_test.py index 7df2366954f3a6f3f37aef447479ba67c263025f..f03ad85f170aa66b15f946cb4a8e7a9760af7efb 100644 --- a/tensorflow/python/kernel_tests/depthtospace_op_test.py +++ b/tensorflow/python/kernel_tests/depthtospace_op_test.py @@ -35,8 +35,8 @@ from tensorflow.python.platform import tf_logging class DepthToSpaceTest(test.TestCase): - def _testOne(self, inputs, block_size, outputs): - input_nhwc = math_ops.to_float(inputs) + def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32): + input_nhwc = math_ops.cast(inputs, dtype) with self.test_session(use_gpu=False): # test NHWC (default) on CPU x_tf = array_ops.depth_to_space(input_nhwc, block_size) @@ -59,6 +59,12 @@ class DepthToSpaceTest(test.TestCase): x_out = [[[[1], [2]], [[3], [4]]]] self._testOne(x_np, block_size, x_out) + def testBasicFloat16(self): + x_np = [[[[1, 2, 3, 4]]]] + block_size = 2 + x_out = [[[[1], [2]], [[3], [4]]]] + self._testOne(x_np, block_size, x_out, dtype=dtypes.float16) + # Tests for larger input dimensions. To make sure elements are # correctly ordered spatially. def testBlockSize2(self): diff --git a/tensorflow/python/kernel_tests/determinant_op_test.py b/tensorflow/python/kernel_tests/determinant_op_test.py index 222038b22ef3c766efd14fd9b1c9044a0b6e9125..a52b2c0dc32c26ecd5ef08aa3f8678f0006cd4fe 100644 --- a/tensorflow/python/kernel_tests/determinant_op_test.py +++ b/tensorflow/python/kernel_tests/determinant_op_test.py @@ -65,7 +65,7 @@ class DeterminantOpTest(test.TestCase): self._compareDeterminantBase(matrix_x, linalg_ops.matrix_determinant(matrix_x)) self._compareLogDeterminantBase( - matrix_x, gen_linalg_ops._log_matrix_determinant(matrix_x)) + matrix_x, gen_linalg_ops.log_matrix_determinant(matrix_x)) def testBasic(self): # 2x2 matrices diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py index a269d722737866fa5e6ae9feee919be0db71bcf1..09812db8166567403dc966ac9cb4304be0740e50 100644 --- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py +++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py @@ -25,7 +25,6 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bernoulli from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.platform import test @@ -291,12 +290,6 @@ class BernoulliTest(test.TestCase): [np.sqrt(var(0.5)), np.sqrt(var(0.4))]], dtype=np.float32)) - def testBernoulliWithSigmoidProbs(self): - p = np.array([8.3, 4.2]) - dist = bernoulli.BernoulliWithSigmoidProbs(logits=p) - with self.test_session(): - self.assertAllClose(math_ops.sigmoid(p).eval(), dist.probs.eval()) - def testBernoulliBernoulliKL(self): with self.test_session() as sess: batch_size = 6 diff --git a/tensorflow/python/kernel_tests/distributions/beta_test.py b/tensorflow/python/kernel_tests/distributions/beta_test.py index 91a451f033ffbb01d54c3dacce952b406564b7b4..ab5041a6eb477ce231acbd1e6041c354ee17409b 100644 --- a/tensorflow/python/kernel_tests/distributions/beta_test.py +++ b/tensorflow/python/kernel_tests/distributions/beta_test.py @@ -107,8 +107,10 @@ class BetaTest(test.TestCase): dist.prob([-1., 0.1, 0.5]).eval() with self.assertRaisesOpError("sample must be positive"): dist.prob([0., 0.1, 0.5]).eval() - with self.assertRaisesOpError("sample must be no larger than `1`"): + with self.assertRaisesOpError("sample must be less than `1`"): dist.prob([.1, .2, 1.2]).eval() + with self.assertRaisesOpError("sample must be less than `1`"): + dist.prob([.1, .2, 1.0]).eval() def testPdfTwoBatches(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py index fedbf9e696923a34968e7a907e4099c520d1447b..5e8937ad2c36afb2b1ddb58ffb238a45e09e4b30 100644 --- a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py @@ -326,6 +326,18 @@ class DynamicPartitionTest(test.TestCase): with self.assertRaises(ValueError): data_flow_ops.dynamic_partition(data, indices, num_partitions=4) + # see https://github.com/tensorflow/tensorflow/issues/17106 + def testCUBBug(self): + x = constant_op.constant(np.random.randn(3072)) + inds = [0]*189 + [1]*184 + [2]*184 + [3]*191 + [4]*192 + [5]*195 + [6]*195 + inds += [7]*195 + [8]*188 + [9]*195 + [10]*188 + [11]*202 + [12]*194 + inds += [13]*194 + [14]*194 + [15]*192 + self.assertEqual(len(inds), x.shape[0]) + partitioned = data_flow_ops.dynamic_partition(x, inds, 16) + with self.test_session() as sess: + res = sess.run(partitioned) + self.assertEqual(res[-1].shape[0], 192) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/fifo_queue_test.py b/tensorflow/python/kernel_tests/fifo_queue_test.py index 748135440ec5e8ad387f910e1433f638abf2260a..ce73e7ad3e5f822363c697609dfa163b6f13751a 100644 --- a/tensorflow/python/kernel_tests/fifo_queue_test.py +++ b/tensorflow/python/kernel_tests/fifo_queue_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import random -import re import time import numpy as np diff --git a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py index feec9934e459590bb1dd0bc5c7cf40013d3d8b88..faac7d8365dfaa1b6b32f8fe66a76c3694aa0d5b 100644 --- a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py +++ b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py @@ -347,7 +347,7 @@ class FractionalAvgPoolGradTest(test.TestCase): Two types of tests for FractionalAvgPoolGrad. 1) Test fractional_avg_pool_grad() directly. - This type of test relies on gen_nn_ops._avg_pool_grad() returns the + This type of test relies on gen_nn_ops.avg_pool_grad() returns the correct result. For example: * input_tensor_shape = (1, 10, 10, 1) * window_size = (1, 2, 2, 1) @@ -404,13 +404,13 @@ class FractionalAvgPoolGradTest(test.TestCase): num_elements *= dim_size output_backprop = (self._PRNG.rand(num_elements) * 1000).reshape(output_data.shape) - input_backprop_tensor = gen_nn_ops._avg_pool_grad( + input_backprop_tensor = gen_nn_ops.avg_pool_grad( input_tensor.get_shape(), output_backprop, window_size, stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows + 1, row_window_size)) col_seq = list(range(0, num_cols + 1, col_window_size)) - fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad( + fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad( input_tensor.get_shape(), output_backprop, row_seq, @@ -443,7 +443,7 @@ class FractionalAvgPoolGradTest(test.TestCase): num_elements *= dim_size output_backprop = (self._PRNG.rand(num_elements) * 1000).reshape(output_data.shape) - input_backprop_tensor = gen_nn_ops._avg_pool_grad( + input_backprop_tensor = gen_nn_ops.avg_pool_grad( input_tensor.get_shape(), output_backprop, window_size, stride_size, padding) input_backprop = input_backprop_tensor.eval() @@ -451,7 +451,7 @@ class FractionalAvgPoolGradTest(test.TestCase): col_seq = list(range(0, num_cols, col_window_size - 1)) row_seq[-1] += 1 col_seq[-1] += 1 - fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad( + fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad( input_tensor.get_shape(), output_backprop, row_seq, diff --git a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py index 5983ae7759dbf3eb2db9867def829ce8dbeb4b73..6477c9ebc4c35fcc5963b27a0f5c50624a73fa09 100644 --- a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py +++ b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py @@ -318,7 +318,7 @@ class FractionalMaxPoolGradTest(test.TestCase): Two types of tests for FractionalMaxPoolGrad. 1) Test fractional_max_pool_grad() directly. - This type of test relies on gen_nn_ops._max_pool_grad() returns the correct + This type of test relies on gen_nn_ops.max_pool_grad() returns the correct result. For example: * input_tensor_shape = (1, 10, 10, 1) * window_size = (1, 2, 2, 1) @@ -384,16 +384,13 @@ class FractionalMaxPoolGradTest(test.TestCase): stride_size, padding) output_data = output_tensor.eval() output_backprop = self._PRNG.randint(100, size=output_data.shape) - input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor, - output_tensor, - output_backprop, - window_size, - stride_size, - padding) + input_backprop_tensor = gen_nn_ops.max_pool_grad( + input_tensor, output_tensor, output_backprop, window_size, + stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows + 1, row_window_size)) col_seq = list(range(0, num_cols + 1, col_window_size)) - fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad( + fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, output_backprop, @@ -422,18 +419,15 @@ class FractionalMaxPoolGradTest(test.TestCase): stride_size, padding) output_data = output_tensor.eval() output_backprop = self._PRNG.randint(100, size=output_data.shape) - input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor, - output_tensor, - output_backprop, - window_size, - stride_size, - padding) + input_backprop_tensor = gen_nn_ops.max_pool_grad( + input_tensor, output_tensor, output_backprop, window_size, + stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows, row_window_size - 1)) col_seq = list(range(0, num_cols, col_window_size - 1)) row_seq[-1] += 1 col_seq[-1] += 1 - fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad( + fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, output_backprop, @@ -591,7 +585,7 @@ class FractionalMaxPoolGradTest(test.TestCase): output_tensor = constant_op.constant( output_data_not_overlapping, shape=output_size) grad = constant_op.constant(output_backprop, shape=output_size) - r = gen_nn_ops._fractional_max_pool_grad( + r = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, grad, @@ -606,7 +600,7 @@ class FractionalMaxPoolGradTest(test.TestCase): # Test when overlapping is True output_tensor = constant_op.constant( output_data_overlapping, shape=output_size) - r = gen_nn_ops._fractional_max_pool_grad( + r = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, grad, row_seq, col_seq, overlapping=True) input_backprop_overlapping = r.eval() self.assertShapeEqual( diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index 19a7d2f9d51fff46ee817ad03ef62383f6727791..c1755985ee85c62005c8d3d5fb916859193aa5f3 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -25,10 +25,13 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.layers import convolutional from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import partitioned_variables +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -571,6 +574,82 @@ class OrthogonalInitializerTest(test.TestCase): np.dot(t, t.T), np.eye(t.shape[0]), rtol=tol, atol=tol) +class ConvolutionDeltaOrthogonalInitializerTest(test.TestCase): + + def testInitializerIdentical(self): + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + self.assertTrue(identicaltest(self, init1, init2, (3, 3, 10, 10))) + + def testInitializerDifferent(self): + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(seed=2, dtype=dtype) + self.assertFalse(identicaltest(self, init1, init2, (3, 3, 10, 10))) + + def testDuplicatedInitializer(self): + init = init_ops.convolutional_delta_orthogonal() + self.assertFalse(duplicated_initializer(self, init, 1, (3, 3, 10, 10))) + + def testInvalidDataType(self): + self.assertRaises( + ValueError, init_ops.convolutional_delta_orthogonal, + dtype=dtypes.string) + + def testInvalidShape(self): + init1 = init_ops.convolutional_delta_orthogonal() + with self.test_session(graph=ops.Graph(), use_gpu=True): + self.assertRaises(ValueError, init1, shape=[3, 3, 6, 5]) + + def testGain(self): + shape = (3, 3, 10, 10) + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(gain=3.14, + seed=1, dtype=dtype) + with self.test_session(graph=ops.Graph(), use_gpu=True): + t1 = init1(shape).eval() + with self.test_session(graph=ops.Graph(), use_gpu=True): + t2 = init2(shape).eval() + return np.allclose(t1, t2 / 3.14, rtol=1e-15, atol=1e-15) + + def testShapesValues(self): + for dtype in [dtypes.float32]: + for kernel_size in [[3], [8], [3, 5], [2, 4], [3, 3, 3], [2, 2, 2]]: + tol = 1e-2 + # Check orthogonality by computing the 2-norms of the inputs and ouputs. + if len(kernel_size) == 1: + shape = [4, 32, 64] + convolution = convolutional.conv1d + elif len(kernel_size) == 2: + convolution = convolutional.conv2d + shape = [4, 32, 32, 64] + else: + shape = [4, 16, 16, 16, 64] + convolution = convolutional.conv3d + inputs = random_ops.random_normal(shape, dtype=dtype) + inputs_2norm = linalg_ops.norm(inputs) + outputs = convolution( + inputs, padding="same", filters=128, + kernel_size=kernel_size, use_bias=False, + kernel_initializer=init_ops.convolutional_delta_orthogonal( + gain=3.14)) + outputs_shape = shape[0:-1] + [128] + outputs_2norm = linalg_ops.norm(outputs) + my_ops = variables.global_variables_initializer() + with self.test_session(use_gpu=True) as sess: + sess.run(my_ops) + # Check the shape of the outputs + t = outputs.eval() + self.assertAllEqual(t.shape, outputs_shape) + # Check isometry of the delta-orthogonal kernel. + self.assertAllClose( + sess.run(inputs_2norm)/np.sqrt(np.prod(shape)), + sess.run(outputs_2norm)/(np.sqrt(np.prod(shape))*np.sqrt(3.14)), + rtol=tol, atol=tol) + + class IdentityInitializerTest(test.TestCase): def testInvalidDataType(self): diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py index 343d158498833dd92361bc41d433e28296fc4c9a..8cb9f9e6213cda8daae7b629fc31d4721fd48fa7 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py @@ -129,7 +129,7 @@ class LinearOperatorDiagTest( with self.test_session() as sess: x = random_ops.random_normal(shape=(2, 2, 3, 4)) - # This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve + # This LinearOperatorDiag will be broadcast to (2, 2, 3, 3) during solve # and matmul with 'x' as the argument. diag = random_ops.random_uniform(shape=(2, 1, 3)) operator = linalg.LinearOperatorDiag(diag, is_self_adjoint=True) diff --git a/tensorflow/python/kernel_tests/list_ops_test.py b/tensorflow/python/kernel_tests/list_ops_test.py index 1577b7bc8021a326eb720bdf059b8d1c568c0cc1..8040ea37a7c4228c4fa4f146c023f0ad30b1636f 100644 --- a/tensorflow/python/kernel_tests/list_ops_test.py +++ b/tensorflow/python/kernel_tests/list_ops_test.py @@ -123,6 +123,16 @@ class ListOpsTest(test_util.TensorFlowTestCase): l_cpu, element_dtype=dtypes.float32)[1], 2.0) + def testGraphStack(self): + with context.graph_mode(), self.test_session(): + tl = list_ops.empty_tensor_list( + element_shape=constant_op.constant([1], dtype=dtypes.int32), + element_dtype=dtypes.int32) + tl = list_ops.tensor_list_push_back(tl, [1]) + self.assertAllEqual( + list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32).eval(), + [[1]]) + def testSerialize(self): # pylint: disable=g-import-not-at-top try: diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index f1fbe1a745bcc851154121e6e2123b92bba6fec1..1123c20a165ba93bd380fa471a8be91f7005d7bb 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -32,11 +33,25 @@ from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.losses import losses +from tensorflow.python.ops.losses import losses_impl from tensorflow.python.ops.losses import util from tensorflow.python.platform import test from tensorflow.python.training import momentum as momentum_lib +safe_div = losses_impl._safe_div # pylint: disable=protected-access + + +class SafeDivTest(test.TestCase): + + def testEager(self): + with context.eager_mode(): + self.assertAllEqual(safe_div(constant_op.constant(1.0), + constant_op.constant(0.0)), 0.0) + self.assertAllEqual(safe_div(constant_op.constant(1.0), + 0.0), 0.0) + + class AbsoluteDifferenceLossTest(test.TestCase): def setUp(self): @@ -953,8 +968,8 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): # Compute the expected loss 'manually'. total = np.zeros((batch_size,)) for b in range(batch_size): - for i in range(dims-1): - for j in range(i+1, dims): + for i in range(dims - 1): + for j in range(i + 1, dims): x = self._predictions[b, i].item() - self._predictions[b, j].item() y = self._labels[b, i].item() - self._labels[b, j].item() diff = (x - y) @@ -1059,8 +1074,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): [[4, 8, 12], [1, 2, 3], [4, 5, 6]], [[8, 1, 3], [7, 8, 9], [10, 11, 12]], ]) - self._test_valid_weights( - labels, predictions, expected_loss=137.5) + self._test_valid_weights(labels, predictions, expected_loss=137.5) def test3dWeightedScalar(self): labels = np.array([ @@ -1073,8 +1087,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) weight = 3.0 self._test_valid_weights( - labels, predictions, expected_loss=weight * 137.5, - weights=weight) + labels, predictions, expected_loss=weight * 137.5, weights=weight) def _test_invalid_weights( self, labels, predictions, weights=1.0): @@ -1124,7 +1137,9 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) self._test_valid_weights( # TODO(ptucker): This doesn't look right. - labels, predictions, expected_loss=9 * 137.5, + labels, + predictions, + expected_loss=9 * 137.5, weights=np.ones((2, 3, 3))) def testLossWithAllZeroBatchSpecificWeights(self): diff --git a/tensorflow/python/kernel_tests/manip_ops_test.py b/tensorflow/python/kernel_tests/manip_ops_test.py index 3044b21aa4271cabda2a174a85b6db16fe13f93b..b8200ac0cb1e4315a56181779c70da1126d8fc15 100644 --- a/tensorflow/python/kernel_tests/manip_ops_test.py +++ b/tensorflow/python/kernel_tests/manip_ops_test.py @@ -17,25 +17,27 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util -from tensorflow.python.ops import manip_ops from tensorflow.python.ops import gradient_checker +from tensorflow.python.ops import manip_ops from tensorflow.python.platform import test as test_lib -import numpy as np - # pylint: disable=g-import-not-at-top try: from distutils.version import StrictVersion as Version # numpy.roll for multiple shifts was introduced in numpy version 1.12.0 - NP_ROLL_CAN_MULTISHIFT = Version(np.version.version) >= Version('1.12.0') + NP_ROLL_CAN_MULTISHIFT = Version(np.version.version) >= Version("1.12.0") except ImportError: NP_ROLL_CAN_MULTISHIFT = False # pylint: enable=g-import-not-at-top + class RollTest(test_util.TensorFlowTestCase): + def _testRoll(self, np_input, shift, axis): expected_roll = np.roll(np_input, shift, axis) with self.test_session(): @@ -62,10 +64,12 @@ class RollTest(test_util.TensorFlowTestCase): for t in [np.int32, np.int64]: self._testAll(np.random.randint(-100, 100, (5)).astype(t), 3, 0) if NP_ROLL_CAN_MULTISHIFT: - self._testAll(np.random.randint(-100, 100, (4, 4, 3)).astype(t), - [1, -2, 3], [0, 1, 2]) - self._testAll(np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t), - [0, 1, -2], [1, 2, 3]) + self._testAll( + np.random.randint(-100, 100, (4, 4, 3)).astype(t), [1, -2, 3], + [0, 1, 2]) + self._testAll( + np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t), [0, 1, -2], + [1, 2, 3]) def testFloatTypes(self): for t in [np.float32, np.float64]: @@ -84,7 +88,6 @@ class RollTest(test_util.TensorFlowTestCase): x = np.random.rand(3, 2, 1, 1).astype(t) self._testAll(x + 1j * x, [2, 1, 1, 0], [0, 3, 1, 2]) - def testRollInputMustVectorHigherRaises(self): tensor = 7 shift = 1 @@ -95,8 +98,7 @@ class RollTest(test_util.TensorFlowTestCase): manip_ops.roll(tensor, shift, axis).eval() def testRollAxisMustBeScalarOrVectorRaises(self): - tensor = [[1, 2], - [3, 4]] + tensor = [[1, 2], [3, 4]] shift = 1 axis = [[0, 1]] with self.test_session(): @@ -105,8 +107,7 @@ class RollTest(test_util.TensorFlowTestCase): manip_ops.roll(tensor, shift, axis).eval() def testRollShiftMustBeScalarOrVectorRaises(self): - tensor = [[1, 2], - [3, 4]] + tensor = [[1, 2], [3, 4]] shift = [[0, 1]] axis = 1 with self.test_session(): @@ -115,8 +116,7 @@ class RollTest(test_util.TensorFlowTestCase): manip_ops.roll(tensor, shift, axis).eval() def testRollShiftAndAxisMustBeSameSizeRaises(self): - tensor = [[1, 2], - [3, 4]] + tensor = [[1, 2], [3, 4]] shift = [1] axis = [0, 1] with self.test_session(): @@ -133,5 +133,6 @@ class RollTest(test_util.TensorFlowTestCase): "is out of range"): manip_ops.roll(tensor, shift, axis).eval() + if __name__ == "__main__": test_lib.main() diff --git a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py index 6203a412d7faec4fe9f6179141301579b5900291..a0c66c77d8850d3144678870983730537a253556 100644 --- a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py @@ -48,7 +48,7 @@ class ExponentialOpTest(test.TestCase): def _verifyExponential(self, x, np_type): inp = x.astype(np_type) with self.test_session(use_gpu=True): - tf_ans = gen_linalg_ops._matrix_exponential(inp) + tf_ans = gen_linalg_ops.matrix_exponential(inp) if x.size == 0: np_ans = np.empty(x.shape, dtype=np_type) else: @@ -116,13 +116,13 @@ class ExponentialOpTest(test.TestCase): # When the exponential of a non-square matrix is attempted we should return # an error with self.assertRaises(ValueError): - gen_linalg_ops._matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]])) + gen_linalg_ops.matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]])) def testWrongDimensions(self): # The input to the exponential should be at least a 2-dimensional tensor. tensor3 = constant_op.constant([1., 2.]) with self.assertRaises(ValueError): - gen_linalg_ops._matrix_exponential(tensor3) + gen_linalg_ops.matrix_exponential(tensor3) def testEmpty(self): self._verifyExponentialReal(np.empty([0, 2, 2])) @@ -143,8 +143,8 @@ class ExponentialOpTest(test.TestCase): with self.test_session(use_gpu=True) as sess: matrix1 = random_ops.random_normal([5, 5], seed=42) matrix2 = random_ops.random_normal([5, 5], seed=42) - expm1 = gen_linalg_ops._matrix_exponential(matrix1) - expm2 = gen_linalg_ops._matrix_exponential(matrix2) + expm1 = gen_linalg_ops.matrix_exponential(matrix1) + expm2 = gen_linalg_ops.matrix_exponential(matrix2) expm = sess.run([expm1, expm2]) self.assertAllEqual(expm[0], expm[1]) @@ -180,7 +180,7 @@ class MatrixExponentialBenchmark(test.Benchmark): session.Session() as sess, \ ops.device("/cpu:0"): matrix = self._GenerateMatrix(shape) - expm = gen_linalg_ops._matrix_exponential(matrix) + expm = gen_linalg_ops.matrix_exponential(matrix) variables.global_variables_initializer().run() self.run_op_benchmark( sess, diff --git a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py index 18ed59828c15f5ad21fe054cd6e40991c02bb356..24edc4f59fe6dd84da6732036eb53e2ad367bd06 100644 --- a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py @@ -39,8 +39,8 @@ class LogarithmOpTest(test.TestCase): inp = x.astype(np_type) with self.test_session(use_gpu=True): # Verify that expm(logm(A)) == A. - tf_ans = gen_linalg_ops._matrix_exponential( - gen_linalg_ops._matrix_logarithm(inp)) + tf_ans = gen_linalg_ops.matrix_exponential( + gen_linalg_ops.matrix_logarithm(inp)) out = tf_ans.eval() self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3) @@ -85,14 +85,14 @@ class LogarithmOpTest(test.TestCase): # When the logarithm of a non-square matrix is attempted we should return # an error with self.assertRaises(ValueError): - gen_linalg_ops._matrix_logarithm( + gen_linalg_ops.matrix_logarithm( np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64)) def testWrongDimensions(self): # The input to the logarithm should be at least a 2-dimensional tensor. tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64) with self.assertRaises(ValueError): - gen_linalg_ops._matrix_logarithm(tensor3) + gen_linalg_ops.matrix_logarithm(tensor3) def testEmpty(self): self._verifyLogarithmComplex(np.empty([0, 2, 2], dtype=np.complex64)) @@ -115,8 +115,8 @@ class LogarithmOpTest(test.TestCase): random_ops.random_normal([5, 5], seed=42), dtypes.complex64) matrix2 = math_ops.cast( random_ops.random_normal([5, 5], seed=42), dtypes.complex64) - logm1 = gen_linalg_ops._matrix_logarithm(matrix1) - logm2 = gen_linalg_ops._matrix_logarithm(matrix2) + logm1 = gen_linalg_ops.matrix_logarithm(matrix1) + logm2 = gen_linalg_ops.matrix_logarithm(matrix2) logm = sess.run([logm1, logm2]) self.assertAllEqual(logm[0], logm[1]) @@ -152,7 +152,7 @@ class MatrixLogarithmBenchmark(test.Benchmark): session.Session() as sess, \ ops.device("/cpu:0"): matrix = self._GenerateMatrix(shape) - logm = gen_linalg_ops._matrix_logarithm(matrix) + logm = gen_linalg_ops.matrix_logarithm(matrix) variables.global_variables_initializer().run() self.run_op_benchmark( sess, diff --git a/tensorflow/python/kernel_tests/metrics_test.py b/tensorflow/python/kernel_tests/metrics_test.py index e0e752147cdf8690d22fa782aca2561b2935fa8e..ad802f7e1f72f6cbc3dda1ca98e46e6da4e5110a 100644 --- a/tensorflow/python/kernel_tests/metrics_test.py +++ b/tensorflow/python/kernel_tests/metrics_test.py @@ -417,7 +417,7 @@ class MeanTensorTest(test.TestCase): self.assertAllClose([[-0.9 / 4., 3.525]], sess.run(mean), 5) - def testWeighted1d(self): + def testBinaryWeighted1d(self): with self.test_session() as sess: # Create the queue that populates the values. values_queue = data_flow_ops.FIFOQueue( @@ -444,6 +444,33 @@ class MeanTensorTest(test.TestCase): sess.run(update_op) self.assertAllClose([[3.25, 0.5]], sess.run(mean), 5) + def testWeighted1d(self): + with self.test_session() as sess: + # Create the queue that populates the values. + values_queue = data_flow_ops.FIFOQueue( + 4, dtypes=dtypes_lib.float32, shapes=(1, 2)) + _enqueue_vector(sess, values_queue, [0, 1]) + _enqueue_vector(sess, values_queue, [-4.2, 9.1]) + _enqueue_vector(sess, values_queue, [6.5, 0]) + _enqueue_vector(sess, values_queue, [-3.2, 4.0]) + values = values_queue.dequeue() + + # Create the queue that populates the weights. + weights_queue = data_flow_ops.FIFOQueue( + 4, dtypes=dtypes_lib.float32, shapes=(1, 1)) + _enqueue_vector(sess, weights_queue, [[0.0025]]) + _enqueue_vector(sess, weights_queue, [[0.005]]) + _enqueue_vector(sess, weights_queue, [[0.01]]) + _enqueue_vector(sess, weights_queue, [[0.0075]]) + weights = weights_queue.dequeue() + + mean, update_op = metrics.mean_tensor(values, weights) + + sess.run(variables.local_variables_initializer()) + for _ in range(4): + sess.run(update_op) + self.assertAllClose([[0.8, 3.52]], sess.run(mean), 5) + def testWeighted2d_1(self): with self.test_session() as sess: # Create the queue that populates the values. diff --git a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py index 30795eed8a063076a69ec2ec7851788775fe4dc6..d8ce9fffbd2bc0d18033339a02e0ad84f8f4c952 100644 --- a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py @@ -148,7 +148,7 @@ class DepthwiseConv2DTest(test.TestCase): print("depthwise conv_2d: ", tensor_in_sizes, "*", filter_in_sizes, ", stride:", stride, ", padding: ", padding, ", max diff: ", np.amax(np.absolute(native_result - interface_result))) - self.assertArrayNear( + self.assertAllClose( np.ravel(native_result), np.ravel(interface_result), 1e-5) self.assertShapeEqual(native_result, conv_native) self.assertShapeEqual(native_result, conv_interface) @@ -213,7 +213,7 @@ class DepthwiseConv2DTest(test.TestCase): t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) print("value = ", value) - self.assertArrayNear(expected, np.ravel(value), 1e-5) + self.assertAllClose(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) def testConv2D2x2Filter(self): diff --git a/tensorflow/python/kernel_tests/pad_op_test.py b/tensorflow/python/kernel_tests/pad_op_test.py index 2c766e364073fc8c92156f19d08753367982e7fc..aaeb3b199e3feb07e56401fb17bd74ce1c54d502 100644 --- a/tensorflow/python/kernel_tests/pad_op_test.py +++ b/tensorflow/python/kernel_tests/pad_op_test.py @@ -238,6 +238,29 @@ class PadOpTest(test.TestCase): x = np.random.rand(3, 2, 1, 1).astype(t) self._testAll(x + 1j * x, [[0, 0], [0, 0], [0, 0], [0, 0]], 0 + 0j) + def testString(self): + # Numpy does not support padding strings so we compare padding manually. + x = ops.convert_to_tensor([["Hello", "World"], + ["Goodnight", "Moon"]]) + + constant = array_ops.pad(x, [[1, 0], [0, 1]], mode="CONSTANT", + constant_values="PAD") + reflect = array_ops.pad(x, [[1, 0], [0, 1]], mode="REFLECT", + constant_values="PAD") + symmetric = array_ops.pad(x, [[1, 0], [0, 1]], mode="SYMMETRIC", + constant_values="PAD") + with self.test_session(use_gpu=True): + self.assertAllEqual([[b"PAD", b"PAD", b"PAD"], + [b"Hello", b"World", b"PAD"], + [b"Goodnight", b"Moon", b"PAD"]], constant.eval()) + self.assertAllEqual([[b"Goodnight", b"Moon", b"Goodnight"], + [b"Hello", b"World", b"Hello"], + [b"Goodnight", b"Moon", b"Goodnight"]], + reflect.eval()) + self.assertAllEqual([[b"Hello", b"World", b"World"], + [b"Hello", b"World", b"World"], + [b"Goodnight", b"Moon", b"Moon"]], symmetric.eval()) + def testShapeFunctionEdgeCases(self): # Unknown paddings shape. inp = constant_op.constant(0.0, shape=[4, 4, 4, 4]) diff --git a/tensorflow/python/kernel_tests/pool_test.py b/tensorflow/python/kernel_tests/pool_test.py index 63848976336f5487cf2a44f7cf62ea316c40d7c8..6ede654aadc7d0d78bc18f13c2d4b3d47fef0402 100644 --- a/tensorflow/python/kernel_tests/pool_test.py +++ b/tensorflow/python/kernel_tests/pool_test.py @@ -96,7 +96,7 @@ def pool_direct_single_axis( def pool_direct( - input, + input, # pylint: disable=redefined-builtin window_shape, pooling_type, padding, # pylint: disable=redefined-builtin diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 4466beeec96509b3761e34d885276e1510c62d10..2f3bea5825f8889c5880c819ebf6b17aaa613f08 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variables import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging @@ -405,7 +406,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 3, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -427,7 +428,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -456,7 +457,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 2, 1], ksize=[1, 1, 2, 1], strides=[1, 1, 1, 1], @@ -485,7 +486,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 1, 2, 1], @@ -494,7 +495,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu, v2=v2) self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 2, 1, 1], @@ -519,7 +520,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 4], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -554,7 +555,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], @@ -565,7 +566,7 @@ class PoolingTest(test.TestCase): def _testMaxPoolEmptyInput(self, use_gpu): self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[0, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], @@ -600,7 +601,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 1, 1, 10], ksize=[1, 1, 1, 2], strides=[1, 1, 1, 2], @@ -626,7 +627,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 2, 6], ksize=[1, 1, 1, 3], strides=[1, 1, 1, 3], @@ -648,7 +649,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 7, 7, 1], ksize=[1, 2, 2, 1], strides=[1, 3, 3, 1], @@ -689,7 +690,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 3, 3, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -699,7 +700,7 @@ class PoolingTest(test.TestCase): v2=v2) self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -731,7 +732,8 @@ class PoolingTest(test.TestCase): [1, 1, 1, 3], "evenly divide") if test.is_gpu_available(): with self.test_session(use_gpu=True): - t = constant_op.constant(1.0, shape=[1, 2, 2, 4]) + t = variables.Variable(np.ones([1, 2, 2, 4])) + variables.global_variables_initializer().run() with self.assertRaisesOpError("for CPU devices"): nn_ops.max_pool( t, ksize=[1, 1, 1, 2], strides=[1, 1, 1, 2], @@ -764,8 +766,8 @@ class PoolingTest(test.TestCase): _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding) argmax = argmax_op.eval() grad_in = constant_op.constant(tensor_output, shape=output_shape) - out_op = gen_nn_ops._max_pool_grad_with_argmax(t, grad_in, argmax, - ksize, strides, padding) + out_op = gen_nn_ops.max_pool_grad_with_argmax(t, grad_in, argmax, ksize, + strides, padding) gpu_val = out_op.eval() self.assertShapeEqual(gpu_val, out_op) with self.test_session(use_gpu=False): @@ -773,8 +775,8 @@ class PoolingTest(test.TestCase): out_op = nn_ops.max_pool(t, ksize, strides, padding) orig_out = out_op.eval() grad_in = constant_op.constant(tensor_output, shape=output_shape) - out_op = gen_nn_ops._max_pool_grad(t, orig_out, grad_in, ksize, strides, - padding) + out_op = gen_nn_ops.max_pool_grad(t, orig_out, grad_in, ksize, strides, + padding) cpu_val = out_op.eval() self.assertShapeEqual(cpu_val, out_op) # The CPU version accumulates its gradient on fp16, so it's less @@ -793,7 +795,7 @@ class PoolingTest(test.TestCase): _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding) argmax = argmax_op.eval() grad_in = constant_op.constant(tensor_input, shape=input_shape) - out_op = gen_nn_ops._max_pool_grad_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_grad_with_argmax( t, grad_in, argmax, ksize, strides, padding) gpu_val = out_op.eval() self.assertShapeEqual(gpu_val, out_op) @@ -802,8 +804,8 @@ class PoolingTest(test.TestCase): out_op = nn_ops.max_pool(t, ksize, strides, padding) orig_out = out_op.eval() grad_in = constant_op.constant(tensor_input, shape=input_shape) - out_op = gen_nn_ops._max_pool_grad_grad(t, orig_out, grad_in, ksize, - strides, padding) + out_op = gen_nn_ops.max_pool_grad_grad(t, orig_out, grad_in, ksize, + strides, padding) cpu_val = out_op.eval() self.assertShapeEqual(cpu_val, out_op) # The CPU version accumulates its gradient on fp16, so it's less @@ -842,7 +844,7 @@ class PoolingTest(test.TestCase): t = constant_op.constant(tensor_input, shape=[1, 2, 2, 1]) argmax = constant_op.constant( tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64) - out_op = gen_nn_ops._max_pool_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_with_argmax( orig_in, t, argmax, @@ -865,7 +867,7 @@ class PoolingTest(test.TestCase): t = constant_op.constant(tensor_input, shape=[1, 3, 3, 1]) argmax = constant_op.constant( tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64) - out_op = gen_nn_ops._max_pool_grad_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_grad_with_argmax( orig_in, t, argmax, @@ -1029,7 +1031,7 @@ class PoolingTest(test.TestCase): self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1043,7 +1045,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_1_6(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 6, 6, 3], @@ -1057,7 +1059,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_1_7(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 7, 7, 3], @@ -1071,7 +1073,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding1_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1085,7 +1087,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 2, 3], @@ -1099,7 +1101,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1113,7 +1115,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding1_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1127,7 +1129,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding2_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1141,7 +1143,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1155,7 +1157,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding3_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 7, 7, 1], @@ -1199,7 +1201,7 @@ class PoolingTest(test.TestCase): Returns: A Tensor. """ - pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops._max_pool_grad + pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops.max_pool_grad return pool_func(orig_input, orig_output, grad, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) @@ -1208,9 +1210,11 @@ class PoolingTest(test.TestCase): expected_input_backprop, input_sizes, output_sizes, window_rows, window_cols, row_stride, col_stride, padding, use_gpu, v2): - pool_func = gen_nn_ops._max_pool_v2 if v2 else nn_ops.max_pool + pool_func = gen_nn_ops.max_pool_v2 if v2 else nn_ops.max_pool with self.test_session(use_gpu=use_gpu): - input_tensor = constant_op.constant(input_data, shape=input_sizes) + input_tensor = variables.Variable( + np.array(input_data, dtype=np.float32).reshape(input_sizes)) + variables.global_variables_initializer().run() output_tensor = pool_func(input_tensor, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) output_backprop_tensor = constant_op.constant( @@ -1504,7 +1508,7 @@ class PoolingTest(test.TestCase): self._testMaxPoolGradDirectWithNans2_2() def _testMaxPoolGradGradValidPadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1518,7 +1522,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_1_6(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 6, 6, 3], @@ -1532,7 +1536,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_1_7(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 7, 7, 3], @@ -1546,7 +1550,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 2, 3], @@ -1560,7 +1564,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1574,7 +1578,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding2_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1588,7 +1592,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1602,7 +1606,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding3_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[1, 7, 7, 1], @@ -1644,7 +1648,7 @@ class PoolingTest(test.TestCase): Returns: A Tensor. """ - return gen_nn_ops._max_pool_grad_grad( + return gen_nn_ops.max_pool_grad_grad( orig_input, orig_output, grad, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index c7181497d891f6d35a788c90bf59a0ce5a536328..36142801d682d4e99bc1beceee7aee76206db4cf 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -18,6 +19,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import re + import numpy as np from six.moves import queue from six.moves import xrange # pylint: disable=redefined-builtin @@ -212,6 +215,16 @@ class PyFuncTest(test.TestCase): value.op.run() self.assertAllEqual(np_array, [1.0, 2.0]) + def testReturnUnicodeString(self): + with self.test_session(): + correct = u"你好 世界" + + def unicode_string(): + return correct + + z, = script_ops.py_func(unicode_string, [], [dtypes.string]) + self.assertEqual(z.eval(), correct.encode("utf8")) + def testBadNumpyReturnType(self): with self.test_session(): @@ -345,12 +358,22 @@ class PyFuncTest(test.TestCase): def _testExceptionHandling(self, py_exp, tf_exp, eager=False): - def raise_exception(): + def inner_exception(): raise py_exp("blah") # pylint: disable=not-callable + def raise_exception(): + inner_exception() + + expected_regexp = r": blah.*" # Error at the top + expected_regexp += r"in raise_exception.*" # Stacktrace outer + expected_regexp += r"in inner_exception.*" # Stacktrace inner + expected_regexp += r": blah" # Stacktrace of raise + def expected_error_check(exception): + return re.search(expected_regexp, str(exception), re.DOTALL) + if eager: - if context.in_eager_mode(): - with self.assertRaisesRegexp(tf_exp, "blah"): + if context.executing_eagerly(): + with self.assertRaisesWithPredicateMatch(tf_exp, expected_error_check): f = script_ops.eager_py_func(raise_exception, [], []) return else: @@ -359,7 +382,7 @@ class PyFuncTest(test.TestCase): f = script_ops.py_func(raise_exception, [], []) with self.test_session(): - with self.assertRaisesRegexp(tf_exp, "blah"): + with self.assertRaisesWithPredicateMatch(tf_exp, expected_error_check): self.evaluate(f) def testExceptionHandling(self): @@ -421,7 +444,7 @@ class PyFuncTest(test.TestCase): output = script_ops.eager_py_func(no_return_value, inp=[], Tout=[]) ret = self.evaluate(output) - if context.in_eager_mode(): + if context.executing_eagerly(): self.assertEquals(len(ret), 0) else: self.assertIsNone(ret) diff --git a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py index c4e16ff6280cc7ce121955474fe8ec45acd57f95..b7a79f239cee04b191b78affd002f687b7de851a 100644 --- a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py +++ b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import random -import re import time import numpy as np diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py index 4231a79b2dcef951048ca54e8c8df2f42b44b1a1..589ea54973c10902c461f552d5c54b6fad6ecf67 100644 --- a/tensorflow/python/kernel_tests/reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/reduction_ops_test.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test # The maximum input rank to test. @@ -110,10 +111,10 @@ class ReductionUnknownShape(test.TestCase): class BaseReductionTest(test.TestCase): - def _tf_reduce(self, x, reduction_axes, keep_dims): + def _tf_reduce(self, x, reduction_axes, keepdims): raise NotImplementedError() - def _np_reduce(self, x, reduction_axes, keep_dims): + def _np_reduce(self, x, reduction_axes, keepdims): raise NotImplementedError() def _makeIncremental(self, shape, dtype): @@ -128,10 +129,10 @@ class BaseReductionTest(test.TestCase): data -= 2j * data return data - def _compare(self, x, reduction_axes, keep_dims, feed_dict=None): - np_ans = self._np_reduce(x, reduction_axes, keep_dims) + def _compare(self, x, reduction_axes, keepdims, feed_dict=None): + np_ans = self._np_reduce(x, reduction_axes, keepdims) with self.test_session(use_gpu=True) as sess: - tf_ans = self._tf_reduce(x, reduction_axes, keep_dims) + tf_ans = self._tf_reduce(x, reduction_axes, keepdims) out = sess.run(tf_ans, feed_dict) self.assertAllClose(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) @@ -140,8 +141,8 @@ class BaseReductionTest(test.TestCase): if reduction_axes is not None and np.shape(reduction_axes) == (1,): # Test scalar reduction_axes argument self._compareAll(x, reduction_axes[0]) - self._compare(x, reduction_axes, keep_dims=False, feed_dict=feed_dict) - self._compare(x, reduction_axes, keep_dims=True, feed_dict=feed_dict) + self._compare(x, reduction_axes, keepdims=False, feed_dict=feed_dict) + self._compare(x, reduction_axes, keepdims=True, feed_dict=feed_dict) def _compareAllAxes(self, x, feed_dict=None): self._compareAll(x, None) @@ -171,14 +172,14 @@ class BaseReductionTest(test.TestCase): class SumReductionTest(BaseReductionTest): - def _tf_reduce(self, x, reduction_axes, keep_dims): - return math_ops.reduce_sum(x, reduction_axes, keep_dims) + def _tf_reduce(self, x, reduction_axes, keepdims): + return math_ops.reduce_sum(x, reduction_axes, keepdims) - def _np_reduce(self, x, reduction_axes, keep_dims): + def _np_reduce(self, x, reduction_axes, keepdims): if isinstance(reduction_axes, list) or isinstance(reduction_axes, np.ndarray): reduction_axes = tuple(reduction_axes) - return np.sum(x, axis=reduction_axes, keepdims=keep_dims) + return np.sum(x, axis=reduction_axes, keepdims=keepdims) def testAxesType(self): for dtype in [dtypes.int64, dtypes.int32]: @@ -212,7 +213,8 @@ class SumReductionTest(BaseReductionTest): arr = np.ones([68000], dtype=np.float16) with self.test_session(graph=ops.Graph(), use_gpu=True) as sess: - tf_arr = array_ops.constant(arr) + tf_arr = variables.Variable(arr) + variables.global_variables_initializer().run() tf_mean = math_ops.reduce_mean(tf_arr, 0, False) tf_out_mean = sess.run(tf_mean) self.assertAllClose(tf_out_mean, 1.) @@ -298,7 +300,7 @@ class SumReductionTest(BaseReductionTest): c_known_rank = array_ops.placeholder(dtypes.float32) c_known_rank.set_shape(tensor_shape.unknown_shape(ndims=3)) s_known_rank = math_ops.reduce_sum( - c_known_rank, reduction_axes, keep_dims=True) + c_known_rank, reduction_axes, keepdims=True) self.assertEqual(3, s_known_rank.get_shape().ndims) np_input = np.random.randn(3, 3, 3) @@ -308,11 +310,11 @@ class SumReductionTest(BaseReductionTest): unknown_indices = array_ops.placeholder(dtypes.int32) c_unknown_indices = constant_op.constant([[10.0], [20.0]]) s_unknown_indices = math_ops.reduce_sum( - c_unknown_indices, unknown_indices, keep_dims=False) + c_unknown_indices, unknown_indices, keepdims=False) self.assertEqual(tensor_shape.unknown_shape(), s_unknown_indices.get_shape()) s_unknown_indices_keep = math_ops.reduce_sum( - c_unknown_indices, unknown_indices, keep_dims=True) + c_unknown_indices, unknown_indices, keepdims=True) self.assertEqual(2, s_unknown_indices_keep.get_shape().ndims) def testWrongShapeForReductionIndices(self): @@ -372,10 +374,10 @@ class SumReductionTest(BaseReductionTest): class MeanReductionTest(BaseReductionTest): - def _tf_reduce(self, x, reduction_axes, keep_dims): - return math_ops.reduce_mean(x, reduction_axes, keep_dims) + def _tf_reduce(self, x, reduction_axes, keepdims): + return math_ops.reduce_mean(x, reduction_axes, keepdims) - def _np_reduce(self, x, reduction_axes, keep_dims): + def _np_reduce(self, x, reduction_axes, keepdims): if isinstance(reduction_axes, list) or isinstance(reduction_axes, np.ndarray): reduction_axes = tuple(reduction_axes) @@ -389,7 +391,7 @@ class MeanReductionTest(BaseReductionTest): # np.mean automatically converts integer inputs to float, while TensorFlow's # reduce_mean does not. For integer inputs, we emulate TensorFlow's behavior # using np.sum and truncating division. - np_sum = np.sum(x, axis=reduction_axes, keepdims=keep_dims) + np_sum = np.sum(x, axis=reduction_axes, keepdims=keepdims) if np.issubdtype(x.dtype, np.integer): return np_sum // count return np_sum / count @@ -458,14 +460,14 @@ class MeanReductionTest(BaseReductionTest): class ProdReductionTest(BaseReductionTest): - def _tf_reduce(self, x, reduction_axes, keep_dims): - return math_ops.reduce_prod(x, reduction_axes, keep_dims) + def _tf_reduce(self, x, reduction_axes, keepdims): + return math_ops.reduce_prod(x, reduction_axes, keepdims) - def _np_reduce(self, x, reduction_axes, keep_dims): + def _np_reduce(self, x, reduction_axes, keepdims): if isinstance(reduction_axes, list) or isinstance(reduction_axes, np.ndarray): reduction_axes = tuple(reduction_axes) - return np.prod(x, axis=reduction_axes, keepdims=keep_dims) + return np.prod(x, axis=reduction_axes, keepdims=keepdims) def testAxesType(self): for dtype in [dtypes.int64, dtypes.int32]: @@ -549,17 +551,17 @@ class ProdReductionTest(BaseReductionTest): class MinReductionTest(test.TestCase): - def _compare(self, x, reduction_axes, keep_dims, use_gpu=False): + def _compare(self, x, reduction_axes, keepdims, use_gpu=False): np_ans = x if reduction_axes is None: - np_ans = np.amin(np_ans, keepdims=keep_dims) + np_ans = np.amin(np_ans, keepdims=keepdims) else: for ra in reduction_axes[::-1]: - np_ans = np.amin(np_ans, axis=ra, keepdims=keep_dims) + np_ans = np.amin(np_ans, axis=ra, keepdims=keepdims) with self.test_session(use_gpu=use_gpu): if reduction_axes is not None: reduction_axes = np.array(reduction_axes).astype(np.int32) - tf_ans = math_ops.reduce_min(x, reduction_axes, keep_dims) + tf_ans = math_ops.reduce_min(x, reduction_axes, keepdims) out = tf_ans.eval() self.assertAllClose(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) @@ -662,17 +664,17 @@ class MinReductionTest(test.TestCase): class MaxReductionTest(test.TestCase): - def _compare(self, x, reduction_axes, keep_dims, use_gpu=False): + def _compare(self, x, reduction_axes, keepdims, use_gpu=False): np_ans = x if reduction_axes is None: - np_ans = np.amax(np_ans, keepdims=keep_dims) + np_ans = np.amax(np_ans, keepdims=keepdims) else: for ra in reduction_axes[::-1]: - np_ans = np.amax(np_ans, axis=ra, keepdims=keep_dims) + np_ans = np.amax(np_ans, axis=ra, keepdims=keepdims) with self.test_session(use_gpu=use_gpu): if reduction_axes is not None: reduction_axes = np.array(reduction_axes).astype(np.int32) - tf_ans = math_ops.reduce_max(x, reduction_axes, keep_dims) + tf_ans = math_ops.reduce_max(x, reduction_axes, keepdims) out = tf_ans.eval() self.assertAllClose(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) @@ -789,17 +791,17 @@ class MaxReductionTest(test.TestCase): class AllReductionTest(test.TestCase): - def _compare(self, x, reduction_axes, keep_dims, use_gpu=False): + def _compare(self, x, reduction_axes, keepdims, use_gpu=False): np_ans = x if reduction_axes is None: - np_ans = np.all(np_ans, keepdims=keep_dims) + np_ans = np.all(np_ans, keepdims=keepdims) else: for ra in reduction_axes[::-1]: - np_ans = np.all(np_ans, axis=ra, keepdims=keep_dims) + np_ans = np.all(np_ans, axis=ra, keepdims=keepdims) with self.test_session(use_gpu=use_gpu): if reduction_axes is not None: reduction_axes = np.array(reduction_axes).astype(np.int32) - tf_ans = math_ops.reduce_all(x, reduction_axes, keep_dims) + tf_ans = math_ops.reduce_all(x, reduction_axes, keepdims) out = tf_ans.eval() self.assertAllEqual(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) @@ -838,17 +840,17 @@ class AllReductionTest(test.TestCase): class AnyReductionTest(test.TestCase): - def _compare(self, x, reduction_axes, keep_dims, use_gpu=False): + def _compare(self, x, reduction_axes, keepdims, use_gpu=False): np_ans = x if reduction_axes is None: - np_ans = np.any(np_ans, keepdims=keep_dims) + np_ans = np.any(np_ans, keepdims=keepdims) else: for ra in reduction_axes[::-1]: - np_ans = np.any(np_ans, axis=ra, keepdims=keep_dims) + np_ans = np.any(np_ans, axis=ra, keepdims=keepdims) with self.test_session(use_gpu=use_gpu): if reduction_axes is not None: reduction_axes = np.array(reduction_axes).astype(np.int32) - tf_ans = math_ops.reduce_any(x, reduction_axes, keep_dims) + tf_ans = math_ops.reduce_any(x, reduction_axes, keepdims) out = tf_ans.eval() self.assertAllEqual(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) @@ -887,21 +889,17 @@ class AnyReductionTest(test.TestCase): class CountNonzeroReductionTest(test.TestCase): - def _compare(self, - x, - reduction_axes, - keep_dims, - use_gpu=False, + def _compare(self, x, reduction_axes, keepdims, use_gpu=False, feed_dict=None): np_ans = (x != 0).astype(np.int32) if reduction_axes is None: - np_ans = np.sum(np_ans, keepdims=keep_dims) + np_ans = np.sum(np_ans, keepdims=keepdims) else: reduction_axes = np.array(reduction_axes).astype(np.int32) for ra in reduction_axes.ravel()[::-1]: - np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims) + np_ans = np.sum(np_ans, axis=ra, keepdims=keepdims) with self.test_session(use_gpu=use_gpu) as sess: - tf_ans = math_ops.count_nonzero(x, reduction_axes, keep_dims) + tf_ans = math_ops.count_nonzero(x, reduction_axes, keepdims) out = sess.run(tf_ans, feed_dict) self.assertAllClose(np_ans, out) self.assertShapeEqual(np_ans, tf_ans) diff --git a/tensorflow/python/kernel_tests/reduction_ops_test_big.py b/tensorflow/python/kernel_tests/reduction_ops_test_big.py index 0959adb026e3934713442e6f3487b30a0b252943..d70360775a03caa32eab995371d54786c3c0a0d9 100644 --- a/tensorflow/python/kernel_tests/reduction_ops_test_big.py +++ b/tensorflow/python/kernel_tests/reduction_ops_test_big.py @@ -27,24 +27,24 @@ from tensorflow.python.platform import test class BaseReductionTest(test.TestCase): - def _tf_reduce(self, x, reduction_axes, keep_dims): + def _tf_reduce(self, x, reduction_axes, keepdims): raise NotImplementedError() class BigReductionTest(BaseReductionTest): """Test reductions for sum and boolean all over a wide range of shapes.""" - def _tf_reduce_max(self, x, reduction_axes, keep_dims): - return math_ops.reduce_max(x, reduction_axes, keep_dims) + def _tf_reduce_max(self, x, reduction_axes, keepdims): + return math_ops.reduce_max(x, reduction_axes, keepdims) - def _tf_reduce_all(self, x, reduction_axes, keep_dims): - return math_ops.reduce_all(x, reduction_axes, keep_dims) + def _tf_reduce_all(self, x, reduction_axes, keepdims): + return math_ops.reduce_all(x, reduction_axes, keepdims) - def _tf_reduce_mean(self, x, reduction_axes, keep_dims): - return math_ops.reduce_mean(x, reduction_axes, keep_dims) + def _tf_reduce_mean(self, x, reduction_axes, keepdims): + return math_ops.reduce_mean(x, reduction_axes, keepdims) - def _tf_reduce_sum(self, x, reduction_axes, keep_dims): - return math_ops.reduce_sum(x, reduction_axes, keep_dims) + def _tf_reduce_sum(self, x, reduction_axes, keepdims): + return math_ops.reduce_sum(x, reduction_axes, keepdims) def testFloat32Sum(self): # make sure we test all possible kernel invocations diff --git a/tensorflow/python/kernel_tests/regex_replace_op_test.py b/tensorflow/python/kernel_tests/regex_replace_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6739ac32245668e98d37673fe9e9fe9d55cc0c5f --- /dev/null +++ b/tensorflow/python/kernel_tests/regex_replace_op_test.py @@ -0,0 +1,71 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for RegexReplace op from string_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import string_ops +from tensorflow.python.platform import test + + +class RegexReplaceOpTest(test.TestCase): + + def testRemovePrefix(self): + values = ["a:foo", "a:bar", "a:foo", "b:baz", "b:qux", "ca:b"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace( + input_vector, "^(a:|b:)", "", replace_global=False).eval() + self.assertAllEqual([b"foo", b"bar", b"foo", b"baz", b"qux", b"ca:b"], + stripped) + + def testRegexReplace(self): + values = ["aba\naba", "abcdabcde"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "a.*a", "(\\0)").eval() + self.assertAllEqual([b"(aba)\n(aba)", b"(abcda)bcde"], stripped) + + def testEmptyMatch(self): + values = ["abc", "1"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "", "x").eval() + self.assertAllEqual([b"xaxbxcx", b"x1x"], stripped) + + def testInvalidPattern(self): + values = ["abc", "1"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + invalid_pattern = "A[" + replace = string_ops.regex_replace(input_vector, invalid_pattern, "x") + with self.assertRaisesOpError("Invalid pattern"): + replace.eval() + + def testGlobal(self): + values = ["ababababab", "abcabcabc", ""] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "ab", "abc", + True).eval() + self.assertAllEqual([b"abcabcabcabcabc", b"abccabccabcc", b""], stripped) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py index 6b4091ae5d3c6e469a9cd5237b978eae4c75485f..25e947f09e137b37ea129ba6015a060aa01f02e4 100644 --- a/tensorflow/python/kernel_tests/relu_op_test.py +++ b/tensorflow/python/kernel_tests/relu_op_test.py @@ -19,12 +19,14 @@ from __future__ import division from __future__ import print_function import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables @@ -87,6 +89,35 @@ class ReluTest(test.TestCase): print("relu (float32) gradient err = ", err) self.assertLess(err, 1e-4) + # The gradient for fp16 is inaccurate due to the low-precision. + # Instead of relying on compute_gradient_error, we compare the fp16 analytical + # gradient against their fp32 counterpart. + def testGradientFloat16(self): + with self.test_session(use_gpu=True) as sess: + # Randomly construct a 1D shape from [1, 40) + shape = random_ops.random_uniform( + [1], minval=1, maxval=40, dtype=dtypes.int32) + + # Construct the fp32 graph and its gradient. + x = random_ops.random_uniform(shape, minval=-1, maxval=1, name="x") + y1 = nn_ops.relu(x, name="relu_fp32") + l1 = nn_ops.l2_loss(y1) + dx_f32 = gradients_impl.gradients(l1, x) + + # Construct the fp16 graph and its gradient. + # It starts with the same x, in fp32. But before it reaches Relu, it is + # cast into fp16. So during backprop, the gradient computation is in fp16. + x2 = math_ops.cast(x, dtype=dtypes.float16, name="cast") + y2 = nn_ops.relu(x2, name="relu_fp16") + l2 = nn_ops.l2_loss(y2) + dx_f16 = gradients_impl.gradients(l2, x) + + # Repeat the experiment for 100 times. All tensor shapes and its tensor + # values are randomly generated for each run. + for _ in xrange(100): + dx_f32_v, dx_f16_v = sess.run([dx_f32, dx_f16]) + self.assertAllClose(dx_f32_v, dx_f16_v, atol=3e-4) + def testGradientFloat64(self): with self.test_session(): x = constant_op.constant( diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index cd94579688130974f640d1dea2afadeadadd551b..d34b751062e7892f312ce704ef7251f27e5141ba 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -64,6 +64,13 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): 0, dtype=dtypes.int32)).run() + def testGPUInt64(self): + if not context.context().num_gpus(): + return + with context.eager_mode(), context.device("gpu:0"): + v = resource_variable_ops.ResourceVariable(1, dtype=dtypes.int64) + self.assertAllEqual(1, v.numpy()) + def testEagerNameNotIdentity(self): with context.eager_mode(): v0 = resource_variable_ops.ResourceVariable(1.0, name="a") @@ -162,14 +169,15 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes(use_gpu=True) def testScatterAdd(self): - handle = resource_variable_ops.var_handle_op( - dtype=dtypes.int32, shape=[1, 1]) - self.evaluate(resource_variable_ops.assign_variable_op( - handle, constant_op.constant([[1]], dtype=dtypes.int32))) - self.evaluate(resource_variable_ops.resource_scatter_add( - handle, [0], constant_op.constant([[2]], dtype=dtypes.int32))) - read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) - self.assertEqual(self.evaluate(read), [[3]]) + with ops.device("cpu:0"): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + self.evaluate(resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1]], dtype=dtypes.int32))) + self.evaluate(resource_variable_ops.resource_scatter_add( + handle, [0], constant_op.constant([[2]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(self.evaluate(read), [[3]]) def testScatterUpdateString(self): handle = resource_variable_ops.var_handle_op( @@ -269,6 +277,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign(2.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign(3.0, read_value=True) + self.assertEqual(3.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign(4.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(4.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testLoad(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") @@ -276,6 +295,32 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): v.load(2.0) self.assertEqual(2.0, self.evaluate(v.value())) + def testVariableDefInitializedInstances(self): + with ops.Graph().as_default(), self.test_session() as sess: + v_def = resource_variable_ops.ResourceVariable( + initial_value=constant_op.constant(3.0)).to_proto() + + with ops.Graph().as_default(), self.test_session() as sess: + # v describes a VariableDef-based variable without an initial value. + v = resource_variable_ops.ResourceVariable(variable_def=v_def) + self.assertEqual(3.0, sess.run(v.initialized_value())) + + # initialized_value should not rerun the initializer_op if the variable + # has already been initialized elsewhere. + sess.run(v.assign(1.0)) + self.assertEqual(1.0, v.initialized_value().eval()) + + v_def.ClearField("initial_value_name") + with ops.Graph().as_default(), self.test_session() as sess: + # Restoring a legacy VariableDef proto that does not have + # initial_value_name set should still work. + v = resource_variable_ops.ResourceVariable(variable_def=v_def) + # We should also be able to re-export the variable to a new meta graph. + self.assertProtoEquals(v_def, v.to_proto()) + # But attempts to use initialized_value will result in errors. + with self.assertRaises(ValueError): + sess.run(v.initialized_value()) + @test_util.run_in_graph_and_eager_modes() def testSparseRead(self): with self.test_session(): @@ -295,6 +340,9 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): w = resource_variable_ops.ResourceVariable.from_proto(v.to_proto()) self.assertEquals(2, math_ops.add(w, 1).eval()) + self.assertEquals(v._handle, w._handle) + self.assertEquals(v._graph_element, w._graph_element) + @test_util.run_in_graph_and_eager_modes() def testAssignAddMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") @@ -302,6 +350,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign_add(1.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign_add(1.0, read_value=True) + self.assertEqual(3.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign_add(1.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(4.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testAssignSubMethod(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") @@ -309,6 +368,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign_sub(1.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign_sub(1.0, read_value=True) + self.assertEqual(1.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign_sub(1.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(0.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testDestroyResource(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") @@ -406,7 +476,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual("(10, 20, 35)", str(v.get_shape())) self.assertEqual("(10, 20, 35)", str(v.value().shape)) self.assertEqual("(3, 20, 35)", str(v.sparse_read([0, 1, 2]).shape)) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( "", str(v.sparse_read(array_ops.placeholder(dtypes.int32)).shape)) @@ -447,7 +517,6 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual(dtypes.int32, v.dtype) self.assertEqual("foo/var7:0", v.name) self.assertAllEqual([10, 20, 35], v.shape.as_list()) - self.assertEqual(context.get_default_context().device_name, v.device) self.assertTrue(isinstance(v.handle, ops.EagerTensor)) self.assertEqual(constraint, v.constraint) self.assertAllEqual(init.numpy(), v.read_value().numpy()) diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index a86b65affec9e491fec13577ad6dc3db610df797..9a0409c796ab60da3d47cf7d46ef6fbd5bd82394 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -23,7 +23,7 @@ import timeit import numpy as np -from six.moves import xrange +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import rnn as contrib_rnn from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session @@ -111,10 +111,10 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testInvalidSequenceLengthShape(self): cell = Plus1RNNCell() - if context.in_graph_mode(): - inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))] - else: + if context.executing_eagerly(): inputs = [constant_op.constant(np.ones((3, 4)))] + else: + inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))] with self.assertRaisesRegexp(ValueError, "must be a vector"): rnn.dynamic_rnn( cell, @@ -125,38 +125,30 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testBatchSizeFromInput(self): cell = Plus1RNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() # With static batch size - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(3, 4, 5)) - initial_state = array_ops.placeholder(dtypes.float32, shape=(3, 5)) - else: + if in_eager_mode: inputs = np.zeros((3, 4, 5), dtype=np.float32) initial_state = np.zeros((3, 5), dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(3, 4, 5)) + initial_state = array_ops.placeholder(dtypes.float32, shape=(3, 5)) # - Without initial_state outputs, state = rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) - if in_graph_mode: - self.assertEqual(3, outputs.shape[0].value) - self.assertEqual(3, state.shape[0].value) - else: - self.assertEqual(3, outputs.shape[0]) - self.assertEqual(3, state.shape[0]) + self.assertEqual(3, outputs.shape[0]) + self.assertEqual(3, state.shape[0]) # - With initial_state outputs, state = rnn.dynamic_rnn( cell, inputs, initial_state=initial_state) - if in_graph_mode: - self.assertEqual(3, outputs.shape[0].value) - self.assertEqual(3, state.shape[0].value) - else: - self.assertEqual(3, outputs.shape[0]) - self.assertEqual(3, state.shape[0]) + self.assertEqual(3, outputs.shape[0]) + self.assertEqual(3, state.shape[0]) # Without static batch size - # Tensor shapes are fully determined in Eager mode, so only run this - # test in graph mode. - if in_graph_mode: + # Tensor shapes are fully determined with eager execution enabled, + # so only run this test for graph construction. + if not in_eager_mode: inputs = array_ops.placeholder(dtypes.float32, shape=(None, 4, 5)) # - Without initial_state outputs, state = rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) @@ -173,56 +165,46 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testScalarStateIsAccepted(self): cell = ScalarStateRNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) - else: + if in_eager_mode: inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) with self.test_session() as sess: outputs, state = rnn.dynamic_rnn( cell, inputs, dtype=dtypes.float32, sequence_length=[4]) - if in_graph_mode: + if not in_eager_mode: outputs, state = sess.run( [outputs, state], feed_dict={inputs: [[[1], [2], [3], [4]]]}) - if in_graph_mode: - self.assertAllEqual(outputs, np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state, 4) - else: - self.assertAllEqual(outputs.numpy(), np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state.numpy(), 4) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs) + self.assertAllEqual(4, state) @test_util.run_in_graph_and_eager_modes() def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) - else: + if in_eager_mode: inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) with self.test_session() as sess: outputs, state = rnn.dynamic_rnn( cell, inputs, dtype=dtypes.float32, sequence_length=[4]) state = (state[0], state[1].stack()) - if in_graph_mode: + if not in_eager_mode: outputs, state = sess.run( [outputs, state], feed_dict={ inputs: [[[1], [2], [3], [4]]] }) - if in_graph_mode: - self.assertAllEqual(outputs, np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state[0], 4) - self.assertAllEqual(state[1], np.array([[[1]], [[2]], [[3]], [[4]]])) - else: - self.assertAllEqual(outputs.numpy(), np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state[0].numpy(), 4) - self.assertAllEqual(state[1].numpy(), - np.array([[[1]], [[2]], [[3]], [[4]]])) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs) + self.assertAllEqual(4, state[0]) + self.assertAllEqual([[[1]], [[2]], [[3]], [[4]]], state[1]) ######### Benchmarking RNN code diff --git a/tensorflow/python/kernel_tests/save_restore_ops_test.py b/tensorflow/python/kernel_tests/save_restore_ops_test.py index 1bdfa9ebd8e1a4495e67004f59adfb51bf3a6602..cb9aa1e34d6eb82efa94e60e7b56c26b181cef04 100644 --- a/tensorflow/python/kernel_tests/save_restore_ops_test.py +++ b/tensorflow/python/kernel_tests/save_restore_ops_test.py @@ -31,11 +31,10 @@ class ShardedFileOpsTest(test.TestCase): with session.Session( target="", config=config_pb2.ConfigProto(device_count={"CPU": 2})): self.assertEqual( - gen_io_ops._sharded_filename("foo", 4, 100).eval(), + gen_io_ops.sharded_filename("foo", 4, 100).eval(), b"foo-00004-of-00100") self.assertEqual( - gen_io_ops._sharded_filespec("foo", 100).eval(), - b"foo-?????-of-00100") + gen_io_ops.sharded_filespec("foo", 100).eval(), b"foo-?????-of-00100") class ShapeInferenceTest(test.TestCase): @@ -53,7 +52,7 @@ class ShapeInferenceTest(test.TestCase): [dtypes.float32, dtypes.float32]) def testRestoreSlice(self): - op = gen_io_ops._restore_slice("model", "var", "3 4 0,1:-", dtypes.float32) + op = gen_io_ops.restore_slice("model", "var", "3 4 0,1:-", dtypes.float32) self.assertEqual([1, 4], op.get_shape()) diff --git a/tensorflow/python/kernel_tests/scalar_test.py b/tensorflow/python/kernel_tests/scalar_test.py index e65241981eac2d42207c1de7a261f7936e588f2a..0d8fd232946883ac1d95c4c2d9744af69175ab90 100644 --- a/tensorflow/python/kernel_tests/scalar_test.py +++ b/tensorflow/python/kernel_tests/scalar_test.py @@ -92,11 +92,11 @@ class ScalarTest(test.TestCase): self.check(array_ops.reshape, (7, 1), 'sizes input must be 1-D', [7]) def testShardedFilename(self): - self.check(gen_io_ops._sharded_filename, ('foo', 4, [100]), + self.check(gen_io_ops.sharded_filename, ('foo', 4, [100]), 'must be a scalar', b'foo-00004-of-00100') def testShardedFilespec(self): - self.check(gen_io_ops._sharded_filespec, ('foo', [100]), 'must be a scalar', + self.check(gen_io_ops.sharded_filespec, ('foo', [100]), 'must be a scalar', b'foo-?????-of-00100') def testUnsortedSegmentSum(self): diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index 5a54f448d092093db668570d055801f9f9cd0f9f..3bca5fadc42693f514911c7ffa8f078de8ef9bcd 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -46,7 +46,8 @@ class SegmentReductionHelper(test.TestCase): return constant_op.constant( np_values, shape=input_shape, dtype=dtype), np_values - def _segmentReduce(self, indices, x, op1, op2=None, num_segments=None): + def _segmentReduce(self, indices, x, op1, op2=None, num_segments=None, + initial_value=0): if not x.size: return np.array([]) indices = np.asarray(indices) @@ -64,13 +65,8 @@ class SegmentReductionHelper(test.TestCase): else: output[index] = x_flat[i] # zero initialize values that are still uncalcuated. - # output = [o if o is not None else np.zeros(slice_shape) for o in output] - if not op1 == np.max: - output = [o if o is not None else np.zeros(slice_shape) for o in output] - else: - zeroslice = np.zeros(slice_shape) - zeroslice.fill(dtype.min) - output = [o if o is not None else zeroslice for o in output] + initial_value_slice = np.ones(slice_shape) * initial_value + output = [o if o is not None else initial_value_slice for o in output] if op2 is not None: output = [op2(o) for o in output] output = [o.reshape(slice_shape) for o in output] @@ -82,6 +78,9 @@ class SegmentReductionHelper(test.TestCase): def _mean_reduce_op(self, x): return x[0] / x[1] if isinstance(x, tuple) else x + def _sqrt_n_reduce_op(self, x): + return x[0] / np.sqrt(x[1]) if isinstance(x, tuple) else x + class SegmentReductionOpTest(SegmentReductionHelper): @@ -244,27 +243,61 @@ class SegmentReductionOpTest(SegmentReductionHelper): self.assertAllClose(jacob_t, jacob_n) -class UnsortedSegmentSumTest(SegmentReductionHelper): +class UnsortedSegmentTest(SegmentReductionHelper): + + def __init__(self, methodName='runTest'): + # Each item is np_op1, np_op2, tf_op, initial_value functor + self.ops_list = [(np.add, None, + math_ops.unsorted_segment_sum, lambda t: 0), + (self._mean_cum_op, self._mean_reduce_op, + math_ops.unsorted_segment_mean, lambda t: 0), + (self._mean_cum_op, self._sqrt_n_reduce_op, + math_ops.unsorted_segment_sqrt_n, lambda t: 0), + (np.ndarray.__mul__, None, + math_ops.unsorted_segment_prod, lambda t: 1), + (np.minimum, None, + math_ops.unsorted_segment_min, lambda t: t.max), + (np.maximum, None, + math_ops.unsorted_segment_max, lambda t: t.min)] + + # A subset of ops has been enabled for complex numbers + self.complex_ops_list = [(np.add, None, + math_ops.unsorted_segment_sum, lambda t: 0)] + self.differentiable_dtypes = [dtypes_lib.float16, dtypes_lib.float32, + dtypes_lib.float64] + self.all_dtypes = (self.differentiable_dtypes + + [dtypes_lib.bfloat16, + dtypes_lib.int64, dtypes_lib.int32, + dtypes_lib.complex64, dtypes_lib.complex128]) + super(UnsortedSegmentTest, self).__init__(methodName=methodName) def testValues(self): - dtypes = [ - dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int64, - dtypes_lib.int32, dtypes_lib.complex64, dtypes_lib.complex128 - ] indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3]) num_segments = 12 for indices in indices_flat, indices_flat.reshape(5, 2): shape = indices.shape + (2,) - for dtype in dtypes: - with self.test_session(use_gpu=True): - tf_x, np_x = self._input(shape, dtype=dtype) - np_ans = self._segmentReduce( - indices, np_x, np.add, op2=None, num_segments=num_segments) - s = math_ops.unsorted_segment_sum( - data=tf_x, segment_ids=indices, num_segments=num_segments) - tf_ans = s.eval() - self.assertAllClose(np_ans, tf_ans) - self.assertShapeEqual(np_ans, s) + for dtype in self.all_dtypes: + ops_list = self.complex_ops_list if dtype.is_complex else self.ops_list + tf_x, np_x = self._input(shape, dtype=dtype) + for use_gpu in [True, False]: + with self.test_session(use_gpu=True): + for np_op1, np_op2, tf_op, init_op in ops_list: + # sqrt_n doesn't support integers + if (np_op2 == self._sqrt_n_reduce_op and dtype.is_integer): + continue + # todo(philjd): enable this test once real_div supports bfloat16 + if (np_op2 in [self._sqrt_n_reduce_op, self._mean_reduce_op] and + dtype == dtypes_lib.bfloat16): + continue + np_ans = self._segmentReduce( + indices, np_x, np_op1, np_op2, num_segments=num_segments, + initial_value=init_op(dtype)) + s = tf_op(tf_x, segment_ids=indices, num_segments=num_segments) + tf_ans = s.eval() + if dtype is dtypes_lib.bfloat16: + tf_ans = tf_ans.astype(np.float32) + self.assertAllClose(np_ans, tf_ans) + self.assertShapeEqual(np_ans, s) def testNumSegmentsTypes(self): dtypes = [dtypes_lib.int32, dtypes_lib.int64] @@ -287,25 +320,51 @@ class UnsortedSegmentSumTest(SegmentReductionHelper): self.assertAllClose(np_ans, tf_ans) self.assertShapeEqual(np_ans, s) - def testGradientSegmentSum(self): + def testGradients(self): num_cols = 2 - indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3]) + indices_flat = np.array([0, 4, 0, -1, 3, -1, 4, 7, 7, 3]) num_segments = max(indices_flat) + 3 - for dtype in [dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.complex64, - dtypes_lib.complex128]: + for dtype in self.differentiable_dtypes: + ops_list = self.complex_ops_list if dtype.is_complex else self.ops_list for indices in indices_flat, indices_flat.reshape(5, 2): shape = indices.shape + (num_cols,) - with self.test_session(use_gpu=True): - tf_x, np_x = self._input(shape, dtype=dtype) - s = math_ops.unsorted_segment_sum( - data=tf_x, segment_ids=indices, num_segments=num_segments) + # test CPU and GPU as tf.gather behaves differently on each device + for use_gpu in [False, True]: + with self.test_session(use_gpu=use_gpu): + for _, _, tf_op, _ in ops_list: + tf_x, np_x = self._input(shape, dtype=dtype) + s = tf_op(tf_x, indices, num_segments) + jacob_t, jacob_n = gradient_checker.compute_gradient( + tf_x, + shape, + s, [num_segments, num_cols], + x_init_value=np_x, + delta=1) + self.assertAllClose(jacob_t, jacob_n) + + def testProdGrad(self): + # additional test for the prod gradient to ensure correct handling of zeros + values = np.array([0, 0, 1, 0, 2, 2, 3, 3, 3], dtype=np.float32) + indices = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=np.int32) + indices_neg = np.array([-1, 0, 0, -1, 1, 1, -1, 2, 2], dtype=np.int32) + values_tf = constant_op.constant(values) + # ground truth partial derivatives + gradients_indices = np.zeros((9, 3), dtype=np.float32) + gradients_indices_neg = np.zeros((9, 3), dtype=np.float32) + # the derivative w.r.t. to the other segments is zero, so here we only + # explicitly set the grad values for the corresponding segment + gradients_indices[range(9), indices] = [0, 0, 0, 4, 0, 0, 9, 9, 9] + gradients_indices_neg[range(9), indices_neg] = [0, 1, 0, 0, 2, 2, 0, 3, 3] + for use_gpu in [False, True]: + with self.test_session(use_gpu=use_gpu): + for ind, grad_gt in [(indices, gradients_indices), + (indices_neg, gradients_indices_neg)]: + s = math_ops.unsorted_segment_prod(values_tf, + constant_op.constant(ind), 3) jacob_t, jacob_n = gradient_checker.compute_gradient( - tf_x, - shape, - s, [num_segments, num_cols], - x_init_value=np_x, - delta=1) - self.assertAllClose(jacob_t, jacob_n) + values_tf, (9,), s, (3,), x_init_value=values, delta=1) + self.assertAllClose(jacob_t, jacob_n) + self.assertAllClose(jacob_t, grad_gt) def testGradientMatchesSegmentSum(self): # Strategy: compute the gradient for UnsortedSegmentSum and SegmentSum @@ -318,8 +377,7 @@ class UnsortedSegmentSumTest(SegmentReductionHelper): num_cols = 2 shape = [n, num_cols] num_segments = max(indices) + 1 - for dtype in [dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.complex64, - dtypes_lib.complex128]: + for dtype in self.differentiable_dtypes: with self.test_session(use_gpu=True): tf_x, np_x = self._input(shape, dtype=dtype) # Results from UnsortedSegmentSum @@ -353,9 +411,8 @@ class UnsortedSegmentSumTest(SegmentReductionHelper): unsorted.eval() def testEmptySecondDimension(self): - dtypes = [ - np.float32, np.float64, np.int64, np.int32, np.complex64, np.complex128 - ] + dtypes = [np.float16, np.float32, np.float64, np.int64, np.int32, + np.complex64, np.complex128] with self.test_session(use_gpu=True): for dtype in dtypes: for itype in (np.int32, np.int64): @@ -364,36 +421,14 @@ class UnsortedSegmentSumTest(SegmentReductionHelper): unsorted = math_ops.unsorted_segment_sum(data, segment_ids, 2) self.assertAllEqual(unsorted.eval(), np.zeros((2, 0), dtype=dtype)) - def testGradientSegmentMax(self): - num_cols = 2 - indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3]) - num_segments = max(indices_flat) + 3 - for indices in indices_flat, indices_flat.reshape(5, 2): - shape = indices.shape + (num_cols,) - with self.test_session(use_gpu=True): - tf_x, np_x = self._input(shape, dtype=dtypes_lib.float64) - s = math_ops.unsorted_segment_max( - data=tf_x, segment_ids=indices, num_segments=num_segments) - jacob_t, jacob_n = gradient_checker.compute_gradient( - tf_x, - shape, - s, - [num_segments, num_cols], - x_init_value=np_x.astype(np.double), delta=1) - self.assertAllClose(jacob_t, jacob_n) - def testDropNegatives(self): # Note: the test is done by replacing segment_ids with 8 to -1 # for index and replace values generated by numpy with 0. - dtypes = [ - dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int64, - dtypes_lib.int32, dtypes_lib.complex64, dtypes_lib.complex128 - ] indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3]) num_segments = 12 for indices in indices_flat, indices_flat.reshape(5, 2): shape = indices.shape + (2,) - for dtype in dtypes: + for dtype in self.all_dtypes: with self.test_session(use_gpu=True): tf_x, np_x = self._input(shape, dtype=dtype) np_ans = self._segmentReduce( @@ -507,6 +542,25 @@ class SparseSegmentReductionOpTest(SparseSegmentReductionHelper): tf_ans = s.eval() self.assertAllClose(np_ans, tf_ans) + def testWithEmptySegments(self): + tf_x = constant_op.constant([], shape=[0, 4], dtype=dtypes_lib.float32) + ops_list = [ + math_ops.sparse_segment_sum_with_num_segments, + math_ops.sparse_segment_mean_with_num_segments + ] + segment_indices = [] + tf_indices = [] + num_segments = 5 + with self.test_session(use_gpu=False): + for tf_op in ops_list: + s = tf_op( + data=tf_x, + indices=tf_indices, + segment_ids=segment_indices, + num_segments=num_segments) + tf_ans = s.eval() + self.assertAllClose(np.zeros([5, 4]), tf_ans) + def testSegmentIdsGreaterThanZero(self): tf_x, np_x = self._input([10, 4], dtype=dtypes_lib.float32) ops_list = [(np.add, None, math_ops.sparse_segment_sum), ( diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py index 051a25080b826de05ee3e24a82fbcd1f47995544..5fc9bef21816e3a12f0d274bab1fc82a83546422 100644 --- a/tensorflow/python/kernel_tests/slice_op_test.py +++ b/tensorflow/python/kernel_tests/slice_op_test.py @@ -283,7 +283,7 @@ class SliceTest(test.TestCase): # unintended behavior is prevented. c = constant_op.constant(5.0) with self.assertRaisesWithPredicateMatch( - TypeError, lambda e: "`Tensor` objects are not iterable" in str(e)): + TypeError, lambda e: "Tensor objects are not iterable" in str(e)): for _ in c: pass diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py index bb3f6970e4f18ce174062e2fce488af738b93a50..2b8e99e18e6881143ee77c4f1ec5096635e5c1b2 100644 --- a/tensorflow/python/kernel_tests/softmax_op_test.py +++ b/tensorflow/python/kernel_tests/softmax_op_test.py @@ -18,11 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import sys - import numpy as np -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util @@ -101,10 +98,10 @@ class SoftmaxTest(test.TestCase): def _testOverflow(self, use_gpu=False): if use_gpu: - type = np.float32 + type = np.float32 # pylint: disable=redefined-builtin else: - type = np.float64 - max = np.finfo(type).max + type = np.float64 # pylint: disable=redefined-builtin + max = np.finfo(type).max # pylint: disable=redefined-builtin features = np.array([[1., 1., 1., 1.], [max, 1., 2., 3.]]).astype(type) with self.test_session(use_gpu=use_gpu): tf_log_softmax = nn_ops.log_softmax(features) @@ -168,7 +165,7 @@ class SoftmaxTest(test.TestCase): def testEmptyInput(self): with self.test_session(): - x = constant_op.constant([[]], shape=[0, 3]) + x = array_ops.placeholder(dtypes.float32, shape=[0, 3]) self.assertEqual(0, array_ops.size(x).eval()) # reshape would raise if logits is empty with self.assertRaises(errors_impl.InvalidArgumentError): diff --git a/tensorflow/python/kernel_tests/spacetobatch_op_test.py b/tensorflow/python/kernel_tests/spacetobatch_op_test.py index b943dfa4e5f2a06eddcb3af03764e5e046b715f4..2a9232b6aecb66328f10a62f2251246c4fcec6e6 100644 --- a/tensorflow/python/kernel_tests/spacetobatch_op_test.py +++ b/tensorflow/python/kernel_tests/spacetobatch_op_test.py @@ -86,11 +86,11 @@ class CppOpImpl(object): @staticmethod def space_to_batch(*args, **kwargs): - return gen_array_ops._space_to_batch(*args, **kwargs) + return gen_array_ops.space_to_batch(*args, **kwargs) @staticmethod def batch_to_space(*args, **kwargs): - return gen_array_ops._batch_to_space(*args, **kwargs) + return gen_array_ops.batch_to_space(*args, **kwargs) class SpaceToBatchTest(test.TestCase, PythonOpImpl): diff --git a/tensorflow/python/kernel_tests/spacetodepth_op_test.py b/tensorflow/python/kernel_tests/spacetodepth_op_test.py index 3c98a685e07a1f2d55c3c1035a99ffaa593d35b3..4af0e6f9dba2fb992e76a4363ce611c365ff15e4 100644 --- a/tensorflow/python/kernel_tests/spacetodepth_op_test.py +++ b/tensorflow/python/kernel_tests/spacetodepth_op_test.py @@ -34,8 +34,8 @@ from tensorflow.python.platform import tf_logging class SpaceToDepthTest(test.TestCase): - def _testOne(self, inputs, block_size, outputs): - input_nhwc = math_ops.to_float(inputs) + def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32): + input_nhwc = math_ops.cast(inputs, dtype) with self.test_session(use_gpu=False): # test NHWC (default) on CPU x_tf = array_ops.space_to_depth(input_nhwc, block_size) @@ -58,6 +58,12 @@ class SpaceToDepthTest(test.TestCase): x_out = [[[[1, 2, 3, 4]]]] self._testOne(x_np, block_size, x_out) + def testBasicFloat16(self): + x_np = [[[[1], [2]], [[3], [4]]]] + block_size = 2 + x_out = [[[[1, 2, 3, 4]]]] + self._testOne(x_np, block_size, x_out, dtype=dtypes.float16) + # Tests for larger input dimensions. To make sure elements are # correctly ordered spatially. def testLargerInput2x2(self): diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py index cd5b711a0ed18aabff543aa4b6ecb1a885618caf..a841fe83a7f585a69ef33c437570359797484a4a 100644 --- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py @@ -64,7 +64,7 @@ class SparseXentTest(test.TestCase): def _testXent(self, np_features, np_labels): np_loss, np_backprop = self._npXent(np_features, np_labels) with self.test_session(use_gpu=True) as sess: - loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( np_features, np_labels) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) @@ -73,7 +73,7 @@ class SparseXentTest(test.TestCase): def testSingleClass(self): for label_dtype in np.int32, np.int64: with self.test_session(use_gpu=True) as sess: - loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( np.array([[1.], [-1.], [0.]]).astype(np.float32), np.array([0, 0, 0]).astype(label_dtype)) tf_loss, tf_backprop = sess.run([loss, backprop]) @@ -87,8 +87,9 @@ class SparseXentTest(test.TestCase): if test.is_built_with_cuda() and test.is_gpu_available(): with self.test_session(use_gpu=True) as sess: - loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - features, labels)) + loss, backprop = ( + gen_nn_ops.sparse_softmax_cross_entropy_with_logits( + features, labels)) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllClose( [[np.nan] * 4, [0.25, 0.25, 0.25, -0.75], @@ -100,8 +101,8 @@ class SparseXentTest(test.TestCase): [np.nan, 1.3862, 3.4420, np.nan], tf_loss, rtol=1e-3, atol=1e-3) with self.test_session(use_gpu=False) as sess: - loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - features, labels)) + loss, backprop = ( + gen_nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels)) with self.assertRaisesOpError("Received a label value of"): sess.run([loss, backprop]) diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py index 6171793b148f8d8f195b9548a13df89d29c5e96e..8cfee3eb933afcea7a58d5632948b87b0c4c10df 100644 --- a/tensorflow/python/kernel_tests/split_op_test.py +++ b/tensorflow/python/kernel_tests/split_op_test.py @@ -336,6 +336,20 @@ class SplitOpTest(test.TestCase): for s in splits: self.assertEqual(None, s.get_shape().ndims) + def testNonexistentDimTensor(self): + x = array_ops.placeholder(dtypes.int32) + values = np.zeros([5, 30]) + splits = array_ops.placeholder(dtypes.int32) + with self.assertRaisesRegexp(ValueError, "Cannot infer"): + y = array_ops.split(values, splits, axis=x) + + splits = array_ops.placeholder(dtypes.int32, [3]) + y = array_ops.split(values, splits, axis=x) + with self.test_session(use_gpu=True) as sess: + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "must have exactly one element"): + sess.run(y, {x: np.array([], dtype=np.int32), splits: [4, 11, 15]}) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/stack_op_test.py b/tensorflow/python/kernel_tests/stack_op_test.py index 347baf81148e9b747a9be4849912d154b220a084..2f27d1839b2218d0cc33d7278116186548ad3420 100644 --- a/tensorflow/python/kernel_tests/stack_op_test.py +++ b/tensorflow/python/kernel_tests/stack_op_test.py @@ -50,7 +50,7 @@ class StackOpTest(test.TestCase): # Convert [data[0], data[1], ...] separately to tensorflow # TODO(irving): Remove list() once we handle maps correctly xs = list(map(constant_op.constant, data)) - # Pack back into a single tensorflow tensor + # Stack back into a single tensorflow tensor c = array_ops.stack(xs) self.assertAllEqual(c.eval(), data) @@ -78,7 +78,7 @@ class StackOpTest(test.TestCase): for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2): for dtype in [np.bool, np.float32, np.int32, np.int64]: data = np.random.randn(*shape).astype(dtype) - # Pack back into a single tensorflow tensor directly using np array + # Stack back into a single tensorflow tensor directly using np array c = array_ops.stack(data) # This is implemented via a Const: self.assertEqual(c.op.type, "Const") @@ -223,7 +223,7 @@ class StackOpTest(test.TestCase): array_ops.stack(t, axis=-3) -class AutomaticPackingTest(test.TestCase): +class AutomaticStackingTest(test.TestCase): def testSimple(self): with self.test_session(use_gpu=True): diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py index aa409336f5c50178e4d0ca946190119fb0e4188e..afd2eaffab992bca4b3ae7b4f65e0370f325b548 100644 --- a/tensorflow/python/kernel_tests/stack_ops_test.py +++ b/tensorflow/python/kernel_tests/stack_ops_test.py @@ -34,11 +34,11 @@ class StackOpTest(test.TestCase): def _testStackPushPop(self, use_gpu): with self.test_session(use_gpu=use_gpu): - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval()) def testStackPushPop(self): @@ -49,11 +49,11 @@ class StackOpTest(test.TestCase): with self.test_session(use_gpu=use_gpu): a = np.arange(2000) x = constant_op.constant(a, dtype=dtypes.float32) - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True) + c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose(a, c1.eval()) def testStackPushPopSwap(self): @@ -63,7 +63,7 @@ class StackOpTest(test.TestCase): def _testStackWhileSwap(self, use_gpu): with self.test_session(use_gpu=use_gpu): n = constant_op.constant(0) - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") def c(x): @@ -72,7 +72,7 @@ class StackOpTest(test.TestCase): def b(x): with ops.control_dependencies([x]): a = constant_op.constant(np.ones(2000), dtype=dtypes.float32) - v = gen_data_flow_ops._stack_push_v2(h, a, swap_memory=True) + v = gen_data_flow_ops.stack_push_v2(h, a, swap_memory=True) with ops.control_dependencies([v]): return math_ops.add(x, 1) @@ -86,7 +86,7 @@ class StackOpTest(test.TestCase): def b1(x, y): nx = math_ops.subtract(x, 1) - ny = y + gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + ny = y + gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) return [nx, ny] _, ry = control_flow_ops.while_loop( @@ -99,16 +99,16 @@ class StackOpTest(test.TestCase): def _testMultiStack(self, use_gpu): with self.test_session(use_gpu=use_gpu): - h1 = gen_data_flow_ops._stack_v2( + h1 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0) + c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - h2 = gen_data_flow_ops._stack_v2( + c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + h2 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval()) @@ -119,17 +119,17 @@ class StackOpTest(test.TestCase): def _testSameNameStacks(self, use_gpu): """Different stacks with the same name do not interfere.""" with self.test_session(use_gpu=use_gpu) as sess: - h1 = gen_data_flow_ops._stack_v2( + h1 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - h2 = gen_data_flow_ops._stack_v2( + h2 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0) + c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0) with ops.control_dependencies([c1]): - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) out1, out2 = sess.run([pop1, pop2]) self.assertAllClose(out1, 4.0) @@ -141,9 +141,9 @@ class StackOpTest(test.TestCase): def _testCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1) def testCloseStack(self): @@ -152,11 +152,11 @@ class StackOpTest(test.TestCase): def _testPushCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1) def testPushCloseStack(self): @@ -170,9 +170,9 @@ class StackOpRefTest(test.TestCase): def _testStackPushPop(self, use_gpu): with self.test_session(use_gpu=use_gpu): h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval()) def testStackPushPop(self): @@ -184,9 +184,9 @@ class StackOpRefTest(test.TestCase): a = np.arange(2000) x = constant_op.constant(a, dtype=dtypes.float32) h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, x, swap_memory=True) + c = gen_data_flow_ops.stack_push(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32) self.assertAllClose(a, c1.eval()) def testStackPushPopSwap(self): @@ -196,13 +196,13 @@ class StackOpRefTest(test.TestCase): def _testMultiStack(self, use_gpu): with self.test_session(use_gpu=use_gpu): h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push(h1, 4.0) + c1 = gen_data_flow_ops.stack_push(h1, 4.0) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop(h1, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h1, dtypes.float32) h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push(h2, 5.0) + c2 = gen_data_flow_ops.stack_push(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval()) @@ -217,7 +217,7 @@ class StackOpRefTest(test.TestCase): def b(x): with ops.control_dependencies([x]): a = constant_op.constant(np.ones(2000), dtype=dtypes.float32) - v = gen_data_flow_ops._stack_push(h, a, swap_memory=True) + v = gen_data_flow_ops.stack_push(h, a, swap_memory=True) with ops.control_dependencies([v]): return math_ops.add(x, 1) @@ -231,7 +231,7 @@ class StackOpRefTest(test.TestCase): def b1(x, y): nx = math_ops.subtract(x, 1) - ny = y + gen_data_flow_ops._stack_pop(h, dtypes.float32) + ny = y + gen_data_flow_ops.stack_pop(h, dtypes.float32) return [nx, ny] _, ry = control_flow_ops.while_loop( @@ -249,9 +249,9 @@ class StackOpRefTest(test.TestCase): def _testSameNameStacks(self, use_gpu): with self.test_session(use_gpu=use_gpu): h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push(h1, 4.0) + c1 = gen_data_flow_ops.stack_push(h1, 4.0) h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c2 = gen_data_flow_ops._stack_push(h2, 5.0) + c2 = gen_data_flow_ops.stack_push(h2, 5.0) _ = c1 + c2 self.assertNotEqual(h1.eval()[1], h2.eval()[1]) @@ -262,7 +262,7 @@ class StackOpRefTest(test.TestCase): def _testCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close(h) + c1 = gen_data_flow_ops.stack_close(h) sess.run(c1) def testCloseStack(self): @@ -272,9 +272,9 @@ class StackOpRefTest(test.TestCase): def _testPushCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close(h) + c1 = gen_data_flow_ops.stack_close(h) sess.run(c1) def testPushCloseStack(self): diff --git a/tensorflow/python/kernel_tests/template_test.py b/tensorflow/python/kernel_tests/template_test.py index 8792ab41a07aac2dc8c9fcb956c378054a309a41..1b935d5286729e9e802c56e90e2ae7ab72a6e080 100644 --- a/tensorflow/python/kernel_tests/template_test.py +++ b/tensorflow/python/kernel_tests/template_test.py @@ -17,10 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools import traceback from tensorflow.python.client import session from tensorflow.python.eager import context +from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops @@ -354,6 +356,10 @@ class TemplateTest(test.TestCase): self.assertEqual("s1_1/nested/dummy:0", v5.name) self.assertEqual("s1_1/nested_1/dummy:0", v6.name) + self.assertEqual(2, len(tmpl1._checkpoint_dependencies)) + self.assertEqual("nested", tmpl1._checkpoint_dependencies[0].name) + self.assertEqual("nested_1", tmpl1._checkpoint_dependencies[1].name) + @test_util.run_in_graph_and_eager_modes() def test_nested_templates_with_defun(self): @@ -412,6 +418,17 @@ class TemplateTest(test.TestCase): self.assertEqual("s1_1/nested/dummy:0", v3[0].name) self.assertEqual("s1_1/nested_1/dummy:0", v3[1].name) + def test_graph_function_no_name(self): + with context.eager_mode(): + + def f(_, y): + return y + 1 + + partial = functools.partial(f, 1.0) + tmpl = template.make_template_internal( + "a", partial, create_graph_function_=True) + self.assertAllEqual(tmpl(ops.convert_to_tensor(1.0)), 2.0) + @test_util.run_in_graph_and_eager_modes() def test_immediate_scope_creation(self): # Create templates in scope a then call in scope b. make_template should @@ -545,7 +562,7 @@ class TemplateTest(test.TestCase): outputs_b, _ = linear1(inputs) self.assertEquals("foo", linear1.variable_scope.name) self.assertEquals("foo/w:0", w1.name) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEquals("foo/add:0", outputs_a.name, "First application of template should get " "same name scope as variables.") @@ -560,7 +577,7 @@ class TemplateTest(test.TestCase): "New template gets a freshly uniquified variable scope " "because 'foo' is already taken.") self.assertEquals("foo_1/w:0", w2.name) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEquals("foo_1_1/add:0", outputs_c.name, "First application of template would get " "same name scope as variables, but 'foo_1' is already " @@ -575,7 +592,7 @@ class TemplateTest(test.TestCase): with variable_scope.variable_scope("foo"): # Create two templates with the same name, ensure scopes are made unique. ta = template.make_template("bar", variable_scoped_function, True) - if context.in_eager_mode(): + if context.executing_eagerly(): tb = template.make_template("s", function_with_side_create, trainable=False) else: diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index aad2443eea7ad87faf481973e91ca3df32ccfb44..a834675828b67aed4057d1857c546a586cee69c9 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -399,28 +399,14 @@ class TensorArrayTest(test.TestCase): def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) - in_graph_mode = context.in_graph_mode() # Test writing the wrong datatype - if in_graph_mode: - with self.assertRaisesOpError( - "TensorArray dtype is float but Op is trying to write " - "dtype string"): - self.evaluate(ta.write(0, "wrong_type_scalar").flow) - else: - with self.assertRaisesOpError( - "TensorArray dtype is float32 but Op is trying to write " - "dtype string"): - self.evaluate(ta.write(0, "wrong_type_scalar").flow) + with self.assertRaisesOpError( + "TensorArray dtype is (float|float32) but Op is trying to write " + "dtype string"): + self.evaluate(ta.write(0, "wrong_type_scalar").flow) - if context.in_graph_mode(): - with self.assertRaisesOpError( - "Tried to write to index -1 but array is not " - "resizeable and size is: 3"): - self.evaluate(ta.write(-1, 3.0).flow) - else: - with self.assertRaisesOpError( - r"Writing to negative indices \(index -1\) is not allowed."): - self.evaluate(ta.write(-1, 3.0).flow) + with self.assertRaisesOpError("index -1"): + self.evaluate(ta.write(-1, 3.0).flow) # Test reading from too large an index with self.assertRaisesOpError( @@ -435,23 +421,17 @@ class TensorArrayTest(test.TestCase): w0 = ta.write(0, [[4.0, 5.0]]) - # Test reading wrong datatype, which is only possible in graph mode - if context.in_graph_mode(): - r0_bad = gen_data_flow_ops._tensor_array_read_v3( + # Test reading wrong datatype (only possible when constructing graphs). + if not context.executing_eagerly(): + r0_bad = gen_data_flow_ops.tensor_array_read_v3( handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow) with self.assertRaisesOpError( "TensorArray dtype is float but Op requested dtype double."): r0_bad.eval() # Test reading from a negative index, which is not allowed - if context.in_graph_mode(): - with self.assertRaisesOpError( - r"Tried to read from index -1 but array size is: 3"): - self.evaluate(ta.read(-1)) - else: - with self.assertRaisesOpError( - r"Reading from negative indices \(index -1\) is not allowed."): - self.evaluate(ta.read(-1)) + with self.assertRaisesOpError("index -1"): + self.evaluate(ta.read(-1)) # Test reading from too large an index with self.assertRaisesOpError( @@ -467,10 +447,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError( "Could not write to TensorArray index 2 because " "it has already been written to."): - if context.in_graph_mode(): - self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - else: - self.evaluate(ta.write(2, 3.0).write(2, 3.0)) + self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) @test_util.run_in_graph_and_eager_modes() def testTensorArrayConcatIncompatibleShapesFails(self): @@ -499,58 +476,40 @@ class TensorArrayTest(test.TestCase): w2 = w1.write(1, [4.0]) w3 = w2.write(2, [[3.0]]) - # The eager-mode implementation just passes up array_op.concat's error - # message. - if context.in_graph_mode(): - with self.assertRaisesOpError( - r"TensorArray has inconsistent shapes. Index 0 has " - r"\(excepting dimension 0\) shape: \[\] but index 2 has " - r"\(excepting dimension 0\) shape: \[1\]"): - self.evaluate(w3.concat()) - else: - with self.assertRaisesOpError( - r".*Ranks of all input tensors should match: shape\[0\] " - r"= \[1\] vs\. shape\[2\] = \[1,1\].*"): - self.evaluate(w3.concat()) + # The exact error messages differ between eager execution and graph + # construction as the former bubbles up the error from array_op.concat. + with self.assertRaisesOpError("shape"): + self.evaluate(w3.concat()) @test_util.run_in_graph_and_eager_modes() def testTensorArraySplitIncompatibleShapesFails(self): with self.test_session(use_gpu=True): - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() ta = _make_ta(3, "foo") with self.assertRaisesOpError( r"Expected lengths to be a vector, received shape: \[\]"): - if in_graph_mode: + if in_eager_mode: + self.evaluate(ta.split([1.0, 2.0, 3.0], 1)) + else: lengths = array_ops.placeholder(dtypes.int64) ta.split([1.0, 2.0, 3.0], lengths).flow.eval(feed_dict={lengths: 1}) - else: - self.evaluate(ta.split([1.0, 2.0, 3.0], 1)) with self.assertRaisesOpError( r"Expected sum of lengths to be equal to values.shape\[0\], " r"but sum of lengths is 1 and value's shape is: \[3\]"): - if in_graph_mode: - self.evaluate(ta.split([1.0, 2.0, 3.0], [1]).flow) - else: - self.evaluate(ta.split([1.0, 2.0, 3.0], [1])) + self.evaluate(ta.split([1.0, 2.0, 3.0], [1]).flow) ta = _make_ta(1, "baz") with self.assertRaisesOpError( r"Expected value to be at least a vector, but received shape: \[\]"): - if in_graph_mode: - self.evaluate(ta.split(1.0, [1]).flow) - else: - self.evaluate(ta.split(1.0, [1])) + self.evaluate(ta.split(1.0, [1]).flow) ta = _make_ta(2, "buz") with self.assertRaisesOpError( r"TensorArray's size is not equal to the size of lengths " r"\(2 vs. 1\), and the TensorArray is not marked as " r"dynamically resizeable"): - if in_graph_mode: - self.evaluate(ta.split([1.0], [1]).flow) - else: - self.evaluate(ta.split([1.0], [1])) + self.evaluate(ta.split([1.0], [1]).flow) def _testTensorArrayWriteGradientAddMultipleAdds(self, dtype): with self.test_session(use_gpu=True): @@ -868,14 +827,14 @@ class TensorArrayTest(test.TestCase): vout = func(v0, state0, var) grad_val = -np.arange(3 * 5, dtype=np_dtype).reshape(3, 5) - if context.in_graph_mode(): + if context.executing_eagerly(): + grad_fn = backprop.gradients_function(func) + v0_grad, state0_grad, var_grad = grad_fn(v0, state0, var, dy=grad_val) + else: v0_grad = gradients_impl.gradients([vout], [v0], [grad_val])[0] state0_grad = gradients_impl.gradients([vout], [state0], [grad_val])[0] var_grad = gradients_impl.gradients([vout], [var], [grad_val])[0] variables.global_variables_initializer().run() - else: - grad_fn = backprop.gradients_function(func) - v0_grad, state0_grad, var_grad = grad_fn(v0, state0, var, dy=grad_val) state0_t, var_t, v0_t, vout_t, v0_grad_t, var_grad_t, state0_grad_t = ( self.evaluate( @@ -959,10 +918,10 @@ class TensorArrayTest(test.TestCase): return r x = constant_op.constant(2.0, name="x") - if context.in_graph_mode(): - grad = gradients_impl.gradients(loop(x), [x])[0] - else: + if context.executing_eagerly(): grad = backprop.gradients_function(loop)(x)[0] + else: + grad = gradients_impl.gradients(loop(x), [x])[0] self.assertAllClose(31.0, self.evaluate(grad)) def testSumOfTwoReadVariablesWithoutRepeatGrad(self): @@ -1158,14 +1117,14 @@ class TensorArrayTest(test.TestCase): infer_shape=True) w0 = ta1.split(value, [1, 2]) r0 = w0.read(0) - if context.in_graph_mode(): + if context.executing_eagerly(): + self.assertEqual((1, 2), r0.get_shape()) + self.assertEqual((2, 2), w0.read(1).get_shape()) + else: self.assertEqual(r0.get_shape().ndims, None) self.assertEqual( tensor_shape.TensorShape( ta1.handle.op.get_attr("element_shape")).ndims, None) - else: - self.assertEqual((1, 2), r0.get_shape()) - self.assertEqual((2, 2), w0.read(1).get_shape()) def testWriteUnknownShape(self): with self.test_session(use_gpu=True): @@ -1297,13 +1256,13 @@ class TensorArrayTest(test.TestCase): g = func(values) grad_ys = [[[2.0, 3.0], [4.0, 5.0]]] # Test combined gradients + aggregation of read(0) - if context.in_graph_mode(): - grad = gradients_impl.gradients(ys=[g], xs=[values], grad_ys=grad_ys) - g_vals, grad_vals = session.run([[g], grad]) - else: + if context.executing_eagerly(): g_vals = [g] grad_vals = backprop.gradients_function(func)( values, dy=constant_op.constant(grad_ys[0], dtype=dtypes.float32)) + else: + grad = gradients_impl.gradients(ys=[g], xs=[values], grad_ys=grad_ys) + g_vals, grad_vals = session.run([[g], grad]) # Gradients for 8 of the 10 unread components are zero. expected_grad = np.zeros((10, 2)) @@ -1453,13 +1412,13 @@ class TensorArrayTest(test.TestCase): # Tests correct properties on new TensorArrays. self.assertEqual(dtypes.float32, ta0.dtype) self.assertEqual(dtypes.int32, ta1.dtype) - if context.in_graph_mode(): - self.assertEqual(tensor_shape.unknown_shape(), read0.get_shape()) + if context.executing_eagerly(): + self.assertEqual(tensor_shape.scalar(), read0.get_shape()) else: - self.assertEqual(tensor_shape.scalar(), read1.get_shape()) + self.assertEqual(tensor_shape.unknown_shape(), read0.get_shape()) self.assertEqual(tensor_shape.scalar(), read1.get_shape()) - if context.in_graph_mode(): + if not context.executing_eagerly(): variables.global_variables_initializer().run() read0_v, read1_v, size0_v, size1_v = self.evaluate((read0, read1, size0, diff --git a/tensorflow/python/kernel_tests/unique_op_test.py b/tensorflow/python/kernel_tests/unique_op_test.py index 6366d2e181c8cfabba8a78b664c25c85debc67ef..bbc040dc13fc151b970f130eeb76fa1639245416 100644 --- a/tensorflow/python/kernel_tests/unique_op_test.py +++ b/tensorflow/python/kernel_tests/unique_op_test.py @@ -66,9 +66,9 @@ class UniqueTest(test.TestCase): for dtype in [np.int32, np.int64]: x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]]) with self.test_session() as sess: - y0, idx0 = gen_array_ops._unique_v2(x, axis=np.array([0], dtype)) + y0, idx0 = gen_array_ops.unique_v2(x, axis=np.array([0], dtype)) tf_y0, tf_idx0 = sess.run([y0, idx0]) - y1, idx1 = gen_array_ops._unique_v2(x, axis=np.array([1], dtype)) + y1, idx1 = gen_array_ops.unique_v2(x, axis=np.array([1], dtype)) tf_y1, tf_idx1 = sess.run([y1, idx1]) self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]])) self.assertAllEqual(tf_idx0, np.array([0, 0, 1])) @@ -80,7 +80,7 @@ class UniqueTest(test.TestCase): # by default, the axis will be wrapped to allow `axis=None`. x = np.random.randint(2, high=10, size=7000) with self.test_session() as sess: - y, idx = gen_array_ops._unique_v2(x, axis=np.array([], np.int32)) + y, idx = gen_array_ops.unique_v2(x, axis=np.array([], np.int32)) tf_y, tf_idx = sess.run([y, idx]) self.assertEqual(len(x), len(tf_idx)) @@ -133,6 +133,39 @@ class UniqueWithCountsTest(test.TestCase): v = [1 if x[i] == value.decode('ascii') else 0 for i in range(7000)] self.assertEqual(count, sum(v)) + def testInt32Axis(self): + for dtype in [np.int32, np.int64]: + x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]]) + with self.test_session() as sess: + y0, idx0, count0 = gen_array_ops.unique_with_counts_v2( + x, axis=np.array([0], dtype)) + tf_y0, tf_idx0, tf_count0 = sess.run([y0, idx0, count0]) + y1, idx1, count1 = gen_array_ops.unique_with_counts_v2( + x, axis=np.array([1], dtype)) + tf_y1, tf_idx1, tf_count1 = sess.run([y1, idx1, count1]) + self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]])) + self.assertAllEqual(tf_idx0, np.array([0, 0, 1])) + self.assertAllEqual(tf_count0, np.array([2, 1])) + self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]])) + self.assertAllEqual(tf_idx1, np.array([0, 1, 1])) + self.assertAllEqual(tf_count1, np.array([1, 2])) + + def testInt32V2(self): + # This test is only temporary, once V2 is used + # by default, the axis will be wrapped to allow `axis=None`. + x = np.random.randint(2, high=10, size=7000) + with self.test_session() as sess: + y, idx, count = gen_array_ops.unique_with_counts_v2( + x, axis=np.array([], np.int32)) + tf_y, tf_idx, tf_count = sess.run([y, idx, count]) + + self.assertEqual(len(x), len(tf_idx)) + self.assertEqual(len(tf_y), len(np.unique(x))) + for i in range(len(x)): + self.assertEqual(x[i], tf_y[tf_idx[i]]) + for value, count in zip(tf_y, tf_count): + self.assertEqual(count, np.sum(x == value)) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/unstack_op_test.py b/tensorflow/python/kernel_tests/unstack_op_test.py index 84818755766a435c873f30e96dc0080af4f78b84..1ee6e0866a6b1c7a9b641a95403d45213f5dc0b4 100644 --- a/tensorflow/python/kernel_tests/unstack_op_test.py +++ b/tensorflow/python/kernel_tests/unstack_op_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Functional tests for Unpack Op.""" +"""Functional tests for Unstack Op.""" from __future__ import absolute_import from __future__ import division @@ -49,7 +49,7 @@ class UnstackOpTest(test.TestCase): data = np.random.randn(*shape).astype(dtype) # Convert data to a single tensorflow tensor x = constant_op.constant(data) - # Unpack into a list of tensors + # Unstack into a list of tensors cs = array_ops.unstack(x, num=shape[0]) self.assertEqual(type(cs), list) self.assertEqual(len(cs), shape[0]) @@ -66,7 +66,7 @@ class UnstackOpTest(test.TestCase): data = np.random.randn(*shape).astype(dtype) # Convert data to a single tensorflow tensor x = constant_op.constant(data) - # Unpack into a list of tensors + # Unstack into a list of tensors cs = array_ops.unstack(x, num=shape[0]) self.assertEqual(type(cs), list) self.assertEqual(len(cs), shape[0]) diff --git a/tensorflow/python/kernel_tests/variable_ops_test.py b/tensorflow/python/kernel_tests/variable_ops_test.py index 79071029fd42374964d12f513e9c510bdc7400eb..cf369c071813120fef685b7220292d50b966cf11 100644 --- a/tensorflow/python/kernel_tests/variable_ops_test.py +++ b/tensorflow/python/kernel_tests/variable_ops_test.py @@ -165,26 +165,26 @@ class VariableOpTest(test.TestCase): def testTemporaryVariable(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="foo") var = state_ops.assign(var, [[4.0, 5.0]]) var = state_ops.assign_add(var, [[6.0, 7.0]]) - final = gen_state_ops._destroy_temporary_variable(var, var_name="foo") + final = gen_state_ops.destroy_temporary_variable(var, var_name="foo") self.assertAllClose([[10.0, 12.0]], final.eval()) def testDestroyNonexistentTemporaryVariable(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable([1, 2], dtypes.float32) - final = gen_state_ops._destroy_temporary_variable(var, var_name="bad") + var = gen_state_ops.temporary_variable([1, 2], dtypes.float32) + final = gen_state_ops.destroy_temporary_variable(var, var_name="bad") with self.assertRaises(errors.NotFoundError): final.eval() def testDuplicateTemporaryVariable(self): with self.test_session(use_gpu=True): - var1 = gen_state_ops._temporary_variable( + var1 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="dup") var1 = state_ops.assign(var1, [[1.0, 2.0]]) - var2 = gen_state_ops._temporary_variable( + var2 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="dup") var2 = state_ops.assign(var2, [[3.0, 4.0]]) final = var1 + var2 @@ -193,25 +193,25 @@ class VariableOpTest(test.TestCase): def testDestroyTemporaryVariableTwice(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable([1, 2], dtypes.float32) - val1 = gen_state_ops._destroy_temporary_variable(var, var_name="dup") - val2 = gen_state_ops._destroy_temporary_variable(var, var_name="dup") + var = gen_state_ops.temporary_variable([1, 2], dtypes.float32) + val1 = gen_state_ops.destroy_temporary_variable(var, var_name="dup") + val2 = gen_state_ops.destroy_temporary_variable(var, var_name="dup") final = val1 + val2 with self.assertRaises(errors.NotFoundError): final.eval() def testTemporaryVariableNoLeak(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="bar") final = array_ops.identity(var) final.eval() def testTwoTemporaryVariablesNoLeaks(self): with self.test_session(use_gpu=True): - var1 = gen_state_ops._temporary_variable( + var1 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="var1") - var2 = gen_state_ops._temporary_variable( + var2 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="var2") final = var1 + var2 final.eval() diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 8527f116f9541942e52ba2ab635ca1212ea38583..531d0cdf9002d966909fe29ffcd538c3510c60ab 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -166,12 +166,10 @@ class VariableScopeTest(test.TestCase): self.evaluate(variables_lib.variables_initializer([w])) self.assertAllClose(self.evaluate(w.value()), [1, 2, 3]) - if context.in_graph_mode(): - with self.assertRaises(TypeError): - variable_scope.get_variable("x4", initializer={}) - else: - with self.assertRaises(ValueError): - variable_scope.get_variable("x4", initializer={}) + # A quirk to be revisited? + error = ValueError if context.executing_eagerly() else TypeError + with self.assertRaises(error): + variable_scope.get_variable("x4", initializer={}) @test_util.run_in_graph_and_eager_modes() def testInitFromNonInitializer(self): @@ -267,7 +265,7 @@ class VariableScopeTest(test.TestCase): self.assertAllClose(self.evaluate(losses[2]), 0.5) with variable_scope.variable_scope("foo", reuse=True): # reuse=True is for now only supported when eager execution is disabled. - if context.in_graph_mode(): + if not context.executing_eagerly(): v = variable_scope.get_variable("v", []) # "v" is alredy there, reused losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) @@ -374,7 +372,7 @@ class VariableScopeTest(test.TestCase): v = variable_scope.get_variable("v", []) self.evaluate(variables_lib.variables_initializer([v])) self.assertAllClose(self.evaluate(v.value()), 0.3) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Check that we can set reuse. variable_scope.get_variable_scope().reuse_variables() with self.assertRaises(ValueError): # Fail, w does not exist yet. @@ -408,7 +406,7 @@ class VariableScopeTest(test.TestCase): with variable_scope.variable_scope("tower") as tower: with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope1/tower/scope2/") - if context.in_graph_mode(): + if not context.executing_eagerly(): with variable_scope.variable_scope( tower): # Re-entering acts like another "tower". with ops.name_scope("scope2") as sc2: @@ -422,7 +420,7 @@ class VariableScopeTest(test.TestCase): with variable_scope.variable_scope("tower"): with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope2/tower/scope2/") - if context.in_graph_mode(): + if not context.executing_eagerly(): with variable_scope.variable_scope(tower): with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope2/tower_1/scope2/") @@ -903,17 +901,15 @@ class VariableScopeTest(test.TestCase): "w", [], collections=["foo"]) self.assertEqual(local_var.name, "outer/w:0") - # Since variable is local, it should be in the local variable collection - # but not the trainable collection. - if context.in_graph_mode(): + if not context.executing_eagerly(): + # Since variable is local, it should be in the local variable collection + # but not the trainable collection. self.assertIn(local_var, ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)) self.assertIn(local_var, ops.get_collection("foo")) self.assertNotIn(local_var, ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - - # Check that local variable respects `reuse`. - if context.in_graph_mode(): + # Check that local variable respects `reuse`. with variable_scope.variable_scope(outer, "default", reuse=True): self.assertEqual( variable_scope.get_local_variable("w", []).name, "outer/w:0") diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py index b16c8c002c98a0351d1fc55fce061695327a18c9..27599868b74be323189b872c2147c6a33f84d170 100644 --- a/tensorflow/python/kernel_tests/variables_test.py +++ b/tensorflow/python/kernel_tests/variables_test.py @@ -687,7 +687,7 @@ class VariableContainerTest(test.TestCase): v1 = variables.Variable([1]) with ops.container("l2"): v2 = variables.Variable([2]) - special_v = gen_state_ops._variable( + special_v = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="VariableInL3", diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py index e152f02d8e983364603053dc5c8d14b5dfaf3605..e3e120a4eb01885ac5ac5e41f82ad3e480a83a77 100644 --- a/tensorflow/python/kernel_tests/xent_op_test.py +++ b/tensorflow/python/kernel_tests/xent_op_test.py @@ -48,7 +48,7 @@ class XentTest(test.TestCase): def _testXent(self, np_features, np_labels, use_gpu=False): np_loss, np_backprop = self._npXent(np_features, np_labels) with self.test_session(use_gpu=use_gpu) as sess: - loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits( np_features, np_labels) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) @@ -71,7 +71,7 @@ class XentTest(test.TestCase): def _testSingleClass(self, use_gpu=False): for dtype in np.float16, np.float32: with self.test_session(use_gpu=use_gpu) as sess: - loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits( np.array([[1.], [-1.], [0.]]).astype(dtype), np.array([[-1.], [0.], [1.]]).astype(dtype)) tf_loss, tf_backprop = sess.run([loss, backprop]) @@ -89,7 +89,7 @@ class XentTest(test.TestCase): np_labels = np.array([[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype) self.assertRaisesRegexp(ValueError, "must be rank 2", - gen_nn_ops._softmax_cross_entropy_with_logits, + gen_nn_ops.softmax_cross_entropy_with_logits, np_features, np_labels) def testNpXent(self): @@ -131,14 +131,14 @@ class XentTest(test.TestCase): def testShapeMismatch(self): with self.test_session(): with self.assertRaises(ValueError): - gen_nn_ops._softmax_cross_entropy_with_logits( + gen_nn_ops.softmax_cross_entropy_with_logits( [[0., 1.], [2., 3.]], [[0., 1., 0.], [1., 0., 0.]]) def testNotMatrix(self): with self.test_session(): with self.assertRaises(ValueError): - gen_nn_ops._softmax_cross_entropy_with_logits([0., 1., 2., 3.], - [0., 1., 0., 1.]) + gen_nn_ops.softmax_cross_entropy_with_logits([0., 1., 2., 3.], + [0., 1., 0., 1.]) def testHalf(self): self._testAll( diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index 5dea732cbaa43a40f6a1bc4beef729f3b84dad5c..e9066d3fdaff95d543be9df81d1c60d5d4197290 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -36,10 +36,13 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export -class Layer(object): +@tf_export('layers.Layer') +class Layer(checkpointable.CheckpointableBase): """Base layer class. This is the class from which all layers inherit, implementing common @@ -112,7 +115,7 @@ class Layer(object): # Provides information about which inputs are compatible with the layer. self.input_spec = None - if activity_regularizer and context.in_eager_mode(): + if activity_regularizer and context.executing_eagerly(): raise ValueError( ('Activity regularization is not supported when executing eagerly. ' 'Got activity_regularizer=%s') % (activity_regularizer,)) @@ -124,14 +127,12 @@ class Layer(object): # return tensors. When using graph execution, _losses is a list of ops. self._losses = [] self._reuse = kwargs.get('_reuse') - self._graph = ops.get_default_graph() - self._per_input_losses = {} - self._per_input_updates = {} + self._graph = None # Will be set at build time. self._dtype = None if dtype is None else dtypes.as_dtype(dtype).name - call_fn_args = estimator_util.fn_args(self.call) - self._compute_previous_mask = ('mask' in call_fn_args or + self._call_fn_args = estimator_util.fn_args(self.call) + self._compute_previous_mask = ('mask' in self._call_fn_args or hasattr(self, 'compute_mask')) - self._call_has_scope_arg = 'scope' in call_fn_args + self._call_has_scope_arg = 'scope' in self._call_fn_args # These lists will be filled via successive calls # to self._add_inbound_node(). @@ -227,7 +228,7 @@ class Layer(object): @property def updates(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.updates not supported in Eager mode.') if not self.trainable and not self.stateful: return [] @@ -250,39 +251,34 @@ class Layer(object): Arguments: updates: Update op, or list/tuple of update ops. - inputs: Optional input tensor(s) that the update(s) depend on. Must - match the `inputs` argument passed to the `__call__` method at the time - the updates are created. If `None` is passed, the updates are assumed - to be unconditional, and will apply across all dataflows of the layer. + inputs: If anything other than None is passed, it signals the updates + are conditional on some of the layer's inputs, + and thus they should only be run where these inputs are available. + This is the case for BatchNormalization updates, for instance. + If None, the updates will be taken into account unconditionally, + and you are responsible for making sure that any dependency they might + have is available at runtime. + A step counter might fall into this category. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return # Updates already applied when in eager mode. + updates = _to_list(updates) - if not updates: - return + updates = [x if isinstance(x, ops.Operation) + else ops.convert_to_tensor(x) for x in updates] self._updates += updates - if inputs is not None: - inputs = nest.flatten(inputs) - if not inputs: - inputs = None - if inputs is not None: - # We compute an ID that uniquely identifies the list of tensors. - # This ID is order-sensitive. - inputs_hash = layers_util.object_list_uid(inputs) + if inputs is None: + for u in updates: + u._unconditional_update = True # pylint: disable=protected-access else: - inputs_hash = None - if inputs_hash not in self._per_input_updates: - self._per_input_updates[inputs_hash] = [] - self._per_input_updates[inputs_hash] += updates + for u in updates: + u._unconditional_update = False # pylint: disable=protected-access def get_updates_for(self, inputs): """Retrieves updates relevant to a specific set of inputs. Arguments: inputs: Input tensor or list/tuple of input tensors. - Must match the `inputs` argument passed to the `__call__` method - at the time the updates were created. - If you pass `inputs=None`, unconditional updates are returned. Returns: List of update ops of the layer that depend on `inputs`. @@ -290,19 +286,25 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): - raise RuntimeError('Layer.get_updates_for not supported in Eager mode.') + if context.executing_eagerly(): + raise RuntimeError('`get_updates_for()` not supported in Eager mode.') + + # Updates disabled if layer is not trainable and not explicitly stateful. if not self.trainable and not self.stateful: return [] - if inputs is not None: - inputs = nest.flatten(inputs) - if not inputs: - inputs = None - if inputs is not None: - inputs_hash = layers_util.object_list_uid(inputs) - else: - inputs_hash = None - return self._per_input_updates.get(inputs_hash, []) + + if inputs is None: + # Requesting unconditional updates. + return [x for x in self.updates if x._unconditional_update] # pylint: disable=protected-access + + # Requesting input-conditional updates. + inputs = nest.flatten(inputs) + reachable = layers_util.get_reachable_from_inputs(inputs, self.updates) + updates = [] + for update in self.updates: + if update in reachable: + updates.append(update) + return updates @property def losses(self): @@ -315,7 +317,7 @@ class Layer(object): Returns: A list of tensors. """ - if context.in_eager_mode(): + if context.executing_eagerly(): # _losses may only contain variable regularization losses when executing # eagerly, and they have been saved as lambdas to be executed when # requested. @@ -342,34 +344,37 @@ class Layer(object): Arguments: losses: Loss tensor, or list/tuple of tensors. - inputs: Optional input tensor(s) that the loss(es) depend on. Must - match the `inputs` argument passed to the `__call__` method at the time - the losses are created. If `None` is passed, the losses are assumed + inputs: If anything other than None is passed, it signals the losses + are conditional on some of the layer's inputs, + and thus they should only be run where these inputs are available. + This is the case for activity regularization losses, for instance. + If `None` is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses). Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): + # TODO(fchollet): it should be possible (and highly desirable) to support + # `add_loss` in eager mode. This allows great convenience and flexibility + # in defining custom losses on the fly (e.g. in VAEs). + # Simply appending the loss value to `self._losses` + # is the correct behavior. + # The only caveat is that we need to force the user to only call + # `add_loss` from inside a model or Layer's `call` method + # (otherwise the loss computation cannot be backproped through). raise RuntimeError('Layer.add_loss not supported in Eager mode.') + losses = _to_list(losses) - if not losses: - return self._losses += losses - if inputs is not None: - inputs = nest.flatten(inputs) - if not inputs: - inputs = None - if inputs is not None: - # We compute an ID that uniquely identifies the list of tensors. - # This ID is order-sensitive. - inputs_hash = layers_util.object_list_uid(inputs) + if inputs is None: + for loss in losses: + loss._unconditional_loss = True # pylint: disable=protected-access else: - inputs_hash = None - if inputs_hash not in self._per_input_losses: - self._per_input_losses[inputs_hash] = [] - self._per_input_losses[inputs_hash] += losses + for loss in losses: + loss._unconditional_loss = False # pylint: disable=protected-access + # TODO(fchollet): deprecate collection below. _add_elements_to_collection(losses, ops.GraphKeys.REGULARIZATION_LOSSES) def get_losses_for(self, inputs): @@ -377,10 +382,6 @@ class Layer(object): Arguments: inputs: Input tensor or list/tuple of input tensors. - Must match the `inputs` argument passed to the `__call__` - method at the time the losses were created. - If you pass `inputs=None`, unconditional losses are returned, - such as weight regularization losses. Returns: List of loss tensors of the layer that depend on `inputs`. @@ -388,17 +389,25 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_losses_for not supported in Eager mode.') - if inputs is not None: - inputs = nest.flatten(inputs) - if not inputs: - inputs = None - if inputs is not None: - inputs_hash = layers_util.object_list_uid(inputs) - else: - inputs_hash = None - return self._per_input_losses.get(inputs_hash, []) + + if inputs is None: + # Requesting unconditional losses. + return [x for x in self.losses if x._unconditional_loss] # pylint: disable=protected-access + + # Requesting input-conditional losses. + inputs = nest.flatten(inputs) + # Retrieve the set of tensors in the TF graph that depend on `inputs`. + # The losses we want to return will be part of this set. + # To avoid unnecessary work, we stop the search in case all of + # `self.losses` have been retrieved. + reachable = layers_util.get_reachable_from_inputs(inputs, self.losses) + losses = [] + for loss in self.losses: + if loss in reachable: + losses.append(loss) + return losses def build(self, _): """Creates the variables of the layer.""" @@ -500,7 +509,7 @@ class Layer(object): # will occur; it should be None if and only if initialization will take # place in the eager context. init_graph = None - if context.in_graph_mode(): + if not context.executing_eagerly(): default_graph = ops.get_default_graph() if default_graph.building_function: with ops.init_scope(): @@ -508,7 +517,7 @@ class Layer(object): # will be lifted; if initialization ops will be lifted into # the eager context, then there is nothing to retrieve, since variable # collections are not supported when eager execution is enabled. - if context.in_graph_mode(): + if not context.executing_eagerly(): init_graph = ops.get_default_graph() existing_variables = set(tf_variables.global_variables()) else: @@ -524,13 +533,17 @@ class Layer(object): with vs.variable_scope( self._scope, reuse=reuse, auxiliary_name_scope=False) as scope: with ops.name_scope(self._name_scope_name(scope)): - variable = vs.get_variable(name, - shape=shape, - initializer=initializer, - dtype=dtypes.as_dtype(dtype), - constraint=constraint, - trainable=trainable and self.trainable, - partitioner=partitioner) + variable = self._add_variable_with_custom_getter( + name=name, + shape=shape, + getter=vs.get_variable, + # Manage errors in Layer rather than Checkpointable. + overwrite=True, + initializer=initializer, + dtype=dtypes.as_dtype(dtype), + constraint=constraint, + trainable=trainable and self.trainable, + partitioner=partitioner) if init_graph is not None: # pylint: disable=protected-access # The variable was created and initialized in a graph. @@ -611,16 +624,17 @@ class Layer(object): self._set_scope(kwargs.pop('scope', None)) input_list = nest.flatten(inputs) - in_graph_mode = context.in_graph_mode() + build_graph = not context.executing_eagerly() in_deferred_mode = isinstance(input_list[0], _DeferredTensor) # Ensure the Layer, if being reused, is working with inputs from # the same graph as where it was created. - if in_graph_mode: + if build_graph: try: - ops._get_graph_from_inputs(input_list, graph=self.graph) # pylint: disable=protected-access + # Set layer's "graph" at build time + self._graph = ops._get_graph_from_inputs(input_list, graph=self._graph) # pylint: disable=protected-access except ValueError as e: raise ValueError('Input graph and Layer graph are not the same: %s' % e) - if in_graph_mode or in_deferred_mode: + if build_graph or in_deferred_mode: user_kwargs = copy.copy(kwargs) # Handle Keras mask propagation from previous layer to current layer. @@ -628,8 +642,9 @@ class Layer(object): if (not hasattr(self, '_compute_previous_mask') or self._compute_previous_mask): previous_mask = _collect_previous_mask(inputs) - if ('mask' in estimator_util.fn_args(self.call) and - 'mask' not in kwargs and + if not hasattr(self, '_call_fn_args'): + self._call_fn_args = estimator_util.fn_args(self.call) + if ('mask' in self._call_fn_args and 'mask' not in kwargs and not _is_all_none(previous_mask)): # The previous layer generated a mask, and mask was not explicitly pass # to __call__, hence we set previous_mask as the default value. @@ -654,13 +669,14 @@ class Layer(object): with scope_context_manager as scope: with ops.name_scope(self._name_scope_name(scope)): if not self.built: - if not in_graph_mode: + if not build_graph: # Activity regularization is currently unsupported in Eager mode. if self._activity_regularizer: - raise ValueError('activity_regularizer currently unsupported in ' - 'Eager mode. Found an activity_regularizer in ' - '%s(%s).' % (self.__class__.__name__, self)) - if not in_graph_mode and not in_deferred_mode: + raise ValueError( + 'activity_regularizer currently unsupported with ' + 'eager execution enabled. Found an activity_regularizer in ' + '%s(%s).' % (self.__class__.__name__, self)) + if not build_graph and not in_deferred_mode: # TODO(agarwal): support _keras_history in Eager mode. for x in input_list: if hasattr(x, '_keras_history'): @@ -685,11 +701,13 @@ class Layer(object): # TODO(agarwal): Fix the sub-classes and avoid this complexity. call_has_scope_arg = self._call_has_scope_arg except AttributeError: - call_has_scope_arg = 'scope' in estimator_util.fn_args(self.call) + self._call_fn_args = estimator_util.fn_args(self.call) + self._call_has_scope_arg = 'scope' in self._call_fn_args + call_has_scope_arg = self._call_has_scope_arg if call_has_scope_arg: kwargs['scope'] = scope # Check input assumptions set after layer building, e.g. input shape. - if in_graph_mode or in_deferred_mode: + if build_graph or in_deferred_mode: self._assert_input_compatibility(inputs) if not in_deferred_mode: @@ -713,7 +731,7 @@ class Layer(object): if len(outputs) == 1: outputs = outputs[0] - if in_graph_mode: + if build_graph: # Apply activity regularization. # Note that it should be applied every time the layer creates a new # output, since it is output-specific. @@ -724,12 +742,10 @@ class Layer(object): activity_regularization = self._activity_regularizer(output) self.add_loss(activity_regularization, inputs=inputs) - if not in_deferred_mode: - # TODO(fchollet): consider how masking will work with deferred mode. - # Handle mask computation and propagation to the next layer. + # TODO(fchollet): consider enabling masking for Eager mode. if hasattr(self, 'compute_mask'): output_mask = self.compute_mask(inputs, previous_mask) - if isinstance(outputs, list): + if isinstance(outputs, (list, tuple)): if output_mask is None: output_mask = [None for _ in range(len(outputs))] for x, m in zip(outputs, output_mask): @@ -737,7 +753,7 @@ class Layer(object): else: outputs._keras_mask = output_mask # pylint: disable=protected-access - if in_graph_mode: + if build_graph: # If all input tensors have history metadata, # we update the output tensors # with corresponding history metadata, thus eventually allowing to use @@ -760,7 +776,7 @@ class Layer(object): # Update global default collections. _add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS) - if in_deferred_mode or in_graph_mode: + if in_deferred_mode or build_graph: if _have_all_keras_metadata(inputs): # Add an inbound node to the layer, so it can keep track of this call. # This updates the layer history of the output tensor(s). @@ -772,7 +788,7 @@ class Layer(object): @property def graph(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.graph not supported in Eager mode.') return self._graph @@ -876,7 +892,7 @@ class Layer(object): mode. ValueError: If the index provided does not match any node. """ - assert context.in_graph_mode() + assert not context.executing_eagerly() if not self._inbound_nodes: raise RuntimeError('The layer has never been called ' 'and thus has no defined ' + attr_name + '.') @@ -906,7 +922,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Layer.get_input_shape_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'input_shapes', @@ -928,7 +944,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Layer.get_output_shape_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'output_shapes', @@ -949,7 +965,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_input_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'input_tensors', 'input') @@ -969,7 +985,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_output_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'output_tensors', 'output') @@ -992,7 +1008,7 @@ class Layer(object): RuntimeError: If called in Eager mode. AttributeError: If no inbound nodes are found. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.input not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + @@ -1014,7 +1030,7 @@ class Layer(object): layers. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.output not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' has no inbound nodes.') @@ -1036,7 +1052,7 @@ class Layer(object): AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.input_shape not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('The layer has never been called ' @@ -1097,7 +1113,7 @@ class Layer(object): AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.output_shape not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('The layer has never been called ' @@ -1228,6 +1244,7 @@ class Layer(object): ', found shape=' + str(shape)) +@tf_export('keras.layers.InputSpec', 'layers.InputSpec') class InputSpec(object): """Specifies the ndim, dtype and shape of every input to a layer. @@ -1454,7 +1471,7 @@ def _to_list(x): def _add_elements_to_collection(elements, collection_list): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Using collections from Layers not supported in Eager ' 'mode. Tried to add %s to %s' % (elements, collection_list)) diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index 06ba214c0fc60202c773f8f231b17c3b728f5c52..9ed4afeaba931c47d2a1e65f08489773f0b9eb1b 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test @@ -43,7 +44,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, []) self.assertEqual(layer.trainable_variables, []) self.assertEqual(layer.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): # updates, losses only supported in GRAPH mode self.assertEqual(layer.updates, []) self.assertEqual(layer.losses, []) @@ -62,7 +63,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, [variable]) self.assertEqual(layer.trainable_variables, [variable]) self.assertEqual(layer.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( layer.variables, ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) @@ -76,7 +77,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, [variable, variable_2]) self.assertEqual(layer.trainable_variables, [variable]) self.assertEqual(layer.non_trainable_variables, [variable_2]) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1) @@ -160,7 +161,7 @@ class BaseLayerTest(test.TestCase): inputs = random_ops.random_uniform((5,), seed=1) outputs = layer.apply(inputs) self.assertEqual(layer.built, True) - if context.in_graph_mode(): + if not context.executing_eagerly(): # op is only supported in GRAPH mode self.assertEqual(outputs.op.name, 'my_layer/Square') @@ -209,7 +210,7 @@ class BaseLayerTest(test.TestCase): inputs = random_ops.random_uniform((5,), seed=1) outputs = layer.apply(inputs) self.assertEqual(layer.built, True) - if context.in_graph_mode(): + if not context.executing_eagerly(): # op only supported in GRAPH mode. self.assertEqual(outputs.op.name, 'my_layer/Square') @@ -279,7 +280,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -306,7 +307,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -334,7 +335,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -429,7 +430,7 @@ class BaseLayerTest(test.TestCase): layer.apply(constant_op.constant(1)) # Works - if context.in_graph_mode(): + if not context.executing_eagerly(): layer.apply(array_ops.placeholder('int32')) layer.apply(array_ops.placeholder('int32', shape=(2, 3))) @@ -452,13 +453,7 @@ class BaseLayerTest(test.TestCase): return {'l' + key: inputs[key] for key in inputs} layer = DictLayer() - if context.in_graph_mode(): - i1 = array_ops.placeholder('int32') - i2 = array_ops.placeholder('float32') - result = layer.apply({'abel': i1, 'ogits': i2}) - self.assertTrue(isinstance(result, dict)) - self.assertEqual(set(['label', 'logits']), set(result.keys())) - else: + if context.executing_eagerly(): i1 = constant_op.constant(3) i2 = constant_op.constant(4.0) result = layer.apply({'abel': i1, 'ogits': i2}) @@ -466,6 +461,12 @@ class BaseLayerTest(test.TestCase): self.assertEqual(set(['label', 'logits']), set(result.keys())) self.assertEqual(3, result['label'].numpy()) self.assertEqual(4.0, result['logits'].numpy()) + else: + i1 = array_ops.placeholder('int32') + i2 = array_ops.placeholder('float32') + result = layer.apply({'abel': i1, 'ogits': i2}) + self.assertTrue(isinstance(result, dict)) + self.assertEqual(set(['label', 'logits']), set(result.keys())) def testActivityRegularizer(self): regularizer = math_ops.reduce_sum @@ -555,6 +556,103 @@ class BaseLayerTest(test.TestCase): self.assertEqual(len(layer.trainable_variables), 1) self.assertEqual(layer.variables[0].graph, outer_graph) + def testGetUpdateFor(self): + + class MyLayer(base_layers.Layer): + + def build(self, input_shape): + self.a = self.add_variable('a', + (), + dtypes.float32, + trainable=False) + self.b = self.add_variable('b', + (), + dtypes.float32, + trainable=False) + self.add_update(state_ops.assign_add(self.a, 1., name='b_update')) + self.built = True + + def call(self, inputs): + self.add_update(state_ops.assign_add(self.a, inputs, name='a_update'), + inputs=True) + return inputs + 1 + + layer = MyLayer() + inputs = array_ops.placeholder(dtypes.float32, (), 'inputs') + intermediate_inputs = inputs + 1 + outputs = layer.apply(intermediate_inputs) + + self.assertEqual(len(layer.updates), 2) + self.assertEqual(len(layer.get_updates_for(None)), 1) + self.assertEqual(len(layer.get_updates_for([inputs])), 1) + self.assertEqual(len(layer.get_updates_for([intermediate_inputs])), 1) + self.assertEqual(len(layer.get_updates_for([outputs])), 0) + + # Call same layer on new input, creating one more conditional update + inputs = array_ops.placeholder(dtypes.float32, (), 'inputs') + intermediate_inputs = inputs + 1 + outputs = layer.apply(intermediate_inputs) + + self.assertEqual(len(layer.updates), 3) + self.assertEqual(len(layer.get_updates_for(None)), 1) + # Check that we are successfully filtering out irrelevant updates + self.assertEqual(len(layer.get_updates_for([inputs])), 1) + self.assertEqual(len(layer.get_updates_for([intermediate_inputs])), 1) + self.assertEqual(len(layer.get_updates_for([outputs])), 0) + + def testGetLossesFor(self): + + class MyLayer(base_layers.Layer): + + def build(self, input_shape): + self.a = self.add_variable('a', + (), + dtypes.float32, + trainable=False) + self.b = self.add_variable('b', + (), + dtypes.float32, + trainable=False) + self.add_loss(self.a) + self.built = True + + def call(self, inputs): + self.add_loss(inputs, inputs=True) + return inputs + 1 + + layer = MyLayer() + inputs = array_ops.placeholder(dtypes.float32, (), 'inputs') + intermediate_inputs = inputs + 1 + outputs = layer.apply(intermediate_inputs) + + self.assertEqual(len(layer.losses), 2) + self.assertEqual(len(layer.get_losses_for(None)), 1) + self.assertEqual(len(layer.get_losses_for([inputs])), 1) + self.assertEqual(len(layer.get_losses_for([intermediate_inputs])), 1) + self.assertEqual(len(layer.get_losses_for([outputs])), 0) + + # Call same layer on new input, creating one more conditional loss + inputs = array_ops.placeholder(dtypes.float32, (), 'inputs') + intermediate_inputs = inputs + 1 + outputs = layer.apply(intermediate_inputs) + + self.assertEqual(len(layer.losses), 3) + self.assertEqual(len(layer.get_losses_for(None)), 1) + # Check that we are successfully filtering out irrelevant losses + self.assertEqual(len(layer.get_losses_for([inputs])), 1) + self.assertEqual(len(layer.get_losses_for([intermediate_inputs])), 1) + self.assertEqual(len(layer.get_losses_for([outputs])), 0) + + def testLayerGraphSetInFirstApply(self): + with ops.Graph().as_default(): + layer = core_layers.Dense(1) # Graph at construction time is ignored + with ops.Graph().as_default(): + layer.apply(constant_op.constant([[1]])) + # layer is now bound to second Graph + with ops.Graph().as_default(), self.assertRaisesRegexp( + ValueError, 'Input graph and Layer graph are not the same'): + layer.apply(constant_op.constant([[1]])) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index e8dba3cea321a415b84e1ec89fd7b021e2b272d0..74e7c63fb364d9c4475af5efe7d5db95cccf8166 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops +from tensorflow.python.util.tf_export import tf_export class _Conv(base.Layer): @@ -222,6 +223,7 @@ class _Conv(base.Layer): new_space) +@tf_export('layers.Conv1D') class Conv1D(_Conv): """1D convolution layer (e.g. temporal convolution). @@ -311,6 +313,7 @@ class Conv1D(_Conv): name=name, **kwargs) +@tf_export('layers.conv1d') def conv1d(inputs, filters, kernel_size, @@ -411,6 +414,7 @@ def conv1d(inputs, return layer.apply(inputs) +@tf_export('layers.Conv2D') class Conv2D(_Conv): """2D convolution layer (e.g. spatial convolution over images). @@ -507,6 +511,7 @@ class Conv2D(_Conv): name=name, **kwargs) +@tf_export('layers.conv2d') def conv2d(inputs, filters, kernel_size, @@ -614,6 +619,7 @@ def conv2d(inputs, return layer.apply(inputs) +@tf_export('layers.Conv3D') class Conv3D(_Conv): """3D convolution layer (e.g. spatial convolution over volumes). @@ -711,6 +717,7 @@ class Conv3D(_Conv): name=name, **kwargs) +@tf_export('layers.conv3d') def conv3d(inputs, filters, kernel_size, @@ -980,6 +987,7 @@ class _SeparableConv(_Conv): raise NotImplementedError +@tf_export('layers.SeparableConv1D') class SeparableConv1D(_SeparableConv): """Depthwise separable 1D convolution. @@ -1088,10 +1096,10 @@ class SeparableConv1D(_SeparableConv): def call(self, inputs): if self.data_format == 'channels_last': - strides = (1, 1) + self.strides + (1,) + strides = (1,) + self.strides * 2 + (1,) spatial_start_dim = 1 else: - strides = (1, 1, 1) + self.strides + strides = (1, 1) + self.strides * 2 spatial_start_dim = 2 # Explicitly broadcast inputs and kernels to 4D. @@ -1123,6 +1131,7 @@ class SeparableConv1D(_SeparableConv): return outputs +@tf_export('layers.SeparableConv2D') class SeparableConv2D(_SeparableConv): """Depthwise separable 2D convolution. @@ -1260,6 +1269,7 @@ class SeparableConv2D(_SeparableConv): return outputs +@tf_export('layers.separable_conv1d') def separable_conv1d(inputs, filters, kernel_size, @@ -1376,6 +1386,7 @@ def separable_conv1d(inputs, return layer.apply(inputs) +@tf_export('layers.separable_conv2d') def separable_conv2d(inputs, filters, kernel_size, @@ -1497,6 +1508,7 @@ def separable_conv2d(inputs, return layer.apply(inputs) +@tf_export('layers.Conv2DTranspose') class Conv2DTranspose(Conv2D): """Transposed 2D convolution layer (sometimes called 2D Deconvolution). @@ -1652,7 +1664,7 @@ class Conv2DTranspose(Conv2D): padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters @@ -1695,6 +1707,7 @@ class Conv2DTranspose(Conv2D): return tensor_shape.TensorShape(output_shape) +@tf_export('layers.conv2d_transpose') def conv2d_transpose(inputs, filters, kernel_size, @@ -1790,6 +1803,7 @@ def conv2d_transpose(inputs, return layer.apply(inputs) +@tf_export('layers.Conv3DTranspose') class Conv3DTranspose(Conv3D): """Transposed 3D convolution layer (sometimes called 3D Deconvolution). @@ -1955,7 +1969,7 @@ class Conv3DTranspose(Conv3D): data_format=utils.convert_data_format(self.data_format, ndim=5), padding=self.padding.upper()) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters @@ -2018,6 +2032,7 @@ class Conv3DTranspose(Conv3D): return tensor_shape.TensorShape(output_shape) +@tf_export('layers.conv3d_transpose') def conv3d_transpose(inputs, filters, kernel_size, diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index 7bf62d45b8e97aaa027467b6c9862ca2a4699fc1..e598d9f83ab21f2dd5fabb3dd37fa0bfb5f003a4 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -35,10 +35,14 @@ from tensorflow.python.layers import utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops from tensorflow.python.ops import standard_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('layers.Dense') class Dense(base.Layer): """Densely-connected layer class. @@ -152,11 +156,11 @@ class Dense(base.Layer): outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1], [0]]) # Reshape the output back to the original ndim of the input. - if context.in_graph_mode(): + if not context.executing_eagerly(): output_shape = shape[:-1] + [self.units] outputs.set_shape(output_shape) else: - outputs = standard_ops.matmul(inputs, self.kernel) + outputs = gen_math_ops.mat_mul(inputs, self.kernel) if self.use_bias: outputs = nn.bias_add(outputs, self.bias) if self.activation is not None: @@ -173,6 +177,7 @@ class Dense(base.Layer): return input_shape[:-1].concatenate(self.units) +@tf_export('layers.dense') def dense( inputs, units, activation=None, @@ -248,6 +253,7 @@ def dense( return layer.apply(inputs) +@tf_export('layers.Dropout') class Dropout(base.Layer): """Applies Dropout to the input. @@ -287,13 +293,7 @@ class Dropout(base.Layer): # shapes with dynamically sized inputs. if self.noise_shape is None: return self.noise_shape - - symbolic_shape = array_ops.shape(inputs) - noise_shape = [ - symbolic_shape[axis] if shape is None else shape - for axis, shape in enumerate(self.noise_shape) - ] - return noise_shape + return nn_ops._get_noise_shape(inputs, self.noise_shape) def call(self, inputs, training=False): @@ -309,6 +309,7 @@ class Dropout(base.Layer): return input_shape +@tf_export('layers.dropout') def dropout(inputs, rate=0.5, noise_shape=None, @@ -350,6 +351,7 @@ def dropout(inputs, return layer.apply(inputs, training=training) +@tf_export('layers.Flatten') class Flatten(base.Layer): """Flattens an input tensor while preserving the batch axis (axis 0). @@ -372,7 +374,7 @@ class Flatten(base.Layer): def call(self, inputs): outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1)) - if context.in_graph_mode(): + if not context.executing_eagerly(): outputs.set_shape(self.compute_output_shape(inputs.get_shape())) return outputs @@ -386,6 +388,7 @@ class Flatten(base.Layer): return tensor_shape.TensorShape(output_shape) +@tf_export('layers.flatten') def flatten(inputs, name=None): """Flattens an input tensor while preserving the batch axis (axis 0). diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index 15ce6cba21fcc78126f7db58ab18934db69c15fd..ae19866d7aa3580e55ecd77f6345dbefd80f7ab9 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -77,7 +77,7 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.trainable_variables, [dense.kernel, dense.bias]) self.assertListEqual(dense.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2) self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') @@ -98,7 +98,7 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.variables, [dense.kernel]) self.assertListEqual(dense.trainable_variables, [dense.kernel]) self.assertListEqual(dense.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1) self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') @@ -113,7 +113,7 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.non_trainable_variables, [dense.kernel, dense.bias]) self.assertListEqual(dense.trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0) @@ -162,13 +162,13 @@ class DenseTest(test.TestCase): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') inputs = random_ops.random_uniform((5, 3), seed=1) outputs = dense(inputs) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(outputs.op.name, 'dense1/Relu') dense = core_layers.Dense(2, name='dense2') inputs = random_ops.random_uniform((5, 3), seed=1) outputs = dense(inputs) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(outputs.op.name, 'dense2/BiasAdd') def testActivityRegularizer(self): @@ -374,7 +374,7 @@ class DropoutTest(test.TestCase): dp = core_layers.Dropout(0.5) inputs = array_ops.ones((5, 3)) dropped = dp.apply(inputs, training=True) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) np_output = self.evaluate(dropped) self.assertAlmostEqual(0., np_output.min()) diff --git a/tensorflow/python/layers/layers.py b/tensorflow/python/layers/layers.py index 1555846efde812b9e31f48315decaf1f86aa4f70..13a8e8e39caaf9c74d1c7d0ea4d6856f725256fd 100644 --- a/tensorflow/python/layers/layers.py +++ b/tensorflow/python/layers/layers.py @@ -68,7 +68,6 @@ from tensorflow.python.util.all_util import remove_undocumented # Base objects. from tensorflow.python.layers.base import Layer from tensorflow.python.layers.base import InputSpec -from tensorflow.python.layers.network import Input # Core layers. from tensorflow.python.layers.core import Dense diff --git a/tensorflow/python/layers/maxout.py b/tensorflow/python/layers/maxout.py deleted file mode 100644 index 20ce6c9770087f9cfb90d40235955dfe1b7ee98b..0000000000000000000000000000000000000000 --- a/tensorflow/python/layers/maxout.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= - -# pylint: disable=unused-import,g-bad-import-order -"""Contains the maxout layer -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.python.eager import context -from tensorflow.python.framework import ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import gen_array_ops - -from tensorflow.python.layers import base - - -def maxout(inputs, num_units, axis=-1, name=None): - """Adds a maxout op from https://arxiv.org/abs/1302.4389 - - "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron - Courville, - Yoshua Bengio - - Usually the operation is performed in the filter/channel dimension. This can - also be - used after fully-connected layers to reduce number of features. - - Arguments: - inputs: Tensor input - num_units: Specifies how many features will remain after maxout in the `axis` - dimension - (usually channel). This must be multiple of number of `axis`. - axis: The dimension where max pooling will be performed. Default is the - last dimension. - name: Optional scope for name_scope. - - Returns: - A `Tensor` representing the results of the pooling operation. - - Raises: - ValueError: if num_units is not multiple of number of features. - """ - return MaxOut(num_units=num_units, axis=axis, name=name)(inputs) - - -class MaxOut(base.Layer): - """Adds a maxout op from https://arxiv.org/abs/1302.4389 - - "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron - Courville, Yoshua - Bengio - - Usually the operation is performed in the filter/channel dimension. This can - also be - used after fully-connected layers to reduce number of features. - - Arguments: - inputs: Tensor input - num_units: Specifies how many features will remain after maxout in the - `axis` dimension - (usually channel). - This must be multiple of number of `axis`. - axis: The dimension where max pooling will be performed. Default is the - last dimension. - name: Optional scope for name_scope. - - Returns: - A `Tensor` representing the results of the pooling operation. - - Raises: - ValueError: if num_units is not multiple of number of features. - """ - - def __init__(self, num_units, axis=-1, name=None, **kwargs): - super(MaxOut, self).__init__(name=name, trainable=False, **kwargs) - self.axis = axis - self.num_units = num_units - - def call(self, inputs): - inputs = ops.convert_to_tensor(inputs) - shape = inputs.get_shape().as_list() - num_channels = shape[self.axis] - if num_channels % self.num_units: - raise ValueError('number of features({}) is not ' - 'a multiple of num_units({})'.format( - num_channels, self.num_units)) - shape[self.axis] = -1 - shape += [num_channels // self.num_units] - - # Dealing with batches with arbitrary sizes - for i in range(len(shape)): - if shape[i] is None: - shape[i] = gen_array_ops.shape(inputs)[i] - outputs = math_ops.reduce_max( - gen_array_ops.reshape(inputs, shape), -1, keep_dims=False) - - return outputs diff --git a/tensorflow/python/layers/maxout_test.py b/tensorflow/python/layers/maxout_test.py deleted file mode 100644 index 26acac57c41da759f288f255c0cd523f9c6b1dbd..0000000000000000000000000000000000000000 --- a/tensorflow/python/layers/maxout_test.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= - -# pylint: disable=unused-import,g-bad-import-order - - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.python.layers import maxout -from tensorflow.python.layers import convolutional as conv_layers -from tensorflow.python.layers import core as core_layers - -from tensorflow.python.ops import random_ops -from tensorflow.python.platform import test -import numpy as np - -""" -Contains the maxout layer tests -""" - - -class MaxOutTest(test.TestCase): - def test_simple(self): - inputs = random_ops.random_uniform((64, 10, 36), seed=1) - graph = maxout.maxout(inputs, num_units=3) - self.assertEqual(graph.get_shape().as_list(), [64, 10, 3]) - - def test_fully_connected(self): - inputs = random_ops.random_uniform((64, 50), seed=1) - graph = core_layers.dense(inputs, 50) - graph = maxout.maxout(graph, num_units=10) - self.assertEqual(graph.get_shape().as_list(), [64, 10]) - - def test_nchw(self): - inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1) - graph = conv_layers.conv2d(inputs, 10, 3, padding="SAME") - graph = maxout.maxout(graph, num_units=1) - self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1]) - - def test_invalid_shape(self): - inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1) - graph = conv_layers.conv2d(inputs, 3, 10, strides=(1, 1)) - with self.assertRaisesRegexp(ValueError, 'number of features'): - graph = maxout.maxout(graph, num_units=2) - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/python/layers/network.py b/tensorflow/python/layers/network.py deleted file mode 100644 index 745843975c487c34ab854ad2bb52b93e617fcaec..0000000000000000000000000000000000000000 --- a/tensorflow/python/layers/network.py +++ /dev/null @@ -1,969 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= -"""Contains Network, a composition of layers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -from tensorflow.python.eager import context -from tensorflow.python.estimator import util as estimator_util -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.layers import base -from tensorflow.python.layers import utils as layers_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import variable_scope as vs -from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.util import nest - - -class InputLayer(base.Layer): - """Layer to be used as an entry point into a Network (a graph of layers). - - It can either wrap an existing tensor (pass an `input_tensor` argument) - or create its a placeholder tensor (pass arguments `input_shape` - as well as `dtype`). - - It is generally recommend to use the functional layer API via `Input`, - (which creates an `InputLayer`) without directly using `InputLayer`. - - Arguments: - input_shape: Shape tuple (not including the batch axis), or `TensorShape` - instance (not including the batch axis). - batch_size: Optional input batch size (integer or None). - dtype: Datatype of the input. - input_tensor: Optional tensor to use as layer input - instead of creating a placeholder. - sparse: Boolean, whether the placeholder created - is meant to be sparse. - name: Name of the layer (string). - - Raises: - RuntimeError: If created in Eager mode. - """ - - def __init__(self, - input_shape=None, - batch_size=None, - dtype=dtypes.float32, - input_tensor=None, - sparse=False, - name=None): - super(InputLayer, self).__init__(dtype=dtype, name=name) - self.built = True - self.sparse = sparse - self.batch_size = batch_size - - if isinstance(input_shape, tensor_shape.TensorShape): - input_shape = tuple(input_shape.as_list()) - - if input_tensor is None: - if input_shape is not None: - batch_input_shape = (batch_size,) + tuple(input_shape) - else: - batch_input_shape = None - - if context.in_eager_mode(): - # In eager mode, create a temporary placeholder to call the layer on. - input_tensor = base._DeferredTensor( # pylint: disable=protected-access - shape=batch_input_shape, - dtype=dtype, - name=self.name) - else: - # In graph mode, create a graph placeholder to call the layer on. - if sparse: - input_tensor = array_ops.sparse_placeholder( - shape=batch_input_shape, - dtype=dtype, - name=self.name) - else: - input_tensor = array_ops.placeholder( - shape=batch_input_shape, - dtype=dtype, - name=self.name) - - # For compatibility with Keras API. - self.is_placeholder = True - self._batch_input_shape = batch_input_shape - else: - # For compatibility with Keras API. - self.is_placeholder = False - self._batch_input_shape = tuple(input_tensor.get_shape().as_list()) - - # Create an input node to add to self.outbound_node - # and set output_tensors' _keras_history. - input_tensor._keras_history = (self, 0, 0) # pylint: disable=protected-access - base.Node( - self, - inbound_layers=[], - node_indices=[], - tensor_indices=[], - input_tensors=[input_tensor], - output_tensors=[input_tensor]) - - -def Input( # pylint: disable=invalid-name - shape=None, - batch_size=None, - name=None, - dtype=dtypes.float32, - sparse=False, - tensor=None): - """`Input()` is used to instantiate an input tensor for use with a `Network`. - - For instance, if a, b and c are tensors created via `Input`, - it becomes possible to do: - - `network = Network(inputs=[a, b], outputs=c)` - - Example: - - ```python - # This is a logistic regression - x = tf.layers.Input(shape=(32,)) - y = tf.layers.Dense(16, activation='softmax')(x) - network = tf.layers.Network(x, y) - ``` - - Arguments: - shape: A shape tuple (integer), not including the batch size. - For instance, `shape=(32,)` indicates that the expected input - will be batches of 32-dimensional vectors. - batch_size: Optional input batch size (integer or None). - name: An optional name string for the layer. - Should be unique in a model (do not reuse the same name twice). - It will be autogenerated if it isn't provided. - dtype: The data type expected by the input, as a string - (`float32`, `float64`, `int32`...) - sparse: A boolean specifying whether the placeholder - to be created is sparse. - tensor: Optional existing tensor to wrap into the `Input` layer. - If set, the layer will not create a placeholder tensor. - - Returns: - A tensor: either a new placeholder (with history metadata) or - `tensor` (if passed), with added history metadata. - - Raises: - RuntimeError: If called in Eager mode. - """ - input_layer = InputLayer( - input_shape=shape, - batch_size=batch_size, - name=name, - dtype=dtype, - sparse=sparse, - input_tensor=tensor) - # Return tensor including `_keras_history` metadata. - # Note that in this case train_output and test_output are the same pointer. - outputs = input_layer._inbound_nodes[0].output_tensors # pylint: disable=protected-access - if len(outputs) == 1: - return outputs[0] - else: - return outputs - - -class GraphNetwork(base.Layer): - """A GraphNetwork is a directed acyclic graph of layers. - - It is the topological form of a `tf.keras.models.Model`. A `Model` is simply a - `GraphNetwork` with added training/evaluation routines. - - A `GraphNetwork` instance implements the full `Layer` API. In particular, a - `GraphNetwork` can be called on new inputs. - - Example: - - ```python - # This is a logistic regression - x = tf.layers.Input(shape=(32,)) - y = tf.layers.Dense(16, activation='softmax')(x) - network = tf.layers.GraphNetwork(x, y) - - # It is then possible to call the network on compatible inputs: - z = tf.layers.Input(shape=(32,)) - w = network(z) - - # It is possible to retrieve the same properties as a layer: - weights = network.trainable_weights - ``` - - Arguments: - inputs: Input tensor or list of input tensors. - Must come from `tf.layers.Input`. - output: Output tensor or list of output tensors. Must come from - tf.layers Layers or Keras layers. - name: Optional name of the model (string). - - Attributes: - GraphNetwork has the same attributes as Layer. On top of it, it also has: - - layers: a list of the children layers of the network, - a list of layer instances, ordered from "earlier in the graph" - to "later in the graph". - - Methods: - GraphNetwork has the same methods as Layer. On top of it, it also has: - - get_layer: retrieves a child layer by name or index in the graph. - - Raises: - RuntimeError: If created in Eager mode. - """ - - def __init__(self, inputs, outputs, name=None): # pylint: disable=super-init-not-called - if context.in_eager_mode(): - # TODO(fchollet): check that all inputs and outputs are DeferredTensors. - pass - - self._init_set_name(name) - self._activity_regularizer = None - with vs.variable_scope( - None, default_name=self._base_name) as captured_scope: - self._scope = captured_scope - call_fn_args = estimator_util.fn_args(self.call) - self._compute_previous_mask = ('mask' in call_fn_args or - hasattr(self, 'compute_mask')) - self._call_has_scope_arg = 'scope' in call_fn_args - - # This acts just like the `trainable` attribute of any layer instance. - # It does not affect users of the underlying layers, only users of the - # GraphNetwork instance. - self.trainable = True - # A GraphNetwork does not create weights of its own, thus it is already - # built. - self.built = True - # A GraphNetwork does not create weights of its own, thus has no dtype. - self._dtype = None - # The following are implemented as property functions: - # self.trainable_weights - # self.non_trainable_weights - # self.input_spec - - # Private attributes to implement compatibility with Layer. - self._per_input_losses = {} - self._per_input_updates = {} - self._updates = [] - self._losses = [] - self._scope = None - self._reuse = None - self._graph = ops.get_default_graph() - - # GraphNetwork-specific properties. - if isinstance(inputs, (list, tuple)): - self.inputs = list(inputs) # Tensor or list of tensors. - else: - self.inputs = [inputs] - if isinstance(outputs, (list, tuple)): - self.outputs = list(outputs) - else: - self.outputs = [outputs] - # All layers in order of horizontal graph traversal. - # Entries are unique. Includes input and output layers. - self.layers = [] - - # Check for redundancy in inputs. - if len(set(self.inputs)) != len(self.inputs): - raise ValueError('The list of inputs passed to the model ' - 'is redundant. ' - 'All inputs should only appear once.' - ' Found: ' + str(self.inputs)) - - # # List of initial layers (1 to 1 mapping with self.inputs, - # # hence the same layer might appear twice) - # self._input_layers = [] - # self._input_layers_node_indices = [] - # self._input_layers_tensor_indices = [] - # # list of layers (1 to 1 mapping with self.inputs, - # # hence the same layer might appear twice) - # self._output_layers = [] - # self._output_layers_node_indices = [] - # self._output_layers_tensor_indices = [] - - self._input_layers = [] - self._output_layers = [] - self._input_coordinates = [] - self._output_coordinates = [] - - # This is for performance optimization when calling the GraphNetwork on new - # inputs. Every time the GraphNetwork is called on a set on input tensors, - # we compute the output tensors, output masks and output shapes in one pass, - # then cache them here. When any of these outputs is queried later, we - # retrieve it from there instead of recomputing it. - self._output_mask_cache = {} - self._output_tensor_cache = {} - self._output_shape_cache = {} - - # User-provided arguments validation. - for x in self.inputs: - # Check that x has appropriate `_keras_history` metadata. - if not hasattr(x, '_keras_history'): - cls_name = self.__class__.__name__ - raise ValueError('Input tensors to a ' + cls_name + ' ' + - 'must come from `tf.layers.Input`. ' - 'Received: ' + str(x) + - ' (missing previous layer metadata).') - # Check that x is an input tensor. - # pylint: disable=protected-access - layer, node_index, tensor_index = x._keras_history - if len(layer._inbound_nodes) > 1 or ( - layer._inbound_nodes and layer._inbound_nodes[0].inbound_layers): - cls_name = self.__class__.__name__ - logging.warning(cls_name + ' inputs must come from ' - '`tf.layers.Input` (thus holding past layer metadata), ' - 'they cannot be the output of ' - 'a previous non-Input layer. ' - 'Here, a tensor specified as ' - 'input to "' + self.name + '" was not an Input tensor, ' - 'it was generated by layer ' + layer.name + '.\n' - 'Note that input tensors are ' - 'instantiated via `tensor = tf.layers.Input(shape)`.\n' - 'The tensor that caused the issue was: ' + str(x.name)) - # pylint: enable=protected-access - for x in self.outputs: - if not hasattr(x, '_keras_history'): - cls_name = self.__class__.__name__ - raise ValueError('Output tensors to a ' + cls_name + ' must be ' - 'the output of a TensorFlow `Layer` ' - '(thus holding past layer metadata). Found: ' + str(x)) - - # Build self._output_layers: - for x in self.outputs: - layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access - self._output_layers.append(layer) - self._output_coordinates.append((layer, node_index, tensor_index)) - - # Build self._input_layers: - for x in self.inputs: - layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access - # It's supposed to be an input layer, so only one node - # and one tensor output. - assert node_index == 0 - assert tensor_index == 0 - self._input_layers.append(layer) - self._input_coordinates.append((layer, node_index, tensor_index)) - - # Network_nodes: set of nodes included in the graph - # (not all nodes included in the layers - # are relevant to the current graph). - network_nodes = set() # ids of all nodes relevant to the GraphNetwork - nodes_depths = {} # dict {node: depth value} - layers_depths = {} # dict {layer: depth value} - layer_indices = {} # dict {layer: index in traversal} - nodes_in_decreasing_depth = [] - - def build_map_of_graph(tensor, - finished_nodes, - nodes_in_progress, - layer, - node_index, - tensor_index): - """Builds a map of the graph of layers. - - This recursively updates the map `layer_indices`, - the list `nodes_in_decreasing_depth` and the set `network_nodes`. - - Arguments: - tensor: Some tensor in a graph. - finished_nodes: Set of nodes whose subgraphs have been traversed - completely. Useful to prevent duplicated work. - nodes_in_progress: Set of nodes that are currently active on the - recursion stack. Useful to detect cycles. - layer: Layer from which `tensor` comes from. If not provided, - will be obtained from `tensor._keras_history`. - node_index: Node index from which `tensor` comes from. - tensor_index: Tensor_index from which `tensor` comes from. - - Raises: - ValueError: if a cycle is detected. - """ - node = layer._inbound_nodes[node_index] # pylint: disable=protected-access - - # Prevent cycles. - if node in nodes_in_progress: - raise ValueError('The tensor ' + str(tensor) + ' at layer "' + - layer.name + '" is part of a cycle.') - - # Don't repeat work for shared subgraphs - if node in finished_nodes: - return - - node_key = _make_node_key(layer.name, node_index) - # Update network_nodes. - network_nodes.add(node_key) - - # Store the traversal order for layer sorting. - if layer not in layer_indices: - layer_indices[layer] = len(layer_indices) - - nodes_in_progress.add(node) - - # Propagate to all previous tensors connected to this node. - for i in range(len(node.inbound_layers)): - x = node.input_tensors[i] - layer = node.inbound_layers[i] - node_index = node.node_indices[i] - tensor_index = node.tensor_indices[i] - build_map_of_graph(x, finished_nodes, nodes_in_progress, layer, - node_index, tensor_index) - - finished_nodes.add(node) - nodes_in_progress.remove(node) - nodes_in_decreasing_depth.append(node) - - finished_nodes = set() - nodes_in_progress = set() - for x in self.outputs: - layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access - build_map_of_graph(x, finished_nodes, nodes_in_progress, - layer=layer, - node_index=node_index, - tensor_index=tensor_index) - - for node in reversed(nodes_in_decreasing_depth): - # If the depth is not set, the node has no outbound nodes (depth 0). - depth = nodes_depths.setdefault(node, 0) - - # Update the depth of the corresponding layer - previous_depth = layers_depths.get(node.outbound_layer, 0) - # If we've seen this layer before at a higher depth, - # we should use that depth instead of the node depth. - # This is necessary for shared layers that have inputs at different - # depth levels in the graph. - depth = max(depth, previous_depth) - layers_depths[node.outbound_layer] = depth - nodes_depths[node] = depth - - # Update the depth of inbound nodes. - # The "depth" of a node is the max of the depths - # of all layers it is connected to. - for i in range(len(node.inbound_layers)): - inbound_layer = node.inbound_layers[i] - node_index = node.node_indices[i] - inbound_node = inbound_layer._inbound_nodes[node_index] # pylint: disable=protected-access - previous_depth = nodes_depths.get(inbound_node, 0) - nodes_depths[inbound_node] = max(depth + 1, previous_depth) - - # Build a dict {depth: list of nodes with this depth} - nodes_by_depth = {} - for node, depth in nodes_depths.items(): - if depth not in nodes_by_depth: - nodes_by_depth[depth] = [] - nodes_by_depth[depth].append(node) - - # Build a dict {depth: list of layers with this depth} - layers_by_depth = {} - for layer, depth in layers_depths.items(): - if depth not in layers_by_depth: - layers_by_depth[depth] = [] - layers_by_depth[depth].append(layer) - - # Get sorted list of layer depths. - depth_keys = list(layers_by_depth.keys()) - depth_keys.sort(reverse=True) - - # Set self.layers and self._layers_by_depth. - layers = [] - for depth in depth_keys: - layers_for_depth = layers_by_depth[depth] - # GraphNetwork.layers needs to have a deterministic order: - # here we order them by traversal order. - layers_for_depth.sort(key=lambda x: layer_indices[x]) - layers.extend(layers_for_depth) - self.layers = layers - self._layers_by_depth = layers_by_depth - - # Get sorted list of node depths. - depth_keys = list(nodes_by_depth.keys()) - depth_keys.sort(reverse=True) - - # Check that all tensors required are computable. - # computable_tensors: all tensors in the graph - # that can be computed from the inputs provided. - computable_tensors = [] - for x in self.inputs: - computable_tensors.append(x) - - layers_with_complete_input = [] # To provide a better error msg. - for depth in depth_keys: - for node in nodes_by_depth[depth]: - layer = node.outbound_layer - if layer: - for x in node.input_tensors: - if x not in computable_tensors: - raise ValueError('Graph disconnected: ' - 'cannot obtain value for tensor ' + str(x) + - ' at layer "' + layer.name + '". ' - 'The following previous layers ' - 'were accessed without issue: ' + - str(layers_with_complete_input)) - for x in node.output_tensors: - computable_tensors.append(x) - layers_with_complete_input.append(layer.name) - - # Keep track of the network's nodes. - self._network_nodes = network_nodes - self._nodes_by_depth = nodes_by_depth - - # Ensure name unicity, which will be crucial for serialization - # (since serialized nodes refer to layers by their name). - all_names = [layer.name for layer in self.layers] - for name in all_names: - if all_names.count(name) != 1: - raise ValueError('The name "' + name + '" is used ' + - str(all_names.count(name)) + ' times in the model. ' - 'All layer names should be unique.') - - # Layer parameters. - # The new network starts with a single inbound node - # for its inputs, and no outbound nodes. - self._outbound_nodes = [] # Will be appended to by future calls to __call__ - self._inbound_nodes = [ - ] # Will be appended to below, and by future calls to __call__ - # Create the node linking internal inputs to internal outputs. - base.Node( - outbound_layer=self, - inbound_layers=[], - node_indices=[], - tensor_indices=[], - input_tensors=self.inputs, - output_tensors=self.outputs) - - def get_layer(self, name=None, index=None): - """Retrieves a layer based on either its name (unique) or index. - - Indices are based on order of horizontal graph traversal (bottom-up). - - Arguments: - name: String, name of layer. - index: Integer, index of layer. - - Returns: - A layer instance. - - Raises: - ValueError: In case of invalid layer name or index. - """ - # TODO(fchollet): We could build a dictionary based on layer names - # since they are constant, but we have not done that yet. - if index is not None: - if len(self.layers) <= index: - raise ValueError('Was asked to retrieve layer at index ' + str(index) + - ' but model only has ' + str(len(self.layers)) + - ' layers.') - else: - return self.layers[index] - else: - if not name: - raise ValueError('Provide either a layer name or layer index.') - for layer in self.layers: - if layer.name == name: - return layer - raise ValueError('No such layer: ' + name) - - @property - def stateful(self): - return any([(hasattr(layer, 'stateful') and layer.stateful) - for layer in self.layers]) - - @property - def updates(self): - """Retrieve the network's updates. - - Will only include updates that are either - unconditional, or conditional on inputs to this model - (e.g. will not include updates that depend on tensors - that aren't inputs to this model). - - Returns: - A list of update ops. - """ - if not self.trainable and not self.stateful: - return [] - updates = [] - for layer in self.layers: - if hasattr(layer, 'updates'): - # Collect updates that are dependent on inputs - # that are part of the model. - for node_index, node in enumerate(layer._inbound_nodes): # pylint: disable=protected-access - node_key = _make_node_key(layer.name, node_index) - if node_key in self._network_nodes: - # The model owns this layer node. - inputs = node.input_tensors - updates += layer.get_updates_for(inputs) - # Collect unconditional updates. - updates += layer.get_updates_for(None) - return updates - - @property - def losses(self): - """Retrieve the network's losses. - - Will only include losses that are either - unconditional, or conditional on inputs to this model - (e.g. will not include losses that depend on tensors - that aren't inputs to this model). - - Returns: - A list of loss tensors. - """ - losses = [] - if context.in_eager_mode(): - for layer in self.layers: - losses += layer.losses - return losses - - # Retrieve losses for all internal layers. - for layer in self.layers: - if hasattr(layer, 'losses'): - # Collect losses that are dependent on inputs - # that are part of the model. - for node_index, node in enumerate(layer._inbound_nodes): # pylint: disable=protected-access - node_key = _make_node_key(layer.name, node_index) - if node_key in self._network_nodes: - # The model owns this layer node. - inputs = node.input_tensors - losses += layer.get_losses_for(inputs) - # Collect unconditional losses. - losses += layer.get_losses_for(None) - # Add any potential unconditional model-level loss. - losses += self.get_losses_for(None) - return losses - - @property - def trainable_weights(self): - if not self.trainable: - return [] - weights = [] - for layer in self.layers: - weights += layer.trainable_weights - return weights - - @property - def non_trainable_weights(self): - weights = [] - for layer in self.layers: - weights += layer.non_trainable_weights - if not self.trainable: - trainable_weights = [] - for layer in self.layers: - trainable_weights += layer.trainable_weights - return trainable_weights + weights - return weights - - @property - def input_spec(self): - """Gets the network's input specs. - - Returns: - A list of `InputSpec` instances (one per input to the model) - or a single instance if the model has only one input. - """ - specs = [] - for layer in self._input_layers: - if layer.input_spec is None: - specs.append(None) - else: - if not isinstance(layer.input_spec, list): - raise TypeError('Layer ' + layer.name + - ' has an input_spec attribute that ' - 'is not a list. We expect a list. ' - 'Found input_spec = ' + str(layer.input_spec)) - specs += layer.input_spec - if len(specs) == 1: - return specs[0] - return specs - - def call(self, inputs, mask=None): - """Call the model on new inputs. - - In this case `call` just reapplies - all ops in the graph to the new inputs - (e.g. build a new computational graph from the provided inputs). - - Arguments: - inputs: A tensor or list of tensors. - mask: A mask or list of masks. A mask can be - either a tensor or None (no mask). - - Returns: - A tensor if there is a single output, or - a list of tensors if there are more than one outputs. - """ - inputs = nest.flatten(inputs) - if mask is None: - masks = [None for _ in range(len(inputs))] - else: - masks = nest.flatten(mask) - - if context.in_graph_mode(): - # Try to retrieve cached outputs if the layer has already been called - # on these exact inputs. - cache_key = (layers_util.object_list_uid(inputs) - + '_' + layers_util.object_list_uid(masks)) - if cache_key in self._output_tensor_cache: - # Cache hit. - return self._output_tensor_cache[cache_key] - # Actually apply the network graph to the new inputs. - outputs, _ = self._run_internal_graph(inputs, masks) - return outputs - - def compute_output_shape(self, input_shape): - if isinstance(input_shape, list): - input_shapes = [] - for shape in input_shape: - if shape is not None: - input_shapes.append(tuple(tensor_shape.TensorShape(shape).as_list())) - else: - input_shapes.append(None) - else: - if input_shape is not None: - input_shapes = [tuple(tensor_shape.TensorShape(input_shape).as_list())] - else: - input_shapes = [None] - - if len(input_shapes) != len(self._input_layers): - raise ValueError('Invalid input_shape argument ' + str(input_shape) + - ': model has ' + str(len(self._input_layers)) + - ' tensor inputs.') - - cache_key = layers_util.object_list_uid(input_shapes) - if cache_key not in self._output_shape_cache: - # Cache miss. We have to run the network graph manually (recursive calls - # to `compute_output_shape`). - layers_to_output_shapes = {} - for i in range(len(input_shapes)): - layer = self._input_layers[i] - input_shape = input_shapes[i] - # It's an input layer: then `compute_output_shape` is identity, - # and there is only one node and one tensor output. - shape_key = layer.name + '_0_0' - layers_to_output_shapes[shape_key] = input_shape - - depth_keys = list(self._nodes_by_depth.keys()) - depth_keys.sort(reverse=True) - # Iterate over nodes, by depth level. - if len(depth_keys) > 1: - for depth in depth_keys: - nodes = self._nodes_by_depth[depth] - for node in nodes: - # This is always a single layer, never a list. - layer = node.outbound_layer - if layer in self._input_layers: - # We've already covered the input layers - # a few lines above. - continue - # Potentially redundant list, - # same size as node.input_tensors. - input_shapes = [] - for j in range(len(node.inbound_layers)): - inbound_layer = node.inbound_layers[j] - node_index = node.node_indices[j] - tensor_index = node.tensor_indices[j] - shape_key = inbound_layer.name + '_%s_%s' % (node_index, - tensor_index) - input_shape = layers_to_output_shapes[shape_key] - input_shapes.append(input_shape) - - if len(input_shapes) == 1: - output_shape = layer.compute_output_shape(input_shapes[0]) - else: - output_shape = layer.compute_output_shape(input_shapes) - if isinstance(output_shape, list): - output_shapes = [ - tuple(tensor_shape.TensorShape(shape).as_list()) - for shape in output_shape - ] - else: - output_shapes = [ - tuple(tensor_shape.TensorShape(output_shape).as_list()) - ] - - node_index = layer._inbound_nodes.index(node) # pylint: disable=protected-access - for j in range(len(output_shapes)): - shape_key = layer.name + '_%s_%s' % (node_index, j) - layers_to_output_shapes[shape_key] = output_shapes[j] - - # Read final output shapes from layers_to_output_shapes. - output_shapes = [] - for i in range(len(self._output_layers)): - layer, node_index, tensor_index = self._output_coordinates[i] - shape_key = layer.name + '_%s_%s' % (node_index, tensor_index) - output_shapes.append(layers_to_output_shapes[shape_key]) - - # Store in cache. - self._output_shape_cache[cache_key] = output_shapes - else: - # Cache hit. - output_shapes = self._output_shape_cache[cache_key] - - if isinstance(output_shapes, list): - if len(output_shapes) == 1: - return tensor_shape.TensorShape(output_shapes[0]) - else: - return [tensor_shape.TensorShape(shape) for shape in output_shapes] - else: - return tensor_shape.TensorShape(output_shapes) - - def _run_internal_graph(self, inputs, masks=None): - """Computes output tensors for new inputs. - - # Note: - - Expects `inputs` to be a list (potentially with 1 element). - - Can be run on non-Keras tensors. - - Arguments: - inputs: List of tensors - masks: List of masks (tensors or None). - - Returns: - Three lists: output_tensors, output_masks, output_shapes - """ - # Note: masking support is relevant mainly for Keras. - # It cannot be factored out without having the fully reimplement the network - # calling logic on the Keras side. We choose to incorporate it in - # GraphNetwork because 1) it may be useful to fully support in tf.layers in - # the future and 2) Keras is a major user of GraphNetwork. If you don't - # use masking, it does not interfere with regular behavior at all and you - # can ignore it. - if masks is None: - masks = [None for _ in range(len(inputs))] - - # Dictionary mapping reference tensors to tuples - # (computed tensor, compute mask) - # we assume a 1:1 mapping from tensor to mask - # TODO(fchollet): raise exception when a `.compute_mask()` call - # does not return a list the same size as `call` - tensor_map = {} - for x, y, mask in zip(self.inputs, inputs, masks): - tensor_map[str(id(x))] = (y, mask) - - depth_keys = list(self._nodes_by_depth.keys()) - depth_keys.sort(reverse=True) - for depth in depth_keys: - nodes = self._nodes_by_depth[depth] - for node in nodes: - # This is always a single layer, never a list. - layer = node.outbound_layer - reference_input_tensors = node.input_tensors - reference_output_tensors = node.output_tensors - - # If all previous input tensors are available in tensor_map, - # then call node.inbound_layer on them. - computed_data = [] # List of tuples (input, mask). - for x in reference_input_tensors: - if str(id(x)) in tensor_map: - computed_data.append(tensor_map[str(id(x))]) - - if len(computed_data) == len(reference_input_tensors): - # Call layer (reapplying ops to new inputs). - with ops.name_scope(layer.name): - if node.arguments: - kwargs = node.arguments - else: - kwargs = {} - if len(computed_data) == 1: - computed_tensor, computed_mask = computed_data[0] - # Ensure mask propagation if applicable. - if 'mask' in estimator_util.fn_args(layer.call): - if 'mask' not in kwargs: - kwargs['mask'] = computed_mask - - output_tensors = nest.flatten( - layer.call(computed_tensor, **kwargs)) - if hasattr(layer, 'compute_mask'): - output_masks = nest.flatten( - layer.compute_mask(computed_tensor, computed_mask)) - else: - output_masks = [None for _ in range(len(output_tensors))] - computed_tensors = [computed_tensor] - computed_masks = [computed_mask] - else: - computed_tensors = [x[0] for x in computed_data] - computed_masks = [x[1] for x in computed_data] - if 'mask' in estimator_util.fn_args(layer.call): - if 'mask' not in kwargs: - kwargs['mask'] = computed_masks - output_tensors = nest.flatten( - layer.call(computed_tensors, **kwargs)) - if hasattr(layer, 'compute_mask'): - output_masks = nest.flatten( - layer.compute_mask(computed_tensors, computed_masks)) - else: - output_masks = [None for _ in range(len(output_tensors))] - - if context.in_graph_mode(): - if layer.activity_regularizer is not None: - regularization_losses = [ - layer.activity_regularizer(x) for x in computed_tensors - ] - # Apply activity regularizer if any: - layer.add_loss(regularization_losses, computed_tensors) - - if context.in_graph_mode(): - # Update model updates and losses: - # Keep track of updates that depend on the inputs - # (e.g. BN updates). - self.add_update(layer.get_updates_for(computed_tensors), inputs) - # Keep track of unconditional updates (e.g. a counter). - self.add_update(layer.get_updates_for(None), None) - # Keep track of losses that depend on the inputs - # (e.g. activity regularizers). - self.add_loss(layer.get_losses_for(computed_tensors), inputs) - # Keep track of unconditional losses - # (e.g. weight regularizers). - self.add_loss(layer.get_losses_for(None), None) - - # Update tensor_map. - for x, y, mask in zip(reference_output_tensors, output_tensors, - output_masks): - tensor_map[str(id(x))] = (y, mask) - - output_tensors = [] - output_masks = [] - output_shapes = [] - for x in self.outputs: - assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x) - tensor, mask = tensor_map[str(id(x))] - output_shapes.append(layers_util.static_shape(x)) - output_tensors.append(tensor) - output_masks.append(mask) - - if len(output_tensors) == 1: - output_tensors = output_tensors[0] - if output_shapes is not None: - output_shapes = output_shapes[0] - if output_masks is not None: - output_masks = output_masks[0] - - if context.in_graph_mode(): - # Update cache; - # keys are based on ids on input tensors and inputs masks. - cache_key = (layers_util.object_list_uid(inputs) - + '_' + layers_util.object_list_uid(masks)) - self._output_tensor_cache[cache_key] = output_tensors - if output_masks is not None: - self._output_mask_cache[cache_key] = output_masks - if output_shapes is not None: - input_shapes = [layers_util.static_shape(x) for x in inputs] - cache_key = layers_util.object_list_uid(input_shapes) - self._output_shape_cache[cache_key] = output_shapes - - return output_tensors, output_masks - - -def _make_node_key(layer_name, node_index): - return layer_name + '_ib-' + str(node_index) diff --git a/tensorflow/python/layers/network_test.py b/tensorflow/python/layers/network_test.py deleted file mode 100644 index 7a2c7fb3fc782f6e59b8b483ec43c4abddf4d023..0000000000000000000000000000000000000000 --- a/tensorflow/python/layers/network_test.py +++ /dev/null @@ -1,525 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for tf.layers.network.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import test_util -from tensorflow.python.layers import base as base_layers -from tensorflow.python.layers import core as core_layers -from tensorflow.python.layers import network as network_layers -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import sparse_ops -from tensorflow.python.platform import test - - -class BaseLayerCompatibilityTest(test.TestCase): - - def test_get_updates_for(self): - a = network_layers.Input(shape=(2,)) - dense_layer = core_layers.Dense(1) - dense_layer.add_update(0, inputs=a) - dense_layer.add_update(1, inputs=None) - - self.assertEqual(dense_layer.get_updates_for(a), [0]) - self.assertEqual(dense_layer.get_updates_for(None), [1]) - - def test_get_losses_for(self): - a = network_layers.Input(shape=(2,)) - dense_layer = core_layers.Dense(1) - dense_layer.add_loss(0, inputs=a) - dense_layer.add_loss(1, inputs=None) - - self.assertEqual(dense_layer.get_losses_for(a), [0]) - self.assertEqual(dense_layer.get_losses_for(None), [1]) - - def testTopologicalAttributes(self): - # test layer attributes / methods related to cross-layer connectivity. - a = network_layers.Input(shape=(32,), name='input_a') - b = network_layers.Input(shape=(32,), name='input_b') - - # test input, output, input_shape, output_shape - test_layer = core_layers.Dense(16, name='test_layer') - a_test = test_layer(a) - self.assertEqual(test_layer.input, a) - self.assertEqual(test_layer.output, a_test) - self.assertEqual(test_layer.input_shape, (None, 32)) - self.assertEqual(test_layer.output_shape, (None, 16)) - - # test `get_*_at` methods - dense = core_layers.Dense(16, name='dense_1') - a_2 = dense(a) - b_2 = dense(b) - - self.assertEqual(dense.get_input_at(0), a) - self.assertEqual(dense.get_input_at(1), b) - self.assertEqual(dense.get_output_at(0), a_2) - self.assertEqual(dense.get_output_at(1), b_2) - self.assertEqual(dense.get_input_shape_at(0), (None, 32)) - self.assertEqual(dense.get_input_shape_at(1), (None, 32)) - self.assertEqual(dense.get_output_shape_at(0), (None, 16)) - self.assertEqual(dense.get_output_shape_at(1), (None, 16)) - - # Test invalid value for attribute retrieval. - with self.assertRaises(ValueError): - dense.get_input_at(2) - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - _ = new_dense.input - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - _ = new_dense.output - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - _ = new_dense.output_shape - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - _ = new_dense.input_shape - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - a = network_layers.Input(shape=(3, 32)) - a = network_layers.Input(shape=(5, 32)) - a_2 = dense(a) - b_2 = dense(b) - _ = new_dense.input_shape - with self.assertRaises(AttributeError): - new_dense = core_layers.Dense(16) - a = network_layers.Input(shape=(3, 32)) - a = network_layers.Input(shape=(5, 32)) - a_2 = dense(a) - b_2 = dense(b) - _ = new_dense.output_shape - - def testTopologicalAttributesMultiOutputLayer(self): - - class PowersLayer(base_layers.Layer): - - def call(self, inputs): - return [inputs**2, inputs**3] - - x = network_layers.Input(shape=(32,)) - test_layer = PowersLayer() - p1, p2 = test_layer(x) # pylint: disable=not-callable - - self.assertEqual(test_layer.input, x) - self.assertEqual(test_layer.output, [p1, p2]) - self.assertEqual(test_layer.input_shape, (None, 32)) - self.assertEqual(test_layer.output_shape, [(None, 32), (None, 32)]) - - def testTopologicalAttributesMultiInputLayer(self): - - class AddLayer(base_layers.Layer): - - def call(self, inputs): - assert len(inputs) == 2 - return inputs[0] + inputs[1] - - a = network_layers.Input(shape=(32,)) - b = network_layers.Input(shape=(32,)) - test_layer = AddLayer() - y = test_layer([a, b]) # pylint: disable=not-callable - - self.assertEqual(test_layer.input, [a, b]) - self.assertEqual(test_layer.output, y) - self.assertEqual(test_layer.input_shape, [(None, 32), (None, 32)]) - self.assertEqual(test_layer.output_shape, (None, 32)) - - -class NetworkTest(test.TestCase): - - def testBasicNetwork(self): - # minimum viable network - x = network_layers.Input(shape=(32,)) - dense = core_layers.Dense(2) - y = dense(x) - network = network_layers.GraphNetwork(x, y, name='dense_network') - - # test basic attributes - self.assertEqual(network.name, 'dense_network') - self.assertEqual(len(network.layers), 2) # InputLayer + Dense - self.assertEqual(network.layers[1], dense) - self.assertEqual(network.weights, dense.weights) - self.assertEqual(network.trainable_weights, dense.trainable_weights) - self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights) - - # test callability on Input - x_2 = network_layers.Input(shape=(32,)) - y_2 = network(x_2) - self.assertEqual(y_2.get_shape().as_list(), [None, 2]) - - # test callability on regular tensor - x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32)) - y_2 = network(x_2) - self.assertEqual(y_2.get_shape().as_list(), [None, 2]) - - # test network `trainable` attribute - network.trainable = False - self.assertEqual(network.weights, dense.weights) - self.assertEqual(network.trainable_weights, []) - self.assertEqual(network.non_trainable_weights, - dense.trainable_weights + dense.non_trainable_weights) - - def test_node_construction(self): - # test graph topology construction basics - a = network_layers.Input(shape=(32,), name='input_a') - b = network_layers.Input(shape=(32,), name='input_b') - - self.assertEqual(a.get_shape().as_list(), [None, 32]) - a_layer, a_node_index, a_tensor_index = a._keras_history - b_layer, _, _ = b._keras_history - self.assertEqual(len(a_layer._inbound_nodes), 1) - self.assertEqual(a_tensor_index, 0) - node = a_layer._inbound_nodes[a_node_index] - self.assertEqual(node.outbound_layer, a_layer) - - self.assertEqual(node.inbound_layers, []) - self.assertEqual(node.input_tensors, [a]) - self.assertEqual(node.input_shapes, [(None, 32)]) - self.assertEqual(node.output_tensors, [a]) - self.assertEqual(node.output_shapes, [(None, 32)]) - - dense = core_layers.Dense(16, name='dense_1') - dense(a) - dense(b) - - self.assertEqual(len(dense._inbound_nodes), 2) - self.assertEqual(len(dense._outbound_nodes), 0) - self.assertEqual(dense._inbound_nodes[0].inbound_layers, [a_layer]) - self.assertEqual(dense._inbound_nodes[0].outbound_layer, dense) - self.assertEqual(dense._inbound_nodes[1].inbound_layers, [b_layer]) - self.assertEqual(dense._inbound_nodes[1].outbound_layer, dense) - self.assertEqual(dense._inbound_nodes[0].input_tensors, [a]) - self.assertEqual(dense._inbound_nodes[1].input_tensors, [b]) - - # Test config - config_0 = dense._inbound_nodes[0].get_config() - self.assertEqual(config_0['outbound_layer'], dense.name) - - def testMultiInputNetwork(self): - a = network_layers.Input(shape=(32,), name='input_a') - b = network_layers.Input(shape=(32,), name='input_b') - - class AddLayer(base_layers.Layer): - - def call(self, inputs): - assert len(inputs) == 2 - return inputs[0] + inputs[1] - - c = AddLayer()([a, b]) # pylint: disable=not-callable - network = network_layers.GraphNetwork([a, b], c) - self.assertEqual(len(network.layers), 3) # 2 * InputLayer + AddLayer - - # Test callability. - a2 = network_layers.Input(shape=(32,)) - b2 = network_layers.Input(shape=(32,)) - c2 = network([a2, b2]) - self.assertEqual(c2.get_shape().as_list(), [None, 32]) - - def testMultiOutputNetwork(self): - x = network_layers.Input(shape=(32,)) - y1 = core_layers.Dense(2)(x) - y2 = core_layers.Dense(3)(x) - network = network_layers.GraphNetwork(x, [y1, y2]) - - self.assertEqual(len(network.layers), 3) # InputLayer + 2 * Dense - - # Test callability. - x2 = network_layers.Input(shape=(32,)) - outputs = network(x2) - - self.assertEqual(type(outputs), list) - self.assertEqual(len(outputs), 2) - self.assertEqual(outputs[0].get_shape().as_list(), [None, 2]) - self.assertEqual(outputs[1].get_shape().as_list(), [None, 3]) - - def testMultiInputMultiOutputNetworkSharedLayer(self): - a = network_layers.Input(shape=(32,), name='input_a') - b = network_layers.Input(shape=(32,), name='input_b') - - dense = core_layers.Dense(2) - - y1 = dense(a) - y2 = dense(b) - network = network_layers.GraphNetwork([a, b], [y1, y2]) - self.assertEqual(len(network.layers), 3) # 2 * InputLayer + Dense - - # Test callability. - a2 = network_layers.Input(shape=(32,)) - b2 = network_layers.Input(shape=(32,)) - outputs = network([a2, b2]) - - self.assertEqual(type(outputs), list) - self.assertEqual(len(outputs), 2) - self.assertEqual(outputs[0].get_shape().as_list(), [None, 2]) - self.assertEqual(outputs[1].get_shape().as_list(), [None, 2]) - - def testCrossDataFlows(self): - # Test the ability to have multi-output layers with outputs that get routed - # to separate layers - - class PowersLayer(base_layers.Layer): - - def call(self, inputs): - return [inputs**2, inputs**3] - - x = network_layers.Input(shape=(32,)) - p1, p2 = PowersLayer()(x) # pylint: disable=not-callable - y1 = core_layers.Dense(2)(p1) - y2 = core_layers.Dense(3)(p2) - network = network_layers.GraphNetwork(x, [y1, y2]) - - self.assertEqual(len(network.layers), 4) # InputLayer + 2 * Dense + PLayer - - # Test callability. - x2 = network_layers.Input(shape=(32,)) - outputs = network(x2) - - self.assertEqual(type(outputs), list) - self.assertEqual(len(outputs), 2) - self.assertEqual(outputs[0].get_shape().as_list(), [None, 2]) - self.assertEqual(outputs[1].get_shape().as_list(), [None, 3]) - - def testNetworkAttributes(self): - x = network_layers.Input(shape=(32,)) - z = core_layers.Dense(2, kernel_regularizer=lambda x: 0.01 * (x**2))(x) - dense = core_layers.Dense(2, name='dense') - dense.add_update(1) - y = dense(z) - net = network_layers.GraphNetwork(x, y) - - # losses - self.assertEqual(len(net.losses), 1) - - # updates - self.assertEqual(len(net.updates), 1) - - # get_layer - self.assertEqual(net.get_layer('dense'), dense) - self.assertEqual(net.get_layer(index=2), dense) - with self.assertRaises(ValueError): - net.get_layer('dense_unknown') - with self.assertRaises(ValueError): - net.get_layer() - with self.assertRaises(ValueError): - net.get_layer(index=4) - - # input, output - self.assertEqual(net.input, x) - self.assertEqual(net.output, y) - - # input_shape, output_shape - self.assertEqual(net.input_shape, (None, 32)) - self.assertEqual(net.output_shape, (None, 2)) - - # get_*_at - self.assertEqual(net.get_input_at(0), x) - self.assertEqual(net.get_output_at(0), y) - - # compute_output_shape - self.assertEqual(net.compute_output_shape((3, 32)).as_list(), [3, 2]) - - def testInvalidNetworks(self): - # redundant inputs - x = network_layers.Input(shape=(32,)) - y = core_layers.Dense(2)(x) - with self.assertRaises(ValueError): - network_layers.GraphNetwork([x, x], y) - - # inputs that don't come from Input - x = array_ops.placeholder(dtype='float32', shape=(None, 32)) - y = core_layers.Dense(2)(x) - with self.assertRaises(ValueError): - network_layers.GraphNetwork(x, y) - - # inputs that don't come from Input but have a layer history - x = network_layers.Input(shape=(32,)) - x = core_layers.Dense(32)(x) - y = core_layers.Dense(2)(x) - with self.assertRaises(ValueError): - network_layers.GraphNetwork(x, y) - - # outputs that don't come from layers - x = network_layers.Input(shape=(32,)) - y = core_layers.Dense(2)(x) - y = 2 * y - with self.assertRaises(ValueError): - network_layers.GraphNetwork(x, y) - - # disconnected graphs - x1 = network_layers.Input(shape=(32,)) - x2 = network_layers.Input(shape=(32,)) - y = core_layers.Dense(2)(x1) - with self.assertRaises(ValueError): - network_layers.GraphNetwork(x2, y) - - # redundant layer names - x = network_layers.Input(shape=(32,)) - z = core_layers.Dense(2, name='dense')(x) - y = core_layers.Dense(2, name='dense')(z) - with self.assertRaises(ValueError): - network_layers.GraphNetwork(x, y) - - def testInputTensorWrapping(self): - x = array_ops.placeholder(dtype='float32', shape=(None, 32)) - x = network_layers.Input(tensor=x) - y = core_layers.Dense(2)(x) - network_layers.GraphNetwork(x, y) - - def testExplicitBatchSize(self): - x = network_layers.Input(shape=(32,), batch_size=3) - y = core_layers.Dense(2)(x) - self.assertEqual(y.get_shape().as_list(), [3, 2]) - - def testNetworkRecursion(self): - # test the ability of networks to be used as layers inside networks. - a = network_layers.Input(shape=(32,)) - b = core_layers.Dense(2)(a) - net = network_layers.GraphNetwork(a, b) - - c = network_layers.Input(shape=(32,)) - d = net(c) - - recursive_net = network_layers.GraphNetwork(c, d) - self.assertEqual(len(recursive_net.layers), 2) - self.assertEqual(recursive_net.layers[1], net) - self.assertEqual(len(recursive_net.weights), 2) - - # test callability - x = array_ops.placeholder(dtype='float32', shape=(None, 32)) - y = recursive_net(x) - self.assertEqual(y.get_shape().as_list(), [None, 2]) - - def testSparseInput(self): - - class SparseSoftmax(base_layers.Layer): - - def call(self, inputs): - return sparse_ops.sparse_softmax(inputs) - - x = network_layers.Input(shape=(32,), sparse=True) - y = SparseSoftmax()(x) # pylint: disable=not-callable - network = network_layers.GraphNetwork(x, y) - - self.assertEqual(len(network.layers), 2) - self.assertEqual(network.layers[0].sparse, True) - - @test_util.run_in_graph_and_eager_modes() - def testMaskingSingleInput(self): - - class MaskedLayer(base_layers.Layer): - - def call(self, inputs, mask=None): - if mask is not None: - return inputs * mask - return inputs - - def compute_mask(self, inputs, mask=None): - return array_ops.ones_like(inputs) - - if context.in_graph_mode(): - x = network_layers.Input(shape=(32,)) - y = MaskedLayer()(x) # pylint: disable=not-callable - network = network_layers.GraphNetwork(x, y) - - # test callability on Input - x_2 = network_layers.Input(shape=(32,)) - y_2 = network(x_2) - self.assertEqual(y_2.get_shape().as_list(), [None, 32]) - - # test callability on regular tensor - x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32)) - y_2 = network(x_2) - self.assertEqual(y_2.get_shape().as_list(), [None, 32]) - else: - a = constant_op.constant([2] * 32) - mask = constant_op.constant([0, 1] * 16) - a._keras_mask = mask - b = MaskedLayer().apply(a) - self.assertTrue(hasattr(b, '_keras_mask')) - self.assertAllEqual(self.evaluate(array_ops.ones_like(mask)), - self.evaluate(getattr(b, '_keras_mask'))) - self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b)) - - -class DeferredModeTest(test.TestCase): - - def testDeferredTensorAttributes(self): - x = base_layers._DeferredTensor(shape=(None, 2), dtype='float32', name='x') - self.assertEqual(str(x), - 'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)') - self.assertEqual(repr(x), - '<_DeferredTensor \'x\' shape=(?, 2) dtype=float32>') - - @test_util.run_in_graph_and_eager_modes() - def testSimpleNetworkBuilding(self): - inputs = network_layers.Input(shape=(32,)) - if context.in_eager_mode(): - self.assertIsInstance(inputs, base_layers._DeferredTensor) - self.assertEqual(inputs.dtype.name, 'float32') - self.assertEqual(inputs.shape.as_list(), [None, 32]) - - x = core_layers.Dense(2)(inputs) - if context.in_eager_mode(): - self.assertIsInstance(x, base_layers._DeferredTensor) - self.assertEqual(x.dtype.name, 'float32') - self.assertEqual(x.shape.as_list(), [None, 2]) - - outputs = core_layers.Dense(4)(x) - network = network_layers.GraphNetwork(inputs, outputs) - self.assertIsInstance(network, network_layers.GraphNetwork) - - if context.in_eager_mode(): - # It should be possible to call such a network on EagerTensors. - inputs = constant_op.constant( - np.random.random((10, 32)).astype('float32')) - outputs = network(inputs) - self.assertEqual(outputs.shape.as_list(), [10, 4]) - - @test_util.run_in_graph_and_eager_modes() - def testMultiIONetworkbuilding(self): - input_a = network_layers.Input(shape=(32,)) - input_b = network_layers.Input(shape=(16,)) - a = core_layers.Dense(16)(input_a) - - class AddLayer(base_layers.Layer): - - def call(self, inputs): - return inputs[0] + inputs[1] - - def compute_output_shape(self, input_shape): - return input_shape[0] - - c = AddLayer()([a, input_b]) # pylint: disable=not-callable - c = core_layers.Dense(2)(c) - - network = network_layers.GraphNetwork([input_a, input_b], [a, c]) - if context.in_eager_mode(): - a_val = constant_op.constant( - np.random.random((10, 32)).astype('float32')) - b_val = constant_op.constant( - np.random.random((10, 16)).astype('float32')) - outputs = network([a_val, b_val]) - self.assertEqual(len(outputs), 2) - self.assertEqual(outputs[0].shape.as_list(), [10, 16]) - self.assertEqual(outputs[1].shape.as_list(), [10, 2]) - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py index 890c12f6e00daabe7e64c00814fcb3ff8f04ae3a..c23d755a8e7b145a36b3c38e088f5dea74afb0e9 100644 --- a/tensorflow/python/layers/normalization.py +++ b/tensorflow/python/layers/normalization.py @@ -39,8 +39,10 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import moving_averages +from tensorflow.python.util.tf_export import tf_export +@tf_export('layers.BatchNormalization') class BatchNormalization(base.Layer): """Batch Normalization layer from http://arxiv.org/abs/1502.03167. @@ -92,8 +94,8 @@ class BatchNormalization(base.Layer): and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that `momentum` is still applied to get the means and variances for inference. - fused: if `True`, use a faster, fused implementation if possible. - If `None`, use the system recommended implementation. + fused: if `None` or `True`, use a faster, fused implementation if possible. + If `False`, use the system recommended implementation. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`, @@ -336,8 +338,9 @@ class BatchNormalization(base.Layer): return var with ops.device(None): - device = ((lambda _: self.moving_mean.device) - if context.in_graph_mode() else self.moving_mean.device) + device = ( + self.moving_mean.device if context.executing_eagerly() else + (lambda _: self.moving_mean.device)) with ops.device(device): self.renorm_mean = _renorm_variable('renorm_mean', param_shape) self.renorm_mean_weight = _renorm_variable('renorm_mean_weight', ()) @@ -345,8 +348,9 @@ class BatchNormalization(base.Layer): # renorm_stddev_weight. This allows us to (1) mix the average # stddev with the minibatch stddev early in training, and (2) compute # the unbiased average stddev by dividing renorm_stddev by the weight. - device = ((lambda _: self.moving_variance.device) - if context.in_graph_mode() else self.moving_variance.device) + device = ( + self.moving_variance.device if context.executing_eagerly() else + (lambda _: self.moving_variance.device)) with ops.device(device): self.renorm_stddev = _renorm_variable('renorm_stddev', param_shape) self.renorm_stddev_weight = _renorm_variable( @@ -418,7 +422,7 @@ class BatchNormalization(base.Layer): one_minus_decay) variance_update = self._assign_moving_average(self.moving_variance, variance, one_minus_decay) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Note that in Eager mode, the updates are already executed when running # assign_moving_averages. So we do not need to put them into # collections. @@ -491,6 +495,7 @@ class BatchNormalization(base.Layer): return (r, d, new_mean, new_variance) def call(self, inputs, training=False): + in_eager_mode = context.executing_eagerly() if self.virtual_batch_size is not None: # Virtual batches (aka ghost batches) can be simulated by reshaping the # Tensor and reusing the existing batch norm implementation @@ -593,6 +598,9 @@ class BatchNormalization(base.Layer): axis=1, keep_dims=True) def _do_update(var, value): + if in_eager_mode and not self.trainable: + return + return moving_averages.assign_moving_average( var, value, self.momentum, zero_debias=False) @@ -604,7 +612,7 @@ class BatchNormalization(base.Layer): training, lambda: _do_update(self.moving_variance, new_variance), lambda: self.moving_variance) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.add_update(mean_update, inputs=inputs) self.add_update(variance_update, inputs=inputs) @@ -629,6 +637,7 @@ class BatchNormalization(base.Layer): return input_shape +@tf_export('layers.batch_normalization') def batch_normalization(inputs, axis=-1, momentum=0.99, @@ -722,8 +731,8 @@ def batch_normalization(inputs, and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that `momentum` is still applied to get the means and variances for inference. - fused: if `True`, use a faster, fused implementation if possible. - If `None`, use the system recommended implementation. + fused: if `None` or `True`, use a faster, fused implementation if possible. + If `False`, use the system recommended implementation. virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`, which means batch normalization is performed across the whole batch. When `virtual_batch_size` is not `None`, instead perform "Ghost Batch diff --git a/tensorflow/python/layers/pooling.py b/tensorflow/python/layers/pooling.py index ab06a3a40826e7d41c040066fd41c56c1ed84ad2..50503ce093fbc251b11c4d5cbccb2a2683d92e7a 100644 --- a/tensorflow/python/layers/pooling.py +++ b/tensorflow/python/layers/pooling.py @@ -26,6 +26,7 @@ from tensorflow.python.layers import base from tensorflow.python.layers import utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn +from tensorflow.python.util.tf_export import tf_export class _Pooling1D(base.Layer): @@ -96,6 +97,7 @@ class _Pooling1D(base.Layer): return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]]) +@tf_export('layers.AveragePooling1D') class AveragePooling1D(_Pooling1D): """Average Pooling layer for 1D inputs. @@ -127,6 +129,7 @@ class AveragePooling1D(_Pooling1D): **kwargs) +@tf_export('layers.average_pooling1d') def average_pooling1d(inputs, pool_size, strides, padding='valid', data_format='channels_last', name=None): @@ -161,6 +164,7 @@ def average_pooling1d(inputs, pool_size, strides, return layer.apply(inputs) +@tf_export('layers.MaxPooling1D') class MaxPooling1D(_Pooling1D): """Max Pooling layer for 1D inputs. @@ -192,6 +196,7 @@ class MaxPooling1D(_Pooling1D): **kwargs) +@tf_export('layers.max_pooling1d') def max_pooling1d(inputs, pool_size, strides, padding='valid', data_format='channels_last', name=None): @@ -297,6 +302,7 @@ class _Pooling2D(base.Layer): [input_shape[0], rows, cols, input_shape[3]]) +@tf_export('layers.AveragePooling2D') class AveragePooling2D(_Pooling2D): """Average pooling layer for 2D inputs (e.g. images). @@ -328,6 +334,7 @@ class AveragePooling2D(_Pooling2D): padding=padding, data_format=data_format, name=name, **kwargs) +@tf_export('layers.average_pooling2d') def average_pooling2d(inputs, pool_size, strides, padding='valid', data_format='channels_last', @@ -365,6 +372,7 @@ def average_pooling2d(inputs, return layer.apply(inputs) +@tf_export('layers.MaxPooling2D') class MaxPooling2D(_Pooling2D): """Max pooling layer for 2D inputs (e.g. images). @@ -396,6 +404,7 @@ class MaxPooling2D(_Pooling2D): padding=padding, data_format=data_format, name=name, **kwargs) +@tf_export('layers.max_pooling2d') def max_pooling2d(inputs, pool_size, strides, padding='valid', data_format='channels_last', @@ -515,6 +524,7 @@ class _Pooling3D(base.Layer): [input_shape[0], len_dim1, len_dim2, len_dim3, input_shape[4]]) +@tf_export('layers.AveragePooling3D') class AveragePooling3D(_Pooling3D): """Average pooling layer for 3D inputs (e.g. volumes). @@ -548,6 +558,7 @@ class AveragePooling3D(_Pooling3D): padding=padding, data_format=data_format, name=name, **kwargs) +@tf_export('layers.average_pooling3d') def average_pooling3d(inputs, pool_size, strides, padding='valid', data_format='channels_last', @@ -587,6 +598,7 @@ def average_pooling3d(inputs, return layer.apply(inputs) +@tf_export('layers.MaxPooling3D') class MaxPooling3D(_Pooling3D): """Max pooling layer for 3D inputs (e.g. volumes). @@ -620,6 +632,7 @@ class MaxPooling3D(_Pooling3D): padding=padding, data_format=data_format, name=name, **kwargs) +@tf_export('layers.max_pooling3d') def max_pooling3d(inputs, pool_size, strides, padding='valid', data_format='channels_last', diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py index 7407d9a7b30268271810a23a7146c92fd28f8d4e..3b156c36a2ff35fb9e05af1406d7b3f6cf883394 100644 --- a/tensorflow/python/layers/utils.py +++ b/tensorflow/python/layers/utils.py @@ -20,9 +20,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.ops import variables from tensorflow.python.ops import control_flow_ops from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond as smart_module from tensorflow.python.framework import tensor_util from tensorflow.python.util import nest @@ -178,67 +180,56 @@ def deconv_output_length(input_length, filter_size, padding, stride): return input_length -def smart_cond(pred, fn1, fn2, name=None): - """Return either `fn1()` or `fn2()` based on the boolean predicate `pred`. +def smart_cond(pred, true_fn=None, false_fn=None, name=None): + """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. - If `pred` is a bool or has a constant value, we return either `fn1()` - or `fn2()`, otherwise we use `tf.cond` to dynamically route to both. + If `pred` is a bool or has a constant value, we return either `true_fn()` + or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. Arguments: - pred: A scalar determining whether to return the result of `fn1` or `fn2`. - fn1: The callable to be performed if pred is true. - fn2: The callable to be performed if pred is false. + pred: A scalar determining whether to return the result of `true_fn` or + `false_fn`. + true_fn: The callable to be performed if pred is true. + false_fn: The callable to be performed if pred is false. name: Optional name prefix when using `tf.cond`. Returns: - Tensors returned by the call to either `fn1` or `fn2`. + Tensors returned by the call to either `true_fn` or `false_fn`. Raises: - TypeError: If `fn1` or `fn2` is not callable. + TypeError: If `true_fn` or `false_fn` is not callable. """ - if not callable(fn1): - raise TypeError('`fn1` must be callable.') - if not callable(fn2): - raise TypeError('`fn2` must be callable.') - - pred_value = constant_value(pred) - if pred_value is not None: - if pred_value: - return fn1() - else: - return fn2() - else: - return control_flow_ops.cond(pred, true_fn=fn1, false_fn=fn2, name=name) + if isinstance(pred, variables.Variable): + return control_flow_ops.cond( + pred, true_fn=true_fn, false_fn=false_fn, name=name) + return smart_module.smart_cond( + pred, true_fn=true_fn, false_fn=false_fn, name=name) def constant_value(pred): """Return the bool value for `pred`, or None if `pred` had a dynamic value. - Arguments: - pred: A scalar, either a Python bool or a TensorFlow boolean variable - or tensor, or the Python integer 1 or 0. + Arguments: + pred: A scalar, either a Python bool or a TensorFlow boolean variable + or tensor, or the Python integer 1 or 0. - Returns: - True or False if `pred` has a constant boolean value, None otherwise. + Returns: + True or False if `pred` has a constant boolean value, None otherwise. - Raises: - TypeError: If `pred` is not a Variable, Tensor or bool. - """ + Raises: + TypeError: If `pred` is not a Variable, Tensor or bool, or Python + interger 1 or 0. + """ # Allow integer booleans. - if pred == 0: - pred = False - elif pred == 1: - pred = True - - if isinstance(pred, bool): - pred_value = pred - elif isinstance(pred, variables.Variable): - pred_value = None - elif isinstance(pred, ops.Tensor): - pred_value = tensor_util.constant_value(pred) - else: - raise TypeError('`pred` must be a Tensor, a Variable, or a Python bool.') - return pred_value + if isinstance(pred, int): + if pred == 1: + pred = True + elif pred == 0: + pred = False + + if isinstance(pred, variables.Variable): + return None + return smart_module.smart_constant_value(pred) def object_list_uid(object_list): @@ -255,3 +246,45 @@ def static_shape(x): return tuple(x.get_shape().as_list()) except ValueError: return None + + +def get_reachable_from_inputs(inputs, targets=None): + """Returns the set of tensors reachable from `inputs`. + + Stops if all targets have been found (target is optional). + + Only valid in Symbolic mode, not Eager mode. + + Args: + inputs: List of tensors. + targets: List of tensors. + + Returns: + A set of tensors reachable from the inputs (includes the inputs themselves). + """ + reachable = set(inputs) + if targets: + targets = set(targets) + queue = inputs[:] + + while queue: + x = queue.pop() + outputs = [] + try: + consumers = x.consumers() + except AttributeError: + # Case where x is a variable type + consumers = [x.op] + for z in consumers: + consumer_outputs = z.outputs + if consumer_outputs: # May be None + outputs += consumer_outputs + + for y in outputs: + if y not in reachable: + reachable.add(y) + queue.insert(0, y) + + if targets and targets.issubset(reachable): + return reachable + return reachable diff --git a/tensorflow/python/layers/utils_test.py b/tensorflow/python/layers/utils_test.py index a560f6b6d21efc0c1070d5a9296a7a8e914e2eb9..c941aad7bc63dbb891fbe78cd2a47dd6805bf231 100644 --- a/tensorflow/python/layers/utils_test.py +++ b/tensorflow/python/layers/utils_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.layers import utils +from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -87,5 +88,34 @@ class ConvUtilsTest(test.TestCase): self.assertEqual(3, utils.deconv_output_length(4, 2, 'full', 1)) self.assertEqual(6, utils.deconv_output_length(4, 2, 'full', 2)) + +class GraphUtilsTest(test.TestCase): + + def testGetReachableFromInputs(self): + + with self.test_session(): + pl_1 = array_ops.placeholder(shape=None, dtype='float32') + pl_2 = array_ops.placeholder(shape=None, dtype='float32') + pl_3 = array_ops.placeholder(shape=None, dtype='float32') + x_1 = pl_1 + pl_2 + x_2 = pl_2 * 2 + x_3 = pl_3 + 1 + x_4 = x_1 + x_2 + x_5 = x_3 * pl_1 + + self.assertEqual( + utils.get_reachable_from_inputs([pl_1]), + {pl_1, x_1, x_4, x_5}) + self.assertEqual( + utils.get_reachable_from_inputs([pl_1, pl_2]), + {pl_1, pl_2, x_1, x_2, x_4, x_5}) + self.assertEqual( + utils.get_reachable_from_inputs([pl_3]), + {pl_3, x_3, x_5}) + self.assertEqual( + utils.get_reachable_from_inputs([x_3]), + {x_3, x_5}) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index e0422ef80add42307268be2743e668eb8c8acb68..343415b2645e00003e51fad18cbb1ec602db472d 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -79,10 +79,11 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { const Tensor& t = call->ins[i]; if (call->eager) { if (call->gpu) { - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, call->device)); + arg = EagerTensorFromHandle( + new TFE_TensorHandle(t, call->device, call->device)); } else { // TFE_TensorHandle assumes that CPU is identified by `nullptr`. - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr)); + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr, nullptr)); } if (arg == nullptr) { return errors::Internal("Unable to procure EagerTensor from Tensor."); diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc index 2635694e23c07dd8e75d4bb0cfb9e83a2042d921..00cbf0c532cf80d3bb27afe168ecde963ba3591d 100644 --- a/tensorflow/python/lib/core/py_util.cc +++ b/tensorflow/python/lib/core/py_util.cc @@ -41,6 +41,55 @@ const char* ClassName(PyObject* py) { } // end namespace +// Returns a PyObject containing a string, or null +void TryAppendTraceback(PyObject* ptype, PyObject* pvalue, PyObject* ptraceback, + string* out) { + // The "traceback" module is assumed to be imported already by script_ops.py. + PyObject* tb_module = PyImport_AddModule("traceback"); + + if (!tb_module) { + return; + } + + PyObject* format_exception = + PyObject_GetAttrString(tb_module, "format_exception"); + + if (!format_exception) { + return; + } + + if (!PyCallable_Check(format_exception)) { + Py_DECREF(format_exception); + return; + } + + PyObject* ret_val = PyObject_CallFunctionObjArgs(format_exception, ptype, + pvalue, ptraceback, nullptr); + Py_DECREF(format_exception); + + if (!ret_val) { + return; + } + + if (!PyList_Check(ret_val)) { + Py_DECREF(ret_val); + return; + } + + Py_ssize_t n = PyList_GET_SIZE(ret_val); + for (Py_ssize_t i = 0; i < n; ++i) { + PyObject* v = PyList_GET_ITEM(ret_val, i); +#if PY_MAJOR_VERSION < 3 + strings::StrAppend(out, PyString_AS_STRING(v), "\n"); +#else + strings::StrAppend(out, PyUnicode_AsUTF8(v), "\n"); +#endif + } + + // Iterate through ret_val. + Py_DECREF(ret_val); +} + string PyExceptionFetch() { CHECK(PyErr_Occurred()) << "Must only call PyExceptionFetch after an exception."; @@ -52,14 +101,20 @@ string PyExceptionFetch() { string err = ClassName(ptype); if (pvalue) { PyObject* str = PyObject_Str(pvalue); + if (str) { #if PY_MAJOR_VERSION < 3 - strings::StrAppend(&err, ": ", PyString_AS_STRING(str)); + strings::StrAppend(&err, ": ", PyString_AS_STRING(str), "\n"); #else - strings::StrAppend(&err, ": ", PyUnicode_AsUTF8(str)); + strings::StrAppend(&err, ": ", PyUnicode_AsUTF8(str), "\n"); #endif Py_DECREF(str); + } else { + strings::StrAppend(&err, "(unknown error message)\n"); } + + TryAppendTraceback(ptype, pvalue, ptraceback, &err); + Py_DECREF(pvalue); } Py_DECREF(ptype); diff --git a/tensorflow/python/lib/io/file_io.i b/tensorflow/python/lib/io/file_io.i index c0c4e035fc3d6a50334acb9228c13c702ef426c0..891a7b0fd0dc177f5ee439707c9e2c99148e177c 100644 --- a/tensorflow/python/lib/io/file_io.i +++ b/tensorflow/python/lib/io/file_io.i @@ -110,21 +110,15 @@ void RecursivelyCreateDir(const string& dirname, TF_Status* out_status) { } } -void CopyFile(const string& oldpath, const string& newpath, bool overwrite, +void CopyFile(const string& src, const string& target, bool overwrite, TF_Status* out_status) { - // If overwrite is false and the newpath file exists then it's an error. - if (!overwrite && tensorflow::Env::Default()->FileExists(newpath).ok()) { + // If overwrite is false and the target file exists then its an error. + if (!overwrite && tensorflow::Env::Default()->FileExists(target).ok()) { TF_SetStatus(out_status, TF_ALREADY_EXISTS, "file already exists"); return; } - string file_content; - tensorflow::Status status = ReadFileToString(tensorflow::Env::Default(), - oldpath, &file_content); - if (!status.ok()) { - Set_TF_Status_from_Status(out_status, status); - return; - } - status = WriteStringToFile(tensorflow::Env::Default(), newpath, file_content); + tensorflow::Status status = + tensorflow::Env::Default()->CopyFile(src, target); if (!status.ok()) { Set_TF_Status_from_Status(out_status, status); } diff --git a/tensorflow/python/ops/accumulate_n_benchmark.py b/tensorflow/python/ops/accumulate_n_benchmark.py index c58d36f39705ecf0f24214ce4ba4574e70a93e77..a709066cae4da2811b3e98d2e93bf44ec12dcee6 100644 --- a/tensorflow/python/ops/accumulate_n_benchmark.py +++ b/tensorflow/python/ops/accumulate_n_benchmark.py @@ -39,7 +39,7 @@ from tensorflow.python.platform import test class AccumulateNBenchmark(test.Benchmark): def _AccumulateNTemplate(self, inputs, init, shape, validate_shape): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( shape=shape, dtype=inputs[0].dtype.base_dtype) ref = state_ops.assign(var, init, validate_shape=validate_shape) update_ops = [ @@ -47,8 +47,7 @@ class AccumulateNBenchmark(test.Benchmark): ref, tensor, use_locking=True).op for tensor in inputs ] with ops.control_dependencies(update_ops): - return gen_state_ops._destroy_temporary_variable( - ref, var_name=var.op.name) + return gen_state_ops.destroy_temporary_variable(ref, var_name=var.op.name) def _AccumulateNInitializedWithFirst(self, inputs): return self._AccumulateNTemplate( @@ -60,7 +59,7 @@ class AccumulateNBenchmark(test.Benchmark): def _AccumulateNInitializedWithMerge(self, inputs): return self._AccumulateNTemplate( inputs, - init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]), + init=array_ops.zeros_like(gen_control_flow_ops.merge(inputs)[0]), shape=tensor_shape.vector(0), validate_shape=False) diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py index c9292184e6a9910db6b41022ab01312ce44e0a96..3c6a5c9e562ff9765c2ef47555871c94cd6feb1e 100644 --- a/tensorflow/python/ops/array_grad.py +++ b/tensorflow/python/ops/array_grad.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import sparse_ops @@ -79,7 +80,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): def _ExtractInputShapes(inputs): """Extract the shapes of a set of input tensors.""" - if not context.in_graph_mode(): + if context.executing_eagerly(): return array_ops.shape_n(inputs) sizes = [] fully_known = True @@ -105,7 +106,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): out_grads = [] if isinstance(grad, ops.Tensor): - if context.in_eager_mode(): + if context.executing_eagerly(): # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. non_neg_concat_dim = ( @@ -115,6 +116,19 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): non_neg_concat_dim) out_grads = array_ops.split(grad, sizes, non_neg_concat_dim) else: + if constant_op.is_constant(concat_dim): + # If concat_dim is a constant defined in a different context, + # then we duplicate it in the current context to avoid passing it + # through an Enter node. + # This is a small optimization in general, but it is required when + # compiling with XLA, as XLA needs the concat input to be folded into a + # constant. + grad_context = control_flow_util.GetOutputContext(grad.op) + dim_context = control_flow_util.GetOutputContext(concat_dim.op) + if dim_context != grad_context: + value = tensor_util.constant_value(concat_dim) + concat_dim = constant_op.constant(value=value, dtype=concat_dim.dtype) + # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0]) @@ -125,7 +139,6 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): # on CPUs and a Maxwell TitanX. A speedup was seen in a large majority of # cases when switching implementations at N=16, but it is possible that # there will be a small number of performance regressions. - # pylint: disable=protected-access if len(sizes) > 16: # extract the size of each input along the concat dimension sizes = array_ops.squeeze( @@ -134,10 +147,9 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): [1, -1])) out_grads = array_ops.split(grad, sizes, non_neg_concat_dim) else: - offset = gen_array_ops._concat_offset(non_neg_concat_dim, sizes) + offset = gen_array_ops.concat_offset(non_neg_concat_dim, sizes) for (begin, size) in zip(offset, sizes): out_grads.append(array_ops.slice(grad, begin, size)) - # pylint: enable=protected-access elif isinstance(grad, ops.IndexedSlices): # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. @@ -416,7 +428,7 @@ def _GatherV2Grad(op, grad): # For axis 0 gathers, build an appropriately shaped IndexedSlices. if axis_static == 0: - if context.in_eager_mode(): + if context.executing_eagerly(): params_tail_shape = params_shape.cpu()[1:] else: params_tail_shape = params_shape[1:] @@ -566,7 +578,7 @@ def _TileGrad(op, grad): axes = math_ops.range(0, array_ops.size(split_shape), 2) input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes) # Fix shape inference - if context.in_graph_mode(): + if not context.executing_eagerly(): input_grad.set_shape(op.inputs[0].get_shape()) return [input_grad, None] @@ -613,9 +625,7 @@ def _ReverseSequenceGrad(op, grad): @ops.RegisterGradient("Reverse") def _ReverseGrad(op, grad): reverse_dims = op.inputs[1] - # pylint: disable=protected-access - return gen_array_ops._reverse(grad, reverse_dims), None - # pylint: enable=protected-access + return gen_array_ops.reverse(grad, reverse_dims), None @ops.RegisterGradient("ReverseV2") @@ -686,17 +696,13 @@ ops.NotDifferentiable("OneHot") @ops.RegisterGradient("MirrorPad") def _MirrorPadGrad(op, grad): mode = op.get_attr("mode") - # pylint: disable=protected-access - return [gen_array_ops._mirror_pad_grad(grad, op.inputs[1], mode=mode), None] - # pylint: enable=protected-access + return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None] @ops.RegisterGradient("MirrorPadGrad") def _MirrorPadGradGrad(op, grad): mode = op.get_attr("mode") - # pylint: disable=protected-access - return [gen_array_ops._mirror_pad(grad, op.inputs[1], mode=mode), None] - # pylint: enable=protected-access + return [gen_array_ops.mirror_pad(grad, op.inputs[1], mode=mode), None] @ops.RegisterGradient("QuantizeAndDequantize") diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index ad409ad7e5a152bbc4312e1d16f324bb8be71c33..ec7c14f7d8697e61d2acb25a82c0ac9b2fcf28f4 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -128,15 +128,18 @@ def identity(input, name=None): # pylint: disable=redefined-builtin Returns: A `Tensor`. Has the same type as `input`. """ - if context.in_graph_mode(): - return gen_array_ops.identity(input, name=name) - else: + if context.executing_eagerly(): input = ops.convert_to_tensor(input) in_device = input.device # TODO(ashankar): Does 'identity' need to invoke execution callbacks? - if context.context().device_name != in_device: + context_device = context.context().device_name + if not context_device: + context_device = "/job:localhost/replica:0/task:0/device:CPU:0" + if context_device != in_device: return input._copy() # pylint: disable=protected-access return input + else: + return gen_array_ops.identity(input, name=name) # pylint: disable=redefined-builtin,protected-access @@ -195,7 +198,7 @@ def expand_dims(input, axis=None, name=None, dim=None): if axis is not None: raise ValueError("can't specify both 'dim' and 'axis'") axis = dim - return gen_array_ops._expand_dims(input, axis, name) + return gen_array_ops.expand_dims(input, axis, name) # pylint: enable=redefined-builtin,protected-access @@ -208,28 +211,25 @@ def expand_dims(input, axis=None, name=None, dim=None): "This op will be removed after the deprecation date. " "Please switch to tf.setdiff1d().") def listdiff(x, y, out_idx=None, name=None): - return gen_array_ops._list_diff(x, y, out_idx, name) + return gen_array_ops.list_diff(x, y, out_idx, name) -listdiff.__doc__ = gen_array_ops._list_diff.__doc__ + "\n" + listdiff.__doc__ +listdiff.__doc__ = gen_array_ops.list_diff.__doc__ + "\n" + listdiff.__doc__ # pylint: enable=protected-access -# pylint: disable=undefined-variable,protected-access +# pylint: disable=undefined-variable @tf_export("setdiff1d") def setdiff1d(x, y, index_dtype=dtypes.int32, name=None): - return gen_array_ops._list_diff(x, y, index_dtype, name) - + return gen_array_ops.list_diff(x, y, index_dtype, name) -setdiff1d.__doc__ = gen_array_ops._list_diff.__doc__ -# pylint: enable=protected-access +setdiff1d.__doc__ = gen_array_ops.list_diff.__doc__ @tf_export("broadcast_dynamic_shape") def broadcast_dynamic_shape(shape_x, shape_y): - # pylint: disable=protected-access """Returns the broadcasted dynamic shape between `shape_x` and `shape_y`. Args: @@ -239,8 +239,7 @@ def broadcast_dynamic_shape(shape_x, shape_y): Returns: A rank 1 integer `Tensor` representing the broadcasted shape. """ - return gen_array_ops._broadcast_args(shape_x, shape_y) - # pylint: enable=protected-access + return gen_array_ops.broadcast_args(shape_x, shape_y) @tf_export("broadcast_static_shape") @@ -306,7 +305,7 @@ def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32): sparse_tensor.SparseTensorValue)): return gen_math_ops.cast(input.dense_shape, out_type) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): input_tensor = ops.convert_to_tensor(input) input_shape = input_tensor.get_shape() if optimize and input_shape.is_fully_defined(): @@ -331,7 +330,7 @@ def shape_n(input, out_type=dtypes.int32, name=None): """ output = gen_array_ops.shape_n(input, out_type=out_type, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): for i, input_tensor in enumerate(input): input_tensor = ops.convert_to_tensor(input_tensor) input_shape = input_tensor.get_shape() @@ -386,16 +385,22 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32): Returns: A `Tensor` of type `out_type`. Defaults to `tf.int32`. """ + if context.executing_eagerly() and not isinstance( + input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)): + return np.prod(ops.convert_to_tensor(input)._shape_tuple()) # pylint: disable=protected-access with ops.name_scope(name, "Size", [input]) as name: if isinstance(input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)): - return gen_math_ops._prod( + return gen_math_ops.prod( gen_math_ops.cast(input.dense_shape, out_type), 0, name=name) else: input_tensor = ops.convert_to_tensor(input) input_shape = input_tensor.get_shape() - if optimize and input_shape.is_fully_defined(): - return constant(input_shape.num_elements(), out_type, name=name) + if optimize: + if input_shape.is_fully_defined(): + return constant(input_shape.num_elements(), out_type, name=name) + if input_shape.dims and any(dim == 0 for dim in input_shape.dims): + return constant(0, out_type, name=name) return gen_array_ops.size(input, name=name, out_type=out_type) @@ -605,7 +610,7 @@ def slice(input_, begin, size, name=None): Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to perform slices, as it allows you to write `foo[3:7, :-2]` instead of - `tf.slice([3, 0], [4, foo.get_shape()[1]-2])`. + `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`. `begin` is zero-based; `size` is one-based. If `size[i]` is -1, all remaining elements in dimension i are included in the @@ -777,7 +782,7 @@ def strided_slice(input_, new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask) - if context.in_graph_mode(): + if not context.executing_eagerly(): # TODO(apassos) In eager mode assignment will be done by overriding # __setitem__ instead. op.assign = assign @@ -788,8 +793,8 @@ def _SliceHelperVar(var, slice_spec): """Creates a slice helper object given a variable. This allows creating a sub-tensor from part of the current contents - of a variable. See ${tf.Tensor$`Tensor.__getitem__`} - for detailed examples of slicing. + of a variable. See @{tf.Tensor.__getitem__} for detailed examples + of slicing. This function in addition also allows assignment to a sliced range. This is similar to `__setitem__` functionality in Python. However, @@ -879,7 +884,7 @@ def parallel_stack(values, name="parallel_stack"): output_shape = tensor_shape.TensorShape([len(values)]) output_shape = output_shape.concatenate(value_shape) # expand_dims converts concat to stack. - return gen_array_ops._parallel_concat( + return gen_array_ops.parallel_concat( [expand_dims(value, 0) for value in values], shape=output_shape) @@ -937,7 +942,7 @@ def stack(values, axis=0, name="stack"): raise ValueError("axis = %d not in [%d, %d)" % (axis, -expanded_num_dims, expanded_num_dims)) - return gen_array_ops._pack(values, axis=axis, name=name) + return gen_array_ops.pack(values, axis=axis, name=name) # pylint: disable=invalid-name @@ -981,7 +986,7 @@ def _autopacking_helper(list_or_tuple, dtype, name): # convertible-to-tensor types, such as numpy arrays. elems_as_tensors.append( constant_op.constant(elem, dtype=dtype, name=str(i))) - return gen_array_ops._pack(elems_as_tensors, name=scope) + return gen_array_ops.pack(elems_as_tensors, name=scope) else: return converted_elems @@ -1076,7 +1081,7 @@ def unstack(value, num=None, axis=0, name="unstack"): num = value_shape[axis].value if num is None: raise ValueError("Cannot infer num from shape %s" % value_shape) - return gen_array_ops._unpack(value, num=num, axis=axis, name=name) + return gen_array_ops.unpack(value, num=num, axis=axis, name=name) @tf_export("concat") @@ -1173,7 +1178,7 @@ def concat(values, axis, name="concat"): dtype=dtypes.int32).get_shape().assert_is_compatible_with( tensor_shape.scalar()) return identity(values[0], name=scope) - return gen_array_ops._concat_v2(values=values, axis=axis, name=name) + return gen_array_ops.concat_v2(values=values, axis=axis, name=name) @tf_export("boolean_mask") @@ -1241,8 +1246,7 @@ def boolean_mask(tensor, mask, name="boolean_mask", axis=None): axis = 0 if axis is None else axis shape_tensor[axis:axis + ndims_mask].assert_is_compatible_with(shape_mask) - leading_size = gen_math_ops._prod( - shape(tensor)[axis:axis + ndims_mask], [0]) + leading_size = gen_math_ops.prod(shape(tensor)[axis:axis + ndims_mask], [0]) tensor = reshape(tensor, concat([ shape(tensor)[:axis], [leading_size], @@ -1306,10 +1310,22 @@ def unique(x, out_idx=dtypes.int32, name=None): # period (3 weeks) pass. # TODO(yongtang): The documentation should also # be updated when switch to v2. - return gen_array_ops._unique(x, out_idx, name) + return gen_array_ops.unique(x, out_idx, name) -unique.__doc__ = gen_array_ops._unique.__doc__ +unique.__doc__ = gen_array_ops.unique.__doc__ + + +@tf_export("unique_with_counts") +def unique_with_counts(x, out_idx=dtypes.int32, name=None): + # TODO(yongtang): switch to v2 once API deprecation + # period (3 weeks) pass. + # TODO(yongtang): The documentation should also + # be updated when switch to v2. + return gen_array_ops.unique_with_counts(x, out_idx, name) + + +unique_with_counts.__doc__ = gen_array_ops.unique_with_counts.__doc__ @tf_export("split") @@ -1363,20 +1379,18 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"): """ size_splits = ops.convert_to_tensor(num_or_size_splits) if size_splits._rank() == 0 and size_splits.dtype.is_integer: - return gen_array_ops._split( + return gen_array_ops.split( axis=axis, num_split=num_or_size_splits, value=value, name=name) if num is None: - num = size_splits._shape_tuple()[0] + size_splits_shape = size_splits._shape_tuple() + if size_splits_shape: + num = size_splits_shape[0] if num is None: raise ValueError("Cannot infer num from shape %s" % num_or_size_splits) - return gen_array_ops._split_v( - value=value, - size_splits=size_splits, - axis=axis, - num_split=num, - name=name) + return gen_array_ops.split_v( + value=value, size_splits=size_splits, axis=axis, num_split=num, name=name) @tf_export("transpose") @@ -1390,6 +1404,14 @@ def transpose(a, perm=None, name="transpose", conjugate=False): `a.dtype` is either `complex64` or `complex128` then the values of `a` are conjugated and transposed. + @compatibility(numpy) + In `numpy` transposes are memory-efficient constant time operations as they + simply return a new view of the same data with adjusted `strides`. + + TensorFlow does not support strides, so `transpose` returns a new tensor with + the items permuted. + @end_compatibility + For example: ```python @@ -1438,7 +1460,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False): """ with ops.name_scope(name, "transpose", [a]) as name: transpose_fn = ( - gen_array_ops._conjugate_transpose + gen_array_ops.conjugate_transpose if (conjugate and a.dtype.is_complex) else gen_array_ops.transpose) if perm is None: rank = gen_array_ops.rank(a) @@ -1446,7 +1468,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False): ret = transpose_fn(a, perm, name=name) # NOTE(mrry): Setting the shape explicitly because # reverse is not handled by the shape function. - if context.in_graph_mode(): + if not context.executing_eagerly(): input_shape = ret.op.inputs[0].get_shape().dims if input_shape is not None: ret.set_shape(input_shape[::-1]) @@ -1490,6 +1512,14 @@ def matrix_transpose(a, name="matrix_transpose", conjugate=False): tf.matmul(matrix, tf.matrix_transpose(b)) ``` + @compatibility(numpy) + In `numpy` transposes are memory-efficient constant time operations as they + simply return a new view of the same data with adjusted `strides`. + + TensorFlow does not support strides, `matrix_transposes` return a new tensor + with the items permuted. + @end_compatibility + Args: a: A `Tensor` with `rank >= 2`. name: A name for the operation (optional). @@ -1603,12 +1633,12 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): with ops.name_scope(name, "zeros_like", [tensor]) as name: tensor = ops.convert_to_tensor(tensor, name="tensor") - if context.in_eager_mode(): + if context.executing_eagerly(): if dtype is not None and dtype != tensor.dtype: return zeros( shape_internal(tensor, optimize=optimize), dtype=dtype, name=name) with ops.device(tensor.device): - return gen_array_ops._zeros_like(tensor, name=name) + return gen_array_ops.zeros_like(tensor, name=name) # For now, variant types must be created via zeros_like; as we need to # pass the input variant object to the proper zeros callback. @@ -1623,7 +1653,7 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): return zeros( shape_internal(tensor, optimize=optimize), dtype=dtype, name=name) else: - return gen_array_ops._zeros_like(tensor, name=name) + return gen_array_ops.zeros_like(tensor, name=name) @tf_export("ones_like") @@ -1659,7 +1689,7 @@ def ones_like(tensor, dtype=None, name=None, optimize=True): if dtype is None: dtype = tensor.dtype ret = ones(ones_shape, dtype=dtype, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): ret.set_shape(tensor.get_shape()) return ret @@ -1740,11 +1770,11 @@ def placeholder(dtype, shape=None, name=None): Raises: RuntimeError: if eager execution is enabled """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("tf.placeholder() is not compatible with " "eager execution.") - return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name) + return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name) # pylint: disable=redefined-outer-name @@ -1803,7 +1833,7 @@ def sparse_placeholder(dtype, shape=None, name=None): Raises: RuntimeError: if eager execution is enabled """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("tf.placeholder() is not compatible with " "eager execution.") @@ -1888,21 +1918,21 @@ def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0): # pyl # TODO(rjryan): Once the forward compatibility period (3 weeks) have passed # remove the "Pad" fallback here. if constant_values != 0: - result = gen_array_ops._pad_v2( + result = gen_array_ops.pad_v2( tensor, paddings, constant_values, name=name) else: - result = gen_array_ops._pad(tensor, paddings, name=name) + result = gen_array_ops.pad(tensor, paddings, name=name) elif mode == "REFLECT": - result = gen_array_ops._mirror_pad( + result = gen_array_ops.mirror_pad( tensor, paddings, mode="REFLECT", name=name) elif mode == "SYMMETRIC": - result = gen_array_ops._mirror_pad( + result = gen_array_ops.mirror_pad( tensor, paddings, mode="SYMMETRIC", name=name) else: raise ValueError("Unknown padding mode: %s" % mode) # Restore shape information where possible. - if context.in_graph_mode(): + if not context.executing_eagerly(): paddings_constant = tensor_util.constant_value( result.op.inputs[1], partial=True) input_shape = result.op.inputs[0].shape @@ -2126,7 +2156,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"): sparse_tensor.SparseTensorValue)): raise TypeError("Truth must be a SparseTensor.") - return gen_array_ops._edit_distance( + return gen_array_ops.edit_distance( hypothesis.indices, hypothesis.values, hypothesis.dense_shape, @@ -2263,7 +2293,7 @@ def space_to_batch(input, paddings, block_size, name=None): # pylint: disable=r return result -space_to_batch.__doc__ = gen_array_ops._space_to_batch.__doc__ +space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__ @tf_export("space_to_depth") @@ -2293,7 +2323,7 @@ def batch_to_space(input, crops, block_size, name=None): # pylint: disable=rede return result -batch_to_space.__doc__ = gen_array_ops._batch_to_space.__doc__ +batch_to_space.__doc__ = gen_array_ops.batch_to_space.__doc__ @tf_export("one_hot") @@ -2437,8 +2467,8 @@ def one_hot(indices, raise TypeError("dtype {0} of on_value does not match " "dtype {1} of off_value".format(on_dtype, off_dtype)) - return gen_array_ops._one_hot(indices, depth, on_value, off_value, axis, - name) + return gen_array_ops.one_hot(indices, depth, on_value, off_value, axis, + name) def _all_dimensions(x): @@ -2566,7 +2596,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None): axis = squeeze_dims if np.isscalar(axis): axis = [axis] - return gen_array_ops._squeeze(input, axis, name) + return gen_array_ops.squeeze(input, axis, name) @tf_export("where") @@ -2617,7 +2647,7 @@ def where(condition, x=None, y=None, name=None): condition, preferred_dtype=dtypes.bool, name="condition") return gen_array_ops.where(condition=condition, name=name) elif x is not None and y is not None: - return gen_math_ops._select(condition=condition, x=x, y=y, name=name) + return gen_math_ops.select(condition=condition, x=x, y=y, name=name) else: raise ValueError("x and y must both be non-None or both be None.") diff --git a/tensorflow/python/ops/batch_norm_benchmark.py b/tensorflow/python/ops/batch_norm_benchmark.py index c2ee2b383231333239c6e2d4e874a0ad1cdf493e..5d68b47aeaef3a90973387ecd5b265eef1e96a5f 100644 --- a/tensorflow/python/ops/batch_norm_benchmark.py +++ b/tensorflow/python/ops/batch_norm_benchmark.py @@ -41,9 +41,8 @@ def batch_norm_op(tensor, mean, variance, beta, gamma, scale): # _batch_norm_with_global_normalization is deprecated in v9 ops.get_default_graph().graph_def_versions.producer = 8 # pylint: disable=protected-access - return gen_nn_ops._batch_norm_with_global_normalization(tensor, mean, - variance, beta, gamma, - 0.001, scale) + return gen_nn_ops._batch_norm_with_global_normalization( + tensor, mean, variance, beta, gamma, 0.001, scale) # pylint: enable=protected-access diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py index f9b025b787e4f49e1dcde6c589f66c59d779fcef..c4cfc0da197edcfd143cfee79fd3c3f9b7a2858b 100644 --- a/tensorflow/python/ops/bitwise_ops_test.py +++ b/tensorflow/python/ops/bitwise_ops_test.py @@ -71,7 +71,7 @@ class BitwiseOpTest(test_util.TensorFlowTestCase): def testInvertOp(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, - dtypes.uint8, dtypes.uint16] + dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64] inputs = [0, 5, 3, 14] with self.test_session(use_gpu=True) as sess: for dtype in dtype_list: diff --git a/tensorflow/python/ops/candidate_sampling_ops.py b/tensorflow/python/ops/candidate_sampling_ops.py index 20445c78a290a4fe67cad668dd714dd2c61c5f3d..9ea1ea9c92c9b016a3f9126c89ee4dc1e73c9f27 100644 --- a/tensorflow/python/ops/candidate_sampling_ops.py +++ b/tensorflow/python/ops/candidate_sampling_ops.py @@ -20,9 +20,9 @@ from __future__ import division from __future__ import print_function from tensorflow.python.framework import random_seed -from tensorflow.python.ops import array_ops +from tensorflow.python.ops import array_ops # pylint: disable=unused-import from tensorflow.python.ops import gen_candidate_sampling_ops -from tensorflow.python.ops import math_ops +from tensorflow.python.ops import math_ops # pylint: disable=unused-import from tensorflow.python.util.tf_export import tf_export @@ -77,7 +77,7 @@ def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._uniform_candidate_sampler( + return gen_candidate_sampling_ops.uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -136,7 +136,7 @@ def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._log_uniform_candidate_sampler( + return gen_candidate_sampling_ops.log_uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -193,7 +193,7 @@ def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._learned_unigram_candidate_sampler( + return gen_candidate_sampling_ops.learned_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -283,7 +283,7 @@ def fixed_unigram_candidate_sampler(true_classes, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._fixed_unigram_candidate_sampler( + return gen_candidate_sampling_ops.fixed_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, vocab_file=vocab_file, distortion=distortion, num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard, @@ -321,7 +321,7 @@ def all_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. All returned values are 1.0. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._all_candidate_sampler( + return gen_candidate_sampling_ops.all_candidate_sampler( true_classes, num_true, num_sampled, unique, seed=seed1, seed2=seed2, name=name) @@ -370,6 +370,6 @@ def compute_accidental_hits(true_classes, sampled_candidates, num_true, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._compute_accidental_hits( + return gen_candidate_sampling_ops.compute_accidental_hits( true_classes, sampled_candidates, num_true, seed=seed1, seed2=seed2, name=name) diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py index 0fd6e29a49c8e4e31e244bfbbfca525d72e4d811..d6d75e4ef9874e2ed4706d48b7bdeed62baef414 100644 --- a/tensorflow/python/ops/check_ops.py +++ b/tensorflow/python/ops/check_ops.py @@ -169,7 +169,7 @@ def assert_negative(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_negative', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -210,7 +210,7 @@ def assert_positive(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_positive', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -251,7 +251,7 @@ def assert_non_negative(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_non_negative', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -293,7 +293,7 @@ def assert_non_positive(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_non_positive', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -334,16 +334,16 @@ def assert_equal(x, y, data=None, summarize=None, message=None, name=None): @compatibility{eager} returns None Raises: - InvalidArgumentError if the check can be performed immediately and - `x == y` is False. The check can be performed immediately during - eager execution or if `x` and `y` are statically known. + InvalidArgumentError: if the check can be performed immediately and + `x == y` is False. The check can be performed immediately during eager + execution or if `x` and `y` are statically known. """ message = message or '' with ops.name_scope(name, 'assert_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): eq = math_ops.equal(x, y) condition = math_ops.reduce_all(eq) if not condition: @@ -435,7 +435,7 @@ def assert_none_equal( with ops.name_scope(name, 'assert_none_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -512,7 +512,7 @@ def assert_near( rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=x.dtype) atol = ops.convert_to_tensor(atol, name='atol', dtype=x.dtype) - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -562,7 +562,7 @@ def assert_less(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_less', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -610,7 +610,7 @@ def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_less_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -658,7 +658,7 @@ def assert_greater(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_greater', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -708,7 +708,7 @@ def assert_greater_equal(x, y, data=None, summarize=None, message=None, with ops.name_scope(name, 'assert_greater_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -808,7 +808,7 @@ def assert_rank(x, rank, data=None, summarize=None, message=None, name=None): static_condition = lambda actual_rank, given_rank: actual_rank == given_rank dynamic_condition = math_ops.equal - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -873,7 +873,7 @@ def assert_rank_at_least( static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank dynamic_condition = math_ops.greater_equal - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -1001,7 +1001,7 @@ def assert_rank_in( ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks]) message = message or '' - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -1054,7 +1054,7 @@ def assert_integer(x, message=None, name=None): with ops.name_scope(name, 'assert_integer', [x]): x = ops.convert_to_tensor(x, name='x') if not x.dtype.is_integer: - if context.in_eager_mode(): + if context.executing_eagerly(): name = 'tensor' else: name = x.name @@ -1087,12 +1087,11 @@ def assert_type(tensor, tf_type, message=None, name=None): with ops.name_scope(name, 'assert_type', [tensor]): tensor = ops.convert_to_tensor(tensor, name='tensor') if tensor.dtype != tf_type: - if context.in_graph_mode(): - raise TypeError( - '%s %s must be of type %s' % (message, tensor.name, tf_type)) + if context.executing_eagerly(): + raise TypeError('%s tensor must be of type %s' % (message, tf_type)) else: - raise TypeError( - '%s tensor must be of type %s' % (message, tf_type)) + raise TypeError('%s %s must be of type %s' % (message, tensor.name, + tf_type)) return control_flow_ops.no_op('statically_determined_correct_type') @@ -1240,7 +1239,7 @@ def assert_scalar(tensor, name=None): tensor = ops.convert_to_tensor(tensor, name=name_scope) shape = tensor.get_shape() if shape.ndims != 0: - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError('Expected scalar shape, saw shape: %s.' % (shape,)) else: diff --git a/tensorflow/python/ops/clip_ops.py b/tensorflow/python/ops/clip_ops.py index dd8c33247c2436413ee8c9a3ceeca4d8a493bb4e..49f8c665313562cb20dbe4494103ded16646c741 100644 --- a/tensorflow/python/ops/clip_ops.py +++ b/tensorflow/python/ops/clip_ops.py @@ -110,7 +110,7 @@ def clip_by_norm(t, clip_norm, axes=None, name=None): t = ops.convert_to_tensor(t, name="t") # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm - l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keep_dims=True)) + l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keepdims=True)) intermediate = t * clip_norm # Assert that the shape is compatible with the initial shape, # to prevent unintentional broadcasting. diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py index e4ce2ab28a15f82e80194ab17ef939411982076a..b9a93c3bedfff1f398e3b42cedf02a2f0a3ddd5c 100644 --- a/tensorflow/python/ops/confusion_matrix.py +++ b/tensorflow/python/ops/confusion_matrix.py @@ -99,19 +99,16 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32, name=None, weights=None): """Computes the confusion matrix from predictions and labels. - Calculate the Confusion Matrix for a pair of prediction and - label 1-D int arrays. - The matrix columns represent the prediction labels and the rows represent the real labels. The confusion matrix is always a 2-D array of shape `[n, n]`, where `n` is the number of valid labels for a given classification task. Both prediction and labels must be 1-D arrays of the same shape in order for this function to work. - If `num_classes` is None, then `num_classes` will be set to the one plus - the maximum value in either predictions or labels. - Class labels are expected to start at 0. E.g., if `num_classes` was - three, then the possible labels would be `[0, 1, 2]`. + If `num_classes` is `None`, then `num_classes` will be set to one plus the + maximum value in either predictions or labels. Class labels are expected to + start at 0. For example, if `num_classes` is 3, then the possible labels + would be `[0, 1, 2]`. If `weights` is not `None`, then each prediction contributes its corresponding weight to the total value of the confusion matrix cell. @@ -141,8 +138,9 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32, weights: An optional `Tensor` whose shape matches `predictions`. Returns: - A k X k matrix representing the confusion matrix, where k is the number of - possible labels in the classification task. + A `Tensor` of type `dtype` with shape `[n, n]` representing the confusion + matrix, where `n` is the number of possible labels in the classification + task. Raises: ValueError: If both predictions and labels are not 1-D vectors and have @@ -188,7 +186,7 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32, weights = math_ops.cast(weights, dtype) shape = array_ops.stack([num_classes, num_classes]) - indices = array_ops.transpose(array_ops.stack([labels, predictions])) + indices = array_ops.stack([labels, predictions], axis=1) values = (array_ops.ones_like(predictions, dtype) if weights is None else weights) cm_sparse = sparse_tensor.SparseTensor( diff --git a/tensorflow/python/ops/control_flow_grad.py b/tensorflow/python/ops/control_flow_grad.py index 97b57177b29986a006df992f4c0c2b79e11467aa..21354b5ae8ff1724bbb2539aff370b3df6da2598 100644 --- a/tensorflow/python/ops/control_flow_grad.py +++ b/tensorflow/python/ops/control_flow_grad.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import math_ops # go/tf-wildcard-import # pylint: disable=wildcard-import,undefined-variable from tensorflow.python.ops.control_flow_ops import * -from tensorflow.python.ops.gen_control_flow_ops import * # pylint: enable=wildcard-import diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index e75eb0843f5fea5d5d512845df1677485757e32a..a2f52de7492782d4289b464560ebb548ad59b744 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -44,6 +44,7 @@ See the @{$python/control_flow_ops} guide. @@add_check_numerics_ops @@Assert @@Print +@@timestamp """ # pylint: disable=g-bad-name from __future__ import absolute_import @@ -55,7 +56,6 @@ import collections import functools import six -from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.protobuf import control_flow_pb2 @@ -152,7 +152,7 @@ def Assert(condition, data, summarize=None, name=None): @compatibility{eager} `tf.errors.InvalidArgumentError` if `condition` is not true """ - if context.in_eager_mode(): + if context.executing_eagerly(): if not condition: xs = ops.convert_n_to_tensor(data) data_str = [_summarize_eager(x, summarize) for x in xs] @@ -178,7 +178,7 @@ def Assert(condition, data, summarize=None, name=None): condition, data, summarize, name="Assert") guarded_assert = cond(condition, no_op, true_assert, name="AssertGuard") - if context.in_eager_mode(): + if context.executing_eagerly(): return return guarded_assert.op @@ -314,7 +314,7 @@ def _Enter(data, return sparse_tensor.SparseTensor(indices, values, dense_shape) -def exit(data, name=None): +def exit(data, name=None): # pylint: disable=redefined-builtin """Exits the current frame to its parent frame. Exit makes its input `data` available to the parent frame. @@ -329,7 +329,7 @@ def exit(data, name=None): data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True) if isinstance(data, ops.Tensor): if data.dtype._is_ref_dtype: # pylint: disable=protected-access - return gen_control_flow_ops._ref_exit(data, name) + return gen_control_flow_ops.ref_exit(data, name) else: return gen_control_flow_ops._exit(data, name) else: @@ -371,17 +371,17 @@ def switch(data, pred, dtype=None, name=None): data, dtype=dtype, name="data", as_ref=True) pred = ops.convert_to_tensor(pred, name="pred") if isinstance(data, ops.Tensor): - return gen_control_flow_ops._switch(data, pred, name=name) + return gen_control_flow_ops.switch(data, pred, name=name) else: if not isinstance(data, (ops.IndexedSlices, sparse_tensor.SparseTensor)): raise TypeError("Type %s not supported" % type(data)) val, ind = data.values, data.indices - val_f, val_t = gen_control_flow_ops._switch(val, pred, name=name) - ind_f, ind_t = gen_control_flow_ops._switch(ind, pred, name="indices") + val_f, val_t = gen_control_flow_ops.switch(val, pred, name=name) + ind_f, ind_t = gen_control_flow_ops.switch(ind, pred, name="indices") if isinstance(data, ops.IndexedSlices): dense_shape = data.dense_shape if dense_shape is not None: - dense_shape_f, dense_shape_t = gen_control_flow_ops._switch( + dense_shape_f, dense_shape_t = gen_control_flow_ops.switch( dense_shape, pred, name="dense_shape") else: dense_shape_f, dense_shape_t = None, None @@ -389,7 +389,7 @@ def switch(data, pred, dtype=None, name=None): ops.IndexedSlices(val_t, ind_t, dense_shape_t)) else: dense_shape = data.dense_shape - dense_shape_f, dense_shape_t = gen_control_flow_ops._switch( + dense_shape_f, dense_shape_t = gen_control_flow_ops.switch( data.dense_shape, pred, name="dense_shape") return (sparse_tensor.SparseTensor(ind_f, val_f, dense_shape_f), sparse_tensor.SparseTensor(ind_t, val_t, dense_shape_t)) @@ -473,15 +473,15 @@ def merge(inputs, name=None): ] if all([isinstance(v, ops.Tensor) for v in inputs]): if all([v.dtype._is_ref_dtype for v in inputs]): # pylint: disable=protected-access - return gen_control_flow_ops._ref_merge(inputs, name) + return gen_control_flow_ops.ref_merge(inputs, name) else: - return gen_control_flow_ops._merge(inputs, name) + return gen_control_flow_ops.merge(inputs, name) elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]): # Only handle the case when all inputs are SparseTensor. values, _ = merge([inp.values for inp in inputs], name=name) - indices, chosen_index = gen_control_flow_ops._merge( + indices, chosen_index = gen_control_flow_ops.merge( [inp.indices for inp in inputs], name="indices") - dense_shape, _ = gen_control_flow_ops._merge( + dense_shape, _ = gen_control_flow_ops.merge( [inp.dense_shape for inp in inputs], name="dense_shape") return (sparse_tensor.SparseTensor(indices, values, dense_shape), chosen_index) @@ -489,13 +489,13 @@ def merge(inputs, name=None): # For now convert all the inputs as IndexedSlices. inputs = math_ops._as_indexed_slices_list(inputs, optimize=False) values, _ = merge([inp.values for inp in inputs], name=name) - indices, chosen_index = gen_control_flow_ops._merge( + indices, chosen_index = gen_control_flow_ops.merge( [inp.indices for inp in inputs], name="indices") if any(inp.dense_shape is not None for inp in inputs): if any(inp.dense_shape is None for inp in inputs): raise ValueError("Either all merged IndexedSlices must have a " "dense_shape, or none must have a dense_shape.") - dense_shape, _ = gen_control_flow_ops._merge( + dense_shape, _ = gen_control_flow_ops.merge( [inp.dense_shape for inp in inputs], name="dense_shape") else: dense_shape = None @@ -1015,10 +1015,8 @@ class GradLoopState(object): else: max_size = GetMaxSizeFromNestedMaximumIterations( value, self.forward_context) - # pylint: disable=protected-access - acc = gen_data_flow_ops._stack_v2( + acc = gen_data_flow_ops.stack_v2( max_size=max_size, elem_type=value.dtype.base_dtype, name="f_acc") - # pylint: enable=protected-access if curr_ctxt: curr_ctxt.Exit() @@ -1031,10 +1029,8 @@ class GradLoopState(object): if value_ctxt == self.forward_context: # value is not nested in the forward context. self.forward_context.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access self.forward_context.Exit() # Protect stack push and order it before forward_index. self.forward_index.op._add_control_input(push.op) @@ -1046,18 +1042,14 @@ class GradLoopState(object): # The special case for creating a zero tensor for a dead # branch of a switch. See ControlFlowState.ZerosLike(). value_ctxt.outer_context.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access value_ctxt.outer_context.Exit() push.op._set_control_flow_context(value_ctxt) else: value_ctxt.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access value_ctxt.Exit() # Protect stack push and order it before forward_sync. self.forward_sync._add_control_input(push.op) @@ -1104,10 +1096,8 @@ class GradLoopState(object): pred = cond_ctxt.pred branch = (1 - cond_ctxt.branch) if dead_branch else cond_ctxt.branch history_value = _SwitchRefOrTensor(history_value, pred)[branch] - # pylint: disable=protected-access - pop = gen_data_flow_ops._stack_pop_v2(history_value, - value.dtype.base_dtype) - # pylint: enable=protected-access + pop = gen_data_flow_ops.stack_pop_v2(history_value, + value.dtype.base_dtype) pop.set_shape(value.get_shape()) self.grad_context.Exit() parallel_iterations = self.grad_context.parallel_iterations @@ -1509,9 +1499,11 @@ class ControlFlowContext(object): if values_def: self._init_values_from_proto(values_def, import_scope=import_scope) else: - # Values that have been already seen in this context. + # The names of tensors that have been already seen in this context. self._values = set() - # Values referenced by but external to this context. + # The keys are the names of tensors referenced by but external to this + # context. Each value is the Tensor that should be used by this context to + # access the key value (e.g. a switch output guarding a cond input value). self._external_values = {} def _init_values_from_proto(self, values_def, import_scope=None): @@ -1633,10 +1625,13 @@ class ControlFlowContext(object): ctxt = util.GetOutputContext(x) if ctxt is not None and ctxt.GetWhileContext() == while_ctxt: internal_control_inputs.append(x) + external_control_inputs = [] if len(internal_control_inputs) != len(op.control_inputs): + external_control_inputs = list(set(op.control_inputs) + - set(internal_control_inputs)) op._remove_all_control_inputs() op._add_control_inputs(internal_control_inputs) - return internal_control_inputs + return internal_control_inputs, external_control_inputs # pylint: enable=protected-access @@ -1695,9 +1690,12 @@ class CondContext(ControlFlowContext): self._pivot = pivot # The predicate tensor in this branch self._branch = branch # 0 or 1 representing this branch - # Values considered to have been already seen in this context. + # Values considered to have been already seen in this context. They are + # not included in this context. self._values.add(pred.name) + self._external_values[pred.name] = pred self._values.add(pivot.name) + self._external_values[pivot.name] = pivot def _init_from_proto(self, context_def, import_scope=None): """Creates a new `CondContext` from protocol buffer. @@ -1715,8 +1713,8 @@ class CondContext(ControlFlowContext): self._pivot = g.as_graph_element( ops.prepend_name_scope(context_def.pivot_name, import_scope)) self._branch = context_def.branch - super(CondContext, self).__init__( - values_def=context_def.values_def, import_scope=import_scope) + super(CondContext, self).__init__(values_def=context_def.values_def, + import_scope=import_scope) @property def pred(self): @@ -1764,13 +1762,9 @@ class CondContext(ControlFlowContext): context_def.branch = self._branch context_def.values_def.MergeFrom(super(CondContext, self)._to_values_def( export_scope)) - # TODO(b/72868227): enable this once the corresponding control_flow.proto - # changes have been checked in (they aren't checked in and this is - # disabled for now to ensure forwards compatibility). - if False: # pylint: disable=using-constant-test - for nested in self._nested_contexts: - nested_def = context_def.nested_contexts.add() - nested.to_control_flow_context_def(nested_def) + for nested in self._nested_contexts: + nested_def = context_def.nested_contexts.add() + nested.to_control_flow_context_def(nested_def) return context_def else: @@ -1782,14 +1776,10 @@ class CondContext(ControlFlowContext): ret = CondContext(context_def=context_def, import_scope=import_scope) - # TODO(b/72868227): remove "if hasattr(...)" once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is here for now to ensure forwards compatibility). - if hasattr(context_def, "nested_contexts"): - ret.Enter() - for nested_def in context_def.nested_contexts: - from_control_flow_context_def(nested_def) - ret.Exit() + ret.Enter() + for nested_def in context_def.nested_contexts: + from_control_flow_context_def(nested_def, import_scope=import_scope) + ret.Exit() return ret def to_control_flow_context_def(self, context_def, export_scope=None): @@ -1808,6 +1798,7 @@ class CondContext(ControlFlowContext): if self._outer_context: result = self._outer_context.AddValue(val) self._values.add(result.name) + self._external_values[result.name] = result with ops.control_dependencies(None): result = _SwitchRefOrTensor(result, self._pred)[self._branch] if self._outer_context: @@ -1833,8 +1824,6 @@ class CondContext(ControlFlowContext): # pylint: disable=protected-access op._add_control_input(self._pivot.op) # pylint: enable=protected-access - for x in op.outputs: - self._values.add(x.name) else: for index in range(len(op.inputs)): x = op.inputs[index] @@ -1845,13 +1834,20 @@ class CondContext(ControlFlowContext): # pylint: enable=protected-access # Remove any external control dependency on this op. self._RemoveExternalControlEdges(op) - for x in op.outputs: - self._values.add(x.name) # pylint: disable=protected-access if op.graph._is_function(op.type) or op.type == "SymbolicGradient": op._add_control_input(self._pivot.op) # pylint: enable=protected-access + # Mark op's outputs as seen by this context and any outer contexts. + output_names = [x.name for x in op.outputs] + ctxt = self + while ctxt is not None: + # pylint: disable=protected-access + ctxt._values.update(output_names) + ctxt = ctxt._outer_context + # pylint: enable=protected-access + if self._outer_context or not util.IsLoopExit(op): op.graph.prevent_fetching(op) @@ -1867,6 +1863,7 @@ class CondContext(ControlFlowContext): if self._outer_context: real_val = self._outer_context.AddValue(val) self._values.add(real_val.name) + self._external_values[real_val.name] = real_val real_val = _SwitchRefOrTensor(real_val, self._pred)[self._branch] self._external_values[val.name] = real_val else: @@ -2028,7 +2025,7 @@ def cond(pred, raise TypeError("false_fn must be callable.") with ops.name_scope(name, "cond", [pred]): - if context.in_eager_mode(): + if context.executing_eagerly(): if pred: return _UnpackIfSingleton(true_fn()) return _UnpackIfSingleton(false_fn()) @@ -2102,10 +2099,7 @@ def cond(pred, # Only add non-nested conds to the collection. Any nested control flow will # be encapsulated in the root context. assert context_t.outer_context == context_f.outer_context - # TODO(b/72868227): remove "if True..." once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if True or context_t.outer_context is None: + if context_t.outer_context is None: ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t) ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f) @@ -2328,13 +2322,9 @@ class WhileContext(ControlFlowContext): context_def.values_def.MergeFrom( super(WhileContext, self)._to_values_def( export_scope=export_scope)) - # TODO(b/72868227): remove "if True..." once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if False: # pylint: disable=using-constant-test - for nested in self._nested_contexts: - nested_def = context_def.nested_contexts.add() - nested.to_control_flow_context_def(nested_def) + for nested in self._nested_contexts: + nested_def = context_def.nested_contexts.add() + nested.to_control_flow_context_def(nested_def) return context_def else: @@ -2356,14 +2346,10 @@ class WhileContext(ControlFlowContext): """ ret = WhileContext(context_def=context_def, import_scope=import_scope) - # TODO(b/72868227): remove "if hasattr(...)" once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if hasattr(context_def, "nested_contexts"): - ret.Enter() - for nested_def in context_def.nested_contexts: - from_control_flow_context_def(nested_def, import_scope=import_scope) - ret.Exit() + ret.Enter() + for nested_def in context_def.nested_contexts: + from_control_flow_context_def(nested_def, import_scope=import_scope) + ret.Exit() return ret def GetWhileContext(self): @@ -2445,14 +2431,12 @@ class WhileContext(ControlFlowContext): def _AddOpInternal(self, op): """Add `op` to the current context. - In the case that op has only external data inputs, we remove all of its - external control inputs so all its inputs are in the same while loop - context. This is valid because op now has an Enter input that has all - the right control dependency. + We move any external control dependencies of the op to the loop pivot, to + ensure they get executed. """ if not op.inputs: # Remove any external control dependency on this op - control_inputs = self._RemoveExternalControlEdges(op) + control_inputs, external_inputs = self._RemoveExternalControlEdges(op) # Add a control edge from the control pivot to this op. if not control_inputs: # pylint: disable=protected-access @@ -2465,14 +2449,23 @@ class WhileContext(ControlFlowContext): x = op.inputs[index] real_x = self.AddValue(x) if real_x != x: - op._update_input(index, real_x) + op._update_input(index, real_x) # pylint: disable=protected-access # Remove any external control dependency on this op. - self._RemoveExternalControlEdges(op) + _, external_inputs = self._RemoveExternalControlEdges(op) # Add a control dependency to prevent loop invariants from # enabling ops that should not be executed. self._MaybeAddControlDependency(op) for x in op.outputs: self._values.add(x.name) + if external_inputs: + # Use an identity to pull control inputs as data inputs. Note that we + # ignore ops which don't have outputs. TODO(apassos): fix that + with ops.control_dependencies(None): + self.Enter() + external_inputs = [array_ops.identity(x.outputs[0]).op + for x in external_inputs if x.outputs] + self.Exit() + op._add_control_inputs(external_inputs) # pylint: disable=protected-access if self._outer_context or not util.IsLoopExit(op): op.graph.prevent_fetching(op) for x in op.outputs: @@ -2940,8 +2933,11 @@ class WhileContext(ControlFlowContext): loop_vars = ops.convert_n_to_tensor_or_indexed_slices(loop_vars) try: self.Enter() - original_body_result, exit_vars = self._BuildLoop( - pred, body, original_loop_vars, loop_vars, shape_invariants) + # _BuildLoop calls _update_input in several places. _lock ensures a + # Session.run call cannot occur between creating and mutating new ops. + with ops.get_default_graph()._lock: # pylint: disable=protected-access + original_body_result, exit_vars = self._BuildLoop( + pred, body, original_loop_vars, loop_vars, shape_invariants) finally: self.Exit() @@ -3111,6 +3107,43 @@ def while_loop(cond, shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])]) ``` + Example which demonstrates non-strict semantics: In the following + example, the final value of the counter `i` does not depend on `x`. So + the `while_loop` can increment the counter parallel to updates of `x`. + However, because the loop counter at one loop iteration depends + on the value at the previous iteration, the loop counter itself cannot + be incremented in parallel. Hence if we just want the final value of the + counter (which we print on the line `print(sess.run(i))`), then + `x` will never be incremented, but the counter will be updated on a + single thread. Conversely, if we want the value of the output (which we + print on the line `print(sess.run(out).shape)`), then the counter may be + incremented on its own thread, while `x` can be incremented in + parallel on a separate thread. In the extreme case, it is conceivable + that the thread incrementing the counter runs until completion before + `x` is incremented even a single time. The only thing that can never + happen is that the thread updating `x` can never get ahead of the + counter thread because the thread incrementing `x` depends on the value + of the counter. + ```python + import tensorflow as tf + + n = 10000 + x = tf.constant(list(range(n))) + c = lambda i, x: i < n + b = lambda i, x: (tf.Print(i + 1, [i]), tf.Print(x + 1, [i], "x:")) + i, out = tf.while_loop(c, b, (0, x)) + with tf.Session() as sess: + print(sess.run(i)) # prints [0] ... [9999] + + # The following line may increment the counter and x in parallel. + # The counter thread may get ahead of the other thread, but not the + # other way around. So you may see things like + # [9996] x:[9987] + # meaning that the counter thread is on iteration 9996, + # while the other thread is on iteration 9987 + print(sess.run(out).shape) + ``` + """ with ops.name_scope(name, "while", loop_vars): if not loop_vars: @@ -3144,7 +3177,7 @@ def while_loop(cond, math_ops.logical_and(i < maximum_iterations, orig_cond(*lv))) body = lambda i, lv: (i + 1, orig_body(*lv)) - if context.in_eager_mode(): + if context.executing_eagerly(): while cond(*loop_vars): loop_vars = body(*loop_vars) if maximum_iterations is not None: @@ -3164,10 +3197,7 @@ def while_loop(cond, swap_memory=swap_memory) # Only add non-nested loops to the collection. Any nested control flow will # be encapsulated in the root context. - # TODO(b/72868227): enable condition once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if True or loop_context.outer_context is None: + if loop_context.outer_context is None: ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context) result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants) if maximum_iterations is not None: @@ -3241,7 +3271,7 @@ def with_dependencies(dependencies, output_tensor, name=None): Raises: TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return output_tensor with ops.name_scope(name, "control_dependency", list(dependencies) + [output_tensor]) as name: @@ -3286,7 +3316,7 @@ def group(*inputs, **kwargs): Raises: ValueError: If an unknown keyword argument is provided. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return None name = kwargs.pop("name", None) if kwargs: @@ -3334,7 +3364,7 @@ def group(*inputs, **kwargs): @tf_export("tuple") -def tuple(tensors, name=None, control_inputs=None): +def tuple(tensors, name=None, control_inputs=None): # pylint: disable=redefined-builtin """Group tensors together. This creates a tuple of tensors with the same values as the `tensors` @@ -3366,10 +3396,15 @@ def tuple(tensors, name=None, control_inputs=None): objects. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return tensors with ops.name_scope(name, "tuple", tensors) as name: - gating_ops = [t.op for t in tensors if t is not None] + tensors = [t if (isinstance(t, ops.Operation) + or tensor_util.is_tensor(t) + or t is None) + else ops.convert_to_tensor(t) for t in tensors] + gating_ops = [t if isinstance(t, ops.Operation) else t.op for t in tensors + if t is not None] if control_inputs: for c in control_inputs: if isinstance(c, ops.Tensor): @@ -3385,8 +3420,11 @@ def tuple(tensors, name=None, control_inputs=None): gate = group(*gating_ops) tpl = [] for t in tensors: - if t is not None: + if tensor_util.is_tensor(t): tpl.append(with_dependencies([gate], t)) + elif isinstance(t, ops.Operation): + with ops.control_dependencies([gate]): + tpl.append(group(t)) else: tpl.append(None) return tpl diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index f942f478f25929699766b7ecbfb46a354ccc8fc5..f22f3059d139d1bb7c7db57a2939184f1089f397 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -189,7 +189,7 @@ class SwitchTestCase(test_util.TensorFlowTestCase): zero = constant_op.constant(0) one = constant_op.constant(1) less_op = math_ops.less(zero, one) - switch_false, switch_true = control_flow_ops.switch(data, less_op) + _, switch_true = control_flow_ops.switch(data, less_op) self.assertAllEqual([1, 2, 3], switch_true.values.eval()) self.assertAllEqual([0, 1], switch_true.indices.eval()) @@ -199,16 +199,17 @@ class SwitchTestCase(test_util.TensorFlowTestCase): "embedding_matrix", [5, 5], initializer=init_ops.random_normal_initializer()) - def Cond(it, _): + def cond(it, _): return it < 5 - def Body(it, cost): + def body(it, cost): embedding = embedding_ops.embedding_lookup(embedding_matrix + 0.0, [0]) cost += math_ops.reduce_sum(embedding) return it + 1, cost _, cost = control_flow_ops.while_loop( - Cond, Body, [constant_op.constant(0), constant_op.constant(0.0)]) + cond, body, [constant_op.constant(0), + constant_op.constant(0.0)]) optimizer = momentum.MomentumOptimizer(0.1, 0.9) train_op = optimizer.minimize(cost) with self.test_session() as sess: @@ -223,16 +224,17 @@ class SwitchTestCase(test_util.TensorFlowTestCase): initializer=[[2.0], [3.0]], use_resource=True) - def Cond(it, _): + def cond(it, _): return it < 5 - def Body(it, cost): + def body(it, cost): embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) cost += math_ops.reduce_sum(embedding) return it + 1, cost _, cost = control_flow_ops.while_loop( - Cond, Body, [constant_op.constant(0), constant_op.constant(0.0)]) + cond, body, [constant_op.constant(0), + constant_op.constant(0.0)]) with self.test_session() as sess: sess.run(variables.global_variables_initializer()) self.assertAllEqual(10.0, cost.eval()) @@ -244,10 +246,10 @@ class SwitchTestCase(test_util.TensorFlowTestCase): initializer=init_ops.random_normal_initializer(), use_resource=use_resource) - def Cond(it, _): + def cond(it, _): return it < 5 - def Body(it, cost): + def body(it, cost): embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) cost = control_flow_ops.cond( math_ops.equal(it, 3), lambda: math_ops.square(cost), @@ -255,7 +257,8 @@ class SwitchTestCase(test_util.TensorFlowTestCase): return it + 1, cost _, cost = control_flow_ops.while_loop( - Cond, Body, [constant_op.constant(0), constant_op.constant(0.0)]) + cond, body, [constant_op.constant(0), + constant_op.constant(0.0)]) dynamic_grads = gradients_impl.gradients(cost, [embedding_matrix])[0] dynamic_grads = math_ops.segment_sum(dynamic_grads.values, @@ -289,15 +292,15 @@ class SwitchTestCase(test_util.TensorFlowTestCase): dtype=dtype, size=num_steps) initial_i = constant_op.constant(0, dtype=dtypes.int32) - def Cond(i, _): + def cond(i, _): return i < num_steps # pylint: disable=cell-var-from-loop - def Body(i, outputs): + def body(i, outputs): x = array_ops.gather(inputs, i) # pylint: disable=cell-var-from-loop outputs = outputs.write(i, x) return i + 1, outputs - _, outputs = control_flow_ops.while_loop(Cond, Body, + _, outputs = control_flow_ops.while_loop(cond, body, [initial_i, initial_outputs]) outputs = math_ops.reduce_sum(outputs.stack()) @@ -316,15 +319,15 @@ class SwitchTestCase(test_util.TensorFlowTestCase): dtype=dtype, dynamic_size=True, size=1) initial_i = constant_op.constant(0, dtype=dtypes.int32) - def Cond(i, _): + def cond(i, _): return i < array_ops.size(inputs) # pylint: disable=cell-var-from-loop - def Body(i, outputs): + def body(i, outputs): x = array_ops.gather(inputs, i) # pylint: disable=cell-var-from-loop outputs = outputs.write(i, x) return i + 1, outputs - _, outputs = control_flow_ops.while_loop(Cond, Body, + _, outputs = control_flow_ops.while_loop(cond, body, [initial_i, initial_outputs]) outputs = math_ops.reduce_sum(outputs.stack()) @@ -460,11 +463,12 @@ class ContextTest(test_util.TensorFlowTestCase): control_flow_ops.while_loop( c, b, [i], maximum_iterations=maximum_iterations) for op in sess.graph.get_operations(): - context = op._get_control_flow_context() - if context: - self.assertProtoEquals(context.to_proto(), - control_flow_ops.WhileContext.from_proto( - context.to_proto()).to_proto()) + control_flow_context = op._get_control_flow_context() + if control_flow_context: + self.assertProtoEquals( + control_flow_context.to_proto(), + control_flow_ops.WhileContext.from_proto( + control_flow_context.to_proto()).to_proto()) def testWhileContext(self): self._testWhileContextHelper() @@ -498,8 +502,9 @@ class ContextTest(test_util.TensorFlowTestCase): c_with_scope._to_values_def(export_scope="test_scope")) -def _GetNestedShape(nested): - def _GetShape(tensor): +def _get_nested_shape(nested): + + def _get_shape(tensor): if isinstance(tensor, tensor_array_ops.TensorArray): return tensor_array_ops.TensorArray elif isinstance(tensor, ops.IndexedSlices): @@ -507,10 +512,10 @@ def _GetNestedShape(nested): else: return tensor.get_shape() - return nest.map_structure(_GetShape, nested) + return nest.map_structure(_get_shape, nested) -def _CreateTensorArray(size, shape): +def _create_tensor_array(size, shape): ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=size, clear_after_read=False) for i in range(size): @@ -518,13 +523,15 @@ def _CreateTensorArray(size, shape): return ta -def _RawNestedShape(nested_shape): - def _RawShape(shape): +def _raw_nested_shape(nested_shape): + + def _raw_shape(shape): if isinstance(shape, tensor_shape.TensorShape) and shape.ndims is not None: return [x.value for x in shape] else: return None - return nest.map_structure(_RawShape, nested_shape) + + return nest.map_structure(_raw_shape, nested_shape) # TODO(yori): Add tests for indexed slices. @@ -543,13 +550,15 @@ class DataTypesTest(test_util.TensorFlowTestCase): condition = array_ops.placeholder(dtypes.bool) output_cond = control_flow_ops.cond(condition, fn_true, fn_false, strict=strict) - self.assertEqual(_RawNestedShape(_GetNestedShape(output_cond)), - _RawNestedShape(expected_shape)) + self.assertEqual( + _raw_nested_shape(_get_nested_shape(output_cond)), + _raw_nested_shape(expected_shape)) output_case = control_flow_ops.case([(condition, fn_true)], fn_false, strict=strict) - self.assertEqual(_RawNestedShape(_GetNestedShape(output_case)), - _RawNestedShape(expected_shape)) + self.assertEqual( + _raw_nested_shape(_get_nested_shape(output_case)), + _raw_nested_shape(expected_shape)) def _testReturnValues(self, fn_true, fn_false, expected_value_true, expected_value_false, strict=False, @@ -626,45 +635,55 @@ class DataTypesTest(test_util.TensorFlowTestCase): control_flow_ops.cond(constant_op.constant(True), fn_tensor, fn_none) def test_tensors(self): - def _BuildTrueBranch(dtype): - def _Build(): + + def _build_true_branch(dtype): + + def _build(): return (array_ops.zeros([2, 2], dtype=dtype), array_ops.ones([3, 3], dtype=dtype)) - return _Build - def _BuildFalseBranch(dtype): - def _Build(): + return _build + + def _build_false_branch(dtype): + + def _build(): return (array_ops.ones([2, 2], dtype=dtype), array_ops.zeros([3, 3], dtype=dtype)) - return _Build + + return _build for dtype in (dtypes.float16, dtypes.int8, dtypes.int32, dtypes.uint8): shape = (tensor_shape.TensorShape([2, 2]), tensor_shape.TensorShape([3, 3])) - fn_true = _BuildTrueBranch(dtype) - fn_false = _BuildFalseBranch(dtype) + fn_true = _build_true_branch(dtype) + fn_false = _build_false_branch(dtype) self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, (np.zeros([2, 2]), np.ones([3, 3])), (np.ones([2, 2]), np.zeros([3, 3]))) def test_tensors_unknown_shape(self): - def _BuildTrueBranch(dtype): + + def _build_true_branch(dtype): tensor = array_ops.placeholder(dtype=dtype, shape=None) - def _Build(): + + def _build(): return tensor - return _Build, tensor - def _BuildFalseBranch(dtype): + return _build, tensor + + def _build_false_branch(dtype): tensor = array_ops.placeholder(dtype=dtype, shape=None) - def _Build(): + + def _build(): return tensor - return _Build, tensor + + return _build, tensor for dtype in (dtypes.float16, dtypes.int8, dtypes.int32, dtypes.uint8): shape = tensor_shape.TensorShape(None) - fn_true, true_tensor = _BuildTrueBranch(dtype) - fn_false, false_tensor = _BuildFalseBranch(dtype) + fn_true, true_tensor = _build_true_branch(dtype) + fn_false, false_tensor = _build_false_branch(dtype) self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, np.zeros([2, 2]), np.ones([2, 2]), @@ -674,11 +693,11 @@ class DataTypesTest(test_util.TensorFlowTestCase): def test_sparse_tensors(self): shape = tensor_shape.TensorShape([None, None]) - def FnTrue(): + def true_fn(): return [sparse_tensor.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])] - def FnFalse(): + def false_fn(): return [sparse_tensor.SparseTensor(indices=[[0, 0], [2, 1]], values=[3, 4], dense_shape=[3, 4])] @@ -686,26 +705,29 @@ class DataTypesTest(test_util.TensorFlowTestCase): values=[1, 2], dense_shape=[3, 4]) value2 = sparse_tensor.SparseTensorValue(indices=[[0, 0], [2, 1]], values=[3, 4], dense_shape=[3, 4]) - self._testShape(FnTrue, FnFalse, shape) - self._testReturnValues(FnTrue, FnFalse, value1, value2) - self._testShape(FnTrue, FnFalse, [shape], strict=True) - self._testReturnValues(FnTrue, FnFalse, [value1], [value2], strict=True) + self._testShape(true_fn, false_fn, shape) + self._testReturnValues(true_fn, false_fn, value1, value2) + self._testShape(true_fn, false_fn, [shape], strict=True) + self._testReturnValues(true_fn, false_fn, [value1], [value2], strict=True) def test_tensors_with_partially_specified_shapes(self): - def _BuildBranch(dtype, shape): + + def _build_branch(dtype, shape): a = array_ops.placeholder(dtype=dtype, shape=shape[0]) b = array_ops.placeholder(dtype=dtype, shape=shape[1]) c = array_ops.placeholder(dtype=dtype, shape=shape[2]) - def _Build(): + + def _build(): return a, b, c - return _Build, (a, b, c) + + return _build, (a, b, c) for dtype in (dtypes.float16, dtypes.int8, dtypes.int32, dtypes.uint8): shape = (tensor_shape.TensorShape([None, 2]), tensor_shape.TensorShape([None]), tensor_shape.TensorShape([3, None])) - fn_true, true_tensors = _BuildBranch(dtype, shape) - fn_false, false_tensors = _BuildBranch(dtype, shape) + fn_true, true_tensors = _build_branch(dtype, shape) + fn_false, false_tensors = _build_branch(dtype, shape) self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, (np.zeros([2, 2]), np.zeros(5), np.ones([3, 3])), @@ -719,8 +741,8 @@ class DataTypesTest(test_util.TensorFlowTestCase): def test_tensor_arrays(self): element_shape = tensor_shape.TensorShape([2]) - ta1 = _CreateTensorArray(4, element_shape) - ta2 = _CreateTensorArray(4, element_shape) + ta1 = _create_tensor_array(4, element_shape) + ta2 = _create_tensor_array(4, element_shape) shape = tensor_array_ops.TensorArray fn_true = lambda: ta1 fn_false = lambda: ta2 @@ -728,7 +750,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): def test_tensor_array_reads(self): shape = tensor_shape.TensorShape([2]) - ta = _CreateTensorArray(4, shape) + ta = _create_tensor_array(4, shape) fn_true = lambda: ta.read(0) fn_false = lambda: ta.read(1) self._testShape(fn_true, fn_false, shape) @@ -827,23 +849,26 @@ class DataTypesTest(test_util.TensorFlowTestCase): tensor_shape.TensorShape([5, 5]), tensor_shape.TensorShape([])] - def FnTrue(): + def true_fn(): return [constant_op.constant(1), TestTuple(constant_op.constant(2), [3, 4]), array_ops.zeros([5, 5]), 6] - def FnFalse(): + def false_fn(): return [constant_op.constant(11), TestTuple(constant_op.constant(12), [13, 14]), array_ops.ones([5, 5]), 16] - self._testShape(FnTrue, FnFalse, shape) - self._testReturnValues(FnTrue, FnFalse, - [1, TestTuple(2, [3, 4]), np.zeros([5, 5]), 6], - [11, TestTuple(12, [13, 14]), np.ones([5, 5]), 16]) + self._testShape(true_fn, false_fn, shape) + self._testReturnValues( + true_fn, false_fn, + [1, TestTuple(2, [3, 4]), np.zeros([5, 5]), 6], + [11, TestTuple(12, [13, 14]), + np.ones([5, 5]), 16]) def test_cond_inside_while_loop(self): - def Body(i, matrix): + + def body(i, matrix): result_tuple, unused_matrix = control_flow_ops.cond( constant_op.constant(True), lambda: (TestTuple(matrix * 2, matrix * 4), matrix), @@ -852,8 +877,9 @@ class DataTypesTest(test_util.TensorFlowTestCase): iteration, matrix = control_flow_ops.while_loop( lambda i, matrix: i < 10, - Body, - loop_vars=[constant_op.constant(0), array_ops.ones([2, 2])]) + body, + loop_vars=[constant_op.constant(0), + array_ops.ones([2, 2])]) self.assertEqual(iteration.get_shape(), tensor_shape.TensorShape([])) self.assertEqual(matrix.get_shape(), tensor_shape.TensorShape([2, 2])) diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py index 83da6739db673644f59fda3044769b18b2138fbc..4b57e2de790af13499bc73cfcfa98e999eab1603 100644 --- a/tensorflow/python/ops/ctc_ops.py +++ b/tensorflow/python/ops/ctc_ops.py @@ -148,7 +148,7 @@ def ctc_loss(labels, inputs, sequence_length, if not time_major: inputs = array_ops.transpose(inputs, [1, 0, 2]) # (B,T,N) => (T,B,N) - loss, _ = gen_ctc_ops._ctc_loss( + loss, _ = gen_ctc_ops.ctc_loss( inputs, labels.indices, labels.values, @@ -224,7 +224,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True): sequence found, the negative of the sum of the greatest logit at each timeframe. """ - outputs = gen_ctc_ops._ctc_greedy_decoder( + outputs = gen_ctc_ops.ctc_greedy_decoder( inputs, sequence_length, merge_repeated=merge_repeated) (decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs return ([sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)], @@ -272,7 +272,7 @@ def ctc_beam_search_decoder(inputs, sequence_length, beam_width=100, """ decoded_ixs, decoded_vals, decoded_shapes, log_probabilities = ( - gen_ctc_ops._ctc_beam_search_decoder( + gen_ctc_ops.ctc_beam_search_decoder( inputs, sequence_length, beam_width=beam_width, top_paths=top_paths, merge_repeated=merge_repeated)) diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..9eacac1b3704c43cbeb5ecd0cbe827cac3a7cc8b --- /dev/null +++ b/tensorflow/python/ops/custom_gradient.py @@ -0,0 +1,134 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Decorator to overrides the gradient for a function.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.eager import tape +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.util import nest +from tensorflow.python.util import tf_decorator +from tensorflow.python.util.tf_export import tf_export + + +@tf_export("custom_gradient") +def custom_gradient(f): + """Decorator to define a function with a custom gradient. + + This decorator allows fine grained control over the gradients of a sequence + for operations. This may be useful for multiple reasons, including providing + a more efficient or numerically stable gradient for a sequence of operations. + + For example, consider the following function that commonly occurs in the + computation of cross entropy and log likelihoods: + + ```python + def log1pexp(x): + return tf.log(1 + tf.exp(x)) + ``` + + Due to numerical instability, the gradient this function evaluated at x=100 is + NaN. For example: + + ```python + x = tf.constant(100.) + y = log1pexp(x) + dy = tf.gradients(y, x) # Will be NaN when evaluated. + ``` + + The gradient expression can be analytically simplified to provide numerical + stability: + + ```python + @tf.custom_gradient + def log1pexp(x): + e = tf.exp(x) + def grad(dy): + return dy * (1 - 1 / (1 + e)) + return tf.log(1 + e), grad + ``` + + With this definition, the gradient at x=100 will be correctly evaluated as + 1.0. + + See also @{tf.RegisterGradient} which registers a gradient function for a + primitive TensorFlow operation. `tf.custom_gradient` on the other hand allows + for fine grained control over the gradient computation of a sequence of + operations. + + Args: + f: function `f(x)` that returns a tuple `(y, grad_fn)` where: + - `x` is a `Tensor` or sequence of `Tensor` inputs to the function. + - `y` is a `Tensor` or sequence of `Tensor` outputs of applying + TensorFlow + operations in `f` to `x`. + - `grad_fn` is a function with the signature `g(grad_ys)` which returns + a list of `Tensor`s - the derivatives of `Tensor`s in `y` with respect + to the `Tensor`s in `x. `grad_ys` is a `Tensor` or sequence of + `Tensor`s the same size as `y` holding the initial value gradients for + each `Tensor` in `y`. + + Returns: + A function `h(x)` which returns the same value as `f(x)[0]` and whose + gradient (as calculated by @{tf.gradients}) is determined by `f(x)[1]`. + """ + + def decorated(*args, **kwargs): + """Decorated function with custom gradient.""" + if not context.executing_eagerly(): + if kwargs: + raise ValueError( + "The custom_gradient decorator currently suports keywords " + "arguments only when eager execution is enabled.") + name = "CustomGradient-%s" % ops.uid() + args = [ops.convert_to_tensor(x) for x in args] + result, grad_fn = f(*args) + flat_result = nest.flatten(result) + all_tensors = flat_result + args + + @ops.RegisterGradient(name) + def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable + gradients = nest.flatten(grad_fn(*result_grads[:len(flat_result)])) + # Need to return one value per input to the IdentityN, so pad the + # gradients of the inputs of the custom_gradient function with the + # gradients of the outputs as well. + return ([None] * len(flat_result)) + gradients + + with ops.get_default_graph().gradient_override_map({"IdentityN": name}): + all_tensors = array_ops.identity_n(all_tensors) + return nest.pack_sequence_as( + structure=result, flat_sequence=all_tensors[:len(flat_result)]) + + input_tensors = [ops.convert_to_tensor(x) for x in args] + + result, grad_fn = f(*args, **kwargs) + flat_result = nest.flatten(result) + # TODO(apassos) consider removing the identity below. + flat_result = [gen_array_ops.identity(x) for x in flat_result] + + def actual_grad_fn(*outputs): + return nest.flatten(grad_fn(*outputs)) + + tape.record_operation(f.__name__, flat_result, input_tensors, + actual_grad_fn) + flat_result = list(flat_result) + return nest.pack_sequence_as(result, flat_result) + + return tf_decorator.make_decorator(f, decorated) diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 95e45bff066d4b2653e5de67684a6277006345f2..d2cc87555f6321432261b32f08431c23ce707eff 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -159,7 +159,7 @@ class QueueBase(object): ValueError: If one of the arguments is invalid. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Queues are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -177,10 +177,10 @@ class QueueBase(object): else: self._names = None self._queue_ref = queue_ref - if context.in_graph_mode(): - self._name = self._queue_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._queue_ref.op.name.split("/")[-1] @staticmethod def from_list(index, queues): @@ -231,9 +231,9 @@ class QueueBase(object): @property def name(self): """The name of the underlying queue.""" - if context.in_graph_mode(): - return self._queue_ref.op.name - return self._name + if context.executing_eagerly(): + return self._name + return self._queue_ref.op.name @property def dtypes(self): @@ -342,10 +342,10 @@ class QueueBase(object): val.get_shape().assert_is_compatible_with(shape) if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_enqueue_v2( + return gen_data_flow_ops.queue_enqueue_v2( self._queue_ref, vals, name=scope) else: - return gen_data_flow_ops._queue_enqueue( + return gen_data_flow_ops.queue_enqueue( self._queue_ref, vals, name=scope) def enqueue_many(self, vals, name=None): @@ -387,7 +387,7 @@ class QueueBase(object): val.get_shape().with_rank_at_least(1)[0]) val.get_shape()[1:].assert_is_compatible_with(shape) - return gen_data_flow_ops._queue_enqueue_many_v2( + return gen_data_flow_ops.queue_enqueue_many_v2( self._queue_ref, vals, name=scope) def _dequeue_return_value(self, tensors): @@ -436,15 +436,15 @@ class QueueBase(object): if name is None: name = "%s_Dequeue" % self._name if self._queue_ref.dtype == _dtypes.resource: - ret = gen_data_flow_ops._queue_dequeue_v2( + ret = gen_data_flow_ops.queue_dequeue_v2( self._queue_ref, self._dtypes, name=name) else: - ret = gen_data_flow_ops._queue_dequeue( + ret = gen_data_flow_ops.queue_dequeue( self._queue_ref, self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the `QueueBase` object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op for output, shape in zip(op.values(), self._shapes): output.set_shape(shape) @@ -474,17 +474,17 @@ class QueueBase(object): name: A name for the operation (optional). Returns: - The tuple of concatenated tensors that was dequeued. + The list of concatenated tensors that was dequeued. """ if name is None: name = "%s_DequeueMany" % self._name - ret = gen_data_flow_ops._queue_dequeue_many_v2( + ret = gen_data_flow_ops.queue_dequeue_many_v2( self._queue_ref, n=n, component_types=self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the Queue object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op batch_dim = tensor_shape.Dimension( tensor_util.constant_value(op.inputs[1])) @@ -523,12 +523,12 @@ class QueueBase(object): if name is None: name = "%s_DequeueUpTo" % self._name - ret = gen_data_flow_ops._queue_dequeue_up_to_v2( + ret = gen_data_flow_ops.queue_dequeue_up_to_v2( self._queue_ref, n=n, component_types=self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the Queue object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op for output, shape in zip(op.values(), self._shapes): output.set_shape(tensor_shape.TensorShape([None]).concatenate(shape)) @@ -560,12 +560,12 @@ class QueueBase(object): if name is None: name = "%s_Close" % self._name if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_close_v2( + return gen_data_flow_ops.queue_close_v2( self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) else: - return gen_data_flow_ops._queue_close( + return gen_data_flow_ops.queue_close( self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) @@ -601,9 +601,9 @@ class QueueBase(object): if name is None: name = "%s_Size" % self._name if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_size_v2(self._queue_ref, name=name) + return gen_data_flow_ops.queue_size_v2(self._queue_ref, name=name) else: - return gen_data_flow_ops._queue_size(self._queue_ref, name=name) + return gen_data_flow_ops.queue_size(self._queue_ref, name=name) @tf_export("RandomShuffleQueue") @@ -683,7 +683,7 @@ class RandomShuffleQueue(QueueBase): # the id of the last op created.) string = (str(seed1) + shared_name).encode("utf-8") seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF - queue_ref = gen_data_flow_ops._random_shuffle_queue_v2( + queue_ref = gen_data_flow_ops.random_shuffle_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -748,7 +748,7 @@ class FIFOQueue(QueueBase): dtypes = _as_type_list(dtypes) shapes = _as_shape_list(shapes, dtypes) names = _as_name_list(names, dtypes) - queue_ref = gen_data_flow_ops._fifo_queue_v2( + queue_ref = gen_data_flow_ops.fifo_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -827,7 +827,7 @@ class PaddingFIFOQueue(QueueBase): "but received %d dtypes and %d shapes." % (len(dtypes), len(shapes))) - queue_ref = gen_data_flow_ops._padding_fifo_queue_v2( + queue_ref = gen_data_flow_ops.padding_fifo_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -895,7 +895,7 @@ class PriorityQueue(QueueBase): types = _as_type_list(types) shapes = _as_shape_list(shapes, types) - queue_ref = gen_data_flow_ops._priority_queue_v2( + queue_ref = gen_data_flow_ops.priority_queue_v2( component_types=types, shapes=shapes, capacity=capacity, @@ -985,15 +985,15 @@ class Barrier(object): else: self._shapes = [tensor_shape.unknown_shape() for _ in self._types] - self._barrier_ref = gen_data_flow_ops._barrier( + self._barrier_ref = gen_data_flow_ops.barrier( component_types=self._types, shapes=self._shapes, shared_name=shared_name, name=name) - if context.in_graph_mode(): - self._name = self._barrier_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._barrier_ref.op.name.split("/")[-1] @property def barrier_ref(self): @@ -1003,9 +1003,9 @@ class Barrier(object): @property def name(self): """The name of the underlying barrier.""" - if context.in_graph_mode(): - return self._barrier_ref.op.name - return self._name + if context.executing_eagerly(): + return self._name + return self._barrier_ref.op.name def insert_many(self, component_index, keys, values, name=None): """For each key, assigns the respective value to the specified component. @@ -1026,7 +1026,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierInsertMany" % self._name - return gen_data_flow_ops._barrier_insert_many( + return gen_data_flow_ops.barrier_insert_many( self._barrier_ref, keys, values, component_index, name=name) def take_many(self, @@ -1073,7 +1073,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierTakeMany" % self._name - ret = gen_data_flow_ops._barrier_take_many( + ret = gen_data_flow_ops.barrier_take_many( self._barrier_ref, num_elements, self._types, @@ -1083,7 +1083,7 @@ class Barrier(object): # NOTE(mrry): Not using a shape function because we need access to # the Barrier object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op if allow_small_batch: batch_dim = None @@ -1122,7 +1122,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierClose" % self._name - return gen_data_flow_ops._barrier_close( + return gen_data_flow_ops.barrier_close( self._barrier_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) @@ -1139,7 +1139,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierReadySize" % self._name - return gen_data_flow_ops._barrier_ready_size(self._barrier_ref, name=name) + return gen_data_flow_ops.barrier_ready_size(self._barrier_ref, name=name) def incomplete_size(self, name=None): """Compute the number of incomplete elements in the given barrier. @@ -1153,7 +1153,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierIncompleteSize" % self._name - return gen_data_flow_ops._barrier_incomplete_size( + return gen_data_flow_ops.barrier_incomplete_size( self._barrier_ref, name=name) @@ -1183,10 +1183,10 @@ class ConditionalAccumulatorBase(object): else: self._shape = tensor_shape.unknown_shape() self._accumulator_ref = accumulator_ref - if context.in_graph_mode(): - self._name = self._accumulator_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._accumulator_ref.op.name.split("/")[-1] @property def accumulator_ref(self): diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py index 1f300b7147be505a316c38ae57cadeae2bd7ea10..68aaf3815e7e2b21c9550562aa49195569c8ea43 100644 --- a/tensorflow/python/ops/distributions/bernoulli.py +++ b/tensorflow/python/ops/distributions/bernoulli.py @@ -22,7 +22,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops @@ -137,21 +136,12 @@ class Bernoulli(distribution.Distribution): return (array_ops.ones_like(event) * logits, array_ops.ones_like(logits) * event) - # First check static shape. - if (event.get_shape().is_fully_defined() and - logits.get_shape().is_fully_defined()): - if event.get_shape() != logits.get_shape(): - logits, event = _broadcast(logits, event) - else: - logits, event = control_flow_ops.cond( - distribution_util.same_dynamic_shape(logits, event), - lambda: (logits, event), - lambda: _broadcast(logits, event)) + if not (event.get_shape().is_fully_defined() and + logits.get_shape().is_fully_defined() and + event.get_shape() == logits.get_shape()): + logits, event = _broadcast(logits, event) return -nn.sigmoid_cross_entropy_with_logits(labels=event, logits=logits) - def _prob(self, event): - return math_ops.exp(self._log_prob(event)) - def _entropy(self): return (-self.logits * (math_ops.sigmoid(self.logits) - 1) + nn.softplus(-self.logits)) @@ -167,26 +157,6 @@ class Bernoulli(distribution.Distribution): return math_ops.cast(self.probs > 0.5, self.dtype) -class BernoulliWithSigmoidProbs(Bernoulli): - """Bernoulli with `probs = nn.sigmoid(logits)`.""" - - def __init__(self, - logits=None, - dtype=dtypes.int32, - validate_args=False, - allow_nan_stats=True, - name="BernoulliWithSigmoidProbs"): - parameters = locals() - with ops.name_scope(name): - super(BernoulliWithSigmoidProbs, self).__init__( - probs=nn.sigmoid(logits, name="sigmoid_probs"), - dtype=dtype, - validate_args=validate_args, - allow_nan_stats=allow_nan_stats, - name=name) - self._parameters = parameters - - @kullback_leibler.RegisterKL(Bernoulli, Bernoulli) def _kl_bernoulli_bernoulli(a, b, name=None): """Calculate the batched KL divergence KL(a || b) with a and b Bernoulli. diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py index 6d6b40b04557a4483f60d8c06c35f937d38a24b9..469bcadb8ea3a0ec2a85d3a72c0ca5ba08796856 100644 --- a/tensorflow/python/ops/distributions/beta.py +++ b/tensorflow/python/ops/distributions/beta.py @@ -304,12 +304,11 @@ class Beta(distribution.Distribution): if not self.validate_args: return x return control_flow_ops.with_dependencies([ - check_ops.assert_positive( - x, - message="sample must be positive"), + check_ops.assert_positive(x, message="sample must be positive"), check_ops.assert_less( - x, array_ops.ones([], self.dtype), - message="sample must be no larger than `1`."), + x, + array_ops.ones([], self.dtype), + message="sample must be less than `1`."), ], x) diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py index 8fb218be3ac7e17e18d85b8e1c100ccd58aa1034..adb1f4f9a879e44cf8cb4cafd22b92554f487712 100644 --- a/tensorflow/python/ops/distributions/gamma.py +++ b/tensorflow/python/ops/distributions/gamma.py @@ -193,12 +193,6 @@ class Gamma(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - - def _log_cdf(self, x): - return math_ops.log(self._cdf(x)) - def _cdf(self, x): x = self._maybe_assert_valid_sample(x) # Note that igamma returns the regularized incomplete gamma function, diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py index 26b5c5aef98fc11b07a8c8357e7ec37819587da9..4ae67a009b0a4052f6e23e2e42262bb7c42f1c14 100644 --- a/tensorflow/python/ops/distributions/multinomial.py +++ b/tensorflow/python/ops/distributions/multinomial.py @@ -238,7 +238,7 @@ class Multinomial(distribution.Distribution): n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32) k = self.event_shape_tensor()[0] - # boardcast the total_count and logits to same shape + # broadcast the total_count and logits to same shape n_draws = array_ops.ones_like( self.logits[..., 0], dtype=n_draws.dtype) * n_draws logits = array_ops.ones_like( diff --git a/tensorflow/python/ops/distributions/normal.py b/tensorflow/python/ops/distributions/normal.py index e7f120ea2da525e20a1ae42e6418cf2ac83686af..32e8a49c81bc4b23d8897639998dd33942b41a80 100644 --- a/tensorflow/python/ops/distributions/normal.py +++ b/tensorflow/python/ops/distributions/normal.py @@ -188,9 +188,6 @@ class Normal(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return special_math.log_ndtr(self._z(x)) diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py index bed4cbb2c1a43b6952861f4fab82957229e23c9c..1d605c5dfcca9b709a9178ccbe56619f6a92f869 100644 --- a/tensorflow/python/ops/distributions/special_math.py +++ b/tensorflow/python/ops/distributions/special_math.py @@ -213,7 +213,7 @@ def _ndtri(p): # Compute x for p <= exp(-2): x = z - log(z)/z - (1/z) P(1/z) / Q(1/z), # where z = sqrt(-2. * log(p)), and P/Q are chosen between two different - # arrays based on wether p < exp(-32). + # arrays based on whether p < exp(-32). z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp)) first_term = z - math_ops.log(z) / z second_term_small_p = (_create_polynomial(1. / z, p2) diff --git a/tensorflow/python/ops/distributions/student_t.py b/tensorflow/python/ops/distributions/student_t.py index 778fefb8c2991153b7e7a1f20df61680153dab2a..9d9e65b4e8d6d2e40bf9c263339f899439c842c3 100644 --- a/tensorflow/python/ops/distributions/student_t.py +++ b/tensorflow/python/ops/distributions/student_t.py @@ -248,9 +248,6 @@ class StudentT(distribution.Distribution): math_ops.lgamma(0.5 * self.df) - math_ops.lgamma(0.5 * (self.df + 1.))) - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _cdf(self, x): # Take Abs(scale) to make subsequent where work correctly. y = (x - self.loc) / math_ops.abs(self.scale) diff --git a/tensorflow/python/ops/distributions/uniform.py b/tensorflow/python/ops/distributions/uniform.py index 3580af18f241d777c81340f1c565074914838029..ec623b55eb0067e16599c18c9c504635da863907 100644 --- a/tensorflow/python/ops/distributions/uniform.py +++ b/tensorflow/python/ops/distributions/uniform.py @@ -45,11 +45,12 @@ class Uniform(distribution.Distribution): Z = b - a ``` - where: - * `low = a`, - * `high = b`, - * `Z` is the normalizing constant, and, - * `I[predicate]` is the [indicator function]( + where + + - `low = a`, + - `high = b`, + - `Z` is the normalizing constant, and + - `I[predicate]` is the [indicator function]( https://en.wikipedia.org/wiki/Indicator_function) for `predicate`. The parameters `low` and `high` must be shaped in a way that supports @@ -164,9 +165,6 @@ class Uniform(distribution.Distribution): seed=seed) return self.low + self.range() * samples - def _log_prob(self, x): - return math_ops.log(self._prob(x)) - def _prob(self, x): broadcasted_x = x * array_ops.ones(self.batch_shape_tensor()) return array_ops.where( @@ -178,9 +176,6 @@ class Uniform(distribution.Distribution): array_ops.zeros_like(broadcasted_x), array_ops.ones_like(broadcasted_x) / self.range())) - def _log_cdf(self, x): - return math_ops.log(self.cdf(x)) - def _cdf(self, x): broadcast_shape = array_ops.broadcast_dynamic_shape( array_ops.shape(x), self.batch_shape_tensor()) diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py index 5bc25128a8d6f77895fc4decc98a8978ae8400f3..0fe6aa30f945dc7682a53fa6495823288cf111b7 100644 --- a/tensorflow/python/ops/distributions/util.py +++ b/tensorflow/python/ops/distributions/util.py @@ -1041,14 +1041,14 @@ def reduce_weighted_logsumexp( with ops.name_scope(name, "reduce_weighted_logsumexp", [logx, w]): logx = ops.convert_to_tensor(logx, name="logx") if w is None: - lswe = math_ops.reduce_logsumexp(logx, axis=axis, keep_dims=keep_dims) + lswe = math_ops.reduce_logsumexp(logx, axis=axis, keepdims=keep_dims) if return_sign: sgn = array_ops.ones_like(lswe) return lswe, sgn return lswe w = ops.convert_to_tensor(w, dtype=logx.dtype, name="w") log_absw_x = logx + math_ops.log(math_ops.abs(w)) - max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keep_dims=True) + max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keepdims=True) # If the largest element is `-inf` or `inf` then we don't bother subtracting # off the max. We do this because otherwise we'd get `inf - inf = NaN`. That # this is ok follows from the fact that we're actually free to subtract any @@ -1060,9 +1060,7 @@ def reduce_weighted_logsumexp( wx_over_max_absw_x = ( math_ops.sign(w) * math_ops.exp(log_absw_x - max_log_absw_x)) sum_wx_over_max_absw_x = math_ops.reduce_sum( - wx_over_max_absw_x, - axis=axis, - keep_dims=keep_dims) + wx_over_max_absw_x, axis=axis, keepdims=keep_dims) if not keep_dims: max_log_absw_x = array_ops.squeeze(max_log_absw_x, axis) sgn = math_ops.sign(sum_wx_over_max_absw_x) @@ -1180,8 +1178,7 @@ def process_quadrature_grid_and_probs( grid = ops.convert_to_tensor(grid, name="grid", dtype=dtype) probs = ops.convert_to_tensor(probs, name="unnormalized_probs", dtype=dtype) - probs /= linalg_ops.norm(probs, ord=1, axis=-1, keep_dims=True, - name="probs") + probs /= linalg_ops.norm(probs, ord=1, axis=-1, keepdims=True, name="probs") def _static_event_size(x): """Returns the static size of a specific dimension or `None`.""" diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index ac03d30fcd2e65f032937d9259bc8fff18626619..8f5673597e66dcc627137c5967fd9020d1d8f7d1 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -41,7 +41,7 @@ from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops.gen_functional_ops import * # pylint: enable=wildcard-import # pylint: disable=unused-import -from tensorflow.python.ops.gen_functional_ops import _symbolic_gradient +from tensorflow.python.ops.gen_functional_ops import symbolic_gradient # pylint: enable=unused-import from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -90,7 +90,7 @@ def foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, if not callable(fn): raise TypeError("fn must be callable.") - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "foldl", [elems]): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -178,7 +178,7 @@ def foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, if not callable(fn): raise TypeError("fn must be callable.") - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "foldr", [elems]): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -343,7 +343,7 @@ def map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, elems_flat = input_flatten(elems) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "map", elems_flat): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -536,7 +536,7 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, elems_flat = input_flatten(elems) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "scan", elems_flat): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager diff --git a/tensorflow/python/ops/gradients.py b/tensorflow/python/ops/gradients.py index 921fd50aa9fdd1a1e493708f4bc8c66996e26e2c..63d9a232224cc70c9ce6f04787a8dcc921b3e2f3 100644 --- a/tensorflow/python/ops/gradients.py +++ b/tensorflow/python/ops/gradients.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import +from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.gradients_impl import AggregationMethod from tensorflow.python.ops.gradients_impl import gradients from tensorflow.python.ops.gradients_impl import hessians @@ -28,6 +29,7 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ # TODO(drpng): find a good place to reference this. "AggregationMethod", + "custom_gradient", "gradients", # tf.gradients.gradients. "hessians", # tf.gradients.hessians ] diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 230b6c5946ab7e0e3529994f7199026865cecde2..44473ec69c8ac6cf565f635621eebff7bc403225 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -35,7 +35,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops -from tensorflow.python.ops import check_ops +from tensorflow.python.ops import check_ops # pylint: disable=unused-import from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util @@ -86,17 +86,19 @@ def _IndexedSlicesToTensor(value, dtype=None, name=None, as_ref=False): % str(value)) # TODO(mrry): Consider adding static shape information to # IndexedSlices, to avoid using numpy here. - dense_shape_value = tensor_util.constant_value(value.dense_shape) - if dense_shape_value is not None: - num_elements = np.prod(dense_shape_value) - if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS: + if not context.executing_eagerly(): + dense_shape_value = tensor_util.constant_value(value.dense_shape) + if dense_shape_value is not None: + num_elements = np.prod(dense_shape_value) + if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS: + warnings.warn( + "Converting sparse IndexedSlices to a dense Tensor with %d " + "elements. This may consume a large amount of memory." % + num_elements) + else: warnings.warn( - "Converting sparse IndexedSlices to a dense Tensor with %d elements. " - "This may consume a large amount of memory." % num_elements) - else: - warnings.warn( - "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " - "This may consume a large amount of memory.") + "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " + "This may consume a large amount of memory.") return math_ops.unsorted_segment_sum( value.values, value.indices, value.dense_shape[0], name=name) @@ -354,7 +356,7 @@ def _SymGrad(op, out_grads): for k in op.node_def.attr: f.attr[k].CopyFrom(op.node_def.attr[k]) # pylint: disable=protected-access - in_grads = functional_ops._symbolic_gradient(input=f_in, Tout=f_types, f=f) + in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f) # pylint: enable=protected-access return in_grads @@ -478,9 +480,21 @@ def gradients(ys, RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): - raise RuntimeError("tf.gradients not supported in EAGER mode. Use " - "functions in tf.contrib.eager.backprop instead.") + # Creating the gradient graph for control flow mutates Operations. _lock + # ensures a Session.run call cannot occur between creating and mutating new + # ops. + with ops.get_default_graph()._lock: # pylint: disable=protected-access + return _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, + gate_gradients, aggregation_method, stop_gradients) + + +def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, + gate_gradients, aggregation_method, stop_gradients): + """Implementation of gradients().""" + if context.executing_eagerly(): + raise RuntimeError("tf.gradients not supported when eager execution " + "is enabled. Use tf.contrib.eager.GradientTape " + "instead.") ys = _AsList(ys) xs = _AsList(xs) stop_gradients = [] if stop_gradients is None else _AsList(stop_gradients) @@ -494,7 +508,7 @@ def gradients(ys, list(ys) + list(xs) + list(stop_gradients) + list(grad_ys)) as grad_scope: ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y") xs = [ - x.handle if isinstance(x, resource_variable_ops.ResourceVariable) else x + x.handle if resource_variable_ops.is_resource_variable(x) else x for x in xs ] xs = ops.internal_convert_n_to_tensor_or_indexed_slices( diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index d39b934819177e3c15af95a0777ba96869c5e9cf..c94f1396b28e2124c6e5123cf711ac86abf174ab 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -35,6 +35,7 @@ from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import data_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import data_flow_ops # pylint: disable=unused-import from tensorflow.python.ops import functional_ops # pylint: disable=unused-import @@ -661,6 +662,7 @@ class HessianTest(test_util.TensorFlowTestCase): self.assertAllEqual((m, n, m, n), hess_actual.shape) self.assertAllClose(hess_value, hess_actual.reshape((m * n, m * n))) + @test_util.with_c_api class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): @@ -741,6 +743,59 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): "of unknown shape. This may consume a large amount of memory." in str(w[0].message)) + def testCustomGradientTrivial(self): + + @custom_gradient.custom_gradient + def MyIdentity(x): + + def Grad(dy): + return [3 * dy] + + return x, Grad + + with ops.Graph().as_default(): + x = constant(3.) + y = MyIdentity(MyIdentity(x)) + dy = gradients.gradients(y, x)[0] + with session.Session(): + self.assertEqual(9., dy.eval()) + + def testCustomGradient(self): + + @custom_gradient.custom_gradient + def MyMultiply(x1, x2): + result = x1 * x2 + + def Grad(dy): + # Switched the ordering here. + return [dy * x1, dy * x2] + + return result, Grad + + with ops.Graph().as_default(): + x1 = constant(3.) + x2 = constant(5.) + y = MyMultiply(x1, x2) + dy = gradients.gradients(y, [x1, x2]) + with session.Session() as sess: + self.assertAllEqual([3., 5.], sess.run(dy)) + + def testCustomGradientErrors(self): + + @custom_gradient.custom_gradient + def F(x): + + def Grad(_): + raise RuntimeError("x") + + return x, Grad + + with ops.Graph().as_default(): + x = constant(1.0) + y = F(x) + with self.assertRaises(RuntimeError): + gradients.gradients(y, x) + @test_util.with_c_api class OnlyRealGradientsTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt index f6ef6f3f3da4389a16a84fa0b3570d3cd1262472..9b8172bf2639cca0efb663ff4075b36d6f4f2245 100644 --- a/tensorflow/python/ops/hidden_ops.txt +++ b/tensorflow/python/ops/hidden_ops.txt @@ -32,6 +32,8 @@ TileGrad # Exported through array_grad instead of array_ops. ZerosLike # TODO(josh11b): Use this instead of the Python version. Unique UniqueV2 +UniqueWithCounts +UniqueWithCountsV2 Unpack # candidate_sampling_ops diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py index f079e56b10ed484225d8f09c6eaf7cf85a02d12a..4a1ef54fb50013881aa832f83674ac66ecccd9bc 100644 --- a/tensorflow/python/ops/histogram_ops.py +++ b/tensorflow/python/ops/histogram_ops.py @@ -32,8 +32,10 @@ from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import math_ops from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import tf_export +@tf_export('histogram_fixed_width_bins') def histogram_fixed_width_bins(values, value_range, nbins=100, @@ -139,5 +141,7 @@ def histogram_fixed_width(values, """ with ops.name_scope(name, 'histogram_fixed_width', [values, value_range, nbins]) as name: - return gen_math_ops._histogram_fixed_width( # pylint: disable=protected-access + # pylint: disable=protected-access + return gen_math_ops._histogram_fixed_width( values, value_range, nbins, dtype=dtype, name=name) + # pylint: enable=protected-access diff --git a/tensorflow/python/ops/image_grad.py b/tensorflow/python/ops/image_grad.py index d17f1a87d9759d5e83393f40e9e027dee8c15979..9f43e3f1466d900ae6d39f3b9ef48043421cb777 100644 --- a/tensorflow/python/ops/image_grad.py +++ b/tensorflow/python/ops/image_grad.py @@ -41,12 +41,10 @@ def _ResizeNearestNeighborGrad(op, grad): else: image_shape = array_ops.shape(image)[1:3] - # pylint: disable=protected-access - grads = gen_image_ops._resize_nearest_neighbor_grad( + grads = gen_image_ops.resize_nearest_neighbor_grad( grad, image_shape, align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access return [grads, None] @@ -61,15 +59,8 @@ def _ResizeBilinearGrad(op, grad): Returns: The gradients w.r.t. the input. """ - allowed_types = [dtypes.float32, dtypes.float64] - grad0 = None - if op.inputs[0].dtype in allowed_types: - # pylint: disable=protected-access - grad0 = gen_image_ops._resize_bilinear_grad( - grad, - op.inputs[0], - align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access + grad0 = gen_image_ops.resize_bilinear_grad( + grad, op.inputs[0], align_corners=op.get_attr("align_corners")) return [grad0, None] @@ -87,10 +78,8 @@ def _ResizeBicubicGrad(op, grad): allowed_types = [dtypes.float32, dtypes.float64] grad0 = None if op.inputs[0].dtype in allowed_types: - # pylint: disable=protected-access - grad0 = gen_image_ops._resize_bicubic_grad( + grad0 = gen_image_ops.resize_bicubic_grad( grad, op.inputs[0], align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access return [grad0, None] diff --git a/tensorflow/python/ops/image_grad_test.py b/tensorflow/python/ops/image_grad_test.py index 05e8fa1d72851caee522bba470bb40f430152464..75d00c8ed17c26c2c1acb4d92961a2206d959ebb 100644 --- a/tensorflow/python/ops/image_grad_test.py +++ b/tensorflow/python/ops/image_grad_test.py @@ -142,18 +142,6 @@ class ResizeBilinearOpTest(test.TestCase): input_tensor, in_shape, resize_out, out_shape, x_init_value=x) self.assertLess(err, 1e-3) - def testGradOnUnsupportedType(self): - in_shape = [1, 4, 6, 1] - out_shape = [1, 2, 3, 1] - - x = np.arange(0, 24).reshape(in_shape).astype(np.uint8) - - with self.test_session(): - input_tensor = constant_op.constant(x, shape=in_shape) - resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3]) - grad = gradients_impl.gradients(input_tensor, [resize_out]) - self.assertEqual([None], grad) - def testCompareGpuVsCpu(self): in_shape = [2, 4, 6, 3] out_shape = [2, 8, 16, 3] @@ -172,6 +160,26 @@ class ResizeBilinearOpTest(test.TestCase): self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4) + def testTypes(self): + in_shape = [1, 4, 6, 1] + out_shape = [1, 2, 3, 1] + x = np.arange(0, 24).reshape(in_shape) + + with self.test_session() as sess: + for dtype in [np.float16, np.float32, np.float64]: + input_tensor = constant_op.constant(x.astype(dtype), shape=in_shape) + resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3]) + grad = sess.run(gradients_impl.gradients(resize_out, input_tensor))[0] + self.assertAllEqual(in_shape, grad.shape) + # Not using gradient_checker.compute_gradient as I didn't work out + # the changes required to compensate for the lower precision of + # float16 when computing the numeric jacobian. + # Instead, we just test the theoretical jacobian. + self.assertAllEqual([[[[1.], [0.], [1.], [0.], [1.], [0.]], [[0.], [ + 0. + ], [0.], [0.], [0.], [0.]], [[1.], [0.], [1.], [0.], [1.], [0.]], + [[0.], [0.], [0.], [0.], [0.], [0.]]]], grad) + class ResizeBicubicOpTest(test.TestCase): diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index de12c5f63f4357e0982dd2e16999caf2de0b30f8..ae52d32fea1c872e588c4122f5e73198e4dfe9ad 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -26,6 +26,7 @@ See the @{$python/image} guide. @@extract_jpeg_shape @@decode_png @@encode_png +@@is_jpeg @@decode_image @@resize_images @@resize_area diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 22636fdbb3f5c512f25ad0c1d7eb4e18056da211..8c472642dcc4940c1fbe579a8e34c7e06ce96402 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -167,6 +167,28 @@ def _Assert3DImage(image): _Check3DImage(image, require_static=False), image) +def _AssertAtLeast3DImage(image): + """Assert that we are working with a properly shaped image. + + Performs the check statically if possible (i.e. if the shape + is statically known). Otherwise adds a control dependency + to an assert op that checks the dynamic shape. + + Args: + image: >= 3-D Tensor of size [*, height, width, depth] + + Raises: + ValueError: if image.shape is not a [>= 3] vector. + + Returns: + If the shape of `image` could be verified statically, `image` is + returned unchanged, otherwise there will be a control dependency + added that asserts the correct dynamic shape. + """ + return control_flow_ops.with_dependencies( + _CheckAtLeast3DImage(image, require_static=False), image) + + def _CheckAtLeast3DImage(image, require_static=True): """Assert that we are working with properly shaped image. @@ -292,108 +314,185 @@ def random_flip_left_right(image, seed=None): def flip_left_right(image): """Flip an image horizontally (left to right). - Outputs the contents of `image` flipped along the second dimension, which is - `width`. + Outputs the contents of `image` flipped along the width dimension. See also `reverse()`. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ - with ops.name_scope(None, 'flip_left_right', [image]) as scope: + with ops.name_scope(None, 'flip_left_right', [image]): image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) - return fix_image_flip_shape(image, array_ops.reverse( - image, [1], name=scope)) + image = _AssertAtLeast3DImage(image) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + return fix_image_flip_shape(image, array_ops.reverse(image, [1])) + elif shape.ndims == 4: + return array_ops.reverse(image, [2]) + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') @tf_export('image.flip_up_down') def flip_up_down(image): """Flip an image vertically (upside down). - Outputs the contents of `image` flipped along the first dimension, which is - `height`. + Outputs the contents of `image` flipped along the height dimension. See also `reverse()`. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ - with ops.name_scope(None, 'flip_up_down', [image]) as scope: + with ops.name_scope(None, 'flip_up_down', [image]): image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) - return fix_image_flip_shape(image, array_ops.reverse( - image, [0], name=scope)) + image = _AssertAtLeast3DImage(image) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + return fix_image_flip_shape(image, array_ops.reverse(image, [0])) + elif shape.ndims == 4: + return array_ops.reverse(image, [1]) + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') @tf_export('image.rot90') def rot90(image, k=1, name=None): - """Rotate an image counter-clockwise by 90 degrees. + """Rotate image(s) counter-clockwise by 90 degrees. Args: - image: A 3-D tensor of shape `[height, width, channels]`. + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. k: A scalar integer. The number of times the image is rotated by 90 degrees. name: A name for this operation (optional). Returns: - A rotated 3-D tensor of the same type and shape as `image`. + A rotated tensor of the same type and shape as `image`. + + Raises: + ValueError: if the shape of `image` not supported. """ with ops.name_scope(name, 'rot90', [image, k]) as scope: image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) + image = _AssertAtLeast3DImage(image) k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k') k.get_shape().assert_has_rank(0) k = math_ops.mod(k, 4) - def _rot90(): - return array_ops.transpose(array_ops.reverse_v2(image, [1]), [1, 0, 2]) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + return _rot90_3D(image, k, scope) + elif shape.ndims == 4: + return _rot90_4D(image, k, scope) + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') + - def _rot180(): - return array_ops.reverse_v2(image, [0, 1]) +def _rot90_3D(image, k, name_scope): + """Rotate image counter-clockwise by 90 degrees `k` times. - def _rot270(): - return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]), [1]) + Args: + image: 3-D Tensor of shape `[height, width, channels]`. + k: A scalar integer. The number of times the image is rotated by 90 degrees. + name_scope: A valid TensorFlow name scope. + + Returns: + A 3-D tensor of the same type and shape as `image`. + + """ + + def _rot90(): + return array_ops.transpose(array_ops.reverse_v2(image, [1]), [1, 0, 2]) + + def _rot180(): + return array_ops.reverse_v2(image, [0, 1]) + + def _rot270(): + return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]), [1]) + + cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180), + (math_ops.equal(k, 3), _rot270)] + + result = control_flow_ops.case( + cases, default=lambda: image, exclusive=True, name=name_scope) + result.set_shape([None, None, image.get_shape()[2]]) + return result + + +def _rot90_4D(images, k, name_scope): + """Rotate batch of images counter-clockwise by 90 degrees `k` times. + + Args: + images: 4-D Tensor of shape `[height, width, channels]`. + k: A scalar integer. The number of times the images are rotated by 90 + degrees. + name_scope: A valid TensorFlow name scope. + + Returns: + A 4-D tensor of the same type and shape as `images`. + + """ - cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180), - (math_ops.equal(k, 3), _rot270)] + def _rot90(): + return array_ops.transpose(array_ops.reverse_v2(images, [2]), [0, 2, 1, 3]) - ret = control_flow_ops.case( - cases, default=lambda: image, exclusive=True, name=scope) - ret.set_shape([None, None, image.get_shape()[2]]) - return ret + def _rot180(): + return array_ops.reverse_v2(images, [1, 2]) + def _rot270(): + return array_ops.reverse_v2(array_ops.transpose(images, [0, 2, 1, 3]), [2]) + cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180), + (math_ops.equal(k, 3), _rot270)] + + result = control_flow_ops.case( + cases, default=lambda: images, exclusive=True, name=name_scope) + shape = result.get_shape() + result.set_shape([shape[0], None, None, shape[3]]) + return result @tf_export('image.transpose_image') def transpose_image(image): - """Transpose an image by swapping the first and second dimension. + """Transpose image(s) by swapping the height and width dimension. See also `transpose()`. Args: - image: 3-D tensor of shape `[height, width, channels]` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. Returns: - A 3-D tensor of shape `[width, height, channels]` + If `image` was 4-D, a 4-D float Tensor of shape + `[batch, width, height, channels]` + If `image` was 3-D, a 3-D float Tensor of shape + `[width, height, channels]` Raises: ValueError: if the shape of `image` not supported. """ - with ops.name_scope(None, 'transpose_image', [image]) as scope: + with ops.name_scope(None, 'transpose_image', [image]): image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) - return array_ops.transpose(image, [1, 0, 2], name=scope) + image = _AssertAtLeast3DImage(image) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + return array_ops.transpose(image, [1, 0, 2], name='transpose_image') + elif shape.ndims == 4: + return array_ops.transpose(image, [0, 2, 1, 3], name='transpose_image') + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') @tf_export('image.central_crop') @@ -1014,10 +1113,8 @@ def adjust_contrast(images, contrast_factor): orig_dtype = images.dtype flt_images = convert_image_dtype(images, dtypes.float32) - # pylint: disable=protected-access - adjusted = gen_image_ops._adjust_contrastv2( + adjusted = gen_image_ops.adjust_contrastv2( flt_images, contrast_factor=contrast_factor, name=name) - # pylint: enable=protected-access return convert_image_dtype(adjusted, orig_dtype, saturate=True) @@ -1026,9 +1123,9 @@ def adjust_contrast(images, contrast_factor): def adjust_gamma(image, gamma=1, gain=1): """Performs Gamma Correction on the input image. - Also known as Power Law Transform. This function transforms the - input image pixelwise according to the equation Out = In**gamma - after scaling each pixel to the range 0 to 1. + Also known as Power Law Transform. This function transforms the + input image pixelwise according to the equation `Out = In**gamma` + after scaling each pixel to the range 0 to 1. Args: image : A Tensor. @@ -1339,6 +1436,26 @@ def adjust_saturation(image, saturation_factor, name=None): orig_dtype) +@tf_export('image.is_jpeg') +def is_jpeg(contents, name=None): + r"""Convenience function to check if the 'contents' encodes a JPEG image. + + Args: + contents: 0-D `string`. The encoded image bytes. + name: A name for the operation (optional) + + Returns: + A scalar boolean tensor indicating if 'contents' may be a JPEG image. + is_jpeg is susceptible to false positives. + """ + # Normal JPEGs start with \xff\xd8\xff\xe0 + # JPEG with EXIF stats with \xff\xd8\xff\xe1 + # Use \xff\xd8\xff to cover both. + with ops.name_scope(name, 'is_jpeg'): + substr = string_ops.substr(contents, 0, 3) + return math_ops.equal(substr, b'\xff\xd8\xff', name=name) + + @tf_export('image.decode_image') def decode_image(contents, channels=None, name=None): """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`, @@ -1427,8 +1544,8 @@ def decode_image(contents, channels=None, name=None): # Decode normal JPEG images (start with \xff\xd8\xff\xe0) # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1). - is_jpeg = math_ops.equal(substr, b'\xff\xd8\xff', name='is_jpeg') - return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg') + return control_flow_ops.cond( + is_jpeg(contents), _jpeg, check_png, name='cond_jpeg') @tf_export('image.total_variation') @@ -1611,7 +1728,7 @@ def sample_distorted_bounding_box(image_size, Provide as input to `tf.image.draw_bounding_boxes`. """ with ops.name_scope(name, 'sample_distorted_bounding_box'): - return gen_image_ops._sample_distorted_bounding_box_v2( # pylint: disable=protected-access + return gen_image_ops.sample_distorted_bounding_box_v2( image_size, bounding_boxes, seed=seed, @@ -1665,14 +1782,12 @@ def non_max_suppression(boxes, """ with ops.name_scope(name, 'non_max_suppression'): iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold') - # pylint: disable=protected-access - return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size, - iou_threshold) - # pylint: enable=protected-access + return gen_image_ops.non_max_suppression_v2(boxes, scores, max_output_size, + iou_threshold) -_rgb_to_yiq_kernel = [[0.299, 0.59590059, 0.2115], - [0.587, -0.27455667, -0.52273617], +_rgb_to_yiq_kernel = [[0.299, 0.59590059, + 0.2115], [0.587, -0.27455667, -0.52273617], [0.114, -0.32134392, 0.31119955]] @@ -1694,11 +1809,10 @@ def rgb_to_yiq(images): kernel = ops.convert_to_tensor( _rgb_to_yiq_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims - return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) -_yiq_to_rgb_kernel = [[1, 1, 1], - [0.95598634, -0.27201283, -1.10674021], +_yiq_to_rgb_kernel = [[1, 1, 1], [0.95598634, -0.27201283, -1.10674021], [0.6208248, -0.64720424, 1.70423049]] @@ -1721,11 +1835,11 @@ def yiq_to_rgb(images): kernel = ops.convert_to_tensor( _yiq_to_rgb_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims - return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) -_rgb_to_yuv_kernel = [[0.299, -0.14714119, 0.61497538], - [0.587, -0.28886916, -0.51496512], +_rgb_to_yuv_kernel = [[0.299, -0.14714119, + 0.61497538], [0.587, -0.28886916, -0.51496512], [0.114, 0.43601035, -0.10001026]] @@ -1747,11 +1861,10 @@ def rgb_to_yuv(images): kernel = ops.convert_to_tensor( _rgb_to_yuv_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims - return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) -_yuv_to_rgb_kernel = [[1, 1, 1], - [0, -0.394642334, 2.03206185], +_yuv_to_rgb_kernel = [[1, 1, 1], [0, -0.394642334, 2.03206185], [1.13988303, -0.58062185, 0]] @@ -1774,5 +1887,4 @@ def yuv_to_rgb(images): kernel = ops.convert_to_tensor( _yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims - return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) - + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 82b77ee8e3792596ec4c50ac24da1a1c38cc634b..b8c4b27c162acdd86d88da641ff8afffaa5a9e6a 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -934,7 +934,7 @@ class AdjustSaturationTest(test_util.TensorFlowTestCase): class FlipTransposeRotateTest(test_util.TensorFlowTestCase): - def testIdempotentLeftRight(self): + def testInvolutionLeftRight(self): x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) @@ -942,6 +942,16 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, x_np) + def testInvolutionLeftRightWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.flip_left_right(image_ops.flip_left_right(x_tf)) + y_tf = y.eval() + self.assertAllEqual(y_tf, x_np) + def testLeftRight(self): x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[3, 2, 1], [3, 2, 1]], dtype=np.uint8).reshape([2, 3, 1]) @@ -953,9 +963,24 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, y_np) + def testLeftRightWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + y_np = np.array( + [[[3, 2, 1], [3, 2, 1]], [[3, 2, 1], [3, 2, 1]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.flip_left_right(x_tf) + y_tf = y.eval() + self.assertAllEqual(y_tf, y_np) + def testRandomFlipLeftRight(self): x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[3, 2, 1], [3, 2, 1]], dtype=np.uint8).reshape([2, 3, 1]) + seed = 42 with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) @@ -964,7 +989,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): count_flipped = 0 count_unflipped = 0 - for _ in range(50): + for _ in range(100): y_tf = y.eval() if y_tf[0][0] == 1: self.assertAllEqual(y_tf, x_np) @@ -972,10 +997,15 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): else: self.assertAllEqual(y_tf, y_np) count_flipped += 1 - self.assertGreaterEqual(count_flipped, 1) - self.assertGreaterEqual(count_unflipped, 1) - def testIdempotentUpDown(self): + # 100 trials + # Mean: 50 + # Std Dev: ~5 + # Six Sigma: 50 - (5 * 6) = 20 + self.assertGreaterEqual(count_flipped, 20) + self.assertGreaterEqual(count_unflipped, 20) + + def testInvolutionUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) with self.test_session(use_gpu=True): @@ -984,6 +1014,17 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, x_np) + def testInvolutionUpDownWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.flip_up_down(image_ops.flip_up_down(x_tf)) + y_tf = y.eval() + self.assertAllEqual(y_tf, x_np) + def testUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) @@ -995,17 +1036,31 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, y_np) + def testUpDownWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + y_np = np.array( + [[[4, 5, 6], [1, 2, 3]], [[10, 11, 12], [7, 8, 9]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.flip_up_down(x_tf) + y_tf = y.eval() + self.assertAllEqual(y_tf, y_np) + def testRandomFlipUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf) + y = image_ops.random_flip_up_down(x_tf, seed=42) self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 - for _ in range(50): + for _ in range(100): y_tf = y.eval() if y_tf[0][0] == 1: self.assertAllEqual(y_tf, x_np) @@ -1013,10 +1068,15 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): else: self.assertAllEqual(y_tf, y_np) count_flipped += 1 - self.assertGreaterEqual(count_flipped, 1) - self.assertGreaterEqual(count_unflipped, 1) - def testIdempotentTranspose(self): + # 100 trials + # Mean: 50 + # Std Dev: ~5 + # Six Sigma: 50 - (5 * 6) = 20 + self.assertGreaterEqual(count_flipped, 20) + self.assertGreaterEqual(count_unflipped, 20) + + def testInvolutionTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) with self.test_session(use_gpu=True): @@ -1025,6 +1085,17 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, x_np) + def testInvolutionTransposeWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.transpose_image(image_ops.transpose_image(x_tf)) + y_tf = y.eval() + self.assertAllEqual(y_tf, x_np) + def testTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[1, 4], [2, 5], [3, 6]], dtype=np.uint8).reshape([3, 2, 1]) @@ -1036,15 +1107,34 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_tf = y.eval() self.assertAllEqual(y_tf, y_np) + def testTransposeWithBatch(self): + x_np = np.array( + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], + dtype=np.uint8).reshape([2, 2, 3, 1]) + + y_np = np.array( + [[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]], + dtype=np.uint8).reshape([2, 3, 2, 1]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.transpose_image(x_tf) + y_tf = y.eval() + self.assertAllEqual(y_tf, y_np) + def testPartialShapes(self): p_unknown_rank = array_ops.placeholder(dtypes.uint8) - p_unknown_dims = array_ops.placeholder( + p_unknown_dims_3 = array_ops.placeholder( dtypes.uint8, shape=[None, None, None]) + p_unknown_dims_4 = array_ops.placeholder( + dtypes.uint8, shape=[None, None, None, None]) p_unknown_width = array_ops.placeholder(dtypes.uint8, shape=[64, None, 3]) - + p_unknown_batch = array_ops.placeholder( + dtypes.uint8, shape=[None, 64, 64, 3]) p_wrong_rank = array_ops.placeholder(dtypes.uint8, shape=[None, None]) p_zero_dim = array_ops.placeholder(dtypes.uint8, shape=[64, 0, 3]) + #Ops that support 3D input for op in [ image_ops.flip_left_right, image_ops.flip_up_down, image_ops.random_flip_left_right, image_ops.random_flip_up_down, @@ -1052,16 +1142,35 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): ]: transformed_unknown_rank = op(p_unknown_rank) self.assertEqual(3, transformed_unknown_rank.get_shape().ndims) - transformed_unknown_dims = op(p_unknown_dims) - self.assertEqual(3, transformed_unknown_dims.get_shape().ndims) + transformed_unknown_dims_3 = op(p_unknown_dims_3) + self.assertEqual(3, transformed_unknown_dims_3.get_shape().ndims) transformed_unknown_width = op(p_unknown_width) self.assertEqual(3, transformed_unknown_width.get_shape().ndims) - with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): - op(p_wrong_rank) with self.assertRaisesRegexp(ValueError, "must be > 0"): op(p_zero_dim) + #Ops that support 4D input + for op in [ + image_ops.flip_left_right, image_ops.flip_up_down, + image_ops.transpose_image, image_ops.rot90 + ]: + transformed_unknown_dims_4 = op(p_unknown_dims_4) + self.assertEqual(4, transformed_unknown_dims_4.get_shape().ndims) + transformed_unknown_batch = op(p_unknown_batch) + self.assertEqual(4, transformed_unknown_batch.get_shape().ndims) + with self.assertRaisesRegexp(ValueError, + "must be at least three-dimensional"): + op(p_wrong_rank) + + for op in [ + image_ops.random_flip_left_right, + image_ops.random_flip_up_down, + ]: + with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): + op(p_wrong_rank) + + def testRot90GroupOrder(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1070,6 +1179,14 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): rotated = image_ops.rot90(rotated) self.assertAllEqual(image, rotated.eval()) + def testRot90GroupOrderWithBatch(self): + image = np.arange(48, dtype=np.uint8).reshape([2, 2, 4, 3]) + with self.test_session(use_gpu=True): + rotated = image + for _ in xrange(4): + rotated = image_ops.rot90(rotated) + self.assertAllEqual(image, rotated.eval()) + def testRot90NumpyEquivalence(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1079,6 +1196,14 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_np = np.rot90(image, k=k) self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) + def testRot90NumpyEquivalenceWithBatch(self): + image = np.arange(48, dtype=np.uint8).reshape([2, 2, 4, 3]) + with self.test_session(use_gpu=True): + k_placeholder = array_ops.placeholder(dtypes.int32, shape=[]) + y_tf = image_ops.rot90(image, k_placeholder) + for k in xrange(4): + y_np = np.rot90(image, k=k, axes=(1, 2)) + self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) class RandomFlipTest(test_util.TensorFlowTestCase): @@ -1869,8 +1994,8 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): image_size = constant_op.constant( [40, 50, 1], shape=[3], dtype=dtypes.int32) bounding_box = constant_op.constant( - [0.0, 0.0, 1.0, 1.0], - shape=[4], + [[[0.0, 0.0, 1.0, 1.0]]], + shape=[1, 1, 4], dtype=dtypes.float32, ) begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box( @@ -1884,6 +2009,10 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): self.assertAllEqual([3], begin.get_shape().as_list()) self.assertAllEqual([3], end.get_shape().as_list()) self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list()) + # Actual run to make sure shape is correct inside Compute(). + begin = begin.eval() + end = end.eval() + bbox_for_drawing = bbox_for_drawing.eval() begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box( image_size=image_size, @@ -1903,9 +2032,10 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): image_size = constant_op.constant( [40, 50, 1], shape=[3], dtype=dtypes.int32) bounding_box = constant_op.constant( - [0.0, 0.0, 1.0, 1.0], - shape=[4], - dtype=dtypes.float32,) + [[[0.0, 0.0, 1.0, 1.0]]], + shape=[1, 1, 4], + dtype=dtypes.float32, + ) begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box( image_size=image_size, bounding_boxes=bounding_box, @@ -1915,6 +2045,10 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): self.assertAllEqual([3], begin.get_shape().as_list()) self.assertAllEqual([3], end.get_shape().as_list()) self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list()) + # Actual run to make sure shape is correct inside Compute(). + begin = begin.eval() + end = end.eval() + bbox_for_drawing = bbox_for_drawing.eval() class ResizeImagesTest(test_util.TensorFlowTestCase): @@ -2821,20 +2955,9 @@ class PngTest(test_util.TensorFlowTestCase): class GifTest(test_util.TensorFlowTestCase): - def testOptimizedGifErrorString(self): - filename = "tensorflow/core/lib/gif/testdata/optimized.gif" - - with self.test_session(use_gpu=True) as sess: - gif = io_ops.read_file(filename) - image = image_ops.decode_gif(gif) - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "can't process optimized gif"): - gif, image = sess.run([gif, image]) - - def testValid(self): + def _testValid(self, filename): # Read some real GIFs prefix = "tensorflow/core/lib/gif/testdata/" - filename = "scan.gif" WIDTH = 20 HEIGHT = 40 STRIDE = 5 @@ -2861,16 +2984,9 @@ class GifTest(test_util.TensorFlowTestCase): self.assertAllClose(frame, gt) - def testInValid(self): - # Read some real GIFs - prefix = "tensorflow/core/lib/gif/testdata/" - filename = "optimized.gif" - - with self.test_session(use_gpu=True) as sess: - gif0 = io_ops.read_file(prefix + filename) - image0 = image_ops.decode_gif(gif0) - with self.assertRaises(errors.InvalidArgumentError): - gif0, image0 = sess.run([gif0, image0]) + def testValid(self): + self._testValid("scan.gif") + self._testValid("optimized.gif") def testShape(self): with self.test_session(use_gpu=True) as sess: @@ -3171,43 +3287,45 @@ class NonMaxSuppressionTest(test_util.TensorFlowTestCase): def testInvalidShape(self): # The boxes should be 2D of shape [num_boxes, 4]. - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 2 but is rank 1'): + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 2 but is rank 1"): boxes = constant_op.constant([0.0, 0.0, 1.0, 1.0]) scores = constant_op.constant([0.9]) - selected_indices = image_ops.non_max_suppression( - boxes, scores, 3, 0.5) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) - with self.assertRaisesRegexp( - ValueError, 'Dimension must be 4 but is 3'): + with self.assertRaisesRegexp(ValueError, "Dimension must be 4 but is 3"): boxes = constant_op.constant([[0.0, 0.0, 1.0]]) scores = constant_op.constant([0.9]) - selected_indices = image_ops.non_max_suppression( - boxes, scores, 3, 0.5) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) + + # The boxes is of shape [num_boxes, 4], and the scores is + # of shape [num_boxes]. So an error will thrown. + with self.assertRaisesRegexp(ValueError, + "Dimensions must be equal, but are 1 and 2"): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([0.9, 0.75]) + selected_indices = image_ops.non_max_suppression(boxes, scores, 3, 0.5) # The scores should be 1D of shape [num_boxes]. - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 1 but is rank 2'): + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 1 but is rank 2"): boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) scores = constant_op.constant([[0.9]]) - selected_indices = image_ops.non_max_suppression( - boxes, scores, 3, 0.5) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) # The max_output_size should be a scaler (0-D). - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 1'): + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 0 but is rank 1"): boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) scores = constant_op.constant([0.9]) - selected_indices = image_ops.non_max_suppression( - boxes, scores, [3], 0.5) + image_ops.non_max_suppression(boxes, scores, [3], 0.5) # The iou_threshold should be a scaler (0-D). - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 2'): + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 0 but is rank 2"): boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) scores = constant_op.constant([0.9]) - selected_indices = image_ops.non_max_suppression( - boxes, scores, 3, [[0.5]]) + image_ops.non_max_suppression(boxes, scores, 3, [[0.5]]) if __name__ == "__main__": diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index c7502d0fda5c38079362d30877a917e3965e6ca0..40ab22951b1aa04a61e09aac155b6449ae358d7b 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -542,6 +542,62 @@ class Orthogonal(Initializer): return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name} +class ConvolutionDeltaOrthogonal(Initializer): + """Initializer that generates a delta orthogonal kernel for ConvNets. + + The shape of the tensor must have length 3, 4 or 5. The number of input + filters must not exceed the number of output filters. The center pixels of the + tensor form an orthogonal matrix. Other pixels are set to be zero. + + Args: + gain: multiplicative factor to apply to the orthogonal matrix. Default is 1. + The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after + applying this convolution. + dtype: The type of the output. + seed: A Python integer. Used to create random seeds. See + @{tf.set_random_seed} + for behavior. + """ + + def __init__(self, gain=1.0, seed=None, dtype=dtypes.float32): + self.gain = gain + self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype)) + self.seed = seed + + def __call__(self, shape, dtype=None, partition_info=None): + if dtype is None: + dtype = self.dtype + # Check the shape + if len(shape) < 3 or len(shape) > 5: + raise ValueError("The tensor to initialize must be at least " + "three-dimensional and at most five-dimensional") + + if shape[-2] > shape[-1]: + raise ValueError("In_filters cannot be greater than out_filters.") + + # Generate a random matrix + a = random_ops.random_normal([shape[-1], shape[-1]], + dtype=dtype, seed=self.seed) + # Compute the qr factorization + q, _ = linalg_ops.qr(a, full_matrices=False) + q = q[:shape[-2], :] + q *= math_ops.sqrt(math_ops.cast(self.gain, dtype=dtype)) + if len(shape) == 3: + weight = array_ops.scatter_nd([[(shape[0]-1)//2]], + array_ops.expand_dims(q, 0), shape) + elif len(shape) == 4: + weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2]], + array_ops.expand_dims(q, 0), shape) + else: + weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2, + (shape[2]-1)//2]], + array_ops.expand_dims(q, 0), shape) + return weight + + def get_config(self): + return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name} + + @tf_export("keras.initializers.Identity", "initializers.identity") class Identity(Initializer): """Initializer that generates the identity matrix. @@ -586,7 +642,7 @@ uniform_unit_scaling_initializer = UniformUnitScaling variance_scaling_initializer = VarianceScaling orthogonal_initializer = Orthogonal identity_initializer = Identity - +convolutional_delta_orthogonal = ConvolutionDeltaOrthogonal # pylint: enable=invalid-name diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py index 5e70b3186f382a0c795b1795b2db27bb2058ee41..f6a25610c5a2ee8b76d06e286365cb957ab643cd 100644 --- a/tensorflow/python/ops/io_ops.py +++ b/tensorflow/python/ops/io_ops.py @@ -111,10 +111,10 @@ def _save(filename, tensor_names, tensors, tensor_slices=None, name="save"): An Operation that saves the tensors. """ if tensor_slices is None: - return gen_io_ops._save(filename, tensor_names, tensors, name=name) + return gen_io_ops.save(filename, tensor_names, tensors, name=name) else: - return gen_io_ops._save_slices(filename, tensor_names, tensor_slices, - tensors, name=name) + return gen_io_ops.save_slices(filename, tensor_names, tensor_slices, + tensors, name=name) def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, @@ -136,7 +136,7 @@ def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, A tensor of type "tensor_type". """ base_type = dtypes.as_dtype(tensor_type).base_dtype - return gen_io_ops._restore_slice( + return gen_io_ops.restore_slice( file_pattern, tensor_name, shape_and_slice, base_type, preferred_shard, name=name) @@ -173,7 +173,7 @@ class ReaderBase(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Readers are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -208,12 +208,12 @@ class ReaderBase(object): else: queue_ref = queue.queue_ref if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_read_v2(self._reader_ref, queue_ref, name=name) + return gen_io_ops.reader_read_v2(self._reader_ref, queue_ref, name=name) else: # For compatibility with pre-resource queues, create a ref(string) tensor # which can be looked up as the same queue by a resource manager. - old_queue_op = gen_data_flow_ops._fake_queue(queue_ref) - return gen_io_ops._reader_read(self._reader_ref, old_queue_op, name=name) + old_queue_op = gen_data_flow_ops.fake_queue(queue_ref) + return gen_io_ops.reader_read(self._reader_ref, old_queue_op, name=name) def read_up_to(self, queue, num_records, # pylint: disable=invalid-name name=None): @@ -240,18 +240,18 @@ class ReaderBase(object): else: queue_ref = queue.queue_ref if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_read_up_to_v2(self._reader_ref, - queue_ref, - num_records, - name=name) + return gen_io_ops.reader_read_up_to_v2(self._reader_ref, + queue_ref, + num_records, + name=name) else: # For compatibility with pre-resource queues, create a ref(string) tensor # which can be looked up as the same queue by a resource manager. - old_queue_op = gen_data_flow_ops._fake_queue(queue_ref) - return gen_io_ops._reader_read_up_to(self._reader_ref, - old_queue_op, - num_records, - name=name) + old_queue_op = gen_data_flow_ops.fake_queue(queue_ref) + return gen_io_ops.reader_read_up_to(self._reader_ref, + old_queue_op, + num_records, + name=name) def num_records_produced(self, name=None): """Returns the number of records this reader has produced. @@ -267,11 +267,11 @@ class ReaderBase(object): """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_num_records_produced_v2(self._reader_ref, - name=name) + return gen_io_ops.reader_num_records_produced_v2(self._reader_ref, + name=name) else: - return gen_io_ops._reader_num_records_produced(self._reader_ref, - name=name) + return gen_io_ops.reader_num_records_produced(self._reader_ref, + name=name) def num_work_units_completed(self, name=None): """Returns the number of work units this reader has finished processing. @@ -283,11 +283,11 @@ class ReaderBase(object): An int64 Tensor. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_num_work_units_completed_v2(self._reader_ref, - name=name) + return gen_io_ops.reader_num_work_units_completed_v2(self._reader_ref, + name=name) else: - return gen_io_ops._reader_num_work_units_completed(self._reader_ref, - name=name) + return gen_io_ops.reader_num_work_units_completed(self._reader_ref, + name=name) def serialize_state(self, name=None): """Produce a string tensor that encodes the state of a reader. @@ -302,9 +302,9 @@ class ReaderBase(object): A string Tensor. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_serialize_state_v2(self._reader_ref, name=name) + return gen_io_ops.reader_serialize_state_v2(self._reader_ref, name=name) else: - return gen_io_ops._reader_serialize_state(self._reader_ref, name=name) + return gen_io_ops.reader_serialize_state(self._reader_ref, name=name) def restore_state(self, state, name=None): """Restore a reader to a previously saved state. @@ -321,11 +321,10 @@ class ReaderBase(object): The created Operation. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_restore_state_v2( + return gen_io_ops.reader_restore_state_v2( self._reader_ref, state, name=name) else: - return gen_io_ops._reader_restore_state( - self._reader_ref, state, name=name) + return gen_io_ops.reader_restore_state(self._reader_ref, state, name=name) @property def supports_serialize(self): @@ -342,9 +341,9 @@ class ReaderBase(object): The created Operation. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_reset_v2(self._reader_ref, name=name) + return gen_io_ops.reader_reset_v2(self._reader_ref, name=name) else: - return gen_io_ops._reader_reset(self._reader_ref, name=name) + return gen_io_ops.reader_reset(self._reader_ref, name=name) ops.NotDifferentiable("ReaderRead") @@ -377,7 +376,7 @@ class WholeFileReader(ReaderBase): Args: name: A name for the operation (optional). """ - rr = gen_io_ops._whole_file_reader_v2(name=name) + rr = gen_io_ops.whole_file_reader_v2(name=name) super(WholeFileReader, self).__init__(rr, supports_serialize=True) @@ -406,8 +405,8 @@ class TextLineReader(ReaderBase): to skip from the beginning of every file. name: A name for the operation (optional). """ - rr = gen_io_ops._text_line_reader_v2(skip_header_lines=skip_header_lines, - name=name) + rr = gen_io_ops.text_line_reader_v2(skip_header_lines=skip_header_lines, + name=name) super(TextLineReader, self).__init__(rr) @@ -444,7 +443,7 @@ class FixedLengthRecordReader(ReaderBase): name: A name for the operation (optional). encoding: The type of encoding for the file. Defaults to none. """ - rr = gen_io_ops._fixed_length_record_reader_v2( + rr = gen_io_ops.fixed_length_record_reader_v2( record_bytes=record_bytes, header_bytes=header_bytes, footer_bytes=footer_bytes, @@ -480,7 +479,7 @@ class TFRecordReader(ReaderBase): compression_type = python_io.TFRecordOptions.get_compression_type_string( options) - rr = gen_io_ops._tf_record_reader_v2( + rr = gen_io_ops.tf_record_reader_v2( name=name, compression_type=compression_type) super(TFRecordReader, self).__init__(rr) @@ -506,7 +505,7 @@ class LMDBReader(ReaderBase): name: A name for the operation (optional). options: A LMDBRecordOptions object (optional). """ - rr = gen_io_ops._lmdb_reader(name=name) + rr = gen_io_ops.lmdb_reader(name=name) super(LMDBReader, self).__init__(rr) @@ -534,7 +533,7 @@ class IdentityReader(ReaderBase): Args: name: A name for the operation (optional). """ - rr = gen_io_ops._identity_reader_v2(name=name) + rr = gen_io_ops.identity_reader_v2(name=name) super(IdentityReader, self).__init__(rr, supports_serialize=True) diff --git a/tensorflow/python/ops/linalg/linalg.py b/tensorflow/python/ops/linalg/linalg.py index 5369007a56c89ef8601f8144c2fe18717e2e78fe..14319025ff275944cf34e30128df96254d06072b 100644 --- a/tensorflow/python/ops/linalg/linalg.py +++ b/tensorflow/python/ops/linalg/linalg.py @@ -41,4 +41,5 @@ del gen_linalg_ops del linalg_ops del math_ops del special_math_ops +del tf_export # pylint: enable=undefined-variable diff --git a/tensorflow/python/ops/linalg/linalg_impl.py b/tensorflow/python/ops/linalg/linalg_impl.py index a5096ffdd9ca1a50c26857ee40f02918fa90eda8..2be2d5a3d4bbb15c73038dea10e1d7f2cbb17bde 100644 --- a/tensorflow/python/ops/linalg/linalg_impl.py +++ b/tensorflow/python/ops/linalg/linalg_impl.py @@ -24,24 +24,23 @@ from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import special_math_ops +from tensorflow.python.util.tf_export import tf_export # Linear algebra ops. band_part = array_ops.matrix_band_part cholesky = linalg_ops.cholesky cholesky_solve = linalg_ops.cholesky_solve det = linalg_ops.matrix_determinant -# pylint: disable=protected-access -slogdet = gen_linalg_ops._log_matrix_determinant -# pylint: disable=protected-access +slogdet = gen_linalg_ops.log_matrix_determinant diag = array_ops.matrix_diag diag_part = array_ops.matrix_diag_part eigh = linalg_ops.self_adjoint_eig eigvalsh = linalg_ops.self_adjoint_eigvals einsum = special_math_ops.einsum -expm = gen_linalg_ops._matrix_exponential +expm = gen_linalg_ops.matrix_exponential eye = linalg_ops.eye inv = linalg_ops.matrix_inverse -logm = gen_linalg_ops._matrix_logarithm +logm = gen_linalg_ops.matrix_logarithm lstsq = linalg_ops.matrix_solve_ls norm = linalg_ops.norm qr = linalg_ops.qr @@ -54,6 +53,7 @@ transpose = array_ops.matrix_transpose triangular_solve = linalg_ops.matrix_triangular_solve +@tf_export('linalg.logdet') def logdet(matrix, name=None): """Computes log of the determinant of a hermitian positive definite matrix. @@ -86,6 +86,7 @@ def logdet(matrix, name=None): reduction_indices=[-1]) +@tf_export('linalg.adjoint') def adjoint(matrix, name=None): """Transposes the last two dimensions of and conjugates tensor `matrix`. diff --git a/tensorflow/python/ops/linalg/linear_operator.py b/tensorflow/python/ops/linalg/linear_operator.py index 8339c940af8ae46ae3cf6ff59931c013863e66be..957a7959181efe3bbc319e62582053329b763dc3 100644 --- a/tensorflow/python/ops/linalg/linear_operator.py +++ b/tensorflow/python/ops/linalg/linear_operator.py @@ -32,11 +32,13 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator_util from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export __all__ = ["LinearOperator"] # TODO(langmore) Use matrix_solve_ls for singular or non-square matrices. +@tf_export("linalg.LinearOperator") class LinearOperator(object): """Base class defining a [batch of] linear operator[s]. diff --git a/tensorflow/python/ops/linalg/linear_operator_composition.py b/tensorflow/python/ops/linalg/linear_operator_composition.py index 14411291d4fddeb2242e243d9a611e9c2fcd171a..ecd30e4d7e4dd7cfd4b109ad6e60aacb172700f6 100644 --- a/tensorflow/python/ops/linalg/linear_operator_composition.py +++ b/tensorflow/python/ops/linalg/linear_operator_composition.py @@ -25,10 +25,12 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.util.tf_export import tf_export __all__ = ["LinearOperatorComposition"] +@tf_export("linalg.LinearOperatorComposition") class LinearOperatorComposition(linear_operator.LinearOperator): """Composes one or more `LinearOperators`. diff --git a/tensorflow/python/ops/linalg/linear_operator_diag.py b/tensorflow/python/ops/linalg/linear_operator_diag.py index 2217bfd54593129712bc1e5a60bf2e1d5f88939b..e180e830263c44fb5ae290d307f1ef80106c31d5 100644 --- a/tensorflow/python/ops/linalg/linear_operator_diag.py +++ b/tensorflow/python/ops/linalg/linear_operator_diag.py @@ -26,10 +26,12 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.util.tf_export import tf_export __all__ = ["LinearOperatorDiag",] +@tf_export("linalg.LinearOperatorDiag") class LinearOperatorDiag(linear_operator.LinearOperator): """`LinearOperator` acting like a [batch] square diagonal matrix. @@ -65,7 +67,7 @@ class LinearOperatorDiag(linear_operator.LinearOperator): operator = LinearOperatorDiag(diag) # Create a shape [2, 1, 4, 2] vector. Note that this shape is compatible - # since the batch dimensions, [2, 1], are brodcast to + # since the batch dimensions, [2, 1], are broadcast to # operator.batch_shape = [2, 3]. y = tf.random_normal(shape=[2, 1, 4, 2]) x = operator.solve(y) diff --git a/tensorflow/python/ops/linalg/linear_operator_full_matrix.py b/tensorflow/python/ops/linalg/linear_operator_full_matrix.py index 8fb59ca1a7e68450b71728c87e8a3c1f314e2ec4..f979fb37d6c69a2683af08a1f6722b98da0b6650 100644 --- a/tensorflow/python/ops/linalg/linear_operator_full_matrix.py +++ b/tensorflow/python/ops/linalg/linear_operator_full_matrix.py @@ -23,10 +23,12 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.util.tf_export import tf_export __all__ = ["LinearOperatorFullMatrix"] +@tf_export("linalg.LinearOperatorFullMatrix") class LinearOperatorFullMatrix(linear_operator.LinearOperator): """`LinearOperator` that wraps a [batch] matrix. diff --git a/tensorflow/python/ops/linalg/linear_operator_identity.py b/tensorflow/python/ops/linalg/linear_operator_identity.py index 740c6c811f2d98f62c200cda7242c6ad00de499d..50f3d407e85e4cca22ad6326931b5a2a736819a8 100644 --- a/tensorflow/python/ops/linalg/linear_operator_identity.py +++ b/tensorflow/python/ops/linalg/linear_operator_identity.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.util.tf_export import tf_export __all__ = [ "LinearOperatorIdentity", @@ -97,6 +98,7 @@ class BaseLinearOperatorIdentity(linear_operator.LinearOperator): return array_ops.ones(shape=d_shape, dtype=self.dtype) +@tf_export("linalg.LinearOperatorIdentity") class LinearOperatorIdentity(BaseLinearOperatorIdentity): """`LinearOperator` acting like a [batch] square identity matrix. @@ -460,6 +462,7 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity): "%s" % self._batch_shape_static) +@tf_export("linalg.LinearOperatorScaledIdentity") class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity): """`LinearOperator` acting like a scaled [batch] identity matrix `A = c I`. diff --git a/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py b/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py index 36eed89db60a16adaf045e9fa332c38844c964d8..be911029095920d424ac90b406e7b85b73884b3b 100644 --- a/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py +++ b/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py @@ -27,12 +27,14 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linear_operator from tensorflow.python.ops.linalg import linear_operator_diag from tensorflow.python.ops.linalg import linear_operator_identity +from tensorflow.python.util.tf_export import tf_export __all__ = [ "LinearOperatorLowRankUpdate", ] +@tf_export("linalg.LinearOperatorLowRankUpdate") class LinearOperatorLowRankUpdate(linear_operator.LinearOperator): """Perturb a `LinearOperator` with a rank `K` update. diff --git a/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py b/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py index 6419030755f59438081c15b8f1e04e3af59061bb..a5130188b681813e1ccd4818dabdffeeb663e20a 100644 --- a/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py +++ b/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py @@ -26,12 +26,14 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.util.tf_export import tf_export __all__ = [ "LinearOperatorLowerTriangular", ] +@tf_export("linalg.LinearOperatorLowerTriangular") class LinearOperatorLowerTriangular(linear_operator.LinearOperator): """`LinearOperator` acting like a [batch] square lower triangular matrix. diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py index 9803eed6aefe072cbe0841dff2de3f640a440dd5..37470e00d7f11b66c7db0785a530721b07c4b859 100644 --- a/tensorflow/python/ops/linalg_ops.py +++ b/tensorflow/python/ops/linalg_ops.py @@ -248,7 +248,7 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None): and l2_regularizer != 0 due to poor accuracy. """ - # pylint: disable=protected-access,long-lambda + # pylint: disable=long-lambda def _use_composite_impl(fast, tensor_shape): """Determines whether to use the composite or specialized CPU kernel. @@ -323,9 +323,8 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None): if _use_composite_impl(fast, tensor_shape): return _composite_impl(matrix, rhs, l2_regularizer) else: - return gen_linalg_ops._matrix_solve_ls( + return gen_linalg_ops.matrix_solve_ls( matrix, rhs, l2_regularizer, fast=fast, name=name) - # pylint: enable=protected-access @tf_export('self_adjoint_eig', 'linalg.eigh') @@ -346,8 +345,7 @@ def self_adjoint_eig(tensor, name=None): v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most matrices contain eigenvectors of the corresponding matrices in `tensor` """ - # pylint: disable=protected-access - e, v = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=True, name=name) + e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name) return e, v @@ -369,8 +367,7 @@ def self_adjoint_eigvals(tensor, name=None): e: Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N` eigenvalues of `tensor[..., :, :]`. """ - # pylint: disable=protected-access - e, _ = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=False, name=name) + e, _ = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=False, name=name) return e @@ -435,10 +432,8 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None): ```` @end_compatibility """ - # pylint: disable=protected-access - s, u, v = gen_linalg_ops._svd( + s, u, v = gen_linalg_ops.svd( tensor, compute_uv=compute_uv, full_matrices=full_matrices, name=name) - # pylint: enable=protected-access if compute_uv: return math_ops.real(s), u, v else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index eadbc1b7c3b6e66aa76c9afd860b2274ac1976ae..a7ea7dc6e100e809caebed5f03027c4d694cfdd0 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -170,7 +170,7 @@ def image_summary(tag, tensor, max_images=3, collections=None, name=None): buffer. """ with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope: - val = gen_logging_ops._image_summary( + val = gen_logging_ops.image_summary( tag=tag, tensor=tensor, max_images=max_images, name=scope) _Collect(val, collections, [ops.GraphKeys.SUMMARIES]) return val @@ -226,11 +226,12 @@ def audio_summary(tag, with ops.name_scope(name, "AudioSummary", [tag, tensor]) as scope: sample_rate = ops.convert_to_tensor(sample_rate, dtype=dtypes.float32, name="sample_rate") - val = gen_logging_ops._audio_summary_v2(tag=tag, - tensor=tensor, - max_outputs=max_outputs, - sample_rate=sample_rate, - name=scope) + val = gen_logging_ops.audio_summary_v2( + tag=tag, + tensor=tensor, + max_outputs=max_outputs, + sample_rate=sample_rate, + name=scope) _Collect(val, collections, [ops.GraphKeys.SUMMARIES]) return val @@ -263,7 +264,7 @@ def merge_summary(inputs, collections=None, name=None): buffer resulting from the merging. """ with ops.name_scope(name, "MergeSummary", inputs): - val = gen_logging_ops._merge_summary(inputs=inputs, name=name) + val = gen_logging_ops.merge_summary(inputs=inputs, name=name) _Collect(val, collections, []) return val @@ -356,3 +357,4 @@ ops.NotDifferentiable("AudioSummary") ops.NotDifferentiable("AudioSummaryV2") ops.NotDifferentiable("MergeSummary") ops.NotDifferentiable("ScalarSummary") +ops.NotDifferentiable("Timestamp") diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py index f539a7bb68da57e31746bc80fb25339a03a4fafe..6f043f60e677eac560004619464905cd616256b2 100644 --- a/tensorflow/python/ops/lookup_ops.py +++ b/tensorflow/python/ops/lookup_ops.py @@ -157,10 +157,10 @@ class InitializableLookupTableBase(LookupInterface): default_value: The value to use if a key is missing in the table. initializer: The table initializer to use. """ - if context.in_graph_mode(): - name = table_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): name = context.context().scope_name + else: + name = table_ref.op.name.split("/")[-1] super(InitializableLookupTableBase, self).__init__(initializer.key_dtype, initializer.value_dtype, name) @@ -196,9 +196,7 @@ class InitializableLookupTableBase(LookupInterface): """ with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as scope: - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=scope) - # pylint: enable=protected-access + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=scope) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -227,10 +225,8 @@ class InitializableLookupTableBase(LookupInterface): with ops.name_scope(name, "%s_Lookup" % self._name, (self._table_ref, key_tensor, self._default_value)) as scope: - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, key_tensor, self._default_value, name=scope) - # pylint: enable=protected-access values.set_shape(key_tensor.get_shape()) if isinstance(keys, sparse_tensor.SparseTensor): @@ -274,13 +270,11 @@ class HashTable(InitializableLookupTableBase): """ with ops.name_scope(name, "hash_table", (initializer, default_value)) as scope: - # pylint: disable=protected-access - table_ref = gen_lookup_ops._hash_table_v2( + table_ref = gen_lookup_ops.hash_table_v2( shared_name=shared_name, key_dtype=initializer.key_dtype, value_dtype=initializer.value_dtype, name=scope) - # pylint: enable=protected-access super(HashTable, self).__init__(table_ref, default_value, initializer) @@ -352,10 +346,8 @@ class KeyValueTensorInitializer(TableInitializerBase): with ops.name_scope( self._name, values=(table.table_ref, self._keys, self._values)) as scope: - # pylint: disable=protected-access - init_op = gen_lookup_ops._initialize_table_v2( + init_op = gen_lookup_ops.initialize_table_v2( table.table_ref, self._keys, self._values, name=scope) - # pylint: enable=protected-access ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op) return init_op @@ -518,8 +510,7 @@ class TextFileInitializer(TableInitializerBase): (table.table_ref,)) as scope: filename = ops.convert_to_tensor( self._filename, dtypes.string, name="asset_filepath") - # pylint: disable=protected-access - init_op = gen_lookup_ops._initialize_table_from_text_file_v2( + init_op = gen_lookup_ops.initialize_table_from_text_file_v2( table.table_ref, filename, self._key_index, @@ -527,11 +518,10 @@ class TextFileInitializer(TableInitializerBase): -1 if self._vocab_size is None else self._vocab_size, self._delimiter, name=scope) - # pylint: enable=protected-access ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op) # If the filename tensor is anything other than a string constant (e.g., if # it is a placeholder) then it does not make sense to track it as an asset. - if context.in_graph_mode() and constant_op.is_constant(filename): + if not context.executing_eagerly() and constant_op.is_constant(filename): ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, filename) return init_op diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 8b3c61b9339734d6a596d92e93f7a69d32dddd12..0840760810c86a6393ea6b4ab0b9410233275f11 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import confusion_matrix @@ -134,6 +135,10 @@ def _num_present(losses, weights, per_batch=False): `per_batch` is `True`, the value is returned as a tensor of size `[batch_size]`. Otherwise, a single scalar tensor is returned. """ + if ((isinstance(weights, float) and weights != 0.0) or + (context.executing_eagerly() and weights._rank() == 0 # pylint: disable=protected-access + and not math_ops.equal(weights, 0.0))): + return _num_elements(losses) with ops.name_scope(None, "num_present", (losses, weights)) as scope: weights = math_ops.to_float(weights) present = array_ops.where( @@ -143,8 +148,10 @@ def _num_present(losses, weights, per_batch=False): present = weights_broadcast_ops.broadcast_weights(present, losses) if per_batch: return math_ops.reduce_sum( - present, axis=math_ops.range(1, array_ops.rank(present)), - keep_dims=True, name=scope) + present, + axis=math_ops.range(1, array_ops.rank(present)), + keepdims=True, + name=scope) return math_ops.reduce_sum(present, name=scope) @@ -311,7 +318,7 @@ def cosine_distance( predictions.get_shape().assert_is_compatible_with(labels.get_shape()) radial_diffs = math_ops.multiply(predictions, labels) - losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keep_dims=True) + losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True) return compute_weighted_loss( losses, weights, scope, loss_collection, reduction=reduction) @@ -377,7 +384,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, `weights` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights` is a tensor of size - [batch_size], then the total loss for each sample of the batch is rescaled + `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `weights` vector. If the shape of `weights` matches the shape of `predictions`, then the loss of each measurable element of `predictions` is scaled by the corresponding value of @@ -421,8 +428,12 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, # expression when abs_error == delta is 0 (for tf.maximum it would be 1). # This is necessary to avoid doubling the gradient, since there is already a # nonzero contribution to the gradient from the quadratic term. - linear = (abs_error - quadratic) - losses = 0.5 * quadratic**2 + delta * linear + linear = math_ops.subtract(abs_error, quadratic) + losses = math_ops.add( + math_ops.multiply( + ops.convert_to_tensor(0.5, dtype=quadratic.dtype), + math_ops.multiply(quadratic, quadratic)), + math_ops.multiply(delta, linear)) return compute_weighted_loss( losses, weights, scope, loss_collection, reduction=reduction) @@ -435,7 +446,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, `weights` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights` is a tensor of size - [batch_size], then the total loss for each sample of the batch is rescaled + `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `weights` vector. If the shape of `weights` matches the shape of `predictions`, then the loss of each measurable element of `predictions` is scaled by the corresponding value of @@ -502,7 +513,7 @@ def mean_pairwise_squared_error( `weights` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights` is a tensor of size - [batch_size], then the total loss for each sample of the batch is rescaled + `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `weights` vector. Args: @@ -542,18 +553,19 @@ def mean_pairwise_squared_error( reduction_indices = math_ops.range(1, array_ops.rank(diffs)) sum_squares_diff_per_batch = math_ops.reduce_sum( - math_ops.square(diffs), reduction_indices=reduction_indices, - keep_dims=True) + math_ops.square(diffs), + reduction_indices=reduction_indices, + keepdims=True) num_present_per_batch = _num_present(diffs, weights, per_batch=True) term1 = 2.0 * _safe_div(sum_squares_diff_per_batch, - num_present_per_batch-1) + num_present_per_batch - 1) sum_diff = math_ops.reduce_sum( - diffs, reduction_indices=reduction_indices, keep_dims=True) + diffs, reduction_indices=reduction_indices, keepdims=True) term2 = 2.0 * _safe_div( math_ops.square(sum_diff), - math_ops.multiply(num_present_per_batch, num_present_per_batch-1)) + math_ops.multiply(num_present_per_batch, num_present_per_batch - 1)) weighted_losses = math_ops.multiply(term1 - term2, weights) loss = math_ops.reduce_sum(weighted_losses) @@ -576,7 +588,7 @@ def mean_squared_error( `weights` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights` is a tensor of size - [batch_size], then the total loss for each sample of the batch is rescaled + `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `weights` vector. If the shape of `weights` matches the shape of `predictions`, then the loss of each measurable element of `predictions` is scaled by the corresponding value of @@ -634,7 +646,7 @@ def sigmoid_cross_entropy( Args: multi_class_labels: `[batch_size, num_classes]` target integer labels in - `(0, 1)`. + `{0, 1}`. logits: Float `[batch_size, num_classes]` logits outputs of the network. weights: Optional `Tensor` whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must @@ -726,9 +738,11 @@ def softmax_cross_entropy( smooth_negatives = label_smoothing / num_classes onehot_labels = onehot_labels * smooth_positives + smooth_negatives - losses = nn.softmax_cross_entropy_with_logits(labels=onehot_labels, - logits=logits, - name="xentropy") + onehot_labels = array_ops.stop_gradient( + onehot_labels, name="labels_stop_gradient") + losses = nn.softmax_cross_entropy_with_logits_v2( + labels=onehot_labels, logits=logits, name="xentropy") + return compute_weighted_loss( losses, weights, scope, loss_collection, reduction=reduction) @@ -793,7 +807,7 @@ def sparse_softmax_cross_entropy( `weights` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights` is a - tensor of shape [`batch_size`], then the loss weights apply to each + tensor of shape `[batch_size]`, then the loss weights apply to each corresponding sample. Args: diff --git a/tensorflow/python/ops/manip_grad.py b/tensorflow/python/ops/manip_grad.py index 573e8c0a0d4e0b6e5e2ca555e34234d9bea760dc..bb2069359dd6fbe4874e228e6f2f58ea8444744d 100644 --- a/tensorflow/python/ops/manip_grad.py +++ b/tensorflow/python/ops/manip_grad.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Gradients for operators defined in manip_ops.py.""" from __future__ import absolute_import diff --git a/tensorflow/python/ops/manip_ops.py b/tensorflow/python/ops/manip_ops.py index c5f39784f436be361ba0d06c87239ae9b409fb9e..91e15b47b9400f29425af2f186c7c44ee6a5a622 100644 --- a/tensorflow/python/ops/manip_ops.py +++ b/tensorflow/python/ops/manip_ops.py @@ -24,10 +24,12 @@ from __future__ import print_function from tensorflow.python.ops import gen_manip_ops as _gen_manip_ops from tensorflow.python.util.all_util import remove_undocumented + # pylint: disable=protected-access -def roll(input, shift, axis): +def roll(input, shift, axis): # pylint: disable=redefined-builtin return _gen_manip_ops.roll(input, shift, axis) + roll.__doc__ = _gen_manip_ops.roll.__doc__ # pylint: enable=protected-access diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 53308484c427e715f649a09f0dbe3f1448f18f5b..d220fe3cce186d731803d3d1c9587d9fe9ea3a28 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -35,6 +35,12 @@ def _safe_shape_div(x, y): return x // math_ops.maximum(y, 1) +@ops.RegisterGradient("ArgMax") +def _ArgMaxGrad(op, grad): + del op, grad + return [None, None] + + @ops.RegisterGradient("Sum") def _SumGrad(op, grad): """Gradient for Sum.""" @@ -46,10 +52,18 @@ def _SumGrad(op, grad): if axes is not None: rank = len(input_0_shape) if np.array_equal(axes, np.arange(rank)): # Reduce all dims. - grad = array_ops.reshape(grad, [1] * rank) + if context.executing_eagerly(): + ctx = context.context() + new_shape = ctx.ones_rank_cache().get(rank) + if new_shape is None: + new_shape = constant_op.constant([1] * rank, dtype=dtypes.int32) + ctx.ones_rank_cache().put(rank, new_shape) + else: + new_shape = [1] * rank + grad = array_ops.reshape(grad, new_shape) # If shape is not fully defined (but rank is), we use Shape. if None not in input_0_shape: - input_shape = input_0_shape + input_shape = constant_op.constant(input_0_shape, dtype=dtypes.int32) else: input_shape = array_ops.shape(op.inputs[0]) return [array_ops.tile(grad, input_shape), None] @@ -228,56 +242,142 @@ def _SparseSegmentSqrtNWithNumSegmentsGrad(op, grad): dim0), None, None, None) -def _SegmentMinOrMaxGrad(op, grad, is_sorted): - """Gradient for SegmentMin and (unsorted) SegmentMax. - - They share similar code. - """ - zeros = array_ops.zeros( - array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype) - +def _SegmentMinOrMaxGrad(op, grad): + """ Gradient for SegmentMin and SegmentMax. """ + zeros = array_ops.zeros_like(op.inputs[0], dtype=op.inputs[0].dtype) # Get the number of selected (minimum or maximum) elements in each segment. gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1]) is_selected = math_ops.equal(op.inputs[0], gathered_outputs) - if is_sorted: - num_selected = math_ops.segment_sum( - math_ops.cast(is_selected, grad.dtype), op.inputs[1]) - else: - num_selected = math_ops.unsorted_segment_sum( - math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2]) - + num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype), + op.inputs[1]) # Compute the gradient for each segment. The gradient for the ith segment is # divided evenly among the selected elements in that segment. weighted_grads = math_ops.div(grad, num_selected) gathered_grads = array_ops.gather(weighted_grads, op.inputs[1]) - - if is_sorted: - return array_ops.where(is_selected, gathered_grads, zeros), None - else: - return array_ops.where(is_selected, gathered_grads, zeros), None, None + return array_ops.where(is_selected, gathered_grads, zeros), None @ops.RegisterGradient("SegmentMin") def _SegmentMinGrad(op, grad): """Gradient for SegmentMin.""" - return _SegmentMinOrMaxGrad(op, grad, True) + return _SegmentMinOrMaxGrad(op, grad) @ops.RegisterGradient("SegmentMax") def _SegmentMaxGrad(op, grad): """Gradient for SegmentMax.""" - return _SegmentMinOrMaxGrad(op, grad, True) + return _SegmentMinOrMaxGrad(op, grad) + + +def _GatherDropNegatives(params, ids, zero_clipped_indices=None, + is_positive=None): + """ Helper function for unsorted segment ops. Gathers params for + positive segment ids and gathers 0 for inputs with negative segment id. + Also returns the clipped indices and a boolean mask with the same shape + as ids where a positive id is masked as true. With this, the latter two + can be passed as arguments to this function to reuse them. + """ + if zero_clipped_indices is None: + zero_clipped_indices = math_ops.maximum(ids, array_ops.zeros_like(ids)) + gathered = array_ops.gather(params, zero_clipped_indices) + if is_positive is None: + is_positive = math_ops.greater_equal(ids, 0) + # tf.where(condition, x, y) requires condition to have the same shape as x + # and y. + # todo(philjd): remove this if tf.where supports broadcasting (#9284) + for _ in range(gathered.shape.ndims - is_positive.shape.ndims): + is_positive = array_ops.expand_dims(is_positive, -1) + is_positive = (is_positive & + array_ops.ones_like(gathered, dtype=dtypes.bool)) + # replace gathered params of negative indices with 0 + zero_slice = array_ops.zeros_like(gathered) + return (array_ops.where(is_positive, gathered, zero_slice), + zero_clipped_indices, is_positive) + + +def _UnsortedSegmentMinOrMaxGrad(op, grad): + """ Gradient for UnsortedSegmentMin and UnsortedSegmentMax. """ + # Get the number of selected (minimum or maximum) elements in each segment. + gathered_outputs, zero_clipped_indices, is_positive = \ + _GatherDropNegatives(op.outputs[0], op.inputs[1]) + is_selected = math_ops.equal(op.inputs[0], gathered_outputs) + is_selected = math_ops.logical_and(is_selected, is_positive) + num_selected = math_ops.unsorted_segment_sum( + math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2]) + # Compute the gradient for each segment. The gradient for the ith segment is + # divided evenly among the selected elements in that segment. + weighted_grads = math_ops.div(grad, num_selected) + gathered_grads, _, _ = _GatherDropNegatives(weighted_grads, None, + zero_clipped_indices, + is_positive) + zeros = array_ops.zeros_like(gathered_grads) + return array_ops.where(is_selected, gathered_grads, zeros), None, None @ops.RegisterGradient("UnsortedSegmentSum") def _UnsortedSegmentSumGrad(op, grad): - """Gradient for SegmentSum.""" - return array_ops.gather(grad, op.inputs[1]), None, None + """Gradient for UnsortedSegmentSum.""" + return _GatherDropNegatives(grad, op.inputs[1])[0], None, None @ops.RegisterGradient("UnsortedSegmentMax") def _UnsortedSegmentMaxGrad(op, grad): - return _SegmentMinOrMaxGrad(op, grad, False) + """ Gradient for UnsortedSegmentMax. """ + return _UnsortedSegmentMinOrMaxGrad(op, grad) + + +@ops.RegisterGradient("UnsortedSegmentMin") +def _UnsortedSegmentMinGrad(op, grad): + """ Gradient for UnsortedSegmentMin. """ + return _UnsortedSegmentMinOrMaxGrad(op, grad) + + +@ops.RegisterGradient("UnsortedSegmentProd") +def _UnsortedSegmentProdGrad(op, grad): + """ Gradient for UnsortedSegmentProd. + The gradient can be expressed for each segment by dividing the segment's + product by each element of the segment input tensor, but this approach can't + deal with zeros in the input. + Unlike reduce_prod we can't use cumsum here as individual segments may have + a different number of elements. Therefore we consider three cases: + 1) A segment input contains no zeros and we can safely divide by the input + tensor. + 2) A segment contains exactly one zero. Then the gradient of each input of + the segment is zero except for the 0-input, there the gradient is + the product of the remaining segment entries. + 3) A segment contains at least two zeros. The gradient is zero for all + segment inputs. + """ + # Note that unsorted_segment_sum will filter out the negative indices, + # so we don't need to do a logical_and with is_positive here + is_zero = math_ops.equal(op.inputs[0], 0) + num_zeros = gen_math_ops.unsorted_segment_sum( + math_ops.cast(is_zero, dtype=dtypes.int32), op.inputs[1], op.inputs[2]) + # handle case 3 and set the gradient to 0 for segments with more than one + # 0 as input + grad = array_ops.where(math_ops.greater(num_zeros, 1), + array_ops.zeros_like(grad), grad) + # replace all zeros with ones and compute the unsorted_segment_prod + non_zero_data = array_ops.where(is_zero, array_ops.ones_like(op.inputs[0]), + op.inputs[0]) + non_zero_prod = gen_math_ops.unsorted_segment_prod( + non_zero_data, op.inputs[1], op.inputs[2]) + # clip the indices for gather to be positive + zero_clipped_indices = math_ops.maximum(op.inputs[1], + array_ops.zeros_like(op.inputs[1])) + gathered_prod = array_ops.gather(op.outputs[0], zero_clipped_indices) + gathered_non_zero_prod = array_ops.gather(non_zero_prod, + zero_clipped_indices) + prod_divided_by_el = gathered_prod / op.inputs[0] # May contain nan/inf. + # Now fetch the individual results for segments containing 0 and those that + # don't. is_zero will also fetch results for entries with negative index + # but the following gather_drop_negatives sets the corresponding entry in + # grad to 0 for these + partial_derivative = array_ops.where(is_zero, gathered_non_zero_prod, + prod_divided_by_el) + gathered_grad = _GatherDropNegatives(grad, op.inputs[1], + zero_clipped_indices)[0] + return gathered_grad * partial_derivative, None, None @ops.RegisterGradient("Abs") @@ -296,16 +396,14 @@ def _NegGrad(_, grad): def _InvGrad(op, grad): """Returns -grad * (1 / x^2).""" y = op.outputs[0] # y = 1 / x - # pylint: disable=protected-access - return gen_math_ops._reciprocal_grad(y, grad) + return gen_math_ops.reciprocal_grad(y, grad) @ops.RegisterGradient("Reciprocal") def _ReciprocalGrad(op, grad): """Returns -grad * (1 / x^2).""" y = op.outputs[0] # y = 1 / x - # pylint: disable=protected-access - return gen_math_ops._reciprocal_grad(y, grad) + return gen_math_ops.reciprocal_grad(y, grad) @ops.RegisterGradient("InvGrad") @@ -315,8 +413,7 @@ def _InvGradGrad(op, grad): with ops.control_dependencies([grad]): ca = math_ops.conj(op.inputs[0]) cg = math_ops.conj(grad) - # pylint: disable=protected-access - return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad) + return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad) @ops.RegisterGradient("ReciprocalGrad") @@ -326,8 +423,7 @@ def _ReciprocalGradGrad(op, grad): with ops.control_dependencies([grad]): ca = math_ops.conj(op.inputs[0]) cg = math_ops.conj(grad) - # pylint: disable=protected-access - return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad) + return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad) @ops.RegisterGradient("Square") @@ -336,15 +432,14 @@ def _SquareGrad(op, grad): # Added control dependencies to prevent 2*x from being computed too early. with ops.control_dependencies([grad]): x = math_ops.conj(x) - return math_ops.multiply(grad, math_ops.multiply(x, 2.0)) + y = constant_op.constant(2.0, dtype=x.dtype) + return math_ops.multiply(grad, math_ops.multiply(x, y)) @ops.RegisterGradient("Sqrt") def _SqrtGrad(op, grad): y = op.outputs[0] # y = x^(1/2) - # pylint: disable=protected-access - return gen_math_ops._sqrt_grad(y, grad) - # pylint: enable=protected-access + return gen_math_ops.sqrt_grad(y, grad) @ops.RegisterGradient("SqrtGrad") @@ -360,9 +455,7 @@ def _SqrtGradGrad(op, grad): def _RsqrtGrad(op, grad): """Returns -0.5 * grad * conj(y)^3.""" y = op.outputs[0] # y = x^(-1/2) - # pylint: disable=protected-access - return gen_math_ops._rsqrt_grad(y, grad) - # pylint: enable=protected-access + return gen_math_ops.rsqrt_grad(y, grad) @ops.RegisterGradient("RsqrtGrad") @@ -374,8 +467,7 @@ def _RsqrtGradGrad(op, grad): ca = math_ops.conj(a) cg = math_ops.conj(grad) grad_a = -1.5 * cg * b * math_ops.square(ca) - # pylint: disable=protected-access - grad_b = gen_math_ops._rsqrt_grad(ca, grad) + grad_b = gen_math_ops.rsqrt_grad(ca, grad) return grad_a, grad_b @@ -440,8 +532,7 @@ def _TanhGrad(op, grad): y = op.outputs[0] # y = tanh(x) with ops.control_dependencies([grad]): y = math_ops.conj(y) - # pylint: disable=protected-access - return gen_math_ops._tanh_grad(y, grad) + return gen_math_ops.tanh_grad(y, grad) @ops.RegisterGradient("Asinh") @@ -479,8 +570,7 @@ def _TanhGradGrad(op, grad): with ops.control_dependencies([grad]): a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) - # pylint: disable=protected-access - return grad * -2.0 * b * a, gen_math_ops._tanh_grad(a, grad) + return grad * -2.0 * b * a, gen_math_ops.tanh_grad(a, grad) @ops.RegisterGradient("Erf") @@ -628,8 +718,7 @@ def _SigmoidGrad(op, grad): y = op.outputs[0] # y = sigmoid(x) with ops.control_dependencies([grad]): y = math_ops.conj(y) - # pylint: disable=protected-access - return gen_math_ops._sigmoid_grad(y, grad) + return gen_math_ops.sigmoid_grad(y, grad) @ops.RegisterGradient("SigmoidGrad") @@ -638,8 +727,7 @@ def _SigmoidGradGrad(op, grad): a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) gb = grad * b - # pylint: disable=protected-access - return gb - 2.0 * gb * a, gen_math_ops._sigmoid_grad(a, grad) + return gb - 2.0 * gb * a, gen_math_ops.sigmoid_grad(a, grad) @ops.RegisterGradient("Sign") @@ -786,16 +874,18 @@ def _MulGrad(op, grad): if (isinstance(grad, ops.Tensor) and _ShapesFullySpecifiedAndEqual(x, y, grad) and grad.dtype in (dtypes.int32, dtypes.float32)): - return gen_math_ops._mul(grad, y), gen_math_ops._mul(grad, x) + return gen_math_ops.mul(grad, y), gen_math_ops.mul(grad, x) assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype) sx = array_ops.shape(x) sy = array_ops.shape(y) rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy) - # pylint: enable=protected-access x = math_ops.conj(x) y = math_ops.conj(y) - return (array_ops.reshape(math_ops.reduce_sum(grad * y, rx), sx), - array_ops.reshape(math_ops.reduce_sum(x * grad, ry), sy)) + return (array_ops.reshape( + math_ops.reduce_sum(gen_math_ops.mul(grad, y), rx), sx), + array_ops.reshape( + math_ops.reduce_sum(gen_math_ops.mul(x, grad), ry), sy)) + # pylint: enable=protected-access @ops.RegisterGradient("Div") @@ -969,17 +1059,17 @@ def _MatMulGrad(op, grad): a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) if not t_a and not t_b: - grad_a = math_ops.matmul(grad, b, transpose_b=True) - grad_b = math_ops.matmul(a, grad, transpose_a=True) + grad_a = gen_math_ops.mat_mul(grad, b, transpose_b=True) + grad_b = gen_math_ops.mat_mul(a, grad, transpose_a=True) elif not t_a and t_b: - grad_a = math_ops.matmul(grad, b) - grad_b = math_ops.matmul(grad, a, transpose_a=True) + grad_a = gen_math_ops.mat_mul(grad, b) + grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True) elif t_a and not t_b: - grad_a = math_ops.matmul(b, grad, transpose_b=True) - grad_b = math_ops.matmul(a, grad) + grad_a = gen_math_ops.mat_mul(b, grad, transpose_b=True) + grad_b = gen_math_ops.mat_mul(a, grad) elif t_a and t_b: - grad_a = math_ops.matmul(b, grad, transpose_a=True, transpose_b=True) - grad_b = math_ops.matmul(grad, a, transpose_a=True, transpose_b=True) + grad_a = gen_math_ops.mat_mul(b, grad, transpose_a=True, transpose_b=True) + grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True, transpose_b=True) return grad_a, grad_b @@ -993,7 +1083,7 @@ def _SparseMatMulGrad(op, grad): op.inputs[0]: op.get_attr("a_is_sparse"), op.inputs[1]: op.get_attr("b_is_sparse"), # Use heuristic to figure out if grad might be sparse - grad: context.in_graph_mode() and (grad.op.type == "ReluGrad") + grad: not context.executing_eagerly() and (grad.op.type == "ReluGrad") } def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False): diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 9a8ac93de9dcc12c513b5ddd07cca9d863d19b8a..b24711c8a6ef065fefbcfad94183568fc44273fa 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -89,8 +89,6 @@ See the @{$python/math_ops} guide. @@matrix_inverse @@cholesky @@cholesky_solve -@@matrix_exponential -@@matrix_logarithm @@matrix_solve @@matrix_triangular_solve @@matrix_solve_ls @@ -131,6 +129,9 @@ See the @{$python/math_ops} guide. @@segment_mean @@unsorted_segment_sum @@unsorted_segment_max +@@unsorted_segment_min +@@unsorted_segment_prod +@@unsorted_segment_sqrt_n @@sparse_segment_sum @@sparse_segment_mean @@sparse_segment_sqrt_n @@ -158,14 +159,11 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gen_control_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gen_sparse_ops from tensorflow.python.ops import gen_spectral_ops -from tensorflow.python.ops import gen_state_ops -from tensorflow.python.ops import state_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_math_ops import * @@ -181,6 +179,11 @@ arg_max = deprecation.deprecated(None, "Use `argmax` instead")(arg_max) # pylin arg_min = deprecation.deprecated(None, "Use `argmin` instead")(arg_min) # pylint: disable=used-before-assignment +# This is set by resource_variable_ops.py. It is included in this way since +# there is a circular dependency between math_ops and resource_variable_ops +_resource_variable_type = None + + def _set_doc(doc): def _decorator(func): @@ -237,7 +240,7 @@ def argmin(input, # pylint: disable=anomalous-backslash-in-string,protected-access # pylint: disable=g-docstring-has-escape @tf_export("abs") -def abs(x, name=None): +def abs(x, name=None): # pylint: disable=redefined-builtin r"""Computes the absolute value of a tensor. Given a tensor `x` of complex numbers, this operation returns a tensor of type @@ -263,7 +266,7 @@ def abs(x, name=None): with ops.name_scope(name, "Abs", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): if x.values.dtype.is_complex: - x_abs = gen_math_ops._complex_abs( + x_abs = gen_math_ops.complex_abs( x.values, Tout=x.values.dtype.real_dtype, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_abs, dense_shape=x.dense_shape) @@ -273,7 +276,7 @@ def abs(x, name=None): else: x = ops.convert_to_tensor(x, name="x") if x.dtype.is_complex: - return gen_math_ops._complex_abs(x, Tout=x.dtype.real_dtype, name=name) + return gen_math_ops.complex_abs(x, Tout=x.dtype.real_dtype, name=name) return gen_math_ops._abs(x, name=name) @@ -282,7 +285,7 @@ def abs(x, name=None): # pylint: disable=redefined-builtin def _bucketize(input, boundaries, name=None): - return gen_math_ops._bucketize(input=input, boundaries=boundaries, name=name) + return gen_math_ops.bucketize(input=input, boundaries=boundaries, name=name) # pylint: enable=redefined-builtin @@ -325,10 +328,10 @@ def divide(x, y, name=None): @tf_export("multiply") def multiply(x, y, name=None): - return gen_math_ops._mul(x, y, name) + return gen_math_ops.mul(x, y, name) -multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`") +multiply.__doc__ = gen_math_ops.mul.__doc__.replace("Mul", "`tf.multiply`") # TODO(aselle): put deprecation in after another round of global code changes @@ -336,19 +339,19 @@ multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`") "2016-12-30", "`tf.mul(x, y)` is deprecated, please use `tf.multiply(x, y)` or `x * y`") def _mul(x, y, name=None): - return gen_math_ops._mul(x, y, name) + return gen_math_ops.mul(x, y, name) _mul.__doc__ = ( - gen_math_ops._mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__)) + gen_math_ops.mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__)) @tf_export("subtract") def subtract(x, y, name=None): - return gen_math_ops._sub(x, y, name) + return gen_math_ops.sub(x, y, name) -subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`") +subtract.__doc__ = gen_math_ops.sub.__doc__.replace("`Sub`", "`tf.subtract`") # TODO(aselle): put deprecation in after another round of global code changes @@ -356,11 +359,11 @@ subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`") "2016-12-30", "`tf.sub(x, y)` is deprecated, please use `tf.subtract(x, y)` or `x - y`") def _sub(x, y, name=None): - return gen_math_ops._sub(x, y, name) + return gen_math_ops.sub(x, y, name) _sub.__doc__ = ( - gen_math_ops._sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__)) + gen_math_ops.sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__)) # pylint: disable=g-docstring-has-escape @@ -380,11 +383,11 @@ def negative(x, name=None): """ with ops.name_scope(name, "Neg", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): - x_neg = gen_math_ops._neg(x.values, name=name) + x_neg = gen_math_ops.neg(x.values, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_neg, dense_shape=x.dense_shape) else: - return gen_math_ops._neg(x, name=name) + return gen_math_ops.neg(x, name=name) # pylint: enable=g-docstring-has-escape @@ -542,7 +545,7 @@ def scalar_mul(scalar, x): @tf_export("pow") -def pow(x, y, name=None): +def pow(x, y, name=None): # pylint: disable=redefined-builtin r"""Computes the power of one value to another. Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for @@ -712,7 +715,7 @@ def angle(input, name=None): @tf_export("round") -def round(x, name=None): +def round(x, name=None): # pylint: disable=redefined-builtin """Rounds the values of a tensor to the nearest integer, element-wise. Rounds half to even. Also known as bankers rounding. If you want to round @@ -898,7 +901,41 @@ def to_bfloat16(x, name="ToBFloat16"): return cast(x, dtypes.bfloat16, name=name) -ops.Tensor._override_operator("__neg__", gen_math_ops._neg) +@tf_export("to_complex64") +def to_complex64(x, name="ToComplex64"): + """Casts a tensor to type `complex64`. + + Args: + x: A `Tensor` or `SparseTensor`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor` with same shape as `x` with type `complex64`. + + Raises: + TypeError: If `x` cannot be cast to the `complex64`. + """ + return cast(x, dtypes.complex64, name=name) + + +@tf_export("to_complex128") +def to_complex128(x, name="ToComplex128"): + """Casts a tensor to type `complex128`. + + Args: + x: A `Tensor` or `SparseTensor`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor` with same shape as `x` with type `complex128`. + + Raises: + TypeError: If `x` cannot be cast to the `complex128`. + """ + return cast(x, dtypes.complex128, name=name) + + +ops.Tensor._override_operator("__neg__", gen_math_ops.neg) ops.Tensor._override_operator("__abs__", abs) # __invert__ corresponds to the ~ operator. Here we follow the numpy convention # ~ marks an elementwise bit-wise inverse. This is only implemented for boolean @@ -1027,7 +1064,7 @@ def _truediv_python3(x, y, name=None): if dtype is not None: x = cast(x, dtype) y = cast(y, dtype) - return gen_math_ops._real_div(x, y, name=name) + return gen_math_ops.real_div(x, y, name=name) def _div_python2(x, y, name=None): @@ -1050,9 +1087,9 @@ def _div_python2(x, y, name=None): raise TypeError("x and y must have the same dtype, got %r != %r" % (x_dtype, y_dtype)) if x_dtype.is_floating or x_dtype.is_complex: - return gen_math_ops._real_div(x, y, name=name) + return gen_math_ops.real_div(x, y, name=name) else: - return gen_math_ops._floor_div(x, y, name=name) + return gen_math_ops.floor_div(x, y, name=name) @tf_export("truediv") @@ -1110,7 +1147,7 @@ def div(x, y, name=None): # TODO(aselle): This should be removed -mod = gen_math_ops._floor_mod +mod = gen_math_ops.floor_mod # TODO(aselle): Deprecate this once all internal functionality uses @@ -1143,22 +1180,22 @@ def floordiv(x, y, name=None): TypeError: If the inputs are complex. """ with ops.name_scope(name, "floordiv", [x, y]) as name: - return gen_math_ops._floor_div(x, y, name=name) + return gen_math_ops.floor_div(x, y, name=name) -realdiv = gen_math_ops._real_div -truncatediv = gen_math_ops._truncate_div +realdiv = gen_math_ops.real_div +truncatediv = gen_math_ops.truncate_div # TODO(aselle): Rename this to floordiv when we can. -floor_div = gen_math_ops._floor_div -truncatemod = gen_math_ops._truncate_mod -floormod = gen_math_ops._floor_mod +floor_div = gen_math_ops.floor_div +truncatemod = gen_math_ops.truncate_mod +floormod = gen_math_ops.floor_mod def _mul_dispatch(x, y, name=None): """Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".""" is_tensor_y = isinstance(y, ops.Tensor) if is_tensor_y: - return gen_math_ops._mul(x, y, name=name) + return gen_math_ops.mul(x, y, name=name) else: assert isinstance(y, sparse_tensor.SparseTensor) # Case: Dense * Sparse. new_vals = gen_sparse_ops.sparse_dense_cwise_mul(y.indices, y.values, @@ -1177,12 +1214,12 @@ _OverrideBinaryOperatorHelper(gen_sparse_ops.sparse_dense_cwise_mul, "mul", sparse_tensor.SparseTensor) _OverrideBinaryOperatorHelper(gen_math_ops.add, "add") -_OverrideBinaryOperatorHelper(gen_math_ops._sub, "sub") +_OverrideBinaryOperatorHelper(gen_math_ops.sub, "sub") _OverrideBinaryOperatorHelper(_mul_dispatch, "mul") _OverrideBinaryOperatorHelper(_div_python2, "div") _OverrideBinaryOperatorHelper(_truediv_python3, "truediv") _OverrideBinaryOperatorHelper(floordiv, "floordiv") -_OverrideBinaryOperatorHelper(gen_math_ops._floor_mod, "mod") +_OverrideBinaryOperatorHelper(gen_math_ops.floor_mod, "mod") _OverrideBinaryOperatorHelper(pow, "pow") @@ -1207,7 +1244,7 @@ ops.Tensor._override_operator("__ge__", gen_math_ops.greater_equal) @tf_export("range") -def range(start, limit=None, delta=1, dtype=None, name="range"): +def range(start, limit=None, delta=1, dtype=None, name="range"): # pylint: disable=redefined-builtin """Creates a sequence of numbers. Creates a sequence of numbers that begins at `start` and extends by @@ -1292,9 +1329,9 @@ def _ReductionDims(x, axis, reduction_indices): return axis else: # Fast path: avoid creating Rank and Range ops if ndims is known. - if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None: + if isinstance(x, ops.Tensor) and x._rank() is not None: # pylint: disable=protected-access return constant_op.constant( - np.arange(x.get_shape().ndims), dtype=dtypes.int32) + np.arange(x._rank()), dtype=dtypes.int32) # pylint: disable=protected-access if (isinstance(x, sparse_tensor.SparseTensor) and x.dense_shape.get_shape().is_fully_defined()): rank = x.dense_shape.get_shape()[0].value # sparse.dense_shape is 1-D. @@ -1504,7 +1541,7 @@ def reduce_mean(input_tensor, if keepdims is None: keepdims = False return _may_reduce_to_scalar(keepdims, axis, reduction_indices, - gen_math_ops._mean( + gen_math_ops.mean( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), @@ -1554,7 +1591,7 @@ def reduce_prod(input_tensor, if keepdims is None: keepdims = False return _may_reduce_to_scalar(keepdims, axis, reduction_indices, - gen_math_ops._prod( + gen_math_ops.prod( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), @@ -2007,8 +2044,15 @@ def matmul(a, if transpose_b and adjoint_b: raise ValueError("Only one of transpose_b and adjoint_b can be True.") - a = ops.convert_to_tensor(a, name="a") - b = ops.convert_to_tensor(b, name="b") + if context.executing_eagerly(): + if not isinstance(a, (ops.EagerTensor, _resource_variable_type)): + a = ops.convert_to_tensor(a, name="a") + if not isinstance(b, (ops.EagerTensor, _resource_variable_type)): + b = ops.convert_to_tensor(b, name="b") + else: + a = ops.convert_to_tensor(a, name="a") + b = ops.convert_to_tensor(b, name="b") + # TODO(apassos) remove _shape_tuple here when it is not needed. a_shape = a._shape_tuple() # pylint: disable=protected-access b_shape = b._shape_tuple() # pylint: disable=protected-access @@ -2023,7 +2067,7 @@ def matmul(a, if transpose_b: b = conj(b) adjoint_b = True - return gen_math_ops._batch_mat_mul( + return gen_math_ops.batch_mat_mul( a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name) # Neither matmul nor sparse_matmul support adjoint, so we conjugate @@ -2060,13 +2104,13 @@ def matmul(a, ret = cast(ret, dtypes.bfloat16) return ret else: - return gen_math_ops._mat_mul( + return gen_math_ops.mat_mul( a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) _OverrideBinaryOperatorHelper(matmul, "matmul") -sparse_matmul = gen_math_ops._sparse_mat_mul +sparse_matmul = gen_math_ops.sparse_mat_mul @ops.RegisterStatistics("MatMul", "flops") @@ -2171,7 +2215,7 @@ def add_n(inputs, name=None): if name: return array_ops.identity(inputs[0], name=name) return inputs[0] - return gen_math_ops._add_n(inputs, name=name) + return gen_math_ops.add_n(inputs, name=name) @tf_export("accumulate_n") @@ -2181,14 +2225,12 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): Optionally, pass `shape` and `tensor_dtype` for shape and type checking, otherwise, these are inferred. - NOTE: This operation is not differentiable and cannot be used if inputs depend - on trainable variables. Please use `tf.add_n` for such cases. + `tf.accumulate_n` performs the same operation as `tf.add_n`, but does not + wait for all of its inputs to be ready before beginning to sum. This can + save memory if inputs are ready at different times, since minimum temporary + storage is proportional to the output size rather than the inputs size. - Aside from differentiability, `tf.accumulate_n` performs the same operation as - `tf.add_n`, but does not wait for all of its inputs to be ready before - beginning to sum. This can save memory if inputs are ready at different times, - since minimum temporary storage is proportional to the output size rather than - the inputs size. + `accumulate_n` is differentiable (but wasn't previous to TensorFlow 1.7). For example: @@ -2198,8 +2240,9 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): tf.accumulate_n([a, b, a]) # [[7, 4], [6, 14]] # Explicitly pass shape and type - tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) # [[7, 4], - # [6, 14]] + tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) + # [[7, 4], + # [6, 14]] ``` Args: @@ -2215,20 +2258,17 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): ValueError: If `inputs` don't all have same shape and dtype or the shape cannot be inferred. """ - if context.in_eager_mode(): - # TODO(apassos) remove this once the lifetime of eager variables gets - # addressed. - raise ValueError("accumulate_n not supported in eager mode") + def _input_error(): + return ValueError( + "inputs must be a list of at least one Tensor with the " + "same dtype and shape") if not inputs or not isinstance(inputs, (list, tuple)): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs) if not all(isinstance(x, ops.Tensor) for x in inputs): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() if not all(x.dtype == inputs[0].dtype for x in inputs): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() if shape is not None: shape = tensor_shape.as_shape(shape) else: @@ -2236,27 +2276,31 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): for input_tensor in inputs: if isinstance(input_tensor, ops.Tensor): shape = shape.merge_with(input_tensor.get_shape()) - if tensor_dtype is None: - tensor_dtype = inputs[0].dtype - if tensor_dtype != inputs[0].dtype: - raise TypeError("tensor_dtype is {}, but input is of type {}".format( - tensor_dtype, inputs[0].dtype)) - if len(inputs) == 1: + + # tensor_dtype is for safety only; operator's output type computed in C++ + if tensor_dtype is not None and tensor_dtype != inputs[0].dtype: + raise TypeError("tensor_dtype is {}, but input is of type {}" + .format(tensor_dtype, inputs[0].dtype)) + + if len(inputs) == 1 and name is None: return inputs[0] - with ops.name_scope(name, "AccumulateN", inputs) as name: - var = gen_state_ops._temporary_variable( - shape=tensor_shape.vector(0), dtype=tensor_dtype) - with ops.colocate_with(var): - zeros = array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]) - zeros.set_shape(shape) - ref = state_ops.assign(var, zeros, validate_shape=False) - update_ops = [ - state_ops.assign_add(ref, input_tensor, use_locking=True) - for input_tensor in inputs - ] - with ops.control_dependencies(update_ops): - return gen_state_ops._destroy_temporary_variable( - ref, var_name=var.op.name, name=name) + elif len(inputs) == 1 and name is not None: + return array_ops.identity(inputs[0], name=name) + elif context.executing_eagerly(): + # TemporaryVariable not currently supported in eager mode; fall back + # onto AddN for now. + # TODO(frreiss) remove this once the lifetime of eager variables gets + # addressed + return add_n(inputs, name=name) + else: + return gen_math_ops.accumulate_nv2(inputs, name=name, shape=shape) # pylint: disable=protected-access + + +@ops.RegisterGradient("AccumulateNV2") +def _accumulate_n_grad(op, grad): + """Same as gradient for AddN. Copies the gradient to all inputs.""" + # Not broadcasting. + return [grad] * len(op.inputs) @tf_export("nn.sigmoid", "sigmoid") @@ -2279,7 +2323,7 @@ def sigmoid(x, name=None): """ with ops.name_scope(name, "Sigmoid", [x]) as name: x = ops.convert_to_tensor(x, name="x") - return gen_math_ops._sigmoid(x, name=name) + return gen_math_ops.sigmoid(x, name=name) @tf_export("log_sigmoid") @@ -2298,7 +2342,7 @@ def log_sigmoid(x, name=None): """ with ops.name_scope(name, "LogSigmoid", [x]) as name: x = ops.convert_to_tensor(x, name="x") - return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) + return gen_math_ops.neg(gen_nn_ops.softplus(-x), name=name) @tf_export("nn.tanh", "tanh") @@ -2315,11 +2359,11 @@ def tanh(x, name=None): """ with ops.name_scope(name, "Tanh", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): - x_tanh = gen_math_ops._tanh(x.values, name=name) + x_tanh = gen_math_ops.tanh(x.values, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_tanh, dense_shape=x.dense_shape) else: - return gen_math_ops._tanh(x, name=name) + return gen_math_ops.tanh(x, name=name) @tf_export("bincount") @@ -2508,7 +2552,7 @@ def conj(x, name=None): with ops.name_scope(name, "Conj", [x]) as name: x = ops.convert_to_tensor(x, name="x") if x.dtype.is_complex or x.dtype == dtypes.variant: - return gen_math_ops._conj(x, name=name) + return gen_math_ops.conj(x, name=name) elif x.dtype.is_floating or x.dtype.is_integer: return x else: @@ -2552,12 +2596,93 @@ def reduced_shape(input_shape, axes): ]) # [1, 1] +def _unsorted_segment_N(data, segment_ids, num_segments): + """ Helper function for unsorted_segment_mean/_sqrtN. Computes the number + of segment entries with 0-entries set to 1 to allow division by N. + """ + # bincount doesn't support negative indices so we use unsorted_segment_sum + ones_tensor = array_ops.ones(segment_ids.shape, dtype=data.dtype) + N = gen_math_ops.unsorted_segment_sum(ones_tensor, segment_ids, num_segments) + # add dimensions for all non-reduced axes + ndims_output = data.shape.ndims - segment_ids.shape.ndims + broadcast_shape = [num_segments] + [1] * ndims_output + N = array_ops.reshape(N, broadcast_shape) + return gen_math_ops.maximum(N, 1) + + +@tf_export("unsorted_segment_mean") +def unsorted_segment_mean(data, segment_ids, num_segments, name=None): + r""" Computes the mean along segments of a tensor. + + Read @{$math_ops#segmentation$the section on segmentation} for an explanation + of segments. + + This operator is similar to the unsorted segment sum operator found + [here](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). + Instead of computing the sum over segments, it computes the mean of all + entries belonging to a segment such that: + + \\(output_i = 1/N_i \sum data_j\\) where the sum is over `j` such + that `segment_ids[j] == i` with \\N_i\\ being the number of occurrences + of id \\i\\. + + If there is no entry for a given segment ID `i`, it outputs 0. + + segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s + first dimension. + + output: Has same shape as data, except for dimension 0 which + has size `num_segments`. + """ + with ops.name_scope(name, "UnsortedSegmentMean"): + data = ops.convert_to_tensor(data) + segment_ids = ops.convert_to_tensor(segment_ids) + N = _unsorted_segment_N(data, segment_ids, num_segments) + summed = gen_math_ops.unsorted_segment_sum(data, segment_ids, num_segments) + return summed / N + + +@tf_export("unsorted_segment_sqrt_n") +def unsorted_segment_sqrt_n(data, segment_ids, num_segments, name=None): + r"""Computes the sum along segments of a tensor divided by the sqrt(N). + + Read @{$math_ops#segmentation$the section on segmentation} for an explanation + of segments. + + This operator is similar to the unsorted segment sum operator found + [here](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). + Additionally to computing the sum over segments, it divides the results by + sqrt(N). + + \\(output_i = 1/sqrt(N_i) \sum data_j\\) where the sum is over `j` such + that `segment_ids[j] == i` with \\N_i\\ being the number of occurrences + of id \\i\\. + + If there is no entry for a given segment ID `i`, it outputs 0. + + Note that this op only supports floating point and complex dtypes, + due to tf.sqrt only supporting these types. + + segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s + first dimension. + + output: Has same shape as data, except for dimension 0 which + has size `num_segments`. + """ + with ops.name_scope(name, "UnsortedSegmentSqrtN"): + data = ops.convert_to_tensor(data) + segment_ids = ops.convert_to_tensor(segment_ids) + N = _unsorted_segment_N(data, segment_ids, num_segments) + summed = gen_math_ops.unsorted_segment_sum(data, segment_ids, num_segments) + return summed / gen_math_ops.sqrt(N) + + @tf_export("sparse_segment_sum") def sparse_segment_sum(data, indices, segment_ids, name=None, num_segments=None): r"""Computes the sum along sparse segments of a tensor. - Read @{$math_ops#segmentation$the section on segmentation} for an explanation + Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of segments. Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first @@ -2632,7 +2757,7 @@ def sparse_segment_mean(data, indices, segment_ids, name=None, num_segments=None): r"""Computes the mean along sparse segments of a tensor. - Read @{$math_ops#segmentation$the section on segmentation} for an explanation + Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of segments. Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index bd26ff66961c858865c8a61469abac0b783ed645..9f85188b3513563a7444f7a0e908f11af985498b 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -60,7 +60,7 @@ class ReduceTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes() def testReduceInvalidAxis(self): - if context.in_eager_mode(): + if context.executing_eagerly(): # The shape check is in run a graph construction time. In eager mode, # it misses the check, magically return result given wrong shape. return @@ -105,7 +105,7 @@ class LogSumExpTest(test_util.TensorFlowTestCase): for dtype in [np.float16, np.float32, np.double]: x_np = np.random.rand(5, 5).astype(dtype) with self.test_session(use_gpu=True): - y_tf_np = math_ops.reduce_logsumexp(x_np, keep_dims=True).eval() + y_tf_np = math_ops.reduce_logsumexp(x_np, keepdims=True).eval() self.assertEqual(y_tf_np.ndim, x_np.ndim) y_np = log(np.sum(exp(x_np), keepdims=True)) self.assertAllClose(y_tf_np, y_np) @@ -249,7 +249,7 @@ class ScalarMulTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes() def testAcceptsRefs(self): - if context.in_eager_mode(): + if context.executing_eagerly(): var = resource_variable_ops.ResourceVariable(10, name="var") else: var = variables.Variable(10) diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py index 7776ff08c4f55c43947010f313d8167596b15db7..9ec49545796cfa7a603b31c23bfd0d495639898d 100644 --- a/tensorflow/python/ops/metrics_impl.py +++ b/tensorflow/python/ops/metrics_impl.py @@ -308,7 +308,7 @@ def mean(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean is not supported when eager execution ' 'is enabled.') @@ -394,7 +394,7 @@ def accuracy(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.accuracy is not supported when eager ' 'execution is enabled.') @@ -644,7 +644,7 @@ def auc(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.auc is not supported when eager execution ' 'is enabled.') @@ -758,7 +758,7 @@ def mean_absolute_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_absolute_error is not supported ' 'when eager execution is enabled.') @@ -818,7 +818,7 @@ def mean_cosine_distance(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_cosine_distance is not supported when ' 'eager execution is enabled.') @@ -891,7 +891,7 @@ def mean_per_class_accuracy(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_per_class_accuracy is not supported ' 'when eager execution is enabled.') @@ -996,7 +996,7 @@ def mean_iou(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_iou is not supported when ' 'eager execution is enabled.') @@ -1098,7 +1098,7 @@ def mean_relative_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_relative_error is not supported when ' 'eager execution is enabled.') @@ -1165,7 +1165,7 @@ def mean_squared_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_squared_error is not supported when ' 'eager execution is enabled.') @@ -1223,7 +1223,7 @@ def mean_tensor(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_tensor is not supported when ' 'eager execution is enabled.') @@ -1247,13 +1247,8 @@ def mean_tensor(values, with ops.control_dependencies([values]): update_count_op = state_ops.assign_add(count, num_values) - def compute_mean(total, count, name): - non_zero_count = math_ops.maximum( - count, array_ops.ones_like(count), name=name) - return math_ops.truediv(total, non_zero_count, name=name) - - mean_t = compute_mean(total, count, 'value') - update_op = compute_mean(update_total_op, update_count_op, 'update_op') + mean_t = _safe_div(total, count, 'value') + update_op = _safe_div(update_total_op, update_count_op, 'update_op') if metrics_collections: ops.add_to_collections(metrics_collections, mean_t) @@ -1309,7 +1304,7 @@ def percentage_below(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.percentage_below is not supported when ' 'eager execution is enabled.') @@ -1402,7 +1397,7 @@ def false_negatives(labels, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_negatives is not supported when ' 'eager execution is enabled.') @@ -1458,7 +1453,7 @@ def false_negatives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_negatives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1512,7 +1507,7 @@ def false_positives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_positives is not supported when ' 'eager execution is enabled.') @@ -1568,7 +1563,7 @@ def false_positives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_positives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1622,7 +1617,7 @@ def true_negatives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_negatives is not ' 'supported when eager execution is enabled.') @@ -1678,7 +1673,7 @@ def true_negatives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_negatives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1732,7 +1727,7 @@ def true_positives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_positives is not ' 'supported when eager execution is enabled.') @@ -1788,7 +1783,7 @@ def true_positives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_positives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1856,7 +1851,7 @@ def precision(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision is not ' 'supported when eager execution is enabled.') @@ -1952,7 +1947,7 @@ def precision_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -2028,7 +2023,7 @@ def recall(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall is not supported is not ' 'supported when eager execution is enabled.') @@ -2405,7 +2400,7 @@ def recall_at_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall_at_k is not ' 'supported when eager execution is enabled.') @@ -2554,7 +2549,7 @@ def recall_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -2631,7 +2626,7 @@ def root_mean_squared_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.root_mean_squared_error is not ' 'supported when eager execution is enabled.') @@ -2712,7 +2707,7 @@ def sensitivity_at_specificity(labels, or `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sensitivity_at_specificity is not ' 'supported when eager execution is enabled.') @@ -3103,7 +3098,7 @@ def average_precision_at_k(labels, ValueError: if k is invalid. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sparse_average_precision_at_k is not ' 'supported when eager execution is enabled.') @@ -3272,7 +3267,7 @@ def precision_at_top_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision_at_top_k is not ' 'supported when eager execution is enabled.') @@ -3401,7 +3396,7 @@ def precision_at_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sparse_precision_at_k is not ' 'supported when eager execution is enabled.') @@ -3478,7 +3473,7 @@ def specificity_at_sensitivity(labels, or `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.specificity_at_sensitivity is not ' 'supported when eager execution is enabled.') diff --git a/tensorflow/python/ops/nn_batchnorm_test.py b/tensorflow/python/ops/nn_batchnorm_test.py index eebfb17085a568f48769f6df7dddd3ae2f799efc..3ac2c8eb17ef31b46638ce50e0e9f9705adce189 100644 --- a/tensorflow/python/ops/nn_batchnorm_test.py +++ b/tensorflow/python/ops/nn_batchnorm_test.py @@ -57,7 +57,6 @@ class BatchNormalizationTest(test.TestCase): test_util.set_producer_version(ops.get_default_graph(), 8) return gen_nn_ops._batch_norm_with_global_normalization( x, m, v, beta, gamma, epsilon, scale_after_normalization) - # pylint: enable=protected-access def _tfBatchNormV1BW(self, x, m, v, beta, gamma, epsilon, scale_after_normalization): @@ -223,7 +222,7 @@ class BatchNormalizationTest(test.TestCase): for scale_after_normalization in [True, False]: # _batch_norm_with_global_normalization_grad is deprecated in v9 test_util.set_producer_version(ops.get_default_graph(), 8) - grad = gen_nn_ops._batch_norm_with_global_normalization_grad( + grad = gen_nn_ops.batch_norm_with_global_normalization_grad( x, m, v, gamma, backprop, epsilon, scale_after_normalization) dx, dm, dv, db, dg = grad self.assertEqual(grad.dx, dx) diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py index 5e6cafd6aad4a80d3436d9c270ac5e2341c2a5aa..4af5bd26dd80b984b1c898411c2a23827bed1b4b 100644 --- a/tensorflow/python/ops/nn_grad.py +++ b/tensorflow/python/ops/nn_grad.py @@ -150,7 +150,7 @@ def _Conv3DBackpropFilterGrad(op, grad): @ops.RegisterGradient("AvgPool3D") def _AvgPool3DGrad(op, grad): - return gen_nn_ops._avg_pool3d_grad( + return gen_nn_ops.avg_pool3d_grad( array_ops.shape(op.inputs[0]), grad, ksize=op.get_attr("ksize"), @@ -172,7 +172,7 @@ def _AvgPool3DGradGrad(op, grad): @ops.RegisterGradient("MaxPool3D") def _MaxPool3DGrad(op, grad): - return gen_nn_ops._max_pool3d_grad( + return gen_nn_ops.max_pool3d_grad( op.inputs[0], op.outputs[0], grad, @@ -188,7 +188,7 @@ def _MaxPool3DGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool3d_grad_grad( + gen_nn_ops.max_pool3d_grad_grad( op.inputs[0], op.inputs[1], grad, @@ -204,7 +204,7 @@ def _MaxPool3DGradGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool3d_grad( + gen_nn_ops.max_pool3d_grad( op.inputs[0], op.inputs[1], grad, @@ -352,13 +352,13 @@ def _BiasAddGradV1(unused_bias_op, received_grad): @ops.RegisterGradient("Relu") def _ReluGrad(op, grad): - return gen_nn_ops._relu_grad(grad, op.outputs[0]) + return gen_nn_ops.relu_grad(grad, op.outputs[0]) @ops.RegisterGradient("EluGrad") def _EluGradGrad(op, grad): elu_x = op.inputs[1] - return (gen_nn_ops._elu_grad(grad, op.outputs[0]), + return (gen_nn_ops.elu_grad(grad, op.outputs[0]), array_ops.where(elu_x < 0, grad * op.inputs[0], array_ops.zeros( shape=array_ops.shape(elu_x), dtype=elu_x.dtype))) @@ -368,63 +368,63 @@ def _EluGradGrad(op, grad): def _SeluGradGrad(op, grad): x = op.inputs[1] scale_alpha = 1.7580993408473768599402175208123 - return (gen_nn_ops._elu_grad(grad, op.outputs[0]), + return (gen_nn_ops.elu_grad(grad, op.outputs[0]), array_ops.where(x < 0., - gen_nn_ops._elu_grad(grad, - op.outputs[0] + scale_alpha), + gen_nn_ops.elu_grad(grad, + op.outputs[0] + scale_alpha), array_ops.zeros( shape=array_ops.shape(x), dtype=x.dtype))) @ops.RegisterGradient("Relu6") def _Relu6Grad(op, grad): - return gen_nn_ops._relu6_grad(grad, op.outputs[0]) # pylint: disable=protected-access + return gen_nn_ops.relu6_grad(grad, op.outputs[0]) @ops.RegisterGradient("Relu6Grad") def _Relu6GradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu6_grad(grad, x), + return (gen_nn_ops.relu6_grad(grad, x), array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) @ops.RegisterGradient("Elu") def _EluGrad(op, grad): - return gen_nn_ops._elu_grad(grad, op.outputs[0]) + return gen_nn_ops.elu_grad(grad, op.outputs[0]) @ops.RegisterGradient("Selu") def _SeluGrad(op, grad): - return gen_nn_ops._selu_grad(grad, op.outputs[0]) + return gen_nn_ops.selu_grad(grad, op.outputs[0]) @ops.RegisterGradient("Softplus") def _SoftplusGrad(op, grad): - return gen_nn_ops._softplus_grad(grad, op.inputs[0]) + return gen_nn_ops.softplus_grad(grad, op.inputs[0]) @ops.RegisterGradient("SoftplusGrad") def _SoftplusGradGrad(op, grad): # Let: # y = tf.nn.softplus(x) - # dx = gen_nn_ops._softplus_grad(dy, x) = dy / (1 + exp(-x)) + # dx = gen_nn_ops.softplus_grad(dy, x) = dy / (1 + exp(-x)) # This op computes (ddy, d2x) from op.inputs == [dy, x] and grad == ddx. dy, x = op.inputs with ops.control_dependencies([grad]): - ddy = gen_nn_ops._softplus_grad(grad, x) # pylint: disable=protected-access + ddy = gen_nn_ops.softplus_grad(grad, x) d2x = grad * dy / (math_ops.exp(-x) + 2.0 + math_ops.exp(x)) return (ddy, d2x) @ops.RegisterGradient("Softsign") def _SoftsignGrad(op, grad): - return gen_nn_ops._softsign_grad(grad, op.inputs[0]) + return gen_nn_ops.softsign_grad(grad, op.inputs[0]) @ops.RegisterGradient("ReluGrad") def _ReluGradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu_grad(grad, x), + return (gen_nn_ops.relu_grad(grad, x), array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) @@ -456,7 +456,7 @@ def _SoftmaxCrossEntropyWithLogitsGrad(op, grad_loss, grad_grad): def IsZero(g): # Some introspection to check if the gradient is feeding zeros - if context.in_eager_mode(): + if context.executing_eagerly(): # TODO(apassos) add an efficient way to detect eager zeros here. return False if g.op.type in ("ZerosLike", "Zeros"): @@ -565,14 +565,14 @@ def _LRNGrad(op, grad): alpha = op.get_attr("alpha") beta = op.get_attr("beta") return [ - gen_nn_ops._lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, - bias, alpha, beta) + gen_nn_ops.lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, bias, + alpha, beta) ] @ops.RegisterGradient("AvgPool") def _AvgPoolGrad(op, grad): - return gen_nn_ops._avg_pool_grad( + return gen_nn_ops.avg_pool_grad( array_ops.shape(op.inputs[0]), grad, op.get_attr("ksize"), @@ -584,7 +584,7 @@ def _AvgPoolGrad(op, grad): @ops.RegisterGradient("AvgPoolGrad") def _AvgPoolGradGrad(op, grad): return (array_ops.stop_gradient(op.inputs[0]), - gen_nn_ops._avg_pool( + gen_nn_ops.avg_pool( grad, op.get_attr("ksize"), op.get_attr("strides"), @@ -594,7 +594,7 @@ def _AvgPoolGradGrad(op, grad): @ops.RegisterGradient("MaxPool") def _MaxPoolGrad(op, grad): - return gen_nn_ops._max_pool_grad( + return gen_nn_ops.max_pool_grad( op.inputs[0], op.outputs[0], grad, @@ -620,7 +620,7 @@ def _MaxPoolGradV2(op, grad): @ops.RegisterGradient("MaxPoolWithArgmax") def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad): - return gen_nn_ops._max_pool_grad_with_argmax( + return gen_nn_ops.max_pool_grad_with_argmax( op.inputs[0], grad, op.outputs[1], @@ -635,7 +635,7 @@ def _MaxPoolGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool_grad_grad( + gen_nn_ops.max_pool_grad_grad( op.inputs[0], op.inputs[1], grad, @@ -669,7 +669,7 @@ def _MaxPoolGradGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool_grad( + gen_nn_ops.max_pool_grad( op.inputs[0], op.inputs[1], grad, @@ -696,8 +696,7 @@ def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2): Returns: Input backprop for FractionalMaxPool op. """ - # pylint: disable=protected-access - return gen_nn_ops._fractional_max_pool_grad( + return gen_nn_ops.fractional_max_pool_grad( op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2], op.get_attr("overlapping")) @@ -719,10 +718,9 @@ def _FractionalAvgPoolGrad(op, grad_0, unused_grad_1, unused_grad_2): Returns: Input backprop for FractionalAvgPool op. """ - # pylint: disable=protected-access - return gen_nn_ops._fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0, - op.outputs[1], op.outputs[2], - op.get_attr("overlapping")) + return gen_nn_ops.fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0, + op.outputs[1], op.outputs[2], + op.get_attr("overlapping")) @ops.RegisterGradient("BatchNormWithGlobalNormalization") @@ -746,7 +744,7 @@ def _BatchNormWithGlobalNormalizationGrad(op, grad): last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ - dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( + dx, dm, dv, db, dg = gen_nn_ops.batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg @@ -863,27 +861,27 @@ def _BatchNormGrad(grad_y, grad_y = math_ops.cast(grad_y, dtypes.float32) if is_training: if data_format == b"NHWC": - keep_dims = False + keepdims = False reduce_axis = [0, 1, 2] else: - keep_dims = True + keepdims = True reduce_axis = [0, 2, 3] shape = [1, array_ops.size(scale), 1, 1] scale = array_ops.reshape(scale, shape) - mean_grad_y = math_ops.reduce_mean(grad_y, reduce_axis, keep_dims=keep_dims) - mean_x = math_ops.reduce_mean(x, reduce_axis, keep_dims=keep_dims) + mean_grad_y = math_ops.reduce_mean(grad_y, reduce_axis, keepdims=keepdims) + mean_x = math_ops.reduce_mean(x, reduce_axis, keepdims=keepdims) var_x = math_ops.reduce_mean( math_ops.squared_difference(x, array_ops.stop_gradient(mean_x)), reduce_axis, - keep_dims=keep_dims) + keepdims=keepdims) grad_y_offset = grad_y - mean_grad_y x_offset = x - mean_x mean = math_ops.reduce_mean( - grad_y * x_offset, axis=reduce_axis, keep_dims=keep_dims) + grad_y * x_offset, axis=reduce_axis, keepdims=keepdims) grad_x = scale * math_ops.rsqrt(var_x + epsilon) * ( grad_y_offset - math_ops.reciprocal(var_x + epsilon) * mean * x_offset) grad_scale = math_ops.rsqrt(var_x + epsilon) * math_ops.reduce_sum( - grad_y * x_offset, axis=reduce_axis, keep_dims=keep_dims) + grad_y * x_offset, axis=reduce_axis, keepdims=keepdims) if data_format == b"NCHW": grad_scale = array_ops.squeeze(grad_scale) grad_offset = math_ops.reduce_sum(grad_y, axis=reduce_axis) @@ -1010,7 +1008,7 @@ def _NthElementGrad(op, grad): A list of two tensors, the first being the gradient w.r.t. the input, the second being the gradient w.r.t. the N (None). """ - input = op.inputs[0] + input = op.inputs[0] # pylint: disable=redefined-builtin output = op.outputs[0] # Compute the number of elements which equal to output in each reduction diff --git a/tensorflow/python/ops/nn_grad_test.py b/tensorflow/python/ops/nn_grad_test.py index aa7539ae9f09163bf1a2cc9f7dfcc6fc06737ae8..49d54beb20073162279576e1e1011e10392378e0 100644 --- a/tensorflow/python/ops/nn_grad_test.py +++ b/tensorflow/python/ops/nn_grad_test.py @@ -24,7 +24,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import nn_grad +from tensorflow.python.ops import nn_grad # pylint: disable=unused-import from tensorflow.python.ops import nn_ops from tensorflow.python.platform import test diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 55fcd176d62009b9c29afb763dc20daf78cdb5d9..9d6f65dbbfd322100cd1047757e3e69df1d328d4 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -27,7 +27,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import candidate_sampling_ops from tensorflow.python.ops import embedding_ops -from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import gen_array_ops # pylint: disable=unused-import from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops @@ -888,12 +888,10 @@ def fused_batch_norm( # TODO(reedwm): In a few weeks, switch to using the V2 version exclusively. We # currently only use the V2 version for float16 inputs, which is not supported # by the V1 version. - # pylint: disable=protected-access if x.dtype == dtypes.float16 or x.dtype == dtypes.bfloat16: - fused_batch_norm_func = gen_nn_ops._fused_batch_norm_v2 + fused_batch_norm_func = gen_nn_ops.fused_batch_norm_v2 else: - fused_batch_norm_func = gen_nn_ops._fused_batch_norm - # pylint: enable=protected-access + fused_batch_norm_func = gen_nn_ops._fused_batch_norm # pylint: disable=protected-access y, batch_mean, batch_var, _, _ = fused_batch_norm_func( x, scale, @@ -1345,4 +1343,4 @@ def sampled_softmax_loss(weights, sampled_losses = nn_ops.softmax_cross_entropy_with_logits( labels=labels, logits=logits) # sampled_losses is a [batch_size] tensor. - return sampled_losses + return sampled_losses \ No newline at end of file diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index a691e281ee7f0ce4ee3253069b095a88df5723c5..fb3fe77b4ddfbebe98ff62f344ad4552a98dd007 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -29,6 +29,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops @@ -48,8 +49,8 @@ local_response_normalization = gen_nn_ops.lrn def _non_atrous_convolution( - input, - filter, + input, # pylint: disable=redefined-builtin + filter, # pylint: disable=redefined-builtin padding, data_format=None, # pylint: disable=redefined-builtin strides=None, @@ -94,9 +95,9 @@ def _non_atrous_convolution( """ with ops.name_scope(name, "non_atrous_convolution", [input, filter]) as scope: - input = ops.convert_to_tensor(input, name="input") + input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() - filter = ops.convert_to_tensor(filter, name="filter") + filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin filter_shape = filter.get_shape() op = _NonAtrousConvolution( input_shape, @@ -348,7 +349,7 @@ def with_space_to_batch( ValueError: if `spatial_dims` are invalid. """ - input = ops.convert_to_tensor(input, name="input") + input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() def build_op(num_spatial_dims, padding): @@ -645,7 +646,7 @@ def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate): @tf_export("nn.convolution") def convolution( - input, + input, # pylint: disable=redefined-builtin filter, # pylint: disable=redefined-builtin padding, strides=None, @@ -766,9 +767,9 @@ def convolution( """ # pylint: enable=line-too-long with ops.name_scope(name, "convolution", [input, filter]) as name: - input = ops.convert_to_tensor(input, name="input") + input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() - filter = ops.convert_to_tensor(filter, name="filter") + filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin filter_shape = filter.get_shape() op = Convolution( input_shape, @@ -962,7 +963,7 @@ def pool( # pylint: enable=line-too-long with ops.name_scope(name, "%s_pool" % (pooling_type.lower()), [input]) as scope: - input = ops.convert_to_tensor(input, name="input") + input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin num_spatial_dims = len(window_shape) if num_spatial_dims < 1 or num_spatial_dims > 3: @@ -1223,7 +1224,7 @@ def conv2d_transpose( if data_format not in ("NCHW", "NHWC"): raise ValueError("data_format has to be either NCHW or NHWC.") value = ops.convert_to_tensor(value, name="value") - filter = ops.convert_to_tensor(filter, name="filter") + filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin axis = 3 if data_format == "NHWC" else 1 if not value.get_shape()[axis].is_compatible_with(filter.get_shape()[3]): raise ValueError("input channels does not match filter's input channels, " @@ -1447,7 +1448,7 @@ def conv3d_transpose( with ops.name_scope(name, "conv3d_transpose", [value, filter, output_shape]) as name: value = ops.convert_to_tensor(value, name="value") - filter = ops.convert_to_tensor(filter, name="filter") + filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin axis = 1 if data_format == "NCDHW" else 4 if not value.get_shape()[axis].is_compatible_with(filter.get_shape()[4]): raise ValueError("input channels does not match filter's input channels, " @@ -1481,7 +1482,6 @@ def conv3d_transpose( name=name) -# pylint: disable=protected-access @tf_export("nn.bias_add") def bias_add(value, bias, data_format=None, name=None): """Adds `bias` to `value`. @@ -1504,12 +1504,12 @@ def bias_add(value, bias, data_format=None, name=None): A `Tensor` with the same type as `value`. """ with ops.name_scope(name, "BiasAdd", [value, bias]) as name: - value = ops.convert_to_tensor(value, name="input") - bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") - return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name) + if not context.executing_eagerly(): + value = ops.convert_to_tensor(value, name="input") + bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") + return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name) -# pylint: disable=protected-access def bias_add_v1(value, bias, name=None): """Adds `bias` to `value`. @@ -1534,7 +1534,7 @@ def bias_add_v1(value, bias, name=None): with ops.name_scope(name, "BiasAddV1", [value, bias]) as name: value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") - return gen_nn_ops._bias_add_v1(value, bias, name=name) + return gen_nn_ops.bias_add_v1(value, bias, name=name) @tf_export("nn.crelu") @@ -1580,7 +1580,7 @@ def relu6(features, name=None): """ with ops.name_scope(name, "Relu6", [features]) as name: features = ops.convert_to_tensor(features, name="features") - return gen_nn_ops._relu6(features, name=name) + return gen_nn_ops.relu6(features, name=name) @tf_export("nn.leaky_relu") @@ -1616,7 +1616,7 @@ def _flatten_outer_dims(logits): output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0)) # Set output shape if known. - if context.in_graph_mode(): + if not context.executing_eagerly(): shape = logits.get_shape() if shape is not None and shape.dims is not None: shape = shape.as_list() @@ -1645,7 +1645,7 @@ def _softmax(logits, compute_op, dim=-1, name=None): Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. - compute_op: Either gen_nn_ops._softmax or gen_nn_ops._log_softmax + compute_op: Either gen_nn_ops.softmax or gen_nn_ops.log_softmax dim: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). @@ -1739,7 +1739,7 @@ def softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 - return _softmax(logits, gen_nn_ops._softmax, axis, name) + return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export("nn.log_softmax") @@ -1769,7 +1769,7 @@ def log_softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 - return _softmax(logits, gen_nn_ops._log_softmax, axis, name) + return _softmax(logits, gen_nn_ops.log_softmax, axis, name) def _ensure_xent_args(name, sentinel, labels, logits): @@ -1871,7 +1871,7 @@ def softmax_cross_entropy_with_logits_v2( # Do the actual op computation. # The second output tensor contains the gradients. We use it in # _CrossEntropyGrad() in nn_grad but not here. - cost, unused_backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits( precise_logits, labels, name=name) # The output cost shape should be the input minus dim. @@ -1881,7 +1881,8 @@ def softmax_cross_entropy_with_logits_v2( # Make shape inference work since reshape and transpose may erase its static # shape. - if context.in_graph_mode() and shape is not None and shape.dims is not None: + if not context.executing_eagerly( + ) and shape is not None and shape.dims is not None: shape = shape.as_list() del shape[dim] cost.set_shape(shape) @@ -2027,6 +2028,9 @@ def sparse_softmax_cross_entropy_with_logits( # Store label shape for result later. labels_static_shape = labels.get_shape() labels_shape = array_ops.shape(labels) + static_shapes_fully_defined = ( + labels_static_shape.is_fully_defined() and + logits.get_shape()[:-1].is_fully_defined()) if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0: raise ValueError( "Logits cannot be scalars - received shape %s." % logits.get_shape()) @@ -2036,29 +2040,44 @@ def sparse_softmax_cross_entropy_with_logits( raise ValueError("Rank mismatch: Rank of labels (received %s) should " "equal rank of logits minus 1 (received %s)." % (labels_static_shape.ndims, logits.get_shape().ndims)) + if (static_shapes_fully_defined and + labels_static_shape != logits.get_shape()[:-1]): + raise ValueError("Shape mismatch: The shape of labels (received %s) " + "should equal the shape of logits except for the last " + "dimension (received %s)." % (labels_static_shape, + logits.get_shape())) # Check if no reshapes are required. if logits.get_shape().ndims == 2: - cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost - # Reshape logits to 2 dim, labels to 1 dim. - num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] - precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) - labels = array_ops.reshape(labels, [-1]) - # The second output tensor contains the gradients. We use it in - # _CrossEntropyGrad() in nn_grad but not here. - cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - precise_logits, labels, name=name) - cost = array_ops.reshape(cost, labels_shape) - cost.set_shape(labels_static_shape) - if logits.dtype == dtypes.float16: - return math_ops.cast(cost, dtypes.float16) - else: - return cost + # Perform a check of the dynamic shapes if the static shapes are not fully + # defined. + shape_checks = [] + if not static_shapes_fully_defined: + shape_checks.append( + check_ops.assert_equal( + array_ops.shape(labels), + array_ops.shape(logits)[:-1])) + with ops.control_dependencies(shape_checks): + # Reshape logits to 2 dim, labels to 1 dim. + num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] + precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) + labels = array_ops.reshape(labels, [-1]) + # The second output tensor contains the gradients. We use it in + # _CrossEntropyGrad() in nn_grad but not here. + cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( + precise_logits, labels, name=name) + cost = array_ops.reshape(cost, labels_shape) + cost.set_shape(labels_static_shape) + if logits.dtype == dtypes.float16: + return math_ops.cast(cost, dtypes.float16) + else: + return cost @tf_export("nn.avg_pool") @@ -2086,7 +2105,7 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "AvgPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._avg_pool( + return gen_nn_ops.avg_pool( value, ksize=ksize, strides=strides, @@ -2116,12 +2135,13 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool(value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops.max_pool( + value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @ops.RegisterStatistics("Conv2D", "flops") @@ -2215,6 +2235,31 @@ def xw_plus_b_v1(x, weights, biases, name=None): # pylint: disable=invalid-name return bias_add_v1(mm, biases, name=name) +def _get_noise_shape(x, noise_shape): + # If noise_shape is none return immediately. + if noise_shape is None: + return array_ops.shape(x) + + try: + # Best effort to figure out the intended shape. + # If not possible, let the op to handle it. + # In eager mode exception will show up. + noise_shape_ = tensor_shape.as_shape(noise_shape) + except (TypeError, ValueError): + return noise_shape + + if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims): + new_dims = [] + for i, dim in enumerate(x.shape.dims): + if noise_shape_.dims[i].value is None and dim.value is not None: + new_dims.append(dim.value) + else: + new_dims.append(noise_shape_.dims[i].value) + return tensor_shape.TensorShape(new_dims) + + return noise_shape + + @tf_export("nn.dropout") def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name """Computes dropout. @@ -2265,7 +2310,8 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di if tensor_util.constant_value(keep_prob) == 1: return x - noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x) + noise_shape = _get_noise_shape(x, noise_shape) + # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += random_ops.random_uniform( @@ -2273,13 +2319,13 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = math_ops.div(x, keep_prob) * binary_tensor - if context.in_graph_mode(): + if not context.executing_eagerly(): ret.set_shape(x.get_shape()) return ret @tf_export("nn.top_k") -def top_k(input, k=1, sorted=True, name=None): +def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-builtin """Finds values and indices of the `k` largest entries for the last dimension. If the input is a vector (rank=1), finds the `k` largest entries in the vector @@ -2305,10 +2351,10 @@ def top_k(input, k=1, sorted=True, name=None): values: The `k` largest elements along each last dimensional slice. indices: The indices of `values` within the last dimension of `input`. """ - return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name) + return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name) -def nth_element(input, n, reverse=False, name=None): +def nth_element(input, n, reverse=False, name=None): # pylint: disable=redefined-builtin r"""Finds values of the `n`-th order statistic for the last dmension. If the input is a vector (rank-1), finds the entries which is the nth-smallest @@ -2380,7 +2426,7 @@ def conv1d(value, Args: value: A 3D `Tensor`. Must be of type `float16` or `float32`. - filters: A 3D `Tensor`. Must have the same type as `input`. + filters: A 3D `Tensor`. Must have the same type as `value`. stride: An `integer`. The number of entries by which the filter is moved right at each step. padding: 'SAME' or 'VALID' @@ -2505,7 +2551,7 @@ def conv1d_transpose( spatial_start_dim = 2 strides = [1, 1, 1, stride] value = array_ops.expand_dims(value, spatial_start_dim) - filter = array_ops.expand_dims(filter, 0) + filter = array_ops.expand_dims(filter, 0) # pylint: disable=redefined-builtin result = gen_nn_ops.conv2d_backprop_input( input_sizes=output_shape_, @@ -2624,4 +2670,4 @@ def in_top_k(predictions, targets, k, name=None): A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`. """ with ops.name_scope(name, "in_top_k"): - return gen_nn_ops._in_top_kv2(predictions, targets, k, name=name) + return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name) diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 5a45bdc1e5e1d38a34176ed9443fcd1713f38e1e..21eea3db25af0d1bcfbc7496665f5535c3f660ea 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -383,6 +383,31 @@ class DropoutTest(test_lib.TestCase): x, keep_prob, noise_shape=array_ops.placeholder(dtypes.int32)) self.assertEqual(x.get_shape(), dropout_x.get_shape()) + def testPartialShapedDropout(self): + x_dim = 40 * 30 + y_dim = 3 + num_iter = 10 + for keep_prob in [0.1, 0.5, 0.8]: + with self.test_session(): + t = constant_op.constant( + 1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) + # Set noise_shape=[None, 1] which means [x_dim, 1]. + dropout = nn_ops.dropout(t, keep_prob, noise_shape=[None, 1]) + self.assertEqual([x_dim, y_dim], dropout.get_shape()) + final_count = 0 + for _ in xrange(0, num_iter): + value = dropout.eval() + final_count += np.count_nonzero(value) + # Verifies that there are only two values: 0 and 1/keep_prob. + sorted_value = np.unique(np.sort(value)) + self.assertEqual(0, sorted_value[0]) + self.assertAllClose(1 / keep_prob, sorted_value[1]) + # Check that we are in the 15% error range + expected_count = x_dim * y_dim * keep_prob * num_iter + rel_error = math.fabs(final_count - expected_count) / expected_count + print(rel_error) + self.assertTrue(rel_error < 0.15) + def testInvalidKeepProb(self): x_dim = 40 y_dim = 30 diff --git a/tensorflow/python/ops/numerics.py b/tensorflow/python/ops/numerics.py index b4ce1cbf25346412e2781a520b7e2cdcf720bcd5..d348e47f57b703138aabfc3463e750b795113335 100644 --- a/tensorflow/python/ops/numerics.py +++ b/tensorflow/python/ops/numerics.py @@ -74,7 +74,7 @@ def add_check_numerics_ops(): the checked operations. @enc_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "add_check_numerics_ops() is not compatible with eager execution. " "To check for Inf's and NaN's under eager execution, call " diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py index b0315ceee268be8ac1813dae5a262a7d9496e154..075b38d743d13329e646c0b268e938b5c5704e47 100644 --- a/tensorflow/python/ops/parsing_ops.py +++ b/tensorflow/python/ops/parsing_ops.py @@ -700,8 +700,7 @@ def _parse_example_raw(serialized, # Finally, convert dense_shapes to TensorShapeProto dense_shapes = [shape.as_proto() for shape in dense_shapes] - # pylint: disable=protected-access - outputs = gen_parsing_ops._parse_example( + outputs = gen_parsing_ops.parse_example( serialized=serialized, names=names, dense_defaults=dense_defaults_vec, @@ -710,7 +709,6 @@ def _parse_example_raw(serialized, dense_keys=dense_keys, dense_shapes=dense_shapes, name=name) - # pylint: enable=protected-access (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs @@ -1132,8 +1130,7 @@ def _parse_single_sequence_example_raw(serialized, feature_list_dense_shapes = [tensor_shape.as_shape(shape).as_proto() for shape in feature_list_dense_shapes] - # pylint: disable=protected-access - outputs = gen_parsing_ops._parse_single_sequence_example( + outputs = gen_parsing_ops.parse_single_sequence_example( serialized=serialized, debug_name=debug_name, context_dense_defaults=context_dense_defaults_vec, @@ -1149,7 +1146,6 @@ def _parse_single_sequence_example_raw(serialized, feature_list_dense_missing_assumed_empty=( feature_list_dense_missing_assumed_empty), name=name) - # pylint: enable=protected-access (context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, @@ -1182,7 +1178,6 @@ def _parse_single_sequence_example_raw(serialized, @tf_export("decode_csv") def decode_csv(records, record_defaults, field_delim=",", use_quote_delim=True, name=None, na_value=""): - # pylint: disable=protected-access """Convert CSV records to tensors. Each column maps to one tensor. RFC 4180 format is expected for the CSV records. @@ -1211,11 +1206,13 @@ def decode_csv(records, record_defaults, field_delim=",", Each tensor will have the same shape as records. """ # TODO(martinwicke), remove the wrapper when new Python API generator is done. - return gen_parsing_ops._decode_csv( - records=records, record_defaults=record_defaults, - field_delim=field_delim, use_quote_delim=use_quote_delim, - na_value=na_value, name=name) - # pylint: enable=protected-access + return gen_parsing_ops.decode_csv( + records=records, + record_defaults=record_defaults, + field_delim=field_delim, + use_quote_delim=use_quote_delim, + na_value=na_value, + name=name) # TODO(b/70890287): Combine the implementation of this op and @@ -1391,7 +1388,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types, # Finally, convert dense_shapes to TensorShapeProto dense_shapes = [shape.as_proto() for shape in dense_shapes] - # pylint: disable=protected-access outputs = gen_parsing_ops.parse_single_example( serialized=serialized, dense_defaults=dense_defaults_vec, @@ -1401,7 +1397,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types, dense_keys=dense_keys, dense_shapes=dense_shapes, name=name) - # pylint: enable=protected-access (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index 2c86358d21b1c280b8d7ade625fd4b7a44c5de26..db8159579a21d9b98b06b6172f8d0df7e8ff95ca 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -43,7 +43,6 @@ def _ShapeTensor(shape): return ops.convert_to_tensor(shape, dtype=dtype, name="shape") -# pylint: disable=protected-access @tf_export("random_normal") def random_normal(shape, mean=0.0, @@ -74,7 +73,7 @@ def random_normal(shape, mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._random_standard_normal( + rnd = gen_random_ops.random_standard_normal( shape_tensor, dtype, seed=seed1, seed2=seed2) mul = rnd * stddev_tensor value = math_ops.add(mul, mean_tensor, name=name) @@ -126,7 +125,7 @@ def parameterized_truncated_normal(shape, minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals") maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._parameterized_truncated_normal( + rnd = gen_random_ops.parameterized_truncated_normal( shape_tensor, means_tensor, stddevs_tensor, @@ -171,7 +170,7 @@ def truncated_normal(shape, mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._truncated_normal( + rnd = gen_random_ops.truncated_normal( shape_tensor, dtype, seed=seed1, seed2=seed2) mul = rnd * stddev_tensor value = math_ops.add(mul, mean_tensor, name=name) @@ -237,11 +236,10 @@ def random_uniform(shape, maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max") seed1, seed2 = random_seed.get_seed(seed) if dtype.is_integer: - return gen_random_ops._random_uniform_int( + return gen_random_ops.random_uniform_int( shape, minval, maxval, seed=seed1, seed2=seed2, name=name) else: - rnd = gen_random_ops._random_uniform( - shape, dtype, seed=seed1, seed2=seed2) + rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2) return math_ops.add(rnd * (maxval - minval), minval, name=name) @@ -275,7 +273,7 @@ def random_shuffle(value, seed=None, name=None): dimension. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_random_ops._random_shuffle( + return gen_random_ops.random_shuffle( value, seed=seed1, seed2=seed2, name=name) @@ -420,7 +418,7 @@ def random_gamma(shape, seed1, seed2 = random_seed.get_seed(seed) return math_ops.maximum( np.finfo(dtype.as_numpy_dtype).tiny, - gen_random_ops._random_gamma( + gen_random_ops.random_gamma( shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta) ops.NotDifferentiable("RandomGamma") diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index cc9f7981e4148cb117693fb3f83153dfba9c5895..54191ee765c642a9fe8f040eb3c22d1abdff293b 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import variable_pb2 +from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import context from tensorflow.python.eager import tape from tensorflow.python.framework import dtypes @@ -30,11 +31,13 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import gen_state_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_resource_variable_ops import * # pylint: enable=wildcard-import +from tensorflow.python.training import checkpointable from tensorflow.python.util import compat @@ -107,13 +110,16 @@ class EagerResourceDeleter(object): """ def __init__(self, handle, handle_device): + if not isinstance(handle, ops.Tensor): + raise ValueError( + ("Passed handle=%s to EagerResourceDeleter. Was expecting a handle " + "Tensor." % (handle,))) self._handle = handle self._handle_device = handle_device def __del__(self): # Resources follow object-identity when executing eagerly, so it is safe to - # delete the resource we have a handle to. Each Graph has a unique container - # name, which prevents resource sharing. + # delete the resource we have a handle to. try: # This resource was created in eager mode. However, this destructor may be # running in graph mode (especially during unit tests). To clean up @@ -129,10 +135,10 @@ class EagerResourceDeleter(object): # valid, and so on. Printing warnings in these cases is silly # (exceptions raised from __del__ are printed as warnings to stderr). pass # 'NoneType' object is not callable when the handle has been - # partially unloaded. + # partially unloaded. except AttributeError: pass # 'NoneType' object has no attribute 'eager_mode' when context has - # been unloaded. Will catch other module unloads as well. + # been unloaded. Will catch other module unloads as well. def shape_safe_assign_variable_handle(handle, shape, value, name=None): @@ -147,7 +153,7 @@ def shape_safe_assign_variable_handle(handle, shape, value, name=None): class ResourceVariable(variables.Variable): """Variable based on resource handles. - See the ${variables} documentation for more details. + See the @{$python/state_ops$`Variables`} documentation for more details. A `ResourceVariable` allows you to maintain state across subsequent calls to session.run. @@ -261,9 +267,9 @@ class ResourceVariable(variables.Variable): if initial_value is not None: raise ValueError("variable_def and initial_value are mutually " "exclusive.") - if not context.in_graph_mode(): - raise ValueError("Creating ResourceVariable from variable_def" - " only supported in GRAPH mode.") + if context.executing_eagerly(): + raise ValueError("Creating ResourceVariable from variable_def is " + "not supported when eager execution is enabled.") self._init_from_proto(variable_def, import_scope=import_scope) else: self._init_from_args( @@ -344,6 +350,11 @@ class ResourceVariable(variables.Variable): if constraint is not None and not callable(constraint): raise ValueError("The `constraint` argument must be a callable.") + if isinstance(initial_value, checkpointable.CheckpointInitialValue): + self._maybe_initialize_checkpointable() + self._update_uid = initial_value.checkpoint_position.restore_uid + initial_value = initial_value.wrapped_value + self._trainable = trainable if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] @@ -352,7 +363,7 @@ class ResourceVariable(variables.Variable): # this graph. self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access with ops.init_scope(): - self._in_graph_mode = context.in_graph_mode() + self._in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "Variable", [] if init_from_fn else [initial_value]) as name: # pylint: disable=protected-access @@ -375,9 +386,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=self._in_graph_mode) - self._handle_device = ( - self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() else: initial_value = initial_value() @@ -390,9 +398,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=False) - self._handle_device = ( - self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() # pylint: enable=protected-access @@ -416,8 +421,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=self._in_graph_mode) - self._handle_device = (self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() self._initial_value = initial_value if self._in_graph_mode else None @@ -440,7 +443,7 @@ class ResourceVariable(variables.Variable): with ops.name_scope("Read"), ops.colocate_with(self._handle): # Manually assign reads to the handle's device to avoid log # messages. - with ops.device(self._handle_device): + with ops.device(self._handle.device): value = self._read_variable_op() self._graph_element = value if caching_device is not None: @@ -467,7 +470,7 @@ class ResourceVariable(variables.Variable): self._cached_value = self._read_variable_op() else: self._cached_value = None - if context.in_graph_mode(): + if not context.executing_eagerly(): ops.add_to_collections(collections, self) elif ops.GraphKeys.GLOBAL_STEP in collections: ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self) @@ -480,12 +483,13 @@ class ResourceVariable(variables.Variable): # cycles being uncollectable, and means that no __del__ will be defined at # all in graph mode. self._handle_deleter = EagerResourceDeleter( - handle=self._handle, handle_device=self._handle_device) + handle=self._handle, handle_device=self._handle.device) + self._cached_shape_as_list = None def _init_from_proto(self, variable_def, import_scope=None): """Initializes from `VariableDef` proto.""" # Note that init_from_proto is currently not supported in Eager mode. - assert context.in_graph_mode() + assert not context.executing_eagerly() self._in_graph_mode = True assert isinstance(variable_def, variable_pb2.VariableDef) if not variable_def.is_resource: @@ -498,11 +502,18 @@ class ResourceVariable(variables.Variable): variable_def.variable_name, import_scope=import_scope)) self._shape = tensor_shape.TensorShape( self._handle.op.get_attr("shape")) - self._handle_device = self._handle.device self._handle_name = self._handle.name self._initializer_op = g.as_graph_element( ops.prepend_name_scope( variable_def.initializer_name, import_scope=import_scope)) + # Check whether initial_value_name exists for backwards compatibility. + if (hasattr(variable_def, "initial_value_name") and + variable_def.initial_value_name): + self._initial_value = g.as_graph_element( + ops.prepend_name_scope(variable_def.initial_value_name, + import_scope=import_scope)) + else: + self._initial_value = None if variable_def.snapshot_name: self._cached_value = g.as_graph_element( ops.prepend_name_scope( @@ -517,8 +528,10 @@ class ResourceVariable(variables.Variable): self._save_slice_info = None self._caching_device = None self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype")) - self._graph_element = self.value() + self._graph_element = g.get_tensor_by_name( + self._handle.op.name + "/Read/ReadVariableOp:0") self._constraint = None + self._cached_shape_as_list = None def __nonzero__(self): return self.__bool__() @@ -534,7 +547,7 @@ class ResourceVariable(variables.Variable): @property def device(self): """The device this variable is on.""" - return self._handle_device + return self._handle.device @property def graph(self): @@ -551,11 +564,26 @@ class ResourceVariable(variables.Variable): """The shape of this variable.""" return self._shape + def _shape_as_list(self): + if self._cached_shape_as_list: + return self._cached_shape_as_list + if self.shape.ndims is None: + return None + self._cached_shape_as_list = [dim.value for dim in self.shape.dims] + return self._cached_shape_as_list + + def _shape_tuple(self): + shape = self._shape_as_list() + if shape is None: + return None + return tuple(shape) + @property def create(self): """The op responsible for initializing this variable.""" if not self._in_graph_mode: - raise RuntimeError("Calling create in EAGER mode not supported.") + raise RuntimeError("Calling create is not supported when eager execution" + " is enabled.") return self._initializer_op @property @@ -568,7 +596,7 @@ class ResourceVariable(variables.Variable): if self._cached_value is not None: return self._cached_value with ops.colocate_with(None, ignore_existing=True): - with ops.device(self._handle_device): + with ops.device(self._handle.device): return self._read_variable_op() def _as_graph_element(self): @@ -583,7 +611,7 @@ class ResourceVariable(variables.Variable): @property def initial_value(self): """Returns the Tensor used as the initial value for the variable.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("initial_value not supported in EAGER mode.") return self._initial_value @@ -604,15 +632,15 @@ class ResourceVariable(variables.Variable): def eval(self, session=None): """Evaluates and returns the value of this variable.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Trying to eval in EAGER mode") return self._graph_element.eval(session=session) def numpy(self): - if context.in_graph_mode(): - raise NotImplementedError( - "numpy() is only available when eager execution is enabled.") - return self.read_value().numpy() + if context.executing_eagerly(): + return self.read_value().numpy() + raise NotImplementedError( + "numpy() is only available when eager execution is enabled.") def count_up_to(self, limit): """Increments this variable until it reaches `limit`. @@ -665,7 +693,7 @@ class ResourceVariable(variables.Variable): """ with ops.name_scope("Read"): # Ensure we read the variable in the same device as the handle. - with ops.device(self._handle_device): + with ops.device(self._handle.device): value = self._read_variable_op() # Return an identity so it can get placed on whatever device the context # specifies instead of the device where the variable is. @@ -693,12 +721,18 @@ class ResourceVariable(variables.Variable): A `VariableDef` protocol buffer, or `None` if the `Variable` is not in the specified name scope. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("to_proto not supported in EAGER mode.") if export_scope is None or self.handle.name.startswith(export_scope): var_def = variable_pb2.VariableDef() var_def.variable_name = ops.strip_name_scope(self.handle.name, export_scope) + if self._initial_value is not None: + # This is inside an if-statement for backwards compatibility, since + # self._initial_value might be None for variables constructed from old + # protos. + var_def.initial_value_name = ops.strip_name_scope( + self._initial_value.name, export_scope) var_def.initializer_name = ops.strip_name_scope(self.initializer.name, export_scope) if self._cached_value is not None: @@ -714,7 +748,7 @@ class ResourceVariable(variables.Variable): @staticmethod def from_proto(variable_def, import_scope=None): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("from_proto not supported in EAGER mode.") return ResourceVariable( variable_def=variable_def, import_scope=import_scope) @@ -765,42 +799,88 @@ class ResourceVariable(variables.Variable): __array_priority__ = 100 - def assign_sub(self, delta, use_locking=None, name=None): + def assign_sub(self, delta, use_locking=None, name=None, read_value=True): + """Subtracts a value from this variable. + + Args: + delta: A `Tensor`. The value to subtract from this variable. + use_locking: If `True`, use locking during the operation. + name: The name to use for the operation. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ # TODO(apassos): this here and below is not atomic. Consider making it # atomic if there's a way to do so without a performance cost for those who # don't need it. - with ops.control_dependencies([ - gen_resource_variable_ops.assign_sub_variable_op( - self.handle, - ops.convert_to_tensor(delta, dtype=self.dtype), - name=name) - ]): - return self.read_value() - - def assign_add(self, delta, use_locking=None, name=None): - with ops.control_dependencies([ - gen_resource_variable_ops.assign_add_variable_op( - self.handle, - ops.convert_to_tensor(delta, dtype=self.dtype), - name=name) - ]): - return self.read_value() - - def assign(self, value, use_locking=None, name=None): + assign_sub_op = gen_resource_variable_ops.assign_sub_variable_op( + self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name) + if read_value: + return self._lazy_read(assign_sub_op) + return assign_sub_op + + def assign_add(self, delta, use_locking=None, name=None, read_value=True): + """Adds a value to this variable. + + Args: + delta: A `Tensor`. The value to add to this variable. + use_locking: If `True`, use locking during the operation. + name: The name to use for the operation. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ + assign_add_op = gen_resource_variable_ops.assign_add_variable_op( + self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name) + if read_value: + return self._lazy_read(assign_add_op) + return assign_add_op + + def _lazy_read(self, op): + if hasattr(self, "_trainable") and self._trainable: + tape.watch_variable(self) + return _UnreadVariable( + self._handle, self.dtype, self._shape, self._in_graph_mode, + self._handle_deleter if not self._in_graph_mode else None, op) + + def assign(self, value, use_locking=None, name=None, read_value=True): + """Assigns a new value to this variable. + + Args: + value: A `Tensor`. The new value for this variable. + use_locking: If `True`, use locking during the assignment. + name: The name to use for the assignment. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ value_tensor = ops.convert_to_tensor(value, dtype=self.dtype) self._shape.assert_is_compatible_with(value_tensor.shape) - with ops.control_dependencies([ - gen_resource_variable_ops.assign_variable_op( - self.handle, - value_tensor, - name=name) - ]): - return self.read_value() + assign_op = gen_resource_variable_ops.assign_variable_op( + self.handle, value_tensor, name=name) + if read_value: + return self._lazy_read(assign_op) + return assign_op def _strided_slice_assign(self, begin, end, strides, value, name, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask): - with ops.control_dependencies([ + return self._lazy_read( gen_array_ops.resource_strided_slice_assign( ref=self.handle, begin=begin, @@ -812,9 +892,12 @@ class ResourceVariable(variables.Variable): end_mask=end_mask, ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask, - shrink_axis_mask=shrink_axis_mask) - ]): - return self.value() + shrink_axis_mask=shrink_axis_mask)) + + def __int__(self): + if self.dtype != dtypes.int32 and self.dtype != dtypes.int64: + raise TypeError("Non-integer variable can't be converted to integer.") + return int(self.value().numpy()) def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False): del name @@ -868,10 +951,69 @@ class ResourceVariable(variables.Variable): "Tensor object.") +pywrap_tensorflow.TFE_Py_RegisterResourceVariableType(ResourceVariable) +math_ops._resource_variable_type = ResourceVariable # pylint: disable=protected-access + + def _dense_var_to_tensor(var, dtype=None, name=None, as_ref=False): return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref) # pylint: disable=protected-access +class _UnreadVariable(ResourceVariable): + """Represents a future for a read of a variable. + + Pretends to be the tensor if anyone looks. + """ + + def __init__(self, handle, dtype, # pylint: disable=super-init-not-called + shape, in_graph_mode, deleter, parent_op): + # We do not call super init on purpose. + self._trainable = False + self._save_slice_info = None + self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access + self._in_graph_mode = in_graph_mode + self._handle = handle + self._shape = shape + self._initial_value = None + if isinstance(self._handle, ops.EagerTensor): + self._handle_name = "" + else: + self._handle_name = self._handle.name + self._dtype = dtype + self._constraint = None + self._cached_value = None + self._is_initialized_op = None + self._initializer_op = None + self._parent_op = parent_op + if context.executing_eagerly(): + self._graph_element = None + else: + self._graph_element = self.read_value() + self._handle_deleter = deleter + + def value(self): + return self._read_variable_op() + + def read_value(self): + return self._read_variable_op() + + def _read_variable_op(self): + with ops.control_dependencies([self._parent_op]): + return gen_resource_variable_ops.read_variable_op(self._handle, + self._dtype) + + def set_shape(self, shape): + self._shape = shape + self._cached_shape_as_list = None + + @property + def op(self): + """The op for this variable.""" + return self._parent_op + +ops.register_tensor_conversion_function(_UnreadVariable, _dense_var_to_tensor) +ops.register_dense_tensor_like_type(_UnreadVariable) + # Register a conversion function which reads the value of the variable, # allowing instances of the class to be used as tensors. @@ -943,3 +1085,9 @@ ops.register_proto_function( proto_type=variable_pb2.VariableDef, to_proto=_to_proto_fn, from_proto=_from_proto_fn) + + +def is_resource_variable(var): + """"Returns True if `var` is to be considered a ResourceVariable.""" + return isinstance(var, ResourceVariable) or hasattr( + var, "_should_act_as_resource_variable") diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index da80e72071c095207dcfaf18681ecf5e5998e0d6..625d433b1f76d245c1aa8dbf344b5b93e7fa7f83 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -83,8 +83,9 @@ def _best_effort_input_batch_size(flat_input): """Get static input batch size if available, with fallback to the dynamic one. Args: - flat_input: An iterable of time major input Tensors of shape [max_time, - batch_size, ...]. All inputs should have compatible batch sizes. + flat_input: An iterable of time major input Tensors of shape + `[max_time, batch_size, ...]`. + All inputs should have compatible batch sizes. Returns: The batch size in Python integer if available, or a scalar Tensor otherwise. @@ -574,7 +575,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -615,7 +616,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, ["Expected shape for Tensor %s is " % x.name, packed_shape, " but saw shape: ", x_shape]) - if context.in_graph_mode() and sequence_length is not None: + if not context.executing_eagerly() and sequence_length is not None: # Perform some shape validation with ops.control_dependencies( [_assert_has_shape(sequence_length, [batch_size])]): @@ -741,7 +742,7 @@ def _dynamic_rnn_loop(cell, element_shape=element_shape, tensor_array_name=base_name + name) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() if in_graph_mode: output_ta = tuple( _create_ta( @@ -1026,7 +1027,7 @@ def raw_rnn(cell, loop_fn, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -1241,7 +1242,7 @@ def static_rnn(cell, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index f1ac3e9bafa09e4647b4a4263e74fad29b643fd5..e61d10835fbebbc5cd2571d3c621da4b91f70974 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -46,6 +46,7 @@ from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -127,7 +128,7 @@ def _zero_state_tensors(state_size, batch_size, dtype): """Combine s with batch_size to get a proper tensor shape.""" c = _concat(batch_size, s) size = array_ops.zeros(c, dtype=dtype) - if context.in_graph_mode(): + if not context.executing_eagerly(): c_static = _concat(batch_size, s, static=True) size.set_shape(c_static) return size @@ -191,12 +192,13 @@ class RNNCell(base_layer.Layer): def _rnn_get_variable(self, getter, *args, **kwargs): variable = getter(*args, **kwargs) - if context.in_graph_mode(): - trainable = (variable in tf_variables.trainable_variables() or - (isinstance(variable, tf_variables.PartitionedVariable) and - list(variable)[0] in tf_variables.trainable_variables())) - else: + if context.executing_eagerly(): trainable = variable._trainable # pylint: disable=protected-access + else: + trainable = ( + variable in tf_variables.trainable_variables() or + (isinstance(variable, tf_variables.PartitionedVariable) and + list(variable)[0] in tf_variables.trainable_variables())) if trainable and variable not in self._trainable_weights: self._trainable_weights.append(variable) elif not trainable and variable not in self._non_trainable_weights: @@ -240,7 +242,7 @@ class RNNCell(base_layer.Layer): # Try to use the last cached zero_state. This is done to avoid recreating # zeros, especially when eager execution is enabled. state_size = self.state_size - is_eager = context.in_eager_mode() + is_eager = context.executing_eagerly() if is_eager and hasattr(self, "_last_zero_state"): (last_state_size, last_batch_size, last_dtype, last_output) = getattr(self, "_last_zero_state") @@ -255,7 +257,7 @@ class RNNCell(base_layer.Layer): return output -class _LayerRNNCell(RNNCell): +class LayerRNNCell(RNNCell): """Subclass of RNNCells that act like proper `tf.Layer` objects. For backwards compatibility purposes, most `RNNCell` instances allow their @@ -297,7 +299,7 @@ class _LayerRNNCell(RNNCell): @tf_export("nn.rnn_cell.BasicRNNCell") -class BasicRNNCell(_LayerRNNCell): +class BasicRNNCell(LayerRNNCell): """The most basic RNN cell. Args: @@ -355,7 +357,7 @@ class BasicRNNCell(_LayerRNNCell): @tf_export("nn.rnn_cell.GRUCell") -class GRUCell(_LayerRNNCell): +class GRUCell(LayerRNNCell): """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078). Args: @@ -473,7 +475,7 @@ class LSTMStateTuple(_LSTMStateTuple): @tf_export("nn.rnn_cell.BasicLSTMCell") -class BasicLSTMCell(_LayerRNNCell): +class BasicLSTMCell(LayerRNNCell): """Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. @@ -598,7 +600,7 @@ class BasicLSTMCell(_LayerRNNCell): @tf_export("nn.rnn_cell.LSTMCell") -class LSTMCell(_LayerRNNCell): +class LSTMCell(LayerRNNCell): """Long short-term memory unit (LSTM) recurrent network cell. The default non-peephole implementation is based on: @@ -1187,6 +1189,12 @@ class MultiRNNCell(RNNCell): "cells must be a list or tuple, but saw: %s." % cells) self._cells = cells + for cell_number, cell in enumerate(self._cells): + # Add Checkpointable dependencies on these cells so their variables get + # saved with this object when using object-based saving. + if isinstance(cell, checkpointable.CheckpointableBase): + # TODO(allenl): Track down non-Checkpointable callers. + self._track_checkpointable(cell, name="cell-%d" % (cell_number,)) self._state_is_tuple = state_is_tuple if not state_is_tuple: if any(nest.is_sequence(c.state_size) for c in self._cells): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 1b9071ee93c21f8d6bdc9ace11dbf57f3eb3e218..fb59bbba5eb7a668b6b959c8519f2b52f0ce26fb 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -25,6 +25,9 @@ from __future__ import print_function import threading +# Used by py_util.cc to get tracebacks. +import traceback # pylint: disable=unused-import + import numpy as np import six @@ -33,6 +36,7 @@ from tensorflow.python.eager import context from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.ops import gen_script_ops +from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -97,7 +101,7 @@ class FuncRegistry(object): components of a tensor have different lengths. This is bad: ignoring the padding is wrong for text data, and removing the padding is wrong for binary data. To avoid this bug, we redo the conversion using an object dtype. - Additionally, we convert unicode strings to (byte-)strings for Python3 + Additionally, we convert unicode strings to (byte-)strings for compatibility. Args: @@ -111,7 +115,7 @@ class FuncRegistry(object): if result.dtype.char == "S" and result is not value: return np.asarray(value, order="C", dtype=object) elif result.dtype.char == "U" and result is not value: - value = np.vectorize(lambda x: x.encode())(value) + value = np.vectorize(lambda x: x.encode("utf8"))(value) return np.asarray(value, order="C", dtype=object) elif result.dtype.char == "U": return result.astype(np.bytes_) @@ -176,7 +180,10 @@ class CleanupFunc(object): self._token = token def __del__(self): - _py_funcs.remove(self._token) + if _py_funcs is not None: + # If _py_funcs is None, the program is most likely in shutdown, and the + # _py_funcs object has been destroyed already. + _py_funcs.remove(self._token) def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): @@ -215,18 +222,16 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): graph._cleanup_py_funcs_used_in_graph.append(cleanup) # pylint: enable=protected-access - # pylint: disable=protected-access if eager: - result = gen_script_ops._eager_py_func( + result = gen_script_ops.eager_py_func( input=inp, token=token, Tout=Tout, name=name) else: if stateful: - result = gen_script_ops._py_func( + result = gen_script_ops.py_func( input=inp, token=token, Tout=Tout, name=name) else: - result = gen_script_ops._py_func_stateless( + result = gen_script_ops.py_func_stateless( input=inp, token=token, Tout=Tout, name=name) - # pylint: enable=protected-access return result if is_list_or_tuple else result[0] @@ -264,7 +269,7 @@ def py_func(func, inp, Tout, stateful=True, name=None): """Wraps a python function and uses it as a TensorFlow op. Given a python function `func`, which takes numpy arrays as its - inputs and returns numpy arrays as its outputs, wrap this function as an + arguments and returns numpy arrays as its outputs, wrap this function as an operation in a TensorFlow graph. The following snippet constructs a simple TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation in the graph: @@ -273,8 +278,8 @@ def py_func(func, inp, Tout, stateful=True, name=None): def my_func(x): # x will be a numpy array with the contents of the placeholder below return np.sinh(x) - inp = tf.placeholder(tf.float32) - y = tf.py_func(my_func, [inp], tf.float32) + input = tf.placeholder(tf.float32) + y = tf.py_func(my_func, [input], tf.float32) ``` **N.B.** The `tf.py_func()` operation has the following known limitations: @@ -290,10 +295,12 @@ def py_func(func, inp, Tout, stateful=True, name=None): server (e.g. using `with tf.device():`). Args: - func: A Python function, which accepts a list of NumPy `ndarray` objects - having element types that match the corresponding `tf.Tensor` objects - in `inp`, and returns a list of `ndarray` objects (or a single `ndarray`) - having element types that match the corresponding values in `Tout`. + func: A Python function, which accepts `ndarray` objects as arguments and + returns a list of `ndarray` objects (or a single `ndarray`). This function + must accept as many arguments as there are tensors in `inp`, and these + argument types will match the corresponding `tf.Tensor` objects + in `inp`. The returns `ndarray`s must match the number and types defined + `Tout`. Important Note: Input and output numpy `ndarray`s of `func` are not guaranteed to be copies. In some cases their underlying memory will be shared with the corresponding TensorFlow tensors. @@ -313,6 +320,12 @@ def py_func(func, inp, Tout, stateful=True, name=None): Returns: A list of `Tensor` or a single `Tensor` which `func` computes. """ + if context.executing_eagerly(): + result = func(*[x.numpy() for x in inp]) + result = nest.flatten(result) + + return [x if x is None else ops.convert_to_tensor(x) for x in result] + return _internal_py_func( func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name) diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py index cedd36c1deed541adcf601ff9447345e2279e8f9..ad38845153c94e9bb31e6e3ee05ebed0a4313efc 100644 --- a/tensorflow/python/ops/session_ops.py +++ b/tensorflow/python/ops/session_ops.py @@ -16,7 +16,6 @@ """Tensor Handle Operations. See the @{$python/session_ops} guide. @@get_session_handle -@@get_session_handle_v2 @@get_session_tensor @@delete_session_tensor """ @@ -182,7 +181,7 @@ def get_session_handle(data, name=None): # Colocate this operation with data. with ops.colocate_with(data): - return gen_data_flow_ops._get_session_handle(data, name=name) # pylint: disable=protected-access + return gen_data_flow_ops.get_session_handle(data, name=name) @tf_export("get_session_tensor") @@ -222,7 +221,7 @@ def get_session_tensor(handle, dtype, name=None): with ops.device(handle_device): holder = array_ops.placeholder(dtypes.string) _register_handle_feeder(holder.graph, holder, dtype) - tensor = gen_data_flow_ops._get_session_tensor(holder, dtype, name=name) + tensor = gen_data_flow_ops.get_session_tensor(holder, dtype, name=name) return (holder, tensor) @@ -246,7 +245,7 @@ def delete_session_tensor(handle, name=None): handle_device = TensorHandle._get_device_name(handle) with ops.device(handle_device): holder = array_ops.placeholder(dtypes.string) - deleter = gen_data_flow_ops._delete_session_tensor(holder, name=name) + deleter = gen_data_flow_ops.delete_session_tensor(holder, name=name) return (holder, deleter) @@ -268,7 +267,7 @@ def _get_handle_reader(graph, handle, dtype): with graph.as_default(), graph.device(handle_device): holder = array_ops.placeholder(dtypes.string) _register_handle_feeder(holder.graph, holder, dtype) - reader = gen_data_flow_ops._get_session_tensor(holder, dtype) + reader = gen_data_flow_ops.get_session_tensor(holder, dtype) result = (holder, reader) graph._handle_readers[graph_key] = result return result @@ -289,7 +288,7 @@ def _get_handle_mover(graph, feeder, handle): # Create mover if we haven't done it. holder, reader = _get_handle_reader(graph, handle, dtype) with graph.as_default(), graph.device(feeder.op.device): - mover = gen_data_flow_ops._get_session_handle(reader) # pylint: disable=protected-access + mover = gen_data_flow_ops.get_session_handle(reader) result = (holder, mover) graph._handle_movers[graph_key] = result return result @@ -303,7 +302,7 @@ def _get_handle_deleter(graph, deleter_key, handle): handle_device = TensorHandle._get_device_name(handle) with graph.as_default(), graph.device(handle_device): holder = array_ops.placeholder(dtypes.string) - deleter = gen_data_flow_ops._delete_session_tensor(holder) + deleter = gen_data_flow_ops.delete_session_tensor(holder) result = (holder, deleter) graph._handle_deleters[deleter_key] = result return result diff --git a/tensorflow/python/ops/sparse_grad.py b/tensorflow/python/ops/sparse_grad.py index 5295e7d21c2b5810422ec36f5aced63c9039feca..97353d6c747cb7e4d3c1fa92ad61af24fb17de91 100644 --- a/tensorflow/python/ops/sparse_grad.py +++ b/tensorflow/python/ops/sparse_grad.py @@ -88,10 +88,8 @@ def _SparseAddGrad(op, *grads): # the non-zero elements of the sum, and we will peek into `sum_indices` in the # gradient op. - # pylint: disable=protected-access - a_val_grad, b_val_grad = gen_sparse_ops._sparse_add_grad(val_grad, a_indices, - b_indices, - sum_indices) + a_val_grad, b_val_grad = gen_sparse_ops.sparse_add_grad( + val_grad, a_indices, b_indices, sum_indices) a_val_grad.set_shape(op.inputs[1].get_shape()) b_val_grad.set_shape(op.inputs[4].get_shape()) # (a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh) @@ -151,7 +149,7 @@ def _SparseTensorDenseMatMulGrad(op, grad): "complex gradients.") # gradient w.r.t. dense - b_grad = gen_sparse_ops._sparse_tensor_dense_mat_mul( # pylint: disable=protected-access + b_grad = gen_sparse_ops.sparse_tensor_dense_mat_mul( a_indices, a_values, a_shape, grad, adjoint_a=not adj_a) if adj_b: b_grad = array_ops.transpose(b_grad) @@ -278,8 +276,7 @@ def _SparseFillEmptyRowsGrad(op, unused_grad_output_indices, output_grad_values, """Gradients for SparseFillEmptyRows.""" reverse_index_map = op.outputs[3] - # pylint: disable=protected-access - d_values, d_default_value = gen_sparse_ops._sparse_fill_empty_rows_grad( + d_values, d_default_value = gen_sparse_ops.sparse_fill_empty_rows_grad( reverse_index_map=reverse_index_map, grad_values=output_grad_values) # d_indices, d_values, d_dense_shape, d_default_value. diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index 0fbbf5a805f1439d85ad53f02bdb665c04248606..c580052c32c8b61467b857af3d237be41718c1a1 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -234,7 +234,7 @@ def sparse_concat(axis, ] output_ind, output_val, output_shape = ( - gen_sparse_ops._sparse_concat(inds, vals, shapes, axis, name=name)) + gen_sparse_ops.sparse_concat(inds, vals, shapes, axis, name=name)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @@ -302,8 +302,8 @@ def sparse_add(a, b, thresh=0): thresh = ops.convert_to_tensor( thresh, dtype=a.values.dtype.real_dtype.base_dtype, name="thresh") output_ind, output_val, output_shape = ( - gen_sparse_ops._sparse_add(a.indices, a.values, a.dense_shape, - b.indices, b.values, b.dense_shape, thresh)) + gen_sparse_ops.sparse_add(a.indices, a.values, a.dense_shape, + b.indices, b.values, b.dense_shape, thresh)) # Attempt to get output_shape statically. a.get_shape().assert_is_compatible_with(b.get_shape()) @@ -317,8 +317,8 @@ def sparse_add(a, b, thresh=0): # swap to make `a` the SparseTensor. if isinstance(b, sparse_classes): a, b = b, a - return gen_sparse_ops._sparse_tensor_dense_add(a.indices, a.values, - a.dense_shape, b) + return gen_sparse_ops.sparse_tensor_dense_add(a.indices, a.values, + a.dense_shape, b) def _sparse_cross(inputs, name=None): @@ -402,7 +402,7 @@ def _sparse_cross_internal(inputs, num_buckets=0, hash_key=None, name=None): - """See gen_sparse_ops._sparse_cross.""" + """See gen_sparse_ops.sparse_cross.""" if not isinstance(inputs, list): raise TypeError("Inputs must be a list") if not all( @@ -432,7 +432,7 @@ def _sparse_cross_internal(inputs, dense_inputs[i] = math_ops.to_int64(dense_inputs[i]) internal_type = dtypes.int64 - indices_out, values_out, shape_out = gen_sparse_ops._sparse_cross( + indices_out, values_out, shape_out = gen_sparse_ops.sparse_cross( indices=indices, values=values, shapes=shapes, @@ -511,7 +511,7 @@ def sparse_reorder(sp_input, name=None): sp_input = _convert_to_sparse_tensor(sp_input) reordered_ind, reordered_val = ( - gen_sparse_ops._sparse_reorder( + gen_sparse_ops.sparse_reorder( sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)) if sp_input.get_shape().is_fully_defined(): @@ -575,7 +575,7 @@ def sparse_reshape(sp_input, shape, name=None): shape = math_ops.cast(shape, dtype=dtypes.int64) with ops.name_scope(name, "SparseReshape", [sp_input]) as name: - reshaped_ind, reshaped_shape = gen_sparse_ops._sparse_reshape( + reshaped_ind, reshaped_shape = gen_sparse_ops.sparse_reshape( sp_input.indices, sp_input.dense_shape, shape, name=name) reshaped_shape_const = tensor_util.constant_value(shape) @@ -671,7 +671,7 @@ def sparse_split(keyword_required=KeywordRequired(), sp_input = _convert_to_sparse_tensor(sp_input) output_inds, output_vals, output_shapes = ( - gen_sparse_ops._sparse_split( + gen_sparse_ops.sparse_split( axis, sp_input.indices, sp_input.values, @@ -782,7 +782,7 @@ def sparse_to_dense(sparse_indices, Dense `Tensor` of shape `output_shape`. Has the same type as `sparse_values`. """ - return gen_sparse_ops._sparse_to_dense( + return gen_sparse_ops.sparse_to_dense( sparse_indices, output_shape, sparse_values, @@ -1412,7 +1412,7 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None): default_value = ops.convert_to_tensor( default_value, dtype=sp_input.values.dtype) (output_indices, output_values, empty_row_indicator, - unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows( + unused_reverse_index_map) = gen_sparse_ops.sparse_fill_empty_rows( indices=sp_input.indices, values=sp_input.values, dense_shape=sp_input.dense_shape, @@ -1441,7 +1441,7 @@ def serialize_sparse(sp_input, name=None, out_type=dtypes.string): """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._serialize_sparse( + return gen_sparse_ops.serialize_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -1476,7 +1476,7 @@ def serialize_many_sparse(sp_input, name=None, out_type=dtypes.string): """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._serialize_many_sparse( + return gen_sparse_ops.serialize_many_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -1541,7 +1541,7 @@ def deserialize_sparse(serialized_sparse, dtype, rank=None, name=None): """ output_indices, output_values, output_shape = ( - gen_sparse_ops._deserialize_sparse(serialized_sparse, dtype, name=name)) + gen_sparse_ops.deserialize_sparse(serialized_sparse, dtype, name=name)) # Feed rank data back in, if available output_indices.set_shape([None, rank]) @@ -1610,7 +1610,7 @@ def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None): All of the serialized `SparseTensor`s must have had the same rank and type. """ output_indices, output_values, output_shape = ( - gen_sparse_ops._deserialize_many_sparse( + gen_sparse_ops.deserialize_many_sparse( serialized_sparse, dtype, name=name)) # Feed rank data back in, if available @@ -1828,7 +1828,7 @@ def sparse_tensor_dense_matmul(sp_a, with ops.name_scope(name, "SparseTensorDenseMatMul", [sp_a.indices, sp_a.values, b]) as name: b = ops.convert_to_tensor(b, name="b") - return gen_sparse_ops._sparse_tensor_dense_mat_mul( + return gen_sparse_ops.sparse_tensor_dense_mat_mul( a_indices=sp_a.indices, a_values=sp_a.values, a_shape=sp_a.dense_shape, @@ -2046,7 +2046,7 @@ def _add_sparse_to_tensors_map(sp_input, """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._add_sparse_to_tensors_map( + return gen_sparse_ops.add_sparse_to_tensors_map( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -2086,7 +2086,7 @@ def _add_many_sparse_to_tensors_map(sp_input, """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._add_many_sparse_to_tensors_map( + return gen_sparse_ops.add_many_sparse_to_tensors_map( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -2167,7 +2167,7 @@ def _take_many_sparse_from_tensors_map(sparse_map_op, with ops.colocate_with(sparse_map_op): shared_name = sparse_map_op.get_attr("shared_name") or sparse_map_op.name output_indices, output_values, output_shape = ( - gen_sparse_ops._take_many_sparse_from_tensors_map( + gen_sparse_ops.take_many_sparse_from_tensors_map( sparse_handles, dtype=sparse_map_op.get_attr("T"), container=sparse_map_op.get_attr("container"), diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index 737b923415f4a36ee7886ec0eb2e6dfa78be1de4..60a98aca7f822802363de0efd0d2974e45dc810e 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -25,6 +25,7 @@ import sys as _sys # Imports the following modules so that @RegisterGradient get executed. from tensorflow.python.ops import array_grad from tensorflow.python.ops import data_flow_grad +from tensorflow.python.ops import manip_grad from tensorflow.python.ops import math_grad from tensorflow.python.ops import manip_grad from tensorflow.python.ops import sparse_grad @@ -43,11 +44,12 @@ from tensorflow.python.ops.special_math_ops import * # TODO(vrv): Switch to import * once we're okay with exposing the module. from tensorflow.python.ops.confusion_matrix import confusion_matrix from tensorflow.python.ops.control_flow_ops import Assert +from tensorflow.python.ops.control_flow_ops import case +from tensorflow.python.ops.control_flow_ops import cond from tensorflow.python.ops.control_flow_ops import group from tensorflow.python.ops.control_flow_ops import no_op -from tensorflow.python.ops.control_flow_ops import tuple -from tensorflow.python.ops.control_flow_ops import cond -from tensorflow.python.ops.control_flow_ops import case +from tensorflow.python.ops.control_flow_ops import tuple # pylint: disable=redefined-builtin +# pylint: enable=redefined-builtin from tensorflow.python.ops.control_flow_ops import while_loop from tensorflow.python.ops.data_flow_ops import * from tensorflow.python.ops.functional_ops import * @@ -58,6 +60,7 @@ from tensorflow.python.ops.io_ops import * from tensorflow.python.ops.linalg_ops import * from tensorflow.python.ops.logging_ops import Print from tensorflow.python.ops.logging_ops import get_summary_op +from tensorflow.python.ops.logging_ops import timestamp from tensorflow.python.ops.lookup_ops import initialize_all_tables from tensorflow.python.ops.lookup_ops import tables_initializer from tensorflow.python.ops.manip_ops import * @@ -183,7 +186,6 @@ _allowed_symbols_array_ops = [ "quantize_and_dequantize", # to-doc # TODO(drpng): legacy symbols to be removed. - "list_diff", # Use tf.listdiff instead. "batch_matrix_diag", "batch_matrix_band_part", "batch_matrix_diag_part", @@ -216,6 +218,7 @@ _allowed_symbols_gradients = [ # Documented in training.py: # Not importing training.py to avoid complex graph dependencies. "AggregationMethod", + "custom_gradient", "gradients", # tf.gradients = gradients.gradients "hessians", ] @@ -230,7 +233,7 @@ _allowed_symbols_clip_ops = [ "global_norm", ] -_allowed_symbols_image_ops = [ +_allowed_symbols_logging_ops = [ # Documented in training.py. # We are not importing training.py to avoid complex dependencies. "audio_summary", @@ -260,42 +263,43 @@ _allowed_symbols = (_allowed_symbols_array_ops + _allowed_symbols_clip_ops + _allowed_symbols_control_flow_ops + _allowed_symbols_functional_ops + - _allowed_symbols_image_ops + _allowed_symbols_gradients + + _allowed_symbols_logging_ops + _allowed_symbols_math_ops + _allowed_symbols_variable_scope_ops + _allowed_symbols_misc + _allowed_symbols_partitioned_variables) -remove_undocumented(__name__, _allowed_symbols, - [_sys.modules[__name__], - _array_ops, - _check_ops, - _clip_ops, - _confusion_matrix, - _control_flow_ops, - _constant_op, - _data_flow_ops, - _functional_ops, - _gradients, - _histogram_ops, - _init_ops, - _io_ops, - _linalg_ops, - _logging_ops, - _manip_ops, - _math_ops, - _numerics, - _parsing_ops, - _partitioned_variables, - _random_ops, - _script_ops, - _session_ops, - _sparse_ops, - _special_math_ops, - _state_ops, - _string_ops, - _template, - _tensor_array_ops, - _variable_scope, - _variables,]) +remove_undocumented(__name__, _allowed_symbols, [ + _sys.modules[__name__], + _array_ops, + _check_ops, + _clip_ops, + _confusion_matrix, + _control_flow_ops, + _constant_op, + _data_flow_ops, + _functional_ops, + _gradients, + _histogram_ops, + _init_ops, + _io_ops, + _linalg_ops, + _logging_ops, + _manip_ops, + _math_ops, + _numerics, + _parsing_ops, + _partitioned_variables, + _random_ops, + _script_ops, + _session_ops, + _sparse_ops, + _special_math_ops, + _state_ops, + _string_ops, + _template, + _tensor_array_ops, + _variable_scope, + _variables, +]) diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index f00213eb88dce8e7bf73264a54780a704b4c4b18..c3ad5831b4dfa2160a198429c60c7a6ac00f6357 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -99,8 +99,8 @@ def variable_op(shape, dtype, name="Variable", set_shape=True, container="", """Deprecated. Used variable_op_v2 instead.""" if not set_shape: shape = tensor_shape.unknown_shape() - ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name, - container=container, shared_name=shared_name) + ret = gen_state_ops.variable(shape=shape, dtype=dtype, name=name, + container=container, shared_name=shared_name) # TODO(mrry): Move this to where it is used, so we can get rid of this op # wrapper? if set_shape: @@ -127,11 +127,12 @@ def variable_op_v2(shape, dtype, name="Variable", container="", shared_name=""): Returns: A variable tensor. """ - return gen_state_ops._variable_v2(shape=shape, - dtype=dtype, - name=name, - container=container, - shared_name=shared_name) + return gen_state_ops.variable_v2( + shape=shape, + dtype=dtype, + name=name, + container=container, + shared_name=shared_name) def init_variable(v, init, name="init"): @@ -185,7 +186,7 @@ def is_variable_initialized(ref, name=None): if ref.dtype._is_ref_dtype: return gen_state_ops.is_variable_initialized(ref=ref, name=name) # Handle resource variables. - if context.in_eager_mode() or ref.op.type == "VarHandleOp": + if context.executing_eagerly() or ref.op.type == "VarHandleOp": return gen_resource_variable_ops.var_is_initialized_op(ref.handle, name=name) @@ -353,11 +354,9 @@ def scatter_update(ref, indices, updates, use_locking=True, name=None): if ref.dtype._is_ref_dtype: return gen_state_ops.scatter_update(ref, indices, updates, use_locking=use_locking, name=name) - with ops.control_dependencies( - [gen_resource_variable_ops.resource_scatter_update( - ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype), - name=name)]): - return ref.read_value() + return ref._lazy_read(gen_resource_variable_ops.resource_scatter_update( # pylint: disable=protected-access + ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype), + name=name)) @tf_export("scatter_nd_update") diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index b8c39d91b41790c6441594b175e8eaa03620e1ec..5bd75b9215fdbccd5882ea39c2b35ccbbe29d5b0 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -17,6 +17,7 @@ See the @{$python/string_ops} guide. +@@regex_replace @@string_to_hash_bucket_fast @@string_to_hash_bucket_strong @@string_to_hash_bucket @@ -93,10 +94,8 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv delimiter = ops.convert_to_tensor(delimiter, dtype=dtypes.string) source = ops.convert_to_tensor(source, dtype=dtypes.string) - # pylint: disable=protected-access - indices, values, shape = gen_string_ops._string_split( + indices, values, shape = gen_string_ops.string_split( source, delimiter=delimiter, skip_empty=skip_empty) - # pylint: enable=protected-access indices.set_shape([None, 2]) values.set_shape([None]) shape.set_shape([2]) @@ -141,6 +140,7 @@ def reduce_join(inputs, axis=None, reduce_join.__doc__ = deprecation.rewrite_argument_docstring( gen_string_ops.reduce_join.__doc__, "reduction_indices", "axis") +ops.NotDifferentiable("RegexReplace") ops.NotDifferentiable("StringToHashBucket") ops.NotDifferentiable("StringToHashBucketFast") ops.NotDifferentiable("StringToHashBucketStrong") diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py index 7f4f4ce5ab4ee2bd309932cb81f05775996371d6..037bc9845a3f734f65b73b0c4b4ca19fb653731d 100644 --- a/tensorflow/python/ops/summary_ops.py +++ b/tensorflow/python/ops/summary_ops.py @@ -13,7 +13,6 @@ # limitations under the License. # ============================================================================== """Summary Operations.""" -# pylint: disable=protected-access from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -74,7 +73,7 @@ def tensor_summary(name, with summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): - val = gen_logging_ops._tensor_summary_v2( + val = gen_logging_ops.tensor_summary_v2( tensor=tensor, tag=tag, name=scope, diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 806fdd3da7aa6de01b7cd4d9d36dbf43f6139db6..0a391d896a388f7b40b574f6e1367e9d22fb1d0a 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -26,6 +26,7 @@ from tensorflow.python.eager import function from tensorflow.python.framework import ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.deprecation import deprecated @@ -203,7 +204,7 @@ def make_template_internal(name_, if kwargs: func_ = tf_decorator.make_decorator(func_, functools.partial( func_, **kwargs)) - if context.in_eager_mode(): + if context.executing_eagerly(): if unique_name_ is not None: raise ValueError( "unique_name_ cannot be used when eager exeuction is enabled.") @@ -230,7 +231,7 @@ def _skip_common_stack_elements(stacktrace, base_case): return stacktrace[-1:] -class Template(object): +class Template(checkpointable.CheckpointableBase): """Wrap a function to aid in variable sharing. Templates are functions that create variables the first time they are called @@ -294,12 +295,115 @@ class Template(object): # which is not the same as whether the scope has been created. self._variables_created = False + @property + def _checkpoint_dependencies(self): + """Sanity checking for object-based saving. + + Does not override Checkpointable dependency tracking, but checks that + variables accessible through Checkpointable dependencies on other `Template` + objects include all of the variable_scope-filtered `Template.variables`. + + Returns: + A list of checkpointable.CheckpointableReference objects. + Raises: + ValueError: If this object is not compatible with object-based saving. + """ + dependencies = super(Template, self)._checkpoint_dependencies + dependency_variables = [] + for _, dependency in dependencies: + if isinstance(dependency, Template): + dependency_variables.extend(dependency.variables) + else: + dependency_variables.append(dependency) + dependency_variables = set(dependency_variables) + not_included_variables = [] + for expected_variable in sorted(self.variables, key=lambda v: v.name): + if expected_variable not in dependency_variables: + not_included_variables.append(expected_variable) + if not_included_variables: + # Trying to save a Template which improperly tracks its variables. + raise ValueError( + ("The Template '%s' references variables which are not included via " + "object-based dependency tracking. Most likely a custom " + "getter/creator was registered which does not call Template's " + "custom variable creator (which is responsible for tracking " + "dependencies).\n\nExpected these variables to be dependencies: %s") + % (self, not_included_variables)) + return dependencies + + def _checkpointable_custom_creator(self, next_creator, name, initial_value, + checkpointable_parent=None, **kwargs): + """A variable creation hook which adds Checkpointable dependencies. + + Set during the `Template`'s first wrapped function execution. Ensures that + (a) `Template` objects depend on `Template`s created inside them which + create variables, and (b) that any variables not in a more deeply nested + `Template` are added as dependencies directly. + + The `checkpointable_parent` argument is passed between `Template` custom + creators but ignored when the variable object itself is created. This + argument indicates (if not `None`) that a more deeply nested `Template` has + already added the variable as a dependency, and that parent `Template`s + should add a dependency on that `Template` rather than on the variable + directly. + + Args: + next_creator: See `variable_scope.variable_creator_scope`; the next + creator in the chain. + name: The (full, scope-influenced) name of the variable. The scope name + for the Template itself is stripped for the purposes of object-based + dependency tracking, but scopes within Templates are respected. + initial_value: See `variable_scope.variable_creator_scope`. Taken + explicitly so the argument can be re-named and used with + `Checkpointable._add_variable_with_custom_getter`. + checkpointable_parent: If not None, a more deeply nested Template object + to add a dependency on (rather than depending on the variable directly). + **kwargs: Passed through to the next creator. + Returns: + The output of `next_creator`: the fetched/created variable object. + """ + def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): + inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which + # we don't want to propagate. + return next_creator( + initial_value=initializer, + name=name, + **inner_kwargs) + if name.startswith(self._variable_scope.name): + scope_stripped_name = name[len(self._variable_scope.name) + 1:] + if not checkpointable_parent: + return self._add_variable_with_custom_getter( + initializer=initial_value, + name=scope_stripped_name, + getter=_call_next_creator_renaming_initializer, + # Disable error checking for Checkpointable. Exceptions are instead + # raised if necessary when the object-based saver tries to + # save/restore the object. + overwrite=True, + checkpointable_parent=self, + **kwargs) + else: + self._track_checkpointable( + checkpointable_parent, + name=checkpointable_parent._variable_scope.name[ # pylint: disable=protected-access + len(self._variable_scope.name) + 1:], + overwrite=True) + return next_creator(name=name, initial_value=initial_value, + checkpointable_parent=self, **kwargs) + def _call_func(self, args, kwargs): try: vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) trainable_at_start = len( ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - result = self._func(*args, **kwargs) + if self._variables_created: + result = self._func(*args, **kwargs) + else: + # The first time we run, restore variables if necessary (via + # Checkpointable). + with variable_scope.variable_creator_scope( + self._checkpointable_custom_creator): + result = self._func(*args, **kwargs) if self._variables_created: # Variables were previously created, implying this is not the first @@ -543,7 +647,7 @@ class EagerTemplate(Template): Raises: RuntimeError: if eager execution is not enabled. """ - if not context.in_eager_mode(): + if not context.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " "tf.Template for graph construction". @@ -557,12 +661,20 @@ class EagerTemplate(Template): # is created in __call__. variable_scope_name = None self._template_store = _EagerTemplateVariableStore(variable_scope_name) + self._variable_scope_context_manager = None def _call_func(self, args, kwargs): try: vars_at_start = self._template_store.variables() trainable_at_start = self._template_store.trainable_variables() - result = self._func(*args, **kwargs) + if self._variables_created: + result = self._func(*args, **kwargs) + else: + # The first time we run, restore variables if necessary (via + # Checkpointable). + with variable_scope.variable_creator_scope( + self._checkpointable_custom_creator): + result = self._func(*args, **kwargs) if self._variables_created: # Variables were previously created, implying this is not the first @@ -611,8 +723,12 @@ class EagerTemplate(Template): # the variable scope is opened in order to ensure that templates nested at # the same level correctly uniquify lower variable scope names. if self._variable_scope: - with variable_scope.variable_scope( - self._variable_scope, reuse=variable_scope.AUTO_REUSE): + # Create a cache for the variable scope context manager the first time + # around so that we don't have to keep recreating it. + if not self._variable_scope_context_manager: + self._variable_scope_context_manager = variable_scope.variable_scope( + self._variable_scope, reuse=variable_scope.AUTO_REUSE) + with self._variable_scope_context_manager: with self._template_store.as_default(): result = self._call_func(args, kwargs) return result diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index 5cdf03509e3c427deec7e26345059211001e2131..2f6badcb532c0ef9d82b211d0c7b11a67e8e3010 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -148,7 +148,7 @@ class _GraphTensorArray(object): # will retroactively set the device value of this op. def create(): """Create the TensorArray op.""" - return gen_data_flow_ops._tensor_array_v3( + return gen_data_flow_ops.tensor_array_v3( dtype=dtype, size=size, element_shape=element_shape, @@ -237,7 +237,7 @@ class _GraphTensorArray(object): flow = self.flow with ops.name_scope(name, "TensorArrayGrad", [self._handle]): with ops.colocate_with(self._handle): - g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3( + g_handle, unused_flow = gen_data_flow_ops.tensor_array_grad_v3( handle=self._handle, source=source, flow_in=flow, name=name) with ops.control_dependencies([g_handle]): flow = array_ops.identity(flow, name="gradient_flow") @@ -252,7 +252,7 @@ class _GraphTensorArray(object): def read(self, index, name=None): """See TensorArray.""" - value = gen_data_flow_ops._tensor_array_read_v3( + value = gen_data_flow_ops.tensor_array_read_v3( handle=self._handle, index=index, flow_in=self._flow, @@ -270,7 +270,7 @@ class _GraphTensorArray(object): if self._infer_shape: self._merge_element_shape(value.shape) with self._maybe_colocate_with(value): - flow_out = gen_data_flow_ops._tensor_array_write_v3( + flow_out = gen_data_flow_ops.tensor_array_write_v3( handle=self._handle, index=index, value=value, @@ -296,7 +296,7 @@ class _GraphTensorArray(object): element_shape = self._element_shape[0] else: element_shape = tensor_shape.TensorShape(None) - value = gen_data_flow_ops._tensor_array_gather_v3( + value = gen_data_flow_ops.tensor_array_gather_v3( handle=self._handle, indices=indices, flow_in=self._flow, @@ -314,7 +314,7 @@ class _GraphTensorArray(object): tensor_shape.TensorShape(self._element_shape[0].dims[1:])) else: element_shape_except0 = tensor_shape.TensorShape(None) - value, _ = gen_data_flow_ops._tensor_array_concat_v3( + value, _ = gen_data_flow_ops.tensor_array_concat_v3( handle=self._handle, flow_in=self._flow, dtype=self._dtype, @@ -338,10 +338,10 @@ class _GraphTensorArray(object): with ops.name_scope(name, "TensorArrayScatter", [self._handle, value, indices]): value = ops.convert_to_tensor(value, name="value") - if self._infer_shape and context.in_graph_mode(): + if self._infer_shape and not context.executing_eagerly(): self._merge_element_shape(value.shape[1:]) with self._maybe_colocate_with(value): - flow_out = gen_data_flow_ops._tensor_array_scatter_v3( + flow_out = gen_data_flow_ops.tensor_array_scatter_v3( handle=self._handle, indices=indices, value=value, @@ -363,14 +363,14 @@ class _GraphTensorArray(object): value = ops.convert_to_tensor(value, name="value") with self._maybe_colocate_with(value): lengths_64 = math_ops.to_int64(lengths) - if self._infer_shape and context.in_graph_mode(): + if self._infer_shape and not context.executing_eagerly(): clengths = tensor_util.constant_value(lengths_64) if value.shape.dims is not None: if clengths is not None and clengths.max() == clengths.min(): self._merge_element_shape( tensor_shape.TensorShape([clengths[0]]).concatenate( value.shape[1:])) - flow_out = gen_data_flow_ops._tensor_array_split_v3( + flow_out = gen_data_flow_ops.tensor_array_split_v3( handle=self._handle, value=value, lengths=lengths_64, @@ -386,13 +386,13 @@ class _GraphTensorArray(object): def size(self, name=None): """See TensorArray.""" - return gen_data_flow_ops._tensor_array_size_v3( + return gen_data_flow_ops.tensor_array_size_v3( handle=self._handle, flow_in=self.flow, name=name) @tf_should_use.should_use_result def close(self, name=None): """See TensorArray.""" - return gen_data_flow_ops._tensor_array_close_v3( + return gen_data_flow_ops.tensor_array_close_v3( handle=self._handle, name=name) # pylint: enable=protected-access @@ -653,7 +653,7 @@ class _EagerTensorArray(object): if len(tensors) > len(self._tensor_array) and not self._dynamic_size: raise ValueError( "Cannot unstack %d tensors into a TensorArray of static size %d" % - (len(tensors), len(self._tensors))) + (len(tensors), len(self._tensor_array))) ta = self._identity_without_array() ta._implementation._tensor_array = tensors # pylint: disable=protected-access return ta @@ -774,10 +774,10 @@ class TensorArray(object): ValueError: if both handle and tensor_array_name are provided. TypeError: if handle is provided but is not a Tensor. """ - if context.in_graph_mode(): - implementation = _GraphTensorArray - else: + if context.executing_eagerly(): implementation = _EagerTensorArray + else: + implementation = _GraphTensorArray self._implementation = implementation( dtype, diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 81565a63774da49628d100ef071b02f6311f6af2..7f650ff6a916c5ff93122cb5086af9ea3cda04c7 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -321,7 +321,7 @@ class _VariableStore(object): raise ValueError( "Passed a custom_getter which is not callable: %s" % custom_getter) - if context.in_eager_mode(): + if context.executing_eagerly(): if not self._store_eager_variables and reuse: raise RuntimeError( "When eager execution is enabled variable reuse is only supported" @@ -518,7 +518,7 @@ class _VariableStore(object): when violating reuse during variable creation, or if an existing sharded variable exists for the given name but with different sharding. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") @@ -798,7 +798,7 @@ class _VariableStore(object): validate_shape=validate_shape, constraint=constraint, use_resource=use_resource) - if context.in_graph_mode() or self._store_eager_variables: + if not context.executing_eagerly() or self._store_eager_variables: # In eager mode we do not want to keep default references to Variable # objects as this will prevent their memory from being released. self._vars[name] = v @@ -811,12 +811,12 @@ class _VariableStore(object): with ops.name_scope(name + "/Regularizer/"): loss = regularizer(v) if loss is not None: - if context.in_graph_mode(): - v_name = v.name - loss_name = loss.name - else: + if context.executing_eagerly(): v_name = "v_%s" % type(v) loss_name = "loss_%s" % type(loss) + else: + v_name = v.name + loss_name = loss.name logging.vlog(1, "Applied regularizer to %s and added the result %s " "to REGULARIZATION_LOSSES.", v_name, loss_name) ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, loss) @@ -920,7 +920,7 @@ class VariableScope(object): self._dtype = dtype self._use_resource = use_resource self._constraint = constraint - if context.in_eager_mode(): + if context.executing_eagerly(): if self._caching_device is not None: raise NotImplementedError("Caching devices is not yet supported " "when eager execution is enabled.") @@ -988,7 +988,7 @@ class VariableScope(object): def set_use_resource(self, use_resource): """Sets whether to use ResourceVariables for this scope.""" - if context.in_eager_mode() and not use_resource: + if context.executing_eagerly() and not use_resource: raise ValueError("When eager execution is enabled, " "use_resource cannot be set to false.") self._use_resource = use_resource @@ -999,14 +999,14 @@ class VariableScope(object): def set_caching_device(self, caching_device): """Set caching_device for this scope.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Caching devices are not yet supported " "when eager execution is enabled.") self._caching_device = caching_device def set_partitioner(self, partitioner): """Set partitioner for this scope.""" - if partitioner and context.in_eager_mode(): + if partitioner and context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") self._partitioner = partitioner @@ -1057,14 +1057,14 @@ class VariableScope(object): partitioner = self._partitioner if custom_getter is None: custom_getter = self._custom_getter - if context.in_graph_mode(): + if context.executing_eagerly(): + reuse = False + use_resource = True + else: if reuse is None: reuse = self._reuse if use_resource is None: use_resource = self._use_resource - else: - reuse = False - use_resource = True full_name = self.name + "/" + name if self.name else name # Variable names only depend on variable_scope (full_name here), @@ -1107,7 +1107,7 @@ class VariableScope(object): use_resource=None, constraint=None): """Gets an existing variable with this name or create a new one.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") if initializer is None: @@ -1871,7 +1871,7 @@ class variable_scope(object): raise ValueError("The reuse parameter must be True or False or None.") if self._values is None: self._values = [] - self._in_graph_mode = not context.in_eager_mode() + self._in_graph_mode = not context.executing_eagerly() if self._in_graph_mode: self._graph = ops._get_graph_from_inputs(self._values) # pylint: disable=protected-access self._cached_pure_variable_scope = None @@ -2111,13 +2111,13 @@ def default_variable_creator(next_creator=None, **kwargs): use_resource = kwargs.get("use_resource", None) if use_resource is None: use_resource = get_variable_scope().use_resource - if use_resource or (use_resource is None and context.in_eager_mode()): + if use_resource or (use_resource is None and context.executing_eagerly()): return resource_variable_ops.ResourceVariable( initial_value=initial_value, trainable=trainable, collections=collections, validate_shape=validate_shape, caching_device=caching_device, name=name, dtype=dtype, constraint=constraint) - elif not use_resource and context.in_eager_mode(): + elif not use_resource and context.executing_eagerly(): raise RuntimeError( "VariableScope should use resource variable when eager execution is" " enabled, but use_resource is False." @@ -2145,7 +2145,7 @@ def variable(initial_value=None, constraint=None, use_resource=None): previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) - for getter in ops.get_default_graph()._get_variable_creator_stack(): # pylint: disable=protected-access + for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access previous_getter = _make_getter(getter, previous_getter) return previous_getter(initial_value=initial_value, trainable=trainable, diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index 19e3298e4019f94132db25ab0dae5ed458bfbeb3..5b9947f4417a0f7621fda885c685021d91701c2b 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import compat from tensorflow.python.util import tf_should_use from tensorflow.python.util.deprecation import deprecated @@ -36,7 +37,7 @@ from tensorflow.python.util.tf_export import tf_export @tf_export("Variable") -class Variable(object): +class Variable(checkpointable.CheckpointableBase): """See the @{$variables$Variables How To} for a high level overview. A variable maintains state in the graph across calls to `run()`. You add a @@ -209,10 +210,11 @@ class Variable(object): for details on how variables work in eager execution. @end_compatibility """ - if not context.in_graph_mode(): - raise RuntimeError("tf.Variable not supported in Eager mode. " - "Please use tfe.Variable instead") - self._in_graph_mode = context.in_graph_mode() + if context.executing_eagerly(): + raise RuntimeError( + "tf.Variable not supported when eager execution is enabled. " + "Please use tf.contrib.eager.Variable instead") + self._in_graph_mode = True if variable_def: # If variable_def is provided, recreates the variable from its fields. if initial_value: @@ -233,7 +235,7 @@ class Variable(object): constraint=constraint) def __repr__(self): - if context.in_eager_mode(): + if context.executing_eagerly(): return "" % ( self.name, self.get_shape(), self.dtype.name, ops.numpy_text(self.read_value(), is_repr=True)) @@ -306,6 +308,14 @@ class Variable(object): if constraint is not None and not callable(constraint): raise ValueError("The `constraint` argument must be a callable.") + # Store the graph key so optimizers know how to only retrieve variables from + # this graph. + self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access + if isinstance(initial_value, checkpointable.CheckpointInitialValue): + self._maybe_initialize_checkpointable() + self._update_uid = initial_value.checkpoint_position.restore_uid + initial_value = initial_value.wrapped_value + if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] with ops.init_scope(): @@ -731,15 +741,15 @@ class Variable(object): Raises: ValueError: Session is not passed and no default session """ - if context.in_graph_mode(): + if context.executing_eagerly(): + self.assign(value) + else: session = session or ops.get_default_session() if session is None: raise ValueError( "Either session argument should be provided or default session " "should be established") session.run(self._initializer_op, {self._initializer_op.inputs[1]: value}) - else: - self.assign(value) # Conversion to tensor. @staticmethod @@ -786,6 +796,10 @@ class Variable(object): setattr(Variable, operator, _run_op) + def _gather_saveables_for_checkpoint(self): + """For implementing `Checkpointable`. This object is saveable on its own.""" + return {checkpointable.VARIABLE_VALUE_KEY: self} + def _try_guard_against_uninitialized_dependencies(self, initial_value): """Attempt to guard against dependencies on uninitialized variables. @@ -1235,9 +1249,9 @@ class PartitionedVariable(object): information does not match `shape`, or `partitions` has invalid values. RuntimeError: If eager execution is enabled """ - if not context.in_graph_mode(): - raise RuntimeError("tf.PartitionedVariable not supported in " - "eager mode. Please use tfe.Variable instead") + if context.executing_eagerly(): + raise RuntimeError( + "tf.PartitionedVariable not supported with eager execution enabled.") if not isinstance(variable_list, (list, tuple)): raise TypeError( "variable_list is not a list or tuple: %s" % variable_list) @@ -1528,7 +1542,7 @@ def variables_initializer(var_list, name="init"): Returns: An Op that run the initializers of all the specified variables. """ - if var_list and context.in_graph_mode(): + if var_list and not context.executing_eagerly(): return control_flow_ops.group(*[v.initializer for v in var_list], name=name) return control_flow_ops.no_op(name=name) @@ -1550,7 +1564,7 @@ def global_variables_initializer(): Returns: An Op that initializes global variables in the graph. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return control_flow_ops.no_op(name="global_variables_initializer") return variables_initializer(global_variables()) @@ -1572,7 +1586,7 @@ def local_variables_initializer(): Returns: An Op that initializes all local variables in the graph. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return control_flow_ops.no_op(name="local_variables_initializer") return variables_initializer(local_variables()) diff --git a/tensorflow/python/profiler/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py index 0e20ca35bba606079ed5b0f225dd3029772b5af3..acf02096fffe8b38e68824878fa698ed69d3895c 100644 --- a/tensorflow/python/profiler/model_analyzer.py +++ b/tensorflow/python/profiler/model_analyzer.py @@ -172,7 +172,7 @@ class Profiler(object): op_log: optional. tensorflow::tfprof::OpLogProto proto. Used to define extra op types. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() self._coverage = 0.0 self._graph = graph @@ -336,7 +336,7 @@ def profile(graph=None, If cmd is 'op' or 'code', returns MultiGraphNodeProto proto. Side effect: stdout/file/timeline.json depending on options['output'] """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() if options == _DEFAULT_PROFILE_OPTIONS: diff --git a/tensorflow/python/profiler/option_builder.py b/tensorflow/python/profiler/option_builder.py index 957ebe6dddc26118024f71cadef861f38f1805e0..2ad7adf76933df65ca795dca361397f436adb995 100644 --- a/tensorflow/python/profiler/option_builder.py +++ b/tensorflow/python/profiler/option_builder.py @@ -300,7 +300,7 @@ class ProfileOptionBuilder(object): # pylint: disable=line-too-long """Only show profiler nodes consuming no less than 'min_float_ops'. - Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profilerg3doc/profile_model_architecture.md + Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/profile_model_architecture.md on the caveats of calculating float operations. Args: diff --git a/tensorflow/python/profiler/profiler.py b/tensorflow/python/profiler/profiler.py index 130dcb5134d6f7e6eb43aebea803b366a5ce27d8..fa7f30b236997cecd6d5df98c334aa7f5cc571e4 100644 --- a/tensorflow/python/profiler/profiler.py +++ b/tensorflow/python/profiler/profiler.py @@ -31,6 +31,7 @@ from tensorflow.python.profiler.option_builder import ProfileOptionBuilder from tensorflow.python.profiler.tfprof_logger import write_op_log from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export _allowed_symbols = [ @@ -48,6 +49,12 @@ _allowed_symbols.extend([ 'OpLogProto', ]) +# Export protos +tf_export('profiler.GraphNodeProto')(GraphNodeProto) +tf_export('profiler.MultiGraphNodeProto')(MultiGraphNodeProto) +tf_export('profiler.AdviceProto')(AdviceProto) +tf_export('profiler.OpLogProto')(OpLogProto) + remove_undocumented(__name__, _allowed_symbols, [ Profiler, profile, diff --git a/tensorflow/python/profiler/tfprof_logger.py b/tensorflow/python/profiler/tfprof_logger.py index 8d121064967f2f87cd0aefaa361bfd6f387a3e6e..e651de32ea3bce32a965bfbeefc76ff08a79ac38 100644 --- a/tensorflow/python/profiler/tfprof_logger.py +++ b/tensorflow/python/profiler/tfprof_logger.py @@ -156,7 +156,7 @@ def merge_default_with_oplog(graph, op_log=None, run_meta=None, Returns: tmp_op_log: Merged OpLogProto proto. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() tmp_op_log = tfprof_log_pb2.OpLogProto() @@ -210,7 +210,7 @@ def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True): add_trace: Whether to add python code trace information. Used to support "code" view. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() op_log = merge_default_with_oplog(graph, op_log, run_meta, add_trace) diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i index 3f25311a8361d11fbc583413708e148648d95906..b481ddf5d4798aeed970d435234fd82de3b93a06 100644 --- a/tensorflow/python/pywrap_tfe.i +++ b/tensorflow/python/pywrap_tfe.i @@ -29,8 +29,12 @@ limitations under the License. %rename("%s") TFE_OpNameGetAttrType; %rename("%s") TFE_Py_InitEagerTensor; %rename("%s") TFE_Py_RegisterExceptionClass; +%rename("%s") TFE_Py_RegisterBackwardFunctionGetter; +%rename("%s") TFE_Py_RegisterFallbackExceptionClass; +%rename("%s") TFE_Py_RegisterResourceVariableType; %rename("%s") TFE_Py_Execute; %rename("%s") TFE_Py_FastPathExecute; +%rename("%s") TFE_Py_RecordGradient; %rename("%s") TFE_Py_UID; %rename("%s") TFE_Py_TapeSetNew; %rename("%s") TFE_Py_TapeSetRemove; diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index e34aa7cc2ca41ecdd7c9ff52ab8f3d552f26fe69..30e0a099d8b2e30cff36b69164ba9f1789dd8916 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -148,6 +148,7 @@ py_test( "//tensorflow/python:math_ops", "//tensorflow/python:saver_test_utils", "//tensorflow/python:state_ops", + "//tensorflow/python:test_ops", "//tensorflow/python:util", "//tensorflow/python:variables", ], diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 62ee53b816c2a38327fa116d2924446e6bf24a1e..3447d917e9bf2dace3de784106dadb1fcc3a9647 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -34,8 +34,10 @@ from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export +@tf_export("saved_model.builder.SavedModelBuilder") class SavedModelBuilder(object): """Builds the `SavedModel` protocol buffer and saves variables and assets. @@ -191,7 +193,8 @@ class SavedModelBuilder(object): def _validate_tensor_info(self, tensor_info): """Validates the `TensorInfo` proto. - Checks if the `name` and `dtype` fields exist and are non-empty. + Checks if the `encoding` (`name` or `coo_sparse`) and `dtype` fields exist + and are non-empty. Args: tensor_info: `TensorInfo` protocol buffer to validate. @@ -204,10 +207,12 @@ class SavedModelBuilder(object): raise AssertionError( "All TensorInfo protos used in the SignatureDefs must have the name " "and dtype fields set.") - if not tensor_info.name: + if tensor_info.WhichOneof("encoding") is None: + # TODO(soergel) validate each of the fields of coo_sparse raise AssertionError( - "All TensorInfo protos used in the SignatureDefs must have the name " - "field set: %s" % tensor_info) + "All TensorInfo protos used in the SignatureDefs must have one of " + "the 'encoding' fields (e.g., name or coo_sparse) set: %s" + % tensor_info) if tensor_info.dtype is types_pb2.DT_INVALID: raise AssertionError( "All TensorInfo protos used in the SignatureDefs must have the dtype " diff --git a/tensorflow/python/saved_model/constants.py b/tensorflow/python/saved_model/constants.py index 7e3e8df47fb0e024eae8add6a788d632709740af..ec49a0539ff52f6cc69bb24483ede657b698ab8d 100644 --- a/tensorflow/python/saved_model/constants.py +++ b/tensorflow/python/saved_model/constants.py @@ -20,33 +20,52 @@ from __future__ import division from __future__ import print_function from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export # Subdirectory name containing the asset files. ASSETS_DIRECTORY = "assets" +tf_export("saved_model.constants.ASSETS_DIRECTORY").export_constant( + __name__, "ASSETS_DIRECTORY") # CollectionDef key containing SavedModel assets. ASSETS_KEY = "saved_model_assets" +tf_export("saved_model.constants.ASSETS_KEY").export_constant( + __name__, "ASSETS_KEY") # CollectionDef key for the legacy init op. LEGACY_INIT_OP_KEY = "legacy_init_op" +tf_export("saved_model.constants.LEGACY_INIT_OP_KEY").export_constant( + __name__, "LEGACY_INIT_OP_KEY") # CollectionDef key for the SavedModel main op. MAIN_OP_KEY = "saved_model_main_op" +tf_export("saved_model.constants.MAIN_OP_KEY").export_constant( + __name__, "MAIN_OP_KEY") # Schema version for SavedModel. SAVED_MODEL_SCHEMA_VERSION = 1 +tf_export("saved_model.constants.SAVED_MODEL_SCHEMA_VERSION").export_constant( + __name__, "SAVED_MODEL_SCHEMA_VERSION") # File name for SavedModel protocol buffer. SAVED_MODEL_FILENAME_PB = "saved_model.pb" +tf_export("saved_model.constants.SAVED_MODEL_FILENAME_PB").export_constant( + __name__, "SAVED_MODEL_FILENAME_PB") # File name for text version of SavedModel protocol buffer. SAVED_MODEL_FILENAME_PBTXT = "saved_model.pbtxt" +tf_export("saved_model.constants.SAVED_MODEL_FILENAME_PBTXT").export_constant( + __name__, "SAVED_MODEL_FILENAME_PBTXT") # Subdirectory name containing the variables/checkpoint files. VARIABLES_DIRECTORY = "variables" +tf_export("saved_model.constants.VARIABLES_DIRECTORY").export_constant( + __name__, "VARIABLES_DIRECTORY") # File name used for variables. VARIABLES_FILENAME = "variables" +tf_export("saved_model.constants.VARIABLES_FILENAME").export_constant( + __name__, "VARIABLES_FILENAME") _allowed_symbols = [ diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index 6e85df0cbf5623691d38b030036958e5955399ee..bebf1d5e0d3cc6ac0e431230577704365d37a437 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -32,6 +32,7 @@ from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export def _parse_saved_model(export_dir): @@ -156,6 +157,7 @@ def _get_legacy_init_op_tensor(meta_graph_def_to_load): return legacy_init_op_tensor +@tf_export("saved_model.loader.maybe_saved_model_directory") def maybe_saved_model_directory(export_dir): """Checks whether the provided export directory could contain a SavedModel. @@ -176,6 +178,7 @@ def maybe_saved_model_directory(export_dir): return file_io.file_exists(txt_path) or file_io.file_exists(pb_path) +@tf_export("saved_model.loader.load") def load(sess, tags, export_dir, **saver_kwargs): """Loads the model from a SavedModel as specified by tags. @@ -232,8 +235,9 @@ def load(sess, tags, export_dir, **saver_kwargs): asset_tensors_dictionary = _get_asset_tensors(export_dir, meta_graph_def_to_load) - main_op_tensor = (_get_main_op_tensor(meta_graph_def_to_load) or - (_get_legacy_init_op_tensor(meta_graph_def_to_load))) + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def_to_load) or + (_get_legacy_init_op_tensor(meta_graph_def_to_load))) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) diff --git a/tensorflow/python/saved_model/main_op_impl.py b/tensorflow/python/saved_model/main_op_impl.py index 355fd57bf1d2166f58a5fdc95d04695ea05b56b3..631ee63729513d24c2ddae71b771f7cf1695358f 100644 --- a/tensorflow/python/saved_model/main_op_impl.py +++ b/tensorflow/python/saved_model/main_op_impl.py @@ -22,8 +22,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import variables +from tensorflow.python.util.tf_export import tf_export +@tf_export('saved_model.main_op.main_op') def main_op(): """Returns a main op to init variables and tables. @@ -40,6 +42,7 @@ def main_op(): # TODO(sukritiramesh): Integrate with Saver for complete restore functionality. +@tf_export('saved_model.main_op.main_op_with_restore') def main_op_with_restore(restore_op_name): """Returns a main op to init variables, tables and restore the graph. diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index f92247d52e4150b2347a95e84d4bbf9c6ffc258a..804255375e7c5215597a5dcca02f3b32f2c0a497 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import os -from tensorflow.core.framework import op_def_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import meta_graph_pb2 @@ -28,8 +27,8 @@ from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import ops +from tensorflow.python.framework import test_ops from tensorflow.python.framework import test_util from tensorflow.python.lib.io import file_io from tensorflow.python.ops import control_flow_ops @@ -95,7 +94,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(expected_asset_file_name, asset.filename) self.assertEqual(expected_asset_tensor_name, asset.tensor_info.name) - def _validate_inputs_tensor_info(self, builder, tensor_info): + def _validate_inputs_tensor_info_fail(self, builder, tensor_info): with self.test_session(graph=ops.Graph()) as sess: self._init_and_validate_variable(sess, "v", 42) @@ -108,7 +107,18 @@ class SavedModelTest(test.TestCase): sess, ["foo"], signature_def_map={"foo_key": foo_signature}) - def _validate_outputs_tensor_info(self, builder, tensor_info): + def _validate_inputs_tensor_info_accept(self, builder, tensor_info): + with self.test_session(graph=ops.Graph()) as sess: + self._init_and_validate_variable(sess, "v", 42) + + foo_signature = signature_def_utils.build_signature_def({ + "foo_inputs": tensor_info + }, dict(), "foo") + builder.add_meta_graph_and_variables( + sess, ["foo"], + signature_def_map={"foo_key": foo_signature}) + + def _validate_outputs_tensor_info_fail(self, builder, tensor_info): with self.test_session(graph=ops.Graph()) as sess: self._init_and_validate_variable(sess, "v", 42) @@ -120,6 +130,16 @@ class SavedModelTest(test.TestCase): sess, ["foo"], signature_def_map={"foo_key": foo_signature}) + def _validate_outputs_tensor_info_accept(self, builder, tensor_info): + with self.test_session(graph=ops.Graph()) as sess: + self._init_and_validate_variable(sess, "v", 42) + + foo_signature = signature_def_utils.build_signature_def( + dict(), {"foo_outputs": tensor_info}, "foo") + builder.add_meta_graph_and_variables( + sess, ["foo"], + signature_def_map={"foo_key": foo_signature}) + def testMaybeSavedModelDir(self): base_path = test.test_src_dir_path("/python/saved_model") self.assertFalse(loader.maybe_saved_model_directory(base_path)) @@ -539,23 +559,50 @@ class SavedModelTest(test.TestCase): self.assertEqual("bar", bar_signature["bar_key"].method_name) self.assertEqual("foo_new", bar_signature["foo_key"].method_name) - def testSignatureDefValidation(self): - export_dir = self._get_export_dir("test_signature_def_validation") + def testSignatureDefValidationFails(self): + export_dir = self._get_export_dir("test_signature_def_validation_fail") builder = saved_model_builder.SavedModelBuilder(export_dir) - tensor_without_name = meta_graph_pb2.TensorInfo() - tensor_without_name.dtype = types_pb2.DT_FLOAT - self._validate_inputs_tensor_info(builder, tensor_without_name) - self._validate_outputs_tensor_info(builder, tensor_without_name) + tensor_without_encoding = meta_graph_pb2.TensorInfo() + tensor_without_encoding.dtype = types_pb2.DT_FLOAT + self._validate_inputs_tensor_info_fail(builder, tensor_without_encoding) + self._validate_outputs_tensor_info_fail(builder, tensor_without_encoding) tensor_without_dtype = meta_graph_pb2.TensorInfo() tensor_without_dtype.name = "x" - self._validate_inputs_tensor_info(builder, tensor_without_dtype) - self._validate_outputs_tensor_info(builder, tensor_without_dtype) + self._validate_inputs_tensor_info_fail(builder, tensor_without_dtype) + self._validate_outputs_tensor_info_fail(builder, tensor_without_dtype) tensor_empty = meta_graph_pb2.TensorInfo() - self._validate_inputs_tensor_info(builder, tensor_empty) - self._validate_outputs_tensor_info(builder, tensor_empty) + self._validate_inputs_tensor_info_fail(builder, tensor_empty) + self._validate_outputs_tensor_info_fail(builder, tensor_empty) + + def testSignatureDefValidationSucceedsWithName(self): + tensor_with_name = meta_graph_pb2.TensorInfo() + tensor_with_name.name = "foo" + tensor_with_name.dtype = types_pb2.DT_FLOAT + + export_dir = self._get_export_dir("test_signature_def_validation_name_1") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_inputs_tensor_info_accept(builder, tensor_with_name) + + export_dir = self._get_export_dir("test_signature_def_validation_name_2") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_outputs_tensor_info_accept(builder, tensor_with_name) + + def testSignatureDefValidationSucceedsWithCoo(self): + tensor_with_coo = meta_graph_pb2.TensorInfo() + # TODO(soergel) test validation of each of the fields of coo_sparse + tensor_with_coo.coo_sparse.values_tensor_name = "foo" + tensor_with_coo.dtype = types_pb2.DT_FLOAT + + export_dir = self._get_export_dir("test_signature_def_validation_coo_1") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_inputs_tensor_info_accept(builder, tensor_with_coo) + + export_dir = self._get_export_dir("test_signature_def_validation_coo_2") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_outputs_tensor_info_accept(builder, tensor_with_coo) def testAssets(self): export_dir = self._get_export_dir("test_assets") @@ -874,8 +921,6 @@ class SavedModelTest(test.TestCase): 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) def testStripDefaultAttrs(self): - if ops._USE_C_API: return # TODO(skyewm): get this working - export_dir = self._get_export_dir("test_strip_default_attrs") builder = saved_model_builder.SavedModelBuilder(export_dir) @@ -945,61 +990,75 @@ class SavedModelTest(test.TestCase): self.assertIn("T", node_def.attr) self.assertIn("Tout", node_def.attr) - def testStripDefaultAttrsInconsistentConsumerDefaults(self): - if ops._USE_C_API: return # TODO(skyewm): get this working - + # Tests the behavior of loading SavedModels that having missing attrs or attrs + # with incorrect types. + def testInconsistentConsumerDefaultAttrs(self): export_dir = self._get_export_dir( "test_strip_default_attrs_no_consumer_defaults") builder = saved_model_builder.SavedModelBuilder(export_dir) - # Add a graph with two float32 variables and a Complex Op composing them - # with strip_default_attrs enabled. This must remove the following - # defaults for the "Complex" Op: - # o "T" : float32. (input type) - # o "Tout" : complex64. (output type) + # Add a graph with a single variable and a test op with a defaultless + # float32 attr, "test_attr". with session.Session(graph=ops.Graph()) as sess: - real_num = variables.Variable(1.0, dtype=dtypes.float32, name="real") - imag_num = variables.Variable(2.0, dtype=dtypes.float32, name="imag") - math_ops.complex(real_num, imag_num, name="complex") + variables.Variable(1.0, dtype=dtypes.float64, name="var") + test_ops.test_attr(T=dtypes.float32, name="test_attr") sess.run(variables.global_variables_initializer()) - builder.add_meta_graph_and_variables( - sess, ["foo"], strip_default_attrs=True) + builder.add_meta_graph_and_variables(sess, ["foo"]) # Save the SavedModel to disk in text format. builder.save(as_text=True) - # Update the Op registry to remove defaults for all attrs("T", "Tout") from - # the "Complex" OpDef. - complex_op_def = op_def_registry.get_registered_ops()["Complex"] - original_complex_op_def = op_def_pb2.OpDef() - original_complex_op_def.CopyFrom(complex_op_def) - for attr_def in complex_op_def.attr: - attr_def.ClearField("default_value") + # Rewrite the SavedModel to remove the T attr from "test_attr". + saved_model_file = os.path.join( + export_dir, constants.SAVED_MODEL_FILENAME_PBTXT) + with open(saved_model_file) as f: + original_saved_model = f.read() + + no_attr_saved_model = original_saved_model.replace(""" + attr { + key: "T" + value { + type: DT_FLOAT + } + }""", "") + with open(saved_model_file, "w") as f: + f.write(no_attr_saved_model) # Loading the SavedModel via the loader must fail because the SavedModel - # does not have any attr values for the "Complex" node and the current - # op registry does not have have any default values for the "Complex" op. + # does not have any attr values for the "TestAttr" node, and there is no + # default specified in the TestAttr OpDef. sess = session.Session(graph=ops.Graph()) - with self.assertRaisesRegexp( - ValueError, - "Expected one attr with name .*T(out)?.* in name: \"complex\".*"): + if ops._USE_C_API: + error_message = "NodeDef missing attr 'T' from Op complex128). - complex_op_def.CopyFrom(original_complex_op_def) - for attr_def in complex_op_def.attr: - if attr_def.name == "Tout": - attr_def.default_value.type = types_pb2.DT_COMPLEX128 - - # Loading the SavedModel via the loader must set "Tout" attr_value for the - # "Complex" node according to the latest defaults (complex128). This is - # expected to fail the model import as there is no OpKernel registered to - # handle attrs "T" (float32) and "Tout" (complex128). + # Rewrite the SavedModel to change the type of the T attr in "test_attr" + bad_type_saved_model = original_saved_model.replace(""" + attr { + key: "T" + value { + type: DT_FLOAT + } + }""", """ + attr { + key: "T" + value { + type: DT_DOUBLE + } + }""") + with open(saved_model_file, "w") as f: + f.write(bad_type_saved_model) + + # Loading the SavedModel via the loader must fail because there is no + # OpKernel registered to handle T = double. sess = session.Session(graph=ops.Graph()) with self.assertRaisesRegexp( errors.InvalidArgumentError, - ".*No OpKernel was registered to support Op \'Complex\' with these " + ".*No OpKernel was registered to support Op \'TestAttr\' with these " "attrs..*"): loader.load(sess, ["foo"], export_dir) diff --git a/tensorflow/python/saved_model/signature_constants.py b/tensorflow/python/saved_model/signature_constants.py index 935a124645bde509a1b5a7751a285a85acbe8cab..6461fe8a7e7bef1a2fc787879da9e3324e2655c8 100644 --- a/tensorflow/python/saved_model/signature_constants.py +++ b/tensorflow/python/saved_model/signature_constants.py @@ -20,51 +20,79 @@ from __future__ import division from __future__ import print_function from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export # Key in the signature def map for `default` serving signatures. The default # signature is used in inference requests where a specific signature was not # specified. DEFAULT_SERVING_SIGNATURE_DEF_KEY = "serving_default" +tf_export("saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY" + ).export_constant(__name__, "DEFAULT_SERVING_SIGNATURE_DEF_KEY") ################################################################################ # Classification API constants. # Classification inputs. CLASSIFY_INPUTS = "inputs" +tf_export("saved_model.signature_constants.CLASSIFY_INPUTS").export_constant( + __name__, "CLASSIFY_INPUTS") # Classification method name used in a SignatureDef. CLASSIFY_METHOD_NAME = "tensorflow/serving/classify" +tf_export( + "saved_model.signature_constants.CLASSIFY_METHOD_NAME").export_constant( + __name__, "CLASSIFY_METHOD_NAME") # Classification classes output. CLASSIFY_OUTPUT_CLASSES = "classes" +tf_export( + "saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES").export_constant( + __name__, "CLASSIFY_OUTPUT_CLASSES") # Classification scores output. CLASSIFY_OUTPUT_SCORES = "scores" +tf_export( + "saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES").export_constant( + __name__, "CLASSIFY_OUTPUT_SCORES") ################################################################################ # Prediction API constants. # Predict inputs. PREDICT_INPUTS = "inputs" +tf_export("saved_model.signature_constants.PREDICT_INPUTS").export_constant( + __name__, "PREDICT_INPUTS") # Prediction method name used in a SignatureDef. PREDICT_METHOD_NAME = "tensorflow/serving/predict" +tf_export( + "saved_model.signature_constants.PREDICT_METHOD_NAME").export_constant( + __name__, "PREDICT_METHOD_NAME") # Predict outputs. PREDICT_OUTPUTS = "outputs" +tf_export("saved_model.signature_constants.PREDICT_OUTPUTS").export_constant( + __name__, "PREDICT_OUTPUTS") ################################################################################ # Regression API constants. # Regression inputs. REGRESS_INPUTS = "inputs" +tf_export("saved_model.signature_constants.REGRESS_INPUTS").export_constant( + __name__, "REGRESS_INPUTS") # Regression method name used in a SignatureDef. REGRESS_METHOD_NAME = "tensorflow/serving/regress" +tf_export( + "saved_model.signature_constants.REGRESS_METHOD_NAME").export_constant( + __name__, "REGRESS_METHOD_NAME") # Regression outputs. REGRESS_OUTPUTS = "outputs" +tf_export("saved_model.signature_constants.REGRESS_OUTPUTS").export_constant( + __name__, "REGRESS_OUTPUTS") ################################################################################ diff --git a/tensorflow/python/saved_model/signature_def_utils_impl.py b/tensorflow/python/saved_model/signature_def_utils_impl.py index 240ea61aa5f8553852044f84b61d010bfbca69d1..d0331591889110df86bdb2ac69c037bc3b968f91 100644 --- a/tensorflow/python/saved_model/signature_def_utils_impl.py +++ b/tensorflow/python/saved_model/signature_def_utils_impl.py @@ -26,8 +26,10 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import utils +from tensorflow.python.util.tf_export import tf_export +@tf_export('saved_model.signature_def_utils.build_signature_def') def build_signature_def(inputs=None, outputs=None, method_name=None): """Utility function to build a SignatureDef protocol buffer. @@ -53,6 +55,7 @@ def build_signature_def(inputs=None, outputs=None, method_name=None): return signature_def +@tf_export('saved_model.signature_def_utils.regression_signature_def') def regression_signature_def(examples, predictions): """Creates regression signature from given examples and predictions. @@ -94,6 +97,7 @@ def regression_signature_def(examples, predictions): return signature_def +@tf_export('saved_model.signature_def_utils.classification_signature_def') def classification_signature_def(examples, classes, scores): """Creates classification signature from given examples and predictions. @@ -146,6 +150,7 @@ def classification_signature_def(examples, classes, scores): return signature_def +@tf_export('saved_model.signature_def_utils.predict_signature_def') def predict_signature_def(inputs, outputs): """Creates prediction signature from given inputs and outputs. @@ -180,6 +185,7 @@ def predict_signature_def(inputs, outputs): return signature_def +@tf_export('saved_model.signature_def_utils.is_valid_signature') def is_valid_signature(signature_def): """Determine whether a SignatureDef can be served by TensorFlow Serving.""" if signature_def is None: diff --git a/tensorflow/python/saved_model/simple_save.py b/tensorflow/python/saved_model/simple_save.py index 1e4cc733706c774107184c2fb19037da3d4e733d..042b8fa8e22703d8ffb5e12de3f844d22fb1b1ce 100644 --- a/tensorflow/python/saved_model/simple_save.py +++ b/tensorflow/python/saved_model/simple_save.py @@ -23,8 +23,10 @@ from tensorflow.python.saved_model import builder from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.saved_model import tag_constants +from tensorflow.python.util.tf_export import tf_export +@tf_export('saved_model.simple_save') def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None): """Convenience function to build a SavedModel suitable for serving. diff --git a/tensorflow/python/saved_model/tag_constants.py b/tensorflow/python/saved_model/tag_constants.py index e2facafda51919d3f1e0ccbe646db522ed0bc49b..d164e2c23f24469d7536f87cb431afe618ddcc06 100644 --- a/tensorflow/python/saved_model/tag_constants.py +++ b/tensorflow/python/saved_model/tag_constants.py @@ -20,19 +20,26 @@ from __future__ import division from __future__ import print_function from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export # Tag for the `serving` graph. SERVING = "serve" +tf_export("saved_model.tag_constants.SERVING").export_constant( + __name__, "SERVING") # Tag for the `training` graph. TRAINING = "train" +tf_export("saved_model.tag_constants.TRAINING").export_constant( + __name__, "TRAINING") # Tag for the `gpu` graph. GPU = "gpu" +tf_export("saved_model.tag_constants.GPU").export_constant(__name__, "GPU") # Tag for the `tpu` graph. TPU = "tpu" +tf_export("saved_model.tag_constants.TPU").export_constant(__name__, "TPU") _allowed_symbols = [ "SERVING", diff --git a/tensorflow/python/saved_model/utils_impl.py b/tensorflow/python/saved_model/utils_impl.py index 73ca8c9c1c6d8fddc8a9c7dbee56682999281c28..cddce29a08a6c4c79a4c7c5dbfb48a86131530b2 100644 --- a/tensorflow/python/saved_model/utils_impl.py +++ b/tensorflow/python/saved_model/utils_impl.py @@ -22,11 +22,13 @@ from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.util.tf_export import tf_export # TensorInfo helpers. +@tf_export("saved_model.utils.build_tensor_info") def build_tensor_info(tensor): """Utility function to build TensorInfo proto. @@ -50,6 +52,7 @@ def build_tensor_info(tensor): return tensor_info +@tf_export("saved_model.utils.get_tensor_from_tensor_info") def get_tensor_from_tensor_info(tensor_info, graph=None, import_scope=None): """Returns the Tensor or SparseTensor described by a TensorInfo proto. diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py index 92c1fcadd29c7858da1d31375c209bf1b21f3103..2a3918b9b421ea125b327c9f62d098de961fff6b 100644 --- a/tensorflow/python/summary/summary.py +++ b/tensorflow/python/summary/summary.py @@ -72,8 +72,10 @@ from tensorflow.python.summary.writer.writer_cache import FileWriterCache from tensorflow.python.util import compat as _compat from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export +@tf_export('summary.scalar') def scalar(name, tensor, collections=None, family=None): """Outputs a `Summary` protocol buffer containing a single scalar value. @@ -102,6 +104,7 @@ def scalar(name, tensor, collections=None, family=None): return val +@tf_export('summary.image') def image(name, tensor, max_outputs=3, collections=None, family=None): """Outputs a `Summary` protocol buffer with images. @@ -149,13 +152,13 @@ def image(name, tensor, max_outputs=3, collections=None, family=None): """ with _summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): - # pylint: disable=protected-access - val = _gen_logging_ops._image_summary( + val = _gen_logging_ops.image_summary( tag=tag, tensor=tensor, max_images=max_outputs, name=scope) _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES]) return val +@tf_export('summary.histogram') def histogram(name, values, collections=None, family=None): # pylint: disable=line-too-long """Outputs a `Summary` protocol buffer with a histogram. @@ -195,6 +198,7 @@ def histogram(name, values, collections=None, family=None): return val +@tf_export('summary.audio') def audio(name, tensor, sample_rate, max_outputs=3, collections=None, family=None): # pylint: disable=line-too-long @@ -232,16 +236,16 @@ def audio(name, tensor, sample_rate, max_outputs=3, collections=None, """ with _summary_op_util.summary_scope( name, family=family, values=[tensor]) as (tag, scope): - # pylint: disable=protected-access sample_rate = _ops.convert_to_tensor( sample_rate, dtype=_dtypes.float32, name='sample_rate') - val = _gen_logging_ops._audio_summary_v2( + val = _gen_logging_ops.audio_summary_v2( tag=tag, tensor=tensor, max_outputs=max_outputs, sample_rate=sample_rate, name=scope) _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES]) return val +@tf_export('summary.merge') def merge(inputs, collections=None, name=None): # pylint: disable=line-too-long """Merges summaries. @@ -274,18 +278,18 @@ def merge(inputs, collections=None, name=None): @end_compatbility """ # pylint: enable=line-too-long - if _context.in_eager_mode(): + if _context.executing_eagerly(): raise RuntimeError( 'Merging tf.summary.* ops is not compatible with eager execution. ' 'Use tf.contrib.summary instead.') name = _summary_op_util.clean_tag(name) with _ops.name_scope(name, 'Merge', inputs): - # pylint: disable=protected-access - val = _gen_logging_ops._merge_summary(inputs=inputs, name=name) + val = _gen_logging_ops.merge_summary(inputs=inputs, name=name) _summary_op_util.collect(val, collections, []) return val +@tf_export('summary.merge_all') def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): """Merges all summaries collected in the default graph. @@ -307,7 +311,7 @@ def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): summaries under eager execution, use `tf.contrib.summary` instead. @end_compatbility """ - if _context.in_eager_mode(): + if _context.executing_eagerly(): raise RuntimeError( 'Merging tf.summary.* ops is not compatible with eager execution. ' 'Use tf.contrib.summary instead.') @@ -318,6 +322,7 @@ def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): return merge(summary_ops) +@tf_export('summary.get_summary_description') def get_summary_description(node_def): """Given a TensorSummary node_def, retrieve its SummaryDescription. diff --git a/tensorflow/python/summary/summary_iterator.py b/tensorflow/python/summary/summary_iterator.py index 6969c4cf1500bf4b1fda900336158e5af4395ea6..321b11ffb73487405428340df94010ed8ddbfcd4 100644 --- a/tensorflow/python/summary/summary_iterator.py +++ b/tensorflow/python/summary/summary_iterator.py @@ -21,8 +21,10 @@ from __future__ import print_function from tensorflow.core.util import event_pb2 from tensorflow.python.lib.io import tf_record +from tensorflow.python.util.tf_export import tf_export +@tf_export('train.summary_iterator') def summary_iterator(path): # pylint: disable=line-too-long """An iterator for reading `Event` protocol buffers from an event file. diff --git a/tensorflow/python/summary/text_summary.py b/tensorflow/python/summary/text_summary.py index 94a85d73e2f77388f9a29b1c135fc6046a8362d0..6418c847f3c819cf2491bb449921d15c39eae288 100644 --- a/tensorflow/python/summary/text_summary.py +++ b/tensorflow/python/summary/text_summary.py @@ -26,10 +26,12 @@ from __future__ import print_function from tensorflow.core.framework import summary_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.ops.summary_ops import tensor_summary +from tensorflow.python.util.tf_export import tf_export PLUGIN_NAME = "text" +@tf_export("summary.text") def text_summary(name, tensor, collections=None): """Summarizes textual data. diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index 1f3f2287043c021d636113b5a8807c9f4adf77aa..57f78c156b1334a5486b29f2ddec957e49156e73 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -343,7 +343,7 @@ class FileWriter(SummaryToEventTransformer): summaries under eager execution, use `tf.contrib.summary` instead. @end_compatbility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "tf.summary.FileWriter is not compatible with eager execution. " "Use tf.contrib.summary instead.") diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py index fd78f44c999e41f404a6499a0addd2131da6d287..a52f325ddbcd90ad011c1c056965912b96f27aaa 100644 --- a/tensorflow/python/tools/freeze_graph.py +++ b/tensorflow/python/tools/freeze_graph.py @@ -101,15 +101,15 @@ def freeze_graph_with_def_protos(input_graph_def, _ = importer.import_graph_def(input_graph_def, name="") with session.Session() as sess: if input_saver_def: - saver = saver_lib.Saver(saver_def=input_saver_def, - write_version=checkpoint_version) + saver = saver_lib.Saver( + saver_def=input_saver_def, write_version=checkpoint_version) saver.restore(sess, input_checkpoint) elif input_meta_graph_def: restorer = saver_lib.import_meta_graph( input_meta_graph_def, clear_devices=True) restorer.restore(sess, input_checkpoint) if initializer_nodes: - sess.run(initializer_nodes.split(",")) + sess.run(initializer_nodes.replace(" ", "").split(",")) elif input_saved_model_dir: if saved_model_tags is None: saved_model_tags = [] @@ -126,29 +126,31 @@ def freeze_graph_with_def_protos(input_graph_def, # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor - saver = saver_lib.Saver(var_list=var_list, - write_version=checkpoint_version) + saver = saver_lib.Saver( + var_list=var_list, write_version=checkpoint_version) saver.restore(sess, input_checkpoint) if initializer_nodes: - sess.run(initializer_nodes.split(",")) + sess.run(initializer_nodes.replace(" ", "").split(",")) - variable_names_whitelist = (variable_names_whitelist.split(",") - if variable_names_whitelist else None) - variable_names_blacklist = (variable_names_blacklist.split(",") - if variable_names_blacklist else None) + variable_names_whitelist = ( + variable_names_whitelist.replace(" ", "").split(",") + if variable_names_whitelist else None) + variable_names_blacklist = ( + variable_names_blacklist.replace(" ", "").split(",") + if variable_names_blacklist else None) if input_meta_graph_def: output_graph_def = graph_util.convert_variables_to_constants( sess, input_meta_graph_def.graph_def, - output_node_names.split(","), + output_node_names.replace(" ", "").split(","), variable_names_whitelist=variable_names_whitelist, variable_names_blacklist=variable_names_blacklist) else: output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, - output_node_names.split(","), + output_node_names.replace(" ", "").split(","), variable_names_whitelist=variable_names_whitelist, variable_names_blacklist=variable_names_blacklist) @@ -237,21 +239,39 @@ def freeze_graph(input_graph, if input_saver: input_saver_def = _parse_input_saver_proto(input_saver, input_binary) freeze_graph_with_def_protos( - input_graph_def, input_saver_def, input_checkpoint, output_node_names, - restore_op_name, filename_tensor_name, output_graph, clear_devices, - initializer_nodes, variable_names_whitelist, variable_names_blacklist, - input_meta_graph_def, input_saved_model_dir, - saved_model_tags.split(","), checkpoint_version=checkpoint_version) + input_graph_def, + input_saver_def, + input_checkpoint, + output_node_names, + restore_op_name, + filename_tensor_name, + output_graph, + clear_devices, + initializer_nodes, + variable_names_whitelist, + variable_names_blacklist, + input_meta_graph_def, + input_saved_model_dir, + saved_model_tags.replace(" ", "").split(","), + checkpoint_version=checkpoint_version) def main(unused_args): + if FLAGS.checkpoint_version == 1: + checkpoint_version = saver_pb2.SaverDef.V1 + elif FLAGS.checkpoint_version == 2: + checkpoint_version = saver_pb2.SaverDef.V2 + else: + print("Invalid checkpoint version (must be '1' or '2'): %d" % + FLAGS.checkpoint_version) + return -1 freeze_graph(FLAGS.input_graph, FLAGS.input_saver, FLAGS.input_binary, FLAGS.input_checkpoint, FLAGS.output_node_names, FLAGS.restore_op_name, FLAGS.filename_tensor_name, FLAGS.output_graph, FLAGS.clear_devices, FLAGS.initializer_nodes, FLAGS.variable_names_whitelist, FLAGS.variable_names_blacklist, FLAGS.input_meta_graph, FLAGS.input_saved_model_dir, - FLAGS.saved_model_tags, FLAGS.checkpoint_version) + FLAGS.saved_model_tags, checkpoint_version) if __name__ == "__main__": @@ -275,7 +295,7 @@ if __name__ == "__main__": parser.add_argument( "--checkpoint_version", type=int, - default=saver_pb2.SaverDef.V2, + default=2, help="Tensorflow variable file format") parser.add_argument( "--output_graph", diff --git a/tensorflow/python/tools/freeze_graph_test.py b/tensorflow/python/tools/freeze_graph_test.py index 342732465d48f40a4ffeac97146fb1b6d564c568..91f0061ebccaebbdbb09f283d9d52d813459f493 100644 --- a/tensorflow/python/tools/freeze_graph_test.py +++ b/tensorflow/python/tools/freeze_graph_test.py @@ -84,9 +84,18 @@ class FreezeGraphTest(test_util.TensorFlowTestCase): input_meta_graph = checkpoint_meta_graph_file freeze_graph.freeze_graph( - input_graph_path, input_saver_def_path, input_binary, checkpoint_path, - output_node_names, restore_op_name, filename_tensor_name, - output_graph_path, clear_devices, "", "", input_meta_graph, + input_graph_path, + input_saver_def_path, + input_binary, + checkpoint_path, + output_node_names, + restore_op_name, + filename_tensor_name, + output_graph_path, + clear_devices, + "", + "", + input_meta_graph, checkpoint_version=saver_write_version) # Now we make sure the variable is now a constant, and that the graph still diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py index 2ef612473b4bb64f611983f87f8cdd619a2d8a38..084a4500f8e1eb7f75e1e01668fae655b5e06763 100644 --- a/tensorflow/python/tools/optimize_for_inference_test.py +++ b/tensorflow/python/tools/optimize_for_inference_test.py @@ -184,8 +184,11 @@ class OptimizeForInferenceTest(test.TestCase): weights_op = constant_op.constant( np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32) conv_op = nn_ops.conv2d( - input_op, weights_op, [1, 1, 1, 1], padding="SAME", - data_format=data_format, name="conv_op") + input_op, + weights_op, [1, 1, 1, 1], + padding="SAME", + data_format=data_format, + name="conv_op") mean_op = constant_op.constant( np.array([10, 20]), shape=[2], dtype=dtypes.float32) variance_op = constant_op.constant( diff --git a/tensorflow/python/tools/print_selective_registration_header_test.py b/tensorflow/python/tools/print_selective_registration_header_test.py index 36978b0860a423569149cd0572629f9f1f280637..4b3d98242caf683693430f08bd8cb74483f4bc74 100644 --- a/tensorflow/python/tools/print_selective_registration_header_test.py +++ b/tensorflow/python/tools/print_selective_registration_header_test.py @@ -24,6 +24,7 @@ import sys from google.protobuf import text_format from tensorflow.core.framework import graph_pb2 +from tensorflow.python.framework import test_util from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.tools import selective_registration_header_lib @@ -93,11 +94,16 @@ class PrintOpFilegroupTest(test.TestCase): ops_and_kernels = selective_registration_header_lib.get_ops_and_kernels( 'rawproto', self.WriteGraphFiles(graphs), default_ops) + matmul_prefix = '' + if test_util.IsMklEnabled(): + matmul_prefix = 'Mkl' + self.assertListEqual( [ ('BiasAdd', 'BiasOp'), # - ('MatMul', 'MatMulOp'), # - ('MatMul', 'MatMulOp'), # + ('MatMul', + matmul_prefix + 'MatMulOp'), # + ('MatMul', matmul_prefix + 'MatMulOp'), # ('NoOp', 'NoOp'), # ('Reshape', 'ReshapeOp'), # ('_Recv', 'RecvOp'), # @@ -112,8 +118,9 @@ class PrintOpFilegroupTest(test.TestCase): self.assertListEqual( [ ('BiasAdd', 'BiasOp'), # - ('MatMul', 'MatMulOp'), # - ('MatMul', 'MatMulOp'), # + ('MatMul', + matmul_prefix + 'MatMulOp'), # + ('MatMul', matmul_prefix + 'MatMulOp'), # ('NoOp', 'NoOp'), # ('Reshape', 'ReshapeOp'), # ('_Recv', 'RecvOp'), # diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 5b0a584c10ea33f345c09324cbd47eb1789466e0..b0e9e3e5ed2117937bbd275784c44aebd2ea2515 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -39,7 +39,7 @@ from tensorflow.core.framework import types_pb2 from tensorflow.python.client import session from tensorflow.python.debug.wrappers import local_cli_wrapper from tensorflow.python.framework import ops as ops_lib -from tensorflow.python.platform import app +from tensorflow.python.platform import app # pylint: disable=unused-import from tensorflow.python.saved_model import loader from tensorflow.python.tools import saved_model_utils @@ -115,7 +115,7 @@ def _get_outputs_tensor_info_from_meta_graph_def(meta_graph_def, signature_def_key).outputs -def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): +def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, indent=0): """Prints input and output TensorInfos. Prints the details of input and output TensorInfos for the SignatureDef mapped @@ -126,6 +126,7 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): tag_set: Group of tag(s) of the MetaGraphDef, in string format, separated by ','. For tag-set contains multiple tags, all tags must be passed in. signature_def_key: A SignatureDef key string. + indent: How far (in increments of 2 spaces) to indent each line of output. """ meta_graph_def = saved_model_utils.get_meta_graph_def(saved_model_dir, tag_set) @@ -134,29 +135,39 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): outputs_tensor_info = _get_outputs_tensor_info_from_meta_graph_def( meta_graph_def, signature_def_key) - print('The given SavedModel SignatureDef contains the following input(s):') + indent_str = " " * indent + def in_print(s): + print(indent_str + s) + + in_print('The given SavedModel SignatureDef contains the following input(s):') for input_key, input_tensor in sorted(inputs_tensor_info.items()): - print('inputs[\'%s\'] tensor_info:' % input_key) - _print_tensor_info(input_tensor) + in_print(' inputs[\'%s\'] tensor_info:' % input_key) + _print_tensor_info(input_tensor, indent+1) - print('The given SavedModel SignatureDef contains the following output(s):') + in_print('The given SavedModel SignatureDef contains the following ' + 'output(s):') for output_key, output_tensor in sorted(outputs_tensor_info.items()): - print('outputs[\'%s\'] tensor_info:' % output_key) - _print_tensor_info(output_tensor) + in_print(' outputs[\'%s\'] tensor_info:' % output_key) + _print_tensor_info(output_tensor, indent+1) - print('Method name is: %s' % - meta_graph_def.signature_def[signature_def_key].method_name) + in_print('Method name is: %s' % + meta_graph_def.signature_def[signature_def_key].method_name) -def _print_tensor_info(tensor_info): +def _print_tensor_info(tensor_info, indent=0): """Prints details of the given tensor_info. Args: tensor_info: TensorInfo object to be printed. + indent: How far (in increments of 2 spaces) to indent each line output """ - print(' dtype: ' + - {value: key - for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype]) + indent_str = " " * indent + def in_print(s): + print(indent_str + s) + + in_print(' dtype: ' + + {value: key + for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype]) # Display shape as tuple. if tensor_info.tensor_shape.unknown_rank: shape = 'unknown_rank' @@ -164,8 +175,8 @@ def _print_tensor_info(tensor_info): dims = [str(dim.size) for dim in tensor_info.tensor_shape.dim] shape = ', '.join(dims) shape = '(' + shape + ')' - print(' shape: ' + shape) - print(' name: ' + tensor_info.name) + in_print(' shape: ' + shape) + in_print(' name: ' + tensor_info.name) def _show_all(saved_model_dir): @@ -186,7 +197,8 @@ def _show_all(saved_model_dir): signature_def_map = get_signature_def_map(saved_model_dir, tag_set) for signature_def_key in sorted(signature_def_map.keys()): print('\nsignature_def[\'' + signature_def_key + '\']:') - _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key) + _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, + indent=1) def get_meta_graph_def(saved_model_dir, tag_set): @@ -614,19 +626,19 @@ def create_parser(): show_msg = ( 'Usage examples:\n' 'To show all tag-sets in a SavedModel:\n' - '$saved_model_cli show --dir /tmp/saved_model\n' + '$saved_model_cli show --dir /tmp/saved_model\n\n' 'To show all available SignatureDef keys in a ' 'MetaGraphDef specified by its tag-set:\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n\n' 'For a MetaGraphDef with multiple tags in the tag-set, all tags must be ' 'passed in, separated by \';\':\n' '$saved_model_cli show --dir /tmp/saved_model --tag_set serve,gpu\n\n' 'To show all inputs and outputs TensorInfo for a specific' ' SignatureDef specified by the SignatureDef key in a' ' MetaGraph.\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve ' - '--signature_def serving_default\n\n' - 'To show all available information in the SavedModel\n:' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve' + ' --signature_def serving_default\n\n' + 'To show all available information in the SavedModel:\n' '$saved_model_cli show --dir /tmp/saved_model --all') parser_show = subparsers.add_parser( 'show', @@ -658,12 +670,14 @@ def create_parser(): run_msg = ('Usage example:\n' 'To run input tensors from files through a MetaGraphDef and save' ' the output tensors to files:\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve ' - '--signature_def serving_default ' - '--inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy ' - '--input_exprs \'input3_key=np.ones(2)\' --input_examples ' - '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' ' - '--outdir=/out\n\n' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve \\\n' + ' --signature_def serving_default \\\n' + ' --inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy ' + '\\\n' + ' --input_exprs \'input3_key=np.ones(2)\' \\\n' + ' --input_examples ' + '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' \\\n' + ' --outdir=/out\n\n' 'For more information about input file format, please see:\n' 'https://www.tensorflow.org/programmers_guide/saved_model_cli\n') parser_run = subparsers.add_parser( diff --git a/tensorflow/python/tools/saved_model_cli_test.py b/tensorflow/python/tools/saved_model_cli_test.py index d6cbc49ba1e08a6b808b228fb8d69fc14f36e3d2..f99c8448458078935fda477c6e4e15dde8d7d4ab 100644 --- a/tensorflow/python/tools/saved_model_cli_test.py +++ b/tensorflow/python/tools/saved_model_cli_test.py @@ -61,83 +61,84 @@ class SavedModelCLITestCase(test.TestCase): exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: signature_def['classify_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/classify signature_def['classify_x_to_y']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/classify signature_def['regress_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/regress signature_def['regress_x_to_y']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/regress signature_def['regress_x_to_y2']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y2:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y2:0 + Method name is: tensorflow/serving/regress signature_def['serving_default']: -The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/predict""" + The given SavedModel SignatureDef contains the following input(s): + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/predict""" # pylint: enable=line-too-long + self.maxDiff = None # Produce a useful error msg if the comparison fails self.assertMultiLineEqual(output, exp_out) self.assertEqual(err.getvalue().strip(), '') @@ -193,11 +194,11 @@ Method name is: tensorflow/serving/predict""" output = out.getvalue().strip() expected_output = ( 'The given SavedModel SignatureDef contains the following input(s):\n' - 'inputs[\'x\'] tensor_info:\n' - ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: x:0\n' + ' inputs[\'x\'] tensor_info:\n' + ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: x:0\n' 'The given SavedModel SignatureDef contains the following output(s):\n' - 'outputs[\'y\'] tensor_info:\n' - ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: y:0\n' + ' outputs[\'y\'] tensor_info:\n' + ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: y:0\n' 'Method name is: tensorflow/serving/predict') self.assertEqual(output, expected_output) self.assertEqual(err.getvalue().strip(), '') diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py index c92f6fc3015960a2b821651231bb94713e0d53dd..006e360389b404a8edd97c9a8bf4b8876c828004 100644 --- a/tensorflow/python/training/adam.py +++ b/tensorflow/python/training/adam.py @@ -106,10 +106,10 @@ class AdamOptimizer(optimizer.Optimizer): self._updated_lr = None def _get_beta_accumulators(self): - if context.in_graph_mode(): - graph = ops.get_default_graph() - else: + if context.executing_eagerly(): graph = None + else: + graph = ops.get_default_graph() return (self._get_non_slot_variable("beta1_power", graph=graph), self._get_non_slot_variable("beta2_power", graph=graph)) diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py index a521f1299e035424d1c3897a469655db732b0dcd..af87d6f0e5e74557523c7816d966c8fbcf69a824 100644 --- a/tensorflow/python/training/adam_test.py +++ b/tensorflow/python/training/adam_test.py @@ -184,7 +184,7 @@ class AdamOptimizerTest(test.TestCase): # Shouldn't return non-slot variables from other graphs. self.assertEqual(0, len(opt.variables())) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -194,7 +194,7 @@ class AdamOptimizerTest(test.TestCase): # Run 3 steps of Adam for t in range(1, 4): - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1])) diff --git a/tensorflow/python/training/checkpoint_ops.py b/tensorflow/python/training/checkpoint_ops.py index 7f92d94d2be369709608d36c109863b0ebfb7bbe..a6e9662b7305a00f1fcf03245685e93b756942d3 100644 --- a/tensorflow/python/training/checkpoint_ops.py +++ b/tensorflow/python/training/checkpoint_ops.py @@ -149,7 +149,7 @@ def _load_and_remap_matrix(ckpt_path, num_rows_present = num_rows_to_load if remap_rows: row_remapping, num_rows_present = ( - gen_checkpoint_ops._generate_vocab_remapping( # pylint: disable=protected-access + gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=new_row_vocab_file, old_vocab_file=old_row_vocab_file, new_vocab_offset=new_row_vocab_offset, @@ -168,7 +168,7 @@ def _load_and_remap_matrix(ckpt_path, num_cols_present = new_col_vocab_size if remap_cols: col_remapping, num_cols_present = ( - gen_checkpoint_ops._generate_vocab_remapping( # pylint: disable=protected-access + gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=new_col_vocab_file, old_vocab_file=old_col_vocab_file, new_vocab_offset=0, # Offset is unused for cols (no partitioning). @@ -178,7 +178,7 @@ def _load_and_remap_matrix(ckpt_path, num_rows_to_load * new_col_vocab_size - num_rows_present * num_cols_present, 1 ]) - return_tensor = gen_checkpoint_ops._load_and_remap_matrix( # pylint: disable=protected-access + return_tensor = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, row_remapping=row_remapping, diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index fa3de6fad27b6cc773f9f2e86e9f95395eb7c285..e7f88de1d2290a49f3b7bdf47417016d7e7c9cea 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -23,6 +23,7 @@ import six from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import ops from tensorflow.python.ops import io_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables @@ -289,10 +290,20 @@ def _set_checkpoint_initializer(variable, name: Name of the operation. """ base_type = variable.dtype.base_dtype - with ops.colocate_with(variable): + # Do not colocate with variable since RestoreV2 op only runs on CPU and + # colocation will force variable (and other ops that colocate with variable) + # to be on CPU as well. It is okay to place the variable's initializer op on + # CPU since it will only be run once at the start. + with ops.device(variable.device), ops.device("/cpu:0"): restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] - variable._initializer_op = state_ops.assign(variable, restore_op) # pylint:disable=protected-access + if isinstance(variable, resource_variable_ops.ResourceVariable): + init_op = variable.assign(restore_op, read_value=False) + else: + init_op = state_ops.assign(variable, restore_op) + variable._initializer_op = init_op # pylint:disable=protected-access + restore_op.set_shape(variable.shape) + variable._initial_value = restore_op # pylint:disable=protected-access def _set_variable_or_list_initializer(variable_or_list, ckpt_file, diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py index cd17faa040d5b85263b54bc53100b18f736a12e0..338436573a3355b77811542d3845e683c38e2521 100644 --- a/tensorflow/python/training/checkpoint_utils_test.py +++ b/tensorflow/python/training/checkpoint_utils_test.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import partitioned_variables +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -145,6 +146,36 @@ class CheckpointsTest(test.TestCase): # Check that tensors are not explicitly in the graph. self.assertLess(len(str(session.graph.as_graph_def())), 29000) + def testInitialValueComesFromCheckpoint(self): + checkpoint_dir = self.get_temp_dir() + with self.test_session() as session: + v1, _, _, _ = _create_checkpoints(session, checkpoint_dir) + + # New graph and session. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as session: + with variable_scope.variable_scope( + "some_scope", initializer=init_ops.zeros_initializer()): + my1 = variable_scope.get_variable("my1", [1, 10]) + + # At this point, my1.initialized_value() will add ops that reference + # the zeros initializer of my1. + before = variables.Variable(my1.initialized_value(), name="before") + + checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1}) + + # At this point, my1.initialized_value() will add ops that reference + # the newly set initializer of my1. + after = variables.Variable(my1.initialized_value(), name="after") + + session.run(variables.global_variables_initializer()) + self.assertAllEqual(session.run(my1), v1) + self.assertAllEqual(session.run(my1.initialized_value()), v1) + self.assertAllClose(session.run(before), [[0.0] * 10]) + self.assertAllClose(session.run(after), v1) + with self.assertRaises(AssertionError): + self.assertAllClose(session.run(before), session.run(after)) + def testInitWithScopeDoesNotCaptureSuffixes(self): checkpoint_dir = self.get_temp_dir() with self.test_session() as session: @@ -176,7 +207,9 @@ class CheckpointsTest(test.TestCase): checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"useful_scope/": "useful_scope/"}) - self.assertEqual(my4._initializer_op.op.inputs[1].device, "/job:ps") + # initializer runs on the same task but always on CPU. + self.assertEqual(my4._initializer_op.op.inputs[1].device, + "/job:ps/device:CPU:0") def testInitFromRootCheckpoint(self): checkpoint_dir = self.get_temp_dir() @@ -332,6 +365,31 @@ class CheckpointsTest(test.TestCase): checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"useful_scope": "some_scope/"}) + def testNoAdditionalReadOpsForResourceVariables(self): + checkpoint_dir = self.get_temp_dir() + with self.test_session() as session: + v1, _, _, _ = _create_checkpoints(session, checkpoint_dir) + + # New graph and session. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as session: + my1 = resource_variable_ops.ResourceVariable([[0.0] * 10], name="my1") + + with ops.name_scope("init_from_checkpoint"): + checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1}) + + # Basic sanity checks: + session.run(variables.global_variables_initializer()) + self.assertAllEqual(session.run(my1), v1) + + ops_in_init_from_checkpoint_scope = [ + op for op in g.get_operations() + if (op.name.startswith("init_from_checkpoint/") and + not op.name.startswith("init_from_checkpoint/checkpoint_initializer" + ) and op.type != "AssignVariableOp") + ] + self.assertEqual(ops_in_init_from_checkpoint_scope, []) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py new file mode 100644 index 0000000000000000000000000000000000000000..e49965703e65aea05dc3bddabf8d1e3a24c65a74 --- /dev/null +++ b/tensorflow/python/training/checkpointable.py @@ -0,0 +1,656 @@ +"""An object-local variable management scheme.""" +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from tensorflow.python.eager import context +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_io_ops as io_ops +from tensorflow.python.util import nest + +# A key indicating a variable's value in an object's checkpointed Tensors +# (Checkpointable._gather_saveables_for_checkpoint). If this is the only key and +# the object has no dependencies, then its value may be restored on object +# creation (avoiding double assignment when executing eagerly). +VARIABLE_VALUE_KEY = "VARIABLE_VALUE" + +CheckpointableReference = collections.namedtuple( + "CheckpointableReference", + [ + # The local name for this dependency. + "name", + # The Checkpointable object being referenced. + "ref" + ]) + + +class CheckpointInitialValue(ops.Tensor): + """Tensor wrapper for managing update UIDs in `Variables`. + + When supplied as an initial value, objects of this type let a `Variable` + (`Variable`, `ResourceVariable`, etc.) know the UID of the restore the initial + value came from. This allows deferred restorations to be sequenced in the + order the user specified them, and lets us fall back on assignment if an + initial value is not set (e.g. due to a custom getter interfering). + + See comments in _add_variable_with_custom_getter for more information about + how `CheckpointInitialValue` is used. + """ + + def __init__(self, checkpoint_position, shape=None): + self.wrapped_value = checkpoint_position.value_tensors()[ + VARIABLE_VALUE_KEY] + if shape: + # We need to set the static shape information on the initializer if + # possible so we don't get a variable with an unknown shape. + self.wrapped_value.set_shape(shape) + self._checkpoint_position = checkpoint_position + + @property + def __class__(self): + return (self.wrapped_value.__class__, CheckpointInitialValue) + + def __getattr__(self, attr): + try: + return getattr(self.wrapped_value, attr) + except AttributeError: + return self.__getattribute__(attr) + + @property + def checkpoint_position(self): + return self._checkpoint_position + + +class _CheckpointPosition(object): + """Indicates a position within a `_Checkpoint`.""" + + def __init__(self, checkpoint, proto_id): + """Specify an object within a checkpoint. + + Args: + checkpoint: A _Checkpoint object. + proto_id: The index of this object in CheckpointableObjectGraph.nodes. + """ + self._checkpoint = checkpoint + self._proto_id = proto_id + + def restore(self, checkpointable): + """Restore this value into `checkpointable`.""" + if self.bind_object(checkpointable): + # This object's correspondence with a checkpointed object is new, so + # process deferred restorations for it and its dependencies. + restore_ops = checkpointable._restore_from_checkpoint_position(self) # pylint: disable=protected-access + if restore_ops: + self._checkpoint.restore_ops.extend(restore_ops) + + def bind_object(self, checkpointable): + """Set a checkpoint<->object correspondence and process slot variables. + + Args: + checkpointable: The object to record a correspondence for. + Returns: + True if this is a new assignment, False if this object has already been + mapped to a checkpointed `Object` proto. + Raises: + AssertionError: If another object is already bound to the `Object` proto. + """ + checkpoint = self.checkpoint + current_assignment = checkpoint.object_by_proto_id.get(self._proto_id, None) + if current_assignment is None: + checkpoint.object_by_proto_id[self._proto_id] = checkpointable + for deferred_slot_restoration in ( + checkpoint.deferred_slot_restorations.pop(self._proto_id, ())): + checkpointable._create_or_restore_slot_variable( # pylint: disable=protected-access + slot_variable_position=_CheckpointPosition( + checkpoint=checkpoint, + proto_id=deferred_slot_restoration.slot_variable_id), + variable=deferred_slot_restoration.original_variable, + slot_name=deferred_slot_restoration.slot_name) + for slot_restoration in checkpoint.slot_restorations.pop( + self._proto_id, ()): + optimizer_object = checkpoint.object_by_proto_id.get( + slot_restoration.optimizer_id, None) + if optimizer_object is None: + # The optimizer has not yet been created or tracked. Record in the + # checkpoint that the slot variables need to be restored when it is. + checkpoint.deferred_slot_restorations.setdefault( + slot_restoration.optimizer_id, []).append( + _DeferredSlotVariableRestoration( + original_variable=checkpointable, + slot_variable_id=slot_restoration.slot_variable_id, + slot_name=slot_restoration.slot_name)) + else: + optimizer_object._create_or_restore_slot_variable( # pylint: disable=protected-access + slot_variable_position=_CheckpointPosition( + checkpoint=checkpoint, + proto_id=slot_restoration.slot_variable_id), + variable=checkpointable, + slot_name=slot_restoration.slot_name) + return True # New assignment + else: + # The object was already mapped for this checkpoint load, which means + # we don't need to do anything besides check that the mapping is + # consistent (if the dependency DAG is not a tree then there are + # multiple paths to the same object). + if current_assignment is not checkpointable: + raise AssertionError( + ("Unable to load the checkpoint into this object graph. Either " + "the Checkpointable object references in the Python program " + "have changed in an incompatible way, or the checkpoint was " + "generated in an incompatible program.\n\nTwo checkpoint " + "references resolved to different objects (%s and %s).") + % (current_assignment, checkpointable)) + return False # Not a new assignment + + def is_simple_variable(self): + """Determine whether this value is restorable with a Tensor initializer.""" + attributes = self.object_proto.attributes + return (len(attributes) == 1 + and attributes[0].name == VARIABLE_VALUE_KEY + and not self.object_proto.children) + + def value_tensors(self): + """Create value `Tensor`s for this object's attributes. + + Does not require that the Python object has been created. Used for + restore-on-create when executing eagerly. + + Returns: + A dictionary mapping from object attribute names to `Tensor`s. + """ + value_tensors = {} + for serialized_tensor in self.object_proto.attributes: + checkpoint_key = serialized_tensor.checkpoint_key + dtype = self._checkpoint.dtype_map[checkpoint_key] + base_type = dtype.base_dtype + with ops.init_scope(): + with ops.device("/cpu:0"): + # Run the restore itself on the CPU. + value, = io_ops.restore_v2( + prefix=self._checkpoint.save_path, + tensor_names=[checkpoint_key], + shape_and_slices=[""], + dtypes=[base_type], + name="%s_checkpoint_read" % (serialized_tensor.name,)) + # Copy the value to the current device if necessary. + value_tensors[serialized_tensor.name] = array_ops.identity(value) + return value_tensors + + def restore_ops(self): + """Create or fetch restore ops for this object's attributes. + + Requires that the `Checkpointable` Python object has been bound to an object + ID in the checkpoint. + + Returns: + A list of operations when graph building, or an empty list when executing + eagerly. + """ + saveables = self.checkpointable._gather_saveables_for_checkpoint() # pylint: disable=protected-access + # Name saveables based on the name this object had when it was checkpointed. + named_saveables = {} + restore_ops = [] + building_graph = not context.executing_eagerly() + for serialized_tensor in self.object_proto.attributes: + saveable_object = saveables.get(serialized_tensor.name, None) + if saveable_object is None: + # Purposefully does not throw an exception if attributes have been added + # or deleted. Stores unused attributes so an exception can be raised if + # the user decides to check that everything in the checkpoint was + # loaded. + self._checkpoint.unused_attributes.setdefault( + self.checkpointable, []).append(serialized_tensor.name) + continue + if building_graph: + existing_ops = self._checkpoint.restore_ops_by_name.get( + serialized_tensor.name, None) + else: + existing_ops = None + if existing_ops is None: + named_saveables[serialized_tensor.checkpoint_key] = saveable_object + if named_saveables: + validated_saveables = ( + self._checkpoint.builder._ValidateAndSliceInputs(named_saveables)) # pylint: disable=protected-access + validated_names = set(saveable.name for saveable in validated_saveables) + if set(named_saveables.keys()) != validated_names: + raise AssertionError( + ("Saveable keys changed when validating. Got back %s, was " + "expecting %s") % (named_saveables.keys(), validated_names)) + all_tensors = self._checkpoint.builder.bulk_restore( + filename_tensor=self._checkpoint.save_path, + saveables=validated_saveables, preferred_shard=-1, + restore_sequentially=False) + saveable_index = 0 + for saveable in validated_saveables: + num_specs = len(saveable.specs) + saveable_tensors = all_tensors[ + saveable_index:saveable_index + num_specs] + saveable_index += num_specs + restore_op = saveable.restore(saveable_tensors, restored_shapes=None) + if building_graph: + assert saveable.name not in self._checkpoint.restore_ops_by_name + self._checkpoint.restore_ops_by_name[saveable.name] = restore_op + restore_ops.append(restore_op) + return restore_ops + + @property + def checkpoint(self): + return self._checkpoint + + @property + def checkpointable(self): + return self._checkpoint.object_by_proto_id[self._proto_id] + + @property + def object_proto(self): + return self._checkpoint.object_graph_proto.nodes[self._proto_id] + + @property + def restore_uid(self): + return self._checkpoint.restore_uid + + def __repr__(self): + return repr(self.object_proto) + + +_DeferredSlotVariableRestoration = collections.namedtuple( + "_DeferredSlotVariableRestoration", + [ + "original_variable", + "slot_variable_id", + "slot_name", + ] +) + +_SlotVariableRestoration = collections.namedtuple( + "_SlotVariableRestoration", + [ + # The checkpoint proto id of the optimizer object. + "optimizer_id", + # The checkpoint proto id of the slot variable. + "slot_variable_id", + "slot_name", + ]) + + +class CheckpointableBase(object): + """Base class for `Checkpointable` objects without automatic dependencies. + + This class has no __setattr__ override for performance reasons. Dependencies + must be added explicitly. Unless attribute assignment is performance-critical, + use `Checkpointable` instead. Use `CheckpointableBase` for `isinstance` + checks. + """ + + def _maybe_initialize_checkpointable(self): + """Initialize dependency management. + + Not __init__, since most objects will forget to call it. + """ + if hasattr(self, "_unconditional_checkpoint_dependencies"): + # __init__ already called. This check means that we don't need + # Checkpointable.__init__() in the constructor of every TensorFlow object. + return + # A list of CheckpointableReference objects. Some classes implementing + # `Checkpointable`, notably `Optimizer`s, may override the + # _checkpoint_dependencies property with conditional dependencies + # (e.g. based on the current graph when saving). + self._unconditional_checkpoint_dependencies = [] + # Maps names -> Checkpointable objects + self._unconditional_dependency_names = {} + # Restorations for other Checkpointable objects on which this object may + # eventually depend. + self._deferred_dependencies = {} # local name -> _CheckpointPosition list + # The UID of the highest assignment to this object. Used to ensure that the + # last requested assignment determines the final value of an object. + if hasattr(self, "_update_uid"): + raise AssertionError( + "Internal error: the object had an update UID set before its " + "initialization code was run.") + self._update_uid = -1 + + @property + def _checkpoint_dependencies(self): + """All dependencies of this object. + + May be overridden to include conditional dependencies. + + Returns: + A list of `CheckpointableReference` objects indicating named + `Checkpointable` dependencies which should be saved along with this + object. + """ + return self._unconditional_checkpoint_dependencies + + def _lookup_dependency(self, name): + """Look up a dependency by name. + + May be overridden to include conditional dependencies. + + Args: + name: The local name of the dependency. + Returns: + A `Checkpointable` object, or `None` if no dependency by this name was + found. + """ + return self._unconditional_dependency_names.get(name, None) + + def _add_variable_with_custom_getter( + self, name, shape=None, dtype=dtypes.float32, + initializer=None, getter=None, overwrite=False, + **kwargs_for_getter): + """Restore-on-create for a variable be saved with this `Checkpointable`. + + If the user has requested that this object or another `Checkpointable` which + depends on this object be restored from a checkpoint (deferred loading + before variable object creation), `initializer` may be ignored and the value + from the checkpoint used instead. + + Args: + name: A name for the variable. Must be unique within this object. + shape: The shape of the variable. + dtype: The data type of the variable. + initializer: The initializer to use. Ignored if there is a deferred + restoration left over from a call to + `_restore_from_checkpoint_position`. + getter: The getter to wrap which actually fetches the variable. + overwrite: If True, disables unique name and type checks. + **kwargs_for_getter: Passed to the getter. + + Returns: + The new variable object. + + Raises: + ValueError: If the variable name is not unique. + """ + self._maybe_initialize_checkpointable() + if not overwrite and self._lookup_dependency(name) is not None: + raise ValueError( + ("A variable named '%s' already exists in this Checkpointable, but " + "Checkpointable._add_variable called to create another with " + "that name. Variable names must be unique within a Checkpointable " + "object.") % (name,)) + if context.executing_eagerly(): + # If this is a variable with a single Tensor stored in the checkpoint, we + # can set that value as an initializer rather than initializing and then + # assigning (when executing eagerly). This call returns None if there is + # nothing to restore. + checkpoint_initializer = self._preload_simple_restoration( + name=name, shape=shape) + else: + checkpoint_initializer = None + if (checkpoint_initializer is not None + and not ( + isinstance(initializer, CheckpointInitialValue) + and initializer.restore_uid > checkpoint_initializer.restore_uid)): + # If multiple Checkpointable objects are "creating" the same variable via + # the magic of custom getters, the one with the highest restore UID (the + # one called last) has to make the final initializer. If another custom + # getter interrupts this process by overwriting the initializer, then + # we'll catch that when we call _track_checkpointable. So this is "best + # effort" to set the initializer with the highest restore UID. + initializer = checkpoint_initializer + shape = None + + new_variable = getter( + name=name, shape=shape, dtype=dtype, initializer=initializer, + **kwargs_for_getter) + + # If we set an initializer and the variable processed it, tracking will not + # assign again. It will add this variable to our dependencies, and if there + # is a non-trivial restoration queued, it will handle that. This also + # handles slot variables. + if not overwrite or isinstance(new_variable, CheckpointableBase): + return self._track_checkpointable(new_variable, name=name, + overwrite=overwrite) + else: + # TODO(allenl): Some variable types are not yet supported. Remove this + # fallback once all get_variable() return types are Checkpointable. + return new_variable + + def _preload_simple_restoration(self, name, shape): + """Return a dependency's value for restore-on-create. + + Note the restoration is not deleted; if for some reason preload is called + and then not assigned to the variable (for example because a custom getter + overrides the initializer), the assignment will still happen once the + variable is tracked (determined based on checkpoint.restore_uid). + + Args: + name: The object-local name of the dependency holding the variable's + value. + shape: The shape of the variable being loaded into. + Returns: + An callable for use as a variable's initializer/initial_value, or None if + one should not be set (either because there was no variable with this name + in the checkpoint or because it needs more complex deserialization). Any + non-trivial deserialization will happen when the variable object is + tracked. + """ + deferred_dependencies_list = self._deferred_dependencies.get(name, ()) + if not deferred_dependencies_list: + # Nothing to do; we don't have a restore for this dependency queued up. + return + for checkpoint_position in deferred_dependencies_list: + if not checkpoint_position.is_simple_variable(): + # If _any_ pending restoration is too complicated to fit in an + # initializer (because it has dependencies, or because there are + # multiple Tensors to restore), bail and let the general tracking code + # handle it. + return None + checkpoint_position = max( + deferred_dependencies_list, + key=lambda restore: restore.checkpoint.restore_uid) + return CheckpointInitialValue( + checkpoint_position=checkpoint_position, shape=shape) + + def _track_checkpointable(self, checkpointable, name, overwrite=False): + """Declare a dependency on another `Checkpointable` object. + + Indicates that checkpoints for this object should include variables from + `checkpointable`. + + Variables in a checkpoint are mapped to `Checkpointable`s based on the names + provided when the checkpoint was written. To avoid breaking existing + checkpoints when modifying a class, neither variable names nor dependency + names (the names passed to `_track_checkpointable`) may change. + + Args: + checkpointable: A `Checkpointable` which this object depends on. + name: A local name for `checkpointable`, used for loading checkpoints into + the correct objects. + overwrite: Boolean, whether silently replacing dependencies is OK. Used + for __setattr__, where throwing an error on attribute reassignment would + be inappropriate. + + Returns: + `checkpointable`, for convenience when declaring a dependency and + assigning to a member variable in one statement. + + Raises: + TypeError: If `checkpointable` does not inherit from `Checkpointable`. + ValueError: If another object is already tracked by this name. + """ + self._maybe_initialize_checkpointable() + if not isinstance(checkpointable, CheckpointableBase): + raise TypeError( + ("Checkpointable._track_checkpointable() passed type %s, not a " + "Checkpointable.") % (type(checkpointable),)) + new_reference = CheckpointableReference(name=name, ref=checkpointable) + current_object = self._lookup_dependency(name) + if (current_object is not None + and current_object is not checkpointable): + if not overwrite: + raise ValueError( + ("Called Checkpointable._track_checkpointable() with name='%s', " + "but a Checkpointable with this name is already declared as a " + "dependency. Names must be unique (or overwrite=True).") % (name,)) + # This is a weird thing to do, but we're not going to stop people from + # using __setattr__. + for index, (old_name, _) in enumerate( + self._unconditional_checkpoint_dependencies): + if name == old_name: + self._unconditional_checkpoint_dependencies[index] = new_reference + else: + self._unconditional_checkpoint_dependencies.append(new_reference) + + self._unconditional_dependency_names[name] = checkpointable + self._handle_deferred_dependencies(name=name, checkpointable=checkpointable) + return checkpointable + + def _handle_deferred_dependencies(self, name, checkpointable): + """Pop and load any deferred checkpoint restores into `checkpointable`. + + This method does not add a new dependency on `checkpointable`, but it does + check if any outstanding/deferred dependencies have been queued waiting for + this dependency to be added (matched based on `name`). If so, + `checkpointable` and its dependencies are restored. The restorations are + considered fulfilled and so are deleted. + + `_track_checkpointable` is more appropriate for adding a + normal/unconditional dependency, and includes handling for deferred + restorations. This method allows objects such as `Optimizer` to use the same + restoration logic while managing conditional dependencies themselves, by + overriding `_checkpoint_dependencies` and `_lookup_dependency` to change the + object's dependencies based on the context it is saved/restored in (a single + optimizer instance can have state associated with multiple graphs). + + Args: + name: The name of the dependency within this object (`self`), used to + match `checkpointable` with values saved in a checkpoint. + checkpointable: The Checkpointable object to restore (inheriting from + `CheckpointableBase`). + """ + deferred_dependencies_list = self._deferred_dependencies.pop(name, ()) + for checkpoint_position in sorted( + deferred_dependencies_list, + key=lambda restore: restore.checkpoint.restore_uid, + reverse=True): + checkpoint_position.restore(checkpointable) + + def _restore_from_checkpoint_position(self, checkpoint_position): + """Restore this object and its dependencies (may be deferred).""" + # Attempt a breadth-first traversal, since presumably the user has more + # control over shorter paths. If we don't have all of the dependencies at + # this point, the end result is not breadth-first (since other deferred + # traversals will happen later). + visit_queue = collections.deque([checkpoint_position]) + restore_ops = [] + while visit_queue: + current_position = visit_queue.popleft() + restore_ops.extend(nest.flatten( + current_position.checkpointable # pylint: disable=protected-access + ._single_restoration_from_checkpoint_position( + checkpoint_position=current_position, + visit_queue=visit_queue))) + return restore_ops + + def _single_restoration_from_checkpoint_position( + self, checkpoint_position, visit_queue): + """Restore this object, and either queue its dependencies or defer them.""" + self._maybe_initialize_checkpointable() + checkpoint = checkpoint_position.checkpoint + # If the UID of this restore is lower than our current update UID, we don't + # need to actually restore the object. However, we should pass the + # restoration on to our dependencies. + if checkpoint.restore_uid > self._update_uid: + restore_ops = checkpoint_position.restore_ops() + # TODO(allenl): Get a list of feeds for saving Python state + self._update_uid = checkpoint.restore_uid + else: + restore_ops = () + for child in checkpoint_position.object_proto.children: + child_position = _CheckpointPosition( + checkpoint=checkpoint, + proto_id=child.node_id) + local_object = self._lookup_dependency(child.local_name) + if local_object is None: + # We don't yet have a dependency registered with this name. Save it + # in case we do. + self._deferred_dependencies.setdefault(child.local_name, []).append( + child_position) + else: + if child_position.bind_object(checkpointable=local_object): + # This object's correspondence is new, so dependencies need to be + # visited. Delay doing it so that we get a breadth-first dependency + # resolution order (shallowest paths first). The caller is responsible + # for emptying visit_queue. + visit_queue.append(child_position) + return restore_ops + + def _gather_saveables_for_checkpoint(self): + """Returns a dictionary of values to checkpoint with this object. + + Keys in the returned dictionary are local to this object and in a separate + namespace from dependencies. Values may either be `SaveableObject`s or + variables easily converted to `SaveableObject`s (as in `tf.train.Saver`'s + `var_list` constructor argument). + + Returned values must be saved only by this object; if any value may be + shared, it should instead be a dependency. For example, variable objects + save their own values with the key `VARIABLE_VALUE_KEY`, but objects which + reference variables simply add a dependency. + """ + return {} + + +class Checkpointable(CheckpointableBase): + """Manages dependencies on other objects. + + `Checkpointable` objects may have dependencies: other `Checkpointable` objects + which should be saved if the object declaring the dependency is saved. A + correctly saveable program has a dependency graph such that if changing a + global variable affects an object (e.g. changes the behavior of any of its + methods) then there is a chain of dependencies from the influenced object to + the variable. + + Dependency edges have names, and are created implicitly when a + `Checkpointable` object is assigned to an attribute of another + `Checkpointable` object. For example: + + ``` + obj = Checkpointable() + obj.v = ResourceVariable(0.) + ``` + + The `Checkpointable` object `obj` now has a dependency named "v" on a + variable. + + `Checkpointable` objects may specify `Tensor`s to be saved and restored + directly (e.g. a `Variable` indicating how to save itself) rather than through + dependencies on other objects. See + `Checkpointable._gather_saveables_for_checkpoint` for details. + """ + + def __setattr__(self, name, value): + """Support self.foo = checkpointable syntax.""" + # Perform the attribute assignment, and potentially call other __setattr__ + # overrides such as that for tf.keras.Model. + super(Checkpointable, self).__setattr__(name, value) + if isinstance(value, CheckpointableBase): + self._track_checkpointable( + value, name=name, + # Allow the user to switch the Checkpointable which is tracked by this + # name, since assigning a new variable to an attribute has + # historically been fine (e.g. Adam did this). + # TODO(allenl): Should this be a warning once Checkpointable save/load + # is usable? + overwrite=True) diff --git a/tensorflow/python/training/checkpointable_test.py b/tensorflow/python/training/checkpointable_test.py new file mode 100644 index 0000000000000000000000000000000000000000..e79acb49758b6a7d69dd084692d434bea808db64 --- /dev/null +++ b/tensorflow/python/training/checkpointable_test.py @@ -0,0 +1,39 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.platform import test +from tensorflow.python.training import checkpointable + + +class InterfaceTests(test.TestCase): + + def testMultipleAssignment(self): + root = checkpointable.Checkpointable() + root.leaf = checkpointable.Checkpointable() + root.leaf = root.leaf + duplicate_name_dep = checkpointable.Checkpointable() + with self.assertRaises(ValueError): + root._track_checkpointable(duplicate_name_dep, name="leaf") + # No error; we're overriding __setattr__, so we can't really stop people + # from doing this while maintaining backward compatibility. + root.leaf = duplicate_name_dep + root._track_checkpointable(duplicate_name_dep, name="leaf", overwrite=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/training/checkpointable_utils.py b/tensorflow/python/training/checkpointable_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..32123f87ef2d12497077ab0e2f7d4d4cad1ec5dd --- /dev/null +++ b/tensorflow/python/training/checkpointable_utils.py @@ -0,0 +1,78 @@ +"""Utilities for saving/loading Checkpointable objects.""" +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import weakref + +from tensorflow.python.framework import ops +from tensorflow.python.training import checkpointable +from tensorflow.python.training import saver as saver_lib + + +class _Checkpoint(object): + """Holds the status of an object-based checkpoint load.""" + + def __init__(self, object_graph_proto, save_path, dtype_map=None): + """Specify the checkpoint being loaded. + + Args: + object_graph_proto: The CheckpointableObjectGraph protocol buffer + associated with this checkpoint. + save_path: A string `Tensor`. The path to the checkpoint, as returned by + `tf.train.latest_checkpoint`. + dtype_map: When executing eagerly, specifies dtypes for creating slot + variables. None when graph building. + """ + self.builder = saver_lib.BulkSaverBuilder() + self.object_graph_proto = object_graph_proto + self.restore_uid = ops.uid() + # Maps from objects to lists of attributes which were in the checkpoint but + # not loaded into any object, for error checking. + self.unused_attributes = weakref.WeakKeyDictionary() + # Dictionary mapping from an id in the protocol buffer flat array to + # Checkpointable Python objects. This mapping may be deferred if a + # checkpoint is restored before all dependencies have been tracked. Uses + # weak references so that partial restorations don't create reference cycles + # (as objects with deferred dependencies will generally have references to + # this object). + self.object_by_proto_id = weakref.WeakValueDictionary() + self.save_path = save_path + self.dtype_map = dtype_map + # When graph building, contains a list of ops to run to restore objects from + # this checkpoint. + self.restore_ops = [] + self.restore_ops_by_name = {} + # A mapping from optimizer proto ids to lists of slot variables to be + # restored when the optimizer is tracked. Only includes slot variables whose + # regular variables have already been created, and only for optimizer + # objects which have not yet been created/tracked. + self.deferred_slot_restorations = {} + # A mapping from variable proto ids to lists of slot variables to be + # restored when the variable is created/tracked. These get shifted over to + # deferred_slot_restorations if the optimizer hasn't been created when that + # happens. + self.slot_restorations = {} + for node_index, node in enumerate(self.object_graph_proto.nodes): + for slot_reference in node.slot_variables: + # `node` refers to an `Optimizer`, since only these have slot variables. + self.slot_restorations.setdefault( + slot_reference.original_variable_node_id, []).append( + checkpointable._SlotVariableRestoration( # pylint: disable=protected-access + optimizer_id=node_index, + slot_variable_id=slot_reference.slot_variable_node_id, + slot_name=slot_reference.slot_name)) diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py index 689088bb41edfd94a1d483ed2b5f7447e9e060e7..d31c375b4ce48dcb9bc2918514707636a647c675 100644 --- a/tensorflow/python/training/device_setter.py +++ b/tensorflow/python/training/device_setter.py @@ -25,6 +25,15 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util.tf_export import tf_export +# This is a tuple of PS ops used by tf.estimator.Esitmator which should work in +# almost all of cases. +STANDARD_PS_OPS = ( + "Variable", "VariableV2", "AutoReloadVariable", "MutableHashTable", + "MutableHashTableV2", "MutableHashTableOfTensors", + "MutableHashTableOfTensorsV2", "MutableDenseHashTable", + "MutableDenseHashTableV2", "VarHandleOp" +) + class _RoundRobinStrategy(object): """Returns the next ps task index for placement in round-robin order. @@ -170,8 +179,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps", than overriding them. cluster: `ClusterDef` proto or `ClusterSpec`. ps_ops: List of strings representing `Operation` types that need to be - placed on `ps` devices. If `None`, defaults to - `["Variable", "VariableV2", "VarHandleOp"]`. + placed on `ps` devices. If `None`, defaults to `STANDARD_PS_OPS`. ps_strategy: A callable invoked for every ps `Operation` (i.e. matched by `ps_ops`), that takes the `Operation` and returns the ps task index to use. If `None`, defaults to a round-robin strategy across all `ps` @@ -201,7 +209,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps", if ps_ops is None: # TODO(sherrym): Variables in the LOCAL_VARIABLES collection should not be # placed in the parameter server. - ps_ops = ["Variable", "VariableV2", "VarHandleOp"] + ps_ops = list(STANDARD_PS_OPS) if not merge_devices: logging.warning( diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py index 9d02e694db15637126f37ee5575638908b351def..4fa081fab72df62107cf4957d4ff68240ced9ee0 100644 --- a/tensorflow/python/training/ftrl.py +++ b/tensorflow/python/training/ftrl.py @@ -53,7 +53,7 @@ class FtrlOptimizer(optimizer.Optimizer): learning_rate: A float value or a constant float `Tensor`. learning_rate_power: A float value, must be less or equal to zero. initial_accumulator_value: The starting value for accumulators. - Only positive values are allowed. + Only zero or positive values are allowed. l1_regularization_strength: A float value, must be greater than or equal to zero. l2_regularization_strength: A float value, must be greater than or @@ -84,9 +84,10 @@ class FtrlOptimizer(optimizer.Optimizer): """ super(FtrlOptimizer, self).__init__(use_locking, name) - if initial_accumulator_value <= 0.0: - raise ValueError("initial_accumulator_value %f needs to be positive" % - initial_accumulator_value) + if initial_accumulator_value < 0.0: + raise ValueError( + "initial_accumulator_value %f needs to be be positive or zero" % + initial_accumulator_value) if learning_rate_power > 0.0: raise ValueError("learning_rate_power %f needs to be negative or zero" % learning_rate_power) diff --git a/tensorflow/python/training/gradient_descent.py b/tensorflow/python/training/gradient_descent.py index 380e14e02497fbe3681d6bae03fe9c636c5d13aa..6caf29d83af546f821314179e17f7bf1a693ff1a 100644 --- a/tensorflow/python/training/gradient_descent.py +++ b/tensorflow/python/training/gradient_descent.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -43,6 +44,7 @@ class GradientDescentOptimizer(optimizer.Optimizer): """ super(GradientDescentOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate + self._learning_rate_tensor = None def _apply_dense(self, grad, var): return training_ops.apply_gradient_descent( @@ -69,5 +71,6 @@ class GradientDescentOptimizer(optimizer.Optimizer): return var.scatter_sub(delta, use_locking=self._use_locking) def _prepare(self): - self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, - name="learning_rate") + if not context.executing_eagerly() or self._learning_rate_tensor is None: + self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, + name="learning_rate") diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py index bd9985a7c5c181c0431e0c0a91186bc36b11c787..44f00a96deff64012705c4c81b185a9c4fac2295 100644 --- a/tensorflow/python/training/input.py +++ b/tensorflow/python/training/input.py @@ -159,7 +159,7 @@ def input_producer(input_tensor, enabled. Please use the `tf.data` API to ingest data under eager execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -737,7 +737,7 @@ def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `batch` and `maybe_batch`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -775,7 +775,7 @@ def _batch_join(tensors_list, batch_size, keep_input, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `batch_join` and `maybe_batch_join`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -810,7 +810,7 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `shuffle_batch` and `maybe_shuffle_batch`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -855,7 +855,7 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `shuffle_batch_join` and `maybe_shuffle_batch_join`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index 1ce8c156a0b126f680bad62267f90e31a23febed..60306e4f1239a759ea1f68492a1211d5f0858997 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -43,8 +43,8 @@ class LRDecayTest(test_util.TensorFlowTestCase): def testStaircase(self): with self.test_session(): - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, + name="step", container="", shared_name="") assign_100 = state_ops.assign(step, 100) assign_1 = state_ops.assign(step, 1) assign_2 = state_ops.assign(step, 2) @@ -113,7 +113,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): learning_rate_decay.piecewise_constant(x, boundaries, values) # Test that ref types are valid. - if context.in_graph_mode(): + if not context.executing_eagerly(): x = variables.Variable(0.0) x_ref = x.op.outputs[0] # float32_ref tensor should be accepted boundaries, values = [1.0, 2.0], [1, 2, 3] @@ -264,8 +264,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, @@ -281,8 +281,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, @@ -304,8 +304,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, @@ -323,8 +323,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, diff --git a/tensorflow/python/training/momentum_test.py b/tensorflow/python/training/momentum_test.py index 6865513b0e4aad18d77887770a11243642958e7a..297a8bbde5447cff9465be36c0bb71f2490c60fc 100644 --- a/tensorflow/python/training/momentum_test.py +++ b/tensorflow/python/training/momentum_test.py @@ -66,7 +66,7 @@ class MomentumOptimizerTest(test.TestCase): mom_update = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -78,13 +78,13 @@ class MomentumOptimizerTest(test.TestCase): self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertFalse(slot0 in variables.trainable_variables()) self.assertFalse(slot1 in variables.trainable_variables()) # Step 1: the momentum accumulators where 0. So we should see a normal # update: v -= grad * learning_rate - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0.1, 0.1]), @@ -99,10 +99,10 @@ class MomentumOptimizerTest(test.TestCase): np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]), self.evaluate(var1)) # Step 2: the momentum accumulators contain the previous update. - if context.in_graph_mode(): - self.evaluate(mom_update) - else: + if context.executing_eagerly(): mom_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + else: + self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), @@ -142,7 +142,7 @@ class MomentumOptimizerTest(test.TestCase): [1.0, 2.0], dtype=dtypes.float32, name="var0") var1 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var1") - if context.in_eager_mode(): + if context.executing_eagerly(): loss = lambda: math_ops.reduce_sum(var0 + var1) else: loss = math_ops.reduce_sum(var0 + var1) @@ -157,7 +157,7 @@ class MomentumOptimizerTest(test.TestCase): [1.0, 2.0], dtype=dtypes.float32, name="var2") var3 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var3") - if context.in_eager_mode(): + if context.executing_eagerly(): loss = lambda: math_ops.reduce_sum(var2 + var3) else: loss = math_ops.reduce_sum(var2 + var3) @@ -247,7 +247,7 @@ class MomentumOptimizerTest(test.TestCase): # pylint: enable=cell-var-from-loop opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0) - sgd_op = opt.minimize(loss if context.in_eager_mode() else loss()) + sgd_op = opt.minimize(loss) self.evaluate(variables.global_variables_initializer()) # Run 1 step of sgd self.evaluate(sgd_op) @@ -262,7 +262,7 @@ class MomentumOptimizerTest(test.TestCase): return math_ops.reduce_sum(embedding_ops.embedding_lookup(var0, [[1]])) opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0) - sgd_op = opt.minimize(loss if context.in_eager_mode() else loss()) + sgd_op = opt.minimize(loss) self.evaluate(variables.global_variables_initializer()) self.evaluate(sgd_op) self.assertAllCloseAccordingToType([[1, 1], [0, 0]], self.evaluate(var0)) diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py index 2d89082ad75ff8b39575711bdbbc3f454f99a70d..b9ecb27df19d051c28ec1c3fe3cd9fd86717a5ed 100644 --- a/tensorflow/python/training/moving_averages.py +++ b/tensorflow/python/training/moving_averages.py @@ -400,7 +400,9 @@ class ExponentialMovingAverage(object): avg = slot_creator.create_zeros_slot( var, self._name, - colocate_with_primary=(var.op.type in ["Variable", "VariableV2"])) + colocate_with_primary=(var.op.type in ["Variable", + "VariableV2", + "VarHandleOp"])) if self._zero_debias: zero_debias_true.add(avg) self._averages[var] = avg diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py index 6efdeb286657e761a4c46634b9408121765a447b..6717811bbb0f05723a5ad0fbcbfba75249d0d43b 100644 --- a/tensorflow/python/training/moving_averages_test.py +++ b/tensorflow/python/training/moving_averages_test.py @@ -376,7 +376,7 @@ class ExponentialMovingAverageTest(test.TestCase): with ops.device("/job:dev_v0"): v0 = variables.Variable(10.0, name="v0") with ops.device("/job:dev_v1"): - v1 = gen_state_ops._variable( + v1 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="v1", diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index 9ec588bac96d8c8404dee994bc5991f897abbf77..9776b90ba4eee7407d869740b8b9cdb28dc08b75 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables +from tensorflow.python.training import checkpointable from tensorflow.python.training import slot_creator from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -41,7 +42,7 @@ from tensorflow.python.util.tf_export import tf_export def _get_variable_for(v): """Returns the ResourceVariable responsible for v, or v if not necessary.""" - if context.in_eager_mode(): + if context.executing_eagerly(): return v if v.op.type == "VarHandleOp": for var in variables.trainable_variables(): @@ -72,7 +73,7 @@ def _deduplicate_indexed_slices(values, indices): def _var_key(var): - if context.in_eager_mode(): + if context.executing_eagerly(): return var._shared_name # pylint: disable=protected-access return (var.op.graph, var.op.name) @@ -97,6 +98,9 @@ class _RefVariableProcessor(_OptimizableVariable): def __init__(self, v): self._v = v + def __str__(self): + return "<_RefVariableProcessor(%s)>" % self._v + def target(self): return self._v._ref() # pylint: disable=protected-access @@ -175,21 +179,48 @@ class _StreamingModelPortProcessor(_OptimizableVariable): return g +class _TensorProcessor(_OptimizableVariable): + """Processor for ordinary Tensors. + + Even though a Tensor can't really be updated, sometimes it is useful to + compute the gradients with respect to a Tensor using the optimizer. Updating + the Tensor is, of course, unsupported. + """ + + def __init__(self, v): + self._v = v + + def target(self): + return self._v + + def update_op(self, optimizer, g): + raise NotImplementedError("Trying to update a Tensor ", self._v) + + def _get_processor(v): """The processor of v.""" - if context.in_eager_mode(): - return _DenseResourceVariableProcessor(v) + if context.executing_eagerly(): + if isinstance(v, ops.Tensor): + return _TensorProcessor(v) + else: + return _DenseResourceVariableProcessor(v) if v.op.type == "VarHandleOp": return _DenseResourceVariableProcessor(v) if isinstance(v, variables.Variable): return _RefVariableProcessor(v) if v.op.type == "SubmodelPort": return _StreamingModelPortProcessor(v) + if isinstance(v, ops.Tensor): + return _TensorProcessor(v) raise NotImplementedError("Trying to optimize unsupported type ", v) @tf_export("train.Optimizer") -class Optimizer(object): +class Optimizer( + # Optimizers inherit from CheckpointableBase rather than Checkpointable + # since they do most of their dependency management themselves (slot + # variables are special-cased, and non-slot variables are keyed to graphs). + checkpointable.CheckpointableBase): """Base class for optimizers. This class defines the API to add Ops to train a model. You never use this @@ -300,9 +331,18 @@ class Optimizer(object): self._use_locking = use_locking self._name = name # Dictionary of slots. - # {slot_name : { variable_to_train: slot_for_the_variable, ...}, ... } + # {slot_name : + # {_var_key(variable_to_train): slot_for_the_variable, ... }, + # ... } self._slots = {} self._non_slot_dict = {} + # For implementing Checkpointable. Stores information about how to restore + # slot variables which have not yet been created + # (checkpointable._CheckpointPosition objects). + # {slot_name : + # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, + # ... } + self._deferred_slot_restorations = {} def get_name(self): return self._name @@ -382,7 +422,9 @@ class Optimizer(object): given variable. Args: - loss: A Tensor containing the value to minimize. + loss: A Tensor containing the value to minimize or a callable taking + no arguments which returns the value to minimize. When eager execution + is enabled it must be a callable. var_list: Optional list or tuple of `tf.Variable` to update to minimize `loss`. Defaults to the list of variables collected in the graph under the key `GraphKeys.TRAINABLE_VARIABLES`. @@ -401,37 +443,27 @@ class Optimizer(object): Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments are invalid. - RuntimeError: If called with eager execution enabled and if `grad_loss` - is not `None` or `loss` is not callable. + RuntimeError: If called with eager execution enabled and `loss` is + not callable. @compatibility(eager) - When eager execution is enabled, `loss` should be a Python function that - takes elements of `var_list` as arguments and computes the value to be - minimized. If `var_list` is None, `loss` should take no arguments. - Gradient computation is done with respect to the elements of `var_list` if - not None, else with respect to any trainable variables created during the - execution of the `loss` function. - `gate_gradients`, `aggregation_method`, `colocate_gradients_with_ops` and - `grad_loss` are ignored when eager execution is enabled. + When eager execution is enabled, `gate_gradients`, `aggregation_method`, + and `colocate_gradients_with_ops` are ignored. @end_compatibility """ - if context.in_eager_mode(): - if grad_loss is not None: - raise RuntimeError( - "`grad_loss` argument to Optimizer.compute_gradients " - "not supported when eager execution is enabled.") - if not callable(loss): - raise RuntimeError( - "`loss` passed to Optimizer.compute_gradients should " - "be a function when eager execution is enabled.") - # TODO(agarwal): consider passing parameters to the `loss` function. + if callable(loss): + with backprop.GradientTape() as tape: + if var_list is not None: + tape.watch(var_list) + loss_value = loss() if var_list is None: - return backprop.implicit_grad(loss)() - else: - var_list = nest.flatten(var_list) - grads = backprop.gradients_function(loss)(*var_list) - grads_and_vars = list(zip(grads, var_list)) - return grads_and_vars + var_list = tape.watched_variables() + grads = tape.gradient(loss_value, var_list, grad_loss) + return list(zip(grads, var_list)) + if context.executing_eagerly(): + raise RuntimeError( + "`loss` passed to Optimizer.compute_gradients should " + "be a function when eager execution is enabled.") if gate_gradients not in [Optimizer.GATE_NONE, Optimizer.GATE_OP, Optimizer.GATE_GRAPH]: raise ValueError("gate_gradients must be one of: Optimizer.GATE_NONE, " @@ -527,7 +559,7 @@ class Optimizer(object): # We colocate all ops created in _apply_dense or _apply_sparse # on the same device as the variable. # TODO(apassos): figure out how to get the variable name here. - scope_name = var.op.name if context.in_graph_mode() else "" + scope_name = "" if context.executing_eagerly() else var.op.name with ops.name_scope("update_" + scope_name), ops.colocate_with(var): update_ops.append(processor.update_op(self, grad)) if global_step is None: @@ -545,7 +577,7 @@ class Optimizer(object): else: apply_updates = state_ops.assign_add(global_step, 1, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): if isinstance(apply_updates, ops.Tensor): apply_updates = apply_updates.op train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) @@ -595,7 +627,7 @@ class Optimizer(object): Returns: A list of variables. """ - executing_eagerly = context.in_eager_mode() + executing_eagerly = context.executing_eagerly() current_graph = ops.get_default_graph() def _from_current_graph(variable): @@ -617,20 +649,54 @@ class Optimizer(object): def _create_non_slot_variable(self, initial_value, name, colocate_with): """Add an extra variable, not associated with a slot.""" - if context.in_graph_mode(): - graph = colocate_with.graph - else: - graph = None + eager = context.executing_eagerly() + graph = None if eager else colocate_with.graph key = (name, graph) v = self._non_slot_dict.get(key, None) if v is None: + self._maybe_initialize_checkpointable() with ops.colocate_with(colocate_with): + if eager: + restored_initial_value = self._preload_simple_restoration( + name=name, shape=None) + if restored_initial_value is not None: + initial_value = restored_initial_value v = variable_scope.variable(initial_value, name=name, trainable=False) + # Restore this variable by name if necessary, but don't add a + # Checkpointable dependency. Optimizers return the current graph's + # non-slot variables from _checkpoint_dependencies explicitly rather + # than unconditionally adding dependencies (since there may be multiple + # non-slot variables with the same name in different graphs, trying to + # save all of them would result in errors). + self._handle_deferred_dependencies(name=name, checkpointable=v) self._non_slot_dict[key] = v return v + @property + def _checkpoint_dependencies(self): + """From Checkpointable. Gather graph-specific non-slot variables to save.""" + current_graph_non_slot_variables = [] + current_graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access + for (name, _), variable_object in sorted(self._non_slot_dict.items(), + # Avoid comparing graphs + key=lambda item: item[0][0]): + if variable_object._graph_key == current_graph_key: # pylint: disable=protected-access + current_graph_non_slot_variables.append( + checkpointable.CheckpointableReference( + name=name, ref=variable_object)) + return (super(Optimizer, self)._checkpoint_dependencies + + current_graph_non_slot_variables) + + def _lookup_dependency(self, name): + """From Checkpointable. Find a non-slot variable in the current graph.""" + unconditional = super(Optimizer, self)._lookup_dependency(name) + if unconditional is not None: + return unconditional + graph = None if context.executing_eagerly() else ops.get_default_graph() + return self._get_non_slot_variable(name, graph=graph) + def _get_non_slot_variable(self, name, graph=None): return self._non_slot_dict.get((name, graph), None) @@ -868,7 +934,11 @@ class Optimizer(object): """ named_slots = self._slot_dict(slot_name) if _var_key(var) not in named_slots: - named_slots[_var_key(var)] = slot_creator.create_slot(var, val, op_name) + new_slot_variable = slot_creator.create_slot(var, val, op_name) + self._restore_slot_variable( + slot_name=slot_name, variable=var, + slot_variable=new_slot_variable) + named_slots[_var_key(var)] = new_slot_variable return named_slots[_var_key(var)] def _get_or_make_slot_with_initializer(self, var, initializer, shape, dtype, @@ -889,8 +959,12 @@ class Optimizer(object): """ named_slots = self._slot_dict(slot_name) if _var_key(var) not in named_slots: - named_slots[_var_key(var)] = slot_creator.create_slot_with_initializer( + new_slot_variable = slot_creator.create_slot_with_initializer( var, initializer, shape, dtype, op_name) + self._restore_slot_variable( + slot_name=slot_name, variable=var, + slot_variable=new_slot_variable) + named_slots[_var_key(var)] = new_slot_variable return named_slots[_var_key(var)] def _zeros_slot(self, var, slot_name, op_name): @@ -907,5 +981,78 @@ class Optimizer(object): """ named_slots = self._slot_dict(slot_name) if _var_key(var) not in named_slots: - named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name) + new_slot_variable = slot_creator.create_zeros_slot(var, op_name) + self._restore_slot_variable( + slot_name=slot_name, variable=var, + slot_variable=new_slot_variable) + named_slots[_var_key(var)] = new_slot_variable return named_slots[_var_key(var)] + + # -------------- + # For implementing the Checkpointable interface. + # -------------- + + def _restore_slot_variable(self, slot_name, variable, slot_variable): + """Restore a newly created slot variable's value.""" + variable_key = _var_key(variable) + deferred_restorations = self._deferred_slot_restorations.get( + slot_name, {}).pop(variable_key, []) + # Iterate over restores, highest restore UID first to minimize the number + # of assignments. + deferred_restorations.sort(key=lambda position: position.restore_uid, + reverse=True) + for checkpoint_position in deferred_restorations: + checkpoint_position.restore(slot_variable) + + def _create_or_restore_slot_variable( + self, slot_variable_position, slot_name, variable): + """Restore a slot variable's value, possibly creating it. + + Called when a variable which has an associated slot variable is created or + restored. When executing eagerly, we create the slot variable with a + restoring initializer. + + No new variables are created when graph building. Instead, + _restore_slot_variable catches these after normal creation and adds restore + ops to the graph. This method is nonetheless important when graph building + for the case when a slot variable has already been created but `variable` + has just been added to a dependency graph (causing us to realize that the + slot variable needs to be restored). + + Args: + slot_variable_position: A `checkpointable._CheckpointPosition` object + indicating the slot variable `Checkpointable` object to be restored. + slot_name: The name of this `Optimizer`'s slot to restore into. + variable: The variable object this slot is being created for. + """ + named_slots = self._slot_dict(slot_name) + variable_key = _var_key(variable) + slot_variable = named_slots.get(variable_key, None) + if (slot_variable is None and context.executing_eagerly() and + slot_variable_position.is_simple_variable()): + initializer = checkpointable.CheckpointInitialValue( + checkpoint_position=slot_variable_position) + slot_variable = self._get_or_make_slot( + var=variable, + val=initializer, + slot_name=slot_name, + op_name=self._name) + # Slot variables are not owned by any one object (because we don't want to + # save the slot variable if the optimizer is saved without the non-slot + # variable, or if the non-slot variable is saved without the optimizer; + # it's a dependency hypergraph with edges of the form (optimizer, non-slot + # variable, variable)). So we don't _track_ slot variables anywhere, and + # instead special-case this dependency and otherwise pretend it's a normal + # graph. + if slot_variable is not None: + # If we've either made this slot variable, or if we've pulled out an + # existing slot variable, we should restore it. + slot_variable_position.restore(slot_variable) + else: + # We didn't make the slot variable. Defer restoring until it gets created + # normally. We keep a list rather than the one with the highest restore + # UID in case slot variables have their own dependencies, in which case + # those could differ between restores. + self._deferred_slot_restorations.setdefault( + slot_name, {}).setdefault(variable_key, []).append( + slot_variable_position) diff --git a/tensorflow/python/training/optimizer_test.py b/tensorflow/python/training/optimizer_test.py index 6bdae39073d48e0bd8b757a2d5145480e92d185f..0cab6410e83ca1880a0a4a80d2cfa5c17517af95 100644 --- a/tensorflow/python/training/optimizer_test.py +++ b/tensorflow/python/training/optimizer_test.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -44,11 +43,10 @@ class OptimizerTest(test.TestCase): name='a_%d' % i) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, name='b_%d' % i) - def loss(v0, v1): - return 5 * v0 + 3 * v1 + def loss(): + return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop # Note that for eager execution, minimize expects a function instead of a # Tensor. - cost = loss if context.in_eager_mode() else loss(var0, var1) global_step = resource_variable_ops.ResourceVariable( array_ops.zeros([], dtypes.int64), name='global_step_%d' % i) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) @@ -58,7 +56,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([1.0, 2.0], self.evaluate(var0)) self.assertAllClose([3.0, 4.0], self.evaluate(var1)) # Run 1 step of sgd through optimizer - opt_op = sgd_op.minimize(cost, global_step, [var0, var1]) + opt_op = sgd_op.minimize(loss, global_step, [var0, var1]) self.evaluate(opt_op) # Validate updated params self.assertAllClose([-14., -13.], self.evaluate(var0)) @@ -125,10 +123,9 @@ class OptimizerTest(test.TestCase): [3.0, 4.0], dtype=dtype, trainable=False, name='b') return 5 * var0 + var1 # pylint: enable=cell-var-from-loop - cost = loss if context.in_eager_mode() else loss() sgd_op = gradient_descent.GradientDescentOptimizer(3.0) with self.assertRaisesRegexp(ValueError, 'No.*variables'): - sgd_op.minimize(cost) + sgd_op.minimize(loss) @test_util.run_in_graph_and_eager_modes() def testNoGradients(self): @@ -140,14 +137,13 @@ class OptimizerTest(test.TestCase): var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, name='b%d' % i) # pylint: disable=cell-var-from-loop - def loss(_): + def loss(): return 5 * var0 # pylint: enable=cell-var-from-loop - cost = loss if context.in_eager_mode() else loss(var1) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) with self.assertRaisesRegexp(ValueError, 'No gradients'): # var1 has no gradient - sgd_op.minimize(cost, var_list=[var1]) + sgd_op.minimize(loss, var_list=[var1]) @test_util.run_in_graph_and_eager_modes() def testNoGradientsForAnyVariables_Minimize(self): @@ -158,13 +154,12 @@ class OptimizerTest(test.TestCase): name='a_%d' % i) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, name='b_%d' % i) - def loss(unused_v1, unused_v2): + def loss(): return constant_op.constant(5.0) - cost = loss if context.in_eager_mode() else loss(var0, var1) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) with self.assertRaisesRegexp(ValueError, 'No gradients provided for any variable'): - sgd_op.minimize(cost, var_list=[var0, var1]) + sgd_op.minimize(loss, var_list=[var0, var1]) @test_util.run_in_graph_and_eager_modes() def testNoGradientsForAnyVariables_ApplyGradients(self): @@ -189,11 +184,10 @@ class OptimizerTest(test.TestCase): name='a%d' % i) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, name='b%d' % i) - def loss(v0, v1): - return 5 * v0 + 3 * v1 - cost = loss if context.in_eager_mode() else loss(var0, var1) + def loss(): + return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop sgd_op = gradient_descent.GradientDescentOptimizer(3.0) - grads_and_vars = sgd_op.compute_gradients(cost, [var0, var1]) + grads_and_vars = sgd_op.compute_gradients(loss, [var0, var1]) # Convert gradients to tf.Variables converted_grads = [ resource_variable_ops.ResourceVariable(array_ops.zeros([2], dtype), @@ -221,6 +215,21 @@ class OptimizerTest(test.TestCase): self.assertAllClose([-14., -13.], self.evaluate(var0)) self.assertAllClose([-6., -5.], self.evaluate(var1)) + @test_util.run_in_graph_and_eager_modes() + def testComputeGradientsWithTensors(self): + x = ops.convert_to_tensor(1.0) + def f(): + return x * x + sgd_op = gradient_descent.GradientDescentOptimizer(3.0) + grads_and_vars = sgd_op.compute_gradients(f, [x]) + self.assertEqual(1, len(grads_and_vars)) + grad, x_as_var = grads_and_vars[0] + self.assertIs(x, x_as_var) + self.assertEqual(2.0, self.evaluate(grad)) + + with self.assertRaises(NotImplementedError): + sgd_op.apply_gradients(grads_and_vars) + def testTrainOp(self): with self.test_session(): var0 = variables.Variable([1.0, 2.0]) diff --git a/tensorflow/python/training/queue_runner_impl.py b/tensorflow/python/training/queue_runner_impl.py index 07afba79abf4d636c9ec2d53bcf2641594a35733..d38c5499c73e1217effbc907077236cb6c8e0ae8 100644 --- a/tensorflow/python/training/queue_runner_impl.py +++ b/tensorflow/python/training/queue_runner_impl.py @@ -89,7 +89,7 @@ class QueueRunner(object): restoring from `queue_runner_def`. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "QueueRunners are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -441,7 +441,7 @@ def start_queue_runners(sess=None, coord=None, daemon=True, start=True, use the `tf.data` API instead. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Queues are not compatible with eager execution.") if sess is None: sess = ops.get_default_session() diff --git a/tensorflow/python/training/rmsprop.py b/tensorflow/python/training/rmsprop.py index 89d1099a49fedf2cd2ae372cb9c5f7422d43acc2..341b970c92e42b4fe392d91f57219d713d2513e5 100644 --- a/tensorflow/python/training/rmsprop.py +++ b/tensorflow/python/training/rmsprop.py @@ -42,6 +42,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer @@ -107,9 +108,13 @@ class RMSPropOptimizer(optimizer.Optimizer): def _create_slots(self, var_list): for v in var_list: - init_rms = init_ops.ones_initializer(dtype=v.dtype) + if v.get_shape().is_fully_defined(): + init_rms = init_ops.ones_initializer(dtype=v.dtype.base_dtype) + else: + init_rms = array_ops.ones_like(v) self._get_or_make_slot_with_initializer(v, init_rms, v.get_shape(), - v.dtype, "rms", self._name) + v.dtype.base_dtype, "rms", + self._name) if self._centered: self._zeros_slot(v, "mg", self._name) self._zeros_slot(v, "momentum", self._name) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 764f8400122118b6abcbad25ce0555954e38d29d..2ce57c4432db00b7f64970adf667f574398686e1 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -50,6 +50,7 @@ from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState from tensorflow.python.util import compat @@ -196,8 +197,8 @@ class BaseSaverBuilder(object): # Copy the restored tensor to the variable's device. with ops.device(self._var_device): restored_tensor = array_ops.identity(restored_tensor) - return resource_variable_ops.shape_safe_assign_variable_handle( - self.handle_op, self._var_shape, restored_tensor) + return resource_variable_ops.shape_safe_assign_variable_handle( + self.handle_op, self._var_shape, restored_tensor) def __init__(self, write_version=saver_pb2.SaverDef.V2): self._write_version = write_version @@ -310,8 +311,7 @@ class BaseSaverBuilder(object): Returns: A string tensor. """ - # pylint: disable=protected-access - return gen_io_ops._sharded_filename(filename_tensor, shard, num_shards) + return gen_io_ops.sharded_filename(filename_tensor, shard, num_shards) def _AddSaveOps(self, filename_tensor, saveables): """Add ops to save variables that are on the same shard. @@ -420,8 +420,7 @@ class BaseSaverBuilder(object): sharded_saves.append(self._AddSaveOps(sharded_filename, saveables)) # Return the sharded name for the save path. with ops.control_dependencies([x.op for x in sharded_saves]): - # pylint: disable=protected-access - return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor) + return gen_io_ops.sharded_filespec(filename_tensor, num_shards_tensor) def _AddRestoreOps(self, filename_tensor, @@ -577,10 +576,31 @@ class BaseSaverBuilder(object): names_to_saveables[name].append(var) else: names_to_saveables[name] = [var] + elif (isinstance(var, checkpointable.CheckpointableBase) + and not isinstance(var, variables.Variable)): + names_to_saveables.update( + BaseSaverBuilder.OpListToDict( + list(var._gather_saveables_for_checkpoint().values()))) else: - if context.in_graph_mode(): + if context.executing_eagerly(): + if not isinstance(var, resource_variable_ops.ResourceVariable): + raise ValueError( + "Can only save/restore ResourceVariables when eager execution " + "is enabled, type: %s." % type(var)) + set_var = names_to_saveables.setdefault(var._shared_name, var) + if set_var is not var: + raise ValueError( + ("Two different ResourceVariable objects with the same " + "shared_name '%s' were passed to the Saver. This likely means " + "that they were created in different Graphs or isolation " + "contexts, and may not be checkpointed together.") % + (var._shared_name,)) + else: if convert_variable_to_tensor: - var = ops.internal_convert_to_tensor(var, as_ref=True) + if isinstance(var, resource_variable_ops.ResourceVariable): + var = var._graph_element # pylint: disable=protected-access + else: + var = ops.internal_convert_to_tensor(var, as_ref=True) if not BaseSaverBuilder._IsVariable(var): raise TypeError("Variable to save is not a Variable: %s" % var) if var.op.type == "ReadVariableOp": @@ -591,18 +611,6 @@ class BaseSaverBuilder(object): raise ValueError("At least two variables have the same name: %s" % name) names_to_saveables[name] = var - else: - if not isinstance(var, resource_variable_ops.ResourceVariable): - raise ValueError("Can only save/restore ResourceVariable eager " - "mode is enabled, type: %s." % type(var)) - set_var = names_to_saveables.setdefault(var._shared_name, var) - if set_var is not var: - raise ValueError( - ("Two different ResourceVariable objects with the same " - "shared_name '%s' were passed to the Saver. This likely means " - "that they were created in different Graphs or isolation " - "contexts, and may not be checkpointed together.") % ( - var._shared_name,)) # pylint: enable=protected-access return names_to_saveables @@ -664,13 +672,16 @@ class BaseSaverBuilder(object): # pylint: enable=protected-access else: # A variable or tensor. - if context.in_eager_mode(): + if context.executing_eagerly(): if not isinstance(op, resource_variable_ops.ResourceVariable): raise ValueError("Can only save/restore ResourceVariable eager " "mode is enabled, type: %s." % type(op)) saveable = BaseSaverBuilder.ResourceVariableSaveable(op, "", name) else: - variable = ops.internal_convert_to_tensor(op, as_ref=True) + if isinstance(op, resource_variable_ops.ResourceVariable): + variable = op._graph_element # pylint: disable=protected-access + else: + variable = ops.internal_convert_to_tensor(op, as_ref=True) if not BaseSaverBuilder._IsVariable(variable): raise TypeError("names_to_saveables must be a dict mapping string " "names to Tensors/Variables. Not a variable: %s" % @@ -768,8 +779,10 @@ class BaseSaverBuilder(object): build_save=True, build_restore=True): """build() with option to only perform save and restore.""" - if context.in_graph_mode() and (not build_save or not build_restore): - raise ValueError("Graph mode needs to build save and restore together.") + if not context.executing_eagerly() and (not build_save or + not build_restore): + raise ValueError("save and restore operations need to be built together " + " when eager execution is not enabled.") saveables = self._ValidateAndSliceInputs(names_to_saveables) if max_to_keep is None: @@ -806,22 +819,22 @@ class BaseSaverBuilder(object): # such usage model makes sense. # # assert restore_op.name.endswith("restore_all"), restore_op.name - if context.in_graph_mode(): + if context.executing_eagerly(): + # Store the tensor values to the tensor_names. + save_tensor_name = save_tensor.numpy() if build_save else "" return saver_pb2.SaverDef( - filename_tensor_name=filename_tensor.name, - save_tensor_name=save_tensor.name, - restore_op_name=restore_op.name, + filename_tensor_name=filename_tensor.numpy(), + save_tensor_name=save_tensor_name, + restore_op_name="", max_to_keep=max_to_keep, sharded=sharded, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, version=self._write_version) else: - # Store the tensor values to the tensor_names. - save_tensor_name = save_tensor.numpy() if build_save else "" return saver_pb2.SaverDef( - filename_tensor_name=filename_tensor.numpy(), - save_tensor_name=save_tensor_name, - restore_op_name="", + filename_tensor_name=filename_tensor.name, + save_tensor_name=save_tensor.name, + restore_op_name=restore_op.name, max_to_keep=max_to_keep, sharded=sharded, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, @@ -1270,7 +1283,7 @@ class Saver(object): raise ValueError( "If `var_list` is provided then build cannot be deferred. " "Either set defer_build=False or var_list=None.") - if context.in_eager_mode() and var_list is None: + if context.executing_eagerly() and var_list is None: raise RuntimeError( "When eager execution is enabled, `var_list` must specify a list or " "dict of variables to save") @@ -1289,7 +1302,12 @@ class Saver(object): self._write_version = write_version self._pad_step_number = pad_step_number self._filename = filename - if not defer_build and context.in_graph_mode(): + self._last_checkpoints = [] + self._checkpoints_to_be_deleted = [] + if context.executing_eagerly(): + self._next_checkpoint_time = ( + time.time() + self._keep_checkpoint_every_n_hours * 3600) + elif not defer_build: self.build() if self.saver_def: self._check_saver_def() @@ -1297,7 +1315,7 @@ class Saver(object): self._save_relative_paths = save_relative_paths def build(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Use save/restore instead of build in eager mode.") self._build(self._filename, build_save=True, build_restore=True) @@ -1307,12 +1325,12 @@ class Saver(object): def _build(self, checkpoint_path, build_save, build_restore): """Builds saver_def.""" - if context.in_graph_mode(): + if not context.executing_eagerly(): if self._is_built: return self._is_built = True - if not self.saver_def or context.in_eager_mode(): + if not self.saver_def or context.executing_eagerly(): if self._builder is None: self._builder = BulkSaverBuilder(self._write_version) @@ -1349,17 +1367,17 @@ class Saver(object): self.saver_def.restore_op_name, self._name) self._check_saver_def() - # Updates next checkpoint time. - self._next_checkpoint_time = ( - time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) - self._last_checkpoints = [] - self._checkpoints_to_be_deleted = [] + if not context.executing_eagerly(): + # Updates next checkpoint time. + # Set in __init__ when executing eagerly. + self._next_checkpoint_time = ( + time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) def _check_saver_def(self): if not isinstance(self.saver_def, saver_pb2.SaverDef): raise ValueError("saver_def must be a saver_pb2.SaverDef: %s" % self.saver_def) - if context.in_graph_mode(): + if not context.executing_eagerly(): if not self.saver_def.save_tensor_name: raise ValueError("saver_def must specify the save_tensor_name: %s" % str(self.saver_def)) @@ -1597,9 +1615,9 @@ class Saver(object): [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). Returns: - A string: path prefix used for the checkpoint files. If checkpoint - format is V1 and the saver is sharded, this string ends with: - '-?????-of-nnnnn' where 'nnnnn' is the number of shards created. + A string: path prefix used for the checkpoint files. If the saver is + sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn' + is the number of shards created. If the saver is empty, returns None. Raises: @@ -1609,7 +1627,7 @@ class Saver(object): RuntimeError: If save and restore ops weren't built. """ # pylint: enable=line-too-long - if not self._is_built and context.in_graph_mode(): + if not self._is_built and not context.executing_eagerly(): raise RuntimeError( "`build()` should be called before save if defer_build==True") if latest_filename is None: @@ -1641,21 +1659,21 @@ class Saver(object): "'latest_filename' collides with 'save_path': '%s' and '%s'" % (latest_filename, save_path)) - if (context.in_graph_mode() and + if (not context.executing_eagerly() and not isinstance(sess, session.SessionInterface)): raise TypeError("'sess' must be a Session; %s" % sess) save_path_parent = os.path.dirname(save_path) if not self._is_empty: try: - if context.in_graph_mode(): - model_checkpoint_path = sess.run( - self.saver_def.save_tensor_name, - {self.saver_def.filename_tensor_name: checkpoint_file}) - else: + if context.executing_eagerly(): self._build_eager( checkpoint_file, build_save=True, build_restore=False) model_checkpoint_path = self.saver_def.save_tensor_name + else: + model_checkpoint_path = sess.run( + self.saver_def.save_tensor_name, + {self.saver_def.filename_tensor_name: checkpoint_file}) model_checkpoint_path = compat.as_str(model_checkpoint_path) if write_state: @@ -1677,7 +1695,7 @@ class Saver(object): if write_meta_graph: meta_graph_filename = self._MetaGraphFilename( checkpoint_file, meta_graph_suffix=meta_graph_suffix) - if context.in_graph_mode(): + if not context.executing_eagerly(): with sess.graph.as_default(): self.export_meta_graph( meta_graph_filename, strip_default_attrs=strip_default_attrs) @@ -1749,17 +1767,12 @@ class Saver(object): return if save_path is None: raise ValueError("Can't load save_path when it is None.") - if (os.path.isfile(save_path) and - self._write_version != saver_pb2.SaverDef.V1): - raise ValueError("The specified path: %s is a file." - " Please specify only the path prefix" - " to the checkpoint files." % save_path) logging.info("Restoring parameters from %s", save_path) - if context.in_graph_mode(): + if context.executing_eagerly(): + self._build_eager(save_path, build_save=False, build_restore=True) + else: sess.run(self.saver_def.restore_op_name, {self.saver_def.filename_tensor_name: save_path}) - else: - self._build_eager(save_path, build_save=False, build_restore=True) @staticmethod def _add_collection_def(meta_graph_def, key, export_scope=None): @@ -1899,7 +1912,7 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, execution is enabled. @end_compatibility """ # pylint: disable=g-doc-exception - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " "execution is enabled.") @@ -1982,7 +1995,7 @@ def export_meta_graph(filename=None, @end_compatibility """ # pylint: enable=line-too-long - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " "execution is enabled.") diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index c5a6f49df599434ab3bc1a9fe3d85db6f824071e..67848f734090592c3e530547a5ecb2f83d802e36 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -53,6 +53,7 @@ from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import partitioned_variables @@ -66,6 +67,7 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary import summary from tensorflow.python.training import adam +from tensorflow.python.training import checkpointable from tensorflow.python.training import gradient_descent from tensorflow.python.training import queue_runner_impl from tensorflow.python.training import saver as saver_module @@ -89,7 +91,7 @@ class SaverTest(test.TestCase): v2_init = v2.insert("k1", 30.0) # Initialize all variables - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate([variables.global_variables_initializer(), v2_init]) # Check that the parameter nodes have been initialized. @@ -117,7 +119,7 @@ class SaverTest(test.TestCase): v2 = saver_test_utils.CheckpointedOp(name="v2") # Assert that the variables are not initialized. - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(variables.report_uninitialized_variables().eval()), 2) self.assertEqual(0, len(v2.keys().eval())) @@ -140,7 +142,7 @@ class SaverTest(test.TestCase): v2_init = v2_2.insert("k1000", 3000.0) # Check that the parameter nodes have been initialized. - if context.in_graph_mode(): + if not context.executing_eagerly(): init_all_op = [variables.global_variables_initializer(), v2_init] self.evaluate(init_all_op) # TODO(xpan): Why _mutable_hash_table_v2 doesn't create empty @@ -249,10 +251,10 @@ class SaverTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: v = resource_variable_ops.ResourceVariable([1], caching_device="/cpu:0", name="v") - if context.in_graph_mode(): - self.evaluate(variables.global_variables_initializer()) - else: + if context.executing_eagerly(): sess = None + else: + self.evaluate(variables.global_variables_initializer()) save = saver_module.Saver([v]) save.save(sess, save_path) @@ -260,6 +262,24 @@ class SaverTest(test.TestCase): save2.restore(sess, save_path) self.assertEquals(self.evaluate(v), [1]) + def testNoAdditionalOpsAddedBySaverForResourceVariablesOutsideSaveScope(self): + with ops_lib.Graph().as_default() as g: + v = resource_variable_ops.ResourceVariable(1.0, name="v") + with ops_lib.name_scope("saver1"): + saver_module.Saver() + with ops_lib.name_scope("saver2"): + saver_module.Saver({"name": v}) + ops_in_saver1_scope_but_not_save_scope = [ + op for op in g.get_operations() + if (op.name.startswith("saver1/") and + not op.name.startswith("saver1/save/"))] + self.assertEqual(ops_in_saver1_scope_but_not_save_scope, []) + ops_in_saver2_scope_but_not_save_scope = [ + op for op in g.get_operations() + if (op.name.startswith("saver2/") and + not op.name.startswith("saver2/save/"))] + self.assertEqual(ops_in_saver2_scope_but_not_save_scope, []) + def testSaveCopyRestoreWithSaveRelativePaths(self): """Save, copy checkpoint dir and restore from copied dir. @@ -497,7 +517,7 @@ class SaverTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: var = resource_variable_ops.ResourceVariable(var_value, name=var_name) save = saver_module.Saver({var_name: var}) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(var.initializer) val = save.save(sess, save_path) self.assertEqual(save_path, val) @@ -657,11 +677,11 @@ class SaverTest(test.TestCase): { var._shared_name: var }, pad_step_number=pad_step_number) - if context.in_graph_mode(): + if context.executing_eagerly(): + sess = None + else: self.evaluate(var.initializer) sess = ops_lib.get_default_session() - else: - sess = None if use_tensor: global_step = constant_op.constant(global_step_int) val = save.save(sess, save_path, global_step=global_step) @@ -1039,6 +1059,77 @@ class MaxToKeepTest(test.TestCase): self.assertEqual(checkpoint_state.all_model_checkpoint_paths, all_model_checkpoint_paths) + def testMaxToKeepEager(self): + with context.eager_mode(): + save_dir = self._get_test_dir("max_to_keep_non_sharded") + + v = variable_scope.variable(10.0, name="v") + save = saver_module.Saver({"v": v}, max_to_keep=2) + self.evaluate(variables.global_variables_initializer()) + if not context.executing_eagerly(): + self.assertEqual([], save.last_checkpoints) + + s1 = save.save(None, os.path.join(save_dir, "s1")) + self.assertEqual([s1], save.last_checkpoints) + self.assertTrue(saver_module.checkpoint_exists(s1)) + self.assertCheckpointState( + model_checkpoint_path=s1, + all_model_checkpoint_paths=[s1], + save_dir=save_dir) + + s2 = save.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s1, s2], save.last_checkpoints) + self.assertTrue(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s2, + all_model_checkpoint_paths=[s1, s2], + save_dir=save_dir) + + s3 = save.save(None, os.path.join(save_dir, "s3")) + self.assertEqual([s2, s3], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertTrue(saver_module.checkpoint_exists(s3)) + self.assertCheckpointState( + model_checkpoint_path=s3, + all_model_checkpoint_paths=[s2, s3], + save_dir=save_dir) + + # Create a second helper, identical to the first. + save2 = saver_module.Saver({"v": v}, max_to_keep=2) + save2.set_last_checkpoints(save.last_checkpoints) + + # Exercise the first helper. + + # Adding s2 again (old s2 is removed first, then new s2 appended) + s2 = save.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s3, s2], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s3)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s2, + all_model_checkpoint_paths=[s3, s2], + save_dir=save_dir) + + # Adding s1 (s3 should now be deleted as oldest in list) + s1 = save.save(None, os.path.join(save_dir, "s1")) + self.assertEqual([s2, s1], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s3)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s1, + all_model_checkpoint_paths=[s2, s1], + save_dir=save_dir) + + s2 = save2.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s3, s2], save2.last_checkpoints) + # Created by the first helper. + self.assertTrue(saver_module.checkpoint_exists(s1)) + # Deleted by the first helper. + self.assertFalse(saver_module.checkpoint_exists(s3)) + def testNonSharded(self): save_dir = self._get_test_dir("max_to_keep_non_sharded") @@ -1301,15 +1392,16 @@ class KeepCheckpointEveryNHoursTest(test.TestCase): gfile.MakeDirs(test_dir) return test_dir + @test_util.run_in_graph_and_eager_modes() @test.mock.patch.object(saver_module, "time") def testNonSharded(self, mock_time): save_dir = self._get_test_dir("keep_checkpoint_every_n_hours") with self.test_session() as sess: - v = variables.Variable([10.0], name="v") + v = variable_scope.variable([10.0], name="v") # Run the initializer NOW to avoid the 0.5s overhead of the first Run() # call, which throws the test timing off in fastbuild mode. - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Create a saver that will keep the last 2 checkpoints plus one every 0.7 # seconds. start_time = time.time() @@ -1387,7 +1479,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): v0 = variable_op(-1.0, name="v0") v1 = variable_op(-1.0, name="v1") - if context.in_graph_mode(): + if not context.executing_eagerly(): with self.assertRaisesOpError("uninitialized"): self.evaluate(v0) with self.assertRaisesOpError("uninitialized"): @@ -1397,7 +1489,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): save.restore(sess, save_path) # Check that the parameter nodes have been restored. - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(10.0, self.evaluate(v0)) self.assertEqual(20.0, self.evaluate(v1)) @@ -1407,7 +1499,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): v0 = variable_op(-1.0, name="restore_prefix/v0") v1 = variable_op(-1.0, name="restore_prefix/v1") - if context.in_graph_mode(): + if not context.executing_eagerly(): with self.assertRaisesOpError("uninitialized"): self.evaluate(v0) with self.assertRaisesOpError("uninitialized"): @@ -2039,6 +2131,107 @@ class MetaGraphTest(test.TestCase): self._testGraphExtensionRestore(test_dir) self._testRestoreFromTrainGraphWithControlContext(test_dir) + def _testGradientSerDes(self, graph_fn): + """Tests that gradients can be computed after exporting and importing. + + Builds a graph, exports it, and verifies that it can be imported and the + gradient can be built and run correctly. + + Args: + graph_fn: takes a single float Tensor argument as input, outputs a single + Tensor + """ + test_dir = self._get_test_dir("nested_control_flow") + filename = os.path.join(test_dir, "metafile") + saver_ckpt = os.path.join(test_dir, "saver.ckpt") + + # Create while loop using `outer_body_fn`. + with ops_lib.Graph().as_default(): + var = variables.Variable(0.0) + var_name = var.name + output = graph_fn(var) + output_name = output.name + init_op = variables.global_variables_initializer() + + # Generate a MetaGraphDef containing the while loop. + with session.Session() as sess: + sess.run(init_op) + sess.run(output) + saver = saver_module.Saver() + saver.save(sess, saver_ckpt) + saver.export_meta_graph(filename) + + # Build and run the gradients of the while loop. We use this below to + # verify that the gradients are correct with an imported MetaGraphDef. + grad = gradients_impl.gradients([output], [var]) + with session.Session() as sess: + sess.run(init_op) + expected_grad_value = sess.run(grad) + + # Restore the MetaGraphDef into a new Graph. + with ops_lib.Graph().as_default(): + with session.Session() as sess: + saver = saver_module.import_meta_graph(filename) + saver.restore(sess, saver_ckpt) + + # Make sure we can still build gradients and get the same result. + var = ops_lib.get_default_graph().get_tensor_by_name(var_name) + output = ops_lib.get_default_graph().get_tensor_by_name(output_name) + grad = gradients_impl.gradients([output], [var]) + + init_op = variables.global_variables_initializer() + + with session.Session() as sess: + sess.run(init_op) + actual_grad_value = sess.run(grad) + self.assertEqual(expected_grad_value, actual_grad_value) + + def _testWhileLoopAndGradientSerDes(self, outer_body_fn): + # Build a while loop with `outer_body_fn`, export it, and verify that it can + # be imported and the gradient can be built and run correctly. + # pylint: disable=g-long-lambda + return self._testGradientSerDes( + lambda x: control_flow_ops.while_loop( + lambda i, y: i < 5, outer_body_fn, [0, x])[1]) + # pylint: enable=g-long-lambda + + def testNestedWhileLoopsSerDes(self): + # Test two simple nested while loops. + def body(i, x): + _, r = control_flow_ops.while_loop(lambda j, y: j < 3, + lambda j, y: (j + 1, y + x), + [0, 0.0]) + return i + 1, x + r + self._testWhileLoopAndGradientSerDes(body) + + def testNestedControlFlowSerDes(self): + # Test while loop in a cond in a while loop. + # pylint: disable=g-long-lambda + def body(i, x): + cond_result = control_flow_ops.cond( + i > 0, + lambda: control_flow_ops.while_loop( + lambda j, y: j < 3, + lambda j, y: (j + 1, y + x), + [0, 0.0])[1], + lambda: x) + return i + 1, cond_result + # pylint: enable=g-long-lambda + self._testWhileLoopAndGradientSerDes(body) + + def testNestedCondsSerDes(self): + # Test conds in a cond. + # pylint: disable=g-long-lambda + self._testGradientSerDes(lambda x: control_flow_ops.cond( + x > 0, + lambda: control_flow_ops.cond(x > 3, + lambda: array_ops.identity(x), + lambda: math_ops.multiply(x, 2.0)), + lambda: control_flow_ops.cond(x < -3, + lambda: constant_op.constant(1.0), + lambda: math_ops.multiply(x, -1.0)))) + # pylint: enable=g-long-lambda + def testStrippedOpListDef(self): with self.test_session(): # Creates a graph. @@ -2660,5 +2853,92 @@ class ScopedGraphTest(test.TestCase): self.assertEqual(2.0, var_dict2["variable2:0"].eval()) +class _OwnsAVariableSimple(checkpointable.CheckpointableBase): + """A Checkpointable object which can be saved using a tf.train.Saver.""" + + def __init__(self): + self.non_dep_variable = variable_scope.get_variable( + name="non_dep_variable", initializer=6., use_resource=True) + + def _gather_saveables_for_checkpoint(self): + return {checkpointable.VARIABLE_VALUE_KEY: self.non_dep_variable} + + # The Saver sorts by name before parsing, so we need a name property. + @property + def name(self): + return self.non_dep_variable.name + + +class _MirroringSaveable( + saver_module.BaseSaverBuilder.ResourceVariableSaveable): + + def __init__(self, primary_variable, mirrored_variable): + self._primary_variable = primary_variable + self._mirrored_variable = mirrored_variable + super(_MirroringSaveable, self).__init__( + self._primary_variable, "", self._primary_variable.name) + + def restore(self, restored_tensors, restored_shapes): + """Restore the same value into both variables.""" + tensor, = restored_tensors + return control_flow_ops.group( + self._primary_variable.assign(tensor), + self._mirrored_variable.assign(tensor)) + + +class _OwnsMirroredVariables(checkpointable.CheckpointableBase): + """A Checkpointable object which returns a more complex SaveableObject.""" + + def __init__(self): + self.non_dep_variable = variable_scope.get_variable( + name="non_dep_variable", initializer=6., use_resource=True) + self.mirrored = variable_scope.get_variable( + name="mirrored", initializer=15., use_resource=True) + + def _gather_saveables_for_checkpoint(self): + saveable = _MirroringSaveable( + primary_variable=self.non_dep_variable, + mirrored_variable=self.mirrored) + return {checkpointable.VARIABLE_VALUE_KEY: saveable} + + # The Saver sorts by name before parsing, so we need a name property. + @property + def name(self): + return self.non_dep_variable.name + + +@test_util.with_c_api +class CheckpointableCompatibilityTests(test.TestCase): + + # TODO(allenl): Track down python3 reference cycles in these tests. + @test_util.run_in_graph_and_eager_modes() + def testNotSaveableButIsCheckpointable(self): + v = _OwnsAVariableSimple() + saver = saver_module.Saver(var_list=[v]) + test_dir = self.get_temp_dir() + prefix = os.path.join(test_dir, "ckpt") + self.evaluate(v.non_dep_variable.assign(42.)) + with self.test_session() as sess: + save_path = saver.save(sess, prefix) + self.evaluate(v.non_dep_variable.assign(43.)) + saver.restore(sess, save_path) + self.assertEqual(42., self.evaluate(v.non_dep_variable)) + + @test_util.run_in_graph_and_eager_modes() + def testMoreComplexSaveableReturned(self): + v = _OwnsMirroredVariables() + saver = saver_module.Saver(var_list=[v]) + test_dir = self.get_temp_dir() + prefix = os.path.join(test_dir, "ckpt") + self.evaluate(v.non_dep_variable.assign(42.)) + with self.test_session() as sess: + save_path = saver.save(sess, prefix) + self.evaluate(v.non_dep_variable.assign(43.)) + self.evaluate(v.mirrored.assign(44.)) + saver.restore(sess, save_path) + self.assertEqual(42., self.evaluate(v.non_dep_variable)) + self.assertEqual(42., self.evaluate(v.mirrored)) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver_test_utils.py b/tensorflow/python/training/saver_test_utils.py index 44b06b357ecbe4c8e330a2ccc49e83ddd4bf8c7d..2bbe5b6d845c304c4dc79fb3619c57211ca0489e 100644 --- a/tensorflow/python/training/saver_test_utils.py +++ b/tensorflow/python/training/saver_test_utils.py @@ -35,12 +35,12 @@ class CheckpointedOp(object): # pylint: disable=protected-access def __init__(self, name, table_ref=None): if table_ref is None: - self.table_ref = gen_lookup_ops._mutable_hash_table_v2( + self.table_ref = gen_lookup_ops.mutable_hash_table_v2( key_dtype=dtypes.string, value_dtype=dtypes.float32, name=name) else: self.table_ref = table_ref self._name = name - if context.in_graph_mode(): + if not context.executing_eagerly(): self._saveable = CheckpointedOp.CustomSaveable(self, name) ops_lib.add_to_collection(ops_lib.GraphKeys.SAVEABLE_OBJECTS, self._saveable) @@ -51,16 +51,16 @@ class CheckpointedOp(object): @property def saveable(self): - if context.in_graph_mode(): - return self._saveable - else: + if context.executing_eagerly(): return CheckpointedOp.CustomSaveable(self, self.name) + else: + return self._saveable def insert(self, keys, values): - return gen_lookup_ops._lookup_table_insert_v2(self.table_ref, keys, values) + return gen_lookup_ops.lookup_table_insert_v2(self.table_ref, keys, values) def lookup(self, keys, default): - return gen_lookup_ops._lookup_table_find_v2(self.table_ref, keys, default) + return gen_lookup_ops.lookup_table_find_v2(self.table_ref, keys, default) def keys(self): return self._export()[0] @@ -69,8 +69,8 @@ class CheckpointedOp(object): return self._export()[1] def _export(self): - return gen_lookup_ops._lookup_table_export_v2(self.table_ref, dtypes.string, - dtypes.float32) + return gen_lookup_ops.lookup_table_export_v2(self.table_ref, dtypes.string, + dtypes.float32) class CustomSaveable(saver_module.BaseSaverBuilder.SaveableObject): """A custom saveable for CheckpointedOp.""" @@ -86,6 +86,6 @@ class CheckpointedOp(object): super(CheckpointedOp.CustomSaveable, self).__init__(table, specs, name) def restore(self, restore_tensors, shapes): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op.table_ref, restore_tensors[0], restore_tensors[1]) # pylint: enable=protected-access diff --git a/tensorflow/python/training/slot_creator.py b/tensorflow/python/training/slot_creator.py index ea28b5ddfc2dbbf65ec60e86d29ff2a9988d2b97..9ac52dd0715d7ed15e2e57ed286be973614b01e5 100644 --- a/tensorflow/python/training/slot_creator.py +++ b/tensorflow/python/training/slot_creator.py @@ -48,11 +48,6 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -def _is_resource(v): - """Returns true if v is something you get from a resource variable.""" - return isinstance(v, resource_variable_ops.ResourceVariable) - - def _create_slot_var(primary, val, scope, validate_shape, shape, dtype): """Helper function for creating a slot variable.""" @@ -60,9 +55,12 @@ def _create_slot_var(primary, val, scope, validate_shape, shape, dtype): # scope. current_partitioner = variable_scope.get_variable_scope().partitioner variable_scope.get_variable_scope().set_partitioner(None) + # When init from val instead of callable initializer, the shape is expected to + # be None, not or any fully defined shape. + shape = shape if callable(val) else None slot = variable_scope.get_variable( scope, initializer=val, trainable=False, - use_resource=_is_resource(primary), + use_resource=resource_variable_ops.is_resource_variable(primary), shape=shape, dtype=dtype, validate_shape=validate_shape) variable_scope.get_variable_scope().set_partitioner(current_partitioner) @@ -108,7 +106,11 @@ def create_slot(primary, val, name, colocate_with_primary=True): # and the same name has been previously used, the scope name will add '_N' # as suffix for unique identifications. validate_shape = val.get_shape().is_fully_defined() - with variable_scope.variable_scope(None, primary.op.name + "/" + name): + if context.executing_eagerly(): + prefix = primary._shared_name # pylint: disable=protected-access + else: + prefix = primary.op.name + with variable_scope.variable_scope(None, prefix + "/" + name): if colocate_with_primary: with ops.colocate_with(primary): return _create_slot_var(primary, val, "", validate_shape, None, None) @@ -140,7 +142,10 @@ def create_slot_with_initializer(primary, initializer, shape, dtype, name, # and the same name has been previously used, the scope name will add '_N' # as suffix for unique identifications. validate_shape = shape.is_fully_defined() - prefix = primary.op.name if context.in_graph_mode() else primary._shared_name # pylint: disable=protected-access + if context.executing_eagerly(): + prefix = primary._shared_name # pylint: disable=protected-access + else: + prefix = primary.op.name with variable_scope.variable_scope(None, prefix + "/" + name): if colocate_with_primary: with ops.colocate_with(primary): diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py index d2ad34773e0615256c340826dcc312cc8a00dc23..7389e344c7d8eef8e26c4d24c0985ff66276deea 100644 --- a/tensorflow/python/training/supervisor.py +++ b/tensorflow/python/training/supervisor.py @@ -45,7 +45,7 @@ class Supervisor(object): """A training helper that checkpoints models and computes summaries. This class is deprecated. Please use - ${tf.train.MonitoredTrainingSession} instead. + @{tf.train.MonitoredTrainingSession} instead. The Supervisor is a small wrapper around a `Coordinator`, a `Saver`, and a `SessionManager` that takes care of common needs of TensorFlow @@ -305,7 +305,7 @@ class Supervisor(object): `Supervisor`s are not supported when eager execution is enabled. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Supervisors are compatible with eager execution.") # Set default values of arguments. if graph is None: @@ -762,7 +762,7 @@ class Supervisor(object): execution is enabled, use the `tf.data` API. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Queues are not compatible with eager execution.") if queue_runners is None: queue_runners = self._graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS) diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py index 03811fa38dd021fd5ff222bfbe32234606d6c681..e623e27a21289bcd3b9b6f91ea6c3ef2d2902198 100644 --- a/tensorflow/python/training/training.py +++ b/tensorflow/python/training/training.py @@ -28,6 +28,7 @@ See the @{$python/train} guide. @@ProximalGradientDescentOptimizer @@ProximalAdagradOptimizer @@RMSPropOptimizer +@@custom_gradient @@gradients @@AggregationMethod @@stop_gradient @@ -135,7 +136,7 @@ from tensorflow.python.training.queue_runner import * # For the module level doc. from tensorflow.python.training import input as _input -from tensorflow.python.training.input import * +from tensorflow.python.training.input import * # pylint: disable=redefined-builtin # pylint: enable=wildcard-import from tensorflow.python.training.basic_session_run_hooks import SecondOrStepTimer @@ -189,6 +190,7 @@ from tensorflow.python.training.training_util import create_global_step from tensorflow.python.training.training_util import get_or_create_global_step from tensorflow.python.pywrap_tensorflow import do_quantize_training_on_graphdef from tensorflow.python.pywrap_tensorflow import NewCheckpointReader +from tensorflow.python.util.tf_export import tf_export # pylint: disable=wildcard-import # Training data protos. @@ -239,6 +241,23 @@ _allowed_symbols = [ "SequenceExample", # from example_pb2. "ServerDef", ] + +# pylint: disable=undefined-variable +tf_export("train.BytesList")(BytesList) +tf_export("train.ClusterDef")(ClusterDef) +tf_export("train.Example")(Example) +tf_export("train.Feature")(Feature) +tf_export("train.Features")(Features) +tf_export("train.FeatureList")(FeatureList) +tf_export("train.FeatureLists")(FeatureLists) +tf_export("train.FloatList")(FloatList) +tf_export("train.Int64List")(Int64List) +tf_export("train.JobDef")(JobDef) +tf_export("train.SaverDef")(SaverDef) +tf_export("train.SequenceExample")(SequenceExample) +tf_export("train.ServerDef")(ServerDef) +# pylint: enable=undefined-variable + # Include extra modules for docstrings because: # * Input methods in tf.train are documented in io_ops. # * Saver methods in tf.train are documented in state_ops. diff --git a/tensorflow/python/training/training_ops.py b/tensorflow/python/training/training_ops.py index e98c32b614418224b1bc14081bc35f175d769965..d7133cfb500ef11e5b94c7c36905e039f9c0bf46 100644 --- a/tensorflow/python/training/training_ops.py +++ b/tensorflow/python/training/training_ops.py @@ -19,7 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.training import gen_training_ops +from tensorflow.python.training import gen_training_ops # pylint: disable=unused-import # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.training.gen_training_ops import * diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py index 499f1feb2dbf8aee26314a43b0a000fb91a1c686..4f1abccc96ff95b6fcf9fe82c7a5d45fc2fd1c0c 100644 --- a/tensorflow/python/training/training_util.py +++ b/tensorflow/python/training/training_util.py @@ -64,7 +64,7 @@ def global_step(sess, global_step_tensor): Returns: The global step value. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return int(global_step_tensor.numpy()) return int(sess.run(global_step_tensor)) @@ -123,7 +123,7 @@ def create_global_step(graph=None): raise ValueError('"global_step" already exists.') # Create in proper graph and base name_scope. with graph.as_default() as g, g.name_scope(None): - if context.in_eager_mode(): + if context.executing_eagerly(): with ops.device('cpu:0'): return variable_scope.get_variable( ops.GraphKeys.GLOBAL_STEP, diff --git a/tensorflow/python/user_ops/user_ops.py b/tensorflow/python/user_ops/user_ops.py index 17dbab706c9243c5f119dc82cc4428f03b90a18d..6f9b5d92bb2ea662c9a5af279f0fcf71f0efccc5 100644 --- a/tensorflow/python/user_ops/user_ops.py +++ b/tensorflow/python/user_ops/user_ops.py @@ -27,4 +27,4 @@ from tensorflow.python.ops.gen_user_ops import * # pylint: disable=wildcard-imp def my_fact(): """Example of overriding the generated code for an Op.""" - return _gen_user_ops._fact() # pylint: disable=protected-access + return _gen_user_ops.fact() diff --git a/tensorflow/python/util/compat.py b/tensorflow/python/util/compat.py index 7e5f192b8f1ae5c86e463c7560553f2bcfd15995..4163fcac79e3d237c4c4c4303e1db2c39e5fe7c6 100644 --- a/tensorflow/python/util/compat.py +++ b/tensorflow/python/util/compat.py @@ -42,6 +42,7 @@ import six as _six from tensorflow.python.util.all_util import remove_undocumented from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import tf_export @tf_export('compat.as_bytes', 'compat.as_str') @@ -112,6 +113,7 @@ def as_str_any(value): return str(value) +@tf_export('compat.path_to_str') def path_to_str(path): """Returns the file system path representation of a `PathLike` object, else as it is. diff --git a/tensorflow/python/util/compat_internal.py b/tensorflow/python/util/compat_internal.py index a299b2fc3c302705d9493904e8ac0f81e4b8d371..1905c3e3832550906c601bd4545e72b5bd135e2c 100644 --- a/tensorflow/python/util/compat_internal.py +++ b/tensorflow/python/util/compat_internal.py @@ -12,16 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Functions for Python 2 vs. 3 compatibility that are private to TensorFlow.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.util.compat import as_str_any + def path_to_str(path): - """Returns the file system path representation of a `PathLike` object, else as it is. + """Returns the file system path representation of a `PathLike` object, + else as it is. Args: path: An object that can be converted to path representation. diff --git a/tensorflow/python/util/decorator_utils.py b/tensorflow/python/util/decorator_utils.py index df259c7f7c29f9a4b674d3e980b33d6dcf323769..7b4363c0e40802779cf47c75c5a5e5a901da37e2 100644 --- a/tensorflow/python/util/decorator_utils.py +++ b/tensorflow/python/util/decorator_utils.py @@ -82,7 +82,7 @@ def add_notice_to_docstring( lines = _normalize_docstring(doc).splitlines() lines[0] += ' ' + suffix_str - notice = [''] + notice + [instructions] + notice = [''] + notice + ([instructions] if instructions else []) if len(lines) > 1: # Make sure that we keep our distance from the main body diff --git a/tensorflow/python/util/deprecation.py b/tensorflow/python/util/deprecation.py index fbec8fd2d8edfcd503a3677583a0853872ec08bc..376be39978fb11463ae8a870492a359c89a9f2ce 100644 --- a/tensorflow/python/util/deprecation.py +++ b/tensorflow/python/util/deprecation.py @@ -22,9 +22,9 @@ import collections import functools import re -from tensorflow.python.eager import context from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import decorator_utils +from tensorflow.python.util import is_in_graph_mode from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -400,7 +400,7 @@ def deprecated_args(date, instructions, *deprecated_arg_names_or_tuples, """Deprecation wrapper.""" # TODO(apassos) figure out a way to have reasonable performance with # deprecation warnings and eager mode. - if context.in_graph_mode() and _PRINT_DEPRECATION_WARNINGS: + if is_in_graph_mode.IS_IN_GRAPH_MODE() and _PRINT_DEPRECATION_WARNINGS: invalid_args = [] named_args = tf_inspect.getcallargs(func, *args, **kwargs) for arg_name, spec in iter(deprecated_positions.items()): diff --git a/tensorflow/contrib/ndlstm/python/__init__.py b/tensorflow/python/util/is_in_graph_mode.py similarity index 67% rename from tensorflow/contrib/ndlstm/python/__init__.py rename to tensorflow/python/util/is_in_graph_mode.py index 1aa51a6ec40c042ca3c26c6b08e5bdb8a42a12bd..9ae89ecb714c25787732f0d6c671d78144bec395 100644 --- a/tensorflow/contrib/ndlstm/python/__init__.py +++ b/tensorflow/python/util/is_in_graph_mode.py @@ -4,7 +4,7 @@ # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # -# http://www.apache.org/licenses/LICENSE-2.0 +# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, @@ -12,14 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Init file, giving convenient access to all ndlstm ops.""" - +"""A function that tells you if the program is running in graph mode.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -# pylint: disable=wildcard-import,g-importing-member -from tensorflow.contrib.ndlstm.python.lstm1d import * -from tensorflow.contrib.ndlstm.python.lstm2d import * -from tensorflow.contrib.ndlstm.python.misc import * -# pylint: enable=wildcard-import +# Call IS_IN_GRAPH_MODE() when you want to know whether the thread is in +# graph mode. By default, we always are. +IS_IN_GRAPH_MODE = lambda: True diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py index c8525ed42039e151f2b44c472690daf1b0727be7..23c2c48f4b5a165bd6e356a6243b234619af1c4c 100644 --- a/tensorflow/python/util/nest.py +++ b/tensorflow/python/util/nest.py @@ -497,7 +497,9 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): shallow_tree: an arbitrarily nested structure. input_tree: an arbitrarily nested structure. check_types: if `True` (default) the sequence types of `shallow_tree` and - `input_tree` have to be the same. + `input_tree` have to be the same. Note that even with check_types==True, + this function will consider two different namedtuple classes with the same + name and _fields attribute to be the same class. Raises: TypeError: If `shallow_tree` is a sequence but `input_tree` is not. @@ -513,10 +515,21 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): "Input has type: %s." % type(input_tree)) if check_types and not isinstance(input_tree, type(shallow_tree)): - raise TypeError( - "The two structures don't have the same sequence type. Input " - "structure has type %s, while shallow structure has type %s." - % (type(input_tree), type(shallow_tree))) + # Duck-typing means that nest should be fine with two different + # namedtuples with identical name and fields. + shallow_is_namedtuple = _is_namedtuple(shallow_tree, False) + input_is_namedtuple = _is_namedtuple(input_tree, False) + if shallow_is_namedtuple and input_is_namedtuple: + if not _same_namedtuples(shallow_tree, input_tree): + raise TypeError( + "The two namedtuples don't have the same sequence type. Input " + "structure has type %s, while shallow structure has type %s." + % (type(input_tree), type(shallow_tree))) + else: + raise TypeError( + "The two structures don't have the same sequence type. Input " + "structure has type %s, while shallow structure has type %s." + % (type(input_tree), type(shallow_tree))) if len(input_tree) != len(shallow_tree): raise ValueError( diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py index 8aaf799fd05420d898a53a11d65e09f3a545e69d..4439d6241ea9607b194cbb17304dbb77dc9f57a8 100644 --- a/tensorflow/python/util/nest_test.py +++ b/tensorflow/python/util/nest_test.py @@ -429,6 +429,15 @@ class NestTest(test.TestCase): inp_ba = collections.OrderedDict([("b", (2, 3)), ("a", 1)]) nest.assert_shallow_structure(inp_ab, inp_ba) + # This assertion is expected to pass: two namedtuples with the same + # name and field names are considered to be identical. + same_name_type_0 = collections.namedtuple("same_name", ("a", "b")) + same_name_type_1 = collections.namedtuple("same_name", ("a", "b")) + inp_shallow = same_name_type_0(1, 2) + inp_deep = same_name_type_1(1, [1, 2, 3]) + nest.assert_shallow_structure(inp_shallow, inp_deep, check_types=False) + nest.assert_shallow_structure(inp_shallow, inp_deep, check_types=True) + def testFlattenUpTo(self): # Shallow tree ends at scalar. input_tree = [[[2, 2], [3, 3]], [[4, 9], [5, 5]]] diff --git a/tensorflow/python/util/port.i b/tensorflow/python/util/port.i index cea4d8468afe8816d71da6581635b8a7ab0c2388..2f730732bee373a6e6ead97fe3320645f37ac220 100644 --- a/tensorflow/python/util/port.i +++ b/tensorflow/python/util/port.i @@ -23,5 +23,6 @@ limitations under the License. %unignore tensorflow; %unignore tensorflow::IsGoogleCudaEnabled; %unignore tensorflow::CudaSupportsHalfMatMulAndConv; +%unignore tensorflow::IsMklEnabled; %include "tensorflow/core/util/port.h" %unignoreall diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py index 1fbc33ba0b89fd6c5e8c9f27801121c846fbbf21..5e74a97cd78abe434e06f2f343e0ed73faea1262 100644 --- a/tensorflow/python/util/tf_inspect.py +++ b/tensorflow/python/util/tf_inspect.py @@ -56,7 +56,10 @@ def getargspec(object): # pylint: disable=redefined-builtin def getfullargspec(obj): # pylint: disable=redefined-builtin - """TFDecorator-aware replacement for inspect.getfullargspec. + """TFDecorator-aware replacement for `inspect.getfullargspec`/`getargspec`. + + This wrapper uses `inspect.getfullargspec` if available and falls back to + `inspect.getargspec` in Python 2. Args: obj: A callable, possibly decorated. @@ -156,6 +159,11 @@ def getmembers(object, predicate=None): # pylint: disable=redefined-builtin return _inspect.getmembers(object, predicate) +def getmodule(object): # pylint: disable=redefined-builtin + """TFDecorator-aware replacement for inspect.getmodule.""" + return _inspect.getmodule(object) + + def getmro(cls): """TFDecorator-aware replacement for inspect.getmro.""" return _inspect.getmro(cls) @@ -166,6 +174,11 @@ def getsource(object): # pylint: disable=redefined-builtin return _inspect.getsource(tf_decorator.unwrap(object)[1]) +def isbuiltin(object): # pylint: disable=redefined-builtin + """TFDecorator-aware replacement for inspect.isbuiltin.""" + return _inspect.isbuiltin(tf_decorator.unwrap(object)[1]) + + def isclass(object): # pylint: disable=redefined-builtin """TFDecorator-aware replacement for inspect.isclass.""" return _inspect.isclass(tf_decorator.unwrap(object)[1]) diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py index a9e8ffb30c3392251c2bf7076e02aafd2338696b..129408449ebb45ac3a322f163a13b705cbb31f0c 100644 --- a/tensorflow/python/util/tf_inspect_test.py +++ b/tensorflow/python/util/tf_inspect_test.py @@ -124,6 +124,17 @@ class TfInspectTest(test.TestCase): inspect.getmembers(TestDecoratedClass), tf_inspect.getmembers(TestDecoratedClass)) + def testGetModule(self): + self.assertEqual( + inspect.getmodule(TestDecoratedClass), + tf_inspect.getmodule(TestDecoratedClass)) + self.assertEqual( + inspect.getmodule(test_decorated_function), + tf_inspect.getmodule(test_decorated_function)) + self.assertEqual( + inspect.getmodule(test_undecorated_function), + tf_inspect.getmodule(test_undecorated_function)) + def testGetSource(self): expected = '''@test_decorator('decorator') def test_decorated_function_with_defaults(a, b=2, c='Hello'): @@ -133,6 +144,19 @@ def test_decorated_function_with_defaults(a, b=2, c='Hello'): self.assertEqual( expected, tf_inspect.getsource(test_decorated_function_with_defaults)) + def testIsBuiltin(self): + self.assertEqual( + tf_inspect.isbuiltin(TestDecoratedClass), + inspect.isbuiltin(TestDecoratedClass)) + self.assertEqual( + tf_inspect.isbuiltin(test_decorated_function), + inspect.isbuiltin(test_decorated_function)) + self.assertEqual( + tf_inspect.isbuiltin(test_undecorated_function), + inspect.isbuiltin(test_undecorated_function)) + self.assertEqual(tf_inspect.isbuiltin(range), inspect.isbuiltin(range)) + self.assertEqual(tf_inspect.isbuiltin(max), inspect.isbuiltin(max)) + def testIsClass(self): self.assertTrue(tf_inspect.isclass(TestDecoratedClass)) self.assertFalse(tf_inspect.isclass(test_decorated_function)) diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py index 37733152e8ec6d7b026bf74e69e33bfe8f9f4e89..28e49afa023904abed076373685bb38f2537b7d4 100644 --- a/tensorflow/python/util/tf_should_use.py +++ b/tensorflow/python/util/tf_should_use.py @@ -47,7 +47,7 @@ def _add_should_use_warning(x, fatal_error=False): if x is None or x == []: # pylint: disable=g-explicit-bool-comparison return x - if context.in_eager_mode(): + if context.executing_eagerly(): # Typically not needed when executing eagerly (the main use case is for ops # which need to be incorporated into the graph), and even the no-op wrapper # creates reference cycles which require garbage collection. diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc index da09d84921e2dd94942b3a62fe7366211c60aed1..31724cf6c9b97e45975b9e053459f7b8f5918dfa 100644 --- a/tensorflow/stream_executor/blas.cc +++ b/tensorflow/stream_executor/blas.cc @@ -79,6 +79,8 @@ string ComputationTypeString(ComputationType ty) { return "f32"; case ComputationType::kF64: return "f64"; + case ComputationType::kI32: + return "i32"; case ComputationType::kComplexF32: return "complex f32"; case ComputationType::kComplexF64: @@ -88,6 +90,10 @@ string ComputationTypeString(ComputationType ty) { } } +std::ostream& operator<<(std::ostream& os, ComputationType ty) { + return os << ComputationTypeString(ty); +} + } // namespace blas } // namespace gputools } // namespace perftools diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h index 072f08554688276a05d9be85718de8750bd874c2..c5f778a5c74519c0f35cea5d59aac3d0d4564c56 100644 --- a/tensorflow/stream_executor/blas.h +++ b/tensorflow/stream_executor/blas.h @@ -104,6 +104,8 @@ enum class ComputationType { // Converts a ComputationType to a string. string ComputationTypeString(ComputationType ty); +std::ostream &operator<<(std::ostream &os, ComputationType ty); + // Opaque identifier for an "algorithm" used by a blas routine. This functions // as a hint to the blas library. typedef int64 AlgorithmType; diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 44a3a745ad86dc24f632e4a36691fba06171c9fb..c563f8f931b0a5689268329386d1252f2a45bdd1 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -13,17 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// Include cuBLAS headers early, and then set EIGEN_HAS_CUDA_FP16 -// if we have new enough CUDA (which we will only know after including -// cuda.h). This ensures that Eigen's Half.h does not attempt to make its own -// __half typedef if CUDA has already defined one (and conversely, that we do -// not include after Half.h has made its typedef). -#include "cuda/include/cuda.h" #include "cuda/include/cublas_v2.h" - -#if CUDA_VERSION >= 7050 -#define EIGEN_HAS_CUDA_FP16 -#endif +#include "cuda/include/cuda.h" #if CUDA_VERSION >= 8000 #define SE_CUDA_DATA_HALF CUDA_R_16F @@ -33,6 +24,34 @@ limitations under the License. #include "tensorflow/stream_executor/cuda/cuda_blas.h" +// Both Eigen Half.h and CUDA cuda_fp16.h provide similar typedef for __half. As +// such, there are two ways to get the typedef for __half: +// +// (1) Includes cuda_fp16.h and defines EIGEN_HAS_CUDA_FP16. +// (2) Neither includes cuda_fp16.h nor defines EIGEN_HAS_CUDA_FP16. +// +// Due to issue b/73793421, when the first approach is used and NVCC is used to +// compile this file, NVCC will complain duplicated definition for +// EIGEN_HAS_CUDA_FP16. On the other hand, when the second approach is used and +// clang is used to compile this file, clang will not understand __half +// due to missing the definition and macro EIGEN_HAS_CUDA_FP16. +// +// Because this file may be compiled with CLANG but will never be compiled with +// NVCC, we choose the first approach for CUDA < 9.0. For CUDA >= 9.0, we have +// to use the second approach because the data member in the __half defined +// by CUDA > 9.0 is `__x` while Eigen expects it to be `x`. +// +// TODO(b/73793421): Remove the following code block to switch to the second +// approach when the issue is fixed. +#if CUDA_VERSION < 9000 +#include "cuda/include/cuda_fp16.h" +#if CUDA_VERSION >= 7050 +#define EIGEN_HAS_CUDA_FP16 +#endif +#endif + +#include "third_party/eigen3/Eigen/Core" + #include #include @@ -2256,6 +2275,14 @@ bool CUDABlas::DoBlasGemmWithAlgorithm( DeviceMemory *c, int ldc, blas::ComputationType computation_type, blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) { + if (computation_type == blas::ComputationType::kF32) { + return DoBlasGemmWithAlgorithmImpl( + stream, transa, transb, m, n, k, static_cast(alpha), a, lda, b, + ldb, static_cast(beta), c, ldc, computation_type, algorithm, + output_profile_result); + } + + CHECK_EQ(computation_type, blas::ComputationType::kF16); return DoBlasGemmWithAlgorithmImpl( stream, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, computation_type, algorithm, output_profile_result); diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 384445e6c1629e5518459b5382aa9b92698fb6ff..0b3b060fe75e1060c0512ced27ac3da7f726a64e 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -109,6 +109,24 @@ string ToString(cudnnStatus_t status) { } } +template +cudnnDataType_t GetCudnnDataType(); + +template <> +cudnnDataType_t GetCudnnDataType() { + return CUDNN_DATA_DOUBLE; +} + +template <> +cudnnDataType_t GetCudnnDataType() { + return CUDNN_DATA_FLOAT; +} + +template <> +cudnnDataType_t GetCudnnDataType() { + return CUDNN_DATA_HALF; +} + namespace wrap { static port::ThreadPool* InitCudnnThreadpool() { @@ -559,7 +577,7 @@ class ScopedFilterDescriptor { // A helper function to decide whether to enable the TENSOR_OP_MATH math type static bool TensorOpMathEnabled() { static bool is_enabled = [] { - bool is_disabled; + bool is_disabled = false; TF_CHECK_OK( tensorflow::ReadBoolFromEnvVar("TF_DISABLE_CUDNN_TENSOR_OP_MATH", /*default_val=*/false, &is_disabled)); @@ -568,6 +586,25 @@ static bool TensorOpMathEnabled() { return is_enabled; } +// A helper function to decide whether to use CUDNN_BATCHNORM_SPATIAL_PERSISTENT +// in batchnorm. This mode can be faster in some tasks because an optimized path +// may be selected for CUDNN_DATA_FLOAT and CUDNN_DATA_HALF data types, compute +// capability 6.0 or higher. The reason we set it to false by default is that +// this mode may use scaled atomic integer reduction that may cause a numerical +// overflow for certain input data range. +// TODO(yangzihao): Use autotune to choose between this mode and +// CUDNN_BATCHNORM_SPATIAL mode. +static bool BatchnormSpatialPersistentEnabled() { + static bool is_enabled = [] { + bool is_enabled = false; + TF_CHECK_OK(tensorflow::ReadBoolFromEnvVar( + "TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT", + /*default_val=*/false, &is_enabled)); + return is_enabled; + }(); + return is_enabled; +} + // Turns a ConvolutionDescriptor structure into a cudnn convolution handle // within a scope. class ScopedConvolutionDescriptor { @@ -2106,7 +2143,6 @@ inline cudnnConvolutionFwdAlgo_t GetCudnnConvolutionForwardAlgo( dnn::AlgorithmDesc GetCudnnConvolutionForwardAlgorithm( Stream* stream, CUDAExecutor* parent, void* dnn_handle, - int cudnn_type, // Actually cudnnDataType_t. const dnn::AlgorithmConfig& algorithm_config, bool is_profiling, const ScopedTensorDescriptor& input_nd, const ScopedFilterDescriptor& filter, @@ -2245,7 +2281,6 @@ struct ConvDoFP32ComputationFP16Input { // A group of helper functions to return the internal compute type for // convolutions in cudnn. -// TODO(yangzihao): Add support for float64. template cudnnDataType_t GetConvComputeType() { return CUDNN_DATA_FLOAT; @@ -2260,12 +2295,17 @@ cudnnDataType_t GetConvComputeType() { } } +template <> +cudnnDataType_t GetConvComputeType() { + return CUDNN_DATA_DOUBLE; +} + } // namespace template bool CudnnSupport::DoConvolveImpl( - Stream* stream, int cudnn_type, // Actually cudnnDataType_t. - const BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, + Stream* stream, const BatchDescriptor& batch_descriptor, + const DeviceMemory& input_data, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const ConvolutionDescriptor& convolution_descriptor, @@ -2273,12 +2313,11 @@ bool CudnnSupport::DoConvolveImpl( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - ScopedTensorDescriptor input_nd{parent_, batch_descriptor, - static_cast(cudnn_type)}; - ScopedTensorDescriptor output_nd{parent_, output_descriptor, - static_cast(cudnn_type)}; + cudnnDataType_t cudnn_type = GetCudnnDataType(); + ScopedTensorDescriptor input_nd{parent_, batch_descriptor, cudnn_type}; + ScopedTensorDescriptor output_nd{parent_, output_descriptor, cudnn_type}; ScopedFilterDescriptor filter{parent_, filter_descriptor, batch_descriptor, - static_cast(cudnn_type)}; + cudnn_type}; ScopedConvolutionDescriptor conv{parent_, convolution_descriptor, GetConvComputeType()}; @@ -2289,9 +2328,15 @@ bool CudnnSupport::DoConvolveImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); const bool is_profiling = output_profile_result != nullptr; cudnnConvolutionFwdAlgo_t algo; @@ -2429,11 +2474,11 @@ bool CudnnSupport::DoConvolveImpl( } status = wrap::cudnnConvolutionForward( parent_, ToHandle(dnn_handle_), - /*alpha=*/&alpha, /*srcDesc=*/input_nd.handle(), + /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), /*srcData=*/input_data.opaque(), /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(), /*convDesc=*/conv.handle(), /*algo=*/algo, /*workSpace=*/scratch.opaque(), - /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/&beta, + /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/beta, /*destDesc=*/output_nd.handle(), /*destData=*/output_data->opaque()); if (is_profiling) { @@ -2505,9 +2550,8 @@ bool CudnnSupport::DoFusedConvolveImpl( const bool is_profiling = output_profile_result != nullptr; DeviceMemory scratch; dnn::AlgorithmDesc algotype = GetCudnnConvolutionForwardAlgorithm( - stream, parent_, dnn_handle_, cudnn_data_type, algorithm_config, - is_profiling, conv_input_nd, filter, conv, output_nd, scratch_allocator, - &scratch); + stream, parent_, dnn_handle_, algorithm_config, is_profiling, + conv_input_nd, filter, conv, output_nd, scratch_allocator, &scratch); if (algotype.is_default()) { if (!is_profiling) { LOG(ERROR) << "No suitable algorithm found"; @@ -2758,6 +2802,11 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl( ScopedTensorDescriptor scale_offset_descriptor{ parent_, scale_offset_desc, ToCudnnDataType(scale_data_type)}; cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL; +#if CUDNN_VERSION >= 7000 + if (BatchnormSpatialPersistentEnabled() && is_training) { + mode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT; + } +#endif float one = 1.0; float zero = 0.0; @@ -2859,6 +2908,11 @@ bool CudnnSupport::DoBatchNormalizationBackwardImpl( parent_, scale_offset_desc, static_cast(cudnn_scale_type)}; cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL; +#if CUDNN_VERSION >= 7000 + if (BatchnormSpatialPersistentEnabled()) { + mode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT; + } +#endif float one = 1.0; float zero = 0.0; @@ -2888,9 +2942,9 @@ bool CudnnSupport::DoConvolve( const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return DoConvolveImpl( - stream, CUDNN_DATA_FLOAT, batch_descriptor, input_data, filter_descriptor, - filter_data, convolution_descriptor, output_descriptor, output_data, - scratch_allocator, algorithm_config, output_profile_result); + stream, batch_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolve( @@ -2899,10 +2953,14 @@ bool CudnnSupport::DoConvolve( const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const ConvolutionDescriptor& convolution_descriptor, - const BatchDescriptor& output_descriptor, - DeviceMemory* output_data) { - LOG(ERROR) << "double-based DNN not yet implemented"; - return false; + const BatchDescriptor& output_descriptor, DeviceMemory* output_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveImpl( + stream, batch_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolve( @@ -2916,9 +2974,9 @@ bool CudnnSupport::DoConvolve( const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return DoConvolveImpl( - stream, CUDNN_DATA_HALF, batch_descriptor, input_data, filter_descriptor, - filter_data, convolution_descriptor, output_descriptor, output_data, - scratch_allocator, algorithm_config, output_profile_result); + stream, batch_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoFusedConvolve( @@ -3027,7 +3085,6 @@ bool CudnnSupport::DoFusedConvolve( template DeviceMemory CudnnSupport::MaybeTransformLayout( Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. BatchDescriptor* output_descriptor, DeviceMemory backward_output_data, std::unique_ptr>* transform_scratch) { @@ -3041,11 +3098,11 @@ DeviceMemory CudnnSupport::MaybeTransformLayout( BatchDescriptor transformed_output_descriptor; transformed_output_descriptor.CloneFrom(*output_descriptor); transformed_output_descriptor.set_layout(dnn::DataLayout::kBatchDepthYX); - ScopedTensorDescriptor orig_out_back_nd{ - parent_, *output_descriptor, static_cast(cudnn_type)}; + cudnnDataType_t cudnn_type = GetCudnnDataType(); + ScopedTensorDescriptor orig_out_back_nd{parent_, *output_descriptor, + cudnn_type}; ScopedTensorDescriptor transformed_out_back_nd{ - parent_, transformed_output_descriptor, - static_cast(cudnn_type)}; + parent_, transformed_output_descriptor, cudnn_type}; float alpha = 1.0f; float beta = 0.0f; @@ -3092,7 +3149,6 @@ bool CudnnSupport::DoTransformTensor(Stream* stream, template bool CudnnSupport::DoConvolveBackwardDataImpl( Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const BatchDescriptor& output_descriptor_in, @@ -3109,25 +3165,29 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } + cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass. BatchDescriptor output_descriptor; output_descriptor.CloneFrom(output_descriptor_in); std::unique_ptr> transform_scratch; backward_output_data = MaybeTransformLayout( - stream, cudnn_type, &output_descriptor, backward_output_data, - &transform_scratch); + stream, &output_descriptor, backward_output_data, &transform_scratch); - ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, - static_cast(cudnn_type)}; - ScopedTensorDescriptor in_back_nd{parent_, input_descriptor, - static_cast(cudnn_type)}; + ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type}; + ScopedTensorDescriptor in_back_nd{parent_, input_descriptor, cudnn_type}; ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor, - static_cast(cudnn_type)}; + cudnn_type}; ScopedConvolutionDescriptor conv{parent_, convolution_descriptor, GetConvComputeType()}; @@ -3270,7 +3330,7 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( status = wrap::cudnnConvolutionBackwardData_v3( #endif parent_, ToHandle(dnn_handle_), - /*alpha=*/&alpha, + /*alpha=*/alpha, /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(), /*diffDesc=*/out_back_nd.handle(), @@ -3279,7 +3339,7 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( /*algo=*/algo, /*workSpace=*/scratch.opaque(), /*workSpaceSizeInBytes=*/scratch.size(), - /*beta=*/&beta, + /*beta=*/beta, /*gradDesc=*/in_back_nd.handle(), /*gradData=*/backward_input_data->opaque()); if (is_profiling) { @@ -3304,10 +3364,28 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( return true; } +bool CudnnSupport::DoConvolveBackwardData( + Stream* stream, const FilterDescriptor& filter_descriptor, + const DeviceMemory& filter_data, + const BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const ConvolutionDescriptor& convolution_descriptor, + const BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, + output_descriptor, backward_output_data, + convolution_descriptor, input_descriptor, + backward_input_data, scratch_allocator, + algorithm_config, output_profile_result); +} + bool CudnnSupport::DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const BatchDescriptor& output_descriptor_in, + const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, const BatchDescriptor& input_descriptor, @@ -3315,17 +3393,17 @@ bool CudnnSupport::DoConvolveBackwardData( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardDataImpl( - stream, CUDNN_DATA_FLOAT, filter_descriptor, filter_data, - output_descriptor_in, backward_output_data, convolution_descriptor, - input_descriptor, backward_input_data, scratch_allocator, - algorithm_config, output_profile_result); + return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, + output_descriptor, backward_output_data, + convolution_descriptor, input_descriptor, + backward_input_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const BatchDescriptor& output_descriptor_in, + const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, const BatchDescriptor& input_descriptor, @@ -3333,17 +3411,16 @@ bool CudnnSupport::DoConvolveBackwardData( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardDataImpl( - stream, CUDNN_DATA_HALF, filter_descriptor, filter_data, - output_descriptor_in, backward_output_data, convolution_descriptor, - input_descriptor, backward_input_data, scratch_allocator, - algorithm_config, output_profile_result); + return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, + output_descriptor, backward_output_data, + convolution_descriptor, input_descriptor, + backward_input_data, scratch_allocator, + algorithm_config, output_profile_result); } template bool CudnnSupport::DoConvolveBackwardFilterImpl( - Stream* stream, int cudnn_type, // Actually cudnnDataType_t. - const dnn::BatchDescriptor& input_descriptor, + Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor_in, DeviceMemory backward_output_data, @@ -3359,26 +3436,29 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } + cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass. BatchDescriptor output_descriptor; output_descriptor.CloneFrom(output_descriptor_in); std::unique_ptr> transform_scratch; backward_output_data = MaybeTransformLayout( - stream, static_cast(cudnn_type), - &output_descriptor, backward_output_data, - &transform_scratch); - - ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, - static_cast(cudnn_type)}; - ScopedTensorDescriptor input_nd{parent_, input_descriptor, - static_cast(cudnn_type)}; + stream, &output_descriptor, backward_output_data, &transform_scratch); + + ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type}; + ScopedTensorDescriptor input_nd{parent_, input_descriptor, cudnn_type}; ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor, - static_cast(cudnn_type)}; + cudnn_type}; ScopedConvolutionDescriptor conv{parent_, convolution_descriptor, GetConvComputeType()}; @@ -3521,7 +3601,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( #else status = wrap::cudnnConvolutionBackwardFilter_v3( #endif - parent_, ToHandle(dnn_handle_), /*alpha=*/&alpha, + parent_, ToHandle(dnn_handle_), /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), /*srcData=*/input_data.opaque(), /*diffDesc=*/out_back_nd.handle(), @@ -3530,7 +3610,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( /*algo=*/algo, /*workSpace=*/scratch.opaque(), /*workSpaceSizeInBytes=*/scratch.size(), - /*beta=*/&beta, + /*beta=*/beta, /*gradDesc=*/filter.handle(), /*gradData=*/backward_filter_data->opaque()); @@ -3556,10 +3636,28 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( return true; } +bool CudnnSupport::DoConvolveBackwardFilter( + Stream* stream, const dnn::BatchDescriptor& input_descriptor, + const DeviceMemory& input_data, + const dnn::BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor& convolution_descriptor, + const dnn::FilterDescriptor& filter_descriptor, + DeviceMemory* backward_filter_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); +} + bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor_in, + const dnn::BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, @@ -3567,17 +3665,17 @@ bool CudnnSupport::DoConvolveBackwardFilter( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardFilterImpl( - stream, CUDNN_DATA_FLOAT, input_descriptor, input_data, - output_descriptor_in, backward_output_data, convolution_descriptor, - filter_descriptor, backward_filter_data, scratch_allocator, - algorithm_config, output_profile_result); + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor_in, + const dnn::BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, @@ -3585,17 +3683,16 @@ bool CudnnSupport::DoConvolveBackwardFilter( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardFilterImpl( - stream, CUDNN_DATA_HALF, input_descriptor, input_data, - output_descriptor_in, backward_output_data, convolution_descriptor, - filter_descriptor, backward_filter_data, scratch_allocator, - algorithm_config, output_profile_result); + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); } template bool CudnnSupport::DoConvolveBackwardBiasImpl( - Stream* stream, int cudnn_type, // Actually cudnnDataType_t. - const dnn::BatchDescriptor& input_descriptor, + Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& bias_descriptor, DeviceMemory* backward_bias_data) { @@ -3606,10 +3703,9 @@ bool CudnnSupport::DoConvolveBackwardBiasImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } - ScopedTensorDescriptor input_nd{parent_, input_descriptor, - static_cast(cudnn_type)}; - ScopedTensorDescriptor bias_nd{parent_, bias_descriptor, - static_cast(cudnn_type)}; + cudnnDataType_t cudnn_type = GetCudnnDataType(); + ScopedTensorDescriptor input_nd{parent_, input_descriptor, cudnn_type}; + ScopedTensorDescriptor bias_nd{parent_, bias_descriptor, cudnn_type}; // Alpha is the scaling factor for input. float alpha = 1.0; @@ -3633,9 +3729,8 @@ bool CudnnSupport::DoConvolveBackwardBias( const DeviceMemory& input_data, const BatchDescriptor& bias_descriptor, DeviceMemory* backward_bias_data) { - return DoConvolveBackwardBiasImpl(stream, CUDNN_DATA_DOUBLE, input_descriptor, - input_data, bias_descriptor, - backward_bias_data); + return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data, + bias_descriptor, backward_bias_data); } bool CudnnSupport::DoConvolveBackwardBias( @@ -3643,9 +3738,8 @@ bool CudnnSupport::DoConvolveBackwardBias( const DeviceMemory& input_data, const BatchDescriptor& bias_descriptor, DeviceMemory* backward_bias_data) { - return DoConvolveBackwardBiasImpl(stream, CUDNN_DATA_FLOAT, input_descriptor, - input_data, bias_descriptor, - backward_bias_data); + return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data, + bias_descriptor, backward_bias_data); } bool CudnnSupport::DoConvolveBackwardBias( @@ -3653,9 +3747,8 @@ bool CudnnSupport::DoConvolveBackwardBias( const DeviceMemory& input_data, const BatchDescriptor& bias_descriptor, DeviceMemory* backward_bias_data) { - return DoConvolveBackwardBiasImpl(stream, CUDNN_DATA_HALF, input_descriptor, - input_data, bias_descriptor, - backward_bias_data); + return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data, + bias_descriptor, backward_bias_data); } bool CudnnSupport::DoMatMul(Stream* stream, diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h index ee28c0bf57a51a63be7ebbce5c8f80e09737bb16..48d56f71e3195a897b6216ab9f5709326d1b86d3 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.h +++ b/tensorflow/stream_executor/cuda/cuda_dnn.h @@ -259,7 +259,10 @@ class CudnnSupport : public dnn::DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data) override; + DeviceMemory* output_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, @@ -371,6 +374,18 @@ class CudnnSupport : public dnn::DnnSupport { return false; } + bool DoConvolveBackwardData( + Stream* stream, const dnn::FilterDescriptor& filter_descriptor, + const DeviceMemory& filter_data, + const dnn::BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor& convolution_descriptor, + const dnn::BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; + bool DoConvolveBackwardData( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, @@ -395,6 +410,18 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; + bool DoConvolveBackwardFilter( + Stream* stream, const dnn::BatchDescriptor& input_descriptor, + const DeviceMemory& input_data, + const dnn::BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor& convolution_descriptor, + const dnn::FilterDescriptor& filter_descriptor, + DeviceMemory* backward_filter_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; + bool DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, @@ -611,7 +638,6 @@ class CudnnSupport : public dnn::DnnSupport { template DeviceMemory MaybeTransformLayout( Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. dnn::BatchDescriptor* output_descriptor, DeviceMemory backward_output_data, std::unique_ptr>* transform_scratch) @@ -644,7 +670,6 @@ class CudnnSupport : public dnn::DnnSupport { template bool DoConvolveImpl(Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. const dnn::BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, const dnn::FilterDescriptor& filter_descriptor, @@ -675,7 +700,6 @@ class CudnnSupport : public dnn::DnnSupport { template bool DoConvolveBackwardDataImpl( Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const dnn::BatchDescriptor& output_descriptor, @@ -688,8 +712,7 @@ class CudnnSupport : public dnn::DnnSupport { template bool DoConvolveBackwardFilterImpl( - Stream* stream, int cudnn_type, // Actually cudnnDataType_t. - const dnn::BatchDescriptor& input_descriptor, + Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor_in, DeviceMemory backward_output_data, @@ -702,7 +725,6 @@ class CudnnSupport : public dnn::DnnSupport { template bool DoConvolveBackwardBiasImpl(Stream* stream, - int cudnn_type, // Actually cudnnDataType_t. const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& bias_descriptor, diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h index f4162b096299ca9405e1f3045e370d0da1acf8da..b41536e638873412a31a0cdbbd3ba3a818dd9cf2 100644 --- a/tensorflow/stream_executor/dnn.h +++ b/tensorflow/stream_executor/dnn.h @@ -896,7 +896,7 @@ class DnnSupport { // offset: offset parameters. // estimated_mean: population mean estimated during training. // Used for inference only; empty for training. - // estimated_variance: population variance estimated during traning, + // estimated_variance: population variance estimated during training, // used for inference only; empty for training. // x_desc: dimensions of the input data, which is the same as the dimensions // of the output. @@ -1172,7 +1172,9 @@ class DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data) = 0; + DeviceMemory* output_data, ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) = 0; // Enqueues a half-precision convolution operation onto the stream. // See DoConvolve above for argument details. @@ -1273,6 +1275,18 @@ class DnnSupport { bool with_winograd_nonfused, int cc_major, int cc_minor, std::vector* out_algorithms); + virtual bool DoConvolveBackwardData( + Stream* stream, const FilterDescriptor& filter_descriptor, + const DeviceMemory& filter_data, + const BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const ConvolutionDescriptor& convolution_descriptor, + const BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + ProfileResult* output_profile_result) = 0; + virtual bool DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, @@ -1322,6 +1336,18 @@ class DnnSupport { bool with_winograd_nonfused, int cc_major, int cc_minor, std::vector* out_algorithms); + virtual bool DoConvolveBackwardFilter( + Stream* stream, const BatchDescriptor& input_descriptor, + const DeviceMemory& input_data, + const BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const ConvolutionDescriptor& convolution_descriptor, + const FilterDescriptor& filter_descriptor, + DeviceMemory* backward_filter_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + ProfileResult* output_profile_result) = 0; + virtual bool DoConvolveBackwardFilter( Stream* stream, const BatchDescriptor& input_descriptor, const DeviceMemory& input_data, diff --git a/tensorflow/stream_executor/dso_loader.cc b/tensorflow/stream_executor/dso_loader.cc index d71938634d6e6fe092d9a1e0861215bb101e824f..95168836278add5d6592ff0c3d0f7245e6f6bc5b 100644 --- a/tensorflow/stream_executor/dso_loader.cc +++ b/tensorflow/stream_executor/dso_loader.cc @@ -33,6 +33,10 @@ limitations under the License. #include "tensorflow/stream_executor/platform/logging.h" #include "tensorflow/stream_executor/platform/port.h" +#if !defined(PLATFORM_GOOGLE) +#include "cuda/cuda_config.h" +#endif + namespace perftools { namespace gputools { namespace internal { @@ -97,11 +101,12 @@ string GetCudnnVersion() { return TF_CUDNN_VERSION; } /* static */ port::Status DsoLoader::GetLibcuptiDsoHandle(void** dso_handle) { #if defined(ANDROID_TEGRA) - // On Android devices the CUDA version number is not added to the library name. - return GetDsoHandle(FindDsoPath(port::Env::Default()->FormatLibraryFileName( - "cupti", ""), - GetCudaCuptiLibraryPath()), - dso_handle); + // On Android devices the CUDA version number is not added to the library + // name. + return GetDsoHandle( + FindDsoPath(port::Env::Default()->FormatLibraryFileName("cupti", ""), + GetCudaCuptiLibraryPath()), + dso_handle); #else return GetDsoHandle(FindDsoPath(port::Env::Default()->FormatLibraryFileName( "cupti", GetCudaVersion()), diff --git a/tensorflow/stream_executor/dso_loader.h b/tensorflow/stream_executor/dso_loader.h index 9495f7253a1d475f0b5321b71419febd086832af..354c7b50b8209755991827b3c36afac790cb952b 100644 --- a/tensorflow/stream_executor/dso_loader.h +++ b/tensorflow/stream_executor/dso_loader.h @@ -28,10 +28,6 @@ limitations under the License. #include "tensorflow/stream_executor/platform.h" #include "tensorflow/stream_executor/platform/mutex.h" -#if !defined(PLATFORM_GOOGLE) -#include "cuda/cuda_config.h" -#endif - namespace perftools { namespace gputools { namespace internal { diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index ba5001e273632c893b05eea64542f1b156e28c47..4d852e6e5a7aeecd6e641bb197f3aa6137cf009e 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -681,6 +681,37 @@ Stream &Stream::ThenFusedConvolveWithAlgorithm( return *this; } +Stream &Stream::ThenConvolveWithAlgorithm( + const dnn::BatchDescriptor &input_descriptor, + const DeviceMemory &input_data, + const dnn::FilterDescriptor &filter_descriptor, + const DeviceMemory &filter_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::BatchDescriptor &output_descriptor, DeviceMemory *output, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(input_descriptor), PARAM(input_data), + PARAM(filter_descriptor), PARAM(filter_data), + PARAM(convolution_descriptor), PARAM(output_descriptor), + PARAM(output), PARAM(algorithm_config)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + auto status = dnn->DoConvolve( + this, input_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output, scratch_allocator, + algorithm_config, output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -890,6 +921,39 @@ Stream &Stream::ThenConvolveBackwardDataWithScratch( return *this; } +Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( + const dnn::FilterDescriptor &filter_descriptor, + const DeviceMemory &filter_data, + const dnn::BatchDescriptor &output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::BatchDescriptor &input_descriptor, + DeviceMemory *backward_input_data, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(filter_descriptor), PARAM(filter_data), + PARAM(output_descriptor), PARAM(backward_output_data), + PARAM(convolution_descriptor), PARAM(input_descriptor), + PARAM(backward_input_data)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + auto status = dnn->DoConvolveBackwardData( + this, filter_descriptor, filter_data, output_descriptor, + backward_output_data, convolution_descriptor, input_descriptor, + backward_input_data, scratch_allocator, algorithm_config, + output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( const dnn::FilterDescriptor &filter_descriptor, const DeviceMemory &filter_data, @@ -1026,6 +1090,39 @@ Stream &Stream::ThenConvolveBackwardFilterWithScratch( return *this; } +Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( + const dnn::BatchDescriptor &input_descriptor, + const DeviceMemory &input_data, + const dnn::BatchDescriptor &output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::FilterDescriptor &filter_descriptor, + DeviceMemory *backward_filter_data, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(input_descriptor), PARAM(input_data), + PARAM(output_descriptor), PARAM(backward_output_data), + PARAM(convolution_descriptor), PARAM(filter_descriptor), + PARAM(backward_filter_data)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + auto status = dnn->DoConvolveBackwardFilter( + this, input_descriptor, input_data, output_descriptor, + backward_output_data, convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, algorithm_config, + output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index a2fb2ea2375d0f245ae3bf3ccb04803d01663def..8cd0a0d3ba33d75005b3a6a90298f32043f38449 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -358,6 +358,17 @@ class Stream { const dnn::BatchDescriptor &output_descriptor, DeviceMemory *output, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveWithAlgorithm( + const dnn::BatchDescriptor &input_descriptor, + const DeviceMemory &input_data, + const dnn::FilterDescriptor &filter_descriptor, + const DeviceMemory &filter_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::BatchDescriptor &output_descriptor, + DeviceMemory *output, ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result); + Stream &ThenConvolveWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -476,6 +487,18 @@ class Stream { DeviceMemory *backward_input_data, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveBackwardDataWithAlgorithm( + const dnn::FilterDescriptor &filter_descriptor, + const DeviceMemory &filter_data, + const dnn::BatchDescriptor &output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::BatchDescriptor &input_descriptor, + DeviceMemory *backward_input_data, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result); + Stream &ThenConvolveBackwardDataWithAlgorithm( const dnn::FilterDescriptor &filter_descriptor, const DeviceMemory &filter_data, @@ -529,6 +552,18 @@ class Stream { DeviceMemory *backward_filter_data, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveBackwardFilterWithAlgorithm( + const dnn::BatchDescriptor &input_descriptor, + const DeviceMemory &input_data, + const dnn::BatchDescriptor &output_descriptor, + DeviceMemory backward_output_data, + const dnn::ConvolutionDescriptor &convolution_descriptor, + const dnn::FilterDescriptor &filter_descriptor, + DeviceMemory *backward_filter_data, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result); + Stream &ThenConvolveBackwardFilterWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 23d11c88ed687d919bd605af2f83f1ef77209370..51ef3235b7fa41342a93102a9fa442941c071226 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -219,6 +219,13 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False): "//conditions:default": ["-pthread"] })) + +def tfe_xla_copts(): + return select({ + "//tensorflow:with_xla_support": ["-DTENSORFLOW_EAGER_USE_XLA"], + "//conditions:default": [], + }) + def tf_opts_nortti_if_android(): return if_android([ "-fno-rtti", @@ -491,6 +498,9 @@ def tf_gen_op_wrappers_cc(name, # is invalid to specify both "hidden" and "op_whitelist". # cc_linkopts: Optional linkopts to be added to tf_cc_binary that contains the # specified ops. +# gen_locally: if True, the genrule to generate the Python library will be run +# without sandboxing. This would help when the genrule depends on symlinks +# which may not be supported in the sandbox. def tf_gen_op_wrapper_py(name, out=None, hidden=None, @@ -501,7 +511,8 @@ def tf_gen_op_wrapper_py(name, generated_target_name=None, op_whitelist=[], cc_linkopts=[], - api_def_srcs=[]): + api_def_srcs=[], + gen_locally=False): if (hidden or hidden_file) and op_whitelist: fail('Cannot pass specify both hidden and op_whitelist.') @@ -556,6 +567,7 @@ def tf_gen_op_wrapper_py(name, outs=[out], srcs=api_def_srcs + [hidden_file], tools=[tool_name] + tf_binary_additional_srcs(), + local = (1 if gen_locally else 0), cmd=("$(location " + tool_name + ") " + api_def_args_str + " @$(location " + hidden_file + ") " + ("1" if require_shape_functions else "0") + " > $@")) @@ -565,6 +577,7 @@ def tf_gen_op_wrapper_py(name, outs=[out], srcs=api_def_srcs, tools=[tool_name] + tf_binary_additional_srcs(), + local = (1 if gen_locally else 0), cmd=("$(location " + tool_name + ") " + api_def_args_str + " " + op_list_arg + " " + ("1" if require_shape_functions else "0") + " " + @@ -605,7 +618,7 @@ def tf_cc_test(name, srcs=srcs + tf_binary_additional_srcs(), copts=tf_copts() + extra_copts, linkopts=select({ - "//tensorflow:android": [ + clean_dep("//tensorflow:android"): [ "-pie", ], clean_dep("//tensorflow:windows"): [], @@ -671,6 +684,7 @@ def tf_cuda_cc_test(name, tags=[], data=[], size="medium", + extra_copts=[], linkstatic=0, args=[], linkopts=[]): @@ -681,6 +695,7 @@ def tf_cuda_cc_test(name, tags=tags + ["manual"], data=data, size=size, + extra_copts=extra_copts, linkstatic=linkstatic, linkopts=linkopts, args=args) @@ -701,6 +716,7 @@ def tf_cuda_cc_test(name, tags=tags + tf_cuda_tests_tags(), data=data, size=size, + extra_copts=extra_copts, linkopts=linkopts, args=args) @@ -1169,6 +1185,22 @@ def tf_custom_op_library_additional_deps(): "@nsync//:nsync_headers", clean_dep("//third_party/eigen3"), clean_dep("//tensorflow/core:framework_headers_lib"), + ] + if_windows(["//tensorflow/python:pywrap_tensorflow_import_lib"]) + +# A list of targets that contains the implemenation of +# tf_custom_op_library_additional_deps. It's used to generate a DEF file for +# exporting symbols from _pywrap_tensorflow.dll on Windows. +def tf_custom_op_library_additional_deps_impl(): + return [ + # for @protobuf_archive//:protobuf_headers + "@protobuf_archive//:protobuf", + # for @nsync//:nsync_headers + "@nsync//:nsync_cpp", + # for //third_party/eigen3 + clean_dep("//third_party/eigen3"), + # for //tensorflow/core:framework_headers_lib + clean_dep("//tensorflow/core:framework"), + clean_dep("//tensorflow/core:reader_base"), ] # Traverse the dependency graph along the "deps" attribute of the @@ -1255,6 +1287,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): deps=deps + if_cuda(cuda_deps), data=[name + "_check_deps"], copts=tf_copts(is_external=True), + features = ["windows_export_all_symbols"], linkopts=linkopts + select({ "//conditions:default": [ "-lm", @@ -1296,6 +1329,46 @@ def tf_extension_linkopts(): def tf_extension_copts(): return [] # No extension c opts +# In tf_py_wrap_cc generated libraries +# module init functions are not exported unless +# they contain one of the keywords in the version file +# this prevents custom python modules. +# This function attempts to append init_module_name to list of +# exported functions in version script +def _append_init_to_versionscript_impl(ctx): + mod_name = ctx.attr.module_name + if ctx.attr.is_version_script: + ctx.actions.expand_template( + template=ctx.file.template_file, + output=ctx.outputs.versionscript, + substitutions={ + "global:":"global:\n init_%s;\n PyInit_*;"%(mod_name), + }, + is_executable=False, + ) + else: + ctx.actions.expand_template( + template=ctx.file.template_file, + output=ctx.outputs.versionscript, + substitutions={ + "*tensorflow*":"*tensorflow*\ninit_%s\nPyInit_*\n"%(mod_name), + }, + is_executable=False, + ) + + +_append_init_to_versionscript= rule( + implementation=_append_init_to_versionscript_impl, + attrs={ + "module_name":attr.string(mandatory=True), + "template_file":attr.label(allow_files=True,single_file=True,mandatory=True), + "is_version_script":attr.bool(default=True, + doc='whether target is a ld version script or exported symbol list', + mandatory=False), + }, + outputs={"versionscript":"%{name}.lds"}, +) + def tf_py_wrap_cc(name, srcs, swig_includes=[], @@ -1317,26 +1390,39 @@ def tf_py_wrap_cc(name, toolchain_deps=["//tools/defaults:crosstool"], module_name=module_name, py_module_name=name) + vscriptname=name+"_versionscript" + _append_init_to_versionscript( + name=vscriptname, + module_name=module_name, + is_version_script=select({ + "@local_config_cuda//cuda:darwin":False, + "//conditions:default":True, + }), + template_file=select({ + "@local_config_cuda//cuda:darwin":clean_dep("//tensorflow:tf_exported_symbols.lds"), + "//conditions:default":clean_dep("//tensorflow:tf_version_script.lds") + }) + ) extra_linkopts = select({ "@local_config_cuda//cuda:darwin": [ "-Wl,-exported_symbols_list", - clean_dep("//tensorflow:tf_exported_symbols.lds") + "%s.lds"%vscriptname, ], clean_dep("//tensorflow:windows"): [], clean_dep("//tensorflow:windows_msvc"): [], "//conditions:default": [ "-Wl,--version-script", - clean_dep("//tensorflow:tf_version_script.lds") + "%s.lds"%vscriptname, ] }) extra_deps += select({ "@local_config_cuda//cuda:darwin": [ - clean_dep("//tensorflow:tf_exported_symbols.lds") + "%s.lds"%vscriptname, ], clean_dep("//tensorflow:windows"): [], clean_dep("//tensorflow:windows_msvc"): [], "//conditions:default": [ - clean_dep("//tensorflow:tf_version_script.lds") + "%s.lds"%vscriptname, ] }) @@ -1348,7 +1434,8 @@ def tf_py_wrap_cc(name, ]) + tf_extension_copts()), linkopts=tf_extension_linkopts() + extra_linkopts, linkstatic=1, - deps=deps + extra_deps) + deps=deps + extra_deps, + **kwargs) native.genrule( name="gen_" + cc_library_pyd_name, srcs=[":" + cc_library_name], diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index 66bbd572a673e3ef2da9abc75348e4f70e0cea47..e731127a63d792825e15a4b95379517117edebb0 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -41,24 +41,41 @@ genrule( # every module exported using tf_export. For e.g. if an op is decorated with # @tf_export('module1.module2', 'module3'). Then, outs should include # api/module1/module2/__init__.py and api/module3/__init__.py. + # keep sorted outs = [ "api/__init__.py", + "api/app/__init__.py", "api/bitwise/__init__.py", + "api/compat/__init__.py", "api/contrib/__init__.py", "api/contrib/stat_summarizer/__init__.py", + "api/data/__init__.py", "api/distributions/__init__.py", "api/distributions/bijectors/__init__.py", "api/errors/__init__.py", - "api/image/__init__.py", - "api/linalg/__init__.py", - "api/nn/__init__.py", - "api/spectral/__init__.py", - "api/train/__init__.py", - "api/app/__init__.py", + "api/estimator/__init__.py", + "api/estimator/export/__init__.py", + "api/estimator/inputs/__init__.py", + "api/feature_column/__init__.py", "api/gfile/__init__.py", "api/graph_util/__init__.py", + "api/image/__init__.py", + "api/initializers/__init__.py", "api/keras/__init__.py", + "api/keras/activations/__init__.py", + "api/keras/applications/__init__.py", + "api/keras/applications/densenet/__init__.py", + "api/keras/applications/inception_resnet_v2/__init__.py", + "api/keras/applications/inception_v3/__init__.py", + "api/keras/applications/mobilenet/__init__.py", + "api/keras/applications/nasnet/__init__.py", + "api/keras/applications/resnet50/__init__.py", + "api/keras/applications/vgg16/__init__.py", + "api/keras/applications/vgg19/__init__.py", + "api/keras/applications/xception/__init__.py", "api/keras/backend/__init__.py", + "api/keras/callbacks/__init__.py", + "api/keras/constraints/__init__.py", "api/keras/datasets/__init__.py", "api/keras/datasets/boston_housing/__init__.py", "api/keras/datasets/cifar10/__init__.py", @@ -66,27 +83,47 @@ genrule( "api/keras/datasets/imdb/__init__.py", "api/keras/datasets/mnist/__init__.py", "api/keras/datasets/reuters/__init__.py", + "api/keras/estimator/__init__.py", + "api/keras/initializers/__init__.py", + "api/keras/layers/__init__.py", + "api/keras/losses/__init__.py", + "api/keras/metrics/__init__.py", + "api/keras/models/__init__.py", + "api/keras/optimizers/__init__.py", + "api/keras/preprocessing/__init__.py", + "api/keras/preprocessing/image/__init__.py", + "api/keras/preprocessing/sequence/__init__.py", + "api/keras/preprocessing/text/__init__.py", + "api/keras/regularizers/__init__.py", "api/keras/utils/__init__.py", + "api/keras/wrappers/__init__.py", + "api/keras/wrappers/scikit_learn/__init__.py", + "api/layers/__init__.py", + "api/linalg/__init__.py", "api/logging/__init__.py", - "api/resource_loader/__init__.py", - "api/sysconfig/__init__.py", - "api/test/__init__.py", - "api/initializers/__init__.py", - "api/keras/initializers/__init__.py", + "api/losses/__init__.py", "api/metrics/__init__.py", + "api/nn/__init__.py", "api/nn/rnn_cell/__init__.py", + "api/profiler/__init__.py", + "api/python_io/__init__.py", + "api/resource_loader/__init__.py", + "api/saved_model/__init__.py", + "api/saved_model/builder/__init__.py", + "api/saved_model/constants/__init__.py", + "api/saved_model/loader/__init__.py", + "api/saved_model/main_op/__init__.py", + "api/saved_model/signature_constants/__init__.py", + "api/saved_model/signature_def_utils/__init__.py", + "api/saved_model/tag_constants/__init__.py", + "api/saved_model/utils/__init__.py", "api/sets/__init__.py", + "api/spectral/__init__.py", "api/summary/__init__.py", + "api/sysconfig/__init__.py", + "api/test/__init__.py", + "api/train/__init__.py", "api/train/queue_runner/__init__.py", - "api/compat/__init__.py", - "api/data/__init__.py", - "api/estimator/__init__.py", - "api/estimator/export/__init__.py", - "api/estimator/inputs/__init__.py", - "api/feature_column/__init__.py", - "api/losses/__init__.py", - "api/profiler/__init__.py", - "api/python_io/__init__.py", ], cmd = "$(location create_python_api) $(OUTS)", tools = ["create_python_api"], diff --git a/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt index 75361803a3991f380d6be2485cfd3d05fd1572e1..cdaeb55e30865e082054085f47d6a071ebf3affd 100644 --- a/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt @@ -130,6 +130,10 @@ tf_class { name: "prevent_fetching" argspec: "args=[\'self\', \'op\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "switch_to_thread_local" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "unique_name" argspec: "args=[\'self\', \'name\', \'mark_as_used\'], varargs=None, keywords=None, defaults=[\'True\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt index bc7cf7267f7d23121402e63903f01ddc6caa2e04..5a02bb2175e2d6ad71722799143090f2735c1a37 100644 --- a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.Variable" tf_class { is_instance: "" + is_instance: "" is_instance: "" member { name: "SaveSliceInfo" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt index 42de5c0c80023ad5bd7f33a564780060998307c1..0900adaf762df1415c8db63c3879ca2fabc28d9f 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt @@ -64,7 +64,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt index e2fc8d6cb1d318cc50828f22e8e575cc28c7aaad..7b16ac90c925beb25e065d26e73ee2a54b06d9dc 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt index 9770389e5ef1e29a80ae1da2725d9862f6521ff9..9cf5f2ae2057ab4a16131527cf2ef2fa6ada28e5 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt @@ -17,7 +17,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\', \'num_parallel_reads\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } member_method { name: "apply" @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt index 7263230c1c7182bb812cb2e433aedd415bcd16c7..8c3d6691439e619c906996a3ddaea4317c4a9597 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt index 874a73f661d782ff5637b751f104fd2209734599..be9ba4ce85bd5b9905a39e3f45873c534594e15f 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt index 8da2a2b6867a3f9a3d82fcdb76ac4a62d5cee825..91fca67b6b5b1187b61f398a152793362c0c6e30 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt index efc441ae2f2a00f663c11f84c1155bece0c8e08a..cd4f72fcf839fa89f25c7ed115ee6c61294283c3 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt index 20ce87987060d9013bd071d6fc9f1f4f33467121..303fd74a64d0c7f5a0292a4eaabec63455c29381 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt index 73211aaf8ba5f925982afe3d17c4b8f009250cb8..c97ea7969eff3e6952a604e72ce140b49d304461 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt index 27a159639d2098aace2e69718d9ac4e38a29fdc3..4b5b5bf0e3599a81e2e853ae8ba34ef12cc63097 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt index 76f527f796e95f342eb144ae3de87ff234338021..42a0d595216ad28363727b9d7c066fc37fddd02c 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt @@ -44,7 +44,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt index c45318b98a034255d32c326179813de14cf1d4c8..2de52d6c57cc70b562c3c10b7f23cd15b63e25f8 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt index 04a2aa080d0704a8b7ec98f8eafda4bd1944e567..e552f33720bb939b8a98d34ef3de78bda7db976c 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt @@ -45,7 +45,7 @@ tf_class { } member_method { name: "predict" - argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " } member_method { name: "train" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4fe92643bf9867765499d7bf475b9cdd1686aec5 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.export.TensorServingInputReceiver" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "features" + mtype: "" + } + member { + name: "receiver_tensors" + mtype: "" + } + member { + name: "receiver_tensors_alternatives" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt index 4d0dddb3bc0305a28fab0c95c31e4869f5db0aa8..bd72f6cd79f7dffb9f0a7f8ae43751c4ecba939d 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "ServingInputReceiver" mtype: "" } + member { + name: "TensorServingInputReceiver" + mtype: "" + } member_method { name: "build_parsing_serving_input_receiver_fn" argspec: "args=[\'feature_spec\', \'default_batch_size\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index baedf596e8fbce921ed7e0570542b8a11655dba4..bda1c2bf85977e69b0969bc8b6056710d88ca910 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -100,6 +100,10 @@ tf_module { name: "hsv_to_rgb" argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "is_jpeg" + argspec: "args=[\'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "non_max_suppression" argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt index 2bf584fa2936990b467b2da9c48620a31814691a..7be2f4f61f6b9637f372591e49efc0c93c7a8c0a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt @@ -1,10 +1,10 @@ path: "tensorflow.keras.Model" tf_class { is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -38,6 +38,10 @@ tf_class { name: "input_spec" mtype: "" } + member { + name: "layers" + mtype: "" + } member { name: "losses" mtype: "" @@ -108,11 +112,11 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'inputs\', \'outputs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_update" @@ -120,7 +124,7 @@ tf_class { } member_method { name: "add_variable" - argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " } member_method { name: "add_weight" @@ -136,11 +140,11 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " } member_method { name: "compile" - argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " } member_method { name: "compute_mask" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt index 0a6096813155d59eb1c7920f2bcd250ed9730982..0f2428d77a537959cf2c46dfa350208abea8cb36 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt @@ -1,11 +1,11 @@ path: "tensorflow.keras.Sequential" tf_class { - is_instance: "" + is_instance: "" is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -39,6 +39,10 @@ tf_class { name: "input_spec" mtype: "" } + member { + name: "layers" + mtype: "" + } member { name: "losses" mtype: "" @@ -125,7 +129,7 @@ tf_class { } member_method { name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_update" @@ -133,7 +137,7 @@ tf_class { } member_method { name: "add_variable" - argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " } member_method { name: "add_weight" @@ -149,7 +153,7 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None" } member_method { name: "compile" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-base-logger.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-base-logger.pbtxt index ea4d5143540611f0585b67910cb319454b8560dc..454823fd23e72c6aa6bf6aa608707fa3b893b986 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-base-logger.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-base-logger.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-progbar-logger.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-progbar-logger.pbtxt index 0e6901f28affdfc73092c2b9f3af07d17db61a9f..543de0ad48b86502fc83374e5e6d82822485f331 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-progbar-logger.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-progbar-logger.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'count_mode\'], varargs=None, keywords=None, defaults=[\'samples\'], " + argspec: "args=[\'self\', \'count_mode\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'samples\', \'None\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt index f4ab075959906cdf350ec5d49dc86f928b7eb7ae..db8f626b98b70fd99f38e696aa16c72e74e86e25 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Activation" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt index eb558cddafc3972127786353072767f0d53bf174..809b3a5430449176a0d7423ec7f4499ceb620890 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ActivityRegularization" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt index 770a107b664d7ab0a8aedf292a34d4258a201859..68d41bb6cc258ca87d4664ac0fb9d5649f89ebaf 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Add" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt index 0ce42b706ec20a8ea1cc83ec95cb64d9be2e5710..970b777e514194db4ac49fe58bea737b35436217 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.AlphaDropout" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt index d6c98fa225ce924bc8e20f8531516eaed4d32ffb..529c64ab293d596012aefd42e0695bd1eb7e44d1 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt index 754fd310c6d8ddb994db0590342b29f8cb7abd71..7e7c330d74fe3b71ecd0eb87e34719e47ae70784 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt index 9b62880c7931d151fb98cc1dc3149dcbd4dd103d..ada8466d7473072b1878861ab36ec40b07fa1914 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt index b371ad148cee16dd243869d929e0c1c002794682..2a5c1cd530a7a532f6cdd3c184f4ee7eb88d23d3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Average" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt index 3e2aba55fd63326bb0e232fdce06f32884db7a0a..9a2cb29815d59f3761ea25e9ea36ff6489c85b88 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt index fb37308cce0124538648c3837e1e802794d7f1ae..f5e991ea42e5ee2723b64574d4598dc8463f1c8c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt index 813470ffc7c87727eb0b958e54806f530399806a..31732214a62524017e39776cdfb9ab629746e8ae 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt index e251ac18e511b58a49816126d9941b98e4f91088..422eddf10db6763e10405dba5537ca161d1b8994 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.BatchNormalization" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt index 2f5e65a0c58eb82c43b013f9c2fbffa4e813c4d2..9053a37916314198842bc21b0608a9b69a64c264 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Bidirectional" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -73,6 +74,10 @@ tf_class { name: "scope_name" mtype: "" } + member { + name: "trainable" + mtype: "" + } member { name: "trainable_variables" mtype: "" @@ -159,7 +164,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" @@ -175,7 +180,7 @@ tf_class { } member_method { name: "get_updates_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_weights" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt index ff08def0a08e5201bc01d61be3f2d66d712c384b..3d536d2182fc4480a2ee5fba177543ca21fbd5ac 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Concatenate" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt index 6db22ca0320519fd9c101456c9c9c0e26a9a11e0..6a7da1aef8db64ad11bb5a5ba357f33eeb99170b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt index 577f206e3510a9995d5d383ac440b4f68ea39fe5..801a0339720919f8b3f6beee0f045d58b2c0a371 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt index 72924c32b43e5edb39938cc0cd909cffefa61be1..13352e264a5305190717bb973a3f2bce4d7f4fff 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt index 16be08d9b2bae8fe1faecf34c4d87ac9b9baf142..f400e4a15c362037e85ac375cee98bb5f6358669 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt index 11e05f884d781166616a9c9a61dacbc8fdae6ae3..b3a9f573b8ba652d2544b21f36f65fe81a6ebb50 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt index 72b72d6b3b1e410dda0b0a529449f0135203fc1b..a9be09c0abd19aeb4df30116ef2befc3948bfbf4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt index ee93247f63ed700dc6058041bd0ea4ff5c879078..be1ef5eb928d16cc6bf78c289aa20d815c728b23 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt index e5023287e5f38553f3553a37b5a908790072b5c7..30034f7eaf6d9073695353e5c8d9ead0cc8de7cc 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt index ba38cb7121c9d312e7ba9d7147bdc67673d1ad2e..189b38054c004facfeeff8ad2ae87848b89040f2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt index 58724a1e1661609ef3c000c7ca1dfe9b3235acff..a76d85c629c1fe620dafd62a0f0e05e9009109e2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt index 98d52c430c659d0fc3e9299f7bede9190dad2fcf..782195d4ad5883d8c0ea6a657cc10258f2080a55 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt index 33b6ebe1af731f66f88a9493502f69049ab34b42..2cb7a39ea595e1ff699b96554cb135377d20a488 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt index 4b241ebb0f68c270a9448b02138d44f82211f418..80803306992bba3b601824a93cb3086ef3947369 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt index 1856a9ee21347ed6ca3dd592517eb644e205a5b7..678f40bbc23db15ff7c1138169478fb4412a449d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt index a8c37af31f649d28ca2ab7614178f2dee58c13fc..fac826109b6a32305ece86c4990f08afe2236ce8 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dense" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt index 07d3f023e54105c606b198c05750ffa78ee5d0c8..285d544af2d69d564afdec748598b39b6b95670f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dot" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt index e2e21b5f123f63fa38cb0e344be9a12fc091f20b..b77976974cccb96fc2373c093d2bdf279560c46f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dropout" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt index 92b9760d53e35d3e5066a730bb5cbda45492cc64..b07714d3f2d158496e0482f8611e55ea0fb0fd51 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ELU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt index 83c528b40117222ac2b3e85ad338459948d0aa8c..e67d4ddfc47077d62319ab097e5333a373cbfc80 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Embedding" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt index 73609752886c8c57a78f6bc02cc46d2c7ff6e996..b2a668e5a88d312656f48ddd0e9f7aa9f6306991 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Flatten" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt index b329f1c46bb07ab7684dec6aaf45a20b98c27ed9..1fd3febad26df16576dedca1df7560bf230c08ec 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GRUCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt index d0f6d2a14f936c47ef78f7bf819c13facea2efcb..f5f41d879dcb840551c00a7272bbcfbe51dbee89 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GRU" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" @@ -227,7 +228,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt index 57596badf1881950270fa6d3c074afb65daaa8eb..f4f1a5d51c5d5689918af4facf907f79d9ca71ec 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GaussianDropout" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt index 3829353cc3c195a750ad862707c5c8563e203fba..e502df5e177d422403d0643c18a9588afb9d9713 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GaussianNoise" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt index e53e78a977b32eaf2e31867044aedd39ab2dd34f..9c8d5bfcd8966384230e7d5cdcc1cac53a0eab9a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt index 48fcd1044e06b2fe61aadb6c3675ce82197ff003..8dd65f1f248daaf120780f19050c45d297b7902e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt index 66c06ed47289eb2d83d97778a7b13dab821722d2..5e30571cc730ee23767a044036b590460deec00b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt index 4f2420f74ab3069952e4a44bf61e5e12b3e80ea3..ba90fa454696d1cb4e77d80a2dc77ff65def4714 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt index 7912a6d933b851521358e0246d04688da410b909..8823857758307c208527b144c0cc73b566f2f115 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt index d5b2d2c274ad97071497045271c0a595f8e0e062..500ced852ba6b19502769ba9052f2e364af7e283 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt index d88ff17eb6df7bbba7d3af4344fc8ddc367ae44c..cf2717ed46b56e639fb774c1e922648e1653ec0d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt index c8cc5a0ddfdd54cbb47de922591a9842abf63396..a86ff1a46997f19b11e6ef03be432b45687a2df2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt index 7956c5a340d963cfd5976e8af56da222848a164a..e01cc7c1b09ad6a40380613d54b771c6a1c89c1c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt index 0a7e16413dfbd80d448eb1bad5771915475d96b2..259c1fb37c787f5318570b7aca6935d2f0ed997f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt index 6c8a58a996f5313ea48e395e7e443a7c21f198ee..0c41bf97f763f1e40e8fac714709ccac1483a00b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt index 7678ce8aab63fcfa76c0ac61346a723c1dfe1ee7..bec8817aa393ba2d8a6410408938402366cbb01d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt index d46fd41a3f33002a9bbe755851278c9729ccd1d1..17be86222901c0f5a9a18c0e5f1c5bcac6c06a17 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt @@ -1,9 +1,9 @@ path: "tensorflow.keras.layers.InputLayer" tf_class { - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt index 3b171b137af699c9608494a17c5651b439fe4545..6d2a8c56196d9b3c80f570c7f1d3ac803253fff6 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LSTMCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt index 0036d6805bb67fe20a1373927f15d8f564bdbe1d..490b5b618c65e28f1ae2e01e8d35e7f3973cc180 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.LSTM" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" @@ -231,7 +232,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt index ca0144929942f7024a4e8bac5552bf0547ceb56d..21a65b838af35e2f540eacab823513e7bf54b434 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Lambda" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt index c52ad727545c0bf4f199714d71180eac3f1bf62a..127b04738e70c11b2dc1071cf174cf5de23c5133 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt @@ -1,7 +1,8 @@ path: "tensorflow.keras.layers.Layer" tf_class { - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt index 8134fb738683b79764662d9ea7f721fe04751162..87e49f2ed5b5d73aee5e9aa2511485b1f3f4bcd9 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LeakyReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt index c5d452300947d7f74e7458e2a04bfdfabb1c1da2..1aa3aad3246b83931a47e69a4aa76fdf2b5aee22 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LocallyConnected1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt index bcbed9241b525a953c8b499197facaefebe8cc44..5e9dc7d4774c651a186a4e320d0cfd088e87b6b3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LocallyConnected2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt index 244e79b4ffe60ddd6aa56d2780d80dfd66c494a9..0d101e5b68cdb2cdf24ed472c724cfc885e3d95d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Masking" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt index 56cbf5df785ef0e2614ea7e9e6cfe1335e148eec..c85cd49ac8ce2c1fc0759671865b7174cd1c1480 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt index 33c2d30e86f9cdc3fb9f4f498bfc2c94497fe2dd..4f59e330c92f96101c65a9a24f66196e84587ccb 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt index 94f91059b7a1e291c38fe0045accc6c03f226603..c0ea0eb0505d20e70d641f2a646a060d7dbfabda 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt index 247230a6d68b8ea93a30a2f5846d8baaa78cb13e..ca37ae51314516ae67c7725eb2ccd3d25154e2ac 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt index 8d61b67e7ce9564d31b0bd904a58540d19c89172..3ede2378347f5eddb0e8fae775a0200ea484d3f8 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt index ad2e30802006e934730e5c75247e958329f7121c..d87e25a7ba8e7cce615431723b53a0106c2b5279 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt index ff0db15f190675d533c50c277eb1cb60e0b95e55..e4df7b48ae6b41400375920a48ef8577bb69376e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Maximum" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt index 1d3f33f04516345ee32f16befe0d7200d2cdad00..6bf7c77743c31b6d74df35d827e9d5bc9a25d303 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Multiply" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt index c86bc49b22a8cc3e004a77f4a21594aacb2c665a..c14be132b7e406c99841576be8d8fa9ab99aa816 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.PReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt index 2043e1a1263f0f0745b7c6446cc670fd6b0f0000..72ffbceae01da900778dba1ec14e646aa17b39e5 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Permute" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt index b29f65d79dc0ffca176a8f2905df9474841f572e..d3e780c8b22ed580f61ffc3d9b2bad7278391402 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.RNN" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -162,7 +163,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt index 4b0e98520a0dd86c085fa7345af445e1ae253d3b..a27980a9d17397e558a4b732e3dc332a0c1e8432 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.RepeatVector" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt index 34bc71af8a26ff6e4d7c81a3877751df5209906f..67f991276c6908ff54fd516e84533542a5f60528 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Reshape" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt index dd67b76523cc50409516e29f963f59d039455bfd..fccea5e8af5ab81e712669ff1b2567d8bde8607e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt index 5d898fb2bd86b39cb8fab755382bb96cce231fa6..d20663bdb0bc2eea323d35b1e3d4d27122f50472 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt index bf62c095e7cc3fbeac95919a0f9fdc545efd3d25..889fa0a1b58bbd3babd293b7b1b45915a9ee3ca4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt index c758d87993b3acba88a13c7bc9eaeee929a22652..c850f3fedc814b20f0f95cc3cf4fd5c973446b5b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt index 6e3cde3e3eaba4f9985411d66a220f7cdd4ee7ad..526d88ccba60eb25c68432e5baa03fd3a878f718 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.SimpleRNNCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt index b875898a8196e0359297f77cb10c1d8177f05d45..7fddae34472411f49d42b4d65d12034d056ec818 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.SimpleRNN" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" @@ -219,7 +220,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt index ee4b2fa39ed34a544ee800e9370e4f34c4a17041..5b9b62fc970238e49e6d4849285606d0a7908b23 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Softmax" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt index e4727072e375b9fc4dc99a1536eaaf3df5415369..769da30999993fad05ae0f7c04e256e6cf01a774 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt index c5ff7043115ccdd3bc4a1147790b20feda410f65..fca2e42a1519fcf3a9f0ec996c50b148b2df05fd 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt index 476a7f362cf88e234e964f6f6645ee4ed0cbaff8..36e8de09a967c5940bf8078234f5980a78ec8009 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt index db9f90caef7d6192ec7b68ddffd784a83ec0ac69..a96f16fae99af9c30959d228202055e9aebfaf58 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.StackedRNNCells" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -122,7 +123,7 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'states\'], varargs=None, keywords=kwargs, defaults=None" + argspec: "args=[\'self\', \'inputs\', \'states\', \'constants\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " } member_method { name: "compute_mask" @@ -158,7 +159,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt index ef31c5443efa0c0e5a7a2e0a422d2a9c9c49baaf..e1cbd0e150ed890ae57c1725249d1340fc2cb663 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ThresholdedReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt index 2a7059d9aa7ac12d8130c30622bc5f190562695c..f0d35728fb1c42d563ff0598dd84da51a766a764 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.TimeDistributed" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -69,6 +70,10 @@ tf_class { name: "scope_name" mtype: "" } + member { + name: "trainable" + mtype: "" + } member { name: "trainable_variables" mtype: "" @@ -155,7 +160,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" @@ -171,7 +176,7 @@ tf_class { } member_method { name: "get_updates_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_weights" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt index a81b83be49e0073f242efc6890e419b4fe172ab2..74efaea6ddb22ec2fe9d41558978c183b0e06671 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt index 5403279d45ec7b93bae7907b891c659a043e96d0..dc5bd5fd5319f9bbd601a3c4083ae566b47e1aaa 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt index 96c337caf28d43fabd0b90df016f4e8ab0c408db..e01ccfb74aead591f1018cdcbb1c888767ecdb20 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt index 58bffa087521517fe7f0b5dcd6cae0a8b39a4e25..7e6f90f7623677244865ac285c134dc79f7b9b69 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Wrapper" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -68,6 +69,10 @@ tf_class { name: "scope_name" mtype: "" } + member { + name: "trainable" + mtype: "" + } member { name: "trainable_variables" mtype: "" @@ -154,7 +159,7 @@ tf_class { } member_method { name: "get_losses_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_output_at" @@ -170,7 +175,7 @@ tf_class { } member_method { name: "get_updates_for" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "get_weights" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt index b81a4b1c50b22f13eacb521cfc8bc288bd40c81f..4d0d402dad442ccf52267f5ce40b05400afbfbc7 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt index 1a26f2f3c9bbaa2aa567e76e1aafe14805ecff38..b353a529bcf8e543d334fee57fca26ebc83036a4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt index 310277fe67433fd870ae3d907984f402576925b2..9fe1256e616dbca4f35101df160dc55bc68bfa8a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt index de285c1aab197ea5cae9c94048a5131f8463ebde..42729e4237685638d38301cece6e93383ddfffba 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt @@ -22,7 +22,7 @@ tf_module { } member_method { name: "deserialize" - argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt index 0b816b58631d12471c2e9db96fc5395796d96ddf..8ccf15f9ab0fcfa59907ff05a962a84d3d86ccb4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt @@ -1,10 +1,10 @@ path: "tensorflow.keras.models.Model" tf_class { is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -38,6 +38,10 @@ tf_class { name: "input_spec" mtype: "" } + member { + name: "layers" + mtype: "" + } member { name: "losses" mtype: "" @@ -108,11 +112,11 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'inputs\', \'outputs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_update" @@ -120,7 +124,7 @@ tf_class { } member_method { name: "add_variable" - argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " } member_method { name: "add_weight" @@ -136,11 +140,11 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " } member_method { name: "compile" - argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " } member_method { name: "compute_mask" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt index 7c1bfcb22558ec3a64c63ebbf0466f9114ef68ee..102eb3220334516e0051f952353920f229f4ff20 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt @@ -1,11 +1,11 @@ path: "tensorflow.keras.models.Sequential" tf_class { - is_instance: "" + is_instance: "" is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" @@ -39,6 +39,10 @@ tf_class { name: "input_spec" mtype: "" } + member { + name: "layers" + mtype: "" + } member { name: "losses" mtype: "" @@ -125,7 +129,7 @@ tf_class { } member_method { name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" } member_method { name: "add_update" @@ -133,7 +137,7 @@ tf_class { } member_method { name: "add_variable" - argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " } member_method { name: "add_weight" @@ -149,7 +153,7 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None" } member_method { name: "compile" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt index 3adc6b6faa6f62330f9ac3d621f29adfc380a09d..16e1cbe650e1662f8694fd7137ad20a48a90675b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt @@ -4,7 +4,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'target\', \'width\', \'verbose\', \'interval\'], varargs=None, keywords=None, defaults=[\'30\', \'1\', \'0.05\'], " + argspec: "args=[\'self\', \'target\', \'width\', \'verbose\', \'interval\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'30\', \'1\', \'0.05\', \'None\'], " } member_method { name: "add" @@ -12,6 +12,6 @@ tf_class { } member_method { name: "update" - argspec: "args=[\'self\', \'current\', \'values\', \'force\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + argspec: "args=[\'self\', \'current\', \'values\'], varargs=None, keywords=None, defaults=[\'None\'], " } } diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt index de81206bc8b25046cd48c79ff8f154041c0e0cb0..1c4f550d7f05b8be33326cb39d7a5f3bf663f5e6 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt index 72d5496464210efd9e423996dfb274dd9564f761..d2db0952693f2989e6a9e8748a254eb4db483206 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt index 595e77ff9f8b64b6606fb075f3edf2281b4c3c1f..34d9a9df281c09a2e2030daf74a2ceb8066085bb 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt index 0c4aa2ff2612269727026141574726ad6df5cdbd..21ad0efecf88c42a3a679910ddfe095585a7933a 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.BatchNormalization" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt index 5f576d0189309442dc4cea3d3617ab3144420165..ed38747c7671a267bb640ecb96a4c5fcc46c5edf 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt index 675a7c76e569d3163ecd2c547841b4c36078b21d..ff453c6059477c20528fc768d93c65d208cdfc4a 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt index eaabbf6aab172aea5c51f8071076890bb6b5bcf7..5583bd22dce18b0a0593b73bde509818b63b3f29 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt index 838e070d79d2d7cfbd631f1a5e9960412cfdae5a..63f0c32a7c8f7e530c76c64fa619102bc12f9ad9 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt index 4bd8cfc1a48cd839e2ffa54d0d0ca863060406d8..b77726252ccca30a7c6555fb569eb65b69e34998 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt index 57eccb03ffeb90652b019b5ce8a519797e4a3a3d..92db9f6dcd2f77c4253eb77df4a26fb632b2a766 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Dense" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt index a1ec00eeeaa98a6199e29b187b0760ddc92db09d..80fa846a24c9162d8521bdb4f098b9cd8e34aedb 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Dropout" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt index a06943d51a52f1951056136445b0d5786d801b5b..f63213b3dde40aa54b165c1c269c26fd2cd9e3b4 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Flatten" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt index 24fda0c87ed0aeabd0fd4a16bb2efab444f8cd8a..4e45b2d513bb72bb47433d72c310d6a34fbc0c01 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.layers.Layer" tf_class { is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt index 4c3d00e0e1ddfe95c56f9ebc7c5d609c79dd44d4..19ec33fce775caa634e71e2295ac945a6f70ade9 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt index f7e2017b0c9438130f1cfb2431eb73ca4d3103c5..76180c333a21c592a3b53bb445df9b12d3596552 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt index 84780926a38ff811a5ab35fadfac690a6dbbbbe2..ded75c8ff09efc6746ddd2284f53d2c021cc473c 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt index 05799ecfc9fdb9ff44620a67dcdbdc4426fddced..3dbfa5453f8e0ebb02429df9c4cbdf98de6b8ced 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt index c2aeb35c4648bcce22ca73c838a85803a6b9cedf..ab171df1d1650e19836018f3316e6919f6d36def 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.pbtxt index 59134f84891ad5518dcb5331ce04475482c8b59e..df74c32e1f10cc7540ef105adef6be681e93d089 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.pbtxt @@ -76,10 +76,6 @@ tf_module { name: "SeparableConv2D" mtype: "" } - member_method { - name: "Input" - argspec: "args=[\'shape\', \'batch_size\', \'name\', \'dtype\', \'sparse\', \'tensor\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"\", \'False\', \'None\'], " - } member_method { name: "average_pooling1d" argspec: "args=[\'inputs\', \'pool_size\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'valid\', \'channels_last\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt index a2e728f94b41341b1a7c2a06d2c92d490f6eeb87..9c71a24d0500e2091e0ae94cc4dd7ed6b788a54f 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.nn.rnn_cell.BasicLSTMCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt index 4211faa1ec615da8938d9a858a19a9e9a76378cd..9e19f96b7452616956fb7fd3ca62d8f4b25a2122 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.nn.rnn_cell.BasicRNNCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt index 0d253e5dd233d6d2b6ad0070a463c283a8769dab..7540aa62861895a7c41840476d4edb79785a77a9 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt index 97edf245f6fbed393a6fb8dbf1e83649e9ac4b4e..fc1ff386690f9c7acb11d4cc0770e394f78350ad 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt index 06fdc638c82b0d19b03857e33f083a94b7fd133b..751122cfff3bf9c55dd9fa264fdf2e1960940724 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.nn.rnn_cell.GRUCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt index ef48cff0c329a7af5009d31fda429cf649c24261..4b6313f395fd8fd4ec2af78365117620263e7a55 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.nn.rnn_cell.LSTMCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt index 9a6c73a079884b8ab92be1c9e89b2a9f34aad851..00e8c71140596ecea237ce05a09feff1fbb49001 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt index 27488f8e73f20456fae911511ecd2e41a60da351..3852f90dd6c4a254e20e789bdeb7796d61cef6bc 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.nn.rnn_cell.RNNCell" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt index 3310836ed26387718115c2454300b9edfe930451..8f3f0f7506ef49014b31cd4bc04f1cb1e0d696fc 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index e8890e9cc0a3c659b3f5f377136a2ca616d55993..123d67fd9b3abcb3845c347adb7630116b06c9b1 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -892,6 +892,10 @@ tf_module { name: "cumsum" argspec: "args=[\'x\', \'axis\', \'exclusive\', \'reverse\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'None\'], " } + member_method { + name: "custom_gradient" + argspec: "args=[\'f\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "decode_base64" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -964,6 +968,10 @@ tf_module { name: "einsum" argspec: "args=[\'equation\'], varargs=inputs, keywords=kwargs, defaults=None" } + member_method { + name: "enable_eager_execution" + argspec: "args=[\'config\', \'device_policy\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "encode_base64" argspec: "args=[\'input\', \'pad\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " @@ -980,6 +988,10 @@ tf_module { name: "erfc" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "executing_eagerly" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } member_method { name: "exp" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -1600,6 +1612,10 @@ tf_module { name: "reduce_sum" argspec: "args=[\'input_tensor\', \'axis\', \'keepdims\', \'name\', \'reduction_indices\', \'keep_dims\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " } + member_method { + name: "regex_replace" + argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } member_method { name: "register_tensor_conversion_function" argspec: "args=[\'base_type\', \'conversion_func\', \'priority\'], varargs=None, keywords=None, defaults=[\'100\'], " @@ -1988,6 +2004,10 @@ tf_module { name: "tile" argspec: "args=[\'input\', \'multiples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "timestamp" + argspec: "args=[\'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "to_bfloat16" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToBFloat16\'], " @@ -2056,6 +2076,18 @@ tf_module { name: "unsorted_segment_max" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "unsorted_segment_min" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_prod" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_sqrt_n" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "unsorted_segment_sum" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt index 863beaea4cf05a67e572c97b556bc1eb598d9ced..16bfbf20d5227d6308248bebcb62f32a2df8ef41 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.AdadeltaOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt index 0a7aa9b6bc14c95e74ab05a3aeb71b770a918f60..61cde9181c2367153b7b289b41bd932482bb92fd 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.AdagradDAOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt index 83724fea55d005e9476801feb1bf58cb004aa141..0a998c1afe4fff6e215360bc1cf8fc135754223c 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.AdagradOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt index e285b27a0531e00d27941fe451570a5056995c17..cc5954152577796ee7a5a6e1cedc873647d64f7c 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.AdamOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt index fc28577d6ed1328ae85970cf22cc458b7cf54344..1add3a902122341a706c38b19ea6ff5882c26445 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.FtrlOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt index bf3c1d81f877e3a8a7e24d5455e9c5bf6a41f764..ef5bbd6ace29abb5c73516176fcc7594a58d493a 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.GradientDescentOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt index a640c8d2c6366951cbba6a15d2000d9369cbbdbf..3d6e87f5eb44de9d6ce1bdd25a54b8df9020cc03 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.MomentumOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt index 6b33c236a35f09422a42a17b3ffddf5ba7b1595f..e73861ff7cb2d90d8efac72cdd7de3b27395f29e 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.train.Optimizer" tf_class { is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt index d23fcaed7b4cee397dcf9c51eb3b521e5461c9e5..301b35b199c87890a0aef4139eb06253592ce0c4 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.ProximalAdagradOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt index b6c03e71d9ffb50bd6377b489fcc444453bd9752..8815befa936a85522011111a4a6270d22cbc25ae 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.ProximalGradientDescentOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt index 4a82db11cb8d85bd0c44135ecaf507c62fae41a1..e9819683ba5ec1bcacb3cdbcb2d787e866a77b6f 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.RMSPropOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt index e9131bf544f2e7f08928f46d2be06a00259690be..3db96aff876b88b80b647570cf68b1ebc0b2da3b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.train.SyncReplicasOptimizer" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "GATE_GRAPH" diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py index c1e09cc531ed8e8995e3e73b87e96b72fba6c038..2a784973e1098bb1f67eb1b002b7b006f69670ff 100644 --- a/tensorflow/tools/api/tests/api_compatibility_test.py +++ b/tensorflow/tools/api/tests/api_compatibility_test.py @@ -165,7 +165,7 @@ class ApiCompatibilityTest(test.TestCase): logging.error('%d differences found between API and golden.', diff_count) messages = verbose_diffs if verbose else diffs for i in range(diff_count): - logging.error('Issue %d\t: %s', i + 1, messages[i]) + print('Issue %d\t: %s' % (i + 1, messages[i]), file=sys.stderr) if update_goldens: # Write files if requested. diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake index ec90c83aacd068e8f9c16e5be8eb6e1cef098ea6..d5dea4f3e41841aed5aeac02fcca850dbfdfaeb3 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cmake +++ b/tensorflow/tools/ci_build/Dockerfile.cmake @@ -23,11 +23,12 @@ RUN /install/install_deb_packages.sh RUN apt-get update RUN apt-get install -y --no-install-recommends python-pip +RUN pip install --upgrade wheel RUN pip install --upgrade astor RUN pip install --upgrade gast RUN pip install --upgrade numpy RUN pip install --upgrade termcolor # Install golang -RUN add-apt-repository -y ppa:ubuntu-lxc/lxd-stable -RUN apt-get install -y golang +RUN apt-get install -t xenial-backports -y golang-1.9 +ENV PATH=${PATH}:/usr/lib/go-1.9/bin diff --git a/tensorflow/tools/ci_build/Dockerfile.rbe.gpu b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu new file mode 100644 index 0000000000000000000000000000000000000000..24ff4765a619701cd614414d2b06f7fa4ce7d8c0 --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu @@ -0,0 +1,26 @@ +FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 + +LABEL maintainer="Nick Lopez " + +# In the Ubuntu 16.04 images, cudnn is placed in system paths. Move them to +# /usr/local/cuda +RUN cp -P /usr/include/cudnn.h /usr/local/cuda/include +RUN cp -P /usr/lib/x86_64-linux-gnu/libcudnn* /usr/local/cuda/lib64 + +# Copy and run the install scripts. +COPY install/*.sh /install/ +ARG DEBIAN_FRONTEND=noninteractive +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa && \ + add-apt-repository -y ppa:george-edison55/cmake-3.x +RUN /install/install_deb_packages.sh +RUN /install/install_pip_packages.sh +RUN /install/install_golang.sh + +# Install clang from pre-built package +RUN cd /tmp && \ + wget https://storage.googleapis.com/clang-builds-stable/clang-ubuntu16_04/clang_r323528.tar.gz && \ + echo "26752d9f5785df07193fac8316ba5d5ba3bec36d970c29a1577360848818ac74 clang_r323528.tar.gz" | sha256sum -c && \ + tar -C /usr/local -xf clang_r323528.tar.gz && \ + rm clang_r323528.tar.gz + diff --git a/tensorflow/tools/ci_build/builds/test_tutorials.sh b/tensorflow/tools/ci_build/builds/test_tutorials.sh index 67e5af556405a5c659000a07a79a6bd9a1d1e542..db335f14ca4f88ade7a540ffab7ed9de67f1248e 100755 --- a/tensorflow/tools/ci_build/builds/test_tutorials.sh +++ b/tensorflow/tools/ci_build/builds/test_tutorials.sh @@ -277,17 +277,6 @@ test_ptb_word_lm() { fi } - -# ----------------------------------------------------------- -# translate_test -test_translate_test() { - LOG_FILE=$1 - - run_in_directory "${TEST_DIR}" "${LOG_FILE}" \ - "${TF_MODELS_DIR}/tutorials/rnn/translate/translate.py" --self_test=True -} - - # Run the tutorial tests test_runner "tutorial test-on-install" \ "${TUT_TESTS}" "${TF_BUILD_TUT_TEST_BLACKLIST}" "${LOGS_DIR}" diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user index 5817716c8dec37dfdfd50defb4b20b1deafced70..d4bf546d401d058bd205a70c147615c8efc4f4ba 100755 --- a/tensorflow/tools/ci_build/builds/with_the_same_user +++ b/tensorflow/tools/ci_build/builds/with_the_same_user @@ -36,8 +36,13 @@ else rm /this_is_writable_file_system fi +if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then + ADDUSER_OPTS="--force-badname" +fi + getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" -getent passwd "${CI_BUILD_UID}" || adduser --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ +getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \ + --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \ --disabled-password --home "${CI_BUILD_HOME}" --quiet "${CI_BUILD_USER}" usermod -a -G sudo "${CI_BUILD_USER}" diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index fd5d0058441c366be76defdde5dac73405837262..aeac085d30aef746366192361f249eb01f95e8da 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -185,7 +185,9 @@ do_pylint() { # C0330 bad-continuation # C0301 line-too-long # C0326 bad-whitespace - grep -E '(\[E|\[W0311|\[W0312|\[C0330|\[C0301|\[C0326)' ${OUTPUT_FILE} > ${ERRORS_FILE} + # W0611 unused-import + # W0622 redefined-builtin + grep -E '(\[E|\[W0311|\[W0312|\[C0330|\[C0301|\[C0326|\[W0611|\[W0622)' ${OUTPUT_FILE} > ${ERRORS_FILE} N_ERRORS=0 while read -r LINE; do diff --git a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh index cfeaebdbf57c01fef7cd81dae76217429336d0ff..d0816c92b7308a1079579e605ee9af491a0533fb 100755 --- a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh +++ b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh @@ -54,3 +54,6 @@ for i in `seq 0 $((TF_GPU_COUNT-1))`; do fi done +echo "Cannot find a free GPU to run the test $* on, exiting with failure..." +exit 1 + diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh index cf8737c2d8c746b6ad6c436745193290e31326ea..3e27a94cf2bf3110ac181d6ef5a57366be17255f 100755 --- a/tensorflow/tools/ci_build/install/install_bazel.sh +++ b/tensorflow/tools/ci_build/install/install_bazel.sh @@ -15,7 +15,7 @@ # ============================================================================== # Select bazel version. -BAZEL_VERSION="0.8.0" +BAZEL_VERSION="0.11.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh b/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh index 509ee38ec4fd584037f8e43726c01391430c1817..5c5a36139f50e85e70ce4bff5ca8054f7570b0f5 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh @@ -31,7 +31,7 @@ export CC_OPT_FLAGS='-mavx' export PYTHON_BIN_PATH=$(which python2) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 \ --test_size_filters=small,medium --config=opt \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh index 05547136704394ed9262f566a2bfb4160b73c7fd..338066131b5d4511ae9f0646a1269b182cf8e1fa 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh @@ -31,7 +31,7 @@ export CC_OPT_FLAGS='-mavx' export PYTHON_BIN_PATH=$(which python2) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 --config=opt \ --test_size_filters=small,medium \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh index 8f839ca110e5bbeba6fb7f0baaeab2fe6f126319..920a261ae3c8d68ec0b0d311fd361e3843eebd86 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh @@ -30,7 +30,7 @@ export TF_NEED_CUDA=0 export PYTHON_BIN_PATH=$(which python3) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 \ --test_size_filters=small,medium \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py index 347d0769a92cc767f2e263fce0e21d7d0bc8e586..52a0da9a14847e863d92fee9ef7e63e4af0cf068 100755 --- a/tensorflow/tools/ci_build/update_version.py +++ b/tensorflow/tools/ci_build/update_version.py @@ -261,14 +261,12 @@ def major_minor_change(old_version, new_version): def update_dockerfiles(old_version, new_version): """Update dockerfiles if there was a major change.""" if major_minor_change(old_version, new_version): - old_r_major_minor = r"r%s\.%s" % (old_version.major, old_version.minor) - old_r_major_minor_string = old_r_major_minor.replace("\\", "") - r_major_minor = r"r%s\.%s" % (new_version.major, new_version.minor) - r_major_minor_string = r_major_minor.replace("\\", "") + old_r_major_minor = "r%s.%s" % (old_version.major, old_version.minor) + r_major_minor = "r%s.%s" % (new_version.major, new_version.minor) print("Detected Major.Minor change.") print("Updating pattern %s to %s in additional files" - % (old_r_major_minor_string, r_major_minor_string)) + % (old_r_major_minor, r_major_minor)) # Update dockerfiles replace_string_in_line(old_r_major_minor, r_major_minor, DEVEL_DOCKERFILE) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 8b8ba31a0dda88ad3c43330e0208a9fa6a7d0276..40189a6d1b1388092dc397251f4b581726995a77 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -65,4 +65,5 @@ bazel test -c opt $BUILD_OPTS -k --test_output=errors \ --define=no_tensorflow_py_deps=true --test_lang_filters=py \ --test_tag_filters=-no_pip,-no_windows,-no_oss \ --build_tag_filters=-no_pip,-no_windows,-no_oss --build_tests_only \ - //${PY_TEST_DIR}/tensorflow/python/... + //${PY_TEST_DIR}/tensorflow/python/... \ + //${PY_TEST_DIR}/tensorflow/contrib/... diff --git a/tensorflow/tools/compatibility/tf_upgrade_test.py b/tensorflow/tools/compatibility/tf_upgrade_test.py index a495f9883b284869d043441d1cfecca01296eda3..3d02eacba6e7a91e6d3c88e8297306de9782f4bf 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_test.py @@ -114,7 +114,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): self.assertEqual(errors, ["test.py:1: tf.reverse requires manual check."]) def testListComprehension(self): - def _test(input, output): + def _test(input, output): # pylint: disable=redefined-builtin _, unused_report, errors, new_text = self._upgrade(input) self.assertEqual(new_text, output) _test("tf.concat(0, \t[x for x in y])\n", diff --git a/tensorflow/tools/def_file_filter/BUILD b/tensorflow/tools/def_file_filter/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tensorflow/tools/def_file_filter/BUILD.tpl b/tensorflow/tools/def_file_filter/BUILD.tpl new file mode 100644 index 0000000000000000000000000000000000000000..3cb72f49797d8014e4df3f44155c967ab0e2f9b3 --- /dev/null +++ b/tensorflow/tools/def_file_filter/BUILD.tpl @@ -0,0 +1,15 @@ +# Description: +# Tools for filtering DEF file for TensorFlow on Windows +# +# On Windows, we use a DEF file generated by Bazel to export +# symbols from the tensorflow dynamic library(_pywrap_tensorflow.dll). +# The maximum number of symbols that can be exported per DLL is 64K, +# so we have to filter some useless symbols through this python script. + +package(default_visibility = ["//visibility:public"]) + +py_binary( + name = "def_file_filter", + srcs = ["def_file_filter.py"], + srcs_version = "PY2AND3", +) diff --git a/tensorflow/tools/def_file_filter/def_file_filter.py.tpl b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl new file mode 100644 index 0000000000000000000000000000000000000000..8bdc03eb0f19fd6daae826727f429bc1255f0eca --- /dev/null +++ b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl @@ -0,0 +1,168 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""def_file_filter.py - tool to filter a windows def file. + +The def file can be used to export symbols from the tensorflow dll to enable +tf.load_library(). + +Because the linker allows only 64K symbols to be exported per dll +we filter the symbols down to the essentials. The regular expressions +we use for this are specific to tensorflow. + +TODO: this works fine but there is an issue with exporting +'const char * const' and importing it from a user_ops. The problem is +on the importing end and using __declspec(dllimport) works around it. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import io +import os +import re +import subprocess +import sys +import tempfile + +# External tools we use that come with visual studio sdk +UNDNAME = "%{undname_bin_path}" + +# Exclude if matched +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") + +# Include if matched before exclude +INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" + r"google::protobuf::internal::ArenaImpl::AllocateAligned|" # for contrib/data/_prefetching_ops + r"google::protobuf::internal::ArenaImpl::AddCleanup|" # for contrib/data/_prefetching_ops + r"google::protobuf::Arena::OnArenaAllocation|" # for contrib/data/_prefetching_ops + r"tensorflow::internal::LogMessage|" + r"tensorflow::internal::LogString|" + r"tensorflow::internal::CheckOpMessageBuilder|" + r"tensorflow::internal::MakeCheckOpValueString|" + r"tensorflow::internal::PickUnusedPortOrDie|" + r"tensorflow::internal::ValidateDevice|" + r"tensorflow::ops::internal::Enter|" + r"tensorflow::strings::internal::AppendPieces|" + r"tensorflow::strings::internal::CatPieces|" + r"tensorflow::io::internal::JoinPathImpl") + +# Include if matched after exclude +INCLUDE_RE = re.compile(r"^(TF_\w*)$|" + r"^(TFE_\w*)$|" + r"nsync::|" + r"tensorflow::|" + r"functor::|" + r"perftools::gputools") + +# We want to identify data members explicitly in the DEF file, so that no one +# can implicitly link against the DLL if they use one of the variables exported +# from the DLL and the header they use does not decorate the symbol with +# __declspec(dllimport). It is easier to detect what a data symbol does +# NOT look like, so doing it with the below regex. +DATA_EXCLUDE_RE = re.compile(r"[)(]|" + r"vftable|" + r"vbtable|" + r"vcall|" + r"RTTI|" + r"protobuf::internal::ExplicitlyConstructed") + +def get_args(): + """Parse command line.""" + filename_list = lambda x: x.split(";") + parser = argparse.ArgumentParser() + parser.add_argument("--input", type=filename_list, + help="paths to input def file", + required=True) + parser.add_argument("--output", help="output deffile", required=True) + parser.add_argument("--target", help="name of the target", required=True) + args = parser.parse_args() + return args + + +def main(): + """main.""" + args = get_args() + + # Pipe dumpbin to extract all linkable symbols from libs. + # Good symbols are collected in candidates and also written to + # a temp file. + candidates = [] + tmpfile = tempfile.NamedTemporaryFile(mode="w", delete=False) + for def_file_path in args.input: + def_file = open(def_file_path, 'r') + for line in def_file: + cols = line.split() + sym = cols[0] + tmpfile.file.write(sym + "\n") + candidates.append(sym) + tmpfile.file.close() + + # Run the symbols through undname to get their undecorated name + # so we can filter on something readable. + with open(args.output, "w") as def_fp: + # track dupes + taken = set() + + # Header for the def file. + def_fp.write("LIBRARY " + args.target + "\n") + def_fp.write("EXPORTS\n") + def_fp.write("\t ??1OpDef@tensorflow@@UEAA@XZ\n") + + # Each symbols returned by undname matches the same position in candidates. + # We compare on undname but use the decorated name from candidates. + dupes = 0 + proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) + for idx, line in enumerate(io.TextIOWrapper(proc.stdout, encoding="utf-8")): + decorated = candidates[idx] + if decorated in taken: + # Symbol is already in output, done. + dupes += 1 + continue + + if not INCLUDEPRE_RE.search(line): + if EXCLUDE_RE.search(line): + continue + if not INCLUDE_RE.search(line): + continue + + if "deleting destructor" in line: + # Some of the symbols convered by INCLUDEPRE_RE export deleting + # destructor symbols, which is a bad idea. + # So we filter out such symbols here. + continue + + if DATA_EXCLUDE_RE.search(line): + def_fp.write("\t" + decorated + "\n") + else: + def_fp.write("\t" + decorated + " DATA\n") + taken.add(decorated) + def_fp.close() + + exit_code = proc.wait() + if exit_code != 0: + print("{} failed, exit={}".format(UNDNAME, exit_code)) + return exit_code + + os.unlink(tmpfile.name) + + print("symbols={}, taken={}, dupes={}" + .format(len(candidates), len(taken), dupes)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl new file mode 100644 index 0000000000000000000000000000000000000000..47539b2423e602bb9771541ae5b01ba76c79f56f --- /dev/null +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -0,0 +1,56 @@ +"""Repository rule for def file filter autoconfiguration. + +This repository reuses Bazel's VC detect mechanism to find undname.exe, +which is a tool used in def_file_filter.py. + +def_file_filter.py is for filtering the DEF file for TensorFlow on Windows. +On Windows, we use a DEF file generated by Bazel to export symbols from the +tensorflow dynamic library(_pywrap_tensorflow.dll). The maximum number of +symbols that can be exported per DLL is 64K, so we have to filter some useless +symbols through this python script. + +`def_file_filter_config` depends on the following environment variables: + * `BAZEL_VC` + * `BAZEL_VS` + * `VS90COMNTOOLS` + * `VS100COMNTOOLS` + * `VS110COMNTOOLS` + * `VS120COMNTOOLS` + * `VS140COMNTOOLS` +""" + +load("@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_vc_path") +load("@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_msvc_tool") +load("@bazel_tools//tools/cpp:lib_cc_configure.bzl", "auto_configure_fail") + +def _def_file_filter_configure_impl(repository_ctx): + if repository_ctx.os.name.lower().find("windows") == -1: + repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") + repository_ctx.file("def_file_filter.py", "") + return + vc_path = find_vc_path(repository_ctx) + if vc_path == "visual-studio-not-found": + auto_configure_fail("Visual C++ build tools not found on your machine") + undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") + + repository_ctx.template( + "def_file_filter.py", + Label("//tensorflow/tools/def_file_filter:def_file_filter.py.tpl"), + { + "%{undname_bin_path}": undname_bin_path, + }) + repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") + + +def_file_filter_configure = repository_rule( + implementation = _def_file_filter_configure_impl, + environ = [ + "BAZEL_VC", + "BAZEL_VS", + "VS90COMNTOOLS", + "VS100COMNTOOLS", + "VS110COMNTOOLS", + "VS120COMNTOOLS", + "VS140COMNTOOLS" + ], +) diff --git a/tensorflow/tools/dist_test/README.md b/tensorflow/tools/dist_test/README.md index c1b1f79bbd4b657768b9bbcab93efa3354774915..228d5ee35d1839c60b51a85bd606c1ba86e46886 100644 --- a/tensorflow/tools/dist_test/README.md +++ b/tensorflow/tools/dist_test/README.md @@ -17,6 +17,14 @@ cesnsu model: ./local_test.sh --model_name CENSUS_WIDENDEEP +You can test specify version of TensorFlow: + +```shell +./local_test.sh ${whl_file_url} +``` + +For example, you can find these TensorFlow python package URLs from [here](https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package) for Ubuntu. + **2) Launch a remote k8s cluster on Google Kubernetes Engine (GKE) and run the test suite on it** diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index 435f9d0dc9c55a3dcfc45e7e46f279b4679a9086..caae7fd5305af9846628eaf00348dd08df4e827f 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -16,12 +16,11 @@ # # Tests distributed TensorFlow on a locally running TF GRPC cluster. # -# This script peforms the following steps: -# 1) Build the docker-in-docker (dind) image capable of running docker and -# Kubernetes (k8s) cluster inside. +# This script performs the following steps: +# 1) Build the docker image capable of running distributed TensorFlow in docker. # 2) Run a container from the aforementioned image and start docker service # in it -# 3) Call a script to launch a k8s TensorFlow GRPC cluster inside the container +# 3) Call a script to launch a distributed TensorFlow GRPC cluster inside the container # and run the distributed test suite. # # Usage: local_test.sh @@ -64,15 +63,9 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" -LOCAL_K8S_CACHE=${HOME}/kubernetes -# Helper function -get_container_id_by_image_name() { - # Get the id of a container by image name - # Usage: get_docker_container_id_by_image_name - - docker ps | grep $1 | awk '{print $1}' -} +# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below +DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" # Parse input arguments LEAVE_CONTAINER_RUNNING=0 @@ -84,7 +77,8 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - die "whl file location is not specified" + WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} + echo "use default whl file location" fi while true; do @@ -121,7 +115,7 @@ DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # Get utility functions source ${DIR}/scripts/utils.sh -# Build docker-in-docker image for local k8s cluster. +# Build docker image for local distributed TensorFlow cluster. NO_CACHE_FLAG="" if [[ ! -z "${TF_DIST_DOCKER_NO_CACHE}" ]] && [[ "${TF_DIST_DOCKER_NO_CACHE}" != "0" ]]; then diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index d16761c3675942838fd2be0ea6e0b7463a3bf249..22c73c3fe13f2cb763295fa25b43e2f82c0e8962 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -57,7 +57,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.8.0 +ENV BAZEL_VERSION 0.11.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 4ef37881bc91aaa58bab031c69b4a96c2a9d8ec1..69ba340f9201266fd2c2f86571e83f6acdcda950 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -66,7 +66,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.8.0 +ENV BAZEL_VERSION 0.11.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docs/generate_1_0.py b/tensorflow/tools/docs/generate_1_0.py index cdc03fdcacf44f7be49e739962b63ba84cf94896..f4384e0ced77718c80d4d146a2d72072588a0541 100644 --- a/tensorflow/tools/docs/generate_1_0.py +++ b/tensorflow/tools/docs/generate_1_0.py @@ -53,7 +53,6 @@ if __name__ == '__main__': 'factorization', 'grid_rnn', 'labeled_tensor', - 'ndlstm', 'quantization', 'session_bundle', 'slim', diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index d9e8069a61e43d032f400611bfef7b621ff2c07c..6185c9ddd7b2c044728daa6f14befee3c8add036 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -218,7 +218,6 @@ def _get_default_do_not_descend_map(): # Block contrib.keras to de-clutter the docs 'keras', 'labeled_tensor', - 'ndlstm', 'quantization', 'session_bundle', 'slim', diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py index 16513d0ee1ad7605e6ccebeccfe67659c8ff3c05..5f2a411baea473c8359ae13b44b04ed28b38d711 100644 --- a/tensorflow/tools/docs/parser.py +++ b/tensorflow/tools/docs/parser.py @@ -111,8 +111,8 @@ SYMBOL_REFERENCE_RE = re.compile( r""" # Start with a literal "@{". @\{ - # Group at least 1 symbol: not "}" or "\n". - ([^}\n]+) + # Group at least 1 symbol, not "}". + ([^}]+) # Followed by a closing "}" \} """, diff --git a/tensorflow/tools/docs/parser_test.py b/tensorflow/tools/docs/parser_test.py index 7d2bf9177a65c0c8b1046e48951cb44ef5b8df73..ae86142f41b101d963ebb4d188ab82509c0d9152 100644 --- a/tensorflow/tools/docs/parser_test.py +++ b/tensorflow/tools/docs/parser_test.py @@ -76,8 +76,9 @@ class ParserTest(googletest.TestCase): pass string = ( - 'A @{tf.reference}, another @{tf.reference}, a member ' - '@{tf.reference.foo}, and a @{tf.third$link `text` with `code` in it}.') + 'A @{tf.reference}, another @{tf.reference$with\nnewline}, a member ' + '@{tf.reference.foo}, and a @{tf.third$link `text` with `code` in ' + 'it}.') duplicate_of = {'tf.third': 'tf.fourth'} index = {'tf.reference': HasOneMember, 'tf.reference.foo': HasOneMember.foo, @@ -93,7 +94,7 @@ class ParserTest(googletest.TestCase): self.assertEqual('A ' 'tf.reference, ' 'another ' - 'tf.reference, ' + 'with\nnewline, ' 'a member ' 'tf.reference.foo, ' 'and a link ' diff --git a/tensorflow/tools/git/gen_git_source.py b/tensorflow/tools/git/gen_git_source.py index 3630dbd740e981971bdc9ff45b756b45095d437d..cbcdbf5b807a585865e2e3f19291e55388d55cb1 100755 --- a/tensorflow/tools/git/gen_git_source.py +++ b/tensorflow/tools/git/gen_git_source.py @@ -114,6 +114,13 @@ def configure(src_base_path, gen_path, debug=False): for target, src in link_map.items(): if src is None: open(os.path.join(gen_path, target), "w").write("") + elif not os.path.exists(src): + # Git repo is configured in a way we don't support such as having + # packed refs. Even though in a git repo, tf.__git_version__ will not + # be accurate. + # TODO(mikecase): Support grabbing git info when using packed refs. + open(os.path.join(gen_path, target), "w").write("") + spec["git"] = False else: try: # In python 3.5, symlink function exists even on Windows. But requires diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD index b5465b7fb32856833fc2a12c8dfea58c2e8e79dd..b7d7fac315367824c6fdb379eaa6b0812d89b28b 100644 --- a/tensorflow/tools/graph_transforms/BUILD +++ b/tensorflow/tools/graph_transforms/BUILD @@ -91,7 +91,6 @@ cc_library( srcs = [ "add_default_attributes.cc", "backports.cc", - "fake_quantize_training.cc", "flatten_atrous.cc", "fold_batch_norms.cc", "fold_constants_lib.cc", @@ -99,22 +98,21 @@ cc_library( "freeze_requantization_ranges.cc", "fuse_convolutions.cc", "insert_logging.cc", - "remove_ema.cc", "obfuscate_names.cc", + "quantize_nodes.cc", + "quantize_weights.cc", "remove_attribute.cc", + "remove_control_dependencies.cc", "remove_device.cc", "remove_nodes.cc", "rename_attribute.cc", "rename_op.cc", + "round_weights.cc", "set_device.cc", "sort_by_execution_order.cc", "sparsify_gather.cc", "strip_unused_nodes.cc", - ] + if_not_windows([ - "quantize_nodes.cc", - "quantize_weights.cc", - "round_weights.cc", - ]), + ], hdrs = [ "fold_constants_lib.h", ], @@ -134,8 +132,8 @@ cc_library( "//tensorflow/core:tensorflow", "//tensorflow/contrib/rnn:gru_ops_op_lib", "//tensorflow/contrib/rnn:lstm_ops_op_lib", + "//tensorflow/core/kernels:quantization_utils", ] + if_not_windows([ - "//tensorflow/core/kernels:quantized_ops", "//tensorflow/core/kernels:remote_fused_graph_rewriter_transform", "//tensorflow/core/kernels/hexagon:hexagon_rewriter_transform", ]), @@ -148,7 +146,6 @@ tf_cc_test( srcs = [ "add_default_attributes_test.cc", "backports_test.cc", - "fake_quantize_training_test.cc", "flatten_atrous_test.cc", "fold_batch_norms_test.cc", "fold_constants_test.cc", @@ -161,7 +158,6 @@ tf_cc_test( "quantize_weights_test.cc", "remove_attribute_test.cc", "remove_device_test.cc", - "remove_ema_test.cc", "remove_nodes_test.cc", "rename_attribute_test.cc", "rename_op_test.cc", diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md index 345d9eadb858cadebe03ecb3297aea52ba54bd37..67badb4869029b684cae05130d3c1e190dfb40d2 100644 --- a/tensorflow/tools/graph_transforms/README.md +++ b/tensorflow/tools/graph_transforms/README.md @@ -639,6 +639,13 @@ specified devices may not be available. In order to work with graphs like these, you can run this transform to wipe the slate clean and delete the device specifier from all ops. +### remove_control_dependencies + +Args: None \ +Prerequisites: None + +Removes all control dependencies from the graph. + ### remove_nodes Args: diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training.cc b/tensorflow/tools/graph_transforms/fake_quantize_training.cc deleted file mode 100644 index 61aecc6e16d817d245421f18fa39c70aa45b2bef..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/fake_quantize_training.cc +++ /dev/null @@ -1,51 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#define EIGEN_USE_THREADS - -#include "tensorflow/core/graph/quantize_training.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// EXPERIMENTAL: This can change without warning. -// Rewrites the GraphDef for quantized training. -// Rewrites the forward pass to include the precision loss with quantization so -// the model can learn to deal with such loss and achieve better accuracy when -// it is quantized later for inference. -// Quantization range information is collected in FakeQuantizeWithMinMaxVars -// ops. -// -// TODO(suharshs): Provide instructions on converting the resulting graph for -// inference. -// TODO(suharshs): Implement this using the GTT rather than calling the old -// prototype function. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def) { - // TODO(suharshs): Make num_bits a parameter. - const int32 num_bits = 8; - // TODO(suharshs): Make quantization op a parameter? - const string quant_op_type = "FakeQuantWithMinMaxVars"; - - return DoQuantizeTrainingOnGraphDef(input_graph_def, num_bits, quant_op_type, - output_graph_def); -} - -REGISTER_GRAPH_TRANSFORM("fake_quantize_training", FakeQuantizeTraining); - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc b/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc deleted file mode 100644 index 5e4ab209e97808c3f42ecf73fb763ef9d7ab1cfe..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/cc/ops/const_op.h" -#include "tensorflow/cc/ops/math_ops.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// Declare here, so we don't need a public header. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -class FakeQuantizeTrainingTest : public ::testing::Test {}; - -// For now, since the fake_quantize_training transform just calls the -// quantize_training rewrite from tensorflow/core/graph/quantize_training.h, -// we just test that the graph has been changed by the transform. -// TODO(suharshs): Once we implement the fake_quantize_training transform -// using the GTT, write proper tests of the transform here. -TEST_F(FakeQuantizeTrainingTest, TransformOccurred) { - auto root = tensorflow::Scope::DisabledShapeInferenceScope(); - using namespace ::tensorflow::ops; // NOLINT(build/namespaces) - - Tensor a_data(DT_FLOAT, TensorShape()); - test::FillIota(&a_data, 1.0f); - Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data)); - - Tensor b_data(DT_FLOAT, TensorShape()); - test::FillIota(&b_data, 1.0f); - Output b_const = Const(root.WithOpName("b"), Input::Initializer(b_data)); - - Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const); - GraphDef graph_def; - TF_ASSERT_OK(root.ToGraphDef(&graph_def)); - - GraphDef result; - TransformFuncContext context; - TF_ASSERT_OK(FakeQuantizeTraining(graph_def, context, &result)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(result.node_size(), graph_def.node_size()); -} - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc index d89afe85c72883323cec3c14342fd60adebd024d..d86f65325be1c3f5151ab8d0a0c3c64afa3dcf0f 100644 --- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc +++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc @@ -182,6 +182,36 @@ Status FuseBatchNormWithConv(const NodeMatch& match, return Status::OK(); } +Status FuseBatchNormWithBatchToSpace(const NodeMatch& match, + std::vector* new_nodes) { + // Calculate the scale and offset values to apply. + std::vector scale_values; + std::vector offset_values; + TF_RETURN_IF_ERROR( + GetScaleAndOffsetValues(match, &scale_values, &offset_values)); + + // Fuse conv weights, and set the final output node name as batch_norm_node. + const NodeDef& batch_norm_node = match.node; + const NodeMatch& batch_to_space_node_match = match.inputs[0]; + const NodeMatch& conv_node_match = batch_to_space_node_match.inputs[0]; + const NodeDef& batch_to_space_node = batch_to_space_node_match.node; + const NodeDef& conv_node = conv_node_match.node; + + string biasadd_name = conv_node.name() + "/biasadd"; + TF_RETURN_IF_ERROR( + FuseScaleOffsetToConvWeights(scale_values, offset_values, conv_node_match, + biasadd_name , new_nodes)); + + NodeDef new_batch_to_space_node = batch_to_space_node; + // reuse batch_norm node name + new_batch_to_space_node.set_name(batch_norm_node.name()); + new_batch_to_space_node.set_input(0, biasadd_name); + new_nodes->push_back(batch_to_space_node_match.inputs[1].node); + new_nodes->push_back(batch_to_space_node_match.inputs[2].node); + new_nodes->push_back(new_batch_to_space_node); + return Status::OK(); +} + Status FuseBatchNormWithConvConcat(const NodeMatch& match, std::vector* new_nodes) { // Calculate the scale and offset values to apply. @@ -284,6 +314,43 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def, current_graph_def = replaced_graph_def; } while (did_graph_change); + do { + did_graph_change = false; + GraphDef replaced_graph_def; + TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( + current_graph_def, // clang-format off + {"BatchNormWithGlobalNormalization|FusedBatchNorm", // batch_norm_node + { + {"BatchToSpaceND", // batch_to_space_node + { + {"Conv2D", // conv_node + { + {"*"}, // input_node + {"Const"}, // weights_node + } + }, + {"Const"}, // block_shape + {"Const"}, // crops + } + }, + {"Const"}, // mean_node + {"Const"}, // variance_node + {"Const"}, // beta_node + {"Const"}, // gamma_node + } + }, // clang-format on + [&did_graph_change](const NodeMatch& match, + const std::set& input_nodes, + const std::set& output_nodes, + std::vector* new_nodes) { + TF_RETURN_IF_ERROR(FuseBatchNormWithBatchToSpace(match, new_nodes)); + did_graph_change = true; + return Status::OK(); + }, + {}, &replaced_graph_def)); + current_graph_def = replaced_graph_def; + } while (did_graph_change); + do { did_graph_change = false; GraphDef replaced_graph_def; diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc index b30ba9ac8b92db68eb3374c51a7f31b69cd1e3cf..272410c693ae8de8bb131ea577700fd16815c42e 100644 --- a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc +++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/cc/ops/const_op.h" #include "tensorflow/cc/ops/image_ops.h" #include "tensorflow/cc/ops/nn_ops.h" +#include "tensorflow/cc/ops/array_ops.h" #include "tensorflow/cc/ops/sendrecv_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/tensor_testutil.h" @@ -298,6 +299,96 @@ class FoldOldBatchNormsTest : public ::testing::Test { } }; +void TestFoldFusedBatchNormsWithBatchToSpace() { + auto root = tensorflow::Scope::NewRootScope(); + using namespace ::tensorflow::ops; // NOLINT(build/namespaces) + + Tensor input_data(DT_FLOAT, TensorShape({2, 1, 3, 2})); + test::FillValues( + &input_data, {1.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f, -1.0f, -4.0f, -2.0f, + -5.0f, -3.0f, -6.0f}); + Output input_op = + Const(root.WithOpName("input_op"), Input::Initializer(input_data)); + + Tensor weights_data(DT_FLOAT, TensorShape({1, 2, 2, 2})); + test::FillValues(&weights_data, + {1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f, 0.3f, 0.4f}); + Output weights_op = + Const(root.WithOpName("weights_op"), Input::Initializer(weights_data)); + + Output conv_op = Conv2D(root.WithOpName("conv_op"), input_op, weights_op, + {1, 1, 1, 1}, "VALID"); + + Tensor block_shape_data(DT_INT32, TensorShape({2})); + test::FillValues(&block_shape_data, {1, 2}); + Output block_shape_op = + Const(root.WithOpName("block_shape_op"), Input::Initializer(block_shape_data)); + + Tensor crops_data(DT_INT32, TensorShape({2, 2})); + test::FillValues(&crops_data, {0, 0, 0, 1}); + Output crops_op = + Const(root.WithOpName("crops_op"), Input::Initializer(crops_data)); + + Output batch_to_space_op = BatchToSpaceND(root.WithOpName("batch_to_space_op"), + conv_op, block_shape_op, crops_data); + + Tensor mean_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&mean_data, {10.0f, 20.0f}); + Output mean_op = + Const(root.WithOpName("mean_op"), Input::Initializer(mean_data)); + + Tensor variance_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&variance_data, {0.25f, 0.5f}); + Output variance_op = Const(root.WithOpName("variance_op"), + Input::Initializer(variance_data)); + + Tensor beta_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&beta_data, {0.1f, 0.6f}); + Output beta_op = + Const(root.WithOpName("beta_op"), Input::Initializer(beta_data)); + + Tensor gamma_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&gamma_data, {1.0f, 2.0f}); + Output gamma_op = + Const(root.WithOpName("gamma_op"), Input::Initializer(gamma_data)); + + GraphDef original_graph_def; + TF_ASSERT_OK(root.ToGraphDef(&original_graph_def)); + + NodeDef batch_norm_node; + batch_norm_node.set_op("FusedBatchNorm"); + batch_norm_node.set_name("output"); + AddNodeInput("batch_to_space_op", &batch_norm_node); + AddNodeInput("gamma_op", &batch_norm_node); + AddNodeInput("beta_op", &batch_norm_node); + AddNodeInput("mean_op", &batch_norm_node); + AddNodeInput("variance_op", &batch_norm_node); + SetNodeAttr("T", DT_FLOAT, &batch_norm_node); + SetNodeAttr("epsilon", 0.00001f, &batch_norm_node); + SetNodeAttr("is_training", false, &batch_norm_node); + *(original_graph_def.mutable_node()->Add()) = batch_norm_node; + + std::unique_ptr original_session(NewSession(SessionOptions())); + TF_ASSERT_OK(original_session->Create(original_graph_def)); + std::vector original_outputs; + TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs)); + + GraphDef fused_graph_def; + TF_ASSERT_OK(FoldOldBatchNorms(original_graph_def, {{}, {"output"}}, + &fused_graph_def)); + + std::unique_ptr fused_session(NewSession(SessionOptions())); + TF_ASSERT_OK(fused_session->Create(fused_graph_def)); + std::vector fused_outputs; + TF_ASSERT_OK(fused_session->Run({}, {"output"}, {}, &fused_outputs)); + + test::ExpectTensorNear(original_outputs[0], fused_outputs[0], 1e-5); + + for (const NodeDef& node : fused_graph_def.node()) { + EXPECT_NE("FusedBatchNormWithBatchToSpace", node.op()); + } +} + TEST_F(FoldOldBatchNormsTest, TestFoldOldBatchNorms) { TestFoldOldBatchNorms(); } @@ -315,5 +406,9 @@ TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsWithConcat) { TestFoldFusedBatchNormsWithConcat(/*split=*/false); } +TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsWithBatchToSpace) { + TestFoldFusedBatchNormsWithBatchToSpace(); +} + } // namespace graph_transforms } // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_control_dependencies.cc b/tensorflow/tools/graph_transforms/remove_control_dependencies.cc new file mode 100644 index 0000000000000000000000000000000000000000..cba6b78fc5c43ca17f4f30930eb74efdb12940a1 --- /dev/null +++ b/tensorflow/tools/graph_transforms/remove_control_dependencies.cc @@ -0,0 +1,46 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/tools/graph_transforms/transform_utils.h" + +namespace tensorflow { +namespace graph_transforms { + +// Remove control depdencies in preparation for inference. +// In the tensorflow graph, control dependencies are represented as extra +// inputs which are referenced with "^tensor_name". +// See node_def.proto for more details. +Status RemoveControlDependencies(const GraphDef& input_graph_def, + const TransformFuncContext& context, + GraphDef* output_graph_def) { + output_graph_def->Clear(); + for (const NodeDef& node : input_graph_def.node()) { + NodeDef* new_node = output_graph_def->mutable_node()->Add(); + *new_node = node; + new_node->clear_input(); + for (const auto& input : node.input()) { + if (input[0] != '^') { + new_node->add_input(input); + } + } + } + return Status::OK(); +} + +REGISTER_GRAPH_TRANSFORM("remove_control_dependencies", RemoveControlDependencies); + +} // namespace graph_transforms +} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_ema.cc b/tensorflow/tools/graph_transforms/remove_ema.cc deleted file mode 100644 index 22e26267025c3fed4f44ffbc09d55d8d355cc448..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/remove_ema.cc +++ /dev/null @@ -1,146 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#define EIGEN_USE_THREADS - -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// EXPERIMENTAL: This can change without warning. -// Given a graph that has gone through the FakeQuantizeTraining transform and -// has been frozen afterwards, RemoveEMA simplifies the FakeQuantize estimated -// moving average subgraphs to make it compatible with the QuantizeNodes -// transform. -Status RemoveEMA(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def) { - TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"FakeQuantWithMinMaxVars", - { - {"*"}, - {"Assign", - { - {"Const"}, - {"Merge", - { - {"Switch", - { - {"Min", - { - {"*"}, - {"Range", - { - {"*"}, - {"*"}, - {"*"}, - } - } - } - }, - {"IsVariableInitialized"} - } - }, - {"Sub", - { - {"Const"}, - {"Mul", - { - {"Sub"}, - {"Sub", - { - {"Const"}, - {"Const"} - } - } - } - } - } - } - } - } - } - }, - {"Assign", - { - {"Const"}, - {"Merge", - { - {"Switch", - { - {"Max"}, - {"IsVariableInitialized"} - } - }, - {"Sub", - { - {"Const"}, - {"Mul", - { - {"Sub"}, - {"Sub", - { - {"Const"}, - {"Const"} - } - } - } - } - } - } - } - } - } - }, - } - }, // clang-format on - [](const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, - std::vector* new_nodes) { - const NodeDef& fake_quant_node = match.node; - const NodeDef& input_node = match.inputs[0].node; - const NodeDef& min_var_node = match.inputs[1].inputs[0].node; - const NodeDef& max_var_node = match.inputs[2].inputs[0].node; - - // Make a new FakeQuantizeWithMinMaxVars operation that uses constants - // for its min/max arguments rather than an entire EMA subgraph. - NodeDef new_fake_quant_node; - new_fake_quant_node.set_op(fake_quant_node.op()); - new_fake_quant_node.set_name(fake_quant_node.name()); - AddNodeInput(input_node.name(), &new_fake_quant_node); - AddNodeInput(min_var_node.name(), &new_fake_quant_node); - AddNodeInput(max_var_node.name(), &new_fake_quant_node); - CopyNodeAttr(fake_quant_node, "narrow_range", "narrow_range", - &new_fake_quant_node); - CopyNodeAttr(fake_quant_node, "num_bits", "num_bits", - &new_fake_quant_node); - - new_nodes->push_back(new_fake_quant_node); - new_nodes->push_back(input_node); - new_nodes->push_back(min_var_node); - new_nodes->push_back(max_var_node); - - return Status::OK(); - }, - {}, output_graph_def)); - return Status::OK(); -} - -REGISTER_GRAPH_TRANSFORM("remove_ema", RemoveEMA); - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_ema_test.cc b/tensorflow/tools/graph_transforms/remove_ema_test.cc deleted file mode 100644 index 27db90e2729487f89324622f7a63aca1c5a58fe7..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/remove_ema_test.cc +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/cc/ops/const_op.h" -#include "tensorflow/cc/ops/math_ops.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/public/session.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// Declare transformations here, so we don't need a public header. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -Status RemoveEMA(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -Status QuantizeNodes(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -class RemoveEMATest : public ::testing::Test {}; - -TEST_F(RemoveEMATest, FakeQuant_RemoveEMA_QuantizeTraining) { - // Build a small graph. - auto root = tensorflow::Scope::NewRootScope(); - using namespace ::tensorflow::ops; // NOLINT(build/namespaces) - - Tensor a_data(DT_FLOAT, TensorShape({1, 1})); - test::FillIota(&a_data, 1.0f); - Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data)); - - Tensor b_data(DT_FLOAT, TensorShape({1, 1})); - test::FillIota(&b_data, 1.0f); - Output b_const = Const(root.WithOpName("b"), Input::Initializer(b_data)); - - Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const); - GraphDef graph_def; - TF_ASSERT_OK(root.ToGraphDef(&graph_def)); - - // (1) FakeQuantize the graph. - GraphDef fake_quantized_graph_def; - TransformFuncContext context; - TF_ASSERT_OK( - FakeQuantizeTraining(graph_def, context, &fake_quantized_graph_def)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(fake_quantized_graph_def.node_size(), graph_def.node_size()); - - // (2) Run the graph to initialize the newly added variables. - std::unique_ptr session(NewSession(SessionOptions())); - TF_ASSERT_OK(session->Create(fake_quantized_graph_def)); - std::vector outputs; - TF_ASSERT_OK(session->Run({}, {"matmul"}, {}, &outputs)); - - // (3) Freeze the graph. Create a "frozen graph" that matches what we would - // expect if we actually froze the above graph. - // TODO(suharshs): Use a c++ freeze graph alternative, when one is available. - GraphDef frozen_graph_def; - for (const NodeDef& node : fake_quantized_graph_def.node()) { - if (node.op() == "Variable" || node.op() == "VariableV2") { - NodeDef const_node; - const_node.set_op("Const"); - const_node.set_name(node.name()); - SetNodeAttr("dtype", DT_FLOAT, &const_node); - Tensor tensor(DT_FLOAT, {}); - tensor.flat()(0) = 1.0f; - SetNodeTensorAttr("value", tensor, &const_node); - *(frozen_graph_def.mutable_node()->Add()) = const_node; - } else { - *(frozen_graph_def.mutable_node()->Add()) = node; - } - } - - // Test that freezing the graph resulted in a graph with the same number of - // nodes. - EXPECT_EQ(frozen_graph_def.node_size(), fake_quantized_graph_def.node_size()); - - // (4) RemoveEMA on the graph to make it compatible with QuantizeNodes. - GraphDef removed_ema_graph_def; - TF_ASSERT_OK(RemoveEMA(frozen_graph_def, context, &removed_ema_graph_def)); - - // Test that the transformation resulted in a graph with less nodes. - EXPECT_LT(removed_ema_graph_def.node_size(), frozen_graph_def.node_size()); - - // (5) QuantizeNodes and inspect the final graph. - // TODO(suharshs): Add a more thorough inspection of the structure of - // the output graph. - GraphDef quantized_graph_def; - TF_ASSERT_OK( - QuantizeNodes(removed_ema_graph_def, context, &quantized_graph_def)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(quantized_graph_def.node_size(), removed_ema_graph_def.node_size()); - - // Make sure that the FakeQuantizeWithMinMaxVars op has been removed. - for (const NodeDef& node : quantized_graph_def.node()) { - EXPECT_NE(node.op(), "FakeQuantWithMinMaxVars"); - } -} - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_nodes.cc b/tensorflow/tools/graph_transforms/remove_nodes.cc index 119b44d6a4a4d066b734ae8a0e655c771087d0db..05f036a86a09b2a6a94e9c1a1220803eabc64da5 100644 --- a/tensorflow/tools/graph_transforms/remove_nodes.cc +++ b/tensorflow/tools/graph_transforms/remove_nodes.cc @@ -81,7 +81,17 @@ Status RemoveNodes(const GraphDef& input_graph_def, return Status::OK(); } const NodeDef& input_node = match.inputs[0].node; - inputs_to_rename[replace_node.name()] = input_node.name(); + string target_name = input_node.name(); + for (const string& input : replace_node.input()) { + if (!input.compare(0, target_name.size(), target_name)) { + if (input.size() == target_name.size() || + input[target_name.size()] == ':') { + target_name = input; + break; + } + } + } + inputs_to_rename[replace_node.name()] = target_name; inputs_to_rename["^" + replace_node.name()] = "^" + input_node.name(); new_nodes->push_back(input_node); diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc index 214ec721e2c9b8bdd761a1cb7a92a74f4a2a42a0..701e350fc39d083665f5420e6b73510c182e12ce 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc @@ -212,6 +212,14 @@ Status RemoveInputAtIndex(NodeDef* n, int index) { return Status::OK(); } +Status RemoveNodeAtIndex(GraphDef* g, int index) { + for (int i = index; i < g->node_size() - 1; i++) { + g->mutable_node()->SwapElements(i, i + 1); + } + g->mutable_node()->RemoveLast(); + return Status::OK(); +} + Status SparsifyGatherInternal( const GraphDef& input_graph_def, const std::unique_ptr >& @@ -493,9 +501,7 @@ Status SparsifyGatherInternal( removed_node_names.push_back(parsed_input); } } - replaced_graph_def.mutable_node()->SwapElements( - i, replaced_graph_def.node_size() - 1); - replaced_graph_def.mutable_node()->RemoveLast(); + TF_RETURN_IF_ERROR(RemoveNodeAtIndex(&replaced_graph_def, i)); continue; } int j = 0; diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD new file mode 100755 index 0000000000000000000000000000000000000000..0acc139df975fe58ad436837cedd711c54752598 --- /dev/null +++ b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD @@ -0,0 +1,67 @@ +package(default_visibility = ["//visibility:public"]) + +load("@rbe_integration_test//skylark:integration_tests.bzl", "sut_component", "integration_test") +load("@rbe_integration_test//skylark:toolchains.bzl", "toolchain_container_images") + +sut_component( + name = "gcs", + docker_image = toolchain_container_images()["tensorflow"], + setups = [{ + "program": "setup.sh", + "args": [ + "gs://tensorflow-test-bucket/tf-gcs-test", + ], + "output_properties": ["gcs_path"], + "timeout_seconds": 100, + }], + teardowns = [{ + "program": "teardown.sh", + "args": ["{gcs_path}"], + "timeout_seconds": 100, + }], +) + +py_binary( + name = "gcs_smoke", + srcs = ["gcs_smoke.py"], +) + +sh_binary( + name = "test_wrapper", + srcs = ["test_wrapper.sh"], + data = [ + "gcs_smoke", + ], +) + +integration_test( + name = "gcs_smoke_test", + sut_deps = { + ":gcs": "gcs", + }, + tags = [ + "manual", + "notap", + ], + test = { + "program": ":test_wrapper", + "args": [ + "--gcs_bucket_url={gcs#gcs_path}", + "--num_examples=20", + ], + "timeout_seconds": 250, + }, + test_docker_image = toolchain_container_images()["tensorflow"], + test_type = "MultiMachine", +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), +) diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py b/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py new file mode 100755 index 0000000000000000000000000000000000000000..8438c2156cb09b4d8c9442d9a5f4de67e59272f2 --- /dev/null +++ b/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py @@ -0,0 +1,253 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Smoke test for reading records from GCS to TensorFlow.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import sys +import time + +import numpy as np +import tensorflow as tf +from tensorflow.core.example import example_pb2 +from tensorflow.python.lib.io import file_io + +flags = tf.app.flags +flags.DEFINE_string("gcs_bucket_url", "", + "The URL to the GCS bucket in which the temporary " + "tfrecord file is to be written and read, e.g., " + "gs://my-gcs-bucket/test-directory") +flags.DEFINE_integer("num_examples", 10, "Number of examples to generate") + +FLAGS = flags.FLAGS + + +def create_examples(num_examples, input_mean): + """Create ExampleProto's containing data.""" + ids = np.arange(num_examples).reshape([num_examples, 1]) + inputs = np.random.randn(num_examples, 1) + input_mean + target = inputs - input_mean + examples = [] + for row in range(num_examples): + ex = example_pb2.Example() + ex.features.feature["id"].bytes_list.value.append(str(ids[row, 0])) + ex.features.feature["target"].float_list.value.append(target[row, 0]) + ex.features.feature["inputs"].float_list.value.append(inputs[row, 0]) + examples.append(ex) + return examples + + +def create_dir_test(): + """Verifies file_io directory handling methods.""" + + # Test directory creation. + starttime_ms = int(round(time.time() * 1000)) + dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms) + print("Creating dir %s" % dir_name) + file_io.create_dir(dir_name) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Created directory in: %d milliseconds" % elapsed_ms) + + # Check that the directory exists. + dir_exists = file_io.is_directory(dir_name) + assert dir_exists + print("%s directory exists: %s" % (dir_name, dir_exists)) + + # Test recursive directory creation. + starttime_ms = int(round(time.time() * 1000)) + recursive_dir_name = "%s/%s/%s" % (dir_name, + "nested_dir1", + "nested_dir2") + print("Creating recursive dir %s" % recursive_dir_name) + file_io.recursive_create_dir(recursive_dir_name) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Created directory recursively in: %d milliseconds" % elapsed_ms) + + # Check that the directory exists. + recursive_dir_exists = file_io.is_directory(recursive_dir_name) + assert recursive_dir_exists + print("%s directory exists: %s" % (recursive_dir_name, recursive_dir_exists)) + + # Create some contents in the just created directory and list the contents. + num_files = 10 + files_to_create = ["file_%d.txt" % n for n in range(num_files)] + for file_num in files_to_create: + file_name = "%s/%s" % (dir_name, file_num) + print("Creating file %s." % file_name) + file_io.write_string_to_file(file_name, "test file.") + + print("Listing directory %s." % dir_name) + starttime_ms = int(round(time.time() * 1000)) + directory_contents = file_io.list_directory(dir_name) + print(directory_contents) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Listed directory %s in %s milliseconds" % (dir_name, elapsed_ms)) + assert set(directory_contents) == set(files_to_create + ["nested_dir1/"]) + + # Test directory renaming. + dir_to_rename = "%s/old_dir" % dir_name + new_dir_name = "%s/new_dir" % dir_name + file_io.create_dir(dir_to_rename) + assert file_io.is_directory(dir_to_rename) + assert not file_io.is_directory(new_dir_name) + + starttime_ms = int(round(time.time() * 1000)) + print("Will try renaming directory %s to %s" % (dir_to_rename, new_dir_name)) + file_io.rename(dir_to_rename, new_dir_name) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Renamed directory %s to %s in %s milliseconds" % ( + dir_to_rename, new_dir_name, elapsed_ms)) + assert not file_io.is_directory(dir_to_rename) + assert file_io.is_directory(new_dir_name) + + # Test Delete directory recursively. + print("Deleting directory recursively %s." % dir_name) + starttime_ms = int(round(time.time() * 1000)) + file_io.delete_recursively(dir_name) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + dir_exists = file_io.is_directory(dir_name) + assert not dir_exists + print("Deleted directory recursively %s in %s milliseconds" % ( + dir_name, elapsed_ms)) + + +def create_object_test(): + """Verifies file_io's object manipulation methods .""" + starttime_ms = int(round(time.time() * 1000)) + dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms) + print("Creating dir %s." % dir_name) + file_io.create_dir(dir_name) + + num_files = 5 + # Create files of 2 different patterns in this directory. + files_pattern_1 = ["%s/test_file_%d.txt" % (dir_name, n) + for n in range(num_files)] + files_pattern_2 = ["%s/testfile%d.txt" % (dir_name, n) + for n in range(num_files)] + + starttime_ms = int(round(time.time() * 1000)) + files_to_create = files_pattern_1 + files_pattern_2 + for file_name in files_to_create: + print("Creating file %s." % file_name) + file_io.write_string_to_file(file_name, "test file creation.") + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Created %d files in %s milliseconds" % + (len(files_to_create), elapsed_ms)) + + # Listing files of pattern1. + list_files_pattern = "%s/test_file*.txt" % dir_name + print("Getting files matching pattern %s." % list_files_pattern) + starttime_ms = int(round(time.time() * 1000)) + files_list = file_io.get_matching_files(list_files_pattern) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Listed files in %s milliseconds" % elapsed_ms) + print(files_list) + assert set(files_list) == set(files_pattern_1) + + # Listing files of pattern2. + list_files_pattern = "%s/testfile*.txt" % dir_name + print("Getting files matching pattern %s." % list_files_pattern) + starttime_ms = int(round(time.time() * 1000)) + files_list = file_io.get_matching_files(list_files_pattern) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("Listed files in %s milliseconds" % elapsed_ms) + print(files_list) + assert set(files_list) == set(files_pattern_2) + + # Test renaming file. + file_to_rename = "%s/oldname.txt" % dir_name + file_new_name = "%s/newname.txt" % dir_name + file_io.write_string_to_file(file_to_rename, "test file.") + assert file_io.file_exists(file_to_rename) + assert not file_io.file_exists(file_new_name) + + print("Will try renaming file %s to %s" % (file_to_rename, file_new_name)) + starttime_ms = int(round(time.time() * 1000)) + file_io.rename(file_to_rename, file_new_name) + elapsed_ms = int(round(time.time() * 1000)) - starttime_ms + print("File %s renamed to %s in %s milliseconds" % ( + file_to_rename, file_new_name, elapsed_ms)) + assert not file_io.file_exists(file_to_rename) + assert file_io.file_exists(file_new_name) + + # Delete directory. + print("Deleting directory %s." % dir_name) + file_io.delete_recursively(dir_name) + + +def main(argv): + del argv # Unused. + # Sanity check on the GCS bucket URL. + if not FLAGS.gcs_bucket_url or not FLAGS.gcs_bucket_url.startswith("gs://"): + print("ERROR: Invalid GCS bucket URL: \"%s\"" % FLAGS.gcs_bucket_url) + sys.exit(1) + + # Verify that writing to the records file in GCS works. + print("\n=== Testing writing and reading of GCS record file... ===") + example_data = create_examples(FLAGS.num_examples, 5) + with tf.python_io.TFRecordWriter(FLAGS.gcs_bucket_url) as hf: + for e in example_data: + hf.write(e.SerializeToString()) + + print("Data written to: %s" % FLAGS.gcs_bucket_url) + + # Verify that reading from the tfrecord file works and that + # tf_record_iterator works. + record_iter = tf.python_io.tf_record_iterator(FLAGS.gcs_bucket_url) + read_count = 0 + for _ in record_iter: + read_count += 1 + print("Read %d records using tf_record_iterator" % read_count) + + if read_count != FLAGS.num_examples: + print("FAIL: The number of records read from tf_record_iterator (%d) " + "differs from the expected number (%d)" % (read_count, + FLAGS.num_examples)) + sys.exit(1) + + # Verify that running the read op in a session works. + print("\n=== Testing TFRecordReader.read op in a session... ===") + with tf.Graph().as_default() as _: + filename_queue = tf.train.string_input_producer([FLAGS.gcs_bucket_url], + num_epochs=1) + reader = tf.TFRecordReader() + _, serialized_example = reader.read(filename_queue) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + sess.run(tf.local_variables_initializer()) + tf.train.start_queue_runners() + index = 0 + for _ in range(FLAGS.num_examples): + print("Read record: %d" % index) + sess.run(serialized_example) + index += 1 + + # Reading one more record should trigger an exception. + try: + sess.run(serialized_example) + print("FAIL: Failed to catch the expected OutOfRangeError while " + "reading one more record than is available") + sys.exit(1) + except tf.errors.OutOfRangeError: + print("Successfully caught the expected OutOfRangeError while " + "reading one more record than is available") + + create_dir_test() + create_object_test() + +if __name__ == "__main__": + tf.app.run(main) diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh new file mode 100755 index 0000000000000000000000000000000000000000..6553ba5e3093c26d3c95f40216cd3922a1fb9e4e --- /dev/null +++ b/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh @@ -0,0 +1,20 @@ +#!/bin/bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +GCS_NUMBER=$(cat /dev/urandom | tr -dc 'A-F0-9' | fold -w 8 | head -n 1) +GCS_PATH="$1"/"$GCS_NUMBER".tfrecord + +echo "gcs_path=$GCS_PATH" > "$_SETUP_OUTPUT" +touch "$_SETUP_DONE" diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh new file mode 100755 index 0000000000000000000000000000000000000000..852486d1677ec597fe56111ffb0e470c333c1cd7 --- /dev/null +++ b/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +GSUTIL_BIN="/var/gcloud/google-cloud-sdk/bin/gsutil" + +echo "Got teardown argument $1" + +if "${GSUTIL_BIN}" rm "$1" +then + echo "Cleaned up new tfrecord file in GCS: '$1'" +else + echo "FAIL: Unable to clean up new tfrecord file in GCS: '$1'" + exit 1 +fi diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh new file mode 100755 index 0000000000000000000000000000000000000000..ef29dee3462c21d6318a6fb7e7e658961f0d88dd --- /dev/null +++ b/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh @@ -0,0 +1,21 @@ +# This is a python2 only test. +#!/bin/bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# Test Tensorflow package installation. +/usr/local/bin/pip install --user tf-nightly + +# Test Tensorflow interaction with GCS. +python tensorflow/tools/integration_test/gcs_smoke_test/gcs_smoke.py "$@" diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD index 7717d8d7de27e827aab5208404f2e2275d60c8d3..3fbdb5cacd1fd0039deaae5ac330b6c2ca006a68 100644 --- a/tensorflow/tools/lib_package/BUILD +++ b/tensorflow/tools/lib_package/BUILD @@ -27,6 +27,7 @@ pkg_tar( ":cheaders", ":clib", ":clicenses", + ":eager_cheaders", ], ) @@ -57,7 +58,6 @@ pkg_tar( name = "cheaders", files = [ "//tensorflow/c:headers", - "//tensorflow/c/eager:headers", ], package_dir = "include/tensorflow/c", # Mark as "manual" till @@ -68,6 +68,20 @@ pkg_tar( tags = ["manual"], ) +pkg_tar( + name = "eager_cheaders", + files = [ + "//tensorflow/c/eager:headers", + ], + package_dir = "include/tensorflow/c/eager", + # Mark as "manual" till + # https://github.com/bazelbuild/bazel/issues/2352 + # and https://github.com/bazelbuild/bazel/issues/1580 + # are resolved, otherwise these rules break when built + # with Python 3. + tags = ["manual"], +) + pkg_tar( name = "clib", files = ["//tensorflow:libtensorflow.so"], @@ -113,6 +127,7 @@ genrule( "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", "@libxsmm_archive//:LICENSE", + "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", "@nasm//:LICENSE", @@ -150,6 +165,7 @@ genrule( "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", "@libxsmm_archive//:LICENSE", + "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", "@nasm//:LICENSE", diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index a9c4a8de42a7633b09985cdd4470495c2c4749e2..9b02b2f94c99d3b9f92b283c917ccfbf3d70bc66 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -11,6 +11,7 @@ load( ) load("//third_party/mkl:build_defs.bzl", "if_mkl") load("//tensorflow:tensorflow.bzl", "if_cuda") +load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_license_deps") # This returns a list of headers of all public header libraries (e.g., @@ -47,37 +48,65 @@ py_binary( deps = ["//tensorflow:tensorflow_py"], ) +COMMON_PIP_DEPS = [ + ":licenses", + "MANIFEST.in", + "README", + "setup.py", + ":included_headers", + "//tensorflow:tensorflow_py", + "//tensorflow/contrib/boosted_trees:boosted_trees_pip", + "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", + "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/ops:contrib_op_loader", + "//tensorflow/contrib/eager/python/examples:examples_pip", + "//tensorflow/contrib/eager/python:checkpointable_utils", + "//tensorflow/contrib/eager/python:evaluator", + "//tensorflow/contrib/gan:gan", + "//tensorflow/contrib/graph_editor:graph_editor_pip", + "//tensorflow/contrib/keras:keras", + "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip", + "//tensorflow/contrib/nn:nn_py", + "//tensorflow/contrib/predictor:predictor_pip", + "//tensorflow/contrib/py2tf:py2tf", + "//tensorflow/contrib/py2tf/converters:converters", + "//tensorflow/contrib/py2tf/converters:test_lib", + "//tensorflow/contrib/py2tf/impl:impl", + "//tensorflow/contrib/py2tf/pyct:pyct", + "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", + "//tensorflow/contrib/receptive_field:receptive_field_pip", + "//tensorflow/contrib/session_bundle:session_bundle_pip", + "//tensorflow/contrib/signal:signal_py", + "//tensorflow/contrib/signal:test_util", + "//tensorflow/contrib/slim:slim", + "//tensorflow/contrib/slim/python/slim/data:data_pip", + "//tensorflow/contrib/slim/python/slim/nets:nets_pip", + "//tensorflow/contrib/specs:specs", + "//tensorflow/contrib/summary:summary_test_util", + "//tensorflow/contrib/tensor_forest:init_py", + "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", + "//tensorflow/contrib/timeseries:timeseries_pip", + "//tensorflow/contrib/tpu", + "//tensorflow/examples/tutorials/mnist:package", + "//tensorflow/python:distributed_framework_test_lib", + "//tensorflow/python:meta_graph_testdata", + "//tensorflow/python:spectral_ops_test_util", + "//tensorflow/python:util_example_parser_configuration", + "//tensorflow/python/debug:debug_pip", + "//tensorflow/python/eager:eager_pip", + "//tensorflow/python/saved_model:saved_model", + "//tensorflow/python/tools:tools_pip", + "//tensorflow/python:test_ops", + "//tensorflow/tools/dist_test/server:grpc_tensorflow_server", +] + # On Windows, python binary is a zip file of runfiles tree. # Add everything to its data dependency for generating a runfiles tree # for building the pip package on Windows. py_binary( name = "simple_console_for_windows", srcs = ["simple_console_for_windows.py"], - data = [ - "MANIFEST.in", - "README", - "setup.py", - ":included_headers", - "//tensorflow/contrib/nn:nn_py", - "//tensorflow/contrib/session_bundle:session_bundle_pip", - "//tensorflow/contrib/signal:signal_py", - "//tensorflow/contrib/slim/python/slim/data:data_pip", - "//tensorflow/python:util_example_parser_configuration", - "//tensorflow/python/debug:debug_pip", - "//tensorflow/python/saved_model", - "//tensorflow/python:spectral_ops_test_util", - "//tensorflow/python/tools:tools_pip", - "//tensorflow/python/eager:eager_pip", - "//tensorflow/contrib/summary:summary_test_util", - # These targets don't build on Windows yet. Exclude them for now. - # "//tensorflow/contrib/ndlstm", - # "//tensorflow/contrib/slim", - # "//tensorflow/contrib/slim/python/slim/nets:nets_pip", - # "//tensorflow/contrib/specs", - # "//tensorflow/contrib/tensor_forest:init_py", - # "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", - # "//tensorflow/examples/tutorials/mnist:package", - ], + data = COMMON_PIP_DEPS, srcs_version = "PY2AND3", deps = ["//tensorflow:tensorflow_py"], ) @@ -108,6 +137,7 @@ filegroup( "@highwayhash//:LICENSE", "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", + "@kafka//:LICENSE", "@libxsmm_archive//:LICENSE", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", @@ -137,63 +167,15 @@ sh_binary( data = select({ "//tensorflow:windows": [":simple_console_for_windows"], "//tensorflow:windows_msvc": [":simple_console_for_windows"], - "//conditions:default": [ - ":licenses", - "MANIFEST.in", - "README", - "setup.py", - ":included_headers", + "//conditions:default": COMMON_PIP_DEPS + [ ":simple_console", - "//tensorflow:tensorflow_py", - "//tensorflow/contrib/boosted_trees:boosted_trees_pip", - "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:prefetching_py", - "//tensorflow/contrib/eager/python/examples:examples_pip", - "//tensorflow/contrib/eager/python:checkpointable", - "//tensorflow/contrib/eager/python:evaluator", - "//tensorflow/contrib/gan:gan", - "//tensorflow/contrib/graph_editor:graph_editor_pip", - "//tensorflow/contrib/keras:keras", - "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip", "//tensorflow/contrib/lite/toco:toco", "//tensorflow/contrib/lite/toco/python:toco_wrapper", "//tensorflow/contrib/lite/toco/python:toco_from_protos", - "//tensorflow/contrib/ndlstm:ndlstm", - "//tensorflow/contrib/nn:nn_py", - "//tensorflow/contrib/predictor:predictor_pip", - "//tensorflow/contrib/py2tf:py2tf", - "//tensorflow/contrib/py2tf/converters:converters", - "//tensorflow/contrib/py2tf/converters:test_lib", - "//tensorflow/contrib/py2tf/impl:impl", - "//tensorflow/contrib/py2tf/pyct:pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", - "//tensorflow/contrib/receptive_field:receptive_field_pip", - "//tensorflow/contrib/session_bundle:session_bundle_pip", - "//tensorflow/contrib/signal:signal_py", - "//tensorflow/contrib/signal:test_util", - "//tensorflow/contrib/slim:slim", - "//tensorflow/contrib/slim/python/slim/data:data_pip", - "//tensorflow/contrib/slim/python/slim/nets:nets_pip", - "//tensorflow/contrib/specs:specs", - "//tensorflow/contrib/summary:summary_test_util", - "//tensorflow/contrib/tensor_forest:init_py", - "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", - "//tensorflow/contrib/timeseries:timeseries_pip", - "//tensorflow/contrib/tpu", - "//tensorflow/examples/tutorials/mnist:package", - "//tensorflow/python:distributed_framework_test_lib", - "//tensorflow/python:meta_graph_testdata", - "//tensorflow/python:spectral_ops_test_util", - "//tensorflow/python:util_example_parser_configuration", - "//tensorflow/python/debug:debug_pip", - "//tensorflow/python/eager:eager_pip", - "//tensorflow/python/saved_model:saved_model", - "//tensorflow/python/tools:tools_pip", - "//tensorflow/python:test_ops", - "//tensorflow/tools/dist_test/server:grpc_tensorflow_server", ], - }) + if_mkl(["//third_party/mkl:intel_binary_blob"]), + }) + if_mkl(["//third_party/mkl:intel_binary_blob"]) + if_tensorrt([ + "//tensorflow/contrib/tensorrt:init_py", + ]), ) # A genrule for generating a marker file for the pip package on Windows diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 20027869990013098c405b4707318a3ce63000fc..815ea8157d16c7b38afa78cb80db158e44a43c49 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,17 +29,17 @@ from setuptools.dist import Distribution # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.6.0-rc0' +_VERSION = '1.6.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', 'astor >= 0.6.0', 'gast >= 0.2.0', 'grpcio >= 1.8.6', - 'numpy >= 1.12.1', + 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'tensorflow-tensorboard >= 1.5.0, < 1.6.0', + 'tensorboard >= 1.6.0, < 1.7.0', 'termcolor >= 1.1.0', ] @@ -62,7 +62,7 @@ else: if 'tf_nightly' in project_name: for i, pkg in enumerate(REQUIRED_PACKAGES): if 'tensorboard' in pkg: - REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.5.0a0, < 1.6.0a0' + REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.7.0a0, < 1.8.0a0' break # weakref.finalize and enum were introduced in Python 3.4 @@ -181,9 +181,10 @@ def find_files(pattern, root): matches = ['../' + x for x in find_files('*', 'external') if '.py' not in x] -so_lib_paths = [i for i in os.listdir('.') - if os.path.isdir(i) - and fnmatch.fnmatch(i, '_solib_*')] +so_lib_paths = [ + i for i in os.listdir('.') + if os.path.isdir(i) and fnmatch.fnmatch(i, '_solib_*') +] for path in so_lib_paths: matches.extend( diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index 77cc9f75f7725438918f681833d58e9ecb4a2f70..edd093510e5483ef946e1177641371f00c92b50b 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -88,6 +88,7 @@ import os import shutil from google.cloud import datastore +from six import text_type def is_real_file(dirpath, fname): @@ -150,7 +151,7 @@ def upload_benchmark_data(client, data): """ test_result = json.loads(data) - test_name = unicode(test_result["name"]) + test_name = text_type(test_result["name"]) start_time = datetime.datetime.utcfromtimestamp( float(test_result["startTime"])) batch = [] @@ -162,7 +163,7 @@ def upload_benchmark_data(client, data): t_val.update({ "test": test_name, "start": start_time, - "info": unicode(data) + "info": text_type(data) }) batch.append(t_val) @@ -170,7 +171,7 @@ def upload_benchmark_data(client, data): # the attribute to be fetched and displayed. The full entry information is # also stored as a non-indexed JSON blob. for ent in test_result["entries"].get("entry", []): - ent_name = unicode(ent["name"]) + ent_name = text_type(ent["name"]) e_key = client.key("Entry") e_val = datastore.Entity(e_key, exclude_from_indexes=["info"]) e_val.update({ @@ -178,7 +179,7 @@ def upload_benchmark_data(client, data): "start": start_time, "entry": ent_name, "timing": ent["wallTime"], - "info": unicode(json.dumps(ent)) + "info": text_type(json.dumps(ent)) }) batch.append(e_val) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 4a2274eb1ade0b95dc999d7ad141e0b060886a6e..ea931e998dd3d09a358f4df01c1f8dc423989f59 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -5,12 +5,15 @@ load("//third_party/tensorrt:tensorrt_configure.bzl", "tensorrt_configure") load("//third_party/mkl:build_defs.bzl", "mkl_repository") load("//third_party/git:git_configure.bzl", "git_configure") load("//third_party/py:python_configure.bzl", "python_configure") + load("//third_party/sycl:sycl_configure.bzl", "sycl_configure") load("//third_party/toolchains/clang6:repo.bzl", "clang6_configure") load("//third_party/toolchains/cpus/arm:arm_compiler_configure.bzl", "arm_compiler_configure") load("//third_party:repo.bzl", "tf_http_archive") load("@io_bazel_rules_closure//closure/private:java_import_external.bzl", "java_import_external") load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external") +load("//tensorflow/tools/def_file_filter:def_file_filter_configure.bzl", + "def_file_filter_configure") def _extract_version_number(bazel_version): """Extracts the semantic version number from a version string @@ -66,7 +69,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): # We must check the bazel version before trying to parse any other BUILD # files, in case the parsing of those build files depends on the bazel # version we require here. - check_bazel_version_at_least("0.5.4") + check_bazel_version_at_least("0.10.0") clang6_configure(name="local_config_clang6") cuda_configure(name="local_config_cuda") tensorrt_configure(name="local_config_tensorrt") @@ -74,6 +77,10 @@ def tf_workspace(path_prefix="", tf_repo_name=""): sycl_configure(name="local_config_sycl") python_configure(name="local_config_python") + # For windows bazel build + # TODO: Remove def file filter when TensorFlow can export symbols properly on Windows. + def_file_filter_configure(name = "local_config_def_file_filter") + # Point //external/local_config_arm_compiler to //external/arm_compiler arm_compiler_configure( name="local_config_arm_compiler", @@ -126,6 +133,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): sha256 = "0cadb31a35b514bf2dfd6b5d38205da94ef326ec6908fc3fd7c269948467214f", strip_prefix = "eigen-eigen-2355b229ea4c", build_file = str(Label("//third_party:eigen.BUILD")), + patch_file = str(Label("//third_party:eigen_fix_cuda_compilation.patch")) ) tf_http_archive( @@ -179,11 +187,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "gemmlowp", urls = [ - "https://mirror.bazel.build/github.com/google/gemmlowp/archive/010bb3e71a26ca1d0884a167081d092b43563996.zip", - "https://github.com/google/gemmlowp/archive/010bb3e71a26ca1d0884a167081d092b43563996.zip", + "https://mirror.bazel.build/github.com/google/gemmlowp/archive/7c7c744640ddc3d0af18fb245b4d23228813a71b.zip", + "https://github.com/google/gemmlowp/archive/7c7c744640ddc3d0af18fb245b4d23228813a71b.zip", ], - sha256 = "dd2557072bde12141419cb8320a9c25e6ec41a8ae53c2ac78c076a347bb46d9d", - strip_prefix = "gemmlowp-010bb3e71a26ca1d0884a167081d092b43563996", + sha256 = "b852cc90259a7357c8a323f108f2cec6e85979fc3b18b5590b99e0130044b2cf", + strip_prefix = "gemmlowp-7c7c744640ddc3d0af18fb245b4d23228813a71b", ) tf_http_archive( @@ -462,11 +470,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/299f8c346e1ab483463da5f02536ffd00b7ad9c6.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/299f8c346e1ab483463da5f02536ffd00b7ad9c6.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/195a164675af86f390f9816e53291013d1b551d7.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/195a164675af86f390f9816e53291013d1b551d7.tar.gz", ], - sha256 = "0556bc6a85000c573d92fe00946b6418cbcd3844912696a81055e4768299dda4", - strip_prefix = "llvm-299f8c346e1ab483463da5f02536ffd00b7ad9c6", + sha256 = "57a8333f8e6095d49f1e597ca18e591aba8a89d417f4b58bceffc5fe1ffcc02b", + strip_prefix = "llvm-195a164675af86f390f9816e53291013d1b551d7", build_file = str(Label("//third_party/llvm:llvm.BUILD")), ) @@ -653,11 +661,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "cub_archive", urls = [ - "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip", - "https://github.com/NVlabs/cub/archive/1.7.4.zip", + "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip", + "https://github.com/NVlabs/cub/archive/1.8.0.zip", ], - sha256 = "20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31", - strip_prefix = "cub-1.7.4", + sha256 = "6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3", + strip_prefix = "cub-1.8.0", build_file = str(Label("//third_party:cub.BUILD")), ) @@ -676,13 +684,23 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "bazel_toolchains", urls = [ - "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/f3b09700fae5d7b6e659d7cefe0dcc6e8498504c.tar.gz", - "https://github.com/bazelbuild/bazel-toolchains/archive/f3b09700fae5d7b6e659d7cefe0dcc6e8498504c.tar.gz", + "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz", + "https://github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz", ], - sha256 = "ed829b5eea8af1f405f4cc3d6ecfc3b1365bb7843171036030a31b5127002311", - strip_prefix = "bazel-toolchains-f3b09700fae5d7b6e659d7cefe0dcc6e8498504c", + strip_prefix = "bazel-toolchains-44200e0c026d86c53470d107b3697a3e46469c43", + sha256 = "699b55a6916c687f4b7dc092dbbf5f64672cde0dc965f79717735ec4e5416556", ) + tf_http_archive( + name = "rbe_integration_test", + urls = [ + "http://mirror.bazel.build/github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz", + "https://github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz", + ], + sha256 = "66d93b3919a165d486c31f5290d312abe9fda2685242f812c110653c124e1db4", + strip_prefix = "rbe-integration-test-78a6194c7dda200b9522cf07707e3bc695804d1e", + ) + tf_http_archive( name = "arm_neon_2_x86_sse", sha256 = "c8d90aa4357f8079d427e87a6f4c493da1fa4140aee926c05902d7ec1533d9a5", diff --git a/third_party/eigen_fix_cuda_compilation.patch b/third_party/eigen_fix_cuda_compilation.patch new file mode 100644 index 0000000000000000000000000000000000000000..b921a7c31d5c96c79cd3033b13c60a8f7e63ba75 --- /dev/null +++ b/third_party/eigen_fix_cuda_compilation.patch @@ -0,0 +1,38 @@ +diff --git a/Eigen/src/Core/ProductEvaluators.h b/Eigen/src/Core/ProductEvaluators.h +--- a/Eigen/src/Core/ProductEvaluators.h ++++ b/Eigen/src/Core/ProductEvaluators.h +@@ -137,7 +137,7 @@ struct Assignment::type> + { + typedef Product SrcXprType; +- static EIGEN_STRONG_INLINE ++ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op &) + { + Index dstRows = src.rows(); +@@ -390,7 +390,7 @@ struct generic_product_impl::Scalar Scalar; + + template +- static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) ++ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // Same as: dst.noalias() = lhs.lazyProduct(rhs); + // but easier on the compiler side +@@ -398,14 +398,14 @@ struct generic_product_impl +- static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) ++ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // dst.noalias() += lhs.lazyProduct(rhs); + call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op()); + } + + template +- static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) ++ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // dst.noalias() -= lhs.lazyProduct(rhs); + call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op()); diff --git a/third_party/examples/eager/spinn/README.md b/third_party/examples/eager/spinn/README.md index 6bd3d53e56d01e15491ecd383dcc763a19d75b88..7f477d19208257474d0481ca04c04679f589b751 100644 --- a/third_party/examples/eager/spinn/README.md +++ b/third_party/examples/eager/spinn/README.md @@ -66,3 +66,44 @@ Other eager execution examples can be found under [tensorflow/contrib/eager/pyth ```bash tensorboard --logdir /tmp/spinn-logs ``` + +- After training, you may use the model to perform inference on input data in + the SNLI data format. The premise and hypotheses sentences are specified with + the command-line flags `--inference_premise` and `--inference_hypothesis`, + respecitvely. Each sentence should include the words, as well as parentheses + representing a binary parsing of the sentence. The words and parentheses + should all be separated by spaces. For instance, + + ```bash + python spinn.py --data_root /tmp/spinn-data --logdir /tmp/spinn-logs \ + --inference_premise '( ( The dog ) ( ( is running ) . ) )' \ + --inference_hypothesis '( ( The dog ) ( moves . ) )' + ``` + + which will generate an output like the following, due to the semantic + consistency of the two sentences. + + ```none + Inference logits: + entailment: 1.101249 (winner) + contradiction: -2.374171 + neutral: -0.296733 + ``` + + By contrast, the following sentence pair: + + ```bash + python spinn.py --data_root /tmp/spinn-data --logdir /tmp/spinn-logs \ + --inference_premise '( ( The dog ) ( ( is running ) . ) )' \ + --inference_hypothesis '( ( The dog ) ( rests . ) )' + ``` + + will give you an output like the following, due to the semantic + contradiction of the two sentences. + + ```none + Inference logits: + entailment: -1.070098 + contradiction: 2.798695 (winner) + neutral: -1.402287 + ``` diff --git a/third_party/examples/eager/spinn/spinn.py b/third_party/examples/eager/spinn/spinn.py index a2fa18eeb1077c8a1ccd4ab0bcd178f952e17270..8a1c7db2ea14365be53a796a79fce77900e668e1 100644 --- a/third_party/examples/eager/spinn/spinn.py +++ b/third_party/examples/eager/spinn/spinn.py @@ -471,6 +471,15 @@ class SNLIClassifierTrainer(object): def learning_rate(self): return self._learning_rate + @property + def model(self): + return self._model + + @property + def variables(self): + return (self._model.variables + [self.learning_rate] + + self._optimizer.variables()) + def _batch_n_correct(logits, label): """Calculate number of correct predictions in a batch. @@ -488,13 +497,12 @@ def _batch_n_correct(logits, label): tf.argmax(logits, axis=1), label)), tf.float32)).numpy() -def _evaluate_on_dataset(snli_data, batch_size, model, trainer, use_gpu): +def _evaluate_on_dataset(snli_data, batch_size, trainer, use_gpu): """Run evaluation on a dataset. Args: snli_data: The `data.SnliData` to use in this evaluation. batch_size: The batch size to use during this evaluation. - model: An instance of `SNLIClassifier` to evaluate. trainer: An instance of `SNLIClassifierTrainer to use for this evaluation. use_gpu: Whether GPU is being used. @@ -509,7 +517,7 @@ def _evaluate_on_dataset(snli_data, batch_size, model, trainer, use_gpu): snli_data, batch_size): if use_gpu: label, prem, hypo = label.gpu(), prem.gpu(), hypo.gpu() - logits = model(prem, prem_trans, hypo, hypo_trans, training=False) + logits = trainer.model(prem, prem_trans, hypo, hypo_trans, training=False) loss_val = trainer.loss(label, logits) batch_size = tf.shape(label)[0] mean_loss(loss_val, weights=batch_size.gpu() if use_gpu else batch_size) @@ -536,13 +544,19 @@ def _get_dataset_iterator(snli_data, batch_size): return tfe.Iterator(dataset) -def train_spinn(embed, train_data, dev_data, test_data, config): - """Train a SPINN model. +def train_or_infer_spinn(embed, + word2index, + train_data, + dev_data, + test_data, + config): + """Perform Training or Inference on a SPINN model. Args: embed: The embedding matrix as a float32 numpy array with shape [vocabulary_size, word_vector_len]. word_vector_len is the length of a word embedding vector. + word2index: A `dict` mapping word to word index. train_data: An instance of `data.SnliData`, for the train split. dev_data: Same as above, for the dev split. test_data: Same as above, for the test split. @@ -550,13 +564,35 @@ def train_spinn(embed, train_data, dev_data, test_data, config): details. Returns: - 1. Final loss value on the test split. - 2. Final fraction of correct classifications on the test split. + If `config.inference_premise ` and `config.inference_hypothesis` are not + `None`, i.e., inference mode: the logits for the possible labels of the + SNLI data set, as a `Tensor` of three floats. + else: + The trainer object. + Raises: + ValueError: if only one of config.inference_premise and + config.inference_hypothesis is specified. """ + # TODO(cais): Refactor this function into separate one for training and + # inference. use_gpu = tfe.num_gpus() > 0 and not config.force_cpu device = "gpu:0" if use_gpu else "cpu:0" print("Using device: %s" % device) + if ((config.inference_premise and not config.inference_hypothesis) or + (not config.inference_premise and config.inference_hypothesis)): + raise ValueError( + "--inference_premise and --inference_hypothesis must be both " + "specified or both unspecified, but only one is specified.") + + if config.inference_premise: + # Inference mode. + inference_sentence_pair = [ + data.encode_sentence(config.inference_premise, word2index), + data.encode_sentence(config.inference_hypothesis, word2index)] + else: + inference_sentence_pair = None + log_header = ( " Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss" " Accuracy Dev/Accuracy") @@ -569,16 +605,36 @@ def train_spinn(embed, train_data, dev_data, test_data, config): summary_writer = tf.contrib.summary.create_file_writer( config.logdir, flush_millis=10000) - train_len = train_data.num_batches(config.batch_size) + with tf.device(device), \ - tfe.restore_variables_on_create( - tf.train.latest_checkpoint(config.logdir)), \ summary_writer.as_default(), \ tf.contrib.summary.always_record_summaries(): - model = SNLIClassifier(config, embed) - global_step = tf.train.get_or_create_global_step() - trainer = SNLIClassifierTrainer(model, config.lr) - + with tfe.restore_variables_on_create( + tf.train.latest_checkpoint(config.logdir)): + model = SNLIClassifier(config, embed) + global_step = tf.train.get_or_create_global_step() + trainer = SNLIClassifierTrainer(model, config.lr) + + if inference_sentence_pair: + # Inference mode. + with tfe.restore_variables_on_create( + tf.train.latest_checkpoint(config.logdir)): + prem, prem_trans = inference_sentence_pair[0] + hypo, hypo_trans = inference_sentence_pair[1] + hypo_trans = inference_sentence_pair[1][1] + inference_logits = model( # pylint: disable=not-callable + tf.constant(prem), tf.constant(prem_trans), + tf.constant(hypo), tf.constant(hypo_trans), training=False) + inference_logits = inference_logits[0][1:] + max_index = tf.argmax(inference_logits) + print("\nInference logits:") + for i, (label, logit) in enumerate( + zip(data.POSSIBLE_LABELS, inference_logits)): + winner_tag = " (winner)" if max_index == i else "" + print(" {0:<16}{1:.6f}{2}".format(label + ":", logit, winner_tag)) + return inference_logits + + train_len = train_data.num_batches(config.batch_size) start = time.time() iterations = 0 mean_loss = tfe.metrics.Mean() @@ -594,23 +650,24 @@ def train_spinn(embed, train_data, dev_data, test_data, config): # remain on CPU. Same in _evaluate_on_dataset(). iterations += 1 - batch_train_loss, batch_train_logits = trainer.train_batch( - label, prem, prem_trans, hypo, hypo_trans) + with tfe.restore_variables_on_create( + tf.train.latest_checkpoint(config.logdir)): + batch_train_loss, batch_train_logits = trainer.train_batch( + label, prem, prem_trans, hypo, hypo_trans) batch_size = tf.shape(label)[0] mean_loss(batch_train_loss.numpy(), weights=batch_size.gpu() if use_gpu else batch_size) accuracy(tf.argmax(batch_train_logits, axis=1), label) if iterations % config.save_every == 0: - all_variables = ( - model.variables + [trainer.learning_rate] + [global_step]) + all_variables = trainer.variables + [global_step] saver = tfe.Saver(all_variables) saver.save(os.path.join(config.logdir, "ckpt"), global_step=global_step) if iterations % config.dev_every == 0: dev_loss, dev_frac_correct = _evaluate_on_dataset( - dev_data, config.batch_size, model, trainer, use_gpu) + dev_data, config.batch_size, trainer, use_gpu) print(dev_log_template.format( time.time() - start, epoch, iterations, 1 + batch_idx, train_len, @@ -638,10 +695,12 @@ def train_spinn(embed, train_data, dev_data, test_data, config): trainer.decay_learning_rate(config.lr_decay_by) test_loss, test_frac_correct = _evaluate_on_dataset( - test_data, config.batch_size, model, trainer, use_gpu) + test_data, config.batch_size, trainer, use_gpu) print("Final test loss: %g; accuracy: %g%%" % (test_loss, test_frac_correct * 100.0)) + return trainer + def main(_): config = FLAGS @@ -650,18 +709,24 @@ def main(_): vocab = data.load_vocabulary(FLAGS.data_root) word2index, embed = data.load_word_vectors(FLAGS.data_root, vocab) - print("Loading train, dev and test data...") - train_data = data.SnliData( - os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_train.txt"), - word2index, sentence_len_limit=FLAGS.sentence_len_limit) - dev_data = data.SnliData( - os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_dev.txt"), - word2index, sentence_len_limit=FLAGS.sentence_len_limit) - test_data = data.SnliData( - os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_test.txt"), - word2index, sentence_len_limit=FLAGS.sentence_len_limit) - - train_spinn(embed, train_data, dev_data, test_data, config) + if not (config.inference_premise or config.inference_hypothesis): + print("Loading train, dev and test data...") + train_data = data.SnliData( + os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_train.txt"), + word2index, sentence_len_limit=FLAGS.sentence_len_limit) + dev_data = data.SnliData( + os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_dev.txt"), + word2index, sentence_len_limit=FLAGS.sentence_len_limit) + test_data = data.SnliData( + os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_test.txt"), + word2index, sentence_len_limit=FLAGS.sentence_len_limit) + else: + train_data = None + dev_data = None + test_data = None + + train_or_infer_spinn( + embed, word2index, train_data, dev_data, test_data, config) if __name__ == "__main__": @@ -678,6 +743,15 @@ if __name__ == "__main__": parser.add_argument("--logdir", type=str, default="/tmp/spinn-logs", help="Directory in which summaries will be written for " "TensorBoard.") + parser.add_argument("--inference_premise", type=str, default=None, + help="Premise sentence for inference. Must be " + "accompanied by --inference_hypothesis. If specified, " + "will override all training parameters and perform " + "inference.") + parser.add_argument("--inference_hypothesis", type=str, default=None, + help="Hypothesis sentence for inference. Must be " + "accompanied by --inference_premise. If specified, will " + "override all training parameters and perform inference.") parser.add_argument("--epochs", type=int, default=50, help="Number of epochs to train.") parser.add_argument("--batch_size", type=int, default=128, diff --git a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl index e4363d604577de09241d635b6990c9dd6429efe0..2f09473ee2ddf9a38ca0c7aa11094690607b532f 100644 --- a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl +++ b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl @@ -49,6 +49,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-lstdc++" } @@ -75,6 +76,7 @@ toolchain { name: "alwayslink" flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,-no-as-needed" @@ -116,6 +118,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-Wl,-z,relro,-z,now" } @@ -161,6 +164,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { # Stamp the binary with a unique identifier. flag: "-Wl,--build-id=md5" @@ -176,6 +180,7 @@ toolchain { action: "c++-compile" action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag:"-no-canonical-prefixes" } @@ -199,6 +204,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-B/usr/bin/" } @@ -246,6 +252,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,--gc-sections" diff --git a/third_party/gpus/cuda/remote.BUILD.tpl b/third_party/gpus/cuda/remote.BUILD.tpl index d88d512b90c352e6a301ed6efe8266d8dd6bf744..f774def5e6cec25e4920ecce0076340a31c70386 100644 --- a/third_party/gpus/cuda/remote.BUILD.tpl +++ b/third_party/gpus/cuda/remote.BUILD.tpl @@ -41,65 +41,65 @@ config_setting( alias( name = "cuda_headers", - actual = "%{remote_cuda_repo}cuda:cuda_headers", + actual = "%{remote_cuda_repo}/cuda:cuda_headers", ) alias( name = "cudart_static", - actual = "%{remote_cuda_repo}cuda:cudart_static", + actual = "%{remote_cuda_repo}/cuda:cudart_static", ) alias( name = "cuda_driver", - actual = "%{remote_cuda_repo}cuda:cuda_driver", + actual = "%{remote_cuda_repo}/cuda:cuda_driver", ) alias( name = "cudart", - actual = "%{remote_cuda_repo}cuda:cudart", + actual = "%{remote_cuda_repo}/cuda:cudart", ) alias( name = "cublas", - actual = "%{remote_cuda_repo}cuda:cublas", + actual = "%{remote_cuda_repo}/cuda:cublas", ) alias( name = "cusolver", - actual = "%{remote_cuda_repo}cuda:cusolver", + actual = "%{remote_cuda_repo}/cuda:cusolver", ) alias( name = "cudnn", - actual = "%{remote_cuda_repo}cuda:cudnn", + actual = "%{remote_cuda_repo}/cuda:cudnn", ) alias( name = "cufft", - actual = "%{remote_cuda_repo}cuda:cufft", + actual = "%{remote_cuda_repo}/cuda:cufft", ) alias( name = "curand", - actual = "%{remote_cuda_repo}cuda:curand", + actual = "%{remote_cuda_repo}/cuda:curand", ) alias( name = "cuda", - actual = "%{remote_cuda_repo}cuda:cuda", + actual = "%{remote_cuda_repo}/cuda:cuda", ) alias( name = "cupti_headers", - actual = "%{remote_cuda_repo}cuda:cupti_headers", + actual = "%{remote_cuda_repo}/cuda:cupti_headers", ) alias( name = "cupti_dsos", - actual = "%{remote_cuda_repo}cuda:cupti_dsos", + actual = "%{remote_cuda_repo}/cuda:cupti_dsos", ) alias( name = "libdevice_root", - actual = "%{remote_cuda_repo}cuda:libdevice_root", + actual = "%{remote_cuda_repo}/cuda:libdevice_root", ) diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl index 255ae0119095ee17babc00f43e93fa4c4931c1fb..6c9c128db635ecb23b7aa930dca1d20bae17f71c 100644 --- a/third_party/gpus/cuda_configure.bzl +++ b/third_party/gpus/cuda_configure.bzl @@ -38,6 +38,64 @@ _DEFAULT_CUDA_TOOLKIT_PATH = "/usr/local/cuda" _DEFAULT_CUDNN_INSTALL_PATH = "/usr/local/cuda" _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"] +# Lookup paths for CUDA / cuDNN libraries, relative to the install directories. +# +# Paths will be tried out in the order listed below. The first successful path +# will be used. For example, when looking for the cudart libraries, the first +# attempt will be lib64/cudart inside the CUDA toolkit. +CUDA_LIB_PATHS = [ + "lib64/", + "lib64/stubs/", + "lib/x86_64-linux-gnu/", + "lib/x64/", + "lib/", + "", +] + +# Lookup paths for cupti.h, relative to the CUDA toolkit directory. +# +# On most systems, the cupti library is not installed in the same directory as +# the other CUDA libraries but rather in a special extras/CUPTI directory. +CUPTI_HEADER_PATHS = [ + "extras/CUPTI/include/", + "include/cuda/CUPTI/", +] + +# Lookup paths for the cupti library, relative to the +# +# On most systems, the cupti library is not installed in the same directory as +# the other CUDA libraries but rather in a special extras/CUPTI directory. +CUPTI_LIB_PATHS = [ + "extras/CUPTI/lib64/", + "lib/x86_64-linux-gnu", + "lib64/", + "extras/CUPTI/libx64/", + "extras/CUPTI/lib/", + "lib/", +] + +# Lookup paths for CUDA headers (cuda.h) relative to the CUDA toolkit directory. +CUDA_INCLUDE_PATHS = [ + "include/", + "include/cuda/" +] + +# Lookup paths for cudnn.h relative to the CUDNN install directory. +CUDNN_INCLUDE_PATHS = [ + "", + "include/", + "include/cuda/", +] + +# Lookup paths for NVVM libdevice relative to the CUDA directory toolkit. +# +# libdevice implements mathematical functions for GPU kernels, and is provided +# in NVVM bitcode (a subset of LLVM bitcode). +NVVM_LIBDEVICE_PATHS = [ + "nvvm/libdevice/", + "share/cuda/", +] + load(":download_clang.bzl", "download_clang") # TODO(dzc): Once these functions have been factored out of Bazel's @@ -367,11 +425,20 @@ def find_cuda_define(repository_ctx, header_dir, header_file, define): if result.stdout.find(define) == -1: auto_configure_fail("Cannot find line containing '%s' in %s" % (define, h_path)) - version = result.stdout - # Remove the new line and '\' character if any. - version = version.replace("\\", " ") - version = version.replace("\n", " ") - version = version.replace(define, "").lstrip() + # Split results to lines + lines = result.stdout.split('\n') + num_lines = len(lines) + for l in range(num_lines): + line = lines[l] + if define in line: # Find the line with define + version = line + if l != num_lines-1 and line[-1] == '\\': # Add next line, if multiline + version = version[:-1] + lines[l+1] + break + # Remove any comments + version = version.split("//")[0] + # Remove define name + version = version.replace(define, "").strip() # Remove the code after the version number. version_end = version.find(" ") if version_end != -1: @@ -513,31 +580,31 @@ def _find_cuda_lib(lib, repository_ctx, cpu_value, basedir, version="", path: The full path to the library. """ file_name = _lib_name(lib, cpu_value, version, static) - if cpu_value == "Linux": - path = repository_ctx.path("%s/lib64/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/lib64/stubs/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path( - "%s/lib/x86_64-linux-gnu/%s" % (basedir, file_name)) + for relative_path in CUDA_LIB_PATHS: + path = repository_ctx.path("%s/%s%s" % (basedir, relative_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) + auto_configure_fail("Cannot find cuda library %s" % file_name) - elif cpu_value == "Windows": - path = repository_ctx.path("%s/lib/x64/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/lib/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) +def _find_cupti_header_dir(repository_ctx, cuda_config): + """Returns the path to the directory containing cupti.h - auto_configure_fail("Cannot find cuda library %s" % file_name) + On most systems, the cupti library is not installed in the same directory as + the other CUDA libraries but rather in a special extras/CUPTI directory. + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the cupti header. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUPTI_HEADER_PATHS: + if repository_ctx.path("%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find cupti.h under %s" % cuda_toolkit_path) def _find_cupti_lib(repository_ctx, cuda_config): @@ -557,35 +624,13 @@ def _find_cupti_lib(repository_ctx, cuda_config): """ file_name = _lib_name("cupti", cuda_config.cpu_value, cuda_config.cuda_version) - if cuda_config.cpu_value == "Linux": - path = repository_ctx.path( - "%s/extras/CUPTI/lib64/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - path = repository_ctx.path( - "%s/lib/x86_64-linux-gnu/%s" % (cuda_config.cuda_toolkit_path, - file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - elif cuda_config.cpu_value == "Windows": + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUPTI_LIB_PATHS: path = repository_ctx.path( - "%s/extras/CUPTI/libx64/%s" % - (cuda_config.cuda_toolkit_path, file_name)) + "%s/%s%s" % (cuda_toolkit_path, relative_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path( - "%s/extras/CUPTI/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - path = repository_ctx.path( - "%s/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - auto_configure_fail("Cannot find cupti library %s" % file_name) def _find_libs(repository_ctx, cuda_config): @@ -626,6 +671,23 @@ def _find_libs(repository_ctx, cuda_config): } +def _find_cuda_include_path(repository_ctx, cuda_config): + """Returns the path to the directory containing cuda.h + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the CUDA headers. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUDA_INCLUDE_PATHS: + if repository_ctx.path("%s/%scuda.h" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find cuda.h under %s" % cuda_toolkit_path) + + def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): """Returns the path to the directory containing cudnn.h @@ -637,15 +699,31 @@ def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): Returns: The path of the directory containing the cudnn header. """ - if repository_ctx.path(cudnn_install_basedir + "/cudnn.h").exists: - return cudnn_install_basedir - if repository_ctx.path(cudnn_install_basedir + "/include/cudnn.h").exists: - return cudnn_install_basedir + "/include" + for relative_path in CUDA_INCLUDE_PATHS: + if repository_ctx.path("%s/%scudnn.h" % (cudnn_install_basedir, relative_path)).exists: + return ("%s/%s" % (cudnn_install_basedir, relative_path))[:-1] if repository_ctx.path("/usr/include/cudnn.h").exists: return "/usr/include" auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir) +def _find_nvvm_libdevice_dir(repository_ctx, cuda_config): + """Returns the path to the directory containing libdevice in bitcode format. + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the CUDA headers. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in NVVM_LIBDEVICE_PATHS: + if repository_ctx.path("%s/%slibdevice.10.bc" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find libdevice.10.bc under %s" % cuda_toolkit_path) + + def _cudart_static_linkopt(cpu_value): """Returns additional platform-specific linkopts for cudart.""" return "" if cpu_value == "Darwin" else "\"-lrt\"," @@ -916,21 +994,22 @@ def _create_local_cuda_repository(repository_ctx): """Creates the repository containing files set up to build with CUDA.""" cuda_config = _get_cuda_config(repository_ctx) + cuda_include_path = _find_cuda_include_path(repository_ctx, cuda_config) cudnn_header_dir = _find_cudnn_header_dir(repository_ctx, cuda_config.cudnn_install_basedir) + cupti_header_dir = _find_cupti_header_dir(repository_ctx, cuda_config) + nvvm_libdevice_dir = _find_nvvm_libdevice_dir(repository_ctx, cuda_config) # Set up symbolic links for the cuda toolkit by creating genrules to do # symlinking. We create one genrule for each directory we want to track under # cuda_toolkit_path cuda_toolkit_path = cuda_config.cuda_toolkit_path - cuda_include_path = cuda_toolkit_path + "/include" genrules = [symlink_genrule_for_dir(repository_ctx, cuda_include_path, "cuda/include", "cuda-include")] genrules.append(symlink_genrule_for_dir(repository_ctx, - cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm")) + nvvm_libdevice_dir, "cuda/nvvm/libdevice", "cuda-nvvm")) genrules.append(symlink_genrule_for_dir(repository_ctx, - cuda_toolkit_path + "/extras/CUPTI/include", - "cuda/extras/CUPTI/include", "cuda-extras")) + cupti_header_dir, "cuda/extras/CUPTI/include", "cuda-extras")) cuda_libs = _find_libs(repository_ctx, cuda_config) cuda_lib_src = [] @@ -1077,6 +1156,7 @@ cuda_configure = repository_rule( _TF_CUDNN_VERSION, _TF_CUDA_COMPUTE_CAPABILITIES, _TF_CUDA_CONFIG_REPO, + "NVVMIR_LIBRARY_DIR", ], ) diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index ca2d38d6878cee81b29e949175c5133f492cf06b..87a23925c4316c3ee107af77272300e34b1bb257 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -145,9 +145,9 @@ cc_library( "jpeglib.h", "jsimd.h", "jsimddct.h", - "simd/jsimd.h", "simd/jccolor-altivec.c", "simd/jcgray-altivec.c", + "simd/jcsample.h", "simd/jcsample-altivec.c", "simd/jdcolor-altivec.c", "simd/jdmerge-altivec.c", @@ -157,15 +157,15 @@ cc_library( "simd/jidctfst-altivec.c", "simd/jidctint-altivec.c", "simd/jquanti-altivec.c", - "simd/jsimd_powerpc.c", + "simd/jsimd.h", "simd/jsimd_altivec.h", - "simd/jcsample.h", + "simd/jsimd_powerpc.c", ], hdrs = [ - "simd/jdmrgext-altivec.c", # should have been named .inc - "simd/jccolext-altivec.c", # should have been named .inc - "simd/jcgryext-altivec.c", # should have been named .inc - "simd/jdcolext-altivec.c", # should have been named .inc + "simd/jccolext-altivec.c", # should have been named .inc + "simd/jcgryext-altivec.c", # should have been named .inc + "simd/jdcolext-altivec.c", # should have been named .inc + "simd/jdmrgext-altivec.c", # should have been named .inc ], copts = libjpegturbo_copts, nocopts = libjpegturbo_nocopts, @@ -545,7 +545,6 @@ config_setting( ) config_setting( - name = "linux_ppc64le", - values = {"cpu": "ppc"}, - + name = "linux_ppc64le", + values = {"cpu": "ppc"}, ) diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD index a61a9e1f6c2b29ad3b992e810c0cab463dfd7feb..a839ca717e695f35fac684b510f0a022010e0710 100644 --- a/third_party/kafka/BUILD +++ b/third_party/kafka/BUILD @@ -130,12 +130,16 @@ cc_library( ], hdrs = [ "config.h", + "src-cpp/rdkafkacpp.h", + "src-cpp/rdkafkacpp_int.h", + "src/lz4.c", + "src/snappy_compat.h", ], - defines = [ + copts = [ + "-Iexternal/kafka/src", + "-Iexternal/kafka/src-cpp", ], - includes = [ - "src", - "src-cpp", + defines = [ ], linkopts = [ "-lpthread", @@ -143,5 +147,6 @@ cc_library( visibility = ["//visibility:public"], deps = [ "@boringssl//:ssl", + "@zlib_archive//:zlib", ], ) diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD index a9e1341a03c2e725e96bd7c8cbd7b09853bb8af4..28293a36593d8fa67a2d85631a0769e03d508354 100644 --- a/third_party/llvm/llvm.BUILD +++ b/third_party/llvm/llvm.BUILD @@ -1024,6 +1024,7 @@ cc_library( deps = [ ":arm_desc", ":arm_info", + ":arm_utils", ":config", ":mc_disassembler", ":support", diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl index de06ad5f27e7c08aade4a8f51ab60ba52d012b7b..1dd8ab433a37a127b98ae7069bffcbfd4f6d8bd1 100644 --- a/third_party/py/BUILD.tpl +++ b/third_party/py/BUILD.tpl @@ -2,20 +2,26 @@ licenses(["restricted"]) package(default_visibility = ["//visibility:public"]) +# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib +# See https://docs.python.org/3/extending/windows.html +cc_import( + name = "python_lib", + interface_library = select({ + ":windows": ":python_import_lib", + # A placeholder for Unix platforms which makes --no_build happy. + "//conditions:default": "not-existing.lib", + }), + system_provided = 1, +) + cc_library( name = "python_headers", hdrs = [":python_include"], - data = select({ - ":windows": [":python_import_lib"], + deps = select({ + ":windows": [":python_lib"], "//conditions:default": [], }), includes = ["python_include"], - linkopts = select({ - # TODO(pcloudy): Ideally, this should just go into deps after resolving - # https://github.com/bazelbuild/bazel/issues/3237, - ":windows": ["$(locations :python_import_lib)"], - "//conditions:default": [], - }), ) cc_library( diff --git a/third_party/tensorrt/BUILD.tpl b/third_party/tensorrt/BUILD.tpl index feaeb0bea63a982139d34f7c5f37a3f4c66d2af5..57682e8735013544d76b14fc2c41dfff3d50f691 100644 --- a/third_party/tensorrt/BUILD.tpl +++ b/third_party/tensorrt/BUILD.tpl @@ -3,6 +3,8 @@ licenses(["notice"]) +exports_files(["LICENSE"]) + load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts") package(default_visibility = ["//visibility:public"]) @@ -32,36 +34,6 @@ cc_library( visibility = ["//visibility:public"], ) -cc_library( - name = "nv_infer_plugin", - srcs = [%{nv_infer_plugin}], - data = [%{nv_infer_plugin}], - includes = [ - "include", - ], - copts= cuda_default_copts(), - deps = [ - "@local_config_cuda//cuda:cuda", - ":nv_infer", - ":tensorrt_headers", - ], - linkstatic = 1, - visibility = ["//visibility:public"], -) - -cc_library( - name = "nv_parsers", - srcs = [%{nv_parsers}], - data = [%{nv_parsers}], - includes = [ - "include", - ], - copts= cuda_default_copts(), - deps = [ - ":tensorrt_headers", - ], - linkstatic = 1, - visibility = ["//visibility:public"], -) %{tensorrt_genrules} + diff --git a/third_party/tensorrt/LICENSE b/third_party/tensorrt/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..146d9b765c5db44c2f5bea8fa5010eef5ec0c68f --- /dev/null +++ b/third_party/tensorrt/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018 The TensorFlow Authors. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2018, The TensorFlow Authors. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/third_party/tensorrt/tensorrt_configure.bzl b/third_party/tensorrt/tensorrt_configure.bzl index 8aa0f28f39d4dd8e24d5f162bf6535edbb030ee6..8e76e5d02aeddab66dacaa495a6c493f18a95a69 100644 --- a/third_party/tensorrt/tensorrt_configure.bzl +++ b/third_party/tensorrt/tensorrt_configure.bzl @@ -19,11 +19,8 @@ load( _TENSORRT_INSTALL_PATH = "TENSORRT_INSTALL_PATH" _TF_TENSORRT_VERSION = "TF_TENSORRT_VERSION" -_TF_TENSORRT_LIBS = ["nvinfer", "nvinfer_plugin", "nvparsers"] -_TF_TENSORRT_HEADERS = [ - "NvInfer.h", "NvInferPlugin.h", "NvCaffeParser.h", "NvUffParser.h", - "NvUtils.h" -] +_TF_TENSORRT_LIBS = ["nvinfer"] +_TF_TENSORRT_HEADERS = ["NvInfer.h", "NvUtils.h"] _DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR" _DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR" diff --git a/third_party/toolchains/gpus/crosstool/BUILD b/third_party/toolchains/gpus/crosstool/BUILD index a8c6b0f0291363f3a7576a70e78b3428fb984957..1f9065007ca884a46bfa391d1ee8a8f0333da235 100644 --- a/third_party/toolchains/gpus/crosstool/BUILD +++ b/third_party/toolchains/gpus/crosstool/BUILD @@ -50,3 +50,8 @@ filegroup( name = "empty", srcs = [], ) + +filegroup( + name = "crosstool_wrapper_driver_is_not_gcc", + srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"], +) diff --git a/third_party/toolchains/gpus/crosstool/CROSSTOOL b/third_party/toolchains/gpus/crosstool/CROSSTOOL index a47e0c7cd74edcea777d76854c2d7e97d69897fa..d6ee7e38c414dd59b76c7b2b4c95c55831bb30a8 100644 --- a/third_party/toolchains/gpus/crosstool/CROSSTOOL +++ b/third_party/toolchains/gpus/crosstool/CROSSTOOL @@ -53,6 +53,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-lstdc++" } @@ -79,6 +80,7 @@ toolchain { name: "alwayslink" flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,-no-as-needed" @@ -120,6 +122,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-Wl,-z,relro,-z,now" } @@ -141,8 +144,8 @@ toolchain { flag_group { # All warnings are enabled. Maybe enable -Werror as well? flag: "-Wall" - # TODO(ngiraldo): Some parts of the codebase set -Werror and hit this - # warning, so switch it off for now. + # Some parts of the codebase set -Werror and hit this warning, so + # switch it off for now. flag: "-Wno-invalid-partial-specialization" } } @@ -165,6 +168,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { # Stamp the binary with a unique identifier. flag: "-Wl,--build-id=md5" @@ -180,6 +184,7 @@ toolchain { action: "c++-compile" action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag:"-no-canonical-prefixes" } @@ -203,6 +208,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-B/usr/bin/" } @@ -250,6 +256,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,--gc-sections" @@ -296,7 +303,7 @@ toolchain { cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu/c++/5.4.0" cxx_builtin_include_directory: "/usr/include/c++/5.4.0/backward" cxx_builtin_include_directory: "/usr/local/include" - cxx_builtin_include_directory: "/usr/local/lib/clang/6.0.0/include" + cxx_builtin_include_directory: "/usr/local/lib/clang/7.0.0/include" cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu" cxx_builtin_include_directory: "/usr/include" } diff --git a/third_party/toolchains/gpus/cuda/BUILD b/third_party/toolchains/gpus/cuda/BUILD index 39136de99c901d6d6a9dafefe3163972511ec122..cfc6930851dbdca5486322bedc839dad9ed8673d 100644 --- a/third_party/toolchains/gpus/cuda/BUILD +++ b/third_party/toolchains/gpus/cuda/BUILD @@ -51,6 +51,7 @@ cc_library( includes = [ ".", "cuda/include", + "cuda/include/crt", ], visibility = ["//visibility:public"], ) @@ -84,8 +85,8 @@ cc_library( cc_library( name = "cudart", - srcs = ["cuda/lib/libcudart.so.8.0"], - data = ["cuda/lib/libcudart.so.8.0"], + srcs = ["cuda/lib/libcudart.so.9.0"], + data = ["cuda/lib/libcudart.so.9.0"], includes = [ ".", "cuda/include", @@ -96,8 +97,8 @@ cc_library( cc_library( name = "cublas", - srcs = ["cuda/lib/libcublas.so.8.0"], - data = ["cuda/lib/libcublas.so.8.0"], + srcs = ["cuda/lib/libcublas.so.9.0"], + data = ["cuda/lib/libcublas.so.9.0"], includes = [ ".", "cuda/include", @@ -108,8 +109,8 @@ cc_library( cc_library( name = "cusolver", - srcs = ["cuda/lib/libcusolver.so.8.0"], - data = ["cuda/lib/libcusolver.so.8.0"], + srcs = ["cuda/lib/libcusolver.so.9.0"], + data = ["cuda/lib/libcusolver.so.9.0"], includes = [ ".", "cuda/include", @@ -121,8 +122,8 @@ cc_library( cc_library( name = "cudnn", - srcs = ["cuda/lib/libcudnn.so.6"], - data = ["cuda/lib/libcudnn.so.6"], + srcs = ["cuda/lib/libcudnn.so.7"], + data = ["cuda/lib/libcudnn.so.7"], includes = [ ".", "cuda/include", @@ -133,8 +134,8 @@ cc_library( cc_library( name = "cufft", - srcs = ["cuda/lib/libcufft.so.8.0"], - data = ["cuda/lib/libcufft.so.8.0"], + srcs = ["cuda/lib/libcufft.so.9.0"], + data = ["cuda/lib/libcufft.so.9.0"], includes = [ ".", "cuda/include", @@ -145,8 +146,8 @@ cc_library( cc_library( name = "curand", - srcs = ["cuda/lib/libcurand.so.8.0"], - data = ["cuda/lib/libcurand.so.8.0"], + srcs = ["cuda/lib/libcurand.so.9.0"], + data = ["cuda/lib/libcurand.so.9.0"], includes = [ ".", "cuda/include", @@ -183,7 +184,7 @@ cc_library( cc_library( name = "cupti_dsos", - data = ["cuda/lib/libcupti.so.8.0"], + data = ["cuda/lib/libcupti.so.9.0"], includes = [ ".", "cuda/include", @@ -200,1063 +201,990 @@ cc_library( genrule( name = "cuda-include", outs = [ - "cuda/include/math_functions.hpp", - "cuda/include/cufft.h", - "cuda/include/nvgraph.h", - "cuda/include/curand_normal.h", - "cuda/include/curand_uniform.h", - "cuda/include/nppi_data_exchange_and_initialization.h", - "cuda/include/cuda_gl_interop.h", - "cuda/include/nppi_compression_functions.h", - "cuda/include/npp.h", + "cuda/include/CL/cl.h", + "cuda/include/CL/cl.hpp", + "cuda/include/CL/cl_egl.h", + "cuda/include/CL/cl_ext.h", + "cuda/include/CL/cl_gl.h", + "cuda/include/CL/cl_gl_ext.h", + "cuda/include/CL/cl_platform.h", + "cuda/include/CL/opencl.h", + "cuda/include/builtin_types.h", + "cuda/include/channel_descriptor.h", + "cuda/include/common_functions.h", + "cuda/include/cooperative_groups.h", + "cuda/include/cooperative_groups_helpers.h", + "cuda/include/crt/common_functions.h", + "cuda/include/crt/device_double_functions.h", + "cuda/include/crt/device_double_functions.hpp", + "cuda/include/crt/device_functions.h", + "cuda/include/crt/device_functions.hpp", + "cuda/include/crt/func_macro.h", + "cuda/include/crt/host_config.h", + "cuda/include/crt/host_defines.h", + "cuda/include/crt/host_runtime.h", + "cuda/include/crt/math_functions.h", + "cuda/include/crt/math_functions.hpp", + "cuda/include/crt/mma.h", + "cuda/include/crt/mma.hpp", + "cuda/include/crt/nvfunctional", + "cuda/include/crt/sm_70_rt.h", + "cuda/include/crt/sm_70_rt.hpp", + "cuda/include/crt/storage_class.h", + "cuda/include/cuComplex.h", + "cuda/include/cublas.h", + "cuda/include/cublasXt.h", + "cuda/include/cublas_api.h", + "cuda/include/cublas_v2.h", "cuda/include/cuda.h", - "cuda/include/nppi_statistics_functions.h", - "cuda/include/vector_functions.hpp", - "cuda/include/sm_32_intrinsics.hpp", - "cuda/include/sm_32_intrinsics.h", - "cuda/include/curand_discrete.h", + "cuda/include/cudaEGL.h", + "cuda/include/cudaGL.h", + "cuda/include/cudaProfiler.h", + "cuda/include/cudaVDPAU.h", + "cuda/include/cuda_device_runtime_api.h", + "cuda/include/cuda_fp16.h", + "cuda/include/cuda_fp16.hpp", + "cuda/include/cuda_gl_interop.h", + "cuda/include/cuda_occupancy.h", + "cuda/include/cuda_profiler_api.h", "cuda/include/cuda_runtime.h", + "cuda/include/cuda_runtime_api.h", + "cuda/include/cuda_surface_types.h", + "cuda/include/cuda_texture_types.h", + "cuda/include/cuda_vdpau_interop.h", + "cuda/include/cudalibxt.h", + "cuda/include/cudnn.h", + "cuda/include/cufft.h", "cuda/include/cufftXt.h", - "cuda/include/sm_61_intrinsics.h", - "cuda/include/texture_fetch_functions.h", + "cuda/include/cufftw.h", + "cuda/include/curand.h", + "cuda/include/curand_discrete.h", + "cuda/include/curand_discrete2.h", + "cuda/include/curand_globals.h", + "cuda/include/curand_kernel.h", + "cuda/include/curand_lognormal.h", "cuda/include/curand_mrg32k3a.h", - "cuda/include/host_defines.h", - "cuda/include/common_functions.h", - "cuda/include/nppi_support_functions.h", - "cuda/include/nppi_linear_transforms.h", - "cuda/include/device_double_functions.hpp", - "cuda/include/math_constants.h", - "cuda/include/nvToolsExtSync.h", - "cuda/include/npps_initialization.h", + "cuda/include/curand_mtgp32.h", + "cuda/include/curand_mtgp32_host.h", + "cuda/include/curand_mtgp32_kernel.h", + "cuda/include/curand_mtgp32dc_p_11213.h", + "cuda/include/curand_normal.h", + "cuda/include/curand_normal_static.h", + "cuda/include/curand_philox4x32_x.h", + "cuda/include/curand_poisson.h", + "cuda/include/curand_precalc.h", + "cuda/include/curand_uniform.h", + "cuda/include/cusolverDn.h", + "cuda/include/cusolverRf.h", + "cuda/include/cusolverSp.h", "cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h", - "cuda/include/texture_indirect_functions.hpp", - "cuda/include/cudaProfiler.h", - "cuda/include/npps_filtering_functions.h", + "cuda/include/cusolver_common.h", + "cuda/include/cusparse.h", "cuda/include/cusparse_v2.h", - "cuda/include/nppi.h", - "cuda/include/surface_indirect_functions.h", - "cuda/include/sm_30_intrinsics.h", + "cuda/include/device_atomic_functions.h", + "cuda/include/device_atomic_functions.hpp", "cuda/include/device_double_functions.h", - "cuda/include/sm_35_intrinsics.h", - "cuda/include/cusolverSp.h", - "cuda/include/library_types.h", - "cuda/include/surface_indirect_functions.hpp", - "cuda/include/cudalibxt.h", - "cuda/include/channel_descriptor.h", + "cuda/include/device_double_functions.hpp", + "cuda/include/device_functions.h", + "cuda/include/device_functions.hpp", "cuda/include/device_functions_decls.h", - "cuda/include/curand_kernel.h", - "cuda/include/curand_mtgp32_host.h", - "cuda/include/nvToolsExtCuda.h", - "cuda/include/nvToolsExt.h", - "cuda/include/cuComplex.h", - "cuda/include/sm_32_atomic_functions.h", - "cuda/include/texture_indirect_functions.h", - "cuda/include/sm_32_atomic_functions.hpp", - "cuda/include/sm_20_intrinsics.hpp", "cuda/include/device_launch_parameters.h", - "cuda/include/curand_mtgp32.h", - "cuda/include/texture_fetch_functions.hpp", - "cuda/include/cuda_occupancy.h", - "cuda/include/CL/opencl.h", - "cuda/include/CL/cl_platform.h", - "cuda/include/CL/cl_egl.h", - "cuda/include/CL/cl_gl.h", - "cuda/include/CL/cl.h", - "cuda/include/CL/cl_gl_ext.h", - "cuda/include/CL/cl_ext.h", - "cuda/include/CL/cl.hpp", + "cuda/include/device_types.h", + "cuda/include/driver_functions.h", + "cuda/include/driver_types.h", + "cuda/include/dynlink_cuda.h", + "cuda/include/dynlink_cuda_cuda.h", + "cuda/include/dynlink_cuviddec.h", + "cuda/include/dynlink_nvcuvid.h", + "cuda/include/fatBinaryCtl.h", + "cuda/include/fatbinary.h", "cuda/include/host_config.h", - "cuda/include/cuda_surface_types.h", + "cuda/include/host_defines.h", + "cuda/include/library_types.h", + "cuda/include/math_constants.h", "cuda/include/math_functions.h", + "cuda/include/math_functions.hpp", + "cuda/include/math_functions_dbl_ptx3.h", + "cuda/include/math_functions_dbl_ptx3.hpp", + "cuda/include/mma.h", + "cuda/include/npp.h", + "cuda/include/nppcore.h", + "cuda/include/nppdefs.h", + "cuda/include/nppi.h", + "cuda/include/nppi_arithmetic_and_logical_operations.h", + "cuda/include/nppi_color_conversion.h", + "cuda/include/nppi_compression_functions.h", + "cuda/include/nppi_computer_vision.h", + "cuda/include/nppi_data_exchange_and_initialization.h", + "cuda/include/nppi_filtering_functions.h", + "cuda/include/nppi_geometry_transforms.h", + "cuda/include/nppi_linear_transforms.h", + "cuda/include/nppi_morphological_operations.h", + "cuda/include/nppi_statistics_functions.h", + "cuda/include/nppi_support_functions.h", + "cuda/include/nppi_threshold_and_compare_operations.h", + "cuda/include/npps.h", + "cuda/include/npps_arithmetic_and_logical_operations.h", + "cuda/include/npps_conversion_functions.h", + "cuda/include/npps_filtering_functions.h", + "cuda/include/npps_initialization.h", + "cuda/include/npps_statistics_functions.h", + "cuda/include/npps_support_functions.h", + "cuda/include/nppversion.h", + "cuda/include/nvToolsExt.h", + "cuda/include/nvToolsExtCuda.h", + "cuda/include/nvToolsExtCudaRt.h", "cuda/include/nvToolsExtMeta.h", + "cuda/include/nvToolsExtSync.h", + "cuda/include/nvblas.h", + "cuda/include/nvfunctional", + "cuda/include/nvgraph.h", + "cuda/include/nvml.h", + "cuda/include/nvrtc.h", + "cuda/include/sm_20_atomic_functions.h", "cuda/include/sm_20_atomic_functions.hpp", - "cuda/include/device_functions.h", - "cuda/include/device_types.h", - "cuda/include/npps_conversion_functions.h", - "cuda/include/curand_precalc.h", - "cuda/include/cusolverRf.h", + "cuda/include/sm_20_intrinsics.h", + "cuda/include/sm_20_intrinsics.hpp", + "cuda/include/sm_30_intrinsics.h", + "cuda/include/sm_30_intrinsics.hpp", + "cuda/include/sm_32_atomic_functions.h", + "cuda/include/sm_32_atomic_functions.hpp", + "cuda/include/sm_32_intrinsics.h", + "cuda/include/sm_32_intrinsics.hpp", + "cuda/include/sm_35_atomic_functions.h", + "cuda/include/sm_35_intrinsics.h", + "cuda/include/sm_60_atomic_functions.h", "cuda/include/sm_60_atomic_functions.hpp", - "cuda/include/cuviddec.h", - "cuda/include/curand_discrete2.h", - "cuda/include/device_functions.hpp", - "cuda/include/thrust/transform_scan.h", - "cuda/include/thrust/system_error.h", - "cuda/include/thrust/device_malloc.h", - "cuda/include/thrust/partition.h", - "cuda/include/thrust/unique.h", - "cuda/include/thrust/device_delete.h", - "cuda/include/thrust/execution_policy.h", + "cuda/include/sm_61_intrinsics.h", + "cuda/include/sm_61_intrinsics.hpp", + "cuda/include/sobol_direction_vectors.h", + "cuda/include/surface_functions.h", + "cuda/include/surface_functions.hpp", + "cuda/include/surface_indirect_functions.h", + "cuda/include/surface_indirect_functions.hpp", + "cuda/include/surface_types.h", + "cuda/include/texture_fetch_functions.h", + "cuda/include/texture_fetch_functions.hpp", + "cuda/include/texture_indirect_functions.h", + "cuda/include/texture_indirect_functions.hpp", + "cuda/include/texture_types.h", "cuda/include/thrust/adjacent_difference.h", - "cuda/include/thrust/sequence.h", - "cuda/include/thrust/merge.h", - "cuda/include/thrust/device_new.h", - "cuda/include/thrust/transform_reduce.h", - "cuda/include/thrust/device_vector.h", - "cuda/include/thrust/gather.h", - "cuda/include/thrust/sort.h", - "cuda/include/thrust/scan.h", - "cuda/include/thrust/detail/temporary_array.h", - "cuda/include/thrust/detail/util/align.h", - "cuda/include/thrust/detail/util/blocking.h", - "cuda/include/thrust/detail/transform.inl", - "cuda/include/thrust/detail/device_vector.inl", + "cuda/include/thrust/advance.h", + "cuda/include/thrust/binary_search.h", + "cuda/include/thrust/complex.h", + "cuda/include/thrust/copy.h", + "cuda/include/thrust/count.h", + "cuda/include/thrust/detail/adjacent_difference.inl", + "cuda/include/thrust/detail/advance.inl", + "cuda/include/thrust/detail/allocator/allocator_traits.h", + "cuda/include/thrust/detail/allocator/allocator_traits.inl", + "cuda/include/thrust/detail/allocator/copy_construct_range.h", + "cuda/include/thrust/detail/allocator/copy_construct_range.inl", + "cuda/include/thrust/detail/allocator/default_construct_range.h", + "cuda/include/thrust/detail/allocator/default_construct_range.inl", + "cuda/include/thrust/detail/allocator/destroy_range.h", + "cuda/include/thrust/detail/allocator/destroy_range.inl", + "cuda/include/thrust/detail/allocator/fill_construct_range.h", + "cuda/include/thrust/detail/allocator/fill_construct_range.inl", + "cuda/include/thrust/detail/allocator/malloc_allocator.h", + "cuda/include/thrust/detail/allocator/malloc_allocator.inl", + "cuda/include/thrust/detail/allocator/no_throw_allocator.h", + "cuda/include/thrust/detail/allocator/tagged_allocator.h", + "cuda/include/thrust/detail/allocator/tagged_allocator.inl", + "cuda/include/thrust/detail/allocator/temporary_allocator.h", + "cuda/include/thrust/detail/allocator/temporary_allocator.inl", "cuda/include/thrust/detail/binary_search.inl", - "cuda/include/thrust/detail/overlapped_copy.h", - "cuda/include/thrust/detail/vector_base.inl", - "cuda/include/thrust/detail/device_reference.inl", - "cuda/include/thrust/detail/functional/actor.h", - "cuda/include/thrust/detail/functional/value.h", - "cuda/include/thrust/detail/functional/operators.h", - "cuda/include/thrust/detail/functional/operators/logical_operators.h", - "cuda/include/thrust/detail/functional/operators/relational_operators.h", - "cuda/include/thrust/detail/functional/operators/assignment_operator.h", - "cuda/include/thrust/detail/functional/operators/bitwise_operators.h", - "cuda/include/thrust/detail/functional/operators/operator_adaptors.h", - "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h", - "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h", - "cuda/include/thrust/detail/functional/argument.h", - "cuda/include/thrust/detail/functional/placeholder.h", - "cuda/include/thrust/detail/functional/actor.inl", - "cuda/include/thrust/detail/functional/composite.h", - "cuda/include/thrust/detail/static_map.h", - "cuda/include/thrust/detail/type_traits/has_nested_type.h", - "cuda/include/thrust/detail/type_traits/is_call_possible.h", - "cuda/include/thrust/detail/type_traits/function_traits.h", - "cuda/include/thrust/detail/type_traits/pointer_traits.h", - "cuda/include/thrust/detail/type_traits/has_member_function.h", - "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h", - "cuda/include/thrust/detail/type_traits/minimum_type.h", - "cuda/include/thrust/detail/type_traits/has_trivial_assign.h", - "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h", - "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h", - "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h", - "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h", - "cuda/include/thrust/detail/reference.h", - "cuda/include/thrust/detail/inner_product.inl", - "cuda/include/thrust/detail/use_default.h", - "cuda/include/thrust/detail/sequence.inl", - "cuda/include/thrust/detail/sort.inl", - "cuda/include/thrust/detail/equal.inl", - "cuda/include/thrust/detail/execution_policy.h", - "cuda/include/thrust/detail/integer_traits.h", - "cuda/include/thrust/detail/type_traits.h", - "cuda/include/thrust/detail/reverse.inl", - "cuda/include/thrust/detail/tabulate.inl", - "cuda/include/thrust/detail/unique.inl", - "cuda/include/thrust/detail/scatter.inl", - "cuda/include/thrust/detail/set_operations.inl", - "cuda/include/thrust/detail/device_malloc.inl", - "cuda/include/thrust/detail/copy_if.inl", - "cuda/include/thrust/detail/fill.inl", - "cuda/include/thrust/detail/temporary_array.inl", - "cuda/include/thrust/detail/transform_scan.inl", - "cuda/include/thrust/detail/minmax.h", - "cuda/include/thrust/detail/swap.inl", - "cuda/include/thrust/detail/pointer.inl", - "cuda/include/thrust/detail/transform_reduce.inl", - "cuda/include/thrust/detail/config.h", - "cuda/include/thrust/detail/distance.inl", - "cuda/include/thrust/detail/pair.inl", - "cuda/include/thrust/detail/allocator/temporary_allocator.h", - "cuda/include/thrust/detail/allocator/tagged_allocator.h", - "cuda/include/thrust/detail/allocator/destroy_range.inl", - "cuda/include/thrust/detail/allocator/destroy_range.h", - "cuda/include/thrust/detail/allocator/no_throw_allocator.h", - "cuda/include/thrust/detail/allocator/default_construct_range.inl", - "cuda/include/thrust/detail/allocator/fill_construct_range.inl", - "cuda/include/thrust/detail/allocator/tagged_allocator.inl", - "cuda/include/thrust/detail/allocator/malloc_allocator.h", - "cuda/include/thrust/detail/allocator/allocator_traits.h", - "cuda/include/thrust/detail/allocator/copy_construct_range.h", - "cuda/include/thrust/detail/allocator/allocator_traits.inl", - "cuda/include/thrust/detail/allocator/default_construct_range.h", - "cuda/include/thrust/detail/allocator/copy_construct_range.inl", - "cuda/include/thrust/detail/allocator/malloc_allocator.inl", - "cuda/include/thrust/detail/allocator/temporary_allocator.inl", - "cuda/include/thrust/detail/allocator/fill_construct_range.h", - "cuda/include/thrust/detail/temporary_buffer.h", - "cuda/include/thrust/detail/reduce.inl", - "cuda/include/thrust/detail/device_new.inl", - "cuda/include/thrust/detail/pointer.h", - "cuda/include/thrust/detail/for_each.inl", - "cuda/include/thrust/detail/generate.inl", - "cuda/include/thrust/detail/dispatch/is_trivial_copy.h", - "cuda/include/thrust/detail/adjacent_difference.inl", - "cuda/include/thrust/detail/tuple_meta_transform.h", - "cuda/include/thrust/detail/functional.inl", - "cuda/include/thrust/detail/remove.inl", - "cuda/include/thrust/detail/tuple_transform.h", - "cuda/include/thrust/detail/merge.inl", - "cuda/include/thrust/detail/extrema.inl", - "cuda/include/thrust/detail/trivial_sequence.h", - "cuda/include/thrust/detail/vector_base.h", - "cuda/include/thrust/detail/count.inl", - "cuda/include/thrust/detail/uninitialized_copy.inl", - "cuda/include/thrust/detail/function.h", - "cuda/include/thrust/detail/swap_ranges.inl", - "cuda/include/thrust/detail/device_delete.inl", - "cuda/include/thrust/detail/static_assert.h", - "cuda/include/thrust/detail/logical.inl", - "cuda/include/thrust/detail/seq.h", - "cuda/include/thrust/detail/mpl/math.h", - "cuda/include/thrust/detail/mismatch.inl", - "cuda/include/thrust/detail/internal_functional.h", - "cuda/include/thrust/detail/get_iterator_value.h", - "cuda/include/thrust/detail/copy.inl", - "cuda/include/thrust/detail/copy.h", + "cuda/include/thrust/detail/complex/arithmetic.h", + "cuda/include/thrust/detail/complex/c99math.h", + "cuda/include/thrust/detail/complex/catrig.h", "cuda/include/thrust/detail/complex/catrigf.h", - "cuda/include/thrust/detail/complex/cpowf.h", - "cuda/include/thrust/detail/complex/csqrtf.h", + "cuda/include/thrust/detail/complex/ccosh.h", "cuda/include/thrust/detail/complex/ccoshf.h", - "cuda/include/thrust/detail/complex/csinhf.h", + "cuda/include/thrust/detail/complex/cexp.h", + "cuda/include/thrust/detail/complex/cexpf.h", + "cuda/include/thrust/detail/complex/clog.h", "cuda/include/thrust/detail/complex/clogf.h", - "cuda/include/thrust/detail/complex/ccosh.h", - "cuda/include/thrust/detail/complex/arithmetic.h", - "cuda/include/thrust/detail/complex/csqrt.h", - "cuda/include/thrust/detail/complex/cpow.h", "cuda/include/thrust/detail/complex/complex.inl", - "cuda/include/thrust/detail/complex/math_private.h", - "cuda/include/thrust/detail/complex/c99math.h", + "cuda/include/thrust/detail/complex/cpow.h", + "cuda/include/thrust/detail/complex/cpowf.h", "cuda/include/thrust/detail/complex/cproj.h", - "cuda/include/thrust/detail/complex/catrig.h", - "cuda/include/thrust/detail/complex/ctanhf.h", - "cuda/include/thrust/detail/complex/cexpf.h", "cuda/include/thrust/detail/complex/csinh.h", - "cuda/include/thrust/detail/complex/stream.h", + "cuda/include/thrust/detail/complex/csinhf.h", + "cuda/include/thrust/detail/complex/csqrt.h", + "cuda/include/thrust/detail/complex/csqrtf.h", "cuda/include/thrust/detail/complex/ctanh.h", - "cuda/include/thrust/detail/complex/cexp.h", - "cuda/include/thrust/detail/complex/clog.h", - "cuda/include/thrust/detail/range/head_flags.h", - "cuda/include/thrust/detail/range/tail_flags.h", - "cuda/include/thrust/detail/execute_with_allocator.h", - "cuda/include/thrust/detail/integer_math.h", - "cuda/include/thrust/detail/swap.h", - "cuda/include/thrust/detail/uninitialized_fill.inl", - "cuda/include/thrust/detail/scan.inl", - "cuda/include/thrust/detail/gather.inl", - "cuda/include/thrust/detail/reference_forward_declaration.h", - "cuda/include/thrust/detail/numeric_traits.h", - "cuda/include/thrust/detail/reference.inl", - "cuda/include/thrust/detail/cstdint.h", - "cuda/include/thrust/detail/device_free.inl", - "cuda/include/thrust/detail/copy_if.h", - "cuda/include/thrust/detail/partition.inl", - "cuda/include/thrust/detail/find.inl", - "cuda/include/thrust/detail/config/forceinline.h", - "cuda/include/thrust/detail/config/debug.h", - "cuda/include/thrust/detail/config/config.h", - "cuda/include/thrust/detail/config/host_device.h", - "cuda/include/thrust/detail/config/host_system.h", + "cuda/include/thrust/detail/complex/ctanhf.h", + "cuda/include/thrust/detail/complex/math_private.h", + "cuda/include/thrust/detail/complex/stream.h", + "cuda/include/thrust/detail/config.h", "cuda/include/thrust/detail/config/compiler.h", - "cuda/include/thrust/detail/config/device_system.h", "cuda/include/thrust/detail/config/compiler_fence.h", + "cuda/include/thrust/detail/config/config.h", + "cuda/include/thrust/detail/config/debug.h", + "cuda/include/thrust/detail/config/device_system.h", "cuda/include/thrust/detail/config/exec_check_disable.h", - "cuda/include/thrust/detail/config/simple_defines.h", + "cuda/include/thrust/detail/config/forceinline.h", "cuda/include/thrust/detail/config/global_workarounds.h", - "cuda/include/thrust/detail/replace.inl", + "cuda/include/thrust/detail/config/host_device.h", + "cuda/include/thrust/detail/config/host_system.h", + "cuda/include/thrust/detail/config/simple_defines.h", + "cuda/include/thrust/detail/contiguous_storage.h", + "cuda/include/thrust/detail/contiguous_storage.inl", + "cuda/include/thrust/detail/copy.h", + "cuda/include/thrust/detail/copy.inl", + "cuda/include/thrust/detail/copy_if.h", + "cuda/include/thrust/detail/copy_if.inl", + "cuda/include/thrust/detail/count.inl", + "cuda/include/thrust/detail/cstdint.h", + "cuda/include/thrust/detail/device_delete.inl", + "cuda/include/thrust/detail/device_free.inl", + "cuda/include/thrust/detail/device_malloc.inl", + "cuda/include/thrust/detail/device_new.inl", "cuda/include/thrust/detail/device_ptr.inl", - "cuda/include/thrust/detail/tuple.inl", - "cuda/include/thrust/detail/malloc_and_free.h", + "cuda/include/thrust/detail/device_reference.inl", + "cuda/include/thrust/detail/device_vector.inl", + "cuda/include/thrust/detail/dispatch/is_trivial_copy.h", + "cuda/include/thrust/detail/distance.inl", + "cuda/include/thrust/detail/equal.inl", + "cuda/include/thrust/detail/execute_with_allocator.h", + "cuda/include/thrust/detail/execution_policy.h", + "cuda/include/thrust/detail/extrema.inl", + "cuda/include/thrust/detail/fill.inl", + "cuda/include/thrust/detail/find.inl", + "cuda/include/thrust/detail/for_each.inl", + "cuda/include/thrust/detail/function.h", + "cuda/include/thrust/detail/functional.inl", + "cuda/include/thrust/detail/functional/actor.h", + "cuda/include/thrust/detail/functional/actor.inl", + "cuda/include/thrust/detail/functional/argument.h", + "cuda/include/thrust/detail/functional/composite.h", + "cuda/include/thrust/detail/functional/operators.h", + "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h", + "cuda/include/thrust/detail/functional/operators/assignment_operator.h", + "cuda/include/thrust/detail/functional/operators/bitwise_operators.h", + "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h", + "cuda/include/thrust/detail/functional/operators/logical_operators.h", + "cuda/include/thrust/detail/functional/operators/operator_adaptors.h", + "cuda/include/thrust/detail/functional/operators/relational_operators.h", + "cuda/include/thrust/detail/functional/placeholder.h", + "cuda/include/thrust/detail/functional/value.h", + "cuda/include/thrust/detail/gather.inl", + "cuda/include/thrust/detail/generate.inl", + "cuda/include/thrust/detail/get_iterator_value.h", "cuda/include/thrust/detail/host_vector.inl", + "cuda/include/thrust/detail/inner_product.inl", + "cuda/include/thrust/detail/integer_math.h", + "cuda/include/thrust/detail/integer_traits.h", + "cuda/include/thrust/detail/internal_functional.h", + "cuda/include/thrust/detail/logical.inl", + "cuda/include/thrust/detail/malloc_and_free.h", + "cuda/include/thrust/detail/merge.inl", + "cuda/include/thrust/detail/minmax.h", + "cuda/include/thrust/detail/mismatch.inl", + "cuda/include/thrust/detail/mpl/math.h", + "cuda/include/thrust/detail/numeric_traits.h", + "cuda/include/thrust/detail/overlapped_copy.h", + "cuda/include/thrust/detail/pair.inl", + "cuda/include/thrust/detail/partition.inl", + "cuda/include/thrust/detail/pointer.h", + "cuda/include/thrust/detail/pointer.inl", + "cuda/include/thrust/detail/range/head_flags.h", + "cuda/include/thrust/detail/range/tail_flags.h", "cuda/include/thrust/detail/raw_pointer_cast.h", - "cuda/include/thrust/detail/advance.inl", - "cuda/include/thrust/detail/contiguous_storage.h", "cuda/include/thrust/detail/raw_reference_cast.h", - "cuda/include/thrust/detail/contiguous_storage.inl", - "cuda/include/thrust/reverse.h", - "cuda/include/thrust/device_malloc_allocator.h", - "cuda/include/thrust/scatter.h", - "cuda/include/thrust/pair.h", - "cuda/include/thrust/advance.h", - "cuda/include/thrust/find.h", - "cuda/include/thrust/device_ptr.h", - "cuda/include/thrust/generate.h", - "cuda/include/thrust/uninitialized_fill.h", - "cuda/include/thrust/system/system_error.h", - "cuda/include/thrust/system/detail/bad_alloc.h", - "cuda/include/thrust/system/detail/adl/transform_scan.h", - "cuda/include/thrust/system/detail/adl/unique_by_key.h", - "cuda/include/thrust/system/detail/adl/partition.h", - "cuda/include/thrust/system/detail/adl/unique.h", - "cuda/include/thrust/system/detail/adl/adjacent_difference.h", - "cuda/include/thrust/system/detail/adl/sequence.h", - "cuda/include/thrust/system/detail/adl/merge.h", - "cuda/include/thrust/system/detail/adl/transform_reduce.h", - "cuda/include/thrust/system/detail/adl/gather.h", - "cuda/include/thrust/system/detail/adl/sort.h", - "cuda/include/thrust/system/detail/adl/scan.h", - "cuda/include/thrust/system/detail/adl/temporary_buffer.h", - "cuda/include/thrust/system/detail/adl/scan_by_key.h", - "cuda/include/thrust/system/detail/adl/reverse.h", - "cuda/include/thrust/system/detail/adl/assign_value.h", - "cuda/include/thrust/system/detail/adl/scatter.h", - "cuda/include/thrust/system/detail/adl/find.h", - "cuda/include/thrust/system/detail/adl/generate.h", - "cuda/include/thrust/system/detail/adl/uninitialized_fill.h", - "cuda/include/thrust/system/detail/adl/remove.h", - "cuda/include/thrust/system/detail/adl/tabulate.h", - "cuda/include/thrust/system/detail/adl/for_each.h", - "cuda/include/thrust/system/detail/adl/reduce_by_key.h", - "cuda/include/thrust/system/detail/adl/reduce.h", - "cuda/include/thrust/system/detail/adl/equal.h", - "cuda/include/thrust/system/detail/adl/copy.h", - "cuda/include/thrust/system/detail/adl/swap_ranges.h", - "cuda/include/thrust/system/detail/adl/uninitialized_copy.h", - "cuda/include/thrust/system/detail/adl/binary_search.h", - "cuda/include/thrust/system/detail/adl/set_operations.h", - "cuda/include/thrust/system/detail/adl/mismatch.h", - "cuda/include/thrust/system/detail/adl/extrema.h", - "cuda/include/thrust/system/detail/adl/count.h", - "cuda/include/thrust/system/detail/adl/replace.h", + "cuda/include/thrust/detail/reduce.inl", + "cuda/include/thrust/detail/reference.h", + "cuda/include/thrust/detail/reference.inl", + "cuda/include/thrust/detail/reference_forward_declaration.h", + "cuda/include/thrust/detail/remove.inl", + "cuda/include/thrust/detail/replace.inl", + "cuda/include/thrust/detail/reverse.inl", + "cuda/include/thrust/detail/scan.inl", + "cuda/include/thrust/detail/scatter.inl", + "cuda/include/thrust/detail/seq.h", + "cuda/include/thrust/detail/sequence.inl", + "cuda/include/thrust/detail/set_operations.inl", + "cuda/include/thrust/detail/sort.inl", + "cuda/include/thrust/detail/static_assert.h", + "cuda/include/thrust/detail/static_map.h", + "cuda/include/thrust/detail/swap.h", + "cuda/include/thrust/detail/swap.inl", + "cuda/include/thrust/detail/swap_ranges.inl", + "cuda/include/thrust/detail/tabulate.inl", + "cuda/include/thrust/detail/temporary_array.h", + "cuda/include/thrust/detail/temporary_array.inl", + "cuda/include/thrust/detail/temporary_buffer.h", + "cuda/include/thrust/detail/transform.inl", + "cuda/include/thrust/detail/transform_reduce.inl", + "cuda/include/thrust/detail/transform_scan.inl", + "cuda/include/thrust/detail/trivial_sequence.h", + "cuda/include/thrust/detail/tuple.inl", + "cuda/include/thrust/detail/tuple_meta_transform.h", + "cuda/include/thrust/detail/tuple_transform.h", + "cuda/include/thrust/detail/type_traits.h", + "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h", + "cuda/include/thrust/detail/type_traits/function_traits.h", + "cuda/include/thrust/detail/type_traits/has_member_function.h", + "cuda/include/thrust/detail/type_traits/has_nested_type.h", + "cuda/include/thrust/detail/type_traits/has_trivial_assign.h", + "cuda/include/thrust/detail/type_traits/is_call_possible.h", + "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h", + "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h", + "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h", + "cuda/include/thrust/detail/type_traits/minimum_type.h", + "cuda/include/thrust/detail/type_traits/pointer_traits.h", + "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h", + "cuda/include/thrust/detail/uninitialized_copy.inl", + "cuda/include/thrust/detail/uninitialized_fill.inl", + "cuda/include/thrust/detail/unique.inl", + "cuda/include/thrust/detail/use_default.h", + "cuda/include/thrust/detail/util/align.h", + "cuda/include/thrust/detail/util/blocking.h", + "cuda/include/thrust/detail/vector_base.h", + "cuda/include/thrust/detail/vector_base.inl", + "cuda/include/thrust/device_allocator.h", + "cuda/include/thrust/device_delete.h", + "cuda/include/thrust/device_free.h", + "cuda/include/thrust/device_malloc.h", + "cuda/include/thrust/device_malloc_allocator.h", + "cuda/include/thrust/device_new.h", + "cuda/include/thrust/device_new_allocator.h", + "cuda/include/thrust/device_ptr.h", + "cuda/include/thrust/device_reference.h", + "cuda/include/thrust/device_vector.h", + "cuda/include/thrust/distance.h", + "cuda/include/thrust/equal.h", + "cuda/include/thrust/execution_policy.h", + "cuda/include/thrust/extrema.h", + "cuda/include/thrust/fill.h", + "cuda/include/thrust/find.h", + "cuda/include/thrust/for_each.h", + "cuda/include/thrust/functional.h", + "cuda/include/thrust/gather.h", + "cuda/include/thrust/generate.h", + "cuda/include/thrust/host_vector.h", + "cuda/include/thrust/inner_product.h", + "cuda/include/thrust/iterator/constant_iterator.h", + "cuda/include/thrust/iterator/counting_iterator.h", + "cuda/include/thrust/iterator/detail/any_assign.h", + "cuda/include/thrust/iterator/detail/any_system_tag.h", + "cuda/include/thrust/iterator/detail/constant_iterator_base.h", + "cuda/include/thrust/iterator/detail/counting_iterator.inl", + "cuda/include/thrust/iterator/detail/device_system_tag.h", + "cuda/include/thrust/iterator/detail/discard_iterator_base.h", + "cuda/include/thrust/iterator/detail/distance_from_result.h", + "cuda/include/thrust/iterator/detail/host_system_tag.h", + "cuda/include/thrust/iterator/detail/is_iterator_category.h", + "cuda/include/thrust/iterator/detail/is_trivial_iterator.h", + "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h", + "cuda/include/thrust/iterator/detail/iterator_category_to_system.h", + "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h", + "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h", + "cuda/include/thrust/iterator/detail/iterator_facade_category.h", + "cuda/include/thrust/iterator/detail/iterator_traits.inl", + "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h", + "cuda/include/thrust/iterator/detail/join_iterator.h", + "cuda/include/thrust/iterator/detail/minimum_category.h", + "cuda/include/thrust/iterator/detail/minimum_system.h", + "cuda/include/thrust/iterator/detail/normal_iterator.h", + "cuda/include/thrust/iterator/detail/permutation_iterator_base.h", + "cuda/include/thrust/iterator/detail/retag.h", + "cuda/include/thrust/iterator/detail/reverse_iterator.inl", + "cuda/include/thrust/iterator/detail/reverse_iterator_base.h", + "cuda/include/thrust/iterator/detail/tagged_iterator.h", + "cuda/include/thrust/iterator/detail/transform_iterator.inl", + "cuda/include/thrust/iterator/detail/transform_output_iterator.inl", + "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h", + "cuda/include/thrust/iterator/detail/universal_categories.h", + "cuda/include/thrust/iterator/detail/zip_iterator.inl", + "cuda/include/thrust/iterator/detail/zip_iterator_base.h", + "cuda/include/thrust/iterator/discard_iterator.h", + "cuda/include/thrust/iterator/iterator_adaptor.h", + "cuda/include/thrust/iterator/iterator_categories.h", + "cuda/include/thrust/iterator/iterator_facade.h", + "cuda/include/thrust/iterator/iterator_traits.h", + "cuda/include/thrust/iterator/permutation_iterator.h", + "cuda/include/thrust/iterator/retag.h", + "cuda/include/thrust/iterator/reverse_iterator.h", + "cuda/include/thrust/iterator/transform_iterator.h", + "cuda/include/thrust/iterator/transform_output_iterator.h", + "cuda/include/thrust/iterator/zip_iterator.h", + "cuda/include/thrust/logical.h", + "cuda/include/thrust/memory.h", + "cuda/include/thrust/merge.h", + "cuda/include/thrust/mismatch.h", + "cuda/include/thrust/pair.h", + "cuda/include/thrust/partition.h", + "cuda/include/thrust/random.h", + "cuda/include/thrust/random/detail/discard_block_engine.inl", + "cuda/include/thrust/random/detail/linear_congruential_engine.inl", + "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h", + "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl", + "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h", + "cuda/include/thrust/random/detail/mod.h", + "cuda/include/thrust/random/detail/normal_distribution.inl", + "cuda/include/thrust/random/detail/normal_distribution_base.h", + "cuda/include/thrust/random/detail/random_core_access.h", + "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl", + "cuda/include/thrust/random/detail/uniform_int_distribution.inl", + "cuda/include/thrust/random/detail/uniform_real_distribution.inl", + "cuda/include/thrust/random/detail/xor_combine_engine.inl", + "cuda/include/thrust/random/detail/xor_combine_engine_max.h", + "cuda/include/thrust/random/discard_block_engine.h", + "cuda/include/thrust/random/linear_congruential_engine.h", + "cuda/include/thrust/random/linear_feedback_shift_engine.h", + "cuda/include/thrust/random/normal_distribution.h", + "cuda/include/thrust/random/subtract_with_carry_engine.h", + "cuda/include/thrust/random/uniform_int_distribution.h", + "cuda/include/thrust/random/uniform_real_distribution.h", + "cuda/include/thrust/random/xor_combine_engine.h", + "cuda/include/thrust/reduce.h", + "cuda/include/thrust/remove.h", + "cuda/include/thrust/replace.h", + "cuda/include/thrust/reverse.h", + "cuda/include/thrust/scan.h", + "cuda/include/thrust/scatter.h", + "cuda/include/thrust/sequence.h", + "cuda/include/thrust/set_operations.h", + "cuda/include/thrust/sort.h", + "cuda/include/thrust/swap.h", + "cuda/include/thrust/system/cpp/detail/adjacent_difference.h", + "cuda/include/thrust/system/cpp/detail/assign_value.h", + "cuda/include/thrust/system/cpp/detail/binary_search.h", + "cuda/include/thrust/system/cpp/detail/copy.h", + "cuda/include/thrust/system/cpp/detail/copy_if.h", + "cuda/include/thrust/system/cpp/detail/count.h", + "cuda/include/thrust/system/cpp/detail/equal.h", + "cuda/include/thrust/system/cpp/detail/execution_policy.h", + "cuda/include/thrust/system/cpp/detail/extrema.h", + "cuda/include/thrust/system/cpp/detail/fill.h", + "cuda/include/thrust/system/cpp/detail/find.h", + "cuda/include/thrust/system/cpp/detail/for_each.h", + "cuda/include/thrust/system/cpp/detail/gather.h", + "cuda/include/thrust/system/cpp/detail/generate.h", + "cuda/include/thrust/system/cpp/detail/get_value.h", + "cuda/include/thrust/system/cpp/detail/inner_product.h", + "cuda/include/thrust/system/cpp/detail/iter_swap.h", + "cuda/include/thrust/system/cpp/detail/logical.h", + "cuda/include/thrust/system/cpp/detail/malloc_and_free.h", + "cuda/include/thrust/system/cpp/detail/memory.inl", + "cuda/include/thrust/system/cpp/detail/merge.h", + "cuda/include/thrust/system/cpp/detail/mismatch.h", + "cuda/include/thrust/system/cpp/detail/par.h", + "cuda/include/thrust/system/cpp/detail/partition.h", + "cuda/include/thrust/system/cpp/detail/reduce.h", + "cuda/include/thrust/system/cpp/detail/reduce_by_key.h", + "cuda/include/thrust/system/cpp/detail/remove.h", + "cuda/include/thrust/system/cpp/detail/replace.h", + "cuda/include/thrust/system/cpp/detail/reverse.h", + "cuda/include/thrust/system/cpp/detail/scan.h", + "cuda/include/thrust/system/cpp/detail/scan_by_key.h", + "cuda/include/thrust/system/cpp/detail/scatter.h", + "cuda/include/thrust/system/cpp/detail/sequence.h", + "cuda/include/thrust/system/cpp/detail/set_operations.h", + "cuda/include/thrust/system/cpp/detail/sort.h", + "cuda/include/thrust/system/cpp/detail/swap_ranges.h", + "cuda/include/thrust/system/cpp/detail/tabulate.h", + "cuda/include/thrust/system/cpp/detail/temporary_buffer.h", + "cuda/include/thrust/system/cpp/detail/transform.h", + "cuda/include/thrust/system/cpp/detail/transform_reduce.h", + "cuda/include/thrust/system/cpp/detail/transform_scan.h", + "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h", + "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h", + "cuda/include/thrust/system/cpp/detail/unique.h", + "cuda/include/thrust/system/cpp/detail/unique_by_key.h", + "cuda/include/thrust/system/cpp/detail/vector.inl", + "cuda/include/thrust/system/cpp/execution_policy.h", + "cuda/include/thrust/system/cpp/memory.h", + "cuda/include/thrust/system/cpp/vector.h", + "cuda/include/thrust/system/cuda/config.h", + "cuda/include/thrust/system/cuda/detail/adjacent_difference.h", + "cuda/include/thrust/system/cuda/detail/assign_value.h", + "cuda/include/thrust/system/cuda/detail/binary_search.h", + "cuda/include/thrust/system/cuda/detail/copy.h", + "cuda/include/thrust/system/cuda/detail/copy_if.h", + "cuda/include/thrust/system/cuda/detail/core/agent_launcher.h", + "cuda/include/thrust/system/cuda/detail/core/alignment.h", + "cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h", + "cuda/include/thrust/system/cuda/detail/core/util.h", + "cuda/include/thrust/system/cuda/detail/count.h", + "cuda/include/thrust/system/cuda/detail/cross_system.h", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh", + "cuda/include/thrust/system/cuda/detail/cub/cub.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh", + "cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh", + "cuda/include/thrust/system/cuda/detail/equal.h", + "cuda/include/thrust/system/cuda/detail/error.inl", + "cuda/include/thrust/system/cuda/detail/execution_policy.h", + "cuda/include/thrust/system/cuda/detail/extrema.h", + "cuda/include/thrust/system/cuda/detail/fill.h", + "cuda/include/thrust/system/cuda/detail/find.h", + "cuda/include/thrust/system/cuda/detail/for_each.h", + "cuda/include/thrust/system/cuda/detail/gather.h", + "cuda/include/thrust/system/cuda/detail/generate.h", + "cuda/include/thrust/system/cuda/detail/get_value.h", + "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h", + "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h", + "cuda/include/thrust/system/cuda/detail/inner_product.h", + "cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h", + "cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h", + "cuda/include/thrust/system/cuda/detail/iter_swap.h", + "cuda/include/thrust/system/cuda/detail/logical.h", + "cuda/include/thrust/system/cuda/detail/malloc_and_free.h", + "cuda/include/thrust/system/cuda/detail/memory.inl", + "cuda/include/thrust/system/cuda/detail/memory_buffer.h", + "cuda/include/thrust/system/cuda/detail/merge.h", + "cuda/include/thrust/system/cuda/detail/mismatch.h", + "cuda/include/thrust/system/cuda/detail/par.h", + "cuda/include/thrust/system/cuda/detail/par_to_seq.h", + "cuda/include/thrust/system/cuda/detail/parallel_for.h", + "cuda/include/thrust/system/cuda/detail/partition.h", + "cuda/include/thrust/system/cuda/detail/reduce.h", + "cuda/include/thrust/system/cuda/detail/reduce_by_key.h", + "cuda/include/thrust/system/cuda/detail/remove.h", + "cuda/include/thrust/system/cuda/detail/replace.h", + "cuda/include/thrust/system/cuda/detail/reverse.h", + "cuda/include/thrust/system/cuda/detail/scan.h", + "cuda/include/thrust/system/cuda/detail/scan_by_key.h", + "cuda/include/thrust/system/cuda/detail/scatter.h", + "cuda/include/thrust/system/cuda/detail/sequence.h", + "cuda/include/thrust/system/cuda/detail/set_operations.h", + "cuda/include/thrust/system/cuda/detail/sort.h", + "cuda/include/thrust/system/cuda/detail/swap_ranges.h", + "cuda/include/thrust/system/cuda/detail/tabulate.h", + "cuda/include/thrust/system/cuda/detail/temporary_buffer.h", + "cuda/include/thrust/system/cuda/detail/terminate.h", + "cuda/include/thrust/system/cuda/detail/transform.h", + "cuda/include/thrust/system/cuda/detail/transform_reduce.h", + "cuda/include/thrust/system/cuda/detail/transform_scan.h", + "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h", + "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h", + "cuda/include/thrust/system/cuda/detail/unique.h", + "cuda/include/thrust/system/cuda/detail/unique_by_key.h", + "cuda/include/thrust/system/cuda/detail/util.h", + "cuda/include/thrust/system/cuda/detail/vector.inl", + "cuda/include/thrust/system/cuda/error.h", + "cuda/include/thrust/system/cuda/execution_policy.h", + "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h", + "cuda/include/thrust/system/cuda/memory.h", + "cuda/include/thrust/system/cuda/vector.h", + "cuda/include/thrust/system/detail/adl/adjacent_difference.h", + "cuda/include/thrust/system/detail/adl/assign_value.h", + "cuda/include/thrust/system/detail/adl/binary_search.h", + "cuda/include/thrust/system/detail/adl/copy.h", + "cuda/include/thrust/system/detail/adl/copy_if.h", + "cuda/include/thrust/system/detail/adl/count.h", + "cuda/include/thrust/system/detail/adl/equal.h", + "cuda/include/thrust/system/detail/adl/extrema.h", + "cuda/include/thrust/system/detail/adl/fill.h", + "cuda/include/thrust/system/detail/adl/find.h", + "cuda/include/thrust/system/detail/adl/for_each.h", + "cuda/include/thrust/system/detail/adl/gather.h", + "cuda/include/thrust/system/detail/adl/generate.h", "cuda/include/thrust/system/detail/adl/get_value.h", "cuda/include/thrust/system/detail/adl/inner_product.h", - "cuda/include/thrust/system/detail/adl/copy_if.h", - "cuda/include/thrust/system/detail/adl/logical.h", "cuda/include/thrust/system/detail/adl/iter_swap.h", + "cuda/include/thrust/system/detail/adl/logical.h", "cuda/include/thrust/system/detail/adl/malloc_and_free.h", - "cuda/include/thrust/system/detail/adl/fill.h", + "cuda/include/thrust/system/detail/adl/merge.h", + "cuda/include/thrust/system/detail/adl/mismatch.h", + "cuda/include/thrust/system/detail/adl/partition.h", + "cuda/include/thrust/system/detail/adl/reduce.h", + "cuda/include/thrust/system/detail/adl/reduce_by_key.h", + "cuda/include/thrust/system/detail/adl/remove.h", + "cuda/include/thrust/system/detail/adl/replace.h", + "cuda/include/thrust/system/detail/adl/reverse.h", + "cuda/include/thrust/system/detail/adl/scan.h", + "cuda/include/thrust/system/detail/adl/scan_by_key.h", + "cuda/include/thrust/system/detail/adl/scatter.h", + "cuda/include/thrust/system/detail/adl/sequence.h", + "cuda/include/thrust/system/detail/adl/set_operations.h", + "cuda/include/thrust/system/detail/adl/sort.h", + "cuda/include/thrust/system/detail/adl/swap_ranges.h", + "cuda/include/thrust/system/detail/adl/tabulate.h", + "cuda/include/thrust/system/detail/adl/temporary_buffer.h", "cuda/include/thrust/system/detail/adl/transform.h", + "cuda/include/thrust/system/detail/adl/transform_reduce.h", + "cuda/include/thrust/system/detail/adl/transform_scan.h", + "cuda/include/thrust/system/detail/adl/uninitialized_copy.h", + "cuda/include/thrust/system/detail/adl/uninitialized_fill.h", + "cuda/include/thrust/system/detail/adl/unique.h", + "cuda/include/thrust/system/detail/adl/unique_by_key.h", + "cuda/include/thrust/system/detail/bad_alloc.h", "cuda/include/thrust/system/detail/errno.h", "cuda/include/thrust/system/detail/error_category.inl", - "cuda/include/thrust/system/detail/sequential/transform_scan.h", - "cuda/include/thrust/system/detail/sequential/unique_by_key.h", - "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h", - "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl", - "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h", - "cuda/include/thrust/system/detail/sequential/sort.inl", - "cuda/include/thrust/system/detail/sequential/partition.h", - "cuda/include/thrust/system/detail/sequential/unique.h", - "cuda/include/thrust/system/detail/sequential/execution_policy.h", - "cuda/include/thrust/system/detail/sequential/adjacent_difference.h", - "cuda/include/thrust/system/detail/sequential/sequence.h", - "cuda/include/thrust/system/detail/sequential/merge.h", - "cuda/include/thrust/system/detail/sequential/transform_reduce.h", - "cuda/include/thrust/system/detail/sequential/gather.h", - "cuda/include/thrust/system/detail/sequential/sort.h", - "cuda/include/thrust/system/detail/sequential/copy_backward.h", - "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl", - "cuda/include/thrust/system/detail/sequential/scan.h", - "cuda/include/thrust/system/detail/sequential/temporary_buffer.h", - "cuda/include/thrust/system/detail/sequential/scan_by_key.h", - "cuda/include/thrust/system/detail/sequential/reverse.h", - "cuda/include/thrust/system/detail/sequential/assign_value.h", - "cuda/include/thrust/system/detail/sequential/scatter.h", - "cuda/include/thrust/system/detail/sequential/find.h", - "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl", - "cuda/include/thrust/system/detail/sequential/merge.inl", - "cuda/include/thrust/system/detail/sequential/generate.h", - "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h", - "cuda/include/thrust/system/detail/sequential/general_copy.h", - "cuda/include/thrust/system/detail/sequential/insertion_sort.h", - "cuda/include/thrust/system/detail/sequential/remove.h", - "cuda/include/thrust/system/detail/sequential/tabulate.h", - "cuda/include/thrust/system/detail/sequential/for_each.h", - "cuda/include/thrust/system/detail/sequential/reduce_by_key.h", - "cuda/include/thrust/system/detail/sequential/reduce.h", - "cuda/include/thrust/system/detail/sequential/equal.h", - "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h", - "cuda/include/thrust/system/detail/sequential/copy.inl", - "cuda/include/thrust/system/detail/sequential/copy.h", - "cuda/include/thrust/system/detail/sequential/swap_ranges.h", - "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h", - "cuda/include/thrust/system/detail/sequential/binary_search.h", - "cuda/include/thrust/system/detail/sequential/set_operations.h", - "cuda/include/thrust/system/detail/sequential/mismatch.h", - "cuda/include/thrust/system/detail/sequential/extrema.h", - "cuda/include/thrust/system/detail/sequential/count.h", - "cuda/include/thrust/system/detail/sequential/trivial_copy.h", - "cuda/include/thrust/system/detail/sequential/replace.h", - "cuda/include/thrust/system/detail/sequential/get_value.h", - "cuda/include/thrust/system/detail/sequential/inner_product.h", - "cuda/include/thrust/system/detail/sequential/copy_if.h", - "cuda/include/thrust/system/detail/sequential/logical.h", - "cuda/include/thrust/system/detail/sequential/iter_swap.h", - "cuda/include/thrust/system/detail/sequential/malloc_and_free.h", - "cuda/include/thrust/system/detail/sequential/fill.h", - "cuda/include/thrust/system/detail/sequential/transform.h", - "cuda/include/thrust/system/detail/error_condition.inl", - "cuda/include/thrust/system/detail/internal/decompose.h", "cuda/include/thrust/system/detail/error_code.inl", - "cuda/include/thrust/system/detail/generic/transform_scan.h", - "cuda/include/thrust/system/detail/generic/memory.inl", - "cuda/include/thrust/system/detail/generic/transform.inl", - "cuda/include/thrust/system/detail/generic/binary_search.inl", - "cuda/include/thrust/system/detail/generic/scan_by_key.inl", - "cuda/include/thrust/system/detail/generic/unique_by_key.h", - "cuda/include/thrust/system/detail/generic/inner_product.inl", - "cuda/include/thrust/system/detail/generic/select_system.h", - "cuda/include/thrust/system/detail/generic/sequence.inl", - "cuda/include/thrust/system/detail/generic/sort.inl", - "cuda/include/thrust/system/detail/generic/equal.inl", - "cuda/include/thrust/system/detail/generic/partition.h", - "cuda/include/thrust/system/detail/generic/unique.h", + "cuda/include/thrust/system/detail/error_condition.inl", "cuda/include/thrust/system/detail/generic/adjacent_difference.h", - "cuda/include/thrust/system/detail/generic/tag.h", - "cuda/include/thrust/system/detail/generic/unique_by_key.inl", - "cuda/include/thrust/system/detail/generic/sequence.h", - "cuda/include/thrust/system/detail/generic/type_traits.h", - "cuda/include/thrust/system/detail/generic/merge.h", - "cuda/include/thrust/system/detail/generic/reverse.inl", - "cuda/include/thrust/system/detail/generic/tabulate.inl", - "cuda/include/thrust/system/detail/generic/unique.inl", - "cuda/include/thrust/system/detail/generic/scatter.inl", - "cuda/include/thrust/system/detail/generic/set_operations.inl", - "cuda/include/thrust/system/detail/generic/copy_if.inl", - "cuda/include/thrust/system/detail/generic/transform_reduce.h", - "cuda/include/thrust/system/detail/generic/transform_scan.inl", - "cuda/include/thrust/system/detail/generic/gather.h", - "cuda/include/thrust/system/detail/generic/reduce_by_key.inl", - "cuda/include/thrust/system/detail/generic/transform_reduce.inl", - "cuda/include/thrust/system/detail/generic/sort.h", - "cuda/include/thrust/system/detail/generic/distance.inl", - "cuda/include/thrust/system/detail/generic/scan.h", - "cuda/include/thrust/system/detail/generic/temporary_buffer.h", - "cuda/include/thrust/system/detail/generic/reduce.inl", - "cuda/include/thrust/system/detail/generic/scan_by_key.h", - "cuda/include/thrust/system/detail/generic/reverse.h", - "cuda/include/thrust/system/detail/generic/temporary_buffer.inl", - "cuda/include/thrust/system/detail/generic/scatter.h", - "cuda/include/thrust/system/detail/generic/generate.inl", "cuda/include/thrust/system/detail/generic/adjacent_difference.inl", - "cuda/include/thrust/system/detail/generic/remove.inl", "cuda/include/thrust/system/detail/generic/advance.h", - "cuda/include/thrust/system/detail/generic/find.h", - "cuda/include/thrust/system/detail/generic/merge.inl", - "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl", - "cuda/include/thrust/system/detail/generic/scalar/binary_search.h", - "cuda/include/thrust/system/detail/generic/extrema.inl", - "cuda/include/thrust/system/detail/generic/generate.h", - "cuda/include/thrust/system/detail/generic/uninitialized_fill.h", + "cuda/include/thrust/system/detail/generic/advance.inl", + "cuda/include/thrust/system/detail/generic/binary_search.h", + "cuda/include/thrust/system/detail/generic/binary_search.inl", + "cuda/include/thrust/system/detail/generic/copy.h", + "cuda/include/thrust/system/detail/generic/copy.inl", + "cuda/include/thrust/system/detail/generic/copy_if.h", + "cuda/include/thrust/system/detail/generic/copy_if.inl", + "cuda/include/thrust/system/detail/generic/count.h", "cuda/include/thrust/system/detail/generic/count.inl", - "cuda/include/thrust/system/detail/generic/remove.h", - "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl", - "cuda/include/thrust/system/detail/generic/tabulate.h", - "cuda/include/thrust/system/detail/generic/for_each.h", "cuda/include/thrust/system/detail/generic/distance.h", - "cuda/include/thrust/system/detail/generic/swap_ranges.inl", - "cuda/include/thrust/system/detail/generic/reduce_by_key.h", - "cuda/include/thrust/system/detail/generic/reduce.h", + "cuda/include/thrust/system/detail/generic/distance.inl", "cuda/include/thrust/system/detail/generic/equal.h", - "cuda/include/thrust/system/detail/generic/mismatch.inl", - "cuda/include/thrust/system/detail/generic/copy.inl", - "cuda/include/thrust/system/detail/generic/copy.h", - "cuda/include/thrust/system/detail/generic/swap_ranges.h", - "cuda/include/thrust/system/detail/generic/uninitialized_copy.h", - "cuda/include/thrust/system/detail/generic/binary_search.h", - "cuda/include/thrust/system/detail/generic/set_operations.h", - "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl", - "cuda/include/thrust/system/detail/generic/mismatch.h", - "cuda/include/thrust/system/detail/generic/scan.inl", - "cuda/include/thrust/system/detail/generic/gather.inl", + "cuda/include/thrust/system/detail/generic/equal.inl", "cuda/include/thrust/system/detail/generic/extrema.h", - "cuda/include/thrust/system/detail/generic/count.h", - "cuda/include/thrust/system/detail/generic/replace.h", + "cuda/include/thrust/system/detail/generic/extrema.inl", + "cuda/include/thrust/system/detail/generic/fill.h", + "cuda/include/thrust/system/detail/generic/find.h", + "cuda/include/thrust/system/detail/generic/find.inl", + "cuda/include/thrust/system/detail/generic/for_each.h", + "cuda/include/thrust/system/detail/generic/gather.h", + "cuda/include/thrust/system/detail/generic/gather.inl", + "cuda/include/thrust/system/detail/generic/generate.h", + "cuda/include/thrust/system/detail/generic/generate.inl", "cuda/include/thrust/system/detail/generic/inner_product.h", - "cuda/include/thrust/system/detail/generic/copy_if.h", + "cuda/include/thrust/system/detail/generic/inner_product.inl", "cuda/include/thrust/system/detail/generic/logical.h", - "cuda/include/thrust/system/detail/generic/partition.inl", "cuda/include/thrust/system/detail/generic/memory.h", - "cuda/include/thrust/system/detail/generic/find.inl", + "cuda/include/thrust/system/detail/generic/memory.inl", + "cuda/include/thrust/system/detail/generic/merge.h", + "cuda/include/thrust/system/detail/generic/merge.inl", + "cuda/include/thrust/system/detail/generic/mismatch.h", + "cuda/include/thrust/system/detail/generic/mismatch.inl", + "cuda/include/thrust/system/detail/generic/partition.h", + "cuda/include/thrust/system/detail/generic/partition.inl", + "cuda/include/thrust/system/detail/generic/reduce.h", + "cuda/include/thrust/system/detail/generic/reduce.inl", + "cuda/include/thrust/system/detail/generic/reduce_by_key.h", + "cuda/include/thrust/system/detail/generic/reduce_by_key.inl", + "cuda/include/thrust/system/detail/generic/remove.h", + "cuda/include/thrust/system/detail/generic/remove.inl", + "cuda/include/thrust/system/detail/generic/replace.h", "cuda/include/thrust/system/detail/generic/replace.inl", - "cuda/include/thrust/system/detail/generic/advance.inl", - "cuda/include/thrust/system/detail/generic/fill.h", + "cuda/include/thrust/system/detail/generic/reverse.h", + "cuda/include/thrust/system/detail/generic/reverse.inl", + "cuda/include/thrust/system/detail/generic/scalar/binary_search.h", + "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl", + "cuda/include/thrust/system/detail/generic/scan.h", + "cuda/include/thrust/system/detail/generic/scan.inl", + "cuda/include/thrust/system/detail/generic/scan_by_key.h", + "cuda/include/thrust/system/detail/generic/scan_by_key.inl", + "cuda/include/thrust/system/detail/generic/scatter.h", + "cuda/include/thrust/system/detail/generic/scatter.inl", + "cuda/include/thrust/system/detail/generic/select_system.h", + "cuda/include/thrust/system/detail/generic/sequence.h", + "cuda/include/thrust/system/detail/generic/sequence.inl", + "cuda/include/thrust/system/detail/generic/set_operations.h", + "cuda/include/thrust/system/detail/generic/set_operations.inl", + "cuda/include/thrust/system/detail/generic/sort.h", + "cuda/include/thrust/system/detail/generic/sort.inl", + "cuda/include/thrust/system/detail/generic/swap_ranges.h", + "cuda/include/thrust/system/detail/generic/swap_ranges.inl", + "cuda/include/thrust/system/detail/generic/tabulate.h", + "cuda/include/thrust/system/detail/generic/tabulate.inl", + "cuda/include/thrust/system/detail/generic/tag.h", + "cuda/include/thrust/system/detail/generic/temporary_buffer.h", + "cuda/include/thrust/system/detail/generic/temporary_buffer.inl", "cuda/include/thrust/system/detail/generic/transform.h", + "cuda/include/thrust/system/detail/generic/transform.inl", + "cuda/include/thrust/system/detail/generic/transform_reduce.h", + "cuda/include/thrust/system/detail/generic/transform_reduce.inl", + "cuda/include/thrust/system/detail/generic/transform_scan.h", + "cuda/include/thrust/system/detail/generic/transform_scan.inl", + "cuda/include/thrust/system/detail/generic/type_traits.h", + "cuda/include/thrust/system/detail/generic/uninitialized_copy.h", + "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl", + "cuda/include/thrust/system/detail/generic/uninitialized_fill.h", + "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl", + "cuda/include/thrust/system/detail/generic/unique.h", + "cuda/include/thrust/system/detail/generic/unique.inl", + "cuda/include/thrust/system/detail/generic/unique_by_key.h", + "cuda/include/thrust/system/detail/generic/unique_by_key.inl", + "cuda/include/thrust/system/detail/internal/decompose.h", + "cuda/include/thrust/system/detail/sequential/adjacent_difference.h", + "cuda/include/thrust/system/detail/sequential/assign_value.h", + "cuda/include/thrust/system/detail/sequential/binary_search.h", + "cuda/include/thrust/system/detail/sequential/copy.h", + "cuda/include/thrust/system/detail/sequential/copy.inl", + "cuda/include/thrust/system/detail/sequential/copy_backward.h", + "cuda/include/thrust/system/detail/sequential/copy_if.h", + "cuda/include/thrust/system/detail/sequential/count.h", + "cuda/include/thrust/system/detail/sequential/equal.h", + "cuda/include/thrust/system/detail/sequential/execution_policy.h", + "cuda/include/thrust/system/detail/sequential/extrema.h", + "cuda/include/thrust/system/detail/sequential/fill.h", + "cuda/include/thrust/system/detail/sequential/find.h", + "cuda/include/thrust/system/detail/sequential/for_each.h", + "cuda/include/thrust/system/detail/sequential/gather.h", + "cuda/include/thrust/system/detail/sequential/general_copy.h", + "cuda/include/thrust/system/detail/sequential/generate.h", + "cuda/include/thrust/system/detail/sequential/get_value.h", + "cuda/include/thrust/system/detail/sequential/inner_product.h", + "cuda/include/thrust/system/detail/sequential/insertion_sort.h", + "cuda/include/thrust/system/detail/sequential/iter_swap.h", + "cuda/include/thrust/system/detail/sequential/logical.h", + "cuda/include/thrust/system/detail/sequential/malloc_and_free.h", + "cuda/include/thrust/system/detail/sequential/merge.h", + "cuda/include/thrust/system/detail/sequential/merge.inl", + "cuda/include/thrust/system/detail/sequential/mismatch.h", + "cuda/include/thrust/system/detail/sequential/partition.h", + "cuda/include/thrust/system/detail/sequential/reduce.h", + "cuda/include/thrust/system/detail/sequential/reduce_by_key.h", + "cuda/include/thrust/system/detail/sequential/remove.h", + "cuda/include/thrust/system/detail/sequential/replace.h", + "cuda/include/thrust/system/detail/sequential/reverse.h", + "cuda/include/thrust/system/detail/sequential/scan.h", + "cuda/include/thrust/system/detail/sequential/scan_by_key.h", + "cuda/include/thrust/system/detail/sequential/scatter.h", + "cuda/include/thrust/system/detail/sequential/sequence.h", + "cuda/include/thrust/system/detail/sequential/set_operations.h", + "cuda/include/thrust/system/detail/sequential/sort.h", + "cuda/include/thrust/system/detail/sequential/sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl", + "cuda/include/thrust/system/detail/sequential/swap_ranges.h", + "cuda/include/thrust/system/detail/sequential/tabulate.h", + "cuda/include/thrust/system/detail/sequential/temporary_buffer.h", + "cuda/include/thrust/system/detail/sequential/transform.h", + "cuda/include/thrust/system/detail/sequential/transform_reduce.h", + "cuda/include/thrust/system/detail/sequential/transform_scan.h", + "cuda/include/thrust/system/detail/sequential/trivial_copy.h", + "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h", + "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h", + "cuda/include/thrust/system/detail/sequential/unique.h", + "cuda/include/thrust/system/detail/sequential/unique_by_key.h", "cuda/include/thrust/system/detail/system_error.inl", - "cuda/include/thrust/system/omp/execution_policy.h", - "cuda/include/thrust/system/omp/vector.h", - "cuda/include/thrust/system/omp/detail/transform_scan.h", - "cuda/include/thrust/system/omp/detail/memory.inl", - "cuda/include/thrust/system/omp/detail/reduce_intervals.inl", - "cuda/include/thrust/system/omp/detail/unique_by_key.h", - "cuda/include/thrust/system/omp/detail/sort.inl", - "cuda/include/thrust/system/omp/detail/partition.h", - "cuda/include/thrust/system/omp/detail/unique.h", - "cuda/include/thrust/system/omp/detail/execution_policy.h", + "cuda/include/thrust/system/error_code.h", "cuda/include/thrust/system/omp/detail/adjacent_difference.h", - "cuda/include/thrust/system/omp/detail/unique_by_key.inl", - "cuda/include/thrust/system/omp/detail/sequence.h", - "cuda/include/thrust/system/omp/detail/merge.h", - "cuda/include/thrust/system/omp/detail/unique.inl", + "cuda/include/thrust/system/omp/detail/assign_value.h", + "cuda/include/thrust/system/omp/detail/binary_search.h", + "cuda/include/thrust/system/omp/detail/copy.h", + "cuda/include/thrust/system/omp/detail/copy.inl", + "cuda/include/thrust/system/omp/detail/copy_if.h", "cuda/include/thrust/system/omp/detail/copy_if.inl", - "cuda/include/thrust/system/omp/detail/transform_reduce.h", - "cuda/include/thrust/system/omp/detail/gather.h", - "cuda/include/thrust/system/omp/detail/reduce_by_key.inl", - "cuda/include/thrust/system/omp/detail/sort.h", - "cuda/include/thrust/system/omp/detail/scan.h", - "cuda/include/thrust/system/omp/detail/temporary_buffer.h", + "cuda/include/thrust/system/omp/detail/count.h", "cuda/include/thrust/system/omp/detail/default_decomposition.h", - "cuda/include/thrust/system/omp/detail/reduce.inl", - "cuda/include/thrust/system/omp/detail/scan_by_key.h", - "cuda/include/thrust/system/omp/detail/reverse.h", - "cuda/include/thrust/system/omp/detail/assign_value.h", - "cuda/include/thrust/system/omp/detail/scatter.h", - "cuda/include/thrust/system/omp/detail/for_each.inl", "cuda/include/thrust/system/omp/detail/default_decomposition.inl", - "cuda/include/thrust/system/omp/detail/remove.inl", - "cuda/include/thrust/system/omp/detail/vector.inl", - "cuda/include/thrust/system/omp/detail/find.h", - "cuda/include/thrust/system/omp/detail/generate.h", - "cuda/include/thrust/system/omp/detail/uninitialized_fill.h", - "cuda/include/thrust/system/omp/detail/remove.h", - "cuda/include/thrust/system/omp/detail/tabulate.h", - "cuda/include/thrust/system/omp/detail/for_each.h", - "cuda/include/thrust/system/omp/detail/reduce_by_key.h", - "cuda/include/thrust/system/omp/detail/reduce.h", "cuda/include/thrust/system/omp/detail/equal.h", - "cuda/include/thrust/system/omp/detail/copy.inl", - "cuda/include/thrust/system/omp/detail/copy.h", - "cuda/include/thrust/system/omp/detail/swap_ranges.h", - "cuda/include/thrust/system/omp/detail/uninitialized_copy.h", - "cuda/include/thrust/system/omp/detail/binary_search.h", - "cuda/include/thrust/system/omp/detail/set_operations.h", - "cuda/include/thrust/system/omp/detail/mismatch.h", + "cuda/include/thrust/system/omp/detail/execution_policy.h", "cuda/include/thrust/system/omp/detail/extrema.h", - "cuda/include/thrust/system/omp/detail/count.h", - "cuda/include/thrust/system/omp/detail/replace.h", + "cuda/include/thrust/system/omp/detail/fill.h", + "cuda/include/thrust/system/omp/detail/find.h", + "cuda/include/thrust/system/omp/detail/for_each.h", + "cuda/include/thrust/system/omp/detail/for_each.inl", + "cuda/include/thrust/system/omp/detail/gather.h", + "cuda/include/thrust/system/omp/detail/generate.h", "cuda/include/thrust/system/omp/detail/get_value.h", "cuda/include/thrust/system/omp/detail/inner_product.h", - "cuda/include/thrust/system/omp/detail/copy_if.h", - "cuda/include/thrust/system/omp/detail/logical.h", - "cuda/include/thrust/system/omp/detail/partition.inl", "cuda/include/thrust/system/omp/detail/iter_swap.h", + "cuda/include/thrust/system/omp/detail/logical.h", + "cuda/include/thrust/system/omp/detail/malloc_and_free.h", + "cuda/include/thrust/system/omp/detail/memory.inl", + "cuda/include/thrust/system/omp/detail/merge.h", + "cuda/include/thrust/system/omp/detail/mismatch.h", "cuda/include/thrust/system/omp/detail/par.h", + "cuda/include/thrust/system/omp/detail/partition.h", + "cuda/include/thrust/system/omp/detail/partition.inl", + "cuda/include/thrust/system/omp/detail/reduce.h", + "cuda/include/thrust/system/omp/detail/reduce.inl", + "cuda/include/thrust/system/omp/detail/reduce_by_key.h", + "cuda/include/thrust/system/omp/detail/reduce_by_key.inl", "cuda/include/thrust/system/omp/detail/reduce_intervals.h", - "cuda/include/thrust/system/omp/detail/malloc_and_free.h", - "cuda/include/thrust/system/omp/detail/fill.h", + "cuda/include/thrust/system/omp/detail/reduce_intervals.inl", + "cuda/include/thrust/system/omp/detail/remove.h", + "cuda/include/thrust/system/omp/detail/remove.inl", + "cuda/include/thrust/system/omp/detail/replace.h", + "cuda/include/thrust/system/omp/detail/reverse.h", + "cuda/include/thrust/system/omp/detail/scan.h", + "cuda/include/thrust/system/omp/detail/scan_by_key.h", + "cuda/include/thrust/system/omp/detail/scatter.h", + "cuda/include/thrust/system/omp/detail/sequence.h", + "cuda/include/thrust/system/omp/detail/set_operations.h", + "cuda/include/thrust/system/omp/detail/sort.h", + "cuda/include/thrust/system/omp/detail/sort.inl", + "cuda/include/thrust/system/omp/detail/swap_ranges.h", + "cuda/include/thrust/system/omp/detail/tabulate.h", + "cuda/include/thrust/system/omp/detail/temporary_buffer.h", "cuda/include/thrust/system/omp/detail/transform.h", - "cuda/include/thrust/system/omp/memory.h", - "cuda/include/thrust/system/tbb/execution_policy.h", - "cuda/include/thrust/system/tbb/vector.h", - "cuda/include/thrust/system/tbb/detail/transform_scan.h", - "cuda/include/thrust/system/tbb/detail/memory.inl", - "cuda/include/thrust/system/tbb/detail/unique_by_key.h", - "cuda/include/thrust/system/tbb/detail/sort.inl", - "cuda/include/thrust/system/tbb/detail/partition.h", - "cuda/include/thrust/system/tbb/detail/unique.h", - "cuda/include/thrust/system/tbb/detail/execution_policy.h", + "cuda/include/thrust/system/omp/detail/transform_reduce.h", + "cuda/include/thrust/system/omp/detail/transform_scan.h", + "cuda/include/thrust/system/omp/detail/uninitialized_copy.h", + "cuda/include/thrust/system/omp/detail/uninitialized_fill.h", + "cuda/include/thrust/system/omp/detail/unique.h", + "cuda/include/thrust/system/omp/detail/unique.inl", + "cuda/include/thrust/system/omp/detail/unique_by_key.h", + "cuda/include/thrust/system/omp/detail/unique_by_key.inl", + "cuda/include/thrust/system/omp/detail/vector.inl", + "cuda/include/thrust/system/omp/execution_policy.h", + "cuda/include/thrust/system/omp/memory.h", + "cuda/include/thrust/system/omp/vector.h", + "cuda/include/thrust/system/system_error.h", "cuda/include/thrust/system/tbb/detail/adjacent_difference.h", - "cuda/include/thrust/system/tbb/detail/unique_by_key.inl", - "cuda/include/thrust/system/tbb/detail/sequence.h", - "cuda/include/thrust/system/tbb/detail/merge.h", - "cuda/include/thrust/system/tbb/detail/unique.inl", - "cuda/include/thrust/system/tbb/detail/copy_if.inl", - "cuda/include/thrust/system/tbb/detail/transform_reduce.h", - "cuda/include/thrust/system/tbb/detail/gather.h", - "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl", - "cuda/include/thrust/system/tbb/detail/sort.h", - "cuda/include/thrust/system/tbb/detail/scan.h", - "cuda/include/thrust/system/tbb/detail/temporary_buffer.h", - "cuda/include/thrust/system/tbb/detail/reduce.inl", - "cuda/include/thrust/system/tbb/detail/scan_by_key.h", - "cuda/include/thrust/system/tbb/detail/reverse.h", "cuda/include/thrust/system/tbb/detail/assign_value.h", - "cuda/include/thrust/system/tbb/detail/scatter.h", - "cuda/include/thrust/system/tbb/detail/for_each.inl", - "cuda/include/thrust/system/tbb/detail/remove.inl", - "cuda/include/thrust/system/tbb/detail/vector.inl", - "cuda/include/thrust/system/tbb/detail/find.h", - "cuda/include/thrust/system/tbb/detail/merge.inl", - "cuda/include/thrust/system/tbb/detail/generate.h", - "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h", - "cuda/include/thrust/system/tbb/detail/remove.h", - "cuda/include/thrust/system/tbb/detail/tabulate.h", - "cuda/include/thrust/system/tbb/detail/for_each.h", - "cuda/include/thrust/system/tbb/detail/reduce_by_key.h", - "cuda/include/thrust/system/tbb/detail/reduce.h", - "cuda/include/thrust/system/tbb/detail/equal.h", - "cuda/include/thrust/system/tbb/detail/copy.inl", - "cuda/include/thrust/system/tbb/detail/copy.h", - "cuda/include/thrust/system/tbb/detail/swap_ranges.h", - "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h", "cuda/include/thrust/system/tbb/detail/binary_search.h", - "cuda/include/thrust/system/tbb/detail/set_operations.h", - "cuda/include/thrust/system/tbb/detail/mismatch.h", - "cuda/include/thrust/system/tbb/detail/scan.inl", - "cuda/include/thrust/system/tbb/detail/extrema.h", + "cuda/include/thrust/system/tbb/detail/copy.h", + "cuda/include/thrust/system/tbb/detail/copy.inl", + "cuda/include/thrust/system/tbb/detail/copy_if.h", + "cuda/include/thrust/system/tbb/detail/copy_if.inl", "cuda/include/thrust/system/tbb/detail/count.h", - "cuda/include/thrust/system/tbb/detail/replace.h", + "cuda/include/thrust/system/tbb/detail/equal.h", + "cuda/include/thrust/system/tbb/detail/execution_policy.h", + "cuda/include/thrust/system/tbb/detail/extrema.h", + "cuda/include/thrust/system/tbb/detail/fill.h", + "cuda/include/thrust/system/tbb/detail/find.h", + "cuda/include/thrust/system/tbb/detail/for_each.h", + "cuda/include/thrust/system/tbb/detail/for_each.inl", + "cuda/include/thrust/system/tbb/detail/gather.h", + "cuda/include/thrust/system/tbb/detail/generate.h", "cuda/include/thrust/system/tbb/detail/get_value.h", "cuda/include/thrust/system/tbb/detail/inner_product.h", - "cuda/include/thrust/system/tbb/detail/copy_if.h", - "cuda/include/thrust/system/tbb/detail/logical.h", - "cuda/include/thrust/system/tbb/detail/partition.inl", "cuda/include/thrust/system/tbb/detail/iter_swap.h", + "cuda/include/thrust/system/tbb/detail/logical.h", + "cuda/include/thrust/system/tbb/detail/malloc_and_free.h", + "cuda/include/thrust/system/tbb/detail/memory.inl", + "cuda/include/thrust/system/tbb/detail/merge.h", + "cuda/include/thrust/system/tbb/detail/merge.inl", + "cuda/include/thrust/system/tbb/detail/mismatch.h", "cuda/include/thrust/system/tbb/detail/par.h", + "cuda/include/thrust/system/tbb/detail/partition.h", + "cuda/include/thrust/system/tbb/detail/partition.inl", + "cuda/include/thrust/system/tbb/detail/reduce.h", + "cuda/include/thrust/system/tbb/detail/reduce.inl", + "cuda/include/thrust/system/tbb/detail/reduce_by_key.h", + "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl", "cuda/include/thrust/system/tbb/detail/reduce_intervals.h", - "cuda/include/thrust/system/tbb/detail/malloc_and_free.h", - "cuda/include/thrust/system/tbb/detail/fill.h", + "cuda/include/thrust/system/tbb/detail/remove.h", + "cuda/include/thrust/system/tbb/detail/remove.inl", + "cuda/include/thrust/system/tbb/detail/replace.h", + "cuda/include/thrust/system/tbb/detail/reverse.h", + "cuda/include/thrust/system/tbb/detail/scan.h", + "cuda/include/thrust/system/tbb/detail/scan.inl", + "cuda/include/thrust/system/tbb/detail/scan_by_key.h", + "cuda/include/thrust/system/tbb/detail/scatter.h", + "cuda/include/thrust/system/tbb/detail/sequence.h", + "cuda/include/thrust/system/tbb/detail/set_operations.h", + "cuda/include/thrust/system/tbb/detail/sort.h", + "cuda/include/thrust/system/tbb/detail/sort.inl", + "cuda/include/thrust/system/tbb/detail/swap_ranges.h", + "cuda/include/thrust/system/tbb/detail/tabulate.h", + "cuda/include/thrust/system/tbb/detail/temporary_buffer.h", "cuda/include/thrust/system/tbb/detail/transform.h", - "cuda/include/thrust/system/tbb/memory.h", - "cuda/include/thrust/system/error_code.h", - "cuda/include/thrust/system/cpp/execution_policy.h", - "cuda/include/thrust/system/cpp/vector.h", - "cuda/include/thrust/system/cpp/detail/transform_scan.h", - "cuda/include/thrust/system/cpp/detail/memory.inl", - "cuda/include/thrust/system/cpp/detail/unique_by_key.h", - "cuda/include/thrust/system/cpp/detail/partition.h", - "cuda/include/thrust/system/cpp/detail/unique.h", - "cuda/include/thrust/system/cpp/detail/execution_policy.h", - "cuda/include/thrust/system/cpp/detail/adjacent_difference.h", - "cuda/include/thrust/system/cpp/detail/sequence.h", - "cuda/include/thrust/system/cpp/detail/merge.h", - "cuda/include/thrust/system/cpp/detail/transform_reduce.h", - "cuda/include/thrust/system/cpp/detail/gather.h", - "cuda/include/thrust/system/cpp/detail/sort.h", - "cuda/include/thrust/system/cpp/detail/scan.h", - "cuda/include/thrust/system/cpp/detail/temporary_buffer.h", - "cuda/include/thrust/system/cpp/detail/scan_by_key.h", - "cuda/include/thrust/system/cpp/detail/reverse.h", - "cuda/include/thrust/system/cpp/detail/assign_value.h", - "cuda/include/thrust/system/cpp/detail/scatter.h", - "cuda/include/thrust/system/cpp/detail/vector.inl", - "cuda/include/thrust/system/cpp/detail/find.h", - "cuda/include/thrust/system/cpp/detail/generate.h", - "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h", - "cuda/include/thrust/system/cpp/detail/remove.h", - "cuda/include/thrust/system/cpp/detail/tabulate.h", - "cuda/include/thrust/system/cpp/detail/for_each.h", - "cuda/include/thrust/system/cpp/detail/reduce_by_key.h", - "cuda/include/thrust/system/cpp/detail/reduce.h", - "cuda/include/thrust/system/cpp/detail/equal.h", - "cuda/include/thrust/system/cpp/detail/copy.h", - "cuda/include/thrust/system/cpp/detail/swap_ranges.h", - "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h", - "cuda/include/thrust/system/cpp/detail/binary_search.h", - "cuda/include/thrust/system/cpp/detail/set_operations.h", - "cuda/include/thrust/system/cpp/detail/mismatch.h", - "cuda/include/thrust/system/cpp/detail/extrema.h", - "cuda/include/thrust/system/cpp/detail/count.h", - "cuda/include/thrust/system/cpp/detail/replace.h", - "cuda/include/thrust/system/cpp/detail/get_value.h", - "cuda/include/thrust/system/cpp/detail/inner_product.h", - "cuda/include/thrust/system/cpp/detail/copy_if.h", - "cuda/include/thrust/system/cpp/detail/logical.h", - "cuda/include/thrust/system/cpp/detail/iter_swap.h", - "cuda/include/thrust/system/cpp/detail/par.h", - "cuda/include/thrust/system/cpp/detail/malloc_and_free.h", - "cuda/include/thrust/system/cpp/detail/fill.h", - "cuda/include/thrust/system/cpp/detail/transform.h", - "cuda/include/thrust/system/cpp/memory.h", - "cuda/include/thrust/system/cuda/execution_policy.h", - "cuda/include/thrust/system/cuda/vector.h", - "cuda/include/thrust/system/cuda/error.h", - "cuda/include/thrust/system/cuda/detail/copy_device_to_device.h", - "cuda/include/thrust/system/cuda/detail/transform_scan.h", - "cuda/include/thrust/system/cuda/detail/memory.inl", - "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh", - "cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh", - "cuda/include/thrust/system/cuda/detail/cub/cub.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.inl", - "cuda/include/thrust/system/cuda/detail/copy_cross_system.inl", - "cuda/include/thrust/system/cuda/detail/unique_by_key.h", - "cuda/include/thrust/system/cuda/detail/bulk.h", - "cuda/include/thrust/system/cuda/detail/sort.inl", - "cuda/include/thrust/system/cuda/detail/partition.h", - "cuda/include/thrust/system/cuda/detail/unique.h", - "cuda/include/thrust/system/cuda/detail/execution_policy.h", - "cuda/include/thrust/system/cuda/detail/cuda_launch_config.h", - "cuda/include/thrust/system/cuda/detail/cub.h", - "cuda/include/thrust/system/cuda/detail/adjacent_difference.h", - "cuda/include/thrust/system/cuda/detail/sequence.h", - "cuda/include/thrust/system/cuda/detail/merge.h", - "cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl", - "cuda/include/thrust/system/cuda/detail/copy_if.inl", - "cuda/include/thrust/system/cuda/detail/transform_reduce.h", - "cuda/include/thrust/system/cuda/detail/error.inl", - "cuda/include/thrust/system/cuda/detail/gather.h", - "cuda/include/thrust/system/cuda/detail/reduce_by_key.inl", - "cuda/include/thrust/system/cuda/detail/sort.h", - "cuda/include/thrust/system/cuda/detail/synchronize.h", - "cuda/include/thrust/system/cuda/detail/scan.h", - "cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h", - "cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h", - "cuda/include/thrust/system/cuda/detail/detail/set_operation.inl", - "cuda/include/thrust/system/cuda/detail/detail/balanced_path.h", - "cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/set_operation.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl", - "cuda/include/thrust/system/cuda/detail/detail/merge.h", - "cuda/include/thrust/system/cuda/detail/detail/alignment.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/launch_closure.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/uninitialized.h", - "cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl", - "cuda/include/thrust/system/cuda/detail/temporary_buffer.h", - "cuda/include/thrust/system/cuda/detail/default_decomposition.h", - "cuda/include/thrust/system/cuda/detail/reduce.inl", - "cuda/include/thrust/system/cuda/detail/scan_by_key.h", - "cuda/include/thrust/system/cuda/detail/reverse.h", - "cuda/include/thrust/system/cuda/detail/assign_value.h", - "cuda/include/thrust/system/cuda/detail/scatter.h", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp", - "cuda/include/thrust/system/cuda/detail/for_each.inl", - "cuda/include/thrust/system/cuda/detail/default_decomposition.inl", - "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h", - "cuda/include/thrust/system/cuda/detail/adjacent_difference.inl", - "cuda/include/thrust/system/cuda/detail/vector.inl", - "cuda/include/thrust/system/cuda/detail/throw_on_error.h", - "cuda/include/thrust/system/cuda/detail/find.h", - "cuda/include/thrust/system/cuda/detail/terminate.h", - "cuda/include/thrust/system/cuda/detail/merge.inl", - "cuda/include/thrust/system/cuda/detail/trivial_copy.inl", - "cuda/include/thrust/system/cuda/detail/generate.h", - "cuda/include/thrust/system/cuda/detail/execute_on_stream.h", - "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h", - "cuda/include/thrust/system/cuda/detail/remove.h", - "cuda/include/thrust/system/cuda/detail/tabulate.h", - "cuda/include/thrust/system/cuda/detail/for_each.h", - "cuda/include/thrust/system/cuda/detail/reduce_by_key.h", - "cuda/include/thrust/system/cuda/detail/decomposition.h", - "cuda/include/thrust/system/cuda/detail/reduce.h", - "cuda/include/thrust/system/cuda/detail/equal.h", - "cuda/include/thrust/system/cuda/detail/runtime_introspection.h", - "cuda/include/thrust/system/cuda/detail/copy.inl", - "cuda/include/thrust/system/cuda/detail/copy.h", - "cuda/include/thrust/system/cuda/detail/swap_ranges.h", - "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h", - "cuda/include/thrust/system/cuda/detail/binary_search.h", - "cuda/include/thrust/system/cuda/detail/runtime_introspection.inl", - "cuda/include/thrust/system/cuda/detail/set_operations.h", - "cuda/include/thrust/system/cuda/detail/mismatch.h", - "cuda/include/thrust/system/cuda/detail/scan.inl", - "cuda/include/thrust/system/cuda/detail/synchronize.inl", - "cuda/include/thrust/system/cuda/detail/extrema.h", - "cuda/include/thrust/system/cuda/detail/set_union.inl", - "cuda/include/thrust/system/cuda/detail/set_intersection.inl", - "cuda/include/thrust/system/cuda/detail/count.h", - "cuda/include/thrust/system/cuda/detail/trivial_copy.h", - "cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl", - "cuda/include/thrust/system/cuda/detail/replace.h", - "cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/async.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/future.hpp", - "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h", - "cuda/include/thrust/system/cuda/detail/get_value.h", - "cuda/include/thrust/system/cuda/detail/inner_product.h", - "cuda/include/thrust/system/cuda/detail/copy_if.h", - "cuda/include/thrust/system/cuda/detail/logical.h", - "cuda/include/thrust/system/cuda/detail/iter_swap.h", - "cuda/include/thrust/system/cuda/detail/block/merge.h", - "cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h", - "cuda/include/thrust/system/cuda/detail/block/merge.inl", - "cuda/include/thrust/system/cuda/detail/block/merging_sort.h", - "cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h", - "cuda/include/thrust/system/cuda/detail/block/reduce.h", - "cuda/include/thrust/system/cuda/detail/block/copy.h", - "cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h", - "cuda/include/thrust/system/cuda/detail/par.h", - "cuda/include/thrust/system/cuda/detail/copy_cross_system.h", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.h", - "cuda/include/thrust/system/cuda/detail/malloc_and_free.h", - "cuda/include/thrust/system/cuda/detail/fill.h", - "cuda/include/thrust/system/cuda/detail/set_difference.inl", - "cuda/include/thrust/system/cuda/detail/transform.h", - "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h", - "cuda/include/thrust/system/cuda/memory.h", - "cuda/include/thrust/remove.h", + "cuda/include/thrust/system/tbb/detail/transform_reduce.h", + "cuda/include/thrust/system/tbb/detail/transform_scan.h", + "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h", + "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h", + "cuda/include/thrust/system/tbb/detail/unique.h", + "cuda/include/thrust/system/tbb/detail/unique.inl", + "cuda/include/thrust/system/tbb/detail/unique_by_key.h", + "cuda/include/thrust/system/tbb/detail/unique_by_key.inl", + "cuda/include/thrust/system/tbb/detail/vector.inl", + "cuda/include/thrust/system/tbb/execution_policy.h", + "cuda/include/thrust/system/tbb/memory.h", + "cuda/include/thrust/system/tbb/vector.h", + "cuda/include/thrust/system_error.h", "cuda/include/thrust/tabulate.h", - "cuda/include/thrust/for_each.h", - "cuda/include/thrust/distance.h", - "cuda/include/thrust/reduce.h", - "cuda/include/thrust/equal.h", - "cuda/include/thrust/complex.h", - "cuda/include/thrust/device_allocator.h", - "cuda/include/thrust/copy.h", + "cuda/include/thrust/transform.h", + "cuda/include/thrust/transform_reduce.h", + "cuda/include/thrust/transform_scan.h", + "cuda/include/thrust/tuple.h", "cuda/include/thrust/uninitialized_copy.h", - "cuda/include/thrust/device_reference.h", - "cuda/include/thrust/binary_search.h", - "cuda/include/thrust/set_operations.h", - "cuda/include/thrust/swap.h", - "cuda/include/thrust/mismatch.h", - "cuda/include/thrust/extrema.h", - "cuda/include/thrust/count.h", - "cuda/include/thrust/device_free.h", - "cuda/include/thrust/random/discard_block_engine.h", - "cuda/include/thrust/random/normal_distribution.h", - "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h", - "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl", - "cuda/include/thrust/random/detail/xor_combine_engine_max.h", - "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h", - "cuda/include/thrust/random/detail/uniform_int_distribution.inl", - "cuda/include/thrust/random/detail/discard_block_engine.inl", - "cuda/include/thrust/random/detail/uniform_real_distribution.inl", - "cuda/include/thrust/random/detail/random_core_access.h", - "cuda/include/thrust/random/detail/mod.h", - "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl", - "cuda/include/thrust/random/detail/linear_congruential_engine.inl", - "cuda/include/thrust/random/detail/xor_combine_engine.inl", - "cuda/include/thrust/random/detail/normal_distribution.inl", - "cuda/include/thrust/random/detail/normal_distribution_base.h", - "cuda/include/thrust/random/uniform_int_distribution.h", - "cuda/include/thrust/random/linear_feedback_shift_engine.h", - "cuda/include/thrust/random/xor_combine_engine.h", - "cuda/include/thrust/random/subtract_with_carry_engine.h", - "cuda/include/thrust/random/linear_congruential_engine.h", - "cuda/include/thrust/random/uniform_real_distribution.h", - "cuda/include/thrust/functional.h", - "cuda/include/thrust/replace.h", - "cuda/include/thrust/device_new_allocator.h", - "cuda/include/thrust/host_vector.h", + "cuda/include/thrust/uninitialized_fill.h", + "cuda/include/thrust/unique.h", "cuda/include/thrust/version.h", - "cuda/include/thrust/inner_product.h", - "cuda/include/thrust/iterator/iterator_traits.h", - "cuda/include/thrust/iterator/discard_iterator.h", - "cuda/include/thrust/iterator/retag.h", - "cuda/include/thrust/iterator/permutation_iterator.h", - "cuda/include/thrust/iterator/transform_iterator.h", - "cuda/include/thrust/iterator/detail/reverse_iterator.inl", - "cuda/include/thrust/iterator/detail/zip_iterator.inl", - "cuda/include/thrust/iterator/detail/counting_iterator.inl", - "cuda/include/thrust/iterator/detail/distance_from_result.h", - "cuda/include/thrust/iterator/detail/host_system_tag.h", - "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h", - "cuda/include/thrust/iterator/detail/retag.h", - "cuda/include/thrust/iterator/detail/tagged_iterator.h", - "cuda/include/thrust/iterator/detail/iterator_traits.inl", - "cuda/include/thrust/iterator/detail/minimum_category.h", - "cuda/include/thrust/iterator/detail/discard_iterator_base.h", - "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h", - "cuda/include/thrust/iterator/detail/zip_iterator_base.h", - "cuda/include/thrust/iterator/detail/normal_iterator.h", - "cuda/include/thrust/iterator/detail/join_iterator.h", - "cuda/include/thrust/iterator/detail/device_system_tag.h", - "cuda/include/thrust/iterator/detail/universal_categories.h", - "cuda/include/thrust/iterator/detail/reverse_iterator_base.h", - "cuda/include/thrust/iterator/detail/minimum_system.h", - "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h", - "cuda/include/thrust/iterator/detail/is_iterator_category.h", - "cuda/include/thrust/iterator/detail/permutation_iterator_base.h", - "cuda/include/thrust/iterator/detail/any_assign.h", - "cuda/include/thrust/iterator/detail/any_system_tag.h", - "cuda/include/thrust/iterator/detail/is_trivial_iterator.h", - "cuda/include/thrust/iterator/detail/iterator_category_to_system.h", - "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h", - "cuda/include/thrust/iterator/detail/constant_iterator_base.h", - "cuda/include/thrust/iterator/detail/transform_iterator.inl", - "cuda/include/thrust/iterator/detail/iterator_facade_category.h", - "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h", - "cuda/include/thrust/iterator/constant_iterator.h", - "cuda/include/thrust/iterator/counting_iterator.h", - "cuda/include/thrust/iterator/iterator_adaptor.h", - "cuda/include/thrust/iterator/iterator_facade.h", - "cuda/include/thrust/iterator/iterator_categories.h", - "cuda/include/thrust/iterator/reverse_iterator.h", - "cuda/include/thrust/iterator/zip_iterator.h", - "cuda/include/thrust/logical.h", - "cuda/include/thrust/tuple.h", - "cuda/include/thrust/memory.h", - "cuda/include/thrust/random.h", - "cuda/include/thrust/fill.h", - "cuda/include/thrust/transform.h", - "cuda/include/texture_types.h", - "cuda/include/nppversion.h", - "cuda/include/cuda_texture_types.h", - "cuda/include/fatbinary.h", - "cuda/include/cublasXt.h", - "cuda/include/cuda_fp16.h", "cuda/include/vector_functions.h", - "cuda/include/cusparse.h", - "cuda/include/nppi_filtering_functions.h", - "cuda/include/nppi_morphological_operations.h", - "cuda/include/sobol_direction_vectors.h", - "cuda/include/nvblas.h", - "cuda/include/curand_mtgp32dc_p_11213.h", - "cuda/include/nvcuvid.h", - "cuda/include/cuda_runtime_api.h", - "cuda/include/curand_mtgp32_kernel.h", - "cuda/include/cublas_v2.h", - "cuda/include/builtin_types.h", - "cuda/include/nppi_geometry_transforms.h", - "cuda/include/npps_support_functions.h", - "cuda/include/cufftw.h", - "cuda/include/cuda_device_runtime_api.h", - "cuda/include/sm_30_intrinsics.hpp", + "cuda/include/vector_functions.hpp", "cuda/include/vector_types.h", - "cuda/include/sm_35_atomic_functions.h", - "cuda/include/sm_20_intrinsics.h", - "cuda/include/driver_types.h", - "cuda/include/nvToolsExtCudaRt.h", - "cuda/include/curand_globals.h", - "cuda/include/device_atomic_functions.h", - "cuda/include/surface_types.h", - "cuda/include/nvrtc.h", - "cuda/include/nppdefs.h", - "cuda/include/sm_60_atomic_functions.h", - "cuda/include/driver_functions.h", - "cuda/include/cusolver_common.h", - "cuda/include/cublas.h", - "cuda/include/curand_lognormal.h", - "cuda/include/device_atomic_functions.hpp", - "cuda/include/crt/device_runtime.h", - "cuda/include/crt/storage_class.h", - "cuda/include/crt/func_macro.h", - "cuda/include/crt/host_runtime.h", - "cuda/include/nppi_arithmetic_and_logical_operations.h", - "cuda/include/npps_arithmetic_and_logical_operations.h", - "cuda/include/nppi_computer_vision.h", - "cuda/include/surface_functions.hpp", - "cuda/include/surface_functions.h", - "cuda/include/curand_normal_static.h", - "cuda/include/curand.h", - "cuda/include/math_functions_dbl_ptx3.h", - "cuda/include/curand_philox4x32_x.h", - "cuda/include/nppi_threshold_and_compare_operations.h", - "cuda/include/nvml.h", - "cuda/include/npps.h", - "cuda/include/cuda_vdpau_interop.h", - "cuda/include/sm_61_intrinsics.hpp", - "cuda/include/cublas_api.h", - "cuda/include/nppi_color_conversion.h", - "cuda/include/math_functions_dbl_ptx3.hpp", - "cuda/include/nppcore.h", - "cuda/include/cudaGL.h", - "cuda/include/fatBinaryCtl.h", - "cuda/include/npps_statistics_functions.h", - "cuda/include/cudaVDPAU.h", - "cuda/include/curand_poisson.h", - "cuda/include/cusolverDn.h", - "cuda/include/cuda_profiler_api.h", - "cuda/include/sm_20_atomic_functions.h", - "cuda/include/nvfunctional", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-9.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-9.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-9.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-9.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-9.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-9.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups.h" "$(@D)/cuda/include/cooperative_groups.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups_helpers.h" "$(@D)/cuda/include/cooperative_groups_helpers.h" && cp "/usr/local/cuda-9.0/include/crt/common_functions.h" "$(@D)/cuda/include/crt/common_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.h" "$(@D)/cuda/include/crt/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.hpp" "$(@D)/cuda/include/crt/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/device_functions.h" "$(@D)/cuda/include/crt/device_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_functions.hpp" "$(@D)/cuda/include/crt/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-9.0/include/crt/host_config.h" "$(@D)/cuda/include/crt/host_config.h" && cp "/usr/local/cuda-9.0/include/crt/host_defines.h" "$(@D)/cuda/include/crt/host_defines.h" && cp "/usr/local/cuda-9.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.h" "$(@D)/cuda/include/crt/math_functions.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.hpp" "$(@D)/cuda/include/crt/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/mma.h" "$(@D)/cuda/include/crt/mma.h" && cp "/usr/local/cuda-9.0/include/crt/mma.hpp" "$(@D)/cuda/include/crt/mma.hpp" && cp "/usr/local/cuda-9.0/include/crt/nvfunctional" "$(@D)/cuda/include/crt/nvfunctional" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.h" "$(@D)/cuda/include/crt/sm_70_rt.h" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.hpp" "$(@D)/cuda/include/crt/sm_70_rt.hpp" && cp "/usr/local/cuda-9.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-9.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-9.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-9.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-9.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-9.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-9.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-9.0/include/cudaEGL.h" "$(@D)/cuda/include/cudaEGL.h" && cp "/usr/local/cuda-9.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-9.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-9.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-9.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.hpp" "$(@D)/cuda/include/cuda_fp16.hpp" && cp "/usr/local/cuda-9.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-9.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-9.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-9.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-9.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-9.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-9.0/include/cudnn.h" "$(@D)/cuda/include/cudnn.h" && cp "/usr/local/cuda-9.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-9.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-9.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-9.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-9.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-9.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-9.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-9.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-9.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-9.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-9.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-9.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-9.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-9.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-9.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-9.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-9.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-9.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-9.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-9.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-9.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-9.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-9.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-9.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-9.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-9.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-9.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda.h" "$(@D)/cuda/include/dynlink_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda_cuda.h" "$(@D)/cuda/include/dynlink_cuda_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuviddec.h" "$(@D)/cuda/include/dynlink_cuviddec.h" && cp "/usr/local/cuda-9.0/include/dynlink_nvcuvid.h" "$(@D)/cuda/include/dynlink_nvcuvid.h" && cp "/usr/local/cuda-9.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-9.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-9.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-9.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-9.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-9.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-9.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-9.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-9.0/include/mma.h" "$(@D)/cuda/include/mma.h" && cp "/usr/local/cuda-9.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-9.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-9.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-9.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-9.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-9.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-9.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-9.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-9.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-9.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-9.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-9.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-9.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-9.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-9.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional" && cp "/usr/local/cuda-9.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-9.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-9.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-9.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-9.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-9.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-9.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-9.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-9.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-9.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-9.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-9.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-9.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_output_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_output_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_output_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-9.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-9.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/config.h" "$(@D)/cuda/include/thrust/system/cuda/config.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/agent_launcher.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/agent_launcher.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/alignment.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/host/mutex.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/memory_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par_to_seq.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par_to_seq.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/parallel_for.h" "$(@D)/cuda/include/thrust/system/cuda/detail/parallel_for.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-9.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-9.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-9.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" """, ) @@ -1264,72 +1192,69 @@ genrule( name = "cuda-nvvm", outs = [ "cuda/nvvm/bin/cicc", - "cuda/nvvm/libdevice/libdevice.compute_50.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_30.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_20.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_35.10.bc", - "cuda/nvvm/lib64/libnvvm.so.3", - "cuda/nvvm/lib64/libnvvm.so", - "cuda/nvvm/lib64/libnvvm.so.3.1.0", "cuda/nvvm/include/nvvm.h", - "cuda/nvvm/libnvvm-samples/ptxgen/README.txt", - "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c", - "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt", + "cuda/nvvm/lib64/libnvvm.so", + "cuda/nvvm/lib64/libnvvm.so.3", + "cuda/nvvm/lib64/libnvvm.so.3.2.0", + "cuda/nvvm/libdevice/libdevice.10.bc", + "cuda/nvvm/libnvvm-samples/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/README.txt", "cuda/nvvm/libnvvm-samples/build.bat", - "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt", - "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu", + "cuda/nvvm/libnvvm-samples/build.sh", + "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h", + "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h", "cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt", "cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp", - "cuda/nvvm/libnvvm-samples/README.txt", - "cuda/nvvm/libnvvm-samples/simple/simple.c", - "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll", + "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu", + "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/ptxgen/README.txt", + "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c", + "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt", "cuda/nvvm/libnvvm-samples/simple/README.txt", + "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll", "cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll", - "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt", - "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h", - "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h", - "cuda/nvvm/libnvvm-samples/build.sh", - "cuda/nvvm/libnvvm-samples/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/simple/simple.c", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-9.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3.2.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.2.0" && cp "/usr/local/cuda-9.0/nvvm/libdevice/libdevice.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.10.bc" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" """, ) genrule( name = "cuda-extras", outs = [ - "cuda/extras/CUPTI/include/cupti_result.h", + "cuda/extras/CUPTI/include/GL/gl.h", + "cuda/extras/CUPTI/include/GL/glew.h", + "cuda/extras/CUPTI/include/GL/glext.h", + "cuda/extras/CUPTI/include/GL/glu.h", + "cuda/extras/CUPTI/include/GL/glut.h", + "cuda/extras/CUPTI/include/GL/glx.h", + "cuda/extras/CUPTI/include/GL/glxext.h", + "cuda/extras/CUPTI/include/GL/wglew.h", + "cuda/extras/CUPTI/include/GL/wglext.h", + "cuda/extras/CUPTI/include/cuda_stdint.h", + "cuda/extras/CUPTI/include/cupti.h", + "cuda/extras/CUPTI/include/cupti_activity.h", + "cuda/extras/CUPTI/include/cupti_callbacks.h", + "cuda/extras/CUPTI/include/cupti_driver_cbid.h", "cuda/extras/CUPTI/include/cupti_events.h", - "cuda/extras/CUPTI/include/openacc/cupti_openacc.h", + "cuda/extras/CUPTI/include/cupti_metrics.h", + "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h", + "cuda/extras/CUPTI/include/cupti_result.h", + "cuda/extras/CUPTI/include/cupti_runtime_cbid.h", "cuda/extras/CUPTI/include/cupti_version.h", - "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h", + "cuda/extras/CUPTI/include/generated_cudaGL_meta.h", "cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h", - "cuda/extras/CUPTI/include/cupti_activity.h", - "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h", + "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h", "cuda/extras/CUPTI/include/generated_cuda_meta.h", - "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h", - "cuda/extras/CUPTI/include/cuda_stdint.h", - "cuda/extras/CUPTI/include/generated_cudaGL_meta.h", + "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h", "cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h", - "cuda/extras/CUPTI/include/cupti_metrics.h", - "cuda/extras/CUPTI/include/cupti_callbacks.h", - "cuda/extras/CUPTI/include/cupti_runtime_cbid.h", - "cuda/extras/CUPTI/include/cupti.h", - "cuda/extras/CUPTI/include/GL/glut.h", - "cuda/extras/CUPTI/include/GL/glu.h", - "cuda/extras/CUPTI/include/GL/glxext.h", - "cuda/extras/CUPTI/include/GL/wglext.h", - "cuda/extras/CUPTI/include/GL/glx.h", - "cuda/extras/CUPTI/include/GL/glext.h", - "cuda/extras/CUPTI/include/GL/wglew.h", - "cuda/extras/CUPTI/include/GL/gl.h", - "cuda/extras/CUPTI/include/GL/glew.h", - "cuda/extras/CUPTI/include/cupti_driver_cbid.h", "cuda/extras/CUPTI/include/generated_nvtx_meta.h", + "cuda/extras/CUPTI/include/openacc/cupti_openacc.h", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" """, ) @@ -1337,26 +1262,21 @@ genrule( name = "cuda-lib", outs = [ "cuda/lib/libcuda.so", - "cuda/lib/libcudart.so.8.0", + "cuda/lib/libcudart.so.9.0", "cuda/lib/libcudart_static.a", - "cuda/lib/libcublas.so.8.0", - "cuda/lib/libcusolver.so.8.0", - "cuda/lib/libcurand.so.8.0", - "cuda/lib/libcufft.so.8.0", - "cuda/lib/libcudnn.so.6", - "cuda/lib/libcupti.so.8.0", + "cuda/lib/libcublas.so.9.0", + "cuda/lib/libcusolver.so.9.0", + "cuda/lib/libcurand.so.9.0", + "cuda/lib/libcufft.so.9.0", + "cuda/lib/libcudnn.so.7", + "cuda/lib/libcupti.so.9.0", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.88" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.6.0.21" "$(@D)/cuda/lib/libcudnn.so.6" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart.so.9.0.176" "$(@D)/cuda/lib/libcudart.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcublas.so.9.0.282" "$(@D)/cuda/lib/libcublas.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcusolver.so.9.0.176" "$(@D)/cuda/lib/libcusolver.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcurand.so.9.0.176" "$(@D)/cuda/lib/libcurand.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcufft.so.9.0.176" "$(@D)/cuda/lib/libcufft.so.9.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.7.0.5" "$(@D)/cuda/lib/libcudnn.so.7" && cp "/usr/local/cuda-9.0/extras/CUPTI/lib64/libcupti.so.9.0.176" "$(@D)/cuda/lib/libcupti.so.9.0" """, ) -genrule( +filegroup( name = "cudnn-include", - outs = [ - "cuda/include/cudnn.h", - ], - cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/include/cudnn.h" "$(@D)/cudnn.h" - """, + srcs = [], ) diff --git a/third_party/toolchains/gpus/py/BUILD b/third_party/toolchains/gpus/py/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..2d5ace93ff5054927cda61b0302af4edd8fe56c1 --- /dev/null +++ b/third_party/toolchains/gpus/py/BUILD @@ -0,0 +1,171 @@ +# A build file to configure python remote repository used with Bazel remote +# execution service +# DO NOT EDIT: automatically generated BUILD file + +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +cc_library( + name = "python_headers", + hdrs = [":python_include"], + data = select({ + ":windows": [":python_import_lib"], + "//conditions:default": [], + }), + includes = ["python_include"], + linkopts = select({ + # TODO(pcloudy): Ideally, this should just go into deps after resolving + # https://github.com/bazelbuild/bazel/issues/3237, + ":windows": ["$(locations :python_import_lib)"], + "//conditions:default": [], + }), +) + +cc_library( + name = "numpy_headers", + hdrs = [":numpy_include"], + includes = ["numpy_include"], +) + +config_setting( + name = "windows", + values = {"cpu": "x64_windows"}, + visibility = ["//visibility:public"], +) + +genrule( + name = "python_include", + outs = [ + "python_include/Python-ast.h", + "python_include/Python.h", + "python_include/abstract.h", + "python_include/asdl.h", + "python_include/ast.h", + "python_include/bitset.h", + "python_include/boolobject.h", + "python_include/bufferobject.h", + "python_include/bytearrayobject.h", + "python_include/bytes_methods.h", + "python_include/bytesobject.h", + "python_include/cStringIO.h", + "python_include/cellobject.h", + "python_include/ceval.h", + "python_include/classobject.h", + "python_include/cobject.h", + "python_include/code.h", + "python_include/codecs.h", + "python_include/compile.h", + "python_include/complexobject.h", + "python_include/datetime.h", + "python_include/descrobject.h", + "python_include/dictobject.h", + "python_include/dtoa.h", + "python_include/enumobject.h", + "python_include/errcode.h", + "python_include/eval.h", + "python_include/fileobject.h", + "python_include/floatobject.h", + "python_include/frameobject.h", + "python_include/funcobject.h", + "python_include/genobject.h", + "python_include/graminit.h", + "python_include/grammar.h", + "python_include/import.h", + "python_include/intobject.h", + "python_include/intrcheck.h", + "python_include/iterobject.h", + "python_include/listobject.h", + "python_include/longintrepr.h", + "python_include/longobject.h", + "python_include/marshal.h", + "python_include/memoryobject.h", + "python_include/metagrammar.h", + "python_include/methodobject.h", + "python_include/modsupport.h", + "python_include/moduleobject.h", + "python_include/node.h", + "python_include/object.h", + "python_include/objimpl.h", + "python_include/opcode.h", + "python_include/osdefs.h", + "python_include/parsetok.h", + "python_include/patchlevel.h", + "python_include/pgen.h", + "python_include/pgenheaders.h", + "python_include/py_curses.h", + "python_include/pyarena.h", + "python_include/pycapsule.h", + "python_include/pyconfig.h", + "python_include/pyctype.h", + "python_include/pydebug.h", + "python_include/pyerrors.h", + "python_include/pyexpat.h", + "python_include/pyfpe.h", + "python_include/pygetopt.h", + "python_include/pymacconfig.h", + "python_include/pymactoolbox.h", + "python_include/pymath.h", + "python_include/pymem.h", + "python_include/pyport.h", + "python_include/pystate.h", + "python_include/pystrcmp.h", + "python_include/pystrtod.h", + "python_include/pythonrun.h", + "python_include/pythread.h", + "python_include/rangeobject.h", + "python_include/setobject.h", + "python_include/sliceobject.h", + "python_include/stringobject.h", + "python_include/structmember.h", + "python_include/structseq.h", + "python_include/symtable.h", + "python_include/sysmodule.h", + "python_include/timefuncs.h", + "python_include/token.h", + "python_include/traceback.h", + "python_include/tupleobject.h", + "python_include/ucnhash.h", + "python_include/unicodeobject.h", + "python_include/warnings.h", + "python_include/weakrefobject.h", + ], + cmd = """ +cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" + """, +) + +genrule( + name = "numpy_include", + outs = [ + "numpy_include/numpy/__multiarray_api.h", + "numpy_include/numpy/__ufunc_api.h", + "numpy_include/numpy/_neighborhood_iterator_imp.h", + "numpy_include/numpy/_numpyconfig.h", + "numpy_include/numpy/arrayobject.h", + "numpy_include/numpy/arrayscalars.h", + "numpy_include/numpy/halffloat.h", + "numpy_include/numpy/multiarray_api.txt", + "numpy_include/numpy/ndarrayobject.h", + "numpy_include/numpy/ndarraytypes.h", + "numpy_include/numpy/noprefix.h", + "numpy_include/numpy/npy_1_7_deprecated_api.h", + "numpy_include/numpy/npy_3kcompat.h", + "numpy_include/numpy/npy_common.h", + "numpy_include/numpy/npy_cpu.h", + "numpy_include/numpy/npy_endian.h", + "numpy_include/numpy/npy_interrupt.h", + "numpy_include/numpy/npy_math.h", + "numpy_include/numpy/npy_no_deprecated_api.h", + "numpy_include/numpy/npy_os.h", + "numpy_include/numpy/numpyconfig.h", + "numpy_include/numpy/old_defines.h", + "numpy_include/numpy/oldnumeric.h", + "numpy_include/numpy/ufunc_api.txt", + "numpy_include/numpy/ufuncobject.h", + "numpy_include/numpy/utils.h", + ], + cmd = """ +cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" + """, +)